{"repo_id":"OneFormer","entity_id":"py:train_net","uri":"program://OneFormer/module/train_net#L1-L442","kind":"module","name":"train_net","path":"train_net.py","language":"python","start_line":1,"end_line":442,"context_start_line":1,"context_end_line":442,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nOneFormer Training Script.\n\nThis script is a simplified version of the training script in detectron2/tools.\n\"\"\"\nimport copy\nimport itertools\nimport logging\nimport os\n\nfrom collections import OrderedDict\nfrom typing import Any, Dict, List, Set\n\nimport torch\nimport warnings\n\nimport detectron2.utils.comm as comm\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.config import get_cfg\nfrom detectron2.data import MetadataCatalog, build_detection_train_loader\nfrom detectron2.engine import (\n DefaultTrainer,\n default_argument_parser,\n default_setup,\n launch,\n)\nfrom detectron2.evaluation import (\n CityscapesSemSegEvaluator,\n COCOPanopticEvaluator,\n DatasetEvaluators,\n SemSegEvaluator,\n DatasetEvaluator,\n inference_on_dataset,\n print_csv_format,\n verify_results,\n)\n\nfrom oneformer.evaluation import (\n COCOEvaluator,\n DetectionCOCOEvaluator,\n CityscapesInstanceEvaluator,\n)\n\nfrom detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler\nfrom detectron2.solver.build import maybe_add_gradient_clipping\nfrom detectron2.utils.logger import setup_logger\n\nfrom oneformer import (\n COCOUnifiedNewBaselineDatasetMapper,\n OneFormerUnifiedDatasetMapper,\n InstanceSegEvaluator,\n SemanticSegmentorWithTTA,\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\n\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter\nfrom oneformer.utils.events import WandbWriter, setup_wandb\nfrom time import sleep\nfrom oneformer.data.build import *\nfrom oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper\n\nclass Trainer(DefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to OneFormer.\n \"\"\"\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n \"\"\"\n Create evaluator(s) for a given dataset.\n This uses the special metadata \"evaluator_type\" associated with each\n builtin dataset. For your own dataset, you can simply create an\n evaluator manually in your script and do not have to worry about the\n hacky if-else logic here.\n \"\"\"\n if output_folder is None:\n output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n evaluator_list = []\n evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type\n # semantic segmentation\n if evaluator_type in [\"sem_seg\", \"ade20k_panoptic_seg\"]:\n evaluator_list.append(\n SemSegEvaluator(\n dataset_name,\n distributed=True,\n output_dir=output_folder,\n )\n )\n # instance segmentation\n if evaluator_type == \"coco\":\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if cfg.MODEL.TEST.DETECTION_ON:\n evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))\n # panoptic segmentation\n if evaluator_type in [\n \"coco_panoptic_seg\",\n \"ade20k_panoptic_seg\",\n \"cityscapes_panoptic_seg\",\n \"mapillary_vistas_panoptic_seg\",\n ]:\n if cfg.MODEL.TEST.PANOPTIC_ON:\n evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))\n # COCO\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.DETECTION_ON:\n evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Cityscapes\n if evaluator_type == \"cityscapes_instance\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesInstanceEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_sem_seg\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesSemSegEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_panoptic_seg\":\n if cfg.MODEL.TEST.SEMANTIC_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))\n if cfg.MODEL.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n WandbWriter(),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")\n model = SemanticSegmentorWithTTA(cfg, model)\n evaluators = [\n cls.build_evaluator(\n cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference_TTA\")\n )\n for name in cfg.DATASETS.TEST_SEMANTIC\n ]\n res = cls.test(cfg, model, evaluators)\n res = OrderedDict({k + \"_TTA\": v for k, v in res.items()})\n return res\n \n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n mapper = DatasetMapper(cfg, False)\n return build_detection_test_loader(cfg, dataset_name, mapper=mapper)\n \n @classmethod\n def test(cls, cfg, model, evaluators=None):\n \"\"\"\n Evaluate the given model. The given model is expected to already contain\n weights to evaluate.\n Args:\n cfg (CfgNode):\n model (nn.Module):\n evaluators (list[DatasetEvaluator] or None): if None, will call\n :meth:`build_evaluator`. Otherwise, must have the same length as\n ``cfg.DATASETS.TEST_{TASK}``.\n Returns:\n dict: a dict of result metrics\n \"\"\"\n logger = logging.getLogger(__name__)\n if isinstance(evaluators, DatasetEvaluator):\n evaluators = [evaluators]\n \n if cfg.MODEL.TEST.TASK == \"panoptic\":\n test_dataset = cfg.DATASETS.TEST_PANOPTIC\n elif cfg.MODEL.TEST.TASK == \"instance\":\n test_dataset = cfg.DATASETS.TEST_INSTANCE\n elif cfg.MODEL.TEST.TASK == \"semantic\":\n test_dataset = cfg.DATASETS.TEST_SEMANTIC\n else:\n warnings.warn(f\"WARNING: No task provided! Setting task to default value: 'panoptic'\")\n test_dataset = cfg.DATASETS.TEST_PANOPTIC\n\n if evaluators is not None:\n assert len(test_dataset) == len(evaluators), \"{} != {}\".format(\n len(test_dataset), len(evaluators)\n )\n \n results = OrderedDict\n\n results = OrderedDict()\n for idx, dataset_name in enumerate(test_dataset):\n data_loader = cls.build_test_loader(cfg, dataset_name)\n # When evaluators are passed in as arguments,\n # implicitly assume that evaluators can be created before data_loader.\n if evaluators is not None:\n evaluator = evaluators[idx]\n else:\n try:\n evaluator = cls.build_evaluator(cfg, dataset_name)\n except NotImplementedError:\n logger.warn(\n \"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, \"\n \"or implement its `build_evaluator` method.\"\n )\n results[dataset_name] = {}\n continue\n results_i = inference_on_dataset(model, data_loader, evaluator)\n\n results[dataset_name] = results_i\n if comm.is_main_process():\n assert isinstance(\n results_i, dict\n ), \"Evaluator must return a dict on the main process. Got {} instead.\".format(\n results_i\n )\n logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n print_csv_format(results_i)\n\n if len(results) == 1:\n results = list(results.values())[0]\n return results\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n if not args.eval_only:\n setup_wandb(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")\n return cfg\n\n\ndef main(args):\n cfg = setup(args)\n\n if args.eval_only:\n model = Trainer.build_model(cfg)\n net_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(\n cfg.MODEL.WEIGHTS, resume=args.resume\n )\n res = Trainer.test(cfg, model)\n if cfg.TEST.AUG.ENABLED:\n res.update(Trainer.test_with_TTA(cfg, model))\n if comm.is_main_process():\n verify_results(cfg, res)\n return res\n\n trainer = Trainer(cfg)\n trainer.resume_or_load(resume=args.resume)\n if args.machine_rank == 0:\n net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n sleep(3)\n return trainer.train()\n\n\nif __name__ == \"__main__\":\n args = default_argument_parser().parse_args()\n print(\"Command Line Args:\", args)\n launch(\n main,\n args.num_gpus,\n num_machines=args.num_machines,\n machine_rank=args.machine_rank,\n dist_url=args.dist_url,\n args=(args,),\n )","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.Trainer","uri":"program://OneFormer/class/train_net.Trainer#L71-L380","kind":"class","name":"Trainer","path":"train_net.py","language":"python","start_line":71,"end_line":380,"context_start_line":51,"context_end_line":400,"code":"from detectron2.utils.logger import setup_logger\n\nfrom oneformer import (\n COCOUnifiedNewBaselineDatasetMapper,\n OneFormerUnifiedDatasetMapper,\n InstanceSegEvaluator,\n SemanticSegmentorWithTTA,\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\n\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter\nfrom oneformer.utils.events import WandbWriter, setup_wandb\nfrom time import sleep\nfrom oneformer.data.build import *\nfrom oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper\n\nclass Trainer(DefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to OneFormer.\n \"\"\"\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n \"\"\"\n Create evaluator(s) for a given dataset.\n This uses the special metadata \"evaluator_type\" associated with each\n builtin dataset. For your own dataset, you can simply create an\n evaluator manually in your script and do not have to worry about the\n hacky if-else logic here.\n \"\"\"\n if output_folder is None:\n output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n evaluator_list = []\n evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type\n # semantic segmentation\n if evaluator_type in [\"sem_seg\", \"ade20k_panoptic_seg\"]:\n evaluator_list.append(\n SemSegEvaluator(\n dataset_name,\n distributed=True,\n output_dir=output_folder,\n )\n )\n # instance segmentation\n if evaluator_type == \"coco\":\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if cfg.MODEL.TEST.DETECTION_ON:\n evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))\n # panoptic segmentation\n if evaluator_type in [\n \"coco_panoptic_seg\",\n \"ade20k_panoptic_seg\",\n \"cityscapes_panoptic_seg\",\n \"mapillary_vistas_panoptic_seg\",\n ]:\n if cfg.MODEL.TEST.PANOPTIC_ON:\n evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))\n # COCO\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.DETECTION_ON:\n evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Cityscapes\n if evaluator_type == \"cityscapes_instance\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesInstanceEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_sem_seg\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesSemSegEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_panoptic_seg\":\n if cfg.MODEL.TEST.SEMANTIC_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))\n if cfg.MODEL.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n WandbWriter(),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")\n model = SemanticSegmentorWithTTA(cfg, model)\n evaluators = [\n cls.build_evaluator(\n cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference_TTA\")\n )\n for name in cfg.DATASETS.TEST_SEMANTIC\n ]\n res = cls.test(cfg, model, evaluators)\n res = OrderedDict({k + \"_TTA\": v for k, v in res.items()})\n return res\n \n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n mapper = DatasetMapper(cfg, False)\n return build_detection_test_loader(cfg, dataset_name, mapper=mapper)\n \n @classmethod\n def test(cls, cfg, model, evaluators=None):\n \"\"\"\n Evaluate the given model. The given model is expected to already contain\n weights to evaluate.\n Args:\n cfg (CfgNode):\n model (nn.Module):\n evaluators (list[DatasetEvaluator] or None): if None, will call\n :meth:`build_evaluator`. Otherwise, must have the same length as\n ``cfg.DATASETS.TEST_{TASK}``.\n Returns:\n dict: a dict of result metrics\n \"\"\"\n logger = logging.getLogger(__name__)\n if isinstance(evaluators, DatasetEvaluator):\n evaluators = [evaluators]\n \n if cfg.MODEL.TEST.TASK == \"panoptic\":\n test_dataset = cfg.DATASETS.TEST_PANOPTIC\n elif cfg.MODEL.TEST.TASK == \"instance\":\n test_dataset = cfg.DATASETS.TEST_INSTANCE\n elif cfg.MODEL.TEST.TASK == \"semantic\":\n test_dataset = cfg.DATASETS.TEST_SEMANTIC\n else:\n warnings.warn(f\"WARNING: No task provided! Setting task to default value: 'panoptic'\")\n test_dataset = cfg.DATASETS.TEST_PANOPTIC\n\n if evaluators is not None:\n assert len(test_dataset) == len(evaluators), \"{} != {}\".format(\n len(test_dataset), len(evaluators)\n )\n \n results = OrderedDict\n\n results = OrderedDict()\n for idx, dataset_name in enumerate(test_dataset):\n data_loader = cls.build_test_loader(cfg, dataset_name)\n # When evaluators are passed in as arguments,\n # implicitly assume that evaluators can be created before data_loader.\n if evaluators is not None:\n evaluator = evaluators[idx]\n else:\n try:\n evaluator = cls.build_evaluator(cfg, dataset_name)\n except NotImplementedError:\n logger.warn(\n \"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, \"\n \"or implement its `build_evaluator` method.\"\n )\n results[dataset_name] = {}\n continue\n results_i = inference_on_dataset(model, data_loader, evaluator)\n\n results[dataset_name] = results_i\n if comm.is_main_process():\n assert isinstance(\n results_i, dict\n ), \"Evaluator must return a dict on the main process. Got {} instead.\".format(\n results_i\n )\n logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n print_csv_format(results_i)\n\n if len(results) == 1:\n results = list(results.values())[0]\n return results\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n if not args.eval_only:\n setup_wandb(cfg, args)","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.setup","uri":"program://OneFormer/function/train_net.setup#L383-L403","kind":"function","name":"setup","path":"train_net.py","language":"python","start_line":383,"end_line":403,"context_start_line":363,"context_end_line":423,"code":" )\n results[dataset_name] = {}\n continue\n results_i = inference_on_dataset(model, data_loader, evaluator)\n\n results[dataset_name] = results_i\n if comm.is_main_process():\n assert isinstance(\n results_i, dict\n ), \"Evaluator must return a dict on the main process. Got {} instead.\".format(\n results_i\n )\n logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n print_csv_format(results_i)\n\n if len(results) == 1:\n results = list(results.values())[0]\n return results\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n if not args.eval_only:\n setup_wandb(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")\n return cfg\n\n\ndef main(args):\n cfg = setup(args)\n\n if args.eval_only:\n model = Trainer.build_model(cfg)\n net_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(\n cfg.MODEL.WEIGHTS, resume=args.resume\n )\n res = Trainer.test(cfg, model)\n if cfg.TEST.AUG.ENABLED:\n res.update(Trainer.test_with_TTA(cfg, model))\n if comm.is_main_process():\n verify_results(cfg, res)\n return res\n\n trainer = Trainer(cfg)","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.main","uri":"program://OneFormer/function/train_net.main#L406-L429","kind":"function","name":"main","path":"train_net.py","language":"python","start_line":406,"end_line":429,"context_start_line":386,"context_end_line":442,"code":" \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n if not args.eval_only:\n setup_wandb(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")\n return cfg\n\n\ndef main(args):\n cfg = setup(args)\n\n if args.eval_only:\n model = Trainer.build_model(cfg)\n net_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(\n cfg.MODEL.WEIGHTS, resume=args.resume\n )\n res = Trainer.test(cfg, model)\n if cfg.TEST.AUG.ENABLED:\n res.update(Trainer.test_with_TTA(cfg, model))\n if comm.is_main_process():\n verify_results(cfg, res)\n return res\n\n trainer = Trainer(cfg)\n trainer.resume_or_load(resume=args.resume)\n if args.machine_rank == 0:\n net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n sleep(3)\n return trainer.train()\n\n\nif __name__ == \"__main__\":\n args = default_argument_parser().parse_args()\n print(\"Command Line Args:\", args)\n launch(\n main,\n args.num_gpus,\n num_machines=args.num_machines,\n machine_rank=args.machine_rank,\n dist_url=args.dist_url,\n args=(args,),\n )","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.build_evaluator","uri":"program://OneFormer/function/train_net.build_evaluator#L77-L155","kind":"function","name":"build_evaluator","path":"train_net.py","language":"python","start_line":77,"end_line":155,"context_start_line":57,"context_end_line":175,"code":" SemanticSegmentorWithTTA,\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\n\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter\nfrom oneformer.utils.events import WandbWriter, setup_wandb\nfrom time import sleep\nfrom oneformer.data.build import *\nfrom oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper\n\nclass Trainer(DefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to OneFormer.\n \"\"\"\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n \"\"\"\n Create evaluator(s) for a given dataset.\n This uses the special metadata \"evaluator_type\" associated with each\n builtin dataset. For your own dataset, you can simply create an\n evaluator manually in your script and do not have to worry about the\n hacky if-else logic here.\n \"\"\"\n if output_folder is None:\n output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n evaluator_list = []\n evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type\n # semantic segmentation\n if evaluator_type in [\"sem_seg\", \"ade20k_panoptic_seg\"]:\n evaluator_list.append(\n SemSegEvaluator(\n dataset_name,\n distributed=True,\n output_dir=output_folder,\n )\n )\n # instance segmentation\n if evaluator_type == \"coco\":\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if cfg.MODEL.TEST.DETECTION_ON:\n evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))\n # panoptic segmentation\n if evaluator_type in [\n \"coco_panoptic_seg\",\n \"ade20k_panoptic_seg\",\n \"cityscapes_panoptic_seg\",\n \"mapillary_vistas_panoptic_seg\",\n ]:\n if cfg.MODEL.TEST.PANOPTIC_ON:\n evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))\n # COCO\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.TEST.DETECTION_ON:\n evaluator_list.append(DetectionCOCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Cityscapes\n if evaluator_type == \"cityscapes_instance\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesInstanceEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_sem_seg\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesSemSegEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_panoptic_seg\":\n if cfg.MODEL.TEST.SEMANTIC_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))\n if cfg.MODEL.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.build_train_loader","uri":"program://OneFormer/function/train_net.build_train_loader#L158-L169","kind":"function","name":"build_train_loader","path":"train_net.py","language":"python","start_line":158,"end_line":169,"context_start_line":138,"context_end_line":189,"code":" if cfg.MODEL.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.build_writers","uri":"program://OneFormer/function/train_net.build_writers#L171-L194","kind":"function","name":"build_writers","path":"train_net.py","language":"python","start_line":171,"end_line":194,"context_start_line":151,"context_end_line":214,"code":" )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n WandbWriter(),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.build_lr_scheduler","uri":"program://OneFormer/function/train_net.build_lr_scheduler#L197-L202","kind":"function","name":"build_lr_scheduler","path":"train_net.py","language":"python","start_line":197,"end_line":202,"context_start_line":177,"context_end_line":222,"code":" your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n WandbWriter(),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.build_optimizer","uri":"program://OneFormer/function/train_net.build_optimizer#L205-L285","kind":"function","name":"build_optimizer","path":"train_net.py","language":"python","start_line":205,"end_line":285,"context_start_line":185,"context_end_line":305,"code":" TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n WandbWriter(),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")\n model = SemanticSegmentorWithTTA(cfg, model)\n evaluators = [\n cls.build_evaluator(\n cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference_TTA\")\n )\n for name in cfg.DATASETS.TEST_SEMANTIC\n ]\n res = cls.test(cfg, model, evaluators)\n res = OrderedDict({k + \"_TTA\": v for k, v in res.items()})\n return res\n \n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.test_with_TTA","uri":"program://OneFormer/function/train_net.test_with_TTA#L288-L301","kind":"function","name":"test_with_TTA","path":"train_net.py","language":"python","start_line":288,"end_line":301,"context_start_line":268,"context_end_line":321,"code":" super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")\n model = SemanticSegmentorWithTTA(cfg, model)\n evaluators = [\n cls.build_evaluator(\n cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference_TTA\")\n )\n for name in cfg.DATASETS.TEST_SEMANTIC\n ]\n res = cls.test(cfg, model, evaluators)\n res = OrderedDict({k + \"_TTA\": v for k, v in res.items()})\n return res\n \n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n mapper = DatasetMapper(cfg, False)\n return build_detection_test_loader(cfg, dataset_name, mapper=mapper)\n \n @classmethod\n def test(cls, cfg, model, evaluators=None):\n \"\"\"\n Evaluate the given model. The given model is expected to already contain\n weights to evaluate.\n Args:\n cfg (CfgNode):\n model (nn.Module):","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.build_test_loader","uri":"program://OneFormer/function/train_net.build_test_loader#L304-L312","kind":"function","name":"build_test_loader","path":"train_net.py","language":"python","start_line":304,"end_line":312,"context_start_line":284,"context_end_line":332,"code":" optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")\n model = SemanticSegmentorWithTTA(cfg, model)\n evaluators = [\n cls.build_evaluator(\n cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference_TTA\")\n )\n for name in cfg.DATASETS.TEST_SEMANTIC\n ]\n res = cls.test(cfg, model, evaluators)\n res = OrderedDict({k + \"_TTA\": v for k, v in res.items()})\n return res\n \n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n mapper = DatasetMapper(cfg, False)\n return build_detection_test_loader(cfg, dataset_name, mapper=mapper)\n \n @classmethod\n def test(cls, cfg, model, evaluators=None):\n \"\"\"\n Evaluate the given model. The given model is expected to already contain\n weights to evaluate.\n Args:\n cfg (CfgNode):\n model (nn.Module):\n evaluators (list[DatasetEvaluator] or None): if None, will call\n :meth:`build_evaluator`. Otherwise, must have the same length as\n ``cfg.DATASETS.TEST_{TASK}``.\n Returns:\n dict: a dict of result metrics\n \"\"\"\n logger = logging.getLogger(__name__)\n if isinstance(evaluators, DatasetEvaluator):\n evaluators = [evaluators]\n \n if cfg.MODEL.TEST.TASK == \"panoptic\":","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.test","uri":"program://OneFormer/function/train_net.test#L315-L380","kind":"function","name":"test","path":"train_net.py","language":"python","start_line":315,"end_line":380,"context_start_line":295,"context_end_line":400,"code":" cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference_TTA\")\n )\n for name in cfg.DATASETS.TEST_SEMANTIC\n ]\n res = cls.test(cfg, model, evaluators)\n res = OrderedDict({k + \"_TTA\": v for k, v in res.items()})\n return res\n \n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n mapper = DatasetMapper(cfg, False)\n return build_detection_test_loader(cfg, dataset_name, mapper=mapper)\n \n @classmethod\n def test(cls, cfg, model, evaluators=None):\n \"\"\"\n Evaluate the given model. The given model is expected to already contain\n weights to evaluate.\n Args:\n cfg (CfgNode):\n model (nn.Module):\n evaluators (list[DatasetEvaluator] or None): if None, will call\n :meth:`build_evaluator`. Otherwise, must have the same length as\n ``cfg.DATASETS.TEST_{TASK}``.\n Returns:\n dict: a dict of result metrics\n \"\"\"\n logger = logging.getLogger(__name__)\n if isinstance(evaluators, DatasetEvaluator):\n evaluators = [evaluators]\n \n if cfg.MODEL.TEST.TASK == \"panoptic\":\n test_dataset = cfg.DATASETS.TEST_PANOPTIC\n elif cfg.MODEL.TEST.TASK == \"instance\":\n test_dataset = cfg.DATASETS.TEST_INSTANCE\n elif cfg.MODEL.TEST.TASK == \"semantic\":\n test_dataset = cfg.DATASETS.TEST_SEMANTIC\n else:\n warnings.warn(f\"WARNING: No task provided! Setting task to default value: 'panoptic'\")\n test_dataset = cfg.DATASETS.TEST_PANOPTIC\n\n if evaluators is not None:\n assert len(test_dataset) == len(evaluators), \"{} != {}\".format(\n len(test_dataset), len(evaluators)\n )\n \n results = OrderedDict\n\n results = OrderedDict()\n for idx, dataset_name in enumerate(test_dataset):\n data_loader = cls.build_test_loader(cfg, dataset_name)\n # When evaluators are passed in as arguments,\n # implicitly assume that evaluators can be created before data_loader.\n if evaluators is not None:\n evaluator = evaluators[idx]\n else:\n try:\n evaluator = cls.build_evaluator(cfg, dataset_name)\n except NotImplementedError:\n logger.warn(\n \"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, \"\n \"or implement its `build_evaluator` method.\"\n )\n results[dataset_name] = {}\n continue\n results_i = inference_on_dataset(model, data_loader, evaluator)\n\n results[dataset_name] = results_i\n if comm.is_main_process():\n assert isinstance(\n results_i, dict\n ), \"Evaluator must return a dict on the main process. Got {} instead.\".format(\n results_i\n )\n logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n print_csv_format(results_i)\n\n if len(results) == 1:\n results = list(results.values())[0]\n return results\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n if not args.eval_only:\n setup_wandb(cfg, args)","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.maybe_add_full_model_gradient_clipping","uri":"program://OneFormer/function/train_net.maybe_add_full_model_gradient_clipping#L253-L270","kind":"function","name":"maybe_add_full_model_gradient_clipping","path":"train_net.py","language":"python","start_line":253,"end_line":270,"context_start_line":233,"context_end_line":290,"code":" # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.FullModelGradientClippingOptimizer","uri":"program://OneFormer/class/train_net.FullModelGradientClippingOptimizer#L262-L268","kind":"class","name":"FullModelGradientClippingOptimizer","path":"train_net.py","language":"python","start_line":262,"end_line":268,"context_start_line":242,"context_end_line":288,"code":" \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:train_net.step","uri":"program://OneFormer/function/train_net.step#L263-L268","kind":"function","name":"step","path":"train_net.py","language":"python","start_line":263,"end_line":268,"context_start_line":243,"context_end_line":288,"code":" or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation","uri":"program://OneFormer/module/oneformer.test_time_augmentation#L1-L107","kind":"module","name":"oneformer.test_time_augmentation","path":"oneformer/test_time_augmentation.py","language":"python","start_line":1,"end_line":107,"context_start_line":1,"context_end_line":107,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/test_time_augmentation.py\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nfrom itertools import count\n\nimport numpy as np\nimport torch\nfrom fvcore.transforms import HFlipTransform\nfrom torch import nn\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom detectron2.data.detection_utils import read_image\nfrom .datasetmapper_tta import DatasetMapperTTA\nimport torch.nn.functional as F\n\n__all__ = [\n \"SemanticSegmentorWithTTA\",\n]\n\n\nclass SemanticSegmentorWithTTA(nn.Module):\n \"\"\"\n A SemanticSegmentor with test-time augmentation enabled.\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\n \"\"\"\n\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\n \"\"\"\n Args:\n cfg (CfgNode):\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\n tta_mapper (callable): takes a dataset dict and returns a list of\n augmented versions of the dataset dict. Defaults to\n `DatasetMapperTTA(cfg)`.\n batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions = self.model([input])[0].pop(\"sem_seg\")\n else:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions += self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions += self.model([input])[0].pop(\"sem_seg\")\n\n final_predictions = final_predictions / count_predictions\n return {\"sem_seg\": final_predictions}\n\n def _get_augmented_inputs(self, input):\n augmented_inputs = self.tta_mapper(input)\n tfms = [x.pop(\"transforms\") for x in augmented_inputs]\n return augmented_inputs, tfms","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation.SemanticSegmentorWithTTA","uri":"program://OneFormer/class/oneformer.test_time_augmentation.SemanticSegmentorWithTTA#L24-L107","kind":"class","name":"SemanticSegmentorWithTTA","path":"oneformer/test_time_augmentation.py","language":"python","start_line":24,"end_line":107,"context_start_line":4,"context_end_line":107,"code":"\nimport copy\nimport logging\nfrom itertools import count\n\nimport numpy as np\nimport torch\nfrom fvcore.transforms import HFlipTransform\nfrom torch import nn\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom detectron2.data.detection_utils import read_image\nfrom .datasetmapper_tta import DatasetMapperTTA\nimport torch.nn.functional as F\n\n__all__ = [\n \"SemanticSegmentorWithTTA\",\n]\n\n\nclass SemanticSegmentorWithTTA(nn.Module):\n \"\"\"\n A SemanticSegmentor with test-time augmentation enabled.\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\n \"\"\"\n\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\n \"\"\"\n Args:\n cfg (CfgNode):\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\n tta_mapper (callable): takes a dataset dict and returns a list of\n augmented versions of the dataset dict. Defaults to\n `DatasetMapperTTA(cfg)`.\n batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions = self.model([input])[0].pop(\"sem_seg\")\n else:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions += self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions += self.model([input])[0].pop(\"sem_seg\")\n\n final_predictions = final_predictions / count_predictions\n return {\"sem_seg\": final_predictions}\n\n def _get_augmented_inputs(self, input):\n augmented_inputs = self.tta_mapper(input)\n tfms = [x.pop(\"transforms\") for x in augmented_inputs]\n return augmented_inputs, tfms","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation.__init__","uri":"program://OneFormer/function/oneformer.test_time_augmentation.__init__#L30-L51","kind":"function","name":"__init__","path":"oneformer/test_time_augmentation.py","language":"python","start_line":30,"end_line":51,"context_start_line":10,"context_end_line":71,"code":"import torch\nfrom fvcore.transforms import HFlipTransform\nfrom torch import nn\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom detectron2.data.detection_utils import read_image\nfrom .datasetmapper_tta import DatasetMapperTTA\nimport torch.nn.functional as F\n\n__all__ = [\n \"SemanticSegmentorWithTTA\",\n]\n\n\nclass SemanticSegmentorWithTTA(nn.Module):\n \"\"\"\n A SemanticSegmentor with test-time augmentation enabled.\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\n \"\"\"\n\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\n \"\"\"\n Args:\n cfg (CfgNode):\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\n tta_mapper (callable): takes a dataset dict and returns a list of\n augmented versions of the dataset dict. Defaults to\n `DatasetMapperTTA(cfg)`.\n batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation.__call__","uri":"program://OneFormer/function/oneformer.test_time_augmentation.__call__#L53-L73","kind":"function","name":"__call__","path":"oneformer/test_time_augmentation.py","language":"python","start_line":53,"end_line":73,"context_start_line":33,"context_end_line":93,"code":" cfg (CfgNode):\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\n tta_mapper (callable): takes a dataset dict and returns a list of\n augmented versions of the dataset dict. Defaults to\n `DatasetMapperTTA(cfg)`.\n batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation._inference_one_image","uri":"program://OneFormer/function/oneformer.test_time_augmentation._inference_one_image#L75-L102","kind":"function","name":"_inference_one_image","path":"oneformer/test_time_augmentation.py","language":"python","start_line":75,"end_line":102,"context_start_line":55,"context_end_line":107,"code":" Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions = self.model([input])[0].pop(\"sem_seg\")\n else:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions += self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions += self.model([input])[0].pop(\"sem_seg\")\n\n final_predictions = final_predictions / count_predictions\n return {\"sem_seg\": final_predictions}\n\n def _get_augmented_inputs(self, input):\n augmented_inputs = self.tta_mapper(input)\n tfms = [x.pop(\"transforms\") for x in augmented_inputs]\n return augmented_inputs, tfms","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation._get_augmented_inputs","uri":"program://OneFormer/function/oneformer.test_time_augmentation._get_augmented_inputs#L104-L107","kind":"function","name":"_get_augmented_inputs","path":"oneformer/test_time_augmentation.py","language":"python","start_line":104,"end_line":107,"context_start_line":84,"context_end_line":107,"code":"\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions = self.model([input])[0].pop(\"sem_seg\")\n else:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions += self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions += self.model([input])[0].pop(\"sem_seg\")\n\n final_predictions = final_predictions / count_predictions\n return {\"sem_seg\": final_predictions}\n\n def _get_augmented_inputs(self, input):\n augmented_inputs = self.tta_mapper(input)\n tfms = [x.pop(\"transforms\") for x in augmented_inputs]\n return augmented_inputs, tfms","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.test_time_augmentation._maybe_read_image","uri":"program://OneFormer/function/oneformer.test_time_augmentation._maybe_read_image#L58-L67","kind":"function","name":"_maybe_read_image","path":"oneformer/test_time_augmentation.py","language":"python","start_line":58,"end_line":67,"context_start_line":38,"context_end_line":87,"code":" batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.config","uri":"program://OneFormer/module/oneformer.config#L1-L210","kind":"module","name":"oneformer.config","path":"oneformer/config.py","language":"python","start_line":1,"end_line":210,"context_start_line":1,"context_end_line":210,"code":"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nfrom detectron2.config import CfgNode as CN\n\n__all__ = [\"add_common_config\", \"add_oneformer_config\", \"add_swin_config\", \n \"add_dinat_config\", \"add_convnext_config\"]\n\ndef add_common_config(cfg):\n \"\"\"\n Add config for common configuration\n \"\"\"\n\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME = \"oneformer_unified\"\n # Color augmentation\n cfg.INPUT.COLOR_AUG_SSD = False\n # We retry random cropping until no single category in semantic segmentation GT occupies more\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\n # Pad image and segmentation GT in dataset mapper.\n cfg.INPUT.SIZE_DIVISIBILITY = -1\n\n cfg.INPUT.TASK_SEQ_LEN = 77\n cfg.INPUT.MAX_SEQ_LEN = 77\n\n cfg.INPUT.TASK_PROB = CN()\n cfg.INPUT.TASK_PROB.SEMANTIC = 0.33\n cfg.INPUT.TASK_PROB.INSTANCE = 0.66\n\n # test dataset\n cfg.DATASETS.TEST_PANOPTIC = (\"\",)\n cfg.DATASETS.TEST_INSTANCE = (\"\",)\n cfg.DATASETS.TEST_SEMANTIC = (\"\",)\n\n # solver config\n # weight decay on embedding\n cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0\n # optimizer\n cfg.SOLVER.OPTIMIZER = \"ADAMW\"\n cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1\n\n # wandb\n cfg.WANDB = CN()\n cfg.WANDB.PROJECT = \"OneFormer\"\n cfg.WANDB.NAME = None\n\n cfg.MODEL.IS_TRAIN = True\n cfg.MODEL.IS_DEMO = False\n\n # text encoder config\n cfg.MODEL.TEXT_ENCODER = CN()\n\n cfg.MODEL.TEXT_ENCODER.WIDTH = 256\n cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77\n cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12\n cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408\n cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2\n cfg.MODEL.TEXT_ENCODER.N_CTX = 16\n\n # oneformer inference config\n cfg.MODEL.TEST = CN()\n cfg.MODEL.TEST.SEMANTIC_ON = True\n cfg.MODEL.TEST.INSTANCE_ON = False\n cfg.MODEL.TEST.PANOPTIC_ON = False\n cfg.MODEL.TEST.DETECTION_ON = False\n cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0\n cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0\n cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False\n cfg.MODEL.TEST.TASK = \"panoptic\"\n\n # TEST AUG Slide\n cfg.TEST.AUG.IS_SLIDE = False\n cfg.TEST.AUG.CROP_SIZE = (640, 640)\n cfg.TEST.AUG.STRIDE = (426, 426)\n cfg.TEST.AUG.SCALE = (2048, 640)\n cfg.TEST.AUG.SETR_MULTI_SCALE = True\n cfg.TEST.AUG.KEEP_RATIO = True\n cfg.TEST.AUG.SIZE_DIVISOR = 32\n\n # pixel decoder config\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\n # adding transformer in pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\n # pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \"BasePixelDecoder\"\n cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256\n cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256\n\n # LSJ aug\n cfg.INPUT.IMAGE_SIZE = 1024\n cfg.INPUT.MIN_SCALE = 0.1\n cfg.INPUT.MAX_SCALE = 2.0\n\n # MSDeformAttn encoder configs\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\n\ndef add_oneformer_config(cfg):\n \"\"\"\n Add config for ONE_FORMER.\n \"\"\"\n\n # oneformer model config\n cfg.MODEL.ONE_FORMER = CN()\n\n # loss\n cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True\n cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1\n cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0\n cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0\n cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0\n cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5\n cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07\n\n # transformer config\n cfg.MODEL.ONE_FORMER.NHEADS = 8\n cfg.MODEL.ONE_FORMER.DROPOUT = 0.1\n cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048\n cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0\n cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2\n cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6\n cfg.MODEL.ONE_FORMER.PRE_NORM = False\n\n cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256\n cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120\n cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16\n cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True\n\n cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = \"res5\"\n cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False\n\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\n # you can use this config to override\n cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32\n\n # transformer module\n cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = \"ContrastiveMultiScaleMaskedTransformerDecoder\"\n\n # point loss configs\n # Number of points sampled during training for a mask point head.\n cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\n # original paper.\n cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\n # the original paper.\n cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75\n\ndef add_swin_config(cfg):\n \"\"\"\n Add config forSWIN Backbone.\n \"\"\"\n \n # swin transformer backbone\n cfg.MODEL.SWIN = CN()\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\n cfg.MODEL.SWIN.PATCH_SIZE = 4\n cfg.MODEL.SWIN.EMBED_DIM = 96\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\n cfg.MODEL.SWIN.QKV_BIAS = True\n cfg.MODEL.SWIN.QK_SCALE = None\n cfg.MODEL.SWIN.DROP_RATE = 0.0\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\n cfg.MODEL.SWIN.APE = False\n cfg.MODEL.SWIN.PATCH_NORM = True\n cfg.MODEL.SWIN.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\n\ndef add_dinat_config(cfg):\n \"\"\"\n Add config for NAT Backbone.\n \"\"\"\n\n # DINAT transformer backbone\n cfg.MODEL.DiNAT = CN()\n cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]\n cfg.MODEL.DiNAT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.DiNAT.EMBED_DIM = 64\n cfg.MODEL.DiNAT.MLP_RATIO = 3.0\n cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]\n cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2\n cfg.MODEL.DiNAT.KERNEL_SIZE = 7\n cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]\n cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)\n cfg.MODEL.DiNAT.QKV_BIAS = True\n cfg.MODEL.DiNAT.QK_SCALE = None\n cfg.MODEL.DiNAT.DROP_RATE = 0\n cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.\n cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4\n\ndef add_convnext_config(cfg):\n \"\"\"\n Add config for ConvNeXt Backbone.\n \"\"\"\n \n # swin transformer backbone\n cfg.MODEL.CONVNEXT = CN()\n cfg.MODEL.CONVNEXT.IN_CHANNELS = 3\n cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]\n cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]\n cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4\n cfg.MODEL.CONVNEXT.LSIT = 1.0\n cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]\n cfg.MODEL.CONVNEXT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.config.add_common_config","uri":"program://OneFormer/function/oneformer.config.add_common_config#L8-L98","kind":"function","name":"add_common_config","path":"oneformer/config.py","language":"python","start_line":8,"end_line":98,"context_start_line":1,"context_end_line":118,"code":"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nfrom detectron2.config import CfgNode as CN\n\n__all__ = [\"add_common_config\", \"add_oneformer_config\", \"add_swin_config\", \n \"add_dinat_config\", \"add_convnext_config\"]\n\ndef add_common_config(cfg):\n \"\"\"\n Add config for common configuration\n \"\"\"\n\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME = \"oneformer_unified\"\n # Color augmentation\n cfg.INPUT.COLOR_AUG_SSD = False\n # We retry random cropping until no single category in semantic segmentation GT occupies more\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\n # Pad image and segmentation GT in dataset mapper.\n cfg.INPUT.SIZE_DIVISIBILITY = -1\n\n cfg.INPUT.TASK_SEQ_LEN = 77\n cfg.INPUT.MAX_SEQ_LEN = 77\n\n cfg.INPUT.TASK_PROB = CN()\n cfg.INPUT.TASK_PROB.SEMANTIC = 0.33\n cfg.INPUT.TASK_PROB.INSTANCE = 0.66\n\n # test dataset\n cfg.DATASETS.TEST_PANOPTIC = (\"\",)\n cfg.DATASETS.TEST_INSTANCE = (\"\",)\n cfg.DATASETS.TEST_SEMANTIC = (\"\",)\n\n # solver config\n # weight decay on embedding\n cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0\n # optimizer\n cfg.SOLVER.OPTIMIZER = \"ADAMW\"\n cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1\n\n # wandb\n cfg.WANDB = CN()\n cfg.WANDB.PROJECT = \"OneFormer\"\n cfg.WANDB.NAME = None\n\n cfg.MODEL.IS_TRAIN = True\n cfg.MODEL.IS_DEMO = False\n\n # text encoder config\n cfg.MODEL.TEXT_ENCODER = CN()\n\n cfg.MODEL.TEXT_ENCODER.WIDTH = 256\n cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77\n cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12\n cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408\n cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2\n cfg.MODEL.TEXT_ENCODER.N_CTX = 16\n\n # oneformer inference config\n cfg.MODEL.TEST = CN()\n cfg.MODEL.TEST.SEMANTIC_ON = True\n cfg.MODEL.TEST.INSTANCE_ON = False\n cfg.MODEL.TEST.PANOPTIC_ON = False\n cfg.MODEL.TEST.DETECTION_ON = False\n cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0\n cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0\n cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False\n cfg.MODEL.TEST.TASK = \"panoptic\"\n\n # TEST AUG Slide\n cfg.TEST.AUG.IS_SLIDE = False\n cfg.TEST.AUG.CROP_SIZE = (640, 640)\n cfg.TEST.AUG.STRIDE = (426, 426)\n cfg.TEST.AUG.SCALE = (2048, 640)\n cfg.TEST.AUG.SETR_MULTI_SCALE = True\n cfg.TEST.AUG.KEEP_RATIO = True\n cfg.TEST.AUG.SIZE_DIVISOR = 32\n\n # pixel decoder config\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\n # adding transformer in pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\n # pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \"BasePixelDecoder\"\n cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256\n cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256\n\n # LSJ aug\n cfg.INPUT.IMAGE_SIZE = 1024\n cfg.INPUT.MIN_SCALE = 0.1\n cfg.INPUT.MAX_SCALE = 2.0\n\n # MSDeformAttn encoder configs\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\n\ndef add_oneformer_config(cfg):\n \"\"\"\n Add config for ONE_FORMER.\n \"\"\"\n\n # oneformer model config\n cfg.MODEL.ONE_FORMER = CN()\n\n # loss\n cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True\n cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1\n cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0\n cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0\n cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0\n cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5\n cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07\n\n # transformer config\n cfg.MODEL.ONE_FORMER.NHEADS = 8","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.config.add_oneformer_config","uri":"program://OneFormer/function/oneformer.config.add_oneformer_config#L100-L149","kind":"function","name":"add_oneformer_config","path":"oneformer/config.py","language":"python","start_line":100,"end_line":149,"context_start_line":80,"context_end_line":169,"code":"\n # pixel decoder config\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\n # adding transformer in pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\n # pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \"BasePixelDecoder\"\n cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256\n cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256\n\n # LSJ aug\n cfg.INPUT.IMAGE_SIZE = 1024\n cfg.INPUT.MIN_SCALE = 0.1\n cfg.INPUT.MAX_SCALE = 2.0\n\n # MSDeformAttn encoder configs\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\n\ndef add_oneformer_config(cfg):\n \"\"\"\n Add config for ONE_FORMER.\n \"\"\"\n\n # oneformer model config\n cfg.MODEL.ONE_FORMER = CN()\n\n # loss\n cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True\n cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1\n cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0\n cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0\n cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0\n cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5\n cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07\n\n # transformer config\n cfg.MODEL.ONE_FORMER.NHEADS = 8\n cfg.MODEL.ONE_FORMER.DROPOUT = 0.1\n cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048\n cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0\n cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2\n cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6\n cfg.MODEL.ONE_FORMER.PRE_NORM = False\n\n cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256\n cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120\n cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16\n cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True\n\n cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = \"res5\"\n cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False\n\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\n # you can use this config to override\n cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32\n\n # transformer module\n cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = \"ContrastiveMultiScaleMaskedTransformerDecoder\"\n\n # point loss configs\n # Number of points sampled during training for a mask point head.\n cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\n # original paper.\n cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\n # the original paper.\n cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75\n\ndef add_swin_config(cfg):\n \"\"\"\n Add config forSWIN Backbone.\n \"\"\"\n \n # swin transformer backbone\n cfg.MODEL.SWIN = CN()\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\n cfg.MODEL.SWIN.PATCH_SIZE = 4\n cfg.MODEL.SWIN.EMBED_DIM = 96\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\n cfg.MODEL.SWIN.QKV_BIAS = True\n cfg.MODEL.SWIN.QK_SCALE = None\n cfg.MODEL.SWIN.DROP_RATE = 0.0\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.config.add_swin_config","uri":"program://OneFormer/function/oneformer.config.add_swin_config#L151-L173","kind":"function","name":"add_swin_config","path":"oneformer/config.py","language":"python","start_line":151,"end_line":173,"context_start_line":131,"context_end_line":193,"code":" cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = \"res5\"\n cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False\n\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\n # you can use this config to override\n cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32\n\n # transformer module\n cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = \"ContrastiveMultiScaleMaskedTransformerDecoder\"\n\n # point loss configs\n # Number of points sampled during training for a mask point head.\n cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\n # original paper.\n cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\n # the original paper.\n cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75\n\ndef add_swin_config(cfg):\n \"\"\"\n Add config forSWIN Backbone.\n \"\"\"\n \n # swin transformer backbone\n cfg.MODEL.SWIN = CN()\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\n cfg.MODEL.SWIN.PATCH_SIZE = 4\n cfg.MODEL.SWIN.EMBED_DIM = 96\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\n cfg.MODEL.SWIN.QKV_BIAS = True\n cfg.MODEL.SWIN.QK_SCALE = None\n cfg.MODEL.SWIN.DROP_RATE = 0.0\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\n cfg.MODEL.SWIN.APE = False\n cfg.MODEL.SWIN.PATCH_NORM = True\n cfg.MODEL.SWIN.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\n\ndef add_dinat_config(cfg):\n \"\"\"\n Add config for NAT Backbone.\n \"\"\"\n\n # DINAT transformer backbone\n cfg.MODEL.DiNAT = CN()\n cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]\n cfg.MODEL.DiNAT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.DiNAT.EMBED_DIM = 64\n cfg.MODEL.DiNAT.MLP_RATIO = 3.0\n cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]\n cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2\n cfg.MODEL.DiNAT.KERNEL_SIZE = 7\n cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]\n cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)\n cfg.MODEL.DiNAT.QKV_BIAS = True\n cfg.MODEL.DiNAT.QK_SCALE = None\n cfg.MODEL.DiNAT.DROP_RATE = 0","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.config.add_dinat_config","uri":"program://OneFormer/function/oneformer.config.add_dinat_config#L175-L195","kind":"function","name":"add_dinat_config","path":"oneformer/config.py","language":"python","start_line":175,"end_line":195,"context_start_line":155,"context_end_line":210,"code":" \n # swin transformer backbone\n cfg.MODEL.SWIN = CN()\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\n cfg.MODEL.SWIN.PATCH_SIZE = 4\n cfg.MODEL.SWIN.EMBED_DIM = 96\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\n cfg.MODEL.SWIN.QKV_BIAS = True\n cfg.MODEL.SWIN.QK_SCALE = None\n cfg.MODEL.SWIN.DROP_RATE = 0.0\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\n cfg.MODEL.SWIN.APE = False\n cfg.MODEL.SWIN.PATCH_NORM = True\n cfg.MODEL.SWIN.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\n\ndef add_dinat_config(cfg):\n \"\"\"\n Add config for NAT Backbone.\n \"\"\"\n\n # DINAT transformer backbone\n cfg.MODEL.DiNAT = CN()\n cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]\n cfg.MODEL.DiNAT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.DiNAT.EMBED_DIM = 64\n cfg.MODEL.DiNAT.MLP_RATIO = 3.0\n cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]\n cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2\n cfg.MODEL.DiNAT.KERNEL_SIZE = 7\n cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]\n cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)\n cfg.MODEL.DiNAT.QKV_BIAS = True\n cfg.MODEL.DiNAT.QK_SCALE = None\n cfg.MODEL.DiNAT.DROP_RATE = 0\n cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.\n cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4\n\ndef add_convnext_config(cfg):\n \"\"\"\n Add config for ConvNeXt Backbone.\n \"\"\"\n \n # swin transformer backbone\n cfg.MODEL.CONVNEXT = CN()\n cfg.MODEL.CONVNEXT.IN_CHANNELS = 3\n cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]\n cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]\n cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4\n cfg.MODEL.CONVNEXT.LSIT = 1.0\n cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]\n cfg.MODEL.CONVNEXT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.config.add_convnext_config","uri":"program://OneFormer/function/oneformer.config.add_convnext_config#L197-L210","kind":"function","name":"add_convnext_config","path":"oneformer/config.py","language":"python","start_line":197,"end_line":210,"context_start_line":177,"context_end_line":210,"code":" Add config for NAT Backbone.\n \"\"\"\n\n # DINAT transformer backbone\n cfg.MODEL.DiNAT = CN()\n cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]\n cfg.MODEL.DiNAT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.DiNAT.EMBED_DIM = 64\n cfg.MODEL.DiNAT.MLP_RATIO = 3.0\n cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]\n cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2\n cfg.MODEL.DiNAT.KERNEL_SIZE = 7\n cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]\n cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)\n cfg.MODEL.DiNAT.QKV_BIAS = True\n cfg.MODEL.DiNAT.QK_SCALE = None\n cfg.MODEL.DiNAT.DROP_RATE = 0\n cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.\n cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4\n\ndef add_convnext_config(cfg):\n \"\"\"\n Add config for ConvNeXt Backbone.\n \"\"\"\n \n # swin transformer backbone\n cfg.MODEL.CONVNEXT = CN()\n cfg.MODEL.CONVNEXT.IN_CHANNELS = 3\n cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]\n cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]\n cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4\n cfg.MODEL.CONVNEXT.LSIT = 1.0\n cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]\n cfg.MODEL.CONVNEXT.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.datasetmapper_tta","uri":"program://OneFormer/module/oneformer.datasetmapper_tta#L1-L88","kind":"module","name":"oneformer.datasetmapper_tta","path":"oneformer/datasetmapper_tta.py","language":"python","start_line":1,"end_line":88,"context_start_line":1,"context_end_line":88,"code":"import copy\nimport numpy as np\nfrom typing import List\nimport torch\nfrom fvcore.transforms import NoOpTransform\nfrom torch import nn\n\nfrom detectron2.config import configurable\nfrom detectron2.data.transforms import (\n RandomFlip,\n ResizeShortestEdge,\n ResizeTransform,\n apply_augmentations,\n)\n\n__all__ = [\"DatasetMapperTTA\"]\n\n\nclass DatasetMapperTTA:\n \"\"\"\n Implement test-time augmentation for detection data.\n It is a callable which takes a dataset dict from a detection dataset,\n and returns a list of dataset dicts where the images\n are augmented from the input image by the transformations defined in the config.\n This is used for test-time augmentation.\n \"\"\"\n\n @configurable\n def __init__(self, min_sizes: List[int], max_size: int, flip: bool):\n \"\"\"\n Args:\n min_sizes: list of short-edge size to resize the image to\n max_size: maximum height or width of resized images\n flip: whether to apply flipping augmentation\n \"\"\"\n self.min_sizes = min_sizes\n self.max_size = max_size\n self.flip = flip\n\n @classmethod\n def from_config(cls, cfg):\n return {\n \"min_sizes\": cfg.TEST.AUG.MIN_SIZES,\n \"max_size\": cfg.TEST.AUG.MAX_SIZE,\n \"flip\": cfg.TEST.AUG.FLIP,\n }\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dict: a dict in standard model input format. See tutorials for details.\n Returns:\n list[dict]:\n a list of dicts, which contain augmented version of the input image.\n The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.\n Each dict has field \"transforms\" which is a TransformList,\n containing the transforms that are used to generate this image.\n \"\"\"\n numpy_image = dataset_dict[\"image\"].permute(1, 2, 0).numpy()\n shape = numpy_image.shape\n orig_shape = (dataset_dict[\"height\"], dataset_dict[\"width\"])\n \n if shape[:2] != orig_shape:\n # It transforms the \"original\" image in the dataset to the input image\n pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])\n else:\n pre_tfm = NoOpTransform()\n\n # Create all combinations of augmentations to use\n aug_candidates = [] # each element is a list[Augmentation]\n for min_size in self.min_sizes:\n resize = ResizeShortestEdge(min_size, self.max_size)\n aug_candidates.append([resize]) # resize only\n if self.flip:\n flip = RandomFlip(prob=1.0)\n aug_candidates.append([resize, flip]) # resize + flip\n\n # Apply all the augmentations\n ret = []\n for aug in aug_candidates:\n new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))\n torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1)))\n\n dic = copy.deepcopy(dataset_dict)\n dic[\"transforms\"] = pre_tfm + tfms\n dic[\"image\"] = torch_image\n ret.append(dic)\n return ret","source_hash":"f4f03c9f61aabf49ce1a2c3594b8741b3eebd1b5193b2064c69a66f747b7cf7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.datasetmapper_tta.DatasetMapperTTA","uri":"program://OneFormer/class/oneformer.datasetmapper_tta.DatasetMapperTTA#L19-L88","kind":"class","name":"DatasetMapperTTA","path":"oneformer/datasetmapper_tta.py","language":"python","start_line":19,"end_line":88,"context_start_line":1,"context_end_line":88,"code":"import copy\nimport numpy as np\nfrom typing import List\nimport torch\nfrom fvcore.transforms import NoOpTransform\nfrom torch import nn\n\nfrom detectron2.config import configurable\nfrom detectron2.data.transforms import (\n RandomFlip,\n ResizeShortestEdge,\n ResizeTransform,\n apply_augmentations,\n)\n\n__all__ = [\"DatasetMapperTTA\"]\n\n\nclass DatasetMapperTTA:\n \"\"\"\n Implement test-time augmentation for detection data.\n It is a callable which takes a dataset dict from a detection dataset,\n and returns a list of dataset dicts where the images\n are augmented from the input image by the transformations defined in the config.\n This is used for test-time augmentation.\n \"\"\"\n\n @configurable\n def __init__(self, min_sizes: List[int], max_size: int, flip: bool):\n \"\"\"\n Args:\n min_sizes: list of short-edge size to resize the image to\n max_size: maximum height or width of resized images\n flip: whether to apply flipping augmentation\n \"\"\"\n self.min_sizes = min_sizes\n self.max_size = max_size\n self.flip = flip\n\n @classmethod\n def from_config(cls, cfg):\n return {\n \"min_sizes\": cfg.TEST.AUG.MIN_SIZES,\n \"max_size\": cfg.TEST.AUG.MAX_SIZE,\n \"flip\": cfg.TEST.AUG.FLIP,\n }\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dict: a dict in standard model input format. See tutorials for details.\n Returns:\n list[dict]:\n a list of dicts, which contain augmented version of the input image.\n The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.\n Each dict has field \"transforms\" which is a TransformList,\n containing the transforms that are used to generate this image.\n \"\"\"\n numpy_image = dataset_dict[\"image\"].permute(1, 2, 0).numpy()\n shape = numpy_image.shape\n orig_shape = (dataset_dict[\"height\"], dataset_dict[\"width\"])\n \n if shape[:2] != orig_shape:\n # It transforms the \"original\" image in the dataset to the input image\n pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])\n else:\n pre_tfm = NoOpTransform()\n\n # Create all combinations of augmentations to use\n aug_candidates = [] # each element is a list[Augmentation]\n for min_size in self.min_sizes:\n resize = ResizeShortestEdge(min_size, self.max_size)\n aug_candidates.append([resize]) # resize only\n if self.flip:\n flip = RandomFlip(prob=1.0)\n aug_candidates.append([resize, flip]) # resize + flip\n\n # Apply all the augmentations\n ret = []\n for aug in aug_candidates:\n new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))\n torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1)))\n\n dic = copy.deepcopy(dataset_dict)\n dic[\"transforms\"] = pre_tfm + tfms\n dic[\"image\"] = torch_image\n ret.append(dic)\n return ret","source_hash":"f4f03c9f61aabf49ce1a2c3594b8741b3eebd1b5193b2064c69a66f747b7cf7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.datasetmapper_tta.__init__","uri":"program://OneFormer/function/oneformer.datasetmapper_tta.__init__#L29-L38","kind":"function","name":"__init__","path":"oneformer/datasetmapper_tta.py","language":"python","start_line":29,"end_line":38,"context_start_line":9,"context_end_line":58,"code":"from detectron2.data.transforms import (\n RandomFlip,\n ResizeShortestEdge,\n ResizeTransform,\n apply_augmentations,\n)\n\n__all__ = [\"DatasetMapperTTA\"]\n\n\nclass DatasetMapperTTA:\n \"\"\"\n Implement test-time augmentation for detection data.\n It is a callable which takes a dataset dict from a detection dataset,\n and returns a list of dataset dicts where the images\n are augmented from the input image by the transformations defined in the config.\n This is used for test-time augmentation.\n \"\"\"\n\n @configurable\n def __init__(self, min_sizes: List[int], max_size: int, flip: bool):\n \"\"\"\n Args:\n min_sizes: list of short-edge size to resize the image to\n max_size: maximum height or width of resized images\n flip: whether to apply flipping augmentation\n \"\"\"\n self.min_sizes = min_sizes\n self.max_size = max_size\n self.flip = flip\n\n @classmethod\n def from_config(cls, cfg):\n return {\n \"min_sizes\": cfg.TEST.AUG.MIN_SIZES,\n \"max_size\": cfg.TEST.AUG.MAX_SIZE,\n \"flip\": cfg.TEST.AUG.FLIP,\n }\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dict: a dict in standard model input format. See tutorials for details.\n Returns:\n list[dict]:\n a list of dicts, which contain augmented version of the input image.\n The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.\n Each dict has field \"transforms\" which is a TransformList,\n containing the transforms that are used to generate this image.\n \"\"\"","source_hash":"f4f03c9f61aabf49ce1a2c3594b8741b3eebd1b5193b2064c69a66f747b7cf7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.datasetmapper_tta.from_config","uri":"program://OneFormer/function/oneformer.datasetmapper_tta.from_config#L41-L46","kind":"function","name":"from_config","path":"oneformer/datasetmapper_tta.py","language":"python","start_line":41,"end_line":46,"context_start_line":21,"context_end_line":66,"code":" Implement test-time augmentation for detection data.\n It is a callable which takes a dataset dict from a detection dataset,\n and returns a list of dataset dicts where the images\n are augmented from the input image by the transformations defined in the config.\n This is used for test-time augmentation.\n \"\"\"\n\n @configurable\n def __init__(self, min_sizes: List[int], max_size: int, flip: bool):\n \"\"\"\n Args:\n min_sizes: list of short-edge size to resize the image to\n max_size: maximum height or width of resized images\n flip: whether to apply flipping augmentation\n \"\"\"\n self.min_sizes = min_sizes\n self.max_size = max_size\n self.flip = flip\n\n @classmethod\n def from_config(cls, cfg):\n return {\n \"min_sizes\": cfg.TEST.AUG.MIN_SIZES,\n \"max_size\": cfg.TEST.AUG.MAX_SIZE,\n \"flip\": cfg.TEST.AUG.FLIP,\n }\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dict: a dict in standard model input format. See tutorials for details.\n Returns:\n list[dict]:\n a list of dicts, which contain augmented version of the input image.\n The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.\n Each dict has field \"transforms\" which is a TransformList,\n containing the transforms that are used to generate this image.\n \"\"\"\n numpy_image = dataset_dict[\"image\"].permute(1, 2, 0).numpy()\n shape = numpy_image.shape\n orig_shape = (dataset_dict[\"height\"], dataset_dict[\"width\"])\n \n if shape[:2] != orig_shape:\n # It transforms the \"original\" image in the dataset to the input image\n pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])\n else:","source_hash":"f4f03c9f61aabf49ce1a2c3594b8741b3eebd1b5193b2064c69a66f747b7cf7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.datasetmapper_tta.__call__","uri":"program://OneFormer/function/oneformer.datasetmapper_tta.__call__#L48-L88","kind":"function","name":"__call__","path":"oneformer/datasetmapper_tta.py","language":"python","start_line":48,"end_line":88,"context_start_line":28,"context_end_line":88,"code":" @configurable\n def __init__(self, min_sizes: List[int], max_size: int, flip: bool):\n \"\"\"\n Args:\n min_sizes: list of short-edge size to resize the image to\n max_size: maximum height or width of resized images\n flip: whether to apply flipping augmentation\n \"\"\"\n self.min_sizes = min_sizes\n self.max_size = max_size\n self.flip = flip\n\n @classmethod\n def from_config(cls, cfg):\n return {\n \"min_sizes\": cfg.TEST.AUG.MIN_SIZES,\n \"max_size\": cfg.TEST.AUG.MAX_SIZE,\n \"flip\": cfg.TEST.AUG.FLIP,\n }\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dict: a dict in standard model input format. See tutorials for details.\n Returns:\n list[dict]:\n a list of dicts, which contain augmented version of the input image.\n The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.\n Each dict has field \"transforms\" which is a TransformList,\n containing the transforms that are used to generate this image.\n \"\"\"\n numpy_image = dataset_dict[\"image\"].permute(1, 2, 0).numpy()\n shape = numpy_image.shape\n orig_shape = (dataset_dict[\"height\"], dataset_dict[\"width\"])\n \n if shape[:2] != orig_shape:\n # It transforms the \"original\" image in the dataset to the input image\n pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])\n else:\n pre_tfm = NoOpTransform()\n\n # Create all combinations of augmentations to use\n aug_candidates = [] # each element is a list[Augmentation]\n for min_size in self.min_sizes:\n resize = ResizeShortestEdge(min_size, self.max_size)\n aug_candidates.append([resize]) # resize only\n if self.flip:\n flip = RandomFlip(prob=1.0)\n aug_candidates.append([resize, flip]) # resize + flip\n\n # Apply all the augmentations\n ret = []\n for aug in aug_candidates:\n new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))\n torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1)))\n\n dic = copy.deepcopy(dataset_dict)\n dic[\"transforms\"] = pre_tfm + tfms\n dic[\"image\"] = torch_image\n ret.append(dic)\n return ret","source_hash":"f4f03c9f61aabf49ce1a2c3594b8741b3eebd1b5193b2064c69a66f747b7cf7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model","uri":"program://OneFormer/module/oneformer.oneformer_model#L1-L486","kind":"module","name":"oneformer.oneformer_model","path":"oneformer/oneformer_model.py","language":"python","start_line":1,"end_line":486,"context_start_line":1,"context_end_line":486,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/maskformer_model.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nfrom typing import Tuple\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head\nfrom detectron2.modeling.backbone import Backbone\nfrom detectron2.modeling.postprocessing import sem_seg_postprocess\nfrom detectron2.structures import Boxes, ImageList, Instances, BitMasks\nfrom detectron2.utils.memory import retry_if_cuda_oom\n\nfrom .modeling.criterion import SetCriterion\nfrom .modeling.matcher import HungarianMatcher\nfrom einops import rearrange\nfrom .modeling.transformer_decoder.text_transformer import TextTransformer\nfrom .modeling.transformer_decoder.oneformer_transformer_decoder import MLP\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n@META_ARCH_REGISTRY.register()\nclass OneFormer(nn.Module):\n \"\"\"\n Main class for mask classification semantic segmentation architectures.\n \"\"\"\n\n @configurable\n def __init__(\n self,\n *,\n backbone: Backbone,\n sem_seg_head: nn.Module,\n task_mlp: nn.Module,\n text_encoder: nn.Module,\n text_projector: nn.Module,\n criterion: nn.Module,\n prompt_ctx: nn.Embedding,\n num_queries: int,\n object_mask_threshold: float,\n overlap_threshold: float,\n metadata,\n size_divisibility: int,\n sem_seg_postprocess_before_inference: bool,\n pixel_mean: Tuple[float],\n pixel_std: Tuple[float],\n # inference\n semantic_on: bool,\n panoptic_on: bool,\n instance_on: bool,\n detection_on: bool,\n test_topk_per_image: int,\n task_seq_len: int,\n max_seq_len: int,\n is_demo: bool,\n ):\n \"\"\"\n Args:\n backbone: a backbone module, must follow detectron2's backbone interface\n sem_seg_head: a module that predicts semantic segmentation from backbone features\n criterion: a module that defines the loss\n num_queries: int, number of queries\n object_mask_threshold: float, threshold to filter query based on classification score\n for panoptic segmentation inference\n overlap_threshold: overlap threshold used in general inference for panoptic segmentation\n metadata: dataset meta, get `thing` and `stuff` category names for panoptic\n segmentation inference\n size_divisibility: Some backbones require the input height and width to be divisible by a\n specific integer. We can use this to override such requirement.\n sem_seg_postprocess_before_inference: whether to resize the prediction back\n to original input size before semantic segmentation inference or after.\n For high-resolution dataset like Mapillary, resizing predictions before\n inference will cause OOM error.\n pixel_mean, pixel_std: list or tuple with #channels element, representing\n the per-channel mean and std to be used to normalize the input image\n semantic_on: bool, whether to output semantic segmentation prediction\n instance_on: bool, whether to output instance segmentation prediction\n panoptic_on: bool, whether to output panoptic segmentation prediction\n test_topk_per_image: int, instance segmentation parameter, keep topk instances per image\n \"\"\"\n super().__init__()\n self.backbone = backbone\n self.sem_seg_head = sem_seg_head\n self.task_mlp = task_mlp\n self.text_encoder = text_encoder\n self.text_projector = text_projector\n self.prompt_ctx = prompt_ctx\n self.criterion = criterion\n self.num_queries = num_queries\n self.overlap_threshold = overlap_threshold\n self.object_mask_threshold = object_mask_threshold\n self.metadata = metadata\n if size_divisibility < 0:\n # use backbone size_divisibility if not set\n size_divisibility = self.backbone.size_divisibility\n self.size_divisibility = size_divisibility\n self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference\n self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n # additional args\n self.semantic_on = semantic_on\n self.instance_on = instance_on\n self.panoptic_on = panoptic_on\n self.detection_on = detection_on\n self.test_topk_per_image = test_topk_per_image\n\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.is_demo = is_demo\n\n self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()]\n\n if not self.semantic_on:\n assert self.sem_seg_postprocess_before_inference\n\n @classmethod\n def from_config(cls, cfg):\n backbone = build_backbone(cfg)\n sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())\n\n if cfg.MODEL.IS_TRAIN:\n text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH,\n width=cfg.MODEL.TEXT_ENCODER.WIDTH,\n layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS,\n vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE)\n text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, \n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS)\n if cfg.MODEL.TEXT_ENCODER.N_CTX > 0:\n prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH)\n else:\n prompt_ctx = None\n else:\n text_encoder = None\n text_projector = None\n prompt_ctx = None\n\n task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,\n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2)\n\n # Loss parameters:\n deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION\n no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT\n\n # loss weights\n class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT\n dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT\n mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT\n contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT\n \n # building criterion\n matcher = HungarianMatcher(\n cost_class=class_weight,\n cost_mask=mask_weight,\n cost_dice=dice_weight,\n num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,\n )\n\n weight_dict = {\"loss_ce\": class_weight, \"loss_mask\": mask_weight, \n \"loss_dice\": dice_weight, \"loss_contrastive\": contrastive_weight}\n\n \n if deep_supervision:\n dec_layers = cfg.MODEL.ONE_FORMER.DEC_LAYERS\n aux_weight_dict = {}\n for i in range(dec_layers - 1):\n aux_weight_dict.update({k + f\"_{i}\": v for k, v in weight_dict.items()})\n weight_dict.update(aux_weight_dict)\n\n losses = [\"labels\", \"masks\", \"contrastive\"]\n\n criterion = SetCriterion(\n sem_seg_head.num_classes,\n matcher=matcher,\n weight_dict=weight_dict,\n eos_coef=no_object_weight,\n contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE,\n losses=losses,\n num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,\n oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO,\n importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO,\n )\n\n return {\n \"backbone\": backbone,\n \"sem_seg_head\": sem_seg_head,\n \"task_mlp\": task_mlp,\n \"prompt_ctx\": prompt_ctx,\n \"text_encoder\": text_encoder,\n \"text_projector\": text_projector,\n \"criterion\": criterion,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES,\n \"object_mask_threshold\": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD,\n \"overlap_threshold\": cfg.MODEL.TEST.OVERLAP_THRESHOLD,\n \"metadata\": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),\n \"size_divisibility\": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY,\n \"sem_seg_postprocess_before_inference\": (\n cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE\n or cfg.MODEL.TEST.PANOPTIC_ON\n or cfg.MODEL.TEST.INSTANCE_ON\n ),\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\n # inference\n \"semantic_on\": cfg.MODEL.TEST.SEMANTIC_ON,\n \"instance_on\": cfg.MODEL.TEST.INSTANCE_ON,\n \"panoptic_on\": cfg.MODEL.TEST.PANOPTIC_ON,\n \"detection_on\": cfg.MODEL.TEST.DETECTION_ON,\n \"test_topk_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"is_demo\": cfg.MODEL.IS_DEMO,\n }\n\n @property\n def device(self):\n return self.pixel_mean.device\n\n def encode_text(self, text):\n assert text.ndim in [2, 3], text.ndim\n b = text.shape[0]\n squeeze_dim = False\n num_text = 1\n if text.ndim == 3:\n num_text = text.shape[1]\n text = rearrange(text, 'b n l -> (b n) l', n=num_text)\n squeeze_dim = True\n\n # [B, C]\n x = self.text_encoder(text)\n\n text_x = self.text_projector(x)\n\n if squeeze_dim:\n text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)\n if self.prompt_ctx is not None:\n text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)\n text_x = torch.cat([text_x, text_ctx], dim=1)\n \n return {\"texts\": text_x}\n \n def forward(self, batched_inputs):\n \"\"\"\n Args:\n batched_inputs: a list, batched outputs of :class:`DatasetMapper`.\n Each item in the list contains the inputs for one image.\n For now, each item in the list is a dict that contains:\n * \"image\": Tensor, image in (C, H, W) format.\n * \"instances\": per-region ground truth\n * Other information that's included in the original dicts, such as:\n \"height\", \"width\" (int): the output resolution of the model (may be different\n from input resolution), used in inference.\n Returns:\n list[dict]:\n each dict has the results for one image. The dict contains the following keys:\n * \"sem_seg\":\n A Tensor that represents the\n per-pixel segmentation prediced by the head.\n The prediction has shape KxHxW that represents the logits of\n each class for each pixel.\n * \"panoptic_seg\":\n A tuple that represent panoptic output\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.\n segments_info (list[dict]): Describe each segment in `panoptic_seg`.\n Each dict contains keys \"id\", \"category_id\", \"isthing\".\n \"\"\"\n images = [x[\"image\"].to(self.device) for x in batched_inputs]\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\n images = ImageList.from_tensors(images, self.size_divisibility)\n\n tasks = torch.cat([self.task_tokenizer(x[\"task\"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)\n tasks = self.task_mlp(tasks.float())\n\n features = self.backbone(images.tensor)\n outputs = self.sem_seg_head(features, tasks)\n\n if self.training:\n texts = torch.cat([self.text_tokenizer(x[\"text\"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)\n texts_x = self.encode_text(texts)\n\n outputs = {**outputs, **texts_x}\n\n # mask classification target\n if \"instances\" in batched_inputs[0]:\n gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\n targets = self.prepare_targets(gt_instances, images)\n else:\n targets = None\n\n # bipartite matching-based loss\n losses = self.criterion(outputs, targets)\n\n for k in list(losses.keys()):\n if k in self.criterion.weight_dict:\n losses[k] *= self.criterion.weight_dict[k]\n else:\n # remove this loss if not specified in `weight_dict`\n losses.pop(k)\n return losses\n else:\n mask_cls_results = outputs[\"pred_logits\"]\n mask_pred_results = outputs[\"pred_masks\"]\n # upsample masks\n mask_pred_results = F.interpolate(\n mask_pred_results,\n size=(images.tensor.shape[-2], images.tensor.shape[-1]),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n del outputs\n\n processed_results = []\n for i, data in enumerate(zip(\n mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes\n )):\n mask_cls_result, mask_pred_result, input_per_image, image_size = data\n height = input_per_image.get(\"height\", image_size[0])\n width = input_per_image.get(\"width\", image_size[1])\n processed_results.append({})\n\n if self.sem_seg_postprocess_before_inference:\n mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(\n mask_pred_result, image_size, height, width\n )\n mask_cls_result = mask_cls_result.to(mask_pred_result)\n\n # semantic segmentation inference\n if self.semantic_on:\n r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result)\n if not self.sem_seg_postprocess_before_inference:\n r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)\n processed_results[-1][\"sem_seg\"] = r\n\n # panoptic segmentation inference\n if self.panoptic_on:\n panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)\n processed_results[-1][\"panoptic_seg\"] = panoptic_r\n \n # instance segmentation inference\n if self.instance_on:\n instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"instances\"] = instance_r\n\n if self.detection_on:\n bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"box_instances\"] = bbox_r\n\n return processed_results\n\n def prepare_targets(self, targets, images):\n h_pad, w_pad = images.tensor.shape[-2:]\n new_targets = []\n for targets_per_image in targets:\n # pad gt\n gt_masks = targets_per_image.gt_masks\n padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)\n padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks\n new_targets.append(\n {\n \"labels\": targets_per_image.gt_classes,\n \"masks\": padded_masks,\n }\n )\n return new_targets\n\n def semantic_inference(self, mask_cls, mask_pred):\n mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]\n mask_pred = mask_pred.sigmoid()\n semseg = torch.einsum(\"qc,qhw->chw\", mask_cls, mask_pred)\n return semseg\n\n def panoptic_inference(self, mask_cls, mask_pred):\n scores, labels = F.softmax(mask_cls, dim=-1).max(-1)\n mask_pred = mask_pred.sigmoid()\n\n keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)\n cur_scores = scores[keep]\n cur_classes = labels[keep]\n cur_masks = mask_pred[keep]\n cur_mask_cls = mask_cls[keep]\n cur_mask_cls = cur_mask_cls[:, :-1]\n\n cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks\n\n h, w = cur_masks.shape[-2:]\n panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)\n segments_info = []\n\n current_segment_id = 0\n\n if cur_masks.shape[0] == 0:\n # We didn't detect any mask :(\n return panoptic_seg, segments_info\n else:\n # take argmax\n cur_mask_ids = cur_prob_masks.argmax(0)\n stuff_memory_list = {}\n for k in range(cur_classes.shape[0]):\n pred_class = cur_classes[k].item()\n isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values()\n mask_area = (cur_mask_ids == k).sum().item()\n original_area = (cur_masks[k] >= 0.5).sum().item()\n mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5)\n\n if mask_area > 0 and original_area > 0 and mask.sum().item() > 0:\n if mask_area / original_area < self.overlap_threshold:\n continue\n\n # merge stuff regions\n if not isthing:\n if int(pred_class) in stuff_memory_list.keys():\n panoptic_seg[mask] = stuff_memory_list[int(pred_class)]\n continue\n else:\n stuff_memory_list[int(pred_class)] = current_segment_id + 1\n\n current_segment_id += 1\n panoptic_seg[mask] = current_segment_id\n\n segments_info.append(\n {\n \"id\": current_segment_id,\n \"isthing\": bool(isthing),\n \"category_id\": int(pred_class),\n }\n )\n\n return panoptic_seg, segments_info\n\n def instance_inference(self, mask_cls, mask_pred, task_type):\n # mask_pred is already processed to have the same shape as original input\n image_size = mask_pred.shape[-2:]\n\n # [Q, K]\n scores = F.softmax(mask_cls, dim=-1)[:, :-1]\n labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)\n \n # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)\n scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)\n labels_per_image = labels[topk_indices]\n\n topk_indices = topk_indices // self.sem_seg_head.num_classes\n # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)\n mask_pred = mask_pred[topk_ind\n# ... truncated ...","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.OneFormer","uri":"program://OneFormer/class/oneformer.oneformer_model.OneFormer#L28-L486","kind":"class","name":"OneFormer","path":"oneformer/oneformer_model.py","language":"python","start_line":28,"end_line":486,"context_start_line":8,"context_end_line":486,"code":"import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head\nfrom detectron2.modeling.backbone import Backbone\nfrom detectron2.modeling.postprocessing import sem_seg_postprocess\nfrom detectron2.structures import Boxes, ImageList, Instances, BitMasks\nfrom detectron2.utils.memory import retry_if_cuda_oom\n\nfrom .modeling.criterion import SetCriterion\nfrom .modeling.matcher import HungarianMatcher\nfrom einops import rearrange\nfrom .modeling.transformer_decoder.text_transformer import TextTransformer\nfrom .modeling.transformer_decoder.oneformer_transformer_decoder import MLP\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n@META_ARCH_REGISTRY.register()\nclass OneFormer(nn.Module):\n \"\"\"\n Main class for mask classification semantic segmentation architectures.\n \"\"\"\n\n @configurable\n def __init__(\n self,\n *,\n backbone: Backbone,\n sem_seg_head: nn.Module,\n task_mlp: nn.Module,\n text_encoder: nn.Module,\n text_projector: nn.Module,\n criterion: nn.Module,\n prompt_ctx: nn.Embedding,\n num_queries: int,\n object_mask_threshold: float,\n overlap_threshold: float,\n metadata,\n size_divisibility: int,\n sem_seg_postprocess_before_inference: bool,\n pixel_mean: Tuple[float],\n pixel_std: Tuple[float],\n # inference\n semantic_on: bool,\n panoptic_on: bool,\n instance_on: bool,\n detection_on: bool,\n test_topk_per_image: int,\n task_seq_len: int,\n max_seq_len: int,\n is_demo: bool,\n ):\n \"\"\"\n Args:\n backbone: a backbone module, must follow detectron2's backbone interface\n sem_seg_head: a module that predicts semantic segmentation from backbone features\n criterion: a module that defines the loss\n num_queries: int, number of queries\n object_mask_threshold: float, threshold to filter query based on classification score\n for panoptic segmentation inference\n overlap_threshold: overlap threshold used in general inference for panoptic segmentation\n metadata: dataset meta, get `thing` and `stuff` category names for panoptic\n segmentation inference\n size_divisibility: Some backbones require the input height and width to be divisible by a\n specific integer. We can use this to override such requirement.\n sem_seg_postprocess_before_inference: whether to resize the prediction back\n to original input size before semantic segmentation inference or after.\n For high-resolution dataset like Mapillary, resizing predictions before\n inference will cause OOM error.\n pixel_mean, pixel_std: list or tuple with #channels element, representing\n the per-channel mean and std to be used to normalize the input image\n semantic_on: bool, whether to output semantic segmentation prediction\n instance_on: bool, whether to output instance segmentation prediction\n panoptic_on: bool, whether to output panoptic segmentation prediction\n test_topk_per_image: int, instance segmentation parameter, keep topk instances per image\n \"\"\"\n super().__init__()\n self.backbone = backbone\n self.sem_seg_head = sem_seg_head\n self.task_mlp = task_mlp\n self.text_encoder = text_encoder\n self.text_projector = text_projector\n self.prompt_ctx = prompt_ctx\n self.criterion = criterion\n self.num_queries = num_queries\n self.overlap_threshold = overlap_threshold\n self.object_mask_threshold = object_mask_threshold\n self.metadata = metadata\n if size_divisibility < 0:\n # use backbone size_divisibility if not set\n size_divisibility = self.backbone.size_divisibility\n self.size_divisibility = size_divisibility\n self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference\n self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n # additional args\n self.semantic_on = semantic_on\n self.instance_on = instance_on\n self.panoptic_on = panoptic_on\n self.detection_on = detection_on\n self.test_topk_per_image = test_topk_per_image\n\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.is_demo = is_demo\n\n self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()]\n\n if not self.semantic_on:\n assert self.sem_seg_postprocess_before_inference\n\n @classmethod\n def from_config(cls, cfg):\n backbone = build_backbone(cfg)\n sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())\n\n if cfg.MODEL.IS_TRAIN:\n text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH,\n width=cfg.MODEL.TEXT_ENCODER.WIDTH,\n layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS,\n vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE)\n text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, \n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS)\n if cfg.MODEL.TEXT_ENCODER.N_CTX > 0:\n prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH)\n else:\n prompt_ctx = None\n else:\n text_encoder = None\n text_projector = None\n prompt_ctx = None\n\n task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,\n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2)\n\n # Loss parameters:\n deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION\n no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT\n\n # loss weights\n class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT\n dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT\n mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT\n contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT\n \n # building criterion\n matcher = HungarianMatcher(\n cost_class=class_weight,\n cost_mask=mask_weight,\n cost_dice=dice_weight,\n num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,\n )\n\n weight_dict = {\"loss_ce\": class_weight, \"loss_mask\": mask_weight, \n \"loss_dice\": dice_weight, \"loss_contrastive\": contrastive_weight}\n\n \n if deep_supervision:\n dec_layers = cfg.MODEL.ONE_FORMER.DEC_LAYERS\n aux_weight_dict = {}\n for i in range(dec_layers - 1):\n aux_weight_dict.update({k + f\"_{i}\": v for k, v in weight_dict.items()})\n weight_dict.update(aux_weight_dict)\n\n losses = [\"labels\", \"masks\", \"contrastive\"]\n\n criterion = SetCriterion(\n sem_seg_head.num_classes,\n matcher=matcher,\n weight_dict=weight_dict,\n eos_coef=no_object_weight,\n contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE,\n losses=losses,\n num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,\n oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO,\n importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO,\n )\n\n return {\n \"backbone\": backbone,\n \"sem_seg_head\": sem_seg_head,\n \"task_mlp\": task_mlp,\n \"prompt_ctx\": prompt_ctx,\n \"text_encoder\": text_encoder,\n \"text_projector\": text_projector,\n \"criterion\": criterion,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES,\n \"object_mask_threshold\": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD,\n \"overlap_threshold\": cfg.MODEL.TEST.OVERLAP_THRESHOLD,\n \"metadata\": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),\n \"size_divisibility\": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY,\n \"sem_seg_postprocess_before_inference\": (\n cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE\n or cfg.MODEL.TEST.PANOPTIC_ON\n or cfg.MODEL.TEST.INSTANCE_ON\n ),\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\n # inference\n \"semantic_on\": cfg.MODEL.TEST.SEMANTIC_ON,\n \"instance_on\": cfg.MODEL.TEST.INSTANCE_ON,\n \"panoptic_on\": cfg.MODEL.TEST.PANOPTIC_ON,\n \"detection_on\": cfg.MODEL.TEST.DETECTION_ON,\n \"test_topk_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"is_demo\": cfg.MODEL.IS_DEMO,\n }\n\n @property\n def device(self):\n return self.pixel_mean.device\n\n def encode_text(self, text):\n assert text.ndim in [2, 3], text.ndim\n b = text.shape[0]\n squeeze_dim = False\n num_text = 1\n if text.ndim == 3:\n num_text = text.shape[1]\n text = rearrange(text, 'b n l -> (b n) l', n=num_text)\n squeeze_dim = True\n\n # [B, C]\n x = self.text_encoder(text)\n\n text_x = self.text_projector(x)\n\n if squeeze_dim:\n text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)\n if self.prompt_ctx is not None:\n text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)\n text_x = torch.cat([text_x, text_ctx], dim=1)\n \n return {\"texts\": text_x}\n \n def forward(self, batched_inputs):\n \"\"\"\n Args:\n batched_inputs: a list, batched outputs of :class:`DatasetMapper`.\n Each item in the list contains the inputs for one image.\n For now, each item in the list is a dict that contains:\n * \"image\": Tensor, image in (C, H, W) format.\n * \"instances\": per-region ground truth\n * Other information that's included in the original dicts, such as:\n \"height\", \"width\" (int): the output resolution of the model (may be different\n from input resolution), used in inference.\n Returns:\n list[dict]:\n each dict has the results for one image. The dict contains the following keys:\n * \"sem_seg\":\n A Tensor that represents the\n per-pixel segmentation prediced by the head.\n The prediction has shape KxHxW that represents the logits of\n each class for each pixel.\n * \"panoptic_seg\":\n A tuple that represent panoptic output\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.\n segments_info (list[dict]): Describe each segment in `panoptic_seg`.\n Each dict contains keys \"id\", \"category_id\", \"isthing\".\n \"\"\"\n images = [x[\"image\"].to(self.device) for x in batched_inputs]\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\n images = ImageList.from_tensors(images, self.size_divisibility)\n\n tasks = torch.cat([self.task_tokenizer(x[\"task\"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)\n tasks = self.task_mlp(tasks.float())\n\n features = self.backbone(images.tensor)\n outputs = self.sem_seg_head(features, tasks)\n\n if self.training:\n texts = torch.cat([self.text_tokenizer(x[\"text\"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)\n texts_x = self.encode_text(texts)\n\n outputs = {**outputs, **texts_x}\n\n # mask classification target\n if \"instances\" in batched_inputs[0]:\n gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\n targets = self.prepare_targets(gt_instances, images)\n else:\n targets = None\n\n # bipartite matching-based loss\n losses = self.criterion(outputs, targets)\n\n for k in list(losses.keys()):\n if k in self.criterion.weight_dict:\n losses[k] *= self.criterion.weight_dict[k]\n else:\n # remove this loss if not specified in `weight_dict`\n losses.pop(k)\n return losses\n else:\n mask_cls_results = outputs[\"pred_logits\"]\n mask_pred_results = outputs[\"pred_masks\"]\n # upsample masks\n mask_pred_results = F.interpolate(\n mask_pred_results,\n size=(images.tensor.shape[-2], images.tensor.shape[-1]),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n del outputs\n\n processed_results = []\n for i, data in enumerate(zip(\n mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes\n )):\n mask_cls_result, mask_pred_result, input_per_image, image_size = data\n height = input_per_image.get(\"height\", image_size[0])\n width = input_per_image.get(\"width\", image_size[1])\n processed_results.append({})\n\n if self.sem_seg_postprocess_before_inference:\n mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(\n mask_pred_result, image_size, height, width\n )\n mask_cls_result = mask_cls_result.to(mask_pred_result)\n\n # semantic segmentation inference\n if self.semantic_on:\n r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result)\n if not self.sem_seg_postprocess_before_inference:\n r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)\n processed_results[-1][\"sem_seg\"] = r\n\n # panoptic segmentation inference\n if self.panoptic_on:\n panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)\n processed_results[-1][\"panoptic_seg\"] = panoptic_r\n \n # instance segmentation inference\n if self.instance_on:\n instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"instances\"] = instance_r\n\n if self.detection_on:\n bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"box_instances\"] = bbox_r\n\n return processed_results\n\n def prepare_targets(self, targets, images):\n h_pad, w_pad = images.tensor.shape[-2:]\n new_targets = []\n for targets_per_image in targets:\n # pad gt\n gt_masks = targets_per_image.gt_masks\n padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)\n padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks\n new_targets.append(\n {\n \"labels\": targets_per_image.gt_classes,\n \"masks\": padded_masks,\n }\n )\n return new_targets\n\n def semantic_inference(self, mask_cls, mask_pred):\n mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]\n mask_pred = mask_pred.sigmoid()\n semseg = torch.einsum(\"qc,qhw->chw\", mask_cls, mask_pred)\n return semseg\n\n def panoptic_inference(self, mask_cls, mask_pred):\n scores, labels = F.softmax(mask_cls, dim=-1).max(-1)\n mask_pred = mask_pred.sigmoid()\n\n keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)\n cur_scores = scores[keep]\n cur_classes = labels[keep]\n cur_masks = mask_pred[keep]\n cur_mask_cls = mask_cls[keep]\n cur_mask_cls = cur_mask_cls[:, :-1]\n\n cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks\n\n h, w = cur_masks.shape[-2:]\n panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)\n segments_info = []\n\n current_segment_id = 0\n\n if cur_masks.shape[0] == 0:\n # We didn't detect any mask :(\n return panoptic_seg, segments_info\n else:\n # take argmax\n cur_mask_ids = cur_prob_masks.argmax(0)\n stuff_memory_list = {}\n for k in range(cur_classes.shape[0]):\n pred_class = cur_classes[k].item()\n isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values()\n mask_area = (cur_mask_ids == k).sum().item()\n original_area = (cur_masks[k] >= 0.5).sum().item()\n mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5)\n\n if mask_area > 0 and original_area > 0 and mask.sum().item() > 0:\n if mask_area / original_area < self.overlap_threshold:\n continue\n\n # merge stuff regions\n if not isthing:\n if int(pred_class) in stuff_memory_list.keys():\n panoptic_seg[mask] = stuff_memory_list[int(pred_class)]\n continue\n else:\n stuff_memory_list[int(pred_class)] = current_segment_id + 1\n\n current_segment_id += 1\n panoptic_seg[mask] = current_segment_id\n\n segments_info.append(\n {\n \"id\": current_segment_id,\n \"isthing\": bool(isthing),\n \"category_id\": int(pred_class),\n }\n )\n\n return panoptic_seg, segments_info\n\n def instance_inference(self, mask_cls, mask_pred, task_type):\n # mask_pred is already processed to have the same shape as original input\n image_size = mask_pred.shape[-2:]\n\n # [Q, K]\n scores = F.softmax(mask_cls, dim=-1)[:, :-1]\n labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)\n \n # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)\n scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)\n labels_per_image = labels[topk_indices]\n\n topk_indices = topk_indices // self.sem_seg_head.num_classes\n # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)\n mask_pred = mask_pred[topk_indices]\n\n # Only consider scores with confidence over [self.object_mask_threshold] for demo\n if self.is_demo:\n keep = scores_per_image > self.object_mask_threshold\n scores_per_image = scores_per_image[keep]\n labels_per_image = labels_per_image[keep]\n mask_pred = mask_pred[keep]\n\n # if this\n# ... truncated ...","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.__init__","uri":"program://OneFormer/function/oneformer.oneformer_model.__init__#L34-L120","kind":"function","name":"__init__","path":"oneformer/oneformer_model.py","language":"python","start_line":34,"end_line":120,"context_start_line":14,"context_end_line":140,"code":"from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head\nfrom detectron2.modeling.backbone import Backbone\nfrom detectron2.modeling.postprocessing import sem_seg_postprocess\nfrom detectron2.structures import Boxes, ImageList, Instances, BitMasks\nfrom detectron2.utils.memory import retry_if_cuda_oom\n\nfrom .modeling.criterion import SetCriterion\nfrom .modeling.matcher import HungarianMatcher\nfrom einops import rearrange\nfrom .modeling.transformer_decoder.text_transformer import TextTransformer\nfrom .modeling.transformer_decoder.oneformer_transformer_decoder import MLP\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n@META_ARCH_REGISTRY.register()\nclass OneFormer(nn.Module):\n \"\"\"\n Main class for mask classification semantic segmentation architectures.\n \"\"\"\n\n @configurable\n def __init__(\n self,\n *,\n backbone: Backbone,\n sem_seg_head: nn.Module,\n task_mlp: nn.Module,\n text_encoder: nn.Module,\n text_projector: nn.Module,\n criterion: nn.Module,\n prompt_ctx: nn.Embedding,\n num_queries: int,\n object_mask_threshold: float,\n overlap_threshold: float,\n metadata,\n size_divisibility: int,\n sem_seg_postprocess_before_inference: bool,\n pixel_mean: Tuple[float],\n pixel_std: Tuple[float],\n # inference\n semantic_on: bool,\n panoptic_on: bool,\n instance_on: bool,\n detection_on: bool,\n test_topk_per_image: int,\n task_seq_len: int,\n max_seq_len: int,\n is_demo: bool,\n ):\n \"\"\"\n Args:\n backbone: a backbone module, must follow detectron2's backbone interface\n sem_seg_head: a module that predicts semantic segmentation from backbone features\n criterion: a module that defines the loss\n num_queries: int, number of queries\n object_mask_threshold: float, threshold to filter query based on classification score\n for panoptic segmentation inference\n overlap_threshold: overlap threshold used in general inference for panoptic segmentation\n metadata: dataset meta, get `thing` and `stuff` category names for panoptic\n segmentation inference\n size_divisibility: Some backbones require the input height and width to be divisible by a\n specific integer. We can use this to override such requirement.\n sem_seg_postprocess_before_inference: whether to resize the prediction back\n to original input size before semantic segmentation inference or after.\n For high-resolution dataset like Mapillary, resizing predictions before\n inference will cause OOM error.\n pixel_mean, pixel_std: list or tuple with #channels element, representing\n the per-channel mean and std to be used to normalize the input image\n semantic_on: bool, whether to output semantic segmentation prediction\n instance_on: bool, whether to output instance segmentation prediction\n panoptic_on: bool, whether to output panoptic segmentation prediction\n test_topk_per_image: int, instance segmentation parameter, keep topk instances per image\n \"\"\"\n super().__init__()\n self.backbone = backbone\n self.sem_seg_head = sem_seg_head\n self.task_mlp = task_mlp\n self.text_encoder = text_encoder\n self.text_projector = text_projector\n self.prompt_ctx = prompt_ctx\n self.criterion = criterion\n self.num_queries = num_queries\n self.overlap_threshold = overlap_threshold\n self.object_mask_threshold = object_mask_threshold\n self.metadata = metadata\n if size_divisibility < 0:\n # use backbone size_divisibility if not set\n size_divisibility = self.backbone.size_divisibility\n self.size_divisibility = size_divisibility\n self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference\n self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n # additional args\n self.semantic_on = semantic_on\n self.instance_on = instance_on\n self.panoptic_on = panoptic_on\n self.detection_on = detection_on\n self.test_topk_per_image = test_topk_per_image\n\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.is_demo = is_demo\n\n self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()]\n\n if not self.semantic_on:\n assert self.sem_seg_postprocess_before_inference\n\n @classmethod\n def from_config(cls, cfg):\n backbone = build_backbone(cfg)\n sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())\n\n if cfg.MODEL.IS_TRAIN:\n text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH,\n width=cfg.MODEL.TEXT_ENCODER.WIDTH,\n layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS,\n vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE)\n text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, \n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS)\n if cfg.MODEL.TEXT_ENCODER.N_CTX > 0:\n prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH)\n else:\n prompt_ctx = None\n else:\n text_encoder = None\n text_projector = None","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.from_config","uri":"program://OneFormer/function/oneformer.oneformer_model.from_config#L123-L218","kind":"function","name":"from_config","path":"oneformer/oneformer_model.py","language":"python","start_line":123,"end_line":218,"context_start_line":103,"context_end_line":238,"code":" self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n # additional args\n self.semantic_on = semantic_on\n self.instance_on = instance_on\n self.panoptic_on = panoptic_on\n self.detection_on = detection_on\n self.test_topk_per_image = test_topk_per_image\n\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.is_demo = is_demo\n\n self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()]\n\n if not self.semantic_on:\n assert self.sem_seg_postprocess_before_inference\n\n @classmethod\n def from_config(cls, cfg):\n backbone = build_backbone(cfg)\n sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())\n\n if cfg.MODEL.IS_TRAIN:\n text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH,\n width=cfg.MODEL.TEXT_ENCODER.WIDTH,\n layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS,\n vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE)\n text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, \n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS)\n if cfg.MODEL.TEXT_ENCODER.N_CTX > 0:\n prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH)\n else:\n prompt_ctx = None\n else:\n text_encoder = None\n text_projector = None\n prompt_ctx = None\n\n task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,\n cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2)\n\n # Loss parameters:\n deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION\n no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT\n\n # loss weights\n class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT\n dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT\n mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT\n contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT\n \n # building criterion\n matcher = HungarianMatcher(\n cost_class=class_weight,\n cost_mask=mask_weight,\n cost_dice=dice_weight,\n num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,\n )\n\n weight_dict = {\"loss_ce\": class_weight, \"loss_mask\": mask_weight, \n \"loss_dice\": dice_weight, \"loss_contrastive\": contrastive_weight}\n\n \n if deep_supervision:\n dec_layers = cfg.MODEL.ONE_FORMER.DEC_LAYERS\n aux_weight_dict = {}\n for i in range(dec_layers - 1):\n aux_weight_dict.update({k + f\"_{i}\": v for k, v in weight_dict.items()})\n weight_dict.update(aux_weight_dict)\n\n losses = [\"labels\", \"masks\", \"contrastive\"]\n\n criterion = SetCriterion(\n sem_seg_head.num_classes,\n matcher=matcher,\n weight_dict=weight_dict,\n eos_coef=no_object_weight,\n contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE,\n losses=losses,\n num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,\n oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO,\n importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO,\n )\n\n return {\n \"backbone\": backbone,\n \"sem_seg_head\": sem_seg_head,\n \"task_mlp\": task_mlp,\n \"prompt_ctx\": prompt_ctx,\n \"text_encoder\": text_encoder,\n \"text_projector\": text_projector,\n \"criterion\": criterion,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES,\n \"object_mask_threshold\": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD,\n \"overlap_threshold\": cfg.MODEL.TEST.OVERLAP_THRESHOLD,\n \"metadata\": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),\n \"size_divisibility\": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY,\n \"sem_seg_postprocess_before_inference\": (\n cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE\n or cfg.MODEL.TEST.PANOPTIC_ON\n or cfg.MODEL.TEST.INSTANCE_ON\n ),\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\n # inference\n \"semantic_on\": cfg.MODEL.TEST.SEMANTIC_ON,\n \"instance_on\": cfg.MODEL.TEST.INSTANCE_ON,\n \"panoptic_on\": cfg.MODEL.TEST.PANOPTIC_ON,\n \"detection_on\": cfg.MODEL.TEST.DETECTION_ON,\n \"test_topk_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"is_demo\": cfg.MODEL.IS_DEMO,\n }\n\n @property\n def device(self):\n return self.pixel_mean.device\n\n def encode_text(self, text):\n assert text.ndim in [2, 3], text.ndim\n b = text.shape[0]\n squeeze_dim = False\n num_text = 1\n if text.ndim == 3:\n num_text = text.shape[1]\n text = rearrange(text, 'b n l -> (b n) l', n=num_text)\n squeeze_dim = True\n\n # [B, C]\n x = self.text_encoder(text)\n\n text_x = self.text_projector(x)\n","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.device","uri":"program://OneFormer/function/oneformer.oneformer_model.device#L221-L222","kind":"function","name":"device","path":"oneformer/oneformer_model.py","language":"python","start_line":221,"end_line":222,"context_start_line":201,"context_end_line":242,"code":" \"size_divisibility\": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY,\n \"sem_seg_postprocess_before_inference\": (\n cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE\n or cfg.MODEL.TEST.PANOPTIC_ON\n or cfg.MODEL.TEST.INSTANCE_ON\n ),\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\n # inference\n \"semantic_on\": cfg.MODEL.TEST.SEMANTIC_ON,\n \"instance_on\": cfg.MODEL.TEST.INSTANCE_ON,\n \"panoptic_on\": cfg.MODEL.TEST.PANOPTIC_ON,\n \"detection_on\": cfg.MODEL.TEST.DETECTION_ON,\n \"test_topk_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"is_demo\": cfg.MODEL.IS_DEMO,\n }\n\n @property\n def device(self):\n return self.pixel_mean.device\n\n def encode_text(self, text):\n assert text.ndim in [2, 3], text.ndim\n b = text.shape[0]\n squeeze_dim = False\n num_text = 1\n if text.ndim == 3:\n num_text = text.shape[1]\n text = rearrange(text, 'b n l -> (b n) l', n=num_text)\n squeeze_dim = True\n\n # [B, C]\n x = self.text_encoder(text)\n\n text_x = self.text_projector(x)\n\n if squeeze_dim:\n text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)\n if self.prompt_ctx is not None:\n text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.encode_text","uri":"program://OneFormer/function/oneformer.oneformer_model.encode_text#L224-L245","kind":"function","name":"encode_text","path":"oneformer/oneformer_model.py","language":"python","start_line":224,"end_line":245,"context_start_line":204,"context_end_line":265,"code":" or cfg.MODEL.TEST.PANOPTIC_ON\n or cfg.MODEL.TEST.INSTANCE_ON\n ),\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\n # inference\n \"semantic_on\": cfg.MODEL.TEST.SEMANTIC_ON,\n \"instance_on\": cfg.MODEL.TEST.INSTANCE_ON,\n \"panoptic_on\": cfg.MODEL.TEST.PANOPTIC_ON,\n \"detection_on\": cfg.MODEL.TEST.DETECTION_ON,\n \"test_topk_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"is_demo\": cfg.MODEL.IS_DEMO,\n }\n\n @property\n def device(self):\n return self.pixel_mean.device\n\n def encode_text(self, text):\n assert text.ndim in [2, 3], text.ndim\n b = text.shape[0]\n squeeze_dim = False\n num_text = 1\n if text.ndim == 3:\n num_text = text.shape[1]\n text = rearrange(text, 'b n l -> (b n) l', n=num_text)\n squeeze_dim = True\n\n # [B, C]\n x = self.text_encoder(text)\n\n text_x = self.text_projector(x)\n\n if squeeze_dim:\n text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)\n if self.prompt_ctx is not None:\n text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)\n text_x = torch.cat([text_x, text_ctx], dim=1)\n \n return {\"texts\": text_x}\n \n def forward(self, batched_inputs):\n \"\"\"\n Args:\n batched_inputs: a list, batched outputs of :class:`DatasetMapper`.\n Each item in the list contains the inputs for one image.\n For now, each item in the list is a dict that contains:\n * \"image\": Tensor, image in (C, H, W) format.\n * \"instances\": per-region ground truth\n * Other information that's included in the original dicts, such as:\n \"height\", \"width\" (int): the output resolution of the model (may be different\n from input resolution), used in inference.\n Returns:\n list[dict]:\n each dict has the results for one image. The dict contains the following keys:\n * \"sem_seg\":\n A Tensor that represents the\n per-pixel segmentation prediced by the head.\n The prediction has shape KxHxW that represents the logits of\n each class for each pixel.","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.forward","uri":"program://OneFormer/function/oneformer.oneformer_model.forward#L247-L354","kind":"function","name":"forward","path":"oneformer/oneformer_model.py","language":"python","start_line":247,"end_line":354,"context_start_line":227,"context_end_line":374,"code":" squeeze_dim = False\n num_text = 1\n if text.ndim == 3:\n num_text = text.shape[1]\n text = rearrange(text, 'b n l -> (b n) l', n=num_text)\n squeeze_dim = True\n\n # [B, C]\n x = self.text_encoder(text)\n\n text_x = self.text_projector(x)\n\n if squeeze_dim:\n text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)\n if self.prompt_ctx is not None:\n text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)\n text_x = torch.cat([text_x, text_ctx], dim=1)\n \n return {\"texts\": text_x}\n \n def forward(self, batched_inputs):\n \"\"\"\n Args:\n batched_inputs: a list, batched outputs of :class:`DatasetMapper`.\n Each item in the list contains the inputs for one image.\n For now, each item in the list is a dict that contains:\n * \"image\": Tensor, image in (C, H, W) format.\n * \"instances\": per-region ground truth\n * Other information that's included in the original dicts, such as:\n \"height\", \"width\" (int): the output resolution of the model (may be different\n from input resolution), used in inference.\n Returns:\n list[dict]:\n each dict has the results for one image. The dict contains the following keys:\n * \"sem_seg\":\n A Tensor that represents the\n per-pixel segmentation prediced by the head.\n The prediction has shape KxHxW that represents the logits of\n each class for each pixel.\n * \"panoptic_seg\":\n A tuple that represent panoptic output\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.\n segments_info (list[dict]): Describe each segment in `panoptic_seg`.\n Each dict contains keys \"id\", \"category_id\", \"isthing\".\n \"\"\"\n images = [x[\"image\"].to(self.device) for x in batched_inputs]\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\n images = ImageList.from_tensors(images, self.size_divisibility)\n\n tasks = torch.cat([self.task_tokenizer(x[\"task\"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)\n tasks = self.task_mlp(tasks.float())\n\n features = self.backbone(images.tensor)\n outputs = self.sem_seg_head(features, tasks)\n\n if self.training:\n texts = torch.cat([self.text_tokenizer(x[\"text\"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)\n texts_x = self.encode_text(texts)\n\n outputs = {**outputs, **texts_x}\n\n # mask classification target\n if \"instances\" in batched_inputs[0]:\n gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\n targets = self.prepare_targets(gt_instances, images)\n else:\n targets = None\n\n # bipartite matching-based loss\n losses = self.criterion(outputs, targets)\n\n for k in list(losses.keys()):\n if k in self.criterion.weight_dict:\n losses[k] *= self.criterion.weight_dict[k]\n else:\n # remove this loss if not specified in `weight_dict`\n losses.pop(k)\n return losses\n else:\n mask_cls_results = outputs[\"pred_logits\"]\n mask_pred_results = outputs[\"pred_masks\"]\n # upsample masks\n mask_pred_results = F.interpolate(\n mask_pred_results,\n size=(images.tensor.shape[-2], images.tensor.shape[-1]),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n del outputs\n\n processed_results = []\n for i, data in enumerate(zip(\n mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes\n )):\n mask_cls_result, mask_pred_result, input_per_image, image_size = data\n height = input_per_image.get(\"height\", image_size[0])\n width = input_per_image.get(\"width\", image_size[1])\n processed_results.append({})\n\n if self.sem_seg_postprocess_before_inference:\n mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(\n mask_pred_result, image_size, height, width\n )\n mask_cls_result = mask_cls_result.to(mask_pred_result)\n\n # semantic segmentation inference\n if self.semantic_on:\n r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result)\n if not self.sem_seg_postprocess_before_inference:\n r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)\n processed_results[-1][\"sem_seg\"] = r\n\n # panoptic segmentation inference\n if self.panoptic_on:\n panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)\n processed_results[-1][\"panoptic_seg\"] = panoptic_r\n \n # instance segmentation inference\n if self.instance_on:\n instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"instances\"] = instance_r\n\n if self.detection_on:\n bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"box_instances\"] = bbox_r\n\n return processed_results\n\n def prepare_targets(self, targets, images):\n h_pad, w_pad = images.tensor.shape[-2:]\n new_targets = []\n for targets_per_image in targets:\n # pad gt\n gt_masks = targets_per_image.gt_masks\n padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)\n padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks\n new_targets.append(\n {\n \"labels\": targets_per_image.gt_classes,\n \"masks\": padded_masks,\n }\n )\n return new_targets\n\n def semantic_inference(self, mask_cls, mask_pred):\n mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]\n mask_pred = mask_pred.sigmoid()","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.prepare_targets","uri":"program://OneFormer/function/oneformer.oneformer_model.prepare_targets#L356-L370","kind":"function","name":"prepare_targets","path":"oneformer/oneformer_model.py","language":"python","start_line":356,"end_line":370,"context_start_line":336,"context_end_line":390,"code":" if not self.sem_seg_postprocess_before_inference:\n r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)\n processed_results[-1][\"sem_seg\"] = r\n\n # panoptic segmentation inference\n if self.panoptic_on:\n panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)\n processed_results[-1][\"panoptic_seg\"] = panoptic_r\n \n # instance segmentation inference\n if self.instance_on:\n instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"instances\"] = instance_r\n\n if self.detection_on:\n bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image[\"task\"])\n processed_results[-1][\"box_instances\"] = bbox_r\n\n return processed_results\n\n def prepare_targets(self, targets, images):\n h_pad, w_pad = images.tensor.shape[-2:]\n new_targets = []\n for targets_per_image in targets:\n # pad gt\n gt_masks = targets_per_image.gt_masks\n padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)\n padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks\n new_targets.append(\n {\n \"labels\": targets_per_image.gt_classes,\n \"masks\": padded_masks,\n }\n )\n return new_targets\n\n def semantic_inference(self, mask_cls, mask_pred):\n mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]\n mask_pred = mask_pred.sigmoid()\n semseg = torch.einsum(\"qc,qhw->chw\", mask_cls, mask_pred)\n return semseg\n\n def panoptic_inference(self, mask_cls, mask_pred):\n scores, labels = F.softmax(mask_cls, dim=-1).max(-1)\n mask_pred = mask_pred.sigmoid()\n\n keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)\n cur_scores = scores[keep]\n cur_classes = labels[keep]\n cur_masks = mask_pred[keep]\n cur_mask_cls = mask_cls[keep]\n cur_mask_cls = cur_mask_cls[:, :-1]\n\n cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks\n","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.semantic_inference","uri":"program://OneFormer/function/oneformer.oneformer_model.semantic_inference#L372-L376","kind":"function","name":"semantic_inference","path":"oneformer/oneformer_model.py","language":"python","start_line":372,"end_line":376,"context_start_line":352,"context_end_line":396,"code":" processed_results[-1][\"box_instances\"] = bbox_r\n\n return processed_results\n\n def prepare_targets(self, targets, images):\n h_pad, w_pad = images.tensor.shape[-2:]\n new_targets = []\n for targets_per_image in targets:\n # pad gt\n gt_masks = targets_per_image.gt_masks\n padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)\n padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks\n new_targets.append(\n {\n \"labels\": targets_per_image.gt_classes,\n \"masks\": padded_masks,\n }\n )\n return new_targets\n\n def semantic_inference(self, mask_cls, mask_pred):\n mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]\n mask_pred = mask_pred.sigmoid()\n semseg = torch.einsum(\"qc,qhw->chw\", mask_cls, mask_pred)\n return semseg\n\n def panoptic_inference(self, mask_cls, mask_pred):\n scores, labels = F.softmax(mask_cls, dim=-1).max(-1)\n mask_pred = mask_pred.sigmoid()\n\n keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)\n cur_scores = scores[keep]\n cur_classes = labels[keep]\n cur_masks = mask_pred[keep]\n cur_mask_cls = mask_cls[keep]\n cur_mask_cls = cur_mask_cls[:, :-1]\n\n cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks\n\n h, w = cur_masks.shape[-2:]\n panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)\n segments_info = []\n\n current_segment_id = 0\n","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.panoptic_inference","uri":"program://OneFormer/function/oneformer.oneformer_model.panoptic_inference#L378-L434","kind":"function","name":"panoptic_inference","path":"oneformer/oneformer_model.py","language":"python","start_line":378,"end_line":434,"context_start_line":358,"context_end_line":454,"code":" new_targets = []\n for targets_per_image in targets:\n # pad gt\n gt_masks = targets_per_image.gt_masks\n padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)\n padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks\n new_targets.append(\n {\n \"labels\": targets_per_image.gt_classes,\n \"masks\": padded_masks,\n }\n )\n return new_targets\n\n def semantic_inference(self, mask_cls, mask_pred):\n mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]\n mask_pred = mask_pred.sigmoid()\n semseg = torch.einsum(\"qc,qhw->chw\", mask_cls, mask_pred)\n return semseg\n\n def panoptic_inference(self, mask_cls, mask_pred):\n scores, labels = F.softmax(mask_cls, dim=-1).max(-1)\n mask_pred = mask_pred.sigmoid()\n\n keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)\n cur_scores = scores[keep]\n cur_classes = labels[keep]\n cur_masks = mask_pred[keep]\n cur_mask_cls = mask_cls[keep]\n cur_mask_cls = cur_mask_cls[:, :-1]\n\n cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks\n\n h, w = cur_masks.shape[-2:]\n panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)\n segments_info = []\n\n current_segment_id = 0\n\n if cur_masks.shape[0] == 0:\n # We didn't detect any mask :(\n return panoptic_seg, segments_info\n else:\n # take argmax\n cur_mask_ids = cur_prob_masks.argmax(0)\n stuff_memory_list = {}\n for k in range(cur_classes.shape[0]):\n pred_class = cur_classes[k].item()\n isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values()\n mask_area = (cur_mask_ids == k).sum().item()\n original_area = (cur_masks[k] >= 0.5).sum().item()\n mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5)\n\n if mask_area > 0 and original_area > 0 and mask.sum().item() > 0:\n if mask_area / original_area < self.overlap_threshold:\n continue\n\n # merge stuff regions\n if not isthing:\n if int(pred_class) in stuff_memory_list.keys():\n panoptic_seg[mask] = stuff_memory_list[int(pred_class)]\n continue\n else:\n stuff_memory_list[int(pred_class)] = current_segment_id + 1\n\n current_segment_id += 1\n panoptic_seg[mask] = current_segment_id\n\n segments_info.append(\n {\n \"id\": current_segment_id,\n \"isthing\": bool(isthing),\n \"category_id\": int(pred_class),\n }\n )\n\n return panoptic_seg, segments_info\n\n def instance_inference(self, mask_cls, mask_pred, task_type):\n # mask_pred is already processed to have the same shape as original input\n image_size = mask_pred.shape[-2:]\n\n # [Q, K]\n scores = F.softmax(mask_cls, dim=-1)[:, :-1]\n labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)\n \n # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)\n scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)\n labels_per_image = labels[topk_indices]\n\n topk_indices = topk_indices // self.sem_seg_head.num_classes\n # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)\n mask_pred = mask_pred[topk_indices]\n\n # Only consider scores with confidence over [self.object_mask_threshold] for demo\n if self.is_demo:\n keep = scores_per_image > self.object_mask_threshold","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.oneformer_model.instance_inference","uri":"program://OneFormer/function/oneformer.oneformer_model.instance_inference#L436-L486","kind":"function","name":"instance_inference","path":"oneformer/oneformer_model.py","language":"python","start_line":436,"end_line":486,"context_start_line":416,"context_end_line":486,"code":" if not isthing:\n if int(pred_class) in stuff_memory_list.keys():\n panoptic_seg[mask] = stuff_memory_list[int(pred_class)]\n continue\n else:\n stuff_memory_list[int(pred_class)] = current_segment_id + 1\n\n current_segment_id += 1\n panoptic_seg[mask] = current_segment_id\n\n segments_info.append(\n {\n \"id\": current_segment_id,\n \"isthing\": bool(isthing),\n \"category_id\": int(pred_class),\n }\n )\n\n return panoptic_seg, segments_info\n\n def instance_inference(self, mask_cls, mask_pred, task_type):\n # mask_pred is already processed to have the same shape as original input\n image_size = mask_pred.shape[-2:]\n\n # [Q, K]\n scores = F.softmax(mask_cls, dim=-1)[:, :-1]\n labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)\n \n # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)\n scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)\n labels_per_image = labels[topk_indices]\n\n topk_indices = topk_indices // self.sem_seg_head.num_classes\n # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)\n mask_pred = mask_pred[topk_indices]\n\n # Only consider scores with confidence over [self.object_mask_threshold] for demo\n if self.is_demo:\n keep = scores_per_image > self.object_mask_threshold\n scores_per_image = scores_per_image[keep]\n labels_per_image = labels_per_image[keep]\n mask_pred = mask_pred[keep]\n\n # if this is panoptic segmentation, we only keep the \"thing\" classes\n if self.panoptic_on:\n keep = torch.zeros_like(scores_per_image).bool()\n for i, lab in enumerate(labels_per_image):\n keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values()\n\n scores_per_image = scores_per_image[keep]\n labels_per_image = labels_per_image[keep]\n mask_pred = mask_pred[keep]\n \n if 'ade20k' in self.metadata.name and not self.is_demo and \"instance\" in task_type:\n for i in range(labels_per_image.shape[0]):\n labels_per_image[i] = self.thing_indices.index(labels_per_image[i].item())\n\n result = Instances(image_size)\n # mask (before sigmoid)\n result.pred_masks = (mask_pred > 0).float()\n if self.detection_on:\n # Uncomment the following to get boxes from masks (this is slow)\n result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes()\n else:\n result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4))\n\n # calculate average mask prob\n mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6)\n result.scores = scores_per_image * mask_scores_per_image\n result.pred_classes = labels_per_image\n return result","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc","uri":"program://OneFormer/module/oneformer.utils.misc#L1-L197","kind":"module","name":"oneformer.utils.misc","path":"oneformer/utils/misc.py","language":"python","start_line":1,"end_line":197,"context_start_line":1,"context_end_line":197,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nfrom typing import List, Optional\n\nimport torch\nimport torch.distributed as dist\nimport torchvision\nfrom torch import Tensor\nimport warnings\nimport torch.nn.functional as F\nimport math\n\ndef inverse_sigmoid(x, eps=1e-3):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1/x2)\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n # Cut & paste from PyTorch official master until it's in a few official releases - RW\n # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n def norm_cdf(x):\n # Computes standard normal cumulative distribution function\n return (1. + math.erf(x / math.sqrt(2.))) / 2.\n\n if (mean < a - 2 * std) or (mean > b + 2 * std):\n warnings.warn(\"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n \"The distribution of values may be incorrect.\",\n stacklevel=2)\n\n with torch.no_grad():\n # Values are generated by using a truncated uniform distribution and\n # then using the inverse CDF for the normal distribution.\n # Get upper and lower cdf values\n l = norm_cdf((a - mean) / std)\n u = norm_cdf((b - mean) / std)\n\n # Uniformly fill tensor with values from [l, u], then translate to\n # [2l-1, 2u-1].\n tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n # Use inverse cdf transform for normal distribution to get truncated\n # standard normal\n tensor.erfinv_()\n\n # Transform to proper mean, std\n tensor.mul_(std * math.sqrt(2.))\n tensor.add_(mean)\n\n # Clamp to ensure it's in the proper range\n tensor.clamp_(min=a, max=b)\n return tensor\n\ndef trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):\n # type: (Tensor, float, float, float, float) -> Tensor\n r\"\"\"Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the\n normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n with values outside :math:`[a, b]` redrawn until they are within\n the bounds. The method used for generating the random values works\n best when :math:`a \\leq \\text{mean} \\leq b`.\n Args:\n tensor: an n-dimensional `torch.Tensor`\n mean: the mean of the normal distribution\n std: the standard deviation of the normal distribution\n a: the minimum cutoff value\n b: the maximum cutoff value\n Examples:\n >>> w = torch.empty(3, 5)\n >>> nn.init.trunc_normal_(w)\n \"\"\"\n return _no_grad_trunc_normal_(tensor, mean, std, a, b)\n\ndef resize(input,\n size=None,\n scale_factor=None,\n mode='nearest',\n align_corners=None,\n warning=True):\n if warning:\n if size is not None and align_corners:\n input_h, input_w = tuple(int(x) for x in input.shape[2:])\n output_h, output_w = tuple(int(x) for x in size)\n if output_h > input_h or output_w > output_h:\n if ((output_h > 1 and output_w > 1 and input_h > 1\n and input_w > 1) and (output_h - 1) % (input_h - 1)\n and (output_w - 1) % (input_w - 1)):\n warnings.warn(\n f'When align_corners={align_corners}, '\n 'the output would more aligned if '\n f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')\n if isinstance(size, torch.Size):\n size = tuple(int(x) for x in size)\n return F.interpolate(input, size, scale_factor, mode, align_corners)\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):\n pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n m[: img.shape[1], : img.shape[2]] = False\n else:\n raise ValueError(\"not supported\")\n return NestedTensor(tensor, mask)\n\n\n# _onnx_nested_tensor_from_tensor_list() is an implementation of\n# nested_tensor_from_tensor_list() that is supported by ONNX tracing.\n@torch.jit.unused\ndef _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:\n max_size = []\n for i in range(tensor_list[0].dim()):\n max_size_i = torch.max(\n torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)\n ).to(torch.int64)\n max_size.append(max_size_i)\n max_size = tuple(max_size)\n\n # work around for\n # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n # m[: img.shape[1], :img.shape[2]] = False\n # which is not yet supported in onnx\n padded_imgs = []\n padded_masks = []\n for img in tensor_list:\n padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]\n padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))\n padded_imgs.append(padded_img)\n\n m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)\n padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), \"constant\", 1)\n padded_masks.append(padded_mask.to(torch.bool))\n\n tensor = torch.stack(padded_imgs)\n mask = torch.stack(padded_masks)\n\n return NestedTensor(tensor, mask=mask)\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.inverse_sigmoid","uri":"program://OneFormer/function/oneformer.utils.misc.inverse_sigmoid#L18-L22","kind":"function","name":"inverse_sigmoid","path":"oneformer/utils/misc.py","language":"python","start_line":18,"end_line":22,"context_start_line":1,"context_end_line":42,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nfrom typing import List, Optional\n\nimport torch\nimport torch.distributed as dist\nimport torchvision\nfrom torch import Tensor\nimport warnings\nimport torch.nn.functional as F\nimport math\n\ndef inverse_sigmoid(x, eps=1e-3):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1/x2)\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n # Cut & paste from PyTorch official master until it's in a few official releases - RW\n # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n def norm_cdf(x):\n # Computes standard normal cumulative distribution function\n return (1. + math.erf(x / math.sqrt(2.))) / 2.\n\n if (mean < a - 2 * std) or (mean > b + 2 * std):\n warnings.warn(\"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n \"The distribution of values may be incorrect.\",\n stacklevel=2)\n\n with torch.no_grad():\n # Values are generated by using a truncated uniform distribution and\n # then using the inverse CDF for the normal distribution.\n # Get upper and lower cdf values\n l = norm_cdf((a - mean) / std)\n u = norm_cdf((b - mean) / std)\n","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc._no_grad_trunc_normal_","uri":"program://OneFormer/function/oneformer.utils.misc._no_grad_trunc_normal_#L24-L57","kind":"function","name":"_no_grad_trunc_normal_","path":"oneformer/utils/misc.py","language":"python","start_line":24,"end_line":57,"context_start_line":4,"context_end_line":77,"code":"Misc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nfrom typing import List, Optional\n\nimport torch\nimport torch.distributed as dist\nimport torchvision\nfrom torch import Tensor\nimport warnings\nimport torch.nn.functional as F\nimport math\n\ndef inverse_sigmoid(x, eps=1e-3):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1/x2)\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n # Cut & paste from PyTorch official master until it's in a few official releases - RW\n # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n def norm_cdf(x):\n # Computes standard normal cumulative distribution function\n return (1. + math.erf(x / math.sqrt(2.))) / 2.\n\n if (mean < a - 2 * std) or (mean > b + 2 * std):\n warnings.warn(\"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n \"The distribution of values may be incorrect.\",\n stacklevel=2)\n\n with torch.no_grad():\n # Values are generated by using a truncated uniform distribution and\n # then using the inverse CDF for the normal distribution.\n # Get upper and lower cdf values\n l = norm_cdf((a - mean) / std)\n u = norm_cdf((b - mean) / std)\n\n # Uniformly fill tensor with values from [l, u], then translate to\n # [2l-1, 2u-1].\n tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n # Use inverse cdf transform for normal distribution to get truncated\n # standard normal\n tensor.erfinv_()\n\n # Transform to proper mean, std\n tensor.mul_(std * math.sqrt(2.))\n tensor.add_(mean)\n\n # Clamp to ensure it's in the proper range\n tensor.clamp_(min=a, max=b)\n return tensor\n\ndef trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):\n # type: (Tensor, float, float, float, float) -> Tensor\n r\"\"\"Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the\n normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n with values outside :math:`[a, b]` redrawn until they are within\n the bounds. The method used for generating the random values works\n best when :math:`a \\leq \\text{mean} \\leq b`.\n Args:\n tensor: an n-dimensional `torch.Tensor`\n mean: the mean of the normal distribution\n std: the standard deviation of the normal distribution\n a: the minimum cutoff value\n b: the maximum cutoff value\n Examples:\n >>> w = torch.empty(3, 5)\n >>> nn.init.trunc_normal_(w)\n \"\"\"\n return _no_grad_trunc_normal_(tensor, mean, std, a, b)","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.trunc_normal_","uri":"program://OneFormer/function/oneformer.utils.misc.trunc_normal_#L59-L77","kind":"function","name":"trunc_normal_","path":"oneformer/utils/misc.py","language":"python","start_line":59,"end_line":77,"context_start_line":39,"context_end_line":97,"code":" # Get upper and lower cdf values\n l = norm_cdf((a - mean) / std)\n u = norm_cdf((b - mean) / std)\n\n # Uniformly fill tensor with values from [l, u], then translate to\n # [2l-1, 2u-1].\n tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n # Use inverse cdf transform for normal distribution to get truncated\n # standard normal\n tensor.erfinv_()\n\n # Transform to proper mean, std\n tensor.mul_(std * math.sqrt(2.))\n tensor.add_(mean)\n\n # Clamp to ensure it's in the proper range\n tensor.clamp_(min=a, max=b)\n return tensor\n\ndef trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):\n # type: (Tensor, float, float, float, float) -> Tensor\n r\"\"\"Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the\n normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n with values outside :math:`[a, b]` redrawn until they are within\n the bounds. The method used for generating the random values works\n best when :math:`a \\leq \\text{mean} \\leq b`.\n Args:\n tensor: an n-dimensional `torch.Tensor`\n mean: the mean of the normal distribution\n std: the standard deviation of the normal distribution\n a: the minimum cutoff value\n b: the maximum cutoff value\n Examples:\n >>> w = torch.empty(3, 5)\n >>> nn.init.trunc_normal_(w)\n \"\"\"\n return _no_grad_trunc_normal_(tensor, mean, std, a, b)\n\ndef resize(input,\n size=None,\n scale_factor=None,\n mode='nearest',\n align_corners=None,\n warning=True):\n if warning:\n if size is not None and align_corners:\n input_h, input_w = tuple(int(x) for x in input.shape[2:])\n output_h, output_w = tuple(int(x) for x in size)\n if output_h > input_h or output_w > output_h:\n if ((output_h > 1 and output_w > 1 and input_h > 1\n and input_w > 1) and (output_h - 1) % (input_h - 1)\n and (output_w - 1) % (input_w - 1)):\n warnings.warn(\n f'When align_corners={align_corners}, '\n 'the output would more aligned if '\n f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.resize","uri":"program://OneFormer/function/oneformer.utils.misc.resize#L79-L100","kind":"function","name":"resize","path":"oneformer/utils/misc.py","language":"python","start_line":79,"end_line":100,"context_start_line":59,"context_end_line":120,"code":"def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):\n # type: (Tensor, float, float, float, float) -> Tensor\n r\"\"\"Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the\n normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n with values outside :math:`[a, b]` redrawn until they are within\n the bounds. The method used for generating the random values works\n best when :math:`a \\leq \\text{mean} \\leq b`.\n Args:\n tensor: an n-dimensional `torch.Tensor`\n mean: the mean of the normal distribution\n std: the standard deviation of the normal distribution\n a: the minimum cutoff value\n b: the maximum cutoff value\n Examples:\n >>> w = torch.empty(3, 5)\n >>> nn.init.trunc_normal_(w)\n \"\"\"\n return _no_grad_trunc_normal_(tensor, mean, std, a, b)\n\ndef resize(input,\n size=None,\n scale_factor=None,\n mode='nearest',\n align_corners=None,\n warning=True):\n if warning:\n if size is not None and align_corners:\n input_h, input_w = tuple(int(x) for x in input.shape[2:])\n output_h, output_w = tuple(int(x) for x in size)\n if output_h > input_h or output_w > output_h:\n if ((output_h > 1 and output_w > 1 and input_h > 1\n and input_w > 1) and (output_h - 1) % (input_h - 1)\n and (output_w - 1) % (input_w - 1)):\n warnings.warn(\n f'When align_corners={align_corners}, '\n 'the output would more aligned if '\n f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')\n if isinstance(size, torch.Size):\n size = tuple(int(x) for x in size)\n return F.interpolate(input, size, scale_factor, mode, align_corners)\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc._max_by_axis","uri":"program://OneFormer/function/oneformer.utils.misc._max_by_axis#L102-L108","kind":"function","name":"_max_by_axis","path":"oneformer/utils/misc.py","language":"python","start_line":102,"end_line":108,"context_start_line":82,"context_end_line":128,"code":" mode='nearest',\n align_corners=None,\n warning=True):\n if warning:\n if size is not None and align_corners:\n input_h, input_w = tuple(int(x) for x in input.shape[2:])\n output_h, output_w = tuple(int(x) for x in size)\n if output_h > input_h or output_w > output_h:\n if ((output_h > 1 and output_w > 1 and input_h > 1\n and input_w > 1) and (output_h - 1) % (input_h - 1)\n and (output_w - 1) % (input_w - 1)):\n warnings.warn(\n f'When align_corners={align_corners}, '\n 'the output would more aligned if '\n f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')\n if isinstance(size, torch.Size):\n size = tuple(int(x) for x in size)\n return F.interpolate(input, size, scale_factor, mode, align_corners)\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.NestedTensor","uri":"program://OneFormer/class/oneformer.utils.misc.NestedTensor#L111-L131","kind":"class","name":"NestedTensor","path":"oneformer/utils/misc.py","language":"python","start_line":111,"end_line":131,"context_start_line":91,"context_end_line":151,"code":" and input_w > 1) and (output_h - 1) % (input_h - 1)\n and (output_w - 1) % (input_w - 1)):\n warnings.warn(\n f'When align_corners={align_corners}, '\n 'the output would more aligned if '\n f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')\n if isinstance(size, torch.Size):\n size = tuple(int(x) for x in size)\n return F.interpolate(input, size, scale_factor, mode, align_corners)\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.nested_tensor_from_tensor_list","uri":"program://OneFormer/function/oneformer.utils.misc.nested_tensor_from_tensor_list#L134-L156","kind":"function","name":"nested_tensor_from_tensor_list","path":"oneformer/utils/misc.py","language":"python","start_line":134,"end_line":156,"context_start_line":114,"context_end_line":176,"code":" self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):\n pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n m[: img.shape[1], : img.shape[2]] = False\n else:\n raise ValueError(\"not supported\")\n return NestedTensor(tensor, mask)\n\n\n# _onnx_nested_tensor_from_tensor_list() is an implementation of\n# nested_tensor_from_tensor_list() that is supported by ONNX tracing.\n@torch.jit.unused\ndef _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:\n max_size = []\n for i in range(tensor_list[0].dim()):\n max_size_i = torch.max(\n torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)\n ).to(torch.int64)\n max_size.append(max_size_i)\n max_size = tuple(max_size)\n\n # work around for\n # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n # m[: img.shape[1], :img.shape[2]] = False\n # which is not yet supported in onnx\n padded_imgs = []\n padded_masks = []","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc._onnx_nested_tensor_from_tensor_list","uri":"program://OneFormer/function/oneformer.utils.misc._onnx_nested_tensor_from_tensor_list#L162-L189","kind":"function","name":"_onnx_nested_tensor_from_tensor_list","path":"oneformer/utils/misc.py","language":"python","start_line":162,"end_line":189,"context_start_line":142,"context_end_line":197,"code":" # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):\n pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n m[: img.shape[1], : img.shape[2]] = False\n else:\n raise ValueError(\"not supported\")\n return NestedTensor(tensor, mask)\n\n\n# _onnx_nested_tensor_from_tensor_list() is an implementation of\n# nested_tensor_from_tensor_list() that is supported by ONNX tracing.\n@torch.jit.unused\ndef _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:\n max_size = []\n for i in range(tensor_list[0].dim()):\n max_size_i = torch.max(\n torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)\n ).to(torch.int64)\n max_size.append(max_size_i)\n max_size = tuple(max_size)\n\n # work around for\n # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n # m[: img.shape[1], :img.shape[2]] = False\n # which is not yet supported in onnx\n padded_imgs = []\n padded_masks = []\n for img in tensor_list:\n padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]\n padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))\n padded_imgs.append(padded_img)\n\n m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)\n padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), \"constant\", 1)\n padded_masks.append(padded_mask.to(torch.bool))\n\n tensor = torch.stack(padded_imgs)\n mask = torch.stack(padded_masks)\n\n return NestedTensor(tensor, mask=mask)\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.is_dist_avail_and_initialized","uri":"program://OneFormer/function/oneformer.utils.misc.is_dist_avail_and_initialized#L192-L197","kind":"function","name":"is_dist_avail_and_initialized","path":"oneformer/utils/misc.py","language":"python","start_line":192,"end_line":197,"context_start_line":172,"context_end_line":197,"code":" # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n # m[: img.shape[1], :img.shape[2]] = False\n # which is not yet supported in onnx\n padded_imgs = []\n padded_masks = []\n for img in tensor_list:\n padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]\n padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))\n padded_imgs.append(padded_img)\n\n m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)\n padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), \"constant\", 1)\n padded_masks.append(padded_mask.to(torch.bool))\n\n tensor = torch.stack(padded_imgs)\n mask = torch.stack(padded_masks)\n\n return NestedTensor(tensor, mask=mask)\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.norm_cdf","uri":"program://OneFormer/function/oneformer.utils.misc.norm_cdf#L27-L29","kind":"function","name":"norm_cdf","path":"oneformer/utils/misc.py","language":"python","start_line":27,"end_line":29,"context_start_line":7,"context_end_line":49,"code":"\"\"\"\nfrom typing import List, Optional\n\nimport torch\nimport torch.distributed as dist\nimport torchvision\nfrom torch import Tensor\nimport warnings\nimport torch.nn.functional as F\nimport math\n\ndef inverse_sigmoid(x, eps=1e-3):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1/x2)\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n # Cut & paste from PyTorch official master until it's in a few official releases - RW\n # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n def norm_cdf(x):\n # Computes standard normal cumulative distribution function\n return (1. + math.erf(x / math.sqrt(2.))) / 2.\n\n if (mean < a - 2 * std) or (mean > b + 2 * std):\n warnings.warn(\"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n \"The distribution of values may be incorrect.\",\n stacklevel=2)\n\n with torch.no_grad():\n # Values are generated by using a truncated uniform distribution and\n # then using the inverse CDF for the normal distribution.\n # Get upper and lower cdf values\n l = norm_cdf((a - mean) / std)\n u = norm_cdf((b - mean) / std)\n\n # Uniformly fill tensor with values from [l, u], then translate to\n # [2l-1, 2u-1].\n tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n # Use inverse cdf transform for normal distribution to get truncated\n # standard normal\n tensor.erfinv_()","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.__init__","uri":"program://OneFormer/function/oneformer.utils.misc.__init__#L112-L114","kind":"function","name":"__init__","path":"oneformer/utils/misc.py","language":"python","start_line":112,"end_line":114,"context_start_line":92,"context_end_line":134,"code":" and (output_w - 1) % (input_w - 1)):\n warnings.warn(\n f'When align_corners={align_corners}, '\n 'the output would more aligned if '\n f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')\n if isinstance(size, torch.Size):\n size = tuple(int(x) for x in size)\n return F.interpolate(input, size, scale_factor, mode, align_corners)\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.to","uri":"program://OneFormer/function/oneformer.utils.misc.to#L116-L125","kind":"function","name":"to","path":"oneformer/utils/misc.py","language":"python","start_line":116,"end_line":125,"context_start_line":96,"context_end_line":145,"code":" f'input size {(input_h, input_w)} is `x+1` and '\n f'out size {(output_h, output_w)} is `nx+1`')\n if isinstance(size, torch.Size):\n size = tuple(int(x) for x in size)\n return F.interpolate(input, size, scale_factor, mode, align_corners)\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.decompose","uri":"program://OneFormer/function/oneformer.utils.misc.decompose#L127-L128","kind":"function","name":"decompose","path":"oneformer/utils/misc.py","language":"python","start_line":127,"end_line":128,"context_start_line":107,"context_end_line":148,"code":" maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.misc.__repr__","uri":"program://OneFormer/function/oneformer.utils.misc.__repr__#L130-L131","kind":"function","name":"__repr__","path":"oneformer/utils/misc.py","language":"python","start_line":130,"end_line":131,"context_start_line":110,"context_end_line":151,"code":"\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops","uri":"program://OneFormer/module/oneformer.utils.box_ops#L1-L133","kind":"module","name":"oneformer.utils.box_ops","path":"oneformer/utils/box_ops.py","language":"python","start_line":1,"end_line":133,"context_start_line":1,"context_end_line":133,"code":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch, os\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2,\n (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n # import ipdb; ipdb.set_trace()\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n\n union = area1[:, None] + area2 - inter\n\n iou = inter / (union + 1e-6)\n return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n The boxes should be in [x0, y0, x1, y1] format\n Returns a [N, M] pairwise matrix, where N = len(boxes1)\n and M = len(boxes2)\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n # except:\n # import ipdb; ipdb.set_trace()\n iou, union = box_iou(boxes1, boxes2)\n\n lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n area = wh[:, :, 0] * wh[:, :, 1]\n\n return iou - (area - union) / (area + 1e-6)\n\n\n\n# modified from torchvision to also return the union\ndef box_iou_pairwise(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]\n rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n inter = wh[:, 0] * wh[:, 1] # [N]\n\n union = area1 + area2 - inter\n\n iou = inter / union\n return iou, union\n\n\ndef generalized_box_iou_pairwise(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n Input:\n - boxes1, boxes2: N,4\n Output:\n - giou: N, 4\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n assert boxes1.shape == boxes2.shape\n iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4\n\n lt = torch.min(boxes1[:, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n area = wh[:, 0] * wh[:, 1]\n\n return iou - (area - union) / area\n\ndef masks_to_boxes(masks):\n \"\"\"Compute the bounding boxes around the provided masks\n The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n Returns a [N, 4] tensors, with the boxes in xyxy format\n \"\"\"\n if masks.numel() == 0:\n return torch.zeros((0, 4), device=masks.device)\n\n h, w = masks.shape[-2:]\n\n y = torch.arange(0, h, dtype=torch.float)\n x = torch.arange(0, w, dtype=torch.float)\n y, x = torch.meshgrid(y, x)\n\n x_mask = (masks * x.unsqueeze(0))\n x_max = x_mask.flatten(1).max(-1)[0]\n x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n y_mask = (masks * y.unsqueeze(0))\n y_max = y_mask.flatten(1).max(-1)[0]\n y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n return torch.stack([x_min, y_min, x_max, y_max], 1)\n\nif __name__ == '__main__':\n x = torch.rand(5, 4)\n y = torch.rand(3, 4)\n iou, union = box_iou(x, y)","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.box_cxcywh_to_xyxy","uri":"program://OneFormer/function/oneformer.utils.box_ops.box_cxcywh_to_xyxy#L9-L13","kind":"function","name":"box_cxcywh_to_xyxy","path":"oneformer/utils/box_ops.py","language":"python","start_line":9,"end_line":13,"context_start_line":1,"context_end_line":33,"code":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch, os\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2,\n (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n # import ipdb; ipdb.set_trace()\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.box_xyxy_to_cxcywh","uri":"program://OneFormer/function/oneformer.utils.box_ops.box_xyxy_to_cxcywh#L16-L20","kind":"function","name":"box_xyxy_to_cxcywh","path":"oneformer/utils/box_ops.py","language":"python","start_line":16,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch, os\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2,\n (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n # import ipdb; ipdb.set_trace()\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n\n union = area1[:, None] + area2 - inter\n\n iou = inter / (union + 1e-6)\n return iou, union\n\n","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.box_iou","uri":"program://OneFormer/function/oneformer.utils.box_ops.box_iou#L24-L38","kind":"function","name":"box_iou","path":"oneformer/utils/box_ops.py","language":"python","start_line":24,"end_line":38,"context_start_line":4,"context_end_line":58,"code":"\"\"\"\nimport torch, os\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2,\n (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n # import ipdb; ipdb.set_trace()\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n\n union = area1[:, None] + area2 - inter\n\n iou = inter / (union + 1e-6)\n return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n The boxes should be in [x0, y0, x1, y1] format\n Returns a [N, M] pairwise matrix, where N = len(boxes1)\n and M = len(boxes2)\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n # except:\n # import ipdb; ipdb.set_trace()\n iou, union = box_iou(boxes1, boxes2)\n\n lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.generalized_box_iou","uri":"program://OneFormer/function/oneformer.utils.box_ops.generalized_box_iou#L41-L62","kind":"function","name":"generalized_box_iou","path":"oneformer/utils/box_ops.py","language":"python","start_line":41,"end_line":62,"context_start_line":21,"context_end_line":82,"code":"\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n # import ipdb; ipdb.set_trace()\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n\n union = area1[:, None] + area2 - inter\n\n iou = inter / (union + 1e-6)\n return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n The boxes should be in [x0, y0, x1, y1] format\n Returns a [N, M] pairwise matrix, where N = len(boxes1)\n and M = len(boxes2)\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n # except:\n # import ipdb; ipdb.set_trace()\n iou, union = box_iou(boxes1, boxes2)\n\n lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n area = wh[:, :, 0] * wh[:, :, 1]\n\n return iou - (area - union) / (area + 1e-6)\n\n\n\n# modified from torchvision to also return the union\ndef box_iou_pairwise(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]\n rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n inter = wh[:, 0] * wh[:, 1] # [N]\n\n union = area1 + area2 - inter\n\n iou = inter / union\n return iou, union\n\n","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.box_iou_pairwise","uri":"program://OneFormer/function/oneformer.utils.box_ops.box_iou_pairwise#L67-L80","kind":"function","name":"box_iou_pairwise","path":"oneformer/utils/box_ops.py","language":"python","start_line":67,"end_line":80,"context_start_line":47,"context_end_line":100,"code":" \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n # except:\n # import ipdb; ipdb.set_trace()\n iou, union = box_iou(boxes1, boxes2)\n\n lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n area = wh[:, :, 0] * wh[:, :, 1]\n\n return iou - (area - union) / (area + 1e-6)\n\n\n\n# modified from torchvision to also return the union\ndef box_iou_pairwise(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]\n rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n inter = wh[:, 0] * wh[:, 1] # [N]\n\n union = area1 + area2 - inter\n\n iou = inter / union\n return iou, union\n\n\ndef generalized_box_iou_pairwise(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n Input:\n - boxes1, boxes2: N,4\n Output:\n - giou: N, 4\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n assert boxes1.shape == boxes2.shape\n iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4\n\n lt = torch.min(boxes1[:, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])\n","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.generalized_box_iou_pairwise","uri":"program://OneFormer/function/oneformer.utils.box_ops.generalized_box_iou_pairwise#L83-L104","kind":"function","name":"generalized_box_iou_pairwise","path":"oneformer/utils/box_ops.py","language":"python","start_line":83,"end_line":104,"context_start_line":63,"context_end_line":124,"code":"\n\n\n# modified from torchvision to also return the union\ndef box_iou_pairwise(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]\n rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n inter = wh[:, 0] * wh[:, 1] # [N]\n\n union = area1 + area2 - inter\n\n iou = inter / union\n return iou, union\n\n\ndef generalized_box_iou_pairwise(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n Input:\n - boxes1, boxes2: N,4\n Output:\n - giou: N, 4\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n assert boxes1.shape == boxes2.shape\n iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4\n\n lt = torch.min(boxes1[:, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n area = wh[:, 0] * wh[:, 1]\n\n return iou - (area - union) / area\n\ndef masks_to_boxes(masks):\n \"\"\"Compute the bounding boxes around the provided masks\n The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n Returns a [N, 4] tensors, with the boxes in xyxy format\n \"\"\"\n if masks.numel() == 0:\n return torch.zeros((0, 4), device=masks.device)\n\n h, w = masks.shape[-2:]\n\n y = torch.arange(0, h, dtype=torch.float)\n x = torch.arange(0, w, dtype=torch.float)\n y, x = torch.meshgrid(y, x)\n\n x_mask = (masks * x.unsqueeze(0))\n x_max = x_mask.flatten(1).max(-1)[0]\n x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n y_mask = (masks * y.unsqueeze(0))","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.box_ops.masks_to_boxes","uri":"program://OneFormer/function/oneformer.utils.box_ops.masks_to_boxes#L106-L128","kind":"function","name":"masks_to_boxes","path":"oneformer/utils/box_ops.py","language":"python","start_line":106,"end_line":128,"context_start_line":86,"context_end_line":133,"code":" Input:\n - boxes1, boxes2: N,4\n Output:\n - giou: N, 4\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n assert boxes1.shape == boxes2.shape\n iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4\n\n lt = torch.min(boxes1[:, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n area = wh[:, 0] * wh[:, 1]\n\n return iou - (area - union) / area\n\ndef masks_to_boxes(masks):\n \"\"\"Compute the bounding boxes around the provided masks\n The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n Returns a [N, 4] tensors, with the boxes in xyxy format\n \"\"\"\n if masks.numel() == 0:\n return torch.zeros((0, 4), device=masks.device)\n\n h, w = masks.shape[-2:]\n\n y = torch.arange(0, h, dtype=torch.float)\n x = torch.arange(0, w, dtype=torch.float)\n y, x = torch.meshgrid(y, x)\n\n x_mask = (masks * x.unsqueeze(0))\n x_max = x_mask.flatten(1).max(-1)[0]\n x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n y_mask = (masks * y.unsqueeze(0))\n y_max = y_mask.flatten(1).max(-1)[0]\n y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n return torch.stack([x_min, y_min, x_max, y_max], 1)\n\nif __name__ == '__main__':\n x = torch.rand(5, 4)\n y = torch.rand(3, 4)\n iou, union = box_iou(x, y)","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.pos_embed","uri":"program://OneFormer/module/oneformer.utils.pos_embed#L1-L122","kind":"module","name":"oneformer.utils.pos_embed","path":"oneformer/utils/pos_embed.py","language":"python","start_line":1,"end_line":122,"context_start_line":1,"context_end_line":122,"code":"# --------------------------------------------------------\n# Position embedding utils\n# --------------------------------------------------------\n\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\n\n\n# --------------------------------------------------------\n# 2D sine-cosine position embedding\n# References:\n# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py\n# MoCo v3: https://github.com/facebookresearch/moco-v3\n# --------------------------------------------------------\ndef get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n grid_h = np.arange(grid_size, dtype=np.float32)\n grid_w = np.arange(grid_size, dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0)\n\n grid = grid.reshape([2, 1, grid_size, grid_size])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token:\n pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (M,)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000 ** omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\n\n# --------------------------------------------------------\n# Interpolate position embeddings for high-resolution\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\ndef interpolate_pos_embed(model, checkpoint_model, pos_embed_key):\n if pos_embed_key in checkpoint_model:\n pos_embed_checkpoint = checkpoint_model[pos_embed_key]\n embedding_size = pos_embed_checkpoint.shape[-1]\n num_patches = model.num_patches\n if pos_embed_key.startswith(\"decoder\"):\n num_extra_tokens = model.decoder_pos_embed.shape[-2] - num_patches\n else:\n num_extra_tokens = model.pos_embed.shape[-2] - num_patches\n # height (== width) for the checkpoint position embedding\n orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n # height (== width) for the new position embedding\n new_size = int(num_patches ** 0.5)\n # class_token and dist_token are kept unchanged\n if orig_size != new_size:\n print(\n \"Position interpolate from %dx%d to %dx%d\"\n % (orig_size, orig_size, new_size, new_size)\n )\n extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n # only the position tokens are interpolated\n pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n pos_tokens = pos_tokens.reshape(\n -1, orig_size, orig_size, embedding_size\n ).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens,\n size=(new_size, new_size),\n mode=\"bicubic\",\n align_corners=False,\n )\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n checkpoint_model[pos_embed_key] = new_pos_embed\n\n\ndef interpolate_pos_embed_online(\n pos_embed, orig_size: Tuple[int], new_size: Tuple[int], num_extra_tokens: int\n):\n extra_tokens = pos_embed[:, :num_extra_tokens]\n pos_tokens = pos_embed[:, num_extra_tokens:]\n embedding_size = pos_tokens.shape[-1]\n pos_tokens = pos_tokens.reshape(\n -1, orig_size[0], orig_size[1], embedding_size\n ).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens, size=new_size, mode=\"bicubic\", align_corners=False,\n )\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n return new_pos_embed","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.pos_embed.get_2d_sincos_pos_embed","uri":"program://OneFormer/function/oneformer.utils.pos_embed.get_2d_sincos_pos_embed#L17-L32","kind":"function","name":"get_2d_sincos_pos_embed","path":"oneformer/utils/pos_embed.py","language":"python","start_line":17,"end_line":32,"context_start_line":1,"context_end_line":52,"code":"# --------------------------------------------------------\n# Position embedding utils\n# --------------------------------------------------------\n\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\n\n\n# --------------------------------------------------------\n# 2D sine-cosine position embedding\n# References:\n# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py\n# MoCo v3: https://github.com/facebookresearch/moco-v3\n# --------------------------------------------------------\ndef get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n grid_h = np.arange(grid_size, dtype=np.float32)\n grid_w = np.arange(grid_size, dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0)\n\n grid = grid.reshape([2, 1, grid_size, grid_size])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token:\n pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (M,)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.pos_embed.get_2d_sincos_pos_embed_from_grid","uri":"program://OneFormer/function/oneformer.utils.pos_embed.get_2d_sincos_pos_embed_from_grid#L35-L43","kind":"function","name":"get_2d_sincos_pos_embed_from_grid","path":"oneformer/utils/pos_embed.py","language":"python","start_line":35,"end_line":43,"context_start_line":15,"context_end_line":63,"code":"# MoCo v3: https://github.com/facebookresearch/moco-v3\n# --------------------------------------------------------\ndef get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n grid_h = np.arange(grid_size, dtype=np.float32)\n grid_w = np.arange(grid_size, dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0)\n\n grid = grid.reshape([2, 1, grid_size, grid_size])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token:\n pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (M,)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000 ** omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.pos_embed.get_1d_sincos_pos_embed_from_grid","uri":"program://OneFormer/function/oneformer.utils.pos_embed.get_1d_sincos_pos_embed_from_grid#L46-L64","kind":"function","name":"get_1d_sincos_pos_embed_from_grid","path":"oneformer/utils/pos_embed.py","language":"python","start_line":46,"end_line":64,"context_start_line":26,"context_end_line":84,"code":" grid = np.stack(grid, axis=0)\n\n grid = grid.reshape([2, 1, grid_size, grid_size])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token:\n pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (M,)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000 ** omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\n\n# --------------------------------------------------------\n# Interpolate position embeddings for high-resolution\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\ndef interpolate_pos_embed(model, checkpoint_model, pos_embed_key):\n if pos_embed_key in checkpoint_model:\n pos_embed_checkpoint = checkpoint_model[pos_embed_key]\n embedding_size = pos_embed_checkpoint.shape[-1]\n num_patches = model.num_patches\n if pos_embed_key.startswith(\"decoder\"):\n num_extra_tokens = model.decoder_pos_embed.shape[-2] - num_patches\n else:\n num_extra_tokens = model.pos_embed.shape[-2] - num_patches\n # height (== width) for the checkpoint position embedding\n orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n # height (== width) for the new position embedding\n new_size = int(num_patches ** 0.5)","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.pos_embed.interpolate_pos_embed","uri":"program://OneFormer/function/oneformer.utils.pos_embed.interpolate_pos_embed#L72-L105","kind":"function","name":"interpolate_pos_embed","path":"oneformer/utils/pos_embed.py","language":"python","start_line":72,"end_line":105,"context_start_line":52,"context_end_line":122,"code":" assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000 ** omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\n\n# --------------------------------------------------------\n# Interpolate position embeddings for high-resolution\n# References:\n# DeiT: https://github.com/facebookresearch/deit\n# --------------------------------------------------------\ndef interpolate_pos_embed(model, checkpoint_model, pos_embed_key):\n if pos_embed_key in checkpoint_model:\n pos_embed_checkpoint = checkpoint_model[pos_embed_key]\n embedding_size = pos_embed_checkpoint.shape[-1]\n num_patches = model.num_patches\n if pos_embed_key.startswith(\"decoder\"):\n num_extra_tokens = model.decoder_pos_embed.shape[-2] - num_patches\n else:\n num_extra_tokens = model.pos_embed.shape[-2] - num_patches\n # height (== width) for the checkpoint position embedding\n orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n # height (== width) for the new position embedding\n new_size = int(num_patches ** 0.5)\n # class_token and dist_token are kept unchanged\n if orig_size != new_size:\n print(\n \"Position interpolate from %dx%d to %dx%d\"\n % (orig_size, orig_size, new_size, new_size)\n )\n extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n # only the position tokens are interpolated\n pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n pos_tokens = pos_tokens.reshape(\n -1, orig_size, orig_size, embedding_size\n ).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens,\n size=(new_size, new_size),\n mode=\"bicubic\",\n align_corners=False,\n )\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n checkpoint_model[pos_embed_key] = new_pos_embed\n\n\ndef interpolate_pos_embed_online(\n pos_embed, orig_size: Tuple[int], new_size: Tuple[int], num_extra_tokens: int\n):\n extra_tokens = pos_embed[:, :num_extra_tokens]\n pos_tokens = pos_embed[:, num_extra_tokens:]\n embedding_size = pos_tokens.shape[-1]\n pos_tokens = pos_tokens.reshape(\n -1, orig_size[0], orig_size[1], embedding_size\n ).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens, size=new_size, mode=\"bicubic\", align_corners=False,\n )\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n return new_pos_embed","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.pos_embed.interpolate_pos_embed_online","uri":"program://OneFormer/function/oneformer.utils.pos_embed.interpolate_pos_embed_online#L108-L122","kind":"function","name":"interpolate_pos_embed_online","path":"oneformer/utils/pos_embed.py","language":"python","start_line":108,"end_line":122,"context_start_line":88,"context_end_line":122,"code":" \"Position interpolate from %dx%d to %dx%d\"\n % (orig_size, orig_size, new_size, new_size)\n )\n extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n # only the position tokens are interpolated\n pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n pos_tokens = pos_tokens.reshape(\n -1, orig_size, orig_size, embedding_size\n ).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens,\n size=(new_size, new_size),\n mode=\"bicubic\",\n align_corners=False,\n )\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n checkpoint_model[pos_embed_key] = new_pos_embed\n\n\ndef interpolate_pos_embed_online(\n pos_embed, orig_size: Tuple[int], new_size: Tuple[int], num_extra_tokens: int\n):\n extra_tokens = pos_embed[:, :num_extra_tokens]\n pos_tokens = pos_embed[:, num_extra_tokens:]\n embedding_size = pos_tokens.shape[-1]\n pos_tokens = pos_tokens.reshape(\n -1, orig_size[0], orig_size[1], embedding_size\n ).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens, size=new_size, mode=\"bicubic\", align_corners=False,\n )\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n return new_pos_embed","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events","uri":"program://OneFormer/module/oneformer.utils.events#L1-L120","kind":"module","name":"oneformer.utils.events","path":"oneformer/utils/events.py","language":"python","start_line":1,"end_line":120,"context_start_line":1,"context_end_line":120,"code":"import os\nimport wandb\nfrom detectron2.utils import comm\nfrom detectron2.utils.events import EventWriter, get_event_storage\n\n\ndef setup_wandb(cfg, args):\n if comm.is_main_process():\n init_args = {\n k.lower(): v\n for k, v in cfg.WANDB.items()\n if isinstance(k, str) and k not in [\"config\"]\n }\n # only include most related part to avoid too big table\n # TODO: add configurable params to select which part of `cfg` should be saved in config\n if \"config_exclude_keys\" in init_args:\n init_args[\"config\"] = cfg\n init_args[\"config\"][\"cfg_file\"] = args.config_file\n else:\n init_args[\"config\"] = {\n \"model\": cfg.MODEL,\n \"solver\": cfg.SOLVER,\n \"cfg_file\": args.config_file,\n }\n if (\"name\" not in init_args) or (init_args[\"name\"] is None):\n init_args[\"name\"] = os.path.basename(args.config_file)\n else:\n init_args[\"name\"] = init_args[\"name\"] + '_' + os.path.basename(args.config_file)\n wandb.init(**init_args)\n\n\nclass BaseRule(object):\n def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):\n\n storage = get_event_storage()\n\n def _group_name(scalar_name):\n for (rule, op) in self._group_rules:\n if rule(scalar_name):\n return op(scalar_name)\n return scalar_name\n\n stats = {\n _group_name(name): scalars[0]\n for name, scalars in storage.latest().items()\n if scalars[1] > self._last_write\n }\n if len(stats) > 0:\n self._last_write = max([v[1] for k, v in storage.latest().items()])\n\n # storage.put_{image,histogram} is only meant to be used by\n # tensorboard writer. So we access its internal fields directly from here.\n if len(storage._vis_data) >= 1:\n stats[\"image\"] = [\n wandb.Image(img, caption=img_name)\n for img_name, img, step_num in storage._vis_data\n ]\n # Storage stores all image data and rely on this writer to clear them.\n # As a result it assumes only one writer will use its image data.\n # An alternative design is to let storage store limited recent\n # data (e.g. only the most recent image) that all writers can access.\n # In that case a writer may not see all image data if its period is long.\n storage.clear_images()\n\n if len(storage._histograms) >= 1:\n\n def create_bar(tag, bucket_limits, bucket_counts, **kwargs):\n data = [\n [label, val] for (label, val) in zip(bucket_limits, bucket_counts)\n ]\n table = wandb.Table(data=data, columns=[\"label\", \"value\"])\n return wandb.plot.bar(table, \"label\", \"value\", title=tag)\n\n stats[\"hist\"] = [create_bar(**params) for params in storage._histograms]\n\n storage.clear_histograms()\n\n if len(stats) == 0:\n return\n wandb.log(stats, step=storage.iter)\n\n def close(self):\n wandb.finish()","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.setup_wandb","uri":"program://OneFormer/function/oneformer.utils.events.setup_wandb#L7-L29","kind":"function","name":"setup_wandb","path":"oneformer/utils/events.py","language":"python","start_line":7,"end_line":29,"context_start_line":1,"context_end_line":49,"code":"import os\nimport wandb\nfrom detectron2.utils import comm\nfrom detectron2.utils.events import EventWriter, get_event_storage\n\n\ndef setup_wandb(cfg, args):\n if comm.is_main_process():\n init_args = {\n k.lower(): v\n for k, v in cfg.WANDB.items()\n if isinstance(k, str) and k not in [\"config\"]\n }\n # only include most related part to avoid too big table\n # TODO: add configurable params to select which part of `cfg` should be saved in config\n if \"config_exclude_keys\" in init_args:\n init_args[\"config\"] = cfg\n init_args[\"config\"][\"cfg_file\"] = args.config_file\n else:\n init_args[\"config\"] = {\n \"model\": cfg.MODEL,\n \"solver\": cfg.SOLVER,\n \"cfg_file\": args.config_file,\n }\n if (\"name\" not in init_args) or (init_args[\"name\"] is None):\n init_args[\"name\"] = os.path.basename(args.config_file)\n else:\n init_args[\"name\"] = init_args[\"name\"] + '_' + os.path.basename(args.config_file)\n wandb.init(**init_args)\n\n\nclass BaseRule(object):\n def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.BaseRule","uri":"program://OneFormer/class/oneformer.utils.events.BaseRule#L32-L34","kind":"class","name":"BaseRule","path":"oneformer/utils/events.py","language":"python","start_line":32,"end_line":34,"context_start_line":12,"context_end_line":54,"code":" if isinstance(k, str) and k not in [\"config\"]\n }\n # only include most related part to avoid too big table\n # TODO: add configurable params to select which part of `cfg` should be saved in config\n if \"config_exclude_keys\" in init_args:\n init_args[\"config\"] = cfg\n init_args[\"config\"][\"cfg_file\"] = args.config_file\n else:\n init_args[\"config\"] = {\n \"model\": cfg.MODEL,\n \"solver\": cfg.SOLVER,\n \"cfg_file\": args.config_file,\n }\n if (\"name\" not in init_args) or (init_args[\"name\"] is None):\n init_args[\"name\"] = os.path.basename(args.config_file)\n else:\n init_args[\"name\"] = init_args[\"name\"] + '_' + os.path.basename(args.config_file)\n wandb.init(**init_args)\n\n\nclass BaseRule(object):\n def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.IsIn","uri":"program://OneFormer/class/oneformer.utils.events.IsIn#L37-L42","kind":"class","name":"IsIn","path":"oneformer/utils/events.py","language":"python","start_line":37,"end_line":42,"context_start_line":17,"context_end_line":62,"code":" init_args[\"config\"] = cfg\n init_args[\"config\"][\"cfg_file\"] = args.config_file\n else:\n init_args[\"config\"] = {\n \"model\": cfg.MODEL,\n \"solver\": cfg.SOLVER,\n \"cfg_file\": args.config_file,\n }\n if (\"name\" not in init_args) or (init_args[\"name\"] is None):\n init_args[\"name\"] = os.path.basename(args.config_file)\n else:\n init_args[\"name\"] = init_args[\"name\"] + '_' + os.path.basename(args.config_file)\n wandb.init(**init_args)\n\n\nclass BaseRule(object):\n def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.Prefix","uri":"program://OneFormer/class/oneformer.utils.events.Prefix#L45-L50","kind":"class","name":"Prefix","path":"oneformer/utils/events.py","language":"python","start_line":45,"end_line":50,"context_start_line":25,"context_end_line":70,"code":" if (\"name\" not in init_args) or (init_args[\"name\"] is None):\n init_args[\"name\"] = os.path.basename(args.config_file)\n else:\n init_args[\"name\"] = init_args[\"name\"] + '_' + os.path.basename(args.config_file)\n wandb.init(**init_args)\n\n\nclass BaseRule(object):\n def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.WandbWriter","uri":"program://OneFormer/class/oneformer.utils.events.WandbWriter#L53-L120","kind":"class","name":"WandbWriter","path":"oneformer/utils/events.py","language":"python","start_line":53,"end_line":120,"context_start_line":33,"context_end_line":120,"code":" def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):\n\n storage = get_event_storage()\n\n def _group_name(scalar_name):\n for (rule, op) in self._group_rules:\n if rule(scalar_name):\n return op(scalar_name)\n return scalar_name\n\n stats = {\n _group_name(name): scalars[0]\n for name, scalars in storage.latest().items()\n if scalars[1] > self._last_write\n }\n if len(stats) > 0:\n self._last_write = max([v[1] for k, v in storage.latest().items()])\n\n # storage.put_{image,histogram} is only meant to be used by\n # tensorboard writer. So we access its internal fields directly from here.\n if len(storage._vis_data) >= 1:\n stats[\"image\"] = [\n wandb.Image(img, caption=img_name)\n for img_name, img, step_num in storage._vis_data\n ]\n # Storage stores all image data and rely on this writer to clear them.\n # As a result it assumes only one writer will use its image data.\n # An alternative design is to let storage store limited recent\n # data (e.g. only the most recent image) that all writers can access.\n # In that case a writer may not see all image data if its period is long.\n storage.clear_images()\n\n if len(storage._histograms) >= 1:\n\n def create_bar(tag, bucket_limits, bucket_counts, **kwargs):\n data = [\n [label, val] for (label, val) in zip(bucket_limits, bucket_counts)\n ]\n table = wandb.Table(data=data, columns=[\"label\", \"value\"])\n return wandb.plot.bar(table, \"label\", \"value\", title=tag)\n\n stats[\"hist\"] = [create_bar(**params) for params in storage._histograms]\n\n storage.clear_histograms()\n\n if len(stats) == 0:\n return\n wandb.log(stats, step=storage.iter)\n\n def close(self):\n wandb.finish()","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.__call__","uri":"program://OneFormer/function/oneformer.utils.events.__call__#L49-L50","kind":"function","name":"__call__","path":"oneformer/utils/events.py","language":"python","start_line":49,"end_line":50,"context_start_line":29,"context_end_line":70,"code":" wandb.init(**init_args)\n\n\nclass BaseRule(object):\n def __call__(self, target):\n return target\n\n\nclass IsIn(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.__init__","uri":"program://OneFormer/function/oneformer.utils.events.__init__#L58-L68","kind":"function","name":"__init__","path":"oneformer/utils/events.py","language":"python","start_line":58,"end_line":68,"context_start_line":38,"context_end_line":88,"code":" def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return self.keyword in target\n\n\nclass Prefix(BaseRule):\n def __init__(self, keyword: str):\n self.keyword = keyword\n\n def __call__(self, target):\n return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):\n\n storage = get_event_storage()\n\n def _group_name(scalar_name):\n for (rule, op) in self._group_rules:\n if rule(scalar_name):\n return op(scalar_name)\n return scalar_name\n\n stats = {\n _group_name(name): scalars[0]\n for name, scalars in storage.latest().items()\n if scalars[1] > self._last_write\n }\n if len(stats) > 0:\n self._last_write = max([v[1] for k, v in storage.latest().items()])\n\n # storage.put_{image,histogram} is only meant to be used by","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.write","uri":"program://OneFormer/function/oneformer.utils.events.write#L70-L117","kind":"function","name":"write","path":"oneformer/utils/events.py","language":"python","start_line":70,"end_line":117,"context_start_line":50,"context_end_line":120,"code":" return \"/\".join([self.keyword, target])\n\n\nclass WandbWriter(EventWriter):\n \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):\n\n storage = get_event_storage()\n\n def _group_name(scalar_name):\n for (rule, op) in self._group_rules:\n if rule(scalar_name):\n return op(scalar_name)\n return scalar_name\n\n stats = {\n _group_name(name): scalars[0]\n for name, scalars in storage.latest().items()\n if scalars[1] > self._last_write\n }\n if len(stats) > 0:\n self._last_write = max([v[1] for k, v in storage.latest().items()])\n\n # storage.put_{image,histogram} is only meant to be used by\n # tensorboard writer. So we access its internal fields directly from here.\n if len(storage._vis_data) >= 1:\n stats[\"image\"] = [\n wandb.Image(img, caption=img_name)\n for img_name, img, step_num in storage._vis_data\n ]\n # Storage stores all image data and rely on this writer to clear them.\n # As a result it assumes only one writer will use its image data.\n # An alternative design is to let storage store limited recent\n # data (e.g. only the most recent image) that all writers can access.\n # In that case a writer may not see all image data if its period is long.\n storage.clear_images()\n\n if len(storage._histograms) >= 1:\n\n def create_bar(tag, bucket_limits, bucket_counts, **kwargs):\n data = [\n [label, val] for (label, val) in zip(bucket_limits, bucket_counts)\n ]\n table = wandb.Table(data=data, columns=[\"label\", \"value\"])\n return wandb.plot.bar(table, \"label\", \"value\", title=tag)\n\n stats[\"hist\"] = [create_bar(**params) for params in storage._histograms]\n\n storage.clear_histograms()\n\n if len(stats) == 0:\n return\n wandb.log(stats, step=storage.iter)\n\n def close(self):\n wandb.finish()","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.close","uri":"program://OneFormer/function/oneformer.utils.events.close#L119-L120","kind":"function","name":"close","path":"oneformer/utils/events.py","language":"python","start_line":119,"end_line":120,"context_start_line":99,"context_end_line":120,"code":" # In that case a writer may not see all image data if its period is long.\n storage.clear_images()\n\n if len(storage._histograms) >= 1:\n\n def create_bar(tag, bucket_limits, bucket_counts, **kwargs):\n data = [\n [label, val] for (label, val) in zip(bucket_limits, bucket_counts)\n ]\n table = wandb.Table(data=data, columns=[\"label\", \"value\"])\n return wandb.plot.bar(table, \"label\", \"value\", title=tag)\n\n stats[\"hist\"] = [create_bar(**params) for params in storage._histograms]\n\n storage.clear_histograms()\n\n if len(stats) == 0:\n return\n wandb.log(stats, step=storage.iter)\n\n def close(self):\n wandb.finish()","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events._group_name","uri":"program://OneFormer/function/oneformer.utils.events._group_name#L74-L78","kind":"function","name":"_group_name","path":"oneformer/utils/events.py","language":"python","start_line":74,"end_line":78,"context_start_line":54,"context_end_line":98,"code":" \"\"\"\n Write all scalars to a tensorboard file.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Args:\n log_dir (str): the directory to save the output events\n kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n \"\"\"\n self._last_write = -1\n self._group_rules = [\n (IsIn(\"/\"), BaseRule()),\n (IsIn(\"loss\"), Prefix(\"train\")),\n ]\n\n def write(self):\n\n storage = get_event_storage()\n\n def _group_name(scalar_name):\n for (rule, op) in self._group_rules:\n if rule(scalar_name):\n return op(scalar_name)\n return scalar_name\n\n stats = {\n _group_name(name): scalars[0]\n for name, scalars in storage.latest().items()\n if scalars[1] > self._last_write\n }\n if len(stats) > 0:\n self._last_write = max([v[1] for k, v in storage.latest().items()])\n\n # storage.put_{image,histogram} is only meant to be used by\n # tensorboard writer. So we access its internal fields directly from here.\n if len(storage._vis_data) >= 1:\n stats[\"image\"] = [\n wandb.Image(img, caption=img_name)\n for img_name, img, step_num in storage._vis_data\n ]\n # Storage stores all image data and rely on this writer to clear them.\n # As a result it assumes only one writer will use its image data.\n # An alternative design is to let storage store limited recent\n # data (e.g. only the most recent image) that all writers can access.","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.utils.events.create_bar","uri":"program://OneFormer/function/oneformer.utils.events.create_bar#L104-L109","kind":"function","name":"create_bar","path":"oneformer/utils/events.py","language":"python","start_line":104,"end_line":109,"context_start_line":84,"context_end_line":120,"code":" }\n if len(stats) > 0:\n self._last_write = max([v[1] for k, v in storage.latest().items()])\n\n # storage.put_{image,histogram} is only meant to be used by\n # tensorboard writer. So we access its internal fields directly from here.\n if len(storage._vis_data) >= 1:\n stats[\"image\"] = [\n wandb.Image(img, caption=img_name)\n for img_name, img, step_num in storage._vis_data\n ]\n # Storage stores all image data and rely on this writer to clear them.\n # As a result it assumes only one writer will use its image data.\n # An alternative design is to let storage store limited recent\n # data (e.g. only the most recent image) that all writers can access.\n # In that case a writer may not see all image data if its period is long.\n storage.clear_images()\n\n if len(storage._histograms) >= 1:\n\n def create_bar(tag, bucket_limits, bucket_counts, **kwargs):\n data = [\n [label, val] for (label, val) in zip(bucket_limits, bucket_counts)\n ]\n table = wandb.Table(data=data, columns=[\"label\", \"value\"])\n return wandb.plot.bar(table, \"label\", \"value\", title=tag)\n\n stats[\"hist\"] = [create_bar(**params) for params in storage._histograms]\n\n storage.clear_histograms()\n\n if len(stats) == 0:\n return\n wandb.log(stats, step=storage.iter)\n\n def close(self):\n wandb.finish()","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer","uri":"program://OneFormer/module/oneformer.data.tokenizer#L1-L193","kind":"module","name":"oneformer.data.tokenizer","path":"oneformer/data/tokenizer.py","language":"python","start_line":1,"end_line":193,"context_start_line":1,"context_end_line":193,"code":"# -------------------------------------------------------------------------\n# MIT License\n#\n# Copyright (c) 2021 OpenAI\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n# Modified by Jiarui Xu\n# -------------------------------------------------------------------------\n\nimport gzip\nimport html\nimport os\nfrom functools import lru_cache\n\nimport ftfy\nimport regex as re\nimport torch\n\n\n@lru_cache()\ndef default_bpe():\n return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"Returns list of utf-8 byte and a corresponding list of unicode strings.\n\n The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab\n if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent\n coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables\n between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\nclass Tokenize:\n\n def __init__(self, tokenizer, max_seq_len=77, truncate=True):\n self.tokenizer = tokenizer\n self.max_seq_len = max_seq_len\n self.truncate = truncate\n\n def __call__(self, texts):\n expanded_dim = False\n if isinstance(texts, str):\n texts = [texts]\n expanded_dim = True\n\n sot_token = self.tokenizer.encoder['<|startoftext|>']\n eot_token = self.tokenizer.encoder['<|endoftext|>']\n all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n if len(tokens) > self.max_seq_len:\n if self.truncate:\n tokens = tokens[:self.max_seq_len]\n tokens[-1] = eot_token\n else:\n raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')\n result[i, :len(tokens)] = torch.tensor(tokens)\n\n if expanded_dim:\n return result[0]\n\n return result\n\n\nclass SimpleTokenizer(object):\n\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n self.pat = re.compile(\n r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n re.IGNORECASE)\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + (token[-1] + '', )\n pairs = get_pairs(word)\n\n if not pairs:\n return token + ''\n\n while True:\n bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except: # noqa: E722\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ')\n return text","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.default_bpe","uri":"program://OneFormer/function/oneformer.data.tokenizer.default_bpe#L38-L39","kind":"function","name":"default_bpe","path":"oneformer/data/tokenizer.py","language":"python","start_line":38,"end_line":39,"context_start_line":18,"context_end_line":59,"code":"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n# Modified by Jiarui Xu\n# -------------------------------------------------------------------------\n\nimport gzip\nimport html\nimport os\nfrom functools import lru_cache\n\nimport ftfy\nimport regex as re\nimport torch\n\n\n@lru_cache()\ndef default_bpe():\n return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"Returns list of utf-8 byte and a corresponding list of unicode strings.\n\n The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab\n if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent\n coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables\n between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.bytes_to_unicode","uri":"program://OneFormer/function/oneformer.data.tokenizer.bytes_to_unicode#L43-L60","kind":"function","name":"bytes_to_unicode","path":"oneformer/data/tokenizer.py","language":"python","start_line":43,"end_line":60,"context_start_line":23,"context_end_line":80,"code":"#\n# Modified by Jiarui Xu\n# -------------------------------------------------------------------------\n\nimport gzip\nimport html\nimport os\nfrom functools import lru_cache\n\nimport ftfy\nimport regex as re\nimport torch\n\n\n@lru_cache()\ndef default_bpe():\n return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"Returns list of utf-8 byte and a corresponding list of unicode strings.\n\n The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab\n if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent\n coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables\n between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.get_pairs","uri":"program://OneFormer/function/oneformer.data.tokenizer.get_pairs#L63-L73","kind":"function","name":"get_pairs","path":"oneformer/data/tokenizer.py","language":"python","start_line":63,"end_line":73,"context_start_line":43,"context_end_line":93,"code":"def bytes_to_unicode():\n \"\"\"Returns list of utf-8 byte and a corresponding list of unicode strings.\n\n The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab\n if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent\n coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables\n between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\nclass Tokenize:\n\n def __init__(self, tokenizer, max_seq_len=77, truncate=True):\n self.tokenizer = tokenizer\n self.max_seq_len = max_seq_len\n self.truncate = truncate\n","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.basic_clean","uri":"program://OneFormer/function/oneformer.data.tokenizer.basic_clean#L76-L79","kind":"function","name":"basic_clean","path":"oneformer/data/tokenizer.py","language":"python","start_line":76,"end_line":79,"context_start_line":56,"context_end_line":99,"code":" bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\nclass Tokenize:\n\n def __init__(self, tokenizer, max_seq_len=77, truncate=True):\n self.tokenizer = tokenizer\n self.max_seq_len = max_seq_len\n self.truncate = truncate\n\n def __call__(self, texts):\n expanded_dim = False\n if isinstance(texts, str):\n texts = [texts]\n expanded_dim = True\n","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.whitespace_clean","uri":"program://OneFormer/function/oneformer.data.tokenizer.whitespace_clean#L82-L85","kind":"function","name":"whitespace_clean","path":"oneformer/data/tokenizer.py","language":"python","start_line":82,"end_line":85,"context_start_line":62,"context_end_line":105,"code":"\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\nclass Tokenize:\n\n def __init__(self, tokenizer, max_seq_len=77, truncate=True):\n self.tokenizer = tokenizer\n self.max_seq_len = max_seq_len\n self.truncate = truncate\n\n def __call__(self, texts):\n expanded_dim = False\n if isinstance(texts, str):\n texts = [texts]\n expanded_dim = True\n\n sot_token = self.tokenizer.encoder['<|startoftext|>']\n eot_token = self.tokenizer.encoder['<|endoftext|>']\n all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.Tokenize","uri":"program://OneFormer/class/oneformer.data.tokenizer.Tokenize#L87-L117","kind":"class","name":"Tokenize","path":"oneformer/data/tokenizer.py","language":"python","start_line":87,"end_line":117,"context_start_line":67,"context_end_line":137,"code":" \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\nclass Tokenize:\n\n def __init__(self, tokenizer, max_seq_len=77, truncate=True):\n self.tokenizer = tokenizer\n self.max_seq_len = max_seq_len\n self.truncate = truncate\n\n def __call__(self, texts):\n expanded_dim = False\n if isinstance(texts, str):\n texts = [texts]\n expanded_dim = True\n\n sot_token = self.tokenizer.encoder['<|startoftext|>']\n eot_token = self.tokenizer.encoder['<|endoftext|>']\n all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n if len(tokens) > self.max_seq_len:\n if self.truncate:\n tokens = tokens[:self.max_seq_len]\n tokens[-1] = eot_token\n else:\n raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')\n result[i, :len(tokens)] = torch.tensor(tokens)\n\n if expanded_dim:\n return result[0]\n\n return result\n\n\nclass SimpleTokenizer(object):\n\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n self.pat = re.compile(","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.SimpleTokenizer","uri":"program://OneFormer/class/oneformer.data.tokenizer.SimpleTokenizer#L120-L193","kind":"class","name":"SimpleTokenizer","path":"oneformer/data/tokenizer.py","language":"python","start_line":120,"end_line":193,"context_start_line":100,"context_end_line":193,"code":" sot_token = self.tokenizer.encoder['<|startoftext|>']\n eot_token = self.tokenizer.encoder['<|endoftext|>']\n all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n if len(tokens) > self.max_seq_len:\n if self.truncate:\n tokens = tokens[:self.max_seq_len]\n tokens[-1] = eot_token\n else:\n raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')\n result[i, :len(tokens)] = torch.tensor(tokens)\n\n if expanded_dim:\n return result[0]\n\n return result\n\n\nclass SimpleTokenizer(object):\n\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n self.pat = re.compile(\n r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n re.IGNORECASE)\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + (token[-1] + '', )\n pairs = get_pairs(word)\n\n if not pairs:\n return token + ''\n\n while True:\n bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except: # noqa: E722\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ')\n return text","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.__init__","uri":"program://OneFormer/function/oneformer.data.tokenizer.__init__#L122-L139","kind":"function","name":"__init__","path":"oneformer/data/tokenizer.py","language":"python","start_line":122,"end_line":139,"context_start_line":102,"context_end_line":159,"code":" all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n if len(tokens) > self.max_seq_len:\n if self.truncate:\n tokens = tokens[:self.max_seq_len]\n tokens[-1] = eot_token\n else:\n raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')\n result[i, :len(tokens)] = torch.tensor(tokens)\n\n if expanded_dim:\n return result[0]\n\n return result\n\n\nclass SimpleTokenizer(object):\n\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n self.pat = re.compile(\n r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n re.IGNORECASE)\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + (token[-1] + '', )\n pairs = get_pairs(word)\n\n if not pairs:\n return token + ''\n\n while True:\n bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.__call__","uri":"program://OneFormer/function/oneformer.data.tokenizer.__call__#L94-L117","kind":"function","name":"__call__","path":"oneformer/data/tokenizer.py","language":"python","start_line":94,"end_line":117,"context_start_line":74,"context_end_line":137,"code":"\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\nclass Tokenize:\n\n def __init__(self, tokenizer, max_seq_len=77, truncate=True):\n self.tokenizer = tokenizer\n self.max_seq_len = max_seq_len\n self.truncate = truncate\n\n def __call__(self, texts):\n expanded_dim = False\n if isinstance(texts, str):\n texts = [texts]\n expanded_dim = True\n\n sot_token = self.tokenizer.encoder['<|startoftext|>']\n eot_token = self.tokenizer.encoder['<|endoftext|>']\n all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n if len(tokens) > self.max_seq_len:\n if self.truncate:\n tokens = tokens[:self.max_seq_len]\n tokens[-1] = eot_token\n else:\n raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')\n result[i, :len(tokens)] = torch.tensor(tokens)\n\n if expanded_dim:\n return result[0]\n\n return result\n\n\nclass SimpleTokenizer(object):\n\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n self.pat = re.compile(","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.bpe","uri":"program://OneFormer/function/oneformer.data.tokenizer.bpe#L141-L180","kind":"function","name":"bpe","path":"oneformer/data/tokenizer.py","language":"python","start_line":141,"end_line":180,"context_start_line":121,"context_end_line":193,"code":"\n def __init__(self, bpe_path: str = default_bpe()):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode('utf-8').split('\\n')\n merges = merges[1:49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + '' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n vocab.extend(['<|startoftext|>', '<|endoftext|>'])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}\n self.pat = re.compile(\n r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n re.IGNORECASE)\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + (token[-1] + '', )\n pairs = get_pairs(word)\n\n if not pairs:\n return token + ''\n\n while True:\n bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except: # noqa: E722\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ')\n return text","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.encode","uri":"program://OneFormer/function/oneformer.data.tokenizer.encode#L182-L188","kind":"function","name":"encode","path":"oneformer/data/tokenizer.py","language":"python","start_line":182,"end_line":188,"context_start_line":162,"context_end_line":193,"code":" except: # noqa: E722\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ')\n return text","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.tokenizer.decode","uri":"program://OneFormer/function/oneformer.data.tokenizer.decode#L190-L193","kind":"function","name":"decode","path":"oneformer/data/tokenizer.py","language":"python","start_line":190,"end_line":193,"context_start_line":170,"context_end_line":193,"code":" new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ')\n return text","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.build","uri":"program://OneFormer/module/oneformer.data.build#L1-L121","kind":"module","name":"oneformer.data.build","path":"oneformer/data/build.py","language":"python","start_line":1,"end_line":121,"context_start_line":1,"context_end_line":121,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nfrom typing import Any, Callable, Dict, List, Optional, Union\nimport torch.utils.data as torchdata\n\nfrom detectron2.config import configurable\n\n\nfrom detectron2.data.common import DatasetFromList, MapDataset\nfrom detectron2.data.dataset_mapper import DatasetMapper\nfrom detectron2.data.samplers import (\n InferenceSampler,\n)\nfrom detectron2.data.build import (\n get_detection_dataset_dicts,\n trivial_batch_collator\n)\n\"\"\"\nThis file contains the default logic to build a dataloader for training or testing.\n\"\"\"\n\n__all__ = [\n \"build_detection_test_loader\",\n]\n\n\ndef _test_loader_from_config(cfg, dataset_name, mapper=None):\n \"\"\"\n Uses the given `dataset_name` argument (instead of the names in cfg), because the\n standard practice is to evaluate each test set individually (not combining them).\n \"\"\"\n if isinstance(dataset_name, str):\n dataset_name = [dataset_name]\n\n dataset = get_detection_dataset_dicts(\n dataset_name,\n filter_empty=False,\n proposal_files=[\n cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name\n ]\n if cfg.MODEL.LOAD_PROPOSALS\n else None,\n )\n if mapper is None:\n mapper = DatasetMapper(cfg, False)\n return {\n \"dataset\": dataset,\n \"mapper\": mapper,\n \"num_workers\": cfg.DATALOADER.NUM_WORKERS,\n \"sampler\": InferenceSampler(len(dataset))\n if not isinstance(dataset, torchdata.IterableDataset)\n else None,\n }\n\n\n@configurable(from_config=_test_loader_from_config)\ndef build_detection_test_loader(\n dataset: Union[List[Any], torchdata.Dataset],\n *,\n mapper: Callable[[Dict[str, Any]], Any],\n sampler: Optional[torchdata.Sampler] = None,\n batch_size: int = 1,\n num_workers: int = 0,\n collate_fn: Optional[Callable[[List[Any]], Any]] = None,\n) -> torchdata.DataLoader:\n \"\"\"\n Similar to `build_detection_train_loader`, with default batch size = 1,\n and sampler = :class:`InferenceSampler`. This sampler coordinates all workers\n to produce the exact set of all samples.\n\n Args:\n dataset: a list of dataset dicts,\n or a pytorch dataset (either map-style or iterable). They can be obtained\n by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.\n mapper: a callable which takes a sample (dict) from dataset\n and returns the format to be consumed by the model.\n When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.\n sampler: a sampler that produces\n indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,\n which splits the dataset across all workers. Sampler must be None\n if `dataset` is iterable.\n batch_size: the batch size of the data loader to be created.\n Default to 1 image per worker since this is the standard when reporting\n inference time in papers.\n num_workers: number of parallel data loading workers\n collate_fn: same as the argument of `torch.utils.data.DataLoader`.\n Defaults to do no collation and return a list of data.\n\n Returns:\n DataLoader: a torch DataLoader, that loads the given detection\n dataset, with test-time transformation and batching.\n\n Examples:\n ::\n data_loader = build_detection_test_loader(\n DatasetRegistry.get(\"my_test\"),\n mapper=DatasetMapper(...))\n\n # or, instantiate with a CfgNode:\n data_loader = build_detection_test_loader(cfg, \"my_test\")\n \"\"\"\n if isinstance(dataset, list):\n dataset = DatasetFromList(dataset, copy=False)\n if mapper is not None:\n dataset = MapDataset(dataset, mapper)\n if isinstance(dataset, torchdata.IterableDataset):\n assert sampler is None, \"sampler must be None if dataset is IterableDataset\"\n else:\n if sampler is None:\n sampler = InferenceSampler(len(dataset))\n return torchdata.DataLoader(\n dataset,\n batch_size=batch_size,\n sampler=sampler,\n drop_last=False,\n num_workers=num_workers,\n collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,\n )","source_hash":"56c5645a25fb4885e90646daff413837ea0fcf4bb12f4b5481d1f2a352de7463","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.build._test_loader_from_config","uri":"program://OneFormer/function/oneformer.data.build._test_loader_from_config#L30-L56","kind":"function","name":"_test_loader_from_config","path":"oneformer/data/build.py","language":"python","start_line":30,"end_line":56,"context_start_line":10,"context_end_line":76,"code":"\n\nfrom detectron2.data.common import DatasetFromList, MapDataset\nfrom detectron2.data.dataset_mapper import DatasetMapper\nfrom detectron2.data.samplers import (\n InferenceSampler,\n)\nfrom detectron2.data.build import (\n get_detection_dataset_dicts,\n trivial_batch_collator\n)\n\"\"\"\nThis file contains the default logic to build a dataloader for training or testing.\n\"\"\"\n\n__all__ = [\n \"build_detection_test_loader\",\n]\n\n\ndef _test_loader_from_config(cfg, dataset_name, mapper=None):\n \"\"\"\n Uses the given `dataset_name` argument (instead of the names in cfg), because the\n standard practice is to evaluate each test set individually (not combining them).\n \"\"\"\n if isinstance(dataset_name, str):\n dataset_name = [dataset_name]\n\n dataset = get_detection_dataset_dicts(\n dataset_name,\n filter_empty=False,\n proposal_files=[\n cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name\n ]\n if cfg.MODEL.LOAD_PROPOSALS\n else None,\n )\n if mapper is None:\n mapper = DatasetMapper(cfg, False)\n return {\n \"dataset\": dataset,\n \"mapper\": mapper,\n \"num_workers\": cfg.DATALOADER.NUM_WORKERS,\n \"sampler\": InferenceSampler(len(dataset))\n if not isinstance(dataset, torchdata.IterableDataset)\n else None,\n }\n\n\n@configurable(from_config=_test_loader_from_config)\ndef build_detection_test_loader(\n dataset: Union[List[Any], torchdata.Dataset],\n *,\n mapper: Callable[[Dict[str, Any]], Any],\n sampler: Optional[torchdata.Sampler] = None,\n batch_size: int = 1,\n num_workers: int = 0,\n collate_fn: Optional[Callable[[List[Any]], Any]] = None,\n) -> torchdata.DataLoader:\n \"\"\"\n Similar to `build_detection_train_loader`, with default batch size = 1,\n and sampler = :class:`InferenceSampler`. This sampler coordinates all workers\n to produce the exact set of all samples.\n\n Args:\n dataset: a list of dataset dicts,\n or a pytorch dataset (either map-style or iterable). They can be obtained","source_hash":"56c5645a25fb4885e90646daff413837ea0fcf4bb12f4b5481d1f2a352de7463","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.build.build_detection_test_loader","uri":"program://OneFormer/function/oneformer.data.build.build_detection_test_loader#L60-L121","kind":"function","name":"build_detection_test_loader","path":"oneformer/data/build.py","language":"python","start_line":60,"end_line":121,"context_start_line":40,"context_end_line":121,"code":" filter_empty=False,\n proposal_files=[\n cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name\n ]\n if cfg.MODEL.LOAD_PROPOSALS\n else None,\n )\n if mapper is None:\n mapper = DatasetMapper(cfg, False)\n return {\n \"dataset\": dataset,\n \"mapper\": mapper,\n \"num_workers\": cfg.DATALOADER.NUM_WORKERS,\n \"sampler\": InferenceSampler(len(dataset))\n if not isinstance(dataset, torchdata.IterableDataset)\n else None,\n }\n\n\n@configurable(from_config=_test_loader_from_config)\ndef build_detection_test_loader(\n dataset: Union[List[Any], torchdata.Dataset],\n *,\n mapper: Callable[[Dict[str, Any]], Any],\n sampler: Optional[torchdata.Sampler] = None,\n batch_size: int = 1,\n num_workers: int = 0,\n collate_fn: Optional[Callable[[List[Any]], Any]] = None,\n) -> torchdata.DataLoader:\n \"\"\"\n Similar to `build_detection_train_loader`, with default batch size = 1,\n and sampler = :class:`InferenceSampler`. This sampler coordinates all workers\n to produce the exact set of all samples.\n\n Args:\n dataset: a list of dataset dicts,\n or a pytorch dataset (either map-style or iterable). They can be obtained\n by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.\n mapper: a callable which takes a sample (dict) from dataset\n and returns the format to be consumed by the model.\n When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.\n sampler: a sampler that produces\n indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,\n which splits the dataset across all workers. Sampler must be None\n if `dataset` is iterable.\n batch_size: the batch size of the data loader to be created.\n Default to 1 image per worker since this is the standard when reporting\n inference time in papers.\n num_workers: number of parallel data loading workers\n collate_fn: same as the argument of `torch.utils.data.DataLoader`.\n Defaults to do no collation and return a list of data.\n\n Returns:\n DataLoader: a torch DataLoader, that loads the given detection\n dataset, with test-time transformation and batching.\n\n Examples:\n ::\n data_loader = build_detection_test_loader(\n DatasetRegistry.get(\"my_test\"),\n mapper=DatasetMapper(...))\n\n # or, instantiate with a CfgNode:\n data_loader = build_detection_test_loader(cfg, \"my_test\")\n \"\"\"\n if isinstance(dataset, list):\n dataset = DatasetFromList(dataset, copy=False)\n if mapper is not None:\n dataset = MapDataset(dataset, mapper)\n if isinstance(dataset, torchdata.IterableDataset):\n assert sampler is None, \"sampler must be None if dataset is IterableDataset\"\n else:\n if sampler is None:\n sampler = InferenceSampler(len(dataset))\n return torchdata.DataLoader(\n dataset,\n batch_size=batch_size,\n sampler=sampler,\n drop_last=False,\n num_workers=num_workers,\n collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,\n )","source_hash":"56c5645a25fb4885e90646daff413837ea0fcf4bb12f4b5481d1f2a352de7463","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas_panoptic","uri":"program://OneFormer/module/oneformer.data.datasets.register_mapillary_vistas_panoptic#L1-L508","kind":"module","name":"oneformer.data.datasets.register_mapillary_vistas_panoptic","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":1,"end_line":508,"context_start_line":1,"context_end_line":508,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\nimport json\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.utils.file_io import PathManager\n\n\nMAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [\n {'color': [165, 42, 42],\n 'id': 1,\n 'isthing': 1,\n 'name': 'Bird',\n 'supercategory': 'animal--bird'},\n {'color': [0, 192, 0],\n 'id': 2,\n 'isthing': 1,\n 'name': 'Ground Animal',\n 'supercategory': 'animal--ground-animal'},\n {'color': [196, 196, 196],\n 'id': 3,\n 'isthing': 0,\n 'name': 'Curb',\n 'supercategory': 'construction--barrier--curb'},\n {'color': [190, 153, 153],\n 'id': 4,\n 'isthing': 0,\n 'name': 'Fence',\n 'supercategory': 'construction--barrier--fence'},\n {'color': [180, 165, 180],\n 'id': 5,\n 'isthing': 0,\n 'name': 'Guard Rail',\n 'supercategory': 'construction--barrier--guard-rail'},\n {'color': [90, 120, 150],\n 'id': 6,\n 'isthing': 0,\n 'name': 'Barrier',\n 'supercategory': 'construction--barrier--other-barrier'},\n {'color': [102, 102, 156],\n 'id': 7,\n 'isthing': 0,\n 'name': 'Wall',\n 'supercategory': 'construction--barrier--wall'},\n {'color': [128, 64, 255],\n 'id': 8,\n 'isthing': 0,\n 'name': 'Bike Lane',\n 'supercategory': 'construction--flat--bike-lane'},\n {'color': [140, 140, 200],\n 'id': 9,\n 'isthing': 1,\n 'name': 'Crosswalk - Plain',\n 'supercategory': 'construction--flat--crosswalk-plain'},\n {'color': [170, 170, 170],\n 'id': 10,\n 'isthing': 0,\n 'name': 'Curb Cut',\n 'supercategory': 'construction--flat--curb-cut'},\n {'color': [250, 170, 160],\n 'id': 11,\n 'isthing': 0,\n 'name': 'Parking',\n 'supercategory': 'construction--flat--parking'},\n {'color': [96, 96, 96],\n 'id': 12,\n 'isthing': 0,\n 'name': 'Pedestrian Area',\n 'supercategory': 'construction--flat--pedestrian-area'},\n {'color': [230, 150, 140],\n 'id': 13,\n 'isthing': 0,\n 'name': 'Rail Track',\n 'supercategory': 'construction--flat--rail-track'},\n {'color': [128, 64, 128],\n 'id': 14,\n 'isthing': 0,\n 'name': 'Road',\n 'supercategory': 'construction--flat--road'},\n {'color': [110, 110, 110],\n 'id': 15,\n 'isthing': 0,\n 'name': 'Service Lane',\n 'supercategory': 'construction--flat--service-lane'},\n {'color': [244, 35, 232],\n 'id': 16,\n 'isthing': 0,\n 'name': 'Sidewalk',\n 'supercategory': 'construction--flat--sidewalk'},\n {'color': [150, 100, 100],\n 'id': 17,\n 'isthing': 0,\n 'name': 'Bridge',\n 'supercategory': 'construction--structure--bridge'},\n {'color': [70, 70, 70],\n 'id': 18,\n 'isthing': 0,\n 'name': 'Building',\n 'supercategory': 'construction--structure--building'},\n {'color': [150, 120, 90],\n 'id': 19,\n 'isthing': 0,\n 'name': 'Tunnel',\n 'supercategory': 'construction--structure--tunnel'},\n {'color': [220, 20, 60],\n 'id': 20,\n 'isthing': 1,\n 'name': 'Person',\n 'supercategory': 'human--person'},\n {'color': [255, 0, 0],\n 'id': 21,\n 'isthing': 1,\n 'name': 'Bicyclist',\n 'supercategory': 'human--rider--bicyclist'},\n {'color': [255, 0, 100],\n 'id': 22,\n 'isthing': 1,\n 'name': 'Motorcyclist',\n 'supercategory': 'human--rider--motorcyclist'},\n {'color': [255, 0, 200],\n 'id': 23,\n 'isthing': 1,\n 'name': 'Other Rider',\n 'supercategory': 'human--rider--other-rider'},\n {'color': [200, 128, 128],\n 'id': 24,\n 'isthing': 1,\n 'name': 'Lane Marking - Crosswalk',\n 'supercategory': 'marking--crosswalk-zebra'},\n {'color': [255, 255, 255],\n 'id': 25,\n 'isthing': 0,\n 'name': 'Lane Marking - General',\n 'supercategory': 'marking--general'},\n {'color': [64, 170, 64],\n 'id': 26,\n 'isthing': 0,\n 'name': 'Mountain',\n 'supercategory': 'nature--mountain'},\n {'color': [230, 160, 50],\n 'id': 27,\n 'isthing': 0,\n 'name': 'Sand',\n 'supercategory': 'nature--sand'},\n {'color': [70, 130, 180],\n 'id': 28,\n 'isthing': 0,\n 'name': 'Sky',\n 'supercategory': 'nature--sky'},\n {'color': [190, 255, 255],\n 'id': 29,\n 'isthing': 0,\n 'name': 'Snow',\n 'supercategory': 'nature--snow'},\n {'color': [152, 251, 152],\n 'id': 30,\n 'isthing': 0,\n 'name': 'Terrain',\n 'supercategory': 'nature--terrain'},\n {'color': [107, 142, 35],\n 'id': 31,\n 'isthing': 0,\n 'name': 'Vegetation',\n 'supercategory': 'nature--vegetation'},\n {'color': [0, 170, 30],\n 'id': 32,\n 'isthing': 0,\n 'name': 'Water',\n 'supercategory': 'nature--water'},\n {'color': [255, 255, 128],\n 'id': 33,\n 'isthing': 1,\n 'name': 'Banner',\n 'supercategory': 'object--banner'},\n {'color': [250, 0, 30],\n 'id': 34,\n 'isthing': 1,\n 'name': 'Bench',\n 'supercategory': 'object--bench'},\n {'color': [100, 140, 180],\n 'id': 35,\n 'isthing': 1,\n 'name': 'Bike Rack',\n 'supercategory': 'object--bike-rack'},\n {'color': [220, 220, 220],\n 'id': 36,\n 'isthing': 1,\n 'name': 'Billboard',\n 'supercategory': 'object--billboard'},\n {'color': [220, 128, 128],\n 'id': 37,\n 'isthing': 1,\n 'name': 'Catch Basin',\n 'supercategory': 'object--catch-basin'},\n {'color': [222, 40, 40],\n 'id': 38,\n 'isthing': 1,\n 'name': 'CCTV Camera',\n 'supercategory': 'object--cctv-camera'},\n {'color': [100, 170, 30],\n 'id': 39,\n 'isthing': 1,\n 'name': 'Fire Hydrant',\n 'supercategory': 'object--fire-hydrant'},\n {'color': [40, 40, 40],\n 'id': 40,\n 'isthing': 1,\n 'name': 'Junction Box',\n 'supercategory': 'object--junction-box'},\n {'color': [33, 33, 33],\n 'id': 41,\n 'isthing': 1,\n 'name': 'Mailbox',\n 'supercategory': 'object--mailbox'},\n {'color': [100, 128, 160],\n 'id': 42,\n 'isthing': 1,\n 'name': 'Manhole',\n 'supercategory': 'object--manhole'},\n {'color': [142, 0, 0],\n 'id': 43,\n 'isthing': 1,\n 'name': 'Phone Booth',\n 'supercategory': 'object--phone-booth'},\n {'color': [70, 100, 150],\n 'id': 44,\n 'isthing': 0,\n 'name': 'Pothole',\n 'supercategory': 'object--pothole'},\n {'color': [210, 170, 100],\n 'id': 45,\n 'isthing': 1,\n 'name': 'Street Light',\n 'supercategory': 'object--street-light'},\n {'color': [153, 153, 153],\n 'id': 46,\n 'isthing': 1,\n 'name': 'Pole',\n 'supercategory': 'object--support--pole'},\n {'color': [128, 128, 128],\n 'id': 47,\n 'isthing': 1,\n 'name': 'Traffic Sign Frame',\n 'supercategory': 'object--support--traffic-sign-frame'},\n {'color': [0, 0, 80],\n 'id': 48,\n 'isthing': 1,\n 'name': 'Utility Pole',\n 'supercategory': 'object--support--utility-pole'},\n {'color': [250, 170, 30],\n 'id': 49,\n 'isthing': 1,\n 'name': 'Traffic Light',\n 'supercategory': 'object--traffic-light'},\n {'color': [192, 192, 192],\n 'id': 50,\n 'isthing': 1,\n 'name': 'Traffic Sign (Back)',\n 'supercategory': 'object--traffic-sign--back'},\n {'color': [220, 220, 0],\n 'id': 51,\n 'isthing': 1,\n 'name': 'Traffic Sign (Front)',\n 'supercategory': 'object--traffic-sign--front'},\n {'color': [140, 140, 20],\n 'id': 52,\n 'isthing': 1,\n 'name': 'Trash Can',\n 'supercategory': 'object--trash-can'},\n {'color': [119, 11, 32],\n 'id': 53,\n 'isthing': 1,\n 'name': 'Bicycle',\n 'supercategory': 'object--vehicle--bicycle'},\n {'color': [150, 0, 255],\n 'id': 54,\n 'isthing': 1,\n 'name': 'Boat',\n 'supercategory': 'object--vehicle--boat'},\n {'color': [0, 60, 100],\n 'id': 55,\n 'isthing': 1,\n 'name': 'Bus',\n 'supercategory': 'object--vehicle--bus'},\n {'color': [0, 0, 142],\n 'id': 56,\n 'isthing': 1,\n 'name': 'Car',\n 'supercategory': 'object--vehicle--car'},\n {'color': [0, 0, 90],\n 'id': 57,\n 'isthing': 1,\n 'name': 'Caravan',\n 'supercategory': 'object--vehicle--caravan'},\n {'color': [0, 0, 230],\n 'id': 58,\n 'isthing': 1,\n 'name': 'Motorcycle',\n 'supercategory': 'object--vehicle--motorcycle'},\n {'color': [0, 80, 100],\n 'id': 59,\n 'isthing': 0,\n 'name': 'On Rails',\n 'supercategory': 'object--vehicle--on-rails'},\n {'color': [128, 64, 64],\n 'id': 60,\n 'isthing': 1,\n 'name': 'Other Vehicle',\n 'supercategory': 'object--vehicle--other-vehicle'},\n {'color': [0, 0, 110],\n 'id': 61,\n 'isthing': 1,\n 'name': 'Trailer',\n 'supercategory': 'object--vehicle--trailer'},\n {'color': [0, 0, 70],\n 'id': 62,\n 'isthing': 1,\n 'name': 'Truck',\n 'supercategory': 'object--vehicle--truck'},\n {'color': [0, 0, 192],\n 'id': 63,\n 'isthing': 1,\n 'name': 'Wheeled Slow',\n 'supercategory': 'object--vehicle--wheeled-slow'},\n {'color': [32, 32, 32],\n 'id': 64,\n 'isthing': 0,\n 'name': 'Car Mount',\n 'supercategory': 'void--car-mount'},\n {'color': [120, 10, 10],\n 'id': 65,\n 'isthing': 0,\n 'name': 'Ego Vehicle',\n 'supercategory': 'void--ego-vehicle'}\n]\n\n\ndef load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n\n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = ann[\"image_id\"]\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_mapillary_vistas_panoptic(\n name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None\n):\n \"\"\"\n Register a \"standard\" version of ADE20k panoptic segmentation dataset named `name`.\n The dictionaries in this registered dataset follows detectron2's standard format.\n Hence it's called \"standard\".\n Args:\n name (str): the name that identifies a dataset,\n e.g. \"ade20k_panoptic_train\"\n metadata (dict): extra metadata associated with this dataset.\n image_root (str): directory which contains all the images\n panoptic_root (str): directory which contains panoptic annotation images in COCO format\n panoptic_json (str): path to the json panoptic annotation file in COCO format\n sem_seg_root (none): not used, to be consistent with\n `register_coco_panoptic_separated`.\n instances_json (str): path to the json instance annotation file\n \"\"\"\n panoptic_name = name\n DatasetCatalog.register(\n panoptic_name,\n lambda: load_mapillary_vistas_panoptic_json(\n panoptic_json, image_root, panoptic_root, semantic_root, metadata\n ),\n )\n MetadataCatalog.get(panoptic_name).set(\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"mapillary_vistas_panoptic_seg\",\n ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65\n label_divisor=1000,\n **metadata,\n )\n\n\n_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {\n \"mapillary_vistas_panoptic_train\": (\n \"mapillary_vistas/training/images\",\n \"mapillary_vistas/training/panoptic\",\n \"mapillary_vistas/training/panoptic/panoptic_2018.json\",\n \"mapillary_vistas/training/labels\",\n ),\n \"mapillary_vistas_panoptic_val\": (\n \"mapillary_vistas/validation/images\",\n \"mapillary_vistas/validation/panoptic\",\n \"mapillary_vistas/validation/panoptic/panoptic_2018.json\",\n \"mapillary_vistas/validation/labels\",\n ),\n}\n\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n thing_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n stuff_classes = [k[\"name\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:\n # category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef register_all_mapillary_vistas_panoptic(root):\n metadata = get_metadata()\n for (\n prefix,\n (image_root, panoptic_root, panoptic_json, semantic_root),\n ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():\n # The \"standard\" version of COCO panoptic segmentation dataset,\n # e.g. used by Panoptic-DeepLab\n register_mapillary_vistas_panoptic(\n prefix,\n metadata,\n os.path.join(root, image_root),\n os.path.join(root, panoptic_root),\n os.path.join(root, semantic_root),\n os.path.join(root, panoptic_json),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_mapillary_vistas_panoptic(_root)","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas_panoptic.load_mapillary_vistas_panoptic_json","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas_panoptic.load_mapillary_vistas_panoptic_json#L338-L389","kind":"function","name":"load_mapillary_vistas_panoptic_json","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":338,"end_line":389,"context_start_line":318,"context_end_line":409,"code":" 'name': 'Truck',\n 'supercategory': 'object--vehicle--truck'},\n {'color': [0, 0, 192],\n 'id': 63,\n 'isthing': 1,\n 'name': 'Wheeled Slow',\n 'supercategory': 'object--vehicle--wheeled-slow'},\n {'color': [32, 32, 32],\n 'id': 64,\n 'isthing': 0,\n 'name': 'Car Mount',\n 'supercategory': 'void--car-mount'},\n {'color': [120, 10, 10],\n 'id': 65,\n 'isthing': 0,\n 'name': 'Ego Vehicle',\n 'supercategory': 'void--ego-vehicle'}\n]\n\n\ndef load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n\n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = ann[\"image_id\"]\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_mapillary_vistas_panoptic(\n name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None\n):\n \"\"\"\n Register a \"standard\" version of ADE20k panoptic segmentation dataset named `name`.\n The dictionaries in this registered dataset follows detectron2's standard format.\n Hence it's called \"standard\".\n Args:\n name (str): the name that identifies a dataset,\n e.g. \"ade20k_panoptic_train\"\n metadata (dict): extra metadata associated with this dataset.\n image_root (str): directory which contains all the images\n panoptic_root (str): directory which contains panoptic annotation images in COCO format\n panoptic_json (str): path to the json panoptic annotation file in COCO format\n sem_seg_root (none): not used, to be consistent with\n `register_coco_panoptic_separated`.\n instances_json (str): path to the json instance annotation file\n \"\"\"","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas_panoptic.register_mapillary_vistas_panoptic","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas_panoptic.register_mapillary_vistas_panoptic#L392-L426","kind":"function","name":"register_mapillary_vistas_panoptic","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":392,"end_line":426,"context_start_line":372,"context_end_line":446,"code":" image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_mapillary_vistas_panoptic(\n name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None\n):\n \"\"\"\n Register a \"standard\" version of ADE20k panoptic segmentation dataset named `name`.\n The dictionaries in this registered dataset follows detectron2's standard format.\n Hence it's called \"standard\".\n Args:\n name (str): the name that identifies a dataset,\n e.g. \"ade20k_panoptic_train\"\n metadata (dict): extra metadata associated with this dataset.\n image_root (str): directory which contains all the images\n panoptic_root (str): directory which contains panoptic annotation images in COCO format\n panoptic_json (str): path to the json panoptic annotation file in COCO format\n sem_seg_root (none): not used, to be consistent with\n `register_coco_panoptic_separated`.\n instances_json (str): path to the json instance annotation file\n \"\"\"\n panoptic_name = name\n DatasetCatalog.register(\n panoptic_name,\n lambda: load_mapillary_vistas_panoptic_json(\n panoptic_json, image_root, panoptic_root, semantic_root, metadata\n ),\n )\n MetadataCatalog.get(panoptic_name).set(\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"mapillary_vistas_panoptic_seg\",\n ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65\n label_divisor=1000,\n **metadata,\n )\n\n\n_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {\n \"mapillary_vistas_panoptic_train\": (\n \"mapillary_vistas/training/images\",\n \"mapillary_vistas/training/panoptic\",\n \"mapillary_vistas/training/panoptic/panoptic_2018.json\",\n \"mapillary_vistas/training/labels\",\n ),\n \"mapillary_vistas_panoptic_val\": (\n \"mapillary_vistas/validation/images\",\n \"mapillary_vistas/validation/panoptic\",\n \"mapillary_vistas/validation/panoptic/panoptic_2018.json\",\n \"mapillary_vistas/validation/labels\",\n ),\n}\n\n\ndef get_metadata():\n meta = {}","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas_panoptic.get_metadata","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas_panoptic.get_metadata#L445-L486","kind":"function","name":"get_metadata","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":445,"end_line":486,"context_start_line":425,"context_end_line":506,"code":" **metadata,\n )\n\n\n_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {\n \"mapillary_vistas_panoptic_train\": (\n \"mapillary_vistas/training/images\",\n \"mapillary_vistas/training/panoptic\",\n \"mapillary_vistas/training/panoptic/panoptic_2018.json\",\n \"mapillary_vistas/training/labels\",\n ),\n \"mapillary_vistas_panoptic_val\": (\n \"mapillary_vistas/validation/images\",\n \"mapillary_vistas/validation/panoptic\",\n \"mapillary_vistas/validation/panoptic/panoptic_2018.json\",\n \"mapillary_vistas/validation/labels\",\n ),\n}\n\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n thing_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n stuff_classes = [k[\"name\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:\n # category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef register_all_mapillary_vistas_panoptic(root):\n metadata = get_metadata()\n for (\n prefix,\n (image_root, panoptic_root, panoptic_json, semantic_root),\n ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():\n # The \"standard\" version of COCO panoptic segmentation dataset,\n # e.g. used by Panoptic-DeepLab\n register_mapillary_vistas_panoptic(\n prefix,\n metadata,\n os.path.join(root, image_root),\n os.path.join(root, panoptic_root),\n os.path.join(root, semantic_root),\n os.path.join(root, panoptic_json),\n )\n\n","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas_panoptic.register_all_mapillary_vistas_panoptic","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas_panoptic.register_all_mapillary_vistas_panoptic#L489-L504","kind":"function","name":"register_all_mapillary_vistas_panoptic","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":489,"end_line":504,"context_start_line":469,"context_end_line":508,"code":" # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef register_all_mapillary_vistas_panoptic(root):\n metadata = get_metadata()\n for (\n prefix,\n (image_root, panoptic_root, panoptic_json, semantic_root),\n ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():\n # The \"standard\" version of COCO panoptic segmentation dataset,\n # e.g. used by Panoptic-DeepLab\n register_mapillary_vistas_panoptic(\n prefix,\n metadata,\n os.path.join(root, image_root),\n os.path.join(root, panoptic_root),\n os.path.join(root, semantic_root),\n os.path.join(root, panoptic_json),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_mapillary_vistas_panoptic(_root)","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas_panoptic._convert_category_id","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas_panoptic._convert_category_id#L349-L360","kind":"function","name":"_convert_category_id","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":349,"end_line":360,"context_start_line":329,"context_end_line":380,"code":" 'supercategory': 'void--car-mount'},\n {'color': [120, 10, 10],\n 'id': 65,\n 'isthing': 0,\n 'name': 'Ego Vehicle',\n 'supercategory': 'void--ego-vehicle'}\n]\n\n\ndef load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n\n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = ann[\"image_id\"]\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic2instance","uri":"program://OneFormer/module/oneformer.data.datasets.register_coco_panoptic2instance#L1-L44","kind":"module","name":"oneformer.data.datasets.register_coco_panoptic2instance","path":"oneformer/data/datasets/register_coco_panoptic2instance.py","language":"python","start_line":1,"end_line":44,"context_start_line":1,"context_end_line":44,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\n\"\"\"\nThis file registers pre-defined datasets at hard-coded paths, and their metadata.\n\nWe hard-code metadata for common datasets. This will enable:\n1. Consistency check when loading the datasets\n2. Use models on these standard datasets directly and run demos,\n without having to download the dataset annotations\n\nWe hard-code some paths to the dataset that's assumed to\nexist in \"./datasets/\".\n\nUsers SHOULD NOT use this file to create new dataset / metadata for new dataset.\nTo add new dataset, refer to the tutorial \"docs/DATASETS.md\".\n\"\"\"\n\nimport os\nfrom detectron2.data.datasets.builtin_meta import _get_builtin_metadata\nfrom detectron2.data.datasets.coco import register_coco_instances\n\n\n_PREDEFINED_SPLITS_COCO = {\n \"coco_2017_val_panoptic2instance\": (\"coco/val2017\", \"coco/annotations/panoptic2instances_val2017.json\"),\n}\n\n\ndef register_panoptic2instances_coco(root):\n for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items():\n # Assume pre-defined datasets live in `./datasets`.\n register_coco_instances(\n key,\n _get_builtin_metadata(\"coco\"),\n os.path.join(root, json_file) if \"://\" not in json_file else json_file,\n os.path.join(root, image_root),\n )\n\n\n_root = os.path.expanduser(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\"))\nregister_panoptic2instances_coco(_root)","source_hash":"e63944245fac85e47a4093d155f94569531b21a08eed4c526939c5d594aeb9c3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic2instance.register_panoptic2instances_coco","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic2instance.register_panoptic2instances_coco#L32-L40","kind":"function","name":"register_panoptic2instances_coco","path":"oneformer/data/datasets/register_coco_panoptic2instance.py","language":"python","start_line":32,"end_line":40,"context_start_line":12,"context_end_line":44,"code":"2. Use models on these standard datasets directly and run demos,\n without having to download the dataset annotations\n\nWe hard-code some paths to the dataset that's assumed to\nexist in \"./datasets/\".\n\nUsers SHOULD NOT use this file to create new dataset / metadata for new dataset.\nTo add new dataset, refer to the tutorial \"docs/DATASETS.md\".\n\"\"\"\n\nimport os\nfrom detectron2.data.datasets.builtin_meta import _get_builtin_metadata\nfrom detectron2.data.datasets.coco import register_coco_instances\n\n\n_PREDEFINED_SPLITS_COCO = {\n \"coco_2017_val_panoptic2instance\": (\"coco/val2017\", \"coco/annotations/panoptic2instances_val2017.json\"),\n}\n\n\ndef register_panoptic2instances_coco(root):\n for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items():\n # Assume pre-defined datasets live in `./datasets`.\n register_coco_instances(\n key,\n _get_builtin_metadata(\"coco\"),\n os.path.join(root, json_file) if \"://\" not in json_file else json_file,\n os.path.join(root, image_root),\n )\n\n\n_root = os.path.expanduser(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\"))\nregister_panoptic2instances_coco(_root)","source_hash":"e63944245fac85e47a4093d155f94569531b21a08eed4c526939c5d594aeb9c3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas","uri":"program://OneFormer/module/oneformer.data.datasets.register_mapillary_vistas#L1-L507","kind":"module","name":"oneformer.data.datasets.register_mapillary_vistas","path":"oneformer/data/datasets/register_mapillary_vistas.py","language":"python","start_line":1,"end_line":507,"context_start_line":1,"context_end_line":507,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets import load_sem_seg\n\nMAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [\n {\n \"color\": [165, 42, 42],\n \"instances\": True,\n \"readable\": \"Bird\",\n \"name\": \"animal--bird\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 192, 0],\n \"instances\": True,\n \"readable\": \"Ground Animal\",\n \"name\": \"animal--ground-animal\",\n \"evaluate\": True,\n },\n {\n \"color\": [196, 196, 196],\n \"instances\": False,\n \"readable\": \"Curb\",\n \"name\": \"construction--barrier--curb\",\n \"evaluate\": True,\n },\n {\n \"color\": [190, 153, 153],\n \"instances\": False,\n \"readable\": \"Fence\",\n \"name\": \"construction--barrier--fence\",\n \"evaluate\": True,\n },\n {\n \"color\": [180, 165, 180],\n \"instances\": False,\n \"readable\": \"Guard Rail\",\n \"name\": \"construction--barrier--guard-rail\",\n \"evaluate\": True,\n },\n {\n \"color\": [90, 120, 150],\n \"instances\": False,\n \"readable\": \"Barrier\",\n \"name\": \"construction--barrier--other-barrier\",\n \"evaluate\": True,\n },\n {\n \"color\": [102, 102, 156],\n \"instances\": False,\n \"readable\": \"Wall\",\n \"name\": \"construction--barrier--wall\",\n \"evaluate\": True,\n },\n {\n \"color\": [128, 64, 255],\n \"instances\": False,\n \"readable\": \"Bike Lane\",\n \"name\": \"construction--flat--bike-lane\",\n \"evaluate\": True,\n },\n {\n \"color\": [140, 140, 200],\n \"instances\": True,\n \"readable\": \"Crosswalk - Plain\",\n \"name\": \"construction--flat--crosswalk-plain\",\n \"evaluate\": True,\n },\n {\n \"color\": [170, 170, 170],\n \"instances\": False,\n \"readable\": \"Curb Cut\",\n \"name\": \"construction--flat--curb-cut\",\n \"evaluate\": True,\n },\n {\n \"color\": [250, 170, 160],\n \"instances\": False,\n \"readable\": \"Parking\",\n \"name\": \"construction--flat--parking\",\n \"evaluate\": True,\n },\n {\n \"color\": [96, 96, 96],\n \"instances\": False,\n \"readable\": \"Pedestrian Area\",\n \"name\": \"construction--flat--pedestrian-area\",\n \"evaluate\": True,\n },\n {\n \"color\": [230, 150, 140],\n \"instances\": False,\n \"readable\": \"Rail Track\",\n \"name\": \"construction--flat--rail-track\",\n \"evaluate\": True,\n },\n {\n \"color\": [128, 64, 128],\n \"instances\": False,\n \"readable\": \"Road\",\n \"name\": \"construction--flat--road\",\n \"evaluate\": True,\n },\n {\n \"color\": [110, 110, 110],\n \"instances\": False,\n \"readable\": \"Service Lane\",\n \"name\": \"construction--flat--service-lane\",\n \"evaluate\": True,\n },\n {\n \"color\": [244, 35, 232],\n \"instances\": False,\n \"readable\": \"Sidewalk\",\n \"name\": \"construction--flat--sidewalk\",\n \"evaluate\": True,\n },\n {\n \"color\": [150, 100, 100],\n \"instances\": False,\n \"readable\": \"Bridge\",\n \"name\": \"construction--structure--bridge\",\n \"evaluate\": True,\n },\n {\n \"color\": [70, 70, 70],\n \"instances\": False,\n \"readable\": \"Building\",\n \"name\": \"construction--structure--building\",\n \"evaluate\": True,\n },\n {\n \"color\": [150, 120, 90],\n \"instances\": False,\n \"readable\": \"Tunnel\",\n \"name\": \"construction--structure--tunnel\",\n \"evaluate\": True,\n },\n {\n \"color\": [220, 20, 60],\n \"instances\": True,\n \"readable\": \"Person\",\n \"name\": \"human--person\",\n \"evaluate\": True,\n },\n {\n \"color\": [255, 0, 0],\n \"instances\": True,\n \"readable\": \"Bicyclist\",\n \"name\": \"human--rider--bicyclist\",\n \"evaluate\": True,\n },\n {\n \"color\": [255, 0, 100],\n \"instances\": True,\n \"readable\": \"Motorcyclist\",\n \"name\": \"human--rider--motorcyclist\",\n \"evaluate\": True,\n },\n {\n \"color\": [255, 0, 200],\n \"instances\": True,\n \"readable\": \"Other Rider\",\n \"name\": \"human--rider--other-rider\",\n \"evaluate\": True,\n },\n {\n \"color\": [200, 128, 128],\n \"instances\": True,\n \"readable\": \"Lane Marking - Crosswalk\",\n \"name\": \"marking--crosswalk-zebra\",\n \"evaluate\": True,\n },\n {\n \"color\": [255, 255, 255],\n \"instances\": False,\n \"readable\": \"Lane Marking - General\",\n \"name\": \"marking--general\",\n \"evaluate\": True,\n },\n {\n \"color\": [64, 170, 64],\n \"instances\": False,\n \"readable\": \"Mountain\",\n \"name\": \"nature--mountain\",\n \"evaluate\": True,\n },\n {\n \"color\": [230, 160, 50],\n \"instances\": False,\n \"readable\": \"Sand\",\n \"name\": \"nature--sand\",\n \"evaluate\": True,\n },\n {\n \"color\": [70, 130, 180],\n \"instances\": False,\n \"readable\": \"Sky\",\n \"name\": \"nature--sky\",\n \"evaluate\": True,\n },\n {\n \"color\": [190, 255, 255],\n \"instances\": False,\n \"readable\": \"Snow\",\n \"name\": \"nature--snow\",\n \"evaluate\": True,\n },\n {\n \"color\": [152, 251, 152],\n \"instances\": False,\n \"readable\": \"Terrain\",\n \"name\": \"nature--terrain\",\n \"evaluate\": True,\n },\n {\n \"color\": [107, 142, 35],\n \"instances\": False,\n \"readable\": \"Vegetation\",\n \"name\": \"nature--vegetation\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 170, 30],\n \"instances\": False,\n \"readable\": \"Water\",\n \"name\": \"nature--water\",\n \"evaluate\": True,\n },\n {\n \"color\": [255, 255, 128],\n \"instances\": True,\n \"readable\": \"Banner\",\n \"name\": \"object--banner\",\n \"evaluate\": True,\n },\n {\n \"color\": [250, 0, 30],\n \"instances\": True,\n \"readable\": \"Bench\",\n \"name\": \"object--bench\",\n \"evaluate\": True,\n },\n {\n \"color\": [100, 140, 180],\n \"instances\": True,\n \"readable\": \"Bike Rack\",\n \"name\": \"object--bike-rack\",\n \"evaluate\": True,\n },\n {\n \"color\": [220, 220, 220],\n \"instances\": True,\n \"readable\": \"Billboard\",\n \"name\": \"object--billboard\",\n \"evaluate\": True,\n },\n {\n \"color\": [220, 128, 128],\n \"instances\": True,\n \"readable\": \"Catch Basin\",\n \"name\": \"object--catch-basin\",\n \"evaluate\": True,\n },\n {\n \"color\": [222, 40, 40],\n \"instances\": True,\n \"readable\": \"CCTV Camera\",\n \"name\": \"object--cctv-camera\",\n \"evaluate\": True,\n },\n {\n \"color\": [100, 170, 30],\n \"instances\": True,\n \"readable\": \"Fire Hydrant\",\n \"name\": \"object--fire-hydrant\",\n \"evaluate\": True,\n },\n {\n \"color\": [40, 40, 40],\n \"instances\": True,\n \"readable\": \"Junction Box\",\n \"name\": \"object--junction-box\",\n \"evaluate\": True,\n },\n {\n \"color\": [33, 33, 33],\n \"instances\": True,\n \"readable\": \"Mailbox\",\n \"name\": \"object--mailbox\",\n \"evaluate\": True,\n },\n {\n \"color\": [100, 128, 160],\n \"instances\": True,\n \"readable\": \"Manhole\",\n \"name\": \"object--manhole\",\n \"evaluate\": True,\n },\n {\n \"color\": [142, 0, 0],\n \"instances\": True,\n \"readable\": \"Phone Booth\",\n \"name\": \"object--phone-booth\",\n \"evaluate\": True,\n },\n {\n \"color\": [70, 100, 150],\n \"instances\": False,\n \"readable\": \"Pothole\",\n \"name\": \"object--pothole\",\n \"evaluate\": True,\n },\n {\n \"color\": [210, 170, 100],\n \"instances\": True,\n \"readable\": \"Street Light\",\n \"name\": \"object--street-light\",\n \"evaluate\": True,\n },\n {\n \"color\": [153, 153, 153],\n \"instances\": True,\n \"readable\": \"Pole\",\n \"name\": \"object--support--pole\",\n \"evaluate\": True,\n },\n {\n \"color\": [128, 128, 128],\n \"instances\": True,\n \"readable\": \"Traffic Sign Frame\",\n \"name\": \"object--support--traffic-sign-frame\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 80],\n \"instances\": True,\n \"readable\": \"Utility Pole\",\n \"name\": \"object--support--utility-pole\",\n \"evaluate\": True,\n },\n {\n \"color\": [250, 170, 30],\n \"instances\": True,\n \"readable\": \"Traffic Light\",\n \"name\": \"object--traffic-light\",\n \"evaluate\": True,\n },\n {\n \"color\": [192, 192, 192],\n \"instances\": True,\n \"readable\": \"Traffic Sign (Back)\",\n \"name\": \"object--traffic-sign--back\",\n \"evaluate\": True,\n },\n {\n \"color\": [220, 220, 0],\n \"instances\": True,\n \"readable\": \"Traffic Sign (Front)\",\n \"name\": \"object--traffic-sign--front\",\n \"evaluate\": True,\n },\n {\n \"color\": [140, 140, 20],\n \"instances\": True,\n \"readable\": \"Trash Can\",\n \"name\": \"object--trash-can\",\n \"evaluate\": True,\n },\n {\n \"color\": [119, 11, 32],\n \"instances\": True,\n \"readable\": \"Bicycle\",\n \"name\": \"object--vehicle--bicycle\",\n \"evaluate\": True,\n },\n {\n \"color\": [150, 0, 255],\n \"instances\": True,\n \"readable\": \"Boat\",\n \"name\": \"object--vehicle--boat\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 60, 100],\n \"instances\": True,\n \"readable\": \"Bus\",\n \"name\": \"object--vehicle--bus\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 142],\n \"instances\": True,\n \"readable\": \"Car\",\n \"name\": \"object--vehicle--car\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 90],\n \"instances\": True,\n \"readable\": \"Caravan\",\n \"name\": \"object--vehicle--caravan\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 230],\n \"instances\": True,\n \"readable\": \"Motorcycle\",\n \"name\": \"object--vehicle--motorcycle\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 80, 100],\n \"instances\": False,\n \"readable\": \"On Rails\",\n \"name\": \"object--vehicle--on-rails\",\n \"evaluate\": True,\n },\n {\n \"color\": [128, 64, 64],\n \"instances\": True,\n \"readable\": \"Other Vehicle\",\n \"name\": \"object--vehicle--other-vehicle\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 110],\n \"instances\": True,\n \"readable\": \"Trailer\",\n \"name\": \"object--vehicle--trailer\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 70],\n \"instances\": True,\n \"readable\": \"Truck\",\n \"name\": \"object--vehicle--truck\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 192],\n \"instances\": True,\n \"readable\": \"Wheeled Slow\",\n \"name\": \"object--vehicle--wheeled-slow\",\n \"evaluate\": True,\n },\n {\n \"color\": [32, 32, 32],\n \"instances\": False,\n \"readable\": \"Car Mount\",\n \"name\": \"void--car-mount\",\n \"evaluate\": True,\n },\n {\n \"color\": [120, 10, 10],\n \"instances\": False,\n \"readable\": \"Ego Vehicle\",\n \"name\": \"void--ego-vehicle\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 0],\n \"instances\": False,\n \"readable\": \"Unlabeled\",\n \"name\": \"void--unlabeled\",\n \"evaluate\": False,\n },\n]\n\n\ndef _get_mapillary_vistas_meta():\n stuff_classes = [k[\"readable\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k[\"evaluate\"]]\n assert len(stuff_classes) == 65\n\n stuff_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k[\"evaluate\"]]\n assert len(stuff_colors) == 65\n\n ret = {\n \"stuff_classes\": stuff_classes,\n \"stuff_colors\": stuff_colors,\n }\n return ret\n\n\ndef register_all_mapillary_vistas(root):\n root = os.path.join(root, \"mapillary_vistas\")\n meta = _get_mapillary_vistas_meta()\n for name, dirname in [(\"train\", \"training\"), (\"val\", \"validation\")]:\n image_dir = os.path.join(root, dirname, \"images\")\n gt_dir = os.path.join(root, dirname, \"labels\")\n name = f\"mapillary_vistas_sem_seg_{name}\"\n DatasetCatalog.register(\n name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext=\"png\", image_ext=\"jpg\")\n )\n MetadataCatalog.get(name).set(\n image_root=image_dir,\n sem_seg_root=gt_dir,\n evaluator_type=\"sem_seg\",\n ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65\n **meta,\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_mapillary_vistas(_root)","source_hash":"cee33a4d7350cee9f8fdccbcb6e963a5d2764563d459c93a64d7a43c16b36971","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas._get_mapillary_vistas_meta","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas._get_mapillary_vistas_meta#L473-L484","kind":"function","name":"_get_mapillary_vistas_meta","path":"oneformer/data/datasets/register_mapillary_vistas.py","language":"python","start_line":473,"end_line":484,"context_start_line":453,"context_end_line":504,"code":" \"name\": \"void--car-mount\",\n \"evaluate\": True,\n },\n {\n \"color\": [120, 10, 10],\n \"instances\": False,\n \"readable\": \"Ego Vehicle\",\n \"name\": \"void--ego-vehicle\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 0, 0],\n \"instances\": False,\n \"readable\": \"Unlabeled\",\n \"name\": \"void--unlabeled\",\n \"evaluate\": False,\n },\n]\n\n\ndef _get_mapillary_vistas_meta():\n stuff_classes = [k[\"readable\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k[\"evaluate\"]]\n assert len(stuff_classes) == 65\n\n stuff_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k[\"evaluate\"]]\n assert len(stuff_colors) == 65\n\n ret = {\n \"stuff_classes\": stuff_classes,\n \"stuff_colors\": stuff_colors,\n }\n return ret\n\n\ndef register_all_mapillary_vistas(root):\n root = os.path.join(root, \"mapillary_vistas\")\n meta = _get_mapillary_vistas_meta()\n for name, dirname in [(\"train\", \"training\"), (\"val\", \"validation\")]:\n image_dir = os.path.join(root, dirname, \"images\")\n gt_dir = os.path.join(root, dirname, \"labels\")\n name = f\"mapillary_vistas_sem_seg_{name}\"\n DatasetCatalog.register(\n name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext=\"png\", image_ext=\"jpg\")\n )\n MetadataCatalog.get(name).set(\n image_root=image_dir,\n sem_seg_root=gt_dir,\n evaluator_type=\"sem_seg\",\n ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65\n **meta,\n )\n","source_hash":"cee33a4d7350cee9f8fdccbcb6e963a5d2764563d459c93a64d7a43c16b36971","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_mapillary_vistas.register_all_mapillary_vistas","uri":"program://OneFormer/function/oneformer.data.datasets.register_mapillary_vistas.register_all_mapillary_vistas#L487-L503","kind":"function","name":"register_all_mapillary_vistas","path":"oneformer/data/datasets/register_mapillary_vistas.py","language":"python","start_line":487,"end_line":503,"context_start_line":467,"context_end_line":507,"code":" \"name\": \"void--unlabeled\",\n \"evaluate\": False,\n },\n]\n\n\ndef _get_mapillary_vistas_meta():\n stuff_classes = [k[\"readable\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k[\"evaluate\"]]\n assert len(stuff_classes) == 65\n\n stuff_colors = [k[\"color\"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k[\"evaluate\"]]\n assert len(stuff_colors) == 65\n\n ret = {\n \"stuff_classes\": stuff_classes,\n \"stuff_colors\": stuff_colors,\n }\n return ret\n\n\ndef register_all_mapillary_vistas(root):\n root = os.path.join(root, \"mapillary_vistas\")\n meta = _get_mapillary_vistas_meta()\n for name, dirname in [(\"train\", \"training\"), (\"val\", \"validation\")]:\n image_dir = os.path.join(root, dirname, \"images\")\n gt_dir = os.path.join(root, dirname, \"labels\")\n name = f\"mapillary_vistas_sem_seg_{name}\"\n DatasetCatalog.register(\n name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext=\"png\", image_ext=\"jpg\")\n )\n MetadataCatalog.get(name).set(\n image_root=image_dir,\n sem_seg_root=gt_dir,\n evaluator_type=\"sem_seg\",\n ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65\n **meta,\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_mapillary_vistas(_root)","source_hash":"cee33a4d7350cee9f8fdccbcb6e963a5d2764563d459c93a64d7a43c16b36971","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_instance","uri":"program://OneFormer/module/oneformer.data.datasets.register_ade20k_instance#L1-L56","kind":"module","name":"oneformer.data.datasets.register_ade20k_instance","path":"oneformer/data/datasets/register_ade20k_instance.py","language":"python","start_line":1,"end_line":56,"context_start_line":1,"context_end_line":56,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_instance.py\n# ------------------------------------------------------------------------------\n\nimport json\nimport logging\nimport numpy as np\nimport os\nfrom PIL import Image\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.coco import load_coco_json, register_coco_instances\nfrom detectron2.utils.file_io import PathManager\n\nADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]\n\n\n_PREDEFINED_SPLITS = {\n # point annotations without masks\n \"ade20k_instance_train\": (\n \"ADEChallengeData2016/images/training\",\n \"ADEChallengeData2016/ade20k_instance_train.json\",\n ),\n \"ade20k_instance_val\": (\n \"ADEChallengeData2016/images/validation\",\n \"ADEChallengeData2016/ade20k_instance_val.json\",\n ),\n}\n\n\ndef _get_ade_instances_meta():\n thing_ids = [k[\"id\"] for k in ADE_CATEGORIES]\n assert len(thing_ids) == 100, len(thing_ids)\n # Mapping from the incontiguous ADE category id to an id in [0, 99]\n thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}\n thing_classes = [k[\"name\"] for k in ADE_CATEGORIES]\n ret = {\n \"thing_dataset_id_to_contiguous_id\": thing_dataset_id_to_contiguous_id,\n \"thing_classes\": thing_classes,\n }\n return ret\n\n\ndef register_all_ade20k_instance(root):\n for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():\n # Assume pre-defined datasets live in `./datasets`.\n register_coco_instances(\n key,\n _get_ade_instances_meta(),\n os.path.join(root, json_file) if \"://\" not in json_file else json_file,\n os.path.join(root, image_root),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_ade20k_instance(_root)","source_hash":"d6dce919a13fa895657f631af55988e0184ea3609190acd5bf823da0573c02d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_instance._get_ade_instances_meta","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_instance._get_ade_instances_meta#L31-L41","kind":"function","name":"_get_ade_instances_meta","path":"oneformer/data/datasets/register_ade20k_instance.py","language":"python","start_line":31,"end_line":41,"context_start_line":11,"context_end_line":56,"code":"from detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.coco import load_coco_json, register_coco_instances\nfrom detectron2.utils.file_io import PathManager\n\nADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]\n\n\n_PREDEFINED_SPLITS = {\n # point annotations without masks\n \"ade20k_instance_train\": (\n \"ADEChallengeData2016/images/training\",\n \"ADEChallengeData2016/ade20k_instance_train.json\",\n ),\n \"ade20k_instance_val\": (\n \"ADEChallengeData2016/images/validation\",\n \"ADEChallengeData2016/ade20k_instance_val.json\",\n ),\n}\n\n\ndef _get_ade_instances_meta():\n thing_ids = [k[\"id\"] for k in ADE_CATEGORIES]\n assert len(thing_ids) == 100, len(thing_ids)\n # Mapping from the incontiguous ADE category id to an id in [0, 99]\n thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}\n thing_classes = [k[\"name\"] for k in ADE_CATEGORIES]\n ret = {\n \"thing_dataset_id_to_contiguous_id\": thing_dataset_id_to_contiguous_id,\n \"thing_classes\": thing_classes,\n }\n return ret\n\n\ndef register_all_ade20k_instance(root):\n for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():\n # Assume pre-defined datasets live in `./datasets`.\n register_coco_instances(\n key,\n _get_ade_instances_meta(),\n os.path.join(root, json_file) if \"://\" not in json_file else json_file,\n os.path.join(root, image_root),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_ade20k_instance(_root)","source_hash":"d6dce919a13fa895657f631af55988e0184ea3609190acd5bf823da0573c02d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_instance.register_all_ade20k_instance","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_instance.register_all_ade20k_instance#L44-L52","kind":"function","name":"register_all_ade20k_instance","path":"oneformer/data/datasets/register_ade20k_instance.py","language":"python","start_line":44,"end_line":52,"context_start_line":24,"context_end_line":56,"code":" \"ade20k_instance_val\": (\n \"ADEChallengeData2016/images/validation\",\n \"ADEChallengeData2016/ade20k_instance_val.json\",\n ),\n}\n\n\ndef _get_ade_instances_meta():\n thing_ids = [k[\"id\"] for k in ADE_CATEGORIES]\n assert len(thing_ids) == 100, len(thing_ids)\n # Mapping from the incontiguous ADE category id to an id in [0, 99]\n thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}\n thing_classes = [k[\"name\"] for k in ADE_CATEGORIES]\n ret = {\n \"thing_dataset_id_to_contiguous_id\": thing_dataset_id_to_contiguous_id,\n \"thing_classes\": thing_classes,\n }\n return ret\n\n\ndef register_all_ade20k_instance(root):\n for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():\n # Assume pre-defined datasets live in `./datasets`.\n register_coco_instances(\n key,\n _get_ade_instances_meta(),\n os.path.join(root, json_file) if \"://\" not in json_file else json_file,\n os.path.join(root, image_root),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_ade20k_instance(_root)","source_hash":"d6dce919a13fa895657f631af55988e0184ea3609190acd5bf823da0573c02d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_cityscapes_panoptic","uri":"program://OneFormer/module/oneformer.data.datasets.register_cityscapes_panoptic#L1-L199","kind":"module","name":"oneformer.data.datasets.register_cityscapes_panoptic","path":"oneformer/data/datasets/register_cityscapes_panoptic.py","language":"python","start_line":1,"end_line":199,"context_start_line":1,"context_end_line":199,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport logging\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES\nfrom detectron2.utils.file_io import PathManager\n\n\"\"\"\nThis file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.\n\"\"\"\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):\n files = []\n # scan through the directory\n cities = PathManager.ls(image_dir)\n logger.info(f\"{len(cities)} cities found in '{image_dir}'.\")\n image_dict = {}\n for city in cities:\n city_img_dir = os.path.join(image_dir, city)\n for basename in PathManager.ls(city_img_dir):\n image_file = os.path.join(city_img_dir, basename)\n\n suffix = \"_leftImg8bit.png\"\n assert basename.endswith(suffix), basename\n basename = os.path.basename(basename)[: -len(suffix)]\n\n image_dict[basename] = image_file\n\n for ann in json_info[\"annotations\"]:\n image_file = image_dict.get(ann[\"image_id\"], None)\n assert image_file is not None, \"No image {} found for annotation {}\".format(\n ann[\"image_id\"], ann[\"file_name\"]\n )\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n segments_info = ann[\"segments_info\"]\n files.append((image_file, label_file, segments_info))\n\n assert len(files), \"No images found in {}\".format(image_dir)\n assert PathManager.isfile(files[0][0]), files[0][0]\n assert PathManager.isfile(files[0][1]), files[0][1]\n return files\n\n\ndef load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/cityscapes/leftImg8bit/train\".\n gt_dir (str): path to the raw annotations. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train\".\n gt_json (str): path to the json file. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train.json\".\n meta (dict): dictionary containing \"thing_dataset_id_to_contiguous_id\"\n and \"stuff_dataset_id_to_contiguous_id\" to map category ids to\n contiguous ids for training.\n\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n return segment_info\n\n assert os.path.exists(\n gt_json\n ), \"Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files.\" # noqa\n\n \n with open(gt_json) as f:\n json_info = json.load(f)\n \n files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)\n ret = []\n for image_file, label_file, segments_info in files:\n sem_label_file = (\n image_file.replace(\"leftImg8bit\", \"gtFine\").split(\".\")[0] + \"_labelTrainIds.png\"\n )\n segments_info = [_convert_category_id(x, meta) for x in segments_info]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": \"_\".join(\n os.path.splitext(os.path.basename(image_file))[0].split(\"_\")[:3]\n ),\n \"sem_seg_file_name\": sem_label_file,\n \"pan_seg_file_name\": label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(\n ret[0][\"sem_seg_file_name\"]\n ), \"Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py\" # noqa\n assert PathManager.isfile(\n ret[0][\"pan_seg_file_name\"]\n ), \"Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py\" # noqa\n return ret\n\n\n_RAW_CITYSCAPES_PANOPTIC_SPLITS = {\n \"cityscapes_fine_panoptic_train\": (\n \"cityscapes/leftImg8bit/train\",\n \"cityscapes/gtFine/cityscapes_panoptic_train\",\n \"cityscapes/gtFine/cityscapes_panoptic_train.json\",\n ),\n \"cityscapes_fine_panoptic_val\": (\n \"cityscapes/leftImg8bit/val\",\n \"cityscapes/gtFine/cityscapes_panoptic_val\",\n \"cityscapes/gtFine/cityscapes_panoptic_val.json\",\n ),\n # \"cityscapes_fine_panoptic_test\": not supported yet\n}\n\n\ndef register_all_cityscapes_panoptic(root):\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in CITYSCAPES_CATEGORIES]\n thing_colors = [k[\"color\"] for k in CITYSCAPES_CATEGORIES]\n stuff_classes = [k[\"name\"] for k in CITYSCAPES_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in CITYSCAPES_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # There are three types of ids in cityscapes panoptic segmentation:\n # (1) category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the classifier\n # (2) instance id: this id is used to differentiate different instances from\n # the same category. For \"stuff\" classes, the instance id is always 0; for\n # \"thing\" classes, the instance id starts from 1 and 0 is reserved for\n # ignored instances (e.g. crowd annotation).\n # (3) panoptic id: this is the compact id that encode both category and\n # instance id by: category_id * 1000 + instance_id.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for k in CITYSCAPES_CATEGORIES:\n if k[\"isthing\"] == 1:\n thing_dataset_id_to_contiguous_id[k[\"id\"]] = k[\"trainId\"]\n else:\n stuff_dataset_id_to_contiguous_id[k[\"id\"]] = k[\"trainId\"]\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():\n image_dir = os.path.join(root, image_dir)\n gt_dir = os.path.join(root, gt_dir)\n gt_json = os.path.join(root, gt_json)\n\n if key in DatasetCatalog.list():\n DatasetCatalog.remove(key)\n\n DatasetCatalog.register(\n key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)\n )\n MetadataCatalog.get(key).set(\n panoptic_root=gt_dir,\n image_root=image_dir,\n panoptic_json=gt_json,\n gt_dir=gt_dir.replace(\"cityscapes_panoptic_\", \"\"),\n evaluator_type=\"cityscapes_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **meta,\n )\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_cityscapes_panoptic(_root)","source_hash":"4ef19980d8d4590b685e333ee853f63ff27ac4ed4f425626cd5ef47df0c1bd7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_cityscapes_panoptic.get_cityscapes_panoptic_files","uri":"program://OneFormer/function/oneformer.data.datasets.register_cityscapes_panoptic.get_cityscapes_panoptic_files#L22-L51","kind":"function","name":"get_cityscapes_panoptic_files","path":"oneformer/data/datasets/register_cityscapes_panoptic.py","language":"python","start_line":22,"end_line":51,"context_start_line":2,"context_end_line":71,"code":"# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport logging\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES\nfrom detectron2.utils.file_io import PathManager\n\n\"\"\"\nThis file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.\n\"\"\"\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):\n files = []\n # scan through the directory\n cities = PathManager.ls(image_dir)\n logger.info(f\"{len(cities)} cities found in '{image_dir}'.\")\n image_dict = {}\n for city in cities:\n city_img_dir = os.path.join(image_dir, city)\n for basename in PathManager.ls(city_img_dir):\n image_file = os.path.join(city_img_dir, basename)\n\n suffix = \"_leftImg8bit.png\"\n assert basename.endswith(suffix), basename\n basename = os.path.basename(basename)[: -len(suffix)]\n\n image_dict[basename] = image_file\n\n for ann in json_info[\"annotations\"]:\n image_file = image_dict.get(ann[\"image_id\"], None)\n assert image_file is not None, \"No image {} found for annotation {}\".format(\n ann[\"image_id\"], ann[\"file_name\"]\n )\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n segments_info = ann[\"segments_info\"]\n files.append((image_file, label_file, segments_info))\n\n assert len(files), \"No images found in {}\".format(image_dir)\n assert PathManager.isfile(files[0][0]), files[0][0]\n assert PathManager.isfile(files[0][1]), files[0][1]\n return files\n\n\ndef load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/cityscapes/leftImg8bit/train\".\n gt_dir (str): path to the raw annotations. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train\".\n gt_json (str): path to the json file. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train.json\".\n meta (dict): dictionary containing \"thing_dataset_id_to_contiguous_id\"\n and \"stuff_dataset_id_to_contiguous_id\" to map category ids to\n contiguous ids for training.\n\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):","source_hash":"4ef19980d8d4590b685e333ee853f63ff27ac4ed4f425626cd5ef47df0c1bd7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_cityscapes_panoptic.load_cityscapes_panoptic","uri":"program://OneFormer/function/oneformer.data.datasets.register_cityscapes_panoptic.load_cityscapes_panoptic#L54-L115","kind":"function","name":"load_cityscapes_panoptic","path":"oneformer/data/datasets/register_cityscapes_panoptic.py","language":"python","start_line":54,"end_line":115,"context_start_line":34,"context_end_line":135,"code":" assert basename.endswith(suffix), basename\n basename = os.path.basename(basename)[: -len(suffix)]\n\n image_dict[basename] = image_file\n\n for ann in json_info[\"annotations\"]:\n image_file = image_dict.get(ann[\"image_id\"], None)\n assert image_file is not None, \"No image {} found for annotation {}\".format(\n ann[\"image_id\"], ann[\"file_name\"]\n )\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n segments_info = ann[\"segments_info\"]\n files.append((image_file, label_file, segments_info))\n\n assert len(files), \"No images found in {}\".format(image_dir)\n assert PathManager.isfile(files[0][0]), files[0][0]\n assert PathManager.isfile(files[0][1]), files[0][1]\n return files\n\n\ndef load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/cityscapes/leftImg8bit/train\".\n gt_dir (str): path to the raw annotations. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train\".\n gt_json (str): path to the json file. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train.json\".\n meta (dict): dictionary containing \"thing_dataset_id_to_contiguous_id\"\n and \"stuff_dataset_id_to_contiguous_id\" to map category ids to\n contiguous ids for training.\n\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n return segment_info\n\n assert os.path.exists(\n gt_json\n ), \"Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files.\" # noqa\n\n \n with open(gt_json) as f:\n json_info = json.load(f)\n \n files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)\n ret = []\n for image_file, label_file, segments_info in files:\n sem_label_file = (\n image_file.replace(\"leftImg8bit\", \"gtFine\").split(\".\")[0] + \"_labelTrainIds.png\"\n )\n segments_info = [_convert_category_id(x, meta) for x in segments_info]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": \"_\".join(\n os.path.splitext(os.path.basename(image_file))[0].split(\"_\")[:3]\n ),\n \"sem_seg_file_name\": sem_label_file,\n \"pan_seg_file_name\": label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(\n ret[0][\"sem_seg_file_name\"]\n ), \"Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py\" # noqa\n assert PathManager.isfile(\n ret[0][\"pan_seg_file_name\"]\n ), \"Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py\" # noqa\n return ret\n\n\n_RAW_CITYSCAPES_PANOPTIC_SPLITS = {\n \"cityscapes_fine_panoptic_train\": (\n \"cityscapes/leftImg8bit/train\",\n \"cityscapes/gtFine/cityscapes_panoptic_train\",\n \"cityscapes/gtFine/cityscapes_panoptic_train.json\",\n ),\n \"cityscapes_fine_panoptic_val\": (\n \"cityscapes/leftImg8bit/val\",\n \"cityscapes/gtFine/cityscapes_panoptic_val\",\n \"cityscapes/gtFine/cityscapes_panoptic_val.json\",\n ),\n # \"cityscapes_fine_panoptic_test\": not supported yet\n}\n\n\ndef register_all_cityscapes_panoptic(root):\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +","source_hash":"4ef19980d8d4590b685e333ee853f63ff27ac4ed4f425626cd5ef47df0c1bd7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_cityscapes_panoptic.register_all_cityscapes_panoptic","uri":"program://OneFormer/function/oneformer.data.datasets.register_cityscapes_panoptic.register_all_cityscapes_panoptic#L133-L196","kind":"function","name":"register_all_cityscapes_panoptic","path":"oneformer/data/datasets/register_cityscapes_panoptic.py","language":"python","start_line":133,"end_line":196,"context_start_line":113,"context_end_line":199,"code":" ret[0][\"pan_seg_file_name\"]\n ), \"Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py\" # noqa\n return ret\n\n\n_RAW_CITYSCAPES_PANOPTIC_SPLITS = {\n \"cityscapes_fine_panoptic_train\": (\n \"cityscapes/leftImg8bit/train\",\n \"cityscapes/gtFine/cityscapes_panoptic_train\",\n \"cityscapes/gtFine/cityscapes_panoptic_train.json\",\n ),\n \"cityscapes_fine_panoptic_val\": (\n \"cityscapes/leftImg8bit/val\",\n \"cityscapes/gtFine/cityscapes_panoptic_val\",\n \"cityscapes/gtFine/cityscapes_panoptic_val.json\",\n ),\n # \"cityscapes_fine_panoptic_test\": not supported yet\n}\n\n\ndef register_all_cityscapes_panoptic(root):\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in CITYSCAPES_CATEGORIES]\n thing_colors = [k[\"color\"] for k in CITYSCAPES_CATEGORIES]\n stuff_classes = [k[\"name\"] for k in CITYSCAPES_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in CITYSCAPES_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # There are three types of ids in cityscapes panoptic segmentation:\n # (1) category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the classifier\n # (2) instance id: this id is used to differentiate different instances from\n # the same category. For \"stuff\" classes, the instance id is always 0; for\n # \"thing\" classes, the instance id starts from 1 and 0 is reserved for\n # ignored instances (e.g. crowd annotation).\n # (3) panoptic id: this is the compact id that encode both category and\n # instance id by: category_id * 1000 + instance_id.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for k in CITYSCAPES_CATEGORIES:\n if k[\"isthing\"] == 1:\n thing_dataset_id_to_contiguous_id[k[\"id\"]] = k[\"trainId\"]\n else:\n stuff_dataset_id_to_contiguous_id[k[\"id\"]] = k[\"trainId\"]\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():\n image_dir = os.path.join(root, image_dir)\n gt_dir = os.path.join(root, gt_dir)\n gt_json = os.path.join(root, gt_json)\n\n if key in DatasetCatalog.list():\n DatasetCatalog.remove(key)\n\n DatasetCatalog.register(\n key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)\n )\n MetadataCatalog.get(key).set(\n panoptic_root=gt_dir,\n image_root=image_dir,\n panoptic_json=gt_json,\n gt_dir=gt_dir.replace(\"cityscapes_panoptic_\", \"\"),\n evaluator_type=\"cityscapes_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **meta,\n )\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_cityscapes_panoptic(_root)","source_hash":"4ef19980d8d4590b685e333ee853f63ff27ac4ed4f425626cd5ef47df0c1bd7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_cityscapes_panoptic._convert_category_id","uri":"program://OneFormer/function/oneformer.data.datasets.register_cityscapes_panoptic._convert_category_id#L71-L80","kind":"function","name":"_convert_category_id","path":"oneformer/data/datasets/register_cityscapes_panoptic.py","language":"python","start_line":71,"end_line":80,"context_start_line":51,"context_end_line":100,"code":" return files\n\n\ndef load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/cityscapes/leftImg8bit/train\".\n gt_dir (str): path to the raw annotations. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train\".\n gt_json (str): path to the json file. e.g.,\n \"~/cityscapes/gtFine/cityscapes_panoptic_train.json\".\n meta (dict): dictionary containing \"thing_dataset_id_to_contiguous_id\"\n and \"stuff_dataset_id_to_contiguous_id\" to map category ids to\n contiguous ids for training.\n\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n return segment_info\n\n assert os.path.exists(\n gt_json\n ), \"Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files.\" # noqa\n\n \n with open(gt_json) as f:\n json_info = json.load(f)\n \n files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)\n ret = []\n for image_file, label_file, segments_info in files:\n sem_label_file = (\n image_file.replace(\"leftImg8bit\", \"gtFine\").split(\".\")[0] + \"_labelTrainIds.png\"\n )\n segments_info = [_convert_category_id(x, meta) for x in segments_info]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": \"_\".join(","source_hash":"4ef19980d8d4590b685e333ee853f63ff27ac4ed4f425626cd5ef47df0c1bd7b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg","uri":"program://OneFormer/module/oneformer.data.datasets.register_coco_panoptic_annos_semseg#L1-L367","kind":"module","name":"oneformer.data.datasets.register_coco_panoptic_annos_semseg","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":1,"end_line":367,"context_start_line":1,"context_end_line":367,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets import load_sem_seg\nfrom detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\nfrom detectron2.utils.file_io import PathManager\nimport contextlib\nimport logging\nimport io\nfrom fvcore.common.timer import Timer\nimport pycocotools.mask as mask_util\nfrom detectron2.structures import BoxMode\n\n\nlogger = logging.getLogger(__name__)\n\n\n_PREDEFINED_SPLITS_COCO_PANOPTIC = {\n \"coco_2017_train_panoptic\": (\n # This is the original panoptic annotation directory\n \"coco/panoptic_train2017\",\n \"coco/annotations/panoptic_train2017.json\",\n # This directory contains semantic annotations that are\n # converted from panoptic annotations.\n # It is used by PanopticFPN.\n # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py\n # to create these directories.\n \"coco/panoptic_semseg_train2017\",\n ),\n \"coco_2017_val_panoptic\": (\n \"coco/panoptic_val2017\",\n \"coco/annotations/panoptic_val2017.json\",\n \"coco/panoptic_semseg_val2017\",\n ),\n}\n\ndef load_coco_instance_json(json_file, image_root, dataset_name=None):\n from pycocotools.coco import COCO\n\n timer = Timer()\n json_file = PathManager.get_local_path(json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n coco_api = COCO(json_file)\n if timer.seconds() > 1:\n logger.info(\"Loading {} takes {:.2f} seconds.\".format(json_file, timer.seconds()))\n\n id_map = None\n if dataset_name is not None:\n meta = MetadataCatalog.get(dataset_name)\n cat_ids = sorted(coco_api.getCatIds())\n cats = coco_api.loadCats(cat_ids)\n # The categories in a custom json file may not be sorted.\n thing_classes = [c[\"name\"] for c in sorted(cats, key=lambda x: x[\"id\"])]\n meta.thing_classes = thing_classes\n\n # In COCO, certain category ids are artificially removed,\n # and by convention they are always ignored.\n # We deal with COCO's id issue and translate\n # the category ids to contiguous ids in [0, 80).\n\n # It works by looking at the \"categories\" field in the json, therefore\n # if users' own json also have incontiguous ids, we'll\n # apply this mapping as well but print a warning.\n if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):\n if \"coco\" not in dataset_name:\n logger.warning(\n \"\"\"\nCategory ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n\"\"\"\n )\n id_map = {v: i for i, v in enumerate(cat_ids)}\n meta.thing_dataset_id_to_contiguous_id = id_map\n\n # sort indices for reproducible results\n img_ids = sorted(coco_api.imgs.keys())\n # imgs is a list of dicts, each looks something like:\n # {'license': 4,\n # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',\n # 'file_name': 'COCO_val2014_000000001268.jpg',\n # 'height': 427,\n # 'width': 640,\n # 'date_captured': '2013-11-17 05:57:24',\n # 'id': 1268}\n imgs = coco_api.loadImgs(img_ids)\n # anns is a list[list[dict]], where each dict is an annotation\n # record for an object. The inner list enumerates the objects in an image\n # and the outer list enumerates over images. Example of anns[0]:\n # [{'segmentation': [[192.81,\n # 247.09,\n # ...\n # 219.03,\n # 249.06]],\n # 'area': 1035.749,\n # 'iscrowd': 0,\n # 'image_id': 1268,\n # 'bbox': [192.81, 224.8, 74.73, 33.43],\n # 'category_id': 16,\n # 'id': 42986},\n # ...]\n anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]\n total_num_valid_anns = sum([len(x) for x in anns])\n total_num_anns = len(coco_api.anns)\n if total_num_valid_anns < total_num_anns:\n logger.warning(\n f\"{json_file} contains {total_num_anns} annotations, but only \"\n f\"{total_num_valid_anns} of them match to images in the file.\"\n )\n\n if \"minival\" not in json_file:\n # The popular valminusminival & minival annotations for COCO2014 contain this bug.\n # However the ratio of buggy annotations there is tiny and does not affect accuracy.\n # Therefore we explicitly white-list them.\n ann_ids = [ann[\"id\"] for anns_per_image in anns for ann in anns_per_image]\n assert len(set(ann_ids)) == len(ann_ids), \"Annotation ids in '{}' are not unique!\".format(\n json_file\n )\n\n imgs_anns = list(zip(imgs, anns))\n logger.info(\"Loaded {} images in COCO format from {}\".format(len(imgs_anns), json_file))\n\n dataset_dicts = {}\n\n ann_keys = [\"iscrowd\", \"bbox\", \"keypoints\", \"category_id\"]\n\n num_instances_without_valid_segmentation = 0\n\n for (img_dict, anno_dict_list) in imgs_anns:\n record = {}\n record[\"file_name\"] = os.path.join(image_root, img_dict[\"file_name\"])\n record[\"height\"] = img_dict[\"height\"]\n record[\"width\"] = img_dict[\"width\"]\n image_id = record[\"image_id\"] = img_dict[\"id\"]\n\n objs = []\n for anno in anno_dict_list:\n # Check that the image_id in this annotation is the same as\n # the image_id we're looking at.\n # This fails only when the data parsing logic or the annotation file is buggy.\n\n # The original COCO valminusminival2014 & minival2014 annotation files\n # actually contains bugs that, together with certain ways of using COCO API,\n # can trigger this assertion.\n assert anno[\"image_id\"] == image_id\n\n assert anno.get(\"ignore\", 0) == 0, '\"ignore\" in COCO json file is not supported.'\n\n obj = {key: anno[key] for key in ann_keys if key in anno}\n if \"bbox\" in obj and len(obj[\"bbox\"]) == 0:\n raise ValueError(\n f\"One annotation of image {image_id} contains empty 'bbox' value! \"\n \"This json does not have valid COCO format.\"\n )\n\n segm = anno.get(\"segmentation\", None)\n if segm: # either list[list[float]] or dict(RLE)\n if isinstance(segm, dict):\n if isinstance(segm[\"counts\"], list):\n # convert to compressed RLE\n segm = mask_util.frPyObjects(segm, *segm[\"size\"])\n else:\n # filter out invalid polygons (< 3 points)\n segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]\n if len(segm) == 0:\n num_instances_without_valid_segmentation += 1\n continue # ignore this instance\n obj[\"segmentation\"] = segm\n\n keypts = anno.get(\"keypoints\", None)\n if keypts: # list[int]\n for idx, v in enumerate(keypts):\n if idx % 3 != 2:\n # COCO's segmentation coordinates are floating points in [0, H or W],\n # but keypoint coordinates are integers in [0, H-1 or W-1]\n # Therefore we assume the coordinates are \"pixel indices\" and\n # add 0.5 to convert to floating point coordinates.\n keypts[idx] = v + 0.5\n obj[\"keypoints\"] = keypts\n\n obj[\"bbox_mode\"] = BoxMode.XYWH_ABS\n if id_map:\n annotation_category_id = obj[\"category_id\"]\n try:\n obj[\"category_id\"] = id_map[annotation_category_id]\n except KeyError as e:\n raise KeyError(\n f\"Encountered category_id={annotation_category_id} \"\n \"but this id does not exist in 'categories' of the json file.\"\n ) from e\n objs.append(obj)\n record[\"annotations\"] = objs\n dataset_dicts[image_id] = record\n\n if num_instances_without_valid_segmentation > 0:\n logger.warning(\n \"Filtered out {} instances without valid segmentation. \".format(\n num_instances_without_valid_segmentation\n )\n + \"There might be issues in your dataset generation process. Please \"\n \"check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully\"\n )\n return dataset_dicts\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in COCO_CATEGORIES if k[\"isthing\"] == 1]\n thing_colors = [k[\"color\"] for k in COCO_CATEGORIES if k[\"isthing\"] == 1]\n stuff_classes = [k[\"name\"] for k in COCO_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in COCO_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:\n # category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(COCO_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n \n instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace(\"panoptic_\", \"\"), instances_name)\n \n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = int(ann[\"image_id\"])\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n \"annotations\": instance_data_dicts[image_id][\"annotations\"],\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_coco_panoptic_annos_sem_seg(\n name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name,\n):\n panoptic_name = name\n delattr(MetadataCatalog.get(panoptic_name), \"thing_classes\")\n delattr(MetadataCatalog.get(panoptic_name), \"thing_colors\")\n MetadataCatalog.get(panoptic_name).set(\n thing_classes=metadata[\"thing_classes\"],\n thing_colors=metadata[\"thing_colors\"],\n # thing_dataset_id_to_contiguous_id=metadata[\"thing_dataset_id_to_contiguous_id\"],\n )\n\n # the name is \"coco_2017_train_panoptic_with_sem_seg\" and \"coco_2017_val_panoptic_with_sem_seg\"\n semantic_name = name + \"_with_sem_seg\"\n DatasetCatalog.register(\n semantic_name,\n lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),\n )\n MetadataCatalog.get(semantic_name).set(\n sem_seg_root=sem_seg_root,\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"coco_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **metadata,\n )\n\n\ndef register_all_coco_panoptic_annos_sem_seg(root):\n for (\n prefix,\n (panoptic_root, panoptic_json, semantic_root),\n ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():\n\n prefix_instances = prefix[: -len(\"_panoptic\")]\n instances_meta = MetadataCatalog.get(prefix_instances)\n image_root, instances_json = instances_meta.image_root, instances_meta.json_file\n\n if 'val' in instances_json:\n instances_json = instances_json.replace('instances_', 'panoptic2instances_')\n\n register_coco_panoptic_annos_sem_seg(\n prefix,\n get_metadata(),\n image_root,\n os.path.join(root, panoptic_root),\n os.path.join(root, panoptic_json),\n os.path.join(root, semantic_root),\n instances_json,\n prefix_instances,\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_coco_panoptic_annos_sem_seg(_root)","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg.load_coco_instance_json","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic_annos_semseg.load_coco_instance_json#L43-L207","kind":"function","name":"load_coco_instance_json","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":43,"end_line":207,"context_start_line":23,"context_end_line":227,"code":"\n_PREDEFINED_SPLITS_COCO_PANOPTIC = {\n \"coco_2017_train_panoptic\": (\n # This is the original panoptic annotation directory\n \"coco/panoptic_train2017\",\n \"coco/annotations/panoptic_train2017.json\",\n # This directory contains semantic annotations that are\n # converted from panoptic annotations.\n # It is used by PanopticFPN.\n # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py\n # to create these directories.\n \"coco/panoptic_semseg_train2017\",\n ),\n \"coco_2017_val_panoptic\": (\n \"coco/panoptic_val2017\",\n \"coco/annotations/panoptic_val2017.json\",\n \"coco/panoptic_semseg_val2017\",\n ),\n}\n\ndef load_coco_instance_json(json_file, image_root, dataset_name=None):\n from pycocotools.coco import COCO\n\n timer = Timer()\n json_file = PathManager.get_local_path(json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n coco_api = COCO(json_file)\n if timer.seconds() > 1:\n logger.info(\"Loading {} takes {:.2f} seconds.\".format(json_file, timer.seconds()))\n\n id_map = None\n if dataset_name is not None:\n meta = MetadataCatalog.get(dataset_name)\n cat_ids = sorted(coco_api.getCatIds())\n cats = coco_api.loadCats(cat_ids)\n # The categories in a custom json file may not be sorted.\n thing_classes = [c[\"name\"] for c in sorted(cats, key=lambda x: x[\"id\"])]\n meta.thing_classes = thing_classes\n\n # In COCO, certain category ids are artificially removed,\n # and by convention they are always ignored.\n # We deal with COCO's id issue and translate\n # the category ids to contiguous ids in [0, 80).\n\n # It works by looking at the \"categories\" field in the json, therefore\n # if users' own json also have incontiguous ids, we'll\n # apply this mapping as well but print a warning.\n if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):\n if \"coco\" not in dataset_name:\n logger.warning(\n \"\"\"\nCategory ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n\"\"\"\n )\n id_map = {v: i for i, v in enumerate(cat_ids)}\n meta.thing_dataset_id_to_contiguous_id = id_map\n\n # sort indices for reproducible results\n img_ids = sorted(coco_api.imgs.keys())\n # imgs is a list of dicts, each looks something like:\n # {'license': 4,\n # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',\n # 'file_name': 'COCO_val2014_000000001268.jpg',\n # 'height': 427,\n # 'width': 640,\n # 'date_captured': '2013-11-17 05:57:24',\n # 'id': 1268}\n imgs = coco_api.loadImgs(img_ids)\n # anns is a list[list[dict]], where each dict is an annotation\n # record for an object. The inner list enumerates the objects in an image\n # and the outer list enumerates over images. Example of anns[0]:\n # [{'segmentation': [[192.81,\n # 247.09,\n # ...\n # 219.03,\n # 249.06]],\n # 'area': 1035.749,\n # 'iscrowd': 0,\n # 'image_id': 1268,\n # 'bbox': [192.81, 224.8, 74.73, 33.43],\n # 'category_id': 16,\n # 'id': 42986},\n # ...]\n anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]\n total_num_valid_anns = sum([len(x) for x in anns])\n total_num_anns = len(coco_api.anns)\n if total_num_valid_anns < total_num_anns:\n logger.warning(\n f\"{json_file} contains {total_num_anns} annotations, but only \"\n f\"{total_num_valid_anns} of them match to images in the file.\"\n )\n\n if \"minival\" not in json_file:\n # The popular valminusminival & minival annotations for COCO2014 contain this bug.\n # However the ratio of buggy annotations there is tiny and does not affect accuracy.\n # Therefore we explicitly white-list them.\n ann_ids = [ann[\"id\"] for anns_per_image in anns for ann in anns_per_image]\n assert len(set(ann_ids)) == len(ann_ids), \"Annotation ids in '{}' are not unique!\".format(\n json_file\n )\n\n imgs_anns = list(zip(imgs, anns))\n logger.info(\"Loaded {} images in COCO format from {}\".format(len(imgs_anns), json_file))\n\n dataset_dicts = {}\n\n ann_keys = [\"iscrowd\", \"bbox\", \"keypoints\", \"category_id\"]\n\n num_instances_without_valid_segmentation = 0\n\n for (img_dict, anno_dict_list) in imgs_anns:\n record = {}\n record[\"file_name\"] = os.path.join(image_root, img_dict[\"file_name\"])\n record[\"height\"] = img_dict[\"height\"]\n record[\"width\"] = img_dict[\"width\"]\n image_id = record[\"image_id\"] = img_dict[\"id\"]\n\n objs = []\n for anno in anno_dict_list:\n # Check that the image_id in this annotation is the same as\n # the image_id we're looking at.\n # This fails only when the data parsing logic or the annotation file is buggy.\n\n # The original COCO valminusminival2014 & minival2014 annotation files\n # actually contains bugs that, together with certain ways of using COCO API,\n # can trigger this assertion.\n assert anno[\"image_id\"] == image_id\n\n assert anno.get(\"ignore\", 0) == 0, '\"ignore\" in COCO json file is not supported.'\n\n obj = {key: anno[key] for key in ann_keys if key in anno}\n if \"bbox\" in obj and len(obj[\"bbox\"]) == 0:\n raise ValueError(\n f\"One annotation of image {image_id} contains empty 'bbox' value! \"\n \"This json does not have valid COCO format.\"\n )\n\n segm = anno.get(\"segmentation\", None)\n if segm: # either list[list[float]] or dict(RLE)\n if isinstance(segm, dict):\n if isinstance(segm[\"counts\"], list):\n # convert to compressed RLE\n segm = mask_util.frPyObjects(segm, *segm[\"size\"])\n else:\n # filter out invalid polygons (< 3 points)\n segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]\n if len(segm) == 0:\n num_instances_without_valid_segmentation += 1\n continue # ignore this instance\n obj[\"segmentation\"] = segm\n\n keypts = anno.get(\"keypoints\", None)\n if keypts: # list[int]\n for idx, v in enumerate(keypts):\n if idx % 3 != 2:\n # COCO's segmentation coordinates are floating points in [0, H or W],\n # but keypoint coordinates are integers in [0, H-1 or W-1]\n # Therefore we assume the coordinates are \"pixel indices\" and\n # add 0.5 to convert to floating point coordinates.\n keypts[idx] = v + 0.5\n obj[\"keypoints\"] = keypts\n\n obj[\"bbox_mode\"] = BoxMode.XYWH_ABS\n if id_map:\n annotation_category_id = obj[\"category_id\"]\n try:\n obj[\"category_id\"] = id_map[annotation_category_id]\n except KeyError as e:\n raise KeyError(\n f\"Encountered category_id={annotation_category_id} \"\n \"but this id does not exist in 'categories' of the json file.\"\n ) from e\n objs.append(obj)\n record[\"annotations\"] = objs\n dataset_dicts[image_id] = record\n\n if num_instances_without_valid_segmentation > 0:\n logger.warning(\n \"Filtered out {} instances without valid segmentation. \".format(\n num_instances_without_valid_segmentation\n )\n + \"There might be issues in your dataset generation process. Please \"\n \"check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully\"\n )\n return dataset_dicts\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in COCO_CATEGORIES if k[\"isthing\"] == 1]\n thing_colors = [k[\"color\"] for k in COCO_CATEGORIES if k[\"isthing\"] == 1]\n stuff_classes = [k[\"name\"] for k in COCO_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in COCO_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg.get_metadata","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic_annos_semseg.get_metadata#L209-L250","kind":"function","name":"get_metadata","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":209,"end_line":250,"context_start_line":189,"context_end_line":270,"code":" obj[\"category_id\"] = id_map[annotation_category_id]\n except KeyError as e:\n raise KeyError(\n f\"Encountered category_id={annotation_category_id} \"\n \"but this id does not exist in 'categories' of the json file.\"\n ) from e\n objs.append(obj)\n record[\"annotations\"] = objs\n dataset_dicts[image_id] = record\n\n if num_instances_without_valid_segmentation > 0:\n logger.warning(\n \"Filtered out {} instances without valid segmentation. \".format(\n num_instances_without_valid_segmentation\n )\n + \"There might be issues in your dataset generation process. Please \"\n \"check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully\"\n )\n return dataset_dicts\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in COCO_CATEGORIES if k[\"isthing\"] == 1]\n thing_colors = [k[\"color\"] for k in COCO_CATEGORIES if k[\"isthing\"] == 1]\n stuff_classes = [k[\"name\"] for k in COCO_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in COCO_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:\n # category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(COCO_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg.load_coco_panoptic_json","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic_annos_semseg.load_coco_panoptic_json#L253-L307","kind":"function","name":"load_coco_panoptic_json","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":253,"end_line":307,"context_start_line":233,"context_end_line":327,"code":" # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(COCO_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n \n instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace(\"panoptic_\", \"\"), instances_name)\n \n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = int(ann[\"image_id\"])\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n \"annotations\": instance_data_dicts[image_id][\"annotations\"],\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_coco_panoptic_annos_sem_seg(\n name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name,\n):\n panoptic_name = name\n delattr(MetadataCatalog.get(panoptic_name), \"thing_classes\")\n delattr(MetadataCatalog.get(panoptic_name), \"thing_colors\")\n MetadataCatalog.get(panoptic_name).set(\n thing_classes=metadata[\"thing_classes\"],\n thing_colors=metadata[\"thing_colors\"],\n # thing_dataset_id_to_contiguous_id=metadata[\"thing_dataset_id_to_contiguous_id\"],\n )\n\n # the name is \"coco_2017_train_panoptic_with_sem_seg\" and \"coco_2017_val_panoptic_with_sem_seg\"\n semantic_name = name + \"_with_sem_seg\"\n DatasetCatalog.register(\n semantic_name,\n lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),\n )","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg.register_coco_panoptic_annos_sem_seg","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic_annos_semseg.register_coco_panoptic_annos_sem_seg#L310-L338","kind":"function","name":"register_coco_panoptic_annos_sem_seg","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":310,"end_line":338,"context_start_line":290,"context_end_line":358,"code":" label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n \"annotations\": instance_data_dicts[image_id][\"annotations\"],\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_coco_panoptic_annos_sem_seg(\n name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name,\n):\n panoptic_name = name\n delattr(MetadataCatalog.get(panoptic_name), \"thing_classes\")\n delattr(MetadataCatalog.get(panoptic_name), \"thing_colors\")\n MetadataCatalog.get(panoptic_name).set(\n thing_classes=metadata[\"thing_classes\"],\n thing_colors=metadata[\"thing_colors\"],\n # thing_dataset_id_to_contiguous_id=metadata[\"thing_dataset_id_to_contiguous_id\"],\n )\n\n # the name is \"coco_2017_train_panoptic_with_sem_seg\" and \"coco_2017_val_panoptic_with_sem_seg\"\n semantic_name = name + \"_with_sem_seg\"\n DatasetCatalog.register(\n semantic_name,\n lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),\n )\n MetadataCatalog.get(semantic_name).set(\n sem_seg_root=sem_seg_root,\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"coco_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **metadata,\n )\n\n\ndef register_all_coco_panoptic_annos_sem_seg(root):\n for (\n prefix,\n (panoptic_root, panoptic_json, semantic_root),\n ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():\n\n prefix_instances = prefix[: -len(\"_panoptic\")]\n instances_meta = MetadataCatalog.get(prefix_instances)\n image_root, instances_json = instances_meta.image_root, instances_meta.json_file\n\n if 'val' in instances_json:\n instances_json = instances_json.replace('instances_', 'panoptic2instances_')\n\n register_coco_panoptic_annos_sem_seg(\n prefix,\n get_metadata(),\n image_root,\n os.path.join(root, panoptic_root),","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg.register_all_coco_panoptic_annos_sem_seg","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic_annos_semseg.register_all_coco_panoptic_annos_sem_seg#L341-L363","kind":"function","name":"register_all_coco_panoptic_annos_sem_seg","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":341,"end_line":363,"context_start_line":321,"context_end_line":367,"code":"\n # the name is \"coco_2017_train_panoptic_with_sem_seg\" and \"coco_2017_val_panoptic_with_sem_seg\"\n semantic_name = name + \"_with_sem_seg\"\n DatasetCatalog.register(\n semantic_name,\n lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),\n )\n MetadataCatalog.get(semantic_name).set(\n sem_seg_root=sem_seg_root,\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"coco_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **metadata,\n )\n\n\ndef register_all_coco_panoptic_annos_sem_seg(root):\n for (\n prefix,\n (panoptic_root, panoptic_json, semantic_root),\n ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():\n\n prefix_instances = prefix[: -len(\"_panoptic\")]\n instances_meta = MetadataCatalog.get(prefix_instances)\n image_root, instances_json = instances_meta.image_root, instances_meta.json_file\n\n if 'val' in instances_json:\n instances_json = instances_json.replace('instances_', 'panoptic2instances_')\n\n register_coco_panoptic_annos_sem_seg(\n prefix,\n get_metadata(),\n image_root,\n os.path.join(root, panoptic_root),\n os.path.join(root, panoptic_json),\n os.path.join(root, semantic_root),\n instances_json,\n prefix_instances,\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_coco_panoptic_annos_sem_seg(_root)","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_coco_panoptic_annos_semseg._convert_category_id","uri":"program://OneFormer/function/oneformer.data.datasets.register_coco_panoptic_annos_semseg._convert_category_id#L264-L275","kind":"function","name":"_convert_category_id","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":264,"end_line":275,"context_start_line":244,"context_end_line":295,"code":" # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n \n instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace(\"panoptic_\", \"\"), instances_name)\n \n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = int(ann[\"image_id\"])\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_panoptic","uri":"program://OneFormer/module/oneformer.data.datasets.register_ade20k_panoptic#L1-L394","kind":"module","name":"oneformer.data.datasets.register_ade20k_panoptic","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":1,"end_line":394,"context_start_line":1,"context_end_line":394,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_panoptic.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.utils.file_io import PathManager\n\nADE20K_150_CATEGORIES = [\n {\"color\": [120, 120, 120], \"id\": 0, \"isthing\": 0, \"name\": \"wall\"},\n {\"color\": [180, 120, 120], \"id\": 1, \"isthing\": 0, \"name\": \"building\"},\n {\"color\": [6, 230, 230], \"id\": 2, \"isthing\": 0, \"name\": \"sky\"},\n {\"color\": [80, 50, 50], \"id\": 3, \"isthing\": 0, \"name\": \"floor\"},\n {\"color\": [4, 200, 3], \"id\": 4, \"isthing\": 0, \"name\": \"tree\"},\n {\"color\": [120, 120, 80], \"id\": 5, \"isthing\": 0, \"name\": \"ceiling\"},\n {\"color\": [140, 140, 140], \"id\": 6, \"isthing\": 0, \"name\": \"road, route\"},\n {\"color\": [204, 5, 255], \"id\": 7, \"isthing\": 1, \"name\": \"bed\"},\n {\"color\": [230, 230, 230], \"id\": 8, \"isthing\": 1, \"name\": \"window \"},\n {\"color\": [4, 250, 7], \"id\": 9, \"isthing\": 0, \"name\": \"grass\"},\n {\"color\": [224, 5, 255], \"id\": 10, \"isthing\": 1, \"name\": \"cabinet\"},\n {\"color\": [235, 255, 7], \"id\": 11, \"isthing\": 0, \"name\": \"sidewalk, pavement\"},\n {\"color\": [150, 5, 61], \"id\": 12, \"isthing\": 1, \"name\": \"person\"},\n {\"color\": [120, 120, 70], \"id\": 13, \"isthing\": 0, \"name\": \"earth, ground\"},\n {\"color\": [8, 255, 51], \"id\": 14, \"isthing\": 1, \"name\": \"door\"},\n {\"color\": [255, 6, 82], \"id\": 15, \"isthing\": 1, \"name\": \"table\"},\n {\"color\": [143, 255, 140], \"id\": 16, \"isthing\": 0, \"name\": \"mountain, mount\"},\n {\"color\": [204, 255, 4], \"id\": 17, \"isthing\": 0, \"name\": \"plant\"},\n {\"color\": [255, 51, 7], \"id\": 18, \"isthing\": 1, \"name\": \"curtain\"},\n {\"color\": [204, 70, 3], \"id\": 19, \"isthing\": 1, \"name\": \"chair\"},\n {\"color\": [0, 102, 200], \"id\": 20, \"isthing\": 1, \"name\": \"car\"},\n {\"color\": [61, 230, 250], \"id\": 21, \"isthing\": 0, \"name\": \"water\"},\n {\"color\": [255, 6, 51], \"id\": 22, \"isthing\": 1, \"name\": \"painting, picture\"},\n {\"color\": [11, 102, 255], \"id\": 23, \"isthing\": 1, \"name\": \"sofa\"},\n {\"color\": [255, 7, 71], \"id\": 24, \"isthing\": 1, \"name\": \"shelf\"},\n {\"color\": [255, 9, 224], \"id\": 25, \"isthing\": 0, \"name\": \"house\"},\n {\"color\": [9, 7, 230], \"id\": 26, \"isthing\": 0, \"name\": \"sea\"},\n {\"color\": [220, 220, 220], \"id\": 27, \"isthing\": 1, \"name\": \"mirror\"},\n {\"color\": [255, 9, 92], \"id\": 28, \"isthing\": 0, \"name\": \"rug\"},\n {\"color\": [112, 9, 255], \"id\": 29, \"isthing\": 0, \"name\": \"field\"},\n {\"color\": [8, 255, 214], \"id\": 30, \"isthing\": 1, \"name\": \"armchair\"},\n {\"color\": [7, 255, 224], \"id\": 31, \"isthing\": 1, \"name\": \"seat\"},\n {\"color\": [255, 184, 6], \"id\": 32, \"isthing\": 1, \"name\": \"fence\"},\n {\"color\": [10, 255, 71], \"id\": 33, \"isthing\": 1, \"name\": \"desk\"},\n {\"color\": [255, 41, 10], \"id\": 34, \"isthing\": 0, \"name\": \"rock, stone\"},\n {\"color\": [7, 255, 255], \"id\": 35, \"isthing\": 1, \"name\": \"wardrobe, closet, press\"},\n {\"color\": [224, 255, 8], \"id\": 36, \"isthing\": 1, \"name\": \"lamp\"},\n {\"color\": [102, 8, 255], \"id\": 37, \"isthing\": 1, \"name\": \"tub\"},\n {\"color\": [255, 61, 6], \"id\": 38, \"isthing\": 1, \"name\": \"rail\"},\n {\"color\": [255, 194, 7], \"id\": 39, \"isthing\": 1, \"name\": \"cushion\"},\n {\"color\": [255, 122, 8], \"id\": 40, \"isthing\": 0, \"name\": \"base, pedestal, stand\"},\n {\"color\": [0, 255, 20], \"id\": 41, \"isthing\": 1, \"name\": \"box\"},\n {\"color\": [255, 8, 41], \"id\": 42, \"isthing\": 1, \"name\": \"column, pillar\"},\n {\"color\": [255, 5, 153], \"id\": 43, \"isthing\": 1, \"name\": \"signboard, sign\"},\n {\n \"color\": [6, 51, 255],\n \"id\": 44,\n \"isthing\": 1,\n \"name\": \"chest of drawers, chest, bureau, dresser\",\n },\n {\"color\": [235, 12, 255], \"id\": 45, \"isthing\": 1, \"name\": \"counter\"},\n {\"color\": [160, 150, 20], \"id\": 46, \"isthing\": 0, \"name\": \"sand\"},\n {\"color\": [0, 163, 255], \"id\": 47, \"isthing\": 1, \"name\": \"sink\"},\n {\"color\": [140, 140, 140], \"id\": 48, \"isthing\": 0, \"name\": \"skyscraper\"},\n {\"color\": [250, 10, 15], \"id\": 49, \"isthing\": 1, \"name\": \"fireplace\"},\n {\"color\": [20, 255, 0], \"id\": 50, \"isthing\": 1, \"name\": \"refrigerator, icebox\"},\n {\"color\": [31, 255, 0], \"id\": 51, \"isthing\": 0, \"name\": \"grandstand, covered stand\"},\n {\"color\": [255, 31, 0], \"id\": 52, \"isthing\": 0, \"name\": \"path\"},\n {\"color\": [255, 224, 0], \"id\": 53, \"isthing\": 1, \"name\": \"stairs\"},\n {\"color\": [153, 255, 0], \"id\": 54, \"isthing\": 0, \"name\": \"runway\"},\n {\"color\": [0, 0, 255], \"id\": 55, \"isthing\": 1, \"name\": \"case, display case, showcase, vitrine\"},\n {\n \"color\": [255, 71, 0],\n \"id\": 56,\n \"isthing\": 1,\n \"name\": \"pool table, billiard table, snooker table\",\n },\n {\"color\": [0, 235, 255], \"id\": 57, \"isthing\": 1, \"name\": \"pillow\"},\n {\"color\": [0, 173, 255], \"id\": 58, \"isthing\": 1, \"name\": \"screen door, screen\"},\n {\"color\": [31, 0, 255], \"id\": 59, \"isthing\": 0, \"name\": \"stairway, staircase\"},\n {\"color\": [11, 200, 200], \"id\": 60, \"isthing\": 0, \"name\": \"river\"},\n {\"color\": [255, 82, 0], \"id\": 61, \"isthing\": 0, \"name\": \"bridge, span\"},\n {\"color\": [0, 255, 245], \"id\": 62, \"isthing\": 1, \"name\": \"bookcase\"},\n {\"color\": [0, 61, 255], \"id\": 63, \"isthing\": 0, \"name\": \"blind, screen\"},\n {\"color\": [0, 255, 112], \"id\": 64, \"isthing\": 1, \"name\": \"coffee table\"},\n {\n \"color\": [0, 255, 133],\n \"id\": 65,\n \"isthing\": 1,\n \"name\": \"toilet, can, commode, crapper, pot, potty, stool, throne\",\n },\n {\"color\": [255, 0, 0], \"id\": 66, \"isthing\": 1, \"name\": \"flower\"},\n {\"color\": [255, 163, 0], \"id\": 67, \"isthing\": 1, \"name\": \"book\"},\n {\"color\": [255, 102, 0], \"id\": 68, \"isthing\": 0, \"name\": \"hill\"},\n {\"color\": [194, 255, 0], \"id\": 69, \"isthing\": 1, \"name\": \"bench\"},\n {\"color\": [0, 143, 255], \"id\": 70, \"isthing\": 1, \"name\": \"countertop\"},\n {\"color\": [51, 255, 0], \"id\": 71, \"isthing\": 1, \"name\": \"stove\"},\n {\"color\": [0, 82, 255], \"id\": 72, \"isthing\": 1, \"name\": \"palm, palm tree\"},\n {\"color\": [0, 255, 41], \"id\": 73, \"isthing\": 1, \"name\": \"kitchen island\"},\n {\"color\": [0, 255, 173], \"id\": 74, \"isthing\": 1, \"name\": \"computer\"},\n {\"color\": [10, 0, 255], \"id\": 75, \"isthing\": 1, \"name\": \"swivel chair\"},\n {\"color\": [173, 255, 0], \"id\": 76, \"isthing\": 1, \"name\": \"boat\"},\n {\"color\": [0, 255, 153], \"id\": 77, \"isthing\": 0, \"name\": \"bar\"},\n {\"color\": [255, 92, 0], \"id\": 78, \"isthing\": 1, \"name\": \"arcade machine\"},\n {\"color\": [255, 0, 255], \"id\": 79, \"isthing\": 0, \"name\": \"hovel, hut, hutch, shack, shanty\"},\n {\"color\": [255, 0, 245], \"id\": 80, \"isthing\": 1, \"name\": \"bus\"},\n {\"color\": [255, 0, 102], \"id\": 81, \"isthing\": 1, \"name\": \"towel\"},\n {\"color\": [255, 173, 0], \"id\": 82, \"isthing\": 1, \"name\": \"light\"},\n {\"color\": [255, 0, 20], \"id\": 83, \"isthing\": 1, \"name\": \"truck\"},\n {\"color\": [255, 184, 184], \"id\": 84, \"isthing\": 0, \"name\": \"tower\"},\n {\"color\": [0, 31, 255], \"id\": 85, \"isthing\": 1, \"name\": \"chandelier\"},\n {\"color\": [0, 255, 61], \"id\": 86, \"isthing\": 1, \"name\": \"awning, sunshade, sunblind\"},\n {\"color\": [0, 71, 255], \"id\": 87, \"isthing\": 1, \"name\": \"street lamp\"},\n {\"color\": [255, 0, 204], \"id\": 88, \"isthing\": 1, \"name\": \"booth\"},\n {\"color\": [0, 255, 194], \"id\": 89, \"isthing\": 1, \"name\": \"tv\"},\n {\"color\": [0, 255, 82], \"id\": 90, \"isthing\": 1, \"name\": \"plane\"},\n {\"color\": [0, 10, 255], \"id\": 91, \"isthing\": 0, \"name\": \"dirt track\"},\n {\"color\": [0, 112, 255], \"id\": 92, \"isthing\": 1, \"name\": \"clothes\"},\n {\"color\": [51, 0, 255], \"id\": 93, \"isthing\": 1, \"name\": \"pole\"},\n {\"color\": [0, 194, 255], \"id\": 94, \"isthing\": 0, \"name\": \"land, ground, soil\"},\n {\n \"color\": [0, 122, 255],\n \"id\": 95,\n \"isthing\": 1,\n \"name\": \"bannister, banister, balustrade, balusters, handrail\",\n },\n {\n \"color\": [0, 255, 163],\n \"id\": 96,\n \"isthing\": 0,\n \"name\": \"escalator, moving staircase, moving stairway\",\n },\n {\n \"color\": [255, 153, 0],\n \"id\": 97,\n \"isthing\": 1,\n \"name\": \"ottoman, pouf, pouffe, puff, hassock\",\n },\n {\"color\": [0, 255, 10], \"id\": 98, \"isthing\": 1, \"name\": \"bottle\"},\n {\"color\": [255, 112, 0], \"id\": 99, \"isthing\": 0, \"name\": \"buffet, counter, sideboard\"},\n {\n \"color\": [143, 255, 0],\n \"id\": 100,\n \"isthing\": 0,\n \"name\": \"poster, posting, placard, notice, bill, card\",\n },\n {\"color\": [82, 0, 255], \"id\": 101, \"isthing\": 0, \"name\": \"stage\"},\n {\"color\": [163, 255, 0], \"id\": 102, \"isthing\": 1, \"name\": \"van\"},\n {\"color\": [255, 235, 0], \"id\": 103, \"isthing\": 1, \"name\": \"ship\"},\n {\"color\": [8, 184, 170], \"id\": 104, \"isthing\": 1, \"name\": \"fountain\"},\n {\n \"color\": [133, 0, 255],\n \"id\": 105,\n \"isthing\": 0,\n \"name\": \"conveyer belt, conveyor belt, conveyer, conveyor, transporter\",\n },\n {\"color\": [0, 255, 92], \"id\": 106, \"isthing\": 0, \"name\": \"canopy\"},\n {\n \"color\": [184, 0, 255],\n \"id\": 107,\n \"isthing\": 1,\n \"name\": \"washer, automatic washer, washing machine\",\n },\n {\"color\": [255, 0, 31], \"id\": 108, \"isthing\": 1, \"name\": \"plaything, toy\"},\n {\"color\": [0, 184, 255], \"id\": 109, \"isthing\": 0, \"name\": \"pool\"},\n {\"color\": [0, 214, 255], \"id\": 110, \"isthing\": 1, \"name\": \"stool\"},\n {\"color\": [255, 0, 112], \"id\": 111, \"isthing\": 1, \"name\": \"barrel, cask\"},\n {\"color\": [92, 255, 0], \"id\": 112, \"isthing\": 1, \"name\": \"basket, handbasket\"},\n {\"color\": [0, 224, 255], \"id\": 113, \"isthing\": 0, \"name\": \"falls\"},\n {\"color\": [112, 224, 255], \"id\": 114, \"isthing\": 0, \"name\": \"tent\"},\n {\"color\": [70, 184, 160], \"id\": 115, \"isthing\": 1, \"name\": \"bag\"},\n {\"color\": [163, 0, 255], \"id\": 116, \"isthing\": 1, \"name\": \"minibike, motorbike\"},\n {\"color\": [153, 0, 255], \"id\": 117, \"isthing\": 0, \"name\": \"cradle\"},\n {\"color\": [71, 255, 0], \"id\": 118, \"isthing\": 1, \"name\": \"oven\"},\n {\"color\": [255, 0, 163], \"id\": 119, \"isthing\": 1, \"name\": \"ball\"},\n {\"color\": [255, 204, 0], \"id\": 120, \"isthing\": 1, \"name\": \"food, solid food\"},\n {\"color\": [255, 0, 143], \"id\": 121, \"isthing\": 1, \"name\": \"step, stair\"},\n {\"color\": [0, 255, 235], \"id\": 122, \"isthing\": 0, \"name\": \"tank, storage tank\"},\n {\"color\": [133, 255, 0], \"id\": 123, \"isthing\": 1, \"name\": \"trade name\"},\n {\"color\": [255, 0, 235], \"id\": 124, \"isthing\": 1, \"name\": \"microwave\"},\n {\"color\": [245, 0, 255], \"id\": 125, \"isthing\": 1, \"name\": \"pot\"},\n {\"color\": [255, 0, 122], \"id\": 126, \"isthing\": 1, \"name\": \"animal\"},\n {\"color\": [255, 245, 0], \"id\": 127, \"isthing\": 1, \"name\": \"bicycle\"},\n {\"color\": [10, 190, 212], \"id\": 128, \"isthing\": 0, \"name\": \"lake\"},\n {\"color\": [214, 255, 0], \"id\": 129, \"isthing\": 1, \"name\": \"dishwasher\"},\n {\"color\": [0, 204, 255], \"id\": 130, \"isthing\": 1, \"name\": \"screen\"},\n {\"color\": [20, 0, 255], \"id\": 131, \"isthing\": 0, \"name\": \"blanket, cover\"},\n {\"color\": [255, 255, 0], \"id\": 132, \"isthing\": 1, \"name\": \"sculpture\"},\n {\"color\": [0, 153, 255], \"id\": 133, \"isthing\": 1, \"name\": \"hood, exhaust hood\"},\n {\"color\": [0, 41, 255], \"id\": 134, \"isthing\": 1, \"name\": \"sconce\"},\n {\"color\": [0, 255, 204], \"id\": 135, \"isthing\": 1, \"name\": \"vase\"},\n {\"color\": [41, 0, 255], \"id\": 136, \"isthing\": 1, \"name\": \"traffic light\"},\n {\"color\": [41, 255, 0], \"id\": 137, \"isthing\": 1, \"name\": \"tray\"},\n {\"color\": [173, 0, 255], \"id\": 138, \"isthing\": 1, \"name\": \"trash can\"},\n {\"color\": [0, 245, 255], \"id\": 139, \"isthing\": 1, \"name\": \"fan\"},\n {\"color\": [71, 0, 255], \"id\": 140, \"isthing\": 0, \"name\": \"pier\"},\n {\"color\": [122, 0, 255], \"id\": 141, \"isthing\": 0, \"name\": \"crt screen\"},\n {\"color\": [0, 255, 184], \"id\": 142, \"isthing\": 1, \"name\": \"plate\"},\n {\"color\": [0, 92, 255], \"id\": 143, \"isthing\": 1, \"name\": \"monitor\"},\n {\"color\": [184, 255, 0], \"id\": 144, \"isthing\": 1, \"name\": \"bulletin board\"},\n {\"color\": [0, 133, 255], \"id\": 145, \"isthing\": 0, \"name\": \"shower\"},\n {\"color\": [255, 214, 0], \"id\": 146, \"isthing\": 1, \"name\": \"radiator\"},\n {\"color\": [25, 194, 194], \"id\": 147, \"isthing\": 1, \"name\": \"glass, drinking glass\"},\n {\"color\": [102, 255, 0], \"id\": 148, \"isthing\": 1, \"name\": \"clock\"},\n {\"color\": [92, 0, 255], \"id\": 149, \"isthing\": 1, \"name\": \"flag\"},\n]\n\nADE20k_COLORS = [k[\"color\"] for k in ADE20K_150_CATEGORIES]\n\nMetadataCatalog.get(\"ade20k_sem_seg_train\").set(\n stuff_colors=ADE20k_COLORS[:],\n)\n\nMetadataCatalog.get(\"ade20k_sem_seg_val\").set(\n stuff_colors=ADE20k_COLORS[:],\n)\n\n\ndef load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n\n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = ann[\"image_id\"]\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_ade20k_panoptic(\n name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None,\n):\n \"\"\"\n Register a \"standard\" version of ADE20k panoptic segmentation dataset named `name`.\n The dictionaries in this registered dataset follows detectron2's standard format.\n Hence it's called \"standard\".\n Args:\n name (str): the name that identifies a dataset,\n e.g. \"ade20k_panoptic_train\"\n metadata (dict): extra metadata associated with this dataset.\n image_root (str): directory which contains all the images\n panoptic_root (str): directory which contains panoptic annotation images in COCO format\n panoptic_json (str): path to the json panoptic annotation file in COCO format\n sem_seg_root (none): not used, to be consistent with\n `register_coco_panoptic_separated`.\n instances_json (str): path to the json instance annotation file\n \"\"\"\n panoptic_name = name\n DatasetCatalog.register(\n panoptic_name,\n lambda: load_ade20k_panoptic_json(\n panoptic_json, image_root, panoptic_root, semantic_root, metadata\n ),\n )\n MetadataCatalog.get(panoptic_name).set(\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"ade20k_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **metadata,\n )\n\n\n_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {\n \"ade20k_panoptic_train\": (\n \"ADEChallengeData2016/images/training\",\n \"ADEChallengeData2016/ade20k_panoptic_train\",\n \"ADEChallengeData2016/ade20k_panoptic_train.json\",\n \"ADEChallengeData2016/annotations_detectron2/training\",\n \"ADEChallengeData2016/ade20k_instance_train.json\",\n ),\n \"ade20k_panoptic_val\": (\n \"ADEChallengeData2016/images/validation\",\n \"ADEChallengeData2016/ade20k_panoptic_val\",\n \"ADEChallengeData2016/ade20k_panoptic_val.json\",\n \"ADEChallengeData2016/annotations_detectron2/validation\",\n \"ADEChallengeData2016/ade20k_instance_val.json\",\n ),\n}\n\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in ADE20K_150_CATEGORIES if k[\"isthing\"] == 1]\n thing_colors = [k[\"color\"] for k in ADE20K_150_CATEGORIES if k[\"isthing\"] == 1]\n stuff_classes = [k[\"name\"] for k in ADE20K_150_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in ADE20K_150_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:\n # category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(ADE20K_150_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef register_all_ade20k_panoptic(root):\n metadata = get_metadata()\n for (\n prefix,\n (image_root, panoptic_root, panoptic_json, semantic_root, instance_json),\n ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():\n # The \"standard\" version of COCO panoptic segmentation dataset,\n # e.g. used by Panoptic-DeepLab\n register_ade20k_panoptic(\n prefix,\n metadata,\n os.path.join(root, image_root),\n os.path.join(root, panoptic_root),\n os.path.join(root, semantic_root),\n os.path.join(root, panoptic_json),\n os.path.join(root, instance_json),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\",\n# ... truncated ...","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_panoptic.load_ade20k_panoptic_json","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_panoptic.load_ade20k_panoptic_json#L221-L272","kind":"function","name":"load_ade20k_panoptic_json","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":221,"end_line":272,"context_start_line":201,"context_end_line":292,"code":" {\"color\": [0, 92, 255], \"id\": 143, \"isthing\": 1, \"name\": \"monitor\"},\n {\"color\": [184, 255, 0], \"id\": 144, \"isthing\": 1, \"name\": \"bulletin board\"},\n {\"color\": [0, 133, 255], \"id\": 145, \"isthing\": 0, \"name\": \"shower\"},\n {\"color\": [255, 214, 0], \"id\": 146, \"isthing\": 1, \"name\": \"radiator\"},\n {\"color\": [25, 194, 194], \"id\": 147, \"isthing\": 1, \"name\": \"glass, drinking glass\"},\n {\"color\": [102, 255, 0], \"id\": 148, \"isthing\": 1, \"name\": \"clock\"},\n {\"color\": [92, 0, 255], \"id\": 149, \"isthing\": 1, \"name\": \"flag\"},\n]\n\nADE20k_COLORS = [k[\"color\"] for k in ADE20K_150_CATEGORIES]\n\nMetadataCatalog.get(\"ade20k_sem_seg_train\").set(\n stuff_colors=ADE20k_COLORS[:],\n)\n\nMetadataCatalog.get(\"ade20k_sem_seg_val\").set(\n stuff_colors=ADE20k_COLORS[:],\n)\n\n\ndef load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n\n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = ann[\"image_id\"]\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_ade20k_panoptic(\n name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None,\n):\n \"\"\"\n Register a \"standard\" version of ADE20k panoptic segmentation dataset named `name`.\n The dictionaries in this registered dataset follows detectron2's standard format.\n Hence it's called \"standard\".\n Args:\n name (str): the name that identifies a dataset,\n e.g. \"ade20k_panoptic_train\"\n metadata (dict): extra metadata associated with this dataset.\n image_root (str): directory which contains all the images\n panoptic_root (str): directory which contains panoptic annotation images in COCO format\n panoptic_json (str): path to the json panoptic annotation file in COCO format\n sem_seg_root (none): not used, to be consistent with\n `register_coco_panoptic_separated`.\n instances_json (str): path to the json instance annotation file\n \"\"\"","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_panoptic.register_ade20k_panoptic","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_panoptic.register_ade20k_panoptic#L275-L309","kind":"function","name":"register_ade20k_panoptic","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":275,"end_line":309,"context_start_line":255,"context_end_line":329,"code":" image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,\n \"sem_seg_file_name\": sem_label_file,\n \"segments_info\": segments_info,\n }\n )\n assert len(ret), f\"No images found in {image_dir}!\"\n assert PathManager.isfile(ret[0][\"file_name\"]), ret[0][\"file_name\"]\n assert PathManager.isfile(ret[0][\"pan_seg_file_name\"]), ret[0][\"pan_seg_file_name\"]\n assert PathManager.isfile(ret[0][\"sem_seg_file_name\"]), ret[0][\"sem_seg_file_name\"]\n return ret\n\n\ndef register_ade20k_panoptic(\n name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None,\n):\n \"\"\"\n Register a \"standard\" version of ADE20k panoptic segmentation dataset named `name`.\n The dictionaries in this registered dataset follows detectron2's standard format.\n Hence it's called \"standard\".\n Args:\n name (str): the name that identifies a dataset,\n e.g. \"ade20k_panoptic_train\"\n metadata (dict): extra metadata associated with this dataset.\n image_root (str): directory which contains all the images\n panoptic_root (str): directory which contains panoptic annotation images in COCO format\n panoptic_json (str): path to the json panoptic annotation file in COCO format\n sem_seg_root (none): not used, to be consistent with\n `register_coco_panoptic_separated`.\n instances_json (str): path to the json instance annotation file\n \"\"\"\n panoptic_name = name\n DatasetCatalog.register(\n panoptic_name,\n lambda: load_ade20k_panoptic_json(\n panoptic_json, image_root, panoptic_root, semantic_root, metadata\n ),\n )\n MetadataCatalog.get(panoptic_name).set(\n panoptic_root=panoptic_root,\n image_root=image_root,\n panoptic_json=panoptic_json,\n json_file=instances_json,\n evaluator_type=\"ade20k_panoptic_seg\",\n ignore_label=255,\n label_divisor=1000,\n **metadata,\n )\n\n\n_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {\n \"ade20k_panoptic_train\": (\n \"ADEChallengeData2016/images/training\",\n \"ADEChallengeData2016/ade20k_panoptic_train\",\n \"ADEChallengeData2016/ade20k_panoptic_train.json\",\n \"ADEChallengeData2016/annotations_detectron2/training\",\n \"ADEChallengeData2016/ade20k_instance_train.json\",\n ),\n \"ade20k_panoptic_val\": (\n \"ADEChallengeData2016/images/validation\",\n \"ADEChallengeData2016/ade20k_panoptic_val\",\n \"ADEChallengeData2016/ade20k_panoptic_val.json\",\n \"ADEChallengeData2016/annotations_detectron2/validation\",\n \"ADEChallengeData2016/ade20k_instance_val.json\",\n ),\n}\n\n","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_panoptic.get_metadata","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_panoptic.get_metadata#L330-L371","kind":"function","name":"get_metadata","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":330,"end_line":371,"context_start_line":310,"context_end_line":391,"code":"\n\n_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {\n \"ade20k_panoptic_train\": (\n \"ADEChallengeData2016/images/training\",\n \"ADEChallengeData2016/ade20k_panoptic_train\",\n \"ADEChallengeData2016/ade20k_panoptic_train.json\",\n \"ADEChallengeData2016/annotations_detectron2/training\",\n \"ADEChallengeData2016/ade20k_instance_train.json\",\n ),\n \"ade20k_panoptic_val\": (\n \"ADEChallengeData2016/images/validation\",\n \"ADEChallengeData2016/ade20k_panoptic_val\",\n \"ADEChallengeData2016/ade20k_panoptic_val.json\",\n \"ADEChallengeData2016/annotations_detectron2/validation\",\n \"ADEChallengeData2016/ade20k_instance_val.json\",\n ),\n}\n\n\ndef get_metadata():\n meta = {}\n # The following metadata maps contiguous id from [0, #thing categories +\n # #stuff categories) to their names and colors. We have to replica of the\n # same name and color under \"thing_*\" and \"stuff_*\" because the current\n # visualization function in D2 handles thing and class classes differently\n # due to some heuristic used in Panoptic FPN. We keep the same naming to\n # enable reusing existing visualization functions.\n thing_classes = [k[\"name\"] for k in ADE20K_150_CATEGORIES if k[\"isthing\"] == 1]\n thing_colors = [k[\"color\"] for k in ADE20K_150_CATEGORIES if k[\"isthing\"] == 1]\n stuff_classes = [k[\"name\"] for k in ADE20K_150_CATEGORIES]\n stuff_colors = [k[\"color\"] for k in ADE20K_150_CATEGORIES]\n\n meta[\"thing_classes\"] = thing_classes\n meta[\"thing_colors\"] = thing_colors\n meta[\"stuff_classes\"] = stuff_classes\n meta[\"stuff_colors\"] = stuff_colors\n\n # Convert category id for training:\n # category id: like semantic segmentation, it is the class id for each\n # pixel. Since there are some classes not used in evaluation, the category\n # id is not always contiguous and thus we have two set of category ids:\n # - original category id: category id in the original dataset, mainly\n # used for evaluation.\n # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(ADE20K_150_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef register_all_ade20k_panoptic(root):\n metadata = get_metadata()\n for (\n prefix,\n (image_root, panoptic_root, panoptic_json, semantic_root, instance_json),\n ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():\n # The \"standard\" version of COCO panoptic segmentation dataset,\n # e.g. used by Panoptic-DeepLab\n register_ade20k_panoptic(\n prefix,\n metadata,\n os.path.join(root, image_root),\n os.path.join(root, panoptic_root),\n os.path.join(root, semantic_root),\n os.path.join(root, panoptic_json),\n os.path.join(root, instance_json),\n )\n","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_panoptic.register_all_ade20k_panoptic","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_panoptic.register_all_ade20k_panoptic#L374-L390","kind":"function","name":"register_all_ade20k_panoptic","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":374,"end_line":390,"context_start_line":354,"context_end_line":394,"code":" # - contiguous category id: [0, #classes), in order to train the linear\n # softmax classifier.\n thing_dataset_id_to_contiguous_id = {}\n stuff_dataset_id_to_contiguous_id = {}\n\n for i, cat in enumerate(ADE20K_150_CATEGORIES):\n if cat[\"isthing\"]:\n thing_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n # else:\n # stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n # in order to use sem_seg evaluator\n stuff_dataset_id_to_contiguous_id[cat[\"id\"]] = i\n\n meta[\"thing_dataset_id_to_contiguous_id\"] = thing_dataset_id_to_contiguous_id\n meta[\"stuff_dataset_id_to_contiguous_id\"] = stuff_dataset_id_to_contiguous_id\n\n return meta\n\n\ndef register_all_ade20k_panoptic(root):\n metadata = get_metadata()\n for (\n prefix,\n (image_root, panoptic_root, panoptic_json, semantic_root, instance_json),\n ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():\n # The \"standard\" version of COCO panoptic segmentation dataset,\n # e.g. used by Panoptic-DeepLab\n register_ade20k_panoptic(\n prefix,\n metadata,\n os.path.join(root, image_root),\n os.path.join(root, panoptic_root),\n os.path.join(root, semantic_root),\n os.path.join(root, panoptic_json),\n os.path.join(root, instance_json),\n )\n\n\n_root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\nregister_all_ade20k_panoptic(_root)","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.datasets.register_ade20k_panoptic._convert_category_id","uri":"program://OneFormer/function/oneformer.data.datasets.register_ade20k_panoptic._convert_category_id#L232-L243","kind":"function","name":"_convert_category_id","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":232,"end_line":243,"context_start_line":212,"context_end_line":263,"code":"MetadataCatalog.get(\"ade20k_sem_seg_train\").set(\n stuff_colors=ADE20k_COLORS[:],\n)\n\nMetadataCatalog.get(\"ade20k_sem_seg_val\").set(\n stuff_colors=ADE20k_COLORS[:],\n)\n\n\ndef load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):\n \"\"\"\n Args:\n image_dir (str): path to the raw dataset. e.g., \"~/coco/train2017\".\n gt_dir (str): path to the raw annotations. e.g., \"~/coco/panoptic_train2017\".\n json_file (str): path to the json file. e.g., \"~/coco/annotations/panoptic_train2017.json\".\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n \"\"\"\n\n def _convert_category_id(segment_info, meta):\n if segment_info[\"category_id\"] in meta[\"thing_dataset_id_to_contiguous_id\"]:\n segment_info[\"category_id\"] = meta[\"thing_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = True\n else:\n segment_info[\"category_id\"] = meta[\"stuff_dataset_id_to_contiguous_id\"][\n segment_info[\"category_id\"]\n ]\n segment_info[\"isthing\"] = False\n return segment_info\n\n with PathManager.open(json_file) as f:\n json_info = json.load(f)\n\n ret = []\n for ann in json_info[\"annotations\"]:\n image_id = ann[\"image_id\"]\n # TODO: currently we assume image and label has the same filename but\n # different extension, and images have extension \".jpg\" for COCO. Need\n # to make image extension a user-provided argument if we extend this\n # function to support other COCO-like datasets.\n image_file = os.path.join(image_dir, os.path.splitext(ann[\"file_name\"])[0] + \".jpg\")\n label_file = os.path.join(gt_dir, ann[\"file_name\"])\n sem_label_file = os.path.join(semseg_dir, ann[\"file_name\"])\n segments_info = [_convert_category_id(x, meta) for x in ann[\"segments_info\"]]\n ret.append(\n {\n \"file_name\": image_file,\n \"image_id\": image_id,\n \"pan_seg_file_name\": label_file,","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper","uri":"program://OneFormer/module/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper#L1-L341","kind":"module","name":"oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":1,"end_line":341,"context_start_line":1,"context_end_line":341,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\n\nimport numpy as np\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances\nfrom oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"COCOUnifiedNewBaselineDatasetMapper\"]\n\n\ndef build_transform_gen(cfg, is_train):\n \"\"\"\n Create a list of default :class:`Augmentation` from config.\n Now it includes resizing and flipping.\n Returns:\n list[Augmentation]\n \"\"\"\n assert is_train, \"Only support training augmentation\"\n image_size = cfg.INPUT.IMAGE_SIZE\n min_scale = cfg.INPUT.MIN_SCALE\n max_scale = cfg.INPUT.MAX_SCALE\n\n augmentation = []\n\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",\n vertical=cfg.INPUT.RANDOM_FLIP == \"vertical\",\n )\n )\n\n augmentation.extend([\n T.ResizeScale(\n min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size\n ),\n T.FixedSizeCrop(crop_size=(image_size, image_size)),\n ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO dataset.\nclass COCOUnifiedNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n num_queries,\n tfm_gens,\n meta,\n image_format,\n max_seq_len,\n task_seq_len,\n semantic_prob,\n instance_prob,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.ignore_label = self.meta.ignore_label\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n \n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:\n idx = classes.index(class_id)\n masks[idx] += mask\n masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)\n label[mask] = class_id\n\n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n \n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)\n else:\n sem_seg_gt = None\n \n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n prob_task = np.random.uniform(0,1.)\n\n num_class_obj = {}\n\n for name in self.class_names:\n num_class_obj[name] = 0\n\n if prob_task < self.semantic_prob:\n task = \"The task is semantic\"\n instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n elif prob_task < self.instance_prob:\n task = \"The task is instance\"\n instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n else:\n task = \"The task is panoptic\"\n instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n\n\n dataset_dict[\"sem_seg\"] = torch.from_numpy(sem_seg).long()\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n\n return dataset_dict","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.build_transform_gen","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.build_transform_gen#L23-L52","kind":"function","name":"build_transform_gen","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":23,"end_line":52,"context_start_line":3,"context_end_line":72,"code":"# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\n\nimport numpy as np\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances\nfrom oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"COCOUnifiedNewBaselineDatasetMapper\"]\n\n\ndef build_transform_gen(cfg, is_train):\n \"\"\"\n Create a list of default :class:`Augmentation` from config.\n Now it includes resizing and flipping.\n Returns:\n list[Augmentation]\n \"\"\"\n assert is_train, \"Only support training augmentation\"\n image_size = cfg.INPUT.IMAGE_SIZE\n min_scale = cfg.INPUT.MIN_SCALE\n max_scale = cfg.INPUT.MAX_SCALE\n\n augmentation = []\n\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",\n vertical=cfg.INPUT.RANDOM_FLIP == \"vertical\",\n )\n )\n\n augmentation.extend([\n T.ResizeScale(\n min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size\n ),\n T.FixedSizeCrop(crop_size=(image_size, image_size)),\n ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO dataset.\nclass COCOUnifiedNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.COCOUnifiedNewBaselineDatasetMapper","uri":"program://OneFormer/class/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.COCOUnifiedNewBaselineDatasetMapper#L56-L341","kind":"class","name":"COCOUnifiedNewBaselineDatasetMapper","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":56,"end_line":341,"context_start_line":36,"context_end_line":341,"code":"\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",\n vertical=cfg.INPUT.RANDOM_FLIP == \"vertical\",\n )\n )\n\n augmentation.extend([\n T.ResizeScale(\n min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size\n ),\n T.FixedSizeCrop(crop_size=(image_size, image_size)),\n ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO dataset.\nclass COCOUnifiedNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n num_queries,\n tfm_gens,\n meta,\n image_format,\n max_seq_len,\n task_seq_len,\n semantic_prob,\n instance_prob,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.ignore_label = self.meta.ignore_label\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n \n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:\n idx = classes.index(class_id)\n masks[idx] += mask\n masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)\n label[mask] = class_id\n\n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n \n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)\n else:\n sem_seg_gt = None\n \n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n prob_task = np.random.uniform(0,1.)\n\n num_class_obj = {}\n\n for name in self.class_names:\n num_class_obj[name] = 0\n\n if prob_task < self.semantic_prob:\n task = \"The task is semantic\"\n instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n elif prob_task < self.instance_prob:\n task = \"The task is instance\"\n instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n else:\n task = \"The task is panoptic\"\n instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n\n\n dataset_dict[\"sem_seg\"] = torch.from_numpy(sem_seg).long()\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n\n return dataset_dict","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.__init__","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.__init__#L72-L114","kind":"function","name":"__init__","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":72,"end_line":114,"context_start_line":52,"context_end_line":134,"code":" return augmentation\n\n\n# This is specifically designed for the COCO dataset.\nclass COCOUnifiedNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n num_queries,\n tfm_gens,\n meta,\n image_format,\n max_seq_len,\n task_seq_len,\n semantic_prob,\n instance_prob,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.ignore_label = self.meta.ignore_label\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.from_config","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.from_config#L117-L134","kind":"function","name":"from_config","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":117,"end_line":134,"context_start_line":97,"context_end_line":154,"code":" str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.ignore_label = self.meta.ignore_label\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n \n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper._get_semantic_dict","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper._get_semantic_dict#L136-L182","kind":"function","name":"_get_semantic_dict","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":136,"end_line":182,"context_start_line":116,"context_end_line":202,"code":" @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n \n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:\n idx = classes.index(class_id)\n masks[idx] += mask\n masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)\n label[mask] = class_id\n\n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper._get_instance_dict","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper._get_instance_dict#L184-L225","kind":"function","name":"_get_instance_dict","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":184,"end_line":225,"context_start_line":164,"context_end_line":245,"code":" if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n ","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper._get_panoptic_dict","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper._get_panoptic_dict#L227-L271","kind":"function","name":"_get_panoptic_dict","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":227,"end_line":271,"context_start_line":207,"context_end_line":291,"code":" for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n instances = Instances(image_shape)\n\n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n \n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.__call__","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.coco_unified_new_baseline_dataset_mapper.__call__#L273-L341","kind":"function","name":"__call__","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":273,"end_line":341,"context_start_line":253,"context_end_line":341,"code":" num += 1\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n \n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)\n else:\n sem_seg_gt = None\n \n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n prob_task = np.random.uniform(0,1.)\n\n num_class_obj = {}\n\n for name in self.class_names:\n num_class_obj[name] = 0\n\n if prob_task < self.semantic_prob:\n task = \"The task is semantic\"\n instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n elif prob_task < self.instance_prob:\n task = \"The task is instance\"\n instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n else:\n task = \"The task is panoptic\"\n instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n\n\n dataset_dict[\"sem_seg\"] = torch.from_numpy(sem_seg).long()\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n\n return dataset_dict","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper","uri":"program://OneFormer/module/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper#L1-L375","kind":"module","name":"oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":1,"end_line":375,"context_start_line":1,"context_end_line":375,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"OneFormerUnifiedDatasetMapper\"]\n\n\nclass OneFormerUnifiedDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for universal segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n semantic_prob,\n instance_prob,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:\n idx = classes.index(class_id)\n masks[idx] += mask\n masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n # panoptic segmentation\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n else:\n pan_seg_gt = None\n segments_info = None\n\n if pan_seg_gt is None:\n raise ValueError(\n \"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n if sem_seg_gt is not None:\n sem_seg_gt = aug_input.sem_seg\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n pan_seg_gt = F.pad(\n pan_seg_gt, padding_size, value=0\n ).contiguous() # 0 is the VOID panoptic label\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Pemantic segmentation dataset should not have 'annotations'.\")\n\n prob_task = np.random.uniform(0,1.)\n\n num_class_obj = {}\n\n for name in self.class_names:\n num_class_obj[name] = 0\n\n if prob_task < self.semantic_prob:\n task = \"The task is semantic\"\n instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n elif prob_task < self.instance_prob:\n task = \"The task is instance\"\n instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n else:\n task = \"The task is panoptic\"\n instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n\n dataset_dict[\"sem_seg\"] = torch.from_numpy(sem_seg).long()\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n \n return dataset_dict","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.OneFormerUnifiedDatasetMapper","uri":"program://OneFormer/class/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.OneFormerUnifiedDatasetMapper#L26-L375","kind":"class","name":"OneFormerUnifiedDatasetMapper","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":26,"end_line":375,"context_start_line":6,"context_end_line":375,"code":"import copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"OneFormerUnifiedDatasetMapper\"]\n\n\nclass OneFormerUnifiedDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for universal segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n semantic_prob,\n instance_prob,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:\n idx = classes.index(class_id)\n masks[idx] += mask\n masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n # panoptic segmentation\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n else:\n pan_seg_gt = None\n segments_info = None\n\n if pan_seg_gt is None:\n raise ValueError(\n \"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n if sem_seg_gt is not None:\n sem_seg_gt = aug_input.sem_seg\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n pan_seg_gt = F.pad(\n pan_seg_gt, padding_size, value=0\n ).contiguous() # 0 is the VOID panoptic label\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Pemantic segmentation dataset should not have 'annotations'.\")\n\n prob_task = np.random.uniform(0,1.)\n\n num_class_obj = {}\n\n for name in self.class_names:\n num_class_obj[name] = 0\n\n if prob_task < self.semantic_prob:\n task = \"The task is semantic\"\n instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n elif prob_task < self.instance_prob:\n task = \"The task is instance\"\n instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n else:\n task = \"The task is panoptic\"\n instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n\n dataset_dict[\"sem_seg\"] = torch.from_numpy(sem_seg).long()\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n \n return dataset_dict","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.__init__","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.__init__#L40-L85","kind":"function","name":"__init__","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":40,"end_line":85,"context_start_line":20,"context_end_line":105,"code":"from oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"OneFormerUnifiedDatasetMapper\"]\n\n\nclass OneFormerUnifiedDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for universal segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n semantic_prob,\n instance_prob,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.from_config","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.from_config#L88-L129","kind":"function","name":"from_config","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":88,"end_line":129,"context_start_line":68,"context_end_line":149,"code":" self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.semantic_prob = semantic_prob\n self.instance_prob = instance_prob\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper._get_semantic_dict","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper._get_semantic_dict#L131-L178","kind":"function","name":"_get_semantic_dict","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":131,"end_line":178,"context_start_line":111,"context_end_line":198,"code":" dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a semantic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n if class_id not in classes:\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n else:\n idx = classes.index(class_id)\n masks[idx] += mask\n masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper._get_instance_dict","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper._get_instance_dict#L180-L222","kind":"function","name":"_get_instance_dict","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":180,"end_line":222,"context_start_line":160,"context_end_line":242,"code":" if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])\n return instances, texts, label\n \n def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"an instance photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if class_id in self.things:\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper._get_panoptic_dict","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper._get_panoptic_dict#L224-L269","kind":"function","name":"_get_panoptic_dict","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":224,"end_line":269,"context_start_line":204,"context_end_line":289,"code":" for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n return instances, texts, label\n \n def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n \n classes = []\n texts = [\"a panoptic photo\"] * self.num_queries\n masks = []\n label = np.ones_like(pan_seg_gt) * self.ignore_label\n\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n mask = pan_seg_gt == segment_info[\"id\"]\n if not np.all(mask == False):\n cls_name = self.class_names[class_id]\n classes.append(class_id)\n masks.append(mask)\n num_class_obj[cls_name] += 1\n label[mask] = class_id\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.__call__","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.oneformer_unified_dataset_mapper.__call__#L271-L375","kind":"function","name":"__call__","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":271,"end_line":375,"context_start_line":251,"context_end_line":375,"code":" num += 1\n \n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_bboxes = torch.zeros((0, 4))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_bboxes = masks_to_boxes(instances.gt_masks)\n for i in range(instances.gt_classes.shape[0]):\n # Placeholder bounding boxes for stuff regions. Note that these are not used during training.\n if instances.gt_classes[i].item() not in self.things:\n instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])\n return instances, texts, label\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n # panoptic segmentation\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n else:\n pan_seg_gt = None\n segments_info = None\n\n if pan_seg_gt is None:\n raise ValueError(\n \"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n if sem_seg_gt is not None:\n sem_seg_gt = aug_input.sem_seg\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n pan_seg_gt = F.pad(\n pan_seg_gt, padding_size, value=0\n ).contiguous() # 0 is the VOID panoptic label\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Pemantic segmentation dataset should not have 'annotations'.\")\n\n prob_task = np.random.uniform(0,1.)\n\n num_class_obj = {}\n\n for name in self.class_names:\n num_class_obj[name] = 0\n\n if prob_task < self.semantic_prob:\n task = \"The task is semantic\"\n instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n elif prob_task < self.instance_prob:\n task = \"The task is instance\"\n instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n else:\n task = \"The task is panoptic\"\n instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)\n\n dataset_dict[\"sem_seg\"] = torch.from_numpy(sem_seg).long()\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n \n return dataset_dict","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.dataset_mapper","uri":"program://OneFormer/module/oneformer.data.dataset_mappers.dataset_mapper#L1-L203","kind":"module","name":"oneformer.data.dataset_mappers.dataset_mapper","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":1,"end_line":203,"context_start_line":1,"context_end_line":203,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport numpy as np\nfrom typing import List, Optional, Union\nimport torch\n\nfrom detectron2.config import configurable\n\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"DatasetMapper\"]\n\n\nclass DatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by the model.\n\n This is the default callable to be used to map your dataset dict into training data.\n You may need to follow it to implement your own one for customized logic,\n such as a different way to read or transform images.\n See :doc:`/tutorials/data_loading` for details.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies cropping/geometric transforms to the image and annotations\n 3. Prepare data and annotations to Tensor and :class:`Instances`\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train: bool,\n *,\n augmentations: List[Union[T.Augmentation, T.Transform]],\n image_format: str,\n task_seq_len: int,\n task: str = \"panoptic\",\n use_instance_mask: bool = False,\n use_keypoint: bool = False,\n instance_mask_format: str = \"polygon\",\n keypoint_hflip_indices: Optional[np.ndarray] = None,\n precomputed_proposal_topk: Optional[int] = None,\n recompute_boxes: bool = False,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n\n Args:\n is_train: whether it's used in training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n use_instance_mask: whether to process instance segmentation annotations, if available\n use_keypoint: whether to process keypoint annotations if available\n instance_mask_format: one of \"polygon\" or \"bitmask\". Process instance segmentation\n masks into this format.\n keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`\n precomputed_proposal_topk: if given, will load pre-computed\n proposals from dataset_dict and keep the top k proposals for each image.\n recompute_boxes: whether to overwrite bounding box annotations\n by computing tight bounding boxes from instance mask annotations.\n \"\"\"\n if recompute_boxes:\n assert use_instance_mask, \"recompute_boxes requires instance masks\"\n # fmt: off\n self.is_train = is_train\n self.augmentations = T.AugmentationList(augmentations)\n self.image_format = image_format\n self.use_instance_mask = use_instance_mask\n self.instance_mask_format = instance_mask_format\n self.use_keypoint = use_keypoint\n self.keypoint_hflip_indices = keypoint_hflip_indices\n self.proposal_topk = precomputed_proposal_topk\n self.recompute_boxes = recompute_boxes\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.task = task\n assert self.task in [\"panoptic\", \"semantic\", \"instance\"]\n\n # fmt: on\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[DatasetMapper] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train: bool = True):\n augs = utils.build_augmentation(cfg, is_train)\n if cfg.INPUT.CROP.ENABLED and is_train:\n augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))\n recompute_boxes = cfg.MODEL.MASK_ON\n else:\n recompute_boxes = False\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"use_instance_mask\": cfg.MODEL.MASK_ON,\n \"instance_mask_format\": cfg.INPUT.MASK_FORMAT,\n \"use_keypoint\": cfg.MODEL.KEYPOINT_ON,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"recompute_boxes\": recompute_boxes,\n \"task\": cfg.MODEL.TEST.TASK,\n }\n\n if cfg.MODEL.KEYPOINT_ON:\n ret[\"keypoint_hflip_indices\"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)\n\n if cfg.MODEL.LOAD_PROPOSALS:\n ret[\"precomputed_proposal_topk\"] = (\n cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN\n if is_train\n else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST\n )\n return ret\n\n def _transform_annotations(self, dataset_dict, transforms, image_shape):\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n if not self.use_instance_mask:\n anno.pop(\"segmentation\", None)\n if not self.use_keypoint:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(\n obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices\n )\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n instances = utils.annotations_to_instances(\n annos, image_shape, mask_format=self.instance_mask_format\n )\n\n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n if self.recompute_boxes:\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n dataset_dict[\"instances\"] = utils.filter_empty_instances(instances)\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n # USER: Write your own image loading if it's not from a file\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.image_format)\n utils.check_image_size(dataset_dict, image)\n \n task = f\"The task is {self.task}\"\n dataset_dict[\"task\"] = task\n\n # USER: Remove if you don't do semantic/panoptic segmentation.\n if \"sem_seg_file_name\" in dataset_dict:\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\"), \"L\").squeeze(2)\n else:\n sem_seg_gt = None\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n transforms = self.augmentations(aug_input)\n image, sem_seg_gt = aug_input.image, aug_input.sem_seg\n\n image_shape = image.shape[:2] # h, w\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n # USER: Remove if you don't use pre-computed proposals.\n # Most users would not need this feature.\n if self.proposal_topk is not None:\n utils.transform_proposals(\n dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk\n )\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n dataset_dict.pop(\"sem_seg_file_name\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n self._transform_annotations(dataset_dict, transforms, image_shape)\n\n return dataset_dict","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.dataset_mapper.DatasetMapper","uri":"program://OneFormer/class/oneformer.data.dataset_mappers.dataset_mapper.DatasetMapper#L21-L203","kind":"class","name":"DatasetMapper","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":21,"end_line":203,"context_start_line":1,"context_end_line":203,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport numpy as np\nfrom typing import List, Optional, Union\nimport torch\n\nfrom detectron2.config import configurable\n\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"DatasetMapper\"]\n\n\nclass DatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by the model.\n\n This is the default callable to be used to map your dataset dict into training data.\n You may need to follow it to implement your own one for customized logic,\n such as a different way to read or transform images.\n See :doc:`/tutorials/data_loading` for details.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies cropping/geometric transforms to the image and annotations\n 3. Prepare data and annotations to Tensor and :class:`Instances`\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train: bool,\n *,\n augmentations: List[Union[T.Augmentation, T.Transform]],\n image_format: str,\n task_seq_len: int,\n task: str = \"panoptic\",\n use_instance_mask: bool = False,\n use_keypoint: bool = False,\n instance_mask_format: str = \"polygon\",\n keypoint_hflip_indices: Optional[np.ndarray] = None,\n precomputed_proposal_topk: Optional[int] = None,\n recompute_boxes: bool = False,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n\n Args:\n is_train: whether it's used in training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n use_instance_mask: whether to process instance segmentation annotations, if available\n use_keypoint: whether to process keypoint annotations if available\n instance_mask_format: one of \"polygon\" or \"bitmask\". Process instance segmentation\n masks into this format.\n keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`\n precomputed_proposal_topk: if given, will load pre-computed\n proposals from dataset_dict and keep the top k proposals for each image.\n recompute_boxes: whether to overwrite bounding box annotations\n by computing tight bounding boxes from instance mask annotations.\n \"\"\"\n if recompute_boxes:\n assert use_instance_mask, \"recompute_boxes requires instance masks\"\n # fmt: off\n self.is_train = is_train\n self.augmentations = T.AugmentationList(augmentations)\n self.image_format = image_format\n self.use_instance_mask = use_instance_mask\n self.instance_mask_format = instance_mask_format\n self.use_keypoint = use_keypoint\n self.keypoint_hflip_indices = keypoint_hflip_indices\n self.proposal_topk = precomputed_proposal_topk\n self.recompute_boxes = recompute_boxes\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.task = task\n assert self.task in [\"panoptic\", \"semantic\", \"instance\"]\n\n # fmt: on\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[DatasetMapper] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train: bool = True):\n augs = utils.build_augmentation(cfg, is_train)\n if cfg.INPUT.CROP.ENABLED and is_train:\n augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))\n recompute_boxes = cfg.MODEL.MASK_ON\n else:\n recompute_boxes = False\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"use_instance_mask\": cfg.MODEL.MASK_ON,\n \"instance_mask_format\": cfg.INPUT.MASK_FORMAT,\n \"use_keypoint\": cfg.MODEL.KEYPOINT_ON,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"recompute_boxes\": recompute_boxes,\n \"task\": cfg.MODEL.TEST.TASK,\n }\n\n if cfg.MODEL.KEYPOINT_ON:\n ret[\"keypoint_hflip_indices\"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)\n\n if cfg.MODEL.LOAD_PROPOSALS:\n ret[\"precomputed_proposal_topk\"] = (\n cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN\n if is_train\n else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST\n )\n return ret\n\n def _transform_annotations(self, dataset_dict, transforms, image_shape):\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n if not self.use_instance_mask:\n anno.pop(\"segmentation\", None)\n if not self.use_keypoint:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(\n obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices\n )\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n instances = utils.annotations_to_instances(\n annos, image_shape, mask_format=self.instance_mask_format\n )\n\n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n if self.recompute_boxes:\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n dataset_dict[\"instances\"] = utils.filter_empty_instances(instances)\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n # USER: Write your own image loading if it's not from a file\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.image_format)\n utils.check_image_size(dataset_dict, image)\n \n task = f\"The task is {self.task}\"\n dataset_dict[\"task\"] = task\n\n # USER: Remove if you don't do semantic/panoptic segmentation.\n if \"sem_seg_file_name\" in dataset_dict:\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\"), \"L\").squeeze(2)\n else:\n sem_seg_gt = None\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n transforms = self.augmentations(aug_input)\n image, sem_seg_gt = aug_input.image, aug_input.sem_seg\n\n image_shape = image.shape[:2] # h, w\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n # USER: Remove if you don't use pre-computed proposals.\n # Most users would not need this feature.\n if self.proposal_topk is not None:\n utils.transform_proposals(\n dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk\n )\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n dataset_dict.pop(\"sem_seg_file_name\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n self._transform_annotations(dataset_dict, transforms, image_shape)\n\n return dataset_dict","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.dataset_mapper.__init__","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.dataset_mapper.__init__#L39-L90","kind":"function","name":"__init__","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":39,"end_line":90,"context_start_line":19,"context_end_line":110,"code":"\n\nclass DatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by the model.\n\n This is the default callable to be used to map your dataset dict into training data.\n You may need to follow it to implement your own one for customized logic,\n such as a different way to read or transform images.\n See :doc:`/tutorials/data_loading` for details.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies cropping/geometric transforms to the image and annotations\n 3. Prepare data and annotations to Tensor and :class:`Instances`\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train: bool,\n *,\n augmentations: List[Union[T.Augmentation, T.Transform]],\n image_format: str,\n task_seq_len: int,\n task: str = \"panoptic\",\n use_instance_mask: bool = False,\n use_keypoint: bool = False,\n instance_mask_format: str = \"polygon\",\n keypoint_hflip_indices: Optional[np.ndarray] = None,\n precomputed_proposal_topk: Optional[int] = None,\n recompute_boxes: bool = False,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n\n Args:\n is_train: whether it's used in training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n use_instance_mask: whether to process instance segmentation annotations, if available\n use_keypoint: whether to process keypoint annotations if available\n instance_mask_format: one of \"polygon\" or \"bitmask\". Process instance segmentation\n masks into this format.\n keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`\n precomputed_proposal_topk: if given, will load pre-computed\n proposals from dataset_dict and keep the top k proposals for each image.\n recompute_boxes: whether to overwrite bounding box annotations\n by computing tight bounding boxes from instance mask annotations.\n \"\"\"\n if recompute_boxes:\n assert use_instance_mask, \"recompute_boxes requires instance masks\"\n # fmt: off\n self.is_train = is_train\n self.augmentations = T.AugmentationList(augmentations)\n self.image_format = image_format\n self.use_instance_mask = use_instance_mask\n self.instance_mask_format = instance_mask_format\n self.use_keypoint = use_keypoint\n self.keypoint_hflip_indices = keypoint_hflip_indices\n self.proposal_topk = precomputed_proposal_topk\n self.recompute_boxes = recompute_boxes\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.task = task\n assert self.task in [\"panoptic\", \"semantic\", \"instance\"]\n\n # fmt: on\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[DatasetMapper] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train: bool = True):\n augs = utils.build_augmentation(cfg, is_train)\n if cfg.INPUT.CROP.ENABLED and is_train:\n augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))\n recompute_boxes = cfg.MODEL.MASK_ON\n else:\n recompute_boxes = False\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"use_instance_mask\": cfg.MODEL.MASK_ON,\n \"instance_mask_format\": cfg.INPUT.MASK_FORMAT,\n \"use_keypoint\": cfg.MODEL.KEYPOINT_ON,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"recompute_boxes\": recompute_boxes,\n \"task\": cfg.MODEL.TEST.TASK,","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.dataset_mapper.from_config","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.dataset_mapper.from_config#L93-L122","kind":"function","name":"from_config","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":93,"end_line":122,"context_start_line":73,"context_end_line":142,"code":" # fmt: off\n self.is_train = is_train\n self.augmentations = T.AugmentationList(augmentations)\n self.image_format = image_format\n self.use_instance_mask = use_instance_mask\n self.instance_mask_format = instance_mask_format\n self.use_keypoint = use_keypoint\n self.keypoint_hflip_indices = keypoint_hflip_indices\n self.proposal_topk = precomputed_proposal_topk\n self.recompute_boxes = recompute_boxes\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n self.task = task\n assert self.task in [\"panoptic\", \"semantic\", \"instance\"]\n\n # fmt: on\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[DatasetMapper] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train: bool = True):\n augs = utils.build_augmentation(cfg, is_train)\n if cfg.INPUT.CROP.ENABLED and is_train:\n augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))\n recompute_boxes = cfg.MODEL.MASK_ON\n else:\n recompute_boxes = False\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"use_instance_mask\": cfg.MODEL.MASK_ON,\n \"instance_mask_format\": cfg.INPUT.MASK_FORMAT,\n \"use_keypoint\": cfg.MODEL.KEYPOINT_ON,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"recompute_boxes\": recompute_boxes,\n \"task\": cfg.MODEL.TEST.TASK,\n }\n\n if cfg.MODEL.KEYPOINT_ON:\n ret[\"keypoint_hflip_indices\"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)\n\n if cfg.MODEL.LOAD_PROPOSALS:\n ret[\"precomputed_proposal_topk\"] = (\n cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN\n if is_train\n else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST\n )\n return ret\n\n def _transform_annotations(self, dataset_dict, transforms, image_shape):\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n if not self.use_instance_mask:\n anno.pop(\"segmentation\", None)\n if not self.use_keypoint:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(\n obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices\n )\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n instances = utils.annotations_to_instances(\n annos, image_shape, mask_format=self.instance_mask_format\n )","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.dataset_mapper._transform_annotations","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.dataset_mapper._transform_annotations#L124-L151","kind":"function","name":"_transform_annotations","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":124,"end_line":151,"context_start_line":104,"context_end_line":171,"code":" \"image_format\": cfg.INPUT.FORMAT,\n \"use_instance_mask\": cfg.MODEL.MASK_ON,\n \"instance_mask_format\": cfg.INPUT.MASK_FORMAT,\n \"use_keypoint\": cfg.MODEL.KEYPOINT_ON,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"recompute_boxes\": recompute_boxes,\n \"task\": cfg.MODEL.TEST.TASK,\n }\n\n if cfg.MODEL.KEYPOINT_ON:\n ret[\"keypoint_hflip_indices\"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)\n\n if cfg.MODEL.LOAD_PROPOSALS:\n ret[\"precomputed_proposal_topk\"] = (\n cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN\n if is_train\n else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST\n )\n return ret\n\n def _transform_annotations(self, dataset_dict, transforms, image_shape):\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n if not self.use_instance_mask:\n anno.pop(\"segmentation\", None)\n if not self.use_keypoint:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(\n obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices\n )\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n instances = utils.annotations_to_instances(\n annos, image_shape, mask_format=self.instance_mask_format\n )\n\n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n if self.recompute_boxes:\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n dataset_dict[\"instances\"] = utils.filter_empty_instances(instances)\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n # USER: Write your own image loading if it's not from a file\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.image_format)\n utils.check_image_size(dataset_dict, image)\n \n task = f\"The task is {self.task}\"\n dataset_dict[\"task\"] = task\n\n # USER: Remove if you don't do semantic/panoptic segmentation.\n if \"sem_seg_file_name\" in dataset_dict:\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\"), \"L\").squeeze(2)","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.data.dataset_mappers.dataset_mapper.__call__","uri":"program://OneFormer/function/oneformer.data.dataset_mappers.dataset_mapper.__call__#L153-L203","kind":"function","name":"__call__","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":153,"end_line":203,"context_start_line":133,"context_end_line":203,"code":" annos = [\n utils.transform_instance_annotations(\n obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices\n )\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n instances = utils.annotations_to_instances(\n annos, image_shape, mask_format=self.instance_mask_format\n )\n\n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n if self.recompute_boxes:\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n dataset_dict[\"instances\"] = utils.filter_empty_instances(instances)\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n # USER: Write your own image loading if it's not from a file\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.image_format)\n utils.check_image_size(dataset_dict, image)\n \n task = f\"The task is {self.task}\"\n dataset_dict[\"task\"] = task\n\n # USER: Remove if you don't do semantic/panoptic segmentation.\n if \"sem_seg_file_name\" in dataset_dict:\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\"), \"L\").squeeze(2)\n else:\n sem_seg_gt = None\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n transforms = self.augmentations(aug_input)\n image, sem_seg_gt = aug_input.image, aug_input.sem_seg\n\n image_shape = image.shape[:2] # h, w\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n # USER: Remove if you don't use pre-computed proposals.\n # Most users would not need this feature.\n if self.proposal_topk is not None:\n utils.transform_proposals(\n dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk\n )\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n dataset_dict.pop(\"sem_seg_file_name\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n self._transform_annotations(dataset_dict, transforms, image_shape)\n\n return dataset_dict","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator","uri":"program://OneFormer/module/oneformer.evaluation.detection_coco_evaluator#L1-L723","kind":"module","name":"oneformer.evaluation.detection_coco_evaluator","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":1,"end_line":723,"context_start_line":1,"context_end_line":723,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport contextlib\nimport copy\nimport io\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import CfgNode\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\nfrom .evaluator import DatasetEvaluator\n\ntry:\n from detectron2.evaluation.fast_eval_api import COCOeval_opt\nexcept ImportError:\n COCOeval_opt = COCOeval\n\n\nclass DetectionCOCOEvaluator(DatasetEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def __init__(\n self,\n dataset_name,\n tasks=None,\n distributed=True,\n output_dir=None,\n *,\n max_dets_per_image=None,\n use_fast_impl=True,\n kpt_oks_sigmas=(),\n allow_cached_coco=True,\n ):\n \"\"\"\n Args:\n dataset_name (str): name of the dataset to be evaluated.\n It must have either the following corresponding metadata:\n\n \"json_file\": the path to the COCO format annotation\n\n Or it must be in detectron2's standard dataset format\n so it can be converted to COCO format automatically.\n tasks (tuple[str]): tasks that can be evaluated under the given\n configuration. A task is one of \"bbox\", \"segm\", \"keypoints\".\n By default, will infer this automatically from predictions.\n distributed (True): if True, will collect results from all ranks and run evaluation\n in the main process.\n Otherwise, will only evaluate the results in the current process.\n output_dir (str): optional, an output directory to dump all\n results predicted on the dataset. The dump contains two files:\n\n 1. \"instances_predictions.pth\" a file that can be loaded with `torch.load` and\n contains all the results in the format they are produced by the model.\n 2. \"coco_instances_results.json\" a json file in COCO's result format.\n max_dets_per_image (int): limit on the maximum number of detections per image.\n By default in COCO, this limit is to 100, but this can be customized\n to be greater, as is needed in evaluation metrics AP fixed and AP pool\n (see https://arxiv.org/pdf/2102.01066.pdf)\n This doesn't affect keypoint evaluation.\n use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.\n Although the results should be very close to the official implementation in COCO\n API, it is still recommended to compute results with the official API for use in\n papers. The faster implementation also uses more RAM.\n kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.\n See http://cocodataset.org/#keypoints-eval\n When empty, it will use the defaults in COCO.\n Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.\n allow_cached_coco (bool): Whether to use cached coco json from previous validation\n runs. You should set this to False if you need to use different validation data.\n Defaults to True.\n \"\"\"\n self._logger = logging.getLogger(__name__)\n self._distributed = distributed\n self._output_dir = output_dir\n\n if use_fast_impl and (COCOeval_opt is COCOeval):\n self._logger.info(\"Fast COCO eval is not built. Falling back to official COCO eval.\")\n use_fast_impl = False\n self._use_fast_impl = use_fast_impl\n\n # COCOeval requires the limit on the number of detections per image (maxDets) to be a list\n # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the\n # 3rd element (100) is used as the limit on the number of detections per image when\n # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,\n # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100]\n else:\n max_dets_per_image = [1, 10, max_dets_per_image]\n self._max_dets_per_image = max_dets_per_image\n\n if tasks is not None and isinstance(tasks, CfgNode):\n kpt_oks_sigmas = (\n tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas\n )\n self._logger.warn(\n \"COCO Evaluator instantiated using config, this is deprecated behavior.\"\n \" Please pass in explicit arguments instead.\"\n )\n self._tasks = None # Infering it from predictions should be better\n else:\n self._tasks = tasks\n\n self._cpu_device = torch.device(\"cpu\")\n\n self._metadata = MetadataCatalog.get(dataset_name)\n if not hasattr(self._metadata, \"json_file\"):\n if output_dir is None:\n raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"box_instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"box_instances\" in output:\n instances = output[\"box_instances\"].to(self._cpu_device)\n prediction[\"box_instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if \"proposals\" in output:\n prediction[\"proposals\"] = output[\"proposals\"].to(self._cpu_device)\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"proposals\" in predictions[0]:\n self._eval_box_proposals(predictions)\n if \"box_instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n tasks = {\"bbox\"}\n for pred in predictions:\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"box_instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has {num_classes} classes and \"\n f\"predicted class id should be in [0, {num_classes - 1}].\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _eval_box_proposals(self, predictions):\n \"\"\"\n Evaluate the box proposals in predictions.\n Fill self._results with the metrics for \"box_proposals\" task.\n \"\"\"\n if self._output_dir:\n # Saving generated box proposals to file.\n # Predicted box_proposals are in XYXY_ABS mode.\n bbox_mode = BoxMode.XYXY_ABS.value\n ids, boxes, objectness_logits = [], [], []\n for prediction in predictions:\n ids.append(prediction[\"image_id\"])\n boxes.append(prediction[\"proposals\"].proposal_boxes.tensor.numpy())\n objectness_logits.append(prediction[\"proposals\"].objectness_logits.numpy())\n\n proposal_data = {\n \"boxes\": boxes,\n \"objectness_logits\": objectness_logits,\n \"ids\": ids,\n \"bbox_mode\": bbox_mode,\n }\n with PathManager.open(os.path.join(self._output_dir, \"box_proposals.pkl\"), \"wb\") as f:\n pickle.dump(proposal_data, f)\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\"Evaluating bbox proposals ...\")\n res = {}\n areas = {\"all\": \"\", \"small\": \"s\", \"medium\": \"m\", \"large\": \"l\"}\n for limit in [100, 1000]:\n for area, suffix in areas.items():\n stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)\n key = \"AR{}@{:d}\".format(suffix, limit)\n res[key] = float(stats[\"ar\"].item() * 100)\n self._logger.info(\"Proposal metrics: \\n\" + create_small_table(res))\n self._results[\"box_proposals\"] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"bbox\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n\n if coco_eval is None:\n self._logger.warn(\"No predictions from the model!\")\n return {metric: float(\"nan\") for metric in metrics}\n\n # the standard metrics\n results = {\n metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else \"nan\")\n for idx, metric in enumerate(metrics)\n }\n self._logger.info(\n \"Evaluation results for {}: \\n\".format(iou_type) + create_small_table(results)\n )\n if not np.isfinite(sum(results.values())):\n self._logger.info(\"Some metrics cannot be computed and is shown as NaN.\")\n\n if class_names is None or len(class_names) <= 1:\n return results\n # Compute per-category AP\n # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa\n precisions = coco_eval.eval[\"precision\"]\n # precision has dims (iou, recall, cls, area range, max dets)\n assert len(class_names) == precisions.shape[2]\n\n results_per_category = []\n for idx, name in enumerate(class_names):\n # area range index 0: all area ranges\n # max dets index -1: typically 100 per image\n precision = precisions[:, :, idx, 0, -1]\n precision = precision[precision > -1]\n ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n boxes = instances.pred_boxes.tensor.numpy()\n boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)\n boxes = boxes.tolist()\n scores = instances.scores.tolist()\n classes = instances.pred_classes.tolist()\n\n has_mask = instances.has(\"pred_masks\")\n if has_mask:\n # use RLE to encode the masks, because they are too large and takes memory\n # since this evaluator stores outputs of the entire dataset\n rles = [\n mask_util.encode(np.array(mask[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n for mask in instances.pred_masks\n ]\n for rle in rles:\n # \"counts\" is an array encoded by mask_util as a byte-stream. Python3's\n # json writer which always produces strings cannot serialize a bytestream\n # unless you decode it. Thankfully, utf-8 works out (which is also what\n # the pycocotools/_mask.pyx does).\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n has_keypoints = instances.has(\"pred_keypoints\")\n if has_keypoints:\n keypoints = instances.pred_keypoints\n\n results = []\n for k in range(num_instance):\n result = {\n \"image_id\": img_id,\n \"category_id\": classes[k],\n \"bbox\": boxes[k],\n \"score\": scores[k],\n }\n if has_mask:\n result[\"segmentation\"] = rles[k]\n if has_keypoints:\n # In COCO annotations,\n # keypoints coordinates are pixel indices.\n # However our predictions are floating point coordinates.\n # Therefore we subtract 0.5 to be consistent with the annotation format.\n # This is the inverse of data loading logic in `datasets/coco.py`.\n keypoints[k][:, :2] -= 0.5\n result[\"keypoints\"] = keypoints[k].flatten().tolist()\n results.append(result)\n return results\n\n\n# inspired from Detectron:\n# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa\ndef _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area=\"all\", limit=None):\n \"\"\"\n Evaluate detection proposal recall metrics. This function is a much\n faster alternative to the official COCO API recall evaluation code. However,\n it produc\n# ... truncated ...","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.DetectionCOCOEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.detection_coco_evaluator.DetectionCOCOEvaluator#L38-L390","kind":"class","name":"DetectionCOCOEvaluator","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":38,"end_line":390,"context_start_line":18,"context_end_line":410,"code":"from pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import CfgNode\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\nfrom .evaluator import DatasetEvaluator\n\ntry:\n from detectron2.evaluation.fast_eval_api import COCOeval_opt\nexcept ImportError:\n COCOeval_opt = COCOeval\n\n\nclass DetectionCOCOEvaluator(DatasetEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def __init__(\n self,\n dataset_name,\n tasks=None,\n distributed=True,\n output_dir=None,\n *,\n max_dets_per_image=None,\n use_fast_impl=True,\n kpt_oks_sigmas=(),\n allow_cached_coco=True,\n ):\n \"\"\"\n Args:\n dataset_name (str): name of the dataset to be evaluated.\n It must have either the following corresponding metadata:\n\n \"json_file\": the path to the COCO format annotation\n\n Or it must be in detectron2's standard dataset format\n so it can be converted to COCO format automatically.\n tasks (tuple[str]): tasks that can be evaluated under the given\n configuration. A task is one of \"bbox\", \"segm\", \"keypoints\".\n By default, will infer this automatically from predictions.\n distributed (True): if True, will collect results from all ranks and run evaluation\n in the main process.\n Otherwise, will only evaluate the results in the current process.\n output_dir (str): optional, an output directory to dump all\n results predicted on the dataset. The dump contains two files:\n\n 1. \"instances_predictions.pth\" a file that can be loaded with `torch.load` and\n contains all the results in the format they are produced by the model.\n 2. \"coco_instances_results.json\" a json file in COCO's result format.\n max_dets_per_image (int): limit on the maximum number of detections per image.\n By default in COCO, this limit is to 100, but this can be customized\n to be greater, as is needed in evaluation metrics AP fixed and AP pool\n (see https://arxiv.org/pdf/2102.01066.pdf)\n This doesn't affect keypoint evaluation.\n use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.\n Although the results should be very close to the official implementation in COCO\n API, it is still recommended to compute results with the official API for use in\n papers. The faster implementation also uses more RAM.\n kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.\n See http://cocodataset.org/#keypoints-eval\n When empty, it will use the defaults in COCO.\n Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.\n allow_cached_coco (bool): Whether to use cached coco json from previous validation\n runs. You should set this to False if you need to use different validation data.\n Defaults to True.\n \"\"\"\n self._logger = logging.getLogger(__name__)\n self._distributed = distributed\n self._output_dir = output_dir\n\n if use_fast_impl and (COCOeval_opt is COCOeval):\n self._logger.info(\"Fast COCO eval is not built. Falling back to official COCO eval.\")\n use_fast_impl = False\n self._use_fast_impl = use_fast_impl\n\n # COCOeval requires the limit on the number of detections per image (maxDets) to be a list\n # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the\n # 3rd element (100) is used as the limit on the number of detections per image when\n # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,\n # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100]\n else:\n max_dets_per_image = [1, 10, max_dets_per_image]\n self._max_dets_per_image = max_dets_per_image\n\n if tasks is not None and isinstance(tasks, CfgNode):\n kpt_oks_sigmas = (\n tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas\n )\n self._logger.warn(\n \"COCO Evaluator instantiated using config, this is deprecated behavior.\"\n \" Please pass in explicit arguments instead.\"\n )\n self._tasks = None # Infering it from predictions should be better\n else:\n self._tasks = tasks\n\n self._cpu_device = torch.device(\"cpu\")\n\n self._metadata = MetadataCatalog.get(dataset_name)\n if not hasattr(self._metadata, \"json_file\"):\n if output_dir is None:\n raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"box_instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"box_instances\" in output:\n instances = output[\"box_instances\"].to(self._cpu_device)\n prediction[\"box_instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if \"proposals\" in output:\n prediction[\"proposals\"] = output[\"proposals\"].to(self._cpu_device)\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"proposals\" in predictions[0]:\n self._eval_box_proposals(predictions)\n if \"box_instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n tasks = {\"bbox\"}\n for pred in predictions:\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"box_instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has {num_classes} classes and \"\n f\"predicted class id should be in [0, {num_classes - 1}].\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _eval_box_proposals(self, predictions):\n \"\"\"\n Evaluate the box proposals in predictions.\n Fill self._results with the metrics for \"box_proposals\" task.\n \"\"\"\n if self._output_dir:\n # Saving generated box proposals to file.\n # Predicted box_proposals are in XYXY_ABS mode.\n bbox_mode = BoxMode.XYXY_ABS.value\n ids, boxes, objectness_logits = [], [], []\n for prediction in predictions:\n ids.append(prediction[\"image_id\"])\n boxes.append(prediction[\"proposals\"].proposal_boxes.tensor.numpy())\n objectness_logits.append(prediction[\"proposals\"].objectness_logits.numpy())\n\n proposal_data = {\n \"boxes\": boxes,\n \"objectness_logits\": objectness_logits,\n \"ids\": ids,\n \"bbox_mode\": bbox_mode,\n }\n with PathManager.open(os.path.join(self._output_dir, \"box_proposals.pkl\"), \"wb\") as f:\n pickle.dump(proposal_data, f)\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\"Evaluating bbox proposals ...\")\n res = {}\n areas = {\"all\": \"\", \"small\": \"s\", \"medium\": \"m\", \"large\": \"l\"}\n for limit in [100, 1000]:\n for area, suffix in areas.items():\n stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)\n key = \"AR{}@{:d}\".format(suffix, limit)\n res[key] = float(stats[\"ar\"].item() * 100)\n self._logger.info(\"Proposal metrics: \\n\" + create_small_table(res))\n self._results[\"box_proposals\"] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"bbox\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n\n if coco_eval is None:\n self._logger.warn(\"No predictions from the model!\")\n return {metric: float(\"nan\") for metric in metrics}\n\n # the standard metrics\n results = {\n metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else \"nan\")\n for idx, metric in enumerate(metrics)\n }\n self._logger.info(\n \"Evaluation results for {}: \\n\".format(iou_type) + create_small_table(results)\n )\n if not np.isfinite(sum(results.values())):\n self._logger.info(\"Some metrics cannot be computed and is shown as NaN.\")\n\n if class_names is None or len(class_names) <= 1:\n return results\n # Compute per-category AP\n # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa\n precisions = coco_eval.eval[\"precision\"]\n # precision has dims (iou, recall, cls, area range, max dets)\n assert len(class_names) == precisions.shape[2]\n\n results_per_category = []\n for idx, name in enumerate(class_names):\n # area range index 0: all area ranges\n # max dets index -1: typically 100 per image\n precision = precisions[:, :, idx, 0, -1]\n precision = precision[precision > -1]\n ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n boxes = instances.pred_boxes.tensor.numpy()\n boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)\n boxes = boxes.tolist()","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.instances_to_coco_json","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.instances_to_coco_json#L393-L452","kind":"function","name":"instances_to_coco_json","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":393,"end_line":452,"context_start_line":373,"context_end_line":472,"code":" ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n boxes = instances.pred_boxes.tensor.numpy()\n boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)\n boxes = boxes.tolist()\n scores = instances.scores.tolist()\n classes = instances.pred_classes.tolist()\n\n has_mask = instances.has(\"pred_masks\")\n if has_mask:\n # use RLE to encode the masks, because they are too large and takes memory\n # since this evaluator stores outputs of the entire dataset\n rles = [\n mask_util.encode(np.array(mask[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n for mask in instances.pred_masks\n ]\n for rle in rles:\n # \"counts\" is an array encoded by mask_util as a byte-stream. Python3's\n # json writer which always produces strings cannot serialize a bytestream\n # unless you decode it. Thankfully, utf-8 works out (which is also what\n # the pycocotools/_mask.pyx does).\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n has_keypoints = instances.has(\"pred_keypoints\")\n if has_keypoints:\n keypoints = instances.pred_keypoints\n\n results = []\n for k in range(num_instance):\n result = {\n \"image_id\": img_id,\n \"category_id\": classes[k],\n \"bbox\": boxes[k],\n \"score\": scores[k],\n }\n if has_mask:\n result[\"segmentation\"] = rles[k]\n if has_keypoints:\n # In COCO annotations,\n # keypoints coordinates are pixel indices.\n # However our predictions are floating point coordinates.\n # Therefore we subtract 0.5 to be consistent with the annotation format.\n # This is the inverse of data loading logic in `datasets/coco.py`.\n keypoints[k][:, :2] -= 0.5\n result[\"keypoints\"] = keypoints[k].flatten().tolist()\n results.append(result)\n return results\n\n\n# inspired from Detectron:\n# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa\ndef _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area=\"all\", limit=None):\n \"\"\"\n Evaluate detection proposal recall metrics. This function is a much\n faster alternative to the official COCO API recall evaluation code. However,\n it produces slightly different results.\n \"\"\"\n # Record max overlap value for each gt box\n # Return vector of overlap values\n areas = {\n \"all\": 0,\n \"small\": 1,\n \"medium\": 2,\n \"large\": 3,\n \"96-128\": 4,\n \"128-256\": 5,\n \"256-512\": 6,","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._evaluate_box_proposals","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._evaluate_box_proposals#L457-L565","kind":"function","name":"_evaluate_box_proposals","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":457,"end_line":565,"context_start_line":437,"context_end_line":585,"code":" \"category_id\": classes[k],\n \"bbox\": boxes[k],\n \"score\": scores[k],\n }\n if has_mask:\n result[\"segmentation\"] = rles[k]\n if has_keypoints:\n # In COCO annotations,\n # keypoints coordinates are pixel indices.\n # However our predictions are floating point coordinates.\n # Therefore we subtract 0.5 to be consistent with the annotation format.\n # This is the inverse of data loading logic in `datasets/coco.py`.\n keypoints[k][:, :2] -= 0.5\n result[\"keypoints\"] = keypoints[k].flatten().tolist()\n results.append(result)\n return results\n\n\n# inspired from Detectron:\n# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa\ndef _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area=\"all\", limit=None):\n \"\"\"\n Evaluate detection proposal recall metrics. This function is a much\n faster alternative to the official COCO API recall evaluation code. However,\n it produces slightly different results.\n \"\"\"\n # Record max overlap value for each gt box\n # Return vector of overlap values\n areas = {\n \"all\": 0,\n \"small\": 1,\n \"medium\": 2,\n \"large\": 3,\n \"96-128\": 4,\n \"128-256\": 5,\n \"256-512\": 6,\n \"512-inf\": 7,\n }\n area_ranges = [\n [0**2, 1e5**2], # all\n [0**2, 32**2], # small\n [32**2, 96**2], # medium\n [96**2, 1e5**2], # large\n [96**2, 128**2], # 96-128\n [128**2, 256**2], # 128-256\n [256**2, 512**2], # 256-512\n [512**2, 1e5**2],\n ] # 512-inf\n assert area in areas, \"Unknown area range: {}\".format(area)\n area_range = area_ranges[areas[area]]\n gt_overlaps = []\n num_pos = 0\n\n for prediction_dict in dataset_predictions:\n predictions = prediction_dict[\"proposals\"]\n\n # sort predictions in descending order\n # TODO maybe remove this and make it explicit in the documentation\n inds = predictions.objectness_logits.sort(descending=True)[1]\n predictions = predictions[inds]\n\n ann_ids = coco_api.getAnnIds(imgIds=prediction_dict[\"image_id\"])\n anno = coco_api.loadAnns(ann_ids)\n gt_boxes = [\n BoxMode.convert(obj[\"bbox\"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)\n for obj in anno\n if obj[\"iscrowd\"] == 0\n ]\n gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes\n gt_boxes = Boxes(gt_boxes)\n gt_areas = torch.as_tensor([obj[\"area\"] for obj in anno if obj[\"iscrowd\"] == 0])\n\n if len(gt_boxes) == 0 or len(predictions) == 0:\n continue\n\n valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])\n gt_boxes = gt_boxes[valid_gt_inds]\n\n num_pos += len(gt_boxes)\n\n if len(gt_boxes) == 0:\n continue\n\n if limit is not None and len(predictions) > limit:\n predictions = predictions[:limit]\n\n overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)\n\n _gt_overlaps = torch.zeros(len(gt_boxes))\n for j in range(min(len(predictions), len(gt_boxes))):\n # find which proposal box maximally covers each gt box\n # and get the iou amount of coverage for each gt box\n max_overlaps, argmax_overlaps = overlaps.max(dim=0)\n\n # find which gt box is 'best' covered (i.e. 'best' = most iou)\n gt_ovr, gt_ind = max_overlaps.max(dim=0)\n assert gt_ovr >= 0\n # find the proposal box that covers the best covered gt box\n box_ind = argmax_overlaps[gt_ind]\n # record the iou coverage of this gt box\n _gt_overlaps[j] = overlaps[box_ind, gt_ind]\n assert _gt_overlaps[j] == gt_ovr\n # mark the proposal box and the gt box as used\n overlaps[box_ind, :] = -1\n overlaps[:, gt_ind] = -1\n\n # append recorded iou coverage level\n gt_overlaps.append(_gt_overlaps)\n gt_overlaps = (\n torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)\n )\n gt_overlaps, _ = torch.sort(gt_overlaps)\n\n if thresholds is None:\n step = 0.05\n thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)\n recalls = torch.zeros_like(thresholds)\n # compute recall for each iou threshold\n for i, t in enumerate(thresholds):\n recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)\n # ar = 2 * np.trapz(recalls, thresholds)\n ar = recalls.mean()\n return {\n \"ar\": ar,\n \"recalls\": recalls,\n \"thresholds\": thresholds,\n \"gt_overlaps\": gt_overlaps,\n \"num_pos\": num_pos,\n }\n\n\ndef _evaluate_predictions_on_coco(\n coco_gt,\n coco_results,\n iou_type,\n kpt_oks_sigmas=None,\n use_fast_impl=True,\n img_ids=None,\n max_dets_per_image=None,\n):\n \"\"\"\n Evaluate the coco results using COCOEval API.\n \"\"\"\n assert len(coco_results) > 0\n\n if iou_type == \"segm\":\n coco_results = copy.deepcopy(coco_results)\n # When evaluating mask AP, if the results contain bbox, cocoapi will\n # use the box area as the area of the instance, instead of the mask area.","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._evaluate_predictions_on_coco","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._evaluate_predictions_on_coco#L568-L632","kind":"function","name":"_evaluate_predictions_on_coco","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":568,"end_line":632,"context_start_line":548,"context_end_line":652,"code":" gt_overlaps, _ = torch.sort(gt_overlaps)\n\n if thresholds is None:\n step = 0.05\n thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)\n recalls = torch.zeros_like(thresholds)\n # compute recall for each iou threshold\n for i, t in enumerate(thresholds):\n recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)\n # ar = 2 * np.trapz(recalls, thresholds)\n ar = recalls.mean()\n return {\n \"ar\": ar,\n \"recalls\": recalls,\n \"thresholds\": thresholds,\n \"gt_overlaps\": gt_overlaps,\n \"num_pos\": num_pos,\n }\n\n\ndef _evaluate_predictions_on_coco(\n coco_gt,\n coco_results,\n iou_type,\n kpt_oks_sigmas=None,\n use_fast_impl=True,\n img_ids=None,\n max_dets_per_image=None,\n):\n \"\"\"\n Evaluate the coco results using COCOEval API.\n \"\"\"\n assert len(coco_results) > 0\n\n if iou_type == \"segm\":\n coco_results = copy.deepcopy(coco_results)\n # When evaluating mask AP, if the results contain bbox, cocoapi will\n # use the box area as the area of the instance, instead of the mask area.\n # This leads to a different definition of small/medium/large.\n # We remove the bbox field to let mask AP use mask area.\n for c in coco_results:\n c.pop(\"bbox\", None)\n\n coco_dt = coco_gt.loadRes(coco_results)\n coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)\n # For COCO, the default max_dets_per_image is [1, 10, 100].\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100] # Default from COCOEval\n else:\n assert (\n len(max_dets_per_image) >= 3\n ), \"COCOeval requires maxDets (and max_dets_per_image) to have length at least 3\"\n # In the case that user supplies a custom input for max_dets_per_image,\n # apply COCOevalMaxDets to evaluate AP with the custom input.\n if max_dets_per_image[2] != 100:\n coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)\n if iou_type != \"keypoints\":\n coco_eval.params.maxDets = max_dets_per_image\n\n if img_ids is not None:\n coco_eval.params.imgIds = img_ids\n\n if iou_type == \"keypoints\":\n # Use the COCO default keypoint OKS sigmas unless overrides are specified\n if kpt_oks_sigmas:\n assert hasattr(coco_eval.params, \"kpt_oks_sigmas\"), \"pycocotools is too old!\"\n coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)\n # COCOAPI requires every detection and every gt to have keypoints, so\n # we just take the first entry from both\n num_keypoints_dt = len(coco_results[0][\"keypoints\"]) // 3\n num_keypoints_gt = len(next(iter(coco_gt.anns.values()))[\"keypoints\"]) // 3\n num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)\n assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (\n f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS, please refer to \"\n \"http://cocodataset.org/#keypoints-eval.\"\n )\n\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.COCOevalMaxDets","uri":"program://OneFormer/class/oneformer.evaluation.detection_coco_evaluator.COCOevalMaxDets#L635-L723","kind":"class","name":"COCOevalMaxDets","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":635,"end_line":723,"context_start_line":615,"context_end_line":723,"code":" # COCOAPI requires every detection and every gt to have keypoints, so\n # we just take the first entry from both\n num_keypoints_dt = len(coco_results[0][\"keypoints\"]) // 3\n num_keypoints_gt = len(next(iter(coco_gt.anns.values()))[\"keypoints\"]) // 3\n num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)\n assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (\n f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS, please refer to \"\n \"http://cocodataset.org/#keypoints-eval.\"\n )\n\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (\n \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n if iouThr is None\n else \"{:0.2f}\".format(iouThr)\n )\n\n aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\" or iouType == \"bbox\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.__init__","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.__init__#L51-L156","kind":"function","name":"__init__","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":51,"end_line":156,"context_start_line":31,"context_end_line":176,"code":"\ntry:\n from detectron2.evaluation.fast_eval_api import COCOeval_opt\nexcept ImportError:\n COCOeval_opt = COCOeval\n\n\nclass DetectionCOCOEvaluator(DatasetEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def __init__(\n self,\n dataset_name,\n tasks=None,\n distributed=True,\n output_dir=None,\n *,\n max_dets_per_image=None,\n use_fast_impl=True,\n kpt_oks_sigmas=(),\n allow_cached_coco=True,\n ):\n \"\"\"\n Args:\n dataset_name (str): name of the dataset to be evaluated.\n It must have either the following corresponding metadata:\n\n \"json_file\": the path to the COCO format annotation\n\n Or it must be in detectron2's standard dataset format\n so it can be converted to COCO format automatically.\n tasks (tuple[str]): tasks that can be evaluated under the given\n configuration. A task is one of \"bbox\", \"segm\", \"keypoints\".\n By default, will infer this automatically from predictions.\n distributed (True): if True, will collect results from all ranks and run evaluation\n in the main process.\n Otherwise, will only evaluate the results in the current process.\n output_dir (str): optional, an output directory to dump all\n results predicted on the dataset. The dump contains two files:\n\n 1. \"instances_predictions.pth\" a file that can be loaded with `torch.load` and\n contains all the results in the format they are produced by the model.\n 2. \"coco_instances_results.json\" a json file in COCO's result format.\n max_dets_per_image (int): limit on the maximum number of detections per image.\n By default in COCO, this limit is to 100, but this can be customized\n to be greater, as is needed in evaluation metrics AP fixed and AP pool\n (see https://arxiv.org/pdf/2102.01066.pdf)\n This doesn't affect keypoint evaluation.\n use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.\n Although the results should be very close to the official implementation in COCO\n API, it is still recommended to compute results with the official API for use in\n papers. The faster implementation also uses more RAM.\n kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.\n See http://cocodataset.org/#keypoints-eval\n When empty, it will use the defaults in COCO.\n Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.\n allow_cached_coco (bool): Whether to use cached coco json from previous validation\n runs. You should set this to False if you need to use different validation data.\n Defaults to True.\n \"\"\"\n self._logger = logging.getLogger(__name__)\n self._distributed = distributed\n self._output_dir = output_dir\n\n if use_fast_impl and (COCOeval_opt is COCOeval):\n self._logger.info(\"Fast COCO eval is not built. Falling back to official COCO eval.\")\n use_fast_impl = False\n self._use_fast_impl = use_fast_impl\n\n # COCOeval requires the limit on the number of detections per image (maxDets) to be a list\n # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the\n # 3rd element (100) is used as the limit on the number of detections per image when\n # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,\n # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100]\n else:\n max_dets_per_image = [1, 10, max_dets_per_image]\n self._max_dets_per_image = max_dets_per_image\n\n if tasks is not None and isinstance(tasks, CfgNode):\n kpt_oks_sigmas = (\n tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas\n )\n self._logger.warn(\n \"COCO Evaluator instantiated using config, this is deprecated behavior.\"\n \" Please pass in explicit arguments instead.\"\n )\n self._tasks = None # Infering it from predictions should be better\n else:\n self._tasks = tasks\n\n self._cpu_device = torch.device(\"cpu\")\n\n self._metadata = MetadataCatalog.get(dataset_name)\n if not hasattr(self._metadata, \"json_file\"):\n if output_dir is None:\n raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"box_instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"box_instances\" in output:\n instances = output[\"box_instances\"].to(self._cpu_device)\n prediction[\"box_instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if \"proposals\" in output:","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.reset","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.reset#L158-L159","kind":"function","name":"reset","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":158,"end_line":159,"context_start_line":138,"context_end_line":179,"code":" raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"box_instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"box_instances\" in output:\n instances = output[\"box_instances\"].to(self._cpu_device)\n prediction[\"box_instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if \"proposals\" in output:\n prediction[\"proposals\"] = output[\"proposals\"].to(self._cpu_device)\n if len(prediction) > 1:\n self._predictions.append(prediction)","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.process","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.process#L161-L179","kind":"function","name":"process","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":161,"end_line":179,"context_start_line":141,"context_end_line":199,"code":" )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"box_instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"box_instances\" in output:\n instances = output[\"box_instances\"].to(self._cpu_device)\n prediction[\"box_instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if \"proposals\" in output:\n prediction[\"proposals\"] = output[\"proposals\"].to(self._cpu_device)\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.evaluate","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.evaluate#L181-L212","kind":"function","name":"evaluate","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":181,"end_line":212,"context_start_line":161,"context_end_line":232,"code":" def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"box_instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"box_instances\" in output:\n instances = output[\"box_instances\"].to(self._cpu_device)\n prediction[\"box_instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if \"proposals\" in output:\n prediction[\"proposals\"] = output[\"proposals\"].to(self._cpu_device)\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"proposals\" in predictions[0]:\n self._eval_box_proposals(predictions)\n if \"box_instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n tasks = {\"bbox\"}\n for pred in predictions:\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"box_instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._tasks_from_predictions","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._tasks_from_predictions#L214-L222","kind":"function","name":"_tasks_from_predictions","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":214,"end_line":222,"context_start_line":194,"context_end_line":242,"code":" predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"proposals\" in predictions[0]:\n self._eval_box_proposals(predictions)\n if \"box_instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n tasks = {\"bbox\"}\n for pred in predictions:\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"box_instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._eval_predictions","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._eval_predictions#L224-L284","kind":"function","name":"_eval_predictions","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":224,"end_line":284,"context_start_line":204,"context_end_line":304,"code":" torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"proposals\" in predictions[0]:\n self._eval_box_proposals(predictions)\n if \"box_instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n tasks = {\"bbox\"}\n for pred in predictions:\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"box_instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has {num_classes} classes and \"\n f\"predicted class id should be in [0, {num_classes - 1}].\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _eval_box_proposals(self, predictions):\n \"\"\"\n Evaluate the box proposals in predictions.\n Fill self._results with the metrics for \"box_proposals\" task.\n \"\"\"\n if self._output_dir:\n # Saving generated box proposals to file.\n # Predicted box_proposals are in XYXY_ABS mode.\n bbox_mode = BoxMode.XYXY_ABS.value\n ids, boxes, objectness_logits = [], [], []\n for prediction in predictions:\n ids.append(prediction[\"image_id\"])\n boxes.append(prediction[\"proposals\"].proposal_boxes.tensor.numpy())\n objectness_logits.append(prediction[\"proposals\"].objectness_logits.numpy())\n\n proposal_data = {\n \"boxes\": boxes,\n \"objectness_logits\": objectness_logits,\n \"ids\": ids,","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._eval_box_proposals","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._eval_box_proposals#L286-L323","kind":"function","name":"_eval_box_proposals","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":286,"end_line":323,"context_start_line":266,"context_end_line":343,"code":" assert task in {\"bbox\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _eval_box_proposals(self, predictions):\n \"\"\"\n Evaluate the box proposals in predictions.\n Fill self._results with the metrics for \"box_proposals\" task.\n \"\"\"\n if self._output_dir:\n # Saving generated box proposals to file.\n # Predicted box_proposals are in XYXY_ABS mode.\n bbox_mode = BoxMode.XYXY_ABS.value\n ids, boxes, objectness_logits = [], [], []\n for prediction in predictions:\n ids.append(prediction[\"image_id\"])\n boxes.append(prediction[\"proposals\"].proposal_boxes.tensor.numpy())\n objectness_logits.append(prediction[\"proposals\"].objectness_logits.numpy())\n\n proposal_data = {\n \"boxes\": boxes,\n \"objectness_logits\": objectness_logits,\n \"ids\": ids,\n \"bbox_mode\": bbox_mode,\n }\n with PathManager.open(os.path.join(self._output_dir, \"box_proposals.pkl\"), \"wb\") as f:\n pickle.dump(proposal_data, f)\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\"Evaluating bbox proposals ...\")\n res = {}\n areas = {\"all\": \"\", \"small\": \"s\", \"medium\": \"m\", \"large\": \"l\"}\n for limit in [100, 1000]:\n for area, suffix in areas.items():\n stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)\n key = \"AR{}@{:d}\".format(suffix, limit)\n res[key] = float(stats[\"ar\"].item() * 100)\n self._logger.info(\"Proposal metrics: \\n\" + create_small_table(res))\n self._results[\"box_proposals\"] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"bbox\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._derive_coco_results","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._derive_coco_results#L325-L390","kind":"function","name":"_derive_coco_results","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":325,"end_line":390,"context_start_line":305,"context_end_line":410,"code":" \"bbox_mode\": bbox_mode,\n }\n with PathManager.open(os.path.join(self._output_dir, \"box_proposals.pkl\"), \"wb\") as f:\n pickle.dump(proposal_data, f)\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\"Evaluating bbox proposals ...\")\n res = {}\n areas = {\"all\": \"\", \"small\": \"s\", \"medium\": \"m\", \"large\": \"l\"}\n for limit in [100, 1000]:\n for area, suffix in areas.items():\n stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)\n key = \"AR{}@{:d}\".format(suffix, limit)\n res[key] = float(stats[\"ar\"].item() * 100)\n self._logger.info(\"Proposal metrics: \\n\" + create_small_table(res))\n self._results[\"box_proposals\"] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"bbox\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n\n if coco_eval is None:\n self._logger.warn(\"No predictions from the model!\")\n return {metric: float(\"nan\") for metric in metrics}\n\n # the standard metrics\n results = {\n metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else \"nan\")\n for idx, metric in enumerate(metrics)\n }\n self._logger.info(\n \"Evaluation results for {}: \\n\".format(iou_type) + create_small_table(results)\n )\n if not np.isfinite(sum(results.values())):\n self._logger.info(\"Some metrics cannot be computed and is shown as NaN.\")\n\n if class_names is None or len(class_names) <= 1:\n return results\n # Compute per-category AP\n # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa\n precisions = coco_eval.eval[\"precision\"]\n # precision has dims (iou, recall, cls, area range, max dets)\n assert len(class_names) == precisions.shape[2]\n\n results_per_category = []\n for idx, name in enumerate(class_names):\n # area range index 0: all area ranges\n # max dets index -1: typically 100 per image\n precision = precisions[:, :, idx, 0, -1]\n precision = precision[precision > -1]\n ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n boxes = instances.pred_boxes.tensor.numpy()\n boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)\n boxes = boxes.tolist()","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.summarize","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.summarize#L641-L720","kind":"function","name":"summarize","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":641,"end_line":720,"context_start_line":621,"context_end_line":723,"code":" f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS, please refer to \"\n \"http://cocodataset.org/#keypoints-eval.\"\n )\n\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (\n \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n if iouThr is None\n else \"{:0.2f}\".format(iouThr)\n )\n\n aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\" or iouType == \"bbox\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator.__str__","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator.__str__#L722-L723","kind":"function","name":"__str__","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":722,"end_line":723,"context_start_line":702,"context_end_line":723,"code":" stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\" or iouType == \"bbox\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._summarize","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._summarize#L647-L680","kind":"function","name":"_summarize","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":647,"end_line":680,"context_start_line":627,"context_end_line":700,"code":"\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (\n \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n if iouThr is None\n else \"{:0.2f}\".format(iouThr)\n )\n\n aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._summarizeDets","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._summarizeDets#L682-L697","kind":"function","name":"_summarizeDets","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":682,"end_line":697,"context_start_line":662,"context_end_line":717,"code":" s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\" or iouType == \"bbox\":\n summarize = _summarizeDets","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.detection_coco_evaluator._summarizeKps","uri":"program://OneFormer/function/oneformer.evaluation.detection_coco_evaluator._summarizeKps#L699-L711","kind":"function","name":"_summarizeKps","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":699,"end_line":711,"context_start_line":679,"context_end_line":723,"code":" print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\" or iouType == \"bbox\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation","uri":"program://OneFormer/module/oneformer.evaluation.cityscapes_evaluation#L1-L201","kind":"module","name":"oneformer.evaluation.cityscapes_evaluation","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":1,"end_line":201,"context_start_line":1,"context_end_line":201,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport glob\nimport logging\nimport numpy as np\nimport os\nimport tempfile\nfrom collections import OrderedDict\nimport torch\nfrom PIL import Image\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.utils import comm\nfrom detectron2.utils.file_io import PathManager\n\nfrom .evaluator import DatasetEvaluator\n\n\nclass CityscapesEvaluator(DatasetEvaluator):\n \"\"\"\n Base class for evaluation using cityscapes API.\n \"\"\"\n\n def __init__(self, dataset_name):\n \"\"\"\n Args:\n dataset_name (str): the name of the dataset.\n It must have the following metadata associated with it:\n \"thing_classes\", \"gt_dir\".\n \"\"\"\n self._metadata = MetadataCatalog.get(dataset_name)\n self._cpu_device = torch.device(\"cpu\")\n self._logger = logging.getLogger(__name__)\n\n def reset(self):\n self._working_dir = tempfile.TemporaryDirectory(prefix=\"cityscapes_eval_\")\n self._temp_dir = self._working_dir.name\n # All workers will write to the same results directory\n # TODO this does not work in distributed training\n assert (\n comm.get_local_size() == comm.get_world_size()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n self._temp_dir = comm.all_gather(self._temp_dir)[0]\n if self._temp_dir != self._working_dir.name:\n self._working_dir.cleanup()\n self._logger.info(\n \"Writing cityscapes results to temporary directory {} ...\".format(self._temp_dir)\n )\n\n\nclass CityscapesInstanceEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate instance segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import name2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_txt = os.path.join(self._temp_dir, basename + \"_pred.txt\")\n\n if \"instances\" in output:\n output = output[\"instances\"].to(self._cpu_device)\n num_instances = len(output)\n with open(pred_txt, \"w\") as fout:\n for i in range(num_instances):\n pred_class = output.pred_classes[i]\n classes = self._metadata.stuff_classes[pred_class]\n class_id = name2label[classes].id\n score = output.scores[i]\n mask = output.pred_masks[i].numpy().astype(\"uint8\")\n png_filename = os.path.join(\n self._temp_dir, basename + \"_{}_{}.png\".format(i, classes)\n )\n\n Image.fromarray(mask * 255).save(png_filename)\n fout.write(\n \"{} {} {}\\n\".format(os.path.basename(png_filename), class_id, score)\n )\n else:\n # Cityscapes requires a prediction file for every ground truth image.\n with open(pred_txt, \"w\") as fout:\n pass\n\n def evaluate(self):\n \"\"\"\n Returns:\n dict: has a key \"segm\", whose value is a dict of \"AP\" and \"AP50\".\n \"\"\"\n comm.synchronize()\n if comm.get_rank() > 0:\n return\n import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval\n\n self._logger.info(\"Evaluating results under {} ...\".format(self._temp_dir))\n\n # set some global states in cityscapes evaluation API, before evaluating\n cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)\n cityscapes_eval.args.predictionWalk = None\n cityscapes_eval.args.JSONOutput = False\n cityscapes_eval.args.colorized = False\n cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, \"gtInstances.json\")\n\n # These lines are adopted from\n # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa\n gt_dir = PathManager.get_local_path(self._metadata.gt_dir)\n groundTruthImgList = glob.glob(os.path.join(gt_dir, \"*\", \"*_gtFine_instanceIds.png\"))\n assert len(\n groundTruthImgList\n ), \"Cannot find any ground truth images to use for evaluation. Searched for: {}\".format(\n cityscapes_eval.args.groundTruthSearch\n )\n predictionImgList = []\n for gt in groundTruthImgList:\n predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))\n results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )[\"averages\"]\n\n ret = OrderedDict()\n ret[\"segm\"] = {\"AP\": results[\"allAp\"] * 100, \"AP50\": results[\"allAp50%\"] * 100}\n self._working_dir.cleanup()\n return ret\n\n\nclass CityscapesSemSegEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import trainId2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_filename = os.path.join(self._temp_dir, basename + \"_pred.png\")\n\n output = output[\"sem_seg\"].argmax(dim=0).to(self._cpu_device).numpy()\n pred = 255 * np.ones(output.shape, dtype=np.uint8)\n for train_id, label in trainId2label.items():\n if label.ignoreInEval:\n continue\n pred[output == train_id] = label.id\n Image.fromarray(pred).save(pred_filename)\n\n def evaluate(self):\n comm.synchronize()\n if comm.get_rank() > 0:\n return\n # Load the Cityscapes eval script *after* setting the required env var,\n # since the script reads CITYSCAPES_DATASET into global variables at load time.\n import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval\n\n self._logger.info(\"Evaluating results under {} ...\".format(self._temp_dir))\n\n # set some global states in cityscapes evaluation API, before evaluating\n cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)\n cityscapes_eval.args.predictionWalk = None\n cityscapes_eval.args.JSONOutput = False\n cityscapes_eval.args.colorized = False\n\n # These lines are adopted from\n # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa\n gt_dir = PathManager.get_local_path(self._metadata.gt_dir)\n groundTruthImgList = glob.glob(os.path.join(gt_dir, \"*\", \"*_gtFine_labelIds.png\"))\n assert len(\n groundTruthImgList\n ), \"Cannot find any ground truth images to use for evaluation. Searched for: {}\".format(\n cityscapes_eval.args.groundTruthSearch\n )\n predictionImgList = []\n for gt in groundTruthImgList:\n predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))\n results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )\n ret = OrderedDict()\n ret[\"sem_seg\"] = {\n \"IoU\": 100.0 * results[\"averageScoreClasses\"],\n \"iIoU\": 100.0 * results[\"averageScoreInstClasses\"],\n \"IoU_sup\": 100.0 * results[\"averageScoreCategories\"],\n \"iIoU_sup\": 100.0 * results[\"averageScoreInstCategories\"],\n }\n self._working_dir.cleanup()\n return ret","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.CityscapesEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.cityscapes_evaluation.CityscapesEvaluator#L22-L51","kind":"class","name":"CityscapesEvaluator","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":22,"end_line":51,"context_start_line":2,"context_end_line":71,"code":"# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport glob\nimport logging\nimport numpy as np\nimport os\nimport tempfile\nfrom collections import OrderedDict\nimport torch\nfrom PIL import Image\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.utils import comm\nfrom detectron2.utils.file_io import PathManager\n\nfrom .evaluator import DatasetEvaluator\n\n\nclass CityscapesEvaluator(DatasetEvaluator):\n \"\"\"\n Base class for evaluation using cityscapes API.\n \"\"\"\n\n def __init__(self, dataset_name):\n \"\"\"\n Args:\n dataset_name (str): the name of the dataset.\n It must have the following metadata associated with it:\n \"thing_classes\", \"gt_dir\".\n \"\"\"\n self._metadata = MetadataCatalog.get(dataset_name)\n self._cpu_device = torch.device(\"cpu\")\n self._logger = logging.getLogger(__name__)\n\n def reset(self):\n self._working_dir = tempfile.TemporaryDirectory(prefix=\"cityscapes_eval_\")\n self._temp_dir = self._working_dir.name\n # All workers will write to the same results directory\n # TODO this does not work in distributed training\n assert (\n comm.get_local_size() == comm.get_world_size()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n self._temp_dir = comm.all_gather(self._temp_dir)[0]\n if self._temp_dir != self._working_dir.name:\n self._working_dir.cleanup()\n self._logger.info(\n \"Writing cityscapes results to temporary directory {} ...\".format(self._temp_dir)\n )\n\n\nclass CityscapesInstanceEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate instance segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import name2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_txt = os.path.join(self._temp_dir, basename + \"_pred.txt\")\n","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.CityscapesInstanceEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.cityscapes_evaluation.CityscapesInstanceEvaluator#L54-L133","kind":"class","name":"CityscapesInstanceEvaluator","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":54,"end_line":133,"context_start_line":34,"context_end_line":153,"code":" self._metadata = MetadataCatalog.get(dataset_name)\n self._cpu_device = torch.device(\"cpu\")\n self._logger = logging.getLogger(__name__)\n\n def reset(self):\n self._working_dir = tempfile.TemporaryDirectory(prefix=\"cityscapes_eval_\")\n self._temp_dir = self._working_dir.name\n # All workers will write to the same results directory\n # TODO this does not work in distributed training\n assert (\n comm.get_local_size() == comm.get_world_size()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n self._temp_dir = comm.all_gather(self._temp_dir)[0]\n if self._temp_dir != self._working_dir.name:\n self._working_dir.cleanup()\n self._logger.info(\n \"Writing cityscapes results to temporary directory {} ...\".format(self._temp_dir)\n )\n\n\nclass CityscapesInstanceEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate instance segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import name2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_txt = os.path.join(self._temp_dir, basename + \"_pred.txt\")\n\n if \"instances\" in output:\n output = output[\"instances\"].to(self._cpu_device)\n num_instances = len(output)\n with open(pred_txt, \"w\") as fout:\n for i in range(num_instances):\n pred_class = output.pred_classes[i]\n classes = self._metadata.stuff_classes[pred_class]\n class_id = name2label[classes].id\n score = output.scores[i]\n mask = output.pred_masks[i].numpy().astype(\"uint8\")\n png_filename = os.path.join(\n self._temp_dir, basename + \"_{}_{}.png\".format(i, classes)\n )\n\n Image.fromarray(mask * 255).save(png_filename)\n fout.write(\n \"{} {} {}\\n\".format(os.path.basename(png_filename), class_id, score)\n )\n else:\n # Cityscapes requires a prediction file for every ground truth image.\n with open(pred_txt, \"w\") as fout:\n pass\n\n def evaluate(self):\n \"\"\"\n Returns:\n dict: has a key \"segm\", whose value is a dict of \"AP\" and \"AP50\".\n \"\"\"\n comm.synchronize()\n if comm.get_rank() > 0:\n return\n import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval\n\n self._logger.info(\"Evaluating results under {} ...\".format(self._temp_dir))\n\n # set some global states in cityscapes evaluation API, before evaluating\n cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)\n cityscapes_eval.args.predictionWalk = None\n cityscapes_eval.args.JSONOutput = False\n cityscapes_eval.args.colorized = False\n cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, \"gtInstances.json\")\n\n # These lines are adopted from\n # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa\n gt_dir = PathManager.get_local_path(self._metadata.gt_dir)\n groundTruthImgList = glob.glob(os.path.join(gt_dir, \"*\", \"*_gtFine_instanceIds.png\"))\n assert len(\n groundTruthImgList\n ), \"Cannot find any ground truth images to use for evaluation. Searched for: {}\".format(\n cityscapes_eval.args.groundTruthSearch\n )\n predictionImgList = []\n for gt in groundTruthImgList:\n predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))\n results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )[\"averages\"]\n\n ret = OrderedDict()\n ret[\"segm\"] = {\"AP\": results[\"allAp\"] * 100, \"AP50\": results[\"allAp50%\"] * 100}\n self._working_dir.cleanup()\n return ret\n\n\nclass CityscapesSemSegEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import trainId2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_filename = os.path.join(self._temp_dir, basename + \"_pred.png\")\n","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.CityscapesSemSegEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.cityscapes_evaluation.CityscapesSemSegEvaluator#L136-L201","kind":"class","name":"CityscapesSemSegEvaluator","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":136,"end_line":201,"context_start_line":116,"context_end_line":201,"code":" gt_dir = PathManager.get_local_path(self._metadata.gt_dir)\n groundTruthImgList = glob.glob(os.path.join(gt_dir, \"*\", \"*_gtFine_instanceIds.png\"))\n assert len(\n groundTruthImgList\n ), \"Cannot find any ground truth images to use for evaluation. Searched for: {}\".format(\n cityscapes_eval.args.groundTruthSearch\n )\n predictionImgList = []\n for gt in groundTruthImgList:\n predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))\n results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )[\"averages\"]\n\n ret = OrderedDict()\n ret[\"segm\"] = {\"AP\": results[\"allAp\"] * 100, \"AP50\": results[\"allAp50%\"] * 100}\n self._working_dir.cleanup()\n return ret\n\n\nclass CityscapesSemSegEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import trainId2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_filename = os.path.join(self._temp_dir, basename + \"_pred.png\")\n\n output = output[\"sem_seg\"].argmax(dim=0).to(self._cpu_device).numpy()\n pred = 255 * np.ones(output.shape, dtype=np.uint8)\n for train_id, label in trainId2label.items():\n if label.ignoreInEval:\n continue\n pred[output == train_id] = label.id\n Image.fromarray(pred).save(pred_filename)\n\n def evaluate(self):\n comm.synchronize()\n if comm.get_rank() > 0:\n return\n # Load the Cityscapes eval script *after* setting the required env var,\n # since the script reads CITYSCAPES_DATASET into global variables at load time.\n import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval\n\n self._logger.info(\"Evaluating results under {} ...\".format(self._temp_dir))\n\n # set some global states in cityscapes evaluation API, before evaluating\n cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)\n cityscapes_eval.args.predictionWalk = None\n cityscapes_eval.args.JSONOutput = False\n cityscapes_eval.args.colorized = False\n\n # These lines are adopted from\n # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa\n gt_dir = PathManager.get_local_path(self._metadata.gt_dir)\n groundTruthImgList = glob.glob(os.path.join(gt_dir, \"*\", \"*_gtFine_labelIds.png\"))\n assert len(\n groundTruthImgList\n ), \"Cannot find any ground truth images to use for evaluation. Searched for: {}\".format(\n cityscapes_eval.args.groundTruthSearch\n )\n predictionImgList = []\n for gt in groundTruthImgList:\n predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))\n results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )\n ret = OrderedDict()\n ret[\"sem_seg\"] = {\n \"IoU\": 100.0 * results[\"averageScoreClasses\"],\n \"iIoU\": 100.0 * results[\"averageScoreInstClasses\"],\n \"IoU_sup\": 100.0 * results[\"averageScoreCategories\"],\n \"iIoU_sup\": 100.0 * results[\"averageScoreInstCategories\"],\n }\n self._working_dir.cleanup()\n return ret","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.__init__","uri":"program://OneFormer/function/oneformer.evaluation.cityscapes_evaluation.__init__#L27-L36","kind":"function","name":"__init__","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":27,"end_line":36,"context_start_line":7,"context_end_line":56,"code":"import logging\nimport numpy as np\nimport os\nimport tempfile\nfrom collections import OrderedDict\nimport torch\nfrom PIL import Image\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.utils import comm\nfrom detectron2.utils.file_io import PathManager\n\nfrom .evaluator import DatasetEvaluator\n\n\nclass CityscapesEvaluator(DatasetEvaluator):\n \"\"\"\n Base class for evaluation using cityscapes API.\n \"\"\"\n\n def __init__(self, dataset_name):\n \"\"\"\n Args:\n dataset_name (str): the name of the dataset.\n It must have the following metadata associated with it:\n \"thing_classes\", \"gt_dir\".\n \"\"\"\n self._metadata = MetadataCatalog.get(dataset_name)\n self._cpu_device = torch.device(\"cpu\")\n self._logger = logging.getLogger(__name__)\n\n def reset(self):\n self._working_dir = tempfile.TemporaryDirectory(prefix=\"cityscapes_eval_\")\n self._temp_dir = self._working_dir.name\n # All workers will write to the same results directory\n # TODO this does not work in distributed training\n assert (\n comm.get_local_size() == comm.get_world_size()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n self._temp_dir = comm.all_gather(self._temp_dir)[0]\n if self._temp_dir != self._working_dir.name:\n self._working_dir.cleanup()\n self._logger.info(\n \"Writing cityscapes results to temporary directory {} ...\".format(self._temp_dir)\n )\n\n\nclass CityscapesInstanceEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate instance segmentation results on cityscapes dataset using cityscapes API.","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.reset","uri":"program://OneFormer/function/oneformer.evaluation.cityscapes_evaluation.reset#L38-L51","kind":"function","name":"reset","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":38,"end_line":51,"context_start_line":18,"context_end_line":71,"code":"\nfrom .evaluator import DatasetEvaluator\n\n\nclass CityscapesEvaluator(DatasetEvaluator):\n \"\"\"\n Base class for evaluation using cityscapes API.\n \"\"\"\n\n def __init__(self, dataset_name):\n \"\"\"\n Args:\n dataset_name (str): the name of the dataset.\n It must have the following metadata associated with it:\n \"thing_classes\", \"gt_dir\".\n \"\"\"\n self._metadata = MetadataCatalog.get(dataset_name)\n self._cpu_device = torch.device(\"cpu\")\n self._logger = logging.getLogger(__name__)\n\n def reset(self):\n self._working_dir = tempfile.TemporaryDirectory(prefix=\"cityscapes_eval_\")\n self._temp_dir = self._working_dir.name\n # All workers will write to the same results directory\n # TODO this does not work in distributed training\n assert (\n comm.get_local_size() == comm.get_world_size()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n self._temp_dir = comm.all_gather(self._temp_dir)[0]\n if self._temp_dir != self._working_dir.name:\n self._working_dir.cleanup()\n self._logger.info(\n \"Writing cityscapes results to temporary directory {} ...\".format(self._temp_dir)\n )\n\n\nclass CityscapesInstanceEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate instance segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import name2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_txt = os.path.join(self._temp_dir, basename + \"_pred.txt\")\n","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.process","uri":"program://OneFormer/function/oneformer.evaluation.cityscapes_evaluation.process#L146-L160","kind":"function","name":"process","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":146,"end_line":160,"context_start_line":126,"context_end_line":180,"code":" results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )[\"averages\"]\n\n ret = OrderedDict()\n ret[\"segm\"] = {\"AP\": results[\"allAp\"] * 100, \"AP50\": results[\"allAp50%\"] * 100}\n self._working_dir.cleanup()\n return ret\n\n\nclass CityscapesSemSegEvaluator(CityscapesEvaluator):\n \"\"\"\n Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.\n\n Note:\n * It does not work in multi-machine distributed training.\n * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import trainId2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_filename = os.path.join(self._temp_dir, basename + \"_pred.png\")\n\n output = output[\"sem_seg\"].argmax(dim=0).to(self._cpu_device).numpy()\n pred = 255 * np.ones(output.shape, dtype=np.uint8)\n for train_id, label in trainId2label.items():\n if label.ignoreInEval:\n continue\n pred[output == train_id] = label.id\n Image.fromarray(pred).save(pred_filename)\n\n def evaluate(self):\n comm.synchronize()\n if comm.get_rank() > 0:\n return\n # Load the Cityscapes eval script *after* setting the required env var,\n # since the script reads CITYSCAPES_DATASET into global variables at load time.\n import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval\n\n self._logger.info(\"Evaluating results under {} ...\".format(self._temp_dir))\n\n # set some global states in cityscapes evaluation API, before evaluating\n cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)\n cityscapes_eval.args.predictionWalk = None\n cityscapes_eval.args.JSONOutput = False\n cityscapes_eval.args.colorized = False\n\n # These lines are adopted from\n # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa\n gt_dir = PathManager.get_local_path(self._metadata.gt_dir)","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.cityscapes_evaluation.evaluate","uri":"program://OneFormer/function/oneformer.evaluation.cityscapes_evaluation.evaluate#L162-L201","kind":"function","name":"evaluate","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":162,"end_line":201,"context_start_line":142,"context_end_line":201,"code":" * It contains a synchronization, therefore has to be used on all ranks.\n * Only the main process runs evaluation.\n \"\"\"\n\n def process(self, inputs, outputs):\n from cityscapesscripts.helpers.labels import trainId2label\n\n for input, output in zip(inputs, outputs):\n file_name = input[\"file_name\"]\n basename = os.path.splitext(os.path.basename(file_name))[0]\n pred_filename = os.path.join(self._temp_dir, basename + \"_pred.png\")\n\n output = output[\"sem_seg\"].argmax(dim=0).to(self._cpu_device).numpy()\n pred = 255 * np.ones(output.shape, dtype=np.uint8)\n for train_id, label in trainId2label.items():\n if label.ignoreInEval:\n continue\n pred[output == train_id] = label.id\n Image.fromarray(pred).save(pred_filename)\n\n def evaluate(self):\n comm.synchronize()\n if comm.get_rank() > 0:\n return\n # Load the Cityscapes eval script *after* setting the required env var,\n # since the script reads CITYSCAPES_DATASET into global variables at load time.\n import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval\n\n self._logger.info(\"Evaluating results under {} ...\".format(self._temp_dir))\n\n # set some global states in cityscapes evaluation API, before evaluating\n cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)\n cityscapes_eval.args.predictionWalk = None\n cityscapes_eval.args.JSONOutput = False\n cityscapes_eval.args.colorized = False\n\n # These lines are adopted from\n # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa\n gt_dir = PathManager.get_local_path(self._metadata.gt_dir)\n groundTruthImgList = glob.glob(os.path.join(gt_dir, \"*\", \"*_gtFine_labelIds.png\"))\n assert len(\n groundTruthImgList\n ), \"Cannot find any ground truth images to use for evaluation. Searched for: {}\".format(\n cityscapes_eval.args.groundTruthSearch\n )\n predictionImgList = []\n for gt in groundTruthImgList:\n predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))\n results = cityscapes_eval.evaluateImgLists(\n predictionImgList, groundTruthImgList, cityscapes_eval.args\n )\n ret = OrderedDict()\n ret[\"sem_seg\"] = {\n \"IoU\": 100.0 * results[\"averageScoreClasses\"],\n \"iIoU\": 100.0 * results[\"averageScoreInstClasses\"],\n \"IoU_sup\": 100.0 * results[\"averageScoreCategories\"],\n \"iIoU_sup\": 100.0 * results[\"averageScoreInstCategories\"],\n }\n self._working_dir.cleanup()\n return ret","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator","uri":"program://OneFormer/module/oneformer.evaluation.coco_evaluator#L1-L563","kind":"module","name":"oneformer.evaluation.coco_evaluator","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":1,"end_line":563,"context_start_line":1,"context_end_line":563,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport contextlib\nimport copy\nimport io\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import CfgNode\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\nfrom .evaluator import DatasetEvaluator\n\ntry:\n from detectron2.evaluation.fast_eval_api import COCOeval_opt\nexcept ImportError:\n COCOeval_opt = COCOeval\n\n\nclass COCOEvaluator(DatasetEvaluator):\n \"\"\"\n Evaluate AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def __init__(\n self,\n dataset_name,\n tasks=None,\n distributed=True,\n output_dir=None,\n *,\n max_dets_per_image=None,\n use_fast_impl=True,\n kpt_oks_sigmas=(),\n allow_cached_coco=True,\n ):\n \"\"\"\n Args:\n dataset_name (str): name of the dataset to be evaluated.\n It must have either the following corresponding metadata:\n\n \"json_file\": the path to the COCO format annotation\n\n Or it must be in detectron2's standard dataset format\n so it can be converted to COCO format automatically.\n tasks (tuple[str]): tasks that can be evaluated under the given\n configuration. A task is one of \"bbox\", \"segm\", \"keypoints\".\n By default, will infer this automatically from predictions.\n distributed (True): if True, will collect results from all ranks and run evaluation\n in the main process.\n Otherwise, will only evaluate the results in the current process.\n output_dir (str): optional, an output directory to dump all\n results predicted on the dataset. The dump contains two files:\n\n 1. \"instances_predictions.pth\" a file that can be loaded with `torch.load` and\n contains all the results in the format they are produced by the model.\n 2. \"coco_instances_results.json\" a json file in COCO's result format.\n max_dets_per_image (int): limit on the maximum number of detections per image.\n By default in COCO, this limit is to 100, but this can be customized\n to be greater, as is needed in evaluation metrics AP fixed and AP pool\n (see https://arxiv.org/pdf/2102.01066.pdf)\n This doesn't affect keypoint evaluation.\n use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.\n Although the results should be very close to the official implementation in COCO\n API, it is still recommended to compute results with the official API for use in\n papers. The faster implementation also uses more RAM.\n kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.\n See http://cocodataset.org/#keypoints-eval\n When empty, it will use the defaults in COCO.\n Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.\n allow_cached_coco (bool): Whether to use cached coco json from previous validation\n runs. You should set this to False if you need to use different validation data.\n Defaults to True.\n \"\"\"\n self._logger = logging.getLogger(__name__)\n self._distributed = distributed\n self._output_dir = output_dir\n\n if use_fast_impl and (COCOeval_opt is COCOeval):\n self._logger.info(\"Fast COCO eval is not built. Falling back to official COCO eval.\")\n use_fast_impl = False\n self._use_fast_impl = use_fast_impl\n\n # COCOeval requires the limit on the number of detections per image (maxDets) to be a list\n # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the\n # 3rd element (100) is used as the limit on the number of detections per image when\n # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,\n # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100]\n else:\n max_dets_per_image = [1, 10, max_dets_per_image]\n self._max_dets_per_image = max_dets_per_image\n\n if tasks is not None and isinstance(tasks, CfgNode):\n kpt_oks_sigmas = (\n tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas\n )\n self._logger.warn(\n \"COCO Evaluator instantiated using config, this is deprecated behavior.\"\n \" Please pass in explicit arguments instead.\"\n )\n self._tasks = None # Infering it from predictions should be better\n else:\n self._tasks = tasks\n\n self._cpu_device = torch.device(\"cpu\")\n\n self._metadata = MetadataCatalog.get(dataset_name)\n if not hasattr(self._metadata, \"json_file\"):\n if output_dir is None:\n raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"instances\" in output:\n instances = output[\"instances\"].to(self._cpu_device)\n prediction[\"instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n for pred in predictions:\n if \"segmentation\" in pred:\n tasks = {\"segm\"}\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has {num_classes} classes and \"\n f\"predicted class id should be in [0, {num_classes - 1}].\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"segm\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n\n if coco_eval is None:\n self._logger.warn(\"No predictions from the model!\")\n return {metric: float(\"nan\") for metric in metrics}\n\n # the standard metrics\n results = {\n metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else \"nan\")\n for idx, metric in enumerate(metrics)\n }\n self._logger.info(\n \"Evaluation results for {}: \\n\".format(iou_type) + create_small_table(results)\n )\n if not np.isfinite(sum(results.values())):\n self._logger.info(\"Some metrics cannot be computed and is shown as NaN.\")\n\n if class_names is None or len(class_names) <= 1:\n return results\n # Compute per-category AP\n # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa\n precisions = coco_eval.eval[\"precision\"]\n # precision has dims (iou, recall, cls, area range, max dets)\n assert len(class_names) == precisions.shape[2]\n\n results_per_category = []\n for idx, name in enumerate(class_names):\n # area range index 0: all area ranges\n # max dets index -1: typically 100 per image\n precision = precisions[:, :, idx, 0, -1]\n precision = precision[precision > -1]\n ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n scores = instances.scores.tolist()\n classes = instances.pred_classes.tolist()\n\n has_mask = instances.has(\"pred_masks\")\n if has_mask:\n # use RLE to encode the masks, because they are too large and takes memory\n # since this evaluator stores outputs of the entire dataset\n rles = [\n mask_util.encode(np.array(mask[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n for mask in instances.pred_masks\n ]\n for rle in rles:\n # \"counts\" is an array encoded by mask_util as a byte-stream. Python3's\n # json writer which always produces strings cannot serialize a bytestream\n # unless you decode it. Thankfully, utf-8 works out (which is also what\n # the pycocotools/_mask.pyx does).\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n has_keypoints = instances.has(\"pred_keypoints\")\n if has_keypoints:\n keypoints = instances.pred_keypoints\n\n results = []\n for k in range(num_instance):\n result = {\n \"image_id\": img_id,\n \"category_id\": classes[k],\n \"score\": scores[k],\n }\n if has_mask:\n result[\"segmentation\"] = rles[k]\n if has_keypoints:\n # In COCO annotations,\n # keypoints coordinates are pixel indices.\n # However our predictions are floating point coordinates.\n # Therefore we subtract 0.5 to be consistent with the annotation format.\n # This is the inverse of data loading logic in `datasets/coco.py`.\n keypoints[k][:, :2] -= 0.5\n result[\"keypoints\"] = keypoints[k].flatten().tolist()\n results.append(result)\n return results\n\ndef _evaluate_predictions_on_coco(\n coco_gt,\n coco_results,\n iou_type,\n kpt_oks_sigmas=None,\n use_fast_impl=True,\n img_ids=None,\n max_dets_per_image=None,\n):\n \"\"\"\n Evaluate the coco results using COCOEval API.\n \"\"\"\n assert len(coco_results) > 0\n\n if iou_type == \"segm\":\n coco_results = copy.deepcopy(coco_results)\n # When evaluating mask AP, if the results contain bbox, cocoapi will\n # use the box area as the area of the instance, instead of the mask area.\n # This leads to a different definition of small/medium/large.\n # We remove the bbox field to let mask AP use mask area.\n for c in coco_results:\n c.pop(\"bbox\", None)\n\n coco_dt = coco_gt.loadRes(coco_results)\n coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)\n # For COCO, the default max_dets_per_image is [1, 10, 100].\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100] # Default from COCOEval\n else:\n assert (\n len(max_dets_per_image) >= 3\n ), \"COCOeval requires maxDets (and max_dets_per_image) to have length at least 3\"\n # In the case that user supplies a custom input for max_dets_per_image,\n # apply COCOevalMaxDets to evaluate AP with the custom input.\n if max_dets_per_image[2] != 100:\n coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)\n if iou_type != \"keypoints\":\n coco_eval.params.maxDets = max_dets_per_image\n\n if img_ids is not None:\n coco_eval.params.imgIds = img_ids\n\n if iou_type == \"keypoints\":\n # Use the COCO default keypoint OKS sigmas unless overrides are specified\n if kpt_oks_sigmas:\n assert hasattr(coco_eval.params, \"kpt_oks_sigmas\"), \"pycocotools is too old!\"\n coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)\n # COCOAPI requires every detection and every gt to have keypoints, so\n # we just take the first entry from both\n num_keypoints_dt = len(coco_results[0][\"keypoints\"]) // 3\n num_keypoints_gt = len(next(iter(coco_gt.anns.values()))[\"keypoints\"]) // 3\n num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)\n assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (\n f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS,\n# ... truncated ...","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.COCOEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.coco_evaluator.COCOEvaluator#L38-L348","kind":"class","name":"COCOEvaluator","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":38,"end_line":348,"context_start_line":18,"context_end_line":368,"code":"from pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import CfgNode\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\nfrom .evaluator import DatasetEvaluator\n\ntry:\n from detectron2.evaluation.fast_eval_api import COCOeval_opt\nexcept ImportError:\n COCOeval_opt = COCOeval\n\n\nclass COCOEvaluator(DatasetEvaluator):\n \"\"\"\n Evaluate AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def __init__(\n self,\n dataset_name,\n tasks=None,\n distributed=True,\n output_dir=None,\n *,\n max_dets_per_image=None,\n use_fast_impl=True,\n kpt_oks_sigmas=(),\n allow_cached_coco=True,\n ):\n \"\"\"\n Args:\n dataset_name (str): name of the dataset to be evaluated.\n It must have either the following corresponding metadata:\n\n \"json_file\": the path to the COCO format annotation\n\n Or it must be in detectron2's standard dataset format\n so it can be converted to COCO format automatically.\n tasks (tuple[str]): tasks that can be evaluated under the given\n configuration. A task is one of \"bbox\", \"segm\", \"keypoints\".\n By default, will infer this automatically from predictions.\n distributed (True): if True, will collect results from all ranks and run evaluation\n in the main process.\n Otherwise, will only evaluate the results in the current process.\n output_dir (str): optional, an output directory to dump all\n results predicted on the dataset. The dump contains two files:\n\n 1. \"instances_predictions.pth\" a file that can be loaded with `torch.load` and\n contains all the results in the format they are produced by the model.\n 2. \"coco_instances_results.json\" a json file in COCO's result format.\n max_dets_per_image (int): limit on the maximum number of detections per image.\n By default in COCO, this limit is to 100, but this can be customized\n to be greater, as is needed in evaluation metrics AP fixed and AP pool\n (see https://arxiv.org/pdf/2102.01066.pdf)\n This doesn't affect keypoint evaluation.\n use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.\n Although the results should be very close to the official implementation in COCO\n API, it is still recommended to compute results with the official API for use in\n papers. The faster implementation also uses more RAM.\n kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.\n See http://cocodataset.org/#keypoints-eval\n When empty, it will use the defaults in COCO.\n Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.\n allow_cached_coco (bool): Whether to use cached coco json from previous validation\n runs. You should set this to False if you need to use different validation data.\n Defaults to True.\n \"\"\"\n self._logger = logging.getLogger(__name__)\n self._distributed = distributed\n self._output_dir = output_dir\n\n if use_fast_impl and (COCOeval_opt is COCOeval):\n self._logger.info(\"Fast COCO eval is not built. Falling back to official COCO eval.\")\n use_fast_impl = False\n self._use_fast_impl = use_fast_impl\n\n # COCOeval requires the limit on the number of detections per image (maxDets) to be a list\n # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the\n # 3rd element (100) is used as the limit on the number of detections per image when\n # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,\n # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100]\n else:\n max_dets_per_image = [1, 10, max_dets_per_image]\n self._max_dets_per_image = max_dets_per_image\n\n if tasks is not None and isinstance(tasks, CfgNode):\n kpt_oks_sigmas = (\n tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas\n )\n self._logger.warn(\n \"COCO Evaluator instantiated using config, this is deprecated behavior.\"\n \" Please pass in explicit arguments instead.\"\n )\n self._tasks = None # Infering it from predictions should be better\n else:\n self._tasks = tasks\n\n self._cpu_device = torch.device(\"cpu\")\n\n self._metadata = MetadataCatalog.get(dataset_name)\n if not hasattr(self._metadata, \"json_file\"):\n if output_dir is None:\n raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"instances\" in output:\n instances = output[\"instances\"].to(self._cpu_device)\n prediction[\"instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n for pred in predictions:\n if \"segmentation\" in pred:\n tasks = {\"segm\"}\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has {num_classes} classes and \"\n f\"predicted class id should be in [0, {num_classes - 1}].\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"segm\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n\n if coco_eval is None:\n self._logger.warn(\"No predictions from the model!\")\n return {metric: float(\"nan\") for metric in metrics}\n\n # the standard metrics\n results = {\n metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else \"nan\")\n for idx, metric in enumerate(metrics)\n }\n self._logger.info(\n \"Evaluation results for {}: \\n\".format(iou_type) + create_small_table(results)\n )\n if not np.isfinite(sum(results.values())):\n self._logger.info(\"Some metrics cannot be computed and is shown as NaN.\")\n\n if class_names is None or len(class_names) <= 1:\n return results\n # Compute per-category AP\n # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa\n precisions = coco_eval.eval[\"precision\"]\n # precision has dims (iou, recall, cls, area range, max dets)\n assert len(class_names) == precisions.shape[2]\n\n results_per_category = []\n for idx, name in enumerate(class_names):\n # area range index 0: all area ranges\n # max dets index -1: typically 100 per image\n precision = precisions[:, :, idx, 0, -1]\n precision = precision[precision > -1]\n ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n scores = instances.scores.tolist()\n classes = instances.pred_classes.tolist()\n","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.instances_to_coco_json","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.instances_to_coco_json#L351-L406","kind":"function","name":"instances_to_coco_json","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":351,"end_line":406,"context_start_line":331,"context_end_line":426,"code":" ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n scores = instances.scores.tolist()\n classes = instances.pred_classes.tolist()\n\n has_mask = instances.has(\"pred_masks\")\n if has_mask:\n # use RLE to encode the masks, because they are too large and takes memory\n # since this evaluator stores outputs of the entire dataset\n rles = [\n mask_util.encode(np.array(mask[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n for mask in instances.pred_masks\n ]\n for rle in rles:\n # \"counts\" is an array encoded by mask_util as a byte-stream. Python3's\n # json writer which always produces strings cannot serialize a bytestream\n # unless you decode it. Thankfully, utf-8 works out (which is also what\n # the pycocotools/_mask.pyx does).\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n\n has_keypoints = instances.has(\"pred_keypoints\")\n if has_keypoints:\n keypoints = instances.pred_keypoints\n\n results = []\n for k in range(num_instance):\n result = {\n \"image_id\": img_id,\n \"category_id\": classes[k],\n \"score\": scores[k],\n }\n if has_mask:\n result[\"segmentation\"] = rles[k]\n if has_keypoints:\n # In COCO annotations,\n # keypoints coordinates are pixel indices.\n # However our predictions are floating point coordinates.\n # Therefore we subtract 0.5 to be consistent with the annotation format.\n # This is the inverse of data loading logic in `datasets/coco.py`.\n keypoints[k][:, :2] -= 0.5\n result[\"keypoints\"] = keypoints[k].flatten().tolist()\n results.append(result)\n return results\n\ndef _evaluate_predictions_on_coco(\n coco_gt,\n coco_results,\n iou_type,\n kpt_oks_sigmas=None,\n use_fast_impl=True,\n img_ids=None,\n max_dets_per_image=None,\n):\n \"\"\"\n Evaluate the coco results using COCOEval API.\n \"\"\"\n assert len(coco_results) > 0\n\n if iou_type == \"segm\":\n coco_results = copy.deepcopy(coco_results)\n # When evaluating mask AP, if the results contain bbox, cocoapi will\n # use the box area as the area of the instance, instead of the mask area.\n # This leads to a different definition of small/medium/large.","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._evaluate_predictions_on_coco","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._evaluate_predictions_on_coco#L408-L472","kind":"function","name":"_evaluate_predictions_on_coco","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":408,"end_line":472,"context_start_line":388,"context_end_line":492,"code":" results = []\n for k in range(num_instance):\n result = {\n \"image_id\": img_id,\n \"category_id\": classes[k],\n \"score\": scores[k],\n }\n if has_mask:\n result[\"segmentation\"] = rles[k]\n if has_keypoints:\n # In COCO annotations,\n # keypoints coordinates are pixel indices.\n # However our predictions are floating point coordinates.\n # Therefore we subtract 0.5 to be consistent with the annotation format.\n # This is the inverse of data loading logic in `datasets/coco.py`.\n keypoints[k][:, :2] -= 0.5\n result[\"keypoints\"] = keypoints[k].flatten().tolist()\n results.append(result)\n return results\n\ndef _evaluate_predictions_on_coco(\n coco_gt,\n coco_results,\n iou_type,\n kpt_oks_sigmas=None,\n use_fast_impl=True,\n img_ids=None,\n max_dets_per_image=None,\n):\n \"\"\"\n Evaluate the coco results using COCOEval API.\n \"\"\"\n assert len(coco_results) > 0\n\n if iou_type == \"segm\":\n coco_results = copy.deepcopy(coco_results)\n # When evaluating mask AP, if the results contain bbox, cocoapi will\n # use the box area as the area of the instance, instead of the mask area.\n # This leads to a different definition of small/medium/large.\n # We remove the bbox field to let mask AP use mask area.\n for c in coco_results:\n c.pop(\"bbox\", None)\n\n coco_dt = coco_gt.loadRes(coco_results)\n coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)\n # For COCO, the default max_dets_per_image is [1, 10, 100].\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100] # Default from COCOEval\n else:\n assert (\n len(max_dets_per_image) >= 3\n ), \"COCOeval requires maxDets (and max_dets_per_image) to have length at least 3\"\n # In the case that user supplies a custom input for max_dets_per_image,\n # apply COCOevalMaxDets to evaluate AP with the custom input.\n if max_dets_per_image[2] != 100:\n coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)\n if iou_type != \"keypoints\":\n coco_eval.params.maxDets = max_dets_per_image\n\n if img_ids is not None:\n coco_eval.params.imgIds = img_ids\n\n if iou_type == \"keypoints\":\n # Use the COCO default keypoint OKS sigmas unless overrides are specified\n if kpt_oks_sigmas:\n assert hasattr(coco_eval.params, \"kpt_oks_sigmas\"), \"pycocotools is too old!\"\n coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)\n # COCOAPI requires every detection and every gt to have keypoints, so\n # we just take the first entry from both\n num_keypoints_dt = len(coco_results[0][\"keypoints\"]) // 3\n num_keypoints_gt = len(next(iter(coco_gt.anns.values()))[\"keypoints\"]) // 3\n num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)\n assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (\n f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS, please refer to \"\n \"http://cocodataset.org/#keypoints-eval.\"\n )\n\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.COCOevalMaxDets","uri":"program://OneFormer/class/oneformer.evaluation.coco_evaluator.COCOevalMaxDets#L475-L563","kind":"class","name":"COCOevalMaxDets","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":475,"end_line":563,"context_start_line":455,"context_end_line":563,"code":" # COCOAPI requires every detection and every gt to have keypoints, so\n # we just take the first entry from both\n num_keypoints_dt = len(coco_results[0][\"keypoints\"]) // 3\n num_keypoints_gt = len(next(iter(coco_gt.anns.values()))[\"keypoints\"]) // 3\n num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)\n assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (\n f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS, please refer to \"\n \"http://cocodataset.org/#keypoints-eval.\"\n )\n\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (\n \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n if iouThr is None\n else \"{:0.2f}\".format(iouThr)\n )\n\n aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.__init__","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.__init__#L51-L156","kind":"function","name":"__init__","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":51,"end_line":156,"context_start_line":31,"context_end_line":176,"code":"\ntry:\n from detectron2.evaluation.fast_eval_api import COCOeval_opt\nexcept ImportError:\n COCOeval_opt = COCOeval\n\n\nclass COCOEvaluator(DatasetEvaluator):\n \"\"\"\n Evaluate AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def __init__(\n self,\n dataset_name,\n tasks=None,\n distributed=True,\n output_dir=None,\n *,\n max_dets_per_image=None,\n use_fast_impl=True,\n kpt_oks_sigmas=(),\n allow_cached_coco=True,\n ):\n \"\"\"\n Args:\n dataset_name (str): name of the dataset to be evaluated.\n It must have either the following corresponding metadata:\n\n \"json_file\": the path to the COCO format annotation\n\n Or it must be in detectron2's standard dataset format\n so it can be converted to COCO format automatically.\n tasks (tuple[str]): tasks that can be evaluated under the given\n configuration. A task is one of \"bbox\", \"segm\", \"keypoints\".\n By default, will infer this automatically from predictions.\n distributed (True): if True, will collect results from all ranks and run evaluation\n in the main process.\n Otherwise, will only evaluate the results in the current process.\n output_dir (str): optional, an output directory to dump all\n results predicted on the dataset. The dump contains two files:\n\n 1. \"instances_predictions.pth\" a file that can be loaded with `torch.load` and\n contains all the results in the format they are produced by the model.\n 2. \"coco_instances_results.json\" a json file in COCO's result format.\n max_dets_per_image (int): limit on the maximum number of detections per image.\n By default in COCO, this limit is to 100, but this can be customized\n to be greater, as is needed in evaluation metrics AP fixed and AP pool\n (see https://arxiv.org/pdf/2102.01066.pdf)\n This doesn't affect keypoint evaluation.\n use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.\n Although the results should be very close to the official implementation in COCO\n API, it is still recommended to compute results with the official API for use in\n papers. The faster implementation also uses more RAM.\n kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.\n See http://cocodataset.org/#keypoints-eval\n When empty, it will use the defaults in COCO.\n Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.\n allow_cached_coco (bool): Whether to use cached coco json from previous validation\n runs. You should set this to False if you need to use different validation data.\n Defaults to True.\n \"\"\"\n self._logger = logging.getLogger(__name__)\n self._distributed = distributed\n self._output_dir = output_dir\n\n if use_fast_impl and (COCOeval_opt is COCOeval):\n self._logger.info(\"Fast COCO eval is not built. Falling back to official COCO eval.\")\n use_fast_impl = False\n self._use_fast_impl = use_fast_impl\n\n # COCOeval requires the limit on the number of detections per image (maxDets) to be a list\n # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the\n # 3rd element (100) is used as the limit on the number of detections per image when\n # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,\n # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.\n if max_dets_per_image is None:\n max_dets_per_image = [1, 10, 100]\n else:\n max_dets_per_image = [1, 10, max_dets_per_image]\n self._max_dets_per_image = max_dets_per_image\n\n if tasks is not None and isinstance(tasks, CfgNode):\n kpt_oks_sigmas = (\n tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas\n )\n self._logger.warn(\n \"COCO Evaluator instantiated using config, this is deprecated behavior.\"\n \" Please pass in explicit arguments instead.\"\n )\n self._tasks = None # Infering it from predictions should be better\n else:\n self._tasks = tasks\n\n self._cpu_device = torch.device(\"cpu\")\n\n self._metadata = MetadataCatalog.get(dataset_name)\n if not hasattr(self._metadata, \"json_file\"):\n if output_dir is None:\n raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"instances\" in output:\n instances = output[\"instances\"].to(self._cpu_device)\n prediction[\"instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if len(prediction) > 1:","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.reset","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.reset#L158-L159","kind":"function","name":"reset","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":158,"end_line":159,"context_start_line":138,"context_end_line":179,"code":" raise ValueError(\n \"output_dir must be provided to COCOEvaluator \"\n \"for datasets not in COCO format.\"\n )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"instances\" in output:\n instances = output[\"instances\"].to(self._cpu_device)\n prediction[\"instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.process","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.process#L161-L177","kind":"function","name":"process","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":161,"end_line":177,"context_start_line":141,"context_end_line":197,"code":" )\n self._logger.info(f\"Trying to convert '{dataset_name}' to COCO format ...\")\n\n cache_path = os.path.join(output_dir, f\"{dataset_name}_coco_format.json\")\n self._metadata.json_file = cache_path\n convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)\n\n json_file = PathManager.get_local_path(self._metadata.json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n self._coco_api = COCO(json_file)\n\n # Test set json files do not contain annotations (evaluation must be\n # performed using the COCO evaluation server).\n self._do_evaluation = \"annotations\" in self._coco_api.dataset\n if self._do_evaluation:\n self._kpt_oks_sigmas = kpt_oks_sigmas\n\n def reset(self):\n self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"instances\" in output:\n instances = output[\"instances\"].to(self._cpu_device)\n prediction[\"instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.evaluate","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.evaluate#L179-L208","kind":"function","name":"evaluate","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":179,"end_line":208,"context_start_line":159,"context_end_line":228,"code":" self._predictions = []\n\n def process(self, inputs, outputs):\n \"\"\"\n Args:\n inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).\n It is a list of dict. Each dict corresponds to an image and\n contains keys like \"height\", \"width\", \"file_name\", \"image_id\".\n outputs: the outputs of a COCO model. It is a list of dicts with key\n \"instances\" that contains :class:`Instances`.\n \"\"\"\n for input, output in zip(inputs, outputs):\n prediction = {\"image_id\": input[\"image_id\"]}\n\n if \"instances\" in output:\n instances = output[\"instances\"].to(self._cpu_device)\n prediction[\"instances\"] = instances_to_coco_json(instances, input[\"image_id\"])\n if len(prediction) > 1:\n self._predictions.append(prediction)\n\n def evaluate(self, img_ids=None):\n \"\"\"\n Args:\n img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset\n \"\"\"\n if self._distributed:\n comm.synchronize()\n predictions = comm.gather(self._predictions, dst=0)\n predictions = list(itertools.chain(*predictions))\n\n if not comm.is_main_process():\n return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n for pred in predictions:\n if \"segmentation\" in pred:\n tasks = {\"segm\"}\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._tasks_from_predictions","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._tasks_from_predictions#L210-L219","kind":"function","name":"_tasks_from_predictions","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":210,"end_line":219,"context_start_line":190,"context_end_line":239,"code":" return {}\n else:\n predictions = self._predictions\n\n if len(predictions) == 0:\n self._logger.warning(\"[COCOEvaluator] Did not receive valid predictions.\")\n return {}\n\n if self._output_dir:\n PathManager.mkdirs(self._output_dir)\n file_path = os.path.join(self._output_dir, \"instances_predictions.pth\")\n with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n for pred in predictions:\n if \"segmentation\" in pred:\n tasks = {\"segm\"}\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._eval_predictions","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._eval_predictions#L221-L281","kind":"function","name":"_eval_predictions","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":221,"end_line":281,"context_start_line":201,"context_end_line":301,"code":" with PathManager.open(file_path, \"wb\") as f:\n torch.save(predictions, f)\n\n self._results = OrderedDict()\n if \"instances\" in predictions[0]:\n self._eval_predictions(predictions, img_ids=img_ids)\n # Copy so the caller can do whatever with results\n return copy.deepcopy(self._results)\n\n def _tasks_from_predictions(self, predictions):\n \"\"\"\n Get COCO API \"tasks\" (i.e. iou_type) from COCO-format predictions.\n \"\"\"\n for pred in predictions:\n if \"segmentation\" in pred:\n tasks = {\"segm\"}\n if \"keypoints\" in pred:\n tasks.add(\"keypoints\")\n return sorted(tasks)\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n num_classes = len(all_contiguous_ids)\n assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n assert category_id < num_classes, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has {num_classes} classes and \"\n f\"predicted class id should be in [0, {num_classes - 1}].\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"segm\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._derive_coco_results","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._derive_coco_results#L283-L348","kind":"function","name":"_derive_coco_results","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":283,"end_line":348,"context_start_line":263,"context_end_line":368,"code":" assert task in {\"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res\n\n def _derive_coco_results(self, coco_eval, iou_type, class_names=None):\n \"\"\"\n Derive the desired score numbers from summarized COCOeval.\n\n Args:\n coco_eval (None or COCOEval): None represents no predictions from model.\n iou_type (str):\n class_names (None or list[str]): if provided, will use it to predict\n per-category AP.\n\n Returns:\n a dict of {metric name: score}\n \"\"\"\n\n metrics = {\n \"segm\": [\"AP\", \"AP50\", \"AP75\", \"APs\", \"APm\", \"APl\"],\n \"keypoints\": [\"AP\", \"AP50\", \"AP75\", \"APm\", \"APl\"],\n }[iou_type]\n\n if coco_eval is None:\n self._logger.warn(\"No predictions from the model!\")\n return {metric: float(\"nan\") for metric in metrics}\n\n # the standard metrics\n results = {\n metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else \"nan\")\n for idx, metric in enumerate(metrics)\n }\n self._logger.info(\n \"Evaluation results for {}: \\n\".format(iou_type) + create_small_table(results)\n )\n if not np.isfinite(sum(results.values())):\n self._logger.info(\"Some metrics cannot be computed and is shown as NaN.\")\n\n if class_names is None or len(class_names) <= 1:\n return results\n # Compute per-category AP\n # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa\n precisions = coco_eval.eval[\"precision\"]\n # precision has dims (iou, recall, cls, area range, max dets)\n assert len(class_names) == precisions.shape[2]\n\n results_per_category = []\n for idx, name in enumerate(class_names):\n # area range index 0: all area ranges\n # max dets index -1: typically 100 per image\n precision = precisions[:, :, idx, 0, -1]\n precision = precision[precision > -1]\n ap = np.mean(precision) if precision.size else float(\"nan\")\n results_per_category.append((\"{}\".format(name), float(ap * 100)))\n\n # tabulate it\n N_COLS = min(6, len(results_per_category) * 2)\n results_flatten = list(itertools.chain(*results_per_category))\n results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])\n table = tabulate(\n results_2d,\n tablefmt=\"pipe\",\n floatfmt=\".3f\",\n headers=[\"category\", \"AP\"] * (N_COLS // 2),\n numalign=\"left\",\n )\n self._logger.info(\"Per-category {} AP: \\n\".format(iou_type) + table)\n\n results.update({\"AP-\" + name: ap for name, ap in results_per_category})\n return results\n\n\ndef instances_to_coco_json(instances, img_id):\n \"\"\"\n Dump an \"Instances\" object to a COCO-format json that's used for evaluation.\n\n Args:\n instances (Instances):\n img_id (int): the image id\n\n Returns:\n list[dict]: list of json annotations in COCO format.\n \"\"\"\n num_instance = len(instances)\n if num_instance == 0:\n return []\n\n scores = instances.scores.tolist()\n classes = instances.pred_classes.tolist()\n","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.summarize","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.summarize#L481-L560","kind":"function","name":"summarize","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":481,"end_line":560,"context_start_line":461,"context_end_line":563,"code":" f\"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. \"\n f\"Ground truth contains {num_keypoints_gt} keypoints. \"\n f\"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. \"\n \"They have to agree with each other. For meaning of OKS, please refer to \"\n \"http://cocodataset.org/#keypoints-eval.\"\n )\n\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (\n \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n if iouThr is None\n else \"{:0.2f}\".format(iouThr)\n )\n\n aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator.__str__","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator.__str__#L562-L563","kind":"function","name":"__str__","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":562,"end_line":563,"context_start_line":542,"context_end_line":563,"code":" stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._summarize","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._summarize#L487-L520","kind":"function","name":"_summarize","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":487,"end_line":520,"context_start_line":467,"context_end_line":540,"code":"\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n\n return coco_eval\n\n\nclass COCOevalMaxDets(COCOeval):\n \"\"\"\n Modified version of COCOeval for evaluating AP with a custom\n maxDets (by default for COCO, maxDets is 100)\n \"\"\"\n\n def summarize(self):\n \"\"\"\n Compute and display summary metrics for evaluation results given\n a custom value for max_dets_per_image\n \"\"\"\n\n def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n p = self.params\n iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n iouStr = (\n \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n if iouThr is None\n else \"{:0.2f}\".format(iouThr)\n )\n\n aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._summarizeDets","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._summarizeDets#L522-L537","kind":"function","name":"_summarizeDets","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":522,"end_line":537,"context_start_line":502,"context_end_line":557,"code":" s = self.eval[\"precision\"]\n # IoU\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval[\"recall\"]\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\":\n summarize = _summarizeDets","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.coco_evaluator._summarizeKps","uri":"program://OneFormer/function/oneformer.evaluation.coco_evaluator._summarizeKps#L539-L551","kind":"function","name":"_summarizeKps","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":539,"end_line":551,"context_start_line":519,"context_end_line":563,"code":" print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n # Evaluate AP using the custom limit on maximum detections per image\n stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng=\"large\", maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng=\"small\", maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng=\"medium\", maxDets=self.params.maxDets[2])\n stats[11] = _summarize(0, areaRng=\"large\", maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n stats = np.zeros((10,))\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=0.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=0.75)\n stats[3] = _summarize(1, maxDets=20, areaRng=\"medium\")\n stats[4] = _summarize(1, maxDets=20, areaRng=\"large\")\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=0.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=0.75)\n stats[8] = _summarize(0, maxDets=20, areaRng=\"medium\")\n stats[9] = _summarize(0, maxDets=20, areaRng=\"large\")\n return stats\n\n if not self.eval:\n raise Exception(\"Please run accumulate() first\")\n iouType = self.params.iouType\n if iouType == \"segm\":\n summarize = _summarizeDets\n elif iouType == \"keypoints\":\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.instance_evaluation","uri":"program://OneFormer/module/oneformer.evaluation.instance_evaluation#L1-L110","kind":"module","name":"oneformer.evaluation.instance_evaluation","path":"oneformer/evaluation/instance_evaluation.py","language":"python","start_line":1,"end_line":110,"context_start_line":1,"context_end_line":110,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py\n# ------------------------------------------------------------------------------\n\nimport contextlib\nimport copy\nimport io\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import CfgNode\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco\nfrom detectron2.evaluation.fast_eval_api import COCOeval_opt\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\n\n# modified from COCOEvaluator for instance segmetnat\nclass InstanceSegEvaluator(COCOEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n # num_classes = len(all_contiguous_ids)\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n # assert category_id < num_classes, (\n # f\"A prediction has class={category_id}, \"\n # f\"but the dataset only has {num_classes} classes and \"\n # f\"predicted class id should be in [0, {num_classes - 1}].\"\n # )\n assert category_id in reverse_id_mapping, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res","source_hash":"a34a7e1b5885592176f67236b3cbc7d74c8c5ab074ccc06c37789aeecd602582","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.instance_evaluation.InstanceSegEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.instance_evaluation.InstanceSegEvaluator#L33-L110","kind":"class","name":"InstanceSegEvaluator","path":"oneformer/evaluation/instance_evaluation.py","language":"python","start_line":33,"end_line":110,"context_start_line":13,"context_end_line":110,"code":"import pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import CfgNode\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco\nfrom detectron2.evaluation.fast_eval_api import COCOeval_opt\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\n\n# modified from COCOEvaluator for instance segmetnat\nclass InstanceSegEvaluator(COCOEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n # num_classes = len(all_contiguous_ids)\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n # assert category_id < num_classes, (\n # f\"A prediction has class={category_id}, \"\n # f\"but the dataset only has {num_classes} classes and \"\n # f\"predicted class id should be in [0, {num_classes - 1}].\"\n # )\n assert category_id in reverse_id_mapping, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res","source_hash":"a34a7e1b5885592176f67236b3cbc7d74c8c5ab074ccc06c37789aeecd602582","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.instance_evaluation._eval_predictions","uri":"program://OneFormer/function/oneformer.evaluation.instance_evaluation._eval_predictions#L46-L110","kind":"function","name":"_eval_predictions","path":"oneformer/evaluation/instance_evaluation.py","language":"python","start_line":46,"end_line":110,"context_start_line":26,"context_end_line":110,"code":"from detectron2.evaluation.fast_eval_api import COCOeval_opt\nfrom detectron2.structures import Boxes, BoxMode, pairwise_iou\nfrom detectron2.utils.file_io import PathManager\nfrom detectron2.utils.logger import create_small_table\n\n\n# modified from COCOEvaluator for instance segmetnat\nclass InstanceSegEvaluator(COCOEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n # num_classes = len(all_contiguous_ids)\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n # assert category_id < num_classes, (\n # f\"A prediction has class={category_id}, \"\n # f\"but the dataset only has {num_classes} classes and \"\n # f\"predicted class id should be in [0, {num_classes - 1}].\"\n # )\n assert category_id in reverse_id_mapping, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res","source_hash":"a34a7e1b5885592176f67236b3cbc7d74c8c5ab074ccc06c37789aeecd602582","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator","uri":"program://OneFormer/module/oneformer.evaluation.evaluator#L1-L228","kind":"module","name":"oneformer.evaluation.evaluator","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":1,"end_line":228,"context_start_line":1,"context_end_line":228,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport datetime\nimport logging\nimport time\nfrom collections import OrderedDict, abc\nfrom contextlib import ExitStack, contextmanager\nfrom typing import List, Union\nimport torch\nfrom torch import nn\n\nfrom detectron2.utils.comm import get_world_size, is_main_process\nfrom detectron2.utils.logger import log_every_n_seconds\n\n\nclass DatasetEvaluator:\n \"\"\"\n Base class for a dataset evaluator.\n\n The function :func:`inference_on_dataset` runs the model over\n all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.\n\n This class will accumulate information of the inputs/outputs (by :meth:`process`),\n and produce evaluation results in the end (by :meth:`evaluate`).\n \"\"\"\n\n def reset(self):\n \"\"\"\n Preparation for a new round of evaluation.\n Should be called before starting a round of evaluation.\n \"\"\"\n pass\n\n def process(self, inputs, outputs):\n \"\"\"\n Process the pair of inputs and outputs.\n If they contain batches, the pairs can be consumed one-by-one using `zip`:\n\n .. code-block:: python\n\n for input_, output in zip(inputs, outputs):\n # do evaluation on single input/output pair\n ...\n\n Args:\n inputs (list): the inputs that's used to call the model.\n outputs (list): the return value of `model(inputs)`\n \"\"\"\n pass\n\n def evaluate(self):\n \"\"\"\n Evaluate/summarize the performance, after processing all input/output pairs.\n\n Returns:\n dict:\n A new evaluator class can return a dict of arbitrary format\n as long as the user can process the results.\n In our train_net.py, we expect the following format:\n\n * key: the name of the task (e.g., bbox)\n * value: a dict of {metric name: score}, e.g.: {\"AP50\": 80}\n \"\"\"\n pass\n\n\nclass DatasetEvaluators(DatasetEvaluator):\n \"\"\"\n Wrapper class to combine multiple :class:`DatasetEvaluator` instances.\n\n This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results\n\n\ndef inference_on_dataset(\n model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]\n):\n \"\"\"\n Run model on the data_loader and evaluate the metrics with evaluator.\n Also benchmark the inference speed of `model.__call__` accurately.\n The model will be used in eval mode.\n\n Args:\n model (callable): a callable which takes an object from\n `data_loader` and returns some outputs.\n\n If it's an nn.Module, it will be temporarily set to `eval` mode.\n If you wish to evaluate a model in `training` mode instead, you can\n wrap the given model and override its behavior of `.eval()` and `.train()`.\n data_loader: an iterable object with a length.\n The elements it generates will be the inputs to the model.\n evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,\n but don't want to do any evaluation.\n\n Returns:\n The return value of `evaluator.evaluate()`\n \"\"\"\n num_devices = get_world_size()\n logger = logging.getLogger(__name__)\n logger.info(\"Start inference on {} batches\".format(len(data_loader)))\n\n total = len(data_loader) # inference data loader must have a fixed length\n if evaluator is None:\n # create a no-op evaluator\n evaluator = DatasetEvaluators([])\n if isinstance(evaluator, abc.MutableSequence):\n evaluator = DatasetEvaluators(evaluator)\n evaluator.reset()\n\n num_warmup = min(5, total - 1)\n start_time = time.perf_counter()\n total_data_time = 0\n total_compute_time = 0\n total_eval_time = 0\n with ExitStack() as stack:\n if isinstance(model, nn.Module):\n stack.enter_context(inference_context(model))\n stack.enter_context(torch.no_grad())\n\n start_data_time = time.perf_counter()\n for idx, inputs in enumerate(data_loader):\n total_data_time += time.perf_counter() - start_data_time\n if idx == num_warmup:\n start_time = time.perf_counter()\n total_data_time = 0\n total_compute_time = 0\n total_eval_time = 0\n\n start_compute_time = time.perf_counter()\n outputs = model(inputs)\n if torch.cuda.is_available():\n torch.cuda.synchronize()\n total_compute_time += time.perf_counter() - start_compute_time\n\n start_eval_time = time.perf_counter()\n evaluator.process(inputs, outputs)\n total_eval_time += time.perf_counter() - start_eval_time\n\n iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)\n data_seconds_per_iter = total_data_time / iters_after_start\n compute_seconds_per_iter = total_compute_time / iters_after_start\n eval_seconds_per_iter = total_eval_time / iters_after_start\n total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start\n if idx >= num_warmup * 2 or compute_seconds_per_iter > 5:\n eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))\n log_every_n_seconds(\n logging.INFO,\n (\n f\"Inference done {idx + 1}/{total}. \"\n f\"Dataloading: {data_seconds_per_iter:.4f} s/iter. \"\n f\"Inference: {compute_seconds_per_iter:.4f} s/iter. \"\n f\"Eval: {eval_seconds_per_iter:.4f} s/iter. \"\n f\"Total: {total_seconds_per_iter:.4f} s/iter. \"\n f\"ETA={eta}\"\n ),\n n=5,\n )\n start_data_time = time.perf_counter()\n\n # Measure the time only for this worker (before the synchronization barrier)\n total_time = time.perf_counter() - start_time\n total_time_str = str(datetime.timedelta(seconds=total_time))\n # NOTE this format is parsed by grep\n logger.info(\n \"Total inference time: {} ({:.6f} s / iter per device, on {} devices)\".format(\n total_time_str, total_time / (total - num_warmup), num_devices\n )\n )\n total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))\n logger.info(\n \"Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)\".format(\n total_compute_time_str, total_compute_time / (total - num_warmup), num_devices\n )\n )\n\n results = evaluator.evaluate()\n # An evaluator may return None when not in main process.\n # Replace it by an empty dict instead to make it easier for downstream code to handle\n if results is None:\n results = {}\n return results\n\n\n@contextmanager\ndef inference_context(model):\n \"\"\"\n A context where the model is temporarily changed to eval mode,\n and restored to previous mode afterwards.\n\n Args:\n model: a torch Module\n \"\"\"\n training_mode = model.training\n model.eval()\n yield\n model.train(training_mode)","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.DatasetEvaluator","uri":"program://OneFormer/class/oneformer.evaluation.evaluator.DatasetEvaluator#L19-L67","kind":"class","name":"DatasetEvaluator","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":19,"end_line":67,"context_start_line":1,"context_end_line":87,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport datetime\nimport logging\nimport time\nfrom collections import OrderedDict, abc\nfrom contextlib import ExitStack, contextmanager\nfrom typing import List, Union\nimport torch\nfrom torch import nn\n\nfrom detectron2.utils.comm import get_world_size, is_main_process\nfrom detectron2.utils.logger import log_every_n_seconds\n\n\nclass DatasetEvaluator:\n \"\"\"\n Base class for a dataset evaluator.\n\n The function :func:`inference_on_dataset` runs the model over\n all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.\n\n This class will accumulate information of the inputs/outputs (by :meth:`process`),\n and produce evaluation results in the end (by :meth:`evaluate`).\n \"\"\"\n\n def reset(self):\n \"\"\"\n Preparation for a new round of evaluation.\n Should be called before starting a round of evaluation.\n \"\"\"\n pass\n\n def process(self, inputs, outputs):\n \"\"\"\n Process the pair of inputs and outputs.\n If they contain batches, the pairs can be consumed one-by-one using `zip`:\n\n .. code-block:: python\n\n for input_, output in zip(inputs, outputs):\n # do evaluation on single input/output pair\n ...\n\n Args:\n inputs (list): the inputs that's used to call the model.\n outputs (list): the return value of `model(inputs)`\n \"\"\"\n pass\n\n def evaluate(self):\n \"\"\"\n Evaluate/summarize the performance, after processing all input/output pairs.\n\n Returns:\n dict:\n A new evaluator class can return a dict of arbitrary format\n as long as the user can process the results.\n In our train_net.py, we expect the following format:\n\n * key: the name of the task (e.g., bbox)\n * value: a dict of {metric name: score}, e.g.: {\"AP50\": 80}\n \"\"\"\n pass\n\n\nclass DatasetEvaluators(DatasetEvaluator):\n \"\"\"\n Wrapper class to combine multiple :class:`DatasetEvaluator` instances.\n\n This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.DatasetEvaluators","uri":"program://OneFormer/class/oneformer.evaluation.evaluator.DatasetEvaluators#L70-L104","kind":"class","name":"DatasetEvaluators","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":70,"end_line":104,"context_start_line":50,"context_end_line":124,"code":" outputs (list): the return value of `model(inputs)`\n \"\"\"\n pass\n\n def evaluate(self):\n \"\"\"\n Evaluate/summarize the performance, after processing all input/output pairs.\n\n Returns:\n dict:\n A new evaluator class can return a dict of arbitrary format\n as long as the user can process the results.\n In our train_net.py, we expect the following format:\n\n * key: the name of the task (e.g., bbox)\n * value: a dict of {metric name: score}, e.g.: {\"AP50\": 80}\n \"\"\"\n pass\n\n\nclass DatasetEvaluators(DatasetEvaluator):\n \"\"\"\n Wrapper class to combine multiple :class:`DatasetEvaluator` instances.\n\n This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results\n\n\ndef inference_on_dataset(\n model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]\n):\n \"\"\"\n Run model on the data_loader and evaluate the metrics with evaluator.\n Also benchmark the inference speed of `model.__call__` accurately.\n The model will be used in eval mode.\n\n Args:\n model (callable): a callable which takes an object from\n `data_loader` and returns some outputs.\n\n If it's an nn.Module, it will be temporarily set to `eval` mode.\n If you wish to evaluate a model in `training` mode instead, you can\n wrap the given model and override its behavior of `.eval()` and `.train()`.\n data_loader: an iterable object with a length.\n The elements it generates will be the inputs to the model.\n evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.inference_on_dataset","uri":"program://OneFormer/function/oneformer.evaluation.evaluator.inference_on_dataset#L107-L213","kind":"function","name":"inference_on_dataset","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":107,"end_line":213,"context_start_line":87,"context_end_line":228,"code":" for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results\n\n\ndef inference_on_dataset(\n model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]\n):\n \"\"\"\n Run model on the data_loader and evaluate the metrics with evaluator.\n Also benchmark the inference speed of `model.__call__` accurately.\n The model will be used in eval mode.\n\n Args:\n model (callable): a callable which takes an object from\n `data_loader` and returns some outputs.\n\n If it's an nn.Module, it will be temporarily set to `eval` mode.\n If you wish to evaluate a model in `training` mode instead, you can\n wrap the given model and override its behavior of `.eval()` and `.train()`.\n data_loader: an iterable object with a length.\n The elements it generates will be the inputs to the model.\n evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,\n but don't want to do any evaluation.\n\n Returns:\n The return value of `evaluator.evaluate()`\n \"\"\"\n num_devices = get_world_size()\n logger = logging.getLogger(__name__)\n logger.info(\"Start inference on {} batches\".format(len(data_loader)))\n\n total = len(data_loader) # inference data loader must have a fixed length\n if evaluator is None:\n # create a no-op evaluator\n evaluator = DatasetEvaluators([])\n if isinstance(evaluator, abc.MutableSequence):\n evaluator = DatasetEvaluators(evaluator)\n evaluator.reset()\n\n num_warmup = min(5, total - 1)\n start_time = time.perf_counter()\n total_data_time = 0\n total_compute_time = 0\n total_eval_time = 0\n with ExitStack() as stack:\n if isinstance(model, nn.Module):\n stack.enter_context(inference_context(model))\n stack.enter_context(torch.no_grad())\n\n start_data_time = time.perf_counter()\n for idx, inputs in enumerate(data_loader):\n total_data_time += time.perf_counter() - start_data_time\n if idx == num_warmup:\n start_time = time.perf_counter()\n total_data_time = 0\n total_compute_time = 0\n total_eval_time = 0\n\n start_compute_time = time.perf_counter()\n outputs = model(inputs)\n if torch.cuda.is_available():\n torch.cuda.synchronize()\n total_compute_time += time.perf_counter() - start_compute_time\n\n start_eval_time = time.perf_counter()\n evaluator.process(inputs, outputs)\n total_eval_time += time.perf_counter() - start_eval_time\n\n iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)\n data_seconds_per_iter = total_data_time / iters_after_start\n compute_seconds_per_iter = total_compute_time / iters_after_start\n eval_seconds_per_iter = total_eval_time / iters_after_start\n total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start\n if idx >= num_warmup * 2 or compute_seconds_per_iter > 5:\n eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))\n log_every_n_seconds(\n logging.INFO,\n (\n f\"Inference done {idx + 1}/{total}. \"\n f\"Dataloading: {data_seconds_per_iter:.4f} s/iter. \"\n f\"Inference: {compute_seconds_per_iter:.4f} s/iter. \"\n f\"Eval: {eval_seconds_per_iter:.4f} s/iter. \"\n f\"Total: {total_seconds_per_iter:.4f} s/iter. \"\n f\"ETA={eta}\"\n ),\n n=5,\n )\n start_data_time = time.perf_counter()\n\n # Measure the time only for this worker (before the synchronization barrier)\n total_time = time.perf_counter() - start_time\n total_time_str = str(datetime.timedelta(seconds=total_time))\n # NOTE this format is parsed by grep\n logger.info(\n \"Total inference time: {} ({:.6f} s / iter per device, on {} devices)\".format(\n total_time_str, total_time / (total - num_warmup), num_devices\n )\n )\n total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))\n logger.info(\n \"Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)\".format(\n total_compute_time_str, total_compute_time / (total - num_warmup), num_devices\n )\n )\n\n results = evaluator.evaluate()\n # An evaluator may return None when not in main process.\n # Replace it by an empty dict instead to make it easier for downstream code to handle\n if results is None:\n results = {}\n return results\n\n\n@contextmanager\ndef inference_context(model):\n \"\"\"\n A context where the model is temporarily changed to eval mode,\n and restored to previous mode afterwards.\n\n Args:\n model: a torch Module\n \"\"\"\n training_mode = model.training\n model.eval()\n yield\n model.train(training_mode)","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.inference_context","uri":"program://OneFormer/function/oneformer.evaluation.evaluator.inference_context#L217-L228","kind":"function","name":"inference_context","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":217,"end_line":228,"context_start_line":197,"context_end_line":228,"code":" \"Total inference time: {} ({:.6f} s / iter per device, on {} devices)\".format(\n total_time_str, total_time / (total - num_warmup), num_devices\n )\n )\n total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))\n logger.info(\n \"Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)\".format(\n total_compute_time_str, total_compute_time / (total - num_warmup), num_devices\n )\n )\n\n results = evaluator.evaluate()\n # An evaluator may return None when not in main process.\n # Replace it by an empty dict instead to make it easier for downstream code to handle\n if results is None:\n results = {}\n return results\n\n\n@contextmanager\ndef inference_context(model):\n \"\"\"\n A context where the model is temporarily changed to eval mode,\n and restored to previous mode afterwards.\n\n Args:\n model: a torch Module\n \"\"\"\n training_mode = model.training\n model.eval()\n yield\n model.train(training_mode)","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.reset","uri":"program://OneFormer/function/oneformer.evaluation.evaluator.reset#L86-L88","kind":"function","name":"reset","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":86,"end_line":88,"context_start_line":66,"context_end_line":108,"code":" \"\"\"\n pass\n\n\nclass DatasetEvaluators(DatasetEvaluator):\n \"\"\"\n Wrapper class to combine multiple :class:`DatasetEvaluator` instances.\n\n This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results\n\n\ndef inference_on_dataset(\n model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.process","uri":"program://OneFormer/function/oneformer.evaluation.evaluator.process#L90-L92","kind":"function","name":"process","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":90,"end_line":92,"context_start_line":70,"context_end_line":112,"code":"class DatasetEvaluators(DatasetEvaluator):\n \"\"\"\n Wrapper class to combine multiple :class:`DatasetEvaluator` instances.\n\n This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results\n\n\ndef inference_on_dataset(\n model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]\n):\n \"\"\"\n Run model on the data_loader and evaluate the metrics with evaluator.\n Also benchmark the inference speed of `model.__call__` accurately.","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.evaluate","uri":"program://OneFormer/function/oneformer.evaluation.evaluator.evaluate#L94-L104","kind":"function","name":"evaluate","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":94,"end_line":104,"context_start_line":74,"context_end_line":124,"code":" This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results\n\n\ndef inference_on_dataset(\n model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]\n):\n \"\"\"\n Run model on the data_loader and evaluate the metrics with evaluator.\n Also benchmark the inference speed of `model.__call__` accurately.\n The model will be used in eval mode.\n\n Args:\n model (callable): a callable which takes an object from\n `data_loader` and returns some outputs.\n\n If it's an nn.Module, it will be temporarily set to `eval` mode.\n If you wish to evaluate a model in `training` mode instead, you can\n wrap the given model and override its behavior of `.eval()` and `.train()`.\n data_loader: an iterable object with a length.\n The elements it generates will be the inputs to the model.\n evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.evaluation.evaluator.__init__","uri":"program://OneFormer/function/oneformer.evaluation.evaluator.__init__#L78-L84","kind":"function","name":"__init__","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":78,"end_line":84,"context_start_line":58,"context_end_line":104,"code":" Returns:\n dict:\n A new evaluator class can return a dict of arbitrary format\n as long as the user can process the results.\n In our train_net.py, we expect the following format:\n\n * key: the name of the task (e.g., bbox)\n * value: a dict of {metric name: score}, e.g.: {\"AP50\": 80}\n \"\"\"\n pass\n\n\nclass DatasetEvaluators(DatasetEvaluator):\n \"\"\"\n Wrapper class to combine multiple :class:`DatasetEvaluator` instances.\n\n This class dispatches every evaluation call to\n all of its :class:`DatasetEvaluator`.\n \"\"\"\n\n def __init__(self, evaluators):\n \"\"\"\n Args:\n evaluators (list): the evaluators to combine.\n \"\"\"\n super().__init__()\n self._evaluators = evaluators\n\n def reset(self):\n for evaluator in self._evaluators:\n evaluator.reset()\n\n def process(self, inputs, outputs):\n for evaluator in self._evaluators:\n evaluator.process(inputs, outputs)\n\n def evaluate(self):\n results = OrderedDict()\n for evaluator in self._evaluators:\n result = evaluator.evaluate()\n if is_main_process() and result is not None:\n for k, v in result.items():\n assert (\n k not in results\n ), \"Different evaluators produce results with the same key {}\".format(k)\n results[k] = v\n return results","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion","uri":"program://OneFormer/module/oneformer.modeling.criterion#L1-L330","kind":"module","name":"oneformer.modeling.criterion","path":"oneformer/modeling/criterion.py","language":"python","start_line":1,"end_line":330,"context_start_line":1,"context_end_line":330,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nOneFormer criterion.\n\"\"\"\nimport logging\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom detectron2.utils.comm import get_world_size\nfrom detectron2.projects.point_rend.point_features import (\n get_uncertain_point_coords_with_randomness,\n point_sample,\n)\n\nfrom ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list\nfrom ..utils import box_ops\nimport torch.distributed as dist\nimport diffdist.functional as diff_dist\nimport numpy as np\n\ndef dist_collect(x):\n \"\"\" collect all tensor from all GPUs\n args:\n x: shape (mini_batch, ...)\n returns:\n shape (mini_batch * num_gpu, ...)\n \"\"\"\n x = x.contiguous()\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())]\n out_list = diff_dist.all_gather(out_list, x)\n return torch.cat(out_list, dim=0).contiguous()\n\ndef dice_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n num_masks: float,\n ):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * (inputs * targets).sum(-1)\n denominator = inputs.sum(-1) + targets.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss.sum() / num_masks\n\n\ndice_loss_jit = torch.jit.script(\n dice_loss\n) # type: torch.jit.ScriptModule\n\n\ndef sigmoid_ce_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n num_masks: float,\n ):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:\n Loss tensor\n \"\"\"\n loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n loss = loss.mean(1)\n return loss.sum() / num_masks\n\n\nsigmoid_ce_loss_jit = torch.jit.script(\n sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\ndef calculate_uncertainty(logits):\n \"\"\"\n We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the\n foreground class in `classes`.\n Args:\n logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or\n class-agnostic, where R is the total number of predicted masks in all images and C is\n the number of foreground classes. The values are logits.\n Returns:\n scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with\n the most uncertain locations having the highest uncertainty score.\n \"\"\"\n assert logits.shape[1] == 1\n gt_class_logits = logits.clone()\n return -(torch.abs(gt_class_logits))\n\n\nclass SetCriterion(nn.Module):\n \"\"\"This class computes the loss for DETR.\n The process happens in two steps:\n 1) we compute hungarian assignment between ground truth boxes and the outputs of the model\n 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\n \"\"\"\n\n def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,\n num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None):\n \"\"\"Create the criterion.\n Parameters:\n num_classes: number of object categories, omitting the special no-object category\n matcher: module able to compute a matching between targets and proposals\n weight_dict: dict containing as key the names of the losses and as values their relative weight.\n eos_coef: relative classification weight applied to the no-object category\n losses: list of all the losses to be applied. See get_loss for list of available losses.\n \"\"\"\n super().__init__()\n self.num_classes = num_classes\n self.matcher = matcher\n self.weight_dict = weight_dict\n self.eos_coef = eos_coef\n self.losses = losses\n empty_weight = torch.ones(self.num_classes + 1)\n empty_weight[-1] = self.eos_coef\n self.register_buffer(\"empty_weight\", empty_weight)\n self.cross_entropy = nn.CrossEntropyLoss()\n\n # pointwise mask loss parameters\n self.num_points = num_points\n self.oversample_ratio = oversample_ratio\n self.importance_sample_ratio = importance_sample_ratio\n self.contrast_temperature = contrast_temperature\n if self.contrast_temperature is not None:\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature))\n \n \n def loss_contrastive(self, outputs, targets, indices, num_masks):\n assert \"contrastive_logits\" in outputs\n assert \"texts\" in outputs\n image_x = outputs[\"contrastive_logits\"].float()\n \n batch_size = image_x.shape[0]\n # get label globally\n if is_dist_avail_and_initialized():\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank()\n else:\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device)\n\n text_x = outputs[\"texts\"]\n\n # [B, C]\n image_x = F.normalize(image_x.flatten(1), dim=-1)\n text_x = F.normalize(text_x.flatten(1), dim=-1)\n\n if is_dist_avail_and_initialized():\n logits_per_img = image_x @ dist_collect(text_x).t()\n logits_per_text = text_x @ dist_collect(image_x).t()\n else:\n logits_per_img = image_x @ text_x.t()\n logits_per_text = text_x @ image_x.t()\n\n logit_scale = torch.clamp(self.logit_scale.exp(), max=100)\n loss_img = self.cross_entropy(logits_per_img * logit_scale, labels)\n loss_text = self.cross_entropy(logits_per_text * logit_scale, labels)\n\n loss_contrastive = loss_img + loss_text\n\n losses = {\"loss_contrastive\": loss_contrastive}\n return losses\n\n def loss_labels(self, outputs, targets, indices, num_masks):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n \"\"\"\n assert \"pred_logits\" in outputs\n src_logits = outputs[\"pred_logits\"].float()\n\n idx = self._get_src_permutation_idx(indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n target_classes = torch.full(\n src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device\n )\n target_classes[idx] = target_classes_o\n \n ce_weight = torch.full(\n src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device\n )\n ce_weight[idx] = torch.tensor(1.).to(target_classes.device)\n\n loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction=\"none\")\n loss_ce = loss_ce.sum(1) / ce_weight.sum()\n loss_ce = loss_ce.sum()\n losses = {\"loss_ce\": loss_ce}\n return losses\n \n def loss_masks(self, outputs, targets, indices, num_masks):\n \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n \"\"\"\n assert \"pred_masks\" in outputs\n\n src_idx = self._get_src_permutation_idx(indices)\n tgt_idx = self._get_tgt_permutation_idx(indices)\n src_masks = outputs[\"pred_masks\"]\n src_masks = src_masks[src_idx]\n masks = [t[\"masks\"] for t in targets]\n # TODO use valid to mask invalid areas due to padding in loss\n target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()\n target_masks = target_masks.to(src_masks)\n target_masks = target_masks[tgt_idx]\n\n # No need to upsample predictions as we are using normalized coordinates :)\n # N x 1 x H x W\n src_masks = src_masks[:, None]\n target_masks = target_masks[:, None]\n\n with torch.no_grad():\n # sample point_coords\n point_coords = get_uncertain_point_coords_with_randomness(\n src_masks,\n lambda logits: calculate_uncertainty(logits),\n self.num_points,\n self.oversample_ratio,\n self.importance_sample_ratio,\n )\n # get gt labels\n point_labels = point_sample(\n target_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n point_logits = point_sample(\n src_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n losses = {\n \"loss_mask\": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),\n \"loss_dice\": dice_loss_jit(point_logits, point_labels, num_masks),\n }\n\n del src_masks\n del target_masks\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"\n return loss_map[loss](outputs, targets, indices, num_masks)\n\n def forward(self, outputs, targets):\n \"\"\"This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != \"aux_outputs\"}\n\n # Retrieve the matching between the outputs of the last layer and the targets\n indices = self.matcher(outputs_without_aux, targets)\n\n # Compute the average number of target boxes accross all nodes, for normalization purposes\n num_masks = sum(len(t[\"labels\"]) for t in targets)\n num_masks = torch.as_tensor(\n [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device\n )\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_masks)\n num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()\n\n # Compute all the requested losses\n losses = {}\n for loss in self.losses:\n losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))\n\n # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n if \"aux_outputs\" in outputs:\n for i, aux_outputs in enumerate(outputs[\"aux_outputs\"]):\n indices = self.matcher(aux_outputs, targets)\n for loss in self.losses:\n if loss == \"contrastive\": \n continue\n l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)\n l_dict = {k + f\"_{i}\": v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n return losses\n\n def __repr__(self):\n head = \"Criterion \" + self.__class__.__name__\n body = [\n \"matcher: {}\".format(self.matcher.__repr__(_repr_indent=8)),\n \"losses: {}\".format(self.losses),\n \"weight_dict: {}\".format(self.weight_dict),\n \"num_classes: {}\".format(self.num_classes),\n \"eos_coef: {}\".format(self.eos_coef),\n \"num_points: {}\".format(self.num_points),\n \"oversample_ratio: {}\".format(self.oversample_ratio),\n \"importance_sample_ratio: {}\".format(self.importance_sample_ratio),\n ]\n _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.dist_collect","uri":"program://OneFormer/function/oneformer.modeling.criterion.dist_collect#L27-L37","kind":"function","name":"dist_collect","path":"oneformer/modeling/criterion.py","language":"python","start_line":27,"end_line":37,"context_start_line":7,"context_end_line":57,"code":"OneFormer criterion.\n\"\"\"\nimport logging\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom detectron2.utils.comm import get_world_size\nfrom detectron2.projects.point_rend.point_features import (\n get_uncertain_point_coords_with_randomness,\n point_sample,\n)\n\nfrom ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list\nfrom ..utils import box_ops\nimport torch.distributed as dist\nimport diffdist.functional as diff_dist\nimport numpy as np\n\ndef dist_collect(x):\n \"\"\" collect all tensor from all GPUs\n args:\n x: shape (mini_batch, ...)\n returns:\n shape (mini_batch * num_gpu, ...)\n \"\"\"\n x = x.contiguous()\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())]\n out_list = diff_dist.all_gather(out_list, x)\n return torch.cat(out_list, dim=0).contiguous()\n\ndef dice_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n num_masks: float,\n ):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * (inputs * targets).sum(-1)\n denominator = inputs.sum(-1) + targets.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.dice_loss","uri":"program://OneFormer/function/oneformer.modeling.criterion.dice_loss#L39-L58","kind":"function","name":"dice_loss","path":"oneformer/modeling/criterion.py","language":"python","start_line":39,"end_line":58,"context_start_line":19,"context_end_line":78,"code":")\n\nfrom ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list\nfrom ..utils import box_ops\nimport torch.distributed as dist\nimport diffdist.functional as diff_dist\nimport numpy as np\n\ndef dist_collect(x):\n \"\"\" collect all tensor from all GPUs\n args:\n x: shape (mini_batch, ...)\n returns:\n shape (mini_batch * num_gpu, ...)\n \"\"\"\n x = x.contiguous()\n out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())]\n out_list = diff_dist.all_gather(out_list, x)\n return torch.cat(out_list, dim=0).contiguous()\n\ndef dice_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n num_masks: float,\n ):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * (inputs * targets).sum(-1)\n denominator = inputs.sum(-1) + targets.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss.sum() / num_masks\n\n\ndice_loss_jit = torch.jit.script(\n dice_loss\n) # type: torch.jit.ScriptModule\n\n\ndef sigmoid_ce_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n num_masks: float,\n ):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.sigmoid_ce_loss","uri":"program://OneFormer/function/oneformer.modeling.criterion.sigmoid_ce_loss#L66-L83","kind":"function","name":"sigmoid_ce_loss","path":"oneformer/modeling/criterion.py","language":"python","start_line":66,"end_line":83,"context_start_line":46,"context_end_line":103,"code":" Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * (inputs * targets).sum(-1)\n denominator = inputs.sum(-1) + targets.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss.sum() / num_masks\n\n\ndice_loss_jit = torch.jit.script(\n dice_loss\n) # type: torch.jit.ScriptModule\n\n\ndef sigmoid_ce_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n num_masks: float,\n ):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:\n Loss tensor\n \"\"\"\n loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n loss = loss.mean(1)\n return loss.sum() / num_masks\n\n\nsigmoid_ce_loss_jit = torch.jit.script(\n sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\ndef calculate_uncertainty(logits):\n \"\"\"\n We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the\n foreground class in `classes`.\n Args:\n logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or\n class-agnostic, where R is the total number of predicted masks in all images and C is\n the number of foreground classes. The values are logits.\n Returns:\n scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with\n the most uncertain locations having the highest uncertainty score.\n \"\"\"\n assert logits.shape[1] == 1","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.calculate_uncertainty","uri":"program://OneFormer/function/oneformer.modeling.criterion.calculate_uncertainty#L91-L105","kind":"function","name":"calculate_uncertainty","path":"oneformer/modeling/criterion.py","language":"python","start_line":91,"end_line":105,"context_start_line":71,"context_end_line":125,"code":" \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:\n Loss tensor\n \"\"\"\n loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n loss = loss.mean(1)\n return loss.sum() / num_masks\n\n\nsigmoid_ce_loss_jit = torch.jit.script(\n sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\ndef calculate_uncertainty(logits):\n \"\"\"\n We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the\n foreground class in `classes`.\n Args:\n logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or\n class-agnostic, where R is the total number of predicted masks in all images and C is\n the number of foreground classes. The values are logits.\n Returns:\n scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with\n the most uncertain locations having the highest uncertainty score.\n \"\"\"\n assert logits.shape[1] == 1\n gt_class_logits = logits.clone()\n return -(torch.abs(gt_class_logits))\n\n\nclass SetCriterion(nn.Module):\n \"\"\"This class computes the loss for DETR.\n The process happens in two steps:\n 1) we compute hungarian assignment between ground truth boxes and the outputs of the model\n 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\n \"\"\"\n\n def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,\n num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None):\n \"\"\"Create the criterion.\n Parameters:\n num_classes: number of object categories, omitting the special no-object category\n matcher: module able to compute a matching between targets and proposals\n weight_dict: dict containing as key the names of the losses and as values their relative weight.\n eos_coef: relative classification weight applied to the no-object category\n losses: list of all the losses to be applied. See get_loss for list of available losses.\n \"\"\"\n super().__init__()","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.SetCriterion","uri":"program://OneFormer/class/oneformer.modeling.criterion.SetCriterion#L108-L330","kind":"class","name":"SetCriterion","path":"oneformer/modeling/criterion.py","language":"python","start_line":108,"end_line":330,"context_start_line":88,"context_end_line":330,"code":") # type: torch.jit.ScriptModule\n\n\ndef calculate_uncertainty(logits):\n \"\"\"\n We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the\n foreground class in `classes`.\n Args:\n logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or\n class-agnostic, where R is the total number of predicted masks in all images and C is\n the number of foreground classes. The values are logits.\n Returns:\n scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with\n the most uncertain locations having the highest uncertainty score.\n \"\"\"\n assert logits.shape[1] == 1\n gt_class_logits = logits.clone()\n return -(torch.abs(gt_class_logits))\n\n\nclass SetCriterion(nn.Module):\n \"\"\"This class computes the loss for DETR.\n The process happens in two steps:\n 1) we compute hungarian assignment between ground truth boxes and the outputs of the model\n 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\n \"\"\"\n\n def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,\n num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None):\n \"\"\"Create the criterion.\n Parameters:\n num_classes: number of object categories, omitting the special no-object category\n matcher: module able to compute a matching between targets and proposals\n weight_dict: dict containing as key the names of the losses and as values their relative weight.\n eos_coef: relative classification weight applied to the no-object category\n losses: list of all the losses to be applied. See get_loss for list of available losses.\n \"\"\"\n super().__init__()\n self.num_classes = num_classes\n self.matcher = matcher\n self.weight_dict = weight_dict\n self.eos_coef = eos_coef\n self.losses = losses\n empty_weight = torch.ones(self.num_classes + 1)\n empty_weight[-1] = self.eos_coef\n self.register_buffer(\"empty_weight\", empty_weight)\n self.cross_entropy = nn.CrossEntropyLoss()\n\n # pointwise mask loss parameters\n self.num_points = num_points\n self.oversample_ratio = oversample_ratio\n self.importance_sample_ratio = importance_sample_ratio\n self.contrast_temperature = contrast_temperature\n if self.contrast_temperature is not None:\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature))\n \n \n def loss_contrastive(self, outputs, targets, indices, num_masks):\n assert \"contrastive_logits\" in outputs\n assert \"texts\" in outputs\n image_x = outputs[\"contrastive_logits\"].float()\n \n batch_size = image_x.shape[0]\n # get label globally\n if is_dist_avail_and_initialized():\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank()\n else:\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device)\n\n text_x = outputs[\"texts\"]\n\n # [B, C]\n image_x = F.normalize(image_x.flatten(1), dim=-1)\n text_x = F.normalize(text_x.flatten(1), dim=-1)\n\n if is_dist_avail_and_initialized():\n logits_per_img = image_x @ dist_collect(text_x).t()\n logits_per_text = text_x @ dist_collect(image_x).t()\n else:\n logits_per_img = image_x @ text_x.t()\n logits_per_text = text_x @ image_x.t()\n\n logit_scale = torch.clamp(self.logit_scale.exp(), max=100)\n loss_img = self.cross_entropy(logits_per_img * logit_scale, labels)\n loss_text = self.cross_entropy(logits_per_text * logit_scale, labels)\n\n loss_contrastive = loss_img + loss_text\n\n losses = {\"loss_contrastive\": loss_contrastive}\n return losses\n\n def loss_labels(self, outputs, targets, indices, num_masks):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n \"\"\"\n assert \"pred_logits\" in outputs\n src_logits = outputs[\"pred_logits\"].float()\n\n idx = self._get_src_permutation_idx(indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n target_classes = torch.full(\n src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device\n )\n target_classes[idx] = target_classes_o\n \n ce_weight = torch.full(\n src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device\n )\n ce_weight[idx] = torch.tensor(1.).to(target_classes.device)\n\n loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction=\"none\")\n loss_ce = loss_ce.sum(1) / ce_weight.sum()\n loss_ce = loss_ce.sum()\n losses = {\"loss_ce\": loss_ce}\n return losses\n \n def loss_masks(self, outputs, targets, indices, num_masks):\n \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n \"\"\"\n assert \"pred_masks\" in outputs\n\n src_idx = self._get_src_permutation_idx(indices)\n tgt_idx = self._get_tgt_permutation_idx(indices)\n src_masks = outputs[\"pred_masks\"]\n src_masks = src_masks[src_idx]\n masks = [t[\"masks\"] for t in targets]\n # TODO use valid to mask invalid areas due to padding in loss\n target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()\n target_masks = target_masks.to(src_masks)\n target_masks = target_masks[tgt_idx]\n\n # No need to upsample predictions as we are using normalized coordinates :)\n # N x 1 x H x W\n src_masks = src_masks[:, None]\n target_masks = target_masks[:, None]\n\n with torch.no_grad():\n # sample point_coords\n point_coords = get_uncertain_point_coords_with_randomness(\n src_masks,\n lambda logits: calculate_uncertainty(logits),\n self.num_points,\n self.oversample_ratio,\n self.importance_sample_ratio,\n )\n # get gt labels\n point_labels = point_sample(\n target_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n point_logits = point_sample(\n src_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n losses = {\n \"loss_mask\": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),\n \"loss_dice\": dice_loss_jit(point_logits, point_labels, num_masks),\n }\n\n del src_masks\n del target_masks\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"\n return loss_map[loss](outputs, targets, indices, num_masks)\n\n def forward(self, outputs, targets):\n \"\"\"This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != \"aux_outputs\"}\n\n # Retrieve the matching between the outputs of the last layer and the targets\n indices = self.matcher(outputs_without_aux, targets)\n\n # Compute the average number of target boxes accross all nodes, for normalization purposes\n num_masks = sum(len(t[\"labels\"]) for t in targets)\n num_masks = torch.as_tensor(\n [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device\n )\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_masks)\n num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()\n\n # Compute all the requested losses\n losses = {}\n for loss in self.losses:\n losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))\n\n # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n if \"aux_outputs\" in outputs:\n for i, aux_outputs in enumerate(outputs[\"aux_outputs\"]):\n indices = self.matcher(aux_outputs, targets)\n for loss in self.losses:\n if loss == \"contrastive\": \n continue\n l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)\n l_dict = {k + f\"_{i}\": v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n return losses\n\n def __repr__(self):\n head = \"Criterion \" + self.__class__.__name__\n body = [\n \"matcher: {}\".format(self.matcher.__repr__(_repr_indent=8)),\n \"losses: {}\".format(self.losses),\n \"weight_dict: {}\".format(self.weight_dict),\n \"num_classes: {}\".format(self.num_classes),\n \"eos_coef: {}\".format(self.eos_coef),\n \"num_points: {}\".format(self.num_points),\n \"oversample_ratio: {}\".format(self.oversample_ratio),\n \"importance_sample_ratio: {}\".format(self.importance_sample_ratio),\n ]\n _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.__init__","uri":"program://OneFormer/function/oneformer.modeling.criterion.__init__#L115-L142","kind":"function","name":"__init__","path":"oneformer/modeling/criterion.py","language":"python","start_line":115,"end_line":142,"context_start_line":95,"context_end_line":162,"code":" Args:\n logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or\n class-agnostic, where R is the total number of predicted masks in all images and C is\n the number of foreground classes. The values are logits.\n Returns:\n scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with\n the most uncertain locations having the highest uncertainty score.\n \"\"\"\n assert logits.shape[1] == 1\n gt_class_logits = logits.clone()\n return -(torch.abs(gt_class_logits))\n\n\nclass SetCriterion(nn.Module):\n \"\"\"This class computes the loss for DETR.\n The process happens in two steps:\n 1) we compute hungarian assignment between ground truth boxes and the outputs of the model\n 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\n \"\"\"\n\n def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,\n num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None):\n \"\"\"Create the criterion.\n Parameters:\n num_classes: number of object categories, omitting the special no-object category\n matcher: module able to compute a matching between targets and proposals\n weight_dict: dict containing as key the names of the losses and as values their relative weight.\n eos_coef: relative classification weight applied to the no-object category\n losses: list of all the losses to be applied. See get_loss for list of available losses.\n \"\"\"\n super().__init__()\n self.num_classes = num_classes\n self.matcher = matcher\n self.weight_dict = weight_dict\n self.eos_coef = eos_coef\n self.losses = losses\n empty_weight = torch.ones(self.num_classes + 1)\n empty_weight[-1] = self.eos_coef\n self.register_buffer(\"empty_weight\", empty_weight)\n self.cross_entropy = nn.CrossEntropyLoss()\n\n # pointwise mask loss parameters\n self.num_points = num_points\n self.oversample_ratio = oversample_ratio\n self.importance_sample_ratio = importance_sample_ratio\n self.contrast_temperature = contrast_temperature\n if self.contrast_temperature is not None:\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature))\n \n \n def loss_contrastive(self, outputs, targets, indices, num_masks):\n assert \"contrastive_logits\" in outputs\n assert \"texts\" in outputs\n image_x = outputs[\"contrastive_logits\"].float()\n \n batch_size = image_x.shape[0]\n # get label globally\n if is_dist_avail_and_initialized():\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank()\n else:\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device)\n\n text_x = outputs[\"texts\"]\n\n # [B, C]\n image_x = F.normalize(image_x.flatten(1), dim=-1)\n text_x = F.normalize(text_x.flatten(1), dim=-1)\n","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.loss_contrastive","uri":"program://OneFormer/function/oneformer.modeling.criterion.loss_contrastive#L145-L177","kind":"function","name":"loss_contrastive","path":"oneformer/modeling/criterion.py","language":"python","start_line":145,"end_line":177,"context_start_line":125,"context_end_line":197,"code":" super().__init__()\n self.num_classes = num_classes\n self.matcher = matcher\n self.weight_dict = weight_dict\n self.eos_coef = eos_coef\n self.losses = losses\n empty_weight = torch.ones(self.num_classes + 1)\n empty_weight[-1] = self.eos_coef\n self.register_buffer(\"empty_weight\", empty_weight)\n self.cross_entropy = nn.CrossEntropyLoss()\n\n # pointwise mask loss parameters\n self.num_points = num_points\n self.oversample_ratio = oversample_ratio\n self.importance_sample_ratio = importance_sample_ratio\n self.contrast_temperature = contrast_temperature\n if self.contrast_temperature is not None:\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature))\n \n \n def loss_contrastive(self, outputs, targets, indices, num_masks):\n assert \"contrastive_logits\" in outputs\n assert \"texts\" in outputs\n image_x = outputs[\"contrastive_logits\"].float()\n \n batch_size = image_x.shape[0]\n # get label globally\n if is_dist_avail_and_initialized():\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank()\n else:\n labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device)\n\n text_x = outputs[\"texts\"]\n\n # [B, C]\n image_x = F.normalize(image_x.flatten(1), dim=-1)\n text_x = F.normalize(text_x.flatten(1), dim=-1)\n\n if is_dist_avail_and_initialized():\n logits_per_img = image_x @ dist_collect(text_x).t()\n logits_per_text = text_x @ dist_collect(image_x).t()\n else:\n logits_per_img = image_x @ text_x.t()\n logits_per_text = text_x @ image_x.t()\n\n logit_scale = torch.clamp(self.logit_scale.exp(), max=100)\n loss_img = self.cross_entropy(logits_per_img * logit_scale, labels)\n loss_text = self.cross_entropy(logits_per_text * logit_scale, labels)\n\n loss_contrastive = loss_img + loss_text\n\n losses = {\"loss_contrastive\": loss_contrastive}\n return losses\n\n def loss_labels(self, outputs, targets, indices, num_masks):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n \"\"\"\n assert \"pred_logits\" in outputs\n src_logits = outputs[\"pred_logits\"].float()\n\n idx = self._get_src_permutation_idx(indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n target_classes = torch.full(\n src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device\n )\n target_classes[idx] = target_classes_o\n \n ce_weight = torch.full(\n src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device\n )\n ce_weight[idx] = torch.tensor(1.).to(target_classes.device)\n","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.loss_labels","uri":"program://OneFormer/function/oneformer.modeling.criterion.loss_labels#L179-L202","kind":"function","name":"loss_labels","path":"oneformer/modeling/criterion.py","language":"python","start_line":179,"end_line":202,"context_start_line":159,"context_end_line":222,"code":" # [B, C]\n image_x = F.normalize(image_x.flatten(1), dim=-1)\n text_x = F.normalize(text_x.flatten(1), dim=-1)\n\n if is_dist_avail_and_initialized():\n logits_per_img = image_x @ dist_collect(text_x).t()\n logits_per_text = text_x @ dist_collect(image_x).t()\n else:\n logits_per_img = image_x @ text_x.t()\n logits_per_text = text_x @ image_x.t()\n\n logit_scale = torch.clamp(self.logit_scale.exp(), max=100)\n loss_img = self.cross_entropy(logits_per_img * logit_scale, labels)\n loss_text = self.cross_entropy(logits_per_text * logit_scale, labels)\n\n loss_contrastive = loss_img + loss_text\n\n losses = {\"loss_contrastive\": loss_contrastive}\n return losses\n\n def loss_labels(self, outputs, targets, indices, num_masks):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n \"\"\"\n assert \"pred_logits\" in outputs\n src_logits = outputs[\"pred_logits\"].float()\n\n idx = self._get_src_permutation_idx(indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n target_classes = torch.full(\n src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device\n )\n target_classes[idx] = target_classes_o\n \n ce_weight = torch.full(\n src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device\n )\n ce_weight[idx] = torch.tensor(1.).to(target_classes.device)\n\n loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction=\"none\")\n loss_ce = loss_ce.sum(1) / ce_weight.sum()\n loss_ce = loss_ce.sum()\n losses = {\"loss_ce\": loss_ce}\n return losses\n \n def loss_masks(self, outputs, targets, indices, num_masks):\n \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n \"\"\"\n assert \"pred_masks\" in outputs\n\n src_idx = self._get_src_permutation_idx(indices)\n tgt_idx = self._get_tgt_permutation_idx(indices)\n src_masks = outputs[\"pred_masks\"]\n src_masks = src_masks[src_idx]\n masks = [t[\"masks\"] for t in targets]\n # TODO use valid to mask invalid areas due to padding in loss\n target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()\n target_masks = target_masks.to(src_masks)\n target_masks = target_masks[tgt_idx]\n\n # No need to upsample predictions as we are using normalized coordinates :)\n # N x 1 x H x W\n src_masks = src_masks[:, None]","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.loss_masks","uri":"program://OneFormer/function/oneformer.modeling.criterion.loss_masks#L204-L254","kind":"function","name":"loss_masks","path":"oneformer/modeling/criterion.py","language":"python","start_line":204,"end_line":254,"context_start_line":184,"context_end_line":274,"code":" src_logits = outputs[\"pred_logits\"].float()\n\n idx = self._get_src_permutation_idx(indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n target_classes = torch.full(\n src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device\n )\n target_classes[idx] = target_classes_o\n \n ce_weight = torch.full(\n src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device\n )\n ce_weight[idx] = torch.tensor(1.).to(target_classes.device)\n\n loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction=\"none\")\n loss_ce = loss_ce.sum(1) / ce_weight.sum()\n loss_ce = loss_ce.sum()\n losses = {\"loss_ce\": loss_ce}\n return losses\n \n def loss_masks(self, outputs, targets, indices, num_masks):\n \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n \"\"\"\n assert \"pred_masks\" in outputs\n\n src_idx = self._get_src_permutation_idx(indices)\n tgt_idx = self._get_tgt_permutation_idx(indices)\n src_masks = outputs[\"pred_masks\"]\n src_masks = src_masks[src_idx]\n masks = [t[\"masks\"] for t in targets]\n # TODO use valid to mask invalid areas due to padding in loss\n target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()\n target_masks = target_masks.to(src_masks)\n target_masks = target_masks[tgt_idx]\n\n # No need to upsample predictions as we are using normalized coordinates :)\n # N x 1 x H x W\n src_masks = src_masks[:, None]\n target_masks = target_masks[:, None]\n\n with torch.no_grad():\n # sample point_coords\n point_coords = get_uncertain_point_coords_with_randomness(\n src_masks,\n lambda logits: calculate_uncertainty(logits),\n self.num_points,\n self.oversample_ratio,\n self.importance_sample_ratio,\n )\n # get gt labels\n point_labels = point_sample(\n target_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n point_logits = point_sample(\n src_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n losses = {\n \"loss_mask\": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),\n \"loss_dice\": dice_loss_jit(point_logits, point_labels, num_masks),\n }\n\n del src_masks\n del target_masks\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion._get_src_permutation_idx","uri":"program://OneFormer/function/oneformer.modeling.criterion._get_src_permutation_idx#L256-L260","kind":"function","name":"_get_src_permutation_idx","path":"oneformer/modeling/criterion.py","language":"python","start_line":256,"end_line":260,"context_start_line":236,"context_end_line":280,"code":" target_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n point_logits = point_sample(\n src_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n losses = {\n \"loss_mask\": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),\n \"loss_dice\": dice_loss_jit(point_logits, point_labels, num_masks),\n }\n\n del src_masks\n del target_masks\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"\n return loss_map[loss](outputs, targets, indices, num_masks)\n\n def forward(self, outputs, targets):\n \"\"\"This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion._get_tgt_permutation_idx","uri":"program://OneFormer/function/oneformer.modeling.criterion._get_tgt_permutation_idx#L262-L266","kind":"function","name":"_get_tgt_permutation_idx","path":"oneformer/modeling/criterion.py","language":"python","start_line":262,"end_line":266,"context_start_line":242,"context_end_line":286,"code":" src_masks,\n point_coords,\n align_corners=False,\n ).squeeze(1)\n\n losses = {\n \"loss_mask\": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),\n \"loss_dice\": dice_loss_jit(point_logits, point_labels, num_masks),\n }\n\n del src_masks\n del target_masks\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"\n return loss_map[loss](outputs, targets, indices, num_masks)\n\n def forward(self, outputs, targets):\n \"\"\"This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != \"aux_outputs\"}\n\n # Retrieve the matching between the outputs of the last layer and the targets","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.get_loss","uri":"program://OneFormer/function/oneformer.modeling.criterion.get_loss#L268-L275","kind":"function","name":"get_loss","path":"oneformer/modeling/criterion.py","language":"python","start_line":268,"end_line":275,"context_start_line":248,"context_end_line":295,"code":" \"loss_mask\": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),\n \"loss_dice\": dice_loss_jit(point_logits, point_labels, num_masks),\n }\n\n del src_masks\n del target_masks\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"\n return loss_map[loss](outputs, targets, indices, num_masks)\n\n def forward(self, outputs, targets):\n \"\"\"This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != \"aux_outputs\"}\n\n # Retrieve the matching between the outputs of the last layer and the targets\n indices = self.matcher(outputs_without_aux, targets)\n\n # Compute the average number of target boxes accross all nodes, for normalization purposes\n num_masks = sum(len(t[\"labels\"]) for t in targets)\n num_masks = torch.as_tensor(\n [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device\n )\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_masks)","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.forward","uri":"program://OneFormer/function/oneformer.modeling.criterion.forward#L277-L314","kind":"function","name":"forward","path":"oneformer/modeling/criterion.py","language":"python","start_line":277,"end_line":314,"context_start_line":257,"context_end_line":330,"code":" # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, indices, num_masks):\n loss_map = {\n 'labels': self.loss_labels,\n 'masks': self.loss_masks,\n 'contrastive': self.loss_contrastive,\n }\n assert loss in loss_map, f\"do you really want to compute {loss} loss?\"\n return loss_map[loss](outputs, targets, indices, num_masks)\n\n def forward(self, outputs, targets):\n \"\"\"This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != \"aux_outputs\"}\n\n # Retrieve the matching between the outputs of the last layer and the targets\n indices = self.matcher(outputs_without_aux, targets)\n\n # Compute the average number of target boxes accross all nodes, for normalization purposes\n num_masks = sum(len(t[\"labels\"]) for t in targets)\n num_masks = torch.as_tensor(\n [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device\n )\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_masks)\n num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()\n\n # Compute all the requested losses\n losses = {}\n for loss in self.losses:\n losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))\n\n # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n if \"aux_outputs\" in outputs:\n for i, aux_outputs in enumerate(outputs[\"aux_outputs\"]):\n indices = self.matcher(aux_outputs, targets)\n for loss in self.losses:\n if loss == \"contrastive\": \n continue\n l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)\n l_dict = {k + f\"_{i}\": v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n return losses\n\n def __repr__(self):\n head = \"Criterion \" + self.__class__.__name__\n body = [\n \"matcher: {}\".format(self.matcher.__repr__(_repr_indent=8)),\n \"losses: {}\".format(self.losses),\n \"weight_dict: {}\".format(self.weight_dict),\n \"num_classes: {}\".format(self.num_classes),\n \"eos_coef: {}\".format(self.eos_coef),\n \"num_points: {}\".format(self.num_points),\n \"oversample_ratio: {}\".format(self.oversample_ratio),\n \"importance_sample_ratio: {}\".format(self.importance_sample_ratio),\n ]\n _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.criterion.__repr__","uri":"program://OneFormer/function/oneformer.modeling.criterion.__repr__#L316-L330","kind":"function","name":"__repr__","path":"oneformer/modeling/criterion.py","language":"python","start_line":316,"end_line":330,"context_start_line":296,"context_end_line":330,"code":" num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()\n\n # Compute all the requested losses\n losses = {}\n for loss in self.losses:\n losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))\n\n # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n if \"aux_outputs\" in outputs:\n for i, aux_outputs in enumerate(outputs[\"aux_outputs\"]):\n indices = self.matcher(aux_outputs, targets)\n for loss in self.losses:\n if loss == \"contrastive\": \n continue\n l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)\n l_dict = {k + f\"_{i}\": v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n return losses\n\n def __repr__(self):\n head = \"Criterion \" + self.__class__.__name__\n body = [\n \"matcher: {}\".format(self.matcher.__repr__(_repr_indent=8)),\n \"losses: {}\".format(self.losses),\n \"weight_dict: {}\".format(self.weight_dict),\n \"num_classes: {}\".format(self.num_classes),\n \"eos_coef: {}\".format(self.eos_coef),\n \"num_points: {}\".format(self.num_points),\n \"oversample_ratio: {}\".format(self.oversample_ratio),\n \"importance_sample_ratio: {}\".format(self.importance_sample_ratio),\n ]\n _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher","uri":"program://OneFormer/module/oneformer.modeling.matcher#L1-L212","kind":"module","name":"oneformer.modeling.matcher","path":"oneformer/modeling/matcher.py","language":"python","start_line":1,"end_line":212,"context_start_line":1,"context_end_line":212,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nModules to compute the matching cost and solve the corresponding LSAP.\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\nfrom torch.cuda.amp import autocast\nimport numpy as np\n\nfrom detectron2.projects.point_rend.point_features import point_sample\n\n\ndef linear_sum_assignment_with_nan(cost_matrix):\n cost_matrix = np.asarray(cost_matrix)\n nan = np.isnan(cost_matrix).any()\n nan_all = np.isnan(cost_matrix).all()\n empty = cost_matrix.size == 0\n\n if not empty:\n if nan_all:\n print('Matrix contains all NaN values!')\n elif nan:\n print('Matrix contains NaN values!')\n\n if nan_all:\n cost_matrix = np.empty(shape=(0, 0))\n elif nan:\n cost_matrix[np.isnan(cost_matrix)] = 100\n\n return linear_sum_assignment(cost_matrix)\n\ndef batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * torch.einsum(\"nc,mc->nm\", inputs, targets)\n denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss\n\n\nbatch_dice_loss_jit = torch.jit.script(\n batch_dice_loss\n) # type: torch.jit.ScriptModule\n\n\ndef batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:\n Loss tensor\n \"\"\"\n hw = inputs.shape[1]\n\n pos = F.binary_cross_entropy_with_logits(\n inputs, torch.ones_like(inputs), reduction=\"none\"\n )\n neg = F.binary_cross_entropy_with_logits(\n inputs, torch.zeros_like(inputs), reduction=\"none\"\n )\n\n loss = torch.einsum(\"nc,mc->nm\", pos, targets) + torch.einsum(\n \"nc,mc->nm\", neg, (1 - targets)\n )\n\n return loss / hw\n\n\nbatch_sigmoid_ce_loss_jit = torch.jit.script(\n batch_sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\nclass HungarianMatcher(nn.Module):\n \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n while the others are un-matched (and thus treated as non-objects).\n \"\"\"\n\n def __init__(self, cost_class: float = 1, cost_mask: float = 1, \n cost_dice: float = 1, num_points: int = 0):\n \"\"\"Creates the matcher\n\n Params:\n cost_class: This is the relative weight of the classification error in the matching cost\n cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost\n cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost\n \"\"\"\n super().__init__()\n self.cost_class = cost_class\n self.cost_mask = cost_mask\n self.cost_dice = cost_dice\n\n assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, \"all costs cant be 0\"\n\n self.num_points = num_points\n\n @torch.no_grad()\n def memory_efficient_forward(self, outputs, targets):\n \"\"\"More memory-friendly matching\"\"\"\n bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n indices = []\n\n # Iterate through batch size\n for b in range(bs):\n out_prob = outputs[\"pred_logits\"][b].softmax(-1) # [num_queries, num_classes]\n tgt_ids = targets[b][\"labels\"]\n\n # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n # but approximate it in 1 - proba[target class].\n # The 1 is a constant that doesn't change the matching, it can be ommitted.\n cost_class = -out_prob[:, tgt_ids]\n\n out_mask = outputs[\"pred_masks\"][b] # [num_queries, H_pred, W_pred]\n # gt masks are already padded when preparing target\n tgt_mask = targets[b][\"masks\"].to(out_mask)\n\n out_mask = out_mask[:, None]\n tgt_mask = tgt_mask[:, None]\n # all masks share the same set of points for efficient matching!\n point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device)\n # get gt labels\n tgt_mask = point_sample(\n tgt_mask,\n point_coords.repeat(tgt_mask.shape[0], 1, 1),\n align_corners=False,\n ).squeeze(1)\n\n out_mask = point_sample(\n out_mask,\n point_coords.repeat(out_mask.shape[0], 1, 1),\n align_corners=False,\n ).squeeze(1)\n\n with autocast(enabled=False):\n out_mask = out_mask.float()\n tgt_mask = tgt_mask.float()\n # Compute the focal loss between masks\n cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)\n # Compute the dice loss betwen masks\n cost_dice = batch_dice_loss(out_mask, tgt_mask)\n \n # Final cost matrix\n C = (\n self.cost_mask * cost_mask\n + self.cost_class * cost_class\n + self.cost_dice * cost_dice\n )\n C = C.reshape(num_queries, -1).cpu()\n\n indices.append(linear_sum_assignment_with_nan(C))\n\n return [\n (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))\n for i, j in indices\n ]\n\n @torch.no_grad()\n def forward(self, outputs, targets):\n \"\"\"Performs the matching\n\n Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_masks\": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n objects in the target) containing the class labels\n \"masks\": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n \"\"\"\n\n return self.memory_efficient_forward(outputs, targets)\n\n def __repr__(self, _repr_indent=4):\n head = \"Matcher \" + self.__class__.__name__\n body = [\n \"cost_class: {}\".format(self.cost_class),\n \"cost_mask: {}\".format(self.cost_mask),\n \"cost_dice: {}\".format(self.cost_dice),\n ]\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.linear_sum_assignment_with_nan","uri":"program://OneFormer/function/oneformer.modeling.matcher.linear_sum_assignment_with_nan#L19-L36","kind":"function","name":"linear_sum_assignment_with_nan","path":"oneformer/modeling/matcher.py","language":"python","start_line":19,"end_line":36,"context_start_line":1,"context_end_line":56,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nModules to compute the matching cost and solve the corresponding LSAP.\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\nfrom torch.cuda.amp import autocast\nimport numpy as np\n\nfrom detectron2.projects.point_rend.point_features import point_sample\n\n\ndef linear_sum_assignment_with_nan(cost_matrix):\n cost_matrix = np.asarray(cost_matrix)\n nan = np.isnan(cost_matrix).any()\n nan_all = np.isnan(cost_matrix).all()\n empty = cost_matrix.size == 0\n\n if not empty:\n if nan_all:\n print('Matrix contains all NaN values!')\n elif nan:\n print('Matrix contains NaN values!')\n\n if nan_all:\n cost_matrix = np.empty(shape=(0, 0))\n elif nan:\n cost_matrix[np.isnan(cost_matrix)] = 100\n\n return linear_sum_assignment(cost_matrix)\n\ndef batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * torch.einsum(\"nc,mc->nm\", inputs, targets)\n denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss\n\n\nbatch_dice_loss_jit = torch.jit.script(","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.batch_dice_loss","uri":"program://OneFormer/function/oneformer.modeling.matcher.batch_dice_loss#L38-L53","kind":"function","name":"batch_dice_loss","path":"oneformer/modeling/matcher.py","language":"python","start_line":38,"end_line":53,"context_start_line":18,"context_end_line":73,"code":"\ndef linear_sum_assignment_with_nan(cost_matrix):\n cost_matrix = np.asarray(cost_matrix)\n nan = np.isnan(cost_matrix).any()\n nan_all = np.isnan(cost_matrix).all()\n empty = cost_matrix.size == 0\n\n if not empty:\n if nan_all:\n print('Matrix contains all NaN values!')\n elif nan:\n print('Matrix contains NaN values!')\n\n if nan_all:\n cost_matrix = np.empty(shape=(0, 0))\n elif nan:\n cost_matrix[np.isnan(cost_matrix)] = 100\n\n return linear_sum_assignment(cost_matrix)\n\ndef batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * torch.einsum(\"nc,mc->nm\", inputs, targets)\n denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss\n\n\nbatch_dice_loss_jit = torch.jit.script(\n batch_dice_loss\n) # type: torch.jit.ScriptModule\n\n\ndef batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:\n Loss tensor\n \"\"\"\n hw = inputs.shape[1]\n","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.batch_sigmoid_ce_loss","uri":"program://OneFormer/function/oneformer.modeling.matcher.batch_sigmoid_ce_loss#L61-L85","kind":"function","name":"batch_sigmoid_ce_loss","path":"oneformer/modeling/matcher.py","language":"python","start_line":61,"end_line":85,"context_start_line":41,"context_end_line":105,"code":" Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n \"\"\"\n inputs = inputs.sigmoid()\n inputs = inputs.flatten(1)\n numerator = 2 * torch.einsum(\"nc,mc->nm\", inputs, targets)\n denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]\n loss = 1 - (numerator + 1) / (denominator + 1)\n return loss\n\n\nbatch_dice_loss_jit = torch.jit.script(\n batch_dice_loss\n) # type: torch.jit.ScriptModule\n\n\ndef batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n Returns:\n Loss tensor\n \"\"\"\n hw = inputs.shape[1]\n\n pos = F.binary_cross_entropy_with_logits(\n inputs, torch.ones_like(inputs), reduction=\"none\"\n )\n neg = F.binary_cross_entropy_with_logits(\n inputs, torch.zeros_like(inputs), reduction=\"none\"\n )\n\n loss = torch.einsum(\"nc,mc->nm\", pos, targets) + torch.einsum(\n \"nc,mc->nm\", neg, (1 - targets)\n )\n\n return loss / hw\n\n\nbatch_sigmoid_ce_loss_jit = torch.jit.script(\n batch_sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\nclass HungarianMatcher(nn.Module):\n \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n while the others are un-matched (and thus treated as non-objects).\n \"\"\"\n\n def __init__(self, cost_class: float = 1, cost_mask: float = 1, \n cost_dice: float = 1, num_points: int = 0):\n \"\"\"Creates the matcher\n\n Params:","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.HungarianMatcher","uri":"program://OneFormer/class/oneformer.modeling.matcher.HungarianMatcher#L93-L212","kind":"class","name":"HungarianMatcher","path":"oneformer/modeling/matcher.py","language":"python","start_line":93,"end_line":212,"context_start_line":73,"context_end_line":212,"code":"\n pos = F.binary_cross_entropy_with_logits(\n inputs, torch.ones_like(inputs), reduction=\"none\"\n )\n neg = F.binary_cross_entropy_with_logits(\n inputs, torch.zeros_like(inputs), reduction=\"none\"\n )\n\n loss = torch.einsum(\"nc,mc->nm\", pos, targets) + torch.einsum(\n \"nc,mc->nm\", neg, (1 - targets)\n )\n\n return loss / hw\n\n\nbatch_sigmoid_ce_loss_jit = torch.jit.script(\n batch_sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\nclass HungarianMatcher(nn.Module):\n \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n while the others are un-matched (and thus treated as non-objects).\n \"\"\"\n\n def __init__(self, cost_class: float = 1, cost_mask: float = 1, \n cost_dice: float = 1, num_points: int = 0):\n \"\"\"Creates the matcher\n\n Params:\n cost_class: This is the relative weight of the classification error in the matching cost\n cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost\n cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost\n \"\"\"\n super().__init__()\n self.cost_class = cost_class\n self.cost_mask = cost_mask\n self.cost_dice = cost_dice\n\n assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, \"all costs cant be 0\"\n\n self.num_points = num_points\n\n @torch.no_grad()\n def memory_efficient_forward(self, outputs, targets):\n \"\"\"More memory-friendly matching\"\"\"\n bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n indices = []\n\n # Iterate through batch size\n for b in range(bs):\n out_prob = outputs[\"pred_logits\"][b].softmax(-1) # [num_queries, num_classes]\n tgt_ids = targets[b][\"labels\"]\n\n # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n # but approximate it in 1 - proba[target class].\n # The 1 is a constant that doesn't change the matching, it can be ommitted.\n cost_class = -out_prob[:, tgt_ids]\n\n out_mask = outputs[\"pred_masks\"][b] # [num_queries, H_pred, W_pred]\n # gt masks are already padded when preparing target\n tgt_mask = targets[b][\"masks\"].to(out_mask)\n\n out_mask = out_mask[:, None]\n tgt_mask = tgt_mask[:, None]\n # all masks share the same set of points for efficient matching!\n point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device)\n # get gt labels\n tgt_mask = point_sample(\n tgt_mask,\n point_coords.repeat(tgt_mask.shape[0], 1, 1),\n align_corners=False,\n ).squeeze(1)\n\n out_mask = point_sample(\n out_mask,\n point_coords.repeat(out_mask.shape[0], 1, 1),\n align_corners=False,\n ).squeeze(1)\n\n with autocast(enabled=False):\n out_mask = out_mask.float()\n tgt_mask = tgt_mask.float()\n # Compute the focal loss between masks\n cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)\n # Compute the dice loss betwen masks\n cost_dice = batch_dice_loss(out_mask, tgt_mask)\n \n # Final cost matrix\n C = (\n self.cost_mask * cost_mask\n + self.cost_class * cost_class\n + self.cost_dice * cost_dice\n )\n C = C.reshape(num_queries, -1).cpu()\n\n indices.append(linear_sum_assignment_with_nan(C))\n\n return [\n (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))\n for i, j in indices\n ]\n\n @torch.no_grad()\n def forward(self, outputs, targets):\n \"\"\"Performs the matching\n\n Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_masks\": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n objects in the target) containing the class labels\n \"masks\": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n \"\"\"\n\n return self.memory_efficient_forward(outputs, targets)\n\n def __repr__(self, _repr_indent=4):\n head = \"Matcher \" + self.__class__.__name__\n body = [\n \"cost_class: {}\".format(self.cost_class),\n \"cost_mask: {}\".format(self.cost_mask),\n \"cost_dice: {}\".format(self.cost_dice),\n ]\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.__init__","uri":"program://OneFormer/function/oneformer.modeling.matcher.__init__#L101-L117","kind":"function","name":"__init__","path":"oneformer/modeling/matcher.py","language":"python","start_line":101,"end_line":117,"context_start_line":81,"context_end_line":137,"code":" loss = torch.einsum(\"nc,mc->nm\", pos, targets) + torch.einsum(\n \"nc,mc->nm\", neg, (1 - targets)\n )\n\n return loss / hw\n\n\nbatch_sigmoid_ce_loss_jit = torch.jit.script(\n batch_sigmoid_ce_loss\n) # type: torch.jit.ScriptModule\n\n\nclass HungarianMatcher(nn.Module):\n \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n while the others are un-matched (and thus treated as non-objects).\n \"\"\"\n\n def __init__(self, cost_class: float = 1, cost_mask: float = 1, \n cost_dice: float = 1, num_points: int = 0):\n \"\"\"Creates the matcher\n\n Params:\n cost_class: This is the relative weight of the classification error in the matching cost\n cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost\n cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost\n \"\"\"\n super().__init__()\n self.cost_class = cost_class\n self.cost_mask = cost_mask\n self.cost_dice = cost_dice\n\n assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, \"all costs cant be 0\"\n\n self.num_points = num_points\n\n @torch.no_grad()\n def memory_efficient_forward(self, outputs, targets):\n \"\"\"More memory-friendly matching\"\"\"\n bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n indices = []\n\n # Iterate through batch size\n for b in range(bs):\n out_prob = outputs[\"pred_logits\"][b].softmax(-1) # [num_queries, num_classes]\n tgt_ids = targets[b][\"labels\"]\n\n # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n # but approximate it in 1 - proba[target class].\n # The 1 is a constant that doesn't change the matching, it can be ommitted.\n cost_class = -out_prob[:, tgt_ids]\n\n out_mask = outputs[\"pred_masks\"][b] # [num_queries, H_pred, W_pred]\n # gt masks are already padded when preparing target","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.memory_efficient_forward","uri":"program://OneFormer/function/oneformer.modeling.matcher.memory_efficient_forward#L120-L178","kind":"function","name":"memory_efficient_forward","path":"oneformer/modeling/matcher.py","language":"python","start_line":120,"end_line":178,"context_start_line":100,"context_end_line":198,"code":"\n def __init__(self, cost_class: float = 1, cost_mask: float = 1, \n cost_dice: float = 1, num_points: int = 0):\n \"\"\"Creates the matcher\n\n Params:\n cost_class: This is the relative weight of the classification error in the matching cost\n cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost\n cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost\n \"\"\"\n super().__init__()\n self.cost_class = cost_class\n self.cost_mask = cost_mask\n self.cost_dice = cost_dice\n\n assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, \"all costs cant be 0\"\n\n self.num_points = num_points\n\n @torch.no_grad()\n def memory_efficient_forward(self, outputs, targets):\n \"\"\"More memory-friendly matching\"\"\"\n bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n indices = []\n\n # Iterate through batch size\n for b in range(bs):\n out_prob = outputs[\"pred_logits\"][b].softmax(-1) # [num_queries, num_classes]\n tgt_ids = targets[b][\"labels\"]\n\n # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n # but approximate it in 1 - proba[target class].\n # The 1 is a constant that doesn't change the matching, it can be ommitted.\n cost_class = -out_prob[:, tgt_ids]\n\n out_mask = outputs[\"pred_masks\"][b] # [num_queries, H_pred, W_pred]\n # gt masks are already padded when preparing target\n tgt_mask = targets[b][\"masks\"].to(out_mask)\n\n out_mask = out_mask[:, None]\n tgt_mask = tgt_mask[:, None]\n # all masks share the same set of points for efficient matching!\n point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device)\n # get gt labels\n tgt_mask = point_sample(\n tgt_mask,\n point_coords.repeat(tgt_mask.shape[0], 1, 1),\n align_corners=False,\n ).squeeze(1)\n\n out_mask = point_sample(\n out_mask,\n point_coords.repeat(out_mask.shape[0], 1, 1),\n align_corners=False,\n ).squeeze(1)\n\n with autocast(enabled=False):\n out_mask = out_mask.float()\n tgt_mask = tgt_mask.float()\n # Compute the focal loss between masks\n cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)\n # Compute the dice loss betwen masks\n cost_dice = batch_dice_loss(out_mask, tgt_mask)\n \n # Final cost matrix\n C = (\n self.cost_mask * cost_mask\n + self.cost_class * cost_class\n + self.cost_dice * cost_dice\n )\n C = C.reshape(num_queries, -1).cpu()\n\n indices.append(linear_sum_assignment_with_nan(C))\n\n return [\n (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))\n for i, j in indices\n ]\n\n @torch.no_grad()\n def forward(self, outputs, targets):\n \"\"\"Performs the matching\n\n Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_masks\": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n objects in the target) containing the class labels\n \"masks\": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.forward","uri":"program://OneFormer/function/oneformer.modeling.matcher.forward#L181-L202","kind":"function","name":"forward","path":"oneformer/modeling/matcher.py","language":"python","start_line":181,"end_line":202,"context_start_line":161,"context_end_line":212,"code":" cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)\n # Compute the dice loss betwen masks\n cost_dice = batch_dice_loss(out_mask, tgt_mask)\n \n # Final cost matrix\n C = (\n self.cost_mask * cost_mask\n + self.cost_class * cost_class\n + self.cost_dice * cost_dice\n )\n C = C.reshape(num_queries, -1).cpu()\n\n indices.append(linear_sum_assignment_with_nan(C))\n\n return [\n (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))\n for i, j in indices\n ]\n\n @torch.no_grad()\n def forward(self, outputs, targets):\n \"\"\"Performs the matching\n\n Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_masks\": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n objects in the target) containing the class labels\n \"masks\": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n \"\"\"\n\n return self.memory_efficient_forward(outputs, targets)\n\n def __repr__(self, _repr_indent=4):\n head = \"Matcher \" + self.__class__.__name__\n body = [\n \"cost_class: {}\".format(self.cost_class),\n \"cost_mask: {}\".format(self.cost_mask),\n \"cost_dice: {}\".format(self.cost_dice),\n ]\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.matcher.__repr__","uri":"program://OneFormer/function/oneformer.modeling.matcher.__repr__#L204-L212","kind":"function","name":"__repr__","path":"oneformer/modeling/matcher.py","language":"python","start_line":204,"end_line":212,"context_start_line":184,"context_end_line":212,"code":" Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_masks\": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n objects in the target) containing the class labels\n \"masks\": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n \"\"\"\n\n return self.memory_efficient_forward(outputs, targets)\n\n def __repr__(self, _repr_indent=4):\n head = \"Matcher \" + self.__class__.__name__\n body = [\n \"cost_class: {}\".format(self.cost_class),\n \"cost_mask: {}\".format(self.cost_mask),\n \"cost_dice: {}\".format(self.cost_dice),\n ]\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn","uri":"program://OneFormer/module/oneformer.modeling.pixel_decoder.fpn#L1-L314","kind":"module","name":"oneformer.modeling.pixel_decoder.fpn","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":1,"end_line":314,"context_start_line":1,"context_end_line":314,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/fpn.py\n# ------------------------------------------------------------------------------\nimport logging\nimport numpy as np\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\nfrom torch.cuda.amp import autocast\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\n\nfrom ..transformer_decoder.position_encoding import PositionEmbeddingSine\nfrom ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn\n\n\ndef build_pixel_decoder(cfg, input_shape):\n \"\"\"\n Build a pixel decoder from `cfg.MODEL.ONE_FORMER.PIXEL_DECODER_NAME`.\n \"\"\"\n name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME\n model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)\n forward_features = getattr(model, \"forward_features\", None)\n if not callable(forward_features):\n raise ValueError(\n \"Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. \"\n f\"Please implement forward_features for {name} to only return mask features.\"\n )\n return model\n\n\n# This is a modified FPN decoder.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass BasePixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__()\n\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n feature_channels = [v.channels for k, v in input_shape]\n\n lateral_convs = []\n output_convs = []\n\n use_bias = norm == \"\"\n for idx, in_channels in enumerate(feature_channels):\n if idx == len(self.in_features) - 1:\n output_norm = get_norm(norm, conv_dim)\n output_conv = Conv2d(\n in_channels,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(None)\n output_convs.append(output_conv)\n else:\n lateral_norm = get_norm(norm, conv_dim)\n output_norm = get_norm(norm, conv_dim)\n\n lateral_conv = Conv2d(\n in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm\n )\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(lateral_conv)\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"adapter_{}\".format(idx + 1), lateral_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(lateral_conv)\n output_convs.append(output_conv)\n # Place convs into top-down order (from low to high resolution)\n # to make the top-down computation in forward clearer.\n self.lateral_convs = lateral_convs[::-1]\n self.output_convs = output_convs[::-1]\n\n self.mask_dim = mask_dim\n self.mask_features = Conv2d(\n conv_dim,\n mask_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n )\n weight_init.c2_xavier_fill(self.mask_features)\n\n self.oneformer_num_feature_levels = 3 # always use 3 scales\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n y = output_conv(x)\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), None, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)\n\n\nclass TransformerEncoderOnly(nn.Module):\n def __init__(\n self,\n d_model=512,\n nhead=8,\n num_encoder_layers=6,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n if mask is not None:\n mask = mask.flatten(1)\n\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n return memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\n# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass TransformerEncoderPixelDecoder(BasePixelDecoder):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n transformer_pre_norm: bool,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n transformer_dropout: dropout probability in transformer\n transformer_nheads: number of heads in transformer\n transformer_dim_feedforward: dimension of feedforward network\n transformer_enc_layers: number of transformer encoder layers\n transformer_pre_norm: whether to use pre-layernorm or not\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm)\n\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n in_channels = feature_channels[len(self.in_features) - 1]\n self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.input_proj)\n self.transformer = TransformerEncoderOnly(\n d_model=conv_dim,\n dropout=transformer_dropout,\n nhead=transformer_nheads,\n dim_feedforward=transformer_dim_feedforward,\n num_encoder_layers=transformer_enc_layers,\n normalize_before=transformer_pre_norm,\n )\n N_steps = conv_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n # update layer\n use_bias = norm == \"\"\n output_norm = get_norm(norm, conv_dim)\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n delattr(self, \"layer_{}\".format(len(self.in_features)))\n self.add_module(\"layer_{}\".format(len(self.in_features)), output_conv)\n self.output_convs[0] = output_conv\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = super().from_config(cfg, input_shape)\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n transformer = self.input_proj(x)\n pos = self.pe_layer(x)\n transformer = self.transformer(transformer, None, pos)\n y = output_conv(transformer)\n # save intermediate feature as input to Transformer decoder\n transformer_encoder_features = transformer\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), transformer_encoder_features, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.build_pixel_decoder","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.fpn.build_pixel_decoder#L23-L35","kind":"function","name":"build_pixel_decoder","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":23,"end_line":35,"context_start_line":3,"context_end_line":55,"code":"# ------------------------------------------------------------------------------\nimport logging\nimport numpy as np\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\nfrom torch.cuda.amp import autocast\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\n\nfrom ..transformer_decoder.position_encoding import PositionEmbeddingSine\nfrom ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn\n\n\ndef build_pixel_decoder(cfg, input_shape):\n \"\"\"\n Build a pixel decoder from `cfg.MODEL.ONE_FORMER.PIXEL_DECODER_NAME`.\n \"\"\"\n name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME\n model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)\n forward_features = getattr(model, \"forward_features\", None)\n if not callable(forward_features):\n raise ValueError(\n \"Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. \"\n f\"Please implement forward_features for {name} to only return mask features.\"\n )\n return model\n\n\n# This is a modified FPN decoder.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass BasePixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.BasePixelDecoder","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.fpn.BasePixelDecoder#L40-L161","kind":"class","name":"BasePixelDecoder","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":40,"end_line":161,"context_start_line":20,"context_end_line":181,"code":"from ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn\n\n\ndef build_pixel_decoder(cfg, input_shape):\n \"\"\"\n Build a pixel decoder from `cfg.MODEL.ONE_FORMER.PIXEL_DECODER_NAME`.\n \"\"\"\n name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME\n model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)\n forward_features = getattr(model, \"forward_features\", None)\n if not callable(forward_features):\n raise ValueError(\n \"Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. \"\n f\"Please implement forward_features for {name} to only return mask features.\"\n )\n return model\n\n\n# This is a modified FPN decoder.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass BasePixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__()\n\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n feature_channels = [v.channels for k, v in input_shape]\n\n lateral_convs = []\n output_convs = []\n\n use_bias = norm == \"\"\n for idx, in_channels in enumerate(feature_channels):\n if idx == len(self.in_features) - 1:\n output_norm = get_norm(norm, conv_dim)\n output_conv = Conv2d(\n in_channels,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(None)\n output_convs.append(output_conv)\n else:\n lateral_norm = get_norm(norm, conv_dim)\n output_norm = get_norm(norm, conv_dim)\n\n lateral_conv = Conv2d(\n in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm\n )\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(lateral_conv)\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"adapter_{}\".format(idx + 1), lateral_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(lateral_conv)\n output_convs.append(output_conv)\n # Place convs into top-down order (from low to high resolution)\n # to make the top-down computation in forward clearer.\n self.lateral_convs = lateral_convs[::-1]\n self.output_convs = output_convs[::-1]\n\n self.mask_dim = mask_dim\n self.mask_features = Conv2d(\n conv_dim,\n mask_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n )\n weight_init.c2_xavier_fill(self.mask_features)\n\n self.oneformer_num_feature_levels = 3 # always use 3 scales\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n y = output_conv(x)\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), None, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)\n\n\nclass TransformerEncoderOnly(nn.Module):\n def __init__(\n self,\n d_model=512,\n nhead=8,\n num_encoder_layers=6,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.TransformerEncoderOnly","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.fpn.TransformerEncoderOnly#L164-L202","kind":"class","name":"TransformerEncoderOnly","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":164,"end_line":202,"context_start_line":144,"context_end_line":222,"code":" lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n y = output_conv(x)\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), None, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)\n\n\nclass TransformerEncoderOnly(nn.Module):\n def __init__(\n self,\n d_model=512,\n nhead=8,\n num_encoder_layers=6,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n if mask is not None:\n mask = mask.flatten(1)\n\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n return memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\n# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass TransformerEncoderPixelDecoder(BasePixelDecoder):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n transformer_pre_norm: bool,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.TransformerEncoderPixelDecoder","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.fpn.TransformerEncoderPixelDecoder#L207-L314","kind":"class","name":"TransformerEncoderPixelDecoder","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":207,"end_line":314,"context_start_line":187,"context_end_line":314,"code":"\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n if mask is not None:\n mask = mask.flatten(1)\n\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n return memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\n# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass TransformerEncoderPixelDecoder(BasePixelDecoder):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n transformer_pre_norm: bool,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n transformer_dropout: dropout probability in transformer\n transformer_nheads: number of heads in transformer\n transformer_dim_feedforward: dimension of feedforward network\n transformer_enc_layers: number of transformer encoder layers\n transformer_pre_norm: whether to use pre-layernorm or not\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm)\n\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n in_channels = feature_channels[len(self.in_features) - 1]\n self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.input_proj)\n self.transformer = TransformerEncoderOnly(\n d_model=conv_dim,\n dropout=transformer_dropout,\n nhead=transformer_nheads,\n dim_feedforward=transformer_dim_feedforward,\n num_encoder_layers=transformer_enc_layers,\n normalize_before=transformer_pre_norm,\n )\n N_steps = conv_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n # update layer\n use_bias = norm == \"\"\n output_norm = get_norm(norm, conv_dim)\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n delattr(self, \"layer_{}\".format(len(self.in_features)))\n self.add_module(\"layer_{}\".format(len(self.in_features)), output_conv)\n self.output_convs[0] = output_conv\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = super().from_config(cfg, input_shape)\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n transformer = self.input_proj(x)\n pos = self.pe_layer(x)\n transformer = self.transformer(transformer, None, pos)\n y = output_conv(transformer)\n # save intermediate feature as input to Transformer decoder\n transformer_encoder_features = transformer\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), transformer_encoder_features, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.__init__","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.fpn.__init__#L209-L272","kind":"function","name":"__init__","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":209,"end_line":272,"context_start_line":189,"context_end_line":292,"code":" for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n if mask is not None:\n mask = mask.flatten(1)\n\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n return memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\n# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass TransformerEncoderPixelDecoder(BasePixelDecoder):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n transformer_pre_norm: bool,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n transformer_dropout: dropout probability in transformer\n transformer_nheads: number of heads in transformer\n transformer_dim_feedforward: dimension of feedforward network\n transformer_enc_layers: number of transformer encoder layers\n transformer_pre_norm: whether to use pre-layernorm or not\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm)\n\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n in_channels = feature_channels[len(self.in_features) - 1]\n self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.input_proj)\n self.transformer = TransformerEncoderOnly(\n d_model=conv_dim,\n dropout=transformer_dropout,\n nhead=transformer_nheads,\n dim_feedforward=transformer_dim_feedforward,\n num_encoder_layers=transformer_enc_layers,\n normalize_before=transformer_pre_norm,\n )\n N_steps = conv_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n # update layer\n use_bias = norm == \"\"\n output_norm = get_norm(norm, conv_dim)\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n delattr(self, \"layer_{}\".format(len(self.in_features)))\n self.add_module(\"layer_{}\".format(len(self.in_features)), output_conv)\n self.output_convs[0] = output_conv\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = super().from_config(cfg, input_shape)\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.from_config","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.fpn.from_config#L275-L284","kind":"function","name":"from_config","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":275,"end_line":284,"context_start_line":255,"context_end_line":304,"code":"\n # update layer\n use_bias = norm == \"\"\n output_norm = get_norm(norm, conv_dim)\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n delattr(self, \"layer_{}\".format(len(self.in_features)))\n self.add_module(\"layer_{}\".format(len(self.in_features)), output_conv)\n self.output_convs[0] = output_conv\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = super().from_config(cfg, input_shape)\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n transformer = self.input_proj(x)\n pos = self.pe_layer(x)\n transformer = self.transformer(transformer, None, pos)\n y = output_conv(transformer)\n # save intermediate feature as input to Transformer decoder\n transformer_encoder_features = transformer\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.forward_features","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.fpn.forward_features#L286-L309","kind":"function","name":"forward_features","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":286,"end_line":309,"context_start_line":266,"context_end_line":314,"code":" norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(output_conv)\n delattr(self, \"layer_{}\".format(len(self.in_features)))\n self.add_module(\"layer_{}\".format(len(self.in_features)), output_conv)\n self.output_convs[0] = output_conv\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = super().from_config(cfg, input_shape)\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n return ret\n\n def forward_features(self, features):\n multi_scale_features = []\n num_cur_levels = 0\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[::-1]):\n x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n transformer = self.input_proj(x)\n pos = self.pe_layer(x)\n transformer = self.transformer(transformer, None, pos)\n y = output_conv(transformer)\n # save intermediate feature as input to Transformer decoder\n transformer_encoder_features = transformer\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), transformer_encoder_features, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn.forward","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.fpn.forward#L311-L314","kind":"function","name":"forward","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":311,"end_line":314,"context_start_line":291,"context_end_line":314,"code":" x = features[f]\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n if lateral_conv is None:\n transformer = self.input_proj(x)\n pos = self.pe_layer(x)\n transformer = self.transformer(transformer, None, pos)\n y = output_conv(transformer)\n # save intermediate feature as input to Transformer decoder\n transformer_encoder_features = transformer\n else:\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode=\"nearest\")\n y = output_conv(y)\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(y)\n num_cur_levels += 1\n return self.mask_features(y), transformer_encoder_features, multi_scale_features\n\n def forward(self, features, targets=None):\n logger = logging.getLogger(__name__)\n logger.warning(\"Calling forward() may cause unpredicted behavior of PixelDecoder module.\")\n return self.forward_features(features)","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.fpn._reset_parameters","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.fpn._reset_parameters#L188-L191","kind":"function","name":"_reset_parameters","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":188,"end_line":191,"context_start_line":168,"context_end_line":211,"code":" nhead=8,\n num_encoder_layers=6,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n if mask is not None:\n mask = mask.flatten(1)\n\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n return memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\n# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.\n@SEM_SEG_HEADS_REGISTRY.register()\nclass TransformerEncoderPixelDecoder(BasePixelDecoder):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn","uri":"program://OneFormer/module/oneformer.modeling.pixel_decoder.msdeformattn#L1-L362","kind":"module","name":"oneformer.modeling.pixel_decoder.msdeformattn","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":1,"end_line":362,"context_start_line":1,"context_end_line":362,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/msdeformattn.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nimport numpy as np\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\nfrom torch.cuda.amp import autocast\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\n\nfrom ..transformer_decoder.position_encoding import PositionEmbeddingSine\nfrom ..transformer_decoder.transformer import _get_clones, _get_activation_fn\nfrom .ops.modules import MSDeformAttn\n\n\n# MSDeformAttn Transformer encoder in deformable detr\nclass MSDeformAttnTransformerEncoderOnly(nn.Module):\n def __init__(self, d_model=256, nhead=8,\n num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,\n activation=\"relu\",\n num_feature_levels=4, enc_n_points=4,\n ):\n super().__init__()\n\n self.d_model = d_model\n self.nhead = nhead\n\n encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, enc_n_points)\n self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)\n\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n normal_(self.level_embed)\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, srcs, pos_embeds):\n masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)\n spatial_shapes.append(spatial_shape)\n src = src.flatten(2).transpose(1, 2)\n mask = mask.flatten(1)\n pos_embed = pos_embed.flatten(2).transpose(1, 2)\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1)\n mask_flatten = torch.cat(mask_flatten, 1)\n lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)\n spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)\n level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))\n valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)\n\n # encoder\n memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)\n\n return memory, spatial_shapes, level_start_index, valid_ratios\n\n\nclass MSDeformAttnTransformerEncoderLayer(nn.Module):\n def __init__(self,\n d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass MSDeformAttnTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass MSDeformAttnPixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n # deformable transformer encoder args\n transformer_in_features: List[str],\n common_stride: int,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n transformer_dropout: dropout probability in transformer\n transformer_nheads: number of heads in transformer\n transformer_dim_feedforward: dimension of feedforward network\n transformer_enc_layers: number of transformer encoder layers\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__()\n transformer_input_shape = {\n k: v for k, v in input_shape.items() if k in transformer_in_features\n }\n\n # this is the input shape of pixel decoder\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n self.feature_strides = [v.stride for k, v in input_shape]\n self.feature_channels = [v.channels for k, v in input_shape]\n \n # this is the input shape of transformer encoder (could use less features than pixel decoder\n transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)\n self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from \"res2\" to \"res5\"\n transformer_in_channels = [v.channels for k, v in transformer_input_shape]\n self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers\n\n self.transformer_num_feature_levels = len(self.transformer_in_features)\n if self.transformer_num_feature_levels > 1:\n input_proj_list = []\n # from low resolution to high resolution (res5 -> res2)\n for in_channels in transformer_in_channels[::-1]:\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, conv_dim, kernel_size=1),\n nn.GroupNorm(32, conv_dim),\n ))\n self.input_proj = nn.ModuleList(input_proj_list)\n else:\n self.input_proj = nn.ModuleList([\n nn.Sequential(\n nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),\n nn.GroupNorm(32, conv_dim),\n )])\n\n for proj in self.input_proj:\n nn.init.xavier_uniform_(proj[0].weight, gain=1)\n nn.init.constant_(proj[0].bias, 0)\n\n self.transformer = MSDeformAttnTransformerEncoderOnly(\n d_model=conv_dim,\n dropout=transformer_dropout,\n nhead=transformer_nheads,\n dim_feedforward=transformer_dim_feedforward,\n num_encoder_layers=transformer_enc_layers,\n num_feature_levels=self.transformer_num_feature_levels,\n )\n N_steps = conv_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n self.mask_dim = mask_dim\n # use 1x1 conv instead\n self.mask_features = Conv2d(\n conv_dim,\n mask_dim,\n kernel_size=1,\n stride=1,\n padding=0,\n )\n weight_init.c2_xavier_fill(self.mask_features)\n \n self.oneformer_num_feature_levels = 3 # always use 3 scales\n self.common_stride = common_stride\n\n # extra fpn levels\n stride = min(self.transformer_feature_strides)\n self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))\n\n lateral_convs = []\n output_convs = []\n\n use_bias = norm == \"\"\n for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):\n lateral_norm = get_norm(norm, conv_dim)\n output_norm = get_norm(norm, conv_dim)\n\n lateral_conv = Conv2d(\n in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm\n )\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(lateral_conv)\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"adapter_{}\".format(idx + 1), lateral_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(lateral_conv)\n output_convs.append(output_conv)\n # Place convs into top-down order (from low to high resolution)\n # to make the top-down computation in forward clearer.\n self.lateral_convs = lateral_convs[::-1]\n self.output_convs = output_convs[::-1]\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n # ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\"transformer_dim_feedforward\"] = 1024 # use 1024 for deformable transformer encoder\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_in_features\"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES\n ret[\"common_stride\"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE\n return ret\n\n @autocast(enabled=False)\n def forward_features(self, features):\n srcs = []\n pos = []\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.transformer_in_features[::-1]):\n x = features[f].float() # deformable detr does not support half precision\n srcs.append(self.input_proj[idx](x))\n pos.append(self.pe_layer(x))\n\n y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos)\n bs = y.shape[0]\n\n split_size_or_sections = [None] * self.transformer_num_feature_levels\n for i in range(self.transformer_num_feature_levels):\n if i < self.transformer_num_feature_levels - 1:\n split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]\n else:\n split_size_or_sections[i] = y.shape[1] - level_start_index[i]\n y = torch.split(y, split_size_or_sections, dim=1)\n\n out = []\n multi_scale_features = []\n num_cur_levels = 0\n for i, z in enumerate(y):\n out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))\n\n # append `out` with extra FPN levels\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):\n x = features[f].float()\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode=\"bilinear\", align_corners=False)\n y = output_conv(y)\n out.append(y)\n\n for o in out:\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(o)\n num_cur_levels += 1\n\n return self.mask_features(out[-1]), out[0], multi_scale_features, spatial_shapes, level_start_index","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnTransformerEncoderOnly","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnTransformerEncoderOnly#L27-L93","kind":"class","name":"MSDeformAttnTransformerEncoderOnly","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":27,"end_line":93,"context_start_line":7,"context_end_line":113,"code":"import numpy as np\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\nfrom torch.cuda.amp import autocast\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\n\nfrom ..transformer_decoder.position_encoding import PositionEmbeddingSine\nfrom ..transformer_decoder.transformer import _get_clones, _get_activation_fn\nfrom .ops.modules import MSDeformAttn\n\n\n# MSDeformAttn Transformer encoder in deformable detr\nclass MSDeformAttnTransformerEncoderOnly(nn.Module):\n def __init__(self, d_model=256, nhead=8,\n num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,\n activation=\"relu\",\n num_feature_levels=4, enc_n_points=4,\n ):\n super().__init__()\n\n self.d_model = d_model\n self.nhead = nhead\n\n encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, enc_n_points)\n self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)\n\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n normal_(self.level_embed)\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, srcs, pos_embeds):\n masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)\n spatial_shapes.append(spatial_shape)\n src = src.flatten(2).transpose(1, 2)\n mask = mask.flatten(1)\n pos_embed = pos_embed.flatten(2).transpose(1, 2)\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1)\n mask_flatten = torch.cat(mask_flatten, 1)\n lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)\n spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)\n level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))\n valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)\n\n # encoder\n memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)\n\n return memory, spatial_shapes, level_start_index, valid_ratios\n\n\nclass MSDeformAttnTransformerEncoderLayer(nn.Module):\n def __init__(self,\n d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnTransformerEncoderLayer","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnTransformerEncoderLayer#L96-L135","kind":"class","name":"MSDeformAttnTransformerEncoderLayer","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":96,"end_line":135,"context_start_line":76,"context_end_line":155,"code":" src = src.flatten(2).transpose(1, 2)\n mask = mask.flatten(1)\n pos_embed = pos_embed.flatten(2).transpose(1, 2)\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1)\n mask_flatten = torch.cat(mask_flatten, 1)\n lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)\n spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)\n level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))\n valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)\n\n # encoder\n memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)\n\n return memory, spatial_shapes, level_start_index, valid_ratios\n\n\nclass MSDeformAttnTransformerEncoderLayer(nn.Module):\n def __init__(self,\n d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass MSDeformAttnTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnTransformerEncoder","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnTransformerEncoder#L138-L165","kind":"class","name":"MSDeformAttnTransformerEncoder","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":138,"end_line":165,"context_start_line":118,"context_end_line":185,"code":" return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass MSDeformAttnTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass MSDeformAttnPixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n # deformable transformer encoder args\n transformer_in_features: List[str],\n common_stride: int,\n ):","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnPixelDecoder","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.msdeformattn.MSDeformAttnPixelDecoder#L169-L362","kind":"class","name":"MSDeformAttnPixelDecoder","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":169,"end_line":362,"context_start_line":149,"context_end_line":362,"code":" ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass MSDeformAttnPixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n # deformable transformer encoder args\n transformer_in_features: List[str],\n common_stride: int,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n transformer_dropout: dropout probability in transformer\n transformer_nheads: number of heads in transformer\n transformer_dim_feedforward: dimension of feedforward network\n transformer_enc_layers: number of transformer encoder layers\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__()\n transformer_input_shape = {\n k: v for k, v in input_shape.items() if k in transformer_in_features\n }\n\n # this is the input shape of pixel decoder\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n self.feature_strides = [v.stride for k, v in input_shape]\n self.feature_channels = [v.channels for k, v in input_shape]\n \n # this is the input shape of transformer encoder (could use less features than pixel decoder\n transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)\n self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from \"res2\" to \"res5\"\n transformer_in_channels = [v.channels for k, v in transformer_input_shape]\n self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers\n\n self.transformer_num_feature_levels = len(self.transformer_in_features)\n if self.transformer_num_feature_levels > 1:\n input_proj_list = []\n # from low resolution to high resolution (res5 -> res2)\n for in_channels in transformer_in_channels[::-1]:\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, conv_dim, kernel_size=1),\n nn.GroupNorm(32, conv_dim),\n ))\n self.input_proj = nn.ModuleList(input_proj_list)\n else:\n self.input_proj = nn.ModuleList([\n nn.Sequential(\n nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),\n nn.GroupNorm(32, conv_dim),\n )])\n\n for proj in self.input_proj:\n nn.init.xavier_uniform_(proj[0].weight, gain=1)\n nn.init.constant_(proj[0].bias, 0)\n\n self.transformer = MSDeformAttnTransformerEncoderOnly(\n d_model=conv_dim,\n dropout=transformer_dropout,\n nhead=transformer_nheads,\n dim_feedforward=transformer_dim_feedforward,\n num_encoder_layers=transformer_enc_layers,\n num_feature_levels=self.transformer_num_feature_levels,\n )\n N_steps = conv_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n self.mask_dim = mask_dim\n # use 1x1 conv instead\n self.mask_features = Conv2d(\n conv_dim,\n mask_dim,\n kernel_size=1,\n stride=1,\n padding=0,\n )\n weight_init.c2_xavier_fill(self.mask_features)\n \n self.oneformer_num_feature_levels = 3 # always use 3 scales\n self.common_stride = common_stride\n\n # extra fpn levels\n stride = min(self.transformer_feature_strides)\n self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))\n\n lateral_convs = []\n output_convs = []\n\n use_bias = norm == \"\"\n for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):\n lateral_norm = get_norm(norm, conv_dim)\n output_norm = get_norm(norm, conv_dim)\n\n lateral_conv = Conv2d(\n in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm\n )\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(lateral_conv)\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"adapter_{}\".format(idx + 1), lateral_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(lateral_conv)\n output_convs.append(output_conv)\n # Place convs into top-down order (from low to high resolution)\n # to make the top-down computation in forward clearer.\n self.lateral_convs = lateral_convs[::-1]\n self.output_convs = output_convs[::-1]\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n # ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\"transformer_dim_feedforward\"] = 1024 # use 1024 for deformable transformer encoder\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_in_features\"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES\n ret[\"common_stride\"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE\n return ret\n\n @autocast(enabled=False)\n def forward_features(self, features):\n srcs = []\n pos = []\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.transformer_in_features[::-1]):\n x = features[f].float() # deformable detr does not support half precision\n srcs.append(self.input_proj[idx](x))\n pos.append(self.pe_layer(x))\n\n y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos)\n bs = y.shape[0]\n\n split_size_or_sections = [None] * self.transformer_num_feature_levels\n for i in range(self.transformer_num_feature_levels):\n if i < self.transformer_num_feature_levels - 1:\n split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]\n else:\n split_size_or_sections[i] = y.shape[1] - level_start_index[i]\n y = torch.split(y, split_size_or_sections, dim=1)\n\n out = []\n multi_scale_features = []\n num_cur_levels = 0\n for i, z in enumerate(y):\n out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))\n\n # append `out` with extra FPN levels\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):\n x = features[f].float()\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode=\"bilinear\", align_corners=False)\n y = output_conv(y)\n out.append(y)\n\n for o in out:\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(o)\n num_cur_levels += 1\n\n return self.mask_features(out[-1]), out[0], multi_scale_features, spatial_shapes, level_start_index","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.__init__","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.__init__#L171-L296","kind":"function","name":"__init__","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":171,"end_line":296,"context_start_line":151,"context_end_line":316,"code":" ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass MSDeformAttnPixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n # deformable transformer encoder args\n transformer_in_features: List[str],\n common_stride: int,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n transformer_dropout: dropout probability in transformer\n transformer_nheads: number of heads in transformer\n transformer_dim_feedforward: dimension of feedforward network\n transformer_enc_layers: number of transformer encoder layers\n conv_dims: number of output channels for the intermediate conv layers.\n mask_dim: number of output channels for the final conv layer.\n norm (str or callable): normalization for all conv layers\n \"\"\"\n super().__init__()\n transformer_input_shape = {\n k: v for k, v in input_shape.items() if k in transformer_in_features\n }\n\n # this is the input shape of pixel decoder\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape] # starting from \"res2\" to \"res5\"\n self.feature_strides = [v.stride for k, v in input_shape]\n self.feature_channels = [v.channels for k, v in input_shape]\n \n # this is the input shape of transformer encoder (could use less features than pixel decoder\n transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)\n self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from \"res2\" to \"res5\"\n transformer_in_channels = [v.channels for k, v in transformer_input_shape]\n self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers\n\n self.transformer_num_feature_levels = len(self.transformer_in_features)\n if self.transformer_num_feature_levels > 1:\n input_proj_list = []\n # from low resolution to high resolution (res5 -> res2)\n for in_channels in transformer_in_channels[::-1]:\n input_proj_list.append(nn.Sequential(\n nn.Conv2d(in_channels, conv_dim, kernel_size=1),\n nn.GroupNorm(32, conv_dim),\n ))\n self.input_proj = nn.ModuleList(input_proj_list)\n else:\n self.input_proj = nn.ModuleList([\n nn.Sequential(\n nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),\n nn.GroupNorm(32, conv_dim),\n )])\n\n for proj in self.input_proj:\n nn.init.xavier_uniform_(proj[0].weight, gain=1)\n nn.init.constant_(proj[0].bias, 0)\n\n self.transformer = MSDeformAttnTransformerEncoderOnly(\n d_model=conv_dim,\n dropout=transformer_dropout,\n nhead=transformer_nheads,\n dim_feedforward=transformer_dim_feedforward,\n num_encoder_layers=transformer_enc_layers,\n num_feature_levels=self.transformer_num_feature_levels,\n )\n N_steps = conv_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n self.mask_dim = mask_dim\n # use 1x1 conv instead\n self.mask_features = Conv2d(\n conv_dim,\n mask_dim,\n kernel_size=1,\n stride=1,\n padding=0,\n )\n weight_init.c2_xavier_fill(self.mask_features)\n \n self.oneformer_num_feature_levels = 3 # always use 3 scales\n self.common_stride = common_stride\n\n # extra fpn levels\n stride = min(self.transformer_feature_strides)\n self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))\n\n lateral_convs = []\n output_convs = []\n\n use_bias = norm == \"\"\n for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):\n lateral_norm = get_norm(norm, conv_dim)\n output_norm = get_norm(norm, conv_dim)\n\n lateral_conv = Conv2d(\n in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm\n )\n output_conv = Conv2d(\n conv_dim,\n conv_dim,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(lateral_conv)\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"adapter_{}\".format(idx + 1), lateral_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(lateral_conv)\n output_convs.append(output_conv)\n # Place convs into top-down order (from low to high resolution)\n # to make the top-down computation in forward clearer.\n self.lateral_convs = lateral_convs[::-1]\n self.output_convs = output_convs[::-1]\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n # ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\"transformer_dim_feedforward\"] = 1024 # use 1024 for deformable transformer encoder\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_in_features\"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES\n ret[\"common_stride\"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE\n return ret","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn._reset_parameters","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn._reset_parameters#L47-L54","kind":"function","name":"_reset_parameters","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":47,"end_line":54,"context_start_line":27,"context_end_line":74,"code":"class MSDeformAttnTransformerEncoderOnly(nn.Module):\n def __init__(self, d_model=256, nhead=8,\n num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,\n activation=\"relu\",\n num_feature_levels=4, enc_n_points=4,\n ):\n super().__init__()\n\n self.d_model = d_model\n self.nhead = nhead\n\n encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, enc_n_points)\n self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)\n\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n normal_(self.level_embed)\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, srcs, pos_embeds):\n masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.get_valid_ratio","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.get_valid_ratio#L56-L63","kind":"function","name":"get_valid_ratio","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":56,"end_line":63,"context_start_line":36,"context_end_line":83,"code":" self.nhead = nhead\n\n encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,\n dropout, activation,\n num_feature_levels, nhead, enc_n_points)\n self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)\n\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n normal_(self.level_embed)\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def forward(self, srcs, pos_embeds):\n masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)\n spatial_shapes.append(spatial_shape)\n src = src.flatten(2).transpose(1, 2)\n mask = mask.flatten(1)\n pos_embed = pos_embed.flatten(2).transpose(1, 2)\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1)","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.forward","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.forward#L159-L165","kind":"function","name":"forward","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":159,"end_line":165,"context_start_line":139,"context_end_line":185,"code":" def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass MSDeformAttnPixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,\n transformer_enc_layers: int,\n conv_dim: int,\n mask_dim: int,\n norm: Optional[Union[str, Callable]] = None,\n # deformable transformer encoder args\n transformer_in_features: List[str],\n common_stride: int,\n ):","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.with_pos_embed","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.with_pos_embed#L117-L118","kind":"function","name":"with_pos_embed","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":117,"end_line":118,"context_start_line":97,"context_end_line":138,"code":" def __init__(self,\n d_model=256, d_ffn=1024,\n dropout=0.1, activation=\"relu\",\n n_levels=4, n_heads=8, n_points=4):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass MSDeformAttnTransformerEncoder(nn.Module):","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.forward_ffn","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.forward_ffn#L120-L124","kind":"function","name":"forward_ffn","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":120,"end_line":124,"context_start_line":100,"context_end_line":144,"code":" n_levels=4, n_heads=8, n_points=4):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass MSDeformAttnTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.get_reference_points","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.get_reference_points#L145-L157","kind":"function","name":"get_reference_points","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":145,"end_line":157,"context_start_line":125,"context_end_line":177,"code":"\n def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):\n # self attention\n src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass MSDeformAttnTransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):\n output = src\n reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)\n for _, layer in enumerate(self.layers):\n output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)\n\n return output\n\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass MSDeformAttnPixelDecoder(nn.Module):\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n transformer_dropout: float,\n transformer_nheads: int,\n transformer_dim_feedforward: int,","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.from_config","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.from_config#L299-L316","kind":"function","name":"from_config","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":299,"end_line":316,"context_start_line":279,"context_end_line":336,"code":" kernel_size=3,\n stride=1,\n padding=1,\n bias=use_bias,\n norm=output_norm,\n activation=F.relu,\n )\n weight_init.c2_xavier_fill(lateral_conv)\n weight_init.c2_xavier_fill(output_conv)\n self.add_module(\"adapter_{}\".format(idx + 1), lateral_conv)\n self.add_module(\"layer_{}\".format(idx + 1), output_conv)\n\n lateral_convs.append(lateral_conv)\n output_convs.append(output_conv)\n # Place convs into top-down order (from low to high resolution)\n # to make the top-down computation in forward clearer.\n self.lateral_convs = lateral_convs[::-1]\n self.output_convs = output_convs[::-1]\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n # ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\"transformer_dim_feedforward\"] = 1024 # use 1024 for deformable transformer encoder\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_in_features\"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES\n ret[\"common_stride\"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE\n return ret\n\n @autocast(enabled=False)\n def forward_features(self, features):\n srcs = []\n pos = []\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.transformer_in_features[::-1]):\n x = features[f].float() # deformable detr does not support half precision\n srcs.append(self.input_proj[idx](x))\n pos.append(self.pe_layer(x))\n\n y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos)\n bs = y.shape[0]\n\n split_size_or_sections = [None] * self.transformer_num_feature_levels\n for i in range(self.transformer_num_feature_levels):\n if i < self.transformer_num_feature_levels - 1:\n split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]\n else:\n split_size_or_sections[i] = y.shape[1] - level_start_index[i]","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.msdeformattn.forward_features","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.msdeformattn.forward_features#L319-L362","kind":"function","name":"forward_features","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":319,"end_line":362,"context_start_line":299,"context_end_line":362,"code":" def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n ret = {}\n ret[\"input_shape\"] = {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n }\n ret[\"conv_dim\"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"norm\"] = cfg.MODEL.SEM_SEG_HEAD.NORM\n ret[\"transformer_dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"transformer_nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n # ret[\"transformer_dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n ret[\"transformer_dim_feedforward\"] = 1024 # use 1024 for deformable transformer encoder\n ret[\n \"transformer_enc_layers\"\n ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config\n ret[\"transformer_in_features\"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES\n ret[\"common_stride\"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE\n return ret\n\n @autocast(enabled=False)\n def forward_features(self, features):\n srcs = []\n pos = []\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.transformer_in_features[::-1]):\n x = features[f].float() # deformable detr does not support half precision\n srcs.append(self.input_proj[idx](x))\n pos.append(self.pe_layer(x))\n\n y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos)\n bs = y.shape[0]\n\n split_size_or_sections = [None] * self.transformer_num_feature_levels\n for i in range(self.transformer_num_feature_levels):\n if i < self.transformer_num_feature_levels - 1:\n split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]\n else:\n split_size_or_sections[i] = y.shape[1] - level_start_index[i]\n y = torch.split(y, split_size_or_sections, dim=1)\n\n out = []\n multi_scale_features = []\n num_cur_levels = 0\n for i, z in enumerate(y):\n out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))\n\n # append `out` with extra FPN levels\n # Reverse feature maps into top-down order (from low to high resolution)\n for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):\n x = features[f].float()\n lateral_conv = self.lateral_convs[idx]\n output_conv = self.output_convs[idx]\n cur_fpn = lateral_conv(x)\n # Following FPN implementation, we use nearest upsampling here\n y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode=\"bilinear\", align_corners=False)\n y = output_conv(y)\n out.append(y)\n\n for o in out:\n if num_cur_levels < self.oneformer_num_feature_levels:\n multi_scale_features.append(o)\n num_cur_levels += 1\n\n return self.mask_features(out[-1]), out[0], multi_scale_features, spatial_shapes, level_start_index","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.setup","uri":"program://OneFormer/module/oneformer.modeling.pixel_decoder.ops.setup#L1-L78","kind":"module","name":"oneformer.modeling.pixel_decoder.ops.setup","path":"oneformer/modeling/pixel_decoder/ops/setup.py","language":"python","start_line":1,"end_line":78,"context_start_line":1,"context_end_line":78,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nimport os\nimport glob\n\nimport torch\n\nfrom torch.utils.cpp_extension import CUDA_HOME\nfrom torch.utils.cpp_extension import CppExtension\nfrom torch.utils.cpp_extension import CUDAExtension\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nrequirements = [\"torch\", \"torchvision\"]\n\ndef get_extensions():\n this_dir = os.path.dirname(os.path.abspath(__file__))\n extensions_dir = os.path.join(this_dir, \"src\")\n\n main_file = glob.glob(os.path.join(extensions_dir, \"*.cpp\"))\n source_cpu = glob.glob(os.path.join(extensions_dir, \"cpu\", \"*.cpp\"))\n source_cuda = glob.glob(os.path.join(extensions_dir, \"cuda\", \"*.cu\"))\n\n sources = main_file + source_cpu\n extension = CppExtension\n extra_compile_args = {\"cxx\": []}\n define_macros = []\n\n # Force cuda since torch ask for a device, not if cuda is in fact available.\n if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:\n extension = CUDAExtension\n sources += source_cuda\n define_macros += [(\"WITH_CUDA\", None)]\n extra_compile_args[\"nvcc\"] = [\n \"-DCUDA_HAS_FP16=1\",\n \"-D__CUDA_NO_HALF_OPERATORS__\",\n \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n \"-D__CUDA_NO_HALF2_OPERATORS__\",\n ]\n else:\n if CUDA_HOME is None:\n raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')\n else:\n raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')\n\n sources = [os.path.join(extensions_dir, s) for s in sources]\n include_dirs = [extensions_dir]\n ext_modules = [\n extension(\n \"MultiScaleDeformableAttention\",\n sources,\n include_dirs=include_dirs,\n define_macros=define_macros,\n extra_compile_args=extra_compile_args,\n )\n ]\n return ext_modules\n\nsetup(\n name=\"MultiScaleDeformableAttention\",\n version=\"1.0\",\n author=\"Weijie Su\",\n url=\"https://github.com/fundamentalvision/Deformable-DETR\",\n description=\"PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention\",\n packages=find_packages(exclude=(\"configs\", \"tests\",)),\n ext_modules=get_extensions(),\n cmdclass={\"build_ext\": torch.utils.cpp_extension.BuildExtension},\n)","source_hash":"43337887620f7ddc5fd892abaa837c1a4fbdb0fb767c9cfda78a013f80997780","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.setup.get_extensions","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.setup.get_extensions#L26-L67","kind":"function","name":"get_extensions","path":"oneformer/modeling/pixel_decoder/ops/setup.py","language":"python","start_line":26,"end_line":67,"context_start_line":6,"context_end_line":78,"code":"# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nimport os\nimport glob\n\nimport torch\n\nfrom torch.utils.cpp_extension import CUDA_HOME\nfrom torch.utils.cpp_extension import CppExtension\nfrom torch.utils.cpp_extension import CUDAExtension\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nrequirements = [\"torch\", \"torchvision\"]\n\ndef get_extensions():\n this_dir = os.path.dirname(os.path.abspath(__file__))\n extensions_dir = os.path.join(this_dir, \"src\")\n\n main_file = glob.glob(os.path.join(extensions_dir, \"*.cpp\"))\n source_cpu = glob.glob(os.path.join(extensions_dir, \"cpu\", \"*.cpp\"))\n source_cuda = glob.glob(os.path.join(extensions_dir, \"cuda\", \"*.cu\"))\n\n sources = main_file + source_cpu\n extension = CppExtension\n extra_compile_args = {\"cxx\": []}\n define_macros = []\n\n # Force cuda since torch ask for a device, not if cuda is in fact available.\n if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:\n extension = CUDAExtension\n sources += source_cuda\n define_macros += [(\"WITH_CUDA\", None)]\n extra_compile_args[\"nvcc\"] = [\n \"-DCUDA_HAS_FP16=1\",\n \"-D__CUDA_NO_HALF_OPERATORS__\",\n \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n \"-D__CUDA_NO_HALF2_OPERATORS__\",\n ]\n else:\n if CUDA_HOME is None:\n raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')\n else:\n raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')\n\n sources = [os.path.join(extensions_dir, s) for s in sources]\n include_dirs = [extensions_dir]\n ext_modules = [\n extension(\n \"MultiScaleDeformableAttention\",\n sources,\n include_dirs=include_dirs,\n define_macros=define_macros,\n extra_compile_args=extra_compile_args,\n )\n ]\n return ext_modules\n\nsetup(\n name=\"MultiScaleDeformableAttention\",\n version=\"1.0\",\n author=\"Weijie Su\",\n url=\"https://github.com/fundamentalvision/Deformable-DETR\",\n description=\"PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention\",\n packages=find_packages(exclude=(\"configs\", \"tests\",)),\n ext_modules=get_extensions(),\n cmdclass={\"build_ext\": torch.utils.cpp_extension.BuildExtension},\n)","source_hash":"43337887620f7ddc5fd892abaa837c1a4fbdb0fb767c9cfda78a013f80997780","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.test","uri":"program://OneFormer/module/oneformer.modeling.pixel_decoder.ops.test#L1-L92","kind":"module","name":"oneformer.modeling.pixel_decoder.ops.test","path":"oneformer/modeling/pixel_decoder/ops/test.py","language":"python","start_line":1,"end_line":92,"context_start_line":1,"context_end_line":92,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport time\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import gradcheck\n\nfrom functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch\n\n\nN, M, D = 1, 2, 2\nLq, L, P = 2, 2, 2\nshapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()\nlevel_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))\nS = sum([(H*W).item() for H, W in shapes])\n\n\ntorch.manual_seed(3)\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_double():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_float():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\ndef check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):\n\n value = torch.rand(N, S, M, channels).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n func = MSDeformAttnFunction.apply\n\n value.requires_grad = grad_value\n sampling_locations.requires_grad = grad_sampling_loc\n attention_weights.requires_grad = grad_attn_weight\n\n gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))\n\n print(f'* {gradok} check_gradient_numerical(D={channels})')\n\n\nif __name__ == '__main__':\n check_forward_equal_with_pytorch_double()\n check_forward_equal_with_pytorch_float()\n\n for channels in [30, 32, 64, 71, 1025, 2048, 3096]:\n check_gradient_numerical(channels, True, True, True)\n\n\n","source_hash":"0eae613a37b1a67f52c2a5a56036fa7787bf398e630a1d386b54a4daee591bde","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.test.check_forward_equal_with_pytorch_double","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.test.check_forward_equal_with_pytorch_double#L35-L47","kind":"function","name":"check_forward_equal_with_pytorch_double","path":"oneformer/modeling/pixel_decoder/ops/test.py","language":"python","start_line":35,"end_line":47,"context_start_line":15,"context_end_line":67,"code":"\nimport time\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import gradcheck\n\nfrom functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch\n\n\nN, M, D = 1, 2, 2\nLq, L, P = 2, 2, 2\nshapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()\nlevel_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))\nS = sum([(H*W).item() for H, W in shapes])\n\n\ntorch.manual_seed(3)\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_double():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_float():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\ndef check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):\n","source_hash":"0eae613a37b1a67f52c2a5a56036fa7787bf398e630a1d386b54a4daee591bde","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.test.check_forward_equal_with_pytorch_float","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.test.check_forward_equal_with_pytorch_float#L51-L63","kind":"function","name":"check_forward_equal_with_pytorch_float","path":"oneformer/modeling/pixel_decoder/ops/test.py","language":"python","start_line":51,"end_line":63,"context_start_line":31,"context_end_line":83,"code":"torch.manual_seed(3)\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_double():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_float():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\ndef check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):\n\n value = torch.rand(N, S, M, channels).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n func = MSDeformAttnFunction.apply\n\n value.requires_grad = grad_value\n sampling_locations.requires_grad = grad_sampling_loc\n attention_weights.requires_grad = grad_attn_weight\n\n gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))\n\n print(f'* {gradok} check_gradient_numerical(D={channels})')\n\n","source_hash":"0eae613a37b1a67f52c2a5a56036fa7787bf398e630a1d386b54a4daee591bde","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.test.check_gradient_numerical","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.test.check_gradient_numerical#L66-L81","kind":"function","name":"check_gradient_numerical","path":"oneformer/modeling/pixel_decoder/ops/test.py","language":"python","start_line":66,"end_line":81,"context_start_line":46,"context_end_line":92,"code":"\n print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\n@torch.no_grad()\ndef check_forward_equal_with_pytorch_float():\n value = torch.rand(N, S, M, D).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()\n output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()\n fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)\n max_abs_err = (output_cuda - output_pytorch).abs().max()\n max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()\n\n print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')\n\n\ndef check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):\n\n value = torch.rand(N, S, M, channels).cuda() * 0.01\n sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()\n attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5\n attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)\n im2col_step = 2\n func = MSDeformAttnFunction.apply\n\n value.requires_grad = grad_value\n sampling_locations.requires_grad = grad_sampling_loc\n attention_weights.requires_grad = grad_attn_weight\n\n gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))\n\n print(f'* {gradok} check_gradient_numerical(D={channels})')\n\n\nif __name__ == '__main__':\n check_forward_equal_with_pytorch_double()\n check_forward_equal_with_pytorch_float()\n\n for channels in [30, 32, 64, 71, 1025, 2048, 3096]:\n check_gradient_numerical(channels, True, True, True)\n\n\n","source_hash":"0eae613a37b1a67f52c2a5a56036fa7787bf398e630a1d386b54a4daee591bde","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func","uri":"program://OneFormer/module/oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func#L1-L75","kind":"module","name":"oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func","path":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","language":"python","start_line":1,"end_line":75,"context_start_line":1,"context_end_line":75,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\n\nif torch.cuda.is_available():\n try:\n import MultiScaleDeformableAttention as MSDA\n except ModuleNotFoundError as e:\n info_string = (\n \"\\n\\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\\n\"\n \"\\t`cd mask2former/modeling/pixel_decoder/ops`\\n\"\n \"\\t`sh make.sh`\\n\"\n )\n raise ModuleNotFoundError(info_string)\nelse:\n MultiScaleDeformableAttention = None\n\n\nclass MSDeformAttnFunction(Function):\n @staticmethod\n def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):\n ctx.im2col_step = im2col_step\n output = MSDA.ms_deform_attn_forward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = \\\n MSDA.ms_deform_attn_backward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):\n # for debug and test only,\n # need to use cuda version instead\n N_, S_, M_, D_ = value.shape\n _, Lq_, M_, L_, P_, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []\n for lid_, (H_, W_) in enumerate(value_spatial_shapes):\n # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_\n value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)\n # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2\n sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)\n # N_*M_, D_, Lq_, P_\n sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,\n mode='bilinear', padding_mode='zeros', align_corners=False)\n sampling_value_list.append(sampling_value_l_)\n # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)\n attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)\n output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)\n return output.transpose(1, 2).contiguous()","source_hash":"3647fbebcb25066c7eda926fbc52099e6a68c8a219ecd6bbf59b8dddf0c504d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.MSDeformAttnFunction","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.MSDeformAttnFunction#L35-L52","kind":"class","name":"MSDeformAttnFunction","path":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","language":"python","start_line":35,"end_line":52,"context_start_line":15,"context_end_line":72,"code":"\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\n\nif torch.cuda.is_available():\n try:\n import MultiScaleDeformableAttention as MSDA\n except ModuleNotFoundError as e:\n info_string = (\n \"\\n\\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\\n\"\n \"\\t`cd mask2former/modeling/pixel_decoder/ops`\\n\"\n \"\\t`sh make.sh`\\n\"\n )\n raise ModuleNotFoundError(info_string)\nelse:\n MultiScaleDeformableAttention = None\n\n\nclass MSDeformAttnFunction(Function):\n @staticmethod\n def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):\n ctx.im2col_step = im2col_step\n output = MSDA.ms_deform_attn_forward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = \\\n MSDA.ms_deform_attn_backward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):\n # for debug and test only,\n # need to use cuda version instead\n N_, S_, M_, D_ = value.shape\n _, Lq_, M_, L_, P_, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []\n for lid_, (H_, W_) in enumerate(value_spatial_shapes):\n # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_\n value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)\n # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2\n sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)\n # N_*M_, D_, Lq_, P_\n sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,\n mode='bilinear', padding_mode='zeros', align_corners=False)\n sampling_value_list.append(sampling_value_l_)\n # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)","source_hash":"3647fbebcb25066c7eda926fbc52099e6a68c8a219ecd6bbf59b8dddf0c504d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.ms_deform_attn_core_pytorch","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.ms_deform_attn_core_pytorch#L55-L75","kind":"function","name":"ms_deform_attn_core_pytorch","path":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","language":"python","start_line":55,"end_line":75,"context_start_line":35,"context_end_line":75,"code":"class MSDeformAttnFunction(Function):\n @staticmethod\n def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):\n ctx.im2col_step = im2col_step\n output = MSDA.ms_deform_attn_forward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = \\\n MSDA.ms_deform_attn_backward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):\n # for debug and test only,\n # need to use cuda version instead\n N_, S_, M_, D_ = value.shape\n _, Lq_, M_, L_, P_, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []\n for lid_, (H_, W_) in enumerate(value_spatial_shapes):\n # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_\n value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)\n # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2\n sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)\n # N_*M_, D_, Lq_, P_\n sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,\n mode='bilinear', padding_mode='zeros', align_corners=False)\n sampling_value_list.append(sampling_value_l_)\n # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)\n attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)\n output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)\n return output.transpose(1, 2).contiguous()","source_hash":"3647fbebcb25066c7eda926fbc52099e6a68c8a219ecd6bbf59b8dddf0c504d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.forward","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.forward#L37-L42","kind":"function","name":"forward","path":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","language":"python","start_line":37,"end_line":42,"context_start_line":17,"context_end_line":62,"code":"import torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\n\nif torch.cuda.is_available():\n try:\n import MultiScaleDeformableAttention as MSDA\n except ModuleNotFoundError as e:\n info_string = (\n \"\\n\\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\\n\"\n \"\\t`cd mask2former/modeling/pixel_decoder/ops`\\n\"\n \"\\t`sh make.sh`\\n\"\n )\n raise ModuleNotFoundError(info_string)\nelse:\n MultiScaleDeformableAttention = None\n\n\nclass MSDeformAttnFunction(Function):\n @staticmethod\n def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):\n ctx.im2col_step = im2col_step\n output = MSDA.ms_deform_attn_forward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = \\\n MSDA.ms_deform_attn_backward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):\n # for debug and test only,\n # need to use cuda version instead\n N_, S_, M_, D_ = value.shape\n _, Lq_, M_, L_, P_, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []","source_hash":"3647fbebcb25066c7eda926fbc52099e6a68c8a219ecd6bbf59b8dddf0c504d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.backward","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.functions.ms_deform_attn_func.backward#L46-L52","kind":"function","name":"backward","path":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","language":"python","start_line":46,"end_line":52,"context_start_line":26,"context_end_line":72,"code":" \"\\n\\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\\n\"\n \"\\t`cd mask2former/modeling/pixel_decoder/ops`\\n\"\n \"\\t`sh make.sh`\\n\"\n )\n raise ModuleNotFoundError(info_string)\nelse:\n MultiScaleDeformableAttention = None\n\n\nclass MSDeformAttnFunction(Function):\n @staticmethod\n def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):\n ctx.im2col_step = im2col_step\n output = MSDA.ms_deform_attn_forward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = \\\n MSDA.ms_deform_attn_backward(\n value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):\n # for debug and test only,\n # need to use cuda version instead\n N_, S_, M_, D_ = value.shape\n _, Lq_, M_, L_, P_, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []\n for lid_, (H_, W_) in enumerate(value_spatial_shapes):\n # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_\n value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)\n # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2\n sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)\n # N_*M_, D_, Lq_, P_\n sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,\n mode='bilinear', padding_mode='zeros', align_corners=False)\n sampling_value_list.append(sampling_value_l_)\n # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)","source_hash":"3647fbebcb25066c7eda926fbc52099e6a68c8a219ecd6bbf59b8dddf0c504d2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn","uri":"program://OneFormer/module/oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn#L1-L126","kind":"module","name":"oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":1,"end_line":126,"context_start_line":1,"context_end_line":126,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport warnings\nimport math\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.init import xavier_uniform_, constant_\n\nif torch.cuda.is_available():\n from ..functions import MSDeformAttnFunction\nelse:\n MSDeformAttnFunction = None\nfrom ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch\n\n\ndef _is_power_of_2(n):\n if (not isinstance(n, int)) or (n < 0):\n raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n return (n & (n-1) == 0) and n != 0\n\n\nclass MSDeformAttn(nn.Module):\n def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):\n \"\"\"\n Multi-Scale Deformable Attention Module\n :param d_model hidden dimension\n :param n_levels number of feature levels\n :param n_heads number of attention heads\n :param n_points number of sampling points per attention head per feature level\n \"\"\"\n super().__init__()\n if d_model % n_heads != 0:\n raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))\n _d_per_head = d_model // n_heads\n # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation\n if not _is_power_of_2(_d_per_head):\n warnings.warn(\"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 \"\n \"which is more efficient in our CUDA implementation.\")\n\n self.im2col_step = 128\n\n self.d_model = d_model\n self.n_levels = n_levels\n self.n_heads = n_heads\n self.n_points = n_points\n\n self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)\n self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)\n self.value_proj = nn.Linear(d_model, d_model)\n self.output_proj = nn.Linear(d_model, d_model)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n constant_(self.sampling_offsets.weight.data, 0.)\n thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)\n for i in range(self.n_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.)\n constant_(self.attention_weights.bias.data, 0.)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.)\n\n def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):\n \"\"\"\n :param query (N, Length_{query}, C)\n :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area\n or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes\n :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C)\n :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]\n :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]\n :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements\n\n :return output (N, Length_{query}, C)\n \"\"\"\n N, Len_q, _ = query.shape\n N, Len_in, _ = input_flatten.shape\n assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in\n\n value = self.value_proj(input_flatten)\n if input_padding_mask is not None:\n value = value.masked_fill(input_padding_mask[..., None], float(0))\n value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)\n sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)\n attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)\n attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)\n # N, Len_q, n_heads, n_levels, n_points, 2\n if reference_points.shape[-1] == 2:\n offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)\n sampling_locations = reference_points[:, :, None, :, None, :] \\\n + sampling_offsets / offset_normalizer[None, None, None, :, None, :]\n elif reference_points.shape[-1] == 4:\n sampling_locations = reference_points[:, :, None, :, None, :2] \\\n + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5\n else:\n raise ValueError(\n 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))\n if torch.cuda.is_available():\n output = MSDeformAttnFunction.apply(\n value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)\n else:\n ## CPU\n output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)\n output = self.output_proj(output)\n return output","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn._is_power_of_2","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn._is_power_of_2#L31-L34","kind":"function","name":"_is_power_of_2","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":31,"end_line":34,"context_start_line":11,"context_end_line":54,"code":"\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport warnings\nimport math\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.init import xavier_uniform_, constant_\n\nif torch.cuda.is_available():\n from ..functions import MSDeformAttnFunction\nelse:\n MSDeformAttnFunction = None\nfrom ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch\n\n\ndef _is_power_of_2(n):\n if (not isinstance(n, int)) or (n < 0):\n raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n return (n & (n-1) == 0) and n != 0\n\n\nclass MSDeformAttn(nn.Module):\n def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):\n \"\"\"\n Multi-Scale Deformable Attention Module\n :param d_model hidden dimension\n :param n_levels number of feature levels\n :param n_heads number of attention heads\n :param n_points number of sampling points per attention head per feature level\n \"\"\"\n super().__init__()\n if d_model % n_heads != 0:\n raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))\n _d_per_head = d_model // n_heads\n # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation\n if not _is_power_of_2(_d_per_head):\n warnings.warn(\"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 \"\n \"which is more efficient in our CUDA implementation.\")\n","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn.MSDeformAttn","uri":"program://OneFormer/class/oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn.MSDeformAttn#L37-L126","kind":"class","name":"MSDeformAttn","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":37,"end_line":126,"context_start_line":17,"context_end_line":126,"code":"import math\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.init import xavier_uniform_, constant_\n\nif torch.cuda.is_available():\n from ..functions import MSDeformAttnFunction\nelse:\n MSDeformAttnFunction = None\nfrom ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch\n\n\ndef _is_power_of_2(n):\n if (not isinstance(n, int)) or (n < 0):\n raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n return (n & (n-1) == 0) and n != 0\n\n\nclass MSDeformAttn(nn.Module):\n def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):\n \"\"\"\n Multi-Scale Deformable Attention Module\n :param d_model hidden dimension\n :param n_levels number of feature levels\n :param n_heads number of attention heads\n :param n_points number of sampling points per attention head per feature level\n \"\"\"\n super().__init__()\n if d_model % n_heads != 0:\n raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))\n _d_per_head = d_model // n_heads\n # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation\n if not _is_power_of_2(_d_per_head):\n warnings.warn(\"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 \"\n \"which is more efficient in our CUDA implementation.\")\n\n self.im2col_step = 128\n\n self.d_model = d_model\n self.n_levels = n_levels\n self.n_heads = n_heads\n self.n_points = n_points\n\n self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)\n self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)\n self.value_proj = nn.Linear(d_model, d_model)\n self.output_proj = nn.Linear(d_model, d_model)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n constant_(self.sampling_offsets.weight.data, 0.)\n thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)\n for i in range(self.n_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.)\n constant_(self.attention_weights.bias.data, 0.)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.)\n\n def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):\n \"\"\"\n :param query (N, Length_{query}, C)\n :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area\n or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes\n :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C)\n :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]\n :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]\n :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements\n\n :return output (N, Length_{query}, C)\n \"\"\"\n N, Len_q, _ = query.shape\n N, Len_in, _ = input_flatten.shape\n assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in\n\n value = self.value_proj(input_flatten)\n if input_padding_mask is not None:\n value = value.masked_fill(input_padding_mask[..., None], float(0))\n value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)\n sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)\n attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)\n attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)\n # N, Len_q, n_heads, n_levels, n_points, 2\n if reference_points.shape[-1] == 2:\n offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)\n sampling_locations = reference_points[:, :, None, :, None, :] \\\n + sampling_offsets / offset_normalizer[None, None, None, :, None, :]\n elif reference_points.shape[-1] == 4:\n sampling_locations = reference_points[:, :, None, :, None, :2] \\\n + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5\n else:\n raise ValueError(\n 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))\n if torch.cuda.is_available():\n output = MSDeformAttnFunction.apply(\n value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)\n else:\n ## CPU\n output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)\n output = self.output_proj(output)\n return output","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn.__init__","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn.__init__#L38-L67","kind":"function","name":"__init__","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":38,"end_line":67,"context_start_line":18,"context_end_line":87,"code":"\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn.init import xavier_uniform_, constant_\n\nif torch.cuda.is_available():\n from ..functions import MSDeformAttnFunction\nelse:\n MSDeformAttnFunction = None\nfrom ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch\n\n\ndef _is_power_of_2(n):\n if (not isinstance(n, int)) or (n < 0):\n raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n return (n & (n-1) == 0) and n != 0\n\n\nclass MSDeformAttn(nn.Module):\n def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):\n \"\"\"\n Multi-Scale Deformable Attention Module\n :param d_model hidden dimension\n :param n_levels number of feature levels\n :param n_heads number of attention heads\n :param n_points number of sampling points per attention head per feature level\n \"\"\"\n super().__init__()\n if d_model % n_heads != 0:\n raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))\n _d_per_head = d_model // n_heads\n # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation\n if not _is_power_of_2(_d_per_head):\n warnings.warn(\"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 \"\n \"which is more efficient in our CUDA implementation.\")\n\n self.im2col_step = 128\n\n self.d_model = d_model\n self.n_levels = n_levels\n self.n_heads = n_heads\n self.n_points = n_points\n\n self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)\n self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)\n self.value_proj = nn.Linear(d_model, d_model)\n self.output_proj = nn.Linear(d_model, d_model)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n constant_(self.sampling_offsets.weight.data, 0.)\n thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)\n for i in range(self.n_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.)\n constant_(self.attention_weights.bias.data, 0.)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.)\n\n def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):\n \"\"\"\n :param query (N, Length_{query}, C)","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn._reset_parameters","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn._reset_parameters#L69-L83","kind":"function","name":"_reset_parameters","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":69,"end_line":83,"context_start_line":49,"context_end_line":103,"code":" _d_per_head = d_model // n_heads\n # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation\n if not _is_power_of_2(_d_per_head):\n warnings.warn(\"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 \"\n \"which is more efficient in our CUDA implementation.\")\n\n self.im2col_step = 128\n\n self.d_model = d_model\n self.n_levels = n_levels\n self.n_heads = n_heads\n self.n_points = n_points\n\n self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)\n self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)\n self.value_proj = nn.Linear(d_model, d_model)\n self.output_proj = nn.Linear(d_model, d_model)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n constant_(self.sampling_offsets.weight.data, 0.)\n thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)\n for i in range(self.n_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.)\n constant_(self.attention_weights.bias.data, 0.)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.)\n\n def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):\n \"\"\"\n :param query (N, Length_{query}, C)\n :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area\n or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes\n :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C)\n :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]\n :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]\n :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements\n\n :return output (N, Length_{query}, C)\n \"\"\"\n N, Len_q, _ = query.shape\n N, Len_in, _ = input_flatten.shape\n assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in\n\n value = self.value_proj(input_flatten)\n if input_padding_mask is not None:\n value = value.masked_fill(input_padding_mask[..., None], float(0))","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn.forward","uri":"program://OneFormer/function/oneformer.modeling.pixel_decoder.ops.modules.ms_deform_attn.forward#L85-L126","kind":"function","name":"forward","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":85,"end_line":126,"context_start_line":65,"context_end_line":126,"code":" self.output_proj = nn.Linear(d_model, d_model)\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n constant_(self.sampling_offsets.weight.data, 0.)\n thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)\n for i in range(self.n_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.)\n constant_(self.attention_weights.bias.data, 0.)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.)\n\n def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):\n \"\"\"\n :param query (N, Length_{query}, C)\n :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area\n or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes\n :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C)\n :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]\n :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]\n :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements\n\n :return output (N, Length_{query}, C)\n \"\"\"\n N, Len_q, _ = query.shape\n N, Len_in, _ = input_flatten.shape\n assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in\n\n value = self.value_proj(input_flatten)\n if input_padding_mask is not None:\n value = value.masked_fill(input_padding_mask[..., None], float(0))\n value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)\n sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)\n attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)\n attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)\n # N, Len_q, n_heads, n_levels, n_points, 2\n if reference_points.shape[-1] == 2:\n offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)\n sampling_locations = reference_points[:, :, None, :, None, :] \\\n + sampling_offsets / offset_normalizer[None, None, None, :, None, :]\n elif reference_points.shape[-1] == 4:\n sampling_locations = reference_points[:, :, None, :, None, :2] \\\n + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5\n else:\n raise ValueError(\n 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))\n if torch.cuda.is_available():\n output = MSDeformAttnFunction.apply(\n value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)\n else:\n ## CPU\n output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)\n output = self.output_proj(output)\n return output","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer","uri":"program://OneFormer/module/oneformer.modeling.transformer_decoder.transformer#L1-L376","kind":"module","name":"oneformer.modeling.transformer_decoder.transformer","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":1,"end_line":376,"context_start_line":1,"context_end_line":376,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/transformer.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nTransformer class.\n\nCopy-paste from torch.nn.Transformer with modifications:\n * positional encodings are passed in MHattention\n * extra LN at the end of encoder is removed\n * decoder returns a stack of activations from all decoding layers\n\"\"\"\nimport copy\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\n\nclass Transformer(nn.Module):\n def __init__(\n self,\n d_model=512,\n nhead=8,\n num_encoder_layers=6,\n num_decoder_layers=6,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n return_intermediate_dec=False,\n ):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n decoder_layer = TransformerDecoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n decoder_norm = nn.LayerNorm(d_model)\n self.decoder = TransformerDecoder(\n decoder_layer,\n num_decoder_layers,\n decoder_norm,\n return_intermediate=return_intermediate_dec,\n )\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, query_embed, pos_embed, task_token=None):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)\n if mask is not None:\n mask = mask.flatten(1)\n \n if task_token is None:\n tgt = torch.zeros_like(query_embed)\n else:\n tgt = task_token.repeat(query_embed.shape[0], 1, 1)\n \n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n hs = self.decoder(\n tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed\n )\n return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\nclass TransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers, norm=None):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n\n def forward(\n self,\n src,\n mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n output = src\n\n for layer in self.layers:\n output = layer(\n output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos\n )\n\n if self.norm is not None:\n output = self.norm(output)\n\n return output\n\n\nclass TransformerDecoder(nn.Module):\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n output = tgt\n\n intermediate = []\n\n for layer in self.layers:\n output = layer(\n output,\n memory,\n tgt_mask=tgt_mask,\n memory_mask=memory_mask,\n tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos,\n query_pos=query_pos,\n )\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate)\n\n return output.unsqueeze(0)\n\n\nclass TransformerEncoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(src, pos)\n src2 = self.self_attn(\n q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask\n )[0]\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n src = src + self.dropout2(src2)\n src = self.norm2(src)\n return src\n\n def forward_pre(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n src2 = self.norm1(src)\n q = k = self.with_pos_embed(src2, pos)\n src2 = self.self_attn(\n q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask\n )[0]\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src\n\n def forward(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def forward_pre(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n return self.forward_post(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.Transformer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.transformer.Transformer#L22-L82","kind":"class","name":"Transformer","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":22,"end_line":82,"context_start_line":2,"context_end_line":102,"code":"# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/transformer.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nTransformer class.\n\nCopy-paste from torch.nn.Transformer with modifications:\n * positional encodings are passed in MHattention\n * extra LN at the end of encoder is removed\n * decoder returns a stack of activations from all decoding layers\n\"\"\"\nimport copy\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\n\nclass Transformer(nn.Module):\n def __init__(\n self,\n d_model=512,\n nhead=8,\n num_encoder_layers=6,\n num_decoder_layers=6,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n return_intermediate_dec=False,\n ):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n decoder_layer = TransformerDecoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n decoder_norm = nn.LayerNorm(d_model)\n self.decoder = TransformerDecoder(\n decoder_layer,\n num_decoder_layers,\n decoder_norm,\n return_intermediate=return_intermediate_dec,\n )\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, query_embed, pos_embed, task_token=None):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)\n if mask is not None:\n mask = mask.flatten(1)\n \n if task_token is None:\n tgt = torch.zeros_like(query_embed)\n else:\n tgt = task_token.repeat(query_embed.shape[0], 1, 1)\n \n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n hs = self.decoder(\n tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed\n )\n return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\nclass TransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers, norm=None):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n\n def forward(\n self,\n src,\n mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n output = src\n\n for layer in self.layers:\n output = layer(","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.TransformerEncoder","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.transformer.TransformerEncoder#L85-L109","kind":"class","name":"TransformerEncoder","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":85,"end_line":109,"context_start_line":65,"context_end_line":129,"code":" # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)\n if mask is not None:\n mask = mask.flatten(1)\n \n if task_token is None:\n tgt = torch.zeros_like(query_embed)\n else:\n tgt = task_token.repeat(query_embed.shape[0], 1, 1)\n \n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n hs = self.decoder(\n tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed\n )\n return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)\n\n\nclass TransformerEncoder(nn.Module):\n def __init__(self, encoder_layer, num_layers, norm=None):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n\n def forward(\n self,\n src,\n mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n output = src\n\n for layer in self.layers:\n output = layer(\n output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos\n )\n\n if self.norm is not None:\n output = self.norm(output)\n\n return output\n\n\nclass TransformerDecoder(nn.Module):\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.TransformerDecoder","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.transformer.TransformerDecoder#L112-L158","kind":"class","name":"TransformerDecoder","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":112,"end_line":158,"context_start_line":92,"context_end_line":178,"code":" def forward(\n self,\n src,\n mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n output = src\n\n for layer in self.layers:\n output = layer(\n output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos\n )\n\n if self.norm is not None:\n output = self.norm(output)\n\n return output\n\n\nclass TransformerDecoder(nn.Module):\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n output = tgt\n\n intermediate = []\n\n for layer in self.layers:\n output = layer(\n output,\n memory,\n tgt_mask=tgt_mask,\n memory_mask=memory_mask,\n tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos,\n query_pos=query_pos,\n )\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate)\n\n return output.unsqueeze(0)\n\n\nclass TransformerEncoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.TransformerEncoderLayer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.transformer.TransformerEncoderLayer#L161-L234","kind":"class","name":"TransformerEncoderLayer","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":161,"end_line":234,"context_start_line":141,"context_end_line":254,"code":" tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos,\n query_pos=query_pos,\n )\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate)\n\n return output.unsqueeze(0)\n\n\nclass TransformerEncoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(src, pos)\n src2 = self.self_attn(\n q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask\n )[0]\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n src = src + self.dropout2(src2)\n src = self.norm2(src)\n return src\n\n def forward_pre(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n src2 = self.norm1(src)\n q = k = self.with_pos_embed(src2, pos)\n src2 = self.self_attn(\n q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask\n )[0]\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src\n\n def forward(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.TransformerDecoderLayer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.transformer.TransformerDecoderLayer#L237-L361","kind":"class","name":"TransformerDecoderLayer","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":237,"end_line":361,"context_start_line":217,"context_end_line":376,"code":" q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask\n )[0]\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src\n\n def forward(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def forward_pre(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n return self.forward_post(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer._get_clones","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer._get_clones#L364-L365","kind":"function","name":"_get_clones","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":364,"end_line":365,"context_start_line":344,"context_end_line":376,"code":" memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n return self.forward_post(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer._get_activation_fn","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer._get_activation_fn#L368-L376","kind":"function","name":"_get_activation_fn","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":368,"end_line":376,"context_start_line":348,"context_end_line":376,"code":" memory_key_padding_mask,\n pos,\n query_pos,\n )\n return self.forward_post(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.__init__","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer.__init__#L238-L263","kind":"function","name":"__init__","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":238,"end_line":263,"context_start_line":218,"context_end_line":283,"code":" )[0]\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src\n\n def forward(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer._reset_parameters","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer._reset_parameters#L59-L62","kind":"function","name":"_reset_parameters","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":59,"end_line":62,"context_start_line":39,"context_end_line":82,"code":" )\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n decoder_layer = TransformerDecoderLayer(\n d_model, nhead, dim_feedforward, dropout, activation, normalize_before\n )\n decoder_norm = nn.LayerNorm(d_model)\n self.decoder = TransformerDecoder(\n decoder_layer,\n num_decoder_layers,\n decoder_norm,\n return_intermediate=return_intermediate_dec,\n )\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, query_embed, pos_embed, task_token=None):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)\n if mask is not None:\n mask = mask.flatten(1)\n \n if task_token is None:\n tgt = torch.zeros_like(query_embed)\n else:\n tgt = task_token.repeat(query_embed.shape[0], 1, 1)\n \n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n hs = self.decoder(\n tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed\n )\n return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.forward","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer.forward#L330-L361","kind":"function","name":"forward","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":330,"end_line":361,"context_start_line":310,"context_end_line":376,"code":" tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n return self.forward_post(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,\n pos,\n query_pos,\n )\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.with_pos_embed","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer.with_pos_embed#L265-L266","kind":"function","name":"with_pos_embed","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":265,"end_line":266,"context_start_line":245,"context_end_line":286,"code":" normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt, query_pos),","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.forward_post","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer.forward_post#L268-L297","kind":"function","name":"forward_post","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":268,"end_line":297,"context_start_line":248,"context_end_line":317,"code":" self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def forward_pre(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.transformer.forward_pre","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.transformer.forward_pre#L299-L328","kind":"function","name":"forward_pre","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":299,"end_line":328,"context_start_line":279,"context_end_line":348,"code":" q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def forward_pre(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(\n q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n )[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(\n query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory,\n attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask,\n )[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n ):\n if self.normalize_before:\n return self.forward_pre(\n tgt,\n memory,\n tgt_mask,\n memory_mask,\n tgt_key_padding_mask,\n memory_key_padding_mask,","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer","uri":"program://OneFormer/module/oneformer.modeling.transformer_decoder.text_transformer#L1-L257","kind":"module","name":"oneformer.modeling.transformer_decoder.text_transformer","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":1,"end_line":257,"context_start_line":1,"context_end_line":257,"code":"# -------------------------------------------------------------------------\n# MIT License\n#\n# Copyright (c) 2021 OpenAI\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n# -------------------------------------------------------------------------\n\nimport torch\nimport torch.utils.checkpoint as checkpoint\nfrom torch import nn\nfrom collections import OrderedDict\nfrom timm.models.layers import trunc_normal_\n\nclass Attention(nn.Module):\n def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n self.scale = qk_scale or head_dim ** -0.5\n\n self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)\n self.k_proj = nn.Linear(dim, dim, bias=qkv_bias)\n self.v_proj = nn.Linear(dim, dim, bias=qkv_bias)\n\n\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, q, k, v):\n B, N, C = q.shape\n assert k.shape == v.shape\n B, M, C = k.shape\n q = self.q_proj(q).reshape(B, N, self.num_heads, C // self.num_heads)\n k = self.k_proj(k).reshape(B, M, self.num_heads, C // self.num_heads)\n v = self.v_proj(v).reshape(B, M, self.num_heads, C // self.num_heads)\n\n attn = torch.einsum('bnkc,bmkc->bknm', q, k) * self.scale\n\n attn = attn.softmax(dim=-1)\n\n x = torch.einsum('bknm,bmkc->bnkc', attn, v).reshape(B, N, C)\n\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dropout=0.1,\n ):\n super().__init__()\n self.self_attn = Attention(d_model, nhead, proj_drop=dropout)\n self.cross_attn = Attention(d_model, nhead, proj_drop=dropout)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.mlp = nn.Sequential(\n nn.Linear(d_model, d_model * 4),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(d_model * 4, d_model)\n )\n\n def forward(self, x, mem):\n q = k = v = self.norm1(x)\n x = x + self.self_attn(q, k, v)\n q = self.norm2(x)\n x = x + self.cross_attn(q, mem, mem)\n x = x + self.dropout(self.mlp(self.norm3(x)))\n return x\n\n\nclass ContextDecoder(nn.Module):\n def __init__(self,\n transformer_width=256,\n transformer_heads=4,\n transformer_layers=6,\n visual_dim=1024,\n dropout=0.1,\n **kwargs):\n super().__init__()\n\n self.memory_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),\n nn.Linear(visual_dim, transformer_width),\n nn.LayerNorm(transformer_width),\n )\n\n self.text_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),\n nn.Linear(visual_dim, transformer_width),\n )\n\n self.decoder = nn.ModuleList([\n TransformerDecoderLayer(transformer_width, transformer_heads, dropout) for _ in range(transformer_layers)\n ])\n \n self.out_proj = nn.Sequential(\n nn.LayerNorm(transformer_width),\n nn.Linear(transformer_width, visual_dim)\n )\n\n self.apply(self._init_weights)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n \n def forward(self, text, visual):\n B, N, C = visual.shape\n visual = self.memory_proj(visual)\n x = self.text_proj(text)\n\n for layer in self.decoder:\n x = layer(x, visual)\n \n return self.out_proj(x)\n\n\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ln_1 = nn.LayerNorm(d_model)\n self.mlp = nn.Sequential(\n OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),\n ('c_proj', nn.Linear(d_model * 4, d_model))]))\n self.ln_2 = nn.LayerNorm(d_model)\n self.attn_mask = attn_mask\n\n def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor):\n self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None\n return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]\n\n def forward(self, x: torch.Tensor, key_padding_mask=None):\n x = x + self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)\n x = x + self.mlp(self.ln_2(x))\n return x\n\nclass Transformer(nn.Module):\n\n def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_checkpoint=False):\n super().__init__()\n self.width = width\n self.layers = layers\n self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])\n proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5)\n attn_std = self.width**-0.5\n fc_std = (2 * self.width)**-0.5\n for block in self.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n self.use_checkpoint = use_checkpoint\n\n def forward(self, x: torch.Tensor):\n for resblock in self.resblocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(resblock, x)\n else:\n x = resblock(x)\n return x\n\n\nclass TextTransformer(nn.Module):\n\n def __init__(\n self,\n context_length: int,\n width: int,\n layers: int,\n vocab_size,\n use_checkpoint=False,\n ):\n\n super().__init__()\n heads = width // 64\n self.context_length = context_length\n self.width = width\n self.transformer = Transformer(\n width=width,\n layers=layers,\n heads=heads,\n attn_mask=self.build_attention_mask(),\n use_checkpoint=use_checkpoint)\n\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))\n self.ln_final = nn.LayerNorm(width)\n self.token_embedding = nn.Embedding(vocab_size, width)\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n\n # initialization\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, text):\n x = self.token_embedding(text)\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]\n\n return x","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.Attention","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.Attention#L32-L65","kind":"class","name":"Attention","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":32,"end_line":65,"context_start_line":12,"context_end_line":85,"code":"#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n# -------------------------------------------------------------------------\n\nimport torch\nimport torch.utils.checkpoint as checkpoint\nfrom torch import nn\nfrom collections import OrderedDict\nfrom timm.models.layers import trunc_normal_\n\nclass Attention(nn.Module):\n def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n self.scale = qk_scale or head_dim ** -0.5\n\n self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)\n self.k_proj = nn.Linear(dim, dim, bias=qkv_bias)\n self.v_proj = nn.Linear(dim, dim, bias=qkv_bias)\n\n\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, q, k, v):\n B, N, C = q.shape\n assert k.shape == v.shape\n B, M, C = k.shape\n q = self.q_proj(q).reshape(B, N, self.num_heads, C // self.num_heads)\n k = self.k_proj(k).reshape(B, M, self.num_heads, C // self.num_heads)\n v = self.v_proj(v).reshape(B, M, self.num_heads, C // self.num_heads)\n\n attn = torch.einsum('bnkc,bmkc->bknm', q, k) * self.scale\n\n attn = attn.softmax(dim=-1)\n\n x = torch.einsum('bknm,bmkc->bnkc', attn, v).reshape(B, N, C)\n\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dropout=0.1,\n ):\n super().__init__()\n self.self_attn = Attention(d_model, nhead, proj_drop=dropout)\n self.cross_attn = Attention(d_model, nhead, proj_drop=dropout)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.mlp = nn.Sequential(\n nn.Linear(d_model, d_model * 4),\n nn.GELU(),","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.TransformerDecoderLayer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.TransformerDecoderLayer#L67-L96","kind":"class","name":"TransformerDecoderLayer","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":67,"end_line":96,"context_start_line":47,"context_end_line":116,"code":" self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, q, k, v):\n B, N, C = q.shape\n assert k.shape == v.shape\n B, M, C = k.shape\n q = self.q_proj(q).reshape(B, N, self.num_heads, C // self.num_heads)\n k = self.k_proj(k).reshape(B, M, self.num_heads, C // self.num_heads)\n v = self.v_proj(v).reshape(B, M, self.num_heads, C // self.num_heads)\n\n attn = torch.einsum('bnkc,bmkc->bknm', q, k) * self.scale\n\n attn = attn.softmax(dim=-1)\n\n x = torch.einsum('bknm,bmkc->bnkc', attn, v).reshape(B, N, C)\n\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\nclass TransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dropout=0.1,\n ):\n super().__init__()\n self.self_attn = Attention(d_model, nhead, proj_drop=dropout)\n self.cross_attn = Attention(d_model, nhead, proj_drop=dropout)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.mlp = nn.Sequential(\n nn.Linear(d_model, d_model * 4),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(d_model * 4, d_model)\n )\n\n def forward(self, x, mem):\n q = k = v = self.norm1(x)\n x = x + self.self_attn(q, k, v)\n q = self.norm2(x)\n x = x + self.cross_attn(q, mem, mem)\n x = x + self.dropout(self.mlp(self.norm3(x)))\n return x\n\n\nclass ContextDecoder(nn.Module):\n def __init__(self,\n transformer_width=256,\n transformer_heads=4,\n transformer_layers=6,\n visual_dim=1024,\n dropout=0.1,\n **kwargs):\n super().__init__()\n\n self.memory_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),\n nn.Linear(visual_dim, transformer_width),\n nn.LayerNorm(transformer_width),\n )\n\n self.text_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.ContextDecoder","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.ContextDecoder#L99-L149","kind":"class","name":"ContextDecoder","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":99,"end_line":149,"context_start_line":79,"context_end_line":169,"code":" self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.mlp = nn.Sequential(\n nn.Linear(d_model, d_model * 4),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(d_model * 4, d_model)\n )\n\n def forward(self, x, mem):\n q = k = v = self.norm1(x)\n x = x + self.self_attn(q, k, v)\n q = self.norm2(x)\n x = x + self.cross_attn(q, mem, mem)\n x = x + self.dropout(self.mlp(self.norm3(x)))\n return x\n\n\nclass ContextDecoder(nn.Module):\n def __init__(self,\n transformer_width=256,\n transformer_heads=4,\n transformer_layers=6,\n visual_dim=1024,\n dropout=0.1,\n **kwargs):\n super().__init__()\n\n self.memory_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),\n nn.Linear(visual_dim, transformer_width),\n nn.LayerNorm(transformer_width),\n )\n\n self.text_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),\n nn.Linear(visual_dim, transformer_width),\n )\n\n self.decoder = nn.ModuleList([\n TransformerDecoderLayer(transformer_width, transformer_heads, dropout) for _ in range(transformer_layers)\n ])\n \n self.out_proj = nn.Sequential(\n nn.LayerNorm(transformer_width),\n nn.Linear(transformer_width, visual_dim)\n )\n\n self.apply(self._init_weights)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n \n def forward(self, text, visual):\n B, N, C = visual.shape\n visual = self.memory_proj(visual)\n x = self.text_proj(text)\n\n for layer in self.decoder:\n x = layer(x, visual)\n \n return self.out_proj(x)\n\n\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ln_1 = nn.LayerNorm(d_model)\n self.mlp = nn.Sequential(\n OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),\n ('c_proj', nn.Linear(d_model * 4, d_model))]))\n self.ln_2 = nn.LayerNorm(d_model)\n self.attn_mask = attn_mask","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.QuickGELU","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.QuickGELU#L152-L155","kind":"class","name":"QuickGELU","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":152,"end_line":155,"context_start_line":132,"context_end_line":175,"code":" if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n \n def forward(self, text, visual):\n B, N, C = visual.shape\n visual = self.memory_proj(visual)\n x = self.text_proj(text)\n\n for layer in self.decoder:\n x = layer(x, visual)\n \n return self.out_proj(x)\n\n\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ln_1 = nn.LayerNorm(d_model)\n self.mlp = nn.Sequential(\n OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),\n ('c_proj', nn.Linear(d_model * 4, d_model))]))\n self.ln_2 = nn.LayerNorm(d_model)\n self.attn_mask = attn_mask\n\n def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor):\n self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None\n return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]\n\n def forward(self, x: torch.Tensor, key_padding_mask=None):","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.ResidualAttentionBlock","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.ResidualAttentionBlock#L158-L178","kind":"class","name":"ResidualAttentionBlock","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":158,"end_line":178,"context_start_line":138,"context_end_line":198,"code":" nn.init.constant_(m.weight, 1.0)\n\n \n def forward(self, text, visual):\n B, N, C = visual.shape\n visual = self.memory_proj(visual)\n x = self.text_proj(text)\n\n for layer in self.decoder:\n x = layer(x, visual)\n \n return self.out_proj(x)\n\n\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ln_1 = nn.LayerNorm(d_model)\n self.mlp = nn.Sequential(\n OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),\n ('c_proj', nn.Linear(d_model * 4, d_model))]))\n self.ln_2 = nn.LayerNorm(d_model)\n self.attn_mask = attn_mask\n\n def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor):\n self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None\n return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]\n\n def forward(self, x: torch.Tensor, key_padding_mask=None):\n x = x + self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)\n x = x + self.mlp(self.ln_2(x))\n return x\n\nclass Transformer(nn.Module):\n\n def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_checkpoint=False):\n super().__init__()\n self.width = width\n self.layers = layers\n self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])\n proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5)\n attn_std = self.width**-0.5\n fc_std = (2 * self.width)**-0.5\n for block in self.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n self.use_checkpoint = use_checkpoint\n\n def forward(self, x: torch.Tensor):","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.Transformer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.Transformer#L180-L204","kind":"class","name":"Transformer","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":180,"end_line":204,"context_start_line":160,"context_end_line":224,"code":" def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ln_1 = nn.LayerNorm(d_model)\n self.mlp = nn.Sequential(\n OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),\n ('c_proj', nn.Linear(d_model * 4, d_model))]))\n self.ln_2 = nn.LayerNorm(d_model)\n self.attn_mask = attn_mask\n\n def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor):\n self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None\n return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]\n\n def forward(self, x: torch.Tensor, key_padding_mask=None):\n x = x + self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)\n x = x + self.mlp(self.ln_2(x))\n return x\n\nclass Transformer(nn.Module):\n\n def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_checkpoint=False):\n super().__init__()\n self.width = width\n self.layers = layers\n self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])\n proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5)\n attn_std = self.width**-0.5\n fc_std = (2 * self.width)**-0.5\n for block in self.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n self.use_checkpoint = use_checkpoint\n\n def forward(self, x: torch.Tensor):\n for resblock in self.resblocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(resblock, x)\n else:\n x = resblock(x)\n return x\n\n\nclass TextTransformer(nn.Module):\n\n def __init__(\n self,\n context_length: int,\n width: int,\n layers: int,\n vocab_size,\n use_checkpoint=False,\n ):\n\n super().__init__()\n heads = width // 64\n self.context_length = context_length\n self.width = width\n self.transformer = Transformer(\n width=width,\n layers=layers,","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.TextTransformer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.text_transformer.TextTransformer#L207-L257","kind":"class","name":"TextTransformer","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":207,"end_line":257,"context_start_line":187,"context_end_line":257,"code":" proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5)\n attn_std = self.width**-0.5\n fc_std = (2 * self.width)**-0.5\n for block in self.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n self.use_checkpoint = use_checkpoint\n\n def forward(self, x: torch.Tensor):\n for resblock in self.resblocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(resblock, x)\n else:\n x = resblock(x)\n return x\n\n\nclass TextTransformer(nn.Module):\n\n def __init__(\n self,\n context_length: int,\n width: int,\n layers: int,\n vocab_size,\n use_checkpoint=False,\n ):\n\n super().__init__()\n heads = width // 64\n self.context_length = context_length\n self.width = width\n self.transformer = Transformer(\n width=width,\n layers=layers,\n heads=heads,\n attn_mask=self.build_attention_mask(),\n use_checkpoint=use_checkpoint)\n\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))\n self.ln_final = nn.LayerNorm(width)\n self.token_embedding = nn.Embedding(vocab_size, width)\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n\n # initialization\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, text):\n x = self.token_embedding(text)\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]\n\n return x","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.__init__","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.text_transformer.__init__#L209-L235","kind":"function","name":"__init__","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":209,"end_line":235,"context_start_line":189,"context_end_line":255,"code":" fc_std = (2 * self.width)**-0.5\n for block in self.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n self.use_checkpoint = use_checkpoint\n\n def forward(self, x: torch.Tensor):\n for resblock in self.resblocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(resblock, x)\n else:\n x = resblock(x)\n return x\n\n\nclass TextTransformer(nn.Module):\n\n def __init__(\n self,\n context_length: int,\n width: int,\n layers: int,\n vocab_size,\n use_checkpoint=False,\n ):\n\n super().__init__()\n heads = width // 64\n self.context_length = context_length\n self.width = width\n self.transformer = Transformer(\n width=width,\n layers=layers,\n heads=heads,\n attn_mask=self.build_attention_mask(),\n use_checkpoint=use_checkpoint)\n\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))\n self.ln_final = nn.LayerNorm(width)\n self.token_embedding = nn.Embedding(vocab_size, width)\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n\n # initialization\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, text):\n x = self.token_embedding(text)\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.forward","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.text_transformer.forward#L245-L257","kind":"function","name":"forward","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":245,"end_line":257,"context_start_line":225,"context_end_line":257,"code":" heads=heads,\n attn_mask=self.build_attention_mask(),\n use_checkpoint=use_checkpoint)\n\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))\n self.ln_final = nn.LayerNorm(width)\n self.token_embedding = nn.Embedding(vocab_size, width)\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n\n # initialization\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, text):\n x = self.token_embedding(text)\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]\n\n return x","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer._init_weights","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.text_transformer._init_weights#L131-L138","kind":"function","name":"_init_weights","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":131,"end_line":138,"context_start_line":111,"context_end_line":158,"code":" nn.Linear(visual_dim, transformer_width),\n nn.LayerNorm(transformer_width),\n )\n\n self.text_proj = nn.Sequential(\n nn.LayerNorm(visual_dim),\n nn.Linear(visual_dim, transformer_width),\n )\n\n self.decoder = nn.ModuleList([\n TransformerDecoderLayer(transformer_width, transformer_heads, dropout) for _ in range(transformer_layers)\n ])\n \n self.out_proj = nn.Sequential(\n nn.LayerNorm(transformer_width),\n nn.Linear(transformer_width, visual_dim)\n )\n\n self.apply(self._init_weights)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n \n def forward(self, text, visual):\n B, N, C = visual.shape\n visual = self.memory_proj(visual)\n x = self.text_proj(text)\n\n for layer in self.decoder:\n x = layer(x, visual)\n \n return self.out_proj(x)\n\n\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.attention","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.text_transformer.attention#L171-L173","kind":"function","name":"attention","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":171,"end_line":173,"context_start_line":151,"context_end_line":193,"code":"\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ln_1 = nn.LayerNorm(d_model)\n self.mlp = nn.Sequential(\n OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),\n ('c_proj', nn.Linear(d_model * 4, d_model))]))\n self.ln_2 = nn.LayerNorm(d_model)\n self.attn_mask = attn_mask\n\n def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor):\n self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None\n return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]\n\n def forward(self, x: torch.Tensor, key_padding_mask=None):\n x = x + self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)\n x = x + self.mlp(self.ln_2(x))\n return x\n\nclass Transformer(nn.Module):\n\n def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_checkpoint=False):\n super().__init__()\n self.width = width\n self.layers = layers\n self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])\n proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5)\n attn_std = self.width**-0.5\n fc_std = (2 * self.width)**-0.5\n for block in self.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.text_transformer.build_attention_mask","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.text_transformer.build_attention_mask#L237-L243","kind":"function","name":"build_attention_mask","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":237,"end_line":243,"context_start_line":217,"context_end_line":257,"code":"\n super().__init__()\n heads = width // 64\n self.context_length = context_length\n self.width = width\n self.transformer = Transformer(\n width=width,\n layers=layers,\n heads=heads,\n attn_mask=self.build_attention_mask(),\n use_checkpoint=use_checkpoint)\n\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))\n self.ln_final = nn.LayerNorm(width)\n self.token_embedding = nn.Embedding(vocab_size, width)\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n\n # initialization\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, text):\n x = self.token_embedding(text)\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]\n\n return x","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.position_encoding","uri":"program://OneFormer/module/oneformer.modeling.transformer_decoder.position_encoding#L1-L67","kind":"module","name":"oneformer.modeling.transformer_decoder.position_encoding","path":"oneformer/modeling/transformer_decoder/position_encoding.py","language":"python","start_line":1,"end_line":67,"context_start_line":1,"context_end_line":67,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/position_encoding.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, x, mask=None):\n if mask is None:\n mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n \n def __repr__(self, _repr_indent=4):\n head = \"Positional encoding \" + self.__class__.__name__\n body = [\n \"num_pos_feats: {}\".format(self.num_pos_feats),\n \"temperature: {}\".format(self.temperature),\n \"normalize: {}\".format(self.normalize),\n \"scale: {}\".format(self.scale),\n ]\n # _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"40a45cfcb390c547acd5a70255b7684ecef80421fc0ba35df3f7eb53c1d36532","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.position_encoding.PositionEmbeddingSine","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.position_encoding.PositionEmbeddingSine#L15-L67","kind":"class","name":"PositionEmbeddingSine","path":"oneformer/modeling/transformer_decoder/position_encoding.py","language":"python","start_line":15,"end_line":67,"context_start_line":1,"context_end_line":67,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/position_encoding.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, x, mask=None):\n if mask is None:\n mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n \n def __repr__(self, _repr_indent=4):\n head = \"Positional encoding \" + self.__class__.__name__\n body = [\n \"num_pos_feats: {}\".format(self.num_pos_feats),\n \"temperature: {}\".format(self.temperature),\n \"normalize: {}\".format(self.normalize),\n \"scale: {}\".format(self.scale),\n ]\n # _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"40a45cfcb390c547acd5a70255b7684ecef80421fc0ba35df3f7eb53c1d36532","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.position_encoding.__init__","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.position_encoding.__init__#L21-L30","kind":"function","name":"__init__","path":"oneformer/modeling/transformer_decoder/position_encoding.py","language":"python","start_line":21,"end_line":30,"context_start_line":1,"context_end_line":50,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/position_encoding.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, x, mask=None):\n if mask is None:\n mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)","source_hash":"40a45cfcb390c547acd5a70255b7684ecef80421fc0ba35df3f7eb53c1d36532","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.position_encoding.forward","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.position_encoding.forward#L32-L55","kind":"function","name":"forward","path":"oneformer/modeling/transformer_decoder/position_encoding.py","language":"python","start_line":32,"end_line":55,"context_start_line":12,"context_end_line":67,"code":"from torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, x, mask=None):\n if mask is None:\n mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n \n def __repr__(self, _repr_indent=4):\n head = \"Positional encoding \" + self.__class__.__name__\n body = [\n \"num_pos_feats: {}\".format(self.num_pos_feats),\n \"temperature: {}\".format(self.temperature),\n \"normalize: {}\".format(self.normalize),\n \"scale: {}\".format(self.scale),\n ]\n # _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"40a45cfcb390c547acd5a70255b7684ecef80421fc0ba35df3f7eb53c1d36532","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.position_encoding.__repr__","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.position_encoding.__repr__#L57-L67","kind":"function","name":"__repr__","path":"oneformer/modeling/transformer_decoder/position_encoding.py","language":"python","start_line":57,"end_line":67,"context_start_line":37,"context_end_line":67,"code":" x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n \n def __repr__(self, _repr_indent=4):\n head = \"Positional encoding \" + self.__class__.__name__\n body = [\n \"num_pos_feats: {}\".format(self.num_pos_feats),\n \"temperature: {}\".format(self.temperature),\n \"normalize: {}\".format(self.normalize),\n \"scale: {}\".format(self.scale),\n ]\n # _repr_indent = 4\n lines = [head] + [\" \" * _repr_indent + line for line in body]\n return \"\\n\".join(lines)","source_hash":"40a45cfcb390c547acd5a70255b7684ecef80421fc0ba35df3f7eb53c1d36532","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder","uri":"program://OneFormer/module/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder#L1-L528","kind":"module","name":"oneformer.modeling.transformer_decoder.oneformer_transformer_decoder","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":1,"end_line":528,"context_start_line":1,"context_end_line":528,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nimport fvcore.nn.weight_init as weight_init\nfrom typing import Optional\nimport torch\nfrom torch import nn, Tensor\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d\n\nfrom .position_encoding import PositionEmbeddingSine\nfrom .transformer import Transformer\n\nfrom detectron2.utils.registry import Registry\n\n\nTRANSFORMER_DECODER_REGISTRY = Registry(\"TRANSFORMER_MODULE\")\nTRANSFORMER_DECODER_REGISTRY.__doc__ = \"\"\"\nRegistry for transformer module in OneFormer.\n\"\"\"\n\n\ndef build_transformer_decoder(cfg, in_channels, mask_classification=True):\n \"\"\"\n Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.\n \"\"\"\n name = cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME\n return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)\n\n\nclass SelfAttentionLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n\n self.norm = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n\n return tgt\n\n def forward_pre(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n \n return tgt\n\n def forward(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, tgt_mask,\n tgt_key_padding_mask, query_pos)\n return self.forward_post(tgt, tgt_mask,\n tgt_key_padding_mask, query_pos)\n\n\nclass CrossAttentionLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n\n self.norm = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n \n return tgt\n\n def forward_pre(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm(tgt)\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n\n return tgt\n\n def forward(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, memory_mask,\n memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, memory_mask,\n memory_key_padding_mask, pos, query_pos)\n\n\nclass FFNLayer(nn.Module):\n\n def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm = nn.LayerNorm(d_model)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\n@TRANSFORMER_DECODER_REGISTRY.register()\nclass ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"static_query\" in k:\n newk = k.replace(\"static_query\", \"query_feat\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n in_channels,\n mask_classification=True,\n *,\n num_classes: int,\n hidden_dim: int,\n num_queries: int,\n nheads: int,\n dropout: float,\n dim_feedforward: int,\n enc_layers: int,\n is_train: bool,\n dec_layers: int,\n class_dec_layers: int,\n pre_norm: bool,\n mask_dim: int,\n enforce_input_project: bool,\n use_task_norm: bool,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n in_channels: channels of the input features\n mask_classification: whether to add mask classifier or not\n num_classes: number of classes\n hidden_dim: Transformer feature dimension\n num_queries: number of queries\n nheads: number of heads\n dim_feedforward: feature dimension in feedforward network\n enc_layers: number of Transformer encoder layers\n dec_layers: number of Transformer decoder layers\n pre_norm: whether to use pre-LayerNorm or not\n mask_dim: mask feature dimension\n enforce_input_project: add input project 1x1 conv even if input\n channels and hidden dim is identical\n \"\"\"\n super().__init__()\n\n assert mask_classification, \"Only support mask classification model\"\n self.mask_classification = mask_classification\n self.is_train = is_train\n self.use_task_norm = use_task_norm\n\n # positional encoding\n N_steps = hidden_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n self.class_transformer = Transformer(\n d_model=hidden_dim,\n dropout=dropout,\n nhead=nheads,\n dim_feedforward=dim_feedforward,\n num_encoder_layers=enc_layers,\n num_decoder_layers=class_dec_layers,\n normalize_before=pre_norm,\n return_intermediate_dec=False,\n )\n\n # define Transformer decoder here\n self.num_heads = nheads\n self.num_layers = dec_layers\n self.transformer_self_attention_layers = nn.ModuleList()\n self.transformer_cross_attention_layers = nn.ModuleList()\n self.transformer_ffn_layers = nn.ModuleList()\n\n for _ in range(self.num_layers):\n self.transformer_self_attention_layers.append(\n SelfAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.transformer_cross_attention_layers.append(\n CrossAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.transformer_ffn_layers.append(\n FFNLayer(\n d_model=hidden_dim,\n dim_feedforward=dim_feedforward,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.decoder_norm = nn.LayerNorm(hidden_dim)\n\n self.num_queries = num_queries\n # learnable query p.e.\n self.query_embed = nn.Embedding(num_queries, hidden_dim)\n\n # level embedding (we always use 3 scales)\n self.num_feature_levels = 3\n self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)\n self.input_proj = nn.ModuleList()\n for _ in range(self.num_feature_levels):\n if in_channels != hidden_dim or enforce_input_project:\n self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))\n weight_init.c2_xavier_fill(self.input_proj[-1])\n else:\n self.input_proj.append(nn.Sequential())\n \n self.class_input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.class_input_proj)\n\n # output FFNs\n if self.mask_classification:\n self.class_embed = nn.Linear(hidden_dim, num_classes + 1)\n self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)\n\n @classmethod\n def from_config(cls, cfg, in_channels, mask_classification):\n ret = {}\n ret[\"in_channels\"] = in_channels\n ret[\"mask_classification\"] = mask_classification\n \n ret[\"num_classes\"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n ret[\"hidden_dim\"] = cfg.MODEL.ONE_FORMER.HIDDEN_DIM\n ret[\"num_queries\"] = cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES\n # Transformer parameters:\n ret[\"nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n\n # NOTE: because we add learnable query features which requires supervision,\n # we add minus 1 to decoder layers to be consistent with our loss\n # implementation: that is, number of auxiliary losses is always\n # equal to number of decoder layers. With learnable query features, the number of\n # auxiliary losses equals number of decoders plus 1.\n assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1\n ret[\"dec_layers\"] = cfg.MODEL.ONE_FORMER.DEC_LAYERS - 1\n ret[\"class_dec_layers\"] = cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS\n ret[\"enc_layers\"] = cfg.MODEL.ONE_FORMER.ENC_LAYERS\n ret[\"dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n ret[\"enforce_input_project\"] = cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ\n ret[\"is_train\"] = cfg.MODEL.IS_TRAIN\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"use_task_norm\"] = cfg.MODEL.ONE_FORMER.USE_TASK_NORM\n\n return ret\n\n def forward(self, x, mask_features, tasks, mask = None):\n # x is a list of multi-scale feature\n assert len(x) == self.num_feature_levels\n src = []\n pos = []\n size_list = []\n\n # disable mask, it does not affect performance\n del mask\n\n for i in range(self.num_feature_levels):\n size_list.append(x[i].shape[-2:])\n pos.append(self.pe_layer(x[i], None).flatten(2))\n src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])\n\n # flatten NxCxHxW to HWxNxC\n pos[-1] = pos[-1].permute(2, 0, 1)\n src[-1] = src[-1].permute(2, 0, 1)\n\n _, bs, _ = src[0].shape\n\n # QxNxC\n query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)\n tasks = tasks.unsqueeze(0)\n if self.use_task_norm:\n tasks = self.decoder_norm(tasks)\n \n feats = self.pe_layer(mask_features, None)\n\n out_t, _ = self.class_transformer(feats, None, \n self.query_embed.weight[:-1], \n self.class_input_proj(mask_features),\n tasks if self.use_task_norm else None)\n out_t = out_t[0].permute(1, 0, 2)\n \n out = torch.cat([out_t, tasks], dim=0)\n\n output = out.clone()\n\n predictions_class = []\n predictions_mask = []\n\n # prediction heads on learnable query features\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], i=0)\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n\n for i in range(self.num_layers):\n level_index = i % self.num_feature_levels\n attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False\n # attention: cross-attention first\n output = self.transformer_cross_attention_layers[i](\n output, src[level_index],\n memory_mask=attn_mask,\n memory_key_padding_mask=None, # here we do not apply masking on padded region\n pos=pos[level_index], query_pos=query_embed\n )\n\n output = self.transformer_self_attention_layers[i](\n output, tgt_mask=None,\n tgt_key_padding_mask=None,\n query_pos=query_embed\n )\n \n # FFN\n output = self.transformer_ffn_layers[i](\n output\n )\n\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], i=i+1)\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n \n assert len(predictions_class) == self.num_layers + 1\n if self.is_train:\n query_class = out.permute(1, 0, 2)\n else:\n query_class = None\n out = {\n 'contrastive_logits': query_class,\n 'pred_logits': predictions_class[-1],\n 'pred_masks': predictions_mask[-1],\n 'aux_outputs': self._set_aux_loss(\n predictions_class if self.mask_classification else None, \n predictions_mask, \n )\n }\n\n return out\n\n def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, i):\n decoder_output = self.decoder_norm(output)\n decoder_output = decoder_output.transpose(0, 1)\n outputs_class = self.class_embed(decoder_output)\n mask_embed = self.mask_embed(decoder_output)\n outputs_mask = torch.einsum(\"bqc,bchw->bqhw\", mask_embed, mask_features)\n\n # NOTE: prediction is of higher-resolution\n # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]\n attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode=\"bilinear\", align_corners=False)\n \n # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool')\n \n # must use bool type\n # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.\n attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()\n attn_mask = attn_mask.detach()\n\n return outputs_class, outputs_mask, attn_mask\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_seg_masks):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't suppo\n# ... truncated ...","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.build_transformer_decoder","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.build_transformer_decoder#L28-L33","kind":"function","name":"build_transformer_decoder","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":28,"end_line":33,"context_start_line":8,"context_end_line":53,"code":"from typing import Optional\nimport torch\nfrom torch import nn, Tensor\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d\n\nfrom .position_encoding import PositionEmbeddingSine\nfrom .transformer import Transformer\n\nfrom detectron2.utils.registry import Registry\n\n\nTRANSFORMER_DECODER_REGISTRY = Registry(\"TRANSFORMER_MODULE\")\nTRANSFORMER_DECODER_REGISTRY.__doc__ = \"\"\"\nRegistry for transformer module in OneFormer.\n\"\"\"\n\n\ndef build_transformer_decoder(cfg, in_channels, mask_classification=True):\n \"\"\"\n Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.\n \"\"\"\n name = cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME\n return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)\n\n\nclass SelfAttentionLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n\n self.norm = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.SelfAttentionLayer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.SelfAttentionLayer#L36-L91","kind":"class","name":"SelfAttentionLayer","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":36,"end_line":91,"context_start_line":16,"context_end_line":111,"code":"from .position_encoding import PositionEmbeddingSine\nfrom .transformer import Transformer\n\nfrom detectron2.utils.registry import Registry\n\n\nTRANSFORMER_DECODER_REGISTRY = Registry(\"TRANSFORMER_MODULE\")\nTRANSFORMER_DECODER_REGISTRY.__doc__ = \"\"\"\nRegistry for transformer module in OneFormer.\n\"\"\"\n\n\ndef build_transformer_decoder(cfg, in_channels, mask_classification=True):\n \"\"\"\n Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.\n \"\"\"\n name = cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME\n return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)\n\n\nclass SelfAttentionLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n\n self.norm = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n\n return tgt\n\n def forward_pre(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n \n return tgt\n\n def forward(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, tgt_mask,\n tgt_key_padding_mask, query_pos)\n return self.forward_post(tgt, tgt_mask,\n tgt_key_padding_mask, query_pos)\n\n\nclass CrossAttentionLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n\n self.norm = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.CrossAttentionLayer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.CrossAttentionLayer#L94-L154","kind":"class","name":"CrossAttentionLayer","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":94,"end_line":154,"context_start_line":74,"context_end_line":174,"code":" query_pos: Optional[Tensor] = None):\n tgt2 = self.norm(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n \n return tgt\n\n def forward(self, tgt,\n tgt_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, tgt_mask,\n tgt_key_padding_mask, query_pos)\n return self.forward_post(tgt, tgt_mask,\n tgt_key_padding_mask, query_pos)\n\n\nclass CrossAttentionLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n\n self.norm = nn.LayerNorm(d_model)\n self.dropout = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n \n return tgt\n\n def forward_pre(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm(tgt)\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n\n return tgt\n\n def forward(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, memory_mask,\n memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, memory_mask,\n memory_key_padding_mask, pos, query_pos)\n\n\nclass FFNLayer(nn.Module):\n\n def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm = nn.LayerNorm(d_model)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.FFNLayer","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.FFNLayer#L157-L197","kind":"class","name":"FFNLayer","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":157,"end_line":197,"context_start_line":137,"context_end_line":217,"code":" tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout(tgt2)\n\n return tgt\n\n def forward(self, tgt, memory,\n memory_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, memory_mask,\n memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, memory_mask,\n memory_key_padding_mask, pos, query_pos)\n\n\nclass FFNLayer(nn.Module):\n\n def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm = nn.LayerNorm(d_model)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._get_activation_fn","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._get_activation_fn#L200-L208","kind":"function","name":"_get_activation_fn","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":200,"end_line":208,"context_start_line":180,"context_end_line":228,"code":" return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\n@TRANSFORMER_DECODER_REGISTRY.register()\nclass ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module):\n","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.MLP","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.MLP#L211-L223","kind":"class","name":"MLP","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":211,"end_line":223,"context_start_line":191,"context_end_line":243,"code":" tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\n@TRANSFORMER_DECODER_REGISTRY.register()\nclass ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"static_query\" in k:\n newk = k.replace(\"static_query\", \"query_feat\")\n if newk != k:","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.ContrastiveMultiScaleMaskedTransformerDecoder","uri":"program://OneFormer/class/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.ContrastiveMultiScaleMaskedTransformerDecoder#L227-L528","kind":"class","name":"ContrastiveMultiScaleMaskedTransformerDecoder","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":227,"end_line":528,"context_start_line":207,"context_end_line":528,"code":" return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\n@TRANSFORMER_DECODER_REGISTRY.register()\nclass ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"static_query\" in k:\n newk = k.replace(\"static_query\", \"query_feat\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n in_channels,\n mask_classification=True,\n *,\n num_classes: int,\n hidden_dim: int,\n num_queries: int,\n nheads: int,\n dropout: float,\n dim_feedforward: int,\n enc_layers: int,\n is_train: bool,\n dec_layers: int,\n class_dec_layers: int,\n pre_norm: bool,\n mask_dim: int,\n enforce_input_project: bool,\n use_task_norm: bool,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n in_channels: channels of the input features\n mask_classification: whether to add mask classifier or not\n num_classes: number of classes\n hidden_dim: Transformer feature dimension\n num_queries: number of queries\n nheads: number of heads\n dim_feedforward: feature dimension in feedforward network\n enc_layers: number of Transformer encoder layers\n dec_layers: number of Transformer decoder layers\n pre_norm: whether to use pre-LayerNorm or not\n mask_dim: mask feature dimension\n enforce_input_project: add input project 1x1 conv even if input\n channels and hidden dim is identical\n \"\"\"\n super().__init__()\n\n assert mask_classification, \"Only support mask classification model\"\n self.mask_classification = mask_classification\n self.is_train = is_train\n self.use_task_norm = use_task_norm\n\n # positional encoding\n N_steps = hidden_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n self.class_transformer = Transformer(\n d_model=hidden_dim,\n dropout=dropout,\n nhead=nheads,\n dim_feedforward=dim_feedforward,\n num_encoder_layers=enc_layers,\n num_decoder_layers=class_dec_layers,\n normalize_before=pre_norm,\n return_intermediate_dec=False,\n )\n\n # define Transformer decoder here\n self.num_heads = nheads\n self.num_layers = dec_layers\n self.transformer_self_attention_layers = nn.ModuleList()\n self.transformer_cross_attention_layers = nn.ModuleList()\n self.transformer_ffn_layers = nn.ModuleList()\n\n for _ in range(self.num_layers):\n self.transformer_self_attention_layers.append(\n SelfAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.transformer_cross_attention_layers.append(\n CrossAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.transformer_ffn_layers.append(\n FFNLayer(\n d_model=hidden_dim,\n dim_feedforward=dim_feedforward,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.decoder_norm = nn.LayerNorm(hidden_dim)\n\n self.num_queries = num_queries\n # learnable query p.e.\n self.query_embed = nn.Embedding(num_queries, hidden_dim)\n\n # level embedding (we always use 3 scales)\n self.num_feature_levels = 3\n self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)\n self.input_proj = nn.ModuleList()\n for _ in range(self.num_feature_levels):\n if in_channels != hidden_dim or enforce_input_project:\n self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))\n weight_init.c2_xavier_fill(self.input_proj[-1])\n else:\n self.input_proj.append(nn.Sequential())\n \n self.class_input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.class_input_proj)\n\n # output FFNs\n if self.mask_classification:\n self.class_embed = nn.Linear(hidden_dim, num_classes + 1)\n self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)\n\n @classmethod\n def from_config(cls, cfg, in_channels, mask_classification):\n ret = {}\n ret[\"in_channels\"] = in_channels\n ret[\"mask_classification\"] = mask_classification\n \n ret[\"num_classes\"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n ret[\"hidden_dim\"] = cfg.MODEL.ONE_FORMER.HIDDEN_DIM\n ret[\"num_queries\"] = cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES\n # Transformer parameters:\n ret[\"nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n\n # NOTE: because we add learnable query features which requires supervision,\n # we add minus 1 to decoder layers to be consistent with our loss\n # implementation: that is, number of auxiliary losses is always\n # equal to number of decoder layers. With learnable query features, the number of\n # auxiliary losses equals number of decoders plus 1.\n assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1\n ret[\"dec_layers\"] = cfg.MODEL.ONE_FORMER.DEC_LAYERS - 1\n ret[\"class_dec_layers\"] = cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS\n ret[\"enc_layers\"] = cfg.MODEL.ONE_FORMER.ENC_LAYERS\n ret[\"dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n ret[\"enforce_input_project\"] = cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ\n ret[\"is_train\"] = cfg.MODEL.IS_TRAIN\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"use_task_norm\"] = cfg.MODEL.ONE_FORMER.USE_TASK_NORM\n\n return ret\n\n def forward(self, x, mask_features, tasks, mask = None):\n # x is a list of multi-scale feature\n assert len(x) == self.num_feature_levels\n src = []\n pos = []\n size_list = []\n\n # disable mask, it does not affect performance\n del mask\n\n for i in range(self.num_feature_levels):\n size_list.append(x[i].shape[-2:])\n pos.append(self.pe_layer(x[i], None).flatten(2))\n src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])\n\n # flatten NxCxHxW to HWxNxC\n pos[-1] = pos[-1].permute(2, 0, 1)\n src[-1] = src[-1].permute(2, 0, 1)\n\n _, bs, _ = src[0].shape\n\n # QxNxC\n query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)\n tasks = tasks.unsqueeze(0)\n if self.use_task_norm:\n tasks = self.decoder_norm(tasks)\n \n feats = self.pe_layer(mask_features, None)\n\n out_t, _ = self.class_transformer(feats, None, \n self.query_embed.weight[:-1], \n self.class_input_proj(mask_features),\n tasks if self.use_task_norm else None)\n out_t = out_t[0].permute(1, 0, 2)\n \n out = torch.cat([out_t, tasks], dim=0)\n\n output = out.clone()\n\n predictions_class = []\n predictions_mask = []\n\n # prediction heads on learnable query features\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], i=0)\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n\n for i in range(self.num_layers):\n level_index = i % self.num_feature_levels\n attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False\n # attention: cross-attention first\n output = self.transformer_cross_attention_layers[i](\n output, src[level_index],\n memory_mask=attn_mask,\n memory_key_padding_mask=None, # here we do not apply masking on padded region\n pos=pos[level_index], query_pos=query_embed\n )\n\n output = self.transformer_self_attention_layers[i](\n output, tgt_mask=None,\n tgt_key_padding_mask=None,\n query_pos=query_embed\n )\n \n # FFN\n output = self.transformer_ffn_layers[i](\n output\n )\n\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], i=i+1)\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n \n assert len(predictions_class) == self.num_layers + 1\n if self.is_train:\n query_class = out.permute(1, 0, 2)\n else:\n query_class = None\n out = {\n 'contrastive_logits': query_class,\n 'pred_logits': predictions_class[-1],\n 'pred_masks': predictions_mask[-1],\n 'aux_outputs': self._set_aux_loss(\n predictions_class if self.mask_classification else None, \n predictions_mask, \n )\n }\n\n return out\n\n def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, i):\n decoder_output = self.decoder_norm(output)\n decoder_output = decoder_output.transpose(0, 1)\n outputs_class = self.class_embed(decoder_output)\n mask_embed = self.mask_embed(decoder_output)\n outputs_mask = torch.einsum(\"bqc,bchw->bqhw\", mask_embed, mask_features)\n\n # NOTE: prediction is of higher-resolution\n # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]\n attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode=\"bilinear\", align_corners=False)\n \n # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool')\n \n # must use bool type\n # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.\n attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()\n attn_mask = attn_mask.detach()\n\n return outputs_class, outputs_mask, attn_mask\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_seg_masks):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n if self.mask_classification:\n aux_list = [\n {\"pred_logits\": a, \"pred_masks\": b}\n for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])\n ]\n else:\n aux_list = [{\"pred_masks\": b} for b, in outputs_seg_masks[:-1]]\n \n return aux_list","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.__init__","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.__init__#L255-L372","kind":"function","name":"__init__","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":255,"end_line":372,"context_start_line":235,"context_end_line":392,"code":" if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"static_query\" in k:\n newk = k.replace(\"static_query\", \"query_feat\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n in_channels,\n mask_classification=True,\n *,\n num_classes: int,\n hidden_dim: int,\n num_queries: int,\n nheads: int,\n dropout: float,\n dim_feedforward: int,\n enc_layers: int,\n is_train: bool,\n dec_layers: int,\n class_dec_layers: int,\n pre_norm: bool,\n mask_dim: int,\n enforce_input_project: bool,\n use_task_norm: bool,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n in_channels: channels of the input features\n mask_classification: whether to add mask classifier or not\n num_classes: number of classes\n hidden_dim: Transformer feature dimension\n num_queries: number of queries\n nheads: number of heads\n dim_feedforward: feature dimension in feedforward network\n enc_layers: number of Transformer encoder layers\n dec_layers: number of Transformer decoder layers\n pre_norm: whether to use pre-LayerNorm or not\n mask_dim: mask feature dimension\n enforce_input_project: add input project 1x1 conv even if input\n channels and hidden dim is identical\n \"\"\"\n super().__init__()\n\n assert mask_classification, \"Only support mask classification model\"\n self.mask_classification = mask_classification\n self.is_train = is_train\n self.use_task_norm = use_task_norm\n\n # positional encoding\n N_steps = hidden_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n self.class_transformer = Transformer(\n d_model=hidden_dim,\n dropout=dropout,\n nhead=nheads,\n dim_feedforward=dim_feedforward,\n num_encoder_layers=enc_layers,\n num_decoder_layers=class_dec_layers,\n normalize_before=pre_norm,\n return_intermediate_dec=False,\n )\n\n # define Transformer decoder here\n self.num_heads = nheads\n self.num_layers = dec_layers\n self.transformer_self_attention_layers = nn.ModuleList()\n self.transformer_cross_attention_layers = nn.ModuleList()\n self.transformer_ffn_layers = nn.ModuleList()\n\n for _ in range(self.num_layers):\n self.transformer_self_attention_layers.append(\n SelfAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.transformer_cross_attention_layers.append(\n CrossAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.transformer_ffn_layers.append(\n FFNLayer(\n d_model=hidden_dim,\n dim_feedforward=dim_feedforward,\n dropout=0.0,\n normalize_before=pre_norm,\n )\n )\n\n self.decoder_norm = nn.LayerNorm(hidden_dim)\n\n self.num_queries = num_queries\n # learnable query p.e.\n self.query_embed = nn.Embedding(num_queries, hidden_dim)\n\n # level embedding (we always use 3 scales)\n self.num_feature_levels = 3\n self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)\n self.input_proj = nn.ModuleList()\n for _ in range(self.num_feature_levels):\n if in_channels != hidden_dim or enforce_input_project:\n self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))\n weight_init.c2_xavier_fill(self.input_proj[-1])\n else:\n self.input_proj.append(nn.Sequential())\n \n self.class_input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.class_input_proj)\n\n # output FFNs\n if self.mask_classification:\n self.class_embed = nn.Linear(hidden_dim, num_classes + 1)\n self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)\n\n @classmethod\n def from_config(cls, cfg, in_channels, mask_classification):\n ret = {}\n ret[\"in_channels\"] = in_channels\n ret[\"mask_classification\"] = mask_classification\n \n ret[\"num_classes\"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n ret[\"hidden_dim\"] = cfg.MODEL.ONE_FORMER.HIDDEN_DIM\n ret[\"num_queries\"] = cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES\n # Transformer parameters:\n ret[\"nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n\n # NOTE: because we add learnable query features which requires supervision,\n # we add minus 1 to decoder layers to be consistent with our loss\n # implementation: that is, number of auxiliary losses is always\n # equal to number of decoder layers. With learnable query features, the number of\n # auxiliary losses equals number of decoders plus 1.\n assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._reset_parameters","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._reset_parameters#L174-L177","kind":"function","name":"_reset_parameters","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":174,"end_line":177,"context_start_line":154,"context_end_line":197,"code":" memory_key_padding_mask, pos, query_pos)\n\n\nclass FFNLayer(nn.Module):\n\n def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm = nn.LayerNorm(d_model)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.with_pos_embed","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.with_pos_embed#L179-L180","kind":"function","name":"with_pos_embed","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":179,"end_line":180,"context_start_line":159,"context_end_line":200,"code":" def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm = nn.LayerNorm(d_model)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward_post","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward_post#L182-L186","kind":"function","name":"forward_post","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":182,"end_line":186,"context_start_line":162,"context_end_line":206,"code":" # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm = nn.LayerNorm(d_model)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward_pre","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward_pre#L188-L192","kind":"function","name":"forward_pre","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":188,"end_line":192,"context_start_line":168,"context_end_line":212,"code":"\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n self._reset_parameters()\n \n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt):\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout(tgt2)\n tgt = self.norm(tgt)\n return tgt\n\n def forward_pre(self, tgt):\n tgt2 = self.norm(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout(tgt2)\n return tgt\n\n def forward(self, tgt):\n if self.normalize_before:\n return self.forward_pre(tgt)\n return self.forward_post(tgt)\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward#L405-L493","kind":"function","name":"forward","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":405,"end_line":493,"context_start_line":385,"context_end_line":513,"code":" ret[\"dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n\n # NOTE: because we add learnable query features which requires supervision,\n # we add minus 1 to decoder layers to be consistent with our loss\n # implementation: that is, number of auxiliary losses is always\n # equal to number of decoder layers. With learnable query features, the number of\n # auxiliary losses equals number of decoders plus 1.\n assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1\n ret[\"dec_layers\"] = cfg.MODEL.ONE_FORMER.DEC_LAYERS - 1\n ret[\"class_dec_layers\"] = cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS\n ret[\"enc_layers\"] = cfg.MODEL.ONE_FORMER.ENC_LAYERS\n ret[\"dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n ret[\"enforce_input_project\"] = cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ\n ret[\"is_train\"] = cfg.MODEL.IS_TRAIN\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"use_task_norm\"] = cfg.MODEL.ONE_FORMER.USE_TASK_NORM\n\n return ret\n\n def forward(self, x, mask_features, tasks, mask = None):\n # x is a list of multi-scale feature\n assert len(x) == self.num_feature_levels\n src = []\n pos = []\n size_list = []\n\n # disable mask, it does not affect performance\n del mask\n\n for i in range(self.num_feature_levels):\n size_list.append(x[i].shape[-2:])\n pos.append(self.pe_layer(x[i], None).flatten(2))\n src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])\n\n # flatten NxCxHxW to HWxNxC\n pos[-1] = pos[-1].permute(2, 0, 1)\n src[-1] = src[-1].permute(2, 0, 1)\n\n _, bs, _ = src[0].shape\n\n # QxNxC\n query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)\n tasks = tasks.unsqueeze(0)\n if self.use_task_norm:\n tasks = self.decoder_norm(tasks)\n \n feats = self.pe_layer(mask_features, None)\n\n out_t, _ = self.class_transformer(feats, None, \n self.query_embed.weight[:-1], \n self.class_input_proj(mask_features),\n tasks if self.use_task_norm else None)\n out_t = out_t[0].permute(1, 0, 2)\n \n out = torch.cat([out_t, tasks], dim=0)\n\n output = out.clone()\n\n predictions_class = []\n predictions_mask = []\n\n # prediction heads on learnable query features\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], i=0)\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n\n for i in range(self.num_layers):\n level_index = i % self.num_feature_levels\n attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False\n # attention: cross-attention first\n output = self.transformer_cross_attention_layers[i](\n output, src[level_index],\n memory_mask=attn_mask,\n memory_key_padding_mask=None, # here we do not apply masking on padded region\n pos=pos[level_index], query_pos=query_embed\n )\n\n output = self.transformer_self_attention_layers[i](\n output, tgt_mask=None,\n tgt_key_padding_mask=None,\n query_pos=query_embed\n )\n \n # FFN\n output = self.transformer_ffn_layers[i](\n output\n )\n\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], i=i+1)\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n \n assert len(predictions_class) == self.num_layers + 1\n if self.is_train:\n query_class = out.permute(1, 0, 2)\n else:\n query_class = None\n out = {\n 'contrastive_logits': query_class,\n 'pred_logits': predictions_class[-1],\n 'pred_masks': predictions_mask[-1],\n 'aux_outputs': self._set_aux_loss(\n predictions_class if self.mask_classification else None, \n predictions_mask, \n )\n }\n\n return out\n\n def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, i):\n decoder_output = self.decoder_norm(output)\n decoder_output = decoder_output.transpose(0, 1)\n outputs_class = self.class_embed(decoder_output)\n mask_embed = self.mask_embed(decoder_output)\n outputs_mask = torch.einsum(\"bqc,bchw->bqhw\", mask_embed, mask_features)\n\n # NOTE: prediction is of higher-resolution\n # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]\n attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode=\"bilinear\", align_corners=False)\n \n # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool')\n \n # must use bool type\n # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.\n attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()\n attn_mask = attn_mask.detach()\n\n return outputs_class, outputs_mask, attn_mask","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._load_from_state_dict","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._load_from_state_dict#L231-L252","kind":"function","name":"_load_from_state_dict","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":231,"end_line":252,"context_start_line":211,"context_end_line":272,"code":"class MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\n@TRANSFORMER_DECODER_REGISTRY.register()\nclass ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"static_query\" in k:\n newk = k.replace(\"static_query\", \"query_feat\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n in_channels,\n mask_classification=True,\n *,\n num_classes: int,\n hidden_dim: int,\n num_queries: int,\n nheads: int,\n dropout: float,\n dim_feedforward: int,\n enc_layers: int,\n is_train: bool,\n dec_layers: int,\n class_dec_layers: int,\n pre_norm: bool,\n mask_dim: int,\n enforce_input_project: bool,","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.from_config","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.from_config#L375-L403","kind":"function","name":"from_config","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":375,"end_line":403,"context_start_line":355,"context_end_line":423,"code":" # level embedding (we always use 3 scales)\n self.num_feature_levels = 3\n self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)\n self.input_proj = nn.ModuleList()\n for _ in range(self.num_feature_levels):\n if in_channels != hidden_dim or enforce_input_project:\n self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))\n weight_init.c2_xavier_fill(self.input_proj[-1])\n else:\n self.input_proj.append(nn.Sequential())\n \n self.class_input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)\n weight_init.c2_xavier_fill(self.class_input_proj)\n\n # output FFNs\n if self.mask_classification:\n self.class_embed = nn.Linear(hidden_dim, num_classes + 1)\n self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)\n\n @classmethod\n def from_config(cls, cfg, in_channels, mask_classification):\n ret = {}\n ret[\"in_channels\"] = in_channels\n ret[\"mask_classification\"] = mask_classification\n \n ret[\"num_classes\"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES\n ret[\"hidden_dim\"] = cfg.MODEL.ONE_FORMER.HIDDEN_DIM\n ret[\"num_queries\"] = cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES\n # Transformer parameters:\n ret[\"nheads\"] = cfg.MODEL.ONE_FORMER.NHEADS\n ret[\"dim_feedforward\"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD\n\n # NOTE: because we add learnable query features which requires supervision,\n # we add minus 1 to decoder layers to be consistent with our loss\n # implementation: that is, number of auxiliary losses is always\n # equal to number of decoder layers. With learnable query features, the number of\n # auxiliary losses equals number of decoders plus 1.\n assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1\n ret[\"dec_layers\"] = cfg.MODEL.ONE_FORMER.DEC_LAYERS - 1\n ret[\"class_dec_layers\"] = cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS\n ret[\"enc_layers\"] = cfg.MODEL.ONE_FORMER.ENC_LAYERS\n ret[\"dropout\"] = cfg.MODEL.ONE_FORMER.DROPOUT\n ret[\"pre_norm\"] = cfg.MODEL.ONE_FORMER.PRE_NORM\n ret[\"enforce_input_project\"] = cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ\n ret[\"is_train\"] = cfg.MODEL.IS_TRAIN\n ret[\"mask_dim\"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n ret[\"use_task_norm\"] = cfg.MODEL.ONE_FORMER.USE_TASK_NORM\n\n return ret\n\n def forward(self, x, mask_features, tasks, mask = None):\n # x is a list of multi-scale feature\n assert len(x) == self.num_feature_levels\n src = []\n pos = []\n size_list = []\n\n # disable mask, it does not affect performance\n del mask\n\n for i in range(self.num_feature_levels):\n size_list.append(x[i].shape[-2:])\n pos.append(self.pe_layer(x[i], None).flatten(2))\n src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])\n\n # flatten NxCxHxW to HWxNxC\n pos[-1] = pos[-1].permute(2, 0, 1)\n src[-1] = src[-1].permute(2, 0, 1)\n","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward_prediction_heads","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder.forward_prediction_heads#L495-L513","kind":"function","name":"forward_prediction_heads","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":495,"end_line":513,"context_start_line":475,"context_end_line":528,"code":" predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n \n assert len(predictions_class) == self.num_layers + 1\n if self.is_train:\n query_class = out.permute(1, 0, 2)\n else:\n query_class = None\n out = {\n 'contrastive_logits': query_class,\n 'pred_logits': predictions_class[-1],\n 'pred_masks': predictions_mask[-1],\n 'aux_outputs': self._set_aux_loss(\n predictions_class if self.mask_classification else None, \n predictions_mask, \n )\n }\n\n return out\n\n def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, i):\n decoder_output = self.decoder_norm(output)\n decoder_output = decoder_output.transpose(0, 1)\n outputs_class = self.class_embed(decoder_output)\n mask_embed = self.mask_embed(decoder_output)\n outputs_mask = torch.einsum(\"bqc,bchw->bqhw\", mask_embed, mask_features)\n\n # NOTE: prediction is of higher-resolution\n # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]\n attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode=\"bilinear\", align_corners=False)\n \n # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool')\n \n # must use bool type\n # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.\n attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()\n attn_mask = attn_mask.detach()\n\n return outputs_class, outputs_mask, attn_mask\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_seg_masks):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n if self.mask_classification:\n aux_list = [\n {\"pred_logits\": a, \"pred_masks\": b}\n for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])\n ]\n else:\n aux_list = [{\"pred_masks\": b} for b, in outputs_seg_masks[:-1]]\n \n return aux_list","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._set_aux_loss","uri":"program://OneFormer/function/oneformer.modeling.transformer_decoder.oneformer_transformer_decoder._set_aux_loss#L516-L528","kind":"function","name":"_set_aux_loss","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":516,"end_line":528,"context_start_line":496,"context_end_line":528,"code":" decoder_output = self.decoder_norm(output)\n decoder_output = decoder_output.transpose(0, 1)\n outputs_class = self.class_embed(decoder_output)\n mask_embed = self.mask_embed(decoder_output)\n outputs_mask = torch.einsum(\"bqc,bchw->bqhw\", mask_embed, mask_features)\n\n # NOTE: prediction is of higher-resolution\n # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]\n attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode=\"bilinear\", align_corners=False)\n \n # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool')\n \n # must use bool type\n # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.\n attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()\n attn_mask = attn_mask.detach()\n\n return outputs_class, outputs_mask, attn_mask\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_seg_masks):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n if self.mask_classification:\n aux_list = [\n {\"pred_logits\": a, \"pred_masks\": b}\n for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])\n ]\n else:\n aux_list = [{\"pred_masks\": b} for b, in outputs_seg_masks[:-1]]\n \n return aux_list","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin","uri":"program://OneFormer/module/oneformer.modeling.backbone.swin#L1-L770","kind":"module","name":"oneformer.modeling.backbone.swin","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":1,"end_line":770,"context_start_line":1,"context_end_line":770,"code":"# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ze Liu, Yutong Lin, Yixuan Wei\n# --------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\n\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\n\nclass Mlp(nn.Module):\n \"\"\"Multilayer perceptron.\"\"\"\n\n def __init__(\n self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0\n ):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n It supports both of shifted and non-shifted window.\n Args:\n dim (int): Number of input channels.\n window_size (tuple[int]): The height and width of the window.\n num_heads (int): Number of attention heads.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n \"\"\"\n\n def __init__(\n self,\n dim,\n window_size,\n num_heads,\n qkv_bias=True,\n qk_scale=None,\n attn_drop=0.0,\n proj_drop=0.0,\n ):\n\n super().__init__()\n self.dim = dim\n self.window_size = window_size # Wh, Ww\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = qk_scale or head_dim ** -0.5\n\n # define a parameter table of relative position bias\n self.relative_position_bias_table = nn.Parameter(\n torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)\n ) # 2*Wh-1 * 2*Ww-1, nH\n\n # get pair-wise relative position index for each token inside the window\n coords_h = torch.arange(self.window_size[0])\n coords_w = torch.arange(self.window_size[1])\n coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww\n coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww\n relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww\n relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2\n relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0\n relative_coords[:, :, 1] += self.window_size[1] - 1\n relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww\n self.register_buffer(\"relative_position_index\", relative_position_index)\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n trunc_normal_(self.relative_position_bias_table, std=0.02)\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x, mask=None):\n \"\"\"Forward function.\n Args:\n x: input features with shape of (num_windows*B, N, C)\n mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n \"\"\"\n B_, N, C = x.shape\n qkv = (\n self.qkv(x)\n .reshape(B_, N, 3, self.num_heads, C // self.num_heads)\n .permute(2, 0, 3, 1, 4)\n )\n q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)\n\n q = q * self.scale\n attn = q @ k.transpose(-2, -1)\n\n relative_position_bias = self.relative_position_bias_table[\n self.relative_position_index.view(-1)\n ].view(\n self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1\n ) # Wh*Ww,Wh*Ww,nH\n relative_position_bias = relative_position_bias.permute(\n 2, 0, 1\n ).contiguous() # nH, Wh*Ww, Wh*Ww\n attn = attn + relative_position_bias.unsqueeze(0)\n\n if mask is not None:\n nW = mask.shape[0]\n attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n attn = attn.view(-1, self.num_heads, N, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass SwinTransformerBlock(nn.Module):\n \"\"\"Swin Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(\n self,\n dim,\n num_heads,\n window_size=7,\n shift_size=0,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop=0.0,\n attn_drop=0.0,\n drop_path=0.0,\n act_layer=nn.GELU,\n norm_layer=nn.LayerNorm,\n ):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.shift_size = shift_size\n self.mlp_ratio = mlp_ratio\n assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n\n self.norm1 = norm_layer(dim)\n self.attn = WindowAttention(\n dim,\n window_size=to_2tuple(self.window_size),\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n attn_drop=attn_drop,\n proj_drop=drop,\n )\n\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n self.norm2 = norm_layer(dim)\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = Mlp(\n in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop\n )\n\n self.H = None\n self.W = None\n\n def forward(self, x, mask_matrix):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n mask_matrix: Attention mask for cyclic shift.\n \"\"\"\n B, L, C = x.shape\n H, W = self.H, self.W\n assert L == H * W, \"input feature has wrong size\"\n\n shortcut = x\n x = self.norm1(x)\n x = x.view(B, H, W, C)\n\n # pad feature maps to multiples of window size\n pad_l = pad_t = 0\n pad_r = (self.window_size - W % self.window_size) % self.window_size\n pad_b = (self.window_size - H % self.window_size) % self.window_size\n x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))\n _, Hp, Wp, _ = x.shape\n\n # cyclic shift\n if self.shift_size > 0:\n shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n attn_mask = mask_matrix\n else:\n shifted_x = x\n attn_mask = None\n\n # partition windows\n x_windows = window_partition(\n shifted_x, self.window_size\n ) # nW*B, window_size, window_size, C\n x_windows = x_windows.view(\n -1, self.window_size * self.window_size, C\n ) # nW*B, window_size*window_size, C\n\n # W-MSA/SW-MSA\n attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C\n\n # merge windows\n attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C\n\n # reverse cyclic shift\n if self.shift_size > 0:\n x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n else:\n x = shifted_x\n\n if pad_r > 0 or pad_b > 0:\n x = x[:, :H, :W, :].contiguous()\n\n x = x.view(B, H * W, C)\n\n # FFN\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n return x\n\n\nclass PatchMerging(nn.Module):\n \"\"\"Patch Merging Layer\n Args:\n dim (int): Number of input channels.\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n self.norm = norm_layer(4 * dim)\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n B, L, C = x.shape\n assert L == H * W, \"input feature has wrong size\"\n\n x = x.view(B, H, W, C)\n\n # padding\n pad_input = (H % 2 == 1) or (W % 2 == 1)\n if pad_input:\n x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))\n\n x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C\n x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C\n x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C\n x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C\n x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C\n x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C\n\n x = self.norm(x)\n x = self.reduction(x)\n\n return x\n\n\nclass BasicLayer(nn.Module):\n \"\"\"A basic Swin Transformer layer for one stage.\n Args:\n dim (int): Number of feature channels\n depth (int): Depths of this stage.\n num_heads (int): Number of attention head.\n window_size (int): Local window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n \"\"\"\n\n def __init__(\n self,\n dim,\n depth,\n num_heads,\n window_size=7,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop=0.0,\n attn_drop=0.0,\n drop_path=0.0,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False,\n ):\n super().__init__()\n self.window_size = window_size\n self.shift_size = window_size // 2\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList(\n [\n SwinTransformerBlock(\n dim=dim,\n num_heads=num_heads,\n window_size=window_size,\n shift_size=0 if (i % 2 == 0) else window_size // 2,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer,\n )\n for i in range(depth)\n ]\n )\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(dim=dim, norm_layer=norm_layer)\n else:\n self.downsample = None\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n\n # calculate attention mask for SW-MSA\n Hp = int(np.ceil(H / self.window_size)) * self.window_size\n Wp = int(np.ceil(W / self.window_size)) * self.window_size\n img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1\n h_slices = (\n slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None),\n )\n w_slices = (\n slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None),\n )\n cnt = 0\n for h in h_slices:\n for w in w_slices:\n img_mask[:, h, w, :] = cnt\n cnt += 1\n\n mask_windows = window_partition(\n img_mask, self.window_size\n ) # nW, window_size, window_size, 1\n mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n attn_mask == 0, float(0.0)\n )\n\n for blk in self.blocks:\n blk.H, blk.W = H, W\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, attn_mask)\n else:\n x = blk(x, attn_mask)\n if self.downsample is not None:\n x_down = self.downsample(x, H, W)\n Wh, Ww = (H + 1) // 2, (W + 1) // 2\n return x, H, W, x_down, Wh, Ww\n else:\n return x, H, W, x, H, W\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"Image to Patch Embedding\n Args:\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n patch_size = to_2tuple(patch_size)\n self.patch_size = patch_size\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n # padding\n _, _, H, W = x.size()\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n x = self.proj(x) # B C Wh Ww\n if self.norm is not None:\n Wh, Ww = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2)\n x = self.norm(x)\n x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n return x\n\n\nclass SwinTransformer(nn.Module):\n \"\"\"Swin Transformer backbone.\n A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -\n https://arxiv.org/pdf/2103.14030\n Args:\n pretrain_img_size (int): Input image size for training the pretrained model,\n used in absolute postion embedding. Default 224.\n patch_size (int | tuple(int)): Patch size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n depths (tuple[int]): Depths of each Swin Transformer stage.\n num_heads (tuple[int]): Number of attention head of each stage.\n window_size (int): Window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.\n drop_rate (float): Dropout rate.\n attn_drop_rate (float): Attention dropout rate. Default: 0.\n drop_path_rate (float): Stochastic depth rate. Default: 0.2.\n norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.\n patch_norm (bool): If True, add normalization after patch embedding. Default: True.\n out_indices (Sequence[int]): Output from which stages.\n frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n -1 means not freezing any parameters.\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n \"\"\"\n\n def __init__(\n self,\n pretrain_img_size=224,\n patch_size=4,\n in_chans=3,\n embed_dim=96,\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 24],\n window_size=7,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.2,\n norm_layer=nn.LayerNorm,\n ape=False,\n patch_norm=True,\n out_indices=(0, 1, 2, 3),\n frozen_stages=-1,\n use_checkpoint=False,\n ):\n su\n# ... truncated ...","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":true} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.Mlp","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.Mlp#L21-L41","kind":"class","name":"Mlp","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":21,"end_line":41,"context_start_line":1,"context_end_line":61,"code":"# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ze Liu, Yutong Lin, Yixuan Wei\n# --------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\n\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\n\nclass Mlp(nn.Module):\n \"\"\"Multilayer perceptron.\"\"\"\n\n def __init__(\n self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0\n ):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.window_partition","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.window_partition#L44-L55","kind":"function","name":"window_partition","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":44,"end_line":55,"context_start_line":24,"context_end_line":75,"code":" def __init__(\n self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0\n ):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.window_reverse","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.window_reverse#L58-L71","kind":"function","name":"window_reverse","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":58,"end_line":71,"context_start_line":38,"context_end_line":91,"code":" x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n It supports both of shifted and non-shifted window.\n Args:\n dim (int): Number of input channels.\n window_size (tuple[int]): The height and width of the window.\n num_heads (int): Number of attention heads.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n \"\"\"\n\n def __init__(\n self,\n dim,\n window_size,\n num_heads,","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.WindowAttention","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.WindowAttention#L74-L171","kind":"class","name":"WindowAttention","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":74,"end_line":171,"context_start_line":54,"context_end_line":191,"code":" windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n It supports both of shifted and non-shifted window.\n Args:\n dim (int): Number of input channels.\n window_size (tuple[int]): The height and width of the window.\n num_heads (int): Number of attention heads.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n \"\"\"\n\n def __init__(\n self,\n dim,\n window_size,\n num_heads,\n qkv_bias=True,\n qk_scale=None,\n attn_drop=0.0,\n proj_drop=0.0,\n ):\n\n super().__init__()\n self.dim = dim\n self.window_size = window_size # Wh, Ww\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = qk_scale or head_dim ** -0.5\n\n # define a parameter table of relative position bias\n self.relative_position_bias_table = nn.Parameter(\n torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)\n ) # 2*Wh-1 * 2*Ww-1, nH\n\n # get pair-wise relative position index for each token inside the window\n coords_h = torch.arange(self.window_size[0])\n coords_w = torch.arange(self.window_size[1])\n coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww\n coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww\n relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww\n relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2\n relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0\n relative_coords[:, :, 1] += self.window_size[1] - 1\n relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww\n self.register_buffer(\"relative_position_index\", relative_position_index)\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n trunc_normal_(self.relative_position_bias_table, std=0.02)\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x, mask=None):\n \"\"\"Forward function.\n Args:\n x: input features with shape of (num_windows*B, N, C)\n mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n \"\"\"\n B_, N, C = x.shape\n qkv = (\n self.qkv(x)\n .reshape(B_, N, 3, self.num_heads, C // self.num_heads)\n .permute(2, 0, 3, 1, 4)\n )\n q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)\n\n q = q * self.scale\n attn = q @ k.transpose(-2, -1)\n\n relative_position_bias = self.relative_position_bias_table[\n self.relative_position_index.view(-1)\n ].view(\n self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1\n ) # Wh*Ww,Wh*Ww,nH\n relative_position_bias = relative_position_bias.permute(\n 2, 0, 1\n ).contiguous() # nH, Wh*Ww, Wh*Ww\n attn = attn + relative_position_bias.unsqueeze(0)\n\n if mask is not None:\n nW = mask.shape[0]\n attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n attn = attn.view(-1, self.num_heads, N, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass SwinTransformerBlock(nn.Module):\n \"\"\"Swin Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.SwinTransformerBlock","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.SwinTransformerBlock#L174-L295","kind":"class","name":"SwinTransformerBlock","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":174,"end_line":295,"context_start_line":154,"context_end_line":315,"code":" 2, 0, 1\n ).contiguous() # nH, Wh*Ww, Wh*Ww\n attn = attn + relative_position_bias.unsqueeze(0)\n\n if mask is not None:\n nW = mask.shape[0]\n attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n attn = attn.view(-1, self.num_heads, N, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass SwinTransformerBlock(nn.Module):\n \"\"\"Swin Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(\n self,\n dim,\n num_heads,\n window_size=7,\n shift_size=0,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop=0.0,\n attn_drop=0.0,\n drop_path=0.0,\n act_layer=nn.GELU,\n norm_layer=nn.LayerNorm,\n ):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.shift_size = shift_size\n self.mlp_ratio = mlp_ratio\n assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n\n self.norm1 = norm_layer(dim)\n self.attn = WindowAttention(\n dim,\n window_size=to_2tuple(self.window_size),\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n attn_drop=attn_drop,\n proj_drop=drop,\n )\n\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n self.norm2 = norm_layer(dim)\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = Mlp(\n in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop\n )\n\n self.H = None\n self.W = None\n\n def forward(self, x, mask_matrix):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n mask_matrix: Attention mask for cyclic shift.\n \"\"\"\n B, L, C = x.shape\n H, W = self.H, self.W\n assert L == H * W, \"input feature has wrong size\"\n\n shortcut = x\n x = self.norm1(x)\n x = x.view(B, H, W, C)\n\n # pad feature maps to multiples of window size\n pad_l = pad_t = 0\n pad_r = (self.window_size - W % self.window_size) % self.window_size\n pad_b = (self.window_size - H % self.window_size) % self.window_size\n x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))\n _, Hp, Wp, _ = x.shape\n\n # cyclic shift\n if self.shift_size > 0:\n shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n attn_mask = mask_matrix\n else:\n shifted_x = x\n attn_mask = None\n\n # partition windows\n x_windows = window_partition(\n shifted_x, self.window_size\n ) # nW*B, window_size, window_size, C\n x_windows = x_windows.view(\n -1, self.window_size * self.window_size, C\n ) # nW*B, window_size*window_size, C\n\n # W-MSA/SW-MSA\n attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C\n\n # merge windows\n attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C\n\n # reverse cyclic shift\n if self.shift_size > 0:\n x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n else:\n x = shifted_x\n\n if pad_r > 0 or pad_b > 0:\n x = x[:, :H, :W, :].contiguous()\n\n x = x.view(B, H * W, C)\n\n # FFN\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n return x\n\n\nclass PatchMerging(nn.Module):\n \"\"\"Patch Merging Layer\n Args:\n dim (int): Number of input channels.\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n self.norm = norm_layer(4 * dim)\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.PatchMerging","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.PatchMerging#L298-L337","kind":"class","name":"PatchMerging","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":298,"end_line":337,"context_start_line":278,"context_end_line":357,"code":" shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C\n\n # reverse cyclic shift\n if self.shift_size > 0:\n x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n else:\n x = shifted_x\n\n if pad_r > 0 or pad_b > 0:\n x = x[:, :H, :W, :].contiguous()\n\n x = x.view(B, H * W, C)\n\n # FFN\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n return x\n\n\nclass PatchMerging(nn.Module):\n \"\"\"Patch Merging Layer\n Args:\n dim (int): Number of input channels.\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n self.norm = norm_layer(4 * dim)\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n B, L, C = x.shape\n assert L == H * W, \"input feature has wrong size\"\n\n x = x.view(B, H, W, C)\n\n # padding\n pad_input = (H % 2 == 1) or (W % 2 == 1)\n if pad_input:\n x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))\n\n x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C\n x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C\n x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C\n x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C\n x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C\n x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C\n\n x = self.norm(x)\n x = self.reduction(x)\n\n return x\n\n\nclass BasicLayer(nn.Module):\n \"\"\"A basic Swin Transformer layer for one stage.\n Args:\n dim (int): Number of feature channels\n depth (int): Depths of this stage.\n num_heads (int): Number of attention head.\n window_size (int): Local window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n \"\"\"\n","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.BasicLayer","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.BasicLayer#L340-L453","kind":"class","name":"BasicLayer","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":340,"end_line":453,"context_start_line":320,"context_end_line":473,"code":" x = x.view(B, H, W, C)\n\n # padding\n pad_input = (H % 2 == 1) or (W % 2 == 1)\n if pad_input:\n x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))\n\n x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C\n x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C\n x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C\n x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C\n x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C\n x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C\n\n x = self.norm(x)\n x = self.reduction(x)\n\n return x\n\n\nclass BasicLayer(nn.Module):\n \"\"\"A basic Swin Transformer layer for one stage.\n Args:\n dim (int): Number of feature channels\n depth (int): Depths of this stage.\n num_heads (int): Number of attention head.\n window_size (int): Local window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n \"\"\"\n\n def __init__(\n self,\n dim,\n depth,\n num_heads,\n window_size=7,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop=0.0,\n attn_drop=0.0,\n drop_path=0.0,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False,\n ):\n super().__init__()\n self.window_size = window_size\n self.shift_size = window_size // 2\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList(\n [\n SwinTransformerBlock(\n dim=dim,\n num_heads=num_heads,\n window_size=window_size,\n shift_size=0 if (i % 2 == 0) else window_size // 2,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer,\n )\n for i in range(depth)\n ]\n )\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(dim=dim, norm_layer=norm_layer)\n else:\n self.downsample = None\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n\n # calculate attention mask for SW-MSA\n Hp = int(np.ceil(H / self.window_size)) * self.window_size\n Wp = int(np.ceil(W / self.window_size)) * self.window_size\n img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1\n h_slices = (\n slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None),\n )\n w_slices = (\n slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None),\n )\n cnt = 0\n for h in h_slices:\n for w in w_slices:\n img_mask[:, h, w, :] = cnt\n cnt += 1\n\n mask_windows = window_partition(\n img_mask, self.window_size\n ) # nW, window_size, window_size, 1\n mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n attn_mask == 0, float(0.0)\n )\n\n for blk in self.blocks:\n blk.H, blk.W = H, W\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, attn_mask)\n else:\n x = blk(x, attn_mask)\n if self.downsample is not None:\n x_down = self.downsample(x, H, W)\n Wh, Ww = (H + 1) // 2, (W + 1) // 2\n return x, H, W, x_down, Wh, Ww\n else:\n return x, H, W, x, H, W\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"Image to Patch Embedding\n Args:\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n patch_size = to_2tuple(patch_size)\n self.patch_size = patch_size\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.PatchEmbed","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.PatchEmbed#L456-L495","kind":"class","name":"PatchEmbed","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":456,"end_line":495,"context_start_line":436,"context_end_line":515,"code":" mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n attn_mask == 0, float(0.0)\n )\n\n for blk in self.blocks:\n blk.H, blk.W = H, W\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, attn_mask)\n else:\n x = blk(x, attn_mask)\n if self.downsample is not None:\n x_down = self.downsample(x, H, W)\n Wh, Ww = (H + 1) // 2, (W + 1) // 2\n return x, H, W, x_down, Wh, Ww\n else:\n return x, H, W, x, H, W\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"Image to Patch Embedding\n Args:\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n patch_size = to_2tuple(patch_size)\n self.patch_size = patch_size\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n # padding\n _, _, H, W = x.size()\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n x = self.proj(x) # B C Wh Ww\n if self.norm is not None:\n Wh, Ww = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2)\n x = self.norm(x)\n x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n return x\n\n\nclass SwinTransformer(nn.Module):\n \"\"\"Swin Transformer backbone.\n A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -\n https://arxiv.org/pdf/2103.14030\n Args:\n pretrain_img_size (int): Input image size for training the pretrained model,\n used in absolute postion embedding. Default 224.\n patch_size (int | tuple(int)): Patch size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n depths (tuple[int]): Depths of each Swin Transformer stage.\n num_heads (tuple[int]): Number of attention head of each stage.\n window_size (int): Window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.\n drop_rate (float): Dropout rate.\n attn_drop_rate (float): Attention dropout rate. Default: 0.","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.SwinTransformer","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.SwinTransformer#L498-L683","kind":"class","name":"SwinTransformer","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":498,"end_line":683,"context_start_line":478,"context_end_line":703,"code":"\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n # padding\n _, _, H, W = x.size()\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n x = self.proj(x) # B C Wh Ww\n if self.norm is not None:\n Wh, Ww = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2)\n x = self.norm(x)\n x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n return x\n\n\nclass SwinTransformer(nn.Module):\n \"\"\"Swin Transformer backbone.\n A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -\n https://arxiv.org/pdf/2103.14030\n Args:\n pretrain_img_size (int): Input image size for training the pretrained model,\n used in absolute postion embedding. Default 224.\n patch_size (int | tuple(int)): Patch size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n depths (tuple[int]): Depths of each Swin Transformer stage.\n num_heads (tuple[int]): Number of attention head of each stage.\n window_size (int): Window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.\n drop_rate (float): Dropout rate.\n attn_drop_rate (float): Attention dropout rate. Default: 0.\n drop_path_rate (float): Stochastic depth rate. Default: 0.2.\n norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.\n patch_norm (bool): If True, add normalization after patch embedding. Default: True.\n out_indices (Sequence[int]): Output from which stages.\n frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n -1 means not freezing any parameters.\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n \"\"\"\n\n def __init__(\n self,\n pretrain_img_size=224,\n patch_size=4,\n in_chans=3,\n embed_dim=96,\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 24],\n window_size=7,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.2,\n norm_layer=nn.LayerNorm,\n ape=False,\n patch_norm=True,\n out_indices=(0, 1, 2, 3),\n frozen_stages=-1,\n use_checkpoint=False,\n ):\n super().__init__()\n\n self.pretrain_img_size = pretrain_img_size\n self.num_layers = len(depths)\n self.embed_dim = embed_dim\n self.ape = ape\n self.patch_norm = patch_norm\n self.out_indices = out_indices\n self.frozen_stages = frozen_stages\n\n # split image into non-overlapping patches\n self.patch_embed = PatchEmbed(\n patch_size=patch_size,\n in_chans=in_chans,\n embed_dim=embed_dim,\n norm_layer=norm_layer if self.patch_norm else None,\n )\n\n # absolute position embedding\n if self.ape:\n pretrain_img_size = to_2tuple(pretrain_img_size)\n patch_size = to_2tuple(patch_size)\n patches_resolution = [\n pretrain_img_size[0] // patch_size[0],\n pretrain_img_size[1] // patch_size[1],\n ]\n\n self.absolute_pos_embed = nn.Parameter(\n torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])\n )\n trunc_normal_(self.absolute_pos_embed, std=0.02)\n\n self.pos_drop = nn.Dropout(p=drop_rate)\n\n # stochastic depth\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n ] # stochastic depth decay rule\n\n # build layers\n self.layers = nn.ModuleList()\n for i_layer in range(self.num_layers):\n layer = BasicLayer(\n dim=int(embed_dim * 2 ** i_layer),\n depth=depths[i_layer],\n num_heads=num_heads[i_layer],\n window_size=window_size,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop_rate,\n attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],\n norm_layer=norm_layer,\n downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,\n use_checkpoint=use_checkpoint,\n )\n self.layers.append(layer)\n\n num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]\n self.num_features = num_features\n\n # add a norm layer for each output\n for i_layer in out_indices:\n layer = norm_layer(num_features[i_layer])\n layer_name = f\"norm{i_layer}\"\n self.add_module(layer_name, layer)\n\n self._freeze_stages()\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 1 and self.ape:\n self.absolute_pos_embed.requires_grad = False\n\n if self.frozen_stages >= 2:\n self.pos_drop.eval()\n for i in range(0, self.frozen_stages - 1):\n m = self.layers[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def init_weights(self, pretrained=None):\n \"\"\"Initialize the weights in backbone.\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n \"\"\"\n\n def _init_weights(m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=0.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)\n\n Wh, Ww = x.size(2), x.size(3)\n if self.ape:\n # interpolate the position embedding to the corresponding size\n absolute_pos_embed = F.interpolate(\n self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n )\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = {}\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n\n if i in self.out_indices:\n norm_layer = getattr(self, f\"norm{i}\")\n x_out = norm_layer(x_out)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs[\"res{}\".format(i + 2)] = out\n\n return outs\n\n def train(self, mode=True):\n \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n super(SwinTransformer, self).train(mode)\n self._freeze_stages()\n\n\n@BACKBONE_REGISTRY.register()\nclass D2SwinTransformer(SwinTransformer, Backbone):\n def __init__(self, cfg, input_shape):\n\n pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE\n patch_size = cfg.MODEL.SWIN.PATCH_SIZE\n in_chans = 3\n embed_dim = cfg.MODEL.SWIN.EMBED_DIM\n depths = cfg.MODEL.SWIN.DEPTHS\n num_heads = cfg.MODEL.SWIN.NUM_HEADS\n window_size = cfg.MODEL.SWIN.WINDOW_SIZE\n mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO\n qkv_bias = cfg.MODEL.SWIN.QKV_BIAS\n qk_scale = cfg.MODEL.SWIN.QK_SCALE\n drop_rate = cfg.MODEL.SWIN.DROP_RATE\n attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE\n drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE\n norm_layer = nn.LayerNorm","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.D2SwinTransformer","uri":"program://OneFormer/class/oneformer.modeling.backbone.swin.D2SwinTransformer#L687-L770","kind":"class","name":"D2SwinTransformer","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":687,"end_line":770,"context_start_line":667,"context_end_line":770,"code":" for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n\n if i in self.out_indices:\n norm_layer = getattr(self, f\"norm{i}\")\n x_out = norm_layer(x_out)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs[\"res{}\".format(i + 2)] = out\n\n return outs\n\n def train(self, mode=True):\n \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n super(SwinTransformer, self).train(mode)\n self._freeze_stages()\n\n\n@BACKBONE_REGISTRY.register()\nclass D2SwinTransformer(SwinTransformer, Backbone):\n def __init__(self, cfg, input_shape):\n\n pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE\n patch_size = cfg.MODEL.SWIN.PATCH_SIZE\n in_chans = 3\n embed_dim = cfg.MODEL.SWIN.EMBED_DIM\n depths = cfg.MODEL.SWIN.DEPTHS\n num_heads = cfg.MODEL.SWIN.NUM_HEADS\n window_size = cfg.MODEL.SWIN.WINDOW_SIZE\n mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO\n qkv_bias = cfg.MODEL.SWIN.QKV_BIAS\n qk_scale = cfg.MODEL.SWIN.QK_SCALE\n drop_rate = cfg.MODEL.SWIN.DROP_RATE\n attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE\n drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE\n norm_layer = nn.LayerNorm\n ape = cfg.MODEL.SWIN.APE\n patch_norm = cfg.MODEL.SWIN.PATCH_NORM\n use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT\n\n super().__init__(\n pretrain_img_size,\n patch_size,\n in_chans,\n embed_dim,\n depths,\n num_heads,\n window_size,\n mlp_ratio,\n qkv_bias,\n qk_scale,\n drop_rate,\n attn_drop_rate,\n drop_path_rate,\n norm_layer,\n ape,\n patch_norm,\n use_checkpoint=use_checkpoint,\n )\n\n self._out_features = cfg.MODEL.SWIN.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.__init__","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.__init__#L688-L741","kind":"function","name":"__init__","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":688,"end_line":741,"context_start_line":668,"context_end_line":761,"code":" layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n\n if i in self.out_indices:\n norm_layer = getattr(self, f\"norm{i}\")\n x_out = norm_layer(x_out)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs[\"res{}\".format(i + 2)] = out\n\n return outs\n\n def train(self, mode=True):\n \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n super(SwinTransformer, self).train(mode)\n self._freeze_stages()\n\n\n@BACKBONE_REGISTRY.register()\nclass D2SwinTransformer(SwinTransformer, Backbone):\n def __init__(self, cfg, input_shape):\n\n pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE\n patch_size = cfg.MODEL.SWIN.PATCH_SIZE\n in_chans = 3\n embed_dim = cfg.MODEL.SWIN.EMBED_DIM\n depths = cfg.MODEL.SWIN.DEPTHS\n num_heads = cfg.MODEL.SWIN.NUM_HEADS\n window_size = cfg.MODEL.SWIN.WINDOW_SIZE\n mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO\n qkv_bias = cfg.MODEL.SWIN.QKV_BIAS\n qk_scale = cfg.MODEL.SWIN.QK_SCALE\n drop_rate = cfg.MODEL.SWIN.DROP_RATE\n attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE\n drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE\n norm_layer = nn.LayerNorm\n ape = cfg.MODEL.SWIN.APE\n patch_norm = cfg.MODEL.SWIN.PATCH_NORM\n use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT\n\n super().__init__(\n pretrain_img_size,\n patch_size,\n in_chans,\n embed_dim,\n depths,\n num_heads,\n window_size,\n mlp_ratio,\n qkv_bias,\n qk_scale,\n drop_rate,\n attn_drop_rate,\n drop_path_rate,\n norm_layer,\n ape,\n patch_norm,\n use_checkpoint=use_checkpoint,\n )\n\n self._out_features = cfg.MODEL.SWIN.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.forward","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.forward#L743-L758","kind":"function","name":"forward","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":743,"end_line":758,"context_start_line":723,"context_end_line":770,"code":" ape,\n patch_norm,\n use_checkpoint=use_checkpoint,\n )\n\n self._out_features = cfg.MODEL.SWIN.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin._freeze_stages","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin._freeze_stages#L618-L633","kind":"function","name":"_freeze_stages","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":618,"end_line":633,"context_start_line":598,"context_end_line":653,"code":" drop=drop_rate,\n attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],\n norm_layer=norm_layer,\n downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,\n use_checkpoint=use_checkpoint,\n )\n self.layers.append(layer)\n\n num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]\n self.num_features = num_features\n\n # add a norm layer for each output\n for i_layer in out_indices:\n layer = norm_layer(num_features[i_layer])\n layer_name = f\"norm{i_layer}\"\n self.add_module(layer_name, layer)\n\n self._freeze_stages()\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 1 and self.ape:\n self.absolute_pos_embed.requires_grad = False\n\n if self.frozen_stages >= 2:\n self.pos_drop.eval()\n for i in range(0, self.frozen_stages - 1):\n m = self.layers[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def init_weights(self, pretrained=None):\n \"\"\"Initialize the weights in backbone.\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n \"\"\"\n\n def _init_weights(m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=0.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.init_weights","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.init_weights#L635-L649","kind":"function","name":"init_weights","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":635,"end_line":649,"context_start_line":615,"context_end_line":669,"code":"\n self._freeze_stages()\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 1 and self.ape:\n self.absolute_pos_embed.requires_grad = False\n\n if self.frozen_stages >= 2:\n self.pos_drop.eval()\n for i in range(0, self.frozen_stages - 1):\n m = self.layers[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def init_weights(self, pretrained=None):\n \"\"\"Initialize the weights in backbone.\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n \"\"\"\n\n def _init_weights(m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=0.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)\n\n Wh, Ww = x.size(2), x.size(3)\n if self.ape:\n # interpolate the position embedding to the corresponding size\n absolute_pos_embed = F.interpolate(\n self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n )\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = {}\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.train","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.train#L680-L683","kind":"function","name":"train","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":680,"end_line":683,"context_start_line":660,"context_end_line":703,"code":" )\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = {}\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n\n if i in self.out_indices:\n norm_layer = getattr(self, f\"norm{i}\")\n x_out = norm_layer(x_out)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs[\"res{}\".format(i + 2)] = out\n\n return outs\n\n def train(self, mode=True):\n \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n super(SwinTransformer, self).train(mode)\n self._freeze_stages()\n\n\n@BACKBONE_REGISTRY.register()\nclass D2SwinTransformer(SwinTransformer, Backbone):\n def __init__(self, cfg, input_shape):\n\n pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE\n patch_size = cfg.MODEL.SWIN.PATCH_SIZE\n in_chans = 3\n embed_dim = cfg.MODEL.SWIN.EMBED_DIM\n depths = cfg.MODEL.SWIN.DEPTHS\n num_heads = cfg.MODEL.SWIN.NUM_HEADS\n window_size = cfg.MODEL.SWIN.WINDOW_SIZE\n mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO\n qkv_bias = cfg.MODEL.SWIN.QKV_BIAS\n qk_scale = cfg.MODEL.SWIN.QK_SCALE\n drop_rate = cfg.MODEL.SWIN.DROP_RATE\n attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE\n drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE\n norm_layer = nn.LayerNorm","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.output_shape","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.output_shape#L760-L766","kind":"function","name":"output_shape","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":760,"end_line":766,"context_start_line":740,"context_end_line":770,"code":" \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin.size_divisibility","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin.size_divisibility#L769-L770","kind":"function","name":"size_divisibility","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":769,"end_line":770,"context_start_line":749,"context_end_line":770,"code":" \"\"\"\n assert (\n x.dim() == 4\n ), f\"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.swin._init_weights","uri":"program://OneFormer/function/oneformer.modeling.backbone.swin._init_weights#L642-L649","kind":"function","name":"_init_weights","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":642,"end_line":649,"context_start_line":622,"context_end_line":669,"code":" param.requires_grad = False\n\n if self.frozen_stages >= 1 and self.ape:\n self.absolute_pos_embed.requires_grad = False\n\n if self.frozen_stages >= 2:\n self.pos_drop.eval()\n for i in range(0, self.frozen_stages - 1):\n m = self.layers[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def init_weights(self, pretrained=None):\n \"\"\"Initialize the weights in backbone.\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n \"\"\"\n\n def _init_weights(m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=0.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)\n\n Wh, Ww = x.size(2), x.size(3)\n if self.ape:\n # interpolate the position embedding to the corresponding size\n absolute_pos_embed = F.interpolate(\n self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n )\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = {}\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext","uri":"program://OneFormer/module/oneformer.modeling.backbone.convnext#L1-L214","kind":"module","name":"oneformer.modeling.backbone.convnext","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":1,"end_line":214,"context_start_line":1,"context_end_line":214,"code":"# Copyright (c) Meta Platforms, Inc. and affiliates.\n\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom timm.models.layers import DropPath\n\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\n\nclass Block(nn.Module):\n r\"\"\" ConvNeXt Block. There are two equivalent implementations:\n (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)\n (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back\n We use (2) as we find it slightly faster in PyTorch\n \n Args:\n dim (int): Number of input channels.\n drop_path (float): Stochastic depth rate. Default: 0.0\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n \"\"\"\n def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):\n super().__init__()\n self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv\n self.norm = LayerNorm(dim, eps=1e-6)\n self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers\n self.act = nn.GELU()\n self.pwconv2 = nn.Linear(4 * dim, dim)\n self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), \n requires_grad=True) if layer_scale_init_value > 0 else None\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n\n def forward(self, x):\n input = x\n x = self.dwconv(x)\n x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)\n x = self.norm(x)\n x = self.pwconv1(x)\n x = self.act(x)\n x = self.pwconv2(x)\n if self.gamma is not None:\n x = self.gamma * x\n x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)\n\n x = input + self.drop_path(x)\n return x\n\nclass LayerNorm(nn.Module):\n r\"\"\" LayerNorm that supports two data formats: channels_last (default) or channels_first. \n The ordering of the dimensions in the inputs. channels_last corresponds to inputs with \n shape (batch_size, height, width, channels) while channels_first corresponds to inputs \n with shape (batch_size, channels, height, width).\n \"\"\"\n def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.bias = nn.Parameter(torch.zeros(normalized_shape))\n self.eps = eps\n self.data_format = data_format\n if self.data_format not in [\"channels_last\", \"channels_first\"]:\n raise NotImplementedError \n self.normalized_shape = (normalized_shape, )\n \n def forward(self, x):\n if self.data_format == \"channels_last\":\n return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n elif self.data_format == \"channels_first\":\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n\n\nclass ConvNeXt(nn.Module):\n r\"\"\" ConvNeXt\n A PyTorch impl of : `A ConvNet for the 2020s` -\n https://arxiv.org/pdf/2201.03545.pdf\n\n Args:\n in_chans (int): Number of input image channels. Default: 3\n num_classes (int): Number of classes for classification head. Default: 1000\n depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]\n dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]\n drop_path_rate (float): Stochastic depth rate. Default: 0.\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.\n \"\"\"\n def __init__(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], \n drop_path_rate=0., layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3],\n ):\n super().__init__()\n\n self.num_features = dims\n\n self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers\n stem = nn.Sequential(\n nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),\n LayerNorm(dims[0], eps=1e-6, data_format=\"channels_first\")\n )\n self.downsample_layers.append(stem)\n for i in range(3):\n downsample_layer = nn.Sequential(\n LayerNorm(dims[i], eps=1e-6, data_format=\"channels_first\"),\n nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),\n )\n self.downsample_layers.append(downsample_layer)\n\n self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks\n dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] \n cur = 0\n for i in range(4):\n stage = nn.Sequential(\n *[Block(dim=dims[i], drop_path=dp_rates[cur + j], \n layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]\n )\n self.stages.append(stage)\n cur += depths[i]\n\n self.out_indices = out_indices\n\n norm_layer = partial(LayerNorm, eps=1e-6, data_format=\"channels_first\")\n for i_layer in range(4):\n layer = norm_layer(dims[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n def forward_features(self, x):\n outs = {}\n for i in range(4):\n x = self.downsample_layers[i](x)\n x = self.stages[i](x)\n if i in self.out_indices:\n norm_layer = getattr(self, f'norm{i}')\n x_out = norm_layer(x)\n outs[\"res{}\".format(i + 2)] = x_out\n\n return outs\n\n def forward(self, x):\n x = self.forward_features(x)\n return x\n\n@BACKBONE_REGISTRY.register()\nclass D2ConvNeXt(ConvNeXt, Backbone):\n def __init__(self, cfg, input_shape):\n\n in_chans = cfg.MODEL.CONVNEXT.IN_CHANNELS\n depths = cfg.MODEL.CONVNEXT.DEPTHS\n dims = cfg.MODEL.CONVNEXT.DIMS\n drop_path_rate = cfg.MODEL.CONVNEXT.DROP_PATH_RATE\n layer_scale_init_value = cfg.MODEL.CONVNEXT.LSIT\n out_indices = cfg.MODEL.CONVNEXT.OUT_INDICES\n\n super().__init__(\n in_chans=in_chans,\n depths=depths,\n dims=dims,\n drop_path_rate=drop_path_rate,\n layer_scale_init_value=layer_scale_init_value,\n out_indices=out_indices,\n )\n\n self._out_features = cfg.MODEL.CONVNEXT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.Block","uri":"program://OneFormer/class/oneformer.modeling.backbone.convnext.Block#L19-L54","kind":"class","name":"Block","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":19,"end_line":54,"context_start_line":1,"context_end_line":74,"code":"# Copyright (c) Meta Platforms, Inc. and affiliates.\n\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom timm.models.layers import DropPath\n\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\n\nclass Block(nn.Module):\n r\"\"\" ConvNeXt Block. There are two equivalent implementations:\n (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)\n (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back\n We use (2) as we find it slightly faster in PyTorch\n \n Args:\n dim (int): Number of input channels.\n drop_path (float): Stochastic depth rate. Default: 0.0\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n \"\"\"\n def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):\n super().__init__()\n self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv\n self.norm = LayerNorm(dim, eps=1e-6)\n self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers\n self.act = nn.GELU()\n self.pwconv2 = nn.Linear(4 * dim, dim)\n self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), \n requires_grad=True) if layer_scale_init_value > 0 else None\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n\n def forward(self, x):\n input = x\n x = self.dwconv(x)\n x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)\n x = self.norm(x)\n x = self.pwconv1(x)\n x = self.act(x)\n x = self.pwconv2(x)\n if self.gamma is not None:\n x = self.gamma * x\n x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)\n\n x = input + self.drop_path(x)\n return x\n\nclass LayerNorm(nn.Module):\n r\"\"\" LayerNorm that supports two data formats: channels_last (default) or channels_first. \n The ordering of the dimensions in the inputs. channels_last corresponds to inputs with \n shape (batch_size, height, width, channels) while channels_first corresponds to inputs \n with shape (batch_size, channels, height, width).\n \"\"\"\n def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.bias = nn.Parameter(torch.zeros(normalized_shape))\n self.eps = eps\n self.data_format = data_format\n if self.data_format not in [\"channels_last\", \"channels_first\"]:\n raise NotImplementedError \n self.normalized_shape = (normalized_shape, )\n \n def forward(self, x):\n if self.data_format == \"channels_last\":\n return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.LayerNorm","uri":"program://OneFormer/class/oneformer.modeling.backbone.convnext.LayerNorm#L56-L80","kind":"class","name":"LayerNorm","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":56,"end_line":80,"context_start_line":36,"context_end_line":100,"code":" self.pwconv2 = nn.Linear(4 * dim, dim)\n self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), \n requires_grad=True) if layer_scale_init_value > 0 else None\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n\n def forward(self, x):\n input = x\n x = self.dwconv(x)\n x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)\n x = self.norm(x)\n x = self.pwconv1(x)\n x = self.act(x)\n x = self.pwconv2(x)\n if self.gamma is not None:\n x = self.gamma * x\n x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)\n\n x = input + self.drop_path(x)\n return x\n\nclass LayerNorm(nn.Module):\n r\"\"\" LayerNorm that supports two data formats: channels_last (default) or channels_first. \n The ordering of the dimensions in the inputs. channels_last corresponds to inputs with \n shape (batch_size, height, width, channels) while channels_first corresponds to inputs \n with shape (batch_size, channels, height, width).\n \"\"\"\n def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.bias = nn.Parameter(torch.zeros(normalized_shape))\n self.eps = eps\n self.data_format = data_format\n if self.data_format not in [\"channels_last\", \"channels_first\"]:\n raise NotImplementedError \n self.normalized_shape = (normalized_shape, )\n \n def forward(self, x):\n if self.data_format == \"channels_last\":\n return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n elif self.data_format == \"channels_first\":\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n\n\nclass ConvNeXt(nn.Module):\n r\"\"\" ConvNeXt\n A PyTorch impl of : `A ConvNet for the 2020s` -\n https://arxiv.org/pdf/2201.03545.pdf\n\n Args:\n in_chans (int): Number of input image channels. Default: 3\n num_classes (int): Number of classes for classification head. Default: 1000\n depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]\n dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]\n drop_path_rate (float): Stochastic depth rate. Default: 0.\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.\n \"\"\"\n def __init__(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], \n drop_path_rate=0., layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3],\n ):\n super().__init__()","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.ConvNeXt","uri":"program://OneFormer/class/oneformer.modeling.backbone.convnext.ConvNeXt#L83-L150","kind":"class","name":"ConvNeXt","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":83,"end_line":150,"context_start_line":63,"context_end_line":170,"code":" super().__init__()\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.bias = nn.Parameter(torch.zeros(normalized_shape))\n self.eps = eps\n self.data_format = data_format\n if self.data_format not in [\"channels_last\", \"channels_first\"]:\n raise NotImplementedError \n self.normalized_shape = (normalized_shape, )\n \n def forward(self, x):\n if self.data_format == \"channels_last\":\n return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n elif self.data_format == \"channels_first\":\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n\n\nclass ConvNeXt(nn.Module):\n r\"\"\" ConvNeXt\n A PyTorch impl of : `A ConvNet for the 2020s` -\n https://arxiv.org/pdf/2201.03545.pdf\n\n Args:\n in_chans (int): Number of input image channels. Default: 3\n num_classes (int): Number of classes for classification head. Default: 1000\n depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]\n dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]\n drop_path_rate (float): Stochastic depth rate. Default: 0.\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.\n \"\"\"\n def __init__(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], \n drop_path_rate=0., layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3],\n ):\n super().__init__()\n\n self.num_features = dims\n\n self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers\n stem = nn.Sequential(\n nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),\n LayerNorm(dims[0], eps=1e-6, data_format=\"channels_first\")\n )\n self.downsample_layers.append(stem)\n for i in range(3):\n downsample_layer = nn.Sequential(\n LayerNorm(dims[i], eps=1e-6, data_format=\"channels_first\"),\n nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),\n )\n self.downsample_layers.append(downsample_layer)\n\n self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks\n dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] \n cur = 0\n for i in range(4):\n stage = nn.Sequential(\n *[Block(dim=dims[i], drop_path=dp_rates[cur + j], \n layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]\n )\n self.stages.append(stage)\n cur += depths[i]\n\n self.out_indices = out_indices\n\n norm_layer = partial(LayerNorm, eps=1e-6, data_format=\"channels_first\")\n for i_layer in range(4):\n layer = norm_layer(dims[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n def forward_features(self, x):\n outs = {}\n for i in range(4):\n x = self.downsample_layers[i](x)\n x = self.stages[i](x)\n if i in self.out_indices:\n norm_layer = getattr(self, f'norm{i}')\n x_out = norm_layer(x)\n outs[\"res{}\".format(i + 2)] = x_out\n\n return outs\n\n def forward(self, x):\n x = self.forward_features(x)\n return x\n\n@BACKBONE_REGISTRY.register()\nclass D2ConvNeXt(ConvNeXt, Backbone):\n def __init__(self, cfg, input_shape):\n\n in_chans = cfg.MODEL.CONVNEXT.IN_CHANNELS\n depths = cfg.MODEL.CONVNEXT.DEPTHS\n dims = cfg.MODEL.CONVNEXT.DIMS\n drop_path_rate = cfg.MODEL.CONVNEXT.DROP_PATH_RATE\n layer_scale_init_value = cfg.MODEL.CONVNEXT.LSIT\n out_indices = cfg.MODEL.CONVNEXT.OUT_INDICES\n\n super().__init__(\n in_chans=in_chans,\n depths=depths,\n dims=dims,\n drop_path_rate=drop_path_rate,\n layer_scale_init_value=layer_scale_init_value,\n out_indices=out_indices,\n )","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.D2ConvNeXt","uri":"program://OneFormer/class/oneformer.modeling.backbone.convnext.D2ConvNeXt#L153-L214","kind":"class","name":"D2ConvNeXt","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":153,"end_line":214,"context_start_line":133,"context_end_line":214,"code":" layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n def forward_features(self, x):\n outs = {}\n for i in range(4):\n x = self.downsample_layers[i](x)\n x = self.stages[i](x)\n if i in self.out_indices:\n norm_layer = getattr(self, f'norm{i}')\n x_out = norm_layer(x)\n outs[\"res{}\".format(i + 2)] = x_out\n\n return outs\n\n def forward(self, x):\n x = self.forward_features(x)\n return x\n\n@BACKBONE_REGISTRY.register()\nclass D2ConvNeXt(ConvNeXt, Backbone):\n def __init__(self, cfg, input_shape):\n\n in_chans = cfg.MODEL.CONVNEXT.IN_CHANNELS\n depths = cfg.MODEL.CONVNEXT.DEPTHS\n dims = cfg.MODEL.CONVNEXT.DIMS\n drop_path_rate = cfg.MODEL.CONVNEXT.DROP_PATH_RATE\n layer_scale_init_value = cfg.MODEL.CONVNEXT.LSIT\n out_indices = cfg.MODEL.CONVNEXT.OUT_INDICES\n\n super().__init__(\n in_chans=in_chans,\n depths=depths,\n dims=dims,\n drop_path_rate=drop_path_rate,\n layer_scale_init_value=layer_scale_init_value,\n out_indices=out_indices,\n )\n\n self._out_features = cfg.MODEL.CONVNEXT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.__init__","uri":"program://OneFormer/function/oneformer.modeling.backbone.convnext.__init__#L154-L185","kind":"function","name":"__init__","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":154,"end_line":185,"context_start_line":134,"context_end_line":205,"code":" self.add_module(layer_name, layer)\n\n def forward_features(self, x):\n outs = {}\n for i in range(4):\n x = self.downsample_layers[i](x)\n x = self.stages[i](x)\n if i in self.out_indices:\n norm_layer = getattr(self, f'norm{i}')\n x_out = norm_layer(x)\n outs[\"res{}\".format(i + 2)] = x_out\n\n return outs\n\n def forward(self, x):\n x = self.forward_features(x)\n return x\n\n@BACKBONE_REGISTRY.register()\nclass D2ConvNeXt(ConvNeXt, Backbone):\n def __init__(self, cfg, input_shape):\n\n in_chans = cfg.MODEL.CONVNEXT.IN_CHANNELS\n depths = cfg.MODEL.CONVNEXT.DEPTHS\n dims = cfg.MODEL.CONVNEXT.DIMS\n drop_path_rate = cfg.MODEL.CONVNEXT.DROP_PATH_RATE\n layer_scale_init_value = cfg.MODEL.CONVNEXT.LSIT\n out_indices = cfg.MODEL.CONVNEXT.OUT_INDICES\n\n super().__init__(\n in_chans=in_chans,\n depths=depths,\n dims=dims,\n drop_path_rate=drop_path_rate,\n layer_scale_init_value=layer_scale_init_value,\n out_indices=out_indices,\n )\n\n self._out_features = cfg.MODEL.CONVNEXT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.forward","uri":"program://OneFormer/function/oneformer.modeling.backbone.convnext.forward#L187-L202","kind":"function","name":"forward","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":187,"end_line":202,"context_start_line":167,"context_end_line":214,"code":" drop_path_rate=drop_path_rate,\n layer_scale_init_value=layer_scale_init_value,\n out_indices=out_indices,\n )\n\n self._out_features = cfg.MODEL.CONVNEXT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.forward_features","uri":"program://OneFormer/function/oneformer.modeling.backbone.convnext.forward_features#L136-L146","kind":"function","name":"forward_features","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":136,"end_line":146,"context_start_line":116,"context_end_line":166,"code":"\n self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks\n dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] \n cur = 0\n for i in range(4):\n stage = nn.Sequential(\n *[Block(dim=dims[i], drop_path=dp_rates[cur + j], \n layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]\n )\n self.stages.append(stage)\n cur += depths[i]\n\n self.out_indices = out_indices\n\n norm_layer = partial(LayerNorm, eps=1e-6, data_format=\"channels_first\")\n for i_layer in range(4):\n layer = norm_layer(dims[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n def forward_features(self, x):\n outs = {}\n for i in range(4):\n x = self.downsample_layers[i](x)\n x = self.stages[i](x)\n if i in self.out_indices:\n norm_layer = getattr(self, f'norm{i}')\n x_out = norm_layer(x)\n outs[\"res{}\".format(i + 2)] = x_out\n\n return outs\n\n def forward(self, x):\n x = self.forward_features(x)\n return x\n\n@BACKBONE_REGISTRY.register()\nclass D2ConvNeXt(ConvNeXt, Backbone):\n def __init__(self, cfg, input_shape):\n\n in_chans = cfg.MODEL.CONVNEXT.IN_CHANNELS\n depths = cfg.MODEL.CONVNEXT.DEPTHS\n dims = cfg.MODEL.CONVNEXT.DIMS\n drop_path_rate = cfg.MODEL.CONVNEXT.DROP_PATH_RATE\n layer_scale_init_value = cfg.MODEL.CONVNEXT.LSIT\n out_indices = cfg.MODEL.CONVNEXT.OUT_INDICES\n\n super().__init__(\n in_chans=in_chans,\n depths=depths,\n dims=dims,","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.output_shape","uri":"program://OneFormer/function/oneformer.modeling.backbone.convnext.output_shape#L204-L210","kind":"function","name":"output_shape","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":204,"end_line":210,"context_start_line":184,"context_end_line":214,"code":" \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.convnext.size_divisibility","uri":"program://OneFormer/function/oneformer.modeling.backbone.convnext.size_divisibility#L213-L214","kind":"function","name":"size_divisibility","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":213,"end_line":214,"context_start_line":193,"context_end_line":214,"code":" \"\"\"\n assert (\n x.dim() == 4\n ), f\"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat","uri":"program://OneFormer/module/oneformer.modeling.backbone.dinat#L1-L296","kind":"module","name":"oneformer.modeling.backbone.dinat","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":1,"end_line":296,"context_start_line":1,"context_end_line":296,"code":"# --------------------------------------------------------\n# Neighborhood Attention Transformer\n# Licensed under The MIT License\n# Written by Ali Hassani\n# --------------------------------------------------------\n\n# Modified by Jitesh Jain\n\nimport torch\nimport torch.nn as nn\nfrom timm.models.layers import DropPath\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\nfrom natten import NeighborhoodAttention2D as NeighborhoodAttention\n\n\nclass ConvTokenizer(nn.Module):\n def __init__(self, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n self.proj = nn.Sequential(\n nn.Conv2d(in_chans, embed_dim // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),\n nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),\n )\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n x = self.proj(x).permute(0, 2, 3, 1)\n if self.norm is not None:\n x = self.norm(x)\n return x\n\n\nclass ConvDownsampler(nn.Module):\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n self.norm = norm_layer(2 * dim)\n\n def forward(self, x):\n x = self.reduction(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)\n x = self.norm(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass NATLayer(nn.Module):\n def __init__(self, dim, num_heads, kernel_size=7, dilation=None,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,\n act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.mlp_ratio = mlp_ratio\n\n self.norm1 = norm_layer(dim)\n self.attn = NeighborhoodAttention(\n dim, kernel_size=kernel_size, dilation=dilation, num_heads=num_heads,\n qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n self.norm2 = norm_layer(dim)\n self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)\n self.layer_scale = False\n if layer_scale is not None and type(layer_scale) in [int, float]:\n self.layer_scale = True\n self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)\n self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)\n\n def forward(self, x):\n if not self.layer_scale:\n shortcut = x\n x = self.norm1(x)\n x = self.attn(x)\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n return x\n shortcut = x\n x = self.norm1(x)\n x = self.attn(x)\n x = shortcut + self.drop_path(self.gamma1 * x)\n x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))\n return x\n\n\n\nclass NATBlock(nn.Module):\n def __init__(self, dim, depth, num_heads, kernel_size, dilations=None,\n downsample=True,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,\n drop_path=0., norm_layer=nn.LayerNorm, layer_scale=None):\n super().__init__()\n self.dim = dim\n self.depth = depth\n\n self.blocks = nn.ModuleList([\n NATLayer(dim=dim,\n num_heads=num_heads,\n kernel_size=kernel_size,\n dilation=None if dilations is None else dilations[i],\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop, attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer,\n layer_scale=layer_scale)\n for i in range(depth)])\n\n self.downsample = None if not downsample else ConvDownsampler(dim=dim, norm_layer=norm_layer)\n\n def forward(self, x):\n for blk in self.blocks:\n x = blk(x)\n if self.downsample is None:\n return x, x\n return self.downsample(x), x\n\n\nclass DiNAT(nn.Module):\n def __init__(self,\n embed_dim,\n mlp_ratio,\n depths,\n num_heads,\n drop_path_rate=0.2,\n in_chans=3,\n kernel_size=7,\n dilations=None,\n out_indices=(0, 1, 2, 3),\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.,\n attn_drop_rate=0.,\n norm_layer=nn.LayerNorm,\n frozen_stages=-1,\n layer_scale=None,\n **kwargs):\n super().__init__()\n self.num_levels = len(depths)\n self.embed_dim = embed_dim\n self.num_features = [int(embed_dim * 2 ** i) for i in range(self.num_levels)]\n self.mlp_ratio = mlp_ratio\n\n self.patch_embed = ConvTokenizer(in_chans=in_chans, embed_dim=embed_dim, norm_layer=norm_layer)\n\n self.pos_drop = nn.Dropout(p=drop_rate)\n\n dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]\n self.levels = nn.ModuleList()\n for i in range(self.num_levels):\n level = NATBlock(dim=int(embed_dim * 2 ** i),\n depth=depths[i],\n num_heads=num_heads[i],\n kernel_size=kernel_size,\n dilations=None if dilations is None else dilations[i],\n mlp_ratio=self.mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop_rate, attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],\n norm_layer=norm_layer,\n downsample=(i < self.num_levels - 1),\n layer_scale=layer_scale)\n self.levels.append(level)\n\n # add a norm layer for each output\n self.out_indices = out_indices\n for i_layer in self.out_indices:\n layer = norm_layer(self.num_features[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n self.frozen_stages = frozen_stages\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 2:\n for i in range(0, self.frozen_stages - 1):\n m = self.network[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def train(self, mode=True):\n super(DiNAT, self).train(mode)\n self._freeze_stages()\n\n def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n\n@BACKBONE_REGISTRY.register()\nclass D2DiNAT(DiNAT, Backbone):\n def __init__(self, cfg, input_shape):\n \n embed_dim = cfg.MODEL.DiNAT.EMBED_DIM\n mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO\n depths = cfg.MODEL.DiNAT.DEPTHS\n num_heads = cfg.MODEL.DiNAT.NUM_HEADS\n drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE\n kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE\n out_indices = cfg.MODEL.DiNAT.OUT_INDICES\n dilations = cfg.MODEL.DiNAT.DILATIONS\n\n super().__init__(\n embed_dim=embed_dim,\n mlp_ratio=mlp_ratio,\n depths=depths,\n num_heads=num_heads,\n drop_path_rate=drop_path_rate,\n kernel_size=kernel_size,\n out_indices=out_indices,\n dilations=dilations,\n )\n\n self._out_features = cfg.MODEL.DiNAT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.ConvTokenizer","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.ConvTokenizer#L17-L33","kind":"class","name":"ConvTokenizer","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":17,"end_line":33,"context_start_line":1,"context_end_line":53,"code":"# --------------------------------------------------------\n# Neighborhood Attention Transformer\n# Licensed under The MIT License\n# Written by Ali Hassani\n# --------------------------------------------------------\n\n# Modified by Jitesh Jain\n\nimport torch\nimport torch.nn as nn\nfrom timm.models.layers import DropPath\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\nfrom natten import NeighborhoodAttention2D as NeighborhoodAttention\n\n\nclass ConvTokenizer(nn.Module):\n def __init__(self, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n self.proj = nn.Sequential(\n nn.Conv2d(in_chans, embed_dim // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),\n nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),\n )\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n x = self.proj(x).permute(0, 2, 3, 1)\n if self.norm is not None:\n x = self.norm(x)\n return x\n\n\nclass ConvDownsampler(nn.Module):\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n self.norm = norm_layer(2 * dim)\n\n def forward(self, x):\n x = self.reduction(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)\n x = self.norm(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.ConvDownsampler","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.ConvDownsampler#L36-L45","kind":"class","name":"ConvDownsampler","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":36,"end_line":45,"context_start_line":16,"context_end_line":65,"code":"\nclass ConvTokenizer(nn.Module):\n def __init__(self, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n self.proj = nn.Sequential(\n nn.Conv2d(in_chans, embed_dim // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),\n nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),\n )\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n x = self.proj(x).permute(0, 2, 3, 1)\n if self.norm is not None:\n x = self.norm(x)\n return x\n\n\nclass ConvDownsampler(nn.Module):\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n self.norm = norm_layer(2 * dim)\n\n def forward(self, x):\n x = self.reduction(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)\n x = self.norm(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.Mlp","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.Mlp#L48-L64","kind":"class","name":"Mlp","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":48,"end_line":64,"context_start_line":28,"context_end_line":84,"code":"\n def forward(self, x):\n x = self.proj(x).permute(0, 2, 3, 1)\n if self.norm is not None:\n x = self.norm(x)\n return x\n\n\nclass ConvDownsampler(nn.Module):\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n self.norm = norm_layer(2 * dim)\n\n def forward(self, x):\n x = self.reduction(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)\n x = self.norm(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass NATLayer(nn.Module):\n def __init__(self, dim, num_heads, kernel_size=7, dilation=None,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,\n act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.mlp_ratio = mlp_ratio\n\n self.norm1 = norm_layer(dim)\n self.attn = NeighborhoodAttention(\n dim, kernel_size=kernel_size, dilation=dilation, num_heads=num_heads,\n qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n self.norm2 = norm_layer(dim)\n self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)\n self.layer_scale = False","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.NATLayer","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.NATLayer#L67-L103","kind":"class","name":"NATLayer","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":67,"end_line":103,"context_start_line":47,"context_end_line":123,"code":"\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass NATLayer(nn.Module):\n def __init__(self, dim, num_heads, kernel_size=7, dilation=None,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,\n act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.mlp_ratio = mlp_ratio\n\n self.norm1 = norm_layer(dim)\n self.attn = NeighborhoodAttention(\n dim, kernel_size=kernel_size, dilation=dilation, num_heads=num_heads,\n qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n self.norm2 = norm_layer(dim)\n self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)\n self.layer_scale = False\n if layer_scale is not None and type(layer_scale) in [int, float]:\n self.layer_scale = True\n self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)\n self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)\n\n def forward(self, x):\n if not self.layer_scale:\n shortcut = x\n x = self.norm1(x)\n x = self.attn(x)\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n return x\n shortcut = x\n x = self.norm1(x)\n x = self.attn(x)\n x = shortcut + self.drop_path(self.gamma1 * x)\n x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))\n return x\n\n\n\nclass NATBlock(nn.Module):\n def __init__(self, dim, depth, num_heads, kernel_size, dilations=None,\n downsample=True,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,\n drop_path=0., norm_layer=nn.LayerNorm, layer_scale=None):\n super().__init__()\n self.dim = dim\n self.depth = depth\n\n self.blocks = nn.ModuleList([\n NATLayer(dim=dim,\n num_heads=num_heads,\n kernel_size=kernel_size,\n dilation=None if dilations is None else dilations[i],\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop, attn_drop=attn_drop,","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.NATBlock","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.NATBlock#L107-L136","kind":"class","name":"NATBlock","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":107,"end_line":136,"context_start_line":87,"context_end_line":156,"code":" self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)\n self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)\n\n def forward(self, x):\n if not self.layer_scale:\n shortcut = x\n x = self.norm1(x)\n x = self.attn(x)\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n return x\n shortcut = x\n x = self.norm1(x)\n x = self.attn(x)\n x = shortcut + self.drop_path(self.gamma1 * x)\n x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))\n return x\n\n\n\nclass NATBlock(nn.Module):\n def __init__(self, dim, depth, num_heads, kernel_size, dilations=None,\n downsample=True,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,\n drop_path=0., norm_layer=nn.LayerNorm, layer_scale=None):\n super().__init__()\n self.dim = dim\n self.depth = depth\n\n self.blocks = nn.ModuleList([\n NATLayer(dim=dim,\n num_heads=num_heads,\n kernel_size=kernel_size,\n dilation=None if dilations is None else dilations[i],\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop, attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer,\n layer_scale=layer_scale)\n for i in range(depth)])\n\n self.downsample = None if not downsample else ConvDownsampler(dim=dim, norm_layer=norm_layer)\n\n def forward(self, x):\n for blk in self.blocks:\n x = blk(x)\n if self.downsample is None:\n return x, x\n return self.downsample(x), x\n\n\nclass DiNAT(nn.Module):\n def __init__(self,\n embed_dim,\n mlp_ratio,\n depths,\n num_heads,\n drop_path_rate=0.2,\n in_chans=3,\n kernel_size=7,\n dilations=None,\n out_indices=(0, 1, 2, 3),\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.,\n attn_drop_rate=0.,\n norm_layer=nn.LayerNorm,\n frozen_stages=-1,\n layer_scale=None,","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.DiNAT","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.DiNAT#L139-L227","kind":"class","name":"DiNAT","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":139,"end_line":227,"context_start_line":119,"context_end_line":247,"code":" kernel_size=kernel_size,\n dilation=None if dilations is None else dilations[i],\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop, attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer,\n layer_scale=layer_scale)\n for i in range(depth)])\n\n self.downsample = None if not downsample else ConvDownsampler(dim=dim, norm_layer=norm_layer)\n\n def forward(self, x):\n for blk in self.blocks:\n x = blk(x)\n if self.downsample is None:\n return x, x\n return self.downsample(x), x\n\n\nclass DiNAT(nn.Module):\n def __init__(self,\n embed_dim,\n mlp_ratio,\n depths,\n num_heads,\n drop_path_rate=0.2,\n in_chans=3,\n kernel_size=7,\n dilations=None,\n out_indices=(0, 1, 2, 3),\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.,\n attn_drop_rate=0.,\n norm_layer=nn.LayerNorm,\n frozen_stages=-1,\n layer_scale=None,\n **kwargs):\n super().__init__()\n self.num_levels = len(depths)\n self.embed_dim = embed_dim\n self.num_features = [int(embed_dim * 2 ** i) for i in range(self.num_levels)]\n self.mlp_ratio = mlp_ratio\n\n self.patch_embed = ConvTokenizer(in_chans=in_chans, embed_dim=embed_dim, norm_layer=norm_layer)\n\n self.pos_drop = nn.Dropout(p=drop_rate)\n\n dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]\n self.levels = nn.ModuleList()\n for i in range(self.num_levels):\n level = NATBlock(dim=int(embed_dim * 2 ** i),\n depth=depths[i],\n num_heads=num_heads[i],\n kernel_size=kernel_size,\n dilations=None if dilations is None else dilations[i],\n mlp_ratio=self.mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop_rate, attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],\n norm_layer=norm_layer,\n downsample=(i < self.num_levels - 1),\n layer_scale=layer_scale)\n self.levels.append(level)\n\n # add a norm layer for each output\n self.out_indices = out_indices\n for i_layer in self.out_indices:\n layer = norm_layer(self.num_features[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n self.frozen_stages = frozen_stages\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 2:\n for i in range(0, self.frozen_stages - 1):\n m = self.network[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def train(self, mode=True):\n super(DiNAT, self).train(mode)\n self._freeze_stages()\n\n def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n\n@BACKBONE_REGISTRY.register()\nclass D2DiNAT(DiNAT, Backbone):\n def __init__(self, cfg, input_shape):\n \n embed_dim = cfg.MODEL.DiNAT.EMBED_DIM\n mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO\n depths = cfg.MODEL.DiNAT.DEPTHS\n num_heads = cfg.MODEL.DiNAT.NUM_HEADS\n drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE\n kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE\n out_indices = cfg.MODEL.DiNAT.OUT_INDICES\n dilations = cfg.MODEL.DiNAT.DILATIONS\n\n super().__init__(\n embed_dim=embed_dim,\n mlp_ratio=mlp_ratio,\n depths=depths,\n num_heads=num_heads,","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.D2DiNAT","uri":"program://OneFormer/class/oneformer.modeling.backbone.dinat.D2DiNAT#L231-L296","kind":"class","name":"D2DiNAT","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":231,"end_line":296,"context_start_line":211,"context_end_line":296,"code":" def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n\n@BACKBONE_REGISTRY.register()\nclass D2DiNAT(DiNAT, Backbone):\n def __init__(self, cfg, input_shape):\n \n embed_dim = cfg.MODEL.DiNAT.EMBED_DIM\n mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO\n depths = cfg.MODEL.DiNAT.DEPTHS\n num_heads = cfg.MODEL.DiNAT.NUM_HEADS\n drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE\n kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE\n out_indices = cfg.MODEL.DiNAT.OUT_INDICES\n dilations = cfg.MODEL.DiNAT.DILATIONS\n\n super().__init__(\n embed_dim=embed_dim,\n mlp_ratio=mlp_ratio,\n depths=depths,\n num_heads=num_heads,\n drop_path_rate=drop_path_rate,\n kernel_size=kernel_size,\n out_indices=out_indices,\n dilations=dilations,\n )\n\n self._out_features = cfg.MODEL.DiNAT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.__init__","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.__init__#L232-L267","kind":"function","name":"__init__","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":232,"end_line":267,"context_start_line":212,"context_end_line":287,"code":" x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n\n@BACKBONE_REGISTRY.register()\nclass D2DiNAT(DiNAT, Backbone):\n def __init__(self, cfg, input_shape):\n \n embed_dim = cfg.MODEL.DiNAT.EMBED_DIM\n mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO\n depths = cfg.MODEL.DiNAT.DEPTHS\n num_heads = cfg.MODEL.DiNAT.NUM_HEADS\n drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE\n kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE\n out_indices = cfg.MODEL.DiNAT.OUT_INDICES\n dilations = cfg.MODEL.DiNAT.DILATIONS\n\n super().__init__(\n embed_dim=embed_dim,\n mlp_ratio=mlp_ratio,\n depths=depths,\n num_heads=num_heads,\n drop_path_rate=drop_path_rate,\n kernel_size=kernel_size,\n out_indices=out_indices,\n dilations=dilations,\n )\n\n self._out_features = cfg.MODEL.DiNAT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.forward","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.forward#L269-L284","kind":"function","name":"forward","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":269,"end_line":284,"context_start_line":249,"context_end_line":296,"code":" kernel_size=kernel_size,\n out_indices=out_indices,\n dilations=dilations,\n )\n\n self._out_features = cfg.MODEL.DiNAT.OUT_FEATURES\n\n self._out_feature_strides = {\n \"res2\": 4,\n \"res3\": 8,\n \"res4\": 16,\n \"res5\": 32,\n }\n self._out_feature_channels = {\n \"res2\": self.num_features[0],\n \"res3\": self.num_features[1],\n \"res4\": self.num_features[2],\n \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat._freeze_stages","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat._freeze_stages#L194-L205","kind":"function","name":"_freeze_stages","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":194,"end_line":205,"context_start_line":174,"context_end_line":225,"code":" kernel_size=kernel_size,\n dilations=None if dilations is None else dilations[i],\n mlp_ratio=self.mlp_ratio,\n qkv_bias=qkv_bias, qk_scale=qk_scale,\n drop=drop_rate, attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],\n norm_layer=norm_layer,\n downsample=(i < self.num_levels - 1),\n layer_scale=layer_scale)\n self.levels.append(level)\n\n # add a norm layer for each output\n self.out_indices = out_indices\n for i_layer in self.out_indices:\n layer = norm_layer(self.num_features[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n self.frozen_stages = frozen_stages\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 2:\n for i in range(0, self.frozen_stages - 1):\n m = self.network[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def train(self, mode=True):\n super(DiNAT, self).train(mode)\n self._freeze_stages()\n\n def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.train","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.train#L207-L209","kind":"function","name":"train","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":207,"end_line":209,"context_start_line":187,"context_end_line":229,"code":" for i_layer in self.out_indices:\n layer = norm_layer(self.num_features[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n self.frozen_stages = frozen_stages\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 2:\n for i in range(0, self.frozen_stages - 1):\n m = self.network[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def train(self, mode=True):\n super(DiNAT, self).train(mode)\n self._freeze_stages()\n\n def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.forward_embeddings","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.forward_embeddings#L211-L213","kind":"function","name":"forward_embeddings","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":211,"end_line":213,"context_start_line":191,"context_end_line":233,"code":"\n self.frozen_stages = frozen_stages\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 2:\n for i in range(0, self.frozen_stages - 1):\n m = self.network[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def train(self, mode=True):\n super(DiNAT, self).train(mode)\n self._freeze_stages()\n\n def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n\n@BACKBONE_REGISTRY.register()\nclass D2DiNAT(DiNAT, Backbone):\n def __init__(self, cfg, input_shape):\n ","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.forward_tokens","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.forward_tokens#L215-L223","kind":"function","name":"forward_tokens","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":215,"end_line":223,"context_start_line":195,"context_end_line":243,"code":" if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 2:\n for i in range(0, self.frozen_stages - 1):\n m = self.network[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def train(self, mode=True):\n super(DiNAT, self).train(mode)\n self._freeze_stages()\n\n def forward_embeddings(self, x):\n x = self.patch_embed(x)\n return x\n\n def forward_tokens(self, x):\n outs = {}\n for idx, level in enumerate(self.levels):\n x, xo = level(x)\n if idx in self.out_indices:\n norm_layer = getattr(self, f'norm{idx}')\n x_out = norm_layer(xo)\n outs[\"res{}\".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()\n return outs\n\n def forward(self, x):\n x = self.forward_embeddings(x)\n return self.forward_tokens(x)\n\n\n@BACKBONE_REGISTRY.register()\nclass D2DiNAT(DiNAT, Backbone):\n def __init__(self, cfg, input_shape):\n \n embed_dim = cfg.MODEL.DiNAT.EMBED_DIM\n mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO\n depths = cfg.MODEL.DiNAT.DEPTHS\n num_heads = cfg.MODEL.DiNAT.NUM_HEADS\n drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE\n kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE\n out_indices = cfg.MODEL.DiNAT.OUT_INDICES\n dilations = cfg.MODEL.DiNAT.DILATIONS\n\n super().__init__(","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.output_shape","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.output_shape#L286-L292","kind":"function","name":"output_shape","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":286,"end_line":292,"context_start_line":266,"context_end_line":296,"code":" \"res5\": self.num_features[3],\n }\n\n def forward(self, x):\n \"\"\"\n Args:\n x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.\n Returns:\n dict[str->Tensor]: names and the corresponding features\n \"\"\"\n assert (\n x.dim() == 4\n ), f\"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.backbone.dinat.size_divisibility","uri":"program://OneFormer/function/oneformer.modeling.backbone.dinat.size_divisibility#L295-L296","kind":"function","name":"size_divisibility","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":295,"end_line":296,"context_start_line":275,"context_end_line":296,"code":" \"\"\"\n assert (\n x.dim() == 4\n ), f\"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!\"\n outputs = {}\n y = super().forward(x)\n for k in y.keys():\n if k in self._out_features:\n outputs[k] = y[k]\n return outputs\n\n def output_shape(self):\n return {\n name: ShapeSpec(\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\n )\n for name in self._out_features\n }\n\n @property\n def size_divisibility(self):\n return 32","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head","uri":"program://OneFormer/module/oneformer.modeling.meta_arch.oneformer_head#L1-L135","kind":"module","name":"oneformer.modeling.meta_arch.oneformer_head","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":1,"end_line":135,"context_start_line":1,"context_end_line":135,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nfrom copy import deepcopy\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\nfrom ..pixel_decoder.fpn import build_pixel_decoder\nfrom ..transformer_decoder.oneformer_transformer_decoder import build_transformer_decoder\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass OneFormerHead(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"sem_seg_head\" in k and not k.startswith(prefix + \"predictor\"):\n newk = k.replace(prefix, prefix + \"pixel_decoder.\")\n # logger.debug(f\"{k} ==> {newk}\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n num_classes: int,\n pixel_decoder: nn.Module,\n loss_weight: float = 1.0,\n ignore_value: int = -1,\n # extra parameters\n transformer_predictor: nn.Module,\n transformer_in_feature: str,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n num_classes: number of classes to predict\n pixel_decoder: the pixel decoder module\n loss_weight: loss weight\n ignore_value: category id to be ignored during training.\n transformer_predictor: the transformer decoder that makes prediction\n transformer_in_feature: input feature name to the transformer_predictor\n \"\"\"\n super().__init__()\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape]\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n self.ignore_value = ignore_value\n self.common_stride = 4\n self.loss_weight = loss_weight\n\n self.pixel_decoder = pixel_decoder\n self.predictor = transformer_predictor\n self.transformer_in_feature = transformer_in_feature\n\n self.num_classes = num_classes\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n # figure out in_channels to transformer predictor\n if cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"transformer_encoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"pixel_embedding\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"multi_scale_pixel_decoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n else:\n transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels\n\n return {\n \"input_shape\": {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n },\n \"ignore_value\": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n \"num_classes\": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,\n \"pixel_decoder\": build_pixel_decoder(cfg, input_shape),\n \"loss_weight\": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,\n \"transformer_in_feature\": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE,\n \"transformer_predictor\": build_transformer_decoder(\n cfg,\n transformer_predictor_in_channels,\n mask_classification=True,\n ),\n }\n\n def forward(self, features, tasks, mask=None):\n return self.layers(features, tasks, mask)\n\n def layers(self, features, tasks, mask=None):\n mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features)\n \n if self.transformer_in_feature == \"multi_scale_pixel_decoder\":\n predictions = self.predictor(multi_scale_features, mask_features, tasks, mask)\n else:\n if self.transformer_in_feature == \"transformer_encoder\":\n assert (\n transformer_encoder_features is not None\n ), \"Please use the TransformerEncoderPixelDecoder.\"\n predictions = self.predictor(transformer_encoder_features, mask_features, mask)\n elif self.transformer_in_feature == \"pixel_embedding\":\n predictions = self.predictor(mask_features, mask_features, mask)\n else:\n predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)\n return predictions","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head.OneFormerHead","uri":"program://OneFormer/class/oneformer.modeling.meta_arch.oneformer_head.OneFormerHead#L21-L135","kind":"class","name":"OneFormerHead","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":21,"end_line":135,"context_start_line":1,"context_end_line":135,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nfrom copy import deepcopy\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\nfrom ..pixel_decoder.fpn import build_pixel_decoder\nfrom ..transformer_decoder.oneformer_transformer_decoder import build_transformer_decoder\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass OneFormerHead(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"sem_seg_head\" in k and not k.startswith(prefix + \"predictor\"):\n newk = k.replace(prefix, prefix + \"pixel_decoder.\")\n # logger.debug(f\"{k} ==> {newk}\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n num_classes: int,\n pixel_decoder: nn.Module,\n loss_weight: float = 1.0,\n ignore_value: int = -1,\n # extra parameters\n transformer_predictor: nn.Module,\n transformer_in_feature: str,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n num_classes: number of classes to predict\n pixel_decoder: the pixel decoder module\n loss_weight: loss weight\n ignore_value: category id to be ignored during training.\n transformer_predictor: the transformer decoder that makes prediction\n transformer_in_feature: input feature name to the transformer_predictor\n \"\"\"\n super().__init__()\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape]\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n self.ignore_value = ignore_value\n self.common_stride = 4\n self.loss_weight = loss_weight\n\n self.pixel_decoder = pixel_decoder\n self.predictor = transformer_predictor\n self.transformer_in_feature = transformer_in_feature\n\n self.num_classes = num_classes\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n # figure out in_channels to transformer predictor\n if cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"transformer_encoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"pixel_embedding\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"multi_scale_pixel_decoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n else:\n transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels\n\n return {\n \"input_shape\": {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n },\n \"ignore_value\": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n \"num_classes\": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,\n \"pixel_decoder\": build_pixel_decoder(cfg, input_shape),\n \"loss_weight\": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,\n \"transformer_in_feature\": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE,\n \"transformer_predictor\": build_transformer_decoder(\n cfg,\n transformer_predictor_in_channels,\n mask_classification=True,\n ),\n }\n\n def forward(self, features, tasks, mask=None):\n return self.layers(features, tasks, mask)\n\n def layers(self, features, tasks, mask=None):\n mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features)\n \n if self.transformer_in_feature == \"multi_scale_pixel_decoder\":\n predictions = self.predictor(multi_scale_features, mask_features, tasks, mask)\n else:\n if self.transformer_in_feature == \"transformer_encoder\":\n assert (\n transformer_encoder_features is not None\n ), \"Please use the TransformerEncoderPixelDecoder.\"\n predictions = self.predictor(transformer_encoder_features, mask_features, mask)\n elif self.transformer_in_feature == \"pixel_embedding\":\n predictions = self.predictor(mask_features, mask_features, mask)\n else:\n predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)\n return predictions","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head._load_from_state_dict","uri":"program://OneFormer/function/oneformer.modeling.meta_arch.oneformer_head._load_from_state_dict#L25-L47","kind":"function","name":"_load_from_state_dict","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":25,"end_line":47,"context_start_line":5,"context_end_line":67,"code":"\nimport logging\nfrom copy import deepcopy\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\nfrom ..pixel_decoder.fpn import build_pixel_decoder\nfrom ..transformer_decoder.oneformer_transformer_decoder import build_transformer_decoder\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass OneFormerHead(nn.Module):\n\n _version = 2\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n version = local_metadata.get(\"version\", None)\n if version is None or version < 2:\n # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"sem_seg_head\" in k and not k.startswith(prefix + \"predictor\"):\n newk = k.replace(prefix, prefix + \"pixel_decoder.\")\n # logger.debug(f\"{k} ==> {newk}\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n num_classes: int,\n pixel_decoder: nn.Module,\n loss_weight: float = 1.0,\n ignore_value: int = -1,\n # extra parameters\n transformer_predictor: nn.Module,\n transformer_in_feature: str,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n num_classes: number of classes to predict\n pixel_decoder: the pixel decoder module","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head.__init__","uri":"program://OneFormer/function/oneformer.modeling.meta_arch.oneformer_head.__init__#L50-L87","kind":"function","name":"__init__","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":50,"end_line":87,"context_start_line":30,"context_end_line":107,"code":" # Do not warn if train from scratch\n scratch = True\n logger = logging.getLogger(__name__)\n for k in list(state_dict.keys()):\n newk = k\n if \"sem_seg_head\" in k and not k.startswith(prefix + \"predictor\"):\n newk = k.replace(prefix, prefix + \"pixel_decoder.\")\n # logger.debug(f\"{k} ==> {newk}\")\n if newk != k:\n state_dict[newk] = state_dict[k]\n del state_dict[k]\n scratch = False\n\n if not scratch:\n logger.warning(\n f\"Weight format of {self.__class__.__name__} have changed! \"\n \"Please upgrade your models. Applying automatic conversion now ...\"\n )\n\n @configurable\n def __init__(\n self,\n input_shape: Dict[str, ShapeSpec],\n *,\n num_classes: int,\n pixel_decoder: nn.Module,\n loss_weight: float = 1.0,\n ignore_value: int = -1,\n # extra parameters\n transformer_predictor: nn.Module,\n transformer_in_feature: str,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n input_shape: shapes (channels and stride) of the input features\n num_classes: number of classes to predict\n pixel_decoder: the pixel decoder module\n loss_weight: loss weight\n ignore_value: category id to be ignored during training.\n transformer_predictor: the transformer decoder that makes prediction\n transformer_in_feature: input feature name to the transformer_predictor\n \"\"\"\n super().__init__()\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape]\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n self.ignore_value = ignore_value\n self.common_stride = 4\n self.loss_weight = loss_weight\n\n self.pixel_decoder = pixel_decoder\n self.predictor = transformer_predictor\n self.transformer_in_feature = transformer_in_feature\n\n self.num_classes = num_classes\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n # figure out in_channels to transformer predictor\n if cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"transformer_encoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"pixel_embedding\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"multi_scale_pixel_decoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n else:\n transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels\n\n return {\n \"input_shape\": {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n },\n \"ignore_value\": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n \"num_classes\": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,\n \"pixel_decoder\": build_pixel_decoder(cfg, input_shape),","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head.from_config","uri":"program://OneFormer/function/oneformer.modeling.meta_arch.oneformer_head.from_config#L90-L115","kind":"function","name":"from_config","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":90,"end_line":115,"context_start_line":70,"context_end_line":135,"code":" transformer_predictor: the transformer decoder that makes prediction\n transformer_in_feature: input feature name to the transformer_predictor\n \"\"\"\n super().__init__()\n input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)\n self.in_features = [k for k, v in input_shape]\n feature_strides = [v.stride for k, v in input_shape]\n feature_channels = [v.channels for k, v in input_shape]\n\n self.ignore_value = ignore_value\n self.common_stride = 4\n self.loss_weight = loss_weight\n\n self.pixel_decoder = pixel_decoder\n self.predictor = transformer_predictor\n self.transformer_in_feature = transformer_in_feature\n\n self.num_classes = num_classes\n\n @classmethod\n def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):\n # figure out in_channels to transformer predictor\n if cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"transformer_encoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"pixel_embedding\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM\n elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == \"multi_scale_pixel_decoder\":\n transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n else:\n transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels\n\n return {\n \"input_shape\": {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n },\n \"ignore_value\": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n \"num_classes\": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,\n \"pixel_decoder\": build_pixel_decoder(cfg, input_shape),\n \"loss_weight\": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,\n \"transformer_in_feature\": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE,\n \"transformer_predictor\": build_transformer_decoder(\n cfg,\n transformer_predictor_in_channels,\n mask_classification=True,\n ),\n }\n\n def forward(self, features, tasks, mask=None):\n return self.layers(features, tasks, mask)\n\n def layers(self, features, tasks, mask=None):\n mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features)\n \n if self.transformer_in_feature == \"multi_scale_pixel_decoder\":\n predictions = self.predictor(multi_scale_features, mask_features, tasks, mask)\n else:\n if self.transformer_in_feature == \"transformer_encoder\":\n assert (\n transformer_encoder_features is not None\n ), \"Please use the TransformerEncoderPixelDecoder.\"\n predictions = self.predictor(transformer_encoder_features, mask_features, mask)\n elif self.transformer_in_feature == \"pixel_embedding\":\n predictions = self.predictor(mask_features, mask_features, mask)\n else:\n predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)\n return predictions","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head.forward","uri":"program://OneFormer/function/oneformer.modeling.meta_arch.oneformer_head.forward#L117-L118","kind":"function","name":"forward","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":117,"end_line":118,"context_start_line":97,"context_end_line":135,"code":" transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM\n else:\n transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels\n\n return {\n \"input_shape\": {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n },\n \"ignore_value\": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n \"num_classes\": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,\n \"pixel_decoder\": build_pixel_decoder(cfg, input_shape),\n \"loss_weight\": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,\n \"transformer_in_feature\": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE,\n \"transformer_predictor\": build_transformer_decoder(\n cfg,\n transformer_predictor_in_channels,\n mask_classification=True,\n ),\n }\n\n def forward(self, features, tasks, mask=None):\n return self.layers(features, tasks, mask)\n\n def layers(self, features, tasks, mask=None):\n mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features)\n \n if self.transformer_in_feature == \"multi_scale_pixel_decoder\":\n predictions = self.predictor(multi_scale_features, mask_features, tasks, mask)\n else:\n if self.transformer_in_feature == \"transformer_encoder\":\n assert (\n transformer_encoder_features is not None\n ), \"Please use the TransformerEncoderPixelDecoder.\"\n predictions = self.predictor(transformer_encoder_features, mask_features, mask)\n elif self.transformer_in_feature == \"pixel_embedding\":\n predictions = self.predictor(mask_features, mask_features, mask)\n else:\n predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)\n return predictions","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:oneformer.modeling.meta_arch.oneformer_head.layers","uri":"program://OneFormer/function/oneformer.modeling.meta_arch.oneformer_head.layers#L120-L135","kind":"function","name":"layers","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":120,"end_line":135,"context_start_line":100,"context_end_line":135,"code":"\n return {\n \"input_shape\": {\n k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES\n },\n \"ignore_value\": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n \"num_classes\": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,\n \"pixel_decoder\": build_pixel_decoder(cfg, input_shape),\n \"loss_weight\": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,\n \"transformer_in_feature\": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE,\n \"transformer_predictor\": build_transformer_decoder(\n cfg,\n transformer_predictor_in_channels,\n mask_classification=True,\n ),\n }\n\n def forward(self, features, tasks, mask=None):\n return self.layers(features, tasks, mask)\n\n def layers(self, features, tasks, mask=None):\n mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features)\n \n if self.transformer_in_feature == \"multi_scale_pixel_decoder\":\n predictions = self.predictor(multi_scale_features, mask_features, tasks, mask)\n else:\n if self.transformer_in_feature == \"transformer_encoder\":\n assert (\n transformer_encoder_features is not None\n ), \"Please use the TransformerEncoderPixelDecoder.\"\n predictions = self.predictor(transformer_encoder_features, mask_features, mask)\n elif self.transformer_in_feature == \"pixel_embedding\":\n predictions = self.predictor(mask_features, mask_features, mask)\n else:\n predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)\n return predictions","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.fg_ids","uri":"program://OneFormer/module/datasets.fg_ids#L1-L108","kind":"module","name":"datasets.fg_ids","path":"datasets/fg_ids.py","language":"python","start_line":1,"end_line":108,"context_start_line":1,"context_end_line":108,"code":"ADE20K_FG_IDS = {\n 1: 8,\n 2:\t9,\n 3:\t11,\n 4:\t13,\n 5:\t15,\n 5:\t15,\n 6:\t16,\n 7:\t19,\n 8:\t20,\n 9:\t21,\n 10:\t23,\n 11:\t24,\n 12:\t25,\n 13:\t28,\n 14:\t31,\n 15:\t32,\n 16:\t33,\n 17:\t34,\n 18:\t36,\n 18:\t36,\n 19:\t37,\n 20:\t38,\n 21:\t39,\n 22:\t40,\n 23:\t42,\n 24:\t43,\n 25:\t44,\n 26:\t45,\n 27:\t46,\n 28:\t48,\n 29:\t50,\n 30:\t51,\n 31:\t54,\n 32:\t56,\n 33:\t57,\n 34:\t58,\n 35:\t59,\n 36:\t63,\n 37:\t65,\n 38:\t66,\n 39:\t67,\n 40:\t68,\n 41:\t70,\n 42:\t71,\n 43:\t72,\n 44:\t73,\n 45:\t74,\n 46:\t75,\n 47:\t76,\n 48:\t77,\n 49:\t79,\n 50:\t81,\n 51:\t82,\n 52:\t83,\n 53:\t84,\n 54:\t86,\n 55:\t87,\n 56:\t88,\n 57:\t89,\n 57:\t89,\n 58:\t90,\n 59:\t91,\n 60:\t93,\n 61:\t94,\n 62:\t96,\n 63:\t98,\n 64:\t99,\n 65:\t103,\n 66:\t104,\n 67:\t105,\n 68:\t108,\n 69:\t109,\n 70:\t111,\n 71:\t112,\n 72:\t113,\n 73:\t116,\n 74:\t117,\n 75:\t119,\n 76:\t120,\n 77:\t121,\n 78:\t122,\n 79:\t124,\n 80:\t125,\n 81:\t126,\n 82:\t127,\n 83:\t128,\n 84:\t130,\n 85:\t131,\n 86:\t133,\n 87:\t134,\n 88:\t135,\n 89:\t136,\n 90:\t137,\n 91:\t138,\n 92:\t139,\n 93:\t140,\n 94:\t143,\n 95:\t144,\n 96:\t145,\n 97:\t147,\n 98:\t148,\n 99:\t149,\n 100: 150\n }\n\n\nCITYSCAPES_FG_NAMES = ['person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle']","source_hash":"f4d3eb06bb04ec3067b02378368d5943a91f3352680fe634bc990a467e7fe9eb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_coco_semantic_annos_from_panoptic_annos","uri":"program://OneFormer/module/datasets.prepare_coco_semantic_annos_from_panoptic_annos#L1-L84","kind":"module","name":"datasets.prepare_coco_semantic_annos_from_panoptic_annos","path":"datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","language":"python","start_line":1,"end_line":84,"context_start_line":1,"context_end_line":84,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport functools\nimport json\nimport multiprocessing as mp\nimport numpy as np\nimport os\nimport time\nfrom fvcore.common.download import download\nfrom panopticapi.utils import rgb2id\nfrom PIL import Image\n\nfrom detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\n\n\ndef _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):\n panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)\n panoptic = rgb2id(panoptic)\n output = np.zeros_like(panoptic, dtype=np.uint8) + 255\n for seg in segments:\n cat_id = seg[\"category_id\"]\n new_cat_id = id_map[cat_id]\n output[panoptic == seg[\"id\"]] = new_cat_id\n Image.fromarray(output).save(output_semantic)\n\n\ndef separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):\n \"\"\"\n Create semantic segmentation annotations from panoptic segmentation\n annotations, to be used by PanopticFPN.\n It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.\n It maps all stuff categories to contiguous ids starting from 1.\n Args:\n panoptic_json (str): path to the panoptic json file, in COCO's format.\n panoptic_root (str): a directory with panoptic annotation files, in COCO's format.\n sem_seg_root (str): a directory to output semantic annotation files\n categories (list[dict]): category metadata. Each dict needs to have:\n \"id\": corresponds to the \"category_id\" in the json annotations\n \"isthing\": 0 or 1\n \"\"\"\n os.makedirs(sem_seg_root, exist_ok=True)\n\n id_map = {} # map from category id to id in the output semantic annotation\n assert len(categories) <= 254\n for i, k in enumerate(categories):\n id_map[k[\"id\"]] = i\n # what is id = 0?\n # id_map[0] = 255\n print(id_map)\n\n with open(panoptic_json) as f:\n obj = json.load(f)\n\n pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))\n\n def iter_annotations():\n for anno in obj[\"annotations\"]:\n file_name = anno[\"file_name\"]\n segments = anno[\"segments_info\"]\n input = os.path.join(panoptic_root, file_name)\n output = os.path.join(sem_seg_root, file_name)\n yield input, output, segments\n\n print(\"Start writing to {} ...\".format(sem_seg_root))\n start = time.time()\n pool.starmap(\n functools.partial(_process_panoptic_to_semantic, id_map=id_map),\n iter_annotations(),\n chunksize=100,\n )\n print(\"Finished. time: {:.2f}s\".format(time.time() - start))\n\n\nif __name__ == \"__main__\":\n dataset_dir = os.path.join(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\"), \"coco\")\n for s in [\"val2017\", \"train2017\"]:\n separate_coco_semantic_from_panoptic(\n os.path.join(dataset_dir, \"annotations/panoptic_{}.json\".format(s)),\n os.path.join(dataset_dir, \"panoptic_{}\".format(s)),\n os.path.join(dataset_dir, \"panoptic_semseg_{}\".format(s)),\n COCO_CATEGORIES,\n )","source_hash":"63a9f06c7833676fe94a285a28b9dbbd5bb530726148eadb365c0a791c502564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_coco_semantic_annos_from_panoptic_annos._process_panoptic_to_semantic","uri":"program://OneFormer/function/datasets.prepare_coco_semantic_annos_from_panoptic_annos._process_panoptic_to_semantic#L18-L26","kind":"function","name":"_process_panoptic_to_semantic","path":"datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","language":"python","start_line":18,"end_line":26,"context_start_line":1,"context_end_line":46,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport functools\nimport json\nimport multiprocessing as mp\nimport numpy as np\nimport os\nimport time\nfrom fvcore.common.download import download\nfrom panopticapi.utils import rgb2id\nfrom PIL import Image\n\nfrom detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\n\n\ndef _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):\n panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)\n panoptic = rgb2id(panoptic)\n output = np.zeros_like(panoptic, dtype=np.uint8) + 255\n for seg in segments:\n cat_id = seg[\"category_id\"]\n new_cat_id = id_map[cat_id]\n output[panoptic == seg[\"id\"]] = new_cat_id\n Image.fromarray(output).save(output_semantic)\n\n\ndef separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):\n \"\"\"\n Create semantic segmentation annotations from panoptic segmentation\n annotations, to be used by PanopticFPN.\n It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.\n It maps all stuff categories to contiguous ids starting from 1.\n Args:\n panoptic_json (str): path to the panoptic json file, in COCO's format.\n panoptic_root (str): a directory with panoptic annotation files, in COCO's format.\n sem_seg_root (str): a directory to output semantic annotation files\n categories (list[dict]): category metadata. Each dict needs to have:\n \"id\": corresponds to the \"category_id\" in the json annotations\n \"isthing\": 0 or 1\n \"\"\"\n os.makedirs(sem_seg_root, exist_ok=True)\n\n id_map = {} # map from category id to id in the output semantic annotation\n assert len(categories) <= 254","source_hash":"63a9f06c7833676fe94a285a28b9dbbd5bb530726148eadb365c0a791c502564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_coco_semantic_annos_from_panoptic_annos.separate_coco_semantic_from_panoptic","uri":"program://OneFormer/function/datasets.prepare_coco_semantic_annos_from_panoptic_annos.separate_coco_semantic_from_panoptic#L29-L73","kind":"function","name":"separate_coco_semantic_from_panoptic","path":"datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","language":"python","start_line":29,"end_line":73,"context_start_line":9,"context_end_line":84,"code":"import os\nimport time\nfrom fvcore.common.download import download\nfrom panopticapi.utils import rgb2id\nfrom PIL import Image\n\nfrom detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\n\n\ndef _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):\n panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)\n panoptic = rgb2id(panoptic)\n output = np.zeros_like(panoptic, dtype=np.uint8) + 255\n for seg in segments:\n cat_id = seg[\"category_id\"]\n new_cat_id = id_map[cat_id]\n output[panoptic == seg[\"id\"]] = new_cat_id\n Image.fromarray(output).save(output_semantic)\n\n\ndef separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):\n \"\"\"\n Create semantic segmentation annotations from panoptic segmentation\n annotations, to be used by PanopticFPN.\n It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.\n It maps all stuff categories to contiguous ids starting from 1.\n Args:\n panoptic_json (str): path to the panoptic json file, in COCO's format.\n panoptic_root (str): a directory with panoptic annotation files, in COCO's format.\n sem_seg_root (str): a directory to output semantic annotation files\n categories (list[dict]): category metadata. Each dict needs to have:\n \"id\": corresponds to the \"category_id\" in the json annotations\n \"isthing\": 0 or 1\n \"\"\"\n os.makedirs(sem_seg_root, exist_ok=True)\n\n id_map = {} # map from category id to id in the output semantic annotation\n assert len(categories) <= 254\n for i, k in enumerate(categories):\n id_map[k[\"id\"]] = i\n # what is id = 0?\n # id_map[0] = 255\n print(id_map)\n\n with open(panoptic_json) as f:\n obj = json.load(f)\n\n pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))\n\n def iter_annotations():\n for anno in obj[\"annotations\"]:\n file_name = anno[\"file_name\"]\n segments = anno[\"segments_info\"]\n input = os.path.join(panoptic_root, file_name)\n output = os.path.join(sem_seg_root, file_name)\n yield input, output, segments\n\n print(\"Start writing to {} ...\".format(sem_seg_root))\n start = time.time()\n pool.starmap(\n functools.partial(_process_panoptic_to_semantic, id_map=id_map),\n iter_annotations(),\n chunksize=100,\n )\n print(\"Finished. time: {:.2f}s\".format(time.time() - start))\n\n\nif __name__ == \"__main__\":\n dataset_dir = os.path.join(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\"), \"coco\")\n for s in [\"val2017\", \"train2017\"]:\n separate_coco_semantic_from_panoptic(\n os.path.join(dataset_dir, \"annotations/panoptic_{}.json\".format(s)),\n os.path.join(dataset_dir, \"panoptic_{}\".format(s)),\n os.path.join(dataset_dir, \"panoptic_semseg_{}\".format(s)),\n COCO_CATEGORIES,\n )","source_hash":"63a9f06c7833676fe94a285a28b9dbbd5bb530726148eadb365c0a791c502564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_coco_semantic_annos_from_panoptic_annos.iter_annotations","uri":"program://OneFormer/function/datasets.prepare_coco_semantic_annos_from_panoptic_annos.iter_annotations#L58-L64","kind":"function","name":"iter_annotations","path":"datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","language":"python","start_line":58,"end_line":64,"context_start_line":38,"context_end_line":84,"code":" sem_seg_root (str): a directory to output semantic annotation files\n categories (list[dict]): category metadata. Each dict needs to have:\n \"id\": corresponds to the \"category_id\" in the json annotations\n \"isthing\": 0 or 1\n \"\"\"\n os.makedirs(sem_seg_root, exist_ok=True)\n\n id_map = {} # map from category id to id in the output semantic annotation\n assert len(categories) <= 254\n for i, k in enumerate(categories):\n id_map[k[\"id\"]] = i\n # what is id = 0?\n # id_map[0] = 255\n print(id_map)\n\n with open(panoptic_json) as f:\n obj = json.load(f)\n\n pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))\n\n def iter_annotations():\n for anno in obj[\"annotations\"]:\n file_name = anno[\"file_name\"]\n segments = anno[\"segments_info\"]\n input = os.path.join(panoptic_root, file_name)\n output = os.path.join(sem_seg_root, file_name)\n yield input, output, segments\n\n print(\"Start writing to {} ...\".format(sem_seg_root))\n start = time.time()\n pool.starmap(\n functools.partial(_process_panoptic_to_semantic, id_map=id_map),\n iter_annotations(),\n chunksize=100,\n )\n print(\"Finished. time: {:.2f}s\".format(time.time() - start))\n\n\nif __name__ == \"__main__\":\n dataset_dir = os.path.join(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\"), \"coco\")\n for s in [\"val2017\", \"train2017\"]:\n separate_coco_semantic_from_panoptic(\n os.path.join(dataset_dir, \"annotations/panoptic_{}.json\".format(s)),\n os.path.join(dataset_dir, \"panoptic_{}\".format(s)),\n os.path.join(dataset_dir, \"panoptic_semseg_{}\".format(s)),\n COCO_CATEGORIES,\n )","source_hash":"63a9f06c7833676fe94a285a28b9dbbd5bb530726148eadb365c0a791c502564","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_ade20k_sem_seg","uri":"program://OneFormer/module/datasets.prepare_ade20k_sem_seg#L1-L27","kind":"module","name":"datasets.prepare_ade20k_sem_seg","path":"datasets/prepare_ade20k_sem_seg.py","language":"python","start_line":1,"end_line":27,"context_start_line":1,"context_end_line":27,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport tqdm\nfrom PIL import Image\n\n\ndef convert(input, output):\n img = np.asarray(Image.open(input))\n assert img.dtype == np.uint8\n img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1\n Image.fromarray(img).save(output)\n\n\nif __name__ == \"__main__\":\n dataset_dir = Path(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")) / \"ADEChallengeData2016\"\n for name in [\"training\", \"validation\"]:\n annotation_dir = dataset_dir / \"annotations\" / name\n output_dir = dataset_dir / \"annotations_detectron2\" / name\n output_dir.mkdir(parents=True, exist_ok=True)\n for file in tqdm.tqdm(list(annotation_dir.iterdir())):\n output_file = output_dir / file.name\n convert(file, output_file)","source_hash":"7d6a6dfe4fc6314716c7a9964edfc08e882b80e9468f666360cb297572378981","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_ade20k_sem_seg.convert","uri":"program://OneFormer/function/datasets.prepare_ade20k_sem_seg.convert#L12-L16","kind":"function","name":"convert","path":"datasets/prepare_ade20k_sem_seg.py","language":"python","start_line":12,"end_line":16,"context_start_line":1,"context_end_line":27,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport tqdm\nfrom PIL import Image\n\n\ndef convert(input, output):\n img = np.asarray(Image.open(input))\n assert img.dtype == np.uint8\n img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1\n Image.fromarray(img).save(output)\n\n\nif __name__ == \"__main__\":\n dataset_dir = Path(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")) / \"ADEChallengeData2016\"\n for name in [\"training\", \"validation\"]:\n annotation_dir = dataset_dir / \"annotations\" / name\n output_dir = dataset_dir / \"annotations_detectron2\" / name\n output_dir.mkdir(parents=True, exist_ok=True)\n for file in tqdm.tqdm(list(annotation_dir.iterdir())):\n output_file = output_dir / file.name\n convert(file, output_file)","source_hash":"7d6a6dfe4fc6314716c7a9964edfc08e882b80e9468f666360cb297572378981","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_ade20k_ins_seg","uri":"program://OneFormer/module/datasets.prepare_ade20k_ins_seg#L1-L112","kind":"module","name":"datasets.prepare_ade20k_ins_seg","path":"datasets/prepare_ade20k_ins_seg.py","language":"python","start_line":1,"end_line":112,"context_start_line":1,"context_end_line":112,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport glob\nimport json\nimport os\nfrom collections import Counter\n\nimport numpy as np\nimport tqdm\nfrom panopticapi.utils import IdGenerator, save_json\nfrom PIL import Image\nimport pycocotools.mask as mask_util\n\n\nif __name__ == \"__main__\":\n dataset_dir = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\n\n for name, dirname in [(\"train\", \"training\"), (\"val\", \"validation\")]:\n image_dir = os.path.join(dataset_dir, f\"ADEChallengeData2016/images/{dirname}/\")\n instance_dir = os.path.join(\n dataset_dir, f\"ADEChallengeData2016/annotations_instance/{dirname}/\"\n )\n\n # img_id = 0\n ann_id = 1\n\n # json\n out_file = os.path.join(dataset_dir, f\"ADEChallengeData2016/ade20k_instance_{name}.json\")\n\n # json config\n instance_config_file = \"datasets/ade20k_instance_imgCatIds.json\"\n with open(instance_config_file) as f:\n category_dict = json.load(f)[\"categories\"]\n\n # load catid mapping\n # it is important to share category id for both instance and panoptic annotations\n mapping_file = \"datasets/ade20k_instance_catid_mapping.txt\"\n with open(mapping_file) as f:\n map_id = {}\n for i, line in enumerate(f.readlines()):\n if i == 0:\n continue\n ins_id, sem_id, _ = line.strip().split()\n # shift id by 1 because we want it to start from 0!\n # ignore_label becomes 255\n map_id[int(ins_id)] = int(sem_id) - 1\n\n for cat in category_dict:\n cat[\"id\"] = map_id[cat[\"id\"]]\n\n filenames = sorted(glob.glob(os.path.join(image_dir, \"*.jpg\")))\n\n ann_dict = {}\n images = []\n annotations = []\n\n for idx, filename in enumerate(tqdm.tqdm(filenames)):\n image = {}\n image_id = os.path.basename(filename).split(\".\")[0]\n\n image[\"id\"] = image_id\n image[\"file_name\"] = os.path.basename(filename)\n\n original_format = np.array(Image.open(filename))\n image[\"width\"] = original_format.shape[1]\n image[\"height\"] = original_format.shape[0]\n\n images.append(image)\n\n filename_instance = os.path.join(instance_dir, image_id + \".png\")\n ins_seg = np.asarray(Image.open(filename_instance))\n assert ins_seg.dtype == np.uint8\n\n instance_cat_ids = ins_seg[..., 0]\n # instance id starts from 1!\n # because 0 is reserved as VOID label\n instance_ins_ids = ins_seg[..., 1]\n\n # process things\n for thing_id in np.unique(instance_ins_ids):\n if thing_id == 0:\n continue\n mask = instance_ins_ids == thing_id\n instance_cat_id = np.unique(instance_cat_ids[mask])\n assert len(instance_cat_id) == 1\n\n anno = {}\n anno['id'] = ann_id\n ann_id += 1\n anno['image_id'] = image['id']\n anno[\"iscrowd\"] = int(0)\n anno[\"category_id\"] = int(map_id[instance_cat_id[0]])\n\n inds = np.nonzero(mask)\n ymin, ymax = inds[0].min(), inds[0].max()\n xmin, xmax = inds[1].min(), inds[1].max()\n anno[\"bbox\"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)]\n # if xmax <= xmin or ymax <= ymin:\n # continue\n rle = mask_util.encode(np.array(mask[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n anno[\"segmentation\"] = rle\n anno[\"area\"] = int(mask_util.area(rle))\n annotations.append(anno)\n\n # save this\n ann_dict['images'] = images\n ann_dict['categories'] = category_dict\n ann_dict['annotations'] = annotations\n\n save_json(ann_dict, out_file)","source_hash":"870b14809b72b1b68fc298faed61158d03ebdff3722f4a6b41988c82dfe41600","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.prepare_ade20k_pan_seg","uri":"program://OneFormer/module/datasets.prepare_ade20k_pan_seg#L1-L500","kind":"module","name":"datasets.prepare_ade20k_pan_seg","path":"datasets/prepare_ade20k_pan_seg.py","language":"python","start_line":1,"end_line":500,"context_start_line":1,"context_end_line":500,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport glob\nimport json\nimport os\nfrom collections import Counter\n\nimport numpy as np\nimport tqdm\nfrom panopticapi.utils import IdGenerator, save_json\nfrom PIL import Image\n\nADE20K_SEM_SEG_CATEGORIES = [\n \"wall\",\n \"building\",\n \"sky\",\n \"floor\",\n \"tree\",\n \"ceiling\",\n \"road, route\",\n \"bed\",\n \"window \",\n \"grass\",\n \"cabinet\",\n \"sidewalk, pavement\",\n \"person\",\n \"earth, ground\",\n \"door\",\n \"table\",\n \"mountain, mount\",\n \"plant\",\n \"curtain\",\n \"chair\",\n \"car\",\n \"water\",\n \"painting, picture\",\n \"sofa\",\n \"shelf\",\n \"house\",\n \"sea\",\n \"mirror\",\n \"rug\",\n \"field\",\n \"armchair\",\n \"seat\",\n \"fence\",\n \"desk\",\n \"rock, stone\",\n \"wardrobe, closet, press\",\n \"lamp\",\n \"tub\",\n \"rail\",\n \"cushion\",\n \"base, pedestal, stand\",\n \"box\",\n \"column, pillar\",\n \"signboard, sign\",\n \"chest of drawers, chest, bureau, dresser\",\n \"counter\",\n \"sand\",\n \"sink\",\n \"skyscraper\",\n \"fireplace\",\n \"refrigerator, icebox\",\n \"grandstand, covered stand\",\n \"path\",\n \"stairs\",\n \"runway\",\n \"case, display case, showcase, vitrine\",\n \"pool table, billiard table, snooker table\",\n \"pillow\",\n \"screen door, screen\",\n \"stairway, staircase\",\n \"river\",\n \"bridge, span\",\n \"bookcase\",\n \"blind, screen\",\n \"coffee table\",\n \"toilet, can, commode, crapper, pot, potty, stool, throne\",\n \"flower\",\n \"book\",\n \"hill\",\n \"bench\",\n \"countertop\",\n \"stove\",\n \"palm, palm tree\",\n \"kitchen island\",\n \"computer\",\n \"swivel chair\",\n \"boat\",\n \"bar\",\n \"arcade machine\",\n \"hovel, hut, hutch, shack, shanty\",\n \"bus\",\n \"towel\",\n \"light\",\n \"truck\",\n \"tower\",\n \"chandelier\",\n \"awning, sunshade, sunblind\",\n \"street lamp\",\n \"booth\",\n \"tv\",\n \"plane\",\n \"dirt track\",\n \"clothes\",\n \"pole\",\n \"land, ground, soil\",\n \"bannister, banister, balustrade, balusters, handrail\",\n \"escalator, moving staircase, moving stairway\",\n \"ottoman, pouf, pouffe, puff, hassock\",\n \"bottle\",\n \"buffet, counter, sideboard\",\n \"poster, posting, placard, notice, bill, card\",\n \"stage\",\n \"van\",\n \"ship\",\n \"fountain\",\n \"conveyer belt, conveyor belt, conveyer, conveyor, transporter\",\n \"canopy\",\n \"washer, automatic washer, washing machine\",\n \"plaything, toy\",\n \"pool\",\n \"stool\",\n \"barrel, cask\",\n \"basket, handbasket\",\n \"falls\",\n \"tent\",\n \"bag\",\n \"minibike, motorbike\",\n \"cradle\",\n \"oven\",\n \"ball\",\n \"food, solid food\",\n \"step, stair\",\n \"tank, storage tank\",\n \"trade name\",\n \"microwave\",\n \"pot\",\n \"animal\",\n \"bicycle\",\n \"lake\",\n \"dishwasher\",\n \"screen\",\n \"blanket, cover\",\n \"sculpture\",\n \"hood, exhaust hood\",\n \"sconce\",\n \"vase\",\n \"traffic light\",\n \"tray\",\n \"trash can\",\n \"fan\",\n \"pier\",\n \"crt screen\",\n \"plate\",\n \"monitor\",\n \"bulletin board\",\n \"shower\",\n \"radiator\",\n \"glass, drinking glass\",\n \"clock\",\n \"flag\", # noqa\n]\n\nPALETTE = [\n [120, 120, 120],\n [180, 120, 120],\n [6, 230, 230],\n [80, 50, 50],\n [4, 200, 3],\n [120, 120, 80],\n [140, 140, 140],\n [204, 5, 255],\n [230, 230, 230],\n [4, 250, 7],\n [224, 5, 255],\n [235, 255, 7],\n [150, 5, 61],\n [120, 120, 70],\n [8, 255, 51],\n [255, 6, 82],\n [143, 255, 140],\n [204, 255, 4],\n [255, 51, 7],\n [204, 70, 3],\n [0, 102, 200],\n [61, 230, 250],\n [255, 6, 51],\n [11, 102, 255],\n [255, 7, 71],\n [255, 9, 224],\n [9, 7, 230],\n [220, 220, 220],\n [255, 9, 92],\n [112, 9, 255],\n [8, 255, 214],\n [7, 255, 224],\n [255, 184, 6],\n [10, 255, 71],\n [255, 41, 10],\n [7, 255, 255],\n [224, 255, 8],\n [102, 8, 255],\n [255, 61, 6],\n [255, 194, 7],\n [255, 122, 8],\n [0, 255, 20],\n [255, 8, 41],\n [255, 5, 153],\n [6, 51, 255],\n [235, 12, 255],\n [160, 150, 20],\n [0, 163, 255],\n [140, 140, 200],\n [250, 10, 15],\n [20, 255, 0],\n [31, 255, 0],\n [255, 31, 0],\n [255, 224, 0],\n [153, 255, 0],\n [0, 0, 255],\n [255, 71, 0],\n [0, 235, 255],\n [0, 173, 255],\n [31, 0, 255],\n [11, 200, 200],\n [255, 82, 0],\n [0, 255, 245],\n [0, 61, 255],\n [0, 255, 112],\n [0, 255, 133],\n [255, 0, 0],\n [255, 163, 0],\n [255, 102, 0],\n [194, 255, 0],\n [0, 143, 255],\n [51, 255, 0],\n [0, 82, 255],\n [0, 255, 41],\n [0, 255, 173],\n [10, 0, 255],\n [173, 255, 0],\n [0, 255, 153],\n [255, 92, 0],\n [255, 0, 255],\n [255, 0, 245],\n [255, 0, 102],\n [255, 173, 0],\n [255, 0, 20],\n [255, 184, 184],\n [0, 31, 255],\n [0, 255, 61],\n [0, 71, 255],\n [255, 0, 204],\n [0, 255, 194],\n [0, 255, 82],\n [0, 10, 255],\n [0, 112, 255],\n [51, 0, 255],\n [0, 194, 255],\n [0, 122, 255],\n [0, 255, 163],\n [255, 153, 0],\n [0, 255, 10],\n [255, 112, 0],\n [143, 255, 0],\n [82, 0, 255],\n [163, 255, 0],\n [255, 235, 0],\n [8, 184, 170],\n [133, 0, 255],\n [0, 255, 92],\n [184, 0, 255],\n [255, 0, 31],\n [0, 184, 255],\n [0, 214, 255],\n [255, 0, 112],\n [92, 255, 0],\n [0, 224, 255],\n [112, 224, 255],\n [70, 184, 160],\n [163, 0, 255],\n [153, 0, 255],\n [71, 255, 0],\n [255, 0, 163],\n [255, 204, 0],\n [255, 0, 143],\n [0, 255, 235],\n [133, 255, 0],\n [255, 0, 235],\n [245, 0, 255],\n [255, 0, 122],\n [255, 245, 0],\n [10, 190, 212],\n [214, 255, 0],\n [0, 204, 255],\n [20, 0, 255],\n [255, 255, 0],\n [0, 153, 255],\n [0, 41, 255],\n [0, 255, 204],\n [41, 0, 255],\n [41, 255, 0],\n [173, 0, 255],\n [0, 245, 255],\n [71, 0, 255],\n [122, 0, 255],\n [0, 255, 184],\n [0, 92, 255],\n [184, 255, 0],\n [0, 133, 255],\n [255, 214, 0],\n [25, 194, 194],\n [102, 255, 0],\n [92, 0, 255],\n]\n\n\nif __name__ == \"__main__\":\n dataset_dir = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\n\n for name, dirname in [(\"train\", \"training\"), (\"val\", \"validation\")]:\n image_dir = os.path.join(dataset_dir, f\"ADEChallengeData2016/images/{dirname}/\")\n semantic_dir = os.path.join(dataset_dir, f\"ADEChallengeData2016/annotations/{dirname}/\")\n instance_dir = os.path.join(\n dataset_dir, f\"ADEChallengeData2016/annotations_instance/{dirname}/\"\n )\n\n # folder to store panoptic PNGs\n out_folder = os.path.join(dataset_dir, f\"ADEChallengeData2016/ade20k_panoptic_{name}/\")\n # json with segmentations information\n out_file = os.path.join(dataset_dir, f\"ADEChallengeData2016/ade20k_panoptic_{name}.json\")\n\n if not os.path.isdir(out_folder):\n print(\"Creating folder {} for panoptic segmentation PNGs\".format(out_folder))\n os.mkdir(out_folder)\n\n # json config\n config_file = \"datasets/ade20k_instance_imgCatIds.json\"\n with open(config_file) as f:\n config = json.load(f)\n\n # load catid mapping\n mapping_file = \"datasets/ade20k_instance_catid_mapping.txt\"\n with open(mapping_file) as f:\n map_id = {}\n for i, line in enumerate(f.readlines()):\n if i == 0:\n continue\n ins_id, sem_id, _ = line.strip().split()\n # shift id by 1 because we want it to start from 0!\n # ignore_label becomes 255\n map_id[int(ins_id) - 1] = int(sem_id) - 1\n\n ADE20K_150_CATEGORIES = []\n for cat_id, cat_name in enumerate(ADE20K_SEM_SEG_CATEGORIES):\n ADE20K_150_CATEGORIES.append(\n {\n \"name\": cat_name,\n \"id\": cat_id,\n \"isthing\": int(cat_id in map_id.values()),\n \"color\": PALETTE[cat_id],\n }\n )\n categories_dict = {cat[\"id\"]: cat for cat in ADE20K_150_CATEGORIES}\n\n panoptic_json_categories = ADE20K_150_CATEGORIES[:]\n panoptic_json_images = []\n panoptic_json_annotations = []\n\n filenames = sorted(glob.glob(os.path.join(image_dir, \"*.jpg\")))\n for idx, filename in enumerate(tqdm.tqdm(filenames)):\n panoptic_json_image = {}\n panoptic_json_annotation = {}\n\n image_id = os.path.basename(filename).split(\".\")[0]\n\n panoptic_json_image[\"id\"] = image_id\n panoptic_json_image[\"file_name\"] = os.path.basename(filename)\n\n original_format = np.array(Image.open(filename))\n panoptic_json_image[\"width\"] = original_format.shape[1]\n panoptic_json_image[\"height\"] = original_format.shape[0]\n\n pan_seg = np.zeros(\n (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8\n )\n id_generator = IdGenerator(categories_dict)\n\n filename_semantic = os.path.join(semantic_dir, image_id + \".png\")\n filename_instance = os.path.join(instance_dir, image_id + \".png\")\n\n sem_seg = np.asarray(Image.open(filename_semantic))\n ins_seg = np.asarray(Image.open(filename_instance))\n\n assert sem_seg.dtype == np.uint8\n assert ins_seg.dtype == np.uint8\n\n semantic_cat_ids = sem_seg - 1\n instance_cat_ids = ins_seg[..., 0] - 1\n # instance id starts from 1!\n # because 0 is reserved as VOID label\n instance_ins_ids = ins_seg[..., 1]\n\n segm_info = []\n\n # NOTE: there is some overlap between semantic and instance annotation\n # thus we paste stuffs first\n\n # process stuffs\n for semantic_cat_id in np.unique(semantic_cat_ids):\n if semantic_cat_id == 255:\n continue\n if categories_dict[semantic_cat_id][\"isthing\"]:\n continue\n mask = semantic_cat_ids == semantic_cat_id\n # should not have any overlap\n assert pan_seg[mask].sum() == 0\n\n segment_id, color = id_generator.get_id_and_color(semantic_cat_id)\n pan_seg[mask] = color\n\n area = np.sum(mask) # segment area computation\n # bbox computation for a segment\n hor = np.sum(mask, axis=0)\n hor_idx = np.nonzero(hor)[0]\n x = hor_idx[0]\n width = hor_idx[-1] - x + 1\n vert = np.sum(mask, axis=1)\n vert_idx = np.nonzero(vert)[0]\n y = vert_idx[0]\n height = vert_idx[-1] - y + 1\n bbox = [int(x), int(y), int(width), int(height)]\n\n segm_info.append(\n {\n \"id\": int(segment_id),\n \"category_id\": int(semantic_cat_id),\n \"area\": int(area),\n \"bbox\": bbox,\n \"iscrowd\": 0,\n }\n )\n\n # process things\n for thing_id in np.unique(instance_ins_ids):\n if thing_id == 0:\n continue\n mask = instance_ins_ids == thing_id\n instance_cat_id = np.unique(instance_cat_ids[mask])\n assert len(instance_cat_id) == 1\n\n semantic_cat_id = map_id[instance_cat_id[0]]\n\n segment_id, color = id_generator.get_id_and_color(semantic_cat_id)\n pan_seg[mask] = color\n\n area = np.sum(mask) # segment area computation\n # bbox computation for a segment\n hor = np.sum(mask, axis=0)\n hor_idx = np.nonzero(hor)[0]\n x = hor_idx[0]\n width = hor_idx[-1] - x + 1\n vert = np.sum(mask, axis=1)\n vert_idx = np.nonzero(vert)[0]\n y = vert_idx[0]\n height = vert_idx[-1] - y + 1\n bbox = [int(x), int(y), int(width), int(height)]\n\n segm_info.append(\n {\n \"id\": int(segment_id),\n \"category_id\": int(semantic_cat_id),\n \"area\": int(area),\n \"bbox\": bbox,\n \"iscrowd\": 0,\n }\n )\n\n panoptic_json_annotation = {\n \"image_id\": image_id,\n \"file_name\": image_id + \".png\",\n \"segments_info\": segm_info,\n }\n\n Image.fromarray(pan_seg).save(os.path.join(out_folder, image_id + \".png\"))\n\n panoptic_json_images.append(panoptic_json_image)\n panoptic_json_annotations.append(panoptic_json_annotation)\n\n # save this\n d = {\n \"images\": panoptic_json_images,\n \"annotations\": panoptic_json_annotations,\n \"categories\": panoptic_json_categories,\n }\n\n save_json(d, out_file)","source_hash":"17776f14ec44650131fdda28233f06e18c3d8bee04375b4206ee1675bf89b292","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.panoptic2detection_coco_format","uri":"program://OneFormer/module/datasets.panoptic2detection_coco_format#L1-L152","kind":"module","name":"datasets.panoptic2detection_coco_format","path":"datasets/panoptic2detection_coco_format.py","language":"python","start_line":1,"end_line":152,"context_start_line":1,"context_end_line":152,"code":"#!/usr/bin/env python\n# ------------------------------------------------------------------------------\n# Reference: https://github.com/cocodataset/panopticapi/blob/master/converters/panoptic2detection_coco_format.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n'''\nThis script converts panoptic COCO format to detection COCO format. More\ninformation about the formats can be found here:\nhttp://cocodataset.org/#format-data. All segments will be stored in RLE format.\n\nAdditional option:\n- using option '--things_only' the script can discard all stuff\nsegments, saving segments of things classes only.\n'''\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nimport os, sys\nimport argparse\nimport numpy as np\nimport json\nimport time\nimport multiprocessing\n\nimport PIL.Image as Image\n\nfrom panopticapi.utils import get_traceback, rgb2id, save_json\n\ntry:\n # set up path for pycocotools\n # sys.path.append('./cocoapi-master/PythonAPI/')\n from pycocotools import mask as COCOmask\nexcept Exception:\n raise Exception(\"Please install pycocotools module from https://github.com/cocodataset/cocoapi\")\n\n@get_traceback\ndef convert_panoptic_to_detection_coco_format_single_core(\n proc_id, annotations_set, categories, segmentations_folder, things_only\n):\n annotations_detection = []\n for working_idx, annotation in enumerate(annotations_set):\n if working_idx % 100 == 0:\n print('Core: {}, {} from {} images processed'.format(proc_id,\n working_idx,\n len(annotations_set)))\n\n file_name = '{}.png'.format(annotation['file_name'].rsplit('.')[0])\n try:\n pan_format = np.array(\n Image.open(os.path.join(segmentations_folder, file_name)), dtype=np.uint32\n )\n except IOError:\n raise KeyError('no prediction png file for id: {}'.format(annotation['image_id']))\n pan = rgb2id(pan_format)\n\n for segm_info in annotation['segments_info']:\n if things_only and categories[segm_info['category_id']]['isthing'] != 1:\n continue\n mask = (pan == segm_info['id']).astype(np.uint8)\n mask = np.expand_dims(mask, axis=2)\n segm_info.pop('id')\n segm_info['image_id'] = annotation['image_id']\n rle = COCOmask.encode(np.asfortranarray(mask))[0]\n rle['counts'] = rle['counts'].decode('utf8')\n segm_info['segmentation'] = rle\n annotations_detection.append(segm_info)\n\n print('Core: {}, all {} images processed'.format(proc_id, len(annotations_set)))\n return annotations_detection\n\n\ndef convert_panoptic_to_detection_coco_format(input_json_file,\n segmentations_folder,\n output_json_file,\n categories_json_file,\n things_only):\n start_time = time.time()\n\n if segmentations_folder is None:\n segmentations_folder = input_json_file.rsplit('.', 1)[0]\n\n print(\"CONVERTING...\")\n print(\"COCO panoptic format:\")\n print(\"\\tSegmentation folder: {}\".format(segmentations_folder))\n print(\"\\tJSON file: {}\".format(input_json_file))\n print(\"TO\")\n print(\"COCO detection format\")\n print(\"\\tJSON file: {}\".format(output_json_file))\n if things_only:\n print(\"Saving only segments of things classes.\")\n print('\\n')\n\n print(\"Reading annotation information from {}\".format(input_json_file))\n with open(input_json_file, 'r') as f:\n d_coco = json.load(f)\n annotations_panoptic = d_coco['annotations']\n\n with open(categories_json_file, 'r') as f:\n categories_list = json.load(f)\n categories = {category['id']: category for category in categories_list}\n\n cpu_num = multiprocessing.cpu_count()\n annotations_split = np.array_split(annotations_panoptic, cpu_num)\n print(\"Number of cores: {}, images per core: {}\".format(cpu_num, len(annotations_split[0])))\n workers = multiprocessing.Pool(processes=cpu_num)\n processes = []\n for proc_id, annotations_set in enumerate(annotations_split):\n p = workers.apply_async(convert_panoptic_to_detection_coco_format_single_core,\n (proc_id, annotations_set, categories, segmentations_folder, things_only))\n processes.append(p)\n annotations_coco_detection = []\n for p in processes:\n annotations_coco_detection.extend(p.get())\n for idx, ann in enumerate(annotations_coco_detection):\n ann['id'] = idx\n\n d_coco['annotations'] = annotations_coco_detection\n categories_coco_detection = []\n for category in d_coco['categories']:\n if things_only and category['isthing'] != 1:\n continue\n category.pop('isthing')\n categories_coco_detection.append(category)\n d_coco['categories'] = categories_coco_detection\n save_json(d_coco, output_json_file)\n\n t_delta = time.time() - start_time\n print(\"Time elapsed: {:0.2f} seconds\".format(t_delta))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\"The script converts panoptic COCO format to detection \\\n COCO format. See this file's head for more information.\"\n )\n parser.add_argument('--things_only', action='store_true',\n help=\"discard stuff classes\")\n args = parser.parse_args()\n \n _root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\n root = os.path.join(_root, \"coco\")\n input_json_file = os.path.join(root, \"annotations\", \"panoptic_val2017.json\")\n output_json_file = os.path.join(root, \"annotations\", \"panoptic2instances_val2017.json\")\n categories_json_file = \"datasets/panoptic_coco_categories.json\"\n segmentations_folder = os.path.join(root, \"panoptic_val2017\")\n \n convert_panoptic_to_detection_coco_format(input_json_file,\n segmentations_folder,\n output_json_file,\n categories_json_file,\n args.things_only)","source_hash":"602084f87b04f7b459cccefe9c1475625e0f8cfef4a5003a65a1f06d7b8f3df5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.panoptic2detection_coco_format.convert_panoptic_to_detection_coco_format_single_core","uri":"program://OneFormer/function/datasets.panoptic2detection_coco_format.convert_panoptic_to_detection_coco_format_single_core#L38-L70","kind":"function","name":"convert_panoptic_to_detection_coco_format_single_core","path":"datasets/panoptic2detection_coco_format.py","language":"python","start_line":38,"end_line":70,"context_start_line":18,"context_end_line":90,"code":"from __future__ import unicode_literals\nimport os, sys\nimport argparse\nimport numpy as np\nimport json\nimport time\nimport multiprocessing\n\nimport PIL.Image as Image\n\nfrom panopticapi.utils import get_traceback, rgb2id, save_json\n\ntry:\n # set up path for pycocotools\n # sys.path.append('./cocoapi-master/PythonAPI/')\n from pycocotools import mask as COCOmask\nexcept Exception:\n raise Exception(\"Please install pycocotools module from https://github.com/cocodataset/cocoapi\")\n\n@get_traceback\ndef convert_panoptic_to_detection_coco_format_single_core(\n proc_id, annotations_set, categories, segmentations_folder, things_only\n):\n annotations_detection = []\n for working_idx, annotation in enumerate(annotations_set):\n if working_idx % 100 == 0:\n print('Core: {}, {} from {} images processed'.format(proc_id,\n working_idx,\n len(annotations_set)))\n\n file_name = '{}.png'.format(annotation['file_name'].rsplit('.')[0])\n try:\n pan_format = np.array(\n Image.open(os.path.join(segmentations_folder, file_name)), dtype=np.uint32\n )\n except IOError:\n raise KeyError('no prediction png file for id: {}'.format(annotation['image_id']))\n pan = rgb2id(pan_format)\n\n for segm_info in annotation['segments_info']:\n if things_only and categories[segm_info['category_id']]['isthing'] != 1:\n continue\n mask = (pan == segm_info['id']).astype(np.uint8)\n mask = np.expand_dims(mask, axis=2)\n segm_info.pop('id')\n segm_info['image_id'] = annotation['image_id']\n rle = COCOmask.encode(np.asfortranarray(mask))[0]\n rle['counts'] = rle['counts'].decode('utf8')\n segm_info['segmentation'] = rle\n annotations_detection.append(segm_info)\n\n print('Core: {}, all {} images processed'.format(proc_id, len(annotations_set)))\n return annotations_detection\n\n\ndef convert_panoptic_to_detection_coco_format(input_json_file,\n segmentations_folder,\n output_json_file,\n categories_json_file,\n things_only):\n start_time = time.time()\n\n if segmentations_folder is None:\n segmentations_folder = input_json_file.rsplit('.', 1)[0]\n\n print(\"CONVERTING...\")\n print(\"COCO panoptic format:\")\n print(\"\\tSegmentation folder: {}\".format(segmentations_folder))\n print(\"\\tJSON file: {}\".format(input_json_file))\n print(\"TO\")\n print(\"COCO detection format\")\n print(\"\\tJSON file: {}\".format(output_json_file))\n if things_only:","source_hash":"602084f87b04f7b459cccefe9c1475625e0f8cfef4a5003a65a1f06d7b8f3df5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.panoptic2detection_coco_format.convert_panoptic_to_detection_coco_format","uri":"program://OneFormer/function/datasets.panoptic2detection_coco_format.convert_panoptic_to_detection_coco_format#L73-L129","kind":"function","name":"convert_panoptic_to_detection_coco_format","path":"datasets/panoptic2detection_coco_format.py","language":"python","start_line":73,"end_line":129,"context_start_line":53,"context_end_line":149,"code":" except IOError:\n raise KeyError('no prediction png file for id: {}'.format(annotation['image_id']))\n pan = rgb2id(pan_format)\n\n for segm_info in annotation['segments_info']:\n if things_only and categories[segm_info['category_id']]['isthing'] != 1:\n continue\n mask = (pan == segm_info['id']).astype(np.uint8)\n mask = np.expand_dims(mask, axis=2)\n segm_info.pop('id')\n segm_info['image_id'] = annotation['image_id']\n rle = COCOmask.encode(np.asfortranarray(mask))[0]\n rle['counts'] = rle['counts'].decode('utf8')\n segm_info['segmentation'] = rle\n annotations_detection.append(segm_info)\n\n print('Core: {}, all {} images processed'.format(proc_id, len(annotations_set)))\n return annotations_detection\n\n\ndef convert_panoptic_to_detection_coco_format(input_json_file,\n segmentations_folder,\n output_json_file,\n categories_json_file,\n things_only):\n start_time = time.time()\n\n if segmentations_folder is None:\n segmentations_folder = input_json_file.rsplit('.', 1)[0]\n\n print(\"CONVERTING...\")\n print(\"COCO panoptic format:\")\n print(\"\\tSegmentation folder: {}\".format(segmentations_folder))\n print(\"\\tJSON file: {}\".format(input_json_file))\n print(\"TO\")\n print(\"COCO detection format\")\n print(\"\\tJSON file: {}\".format(output_json_file))\n if things_only:\n print(\"Saving only segments of things classes.\")\n print('\\n')\n\n print(\"Reading annotation information from {}\".format(input_json_file))\n with open(input_json_file, 'r') as f:\n d_coco = json.load(f)\n annotations_panoptic = d_coco['annotations']\n\n with open(categories_json_file, 'r') as f:\n categories_list = json.load(f)\n categories = {category['id']: category for category in categories_list}\n\n cpu_num = multiprocessing.cpu_count()\n annotations_split = np.array_split(annotations_panoptic, cpu_num)\n print(\"Number of cores: {}, images per core: {}\".format(cpu_num, len(annotations_split[0])))\n workers = multiprocessing.Pool(processes=cpu_num)\n processes = []\n for proc_id, annotations_set in enumerate(annotations_split):\n p = workers.apply_async(convert_panoptic_to_detection_coco_format_single_core,\n (proc_id, annotations_set, categories, segmentations_folder, things_only))\n processes.append(p)\n annotations_coco_detection = []\n for p in processes:\n annotations_coco_detection.extend(p.get())\n for idx, ann in enumerate(annotations_coco_detection):\n ann['id'] = idx\n\n d_coco['annotations'] = annotations_coco_detection\n categories_coco_detection = []\n for category in d_coco['categories']:\n if things_only and category['isthing'] != 1:\n continue\n category.pop('isthing')\n categories_coco_detection.append(category)\n d_coco['categories'] = categories_coco_detection\n save_json(d_coco, output_json_file)\n\n t_delta = time.time() - start_time\n print(\"Time elapsed: {:0.2f} seconds\".format(t_delta))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\"The script converts panoptic COCO format to detection \\\n COCO format. See this file's head for more information.\"\n )\n parser.add_argument('--things_only', action='store_true',\n help=\"discard stuff classes\")\n args = parser.parse_args()\n \n _root = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\n root = os.path.join(_root, \"coco\")\n input_json_file = os.path.join(root, \"annotations\", \"panoptic_val2017.json\")\n output_json_file = os.path.join(root, \"annotations\", \"panoptic2instances_val2017.json\")\n categories_json_file = \"datasets/panoptic_coco_categories.json\"\n segmentations_folder = os.path.join(root, \"panoptic_val2017\")\n \n convert_panoptic_to_detection_coco_format(input_json_file,\n segmentations_folder,","source_hash":"602084f87b04f7b459cccefe9c1475625e0f8cfef4a5003a65a1f06d7b8f3df5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper","uri":"program://OneFormer/module/datasets.custom_datasets.instance_coco_custom_dataset_mapper#L1-L235","kind":"module","name":"datasets.custom_datasets.instance_coco_custom_dataset_mapper","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":1,"end_line":235,"context_start_line":1,"context_end_line":235,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\n\nimport numpy as np\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nfrom pycocotools import mask as coco_mask\n\n__all__ = [\"InstanceCOCOCustomNewBaselineDatasetMapper\"]\n\n\ndef convert_coco_poly_to_mask(segmentations, height, width):\n masks = []\n for polygons in segmentations:\n rles = coco_mask.frPyObjects(polygons, height, width)\n mask = coco_mask.decode(rles)\n if len(mask.shape) < 3:\n mask = mask[..., None]\n mask = torch.as_tensor(mask, dtype=torch.uint8)\n mask = mask.any(dim=2)\n masks.append(mask)\n if masks:\n masks = torch.stack(masks, dim=0)\n else:\n masks = torch.zeros((0, height, width), dtype=torch.uint8)\n return masks\n\n\ndef build_transform_gen(cfg, is_train):\n \"\"\"\n Create a list of default :class:`Augmentation` from config.\n Now it includes resizing and flipping.\n Returns:\n list[Augmentation]\n \"\"\"\n assert is_train, \"Only support training augmentation\"\n image_size = cfg.INPUT.IMAGE_SIZE\n min_scale = cfg.INPUT.MIN_SCALE\n max_scale = cfg.INPUT.MAX_SCALE\n\n augmentation = []\n\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",\n vertical=cfg.INPUT.RANDOM_FLIP == \"vertical\",\n )\n )\n\n augmentation.extend([\n T.ResizeScale(\n min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size\n ),\n T.FixedSizeCrop(crop_size=(image_size, image_size)),\n ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO Instance Segmentation dataset.\nclass InstanceCOCOCustomNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for custom instance segmentation using COCO format.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n num_queries,\n tfm_gens,\n meta,\n image_format,\n max_seq_len,\n task_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[InstanceCOCOCustomNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n }\n return ret\n \n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # TODO: get padding mask\n # by feeding a \"segmentation mask\" to the same transforms\n padding_mask = np.ones(image.shape[:2])\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n # the crop transformation has default padding value 0 for segmentation\n padding_mask = transforms.apply_segmentation(padding_mask)\n padding_mask = ~ padding_mask.astype(bool)\n\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n dataset_dict[\"padding_mask\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(obj, transforms, image_shape)\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n instances = utils.annotations_to_instances(annos, image_shape)\n \n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n # Need to filter empty instances first (due to augmentation)\n instances = utils.filter_empty_instances(instances)\n # Generate masks from polygon\n h, w = instances.image_size\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\n if hasattr(instances, 'gt_masks'):\n gt_masks = instances.gt_masks\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\n instances.gt_masks = gt_masks\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is instance\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n\n return dataset_dict","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper.convert_coco_poly_to_mask","uri":"program://OneFormer/function/datasets.custom_datasets.instance_coco_custom_dataset_mapper.convert_coco_poly_to_mask#L22-L36","kind":"function","name":"convert_coco_poly_to_mask","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":22,"end_line":36,"context_start_line":2,"context_end_line":56,"code":"# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\n\nimport numpy as np\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nfrom pycocotools import mask as coco_mask\n\n__all__ = [\"InstanceCOCOCustomNewBaselineDatasetMapper\"]\n\n\ndef convert_coco_poly_to_mask(segmentations, height, width):\n masks = []\n for polygons in segmentations:\n rles = coco_mask.frPyObjects(polygons, height, width)\n mask = coco_mask.decode(rles)\n if len(mask.shape) < 3:\n mask = mask[..., None]\n mask = torch.as_tensor(mask, dtype=torch.uint8)\n mask = mask.any(dim=2)\n masks.append(mask)\n if masks:\n masks = torch.stack(masks, dim=0)\n else:\n masks = torch.zeros((0, height, width), dtype=torch.uint8)\n return masks\n\n\ndef build_transform_gen(cfg, is_train):\n \"\"\"\n Create a list of default :class:`Augmentation` from config.\n Now it includes resizing and flipping.\n Returns:\n list[Augmentation]\n \"\"\"\n assert is_train, \"Only support training augmentation\"\n image_size = cfg.INPUT.IMAGE_SIZE\n min_scale = cfg.INPUT.MIN_SCALE\n max_scale = cfg.INPUT.MAX_SCALE\n\n augmentation = []\n\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper.build_transform_gen","uri":"program://OneFormer/function/datasets.custom_datasets.instance_coco_custom_dataset_mapper.build_transform_gen#L39-L68","kind":"function","name":"build_transform_gen","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":39,"end_line":68,"context_start_line":19,"context_end_line":88,"code":"__all__ = [\"InstanceCOCOCustomNewBaselineDatasetMapper\"]\n\n\ndef convert_coco_poly_to_mask(segmentations, height, width):\n masks = []\n for polygons in segmentations:\n rles = coco_mask.frPyObjects(polygons, height, width)\n mask = coco_mask.decode(rles)\n if len(mask.shape) < 3:\n mask = mask[..., None]\n mask = torch.as_tensor(mask, dtype=torch.uint8)\n mask = mask.any(dim=2)\n masks.append(mask)\n if masks:\n masks = torch.stack(masks, dim=0)\n else:\n masks = torch.zeros((0, height, width), dtype=torch.uint8)\n return masks\n\n\ndef build_transform_gen(cfg, is_train):\n \"\"\"\n Create a list of default :class:`Augmentation` from config.\n Now it includes resizing and flipping.\n Returns:\n list[Augmentation]\n \"\"\"\n assert is_train, \"Only support training augmentation\"\n image_size = cfg.INPUT.IMAGE_SIZE\n min_scale = cfg.INPUT.MIN_SCALE\n max_scale = cfg.INPUT.MAX_SCALE\n\n augmentation = []\n\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",\n vertical=cfg.INPUT.RANDOM_FLIP == \"vertical\",\n )\n )\n\n augmentation.extend([\n T.ResizeScale(\n min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size\n ),\n T.FixedSizeCrop(crop_size=(image_size, image_size)),\n ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO Instance Segmentation dataset.\nclass InstanceCOCOCustomNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for custom instance segmentation using COCO format.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper.InstanceCOCOCustomNewBaselineDatasetMapper","uri":"program://OneFormer/class/datasets.custom_datasets.instance_coco_custom_dataset_mapper.InstanceCOCOCustomNewBaselineDatasetMapper#L72-L235","kind":"class","name":"InstanceCOCOCustomNewBaselineDatasetMapper","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":72,"end_line":235,"context_start_line":52,"context_end_line":235,"code":"\n if cfg.INPUT.RANDOM_FLIP != \"none\":\n augmentation.append(\n T.RandomFlip(\n horizontal=cfg.INPUT.RANDOM_FLIP == \"horizontal\",\n vertical=cfg.INPUT.RANDOM_FLIP == \"vertical\",\n )\n )\n\n augmentation.extend([\n T.ResizeScale(\n min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size\n ),\n T.FixedSizeCrop(crop_size=(image_size, image_size)),\n ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO Instance Segmentation dataset.\nclass InstanceCOCOCustomNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for custom instance segmentation using COCO format.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n num_queries,\n tfm_gens,\n meta,\n image_format,\n max_seq_len,\n task_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[InstanceCOCOCustomNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n }\n return ret\n \n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # TODO: get padding mask\n # by feeding a \"segmentation mask\" to the same transforms\n padding_mask = np.ones(image.shape[:2])\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n # the crop transformation has default padding value 0 for segmentation\n padding_mask = transforms.apply_segmentation(padding_mask)\n padding_mask = ~ padding_mask.astype(bool)\n\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n dataset_dict[\"padding_mask\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(obj, transforms, image_shape)\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n instances = utils.annotations_to_instances(annos, image_shape)\n \n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n # Need to filter empty instances first (due to augmentation)\n instances = utils.filter_empty_instances(instances)\n # Generate masks from polygon\n h, w = instances.image_size\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\n if hasattr(instances, 'gt_masks'):\n gt_masks = instances.gt_masks\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\n instances.gt_masks = gt_masks\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is instance\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n\n return dataset_dict","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper.__init__","uri":"program://OneFormer/function/datasets.custom_datasets.instance_coco_custom_dataset_mapper.__init__#L86-L123","kind":"function","name":"__init__","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":86,"end_line":123,"context_start_line":66,"context_end_line":143,"code":" ])\n\n return augmentation\n\n\n# This is specifically designed for the COCO Instance Segmentation dataset.\nclass InstanceCOCOCustomNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer for custom instance segmentation using COCO format.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n num_queries,\n tfm_gens,\n meta,\n image_format,\n max_seq_len,\n task_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[InstanceCOCOCustomNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n }\n return ret\n \n def _get_texts(self, classes, num_class_obj):","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper.from_config","uri":"program://OneFormer/function/datasets.custom_datasets.instance_coco_custom_dataset_mapper.from_config#L126-L141","kind":"function","name":"from_config","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":126,"end_line":141,"context_start_line":106,"context_end_line":161,"code":" self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[InstanceCOCOCustomNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n self.meta = meta\n self.num_queries = num_queries\n\n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n }\n return ret\n \n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper._get_texts","uri":"program://OneFormer/function/datasets.custom_datasets.instance_coco_custom_dataset_mapper._get_texts#L143-L161","kind":"function","name":"_get_texts","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":143,"end_line":161,"context_start_line":123,"context_end_line":181,"code":" self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n }\n return ret\n \n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # TODO: get padding mask\n # by feeding a \"segmentation mask\" to the same transforms\n padding_mask = np.ones(image.shape[:2])\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n # the crop transformation has default padding value 0 for segmentation\n padding_mask = transforms.apply_segmentation(padding_mask)","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_coco_custom_dataset_mapper.__call__","uri":"program://OneFormer/function/datasets.custom_datasets.instance_coco_custom_dataset_mapper.__call__#L163-L235","kind":"function","name":"__call__","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":163,"end_line":235,"context_start_line":143,"context_end_line":235,"code":" def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # TODO: get padding mask\n # by feeding a \"segmentation mask\" to the same transforms\n padding_mask = np.ones(image.shape[:2])\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n # the crop transformation has default padding value 0 for segmentation\n padding_mask = transforms.apply_segmentation(padding_mask)\n padding_mask = ~ padding_mask.astype(bool)\n\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n dataset_dict[\"padding_mask\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(obj, transforms, image_shape)\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n instances = utils.annotations_to_instances(annos, image_shape)\n \n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n # Need to filter empty instances first (due to augmentation)\n instances = utils.filter_empty_instances(instances)\n # Generate masks from polygon\n h, w = instances.image_size\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\n if hasattr(instances, 'gt_masks'):\n gt_masks = instances.gt_masks\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\n instances.gt_masks = gt_masks\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is instance\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n\n return dataset_dict","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper","uri":"program://OneFormer/module/datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper#L1-L238","kind":"module","name":"datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":1,"end_line":238,"context_start_line":1,"context_end_line":238,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances, polygons_to_bitmask\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util\n\n__all__ = [\"SemanticOneFormerCustomDatasetMapper\"]\n\n\nclass SemanticOneFormerCustomDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer custom semantic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an semantic photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"SemanticOneFormerCustomDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n if sem_seg_gt is None:\n raise ValueError(\n \"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n sem_seg_gt = aug_input.sem_seg\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Semantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n if sem_seg_gt is not None:\n sem_seg_gt = sem_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = np.unique(sem_seg_gt)\n # remove ignored region\n classes = classes[classes != self.ignore_label]\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n\n masks = []\n for class_id in classes:\n masks.append(sem_seg_gt == class_id)\n\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is semantic\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n \n return dataset_dict","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.SemanticOneFormerCustomDatasetMapper","uri":"program://OneFormer/class/datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.SemanticOneFormerCustomDatasetMapper#L26-L238","kind":"class","name":"SemanticOneFormerCustomDatasetMapper","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":26,"end_line":238,"context_start_line":6,"context_end_line":238,"code":"import copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances, polygons_to_bitmask\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util\n\n__all__ = [\"SemanticOneFormerCustomDatasetMapper\"]\n\n\nclass SemanticOneFormerCustomDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer custom semantic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an semantic photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"SemanticOneFormerCustomDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n if sem_seg_gt is None:\n raise ValueError(\n \"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n sem_seg_gt = aug_input.sem_seg\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Semantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n if sem_seg_gt is not None:\n sem_seg_gt = sem_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = np.unique(sem_seg_gt)\n # remove ignored region\n classes = classes[classes != self.ignore_label]\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n\n masks = []\n for class_id in classes:\n masks.append(sem_seg_gt == class_id)\n\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is semantic\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n \n return dataset_dict","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.__init__","uri":"program://OneFormer/function/datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.__init__#L40-L78","kind":"function","name":"__init__","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":40,"end_line":78,"context_start_line":20,"context_end_line":98,"code":"from oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util\n\n__all__ = [\"SemanticOneFormerCustomDatasetMapper\"]\n\n\nclass SemanticOneFormerCustomDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer custom semantic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.from_config","uri":"program://OneFormer/function/datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.from_config#L81-L120","kind":"function","name":"from_config","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":81,"end_line":120,"context_start_line":61,"context_end_line":140,"code":" size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n self.class_names = self.meta.stuff_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop_CategoryAreaConstraint(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an semantic photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper._get_texts","uri":"program://OneFormer/function/datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper._get_texts#L122-L140","kind":"function","name":"_get_texts","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":122,"end_line":140,"context_start_line":102,"context_end_line":160,"code":"\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an semantic photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"SemanticOneFormerCustomDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.__call__","uri":"program://OneFormer/function/datasets.custom_datasets.semantic_oneformer_custom_dataset_mapper.__call__#L142-L238","kind":"function","name":"__call__","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":142,"end_line":238,"context_start_line":122,"context_end_line":238,"code":" def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an semantic photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"SemanticOneFormerCustomDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n if sem_seg_gt is None:\n raise ValueError(\n \"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n sem_seg_gt = aug_input.sem_seg\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Semantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n if sem_seg_gt is not None:\n sem_seg_gt = sem_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = np.unique(sem_seg_gt)\n # remove ignored region\n classes = classes[classes != self.ignore_label]\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n\n masks = []\n for class_id in classes:\n masks.append(sem_seg_gt == class_id)\n\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is semantic\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n \n return dataset_dict","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_oneformer_custom_dataset_mapper","uri":"program://OneFormer/module/datasets.custom_datasets.instance_oneformer_custom_dataset_mapper#L1-L245","kind":"module","name":"datasets.custom_datasets.instance_oneformer_custom_dataset_mapper","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":1,"end_line":245,"context_start_line":1,"context_end_line":245,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances, polygons_to_bitmask\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util\n\n__all__ = [\"InstanceOneFormerCustomDatasetMapper\"]\n\n\nclass InstanceOneFormerCustomDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer custom instance segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n aug_input = T.AugInput(image)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n\n # transform instnace masks\n assert \"annotations\" in dataset_dict\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n annos = [\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n if len(annos):\n assert \"segmentation\" in annos[0]\n segms = [obj[\"segmentation\"] for obj in annos]\n masks = []\n for segm in segms:\n if isinstance(segm, list):\n # polygon\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\n elif isinstance(segm, dict):\n # COCO RLE\n masks.append(mask_util.decode(segm))\n elif isinstance(segm, np.ndarray):\n assert segm.ndim == 2, \"Expect segmentation of 2 dimensions, got {}.\".format(\n segm.ndim\n )\n # mask array\n masks.append(segm)\n else:\n raise ValueError(\n \"Cannot convert segmentation of type '{}' to BitMasks!\"\n \"Supported types are: polygons as list[list[float] or ndarray],\"\n \" COCO-style RLE as a dict, or a binary segmentation mask \"\n \" in a 2D numpy array of shape HxW.\".format(type(segm))\n )\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\n\n classes = [int(obj[\"category_id\"]) for obj in annos]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n # pad image\n image = F.pad(image, padding_size, value=128).contiguous()\n # pad mask\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n # Prepare per-category binary masks\n instances = Instances(image_shape)\n instances.gt_classes = classes\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\n else:\n masks = BitMasks(torch.stack(masks))\n instances.gt_masks = masks.tensor\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is instance\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n \n return dataset_dict","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.InstanceOneFormerCustomDatasetMapper","uri":"program://OneFormer/class/datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.InstanceOneFormerCustomDatasetMapper#L26-L245","kind":"class","name":"InstanceOneFormerCustomDatasetMapper","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":26,"end_line":245,"context_start_line":6,"context_end_line":245,"code":"import copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances, polygons_to_bitmask\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util\n\n__all__ = [\"InstanceOneFormerCustomDatasetMapper\"]\n\n\nclass InstanceOneFormerCustomDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer custom instance segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n aug_input = T.AugInput(image)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n\n # transform instnace masks\n assert \"annotations\" in dataset_dict\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n annos = [\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n if len(annos):\n assert \"segmentation\" in annos[0]\n segms = [obj[\"segmentation\"] for obj in annos]\n masks = []\n for segm in segms:\n if isinstance(segm, list):\n # polygon\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\n elif isinstance(segm, dict):\n # COCO RLE\n masks.append(mask_util.decode(segm))\n elif isinstance(segm, np.ndarray):\n assert segm.ndim == 2, \"Expect segmentation of 2 dimensions, got {}.\".format(\n segm.ndim\n )\n # mask array\n masks.append(segm)\n else:\n raise ValueError(\n \"Cannot convert segmentation of type '{}' to BitMasks!\"\n \"Supported types are: polygons as list[list[float] or ndarray],\"\n \" COCO-style RLE as a dict, or a binary segmentation mask \"\n \" in a 2D numpy array of shape HxW.\".format(type(segm))\n )\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\n\n classes = [int(obj[\"category_id\"]) for obj in annos]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n # pad image\n image = F.pad(image, padding_size, value=128).contiguous()\n # pad mask\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n # Prepare per-category binary masks\n instances = Instances(image_shape)\n instances.gt_classes = classes\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\n else:\n masks = BitMasks(torch.stack(masks))\n instances.gt_masks = masks.tensor\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is instance\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n \n return dataset_dict","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.__init__","uri":"program://OneFormer/function/datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.__init__#L40-L79","kind":"function","name":"__init__","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":40,"end_line":79,"context_start_line":20,"context_end_line":99,"code":"from oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util\n\n__all__ = [\"InstanceOneFormerCustomDatasetMapper\"]\n\n\nclass InstanceOneFormerCustomDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by OneFormer custom instance segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n name,\n num_queries,\n meta,\n augmentations,\n image_format,\n size_divisibility,\n task_seq_len,\n max_seq_len,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.from_config","uri":"program://OneFormer/function/datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.from_config#L82-L119","kind":"function","name":"from_config","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":82,"end_line":119,"context_start_line":62,"context_end_line":139,"code":" self.is_train = is_train\n self.meta = meta\n self.name = name\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.size_divisibility = size_divisibility\n self.num_queries = num_queries\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n \n self.things = []\n for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():\n self.things.append(v)\n self.class_names = self.meta.thing_classes\n self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)\n self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n augs = [\n T.ResizeShortestEdge(\n cfg.INPUT.MIN_SIZE_TRAIN,\n cfg.INPUT.MAX_SIZE_TRAIN,\n cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n )\n ]\n if cfg.INPUT.CROP.ENABLED:\n augs.append(\n T.RandomCrop(\n cfg.INPUT.CROP.TYPE,\n cfg.INPUT.CROP.SIZE,\n )\n )\n if cfg.INPUT.COLOR_AUG_SSD:\n augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n augs.append(T.RandomFlip())\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_oneformer_custom_dataset_mapper._get_texts","uri":"program://OneFormer/function/datasets.custom_datasets.instance_oneformer_custom_dataset_mapper._get_texts#L121-L139","kind":"function","name":"_get_texts","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":121,"end_line":139,"context_start_line":101,"context_end_line":159,"code":"\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n\n ret = {\n \"is_train\": is_train,\n \"meta\": meta,\n \"name\": dataset_names[0],\n \"num_queries\": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,\n \"task_seq_len\": cfg.INPUT.TASK_SEQ_LEN,\n \"max_seq_len\": cfg.INPUT.MAX_SEQ_LEN,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n \"semantic_prob\": cfg.INPUT.TASK_PROB.SEMANTIC,\n \"instance_prob\": cfg.INPUT.TASK_PROB.INSTANCE,\n }\n return ret\n\n def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n aug_input = T.AugInput(image)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n\n # transform instnace masks","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"py:datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.__call__","uri":"program://OneFormer/function/datasets.custom_datasets.instance_oneformer_custom_dataset_mapper.__call__#L141-L245","kind":"function","name":"__call__","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":141,"end_line":245,"context_start_line":121,"context_end_line":245,"code":" def _get_texts(self, classes, num_class_obj):\n \n classes = list(np.array(classes))\n texts = [\"an instance photo\"] * self.num_queries\n \n for class_id in classes:\n cls_name = self.class_names[class_id]\n num_class_obj[cls_name] += 1\n \n num = 0\n for i, cls_name in enumerate(self.class_names):\n if num_class_obj[cls_name] > 0:\n for _ in range(num_class_obj[cls_name]):\n if num >= len(texts):\n break\n texts[num] = f\"a photo with a {cls_name}\"\n num += 1\n\n return texts\n \n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"OneFormerDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n aug_input = T.AugInput(image)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n\n # transform instnace masks\n assert \"annotations\" in dataset_dict\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n annos = [\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n if len(annos):\n assert \"segmentation\" in annos[0]\n segms = [obj[\"segmentation\"] for obj in annos]\n masks = []\n for segm in segms:\n if isinstance(segm, list):\n # polygon\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\n elif isinstance(segm, dict):\n # COCO RLE\n masks.append(mask_util.decode(segm))\n elif isinstance(segm, np.ndarray):\n assert segm.ndim == 2, \"Expect segmentation of 2 dimensions, got {}.\".format(\n segm.ndim\n )\n # mask array\n masks.append(segm)\n else:\n raise ValueError(\n \"Cannot convert segmentation of type '{}' to BitMasks!\"\n \"Supported types are: polygons as list[list[float] or ndarray],\"\n \" COCO-style RLE as a dict, or a binary segmentation mask \"\n \" in a 2D numpy array of shape HxW.\".format(type(segm))\n )\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\n\n classes = [int(obj[\"category_id\"]) for obj in annos]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n # pad image\n image = F.pad(image, padding_size, value=128).contiguous()\n # pad mask\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n # Prepare per-category binary masks\n instances = Instances(image_shape)\n instances.gt_classes = classes\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\n else:\n masks = BitMasks(torch.stack(masks))\n instances.gt_masks = masks.tensor\n\n num_class_obj = {}\n for name in self.class_names:\n num_class_obj[name] = 0\n\n task = \"The task is instance\"\n text = self._get_texts(instances.gt_classes, num_class_obj)\n\n dataset_dict[\"instances\"] = instances\n dataset_dict[\"orig_shape\"] = image_shape\n dataset_dict[\"task\"] = task\n dataset_dict[\"text\"] = text\n dataset_dict[\"thing_ids\"] = self.things\n \n return dataset_dict","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.demo","uri":"program://OneFormer/module/demo.demo#L1-L138","kind":"module","name":"demo.demo","path":"demo/demo.py","language":"python","start_line":1,"end_line":138,"context_start_line":1,"context_end_line":138,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/demo/demo.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport argparse\nimport multiprocessing as mp\nimport os\nimport torch\nimport random\n# fmt: off\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n# fmt: on\n\nimport time\nimport cv2\nimport numpy as np\nimport tqdm\n\nfrom detectron2.config import get_cfg\nfrom detectron2.data.detection_utils import read_image\nfrom detectron2.projects.deeplab import add_deeplab_config\nfrom detectron2.utils.logger import setup_logger\n\nfrom oneformer import (\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\nfrom predictor import VisualizationDemo\n\n# constants\nWINDOW_NAME = \"OneFormer Demo\"\n\ndef setup_cfg(args):\n # load config from file and command-line arguments\n cfg = get_cfg()\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n return cfg\n\n\ndef get_parser():\n parser = argparse.ArgumentParser(description=\"oneformer demo for builtin configs\")\n parser.add_argument(\n \"--config-file\",\n default=\"../configs/ade20k/swin/oneformer_swin_large_IN21k_384_bs16_160k.yaml\",\n metavar=\"FILE\",\n help=\"path to config file\",\n )\n parser.add_argument(\"--task\", help=\"Task type\")\n parser.add_argument(\n \"--input\",\n nargs=\"+\",\n help=\"A list of space separated input images; \"\n \"or a single glob pattern such as 'directory/*.jpg'\",\n )\n parser.add_argument(\n \"--output\",\n help=\"A file or directory to save output visualizations. \"\n \"If not given, will show output in an OpenCV window.\",\n )\n\n parser.add_argument(\n \"--confidence-threshold\",\n type=float,\n default=0.5,\n help=\"Minimum score for instance predictions to be shown\",\n )\n parser.add_argument(\n \"--opts\",\n help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n default=[],\n nargs=argparse.REMAINDER,\n )\n return parser\n\nif __name__ == \"__main__\":\n seed = 0\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n mp.set_start_method(\"spawn\", force=True)\n args = get_parser().parse_args()\n setup_logger(name=\"fvcore\")\n logger = setup_logger()\n logger.info(\"Arguments: \" + str(args))\n\n cfg = setup_cfg(args)\n\n demo = VisualizationDemo(cfg)\n\n if args.input:\n for path in tqdm.tqdm(args.input, disable=not args.output):\n # use PIL, to be consistent with evaluation\n \n img = read_image(path, format=\"BGR\")\n start_time = time.time()\n predictions, visualized_output = demo.run_on_image(img, args.task)\n logger.info(\n \"{}: {} in {:.2f}s\".format(\n path,\n \"detected {} instances\".format(len(predictions[\"instances\"]))\n if \"instances\" in predictions\n else \"finished\",\n time.time() - start_time,\n )\n )\n if args.output:\n if len(args.input) == 1:\n for k in visualized_output.keys():\n os.makedirs(k, exist_ok=True)\n out_filename = os.path.join(k, args.output)\n visualized_output[k].save(out_filename) \n else:\n for k in visualized_output.keys():\n opath = os.path.join(args.output, k) \n os.makedirs(opath, exist_ok=True)\n out_filename = os.path.join(opath, os.path.basename(path))\n visualized_output[k].save(out_filename) \n else:\n raise ValueError(\"Please specify an output path!\")\n else:\n raise ValueError(\"No Input Given\")","source_hash":"3f480cbef43b58982e78ff61826a3f051c76cc64e82701d18276c0df6c57bcf6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.demo.setup_cfg","uri":"program://OneFormer/function/demo.demo.setup_cfg#L38-L50","kind":"function","name":"setup_cfg","path":"demo/demo.py","language":"python","start_line":38,"end_line":50,"context_start_line":18,"context_end_line":70,"code":"import numpy as np\nimport tqdm\n\nfrom detectron2.config import get_cfg\nfrom detectron2.data.detection_utils import read_image\nfrom detectron2.projects.deeplab import add_deeplab_config\nfrom detectron2.utils.logger import setup_logger\n\nfrom oneformer import (\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\nfrom predictor import VisualizationDemo\n\n# constants\nWINDOW_NAME = \"OneFormer Demo\"\n\ndef setup_cfg(args):\n # load config from file and command-line arguments\n cfg = get_cfg()\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n return cfg\n\n\ndef get_parser():\n parser = argparse.ArgumentParser(description=\"oneformer demo for builtin configs\")\n parser.add_argument(\n \"--config-file\",\n default=\"../configs/ade20k/swin/oneformer_swin_large_IN21k_384_bs16_160k.yaml\",\n metavar=\"FILE\",\n help=\"path to config file\",\n )\n parser.add_argument(\"--task\", help=\"Task type\")\n parser.add_argument(\n \"--input\",\n nargs=\"+\",\n help=\"A list of space separated input images; \"\n \"or a single glob pattern such as 'directory/*.jpg'\",\n )\n parser.add_argument(\n \"--output\",\n help=\"A file or directory to save output visualizations. \"","source_hash":"3f480cbef43b58982e78ff61826a3f051c76cc64e82701d18276c0df6c57bcf6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.demo.get_parser","uri":"program://OneFormer/function/demo.demo.get_parser#L53-L86","kind":"function","name":"get_parser","path":"demo/demo.py","language":"python","start_line":53,"end_line":86,"context_start_line":33,"context_end_line":106,"code":"from predictor import VisualizationDemo\n\n# constants\nWINDOW_NAME = \"OneFormer Demo\"\n\ndef setup_cfg(args):\n # load config from file and command-line arguments\n cfg = get_cfg()\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_convnext_config(cfg)\n add_oneformer_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n return cfg\n\n\ndef get_parser():\n parser = argparse.ArgumentParser(description=\"oneformer demo for builtin configs\")\n parser.add_argument(\n \"--config-file\",\n default=\"../configs/ade20k/swin/oneformer_swin_large_IN21k_384_bs16_160k.yaml\",\n metavar=\"FILE\",\n help=\"path to config file\",\n )\n parser.add_argument(\"--task\", help=\"Task type\")\n parser.add_argument(\n \"--input\",\n nargs=\"+\",\n help=\"A list of space separated input images; \"\n \"or a single glob pattern such as 'directory/*.jpg'\",\n )\n parser.add_argument(\n \"--output\",\n help=\"A file or directory to save output visualizations. \"\n \"If not given, will show output in an OpenCV window.\",\n )\n\n parser.add_argument(\n \"--confidence-threshold\",\n type=float,\n default=0.5,\n help=\"Minimum score for instance predictions to be shown\",\n )\n parser.add_argument(\n \"--opts\",\n help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n default=[],\n nargs=argparse.REMAINDER,\n )\n return parser\n\nif __name__ == \"__main__\":\n seed = 0\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n mp.set_start_method(\"spawn\", force=True)\n args = get_parser().parse_args()\n setup_logger(name=\"fvcore\")\n logger = setup_logger()\n logger.info(\"Arguments: \" + str(args))\n\n cfg = setup_cfg(args)\n\n demo = VisualizationDemo(cfg)\n","source_hash":"3f480cbef43b58982e78ff61826a3f051c76cc64e82701d18276c0df6c57bcf6","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer","uri":"program://OneFormer/module/demo.visualizer#L1-L1345","kind":"module","name":"demo.visualizer","path":"demo/visualizer.py","language":"python","start_line":1,"end_line":1345,"context_start_line":1,"context_end_line":1345,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport colorsys\nimport logging\nimport math\nimport numpy as np\nfrom enum import Enum, unique\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.colors as mplc\nimport matplotlib.figure as mplfigure\nimport pycocotools.mask as mask_util\nimport torch\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom PIL import Image\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes\nfrom detectron2.utils.file_io import PathManager\nimport random\nrandom.seed(0)\nfrom colormap import random_color, _COLORS\nlogger = logging.getLogger(__name__)\n\n__all__ = [\"ColorMode\", \"VisImage\", \"Visualizer\"]\n\n\n_SMALL_OBJECT_AREA_THRESH = 1000\n_LARGE_MASK_AREA_THRESH = 120000\n_OFF_WHITE = (1.0, 1.0, 240.0 / 255)\n_BLACK = (0, 0, 0)\n_RED = (1.0, 0, 0)\n\n_KEYPOINT_THRESHOLD = 0.05\n\n\ndef instance_color(rgb=False, idx=1, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n@unique\nclass ColorMode(Enum):\n \"\"\"\n Enum of different color modes to use for instance visualizations.\n \"\"\"\n\n IMAGE = 0\n \"\"\"\n Picks a random color for every instance and overlay segmentations with low opacity.\n \"\"\"\n SEGMENTATION = 1\n \"\"\"\n Let instances of the same category have similar colors\n (from metadata.thing_colors), and overlay them with\n high opacity. This provides more attention on the quality of segmentation.\n \"\"\"\n IMAGE_BW = 2\n \"\"\"\n Same as IMAGE, but convert all areas without masks to gray-scale.\n Only available for drawing per-instance mask predictions.\n \"\"\"\n\n\nclass GenericMask:\n \"\"\"\n Attribute:\n polygons (list[ndarray]): list[ndarray]: polygons for this mask.\n Each ndarray has format [x, y, x, y, ...]\n mask (ndarray): a binary mask\n \"\"\"\n\n def __init__(self, mask_or_polygons, height, width):\n self._mask = self._polygons = self._has_holes = None\n self.height = height\n self.width = width\n\n m = mask_or_polygons\n if isinstance(m, dict):\n # RLEs\n assert \"counts\" in m and \"size\" in m\n if isinstance(m[\"counts\"], list): # uncompressed RLEs\n h, w = m[\"size\"]\n assert h == height and w == width\n m = mask_util.frPyObjects(m, h, w)\n self._mask = mask_util.decode(m)[:, :]\n return\n\n if isinstance(m, list): # list[ndarray]\n self._polygons = [np.asarray(x).reshape(-1) for x in m]\n return\n\n if isinstance(m, np.ndarray): # assumed to be a binary mask\n assert m.shape[1] != 2, m.shape\n assert m.shape == (\n height,\n width,\n ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n self._mask = m.astype(\"uint8\")\n return\n\n raise ValueError(\"GenericMask cannot handle object {} of type '{}'\".format(m, type(m)))\n\n @property\n def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:\n assert metadata is not None\n # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n # H*W int32 image storing the panoptic_id in the format of\n # category_id * label_divisor + instance_id. We reserve -1 for\n # VOID label.\n label_divisor = metadata.label_divisor\n segments_info = []\n for panoptic_label in np.unique(panoptic_seg.numpy()):\n if panoptic_label == -1:\n # VOID region.\n continue\n pred_class = panoptic_label // label_divisor\n isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()\n segments_info.append(\n {\n \"id\": int(panoptic_label),\n \"category_id\": int(pred_class),\n \"isthing\": bool(isthing),\n }\n )\n del metadata\n\n self._seg = panoptic_seg\n\n self._sinfo = {s[\"id\"]: s for s in segments_info} # seg id -> seg info\n segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)\n areas = areas.numpy()\n sorted_idxs = np.argsort(-areas)\n self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]\n self._seg_ids = self._seg_ids.tolist()\n for sid, area in zip(self._seg_ids, self._seg_areas):\n if sid in self._sinfo:\n self._sinfo[sid][\"area\"] = float(area)\n\n def non_empty_mask(self):\n \"\"\"\n Returns:\n (H, W) array, a mask for all pixels that have a prediction\n \"\"\"\n empty_ids = []\n for id in self._seg_ids:\n if id not in self._sinfo:\n empty_ids.append(id)\n if len(empty_ids) == 0:\n return np.zeros(self._seg.shape, dtype=np.uint8)\n assert (\n len(empty_ids) == 1\n ), \">1 ids corresponds to no labels. This is currently not supported\"\n return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(np.bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n \"\"\"\n Args:\n classes (list[int] or None):\n scores (list[float] or None):\n class_names (list[str] or None):\n is_crowd (list[bool] or None):\n Returns:\n list[str] or None\n \"\"\"\n labels = None\n if classes is not None:\n if class_names is not None and len(class_names) > 0:\n labels = [class_names[i] for i in classes]\n else:\n labels = [str(i) for i in classes]\n if scores is not None:\n if labels is None:\n labels = [\"{:.0f}%\".format(s * 100) for s in scores]\n else:\n labels = [\"{} {:.0f}%\".format(l, s * 100) for l, s in zip(labels, scores)]\n if labels is not None and is_crowd is not None:\n labels = [l + (\"|crowd\" if crowd else \"\") for l, crowd in zip(labels, is_crowd)]\n return labels\n\n\nclass VisImage:\n def __init__(self, img, scale=1.0):\n \"\"\"\n Args:\n img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].\n scale (float): scale the input image\n \"\"\"\n self.img = img\n self.scale = scale\n self.width, self.height = img.shape[1], img.shape[0]\n self._setup_figure(img)\n\n def _setup_figure(self, img):\n \"\"\"\n Args:\n Same as in :meth:`__init__()`.\n Returns:\n fig (matplotlib.pyplot.figure): top level container for all the image plot elements.\n ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.\n \"\"\"\n fig = mplfigure.Figure(frameon=False)\n self.dpi = fig.get_dpi()\n # add a small 1e-2 to avoid precision lost due to matplotlib's truncation\n # (https://github.com/matplotlib/matplotlib/issues/15363)\n fig.set_size_inches(\n (self.width * self.scale + 1e-2) / self.dpi,\n (self.height * self.scale + 1e-2) / self.dpi,\n )\n self.canvas = FigureCanvasAgg(fig)\n # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n ax.axis(\"off\")\n self.fig = fig\n self.ax = ax\n self.reset_image(img)\n\n def reset_image(self, img):\n \"\"\"\n Args:\n img: same as in __init__\n \"\"\"\n img = img.astype(\"uint8\")\n self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\")\n\n def save(self, filepath):\n \"\"\"\n Args:\n filepath (str): a string that contains the absolute path, including the file name, where\n the visualized image will be saved.\n \"\"\"\n self.fig.savefig(filepath)\n\n def get_image(self):\n \"\"\"\n Returns:\n ndarray:\n the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n The shape is scaled w.r.t the input image using the given `scale` argument.\n \"\"\"\n canvas = self.canvas\n s, (width, height) = canvas.print_to_buffer()\n # buf = io.BytesIO() # works for cairo backend\n # canvas.print_rgba(buf)\n # width, height = self.width, self.height\n # s = buf.getvalue()\n\n buffer = np.frombuffer(s, dtype=\"uint8\")\n\n img_rgba = buffer.reshape(height, width, 4)\n rgb, alpha = np.split(img_rgba, [3], axis=2)\n return rgb.astype(\"uint8\")\n\n\nclass Visualizer:\n \"\"\"\n Visualizer that draws data about detection/segmentation on images.\n It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n that draw primitive objects to images, as well as high-level wrappers like\n `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`\n that draw composite data in some pre-defined style.\n Note that the exact visualization style for the high-level wrappers are subject to change.\n Style such as color, opacity, label contents, visibility of labels, or even the visibility\n of objects themselves (e.g. when the object is too small) may change according\n to different heuristics, as long as the results still look visually reasonable.\n To obtain a consistent style, you can implement custom drawing functions with the\n abovementioned primitive methods instead. If you need more customized visualization\n styles, you can process the data yourself following their format documented in\n tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not\n intend to satisfy everyone's preference on drawing styles.\n This visualizer focuses on high rendering quality rather than performance. It is not\n designed to be used for real-time applications.\n \"\"\"\n\n # TODO implement a fast, rasterized version using OpenCV\n\n def __init__(self, img_rgb, is_seg=True, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):\n \"\"\"\n Args:\n img_rgb: a numpy array of shape (H, W, C), where H and W correspond to\n the height and width of the image respectively. C is the number of\n color channels. The image is required to be in RGB format since that\n is a requirement of the Matplotlib library. The image is also expected\n to be in the range [0, 255].\n metadata (Metadata): dataset metadata (e.g. class names and colors)\n instance_mode (ColorMode): defines one of the pre-defined style for drawing\n instances on an image.\n \"\"\"\n self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n if metadata is None:\n metadata = MetadataCatalog.get(\"__nonexist__\")\n self.metadata = metadata\n self.output = VisImage(self.img, scale=scale)\n self.cpu_device = torch.device(\"cpu\")\n\n # too small texts are useless, therefore clamp to 9\n self._default_font_size = max(\n np.sqrt(self.output.height * self.output.width) // 90, 10 // scale\n )\n self._instance_mode = instance_mode\n self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n def get_image(self, img):\n img = np.asarray(img).clip(0, 255).astype(np.uint8)\n return VisImage(img, scale=1.0)\n \n def draw_box_predictions(\n self,\n boxes=None,\n labels=None,\n scores=None,\n assigned_colors=None\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = 0\n boxes = self._convert_boxes(boxes)\n classes = labels.tolist()\n scores = scores.tolist()\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n num_instances = len(boxes)\n assert len(labels) == num_instances\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:\n return self.output\n\n # Display in largest to smallest order to reduce occlusion.\n areas = None\n areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n\n if areas is not None:\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs] if boxes is not None else None\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n for i in range(num_instances):\n color = assigned_colors[i]\n if boxes is not None:\n self.draw_box(boxes[i], edge_color=color)\n\n if labels is not None:\n # first get a box\n if boxes is not None:\n x0, y0, x1, y1 = boxes[i]\n text_pos = (x0, y0) # if drawing boxes, put text on the box corner.\n horiz_align = \"left\"\n else:\n continue # drawing the box confidence for keypoints isn't very useful.\n # for small objects, draw text at the side to avoid occlusion\n instance_area = (y1 - y0) * (x1 - x0)\n if (\n instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale\n or y1 - y0 < 40 * self.output.scale\n ):\n if y1 >= self.output.height - 5:\n text_pos = (x1, y0)\n else:\n text_pos = (x0, y1)\n\n height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n return self.output\n \n \n def draw_instance_predictions(self, predictions, alpha=0.8):\n \"\"\"\n Draw instance-level prediction results on an image.\n Args:\n predictions (Instances): the output of an instance detection/segmentation\n model. Following fields will be used to draw:\n \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n boxes = predictions.pred_boxes if predictions.has(\"pred_boxes\") else None\n scores = predictions.scores if predictions.has(\"scores\") else None\n classes = predictions.pred_classes.tolist() if predictions.has(\"pred_classes\") else None\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n keypoints = predictions.pred_keypoints if predictions.has(\"pred_keypoints\") else None\n\n if predictions.has(\"pred_masks\"):\n masks = np.asarray(predictions.pred_masks)\n masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]\n else:\n masks = None\n\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"s\n# ... truncated ...","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":true} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.instance_color","uri":"program://OneFormer/function/demo.visualizer.instance_color#L40-L51","kind":"function","name":"instance_color","path":"demo/visualizer.py","language":"python","start_line":40,"end_line":51,"context_start_line":20,"context_end_line":71,"code":"from detectron2.data import MetadataCatalog\nfrom detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes\nfrom detectron2.utils.file_io import PathManager\nimport random\nrandom.seed(0)\nfrom colormap import random_color, _COLORS\nlogger = logging.getLogger(__name__)\n\n__all__ = [\"ColorMode\", \"VisImage\", \"Visualizer\"]\n\n\n_SMALL_OBJECT_AREA_THRESH = 1000\n_LARGE_MASK_AREA_THRESH = 120000\n_OFF_WHITE = (1.0, 1.0, 240.0 / 255)\n_BLACK = (0, 0, 0)\n_RED = (1.0, 0, 0)\n\n_KEYPOINT_THRESHOLD = 0.05\n\n\ndef instance_color(rgb=False, idx=1, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n@unique\nclass ColorMode(Enum):\n \"\"\"\n Enum of different color modes to use for instance visualizations.\n \"\"\"\n\n IMAGE = 0\n \"\"\"\n Picks a random color for every instance and overlay segmentations with low opacity.\n \"\"\"\n SEGMENTATION = 1\n \"\"\"\n Let instances of the same category have similar colors\n (from metadata.thing_colors), and overlay them with\n high opacity. This provides more attention on the quality of segmentation.\n \"\"\"\n IMAGE_BW = 2\n \"\"\"\n Same as IMAGE, but convert all areas without masks to gray-scale.","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.ColorMode","uri":"program://OneFormer/class/demo.visualizer.ColorMode#L54-L73","kind":"class","name":"ColorMode","path":"demo/visualizer.py","language":"python","start_line":54,"end_line":73,"context_start_line":34,"context_end_line":93,"code":"_BLACK = (0, 0, 0)\n_RED = (1.0, 0, 0)\n\n_KEYPOINT_THRESHOLD = 0.05\n\n\ndef instance_color(rgb=False, idx=1, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n@unique\nclass ColorMode(Enum):\n \"\"\"\n Enum of different color modes to use for instance visualizations.\n \"\"\"\n\n IMAGE = 0\n \"\"\"\n Picks a random color for every instance and overlay segmentations with low opacity.\n \"\"\"\n SEGMENTATION = 1\n \"\"\"\n Let instances of the same category have similar colors\n (from metadata.thing_colors), and overlay them with\n high opacity. This provides more attention on the quality of segmentation.\n \"\"\"\n IMAGE_BW = 2\n \"\"\"\n Same as IMAGE, but convert all areas without masks to gray-scale.\n Only available for drawing per-instance mask predictions.\n \"\"\"\n\n\nclass GenericMask:\n \"\"\"\n Attribute:\n polygons (list[ndarray]): list[ndarray]: polygons for this mask.\n Each ndarray has format [x, y, x, y, ...]\n mask (ndarray): a binary mask\n \"\"\"\n\n def __init__(self, mask_or_polygons, height, width):\n self._mask = self._polygons = self._has_holes = None\n self.height = height\n self.width = width\n\n m = mask_or_polygons\n if isinstance(m, dict):\n # RLEs\n assert \"counts\" in m and \"size\" in m\n if isinstance(m[\"counts\"], list): # uncompressed RLEs","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.GenericMask","uri":"program://OneFormer/class/demo.visualizer.GenericMask#L76-L169","kind":"class","name":"GenericMask","path":"demo/visualizer.py","language":"python","start_line":76,"end_line":169,"context_start_line":56,"context_end_line":189,"code":" Enum of different color modes to use for instance visualizations.\n \"\"\"\n\n IMAGE = 0\n \"\"\"\n Picks a random color for every instance and overlay segmentations with low opacity.\n \"\"\"\n SEGMENTATION = 1\n \"\"\"\n Let instances of the same category have similar colors\n (from metadata.thing_colors), and overlay them with\n high opacity. This provides more attention on the quality of segmentation.\n \"\"\"\n IMAGE_BW = 2\n \"\"\"\n Same as IMAGE, but convert all areas without masks to gray-scale.\n Only available for drawing per-instance mask predictions.\n \"\"\"\n\n\nclass GenericMask:\n \"\"\"\n Attribute:\n polygons (list[ndarray]): list[ndarray]: polygons for this mask.\n Each ndarray has format [x, y, x, y, ...]\n mask (ndarray): a binary mask\n \"\"\"\n\n def __init__(self, mask_or_polygons, height, width):\n self._mask = self._polygons = self._has_holes = None\n self.height = height\n self.width = width\n\n m = mask_or_polygons\n if isinstance(m, dict):\n # RLEs\n assert \"counts\" in m and \"size\" in m\n if isinstance(m[\"counts\"], list): # uncompressed RLEs\n h, w = m[\"size\"]\n assert h == height and w == width\n m = mask_util.frPyObjects(m, h, w)\n self._mask = mask_util.decode(m)[:, :]\n return\n\n if isinstance(m, list): # list[ndarray]\n self._polygons = [np.asarray(x).reshape(-1) for x in m]\n return\n\n if isinstance(m, np.ndarray): # assumed to be a binary mask\n assert m.shape[1] != 2, m.shape\n assert m.shape == (\n height,\n width,\n ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n self._mask = m.astype(\"uint8\")\n return\n\n raise ValueError(\"GenericMask cannot handle object {} of type '{}'\".format(m, type(m)))\n\n @property\n def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:\n assert metadata is not None\n # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n # H*W int32 image storing the panoptic_id in the format of\n # category_id * label_divisor + instance_id. We reserve -1 for\n # VOID label.\n label_divisor = metadata.label_divisor\n segments_info = []\n for panoptic_label in np.unique(panoptic_seg.numpy()):\n if panoptic_label == -1:\n # VOID region.\n continue","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._PanopticPrediction","uri":"program://OneFormer/class/demo.visualizer._PanopticPrediction#L172-L244","kind":"class","name":"_PanopticPrediction","path":"demo/visualizer.py","language":"python","start_line":172,"end_line":244,"context_start_line":152,"context_end_line":264,"code":" res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:\n assert metadata is not None\n # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n # H*W int32 image storing the panoptic_id in the format of\n # category_id * label_divisor + instance_id. We reserve -1 for\n # VOID label.\n label_divisor = metadata.label_divisor\n segments_info = []\n for panoptic_label in np.unique(panoptic_seg.numpy()):\n if panoptic_label == -1:\n # VOID region.\n continue\n pred_class = panoptic_label // label_divisor\n isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()\n segments_info.append(\n {\n \"id\": int(panoptic_label),\n \"category_id\": int(pred_class),\n \"isthing\": bool(isthing),\n }\n )\n del metadata\n\n self._seg = panoptic_seg\n\n self._sinfo = {s[\"id\"]: s for s in segments_info} # seg id -> seg info\n segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)\n areas = areas.numpy()\n sorted_idxs = np.argsort(-areas)\n self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]\n self._seg_ids = self._seg_ids.tolist()\n for sid, area in zip(self._seg_ids, self._seg_areas):\n if sid in self._sinfo:\n self._sinfo[sid][\"area\"] = float(area)\n\n def non_empty_mask(self):\n \"\"\"\n Returns:\n (H, W) array, a mask for all pixels that have a prediction\n \"\"\"\n empty_ids = []\n for id in self._seg_ids:\n if id not in self._sinfo:\n empty_ids.append(id)\n if len(empty_ids) == 0:\n return np.zeros(self._seg.shape, dtype=np.uint8)\n assert (\n len(empty_ids) == 1\n ), \">1 ids corresponds to no labels. This is currently not supported\"\n return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(np.bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n \"\"\"\n Args:\n classes (list[int] or None):\n scores (list[float] or None):\n class_names (list[str] or None):\n is_crowd (list[bool] or None):\n Returns:\n list[str] or None\n \"\"\"\n labels = None\n if classes is not None:\n if class_names is not None and len(class_names) > 0:\n labels = [class_names[i] for i in classes]\n else:\n labels = [str(i) for i in classes]\n if scores is not None:\n if labels is None:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._create_text_labels","uri":"program://OneFormer/function/demo.visualizer._create_text_labels#L247-L270","kind":"function","name":"_create_text_labels","path":"demo/visualizer.py","language":"python","start_line":247,"end_line":270,"context_start_line":227,"context_end_line":290,"code":" return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(np.bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n \"\"\"\n Args:\n classes (list[int] or None):\n scores (list[float] or None):\n class_names (list[str] or None):\n is_crowd (list[bool] or None):\n Returns:\n list[str] or None\n \"\"\"\n labels = None\n if classes is not None:\n if class_names is not None and len(class_names) > 0:\n labels = [class_names[i] for i in classes]\n else:\n labels = [str(i) for i in classes]\n if scores is not None:\n if labels is None:\n labels = [\"{:.0f}%\".format(s * 100) for s in scores]\n else:\n labels = [\"{} {:.0f}%\".format(l, s * 100) for l, s in zip(labels, scores)]\n if labels is not None and is_crowd is not None:\n labels = [l + (\"|crowd\" if crowd else \"\") for l, crowd in zip(labels, is_crowd)]\n return labels\n\n\nclass VisImage:\n def __init__(self, img, scale=1.0):\n \"\"\"\n Args:\n img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].\n scale (float): scale the input image\n \"\"\"\n self.img = img\n self.scale = scale\n self.width, self.height = img.shape[1], img.shape[0]\n self._setup_figure(img)\n\n def _setup_figure(self, img):\n \"\"\"\n Args:\n Same as in :meth:`__init__()`.\n Returns:\n fig (matplotlib.pyplot.figure): top level container for all the image plot elements.","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.VisImage","uri":"program://OneFormer/class/demo.visualizer.VisImage#L273-L343","kind":"class","name":"VisImage","path":"demo/visualizer.py","language":"python","start_line":273,"end_line":343,"context_start_line":253,"context_end_line":363,"code":" is_crowd (list[bool] or None):\n Returns:\n list[str] or None\n \"\"\"\n labels = None\n if classes is not None:\n if class_names is not None and len(class_names) > 0:\n labels = [class_names[i] for i in classes]\n else:\n labels = [str(i) for i in classes]\n if scores is not None:\n if labels is None:\n labels = [\"{:.0f}%\".format(s * 100) for s in scores]\n else:\n labels = [\"{} {:.0f}%\".format(l, s * 100) for l, s in zip(labels, scores)]\n if labels is not None and is_crowd is not None:\n labels = [l + (\"|crowd\" if crowd else \"\") for l, crowd in zip(labels, is_crowd)]\n return labels\n\n\nclass VisImage:\n def __init__(self, img, scale=1.0):\n \"\"\"\n Args:\n img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].\n scale (float): scale the input image\n \"\"\"\n self.img = img\n self.scale = scale\n self.width, self.height = img.shape[1], img.shape[0]\n self._setup_figure(img)\n\n def _setup_figure(self, img):\n \"\"\"\n Args:\n Same as in :meth:`__init__()`.\n Returns:\n fig (matplotlib.pyplot.figure): top level container for all the image plot elements.\n ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.\n \"\"\"\n fig = mplfigure.Figure(frameon=False)\n self.dpi = fig.get_dpi()\n # add a small 1e-2 to avoid precision lost due to matplotlib's truncation\n # (https://github.com/matplotlib/matplotlib/issues/15363)\n fig.set_size_inches(\n (self.width * self.scale + 1e-2) / self.dpi,\n (self.height * self.scale + 1e-2) / self.dpi,\n )\n self.canvas = FigureCanvasAgg(fig)\n # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n ax.axis(\"off\")\n self.fig = fig\n self.ax = ax\n self.reset_image(img)\n\n def reset_image(self, img):\n \"\"\"\n Args:\n img: same as in __init__\n \"\"\"\n img = img.astype(\"uint8\")\n self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\")\n\n def save(self, filepath):\n \"\"\"\n Args:\n filepath (str): a string that contains the absolute path, including the file name, where\n the visualized image will be saved.\n \"\"\"\n self.fig.savefig(filepath)\n\n def get_image(self):\n \"\"\"\n Returns:\n ndarray:\n the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n The shape is scaled w.r.t the input image using the given `scale` argument.\n \"\"\"\n canvas = self.canvas\n s, (width, height) = canvas.print_to_buffer()\n # buf = io.BytesIO() # works for cairo backend\n # canvas.print_rgba(buf)\n # width, height = self.width, self.height\n # s = buf.getvalue()\n\n buffer = np.frombuffer(s, dtype=\"uint8\")\n\n img_rgba = buffer.reshape(height, width, 4)\n rgb, alpha = np.split(img_rgba, [3], axis=2)\n return rgb.astype(\"uint8\")\n\n\nclass Visualizer:\n \"\"\"\n Visualizer that draws data about detection/segmentation on images.\n It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n that draw primitive objects to images, as well as high-level wrappers like\n `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`\n that draw composite data in some pre-defined style.\n Note that the exact visualization style for the high-level wrappers are subject to change.\n Style such as color, opacity, label contents, visibility of labels, or even the visibility\n of objects themselves (e.g. when the object is too small) may change according\n to different heuristics, as long as the results still look visually reasonable.\n To obtain a consistent style, you can implement custom drawing functions with the\n abovementioned primitive methods instead. If you need more customized visualization\n styles, you can process the data yourself following their format documented in\n tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not\n intend to satisfy everyone's preference on drawing styles.\n This visualizer focuses on high rendering quality rather than performance. It is not\n designed to be used for real-time applications.","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.Visualizer","uri":"program://OneFormer/class/demo.visualizer.Visualizer#L346-L1345","kind":"class","name":"Visualizer","path":"demo/visualizer.py","language":"python","start_line":346,"end_line":1345,"context_start_line":326,"context_end_line":1345,"code":" \"\"\"\n Returns:\n ndarray:\n the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n The shape is scaled w.r.t the input image using the given `scale` argument.\n \"\"\"\n canvas = self.canvas\n s, (width, height) = canvas.print_to_buffer()\n # buf = io.BytesIO() # works for cairo backend\n # canvas.print_rgba(buf)\n # width, height = self.width, self.height\n # s = buf.getvalue()\n\n buffer = np.frombuffer(s, dtype=\"uint8\")\n\n img_rgba = buffer.reshape(height, width, 4)\n rgb, alpha = np.split(img_rgba, [3], axis=2)\n return rgb.astype(\"uint8\")\n\n\nclass Visualizer:\n \"\"\"\n Visualizer that draws data about detection/segmentation on images.\n It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n that draw primitive objects to images, as well as high-level wrappers like\n `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`\n that draw composite data in some pre-defined style.\n Note that the exact visualization style for the high-level wrappers are subject to change.\n Style such as color, opacity, label contents, visibility of labels, or even the visibility\n of objects themselves (e.g. when the object is too small) may change according\n to different heuristics, as long as the results still look visually reasonable.\n To obtain a consistent style, you can implement custom drawing functions with the\n abovementioned primitive methods instead. If you need more customized visualization\n styles, you can process the data yourself following their format documented in\n tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not\n intend to satisfy everyone's preference on drawing styles.\n This visualizer focuses on high rendering quality rather than performance. It is not\n designed to be used for real-time applications.\n \"\"\"\n\n # TODO implement a fast, rasterized version using OpenCV\n\n def __init__(self, img_rgb, is_seg=True, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):\n \"\"\"\n Args:\n img_rgb: a numpy array of shape (H, W, C), where H and W correspond to\n the height and width of the image respectively. C is the number of\n color channels. The image is required to be in RGB format since that\n is a requirement of the Matplotlib library. The image is also expected\n to be in the range [0, 255].\n metadata (Metadata): dataset metadata (e.g. class names and colors)\n instance_mode (ColorMode): defines one of the pre-defined style for drawing\n instances on an image.\n \"\"\"\n self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n if metadata is None:\n metadata = MetadataCatalog.get(\"__nonexist__\")\n self.metadata = metadata\n self.output = VisImage(self.img, scale=scale)\n self.cpu_device = torch.device(\"cpu\")\n\n # too small texts are useless, therefore clamp to 9\n self._default_font_size = max(\n np.sqrt(self.output.height * self.output.width) // 90, 10 // scale\n )\n self._instance_mode = instance_mode\n self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n def get_image(self, img):\n img = np.asarray(img).clip(0, 255).astype(np.uint8)\n return VisImage(img, scale=1.0)\n \n def draw_box_predictions(\n self,\n boxes=None,\n labels=None,\n scores=None,\n assigned_colors=None\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = 0\n boxes = self._convert_boxes(boxes)\n classes = labels.tolist()\n scores = scores.tolist()\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n num_instances = len(boxes)\n assert len(labels) == num_instances\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:\n return self.output\n\n # Display in largest to smallest order to reduce occlusion.\n areas = None\n areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n\n if areas is not None:\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs] if boxes is not None else None\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n for i in range(num_instances):\n color = assigned_colors[i]\n if boxes is not None:\n self.draw_box(boxes[i], edge_color=color)\n\n if labels is not None:\n # first get a box\n if boxes is not None:\n x0, y0, x1, y1 = boxes[i]\n text_pos = (x0, y0) # if drawing boxes, put text on the box corner.\n horiz_align = \"left\"\n else:\n continue # drawing the box confidence for keypoints isn't very useful.\n # for small objects, draw text at the side to avoid occlusion\n instance_area = (y1 - y0) * (x1 - x0)\n if (\n instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale\n or y1 - y0 < 40 * self.output.scale\n ):\n if y1 >= self.output.height - 5:\n text_pos = (x1, y0)\n else:\n text_pos = (x0, y1)\n\n height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n return self.output\n \n \n def draw_instance_predictions(self, predictions, alpha=0.8):\n \"\"\"\n Draw instance-level prediction results on an image.\n Args:\n predictions (Instances): the output of an instance detection/segmentation\n model. Following fields will be used to draw:\n \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n boxes = predictions.pred_boxes if predictions.has(\"pred_boxes\") else None\n scores = predictions.scores if predictions.has(\"scores\") else None\n classes = predictions.pred_classes.tolist() if predictions.has(\"pred_classes\") else None\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n keypoints = predictions.pred_keypoints if predictions.has(\"pred_keypoints\") else None\n\n if predictions.has(\"pred_masks\"):\n masks = np.asarray(predictions.pred_masks)\n masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]\n else:\n masks = None\n\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"stuff_colors\"):\n # colors = [\n # self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes\n # ]\n colors = [\n instance_color(rgb=True, idx=c, maximum=1) for c in classes\n ]\n else:\n colors = None\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(\n self._create_grayscale_image(\n (predictions.pred_masks.any(dim=0) > 0).numpy()\n if predictions.has(\"pred_masks\")\n else None\n )\n )\n\n self.overlay_instances(\n masks=masks,\n boxes=boxes,\n labels=labels,\n keypoints=keypoints,\n assigned_colors=colors,\n alpha=alpha,\n )\n return self.output\n\n def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):\n \"\"\"\n Draw semantic segmentation predictions/labels.\n Args:\n sem_seg (Tensor or ndarray): the segmentation of shape (H, W).\n Each value is the integer label of the pixel.\n area_threshold (int): segments with less than `area_threshold` are not drawn.\n alpha (float): the larger it is, the more opaque the segmentations are.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n if isinstance(sem_seg, torch.Tensor):\n sem_seg = sem_seg.numpy()\n labels, areas = np.unique(sem_seg, return_counts=True)\n sorted_idxs = np.argsort(-areas).tolist()\n labels = labels[sorted_idxs]\n for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]\n except (AttributeError, IndexError):\n mask_color = None\n\n binary_mask = (sem_seg == label).astype(np.uint8)\n text = self.metadata.stuff_classes[label]\n self.draw_binary_mask(\n binary_mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n return self.output\n\n def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):\n \"\"\"\n Draw panoptic prediction annotations or results.\n Args:\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each\n segment.\n segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.\n If it is a ``list[dict]``, each dict contains keys \"id\", \"category_id\".\n If None, category id of each pixel is computed by\n ``pixel // metadata.label_divisor``.\n area_threshold (int): stuff segments with less than `area_threshold` are not drawn.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))\n\n # draw mask for all semantic segments first i.e. \"stuff\"\n for mask, sinfo in pred.semantic_masks():\n category_idx = sinfo[\"category_id\"]\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]\n except AttributeError:\n mask_color = None\n\n text = self.metadata.stuff_classes[category_idx]\n self.draw_binary_mask(\n mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n\n # draw mask for all instances second\n all_instances = list(pred.instance_masks())\n if len(all_instances) == 0:\n return self.output\n masks, sinfo = list(zip(*all_instances))\n category_ids = [x[\"category_id\"] for x in sinfo]\n\n try:\n scores = [x[\"score\"] for x in sinfo]\n except KeyError:\n scores = None\n labels = _create_text_labels(\n category_ids, scores, self.metadata.stuff_classes, [x.get(\"iscrowd\", 0) for x in sinfo]\n )\n\n try:\n colors = [\n self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) for c in category_ids\n ]\n except AttributeError:\n colors = None\n self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)\n\n return self.output\n\n draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility\n\n def draw_dataset_dict(self, dic):\n \"\"\"\n Draw annotations/segmentaions in Detectron2 Dataset format.\n Args:\n dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n annos = dic.get(\"annotations\", None)\n if annos:\n if \"segmentation\" in annos[0]:\n masks = [x[\"segmentation\"] for x in annos]\n else:\n masks = None\n if \"keypoints\" in annos[0]:\n keypts = [x[\"keypoints\"] for x in annos]\n keypts = np.array(keypts).reshape(len(annos), -1, 3)\n else:\n keypts = None\n\n boxes = [\n BoxMode.convert(x[\"bbox\"], x[\"bbox_mode\"], BoxMode.XYXY_ABS)\n if len(x[\"bbox\"]) == 4\n else x[\"bbox\"]\n for x in annos\n ]\n\n colors = None\n category_ids = [x[\"category_id\"] for x in annos]\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"stuff_colors\"):\n colors = [\n self._jitter([x / 255 for x in self.metadata.stuff_colors[c]])\n for c in category_ids\n ]\n names = self.metadata.get(\"stuff_classes\", None)\n labels = _create_text_labels(\n category_ids,\n scores=None,\n class_names=names,\n is_crowd=[x.get(\"iscrowd\", 0) for x in annos],\n )\n self.overlay_instances(\n labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors\n )\n\n sem_seg = dic.get(\"sem_seg\", None)\n if sem_seg is None and \"sem_seg_file_name\" in dic:\n with PathManager.open(dic[\"sem_seg_file_name\"], \"rb\") as f:\n sem_seg = Image.open(f)\n sem_seg = np.asarray(sem_seg, dtype=\"uint8\")\n if sem_seg is not None:\n self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)\n\n pan_seg = dic.get(\"pan_seg\", None)\n if pan_seg is None and \"pan_seg_file_name\" in dic:\n with PathManager.open(dic[\"pan_seg_file_name\"], \"rb\") as f:\n pan_seg = Image.open(f)\n pan_seg = np.asarray(pan_seg)\n from panopticapi.utils import rgb2id\n\n pan_seg = rgb2id(pan_seg)\n if pan_seg is not None:\n segments_info = dic[\"segments_info\"]\n pan_seg = torch.tensor(pan_seg)\n self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)\n return self.output\n\n def overlay_instances(\n self,\n *,\n boxes=None,\n labels=None,\n masks=None,\n keypoints=None,\n assigned_colors=None,\n alpha=0.5,\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n masks (masks-like object): Supported types are:\n * :class:`detectron2.structures.PolygonMasks`,\n :class:`detectron2.structures.BitMasks`.\n * list[list[ndarray]]: contains the segmentation masks for all objects in one image.\n The first level of the list corresponds to individual instances. The second\n level to all the polygon that compose the instance, and the third level\n to the polygon coordinates. The third level should have the format of\n [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\n * list[ndarray]: each ndarray is a binary mask of shape (H, W).\n * list[dict]: each dict is a COCO-style RLE.\n keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),\n where the N is the number of instances and K is the number of keypoints.\n The last dimension corresponds to (x, y, visibility or score).\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = 0\n if boxes is not None:\n boxes = self._convert_boxes(boxes)\n num_instances = len(boxes)\n if masks is not None:\n masks = self._convert_masks(masks)\n if num_instances:\n assert len(masks) == num_instances\n else:\n num_instances = len(masks)\n if keypoints is not None:\n if num_instances:\n assert len(keypoints) == num_instances\n else:\n num_instances = len(keypoints)\n keypoints = self._convert_keypoints(keypoints)\n if labels is not None:\n assert len(labels) == num_instances\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:\n return self.output\n if boxes is not None and boxes.shape[1] == 5:\n return self.overlay_rotated_instances(\n boxes=boxes, labels=labels, assigned_colors=assigned_colors\n )\n\n # Display in largest to smallest order to reduce occlusion.\n areas = None\n if boxes is not None:\n areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n elif masks is not None:\n areas = np.asarray([x.area() for x in masks])\n\n if areas is not None:\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs] if boxes is not None else None\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n masks = [masks[idx] for\n# ... truncated ...","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":true} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.__init__","uri":"program://OneFormer/function/demo.visualizer.__init__#L368-L392","kind":"function","name":"__init__","path":"demo/visualizer.py","language":"python","start_line":368,"end_line":392,"context_start_line":348,"context_end_line":412,"code":" Visualizer that draws data about detection/segmentation on images.\n It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n that draw primitive objects to images, as well as high-level wrappers like\n `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`\n that draw composite data in some pre-defined style.\n Note that the exact visualization style for the high-level wrappers are subject to change.\n Style such as color, opacity, label contents, visibility of labels, or even the visibility\n of objects themselves (e.g. when the object is too small) may change according\n to different heuristics, as long as the results still look visually reasonable.\n To obtain a consistent style, you can implement custom drawing functions with the\n abovementioned primitive methods instead. If you need more customized visualization\n styles, you can process the data yourself following their format documented in\n tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not\n intend to satisfy everyone's preference on drawing styles.\n This visualizer focuses on high rendering quality rather than performance. It is not\n designed to be used for real-time applications.\n \"\"\"\n\n # TODO implement a fast, rasterized version using OpenCV\n\n def __init__(self, img_rgb, is_seg=True, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):\n \"\"\"\n Args:\n img_rgb: a numpy array of shape (H, W, C), where H and W correspond to\n the height and width of the image respectively. C is the number of\n color channels. The image is required to be in RGB format since that\n is a requirement of the Matplotlib library. The image is also expected\n to be in the range [0, 255].\n metadata (Metadata): dataset metadata (e.g. class names and colors)\n instance_mode (ColorMode): defines one of the pre-defined style for drawing\n instances on an image.\n \"\"\"\n self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n if metadata is None:\n metadata = MetadataCatalog.get(\"__nonexist__\")\n self.metadata = metadata\n self.output = VisImage(self.img, scale=scale)\n self.cpu_device = torch.device(\"cpu\")\n\n # too small texts are useless, therefore clamp to 9\n self._default_font_size = max(\n np.sqrt(self.output.height * self.output.width) // 90, 10 // scale\n )\n self._instance_mode = instance_mode\n self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n def get_image(self, img):\n img = np.asarray(img).clip(0, 255).astype(np.uint8)\n return VisImage(img, scale=1.0)\n \n def draw_box_predictions(\n self,\n boxes=None,\n labels=None,\n scores=None,\n assigned_colors=None\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.mask","uri":"program://OneFormer/function/demo.visualizer.mask#L116-L119","kind":"function","name":"mask","path":"demo/visualizer.py","language":"python","start_line":116,"end_line":119,"context_start_line":96,"context_end_line":139,"code":" m = mask_util.frPyObjects(m, h, w)\n self._mask = mask_util.decode(m)[:, :]\n return\n\n if isinstance(m, list): # list[ndarray]\n self._polygons = [np.asarray(x).reshape(-1) for x in m]\n return\n\n if isinstance(m, np.ndarray): # assumed to be a binary mask\n assert m.shape[1] != 2, m.shape\n assert m.shape == (\n height,\n width,\n ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n self._mask = m.astype(\"uint8\")\n return\n\n raise ValueError(\"GenericMask cannot handle object {} of type '{}'\".format(m, type(m)))\n\n @property\n def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.polygons","uri":"program://OneFormer/function/demo.visualizer.polygons#L122-L125","kind":"function","name":"polygons","path":"demo/visualizer.py","language":"python","start_line":122,"end_line":125,"context_start_line":102,"context_end_line":145,"code":" return\n\n if isinstance(m, np.ndarray): # assumed to be a binary mask\n assert m.shape[1] != 2, m.shape\n assert m.shape == (\n height,\n width,\n ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n self._mask = m.astype(\"uint8\")\n return\n\n raise ValueError(\"GenericMask cannot handle object {} of type '{}'\".format(m, type(m)))\n\n @property\n def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.has_holes","uri":"program://OneFormer/function/demo.visualizer.has_holes#L128-L134","kind":"function","name":"has_holes","path":"demo/visualizer.py","language":"python","start_line":128,"end_line":134,"context_start_line":108,"context_end_line":154,"code":" width,\n ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n self._mask = m.astype(\"uint8\")\n return\n\n raise ValueError(\"GenericMask cannot handle object {} of type '{}'\".format(m, type(m)))\n\n @property\n def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.mask_to_polygons","uri":"program://OneFormer/function/demo.visualizer.mask_to_polygons#L136-L153","kind":"function","name":"mask_to_polygons","path":"demo/visualizer.py","language":"python","start_line":136,"end_line":153,"context_start_line":116,"context_end_line":173,"code":" def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.polygons_to_mask","uri":"program://OneFormer/function/demo.visualizer.polygons_to_mask#L155-L158","kind":"function","name":"polygons_to_mask","path":"demo/visualizer.py","language":"python","start_line":155,"end_line":158,"context_start_line":135,"context_end_line":178,"code":"\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.area","uri":"program://OneFormer/function/demo.visualizer.area#L160-L161","kind":"function","name":"area","path":"demo/visualizer.py","language":"python","start_line":160,"end_line":161,"context_start_line":140,"context_end_line":181,"code":" # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:\n assert metadata is not None\n # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n # H*W int32 image storing the panoptic_id in the format of","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.bbox","uri":"program://OneFormer/function/demo.visualizer.bbox#L163-L169","kind":"function","name":"bbox","path":"demo/visualizer.py","language":"python","start_line":163,"end_line":169,"context_start_line":143,"context_end_line":189,"code":" hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:\n assert metadata is not None\n # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n # H*W int32 image storing the panoptic_id in the format of\n # category_id * label_divisor + instance_id. We reserve -1 for\n # VOID label.\n label_divisor = metadata.label_divisor\n segments_info = []\n for panoptic_label in np.unique(panoptic_seg.numpy()):\n if panoptic_label == -1:\n # VOID region.\n continue","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.non_empty_mask","uri":"program://OneFormer/function/demo.visualizer.non_empty_mask#L213-L227","kind":"function","name":"non_empty_mask","path":"demo/visualizer.py","language":"python","start_line":213,"end_line":227,"context_start_line":193,"context_end_line":247,"code":" {\n \"id\": int(panoptic_label),\n \"category_id\": int(pred_class),\n \"isthing\": bool(isthing),\n }\n )\n del metadata\n\n self._seg = panoptic_seg\n\n self._sinfo = {s[\"id\"]: s for s in segments_info} # seg id -> seg info\n segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)\n areas = areas.numpy()\n sorted_idxs = np.argsort(-areas)\n self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]\n self._seg_ids = self._seg_ids.tolist()\n for sid, area in zip(self._seg_ids, self._seg_areas):\n if sid in self._sinfo:\n self._sinfo[sid][\"area\"] = float(area)\n\n def non_empty_mask(self):\n \"\"\"\n Returns:\n (H, W) array, a mask for all pixels that have a prediction\n \"\"\"\n empty_ids = []\n for id in self._seg_ids:\n if id not in self._sinfo:\n empty_ids.append(id)\n if len(empty_ids) == 0:\n return np.zeros(self._seg.shape, dtype=np.uint8)\n assert (\n len(empty_ids) == 1\n ), \">1 ids corresponds to no labels. This is currently not supported\"\n return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(np.bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.semantic_masks","uri":"program://OneFormer/function/demo.visualizer.semantic_masks#L229-L235","kind":"function","name":"semantic_masks","path":"demo/visualizer.py","language":"python","start_line":229,"end_line":235,"context_start_line":209,"context_end_line":255,"code":" for sid, area in zip(self._seg_ids, self._seg_areas):\n if sid in self._sinfo:\n self._sinfo[sid][\"area\"] = float(area)\n\n def non_empty_mask(self):\n \"\"\"\n Returns:\n (H, W) array, a mask for all pixels that have a prediction\n \"\"\"\n empty_ids = []\n for id in self._seg_ids:\n if id not in self._sinfo:\n empty_ids.append(id)\n if len(empty_ids) == 0:\n return np.zeros(self._seg.shape, dtype=np.uint8)\n assert (\n len(empty_ids) == 1\n ), \">1 ids corresponds to no labels. This is currently not supported\"\n return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(np.bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n \"\"\"\n Args:\n classes (list[int] or None):\n scores (list[float] or None):\n class_names (list[str] or None):\n is_crowd (list[bool] or None):\n Returns:\n list[str] or None","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.instance_masks","uri":"program://OneFormer/function/demo.visualizer.instance_masks#L237-L244","kind":"function","name":"instance_masks","path":"demo/visualizer.py","language":"python","start_line":237,"end_line":244,"context_start_line":217,"context_end_line":264,"code":" \"\"\"\n empty_ids = []\n for id in self._seg_ids:\n if id not in self._sinfo:\n empty_ids.append(id)\n if len(empty_ids) == 0:\n return np.zeros(self._seg.shape, dtype=np.uint8)\n assert (\n len(empty_ids) == 1\n ), \">1 ids corresponds to no labels. This is currently not supported\"\n return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(np.bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n \"\"\"\n Args:\n classes (list[int] or None):\n scores (list[float] or None):\n class_names (list[str] or None):\n is_crowd (list[bool] or None):\n Returns:\n list[str] or None\n \"\"\"\n labels = None\n if classes is not None:\n if class_names is not None and len(class_names) > 0:\n labels = [class_names[i] for i in classes]\n else:\n labels = [str(i) for i in classes]\n if scores is not None:\n if labels is None:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._setup_figure","uri":"program://OneFormer/function/demo.visualizer._setup_figure#L285-L307","kind":"function","name":"_setup_figure","path":"demo/visualizer.py","language":"python","start_line":285,"end_line":307,"context_start_line":265,"context_end_line":327,"code":" labels = [\"{:.0f}%\".format(s * 100) for s in scores]\n else:\n labels = [\"{} {:.0f}%\".format(l, s * 100) for l, s in zip(labels, scores)]\n if labels is not None and is_crowd is not None:\n labels = [l + (\"|crowd\" if crowd else \"\") for l, crowd in zip(labels, is_crowd)]\n return labels\n\n\nclass VisImage:\n def __init__(self, img, scale=1.0):\n \"\"\"\n Args:\n img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].\n scale (float): scale the input image\n \"\"\"\n self.img = img\n self.scale = scale\n self.width, self.height = img.shape[1], img.shape[0]\n self._setup_figure(img)\n\n def _setup_figure(self, img):\n \"\"\"\n Args:\n Same as in :meth:`__init__()`.\n Returns:\n fig (matplotlib.pyplot.figure): top level container for all the image plot elements.\n ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.\n \"\"\"\n fig = mplfigure.Figure(frameon=False)\n self.dpi = fig.get_dpi()\n # add a small 1e-2 to avoid precision lost due to matplotlib's truncation\n # (https://github.com/matplotlib/matplotlib/issues/15363)\n fig.set_size_inches(\n (self.width * self.scale + 1e-2) / self.dpi,\n (self.height * self.scale + 1e-2) / self.dpi,\n )\n self.canvas = FigureCanvasAgg(fig)\n # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n ax.axis(\"off\")\n self.fig = fig\n self.ax = ax\n self.reset_image(img)\n\n def reset_image(self, img):\n \"\"\"\n Args:\n img: same as in __init__\n \"\"\"\n img = img.astype(\"uint8\")\n self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\")\n\n def save(self, filepath):\n \"\"\"\n Args:\n filepath (str): a string that contains the absolute path, including the file name, where\n the visualized image will be saved.\n \"\"\"\n self.fig.savefig(filepath)\n\n def get_image(self):\n \"\"\"\n Returns:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.reset_image","uri":"program://OneFormer/function/demo.visualizer.reset_image#L309-L315","kind":"function","name":"reset_image","path":"demo/visualizer.py","language":"python","start_line":309,"end_line":315,"context_start_line":289,"context_end_line":335,"code":" Returns:\n fig (matplotlib.pyplot.figure): top level container for all the image plot elements.\n ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.\n \"\"\"\n fig = mplfigure.Figure(frameon=False)\n self.dpi = fig.get_dpi()\n # add a small 1e-2 to avoid precision lost due to matplotlib's truncation\n # (https://github.com/matplotlib/matplotlib/issues/15363)\n fig.set_size_inches(\n (self.width * self.scale + 1e-2) / self.dpi,\n (self.height * self.scale + 1e-2) / self.dpi,\n )\n self.canvas = FigureCanvasAgg(fig)\n # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n ax.axis(\"off\")\n self.fig = fig\n self.ax = ax\n self.reset_image(img)\n\n def reset_image(self, img):\n \"\"\"\n Args:\n img: same as in __init__\n \"\"\"\n img = img.astype(\"uint8\")\n self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\")\n\n def save(self, filepath):\n \"\"\"\n Args:\n filepath (str): a string that contains the absolute path, including the file name, where\n the visualized image will be saved.\n \"\"\"\n self.fig.savefig(filepath)\n\n def get_image(self):\n \"\"\"\n Returns:\n ndarray:\n the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n The shape is scaled w.r.t the input image using the given `scale` argument.\n \"\"\"\n canvas = self.canvas\n s, (width, height) = canvas.print_to_buffer()\n # buf = io.BytesIO() # works for cairo backend\n # canvas.print_rgba(buf)","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.save","uri":"program://OneFormer/function/demo.visualizer.save#L317-L323","kind":"function","name":"save","path":"demo/visualizer.py","language":"python","start_line":317,"end_line":323,"context_start_line":297,"context_end_line":343,"code":" fig.set_size_inches(\n (self.width * self.scale + 1e-2) / self.dpi,\n (self.height * self.scale + 1e-2) / self.dpi,\n )\n self.canvas = FigureCanvasAgg(fig)\n # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n ax.axis(\"off\")\n self.fig = fig\n self.ax = ax\n self.reset_image(img)\n\n def reset_image(self, img):\n \"\"\"\n Args:\n img: same as in __init__\n \"\"\"\n img = img.astype(\"uint8\")\n self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\")\n\n def save(self, filepath):\n \"\"\"\n Args:\n filepath (str): a string that contains the absolute path, including the file name, where\n the visualized image will be saved.\n \"\"\"\n self.fig.savefig(filepath)\n\n def get_image(self):\n \"\"\"\n Returns:\n ndarray:\n the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n The shape is scaled w.r.t the input image using the given `scale` argument.\n \"\"\"\n canvas = self.canvas\n s, (width, height) = canvas.print_to_buffer()\n # buf = io.BytesIO() # works for cairo backend\n # canvas.print_rgba(buf)\n # width, height = self.width, self.height\n # s = buf.getvalue()\n\n buffer = np.frombuffer(s, dtype=\"uint8\")\n\n img_rgba = buffer.reshape(height, width, 4)\n rgb, alpha = np.split(img_rgba, [3], axis=2)\n return rgb.astype(\"uint8\")","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.get_image","uri":"program://OneFormer/function/demo.visualizer.get_image#L394-L396","kind":"function","name":"get_image","path":"demo/visualizer.py","language":"python","start_line":394,"end_line":396,"context_start_line":374,"context_end_line":416,"code":" is a requirement of the Matplotlib library. The image is also expected\n to be in the range [0, 255].\n metadata (Metadata): dataset metadata (e.g. class names and colors)\n instance_mode (ColorMode): defines one of the pre-defined style for drawing\n instances on an image.\n \"\"\"\n self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n if metadata is None:\n metadata = MetadataCatalog.get(\"__nonexist__\")\n self.metadata = metadata\n self.output = VisImage(self.img, scale=scale)\n self.cpu_device = torch.device(\"cpu\")\n\n # too small texts are useless, therefore clamp to 9\n self._default_font_size = max(\n np.sqrt(self.output.height * self.output.width) // 90, 10 // scale\n )\n self._instance_mode = instance_mode\n self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n def get_image(self, img):\n img = np.asarray(img).clip(0, 255).astype(np.uint8)\n return VisImage(img, scale=1.0)\n \n def draw_box_predictions(\n self,\n boxes=None,\n labels=None,\n scores=None,\n assigned_colors=None\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_box_predictions","uri":"program://OneFormer/function/demo.visualizer.draw_box_predictions#L398-L482","kind":"function","name":"draw_box_predictions","path":"demo/visualizer.py","language":"python","start_line":398,"end_line":482,"context_start_line":378,"context_end_line":502,"code":" instances on an image.\n \"\"\"\n self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n if metadata is None:\n metadata = MetadataCatalog.get(\"__nonexist__\")\n self.metadata = metadata\n self.output = VisImage(self.img, scale=scale)\n self.cpu_device = torch.device(\"cpu\")\n\n # too small texts are useless, therefore clamp to 9\n self._default_font_size = max(\n np.sqrt(self.output.height * self.output.width) // 90, 10 // scale\n )\n self._instance_mode = instance_mode\n self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n def get_image(self, img):\n img = np.asarray(img).clip(0, 255).astype(np.uint8)\n return VisImage(img, scale=1.0)\n \n def draw_box_predictions(\n self,\n boxes=None,\n labels=None,\n scores=None,\n assigned_colors=None\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = 0\n boxes = self._convert_boxes(boxes)\n classes = labels.tolist()\n scores = scores.tolist()\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n num_instances = len(boxes)\n assert len(labels) == num_instances\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:\n return self.output\n\n # Display in largest to smallest order to reduce occlusion.\n areas = None\n areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n\n if areas is not None:\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs] if boxes is not None else None\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n for i in range(num_instances):\n color = assigned_colors[i]\n if boxes is not None:\n self.draw_box(boxes[i], edge_color=color)\n\n if labels is not None:\n # first get a box\n if boxes is not None:\n x0, y0, x1, y1 = boxes[i]\n text_pos = (x0, y0) # if drawing boxes, put text on the box corner.\n horiz_align = \"left\"\n else:\n continue # drawing the box confidence for keypoints isn't very useful.\n # for small objects, draw text at the side to avoid occlusion\n instance_area = (y1 - y0) * (x1 - x0)\n if (\n instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale\n or y1 - y0 < 40 * self.output.scale\n ):\n if y1 >= self.output.height - 5:\n text_pos = (x1, y0)\n else:\n text_pos = (x0, y1)\n\n height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n return self.output\n \n \n def draw_instance_predictions(self, predictions, alpha=0.8):\n \"\"\"\n Draw instance-level prediction results on an image.\n Args:\n predictions (Instances): the output of an instance detection/segmentation\n model. Following fields will be used to draw:\n \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n boxes = predictions.pred_boxes if predictions.has(\"pred_boxes\") else None\n scores = predictions.scores if predictions.has(\"scores\") else None\n classes = predictions.pred_classes.tolist() if predictions.has(\"pred_classes\") else None\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n keypoints = predictions.pred_keypoints if predictions.has(\"pred_keypoints\") else None\n\n if predictions.has(\"pred_masks\"):\n masks = np.asarray(predictions.pred_masks)","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_instance_predictions","uri":"program://OneFormer/function/demo.visualizer.draw_instance_predictions#L485-L534","kind":"function","name":"draw_instance_predictions","path":"demo/visualizer.py","language":"python","start_line":485,"end_line":534,"context_start_line":465,"context_end_line":554,"code":" text_pos = (x0, y1)\n\n height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n return self.output\n \n \n def draw_instance_predictions(self, predictions, alpha=0.8):\n \"\"\"\n Draw instance-level prediction results on an image.\n Args:\n predictions (Instances): the output of an instance detection/segmentation\n model. Following fields will be used to draw:\n \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n boxes = predictions.pred_boxes if predictions.has(\"pred_boxes\") else None\n scores = predictions.scores if predictions.has(\"scores\") else None\n classes = predictions.pred_classes.tolist() if predictions.has(\"pred_classes\") else None\n labels = _create_text_labels(classes, scores, self.metadata.get(\"stuff_classes\", None))\n keypoints = predictions.pred_keypoints if predictions.has(\"pred_keypoints\") else None\n\n if predictions.has(\"pred_masks\"):\n masks = np.asarray(predictions.pred_masks)\n masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]\n else:\n masks = None\n\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"stuff_colors\"):\n # colors = [\n # self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes\n # ]\n colors = [\n instance_color(rgb=True, idx=c, maximum=1) for c in classes\n ]\n else:\n colors = None\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(\n self._create_grayscale_image(\n (predictions.pred_masks.any(dim=0) > 0).numpy()\n if predictions.has(\"pred_masks\")\n else None\n )\n )\n\n self.overlay_instances(\n masks=masks,\n boxes=boxes,\n labels=labels,\n keypoints=keypoints,\n assigned_colors=colors,\n alpha=alpha,\n )\n return self.output\n\n def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):\n \"\"\"\n Draw semantic segmentation predictions/labels.\n Args:\n sem_seg (Tensor or ndarray): the segmentation of shape (H, W).\n Each value is the integer label of the pixel.\n area_threshold (int): segments with less than `area_threshold` are not drawn.\n alpha (float): the larger it is, the more opaque the segmentations are.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n if isinstance(sem_seg, torch.Tensor):\n sem_seg = sem_seg.numpy()\n labels, areas = np.unique(sem_seg, return_counts=True)\n sorted_idxs = np.argsort(-areas).tolist()\n labels = labels[sorted_idxs]\n for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_sem_seg","uri":"program://OneFormer/function/demo.visualizer.draw_sem_seg#L536-L568","kind":"function","name":"draw_sem_seg","path":"demo/visualizer.py","language":"python","start_line":536,"end_line":568,"context_start_line":516,"context_end_line":588,"code":"\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(\n self._create_grayscale_image(\n (predictions.pred_masks.any(dim=0) > 0).numpy()\n if predictions.has(\"pred_masks\")\n else None\n )\n )\n\n self.overlay_instances(\n masks=masks,\n boxes=boxes,\n labels=labels,\n keypoints=keypoints,\n assigned_colors=colors,\n alpha=alpha,\n )\n return self.output\n\n def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):\n \"\"\"\n Draw semantic segmentation predictions/labels.\n Args:\n sem_seg (Tensor or ndarray): the segmentation of shape (H, W).\n Each value is the integer label of the pixel.\n area_threshold (int): segments with less than `area_threshold` are not drawn.\n alpha (float): the larger it is, the more opaque the segmentations are.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n if isinstance(sem_seg, torch.Tensor):\n sem_seg = sem_seg.numpy()\n labels, areas = np.unique(sem_seg, return_counts=True)\n sorted_idxs = np.argsort(-areas).tolist()\n labels = labels[sorted_idxs]\n for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]\n except (AttributeError, IndexError):\n mask_color = None\n\n binary_mask = (sem_seg == label).astype(np.uint8)\n text = self.metadata.stuff_classes[label]\n self.draw_binary_mask(\n binary_mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n return self.output\n\n def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):\n \"\"\"\n Draw panoptic prediction annotations or results.\n Args:\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each\n segment.\n segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.\n If it is a ``list[dict]``, each dict contains keys \"id\", \"category_id\".\n If None, category id of each pixel is computed by\n ``pixel // metadata.label_divisor``.\n area_threshold (int): stuff segments with less than `area_threshold` are not drawn.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))\n","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_panoptic_seg","uri":"program://OneFormer/function/demo.visualizer.draw_panoptic_seg#L570-L630","kind":"function","name":"draw_panoptic_seg","path":"demo/visualizer.py","language":"python","start_line":570,"end_line":630,"context_start_line":550,"context_end_line":650,"code":" sorted_idxs = np.argsort(-areas).tolist()\n labels = labels[sorted_idxs]\n for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]\n except (AttributeError, IndexError):\n mask_color = None\n\n binary_mask = (sem_seg == label).astype(np.uint8)\n text = self.metadata.stuff_classes[label]\n self.draw_binary_mask(\n binary_mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n return self.output\n\n def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):\n \"\"\"\n Draw panoptic prediction annotations or results.\n Args:\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each\n segment.\n segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.\n If it is a ``list[dict]``, each dict contains keys \"id\", \"category_id\".\n If None, category id of each pixel is computed by\n ``pixel // metadata.label_divisor``.\n area_threshold (int): stuff segments with less than `area_threshold` are not drawn.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))\n\n # draw mask for all semantic segments first i.e. \"stuff\"\n for mask, sinfo in pred.semantic_masks():\n category_idx = sinfo[\"category_id\"]\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]\n except AttributeError:\n mask_color = None\n\n text = self.metadata.stuff_classes[category_idx]\n self.draw_binary_mask(\n mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n\n # draw mask for all instances second\n all_instances = list(pred.instance_masks())\n if len(all_instances) == 0:\n return self.output\n masks, sinfo = list(zip(*all_instances))\n category_ids = [x[\"category_id\"] for x in sinfo]\n\n try:\n scores = [x[\"score\"] for x in sinfo]\n except KeyError:\n scores = None\n labels = _create_text_labels(\n category_ids, scores, self.metadata.stuff_classes, [x.get(\"iscrowd\", 0) for x in sinfo]\n )\n\n try:\n colors = [\n self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) for c in category_ids\n ]\n except AttributeError:\n colors = None\n self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)\n\n return self.output\n\n draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility\n\n def draw_dataset_dict(self, dic):\n \"\"\"\n Draw annotations/segmentaions in Detectron2 Dataset format.\n Args:\n dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n annos = dic.get(\"annotations\", None)\n if annos:\n if \"segmentation\" in annos[0]:\n masks = [x[\"segmentation\"] for x in annos]\n else:\n masks = None\n if \"keypoints\" in annos[0]:\n keypts = [x[\"keypoints\"] for x in annos]\n keypts = np.array(keypts).reshape(len(annos), -1, 3)","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_dataset_dict","uri":"program://OneFormer/function/demo.visualizer.draw_dataset_dict#L634-L699","kind":"function","name":"draw_dataset_dict","path":"demo/visualizer.py","language":"python","start_line":634,"end_line":699,"context_start_line":614,"context_end_line":719,"code":" try:\n scores = [x[\"score\"] for x in sinfo]\n except KeyError:\n scores = None\n labels = _create_text_labels(\n category_ids, scores, self.metadata.stuff_classes, [x.get(\"iscrowd\", 0) for x in sinfo]\n )\n\n try:\n colors = [\n self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) for c in category_ids\n ]\n except AttributeError:\n colors = None\n self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)\n\n return self.output\n\n draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility\n\n def draw_dataset_dict(self, dic):\n \"\"\"\n Draw annotations/segmentaions in Detectron2 Dataset format.\n Args:\n dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n annos = dic.get(\"annotations\", None)\n if annos:\n if \"segmentation\" in annos[0]:\n masks = [x[\"segmentation\"] for x in annos]\n else:\n masks = None\n if \"keypoints\" in annos[0]:\n keypts = [x[\"keypoints\"] for x in annos]\n keypts = np.array(keypts).reshape(len(annos), -1, 3)\n else:\n keypts = None\n\n boxes = [\n BoxMode.convert(x[\"bbox\"], x[\"bbox_mode\"], BoxMode.XYXY_ABS)\n if len(x[\"bbox\"]) == 4\n else x[\"bbox\"]\n for x in annos\n ]\n\n colors = None\n category_ids = [x[\"category_id\"] for x in annos]\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"stuff_colors\"):\n colors = [\n self._jitter([x / 255 for x in self.metadata.stuff_colors[c]])\n for c in category_ids\n ]\n names = self.metadata.get(\"stuff_classes\", None)\n labels = _create_text_labels(\n category_ids,\n scores=None,\n class_names=names,\n is_crowd=[x.get(\"iscrowd\", 0) for x in annos],\n )\n self.overlay_instances(\n labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors\n )\n\n sem_seg = dic.get(\"sem_seg\", None)\n if sem_seg is None and \"sem_seg_file_name\" in dic:\n with PathManager.open(dic[\"sem_seg_file_name\"], \"rb\") as f:\n sem_seg = Image.open(f)\n sem_seg = np.asarray(sem_seg, dtype=\"uint8\")\n if sem_seg is not None:\n self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)\n\n pan_seg = dic.get(\"pan_seg\", None)\n if pan_seg is None and \"pan_seg_file_name\" in dic:\n with PathManager.open(dic[\"pan_seg_file_name\"], \"rb\") as f:\n pan_seg = Image.open(f)\n pan_seg = np.asarray(pan_seg)\n from panopticapi.utils import rgb2id\n\n pan_seg = rgb2id(pan_seg)\n if pan_seg is not None:\n segments_info = dic[\"segments_info\"]\n pan_seg = torch.tensor(pan_seg)\n self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)\n return self.output\n\n def overlay_instances(\n self,\n *,\n boxes=None,\n labels=None,\n masks=None,\n keypoints=None,\n assigned_colors=None,\n alpha=0.5,\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n masks (masks-like object): Supported types are:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.overlay_instances","uri":"program://OneFormer/function/demo.visualizer.overlay_instances#L701-L841","kind":"function","name":"overlay_instances","path":"demo/visualizer.py","language":"python","start_line":701,"end_line":841,"context_start_line":681,"context_end_line":861,"code":" with PathManager.open(dic[\"sem_seg_file_name\"], \"rb\") as f:\n sem_seg = Image.open(f)\n sem_seg = np.asarray(sem_seg, dtype=\"uint8\")\n if sem_seg is not None:\n self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)\n\n pan_seg = dic.get(\"pan_seg\", None)\n if pan_seg is None and \"pan_seg_file_name\" in dic:\n with PathManager.open(dic[\"pan_seg_file_name\"], \"rb\") as f:\n pan_seg = Image.open(f)\n pan_seg = np.asarray(pan_seg)\n from panopticapi.utils import rgb2id\n\n pan_seg = rgb2id(pan_seg)\n if pan_seg is not None:\n segments_info = dic[\"segments_info\"]\n pan_seg = torch.tensor(pan_seg)\n self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)\n return self.output\n\n def overlay_instances(\n self,\n *,\n boxes=None,\n labels=None,\n masks=None,\n keypoints=None,\n assigned_colors=None,\n alpha=0.5,\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n masks (masks-like object): Supported types are:\n * :class:`detectron2.structures.PolygonMasks`,\n :class:`detectron2.structures.BitMasks`.\n * list[list[ndarray]]: contains the segmentation masks for all objects in one image.\n The first level of the list corresponds to individual instances. The second\n level to all the polygon that compose the instance, and the third level\n to the polygon coordinates. The third level should have the format of\n [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\n * list[ndarray]: each ndarray is a binary mask of shape (H, W).\n * list[dict]: each dict is a COCO-style RLE.\n keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),\n where the N is the number of instances and K is the number of keypoints.\n The last dimension corresponds to (x, y, visibility or score).\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = 0\n if boxes is not None:\n boxes = self._convert_boxes(boxes)\n num_instances = len(boxes)\n if masks is not None:\n masks = self._convert_masks(masks)\n if num_instances:\n assert len(masks) == num_instances\n else:\n num_instances = len(masks)\n if keypoints is not None:\n if num_instances:\n assert len(keypoints) == num_instances\n else:\n num_instances = len(keypoints)\n keypoints = self._convert_keypoints(keypoints)\n if labels is not None:\n assert len(labels) == num_instances\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:\n return self.output\n if boxes is not None and boxes.shape[1] == 5:\n return self.overlay_rotated_instances(\n boxes=boxes, labels=labels, assigned_colors=assigned_colors\n )\n\n # Display in largest to smallest order to reduce occlusion.\n areas = None\n if boxes is not None:\n areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n elif masks is not None:\n areas = np.asarray([x.area() for x in masks])\n\n if areas is not None:\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs] if boxes is not None else None\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None\n assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]\n keypoints = keypoints[sorted_idxs] if keypoints is not None else None\n\n for i in range(num_instances):\n color = assigned_colors[i]\n if boxes is not None:\n self.draw_box(boxes[i], edge_color=color)\n\n if masks is not None:\n for segment in masks[i].polygons:\n self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)\n\n if labels is not None:\n # first get a box\n if boxes is not None:\n x0, y0, x1, y1 = boxes[i]\n text_pos = (x0, y0) # if drawing boxes, put text on the box corner.\n horiz_align = \"left\"\n elif masks is not None:\n # skip small mask without polygon\n if len(masks[i].polygons) == 0:\n continue\n\n x0, y0, x1, y1 = masks[i].bbox()\n\n # draw text in the center (defined by median) when box is not drawn\n # median is less sensitive to outliers.\n text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]\n horiz_align = \"center\"\n else:\n continue # drawing the box confidence for keypoints isn't very useful.\n # for small objects, draw text at the side to avoid occlusion\n instance_area = (y1 - y0) * (x1 - x0)\n if (\n instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale\n or y1 - y0 < 40 * self.output.scale\n ):\n if y1 >= self.output.height - 5:\n text_pos = (x1, y0)\n else:\n text_pos = (x0, y1)\n\n height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n # draw keypoints\n if keypoints is not None:\n for keypoints_per_instance in keypoints:\n self.draw_and_connect_keypoints(keypoints_per_instance)\n\n return self.output\n\n def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):\n \"\"\"\n Args:\n boxes (ndarray): an Nx5 numpy array of\n (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image.\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = len(boxes)\n\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.overlay_rotated_instances","uri":"program://OneFormer/function/demo.visualizer.overlay_rotated_instances#L843-L879","kind":"function","name":"overlay_rotated_instances","path":"demo/visualizer.py","language":"python","start_line":843,"end_line":879,"context_start_line":823,"context_end_line":899,"code":" font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n # draw keypoints\n if keypoints is not None:\n for keypoints_per_instance in keypoints:\n self.draw_and_connect_keypoints(keypoints_per_instance)\n\n return self.output\n\n def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):\n \"\"\"\n Args:\n boxes (ndarray): an Nx5 numpy array of\n (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image.\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = len(boxes)\n\n if assigned_colors is None:\n # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]\n if num_instances == 0:\n return self.output\n\n # Display in largest to smallest order to reduce occlusion.\n if boxes is not None:\n areas = boxes[:, 2] * boxes[:, 3]\n\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs]\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n for i in range(num_instances):\n self.draw_rotated_box_with_label(\n boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None\n )\n\n return self.output\n\n def draw_and_connect_keypoints(self, keypoints):\n \"\"\"\n Draws keypoints of an instance and follows the rules for keypoint connections\n to draw lines between appropriate keypoints. This follows color heuristics for\n line color.\n Args:\n keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints\n and the last dimension corresponds to (x, y, probability).\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n visible = {}\n keypoint_names = self.metadata.get(\"keypoint_names\")\n for idx, keypoint in enumerate(keypoints):\n\n # draw keypoint\n x, y, prob = keypoint\n if prob > self.keypoint_threshold:\n self.draw_circle((x, y), color=_RED)","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_and_connect_keypoints","uri":"program://OneFormer/function/demo.visualizer.draw_and_connect_keypoints#L881-L936","kind":"function","name":"draw_and_connect_keypoints","path":"demo/visualizer.py","language":"python","start_line":881,"end_line":936,"context_start_line":861,"context_end_line":956,"code":" if num_instances == 0:\n return self.output\n\n # Display in largest to smallest order to reduce occlusion.\n if boxes is not None:\n areas = boxes[:, 2] * boxes[:, 3]\n\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs]\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n for i in range(num_instances):\n self.draw_rotated_box_with_label(\n boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None\n )\n\n return self.output\n\n def draw_and_connect_keypoints(self, keypoints):\n \"\"\"\n Draws keypoints of an instance and follows the rules for keypoint connections\n to draw lines between appropriate keypoints. This follows color heuristics for\n line color.\n Args:\n keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints\n and the last dimension corresponds to (x, y, probability).\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n visible = {}\n keypoint_names = self.metadata.get(\"keypoint_names\")\n for idx, keypoint in enumerate(keypoints):\n\n # draw keypoint\n x, y, prob = keypoint\n if prob > self.keypoint_threshold:\n self.draw_circle((x, y), color=_RED)\n if keypoint_names:\n keypoint_name = keypoint_names[idx]\n visible[keypoint_name] = (x, y)\n\n if self.metadata.get(\"keypoint_connection_rules\"):\n for kp0, kp1, color in self.metadata.keypoint_connection_rules:\n if kp0 in visible and kp1 in visible:\n x0, y0 = visible[kp0]\n x1, y1 = visible[kp1]\n color = tuple(x / 255.0 for x in color)\n self.draw_line([x0, x1], [y0, y1], color=color)\n\n # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip\n # Note that this strategy is specific to person keypoints.\n # For other keypoints, it should just do nothing\n try:\n ls_x, ls_y = visible[\"left_shoulder\"]\n rs_x, rs_y = visible[\"right_shoulder\"]\n mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2\n except KeyError:\n pass\n else:\n # draw line from nose to mid-shoulder\n nose_x, nose_y = visible.get(\"nose\", (None, None))\n if nose_x is not None:\n self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)\n\n try:\n # draw line from mid-shoulder to mid-hip\n lh_x, lh_y = visible[\"left_hip\"]\n rh_x, rh_y = visible[\"right_hip\"]\n except KeyError:\n pass\n else:\n mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2\n self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)\n return self.output\n\n \"\"\"\n Primitive drawing functions:\n \"\"\"\n\n def draw_text(\n self,\n text,\n position,\n *,\n font_size=None,\n color=\"g\",\n horizontal_alignment=\"center\",\n rotation=0,\n ):\n \"\"\"\n Args:\n text (str): class label\n position (tuple): a tuple of the x and y coordinates to place text on image.\n font_size (int, optional): font of the text. If not provided, a font size","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_text","uri":"program://OneFormer/function/demo.visualizer.draw_text#L942-L986","kind":"function","name":"draw_text","path":"demo/visualizer.py","language":"python","start_line":942,"end_line":986,"context_start_line":922,"context_end_line":1006,"code":" # draw line from nose to mid-shoulder\n nose_x, nose_y = visible.get(\"nose\", (None, None))\n if nose_x is not None:\n self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)\n\n try:\n # draw line from mid-shoulder to mid-hip\n lh_x, lh_y = visible[\"left_hip\"]\n rh_x, rh_y = visible[\"right_hip\"]\n except KeyError:\n pass\n else:\n mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2\n self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)\n return self.output\n\n \"\"\"\n Primitive drawing functions:\n \"\"\"\n\n def draw_text(\n self,\n text,\n position,\n *,\n font_size=None,\n color=\"g\",\n horizontal_alignment=\"center\",\n rotation=0,\n ):\n \"\"\"\n Args:\n text (str): class label\n position (tuple): a tuple of the x and y coordinates to place text on image.\n font_size (int, optional): font of the text. If not provided, a font size\n proportional to the image width is calculated and used.\n color: color of the text. Refer to `matplotlib.colors` for full list\n of formats that are accepted.\n horizontal_alignment (str): see `matplotlib.text.Text`\n rotation: rotation angle in degrees CCW\n Returns:\n output (VisImage): image object with text drawn.\n \"\"\"\n if not font_size:\n font_size = self._default_font_size\n\n # since the text background is dark, we don't want the text to be dark\n color = np.maximum(list(mplc.to_rgb(color)), 0.2)\n color[np.argmax(color)] = max(0.8, np.max(color))\n\n x, y = position\n self.output.ax.text(\n x,\n y,\n text,\n size=font_size * self.output.scale,\n family=\"sans-serif\",\n bbox={\"facecolor\": \"black\", \"alpha\": 0.8, \"pad\": 0.7, \"edgecolor\": \"none\"},\n verticalalignment=\"top\",\n horizontalalignment=horizontal_alignment,\n color=color,\n zorder=10,\n rotation=rotation,\n )\n return self.output\n\n def draw_box(self, box_coord, alpha=1.0, edge_color=\"g\", line_style=\"-\"):\n \"\"\"\n Args:\n box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0\n are the coordinates of the image's top left corner. x1 and y1 are the\n coordinates of the image's bottom right corner.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n for full list of formats that are accepted.\n line_style (string): the string to use to create the outline of the boxes.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x0, y0, x1, y1 = box_coord\n width = x1 - x0\n height = y1 - y0\n\n linewidth = 2\n","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_box","uri":"program://OneFormer/function/demo.visualizer.draw_box#L988-L1019","kind":"function","name":"draw_box","path":"demo/visualizer.py","language":"python","start_line":988,"end_line":1019,"context_start_line":968,"context_end_line":1039,"code":" # since the text background is dark, we don't want the text to be dark\n color = np.maximum(list(mplc.to_rgb(color)), 0.2)\n color[np.argmax(color)] = max(0.8, np.max(color))\n\n x, y = position\n self.output.ax.text(\n x,\n y,\n text,\n size=font_size * self.output.scale,\n family=\"sans-serif\",\n bbox={\"facecolor\": \"black\", \"alpha\": 0.8, \"pad\": 0.7, \"edgecolor\": \"none\"},\n verticalalignment=\"top\",\n horizontalalignment=horizontal_alignment,\n color=color,\n zorder=10,\n rotation=rotation,\n )\n return self.output\n\n def draw_box(self, box_coord, alpha=1.0, edge_color=\"g\", line_style=\"-\"):\n \"\"\"\n Args:\n box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0\n are the coordinates of the image's top left corner. x1 and y1 are the\n coordinates of the image's bottom right corner.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n for full list of formats that are accepted.\n line_style (string): the string to use to create the outline of the boxes.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x0, y0, x1, y1 = box_coord\n width = x1 - x0\n height = y1 - y0\n\n linewidth = 2\n\n self.output.ax.add_patch(\n mpl.patches.Rectangle(\n (x0, y0),\n width,\n height,\n fill=False,\n edgecolor=edge_color,\n linewidth=linewidth * self.output.scale,\n alpha=alpha,\n linestyle=line_style,\n )\n )\n return self.output\n\n def draw_rotated_box_with_label(\n self, rotated_box, alpha=0.5, edge_color=\"g\", line_style=\"-\", label=None\n ):\n \"\"\"\n Draw a rotated box with label on its top-left corner.\n Args:\n rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),\n where cnt_x and cnt_y are the center coordinates of the box.\n w and h are the width and height of the box. angle represents how\n many degrees the box is rotated CCW with regard to the 0-degree box.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n for full list of formats that are accepted.\n line_style (string): the string to use to create the outline of the boxes.\n label (string): label for rotated box. It will not be rendered when set to None.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n cnt_x, cnt_y, w, h, angle = rotated_box","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_rotated_box_with_label","uri":"program://OneFormer/function/demo.visualizer.draw_rotated_box_with_label#L1021-L1072","kind":"function","name":"draw_rotated_box_with_label","path":"demo/visualizer.py","language":"python","start_line":1021,"end_line":1072,"context_start_line":1001,"context_end_line":1092,"code":" x0, y0, x1, y1 = box_coord\n width = x1 - x0\n height = y1 - y0\n\n linewidth = 2\n\n self.output.ax.add_patch(\n mpl.patches.Rectangle(\n (x0, y0),\n width,\n height,\n fill=False,\n edgecolor=edge_color,\n linewidth=linewidth * self.output.scale,\n alpha=alpha,\n linestyle=line_style,\n )\n )\n return self.output\n\n def draw_rotated_box_with_label(\n self, rotated_box, alpha=0.5, edge_color=\"g\", line_style=\"-\", label=None\n ):\n \"\"\"\n Draw a rotated box with label on its top-left corner.\n Args:\n rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),\n where cnt_x and cnt_y are the center coordinates of the box.\n w and h are the width and height of the box. angle represents how\n many degrees the box is rotated CCW with regard to the 0-degree box.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n for full list of formats that are accepted.\n line_style (string): the string to use to create the outline of the boxes.\n label (string): label for rotated box. It will not be rendered when set to None.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n cnt_x, cnt_y, w, h, angle = rotated_box\n area = w * h\n # use thinner lines when the box is small\n linewidth = self._default_font_size / (\n 6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3\n )\n\n theta = angle * math.pi / 180.0\n c = math.cos(theta)\n s = math.sin(theta)\n rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]\n # x: left->right ; y: top->down\n rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]\n for k in range(4):\n j = (k + 1) % 4\n self.draw_line(\n [rotated_rect[k][0], rotated_rect[j][0]],\n [rotated_rect[k][1], rotated_rect[j][1]],\n color=edge_color,\n linestyle=\"--\" if k == 1 else line_style,\n linewidth=linewidth,\n )\n\n if label is not None:\n text_pos = rotated_rect[1] # topleft corner\n\n height_ratio = h / np.sqrt(self.output.height * self.output.width)\n label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size\n )\n self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)\n\n return self.output\n\n def draw_circle(self, circle_coord, color, radius=3):\n \"\"\"\n Args:\n circle_coord (list(int) or tuple(int)): contains the x and y coordinates\n of the center of the circle.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n radius (int): radius of the circle.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x, y = circle_coord\n self.output.ax.add_patch(\n mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)\n )\n return self.output\n\n def draw_line(self, x_data, y_data, color, linestyle=\"-\", linewidth=None):\n \"\"\"","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_circle","uri":"program://OneFormer/function/demo.visualizer.draw_circle#L1074-L1089","kind":"function","name":"draw_circle","path":"demo/visualizer.py","language":"python","start_line":1074,"end_line":1089,"context_start_line":1054,"context_end_line":1109,"code":" self.draw_line(\n [rotated_rect[k][0], rotated_rect[j][0]],\n [rotated_rect[k][1], rotated_rect[j][1]],\n color=edge_color,\n linestyle=\"--\" if k == 1 else line_style,\n linewidth=linewidth,\n )\n\n if label is not None:\n text_pos = rotated_rect[1] # topleft corner\n\n height_ratio = h / np.sqrt(self.output.height * self.output.width)\n label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size\n )\n self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)\n\n return self.output\n\n def draw_circle(self, circle_coord, color, radius=3):\n \"\"\"\n Args:\n circle_coord (list(int) or tuple(int)): contains the x and y coordinates\n of the center of the circle.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n radius (int): radius of the circle.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x, y = circle_coord\n self.output.ax.add_patch(\n mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)\n )\n return self.output\n\n def draw_line(self, x_data, y_data, color, linestyle=\"-\", linewidth=None):\n \"\"\"\n Args:\n x_data (list[int]): a list containing x values of all the points being drawn.\n Length of list should match the length of y_data.\n y_data (list[int]): a list containing y values of all the points being drawn.\n Length of list should match the length of x_data.\n color: color of the line. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n linestyle: style of the line. Refer to `matplotlib.lines.Line2D`\n for a full list of formats that are accepted.\n linewidth (float or None): width of the line. When it's None,\n a default value will be computed and used.\n Returns:\n output (VisImage): image object with line drawn.\n \"\"\"\n if linewidth is None:\n linewidth = self._default_font_size / 3\n linewidth = max(linewidth, 1)","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_line","uri":"program://OneFormer/function/demo.visualizer.draw_line#L1091-L1119","kind":"function","name":"draw_line","path":"demo/visualizer.py","language":"python","start_line":1091,"end_line":1119,"context_start_line":1071,"context_end_line":1139,"code":"\n return self.output\n\n def draw_circle(self, circle_coord, color, radius=3):\n \"\"\"\n Args:\n circle_coord (list(int) or tuple(int)): contains the x and y coordinates\n of the center of the circle.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n radius (int): radius of the circle.\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x, y = circle_coord\n self.output.ax.add_patch(\n mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)\n )\n return self.output\n\n def draw_line(self, x_data, y_data, color, linestyle=\"-\", linewidth=None):\n \"\"\"\n Args:\n x_data (list[int]): a list containing x values of all the points being drawn.\n Length of list should match the length of y_data.\n y_data (list[int]): a list containing y values of all the points being drawn.\n Length of list should match the length of x_data.\n color: color of the line. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n linestyle: style of the line. Refer to `matplotlib.lines.Line2D`\n for a full list of formats that are accepted.\n linewidth (float or None): width of the line. When it's None,\n a default value will be computed and used.\n Returns:\n output (VisImage): image object with line drawn.\n \"\"\"\n if linewidth is None:\n linewidth = self._default_font_size / 3\n linewidth = max(linewidth, 1)\n self.output.ax.add_line(\n mpl.lines.Line2D(\n x_data,\n y_data,\n linewidth=linewidth * self.output.scale,\n color=color,\n linestyle=linestyle,\n )\n )\n return self.output\n\n def draw_binary_mask(\n self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10\n ):\n \"\"\"\n Args:\n binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and\n W is the image width. Each value in the array is either a 0 or 1 value of uint8\n type.\n color: color of the mask. Refer to `matplotlib.colors` for a full list of\n formats that are accepted. If None, will pick a random color.\n edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n full list of formats that are accepted.\n text (str): if None, will be drawn on the object\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n area_threshold (float): a connected component smaller than this area will not be shown.\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_binary_mask","uri":"program://OneFormer/function/demo.visualizer.draw_binary_mask#L1121-L1169","kind":"function","name":"draw_binary_mask","path":"demo/visualizer.py","language":"python","start_line":1121,"end_line":1169,"context_start_line":1101,"context_end_line":1189,"code":" for a full list of formats that are accepted.\n linewidth (float or None): width of the line. When it's None,\n a default value will be computed and used.\n Returns:\n output (VisImage): image object with line drawn.\n \"\"\"\n if linewidth is None:\n linewidth = self._default_font_size / 3\n linewidth = max(linewidth, 1)\n self.output.ax.add_line(\n mpl.lines.Line2D(\n x_data,\n y_data,\n linewidth=linewidth * self.output.scale,\n color=color,\n linestyle=linestyle,\n )\n )\n return self.output\n\n def draw_binary_mask(\n self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10\n ):\n \"\"\"\n Args:\n binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and\n W is the image width. Each value in the array is either a 0 or 1 value of uint8\n type.\n color: color of the mask. Refer to `matplotlib.colors` for a full list of\n formats that are accepted. If None, will pick a random color.\n edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n full list of formats that are accepted.\n text (str): if None, will be drawn on the object\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n area_threshold (float): a connected component smaller than this area will not be shown.\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:\n color = random_color(rgb=True, maximum=1)\n color = mplc.to_rgb(color)\n\n has_valid_segment = False\n binary_mask = binary_mask.astype(\"uint8\") # opencv needs uint8\n mask = GenericMask(binary_mask, self.output.height, self.output.width)\n shape2d = (binary_mask.shape[0], binary_mask.shape[1])\n\n if not mask.has_holes:\n # draw polygons for regular masks\n for segment in mask.polygons:\n area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))\n if area < (area_threshold or 0):\n continue\n has_valid_segment = True\n segment = segment.reshape(-1, 2)\n self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)\n else:\n # TODO: Use Path/PathPatch to draw vector graphics:\n # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = (mask.mask == 1).astype(\"float32\") * alpha\n has_valid_segment = True\n self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))\n\n if text is not None and has_valid_segment:\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n self._draw_text_in_mask(binary_mask, text, lighter_color)\n return self.output\n\n def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):\n \"\"\"\n Args:\n soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].\n color: color of the mask. Refer to `matplotlib.colors` for a full list of\n formats that are accepted. If None, will pick a random color.\n text (str): if None, will be drawn on the object\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:\n color = random_color(rgb=True, maximum=1)\n color = mplc.to_rgb(color)\n\n shape2d = (soft_mask.shape[0], soft_mask.shape[1])\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = soft_mask * alpha","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_soft_mask","uri":"program://OneFormer/function/demo.visualizer.draw_soft_mask#L1171-L1196","kind":"function","name":"draw_soft_mask","path":"demo/visualizer.py","language":"python","start_line":1171,"end_line":1196,"context_start_line":1151,"context_end_line":1216,"code":" area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))\n if area < (area_threshold or 0):\n continue\n has_valid_segment = True\n segment = segment.reshape(-1, 2)\n self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)\n else:\n # TODO: Use Path/PathPatch to draw vector graphics:\n # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = (mask.mask == 1).astype(\"float32\") * alpha\n has_valid_segment = True\n self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))\n\n if text is not None and has_valid_segment:\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n self._draw_text_in_mask(binary_mask, text, lighter_color)\n return self.output\n\n def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):\n \"\"\"\n Args:\n soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].\n color: color of the mask. Refer to `matplotlib.colors` for a full list of\n formats that are accepted. If None, will pick a random color.\n text (str): if None, will be drawn on the object\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:\n color = random_color(rgb=True, maximum=1)\n color = mplc.to_rgb(color)\n\n shape2d = (soft_mask.shape[0], soft_mask.shape[1])\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = soft_mask * alpha\n self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))\n\n if text is not None:\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n binary_mask = (soft_mask > 0.5).astype(\"uint8\")\n # self._draw_text_in_mask(binary_mask, text, lighter_color)\n return self.output\n\n def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):\n \"\"\"\n Args:\n segment: numpy array of shape Nx2, containing all the points in the polygon.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n full list of formats that are accepted. If not provided, a darker shade\n of the polygon color will be used instead.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n Returns:\n output (VisImage): image object with polygon drawn.\n \"\"\"\n if edge_color is None:\n # make edge color darker than the polygon color\n if alpha > 0.8:\n edge_color = self._change_color_brightness(color, brightness_factor=-0.7)\n else:\n edge_color = color","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.draw_polygon","uri":"program://OneFormer/function/demo.visualizer.draw_polygon#L1198-L1227","kind":"function","name":"draw_polygon","path":"demo/visualizer.py","language":"python","start_line":1198,"end_line":1227,"context_start_line":1178,"context_end_line":1247,"code":" alpha (float): blending efficient. Smaller values lead to more transparent masks.\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:\n color = random_color(rgb=True, maximum=1)\n color = mplc.to_rgb(color)\n\n shape2d = (soft_mask.shape[0], soft_mask.shape[1])\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = soft_mask * alpha\n self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))\n\n if text is not None:\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n binary_mask = (soft_mask > 0.5).astype(\"uint8\")\n # self._draw_text_in_mask(binary_mask, text, lighter_color)\n return self.output\n\n def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):\n \"\"\"\n Args:\n segment: numpy array of shape Nx2, containing all the points in the polygon.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n full list of formats that are accepted. If not provided, a darker shade\n of the polygon color will be used instead.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n Returns:\n output (VisImage): image object with polygon drawn.\n \"\"\"\n if edge_color is None:\n # make edge color darker than the polygon color\n if alpha > 0.8:\n edge_color = self._change_color_brightness(color, brightness_factor=-0.7)\n else:\n edge_color = color\n edge_color = mplc.to_rgb(edge_color) + (1,)\n\n polygon = mpl.patches.Polygon(\n segment,\n fill=True,\n facecolor=mplc.to_rgb(color) + (alpha,),\n edgecolor=edge_color,\n linewidth=max(self._default_font_size // 15 * self.output.scale, 1),\n )\n self.output.ax.add_patch(polygon)\n return self.output\n\n \"\"\"\n Internal methods:\n \"\"\"\n\n def _jitter(self, color):\n \"\"\"\n Randomly modifies given color to produce a slightly different color than the color given.\n Args:\n color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color\n picked. The values in the list are in the [0.0, 1.0] range.\n Returns:\n jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the\n color after being jittered. The values in the list are in the [0.0, 1.0] range.\n \"\"\"\n color = mplc.to_rgb(color)\n vec = np.random.rand(3)\n # better to do it in another color space\n vec = vec / np.linalg.norm(vec) * 0.5\n res = np.clip(vec + color, 0, 1)","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._jitter","uri":"program://OneFormer/function/demo.visualizer._jitter#L1233-L1248","kind":"function","name":"_jitter","path":"demo/visualizer.py","language":"python","start_line":1233,"end_line":1248,"context_start_line":1213,"context_end_line":1268,"code":" if alpha > 0.8:\n edge_color = self._change_color_brightness(color, brightness_factor=-0.7)\n else:\n edge_color = color\n edge_color = mplc.to_rgb(edge_color) + (1,)\n\n polygon = mpl.patches.Polygon(\n segment,\n fill=True,\n facecolor=mplc.to_rgb(color) + (alpha,),\n edgecolor=edge_color,\n linewidth=max(self._default_font_size // 15 * self.output.scale, 1),\n )\n self.output.ax.add_patch(polygon)\n return self.output\n\n \"\"\"\n Internal methods:\n \"\"\"\n\n def _jitter(self, color):\n \"\"\"\n Randomly modifies given color to produce a slightly different color than the color given.\n Args:\n color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color\n picked. The values in the list are in the [0.0, 1.0] range.\n Returns:\n jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the\n color after being jittered. The values in the list are in the [0.0, 1.0] range.\n \"\"\"\n color = mplc.to_rgb(color)\n vec = np.random.rand(3)\n # better to do it in another color space\n vec = vec / np.linalg.norm(vec) * 0.5\n res = np.clip(vec + color, 0, 1)\n return tuple(res)\n\n def _create_grayscale_image(self, mask=None):\n \"\"\"\n Create a grayscale version of the original image.\n The colors in masked area, if given, will be kept.\n \"\"\"\n img_bw = self.img.astype(\"f4\").mean(axis=2)\n img_bw = np.stack([img_bw] * 3, axis=2)\n if mask is not None:\n img_bw[mask] = self.img[mask]\n return img_bw\n\n def _change_color_brightness(self, color, brightness_factor):\n \"\"\"\n Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n less or more saturation than the original color.\n Args:\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._create_grayscale_image","uri":"program://OneFormer/function/demo.visualizer._create_grayscale_image#L1250-L1259","kind":"function","name":"_create_grayscale_image","path":"demo/visualizer.py","language":"python","start_line":1250,"end_line":1259,"context_start_line":1230,"context_end_line":1279,"code":" Internal methods:\n \"\"\"\n\n def _jitter(self, color):\n \"\"\"\n Randomly modifies given color to produce a slightly different color than the color given.\n Args:\n color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color\n picked. The values in the list are in the [0.0, 1.0] range.\n Returns:\n jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the\n color after being jittered. The values in the list are in the [0.0, 1.0] range.\n \"\"\"\n color = mplc.to_rgb(color)\n vec = np.random.rand(3)\n # better to do it in another color space\n vec = vec / np.linalg.norm(vec) * 0.5\n res = np.clip(vec + color, 0, 1)\n return tuple(res)\n\n def _create_grayscale_image(self, mask=None):\n \"\"\"\n Create a grayscale version of the original image.\n The colors in masked area, if given, will be kept.\n \"\"\"\n img_bw = self.img.astype(\"f4\").mean(axis=2)\n img_bw = np.stack([img_bw] * 3, axis=2)\n if mask is not None:\n img_bw[mask] = self.img[mask]\n return img_bw\n\n def _change_color_brightness(self, color, brightness_factor):\n \"\"\"\n Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n less or more saturation than the original color.\n Args:\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n 0 will correspond to no change, a factor in [-1.0, 0) range will result in\n a darker color and a factor in (0, 1.0] range will result in a lighter color.\n Returns:\n modified_color (tuple[double]): a tuple containing the RGB values of the\n modified color. Each value in the tuple is in the [0.0, 1.0] range.\n \"\"\"\n assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n color = mplc.to_rgb(color)\n polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._change_color_brightness","uri":"program://OneFormer/function/demo.visualizer._change_color_brightness#L1261-L1282","kind":"function","name":"_change_color_brightness","path":"demo/visualizer.py","language":"python","start_line":1261,"end_line":1282,"context_start_line":1241,"context_end_line":1302,"code":" color after being jittered. The values in the list are in the [0.0, 1.0] range.\n \"\"\"\n color = mplc.to_rgb(color)\n vec = np.random.rand(3)\n # better to do it in another color space\n vec = vec / np.linalg.norm(vec) * 0.5\n res = np.clip(vec + color, 0, 1)\n return tuple(res)\n\n def _create_grayscale_image(self, mask=None):\n \"\"\"\n Create a grayscale version of the original image.\n The colors in masked area, if given, will be kept.\n \"\"\"\n img_bw = self.img.astype(\"f4\").mean(axis=2)\n img_bw = np.stack([img_bw] * 3, axis=2)\n if mask is not None:\n img_bw[mask] = self.img[mask]\n return img_bw\n\n def _change_color_brightness(self, color, brightness_factor):\n \"\"\"\n Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n less or more saturation than the original color.\n Args:\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n 0 will correspond to no change, a factor in [-1.0, 0) range will result in\n a darker color and a factor in (0, 1.0] range will result in a lighter color.\n Returns:\n modified_color (tuple[double]): a tuple containing the RGB values of the\n modified color. Each value in the tuple is in the [0.0, 1.0] range.\n \"\"\"\n assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n color = mplc.to_rgb(color)\n polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])\n return modified_color\n\n def _convert_boxes(self, boxes):\n \"\"\"\n Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.\n \"\"\"\n if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):\n return boxes.tensor.detach().numpy()\n else:\n return np.asarray(boxes)\n\n def _convert_masks(self, masks_or_polygons):\n \"\"\"\n Convert different format of masks or polygons to a tuple of masks and polygons.\n Returns:\n list[GenericMask]:\n \"\"\"\n\n m = masks_or_polygons\n if isinstance(m, PolygonMasks):\n m = m.polygons","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._convert_boxes","uri":"program://OneFormer/function/demo.visualizer._convert_boxes#L1284-L1291","kind":"function","name":"_convert_boxes","path":"demo/visualizer.py","language":"python","start_line":1284,"end_line":1291,"context_start_line":1264,"context_end_line":1311,"code":" less or more saturation than the original color.\n Args:\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n 0 will correspond to no change, a factor in [-1.0, 0) range will result in\n a darker color and a factor in (0, 1.0] range will result in a lighter color.\n Returns:\n modified_color (tuple[double]): a tuple containing the RGB values of the\n modified color. Each value in the tuple is in the [0.0, 1.0] range.\n \"\"\"\n assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n color = mplc.to_rgb(color)\n polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])\n return modified_color\n\n def _convert_boxes(self, boxes):\n \"\"\"\n Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.\n \"\"\"\n if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):\n return boxes.tensor.detach().numpy()\n else:\n return np.asarray(boxes)\n\n def _convert_masks(self, masks_or_polygons):\n \"\"\"\n Convert different format of masks or polygons to a tuple of masks and polygons.\n Returns:\n list[GenericMask]:\n \"\"\"\n\n m = masks_or_polygons\n if isinstance(m, PolygonMasks):\n m = m.polygons\n if isinstance(m, BitMasks):\n m = m.tensor.numpy()\n if isinstance(m, torch.Tensor):\n m = m.numpy()\n ret = []\n for x in m:\n if isinstance(x, GenericMask):\n ret.append(x)\n else:","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._convert_masks","uri":"program://OneFormer/function/demo.visualizer._convert_masks#L1293-L1313","kind":"function","name":"_convert_masks","path":"demo/visualizer.py","language":"python","start_line":1293,"end_line":1313,"context_start_line":1273,"context_end_line":1333,"code":" modified color. Each value in the tuple is in the [0.0, 1.0] range.\n \"\"\"\n assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n color = mplc.to_rgb(color)\n polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])\n return modified_color\n\n def _convert_boxes(self, boxes):\n \"\"\"\n Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.\n \"\"\"\n if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):\n return boxes.tensor.detach().numpy()\n else:\n return np.asarray(boxes)\n\n def _convert_masks(self, masks_or_polygons):\n \"\"\"\n Convert different format of masks or polygons to a tuple of masks and polygons.\n Returns:\n list[GenericMask]:\n \"\"\"\n\n m = masks_or_polygons\n if isinstance(m, PolygonMasks):\n m = m.polygons\n if isinstance(m, BitMasks):\n m = m.tensor.numpy()\n if isinstance(m, torch.Tensor):\n m = m.numpy()\n ret = []\n for x in m:\n if isinstance(x, GenericMask):\n ret.append(x)\n else:\n ret.append(GenericMask(x, self.output.height, self.output.width))\n return ret\n\n def _draw_text_in_mask(self, binary_mask, text, color):\n \"\"\"\n Find proper places to draw text given a binary mask.\n \"\"\"\n # TODO sometimes drawn on wrong objects. the heuristics here can improve.\n _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n if stats[1:, -1].size == 0:\n return\n largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n # draw text on the largest component, as well as other very large components.\n for cid in range(1, _num_cc):\n if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n # median is more stable than centroid\n # center = centroids[largest_component_id]\n center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n self.draw_text(text, center, color=color)\n\n def _convert_keypoints(self, keypoints):","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._draw_text_in_mask","uri":"program://OneFormer/function/demo.visualizer._draw_text_in_mask#L1315-L1331","kind":"function","name":"_draw_text_in_mask","path":"demo/visualizer.py","language":"python","start_line":1315,"end_line":1331,"context_start_line":1295,"context_end_line":1345,"code":" Convert different format of masks or polygons to a tuple of masks and polygons.\n Returns:\n list[GenericMask]:\n \"\"\"\n\n m = masks_or_polygons\n if isinstance(m, PolygonMasks):\n m = m.polygons\n if isinstance(m, BitMasks):\n m = m.tensor.numpy()\n if isinstance(m, torch.Tensor):\n m = m.numpy()\n ret = []\n for x in m:\n if isinstance(x, GenericMask):\n ret.append(x)\n else:\n ret.append(GenericMask(x, self.output.height, self.output.width))\n return ret\n\n def _draw_text_in_mask(self, binary_mask, text, color):\n \"\"\"\n Find proper places to draw text given a binary mask.\n \"\"\"\n # TODO sometimes drawn on wrong objects. the heuristics here can improve.\n _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n if stats[1:, -1].size == 0:\n return\n largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n # draw text on the largest component, as well as other very large components.\n for cid in range(1, _num_cc):\n if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n # median is more stable than centroid\n # center = centroids[largest_component_id]\n center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n self.draw_text(text, center, color=color)\n\n def _convert_keypoints(self, keypoints):\n if isinstance(keypoints, Keypoints):\n keypoints = keypoints.tensor\n keypoints = np.asarray(keypoints)\n return keypoints\n\n def get_output(self):\n \"\"\"\n Returns:\n output (VisImage): the image output containing the visualizations added\n to the image.\n \"\"\"\n return self.output","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer._convert_keypoints","uri":"program://OneFormer/function/demo.visualizer._convert_keypoints#L1333-L1337","kind":"function","name":"_convert_keypoints","path":"demo/visualizer.py","language":"python","start_line":1333,"end_line":1337,"context_start_line":1313,"context_end_line":1345,"code":" return ret\n\n def _draw_text_in_mask(self, binary_mask, text, color):\n \"\"\"\n Find proper places to draw text given a binary mask.\n \"\"\"\n # TODO sometimes drawn on wrong objects. the heuristics here can improve.\n _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n if stats[1:, -1].size == 0:\n return\n largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n # draw text on the largest component, as well as other very large components.\n for cid in range(1, _num_cc):\n if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n # median is more stable than centroid\n # center = centroids[largest_component_id]\n center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n self.draw_text(text, center, color=color)\n\n def _convert_keypoints(self, keypoints):\n if isinstance(keypoints, Keypoints):\n keypoints = keypoints.tensor\n keypoints = np.asarray(keypoints)\n return keypoints\n\n def get_output(self):\n \"\"\"\n Returns:\n output (VisImage): the image output containing the visualizations added\n to the image.\n \"\"\"\n return self.output","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.visualizer.get_output","uri":"program://OneFormer/function/demo.visualizer.get_output#L1339-L1345","kind":"function","name":"get_output","path":"demo/visualizer.py","language":"python","start_line":1339,"end_line":1345,"context_start_line":1319,"context_end_line":1345,"code":" # TODO sometimes drawn on wrong objects. the heuristics here can improve.\n _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n if stats[1:, -1].size == 0:\n return\n largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n # draw text on the largest component, as well as other very large components.\n for cid in range(1, _num_cc):\n if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n # median is more stable than centroid\n # center = centroids[largest_component_id]\n center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n self.draw_text(text, center, color=color)\n\n def _convert_keypoints(self, keypoints):\n if isinstance(keypoints, Keypoints):\n keypoints = keypoints.tensor\n keypoints = np.asarray(keypoints)\n return keypoints\n\n def get_output(self):\n \"\"\"\n Returns:\n output (VisImage): the image output containing the visualizations added\n to the image.\n \"\"\"\n return self.output","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor","uri":"program://OneFormer/module/demo.predictor#L1-L168","kind":"module","name":"demo.predictor","path":"demo/predictor.py","language":"python","start_line":1,"end_line":168,"context_start_line":1,"context_end_line":168,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport atexit\nimport bisect\nimport multiprocessing as mp\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom defaults import DefaultPredictor\nfrom visualizer import ColorMode, Visualizer\n\n\nclass VisualizationDemo(object):\n def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):\n \"\"\"\n Args:\n cfg (CfgNode):\n instance_mode (ColorMode):\n parallel (bool): whether to run the model in different processes from visualization.\n Useful since the visualization logic can be slow.\n \"\"\"\n self.metadata = MetadataCatalog.get(\n cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else \"__unused\"\n )\n if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]:\n from cityscapesscripts.helpers.labels import labels\n stuff_colors = [k.color for k in labels if k.trainId != 255]\n self.metadata = self.metadata.set(stuff_colors=stuff_colors)\n self.cpu_device = torch.device(\"cpu\")\n self.instance_mode = instance_mode\n\n self.parallel = parallel\n if parallel:\n num_gpu = torch.cuda.device_count()\n self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)\n else:\n self.predictor = DefaultPredictor(cfg)\n\n def run_on_image(self, image, task):\n \"\"\"\n Args:\n image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n This is the format used by OpenCV.\n Returns:\n predictions (dict): the output of the model.\n vis_output (VisImage): the visualized image output.\n \"\"\"\n vis_output = None\n # Convert image from OpenCV BGR format to Matplotlib RGB format.\n image = image[:, :, ::-1]\n vis_output = {}\n \n if task == 'panoptic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE)\n predictions = self.predictor(image, task)\n panoptic_seg, segments_info = predictions[\"panoptic_seg\"]\n vis_output['panoptic_inference'] = visualizer.draw_panoptic_seg_predictions(\n panoptic_seg.to(self.cpu_device), segments_info, alpha=0.7\n )\n\n if task == 'panoptic' or task == 'semantic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n vis_output['semantic_inference'] = visualizer.draw_sem_seg(\n predictions[\"sem_seg\"].argmax(dim=0).to(self.cpu_device), alpha=0.7\n )\n\n if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):\n predictor = DefaultPredictor(self.cfg)\n\n while True:\n task = self.task_queue.get()\n if isinstance(task, AsyncPredictor._StopToken):\n break\n idx, data = task\n result = predictor(data)\n self.result_queue.put((idx, result))\n\n def __init__(self, cfg, num_gpus: int = 1):\n \"\"\"\n Args:\n cfg (CfgNode):\n num_gpus (int): if 0, will run on CPU\n \"\"\"\n num_workers = max(num_gpus, 1)\n self.task_queue = mp.Queue(maxsize=num_workers * 3)\n self.result_queue = mp.Queue(maxsize=num_workers * 3)\n self.procs = []\n for gpuid in range(max(num_gpus, 1)):\n cfg = cfg.clone()\n cfg.defrost()\n cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n self.procs.append(\n AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n )\n\n self.put_idx = 0\n self.get_idx = 0\n self.result_rank = []\n self.result_data = []\n\n for p in self.procs:\n p.start()\n atexit.register(self.shutdown)\n\n def put(self, image):\n self.put_idx += 1\n self.task_queue.put((self.put_idx, image))\n\n def get(self):\n self.get_idx += 1 # the index needed for this request\n if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.VisualizationDemo","uri":"program://OneFormer/class/demo.predictor.VisualizationDemo#L16-L77","kind":"class","name":"VisualizationDemo","path":"demo/predictor.py","language":"python","start_line":16,"end_line":77,"context_start_line":1,"context_end_line":97,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport atexit\nimport bisect\nimport multiprocessing as mp\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom defaults import DefaultPredictor\nfrom visualizer import ColorMode, Visualizer\n\n\nclass VisualizationDemo(object):\n def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):\n \"\"\"\n Args:\n cfg (CfgNode):\n instance_mode (ColorMode):\n parallel (bool): whether to run the model in different processes from visualization.\n Useful since the visualization logic can be slow.\n \"\"\"\n self.metadata = MetadataCatalog.get(\n cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else \"__unused\"\n )\n if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]:\n from cityscapesscripts.helpers.labels import labels\n stuff_colors = [k.color for k in labels if k.trainId != 255]\n self.metadata = self.metadata.set(stuff_colors=stuff_colors)\n self.cpu_device = torch.device(\"cpu\")\n self.instance_mode = instance_mode\n\n self.parallel = parallel\n if parallel:\n num_gpu = torch.cuda.device_count()\n self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)\n else:\n self.predictor = DefaultPredictor(cfg)\n\n def run_on_image(self, image, task):\n \"\"\"\n Args:\n image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n This is the format used by OpenCV.\n Returns:\n predictions (dict): the output of the model.\n vis_output (VisImage): the visualized image output.\n \"\"\"\n vis_output = None\n # Convert image from OpenCV BGR format to Matplotlib RGB format.\n image = image[:, :, ::-1]\n vis_output = {}\n \n if task == 'panoptic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE)\n predictions = self.predictor(image, task)\n panoptic_seg, segments_info = predictions[\"panoptic_seg\"]\n vis_output['panoptic_inference'] = visualizer.draw_panoptic_seg_predictions(\n panoptic_seg.to(self.cpu_device), segments_info, alpha=0.7\n )\n\n if task == 'panoptic' or task == 'semantic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n vis_output['semantic_inference'] = visualizer.draw_sem_seg(\n predictions[\"sem_seg\"].argmax(dim=0).to(self.cpu_device), alpha=0.7\n )\n\n if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.AsyncPredictor","uri":"program://OneFormer/class/demo.predictor.AsyncPredictor#L80-L168","kind":"class","name":"AsyncPredictor","path":"demo/predictor.py","language":"python","start_line":80,"end_line":168,"context_start_line":60,"context_end_line":168,"code":" vis_output['panoptic_inference'] = visualizer.draw_panoptic_seg_predictions(\n panoptic_seg.to(self.cpu_device), segments_info, alpha=0.7\n )\n\n if task == 'panoptic' or task == 'semantic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n vis_output['semantic_inference'] = visualizer.draw_sem_seg(\n predictions[\"sem_seg\"].argmax(dim=0).to(self.cpu_device), alpha=0.7\n )\n\n if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):\n predictor = DefaultPredictor(self.cfg)\n\n while True:\n task = self.task_queue.get()\n if isinstance(task, AsyncPredictor._StopToken):\n break\n idx, data = task\n result = predictor(data)\n self.result_queue.put((idx, result))\n\n def __init__(self, cfg, num_gpus: int = 1):\n \"\"\"\n Args:\n cfg (CfgNode):\n num_gpus (int): if 0, will run on CPU\n \"\"\"\n num_workers = max(num_gpus, 1)\n self.task_queue = mp.Queue(maxsize=num_workers * 3)\n self.result_queue = mp.Queue(maxsize=num_workers * 3)\n self.procs = []\n for gpuid in range(max(num_gpus, 1)):\n cfg = cfg.clone()\n cfg.defrost()\n cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n self.procs.append(\n AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n )\n\n self.put_idx = 0\n self.get_idx = 0\n self.result_rank = []\n self.result_data = []\n\n for p in self.procs:\n p.start()\n atexit.register(self.shutdown)\n\n def put(self, image):\n self.put_idx += 1\n self.task_queue.put((self.put_idx, image))\n\n def get(self):\n self.get_idx += 1 # the index needed for this request\n if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.__init__","uri":"program://OneFormer/function/demo.predictor.__init__#L91-L95","kind":"function","name":"__init__","path":"demo/predictor.py","language":"python","start_line":91,"end_line":95,"context_start_line":71,"context_end_line":115,"code":" if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):\n predictor = DefaultPredictor(self.cfg)\n\n while True:\n task = self.task_queue.get()\n if isinstance(task, AsyncPredictor._StopToken):\n break\n idx, data = task\n result = predictor(data)\n self.result_queue.put((idx, result))\n\n def __init__(self, cfg, num_gpus: int = 1):\n \"\"\"\n Args:\n cfg (CfgNode):\n num_gpus (int): if 0, will run on CPU\n \"\"\"\n num_workers = max(num_gpus, 1)\n self.task_queue = mp.Queue(maxsize=num_workers * 3)","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.run_on_image","uri":"program://OneFormer/function/demo.predictor.run_on_image#L42-L77","kind":"function","name":"run_on_image","path":"demo/predictor.py","language":"python","start_line":42,"end_line":77,"context_start_line":22,"context_end_line":97,"code":" parallel (bool): whether to run the model in different processes from visualization.\n Useful since the visualization logic can be slow.\n \"\"\"\n self.metadata = MetadataCatalog.get(\n cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else \"__unused\"\n )\n if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]:\n from cityscapesscripts.helpers.labels import labels\n stuff_colors = [k.color for k in labels if k.trainId != 255]\n self.metadata = self.metadata.set(stuff_colors=stuff_colors)\n self.cpu_device = torch.device(\"cpu\")\n self.instance_mode = instance_mode\n\n self.parallel = parallel\n if parallel:\n num_gpu = torch.cuda.device_count()\n self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)\n else:\n self.predictor = DefaultPredictor(cfg)\n\n def run_on_image(self, image, task):\n \"\"\"\n Args:\n image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n This is the format used by OpenCV.\n Returns:\n predictions (dict): the output of the model.\n vis_output (VisImage): the visualized image output.\n \"\"\"\n vis_output = None\n # Convert image from OpenCV BGR format to Matplotlib RGB format.\n image = image[:, :, ::-1]\n vis_output = {}\n \n if task == 'panoptic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE)\n predictions = self.predictor(image, task)\n panoptic_seg, segments_info = predictions[\"panoptic_seg\"]\n vis_output['panoptic_inference'] = visualizer.draw_panoptic_seg_predictions(\n panoptic_seg.to(self.cpu_device), segments_info, alpha=0.7\n )\n\n if task == 'panoptic' or task == 'semantic':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n vis_output['semantic_inference'] = visualizer.draw_sem_seg(\n predictions[\"sem_seg\"].argmax(dim=0).to(self.cpu_device), alpha=0.7\n )\n\n if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor._StopToken","uri":"program://OneFormer/class/demo.predictor._StopToken#L87-L88","kind":"class","name":"_StopToken","path":"demo/predictor.py","language":"python","start_line":87,"end_line":88,"context_start_line":67,"context_end_line":108,"code":" vis_output['semantic_inference'] = visualizer.draw_sem_seg(\n predictions[\"sem_seg\"].argmax(dim=0).to(self.cpu_device), alpha=0.7\n )\n\n if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):\n predictor = DefaultPredictor(self.cfg)\n\n while True:\n task = self.task_queue.get()\n if isinstance(task, AsyncPredictor._StopToken):\n break\n idx, data = task\n result = predictor(data)\n self.result_queue.put((idx, result))\n\n def __init__(self, cfg, num_gpus: int = 1):","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor._PredictWorker","uri":"program://OneFormer/class/demo.predictor._PredictWorker#L90-L106","kind":"class","name":"_PredictWorker","path":"demo/predictor.py","language":"python","start_line":90,"end_line":106,"context_start_line":70,"context_end_line":126,"code":"\n if task == 'panoptic' or task == 'instance':\n visualizer = Visualizer(image, metadata=self.metadata, instance_mode=ColorMode.IMAGE_BW)\n predictions = self.predictor(image, task)\n instances = predictions[\"instances\"].to(self.cpu_device)\n vis_output['instance_inference'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)\n\n return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):\n predictor = DefaultPredictor(self.cfg)\n\n while True:\n task = self.task_queue.get()\n if isinstance(task, AsyncPredictor._StopToken):\n break\n idx, data = task\n result = predictor(data)\n self.result_queue.put((idx, result))\n\n def __init__(self, cfg, num_gpus: int = 1):\n \"\"\"\n Args:\n cfg (CfgNode):\n num_gpus (int): if 0, will run on CPU\n \"\"\"\n num_workers = max(num_gpus, 1)\n self.task_queue = mp.Queue(maxsize=num_workers * 3)\n self.result_queue = mp.Queue(maxsize=num_workers * 3)\n self.procs = []\n for gpuid in range(max(num_gpus, 1)):\n cfg = cfg.clone()\n cfg.defrost()\n cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n self.procs.append(\n AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n )\n\n self.put_idx = 0","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.put","uri":"program://OneFormer/function/demo.predictor.put#L135-L137","kind":"function","name":"put","path":"demo/predictor.py","language":"python","start_line":135,"end_line":137,"context_start_line":115,"context_end_line":157,"code":" self.task_queue = mp.Queue(maxsize=num_workers * 3)\n self.result_queue = mp.Queue(maxsize=num_workers * 3)\n self.procs = []\n for gpuid in range(max(num_gpus, 1)):\n cfg = cfg.clone()\n cfg.defrost()\n cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n self.procs.append(\n AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n )\n\n self.put_idx = 0\n self.get_idx = 0\n self.result_rank = []\n self.result_data = []\n\n for p in self.procs:\n p.start()\n atexit.register(self.shutdown)\n\n def put(self, image):\n self.put_idx += 1\n self.task_queue.put((self.put_idx, image))\n\n def get(self):\n self.get_idx += 1 # the index needed for this request\n if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.get","uri":"program://OneFormer/function/demo.predictor.get#L139-L153","kind":"function","name":"get","path":"demo/predictor.py","language":"python","start_line":139,"end_line":153,"context_start_line":119,"context_end_line":168,"code":" cfg = cfg.clone()\n cfg.defrost()\n cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n self.procs.append(\n AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n )\n\n self.put_idx = 0\n self.get_idx = 0\n self.result_rank = []\n self.result_data = []\n\n for p in self.procs:\n p.start()\n atexit.register(self.shutdown)\n\n def put(self, image):\n self.put_idx += 1\n self.task_queue.put((self.put_idx, image))\n\n def get(self):\n self.get_idx += 1 # the index needed for this request\n if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.__len__","uri":"program://OneFormer/function/demo.predictor.__len__#L155-L156","kind":"function","name":"__len__","path":"demo/predictor.py","language":"python","start_line":155,"end_line":156,"context_start_line":135,"context_end_line":168,"code":" def put(self, image):\n self.put_idx += 1\n self.task_queue.put((self.put_idx, image))\n\n def get(self):\n self.get_idx += 1 # the index needed for this request\n if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.__call__","uri":"program://OneFormer/function/demo.predictor.__call__#L158-L160","kind":"function","name":"__call__","path":"demo/predictor.py","language":"python","start_line":158,"end_line":160,"context_start_line":138,"context_end_line":168,"code":"\n def get(self):\n self.get_idx += 1 # the index needed for this request\n if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.shutdown","uri":"program://OneFormer/function/demo.predictor.shutdown#L162-L164","kind":"function","name":"shutdown","path":"demo/predictor.py","language":"python","start_line":162,"end_line":164,"context_start_line":142,"context_end_line":168,"code":" res = self.result_data[0]\n del self.result_data[0], self.result_rank[0]\n return res\n\n while True:\n # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.default_buffer_size","uri":"program://OneFormer/function/demo.predictor.default_buffer_size#L167-L168","kind":"function","name":"default_buffer_size","path":"demo/predictor.py","language":"python","start_line":167,"end_line":168,"context_start_line":147,"context_end_line":168,"code":" # make sure the results are returned in the correct order\n idx, res = self.result_queue.get()\n if idx == self.get_idx:\n return res\n insert = bisect.bisect(self.result_rank, idx)\n self.result_rank.insert(insert, idx)\n self.result_data.insert(insert, res)\n\n def __len__(self):\n return self.put_idx - self.get_idx\n\n def __call__(self, image):\n self.put(image)\n return self.get()\n\n def shutdown(self):\n for _ in self.procs:\n self.task_queue.put(AsyncPredictor._StopToken())\n\n @property\n def default_buffer_size(self):\n return len(self.procs) * 5","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.predictor.run","uri":"program://OneFormer/function/demo.predictor.run#L97-L106","kind":"function","name":"run","path":"demo/predictor.py","language":"python","start_line":97,"end_line":106,"context_start_line":77,"context_end_line":126,"code":" return predictions, vis_output\n\n\nclass AsyncPredictor:\n \"\"\"\n A predictor that runs the model asynchronously, possibly on >1 GPUs.\n Because rendering the visualization takes considerably amount of time,\n this helps improve throughput a little bit when rendering videos.\n \"\"\"\n\n class _StopToken:\n pass\n\n class _PredictWorker(mp.Process):\n def __init__(self, cfg, task_queue, result_queue):\n self.cfg = cfg\n self.task_queue = task_queue\n self.result_queue = result_queue\n super().__init__()\n\n def run(self):\n predictor = DefaultPredictor(self.cfg)\n\n while True:\n task = self.task_queue.get()\n if isinstance(task, AsyncPredictor._StopToken):\n break\n idx, data = task\n result = predictor(data)\n self.result_queue.put((idx, result))\n\n def __init__(self, cfg, num_gpus: int = 1):\n \"\"\"\n Args:\n cfg (CfgNode):\n num_gpus (int): if 0, will run on CPU\n \"\"\"\n num_workers = max(num_gpus, 1)\n self.task_queue = mp.Queue(maxsize=num_workers * 3)\n self.result_queue = mp.Queue(maxsize=num_workers * 3)\n self.procs = []\n for gpuid in range(max(num_gpus, 1)):\n cfg = cfg.clone()\n cfg.defrost()\n cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n self.procs.append(\n AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n )\n\n self.put_idx = 0","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.colormap","uri":"program://OneFormer/module/demo.colormap#L1-L90","kind":"module","name":"demo.colormap","path":"demo/colormap.py","language":"python","start_line":1,"end_line":90,"context_start_line":1,"context_end_line":90,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/colormap.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nAn awesome colormap for really neat visualizations.\nCopied from Detectron, and removed gray colors.\n\"\"\"\n\nimport numpy as np\nimport random\nrandom.seed(0)\n\n__all__ = [\"colormap\", \"random_color\", \"random_colors\"]\n\n_COLORS = []\n\ndef gen_color():\n color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3))\n if color not in _COLORS and np.mean(color) != 0.0:\n _COLORS.append(color)\n else:\n gen_color()\n\n\nfor _ in range(300):\n gen_color()\n\n\ndef colormap(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]\n \"\"\"\n assert maximum in [255, 1], maximum\n c = _COLORS * maximum\n if not rgb:\n c = c[:, ::-1]\n return c\n\n\ndef random_color(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n idx = np.random.randint(0, len(_COLORS))\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n\ndef random_colors(N, rgb=False, maximum=255):\n \"\"\"\n Args:\n N (int): number of unique colors needed\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a list of random_color\n \"\"\"\n indices = random.sample(range(len(_COLORS)), N)\n ret = [_COLORS[i] * maximum for i in indices]\n if not rgb:\n ret = [x[::-1] for x in ret]\n return ret\n\n\nif __name__ == \"__main__\":\n import cv2\n\n size = 100\n H, W = 10, 10\n canvas = np.random.rand(H * size, W * size, 3).astype(\"float32\")\n for h in range(H):\n for w in range(W):\n idx = h * W + w\n if idx >= len(_COLORS):\n break\n canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]\n cv2.imshow(\"a\", canvas)\n cv2.waitKey(0)","source_hash":"a3ac26ee24dc1f0459cb9646f1c9e6c5c6ca21c73677205d4b8d8eb832ff80bd","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.colormap.gen_color","uri":"program://OneFormer/function/demo.colormap.gen_color#L19-L24","kind":"function","name":"gen_color","path":"demo/colormap.py","language":"python","start_line":19,"end_line":24,"context_start_line":1,"context_end_line":44,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/colormap.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nAn awesome colormap for really neat visualizations.\nCopied from Detectron, and removed gray colors.\n\"\"\"\n\nimport numpy as np\nimport random\nrandom.seed(0)\n\n__all__ = [\"colormap\", \"random_color\", \"random_colors\"]\n\n_COLORS = []\n\ndef gen_color():\n color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3))\n if color not in _COLORS and np.mean(color) != 0.0:\n _COLORS.append(color)\n else:\n gen_color()\n\n\nfor _ in range(300):\n gen_color()\n\n\ndef colormap(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]\n \"\"\"\n assert maximum in [255, 1], maximum\n c = _COLORS * maximum\n if not rgb:\n c = c[:, ::-1]\n return c\n","source_hash":"a3ac26ee24dc1f0459cb9646f1c9e6c5c6ca21c73677205d4b8d8eb832ff80bd","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.colormap.colormap","uri":"program://OneFormer/function/demo.colormap.colormap#L31-L43","kind":"function","name":"colormap","path":"demo/colormap.py","language":"python","start_line":31,"end_line":43,"context_start_line":11,"context_end_line":63,"code":"import numpy as np\nimport random\nrandom.seed(0)\n\n__all__ = [\"colormap\", \"random_color\", \"random_colors\"]\n\n_COLORS = []\n\ndef gen_color():\n color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3))\n if color not in _COLORS and np.mean(color) != 0.0:\n _COLORS.append(color)\n else:\n gen_color()\n\n\nfor _ in range(300):\n gen_color()\n\n\ndef colormap(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]\n \"\"\"\n assert maximum in [255, 1], maximum\n c = _COLORS * maximum\n if not rgb:\n c = c[:, ::-1]\n return c\n\n\ndef random_color(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n idx = np.random.randint(0, len(_COLORS))\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n\ndef random_colors(N, rgb=False, maximum=255):\n \"\"\"\n Args:","source_hash":"a3ac26ee24dc1f0459cb9646f1c9e6c5c6ca21c73677205d4b8d8eb832ff80bd","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.colormap.random_color","uri":"program://OneFormer/function/demo.colormap.random_color#L46-L58","kind":"function","name":"random_color","path":"demo/colormap.py","language":"python","start_line":46,"end_line":58,"context_start_line":26,"context_end_line":78,"code":"\nfor _ in range(300):\n gen_color()\n\n\ndef colormap(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]\n \"\"\"\n assert maximum in [255, 1], maximum\n c = _COLORS * maximum\n if not rgb:\n c = c[:, ::-1]\n return c\n\n\ndef random_color(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n idx = np.random.randint(0, len(_COLORS))\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n\ndef random_colors(N, rgb=False, maximum=255):\n \"\"\"\n Args:\n N (int): number of unique colors needed\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a list of random_color\n \"\"\"\n indices = random.sample(range(len(_COLORS)), N)\n ret = [_COLORS[i] * maximum for i in indices]\n if not rgb:\n ret = [x[::-1] for x in ret]\n return ret\n\n\nif __name__ == \"__main__\":\n import cv2","source_hash":"a3ac26ee24dc1f0459cb9646f1c9e6c5c6ca21c73677205d4b8d8eb832ff80bd","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.colormap.random_colors","uri":"program://OneFormer/function/demo.colormap.random_colors#L61-L74","kind":"function","name":"random_colors","path":"demo/colormap.py","language":"python","start_line":61,"end_line":74,"context_start_line":41,"context_end_line":90,"code":" if not rgb:\n c = c[:, ::-1]\n return c\n\n\ndef random_color(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n idx = np.random.randint(0, len(_COLORS))\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret\n\n\ndef random_colors(N, rgb=False, maximum=255):\n \"\"\"\n Args:\n N (int): number of unique colors needed\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n Returns:\n ndarray: a list of random_color\n \"\"\"\n indices = random.sample(range(len(_COLORS)), N)\n ret = [_COLORS[i] * maximum for i in indices]\n if not rgb:\n ret = [x[::-1] for x in ret]\n return ret\n\n\nif __name__ == \"__main__\":\n import cv2\n\n size = 100\n H, W = 10, 10\n canvas = np.random.rand(H * size, W * size, 3).astype(\"float32\")\n for h in range(H):\n for w in range(W):\n idx = h * W + w\n if idx >= len(_COLORS):\n break\n canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]\n cv2.imshow(\"a\", canvas)\n cv2.waitKey(0)","source_hash":"a3ac26ee24dc1f0459cb9646f1c9e6c5c6ca21c73677205d4b8d8eb832ff80bd","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.defaults","uri":"program://OneFormer/module/demo.defaults#L1-L82","kind":"module","name":"demo.defaults","path":"demo/defaults.py","language":"python","start_line":1,"end_line":82,"context_start_line":1,"context_end_line":82,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/defaults.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport torch\nimport detectron2.data.transforms as T\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.data import (\n MetadataCatalog,\n)\nfrom detectron2.modeling import build_model\n\n\n__all__ = [\n \"DefaultPredictor\",\n]\n\n\nclass DefaultPredictor:\n \"\"\"\n Create a simple end-to-end predictor with the given config that runs on\n single device for a single input image.\n Compared to using the model directly, this class does the following additions:\n 1. Load checkpoint from `cfg.MODEL.WEIGHTS`.\n 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`.\n 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`.\n 4. Take one input image and produce a single output, instead of a batch.\n This is meant for simple demo purposes, so it does the above steps automatically.\n This is not meant for benchmarks or running complicated inference logic.\n If you'd like to do anything more complicated, please refer to its source code as\n examples to build and use the model manually.\n Attributes:\n metadata (Metadata): the metadata of the underlying dataset, obtained from\n cfg.DATASETS.TEST.\n Examples:\n ::\n pred = DefaultPredictor(cfg)\n inputs = cv2.imread(\"input.jpg\")\n outputs = pred(inputs)\n \"\"\"\n\n def __init__(self, cfg):\n self.cfg = cfg.clone() # cfg can be modified by model\n self.model = build_model(self.cfg)\n self.model.eval()\n if len(cfg.DATASETS.TEST):\n self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])\n\n checkpointer = DetectionCheckpointer(self.model)\n checkpointer.load(cfg.MODEL.WEIGHTS)\n\n self.aug = T.ResizeShortestEdge(\n [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST\n )\n\n self.input_format = cfg.INPUT.FORMAT\n assert self.input_format in [\"RGB\", \"BGR\"], self.input_format\n\n def __call__(self, original_image, task):\n \"\"\"\n Args:\n original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n Returns:\n predictions (dict):\n the output of the model for one image only.\n See :doc:`/tutorials/models` for details about the format.\n \"\"\"\n with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258\n # Apply pre-processing to image.\n if self.input_format == \"RGB\":\n # whether the model expects BGR inputs or RGB\n original_image = original_image[:, :, ::-1]\n height, width = original_image.shape[:2]\n image = self.aug.get_transform(original_image).apply_image(original_image)\n image = torch.as_tensor(image.astype(\"float32\").transpose(2, 0, 1))\n \n task = f\"The task is {task}\"\n\n inputs = {\"image\": image, \"height\": height, \"width\": width, \"task\": task}\n predictions = self.model([inputs])[0]\n return predictions","source_hash":"f3c32ff13f297ac1a70c83ec06a49fa2dda15f27a7f80fe6add1b8ab98217dbf","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.defaults.DefaultPredictor","uri":"program://OneFormer/class/demo.defaults.DefaultPredictor#L20-L82","kind":"class","name":"DefaultPredictor","path":"demo/defaults.py","language":"python","start_line":20,"end_line":82,"context_start_line":1,"context_end_line":82,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/defaults.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport torch\nimport detectron2.data.transforms as T\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.data import (\n MetadataCatalog,\n)\nfrom detectron2.modeling import build_model\n\n\n__all__ = [\n \"DefaultPredictor\",\n]\n\n\nclass DefaultPredictor:\n \"\"\"\n Create a simple end-to-end predictor with the given config that runs on\n single device for a single input image.\n Compared to using the model directly, this class does the following additions:\n 1. Load checkpoint from `cfg.MODEL.WEIGHTS`.\n 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`.\n 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`.\n 4. Take one input image and produce a single output, instead of a batch.\n This is meant for simple demo purposes, so it does the above steps automatically.\n This is not meant for benchmarks or running complicated inference logic.\n If you'd like to do anything more complicated, please refer to its source code as\n examples to build and use the model manually.\n Attributes:\n metadata (Metadata): the metadata of the underlying dataset, obtained from\n cfg.DATASETS.TEST.\n Examples:\n ::\n pred = DefaultPredictor(cfg)\n inputs = cv2.imread(\"input.jpg\")\n outputs = pred(inputs)\n \"\"\"\n\n def __init__(self, cfg):\n self.cfg = cfg.clone() # cfg can be modified by model\n self.model = build_model(self.cfg)\n self.model.eval()\n if len(cfg.DATASETS.TEST):\n self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])\n\n checkpointer = DetectionCheckpointer(self.model)\n checkpointer.load(cfg.MODEL.WEIGHTS)\n\n self.aug = T.ResizeShortestEdge(\n [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST\n )\n\n self.input_format = cfg.INPUT.FORMAT\n assert self.input_format in [\"RGB\", \"BGR\"], self.input_format\n\n def __call__(self, original_image, task):\n \"\"\"\n Args:\n original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n Returns:\n predictions (dict):\n the output of the model for one image only.\n See :doc:`/tutorials/models` for details about the format.\n \"\"\"\n with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258\n # Apply pre-processing to image.\n if self.input_format == \"RGB\":\n # whether the model expects BGR inputs or RGB\n original_image = original_image[:, :, ::-1]\n height, width = original_image.shape[:2]\n image = self.aug.get_transform(original_image).apply_image(original_image)\n image = torch.as_tensor(image.astype(\"float32\").transpose(2, 0, 1))\n \n task = f\"The task is {task}\"\n\n inputs = {\"image\": image, \"height\": height, \"width\": width, \"task\": task}\n predictions = self.model([inputs])[0]\n return predictions","source_hash":"f3c32ff13f297ac1a70c83ec06a49fa2dda15f27a7f80fe6add1b8ab98217dbf","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.defaults.__init__","uri":"program://OneFormer/function/demo.defaults.__init__#L43-L58","kind":"function","name":"__init__","path":"demo/defaults.py","language":"python","start_line":43,"end_line":58,"context_start_line":23,"context_end_line":78,"code":" single device for a single input image.\n Compared to using the model directly, this class does the following additions:\n 1. Load checkpoint from `cfg.MODEL.WEIGHTS`.\n 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`.\n 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`.\n 4. Take one input image and produce a single output, instead of a batch.\n This is meant for simple demo purposes, so it does the above steps automatically.\n This is not meant for benchmarks or running complicated inference logic.\n If you'd like to do anything more complicated, please refer to its source code as\n examples to build and use the model manually.\n Attributes:\n metadata (Metadata): the metadata of the underlying dataset, obtained from\n cfg.DATASETS.TEST.\n Examples:\n ::\n pred = DefaultPredictor(cfg)\n inputs = cv2.imread(\"input.jpg\")\n outputs = pred(inputs)\n \"\"\"\n\n def __init__(self, cfg):\n self.cfg = cfg.clone() # cfg can be modified by model\n self.model = build_model(self.cfg)\n self.model.eval()\n if len(cfg.DATASETS.TEST):\n self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])\n\n checkpointer = DetectionCheckpointer(self.model)\n checkpointer.load(cfg.MODEL.WEIGHTS)\n\n self.aug = T.ResizeShortestEdge(\n [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST\n )\n\n self.input_format = cfg.INPUT.FORMAT\n assert self.input_format in [\"RGB\", \"BGR\"], self.input_format\n\n def __call__(self, original_image, task):\n \"\"\"\n Args:\n original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n Returns:\n predictions (dict):\n the output of the model for one image only.\n See :doc:`/tutorials/models` for details about the format.\n \"\"\"\n with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258\n # Apply pre-processing to image.\n if self.input_format == \"RGB\":\n # whether the model expects BGR inputs or RGB\n original_image = original_image[:, :, ::-1]\n height, width = original_image.shape[:2]\n image = self.aug.get_transform(original_image).apply_image(original_image)\n image = torch.as_tensor(image.astype(\"float32\").transpose(2, 0, 1))\n \n task = f\"The task is {task}\"","source_hash":"f3c32ff13f297ac1a70c83ec06a49fa2dda15f27a7f80fe6add1b8ab98217dbf","truncated":false} {"repo_id":"OneFormer","entity_id":"py:demo.defaults.__call__","uri":"program://OneFormer/function/demo.defaults.__call__#L60-L82","kind":"function","name":"__call__","path":"demo/defaults.py","language":"python","start_line":60,"end_line":82,"context_start_line":40,"context_end_line":82,"code":" outputs = pred(inputs)\n \"\"\"\n\n def __init__(self, cfg):\n self.cfg = cfg.clone() # cfg can be modified by model\n self.model = build_model(self.cfg)\n self.model.eval()\n if len(cfg.DATASETS.TEST):\n self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])\n\n checkpointer = DetectionCheckpointer(self.model)\n checkpointer.load(cfg.MODEL.WEIGHTS)\n\n self.aug = T.ResizeShortestEdge(\n [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST\n )\n\n self.input_format = cfg.INPUT.FORMAT\n assert self.input_format in [\"RGB\", \"BGR\"], self.input_format\n\n def __call__(self, original_image, task):\n \"\"\"\n Args:\n original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n Returns:\n predictions (dict):\n the output of the model for one image only.\n See :doc:`/tutorials/models` for details about the format.\n \"\"\"\n with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258\n # Apply pre-processing to image.\n if self.input_format == \"RGB\":\n # whether the model expects BGR inputs or RGB\n original_image = original_image[:, :, ::-1]\n height, width = original_image.shape[:2]\n image = self.aug.get_transform(original_image).apply_image(original_image)\n image = torch.as_tensor(image.astype(\"float32\").transpose(2, 0, 1))\n \n task = f\"The task is {task}\"\n\n inputs = {\"image\": image, \"height\": height, \"width\": width, \"task\": task}\n predictions = self.model([inputs])[0]\n return predictions","source_hash":"f3c32ff13f297ac1a70c83ec06a49fa2dda15f27a7f80fe6add1b8ab98217dbf","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.setup_detectron2","uri":"program://OneFormer/module/tools.setup_detectron2#L1-L11","kind":"module","name":"tools.setup_detectron2","path":"tools/setup_detectron2.py","language":"python","start_line":1,"end_line":11,"context_start_line":1,"context_end_line":11,"code":"import sys, os, distutils.core, subprocess\n\nif not os.path.exists('./detectron2'):\n subprocess.run(['git', 'clone', 'https://github.com/facebookresearch/detectron2'])\n\ndist = distutils.core.run_setup(\"./detectron2/setup.py\")\n\nfor x in dist.install_requires:\n subprocess.run(['python', '-m', 'pip', 'install', x])\n\nsys.path.insert(0, os.path.abspath('./detectron2'))","source_hash":"8aca89a7a5271c6a9f20fe058e3beb422cdb6a6e0ee2c8bc107bc3000c1983d1","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.convert-pretrained-model-to-d2","uri":"program://OneFormer/module/tools.convert-pretrained-model-to-d2#L1-L30","kind":"module","name":"tools.convert-pretrained-model-to-d2","path":"tools/convert-pretrained-model-to-d2.py","language":"python","start_line":1,"end_line":30,"context_start_line":1,"context_end_line":30,"code":"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport pickle as pkl\nimport sys\n\nimport torch\n\n\"\"\"\nUsage:\n # download pretrained swin model:\n wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth\n # run the conversion\n ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl\n # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:\nMODEL:\n WEIGHTS: \"/path/to/swin_tiny_patch4_window7_224.pkl\"\nINPUT:\n FORMAT: \"RGB\"\n\"\"\"\n\nif __name__ == \"__main__\":\n input = sys.argv[1]\n\n obj = torch.load(input, map_location=\"cpu\")[\"model\"]\n\n res = {\"model\": obj, \"__author__\": \"third_party\", \"matching_heuristics\": True}\n\n with open(sys.argv[2], \"wb\") as f:\n pkl.dump(res, f)","source_hash":"6d03a428eaf86eaf0ff7da22a722aeb22e347602d4430904537d5395b9403e26","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.convert-pretrained-nat-model-to-d2","uri":"program://OneFormer/module/tools.convert-pretrained-nat-model-to-d2#L1-L30","kind":"module","name":"tools.convert-pretrained-nat-model-to-d2","path":"tools/convert-pretrained-nat-model-to-d2.py","language":"python","start_line":1,"end_line":30,"context_start_line":1,"context_end_line":30,"code":"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport pickle as pkl\nimport sys\n\nimport torch\n\n\"\"\"\nUsage:\n # download pretrained swin model:\n wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth\n # run the conversion\n ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl\n # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:\nMODEL:\n WEIGHTS: \"/path/to/swin_tiny_patch4_window7_224.pkl\"\nINPUT:\n FORMAT: \"RGB\"\n\"\"\"\n\nif __name__ == \"__main__\":\n input = sys.argv[1]\n\n obj = torch.load(input, map_location=\"cpu\")\n\n res = {\"model\": obj, \"__author__\": \"third_party\", \"matching_heuristics\": True}\n\n with open(sys.argv[2], \"wb\") as f:\n pkl.dump(res, f)","source_hash":"e89291607776a84bae9cf3d1a86600223dfa4c005c1fafc07da236d01bab523f","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput","uri":"program://OneFormer/module/tools.calc_throughput#L1-L218","kind":"module","name":"tools.calc_throughput","path":"tools/calc_throughput.py","language":"python","start_line":1,"end_line":218,"context_start_line":1,"context_end_line":218,"code":"import copy\nimport itertools\nimport os\n\nfrom typing import Any, Dict, List, Set\n\nimport torch\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import get_cfg\nfrom detectron2.data import build_detection_train_loader\nfrom detectron2.engine import (\n default_argument_parser,\n default_setup,\n launch,\n)\nfrom detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler\nfrom detectron2.solver.build import maybe_add_gradient_clipping\nfrom detectron2.utils.logger import setup_logger\nfrom trainers.trainer import TPDefaultTrainer\n\n# fmt: off\nimport os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n# fmt: on\n\nfrom oneformer import (\n COCOUnifiedNewBaselineDatasetMapper,\n OneFormerUnifiedDatasetMapper,\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\n\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter\nfrom time import sleep\n\nclass Trainer(TPDefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")\n return cfg\n\n\ndef main(args):\n cfg = setup(args)\n trainer = Trainer(cfg)\n trainer.resume_or_load(resume=args.resume)\n if args.machine_rank == 0:\n net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n sleep(3)\n return trainer.train()\n\n\nif __name__ == \"__main__\":\n args = default_argument_parser().parse_args()\n print(\"Command Line Args:\", args)\n launch(\n main,\n args.num_gpus,\n num_machines=args.num_machines,\n machine_rank=args.machine_rank,\n dist_url=args.dist_url,\n args=(args,),\n )","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.Trainer","uri":"program://OneFormer/class/tools.calc_throughput.Trainer#L41-L173","kind":"class","name":"Trainer","path":"tools/calc_throughput.py","language":"python","start_line":41,"end_line":173,"context_start_line":21,"context_end_line":193,"code":"\n# fmt: off\nimport os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n# fmt: on\n\nfrom oneformer import (\n COCOUnifiedNewBaselineDatasetMapper,\n OneFormerUnifiedDatasetMapper,\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\n\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter\nfrom time import sleep\n\nclass Trainer(TPDefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.setup","uri":"program://OneFormer/function/tools.calc_throughput.setup#L176-L194","kind":"function","name":"setup","path":"tools/calc_throughput.py","language":"python","start_line":176,"end_line":194,"context_start_line":156,"context_end_line":214,"code":" super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")\n return cfg\n\n\ndef main(args):\n cfg = setup(args)\n trainer = Trainer(cfg)\n trainer.resume_or_load(resume=args.resume)\n if args.machine_rank == 0:\n net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n sleep(3)\n return trainer.train()\n\n\nif __name__ == \"__main__\":\n args = default_argument_parser().parse_args()\n print(\"Command Line Args:\", args)\n launch(\n main,\n args.num_gpus,\n num_machines=args.num_machines,","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.main","uri":"program://OneFormer/function/tools.calc_throughput.main#L197-L205","kind":"function","name":"main","path":"tools/calc_throughput.py","language":"python","start_line":197,"end_line":205,"context_start_line":177,"context_end_line":218,"code":" \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")\n return cfg\n\n\ndef main(args):\n cfg = setup(args)\n trainer = Trainer(cfg)\n trainer.resume_or_load(resume=args.resume)\n if args.machine_rank == 0:\n net_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)\n print(\"Total Params: {} M\".format(net_params/1e6))\n sleep(3)\n return trainer.train()\n\n\nif __name__ == \"__main__\":\n args = default_argument_parser().parse_args()\n print(\"Command Line Args:\", args)\n launch(\n main,\n args.num_gpus,\n num_machines=args.num_machines,\n machine_rank=args.machine_rank,\n dist_url=args.dist_url,\n args=(args,),\n )","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.build_train_loader","uri":"program://OneFormer/function/tools.calc_throughput.build_train_loader#L47-L58","kind":"function","name":"build_train_loader","path":"tools/calc_throughput.py","language":"python","start_line":47,"end_line":58,"context_start_line":27,"context_end_line":78,"code":"\nfrom oneformer import (\n COCOUnifiedNewBaselineDatasetMapper,\n OneFormerUnifiedDatasetMapper,\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_convnext_config,\n)\n\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter\nfrom time import sleep\n\nclass Trainer(TPDefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.build_writers","uri":"program://OneFormer/function/tools.calc_throughput.build_writers#L60-L82","kind":"function","name":"build_writers","path":"tools/calc_throughput.py","language":"python","start_line":60,"end_line":82,"context_start_line":40,"context_end_line":102,"code":"\nclass Trainer(TPDefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Unified segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"oneformer_unified\":\n mapper = OneFormerUnifiedDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco unified segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_unified_lsj\":\n mapper = COCOUnifiedNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n \n def build_writers(self):\n \"\"\"\n Build a list of writers to be used. By default it contains\n writers that write metrics to the screen,\n a json file, and a tensorboard event file respectively.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.build_lr_scheduler","uri":"program://OneFormer/function/tools.calc_throughput.build_lr_scheduler#L85-L90","kind":"function","name":"build_lr_scheduler","path":"tools/calc_throughput.py","language":"python","start_line":85,"end_line":90,"context_start_line":65,"context_end_line":110,"code":" If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n It is now implemented by:\n ::\n return [\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.build_optimizer","uri":"program://OneFormer/function/tools.calc_throughput.build_optimizer#L93-L173","kind":"function","name":"build_optimizer","path":"tools/calc_throughput.py","language":"python","start_line":93,"end_line":173,"context_start_line":73,"context_end_line":193,"code":" JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n TensorboardXWriter(self.cfg.OUTPUT_DIR),\n ]\n \"\"\"\n # Here the default print/log frequency of each writer is used.\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(self.max_iter),\n JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n ]\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.\n \"\"\"\n cfg = get_cfg()\n # for poly lr schedule\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n default_setup(cfg, args)\n # Setup logger for \"oneformer\" module\n setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name=\"oneformer\")","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.maybe_add_full_model_gradient_clipping","uri":"program://OneFormer/function/tools.calc_throughput.maybe_add_full_model_gradient_clipping#L141-L158","kind":"function","name":"maybe_add_full_model_gradient_clipping","path":"tools/calc_throughput.py","language":"python","start_line":141,"end_line":158,"context_start_line":121,"context_end_line":178,"code":" # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):\n \"\"\"\n Create configs and perform basic setups.","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.FullModelGradientClippingOptimizer","uri":"program://OneFormer/class/tools.calc_throughput.FullModelGradientClippingOptimizer#L150-L156","kind":"class","name":"FullModelGradientClippingOptimizer","path":"tools/calc_throughput.py","language":"python","start_line":150,"end_line":156,"context_start_line":130,"context_end_line":176,"code":" \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.calc_throughput.step","uri":"program://OneFormer/function/tools.calc_throughput.step#L151-L156","kind":"function","name":"step","path":"tools/calc_throughput.py","language":"python","start_line":151,"end_line":156,"context_start_line":131,"context_end_line":176,"code":" or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n for p in all_params:\n torch.nan_to_num(p.grad, nan=0.0, posinf=1e5, neginf=-1e5, out=p.grad)\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n\ndef setup(args):","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model","uri":"program://OneFormer/module/tools.analyze_model#L1-L227","kind":"module","name":"tools.analyze_model","path":"tools/analyze_model.py","language":"python","start_line":1,"end_line":227,"context_start_line":1,"context_end_line":227,"code":"import logging\nimport numpy as np\nfrom collections import Counter\nimport tqdm\nfrom fvcore.nn import flop_count_table # can also try flop_count_str\n\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate\nfrom detectron2.engine import default_argument_parser\nfrom detectron2.modeling import build_model\nfrom detectron2.projects.deeplab import add_deeplab_config\nfrom detectron2.utils.analysis import (\n FlopCountAnalysis,\n activation_count_operators,\n parameter_count_table,\n)\nfrom detectron2.utils.logger import setup_logger\n\n# fmt: off\nimport os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n# fmt: on\n\nfrom oneformer.data.build import *\nfrom oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper\nfrom oneformer import (\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_beit_adapter_config,\n add_convnext_config,\n)\n\nlogger = logging.getLogger(\"detectron2\")\n\n\ndef setup(args):\n if args.config_file.endswith(\".yaml\"):\n cfg = get_cfg()\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_beit_adapter_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.DATALOADER.NUM_WORKERS = 0\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n else:\n cfg = LazyConfig.load(args.config_file)\n cfg = LazyConfig.apply_overrides(cfg, args.opts)\n setup_logger(name=\"fvcore\")\n setup_logger()\n return cfg\n\n\ndef do_flop(cfg):\n if isinstance(cfg, CfgNode):\n mapper = DatasetMapper(cfg, False)\n data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST_PANOPTIC[0], mapper=mapper)\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n data_loader = instantiate(cfg.dataloader.test)\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_flops = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n if args.use_fixed_input_size and isinstance(cfg, CfgNode):\n import torch\n crop_size = cfg.INPUT.CROP.SIZE\n data[0][\"image\"] = torch.zeros((3, crop_size[0], crop_size[1]))\n flops = FlopCountAnalysis(model, data)\n if idx > 0:\n flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False)\n counts += flops.by_operator()\n total_flops.append(flops.total())\n\n logger.info(\"Flops table computed from only one input sample:\\n\" + flop_count_table(flops))\n logger.info(\n \"Average GFlops for each type of operators:\\n\"\n + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()])\n )\n logger.info(\n \"Total GFlops: {:.1f}±{:.1f}\".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9)\n )\n\n\ndef do_activation(cfg):\n if isinstance(cfg, CfgNode):\n mapper = DatasetMapper(cfg, False)\n data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST_PANOPTIC[0], mapper=mapper)\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n data_loader = instantiate(cfg.dataloader.test)\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_activations = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n count = activation_count_operators(model, data)\n counts += count\n total_activations.append(sum(count.values()))\n logger.info(\n \"(Million) Activations for Each Type of Operators:\\n\"\n + str([(k, v / idx) for k, v in counts.items()])\n )\n logger.info(\n \"Total (Million) Activations: {}±{}\".format(\n np.mean(total_activations), np.std(total_activations)\n )\n )\n\ndef do_speed(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n import torch\n crop_size = cfg.INPUT.CROP.SIZE\n data = [{}]\n data[0][\"image\"] = torch.zeros((3, crop_size[0], crop_size[1]))\n data[0][\"task\"] = \"the task is panoptic\"\n total_times = []\n for _ in tqdm.trange(100): # noqa\n model(data)\n torch.cuda.synchronize()\n tstart = torch.cuda.Event(enable_timing=True)\n tend = torch.cuda.Event(enable_timing=True)\n fps = []\n times = []\n for _ in range(5):\n for _ in tqdm.trange(args.num_inputs): # noqa \n tstart.record()\n model(data)\n tend.record()\n torch.cuda.synchronize()\n total_times.append(tstart.elapsed_time(tend))\n times.append(np.mean(total_times))\n fps.append(1000/np.mean(total_times))\n\n logger.info(\n \"Average Time per {}x{} Image : {:.1f} ± {:.1f} milli-seconds\".format(crop_size, crop_size, np.mean(times), np.std(times))\n )\n logger.info(\n \"FPS : {:.2f} ± {:.2f}\".format(np.mean(fps), np.std(fps))\n )\n\ndef do_parameter(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Parameter Count:\\n\" + parameter_count_table(model, max_depth=5))\n\n\ndef do_structure(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Model Structure:\\n\" + str(model))\n\n\nif __name__ == \"__main__\":\n parser = default_argument_parser(\n epilog=\"\"\"\nExamples:\nTo show parameters of a model:\n$ ./analyze_model.py --tasks parameter \\\\\n --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml\nFlops and activations are data-dependent, therefore inputs and model weights\nare needed to count them:\n$ ./analyze_model.py --num-inputs 100 --tasks flop \\\\\n --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\\\\n MODEL.WEIGHTS /path/to/model.pkl\n\"\"\"\n )\n parser.add_argument(\n \"--tasks\",\n choices=[\"flop\", \"speed\", \"activation\", \"parameter\", \"structure\"],\n required=True,\n nargs=\"+\",\n )\n parser.add_argument(\n \"-n\",\n \"--num-inputs\",\n default=100,\n type=int,\n help=\"number of inputs used to compute statistics for flops/activations, \"\n \"both are data dependent.\",\n )\n parser.add_argument(\n \"--use-fixed-input-size\",\n action=\"store_true\",\n help=\"use fixed input size when calculating flops\",\n )\n args = parser.parse_args()\n assert not args.eval_only\n assert args.num_gpus == 1\n\n cfg = setup(args)\n\n for task in args.tasks:\n {\n \"flop\": do_flop,\n \"speed\": do_speed,\n \"activation\": do_activation,\n \"parameter\": do_parameter,\n \"structure\": do_structure,\n }[task](cfg)","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model.setup","uri":"program://OneFormer/function/tools.analyze_model.setup#L39-L58","kind":"function","name":"setup","path":"tools/analyze_model.py","language":"python","start_line":39,"end_line":58,"context_start_line":19,"context_end_line":78,"code":"# fmt: off\nimport os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n# fmt: on\n\nfrom oneformer.data.build import *\nfrom oneformer.data.dataset_mappers.dataset_mapper import DatasetMapper\nfrom oneformer import (\n add_oneformer_config,\n add_common_config,\n add_swin_config,\n add_dinat_config,\n add_beit_adapter_config,\n add_convnext_config,\n)\n\nlogger = logging.getLogger(\"detectron2\")\n\n\ndef setup(args):\n if args.config_file.endswith(\".yaml\"):\n cfg = get_cfg()\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_beit_adapter_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.DATALOADER.NUM_WORKERS = 0\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n else:\n cfg = LazyConfig.load(args.config_file)\n cfg = LazyConfig.apply_overrides(cfg, args.opts)\n setup_logger(name=\"fvcore\")\n setup_logger()\n return cfg\n\n\ndef do_flop(cfg):\n if isinstance(cfg, CfgNode):\n mapper = DatasetMapper(cfg, False)\n data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST_PANOPTIC[0], mapper=mapper)\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n data_loader = instantiate(cfg.dataloader.test)\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_flops = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n if args.use_fixed_input_size and isinstance(cfg, CfgNode):\n import torch","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model.do_flop","uri":"program://OneFormer/function/tools.analyze_model.do_flop#L61-L94","kind":"function","name":"do_flop","path":"tools/analyze_model.py","language":"python","start_line":61,"end_line":94,"context_start_line":41,"context_end_line":114,"code":" cfg = get_cfg()\n add_deeplab_config(cfg)\n add_common_config(cfg)\n add_swin_config(cfg)\n add_dinat_config(cfg)\n add_beit_adapter_config(cfg)\n add_oneformer_config(cfg)\n add_convnext_config(cfg)\n cfg.merge_from_file(args.config_file)\n cfg.DATALOADER.NUM_WORKERS = 0\n cfg.merge_from_list(args.opts)\n cfg.freeze()\n else:\n cfg = LazyConfig.load(args.config_file)\n cfg = LazyConfig.apply_overrides(cfg, args.opts)\n setup_logger(name=\"fvcore\")\n setup_logger()\n return cfg\n\n\ndef do_flop(cfg):\n if isinstance(cfg, CfgNode):\n mapper = DatasetMapper(cfg, False)\n data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST_PANOPTIC[0], mapper=mapper)\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n data_loader = instantiate(cfg.dataloader.test)\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_flops = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n if args.use_fixed_input_size and isinstance(cfg, CfgNode):\n import torch\n crop_size = cfg.INPUT.CROP.SIZE\n data[0][\"image\"] = torch.zeros((3, crop_size[0], crop_size[1]))\n flops = FlopCountAnalysis(model, data)\n if idx > 0:\n flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False)\n counts += flops.by_operator()\n total_flops.append(flops.total())\n\n logger.info(\"Flops table computed from only one input sample:\\n\" + flop_count_table(flops))\n logger.info(\n \"Average GFlops for each type of operators:\\n\"\n + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()])\n )\n logger.info(\n \"Total GFlops: {:.1f}±{:.1f}\".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9)\n )\n\n\ndef do_activation(cfg):\n if isinstance(cfg, CfgNode):\n mapper = DatasetMapper(cfg, False)\n data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST_PANOPTIC[0], mapper=mapper)\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n data_loader = instantiate(cfg.dataloader.test)\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_activations = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n count = activation_count_operators(model, data)\n counts += count","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model.do_activation","uri":"program://OneFormer/function/tools.analyze_model.do_activation#L97-L124","kind":"function","name":"do_activation","path":"tools/analyze_model.py","language":"python","start_line":97,"end_line":124,"context_start_line":77,"context_end_line":144,"code":" if args.use_fixed_input_size and isinstance(cfg, CfgNode):\n import torch\n crop_size = cfg.INPUT.CROP.SIZE\n data[0][\"image\"] = torch.zeros((3, crop_size[0], crop_size[1]))\n flops = FlopCountAnalysis(model, data)\n if idx > 0:\n flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False)\n counts += flops.by_operator()\n total_flops.append(flops.total())\n\n logger.info(\"Flops table computed from only one input sample:\\n\" + flop_count_table(flops))\n logger.info(\n \"Average GFlops for each type of operators:\\n\"\n + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()])\n )\n logger.info(\n \"Total GFlops: {:.1f}±{:.1f}\".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9)\n )\n\n\ndef do_activation(cfg):\n if isinstance(cfg, CfgNode):\n mapper = DatasetMapper(cfg, False)\n data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST_PANOPTIC[0], mapper=mapper)\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n data_loader = instantiate(cfg.dataloader.test)\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_activations = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n count = activation_count_operators(model, data)\n counts += count\n total_activations.append(sum(count.values()))\n logger.info(\n \"(Million) Activations for Each Type of Operators:\\n\"\n + str([(k, v / idx) for k, v in counts.items()])\n )\n logger.info(\n \"Total (Million) Activations: {}±{}\".format(\n np.mean(total_activations), np.std(total_activations)\n )\n )\n\ndef do_speed(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n import torch\n crop_size = cfg.INPUT.CROP.SIZE\n data = [{}]\n data[0][\"image\"] = torch.zeros((3, crop_size[0], crop_size[1]))\n data[0][\"task\"] = \"the task is panoptic\"\n total_times = []\n for _ in tqdm.trange(100): # noqa\n model(data)\n torch.cuda.synchronize()\n tstart = torch.cuda.Event(enable_timing=True)","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model.do_speed","uri":"program://OneFormer/function/tools.analyze_model.do_speed#L126-L163","kind":"function","name":"do_speed","path":"tools/analyze_model.py","language":"python","start_line":126,"end_line":163,"context_start_line":106,"context_end_line":183,"code":" model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n\n counts = Counter()\n total_activations = []\n for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa\n count = activation_count_operators(model, data)\n counts += count\n total_activations.append(sum(count.values()))\n logger.info(\n \"(Million) Activations for Each Type of Operators:\\n\"\n + str([(k, v / idx) for k, v in counts.items()])\n )\n logger.info(\n \"Total (Million) Activations: {}±{}\".format(\n np.mean(total_activations), np.std(total_activations)\n )\n )\n\ndef do_speed(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)\n else:\n model = instantiate(cfg.model)\n model.to(cfg.train.device)\n DetectionCheckpointer(model).load(cfg.train.init_checkpoint)\n model.eval()\n import torch\n crop_size = cfg.INPUT.CROP.SIZE\n data = [{}]\n data[0][\"image\"] = torch.zeros((3, crop_size[0], crop_size[1]))\n data[0][\"task\"] = \"the task is panoptic\"\n total_times = []\n for _ in tqdm.trange(100): # noqa\n model(data)\n torch.cuda.synchronize()\n tstart = torch.cuda.Event(enable_timing=True)\n tend = torch.cuda.Event(enable_timing=True)\n fps = []\n times = []\n for _ in range(5):\n for _ in tqdm.trange(args.num_inputs): # noqa \n tstart.record()\n model(data)\n tend.record()\n torch.cuda.synchronize()\n total_times.append(tstart.elapsed_time(tend))\n times.append(np.mean(total_times))\n fps.append(1000/np.mean(total_times))\n\n logger.info(\n \"Average Time per {}x{} Image : {:.1f} ± {:.1f} milli-seconds\".format(crop_size, crop_size, np.mean(times), np.std(times))\n )\n logger.info(\n \"FPS : {:.2f} ± {:.2f}\".format(np.mean(fps), np.std(fps))\n )\n\ndef do_parameter(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Parameter Count:\\n\" + parameter_count_table(model, max_depth=5))\n\n\ndef do_structure(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Model Structure:\\n\" + str(model))\n\n\nif __name__ == \"__main__\":\n parser = default_argument_parser(\n epilog=\"\"\"","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model.do_parameter","uri":"program://OneFormer/function/tools.analyze_model.do_parameter#L165-L170","kind":"function","name":"do_parameter","path":"tools/analyze_model.py","language":"python","start_line":165,"end_line":170,"context_start_line":145,"context_end_line":190,"code":" tend = torch.cuda.Event(enable_timing=True)\n fps = []\n times = []\n for _ in range(5):\n for _ in tqdm.trange(args.num_inputs): # noqa \n tstart.record()\n model(data)\n tend.record()\n torch.cuda.synchronize()\n total_times.append(tstart.elapsed_time(tend))\n times.append(np.mean(total_times))\n fps.append(1000/np.mean(total_times))\n\n logger.info(\n \"Average Time per {}x{} Image : {:.1f} ± {:.1f} milli-seconds\".format(crop_size, crop_size, np.mean(times), np.std(times))\n )\n logger.info(\n \"FPS : {:.2f} ± {:.2f}\".format(np.mean(fps), np.std(fps))\n )\n\ndef do_parameter(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Parameter Count:\\n\" + parameter_count_table(model, max_depth=5))\n\n\ndef do_structure(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Model Structure:\\n\" + str(model))\n\n\nif __name__ == \"__main__\":\n parser = default_argument_parser(\n epilog=\"\"\"\nExamples:\nTo show parameters of a model:\n$ ./analyze_model.py --tasks parameter \\\\\n --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml\nFlops and activations are data-dependent, therefore inputs and model weights\nare needed to count them:\n$ ./analyze_model.py --num-inputs 100 --tasks flop \\\\","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.analyze_model.do_structure","uri":"program://OneFormer/function/tools.analyze_model.do_structure#L173-L178","kind":"function","name":"do_structure","path":"tools/analyze_model.py","language":"python","start_line":173,"end_line":178,"context_start_line":153,"context_end_line":198,"code":" torch.cuda.synchronize()\n total_times.append(tstart.elapsed_time(tend))\n times.append(np.mean(total_times))\n fps.append(1000/np.mean(total_times))\n\n logger.info(\n \"Average Time per {}x{} Image : {:.1f} ± {:.1f} milli-seconds\".format(crop_size, crop_size, np.mean(times), np.std(times))\n )\n logger.info(\n \"FPS : {:.2f} ± {:.2f}\".format(np.mean(fps), np.std(fps))\n )\n\ndef do_parameter(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Parameter Count:\\n\" + parameter_count_table(model, max_depth=5))\n\n\ndef do_structure(cfg):\n if isinstance(cfg, CfgNode):\n model = build_model(cfg)\n else:\n model = instantiate(cfg.model)\n logger.info(\"Model Structure:\\n\" + str(model))\n\n\nif __name__ == \"__main__\":\n parser = default_argument_parser(\n epilog=\"\"\"\nExamples:\nTo show parameters of a model:\n$ ./analyze_model.py --tasks parameter \\\\\n --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml\nFlops and activations are data-dependent, therefore inputs and model weights\nare needed to count them:\n$ ./analyze_model.py --num-inputs 100 --tasks flop \\\\\n --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\\\\n MODEL.WEIGHTS /path/to/model.pkl\n\"\"\"\n )\n parser.add_argument(\n \"--tasks\",\n choices=[\"flop\", \"speed\", \"activation\", \"parameter\", \"structure\"],\n required=True,","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.convert-torchvision-to-d2","uri":"program://OneFormer/module/tools.convert-torchvision-to-d2#L1-L51","kind":"module","name":"tools.convert-torchvision-to-d2","path":"tools/convert-torchvision-to-d2.py","language":"python","start_line":1,"end_line":51,"context_start_line":1,"context_end_line":51,"code":"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport pickle as pkl\nimport sys\n\nimport torch\n\n\"\"\"\nUsage:\n # download one of the ResNet{18,34,50,101,152} models from torchvision:\n wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth\n # run the conversion\n ./convert-torchvision-to-d2.py r50.pth r50.pkl\n # Then, use r50.pkl with the following changes in config:\nMODEL:\n WEIGHTS: \"/path/to/r50.pkl\"\n PIXEL_MEAN: [123.675, 116.280, 103.530]\n PIXEL_STD: [58.395, 57.120, 57.375]\n RESNETS:\n DEPTH: 50\n STRIDE_IN_1X1: False\nINPUT:\n FORMAT: \"RGB\"\n\"\"\"\n\nif __name__ == \"__main__\":\n input = sys.argv[1]\n\n obj = torch.load(input, map_location=\"cpu\")\n\n newmodel = {}\n for k in list(obj.keys()):\n old_k = k\n if \"layer\" not in k:\n k = \"stem.\" + k\n for t in [1, 2, 3, 4]:\n k = k.replace(\"layer{}\".format(t), \"res{}\".format(t + 1))\n for t in [1, 2, 3]:\n k = k.replace(\"bn{}\".format(t), \"conv{}.norm\".format(t))\n k = k.replace(\"downsample.0\", \"shortcut\")\n k = k.replace(\"downsample.1\", \"shortcut.norm\")\n print(old_k, \"->\", k)\n newmodel[k] = obj.pop(old_k).detach().numpy()\n\n res = {\"model\": newmodel, \"__author__\": \"torchvision\", \"matching_heuristics\": True}\n\n with open(sys.argv[2], \"wb\") as f:\n pkl.dump(res, f)\n if obj:\n print(\"Unconverted keys:\", obj.keys())","source_hash":"310d02bff605561bf1de3fedb16f610b67d0d84eea34ecc3933d8782f920ab6a","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base","uri":"program://OneFormer/module/tools.trainers.trainer_base#L1-L388","kind":"module","name":"tools.trainers.trainer_base","path":"tools/trainers/trainer_base.py","language":"python","start_line":1,"end_line":388,"context_start_line":1,"context_end_line":388,"code":"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport logging\nimport numpy as np\nimport time\nimport weakref\nfrom typing import List, Mapping, Optional\nimport torch\nfrom torch.nn.parallel import DataParallel, DistributedDataParallel\n\nimport detectron2.utils.comm as comm\nfrom detectron2.utils.events import EventStorage, get_event_storage\nfrom detectron2.utils.logger import _log_api_usage\nfrom detectron2.engine import HookBase\nimport torch.distributed as dist\n\n__all__ = [\"TPTrainerBase\", \"TPSimpleTrainer\", \"TPAMPTrainer\"]\n\n\nclass TPTrainerBase:\n \"\"\"\n Base class for iterative trainer with hooks.\n\n The only assumption we made here is: the training runs in a loop.\n A subclass can implement what the loop is.\n We made no assumptions about the existence of dataloader, optimizer, model, etc.\n\n Attributes:\n iter(int): the current iteration.\n\n start_iter(int): The iteration to start with.\n By convention the minimum possible value is 0.\n\n max_iter(int): The iteration to end training.\n\n storage(EventStorage): An EventStorage that's opened during the course of training.\n \"\"\"\n\n def __init__(self, cfg) -> None:\n self._hooks: List[HookBase] = []\n self.iter: int = 0\n self.start_iter: int = 0\n self.max_iter: int\n self.storage: EventStorage\n self.cfg = cfg\n self.fwd_times = []\n self.bwd_times = []\n self.ave_start = None\n self.ave_end = None\n _log_api_usage(\"trainer.\" + self.__class__.__name__)\n self._logger = logging.getLogger(__name__)\n\n def register_hooks(self, hooks: List[Optional[HookBase]]) -> None:\n \"\"\"\n Register hooks to the trainer. The hooks are executed in the order\n they are registered.\n\n Args:\n hooks (list[Optional[HookBase]]): list of hooks\n \"\"\"\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)\n\n def train(self, start_iter: int, max_iter: int):\n \"\"\"\n Args:\n start_iter, max_iter (int): See docs above\n \"\"\"\n \n self._logger.info(\"Starting training from iteration {}\".format(start_iter))\n\n self.iter = self.start_iter = start_iter\n self.max_iter = max_iter\n\n with EventStorage(start_iter) as self.storage:\n try:\n self.before_train()\n for self.iter in range(start_iter, max_iter):\n if self.iter==4:\n self.ave_start=time.perf_counter()\n self.before_step()\n self.run_step()\n self.after_step()\n self.iter += 1\n except Exception:\n self._logger.exception(\"Exception during training:\")\n raise\n self.ave_end = time.perf_counter()\n \n total_time = np.round((self.ave_end-self.ave_start), 2)\n images = (self.iter-5) * self.cfg.SOLVER.IMS_PER_BATCH\n throughput = np.round(images / total_time, 2)\n \n if dist.get_rank() == 0:\n print('\\n-------------------')\n print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:\n h.after_step()\n\n def run_step(self):\n raise NotImplementedError\n\n def state_dict(self):\n ret = {\"iteration\": self.iter}\n hooks_state = {}\n for h in self._hooks:\n sd = h.state_dict()\n if sd:\n name = type(h).__qualname__\n if name in hooks_state:\n # TODO handle repetitive stateful hooks\n continue\n hooks_state[name] = sd\n if hooks_state:\n ret[\"hooks\"] = hooks_state\n return ret\n\n def load_state_dict(self, state_dict):\n logger = logging.getLogger(__name__)\n self.iter = state_dict[\"iteration\"]\n for key, value in state_dict.get(\"hooks\", {}).items():\n for h in self._hooks:\n try:\n name = type(h).__qualname__\n except AttributeError:\n continue\n if name == key:\n h.load_state_dict(value)\n break\n else:\n logger.warning(f\"Cannot find the hook '{key}', its state_dict is ignored.\")\n\n\nclass TPSimpleTrainer(TPTrainerBase):\n \"\"\"\n A simple trainer for the most common type of task:\n single-cost single-optimizer single-data-source iterative optimization,\n optionally using data-parallelism.\n It assumes that every step, you:\n\n 1. Compute the loss with a data from the data_loader.\n 2. Compute the gradients with the above loss.\n 3. Update the model with the optimizer.\n\n All other tasks during training (checkpointing, logging, evaluation, LR schedule)\n are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`.\n\n If you want to do anything fancier than this,\n either subclass TrainerBase and implement your own `run_step`,\n or write your own training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer):\n \"\"\"\n Args:\n model: a torch Module. Takes a data from data_loader and returns a\n dict of losses.\n data_loader: an iterable. Contains data to be used to call model.\n optimizer: a torch optimizer.\n \"\"\"\n super().__init__(cfg)\n\n \"\"\"\n We set the model to training mode in the trainer.\n However it's valid to train a model that's in eval mode.\n If you want your model (or a submodule of it) to behave\n like evaluation during training, you can overwrite its train() method.\n \"\"\"\n model.train()\n\n self.model = model\n self.data_loader = data_loader\n # to access the data loader iterator, call `self._data_loader_iter`\n self._data_loader_iter_obj = None\n self.optimizer = optimizer\n self.cfg = cfg\n self.batch_size = self.cfg.SOLVER.IMS_PER_BATCH\n\n def run_step(self):\n \"\"\"\n Implement the standard training logic described above.\n \"\"\"\n assert self.model.training, \"[SimpleTrainer] model was changed to eval mode!\"\n start = time.perf_counter()\n \"\"\"\n If you want to do something with the data, you can wrap the dataloader.\n \"\"\"\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n \"\"\"\n If you want to do something with the losses, you can wrap the model.\n \"\"\"\n \n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \"\"\"\n If you need to accumulate gradients or do something similar, you can\n wrap the optimizer with your custom `zero_grad()` method.\n \"\"\"\n self.optimizer.zero_grad()\n losses.backward()\n \n\n \"\"\"\n If you need gradient clipping/scaling or other processing, you can\n wrap the optimizer with your custom `step()` method. But it is\n suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4\n \"\"\"\n \n self._write_metrics(loss_dict, data_time)\n self.optimizer.step()\n\n @property\n def _data_loader_iter(self):\n # only create the data loader iterator when it is used\n if self._data_loader_iter_obj is None:\n self._data_loader_iter_obj = iter(self.data_loader)\n return self._data_loader_iter_obj\n\n def reset_data_loader(self, data_loader_builder):\n \"\"\"\n Delete and replace the current data loader with a new one, which will be created\n by calling `data_loader_builder` (without argument).\n \"\"\"\n del self.data_loader\n data_loader = data_loader_builder()\n self.data_loader = data_loader\n self._data_loader_iter_obj = None\n\n def _write_metrics(\n self,\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n TPSimpleTrainer.write_metrics(loss_dict, data_time, prefix)\n\n @staticmethod\n def write_metrics(\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n \"\"\"\n Args:\n loss_dict (dict): dict of scalar losses\n data_time (float): time taken by the dataloader iteration\n prefix (str): prefix for logging keys\n \"\"\"\n metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}\n metrics_dict[\"data_time\"] = data_time\n\n # Gather metrics among all workers for logging\n # This assumes we do DDP-style training, which is currently the only\n # supported method in detectron2.\n all_metrics_dict = comm.gather(metrics_dict)\n\n if comm.is_main_process():\n storage = get_event_storage()\n\n # data_time among workers can have high variance. The actual latency\n # caused by data_time is the maximum among workers.\n data_time = np.max([x.pop(\"data_time\") for x in all_metrics_dict])\n storage.put_scalar(\"data_time\", data_time)\n\n # average the rest metrics\n metrics_dict = {\n k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()\n }\n total_losses_reduced = sum(metrics_dict.values())\n if not np.isfinite(total_losses_reduced):\n raise FloatingPointError(\n f\"Loss became infinite or NaN at iteration={storage.iter}!\\n\"\n f\"loss_dict = {metrics_dict}\"\n )\n\n storage.put_scalar(\"{}total_loss\".format(prefix), total_losses_reduced)\n if len(metrics_dict) > 1:\n storage.put_scalars(**metrics_dict)\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"optimizer\"] = self.optimizer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.optimizer.load_state_dict(state_dict[\"optimizer\"])\n\n\nclass TPAMPTrainer(TPSimpleTrainer):\n \"\"\"\n Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision\n in the training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer, grad_scaler=None):\n \"\"\"\n Args:\n model, data_loader, optimizer: same as in :class:`SimpleTrainer`.\n grad_scaler: torch GradScaler to automatically scale gradients.\n \"\"\"\n unsupported = \"AMPTrainer does not support single-process multi-device training!\"\n if isinstance(model, DistributedDataParallel):\n assert not (model.device_ids and len(model.device_ids) > 1), unsupported\n assert not isinstance(model, DataParallel), unsupported\n\n super().__init__(cfg, model, data_loader, optimizer)\n\n if grad_scaler is None:\n from torch.cuda.amp import GradScaler\n\n grad_scaler = GradScaler()\n self.grad_scaler = grad_scaler\n self.cfg = cfg\n self.batch_size = self.cfg.SOLVER.IMS_PER_BATCH\n\n def run_step(self):\n \"\"\"\n Implement the AMP training logic.\n \"\"\"\n assert self.model.training, \"[AMPTrainer] model was changed to eval mode!\"\n assert torch.cuda.is_available(), \"[AMPTrainer] CUDA is required for AMP training!\"\n from torch.cuda.amp import autocast\n\n start = time.perf_counter()\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n with autocast():\n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \n self.optimizer.zero_grad()\n self.grad_scaler.scale(losses).backward()\n\n self._write_metrics(loss_dict, data_time)\n\n self.grad_scaler.step(self.optimizer)\n self.grad_scaler.update()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"grad_scaler\"] = self.grad_scaler.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.grad_scaler.load_state_dict(state_dict[\"grad_scaler\"])","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.TPTrainerBase","uri":"program://OneFormer/class/tools.trainers.trainer_base.TPTrainerBase#L21-L159","kind":"class","name":"TPTrainerBase","path":"tools/trainers/trainer_base.py","language":"python","start_line":21,"end_line":159,"context_start_line":1,"context_end_line":179,"code":"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport logging\nimport numpy as np\nimport time\nimport weakref\nfrom typing import List, Mapping, Optional\nimport torch\nfrom torch.nn.parallel import DataParallel, DistributedDataParallel\n\nimport detectron2.utils.comm as comm\nfrom detectron2.utils.events import EventStorage, get_event_storage\nfrom detectron2.utils.logger import _log_api_usage\nfrom detectron2.engine import HookBase\nimport torch.distributed as dist\n\n__all__ = [\"TPTrainerBase\", \"TPSimpleTrainer\", \"TPAMPTrainer\"]\n\n\nclass TPTrainerBase:\n \"\"\"\n Base class for iterative trainer with hooks.\n\n The only assumption we made here is: the training runs in a loop.\n A subclass can implement what the loop is.\n We made no assumptions about the existence of dataloader, optimizer, model, etc.\n\n Attributes:\n iter(int): the current iteration.\n\n start_iter(int): The iteration to start with.\n By convention the minimum possible value is 0.\n\n max_iter(int): The iteration to end training.\n\n storage(EventStorage): An EventStorage that's opened during the course of training.\n \"\"\"\n\n def __init__(self, cfg) -> None:\n self._hooks: List[HookBase] = []\n self.iter: int = 0\n self.start_iter: int = 0\n self.max_iter: int\n self.storage: EventStorage\n self.cfg = cfg\n self.fwd_times = []\n self.bwd_times = []\n self.ave_start = None\n self.ave_end = None\n _log_api_usage(\"trainer.\" + self.__class__.__name__)\n self._logger = logging.getLogger(__name__)\n\n def register_hooks(self, hooks: List[Optional[HookBase]]) -> None:\n \"\"\"\n Register hooks to the trainer. The hooks are executed in the order\n they are registered.\n\n Args:\n hooks (list[Optional[HookBase]]): list of hooks\n \"\"\"\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)\n\n def train(self, start_iter: int, max_iter: int):\n \"\"\"\n Args:\n start_iter, max_iter (int): See docs above\n \"\"\"\n \n self._logger.info(\"Starting training from iteration {}\".format(start_iter))\n\n self.iter = self.start_iter = start_iter\n self.max_iter = max_iter\n\n with EventStorage(start_iter) as self.storage:\n try:\n self.before_train()\n for self.iter in range(start_iter, max_iter):\n if self.iter==4:\n self.ave_start=time.perf_counter()\n self.before_step()\n self.run_step()\n self.after_step()\n self.iter += 1\n except Exception:\n self._logger.exception(\"Exception during training:\")\n raise\n self.ave_end = time.perf_counter()\n \n total_time = np.round((self.ave_end-self.ave_start), 2)\n images = (self.iter-5) * self.cfg.SOLVER.IMS_PER_BATCH\n throughput = np.round(images / total_time, 2)\n \n if dist.get_rank() == 0:\n print('\\n-------------------')\n print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:\n h.after_step()\n\n def run_step(self):\n raise NotImplementedError\n\n def state_dict(self):\n ret = {\"iteration\": self.iter}\n hooks_state = {}\n for h in self._hooks:\n sd = h.state_dict()\n if sd:\n name = type(h).__qualname__\n if name in hooks_state:\n # TODO handle repetitive stateful hooks\n continue\n hooks_state[name] = sd\n if hooks_state:\n ret[\"hooks\"] = hooks_state\n return ret\n\n def load_state_dict(self, state_dict):\n logger = logging.getLogger(__name__)\n self.iter = state_dict[\"iteration\"]\n for key, value in state_dict.get(\"hooks\", {}).items():\n for h in self._hooks:\n try:\n name = type(h).__qualname__\n except AttributeError:\n continue\n if name == key:\n h.load_state_dict(value)\n break\n else:\n logger.warning(f\"Cannot find the hook '{key}', its state_dict is ignored.\")\n\n\nclass TPSimpleTrainer(TPTrainerBase):\n \"\"\"\n A simple trainer for the most common type of task:\n single-cost single-optimizer single-data-source iterative optimization,\n optionally using data-parallelism.\n It assumes that every step, you:\n\n 1. Compute the loss with a data from the data_loader.\n 2. Compute the gradients with the above loss.\n 3. Update the model with the optimizer.\n\n All other tasks during training (checkpointing, logging, evaluation, LR schedule)\n are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`.\n\n If you want to do anything fancier than this,\n either subclass TrainerBase and implement your own `run_step`,\n or write your own training loop.\n \"\"\"","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.TPSimpleTrainer","uri":"program://OneFormer/class/tools.trainers.trainer_base.TPSimpleTrainer#L162-L322","kind":"class","name":"TPSimpleTrainer","path":"tools/trainers/trainer_base.py","language":"python","start_line":162,"end_line":322,"context_start_line":142,"context_end_line":342,"code":" if hooks_state:\n ret[\"hooks\"] = hooks_state\n return ret\n\n def load_state_dict(self, state_dict):\n logger = logging.getLogger(__name__)\n self.iter = state_dict[\"iteration\"]\n for key, value in state_dict.get(\"hooks\", {}).items():\n for h in self._hooks:\n try:\n name = type(h).__qualname__\n except AttributeError:\n continue\n if name == key:\n h.load_state_dict(value)\n break\n else:\n logger.warning(f\"Cannot find the hook '{key}', its state_dict is ignored.\")\n\n\nclass TPSimpleTrainer(TPTrainerBase):\n \"\"\"\n A simple trainer for the most common type of task:\n single-cost single-optimizer single-data-source iterative optimization,\n optionally using data-parallelism.\n It assumes that every step, you:\n\n 1. Compute the loss with a data from the data_loader.\n 2. Compute the gradients with the above loss.\n 3. Update the model with the optimizer.\n\n All other tasks during training (checkpointing, logging, evaluation, LR schedule)\n are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`.\n\n If you want to do anything fancier than this,\n either subclass TrainerBase and implement your own `run_step`,\n or write your own training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer):\n \"\"\"\n Args:\n model: a torch Module. Takes a data from data_loader and returns a\n dict of losses.\n data_loader: an iterable. Contains data to be used to call model.\n optimizer: a torch optimizer.\n \"\"\"\n super().__init__(cfg)\n\n \"\"\"\n We set the model to training mode in the trainer.\n However it's valid to train a model that's in eval mode.\n If you want your model (or a submodule of it) to behave\n like evaluation during training, you can overwrite its train() method.\n \"\"\"\n model.train()\n\n self.model = model\n self.data_loader = data_loader\n # to access the data loader iterator, call `self._data_loader_iter`\n self._data_loader_iter_obj = None\n self.optimizer = optimizer\n self.cfg = cfg\n self.batch_size = self.cfg.SOLVER.IMS_PER_BATCH\n\n def run_step(self):\n \"\"\"\n Implement the standard training logic described above.\n \"\"\"\n assert self.model.training, \"[SimpleTrainer] model was changed to eval mode!\"\n start = time.perf_counter()\n \"\"\"\n If you want to do something with the data, you can wrap the dataloader.\n \"\"\"\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n \"\"\"\n If you want to do something with the losses, you can wrap the model.\n \"\"\"\n \n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \"\"\"\n If you need to accumulate gradients or do something similar, you can\n wrap the optimizer with your custom `zero_grad()` method.\n \"\"\"\n self.optimizer.zero_grad()\n losses.backward()\n \n\n \"\"\"\n If you need gradient clipping/scaling or other processing, you can\n wrap the optimizer with your custom `step()` method. But it is\n suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4\n \"\"\"\n \n self._write_metrics(loss_dict, data_time)\n self.optimizer.step()\n\n @property\n def _data_loader_iter(self):\n # only create the data loader iterator when it is used\n if self._data_loader_iter_obj is None:\n self._data_loader_iter_obj = iter(self.data_loader)\n return self._data_loader_iter_obj\n\n def reset_data_loader(self, data_loader_builder):\n \"\"\"\n Delete and replace the current data loader with a new one, which will be created\n by calling `data_loader_builder` (without argument).\n \"\"\"\n del self.data_loader\n data_loader = data_loader_builder()\n self.data_loader = data_loader\n self._data_loader_iter_obj = None\n\n def _write_metrics(\n self,\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n TPSimpleTrainer.write_metrics(loss_dict, data_time, prefix)\n\n @staticmethod\n def write_metrics(\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n \"\"\"\n Args:\n loss_dict (dict): dict of scalar losses\n data_time (float): time taken by the dataloader iteration\n prefix (str): prefix for logging keys\n \"\"\"\n metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}\n metrics_dict[\"data_time\"] = data_time\n\n # Gather metrics among all workers for logging\n # This assumes we do DDP-style training, which is currently the only\n # supported method in detectron2.\n all_metrics_dict = comm.gather(metrics_dict)\n\n if comm.is_main_process():\n storage = get_event_storage()\n\n # data_time among workers can have high variance. The actual latency\n # caused by data_time is the maximum among workers.\n data_time = np.max([x.pop(\"data_time\") for x in all_metrics_dict])\n storage.put_scalar(\"data_time\", data_time)\n\n # average the rest metrics\n metrics_dict = {\n k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()\n }\n total_losses_reduced = sum(metrics_dict.values())\n if not np.isfinite(total_losses_reduced):\n raise FloatingPointError(\n f\"Loss became infinite or NaN at iteration={storage.iter}!\\n\"\n f\"loss_dict = {metrics_dict}\"\n )\n\n storage.put_scalar(\"{}total_loss\".format(prefix), total_losses_reduced)\n if len(metrics_dict) > 1:\n storage.put_scalars(**metrics_dict)\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"optimizer\"] = self.optimizer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.optimizer.load_state_dict(state_dict[\"optimizer\"])\n\n\nclass TPAMPTrainer(TPSimpleTrainer):\n \"\"\"\n Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision\n in the training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer, grad_scaler=None):\n \"\"\"\n Args:\n model, data_loader, optimizer: same as in :class:`SimpleTrainer`.\n grad_scaler: torch GradScaler to automatically scale gradients.\n \"\"\"\n unsupported = \"AMPTrainer does not support single-process multi-device training!\"\n if isinstance(model, DistributedDataParallel):\n assert not (model.device_ids and len(model.device_ids) > 1), unsupported\n assert not isinstance(model, DataParallel), unsupported\n\n super().__init__(cfg, model, data_loader, optimizer)","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.TPAMPTrainer","uri":"program://OneFormer/class/tools.trainers.trainer_base.TPAMPTrainer#L325-L388","kind":"class","name":"TPAMPTrainer","path":"tools/trainers/trainer_base.py","language":"python","start_line":325,"end_line":388,"context_start_line":305,"context_end_line":388,"code":" if not np.isfinite(total_losses_reduced):\n raise FloatingPointError(\n f\"Loss became infinite or NaN at iteration={storage.iter}!\\n\"\n f\"loss_dict = {metrics_dict}\"\n )\n\n storage.put_scalar(\"{}total_loss\".format(prefix), total_losses_reduced)\n if len(metrics_dict) > 1:\n storage.put_scalars(**metrics_dict)\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"optimizer\"] = self.optimizer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.optimizer.load_state_dict(state_dict[\"optimizer\"])\n\n\nclass TPAMPTrainer(TPSimpleTrainer):\n \"\"\"\n Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision\n in the training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer, grad_scaler=None):\n \"\"\"\n Args:\n model, data_loader, optimizer: same as in :class:`SimpleTrainer`.\n grad_scaler: torch GradScaler to automatically scale gradients.\n \"\"\"\n unsupported = \"AMPTrainer does not support single-process multi-device training!\"\n if isinstance(model, DistributedDataParallel):\n assert not (model.device_ids and len(model.device_ids) > 1), unsupported\n assert not isinstance(model, DataParallel), unsupported\n\n super().__init__(cfg, model, data_loader, optimizer)\n\n if grad_scaler is None:\n from torch.cuda.amp import GradScaler\n\n grad_scaler = GradScaler()\n self.grad_scaler = grad_scaler\n self.cfg = cfg\n self.batch_size = self.cfg.SOLVER.IMS_PER_BATCH\n\n def run_step(self):\n \"\"\"\n Implement the AMP training logic.\n \"\"\"\n assert self.model.training, \"[AMPTrainer] model was changed to eval mode!\"\n assert torch.cuda.is_available(), \"[AMPTrainer] CUDA is required for AMP training!\"\n from torch.cuda.amp import autocast\n\n start = time.perf_counter()\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n with autocast():\n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \n self.optimizer.zero_grad()\n self.grad_scaler.scale(losses).backward()\n\n self._write_metrics(loss_dict, data_time)\n\n self.grad_scaler.step(self.optimizer)\n self.grad_scaler.update()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"grad_scaler\"] = self.grad_scaler.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.grad_scaler.load_state_dict(state_dict[\"grad_scaler\"])","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.__init__","uri":"program://OneFormer/function/tools.trainers.trainer_base.__init__#L331-L350","kind":"function","name":"__init__","path":"tools/trainers/trainer_base.py","language":"python","start_line":331,"end_line":350,"context_start_line":311,"context_end_line":370,"code":" storage.put_scalar(\"{}total_loss\".format(prefix), total_losses_reduced)\n if len(metrics_dict) > 1:\n storage.put_scalars(**metrics_dict)\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"optimizer\"] = self.optimizer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.optimizer.load_state_dict(state_dict[\"optimizer\"])\n\n\nclass TPAMPTrainer(TPSimpleTrainer):\n \"\"\"\n Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision\n in the training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer, grad_scaler=None):\n \"\"\"\n Args:\n model, data_loader, optimizer: same as in :class:`SimpleTrainer`.\n grad_scaler: torch GradScaler to automatically scale gradients.\n \"\"\"\n unsupported = \"AMPTrainer does not support single-process multi-device training!\"\n if isinstance(model, DistributedDataParallel):\n assert not (model.device_ids and len(model.device_ids) > 1), unsupported\n assert not isinstance(model, DataParallel), unsupported\n\n super().__init__(cfg, model, data_loader, optimizer)\n\n if grad_scaler is None:\n from torch.cuda.amp import GradScaler\n\n grad_scaler = GradScaler()\n self.grad_scaler = grad_scaler\n self.cfg = cfg\n self.batch_size = self.cfg.SOLVER.IMS_PER_BATCH\n\n def run_step(self):\n \"\"\"\n Implement the AMP training logic.\n \"\"\"\n assert self.model.training, \"[AMPTrainer] model was changed to eval mode!\"\n assert torch.cuda.is_available(), \"[AMPTrainer] CUDA is required for AMP training!\"\n from torch.cuda.amp import autocast\n\n start = time.perf_counter()\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n with autocast():\n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.register_hooks","uri":"program://OneFormer/function/tools.trainers.trainer_base.register_hooks#L54-L70","kind":"function","name":"register_hooks","path":"tools/trainers/trainer_base.py","language":"python","start_line":54,"end_line":70,"context_start_line":34,"context_end_line":90,"code":"\n max_iter(int): The iteration to end training.\n\n storage(EventStorage): An EventStorage that's opened during the course of training.\n \"\"\"\n\n def __init__(self, cfg) -> None:\n self._hooks: List[HookBase] = []\n self.iter: int = 0\n self.start_iter: int = 0\n self.max_iter: int\n self.storage: EventStorage\n self.cfg = cfg\n self.fwd_times = []\n self.bwd_times = []\n self.ave_start = None\n self.ave_end = None\n _log_api_usage(\"trainer.\" + self.__class__.__name__)\n self._logger = logging.getLogger(__name__)\n\n def register_hooks(self, hooks: List[Optional[HookBase]]) -> None:\n \"\"\"\n Register hooks to the trainer. The hooks are executed in the order\n they are registered.\n\n Args:\n hooks (list[Optional[HookBase]]): list of hooks\n \"\"\"\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)\n\n def train(self, start_iter: int, max_iter: int):\n \"\"\"\n Args:\n start_iter, max_iter (int): See docs above\n \"\"\"\n \n self._logger.info(\"Starting training from iteration {}\".format(start_iter))\n\n self.iter = self.start_iter = start_iter\n self.max_iter = max_iter\n\n with EventStorage(start_iter) as self.storage:\n try:\n self.before_train()\n for self.iter in range(start_iter, max_iter):\n if self.iter==4:\n self.ave_start=time.perf_counter()\n self.before_step()\n self.run_step()","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.train","uri":"program://OneFormer/function/tools.trainers.trainer_base.train#L72-L105","kind":"function","name":"train","path":"tools/trainers/trainer_base.py","language":"python","start_line":72,"end_line":105,"context_start_line":52,"context_end_line":125,"code":" self._logger = logging.getLogger(__name__)\n\n def register_hooks(self, hooks: List[Optional[HookBase]]) -> None:\n \"\"\"\n Register hooks to the trainer. The hooks are executed in the order\n they are registered.\n\n Args:\n hooks (list[Optional[HookBase]]): list of hooks\n \"\"\"\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)\n\n def train(self, start_iter: int, max_iter: int):\n \"\"\"\n Args:\n start_iter, max_iter (int): See docs above\n \"\"\"\n \n self._logger.info(\"Starting training from iteration {}\".format(start_iter))\n\n self.iter = self.start_iter = start_iter\n self.max_iter = max_iter\n\n with EventStorage(start_iter) as self.storage:\n try:\n self.before_train()\n for self.iter in range(start_iter, max_iter):\n if self.iter==4:\n self.ave_start=time.perf_counter()\n self.before_step()\n self.run_step()\n self.after_step()\n self.iter += 1\n except Exception:\n self._logger.exception(\"Exception during training:\")\n raise\n self.ave_end = time.perf_counter()\n \n total_time = np.round((self.ave_end-self.ave_start), 2)\n images = (self.iter-5) * self.cfg.SOLVER.IMS_PER_BATCH\n throughput = np.round(images / total_time, 2)\n \n if dist.get_rank() == 0:\n print('\\n-------------------')\n print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.before_train","uri":"program://OneFormer/function/tools.trainers.trainer_base.before_train#L107-L109","kind":"function","name":"before_train","path":"tools/trainers/trainer_base.py","language":"python","start_line":107,"end_line":109,"context_start_line":87,"context_end_line":129,"code":" if self.iter==4:\n self.ave_start=time.perf_counter()\n self.before_step()\n self.run_step()\n self.after_step()\n self.iter += 1\n except Exception:\n self._logger.exception(\"Exception during training:\")\n raise\n self.ave_end = time.perf_counter()\n \n total_time = np.round((self.ave_end-self.ave_start), 2)\n images = (self.iter-5) * self.cfg.SOLVER.IMS_PER_BATCH\n throughput = np.round(images / total_time, 2)\n \n if dist.get_rank() == 0:\n print('\\n-------------------')\n print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:\n h.after_step()\n\n def run_step(self):\n raise NotImplementedError","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.after_train","uri":"program://OneFormer/function/tools.trainers.trainer_base.after_train#L111-L114","kind":"function","name":"after_train","path":"tools/trainers/trainer_base.py","language":"python","start_line":111,"end_line":114,"context_start_line":91,"context_end_line":134,"code":" self.after_step()\n self.iter += 1\n except Exception:\n self._logger.exception(\"Exception during training:\")\n raise\n self.ave_end = time.perf_counter()\n \n total_time = np.round((self.ave_end-self.ave_start), 2)\n images = (self.iter-5) * self.cfg.SOLVER.IMS_PER_BATCH\n throughput = np.round(images / total_time, 2)\n \n if dist.get_rank() == 0:\n print('\\n-------------------')\n print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:\n h.after_step()\n\n def run_step(self):\n raise NotImplementedError\n\n def state_dict(self):\n ret = {\"iteration\": self.iter}\n hooks_state = {}\n for h in self._hooks:","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.before_step","uri":"program://OneFormer/function/tools.trainers.trainer_base.before_step#L116-L122","kind":"function","name":"before_step","path":"tools/trainers/trainer_base.py","language":"python","start_line":116,"end_line":122,"context_start_line":96,"context_end_line":142,"code":" self.ave_end = time.perf_counter()\n \n total_time = np.round((self.ave_end-self.ave_start), 2)\n images = (self.iter-5) * self.cfg.SOLVER.IMS_PER_BATCH\n throughput = np.round(images / total_time, 2)\n \n if dist.get_rank() == 0:\n print('\\n-------------------')\n print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:\n h.after_step()\n\n def run_step(self):\n raise NotImplementedError\n\n def state_dict(self):\n ret = {\"iteration\": self.iter}\n hooks_state = {}\n for h in self._hooks:\n sd = h.state_dict()\n if sd:\n name = type(h).__qualname__\n if name in hooks_state:\n # TODO handle repetitive stateful hooks\n continue\n hooks_state[name] = sd\n if hooks_state:","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.after_step","uri":"program://OneFormer/function/tools.trainers.trainer_base.after_step#L124-L126","kind":"function","name":"after_step","path":"tools/trainers/trainer_base.py","language":"python","start_line":124,"end_line":126,"context_start_line":104,"context_end_line":146,"code":" print(f\"Throughput: {throughput} img/sec\")\n print('-------------------')\n\n def before_train(self):\n for h in self._hooks:\n h.before_train()\n\n def after_train(self):\n self.storage.iter = self.iter\n for h in self._hooks:\n h.after_train()\n\n def before_step(self):\n # Maintain the invariant that storage.iter == trainer.iter\n # for the entire execution of each step\n self.storage.iter = self.iter\n\n for h in self._hooks:\n h.before_step()\n\n def after_step(self):\n for h in self._hooks:\n h.after_step()\n\n def run_step(self):\n raise NotImplementedError\n\n def state_dict(self):\n ret = {\"iteration\": self.iter}\n hooks_state = {}\n for h in self._hooks:\n sd = h.state_dict()\n if sd:\n name = type(h).__qualname__\n if name in hooks_state:\n # TODO handle repetitive stateful hooks\n continue\n hooks_state[name] = sd\n if hooks_state:\n ret[\"hooks\"] = hooks_state\n return ret\n\n def load_state_dict(self, state_dict):","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.run_step","uri":"program://OneFormer/function/tools.trainers.trainer_base.run_step#L352-L379","kind":"function","name":"run_step","path":"tools/trainers/trainer_base.py","language":"python","start_line":352,"end_line":379,"context_start_line":332,"context_end_line":388,"code":" \"\"\"\n Args:\n model, data_loader, optimizer: same as in :class:`SimpleTrainer`.\n grad_scaler: torch GradScaler to automatically scale gradients.\n \"\"\"\n unsupported = \"AMPTrainer does not support single-process multi-device training!\"\n if isinstance(model, DistributedDataParallel):\n assert not (model.device_ids and len(model.device_ids) > 1), unsupported\n assert not isinstance(model, DataParallel), unsupported\n\n super().__init__(cfg, model, data_loader, optimizer)\n\n if grad_scaler is None:\n from torch.cuda.amp import GradScaler\n\n grad_scaler = GradScaler()\n self.grad_scaler = grad_scaler\n self.cfg = cfg\n self.batch_size = self.cfg.SOLVER.IMS_PER_BATCH\n\n def run_step(self):\n \"\"\"\n Implement the AMP training logic.\n \"\"\"\n assert self.model.training, \"[AMPTrainer] model was changed to eval mode!\"\n assert torch.cuda.is_available(), \"[AMPTrainer] CUDA is required for AMP training!\"\n from torch.cuda.amp import autocast\n\n start = time.perf_counter()\n data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n with autocast():\n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \n self.optimizer.zero_grad()\n self.grad_scaler.scale(losses).backward()\n\n self._write_metrics(loss_dict, data_time)\n\n self.grad_scaler.step(self.optimizer)\n self.grad_scaler.update()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"grad_scaler\"] = self.grad_scaler.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.grad_scaler.load_state_dict(state_dict[\"grad_scaler\"])","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.state_dict","uri":"program://OneFormer/function/tools.trainers.trainer_base.state_dict#L381-L384","kind":"function","name":"state_dict","path":"tools/trainers/trainer_base.py","language":"python","start_line":381,"end_line":384,"context_start_line":361,"context_end_line":388,"code":" data = next(self._data_loader_iter)\n data_time = time.perf_counter() - start\n\n with autocast():\n loss_dict = self.model(data)\n if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \n self.optimizer.zero_grad()\n self.grad_scaler.scale(losses).backward()\n\n self._write_metrics(loss_dict, data_time)\n\n self.grad_scaler.step(self.optimizer)\n self.grad_scaler.update()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"grad_scaler\"] = self.grad_scaler.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.grad_scaler.load_state_dict(state_dict[\"grad_scaler\"])","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.load_state_dict","uri":"program://OneFormer/function/tools.trainers.trainer_base.load_state_dict#L386-L388","kind":"function","name":"load_state_dict","path":"tools/trainers/trainer_base.py","language":"python","start_line":386,"end_line":388,"context_start_line":366,"context_end_line":388,"code":" if isinstance(loss_dict, torch.Tensor):\n losses = loss_dict\n loss_dict = {\"total_loss\": loss_dict}\n else:\n losses = sum(loss_dict.values())\n\n \n self.optimizer.zero_grad()\n self.grad_scaler.scale(losses).backward()\n\n self._write_metrics(loss_dict, data_time)\n\n self.grad_scaler.step(self.optimizer)\n self.grad_scaler.update()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"grad_scaler\"] = self.grad_scaler.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.grad_scaler.load_state_dict(state_dict[\"grad_scaler\"])","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base._data_loader_iter","uri":"program://OneFormer/function/tools.trainers.trainer_base._data_loader_iter#L248-L252","kind":"function","name":"_data_loader_iter","path":"tools/trainers/trainer_base.py","language":"python","start_line":248,"end_line":252,"context_start_line":228,"context_end_line":272,"code":" losses = sum(loss_dict.values())\n\n \"\"\"\n If you need to accumulate gradients or do something similar, you can\n wrap the optimizer with your custom `zero_grad()` method.\n \"\"\"\n self.optimizer.zero_grad()\n losses.backward()\n \n\n \"\"\"\n If you need gradient clipping/scaling or other processing, you can\n wrap the optimizer with your custom `step()` method. But it is\n suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4\n \"\"\"\n \n self._write_metrics(loss_dict, data_time)\n self.optimizer.step()\n\n @property\n def _data_loader_iter(self):\n # only create the data loader iterator when it is used\n if self._data_loader_iter_obj is None:\n self._data_loader_iter_obj = iter(self.data_loader)\n return self._data_loader_iter_obj\n\n def reset_data_loader(self, data_loader_builder):\n \"\"\"\n Delete and replace the current data loader with a new one, which will be created\n by calling `data_loader_builder` (without argument).\n \"\"\"\n del self.data_loader\n data_loader = data_loader_builder()\n self.data_loader = data_loader\n self._data_loader_iter_obj = None\n\n def _write_metrics(\n self,\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n TPSimpleTrainer.write_metrics(loss_dict, data_time, prefix)\n\n @staticmethod","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.reset_data_loader","uri":"program://OneFormer/function/tools.trainers.trainer_base.reset_data_loader#L254-L262","kind":"function","name":"reset_data_loader","path":"tools/trainers/trainer_base.py","language":"python","start_line":254,"end_line":262,"context_start_line":234,"context_end_line":282,"code":" self.optimizer.zero_grad()\n losses.backward()\n \n\n \"\"\"\n If you need gradient clipping/scaling or other processing, you can\n wrap the optimizer with your custom `step()` method. But it is\n suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4\n \"\"\"\n \n self._write_metrics(loss_dict, data_time)\n self.optimizer.step()\n\n @property\n def _data_loader_iter(self):\n # only create the data loader iterator when it is used\n if self._data_loader_iter_obj is None:\n self._data_loader_iter_obj = iter(self.data_loader)\n return self._data_loader_iter_obj\n\n def reset_data_loader(self, data_loader_builder):\n \"\"\"\n Delete and replace the current data loader with a new one, which will be created\n by calling `data_loader_builder` (without argument).\n \"\"\"\n del self.data_loader\n data_loader = data_loader_builder()\n self.data_loader = data_loader\n self._data_loader_iter_obj = None\n\n def _write_metrics(\n self,\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n TPSimpleTrainer.write_metrics(loss_dict, data_time, prefix)\n\n @staticmethod\n def write_metrics(\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n \"\"\"\n Args:\n loss_dict (dict): dict of scalar losses\n data_time (float): time taken by the dataloader iteration\n prefix (str): prefix for logging keys","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base._write_metrics","uri":"program://OneFormer/function/tools.trainers.trainer_base._write_metrics#L264-L270","kind":"function","name":"_write_metrics","path":"tools/trainers/trainer_base.py","language":"python","start_line":264,"end_line":270,"context_start_line":244,"context_end_line":290,"code":" self._write_metrics(loss_dict, data_time)\n self.optimizer.step()\n\n @property\n def _data_loader_iter(self):\n # only create the data loader iterator when it is used\n if self._data_loader_iter_obj is None:\n self._data_loader_iter_obj = iter(self.data_loader)\n return self._data_loader_iter_obj\n\n def reset_data_loader(self, data_loader_builder):\n \"\"\"\n Delete and replace the current data loader with a new one, which will be created\n by calling `data_loader_builder` (without argument).\n \"\"\"\n del self.data_loader\n data_loader = data_loader_builder()\n self.data_loader = data_loader\n self._data_loader_iter_obj = None\n\n def _write_metrics(\n self,\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n TPSimpleTrainer.write_metrics(loss_dict, data_time, prefix)\n\n @staticmethod\n def write_metrics(\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n \"\"\"\n Args:\n loss_dict (dict): dict of scalar losses\n data_time (float): time taken by the dataloader iteration\n prefix (str): prefix for logging keys\n \"\"\"\n metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}\n metrics_dict[\"data_time\"] = data_time\n\n # Gather metrics among all workers for logging\n # This assumes we do DDP-style training, which is currently the only\n # supported method in detectron2.\n all_metrics_dict = comm.gather(metrics_dict)","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer_base.write_metrics","uri":"program://OneFormer/function/tools.trainers.trainer_base.write_metrics#L273-L313","kind":"function","name":"write_metrics","path":"tools/trainers/trainer_base.py","language":"python","start_line":273,"end_line":313,"context_start_line":253,"context_end_line":333,"code":"\n def reset_data_loader(self, data_loader_builder):\n \"\"\"\n Delete and replace the current data loader with a new one, which will be created\n by calling `data_loader_builder` (without argument).\n \"\"\"\n del self.data_loader\n data_loader = data_loader_builder()\n self.data_loader = data_loader\n self._data_loader_iter_obj = None\n\n def _write_metrics(\n self,\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n TPSimpleTrainer.write_metrics(loss_dict, data_time, prefix)\n\n @staticmethod\n def write_metrics(\n loss_dict: Mapping[str, torch.Tensor],\n data_time: float,\n prefix: str = \"\",\n ) -> None:\n \"\"\"\n Args:\n loss_dict (dict): dict of scalar losses\n data_time (float): time taken by the dataloader iteration\n prefix (str): prefix for logging keys\n \"\"\"\n metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}\n metrics_dict[\"data_time\"] = data_time\n\n # Gather metrics among all workers for logging\n # This assumes we do DDP-style training, which is currently the only\n # supported method in detectron2.\n all_metrics_dict = comm.gather(metrics_dict)\n\n if comm.is_main_process():\n storage = get_event_storage()\n\n # data_time among workers can have high variance. The actual latency\n # caused by data_time is the maximum among workers.\n data_time = np.max([x.pop(\"data_time\") for x in all_metrics_dict])\n storage.put_scalar(\"data_time\", data_time)\n\n # average the rest metrics\n metrics_dict = {\n k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()\n }\n total_losses_reduced = sum(metrics_dict.values())\n if not np.isfinite(total_losses_reduced):\n raise FloatingPointError(\n f\"Loss became infinite or NaN at iteration={storage.iter}!\\n\"\n f\"loss_dict = {metrics_dict}\"\n )\n\n storage.put_scalar(\"{}total_loss\".format(prefix), total_losses_reduced)\n if len(metrics_dict) > 1:\n storage.put_scalars(**metrics_dict)\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"optimizer\"] = self.optimizer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self.optimizer.load_state_dict(state_dict[\"optimizer\"])\n\n\nclass TPAMPTrainer(TPSimpleTrainer):\n \"\"\"\n Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision\n in the training loop.\n \"\"\"\n\n def __init__(self, cfg, model, data_loader, optimizer, grad_scaler=None):\n \"\"\"\n Args:","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer","uri":"program://OneFormer/module/tools.trainers.trainer#L1-L364","kind":"module","name":"tools.trainers.trainer","path":"tools/trainers/trainer.py","language":"python","start_line":1,"end_line":364,"context_start_line":1,"context_end_line":364,"code":"\nimport logging\nimport weakref\nfrom collections import OrderedDict\nfrom fvcore.nn.precise_bn import get_bn_modules\n\nimport detectron2.data.transforms as T\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.data import (\n build_detection_test_loader,\n build_detection_train_loader,\n)\nfrom detectron2.evaluation import (\n DatasetEvaluator,\n inference_on_dataset,\n print_csv_format,\n)\nfrom detectron2.modeling import build_model\nfrom detectron2.solver import build_lr_scheduler, build_optimizer\nfrom detectron2.utils import comm\nfrom detectron2.utils.logger import setup_logger\nimport os\nimport detectron2.engine.hooks as hooks\n\nimport logging\nimport weakref\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.utils import comm\nfrom detectron2.utils.logger import setup_logger\nfrom .trainer_base import TPAMPTrainer, TPSimpleTrainer, TPTrainerBase\nfrom detectron2.engine import (\n create_ddp_model\n)\nimport numpy as np\nimport detectron2.utils.comm as comm\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter\nfrom detectron2.utils.file_io import PathManager\nfrom typing import Optional\n\ndef default_writers(output_dir: str, max_iter: Optional[int] = None):\n \"\"\"\n Build a list of :class:`EventWriter` to be used.\n It now consists of a :class:`CommonMetricPrinter`,\n :class:`TensorboardXWriter` and :class:`JSONWriter`.\n Args:\n output_dir: directory to store JSON metrics and tensorboard events\n max_iter: the total number of iterations\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n PathManager.mkdirs(output_dir)\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(max_iter),\n JSONWriter(os.path.join(output_dir, \"metrics.json\")),\n TensorboardXWriter(output_dir),\n ]\n\nclass TPDefaultTrainer(TPTrainerBase):\n \"\"\"\n A trainer with default training logic. It does the following:\n 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader\n defined by the given config. Create a LR scheduler defined by the config.\n 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when\n `resume_or_load` is called.\n 3. Register a few common hooks defined by the config.\n It is created to simplify the **standard model training workflow** and reduce code boilerplate\n for users who only need the standard training workflow, with standard features.\n It means this class makes *many assumptions* about your training logic that\n may easily become invalid in a new research. In fact, any assumptions beyond those made in the\n :class:`SimpleTrainer` are too much for research.\n The code of this class has been annotated about restrictive assumptions it makes.\n When they do not work for you, you're encouraged to:\n 1. Overwrite methods of this class, OR:\n 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and\n nothing else. You can then add your own hooks if needed. OR:\n 3. Write your own training loop similar to `tools/plain_train_net.py`.\n See the :doc:`/tutorials/training` tutorials for more details.\n Note that the behavior of this class, like other functions/classes in\n this file, is not stable, since it is meant to represent the \"common default behavior\".\n It is only guaranteed to work well with the standard models and training workflow in detectron2.\n To obtain more stable behavior, write your own training logic with other public APIs.\n Examples:\n ::\n trainer = DefaultTrainer(cfg)\n trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS\n trainer.train()\n Attributes:\n scheduler:\n checkpointer (DetectionCheckpointer):\n cfg (CfgNode):\n \"\"\"\n\n def __init__(self, cfg):\n \"\"\"\n Args:\n cfg (CfgNode):\n \"\"\"\n super().__init__(cfg)\n logger = logging.getLogger(\"detectron2\")\n if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2\n setup_logger()\n cfg = TPDefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())\n\n # Assume these objects must be constructed in this order.\n model = self.build_model(cfg)\n optimizer = self.build_optimizer(cfg, model)\n data_loader = self.build_train_loader(cfg)\n\n model = create_ddp_model(model, broadcast_buffers=False)\n self._trainer = (TPAMPTrainer if cfg.SOLVER.AMP.ENABLED else TPSimpleTrainer)(\n cfg, model, data_loader, optimizer\n )\n\n self.scheduler = self.build_lr_scheduler(cfg, optimizer)\n self.checkpointer = DetectionCheckpointer(\n # Assume you want to save checkpoints together with logs/statistics\n model,\n cfg.OUTPUT_DIR,\n trainer=weakref.proxy(self),\n )\n self.start_iter = 0\n self.max_iter = cfg.SOLVER.MAX_ITER\n self.cfg = cfg\n\n self.register_hooks(self.build_hooks())\n\n def resume_or_load(self, resume=True):\n \"\"\"\n If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by\n a `last_checkpoint` file), resume from the file. Resuming means loading all\n available states (eg. optimizer and scheduler) and update iteration counter\n from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.\n Otherwise, this is considered as an independent training. The method will load model\n weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start\n from iteration 0.\n Args:\n resume (bool): whether to do resume or not\n \"\"\"\n self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)\n if resume and self.checkpointer.has_checkpoint():\n # The checkpoint stores the training iteration that just finished, thus we start\n # at the next iteration\n self.start_iter = self.iter + 1\n\n def build_hooks(self):\n \"\"\"\n Build a list of default hooks, including timing, evaluation,\n checkpointing, lr scheduling, precise BN, writing events.\n Returns:\n list[HookBase]:\n \"\"\"\n cfg = self.cfg.clone()\n cfg.defrost()\n cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN\n\n ret = [\n hooks.IterationTimer(),\n hooks.LRScheduler(),\n hooks.PreciseBN(\n # Run at the same freq as (but before) evaluation.\n cfg.TEST.EVAL_PERIOD,\n self.model,\n # Build a new data loader to not affect training\n self.build_train_loader(cfg),\n cfg.TEST.PRECISE_BN.NUM_ITER,\n )\n if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)\n else None,\n ]\n\n # Do PreciseBN before checkpointer, because it updates the model and need to\n # be saved by checkpointer.\n # This is not always the best: if checkpointing has a different frequency,\n # some checkpoints may have more precise statistics than others.\n if comm.is_main_process():\n ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))\n\n def test_and_save_results():\n self._last_eval_results = self.test(self.cfg, self.model)\n return self._last_eval_results\n\n # Do evaluation after checkpointer, because then if it fails,\n # we can use the saved checkpoint to debug.\n ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n if comm.is_main_process():\n # Here the default print/log frequency of each writer is used.\n # run writers in the end, so that evaluation metrics are written\n ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))\n return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.\n Overwrite it if you'd like a different optimizer.\n \"\"\"\n return build_optimizer(cfg, model)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n DatasetEvaluator or None\n It is not implemented by default.\n \"\"\"\n raise NotImplementedError(\n \"\"\"\nIf you want DefaultTrainer to automatically run evaluation,\nplease implement `build_evaluator()` in subclasses (see train_net.py for example).\nAlternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example).\n\"\"\"\n )\n\n @staticmethod\n def auto_scale_workers(cfg, num_workers: int):\n \"\"\"\n When the config is defined for certain number of workers (according to\n ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of\n workers currently in use, returns a new cfg where the total batch size\n is scaled so that the per-GPU batch size stays the same as the\n original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.\n Other config options are also scaled accordingly:\n * training steps and warmup steps are scaled inverse proportionally.\n * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.\n For example, with the original config like the following:\n .. code-block:: yaml\n IMS_PER_BATCH: 16\n BASE_LR: 0.1\n REFERENCE_WORLD_SIZE: 8\n MAX_ITER: 5000\n STEPS: (4000,)\n CHECKPOINT_PERIOD: 1000\n When this config is used on 16 GPUs instead of the reference number 8,\n calling this method will return a new config with:\n .. code-block:: yaml\n IMS_PER_BATCH: 32\n BASE_LR: 0.2\n REFERENCE_WORLD_SIZE: 16\n MAX_ITER: 2500\n STEPS: (2000,)\n CHECKPOINT_PERIOD: 500\n Note that both the original config and this new config can be trained on 16 GPUs.\n It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``).\n Returns:\n CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.\n \"\"\"\n old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE\n if old_world_size == 0 or old_world_size == num_workers:\n return cfg\n cfg = cfg.clone()\n frozen = cfg.is_frozen()\n cfg.defrost()\n\n assert (\n cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0\n ), \"Invalid REFERENCE_WORLD_SIZE in config!\"\n scale = num_workers / old_world_size\n bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale))\n lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale\n max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale))\n warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale))\n cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS)\n cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale))\n cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale))\n cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant\n logger = logging.getLogger(__name__)\n logger.info(\n f\"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, \"\n f\"max_iter={max_iter}, warmup={warmup_iter}.\"\n )\n\n if frozen:\n cfg.freeze()\n return cfg\n\n\n# Access basic attributes from the underlying trainer\nfor _attr in [\"model\", \"data_loader\", \"optimizer\"]:\n setattr(\n TPDefaultTrainer,\n _attr,\n property(\n # getter\n lambda self, x=_attr: getattr(self._trainer, x),\n # setter\n lambda self, value, x=_attr: setattr(self._trainer, x, value),\n ),\n )","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.default_writers","uri":"program://OneFormer/function/tools.trainers.trainer.default_writers#L40-L57","kind":"function","name":"default_writers","path":"tools/trainers/trainer.py","language":"python","start_line":40,"end_line":57,"context_start_line":20,"context_end_line":77,"code":"from detectron2.utils import comm\nfrom detectron2.utils.logger import setup_logger\nimport os\nimport detectron2.engine.hooks as hooks\n\nimport logging\nimport weakref\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.utils import comm\nfrom detectron2.utils.logger import setup_logger\nfrom .trainer_base import TPAMPTrainer, TPSimpleTrainer, TPTrainerBase\nfrom detectron2.engine import (\n create_ddp_model\n)\nimport numpy as np\nimport detectron2.utils.comm as comm\nfrom detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter\nfrom detectron2.utils.file_io import PathManager\nfrom typing import Optional\n\ndef default_writers(output_dir: str, max_iter: Optional[int] = None):\n \"\"\"\n Build a list of :class:`EventWriter` to be used.\n It now consists of a :class:`CommonMetricPrinter`,\n :class:`TensorboardXWriter` and :class:`JSONWriter`.\n Args:\n output_dir: directory to store JSON metrics and tensorboard events\n max_iter: the total number of iterations\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n PathManager.mkdirs(output_dir)\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(max_iter),\n JSONWriter(os.path.join(output_dir, \"metrics.json\")),\n TensorboardXWriter(output_dir),\n ]\n\nclass TPDefaultTrainer(TPTrainerBase):\n \"\"\"\n A trainer with default training logic. It does the following:\n 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader\n defined by the given config. Create a LR scheduler defined by the config.\n 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when\n `resume_or_load` is called.\n 3. Register a few common hooks defined by the config.\n It is created to simplify the **standard model training workflow** and reduce code boilerplate\n for users who only need the standard training workflow, with standard features.\n It means this class makes *many assumptions* about your training logic that\n may easily become invalid in a new research. In fact, any assumptions beyond those made in the\n :class:`SimpleTrainer` are too much for research.\n The code of this class has been annotated about restrictive assumptions it makes.\n When they do not work for you, you're encouraged to:\n 1. Overwrite methods of this class, OR:\n 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and\n nothing else. You can then add your own hooks if needed. OR:\n 3. Write your own training loop similar to `tools/plain_train_net.py`.","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.TPDefaultTrainer","uri":"program://OneFormer/class/tools.trainers.trainer.TPDefaultTrainer#L59-L350","kind":"class","name":"TPDefaultTrainer","path":"tools/trainers/trainer.py","language":"python","start_line":59,"end_line":350,"context_start_line":39,"context_end_line":364,"code":"\ndef default_writers(output_dir: str, max_iter: Optional[int] = None):\n \"\"\"\n Build a list of :class:`EventWriter` to be used.\n It now consists of a :class:`CommonMetricPrinter`,\n :class:`TensorboardXWriter` and :class:`JSONWriter`.\n Args:\n output_dir: directory to store JSON metrics and tensorboard events\n max_iter: the total number of iterations\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n PathManager.mkdirs(output_dir)\n return [\n # It may not always print what you want to see, since it prints \"common\" metrics only.\n CommonMetricPrinter(max_iter),\n JSONWriter(os.path.join(output_dir, \"metrics.json\")),\n TensorboardXWriter(output_dir),\n ]\n\nclass TPDefaultTrainer(TPTrainerBase):\n \"\"\"\n A trainer with default training logic. It does the following:\n 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader\n defined by the given config. Create a LR scheduler defined by the config.\n 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when\n `resume_or_load` is called.\n 3. Register a few common hooks defined by the config.\n It is created to simplify the **standard model training workflow** and reduce code boilerplate\n for users who only need the standard training workflow, with standard features.\n It means this class makes *many assumptions* about your training logic that\n may easily become invalid in a new research. In fact, any assumptions beyond those made in the\n :class:`SimpleTrainer` are too much for research.\n The code of this class has been annotated about restrictive assumptions it makes.\n When they do not work for you, you're encouraged to:\n 1. Overwrite methods of this class, OR:\n 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and\n nothing else. You can then add your own hooks if needed. OR:\n 3. Write your own training loop similar to `tools/plain_train_net.py`.\n See the :doc:`/tutorials/training` tutorials for more details.\n Note that the behavior of this class, like other functions/classes in\n this file, is not stable, since it is meant to represent the \"common default behavior\".\n It is only guaranteed to work well with the standard models and training workflow in detectron2.\n To obtain more stable behavior, write your own training logic with other public APIs.\n Examples:\n ::\n trainer = DefaultTrainer(cfg)\n trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS\n trainer.train()\n Attributes:\n scheduler:\n checkpointer (DetectionCheckpointer):\n cfg (CfgNode):\n \"\"\"\n\n def __init__(self, cfg):\n \"\"\"\n Args:\n cfg (CfgNode):\n \"\"\"\n super().__init__(cfg)\n logger = logging.getLogger(\"detectron2\")\n if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2\n setup_logger()\n cfg = TPDefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())\n\n # Assume these objects must be constructed in this order.\n model = self.build_model(cfg)\n optimizer = self.build_optimizer(cfg, model)\n data_loader = self.build_train_loader(cfg)\n\n model = create_ddp_model(model, broadcast_buffers=False)\n self._trainer = (TPAMPTrainer if cfg.SOLVER.AMP.ENABLED else TPSimpleTrainer)(\n cfg, model, data_loader, optimizer\n )\n\n self.scheduler = self.build_lr_scheduler(cfg, optimizer)\n self.checkpointer = DetectionCheckpointer(\n # Assume you want to save checkpoints together with logs/statistics\n model,\n cfg.OUTPUT_DIR,\n trainer=weakref.proxy(self),\n )\n self.start_iter = 0\n self.max_iter = cfg.SOLVER.MAX_ITER\n self.cfg = cfg\n\n self.register_hooks(self.build_hooks())\n\n def resume_or_load(self, resume=True):\n \"\"\"\n If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by\n a `last_checkpoint` file), resume from the file. Resuming means loading all\n available states (eg. optimizer and scheduler) and update iteration counter\n from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.\n Otherwise, this is considered as an independent training. The method will load model\n weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start\n from iteration 0.\n Args:\n resume (bool): whether to do resume or not\n \"\"\"\n self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)\n if resume and self.checkpointer.has_checkpoint():\n # The checkpoint stores the training iteration that just finished, thus we start\n # at the next iteration\n self.start_iter = self.iter + 1\n\n def build_hooks(self):\n \"\"\"\n Build a list of default hooks, including timing, evaluation,\n checkpointing, lr scheduling, precise BN, writing events.\n Returns:\n list[HookBase]:\n \"\"\"\n cfg = self.cfg.clone()\n cfg.defrost()\n cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN\n\n ret = [\n hooks.IterationTimer(),\n hooks.LRScheduler(),\n hooks.PreciseBN(\n # Run at the same freq as (but before) evaluation.\n cfg.TEST.EVAL_PERIOD,\n self.model,\n # Build a new data loader to not affect training\n self.build_train_loader(cfg),\n cfg.TEST.PRECISE_BN.NUM_ITER,\n )\n if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)\n else None,\n ]\n\n # Do PreciseBN before checkpointer, because it updates the model and need to\n # be saved by checkpointer.\n # This is not always the best: if checkpointing has a different frequency,\n # some checkpoints may have more precise statistics than others.\n if comm.is_main_process():\n ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))\n\n def test_and_save_results():\n self._last_eval_results = self.test(self.cfg, self.model)\n return self._last_eval_results\n\n # Do evaluation after checkpointer, because then if it fails,\n # we can use the saved checkpoint to debug.\n ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n if comm.is_main_process():\n # Here the default print/log frequency of each writer is used.\n # run writers in the end, so that evaluation metrics are written\n ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))\n return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.\n Overwrite it if you'd like a different optimizer.\n \"\"\"\n return build_optimizer(cfg, model)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n DatasetEvaluator or None\n It is not implemented by default.\n \"\"\"\n raise NotImplementedError(\n \"\"\"\nIf you want DefaultTrainer to automatically run evaluation,\nplease implement `build_evaluator()` in subclasses (see train_net.py for example).\nAlternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example).\n\"\"\"\n )\n\n @staticmethod\n def auto_scale_workers(cfg, num_workers: int):\n \"\"\"\n When the config is defined for certain number of workers (according to\n ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of\n workers currently in use, returns a new cfg where the total batch size\n is scaled so that the per-GPU batch size stays the same as the\n original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.\n Other config options are also scaled accordingly:\n * training steps and warmup steps are scaled inverse proportionally.\n * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.\n For example, with the original config like the following:\n .. code-block:: yaml\n IMS_PER_BATCH: 16\n BASE_LR: 0.1\n REFERENCE_WORLD_SIZE: 8\n MAX_ITER: 5000\n STEPS: (4000,)\n CHECKPOINT_PERIOD: 1000\n When this config is used on 16 GPUs instead of the reference number 8,\n calling this method will return a new config with:\n .. code-block:: yaml\n IMS_PER_BATCH: 32\n BASE_LR: 0.2\n REFERENCE_WORLD_SIZE: 16\n MAX_ITER: 2500\n STEPS: (2000,)\n CHECKPOINT_PERIOD: 500\n Note that both the original config and this new config can be trained on 16 GPUs.\n It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``).\n Returns:\n CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.\n \"\"\"\n old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE\n if old_world_size == 0 or old_world_size == num_workers:\n return cfg\n cfg = cfg.clone()\n frozen = cfg.is_frozen()\n cfg.defrost()\n\n assert (\n cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0\n ), \"Invalid REFERENCE_WORLD_SIZE in config!\"\n scale = num_workers / old_world_size\n bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale))\n lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale\n max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale))\n warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale))\n cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS)\n cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale))\n cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale))\n cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant\n logger = logging.getLogger(__name__)\n logger.info(\n f\"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, \"\n f\"max_iter={max_iter}, warmup={warmup_iter}.\"\n )\n\n if frozen:\n cfg.freeze()\n return cfg\n\n\n# Access basic attributes from the underlying trainer\nfor _attr in [\"model\", \"data_loader\", \"optimizer\"]:\n setattr(\n TPDefaultTrainer,\n _attr,\n property(\n # getter\n lambda self, x=_attr: getattr(self._trainer, x),\n # setter\n lambda self, value, x=_attr: setattr(self._trainer, x, value),\n ),\n )","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.__init__","uri":"program://OneFormer/function/tools.trainers.trainer.__init__#L94-L126","kind":"function","name":"__init__","path":"tools/trainers/trainer.py","language":"python","start_line":94,"end_line":126,"context_start_line":74,"context_end_line":146,"code":" 1. Overwrite methods of this class, OR:\n 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and\n nothing else. You can then add your own hooks if needed. OR:\n 3. Write your own training loop similar to `tools/plain_train_net.py`.\n See the :doc:`/tutorials/training` tutorials for more details.\n Note that the behavior of this class, like other functions/classes in\n this file, is not stable, since it is meant to represent the \"common default behavior\".\n It is only guaranteed to work well with the standard models and training workflow in detectron2.\n To obtain more stable behavior, write your own training logic with other public APIs.\n Examples:\n ::\n trainer = DefaultTrainer(cfg)\n trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS\n trainer.train()\n Attributes:\n scheduler:\n checkpointer (DetectionCheckpointer):\n cfg (CfgNode):\n \"\"\"\n\n def __init__(self, cfg):\n \"\"\"\n Args:\n cfg (CfgNode):\n \"\"\"\n super().__init__(cfg)\n logger = logging.getLogger(\"detectron2\")\n if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2\n setup_logger()\n cfg = TPDefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())\n\n # Assume these objects must be constructed in this order.\n model = self.build_model(cfg)\n optimizer = self.build_optimizer(cfg, model)\n data_loader = self.build_train_loader(cfg)\n\n model = create_ddp_model(model, broadcast_buffers=False)\n self._trainer = (TPAMPTrainer if cfg.SOLVER.AMP.ENABLED else TPSimpleTrainer)(\n cfg, model, data_loader, optimizer\n )\n\n self.scheduler = self.build_lr_scheduler(cfg, optimizer)\n self.checkpointer = DetectionCheckpointer(\n # Assume you want to save checkpoints together with logs/statistics\n model,\n cfg.OUTPUT_DIR,\n trainer=weakref.proxy(self),\n )\n self.start_iter = 0\n self.max_iter = cfg.SOLVER.MAX_ITER\n self.cfg = cfg\n\n self.register_hooks(self.build_hooks())\n\n def resume_or_load(self, resume=True):\n \"\"\"\n If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by\n a `last_checkpoint` file), resume from the file. Resuming means loading all\n available states (eg. optimizer and scheduler) and update iteration counter\n from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.\n Otherwise, this is considered as an independent training. The method will load model\n weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start\n from iteration 0.\n Args:\n resume (bool): whether to do resume or not\n \"\"\"\n self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)\n if resume and self.checkpointer.has_checkpoint():\n # The checkpoint stores the training iteration that just finished, thus we start\n # at the next iteration\n self.start_iter = self.iter + 1\n\n def build_hooks(self):","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.resume_or_load","uri":"program://OneFormer/function/tools.trainers.trainer.resume_or_load#L128-L144","kind":"function","name":"resume_or_load","path":"tools/trainers/trainer.py","language":"python","start_line":128,"end_line":144,"context_start_line":108,"context_end_line":164,"code":" data_loader = self.build_train_loader(cfg)\n\n model = create_ddp_model(model, broadcast_buffers=False)\n self._trainer = (TPAMPTrainer if cfg.SOLVER.AMP.ENABLED else TPSimpleTrainer)(\n cfg, model, data_loader, optimizer\n )\n\n self.scheduler = self.build_lr_scheduler(cfg, optimizer)\n self.checkpointer = DetectionCheckpointer(\n # Assume you want to save checkpoints together with logs/statistics\n model,\n cfg.OUTPUT_DIR,\n trainer=weakref.proxy(self),\n )\n self.start_iter = 0\n self.max_iter = cfg.SOLVER.MAX_ITER\n self.cfg = cfg\n\n self.register_hooks(self.build_hooks())\n\n def resume_or_load(self, resume=True):\n \"\"\"\n If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by\n a `last_checkpoint` file), resume from the file. Resuming means loading all\n available states (eg. optimizer and scheduler) and update iteration counter\n from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.\n Otherwise, this is considered as an independent training. The method will load model\n weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start\n from iteration 0.\n Args:\n resume (bool): whether to do resume or not\n \"\"\"\n self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)\n if resume and self.checkpointer.has_checkpoint():\n # The checkpoint stores the training iteration that just finished, thus we start\n # at the next iteration\n self.start_iter = self.iter + 1\n\n def build_hooks(self):\n \"\"\"\n Build a list of default hooks, including timing, evaluation,\n checkpointing, lr scheduling, precise BN, writing events.\n Returns:\n list[HookBase]:\n \"\"\"\n cfg = self.cfg.clone()\n cfg.defrost()\n cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN\n\n ret = [\n hooks.IterationTimer(),\n hooks.LRScheduler(),\n hooks.PreciseBN(\n # Run at the same freq as (but before) evaluation.\n cfg.TEST.EVAL_PERIOD,\n self.model,\n # Build a new data loader to not affect training","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_hooks","uri":"program://OneFormer/function/tools.trainers.trainer.build_hooks#L146-L191","kind":"function","name":"build_hooks","path":"tools/trainers/trainer.py","language":"python","start_line":146,"end_line":191,"context_start_line":126,"context_end_line":211,"code":" self.register_hooks(self.build_hooks())\n\n def resume_or_load(self, resume=True):\n \"\"\"\n If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by\n a `last_checkpoint` file), resume from the file. Resuming means loading all\n available states (eg. optimizer and scheduler) and update iteration counter\n from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.\n Otherwise, this is considered as an independent training. The method will load model\n weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start\n from iteration 0.\n Args:\n resume (bool): whether to do resume or not\n \"\"\"\n self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)\n if resume and self.checkpointer.has_checkpoint():\n # The checkpoint stores the training iteration that just finished, thus we start\n # at the next iteration\n self.start_iter = self.iter + 1\n\n def build_hooks(self):\n \"\"\"\n Build a list of default hooks, including timing, evaluation,\n checkpointing, lr scheduling, precise BN, writing events.\n Returns:\n list[HookBase]:\n \"\"\"\n cfg = self.cfg.clone()\n cfg.defrost()\n cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN\n\n ret = [\n hooks.IterationTimer(),\n hooks.LRScheduler(),\n hooks.PreciseBN(\n # Run at the same freq as (but before) evaluation.\n cfg.TEST.EVAL_PERIOD,\n self.model,\n # Build a new data loader to not affect training\n self.build_train_loader(cfg),\n cfg.TEST.PRECISE_BN.NUM_ITER,\n )\n if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)\n else None,\n ]\n\n # Do PreciseBN before checkpointer, because it updates the model and need to\n # be saved by checkpointer.\n # This is not always the best: if checkpointing has a different frequency,\n # some checkpoints may have more precise statistics than others.\n if comm.is_main_process():\n ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))\n\n def test_and_save_results():\n self._last_eval_results = self.test(self.cfg, self.model)\n return self._last_eval_results\n\n # Do evaluation after checkpointer, because then if it fails,\n # we can use the saved checkpoint to debug.\n ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n if comm.is_main_process():\n # Here the default print/log frequency of each writer is used.\n # run writers in the end, so that evaluation metrics are written\n ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))\n return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_writers","uri":"program://OneFormer/function/tools.trainers.trainer.build_writers#L193-L201","kind":"function","name":"build_writers","path":"tools/trainers/trainer.py","language":"python","start_line":193,"end_line":201,"context_start_line":173,"context_end_line":221,"code":" # be saved by checkpointer.\n # This is not always the best: if checkpointing has a different frequency,\n # some checkpoints may have more precise statistics than others.\n if comm.is_main_process():\n ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))\n\n def test_and_save_results():\n self._last_eval_results = self.test(self.cfg, self.model)\n return self._last_eval_results\n\n # Do evaluation after checkpointer, because then if it fails,\n # we can use the saved checkpoint to debug.\n ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n if comm.is_main_process():\n # Here the default print/log frequency of each writer is used.\n # run writers in the end, so that evaluation metrics are written\n ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))\n return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.train","uri":"program://OneFormer/function/tools.trainers.trainer.train#L203-L209","kind":"function","name":"train","path":"tools/trainers/trainer.py","language":"python","start_line":203,"end_line":209,"context_start_line":183,"context_end_line":229,"code":" # Do evaluation after checkpointer, because then if it fails,\n # we can use the saved checkpoint to debug.\n ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n if comm.is_main_process():\n # Here the default print/log frequency of each writer is used.\n # run writers in the end, so that evaluation metrics are written\n ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))\n return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.run_step","uri":"program://OneFormer/function/tools.trainers.trainer.run_step#L211-L213","kind":"function","name":"run_step","path":"tools/trainers/trainer.py","language":"python","start_line":211,"end_line":213,"context_start_line":191,"context_end_line":233,"code":" return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.state_dict","uri":"program://OneFormer/function/tools.trainers.trainer.state_dict#L215-L218","kind":"function","name":"state_dict","path":"tools/trainers/trainer.py","language":"python","start_line":215,"end_line":218,"context_start_line":195,"context_end_line":238,"code":" Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.load_state_dict","uri":"program://OneFormer/function/tools.trainers.trainer.load_state_dict#L220-L222","kind":"function","name":"load_state_dict","path":"tools/trainers/trainer.py","language":"python","start_line":220,"end_line":222,"context_start_line":200,"context_end_line":242,"code":" \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)\n\n def train(self):\n \"\"\"\n Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_model","uri":"program://OneFormer/function/tools.trainers.trainer.build_model#L225-L235","kind":"function","name":"build_model","path":"tools/trainers/trainer.py","language":"python","start_line":225,"end_line":235,"context_start_line":205,"context_end_line":255,"code":" Run training.\n Returns:\n OrderedDict of results, if evaluation is enabled. Otherwise None.\n \"\"\"\n super().train(self.start_iter, self.max_iter)\n\n def run_step(self):\n self._trainer.iter = self.iter\n self._trainer.run_step()\n\n def state_dict(self):\n ret = super().state_dict()\n ret[\"_trainer\"] = self._trainer.state_dict()\n return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.\n Overwrite it if you'd like a different optimizer.\n \"\"\"\n return build_optimizer(cfg, model)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_optimizer","uri":"program://OneFormer/function/tools.trainers.trainer.build_optimizer#L238-L245","kind":"function","name":"build_optimizer","path":"tools/trainers/trainer.py","language":"python","start_line":238,"end_line":245,"context_start_line":218,"context_end_line":265,"code":" return ret\n\n def load_state_dict(self, state_dict):\n super().load_state_dict(state_dict)\n self._trainer.load_state_dict(state_dict[\"_trainer\"])\n\n @classmethod\n def build_model(cls, cfg):\n \"\"\"\n Returns:\n torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.\n Overwrite it if you'd like a different optimizer.\n \"\"\"\n return build_optimizer(cfg, model)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_lr_scheduler","uri":"program://OneFormer/function/tools.trainers.trainer.build_lr_scheduler#L248-L253","kind":"function","name":"build_lr_scheduler","path":"tools/trainers/trainer.py","language":"python","start_line":248,"end_line":253,"context_start_line":228,"context_end_line":273,"code":" torch.nn.Module:\n It now calls :func:`detectron2.modeling.build_model`.\n Overwrite it if you'd like a different model.\n \"\"\"\n model = build_model(cfg)\n logger = logging.getLogger(__name__)\n logger.info(\"Model:\\n{}\".format(model))\n return model\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.\n Overwrite it if you'd like a different optimizer.\n \"\"\"\n return build_optimizer(cfg, model)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_train_loader","uri":"program://OneFormer/function/tools.trainers.trainer.build_train_loader#L256-L263","kind":"function","name":"build_train_loader","path":"tools/trainers/trainer.py","language":"python","start_line":256,"end_line":263,"context_start_line":236,"context_end_line":283,"code":"\n @classmethod\n def build_optimizer(cls, cfg, model):\n \"\"\"\n Returns:\n torch.optim.Optimizer:\n It now calls :func:`detectron2.solver.build_optimizer`.\n Overwrite it if you'd like a different optimizer.\n \"\"\"\n return build_optimizer(cfg, model)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n DatasetEvaluator or None\n It is not implemented by default.\n \"\"\"\n raise NotImplementedError(\n \"\"\"","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_test_loader","uri":"program://OneFormer/function/tools.trainers.trainer.build_test_loader#L266-L273","kind":"function","name":"build_test_loader","path":"tools/trainers/trainer.py","language":"python","start_line":266,"end_line":273,"context_start_line":246,"context_end_line":293,"code":"\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n DatasetEvaluator or None\n It is not implemented by default.\n \"\"\"\n raise NotImplementedError(\n \"\"\"\nIf you want DefaultTrainer to automatically run evaluation,\nplease implement `build_evaluator()` in subclasses (see train_net.py for example).\nAlternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example).\n\"\"\"\n )\n\n @staticmethod\n def auto_scale_workers(cfg, num_workers: int):\n \"\"\"\n When the config is defined for certain number of workers (according to","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.build_evaluator","uri":"program://OneFormer/function/tools.trainers.trainer.build_evaluator#L276-L288","kind":"function","name":"build_evaluator","path":"tools/trainers/trainer.py","language":"python","start_line":276,"end_line":288,"context_start_line":256,"context_end_line":308,"code":" def build_train_loader(cls, cfg):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_train_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_train_loader(cfg)\n\n @classmethod\n def build_test_loader(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n iterable\n It now calls :func:`detectron2.data.build_detection_test_loader`.\n Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n DatasetEvaluator or None\n It is not implemented by default.\n \"\"\"\n raise NotImplementedError(\n \"\"\"\nIf you want DefaultTrainer to automatically run evaluation,\nplease implement `build_evaluator()` in subclasses (see train_net.py for example).\nAlternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example).\n\"\"\"\n )\n\n @staticmethod\n def auto_scale_workers(cfg, num_workers: int):\n \"\"\"\n When the config is defined for certain number of workers (according to\n ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of\n workers currently in use, returns a new cfg where the total batch size\n is scaled so that the per-GPU batch size stays the same as the\n original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.\n Other config options are also scaled accordingly:\n * training steps and warmup steps are scaled inverse proportionally.\n * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.\n For example, with the original config like the following:\n .. code-block:: yaml\n IMS_PER_BATCH: 16\n BASE_LR: 0.1\n REFERENCE_WORLD_SIZE: 8\n MAX_ITER: 5000\n STEPS: (4000,)\n CHECKPOINT_PERIOD: 1000","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.auto_scale_workers","uri":"program://OneFormer/function/tools.trainers.trainer.auto_scale_workers#L291-L350","kind":"function","name":"auto_scale_workers","path":"tools/trainers/trainer.py","language":"python","start_line":291,"end_line":350,"context_start_line":271,"context_end_line":364,"code":" Overwrite it if you'd like a different data loader.\n \"\"\"\n return build_detection_test_loader(cfg, dataset_name)\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name):\n \"\"\"\n Returns:\n DatasetEvaluator or None\n It is not implemented by default.\n \"\"\"\n raise NotImplementedError(\n \"\"\"\nIf you want DefaultTrainer to automatically run evaluation,\nplease implement `build_evaluator()` in subclasses (see train_net.py for example).\nAlternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example).\n\"\"\"\n )\n\n @staticmethod\n def auto_scale_workers(cfg, num_workers: int):\n \"\"\"\n When the config is defined for certain number of workers (according to\n ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of\n workers currently in use, returns a new cfg where the total batch size\n is scaled so that the per-GPU batch size stays the same as the\n original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.\n Other config options are also scaled accordingly:\n * training steps and warmup steps are scaled inverse proportionally.\n * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.\n For example, with the original config like the following:\n .. code-block:: yaml\n IMS_PER_BATCH: 16\n BASE_LR: 0.1\n REFERENCE_WORLD_SIZE: 8\n MAX_ITER: 5000\n STEPS: (4000,)\n CHECKPOINT_PERIOD: 1000\n When this config is used on 16 GPUs instead of the reference number 8,\n calling this method will return a new config with:\n .. code-block:: yaml\n IMS_PER_BATCH: 32\n BASE_LR: 0.2\n REFERENCE_WORLD_SIZE: 16\n MAX_ITER: 2500\n STEPS: (2000,)\n CHECKPOINT_PERIOD: 500\n Note that both the original config and this new config can be trained on 16 GPUs.\n It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``).\n Returns:\n CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.\n \"\"\"\n old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE\n if old_world_size == 0 or old_world_size == num_workers:\n return cfg\n cfg = cfg.clone()\n frozen = cfg.is_frozen()\n cfg.defrost()\n\n assert (\n cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0\n ), \"Invalid REFERENCE_WORLD_SIZE in config!\"\n scale = num_workers / old_world_size\n bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale))\n lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale\n max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale))\n warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale))\n cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS)\n cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale))\n cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale))\n cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant\n logger = logging.getLogger(__name__)\n logger.info(\n f\"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, \"\n f\"max_iter={max_iter}, warmup={warmup_iter}.\"\n )\n\n if frozen:\n cfg.freeze()\n return cfg\n\n\n# Access basic attributes from the underlying trainer\nfor _attr in [\"model\", \"data_loader\", \"optimizer\"]:\n setattr(\n TPDefaultTrainer,\n _attr,\n property(\n # getter\n lambda self, x=_attr: getattr(self._trainer, x),\n # setter\n lambda self, value, x=_attr: setattr(self._trainer, x, value),\n ),\n )","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"py:tools.trainers.trainer.test_and_save_results","uri":"program://OneFormer/function/tools.trainers.trainer.test_and_save_results#L179-L181","kind":"function","name":"test_and_save_results","path":"tools/trainers/trainer.py","language":"python","start_line":179,"end_line":181,"context_start_line":159,"context_end_line":201,"code":" hooks.LRScheduler(),\n hooks.PreciseBN(\n # Run at the same freq as (but before) evaluation.\n cfg.TEST.EVAL_PERIOD,\n self.model,\n # Build a new data loader to not affect training\n self.build_train_loader(cfg),\n cfg.TEST.PRECISE_BN.NUM_ITER,\n )\n if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)\n else None,\n ]\n\n # Do PreciseBN before checkpointer, because it updates the model and need to\n # be saved by checkpointer.\n # This is not always the best: if checkpointing has a different frequency,\n # some checkpoints may have more precise statistics than others.\n if comm.is_main_process():\n ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))\n\n def test_and_save_results():\n self._last_eval_results = self.test(self.cfg, self.model)\n return self._last_eval_results\n\n # Do evaluation after checkpointer, because then if it fails,\n # we can use the saved checkpoint to debug.\n ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n if comm.is_main_process():\n # Here the default print/log frequency of each writer is used.\n # run writers in the end, so that evaluation metrics are written\n ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))\n return ret\n\n def build_writers(self):\n \"\"\"\n Build a list of writers to be used using :func:`default_writers()`.\n If you'd like a different list of writers, you can overwrite it in\n your trainer.\n Returns:\n list[EventWriter]: a list of :class:`EventWriter` objects.\n \"\"\"\n return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false} {"repo_id":"OneFormer","entity_id":"file:train_net.py","uri":"program://OneFormer/file/train_net.py","kind":"file","name":"train_net.py","path":"train_net.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nOneFormer Training Script.\n\nThis script is a simplified version of the training script in detectron2/tools.\n\"\"\"\nimport copy\nimport itertools\nimport logging\nimport os\n\nfrom collections import OrderedDict\nfrom typing import Any, Dict, List, Set\n\nimport torch\nimport warnings\n","source_hash":"6529a3641fc98c69d727251bbb9d9d650651f3fb6382c6f0f3c943bf2d32a820","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/test_time_augmentation.py","uri":"program://OneFormer/file/oneformer/test_time_augmentation.py","kind":"file","name":"oneformer/test_time_augmentation.py","path":"oneformer/test_time_augmentation.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/test_time_augmentation.py\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nfrom itertools import count\n\nimport numpy as np\nimport torch\nfrom fvcore.transforms import HFlipTransform\nfrom torch import nn\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom detectron2.data.detection_utils import read_image\nfrom .datasetmapper_tta import DatasetMapperTTA\nimport torch.nn.functional as F\n\n__all__ = [\n \"SemanticSegmentorWithTTA\",\n]","source_hash":"2a5b1394731d8561ed40eef0823883cfbc48cfd7219d37f5c399d6c59758972a","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/config.py","uri":"program://OneFormer/file/oneformer/config.py","kind":"file","name":"oneformer/config.py","path":"oneformer/config.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nfrom detectron2.config import CfgNode as CN\n\n__all__ = [\"add_common_config\", \"add_oneformer_config\", \"add_swin_config\", \n \"add_dinat_config\", \"add_convnext_config\"]\n\ndef add_common_config(cfg):\n \"\"\"\n Add config for common configuration\n \"\"\"\n\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME = \"oneformer_unified\"\n # Color augmentation\n cfg.INPUT.COLOR_AUG_SSD = False\n # We retry random cropping until no single category in semantic segmentation GT occupies more\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\n # Pad image and segmentation GT in dataset mapper.","source_hash":"17287c6fa9b251c6cd3de0e2dd2d76f0358e5d531fd900de935888012e5e6bd0","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/datasetmapper_tta.py","uri":"program://OneFormer/file/oneformer/datasetmapper_tta.py","kind":"file","name":"oneformer/datasetmapper_tta.py","path":"oneformer/datasetmapper_tta.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import copy\nimport numpy as np\nfrom typing import List\nimport torch\nfrom fvcore.transforms import NoOpTransform\nfrom torch import nn\n\nfrom detectron2.config import configurable\nfrom detectron2.data.transforms import (\n RandomFlip,\n ResizeShortestEdge,\n ResizeTransform,\n apply_augmentations,\n)\n\n__all__ = [\"DatasetMapperTTA\"]\n\n\nclass DatasetMapperTTA:\n \"\"\"\n Implement test-time augmentation for detection data.","source_hash":"f4f03c9f61aabf49ce1a2c3594b8741b3eebd1b5193b2064c69a66f747b7cf7b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/__init__.py","uri":"program://OneFormer/file/oneformer/__init__.py","kind":"file","name":"oneformer/__init__.py","path":"oneformer/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":18,"code":"from . import data # register all new datasets\nfrom . import modeling\n\n# config\nfrom .config import *\n\n# dataset loading\nfrom .data.dataset_mappers.coco_unified_new_baseline_dataset_mapper import COCOUnifiedNewBaselineDatasetMapper\nfrom .data.dataset_mappers.oneformer_unified_dataset_mapper import (\n OneFormerUnifiedDatasetMapper,\n)\n\n# models\nfrom .oneformer_model import OneFormer\nfrom .test_time_augmentation import SemanticSegmentorWithTTA\n\n# evaluation\nfrom .evaluation.instance_evaluation import InstanceSegEvaluator","source_hash":"32733784ec702aba9fd85425951fa46248d6305ecfe18c732fc8aae4bfd2973f","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/oneformer_model.py","uri":"program://OneFormer/file/oneformer/oneformer_model.py","kind":"file","name":"oneformer/oneformer_model.py","path":"oneformer/oneformer_model.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/maskformer_model.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nfrom typing import Tuple\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head\nfrom detectron2.modeling.backbone import Backbone\nfrom detectron2.modeling.postprocessing import sem_seg_postprocess\nfrom detectron2.structures import Boxes, ImageList, Instances, BitMasks\nfrom detectron2.utils.memory import retry_if_cuda_oom\n\nfrom .modeling.criterion import SetCriterion\nfrom .modeling.matcher import HungarianMatcher","source_hash":"20c0e5ccbe68ea307a3dd29aac02add0b20c6abf325878b0d106fa947cead4ce","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/utils/misc.py","uri":"program://OneFormer/file/oneformer/utils/misc.py","kind":"file","name":"oneformer/utils/misc.py","path":"oneformer/utils/misc.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nfrom typing import List, Optional\n\nimport torch\nimport torch.distributed as dist\nimport torchvision\nfrom torch import Tensor\nimport warnings\nimport torch.nn.functional as F\nimport math\n\ndef inverse_sigmoid(x, eps=1e-3):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)","source_hash":"ba4573daadcec4414ac43056e412934304349d00441d78132127e9a2a257664f","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/utils/box_ops.py","uri":"program://OneFormer/file/oneformer/utils/box_ops.py","kind":"file","name":"oneformer/utils/box_ops.py","path":"oneformer/utils/box_ops.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch, os\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2,\n (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n","source_hash":"8a55dc67991a9bb0e1a3ae194e033994a00ea5215df873ba9df0113740e340d8","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/utils/__init__.py","uri":"program://OneFormer/file/oneformer/utils/__init__.py","kind":"file","name":"oneformer/utils/__init__.py","path":"oneformer/utils/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":2,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\nfrom .events import setup_wandb, WandbWriter","source_hash":"73e7db8951ddd7ca86e9aa0cb371986879974d2676a8b0125e525c20dd3ef8d5","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/utils/pos_embed.py","uri":"program://OneFormer/file/oneformer/utils/pos_embed.py","kind":"file","name":"oneformer/utils/pos_embed.py","path":"oneformer/utils/pos_embed.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# --------------------------------------------------------\n# Position embedding utils\n# --------------------------------------------------------\n\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\n\n\n# --------------------------------------------------------\n# 2D sine-cosine position embedding\n# References:\n# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py\n# MoCo v3: https://github.com/facebookresearch/moco-v3\n# --------------------------------------------------------\ndef get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)","source_hash":"df70889139b5e7c5b240b48462913c402bcb561d67a09eb7364965987415e264","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/utils/events.py","uri":"program://OneFormer/file/oneformer/utils/events.py","kind":"file","name":"oneformer/utils/events.py","path":"oneformer/utils/events.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nimport wandb\nfrom detectron2.utils import comm\nfrom detectron2.utils.events import EventWriter, get_event_storage\n\n\ndef setup_wandb(cfg, args):\n if comm.is_main_process():\n init_args = {\n k.lower(): v\n for k, v in cfg.WANDB.items()\n if isinstance(k, str) and k not in [\"config\"]\n }\n # only include most related part to avoid too big table\n # TODO: add configurable params to select which part of `cfg` should be saved in config\n if \"config_exclude_keys\" in init_args:\n init_args[\"config\"] = cfg\n init_args[\"config\"][\"cfg_file\"] = args.config_file\n else:\n init_args[\"config\"] = {\n \"model\": cfg.MODEL,","source_hash":"58ede499d7b84e43f4cfb82096b9ec4445c20f78bdb31109784d1347d6a2e84f","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/__init__.py","uri":"program://OneFormer/file/oneformer/data/__init__.py","kind":"file","name":"oneformer/data/__init__.py","path":"oneformer/data/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"from . import datasets","source_hash":"8b98456e1418a7b53752e01d1e9c382a18ee4a87ef99769320e70ecc188302da","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/tokenizer.py","uri":"program://OneFormer/file/oneformer/data/tokenizer.py","kind":"file","name":"oneformer/data/tokenizer.py","path":"oneformer/data/tokenizer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# -------------------------------------------------------------------------\n# MIT License\n#\n# Copyright (c) 2021 OpenAI\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE","source_hash":"5149d94ff273ce4b4fa954cbfa7adf064a700e14f983b2c0191930e6e9354faa","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/build.py","uri":"program://OneFormer/file/oneformer/data/build.py","kind":"file","name":"oneformer/data/build.py","path":"oneformer/data/build.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nfrom typing import Any, Callable, Dict, List, Optional, Union\nimport torch.utils.data as torchdata\n\nfrom detectron2.config import configurable\n\n\nfrom detectron2.data.common import DatasetFromList, MapDataset\nfrom detectron2.data.dataset_mapper import DatasetMapper\nfrom detectron2.data.samplers import (\n InferenceSampler,\n)\nfrom detectron2.data.build import (\n get_detection_dataset_dicts,\n trivial_batch_collator\n)\n\"\"\"","source_hash":"56c5645a25fb4885e90646daff413837ea0fcf4bb12f4b5481d1f2a352de7463","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_mapillary_vistas_panoptic.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_mapillary_vistas_panoptic.py","kind":"file","name":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","path":"oneformer/data/datasets/register_mapillary_vistas_panoptic.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\nimport json\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.utils.file_io import PathManager\n\n\nMAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [\n {'color': [165, 42, 42],\n 'id': 1,\n 'isthing': 1,\n 'name': 'Bird',\n 'supercategory': 'animal--bird'},\n {'color': [0, 192, 0],\n 'id': 2,\n 'isthing': 1,\n 'name': 'Ground Animal',\n 'supercategory': 'animal--ground-animal'},\n {'color': [196, 196, 196],\n 'id': 3,","source_hash":"5d2cd4eb208aea80bc6b398c67b6cce1608185890b0cd2752bdc0c7af4be2ee6","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_coco_panoptic2instance.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_coco_panoptic2instance.py","kind":"file","name":"oneformer/data/datasets/register_coco_panoptic2instance.py","path":"oneformer/data/datasets/register_coco_panoptic2instance.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\n\"\"\"\nThis file registers pre-defined datasets at hard-coded paths, and their metadata.\n\nWe hard-code metadata for common datasets. This will enable:\n1. Consistency check when loading the datasets\n2. Use models on these standard datasets directly and run demos,\n without having to download the dataset annotations\n\nWe hard-code some paths to the dataset that's assumed to\nexist in \"./datasets/\".\n\nUsers SHOULD NOT use this file to create new dataset / metadata for new dataset.\nTo add new dataset, refer to the tutorial \"docs/DATASETS.md\".\n\"\"\"\n","source_hash":"e63944245fac85e47a4093d155f94569531b21a08eed4c526939c5d594aeb9c3","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_mapillary_vistas.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_mapillary_vistas.py","kind":"file","name":"oneformer/data/datasets/register_mapillary_vistas.py","path":"oneformer/data/datasets/register_mapillary_vistas.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets import load_sem_seg\n\nMAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [\n {\n \"color\": [165, 42, 42],\n \"instances\": True,\n \"readable\": \"Bird\",\n \"name\": \"animal--bird\",\n \"evaluate\": True,\n },\n {\n \"color\": [0, 192, 0],\n \"instances\": True,\n \"readable\": \"Ground Animal\",\n \"name\": \"animal--ground-animal\",\n \"evaluate\": True,\n },","source_hash":"cee33a4d7350cee9f8fdccbcb6e963a5d2764563d459c93a64d7a43c16b36971","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/__init__.py","uri":"program://OneFormer/file/oneformer/data/datasets/__init__.py","kind":"file","name":"oneformer/data/datasets/__init__.py","path":"oneformer/data/datasets/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":9,"code":"from . import (\n register_ade20k_panoptic,\n register_cityscapes_panoptic,\n register_coco_panoptic_annos_semseg,\n register_ade20k_instance,\n register_coco_panoptic2instance,\n register_mapillary_vistas,\n register_mapillary_vistas_panoptic,\n)","source_hash":"dab22141eee08040ae872ea7249374c0320bafb2f122907f32e4efb9a7554f36","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_ade20k_instance.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_ade20k_instance.py","kind":"file","name":"oneformer/data/datasets/register_ade20k_instance.py","path":"oneformer/data/datasets/register_ade20k_instance.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_instance.py\n# ------------------------------------------------------------------------------\n\nimport json\nimport logging\nimport numpy as np\nimport os\nfrom PIL import Image\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.coco import load_coco_json, register_coco_instances\nfrom detectron2.utils.file_io import PathManager\n\nADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]\n\n\n_PREDEFINED_SPLITS = {\n # point annotations without masks\n \"ade20k_instance_train\": (\n \"ADEChallengeData2016/images/training\",","source_hash":"d6dce919a13fa895657f631af55988e0184ea3609190acd5bf823da0573c02d2","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_cityscapes_panoptic.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_cityscapes_panoptic.py","kind":"file","name":"oneformer/data/datasets/register_cityscapes_panoptic.py","path":"oneformer/data/datasets/register_cityscapes_panoptic.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport logging\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES\nfrom detectron2.utils.file_io import PathManager\n\n\"\"\"\nThis file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.\n\"\"\"\n\n\nlogger = logging.getLogger(__name__)\n\n","source_hash":"4ef19980d8d4590b685e333ee853f63ff27ac4ed4f425626cd5ef47df0c1bd7b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","kind":"file","name":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","path":"oneformer/data/datasets/register_coco_panoptic_annos_semseg.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets import load_sem_seg\nfrom detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\nfrom detectron2.utils.file_io import PathManager\nimport contextlib\nimport logging\nimport io\nfrom fvcore.common.timer import Timer\nimport pycocotools.mask as mask_util\nfrom detectron2.structures import BoxMode\n\n\nlogger = logging.getLogger(__name__)","source_hash":"8b95344493def29295d010b0b615db3ff48d03b27258cfe8fdc50451347febea","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/datasets/register_ade20k_panoptic.py","uri":"program://OneFormer/file/oneformer/data/datasets/register_ade20k_panoptic.py","kind":"file","name":"oneformer/data/datasets/register_ade20k_panoptic.py","path":"oneformer/data/datasets/register_ade20k_panoptic.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_panoptic.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport json\nimport os\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.utils.file_io import PathManager\n\nADE20K_150_CATEGORIES = [\n {\"color\": [120, 120, 120], \"id\": 0, \"isthing\": 0, \"name\": \"wall\"},\n {\"color\": [180, 120, 120], \"id\": 1, \"isthing\": 0, \"name\": \"building\"},\n {\"color\": [6, 230, 230], \"id\": 2, \"isthing\": 0, \"name\": \"sky\"},\n {\"color\": [80, 50, 50], \"id\": 3, \"isthing\": 0, \"name\": \"floor\"},\n {\"color\": [4, 200, 3], \"id\": 4, \"isthing\": 0, \"name\": \"tree\"},\n {\"color\": [120, 120, 80], \"id\": 5, \"isthing\": 0, \"name\": \"ceiling\"},\n {\"color\": [140, 140, 140], \"id\": 6, \"isthing\": 0, \"name\": \"road, route\"},\n {\"color\": [204, 5, 255], \"id\": 7, \"isthing\": 1, \"name\": \"bed\"},\n {\"color\": [230, 230, 230], \"id\": 8, \"isthing\": 1, \"name\": \"window \"},","source_hash":"308c0d2026c1617366e0590f002acc8898184154ffedeac0fcb89a0278b6c564","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","uri":"program://OneFormer/file/oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","kind":"file","name":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","path":"oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\n\nimport numpy as np\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances\nfrom oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"COCOUnifiedNewBaselineDatasetMapper\"]\n","source_hash":"765962fa7ac16ca383e0ba5fee41e2db2dab65d9ae4f93e9043cc783b191bdb3","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","uri":"program://OneFormer/file/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","kind":"file","name":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","path":"oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.utils.box_ops import masks_to_boxes\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize","source_hash":"a047d580da48482855afedef3c28320e97295aec9678c31282d16c293a907ada","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/dataset_mappers/__init__.py","uri":"program://OneFormer/file/oneformer/data/dataset_mappers/__init__.py","kind":"file","name":"oneformer/data/dataset_mappers/__init__.py","path":"oneformer/data/dataset_mappers/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"","source_hash":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/data/dataset_mappers/dataset_mapper.py","uri":"program://OneFormer/file/oneformer/data/dataset_mappers/dataset_mapper.py","kind":"file","name":"oneformer/data/dataset_mappers/dataset_mapper.py","path":"oneformer/data/dataset_mappers/dataset_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport numpy as np\nfrom typing import List, Optional, Union\nimport torch\n\nfrom detectron2.config import configurable\n\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\n\n__all__ = [\"DatasetMapper\"]\n\n\nclass DatasetMapper:","source_hash":"0f54ba6f57967799abe80f81069201ec0ce632385dec3731341ef055ed0aefd2","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/evaluation/detection_coco_evaluator.py","uri":"program://OneFormer/file/oneformer/evaluation/detection_coco_evaluator.py","kind":"file","name":"oneformer/evaluation/detection_coco_evaluator.py","path":"oneformer/evaluation/detection_coco_evaluator.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport contextlib\nimport copy\nimport io\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n","source_hash":"b6293a9678a901dbe83cead20b4b6e3b1836246aaaa01955e20af989fed4e707","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/evaluation/cityscapes_evaluation.py","uri":"program://OneFormer/file/oneformer/evaluation/cityscapes_evaluation.py","kind":"file","name":"oneformer/evaluation/cityscapes_evaluation.py","path":"oneformer/evaluation/cityscapes_evaluation.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport glob\nimport logging\nimport numpy as np\nimport os\nimport tempfile\nfrom collections import OrderedDict\nimport torch\nfrom PIL import Image\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.utils import comm\nfrom detectron2.utils.file_io import PathManager\n\nfrom .evaluator import DatasetEvaluator\n\n","source_hash":"3d72030322780a619e23c538e363dc1088fd6810b3c7b1189a829aab2073d39d","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/evaluation/coco_evaluator.py","uri":"program://OneFormer/file/oneformer/evaluation/coco_evaluator.py","kind":"file","name":"oneformer/evaluation/coco_evaluator.py","path":"oneformer/evaluation/coco_evaluator.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport contextlib\nimport copy\nimport io\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n","source_hash":"ba520a3d8924d65a79278af2cef5ac833f76ab59e0f70c0c0a4f2100ea44b936","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/evaluation/__init__.py","uri":"program://OneFormer/file/oneformer/evaluation/__init__.py","kind":"file","name":"oneformer/evaluation/__init__.py","path":"oneformer/evaluation/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":3,"code":"from .detection_coco_evaluator import *\nfrom .coco_evaluator import *\nfrom .cityscapes_evaluation import CityscapesInstanceEvaluator","source_hash":"cab7c6b79074ad2277243e7a07e61c1555b1b8ef1e63fdd4650a2422ead42a3b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/evaluation/instance_evaluation.py","uri":"program://OneFormer/file/oneformer/evaluation/instance_evaluation.py","kind":"file","name":"oneformer/evaluation/instance_evaluation.py","path":"oneformer/evaluation/instance_evaluation.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py\n# ------------------------------------------------------------------------------\n\nimport contextlib\nimport copy\nimport io\nimport itertools\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pickle\nfrom collections import OrderedDict\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom tabulate import tabulate\n\nimport detectron2.utils.comm as comm","source_hash":"a34a7e1b5885592176f67236b3cbc7d74c8c5ab074ccc06c37789aeecd602582","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/evaluation/evaluator.py","uri":"program://OneFormer/file/oneformer/evaluation/evaluator.py","kind":"file","name":"oneformer/evaluation/evaluator.py","path":"oneformer/evaluation/evaluator.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport datetime\nimport logging\nimport time\nfrom collections import OrderedDict, abc\nfrom contextlib import ExitStack, contextmanager\nfrom typing import List, Union\nimport torch\nfrom torch import nn\n\nfrom detectron2.utils.comm import get_world_size, is_main_process\nfrom detectron2.utils.logger import log_every_n_seconds\n\n\nclass DatasetEvaluator:\n \"\"\"\n Base class for a dataset evaluator.","source_hash":"557c61815c99407de37c14085a4c3579277dfd5996b7eaa598b6416a2834877e","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/criterion.py","uri":"program://OneFormer/file/oneformer/modeling/criterion.py","kind":"file","name":"oneformer/modeling/criterion.py","path":"oneformer/modeling/criterion.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nOneFormer criterion.\n\"\"\"\nimport logging\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom detectron2.utils.comm import get_world_size\nfrom detectron2.projects.point_rend.point_features import (\n get_uncertain_point_coords_with_randomness,\n point_sample,\n)\n\nfrom ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list","source_hash":"72fcd00775526c7b9f2384e2297b5d0d007875b9d3bea21ed75adeb9da129744","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/__init__.py","kind":"file","name":"oneformer/modeling/__init__.py","path":"oneformer/modeling/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":6,"code":"from .backbone.swin import D2SwinTransformer\nfrom .backbone.dinat import D2DiNAT\nfrom .backbone.convnext import D2ConvNeXt\nfrom .pixel_decoder.fpn import BasePixelDecoder\nfrom .pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder\nfrom .meta_arch.oneformer_head import OneFormerHead","source_hash":"a7b1da762e26d611dbb6373d2a17fb5f655b68f3b24e732dc1f02e17664c255e","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/matcher.py","uri":"program://OneFormer/file/oneformer/modeling/matcher.py","kind":"file","name":"oneformer/modeling/matcher.py","path":"oneformer/modeling/matcher.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nModules to compute the matching cost and solve the corresponding LSAP.\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\nfrom torch.cuda.amp import autocast\nimport numpy as np\n\nfrom detectron2.projects.point_rend.point_features import point_sample\n\n\ndef linear_sum_assignment_with_nan(cost_matrix):\n cost_matrix = np.asarray(cost_matrix)\n nan = np.isnan(cost_matrix).any()","source_hash":"3f4b45b6f9af4dfac841b3b360d966e3549d8b9ea336ae1be8c8d286aa794290","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/__init__.py","kind":"file","name":"oneformer/modeling/pixel_decoder/__init__.py","path":"oneformer/modeling/pixel_decoder/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"","source_hash":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/fpn.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/fpn.py","kind":"file","name":"oneformer/modeling/pixel_decoder/fpn.py","path":"oneformer/modeling/pixel_decoder/fpn.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/fpn.py\n# ------------------------------------------------------------------------------\nimport logging\nimport numpy as np\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\nfrom torch.cuda.amp import autocast\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\n\nfrom ..transformer_decoder.position_encoding import PositionEmbeddingSine\nfrom ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn\n","source_hash":"4323e0e84ae253d8f556ff56390d0856b869c2d98708774a6d8d23c1b870eeb7","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/msdeformattn.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/msdeformattn.py","kind":"file","name":"oneformer/modeling/pixel_decoder/msdeformattn.py","path":"oneformer/modeling/pixel_decoder/msdeformattn.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/msdeformattn.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nimport numpy as np\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.init import xavier_uniform_, constant_, uniform_, normal_\nfrom torch.cuda.amp import autocast\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\n\nfrom ..transformer_decoder.position_encoding import PositionEmbeddingSine","source_hash":"538eb02ed12da8d4ffe835e9ab952c41fca37012fc3d534aaecd56bd01db498b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/ops/setup.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/ops/setup.py","kind":"file","name":"oneformer/modeling/pixel_decoder/ops/setup.py","path":"oneformer/modeling/pixel_decoder/ops/setup.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nimport os\nimport glob\n\nimport torch\n\nfrom torch.utils.cpp_extension import CUDA_HOME\nfrom torch.utils.cpp_extension import CppExtension\nfrom torch.utils.cpp_extension import CUDAExtension\n\nfrom setuptools import find_packages","source_hash":"43337887620f7ddc5fd892abaa837c1a4fbdb0fb767c9cfda78a013f80997780","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/ops/test.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/ops/test.py","kind":"file","name":"oneformer/modeling/pixel_decoder/ops/test.py","path":"oneformer/modeling/pixel_decoder/ops/test.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport time\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import gradcheck\n\nfrom functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch","source_hash":"0eae613a37b1a67f52c2a5a56036fa7787bf398e630a1d386b54a4daee591bde","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","kind":"file","name":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","path":"oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\n\nif torch.cuda.is_available():","source_hash":"3647fbebcb25066c7eda926fbc52099e6a68c8a219ecd6bbf59b8dddf0c504d2","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/ops/functions/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/ops/functions/__init__.py","kind":"file","name":"oneformer/modeling/pixel_decoder/ops/functions/__init__.py","path":"oneformer/modeling/pixel_decoder/ops/functions/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":10,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\nfrom .ms_deform_attn_func import MSDeformAttnFunction\n","source_hash":"e1a39c1cf23562b11d696d81f95cf8bef368c48149b681d1eadc7f6d188edd3a","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","kind":"file","name":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","path":"oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport warnings\nimport math\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F","source_hash":"ad11fb537f00fcc0a2907e59da2610394fe6180174869036bb1c1ead96feca5e","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/pixel_decoder/ops/modules/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/pixel_decoder/ops/modules/__init__.py","kind":"file","name":"oneformer/modeling/pixel_decoder/ops/modules/__init__.py","path":"oneformer/modeling/pixel_decoder/ops/modules/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":10,"code":"# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n# ------------------------------------------------------------------------------------------------\n\n\nfrom .ms_deform_attn import MSDeformAttn","source_hash":"441ddadc7fea6ae6b251ac8388ec953e384312fddfb66cce1fd4b7ffbdf23c83","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/transformer_decoder/transformer.py","uri":"program://OneFormer/file/oneformer/modeling/transformer_decoder/transformer.py","kind":"file","name":"oneformer/modeling/transformer_decoder/transformer.py","path":"oneformer/modeling/transformer_decoder/transformer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/transformer.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nTransformer class.\n\nCopy-paste from torch.nn.Transformer with modifications:\n * positional encodings are passed in MHattention\n * extra LN at the end of encoder is removed\n * decoder returns a stack of activations from all decoding layers\n\"\"\"\nimport copy\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\n","source_hash":"2442a856f9b0bd94c2ffa04a1d89ea83e3ee31472a5250afa5c6b3dff6b0054b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/transformer_decoder/text_transformer.py","uri":"program://OneFormer/file/oneformer/modeling/transformer_decoder/text_transformer.py","kind":"file","name":"oneformer/modeling/transformer_decoder/text_transformer.py","path":"oneformer/modeling/transformer_decoder/text_transformer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# -------------------------------------------------------------------------\n# MIT License\n#\n# Copyright (c) 2021 OpenAI\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE","source_hash":"08d840eacfa066377fab6ebdd7c5a0bb226c7c3bdac60f3450fd9d5ba1cdf465","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/transformer_decoder/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/transformer_decoder/__init__.py","kind":"file","name":"oneformer/modeling/transformer_decoder/__init__.py","path":"oneformer/modeling/transformer_decoder/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":2,"code":"# Copyright (c) Facebook, Inc. and its affiliates.\nfrom .oneformer_transformer_decoder import ContrastiveMultiScaleMaskedTransformerDecoder","source_hash":"7ad6018a57d72180fcb577bb0eda9d6c886b81b5b8031c7fdd6927b2201332b6","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/transformer_decoder/position_encoding.py","uri":"program://OneFormer/file/oneformer/modeling/transformer_decoder/position_encoding.py","kind":"file","name":"oneformer/modeling/transformer_decoder/position_encoding.py","path":"oneformer/modeling/transformer_decoder/position_encoding.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/position_encoding.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):","source_hash":"40a45cfcb390c547acd5a70255b7684ecef80421fc0ba35df3f7eb53c1d36532","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","uri":"program://OneFormer/file/oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","kind":"file","name":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","path":"oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nimport fvcore.nn.weight_init as weight_init\nfrom typing import Optional\nimport torch\nfrom torch import nn, Tensor\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d\n\nfrom .position_encoding import PositionEmbeddingSine\nfrom .transformer import Transformer\n\nfrom detectron2.utils.registry import Registry\n\n","source_hash":"1dabf94ff3068ca95365e496aa6d43f9cfe27453b107280842b571d3b4428aa3","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/backbone/swin.py","uri":"program://OneFormer/file/oneformer/modeling/backbone/swin.py","kind":"file","name":"oneformer/modeling/backbone/swin.py","path":"oneformer/modeling/backbone/swin.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ze Liu, Yutong Lin, Yixuan Wei\n# --------------------------------------------------------\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\n\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\n\nclass Mlp(nn.Module):","source_hash":"635c15c182571864acb9e60a5d1ce711d7f65089db036fcb90bad914b805bf61","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/backbone/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/backbone/__init__.py","kind":"file","name":"oneformer/modeling/backbone/__init__.py","path":"oneformer/modeling/backbone/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"# Copyright (c) Facebook, Inc. and its affiliates.","source_hash":"1f45f6a979b53c81ac85894aae6f474f070379b5619abd6ce74204188f97bbf3","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/backbone/convnext.py","uri":"program://OneFormer/file/oneformer/modeling/backbone/convnext.py","kind":"file","name":"oneformer/modeling/backbone/convnext.py","path":"oneformer/modeling/backbone/convnext.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Meta Platforms, Inc. and affiliates.\n\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom timm.models.layers import DropPath\n\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\n\nclass Block(nn.Module):\n r\"\"\" ConvNeXt Block. There are two equivalent implementations:\n (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)","source_hash":"2c35e4a72b65713581f357d8603930c69cd60e69aae13d589ffdb737cb0769f9","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/backbone/dinat.py","uri":"program://OneFormer/file/oneformer/modeling/backbone/dinat.py","kind":"file","name":"oneformer/modeling/backbone/dinat.py","path":"oneformer/modeling/backbone/dinat.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# --------------------------------------------------------\n# Neighborhood Attention Transformer\n# Licensed under The MIT License\n# Written by Ali Hassani\n# --------------------------------------------------------\n\n# Modified by Jitesh Jain\n\nimport torch\nimport torch.nn as nn\nfrom timm.models.layers import DropPath\nfrom detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec\n\nfrom natten import NeighborhoodAttention2D as NeighborhoodAttention\n\n\nclass ConvTokenizer(nn.Module):\n def __init__(self, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n self.proj = nn.Sequential(\n nn.Conv2d(in_chans, embed_dim // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),","source_hash":"33d5c710ba83a8946a88f02207132f6ca7832d85ddcb9a05155b7215f8a3245a","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/meta_arch/oneformer_head.py","uri":"program://OneFormer/file/oneformer/modeling/meta_arch/oneformer_head.py","kind":"file","name":"oneformer/modeling/meta_arch/oneformer_head.py","path":"oneformer/modeling/meta_arch/oneformer_head.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport logging\nfrom copy import deepcopy\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport fvcore.nn.weight_init as weight_init\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.layers import Conv2d, ShapeSpec, get_norm\nfrom detectron2.modeling import SEM_SEG_HEADS_REGISTRY\nfrom ..pixel_decoder.fpn import build_pixel_decoder\nfrom ..transformer_decoder.oneformer_transformer_decoder import build_transformer_decoder\n\n@SEM_SEG_HEADS_REGISTRY.register()\nclass OneFormerHead(nn.Module):","source_hash":"dd2cac2bd23d9cabb6f0b83390203aafb8421f86a7c4acbc4810263fd2ef2567","truncated":false} {"repo_id":"OneFormer","entity_id":"file:oneformer/modeling/meta_arch/__init__.py","uri":"program://OneFormer/file/oneformer/modeling/meta_arch/__init__.py","kind":"file","name":"oneformer/modeling/meta_arch/__init__.py","path":"oneformer/modeling/meta_arch/__init__.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":1,"code":"","source_hash":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/fg_ids.py","uri":"program://OneFormer/file/datasets/fg_ids.py","kind":"file","name":"datasets/fg_ids.py","path":"datasets/fg_ids.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"ADE20K_FG_IDS = {\n 1: 8,\n 2:\t9,\n 3:\t11,\n 4:\t13,\n 5:\t15,\n 5:\t15,\n 6:\t16,\n 7:\t19,\n 8:\t20,\n 9:\t21,\n 10:\t23,\n 11:\t24,\n 12:\t25,\n 13:\t28,\n 14:\t31,\n 15:\t32,\n 16:\t33,\n 17:\t34,\n 18:\t36,\n 18:\t36,","source_hash":"f4d3eb06bb04ec3067b02378368d5943a91f3352680fe634bc990a467e7fe9eb","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","uri":"program://OneFormer/file/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","kind":"file","name":"datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","path":"datasets/prepare_coco_semantic_annos_from_panoptic_annos.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport functools\nimport json\nimport multiprocessing as mp\nimport numpy as np\nimport os\nimport time\nfrom fvcore.common.download import download\nfrom panopticapi.utils import rgb2id\nfrom PIL import Image\n\nfrom detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\n\n\ndef _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):\n panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)\n panoptic = rgb2id(panoptic)\n output = np.zeros_like(panoptic, dtype=np.uint8) + 255","source_hash":"63a9f06c7833676fe94a285a28b9dbbd5bb530726148eadb365c0a791c502564","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/prepare_ade20k_sem_seg.py","uri":"program://OneFormer/file/datasets/prepare_ade20k_sem_seg.py","kind":"file","name":"datasets/prepare_ade20k_sem_seg.py","path":"datasets/prepare_ade20k_sem_seg.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport tqdm\nfrom PIL import Image\n\n\ndef convert(input, output):\n img = np.asarray(Image.open(input))\n assert img.dtype == np.uint8\n img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1\n Image.fromarray(img).save(output)\n\n\nif __name__ == \"__main__\":\n dataset_dir = Path(os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")) / \"ADEChallengeData2016\"\n for name in [\"training\", \"validation\"]:","source_hash":"7d6a6dfe4fc6314716c7a9964edfc08e882b80e9468f666360cb297572378981","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/prepare_ade20k_ins_seg.py","uri":"program://OneFormer/file/datasets/prepare_ade20k_ins_seg.py","kind":"file","name":"datasets/prepare_ade20k_ins_seg.py","path":"datasets/prepare_ade20k_ins_seg.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport glob\nimport json\nimport os\nfrom collections import Counter\n\nimport numpy as np\nimport tqdm\nfrom panopticapi.utils import IdGenerator, save_json\nfrom PIL import Image\nimport pycocotools.mask as mask_util\n\n\nif __name__ == \"__main__\":\n dataset_dir = os.getenv(\"DETECTRON2_DATASETS\", \"datasets\")\n\n for name, dirname in [(\"train\", \"training\"), (\"val\", \"validation\")]:\n image_dir = os.path.join(dataset_dir, f\"ADEChallengeData2016/images/{dirname}/\")\n instance_dir = os.path.join(","source_hash":"870b14809b72b1b68fc298faed61158d03ebdff3722f4a6b41988c82dfe41600","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/prepare_ade20k_pan_seg.py","uri":"program://OneFormer/file/datasets/prepare_ade20k_pan_seg.py","kind":"file","name":"datasets/prepare_ade20k_pan_seg.py","path":"datasets/prepare_ade20k_pan_seg.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\nimport glob\nimport json\nimport os\nfrom collections import Counter\n\nimport numpy as np\nimport tqdm\nfrom panopticapi.utils import IdGenerator, save_json\nfrom PIL import Image\n\nADE20K_SEM_SEG_CATEGORIES = [\n \"wall\",\n \"building\",\n \"sky\",\n \"floor\",\n \"tree\",\n \"ceiling\",\n \"road, route\",","source_hash":"17776f14ec44650131fdda28233f06e18c3d8bee04375b4206ee1675bf89b292","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/panoptic2detection_coco_format.py","uri":"program://OneFormer/file/datasets/panoptic2detection_coco_format.py","kind":"file","name":"datasets/panoptic2detection_coco_format.py","path":"datasets/panoptic2detection_coco_format.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python\n# ------------------------------------------------------------------------------\n# Reference: https://github.com/cocodataset/panopticapi/blob/master/converters/panoptic2detection_coco_format.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n'''\nThis script converts panoptic COCO format to detection COCO format. More\ninformation about the formats can be found here:\nhttp://cocodataset.org/#format-data. All segments will be stored in RLE format.\n\nAdditional option:\n- using option '--things_only' the script can discard all stuff\nsegments, saving segments of things classes only.\n'''\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nimport os, sys\nimport argparse\nimport numpy as np","source_hash":"602084f87b04f7b459cccefe9c1475625e0f8cfef4a5003a65a1f06d7b8f3df5","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","uri":"program://OneFormer/file/datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","kind":"file","name":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","path":"datasets/custom_datasets/instance_coco_custom_dataset_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\n\nimport numpy as np\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nfrom pycocotools import mask as coco_mask\n\n__all__ = [\"InstanceCOCOCustomNewBaselineDatasetMapper\"]\n\n","source_hash":"29437e0704e34b0bd224ca10ba9ad34dd588ea9df8b168b09a3e5fb668cb832d","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","uri":"program://OneFormer/file/datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","kind":"file","name":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","path":"datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances, polygons_to_bitmask\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util","source_hash":"a65e7e26e51b0880f7d176d074b9990eb467570f7c3aaba109a14b523d66f8c4","truncated":false} {"repo_id":"OneFormer","entity_id":"file:datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","uri":"program://OneFormer/file/datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","kind":"file","name":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","path":"datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport copy\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\nfrom detectron2.config import configurable\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.structures import BitMasks, Instances, polygons_to_bitmask\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.projects.point_rend import ColorAugSSDTransform\nfrom oneformer.data.tokenizer import SimpleTokenizer, Tokenize\nimport pycocotools.mask as mask_util","source_hash":"ca9ad8072dc114801f775cd05f23c6d9c98c442f459ba0519601905a2895ae38","truncated":false} {"repo_id":"OneFormer","entity_id":"file:demo/demo.py","uri":"program://OneFormer/file/demo/demo.py","kind":"file","name":"demo/demo.py","path":"demo/demo.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/demo/demo.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport argparse\nimport multiprocessing as mp\nimport os\nimport torch\nimport random\n# fmt: off\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n# fmt: on\n\nimport time\nimport cv2\nimport numpy as np\nimport tqdm\n\nfrom detectron2.config import get_cfg","source_hash":"3f480cbef43b58982e78ff61826a3f051c76cc64e82701d18276c0df6c57bcf6","truncated":false} {"repo_id":"OneFormer","entity_id":"file:demo/visualizer.py","uri":"program://OneFormer/file/demo/visualizer.py","kind":"file","name":"demo/visualizer.py","path":"demo/visualizer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport colorsys\nimport logging\nimport math\nimport numpy as np\nfrom enum import Enum, unique\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.colors as mplc\nimport matplotlib.figure as mplfigure\nimport pycocotools.mask as mask_util\nimport torch\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom PIL import Image\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes","source_hash":"e5f02ca3ce2df821a7761b20057ae6e60768ac05809b972bfccb2e62c996039b","truncated":false} {"repo_id":"OneFormer","entity_id":"file:demo/predictor.py","uri":"program://OneFormer/file/demo/predictor.py","kind":"file","name":"demo/predictor.py","path":"demo/predictor.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport atexit\nimport bisect\nimport multiprocessing as mp\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom defaults import DefaultPredictor\nfrom visualizer import ColorMode, Visualizer\n\n\nclass VisualizationDemo(object):\n def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):\n \"\"\"\n Args:\n cfg (CfgNode):\n instance_mode (ColorMode):","source_hash":"28ea182ee4aef27aeb2b05a29a0dc6d96f419ab247ea7be9d805ef2507a03591","truncated":false} {"repo_id":"OneFormer","entity_id":"file:demo/colormap.py","uri":"program://OneFormer/file/demo/colormap.py","kind":"file","name":"demo/colormap.py","path":"demo/colormap.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/colormap.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\n\"\"\"\nAn awesome colormap for really neat visualizations.\nCopied from Detectron, and removed gray colors.\n\"\"\"\n\nimport numpy as np\nimport random\nrandom.seed(0)\n\n__all__ = [\"colormap\", \"random_color\", \"random_colors\"]\n\n_COLORS = []\n\ndef gen_color():\n color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3))\n if color not in _COLORS and np.mean(color) != 0.0:","source_hash":"a3ac26ee24dc1f0459cb9646f1c9e6c5c6ca21c73677205d4b8d8eb832ff80bd","truncated":false} {"repo_id":"OneFormer","entity_id":"file:demo/defaults.py","uri":"program://OneFormer/file/demo/defaults.py","kind":"file","name":"demo/defaults.py","path":"demo/defaults.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# ------------------------------------------------------------------------------\n# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/defaults.py\n# Modified by Jitesh Jain (https://github.com/praeclarumjj3)\n# ------------------------------------------------------------------------------\n\nimport torch\nimport detectron2.data.transforms as T\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.data import (\n MetadataCatalog,\n)\nfrom detectron2.modeling import build_model\n\n\n__all__ = [\n \"DefaultPredictor\",\n]\n\n\nclass DefaultPredictor:\n \"\"\"","source_hash":"f3c32ff13f297ac1a70c83ec06a49fa2dda15f27a7f80fe6add1b8ab98217dbf","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/setup_detectron2.py","uri":"program://OneFormer/file/tools/setup_detectron2.py","kind":"file","name":"tools/setup_detectron2.py","path":"tools/setup_detectron2.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":11,"code":"import sys, os, distutils.core, subprocess\n\nif not os.path.exists('./detectron2'):\n subprocess.run(['git', 'clone', 'https://github.com/facebookresearch/detectron2'])\n\ndist = distutils.core.run_setup(\"./detectron2/setup.py\")\n\nfor x in dist.install_requires:\n subprocess.run(['python', '-m', 'pip', 'install', x])\n\nsys.path.insert(0, os.path.abspath('./detectron2'))","source_hash":"8aca89a7a5271c6a9f20fe058e3beb422cdb6a6e0ee2c8bc107bc3000c1983d1","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/convert-pretrained-model-to-d2.py","uri":"program://OneFormer/file/tools/convert-pretrained-model-to-d2.py","kind":"file","name":"tools/convert-pretrained-model-to-d2.py","path":"tools/convert-pretrained-model-to-d2.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport pickle as pkl\nimport sys\n\nimport torch\n\n\"\"\"\nUsage:\n # download pretrained swin model:\n wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth\n # run the conversion\n ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl\n # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:\nMODEL:\n WEIGHTS: \"/path/to/swin_tiny_patch4_window7_224.pkl\"\nINPUT:\n FORMAT: \"RGB\"\n\"\"\"\n","source_hash":"6d03a428eaf86eaf0ff7da22a722aeb22e347602d4430904537d5395b9403e26","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/convert-pretrained-nat-model-to-d2.py","uri":"program://OneFormer/file/tools/convert-pretrained-nat-model-to-d2.py","kind":"file","name":"tools/convert-pretrained-nat-model-to-d2.py","path":"tools/convert-pretrained-nat-model-to-d2.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport pickle as pkl\nimport sys\n\nimport torch\n\n\"\"\"\nUsage:\n # download pretrained swin model:\n wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth\n # run the conversion\n ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl\n # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:\nMODEL:\n WEIGHTS: \"/path/to/swin_tiny_patch4_window7_224.pkl\"\nINPUT:\n FORMAT: \"RGB\"\n\"\"\"\n","source_hash":"e89291607776a84bae9cf3d1a86600223dfa4c005c1fafc07da236d01bab523f","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/calc_throughput.py","uri":"program://OneFormer/file/tools/calc_throughput.py","kind":"file","name":"tools/calc_throughput.py","path":"tools/calc_throughput.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import copy\nimport itertools\nimport os\n\nfrom typing import Any, Dict, List, Set\n\nimport torch\n\nimport detectron2.utils.comm as comm\nfrom detectron2.config import get_cfg\nfrom detectron2.data import build_detection_train_loader\nfrom detectron2.engine import (\n default_argument_parser,\n default_setup,\n launch,\n)\nfrom detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler\nfrom detectron2.solver.build import maybe_add_gradient_clipping\nfrom detectron2.utils.logger import setup_logger\nfrom trainers.trainer import TPDefaultTrainer\n","source_hash":"cc591cdd12545172875d17fe50661082b639a49b031e39084bb2ab1e3ffa37e5","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/analyze_model.py","uri":"program://OneFormer/file/tools/analyze_model.py","kind":"file","name":"tools/analyze_model.py","path":"tools/analyze_model.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import logging\nimport numpy as np\nfrom collections import Counter\nimport tqdm\nfrom fvcore.nn import flop_count_table # can also try flop_count_str\n\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate\nfrom detectron2.engine import default_argument_parser\nfrom detectron2.modeling import build_model\nfrom detectron2.projects.deeplab import add_deeplab_config\nfrom detectron2.utils.analysis import (\n FlopCountAnalysis,\n activation_count_operators,\n parameter_count_table,\n)\nfrom detectron2.utils.logger import setup_logger\n\n# fmt: off\nimport os\nimport sys","source_hash":"45dd36d31dce9322b65fe33625e19ee6b2b83c6561f45262e2dda6b7dc0b2ac2","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/convert-torchvision-to-d2.py","uri":"program://OneFormer/file/tools/convert-torchvision-to-d2.py","kind":"file","name":"tools/convert-torchvision-to-d2.py","path":"tools/convert-torchvision-to-d2.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport pickle as pkl\nimport sys\n\nimport torch\n\n\"\"\"\nUsage:\n # download one of the ResNet{18,34,50,101,152} models from torchvision:\n wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth\n # run the conversion\n ./convert-torchvision-to-d2.py r50.pth r50.pkl\n # Then, use r50.pkl with the following changes in config:\nMODEL:\n WEIGHTS: \"/path/to/r50.pkl\"\n PIXEL_MEAN: [123.675, 116.280, 103.530]\n PIXEL_STD: [58.395, 57.120, 57.375]\n RESNETS:\n DEPTH: 50","source_hash":"310d02bff605561bf1de3fedb16f610b67d0d84eea34ecc3933d8782f920ab6a","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/trainers/trainer_base.py","uri":"program://OneFormer/file/tools/trainers/trainer_base.py","kind":"file","name":"tools/trainers/trainer_base.py","path":"tools/trainers/trainer_base.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport logging\nimport numpy as np\nimport time\nimport weakref\nfrom typing import List, Mapping, Optional\nimport torch\nfrom torch.nn.parallel import DataParallel, DistributedDataParallel\n\nimport detectron2.utils.comm as comm\nfrom detectron2.utils.events import EventStorage, get_event_storage\nfrom detectron2.utils.logger import _log_api_usage\nfrom detectron2.engine import HookBase\nimport torch.distributed as dist\n\n__all__ = [\"TPTrainerBase\", \"TPSimpleTrainer\", \"TPAMPTrainer\"]\n\n\nclass TPTrainerBase:","source_hash":"e400012ea9a3855c9b8ff1d009d3ffad6ed91850f0ca97c58a504d14fcca72cb","truncated":false} {"repo_id":"OneFormer","entity_id":"file:tools/trainers/trainer.py","uri":"program://OneFormer/file/tools/trainers/trainer.py","kind":"file","name":"tools/trainers/trainer.py","path":"tools/trainers/trainer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\nimport logging\nimport weakref\nfrom collections import OrderedDict\nfrom fvcore.nn.precise_bn import get_bn_modules\n\nimport detectron2.data.transforms as T\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.data import (\n build_detection_test_loader,\n build_detection_train_loader,\n)\nfrom detectron2.evaluation import (\n DatasetEvaluator,\n inference_on_dataset,\n print_csv_format,\n)\nfrom detectron2.modeling import build_model\nfrom detectron2.solver import build_lr_scheduler, build_optimizer\nfrom detectron2.utils import comm\nfrom detectron2.utils.logger import setup_logger","source_hash":"9f6e80dda2a440b78967bf65ed45aa0fece2000fcd302fc62277736135410ed4","truncated":false}