aRng[1] for d in dt]).reshape((1, len(dt)))
+ dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0)))
+ # store results for given image and category
+ return {
+ 'video_id': vidId,
+ 'category_id': catId,
+ 'aRng': aRng,
+ 'maxDet': maxDet,
+ 'dtIds': [d['id'] for d in dt],
+ 'gtIds': [g['id'] for g in gt],
+ 'dtMatches': dtm,
+ 'gtMatches': gtm,
+ 'dtScores': [d['score'] for d in dt],
+ 'gtIgnore': gtIg,
+ 'dtIgnore': dtIg,
+ }
+
+ def accumulate(self, p = None):
+ '''
+ Accumulate per image evaluation results and store the result in self.eval
+ :param p: input params for evaluation
+ :return: None
+ '''
+ print('Accumulating evaluation results...')
+ tic = time.time()
+ if not self.evalImgs:
+ print('Please run evaluate() first')
+ # allows input customized parameters
+ if p is None:
+ p = self.params
+ p.catIds = p.catIds if p.useCats == 1 else [-1]
+ T = len(p.iouThrs)
+ R = len(p.recThrs)
+ K = len(p.catIds) if p.useCats else 1
+ A = len(p.areaRng)
+ M = len(p.maxDets)
+ precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
+ recall = -np.ones((T,K,A,M))
+ scores = -np.ones((T,R,K,A,M))
+
+ # create dictionary for future indexing
+ _pe = self._paramsEval
+ catIds = _pe.catIds if _pe.useCats else [-1]
+ setK = set(catIds)
+ setA = set(map(tuple, _pe.areaRng))
+ setM = set(_pe.maxDets)
+ setI = set(_pe.vidIds)
+ # get inds to evaluate
+ k_list = [n for n, k in enumerate(p.catIds) if k in setK]
+ m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
+ a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
+ i_list = [n for n, i in enumerate(p.vidIds) if i in setI]
+ I0 = len(_pe.vidIds)
+ A0 = len(_pe.areaRng)
+ # retrieve E at each category, area range, and max number of detections
+ for k, k0 in enumerate(k_list):
+ Nk = k0*A0*I0
+ for a, a0 in enumerate(a_list):
+ Na = a0*I0
+ for m, maxDet in enumerate(m_list):
+ E = [self.evalImgs[Nk + Na + i] for i in i_list]
+ E = [e for e in E if not e is None]
+ if len(E) == 0:
+ continue
+ dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])
+
+ # different sorting method generates slightly different results.
+ # mergesort is used to be consistent as Matlab implementation.
+ inds = np.argsort(-dtScores, kind='mergesort')
+ dtScoresSorted = dtScores[inds]
+
+ dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
+ dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds]
+ gtIg = np.concatenate([e['gtIgnore'] for e in E])
+ npig = np.count_nonzero(gtIg==0 )
+ if npig == 0:
+ continue
+ tps = np.logical_and( dtm, np.logical_not(dtIg) )
+ fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )
+
+ tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
+ fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
+ for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
+ tp = np.array(tp)
+ fp = np.array(fp)
+ nd = len(tp)
+ rc = tp / npig
+ pr = tp / (fp+tp+np.spacing(1))
+ q = np.zeros((R,))
+ ss = np.zeros((R,))
+
+ if nd:
+ recall[t,k,a,m] = rc[-1]
+ else:
+ recall[t,k,a,m] = 0
+
+ # numpy is slow without cython optimization for accessing elements
+ # use python array gets significant speed improvement
+ pr = pr.tolist(); q = q.tolist()
+
+ for i in range(nd-1, 0, -1):
+ if pr[i] > pr[i-1]:
+ pr[i-1] = pr[i]
+
+ inds = np.searchsorted(rc, p.recThrs, side='left')
+ try:
+ for ri, pi in enumerate(inds):
+ q[ri] = pr[pi]
+ ss[ri] = dtScoresSorted[pi]
+ except:
+ pass
+ precision[t,:,k,a,m] = np.array(q)
+ scores[t,:,k,a,m] = np.array(ss)
+ self.eval = {
+ 'params': p,
+ 'counts': [T, R, K, A, M],
+ 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+ 'precision': precision,
+ 'recall': recall,
+ 'scores': scores,
+ }
+ toc = time.time()
+ print('DONE (t={:0.2f}s).'.format( toc-tic))
+
+ def summarize(self):
+ '''
+ Compute and display summary metrics for evaluation results.
+ Note this functin can *only* be applied on the default parameter setting
+ '''
+ def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
+ p = self.params
+ iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}'
+ titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
+ typeStr = '(AP)' if ap==1 else '(AR)'
+ iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \
+ if iouThr is None else '{:0.2f}'.format(iouThr)
+
+ aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
+ mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
+ if ap == 1:
+ # dimension of precision: [TxRxKxAxM]
+ s = self.eval['precision']
+ # IoU
+ if iouThr is not None:
+ t = np.where(iouThr == p.iouThrs)[0]
+ s = s[t]
+ s = s[:,:,:,aind,mind]
+ else:
+ # dimension of recall: [TxKxAxM]
+ s = self.eval['recall']
+ if iouThr is not None:
+ t = np.where(iouThr == p.iouThrs)[0]
+ s = s[t]
+ s = s[:,:,aind,mind]
+ if len(s[s>-1])==0:
+ mean_s = -1
+ else:
+ mean_s = np.mean(s[s>-1])
+ print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
+ return mean_s
+ def _summarizeDets():
+ stats = np.zeros((12,))
+ stats[0] = _summarize(1)
+ stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2])
+ stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2])
+ stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2])
+ stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2])
+ stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2])
+ stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
+ stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
+ stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
+ stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2])
+ stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2])
+ stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2])
+ return stats
+ def _summarizeKps():
+ stats = np.zeros((10,))
+ stats[0] = _summarize(1, maxDets=20)
+ stats[1] = _summarize(1, maxDets=20, iouThr=.5)
+ stats[2] = _summarize(1, maxDets=20, iouThr=.75)
+ stats[3] = _summarize(1, maxDets=20, areaRng='medium')
+ stats[4] = _summarize(1, maxDets=20, areaRng='large')
+ stats[5] = _summarize(0, maxDets=20)
+ stats[6] = _summarize(0, maxDets=20, iouThr=.5)
+ stats[7] = _summarize(0, maxDets=20, iouThr=.75)
+ stats[8] = _summarize(0, maxDets=20, areaRng='medium')
+ stats[9] = _summarize(0, maxDets=20, areaRng='large')
+ return stats
+ if not self.eval:
+ raise Exception('Please run accumulate() first')
+ iouType = self.params.iouType
+ if iouType == 'segm' or iouType == 'bbox':
+ summarize = _summarizeDets
+ elif iouType == 'keypoints':
+ summarize = _summarizeKps
+ self.stats = summarize()
+
+ def __str__(self):
+ self.summarize()
+
+class Params:
+ '''
+ Params for coco evaluation api
+ '''
+ def setDetParams(self):
+ self.vidIds = []
+ self.catIds = []
+ # np.arange causes trouble. the data point on arange is slightly larger than the true value
+ #self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
+ #self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
+ self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
+ self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True)
+ self.maxDets = [1, 10, 100]
+ self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 128 ** 2], [ 128 ** 2, 256 ** 2], [256 ** 2, 1e5 ** 2]]
+ self.areaRngLbl = ['all', 'small', 'medium', 'large']
+ self.useCats = 1
+
+ def setKpParams(self):
+ self.vidIds = []
+ self.catIds = []
+ # np.arange causes trouble. the data point on arange is slightly larger than the true value
+ self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
+ self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
+ self.maxDets = [20]
+ self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
+ self.areaRngLbl = ['all', 'medium', 'large']
+ self.useCats = 1
+
+ def __init__(self, iouType='segm'):
+ if iouType == 'segm' or iouType == 'bbox':
+ self.setDetParams()
+ elif iouType == 'keypoints':
+ self.setKpParams()
+ else:
+ raise Exception('iouType not supported')
+ self.iouType = iouType
+ # useSegm is deprecated
+ self.useSegm = None
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/data_video/ytvis_eval.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/data_video/ytvis_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e9c2865dcd1914ffc9ca0acaccb03632c22008e
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/data_video/ytvis_eval.py
@@ -0,0 +1,327 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC
+
+import contextlib
+import copy
+import io
+import itertools
+import json
+import logging
+import numpy as np
+import os
+from collections import OrderedDict
+import pycocotools.mask as mask_util
+import torch
+from .datasets.ytvis_api.ytvos import YTVOS
+from .datasets.ytvis_api.ytvoseval import YTVOSeval
+from tabulate import tabulate
+
+import detectron2.utils.comm as comm
+from detectron2.config import CfgNode
+from detectron2.data import MetadataCatalog
+from detectron2.evaluation import DatasetEvaluator
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import create_small_table
+
+
+class YTVISEvaluator(DatasetEvaluator):
+ """
+ Evaluate AR for object proposals, AP for instance detection/segmentation, AP
+ for keypoint detection outputs using COCO's metrics.
+ See http://cocodataset.org/#detection-eval and
+ http://cocodataset.org/#keypoints-eval to understand its metrics.
+
+ In addition to COCO, this evaluator is able to support any bounding box detection,
+ instance segmentation, or keypoint detection dataset.
+ """
+
+ def __init__(
+ self,
+ dataset_name,
+ tasks=None,
+ distributed=True,
+ output_dir=None,
+ *,
+ use_fast_impl=True,
+ ):
+ """
+ Args:
+ dataset_name (str): name of the dataset to be evaluated.
+ It must have either the following corresponding metadata:
+
+ "json_file": the path to the COCO format annotation
+
+ Or it must be in detectron2's standard dataset format
+ so it can be converted to COCO format automatically.
+ tasks (tuple[str]): tasks that can be evaluated under the given
+ configuration. A task is one of "bbox", "segm", "keypoints".
+ By default, will infer this automatically from predictions.
+ distributed (True): if True, will collect results from all ranks and run evaluation
+ in the main process.
+ Otherwise, will only evaluate the results in the current process.
+ output_dir (str): optional, an output directory to dump all
+ results predicted on the dataset. The dump contains two files:
+
+ 1. "instances_predictions.pth" a file in torch serialization
+ format that contains all the raw original predictions.
+ 2. "coco_instances_results.json" a json file in COCO's result
+ format.
+ use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
+ Although the results should be very close to the official implementation in COCO
+ API, it is still recommended to compute results with the official API for use in
+ papers. The faster implementation also uses more RAM.
+ """
+ self._logger = logging.getLogger(__name__)
+ self._distributed = distributed
+ self._output_dir = output_dir
+ self._use_fast_impl = use_fast_impl
+
+ if tasks is not None and isinstance(tasks, CfgNode):
+ self._logger.warning(
+ "COCO Evaluator instantiated using config, this is deprecated behavior."
+ " Please pass in explicit arguments instead."
+ )
+ self._tasks = None # Infering it from predictions should be better
+ else:
+ self._tasks = tasks
+
+ self._cpu_device = torch.device("cpu")
+
+ self._metadata = MetadataCatalog.get(dataset_name)
+
+ json_file = PathManager.get_local_path(self._metadata.json_file)
+ with contextlib.redirect_stdout(io.StringIO()):
+ self._ytvis_api = YTVOS(json_file)
+
+ # Test set json files do not contain annotations (evaluation must be
+ # performed using the COCO evaluation server).
+ self._do_evaluation = "annotations" in self._ytvis_api.dataset
+
+ def reset(self):
+ self._predictions = []
+
+ def process(self, inputs, outputs):
+ """
+ Args:
+ inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
+ It is a list of dict. Each dict corresponds to an image and
+ contains keys like "height", "width", "file_name", "image_id".
+ outputs: the outputs of a COCO model. It is a list of dicts with key
+ "instances" that contains :class:`Instances`.
+ """
+ prediction = instances_to_coco_json_video(inputs, outputs)
+ self._predictions.extend(prediction)
+
+ def evaluate(self):
+ """
+ Args:
+ img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
+ """
+ if self._distributed:
+ comm.synchronize()
+ predictions = comm.gather(self._predictions, dst=0)
+ predictions = list(itertools.chain(*predictions))
+
+ if not comm.is_main_process():
+ return {}
+ else:
+ predictions = self._predictions
+
+ if len(predictions) == 0:
+ self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
+ return {}
+
+ if self._output_dir:
+ PathManager.mkdirs(self._output_dir)
+ file_path = os.path.join(self._output_dir, "instances_predictions.pth")
+ with PathManager.open(file_path, "wb") as f:
+ torch.save(predictions, f)
+
+ self._results = OrderedDict()
+ self._eval_predictions(predictions)
+ # Copy so the caller can do whatever with results
+ return copy.deepcopy(self._results)
+
+ def _eval_predictions(self, predictions):
+ """
+ Evaluate predictions. Fill self._results with the metrics of the tasks.
+ """
+ self._logger.info("Preparing results for YTVIS format ...")
+
+ # unmap the category ids for COCO
+ if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
+ dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
+ all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
+ num_classes = len(all_contiguous_ids)
+ assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
+
+ reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
+ for result in predictions:
+ category_id = result["category_id"]
+ assert category_id < num_classes, (
+ f"A prediction has class={category_id}, "
+ f"but the dataset only has {num_classes} classes and "
+ f"predicted class id should be in [0, {num_classes - 1}]."
+ )
+ result["category_id"] = reverse_id_mapping[category_id]
+
+ if self._output_dir:
+ file_path = os.path.join(self._output_dir, "results.json")
+ self._logger.info("Saving results to {}".format(file_path))
+ with PathManager.open(file_path, "w") as f:
+ f.write(json.dumps(predictions))
+ f.flush()
+
+ if not self._do_evaluation:
+ self._logger.info("Annotations are not available for evaluation.")
+ return
+
+ coco_eval = (
+ _evaluate_predictions_on_coco(
+ self._ytvis_api,
+ predictions,
+ )
+ if len(predictions) > 0
+ else None # cocoapi does not handle empty results very well
+ )
+
+ res = self._derive_coco_results(
+ coco_eval, class_names=self._metadata.get("thing_classes")
+ )
+ self._results["segm"] = res
+
+ def _derive_coco_results(self, coco_eval, class_names=None):
+ """
+ Derive the desired score numbers from summarized COCOeval.
+ Args:
+ coco_eval (None or COCOEval): None represents no predictions from model.
+ iou_type (str):
+ class_names (None or list[str]): if provided, will use it to predict
+ per-category AP.
+ Returns:
+ a dict of {metric name: score}
+ """
+
+ metrics = ["AP", "AP50", "AP75", "APs", "APm", "APl", "AR1", "AR10"]
+
+ if coco_eval is None:
+ self._logger.warn("No predictions from the model!")
+ return {metric: float("nan") for metric in metrics}
+
+ # the standard metrics
+ results = {
+ metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
+ for idx, metric in enumerate(metrics)
+ }
+ self._logger.info(
+ "Evaluation results for {}: \n".format("segm") + create_small_table(results)
+ )
+ if not np.isfinite(sum(results.values())):
+ self._logger.info("Some metrics cannot be computed and is shown as NaN.")
+
+ if class_names is None or len(class_names) <= 1:
+ return results
+ # Compute per-category AP
+ # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
+ precisions = coco_eval.eval["precision"]
+ # precision has dims (iou, recall, cls, area range, max dets)
+ assert len(class_names) == precisions.shape[2]
+
+ results_per_category = []
+ for idx, name in enumerate(class_names):
+ # area range index 0: all area ranges
+ # max dets index -1: typically 100 per image
+ precision = precisions[:, :, idx, 0, -1]
+ precision = precision[precision > -1]
+ ap = np.mean(precision) if precision.size else float("nan")
+ results_per_category.append(("{}".format(name), float(ap * 100)))
+
+ # tabulate it
+ N_COLS = min(6, len(results_per_category) * 2)
+ results_flatten = list(itertools.chain(*results_per_category))
+ results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
+ table = tabulate(
+ results_2d,
+ tablefmt="pipe",
+ floatfmt=".3f",
+ headers=["category", "AP"] * (N_COLS // 2),
+ numalign="left",
+ )
+ self._logger.info("Per-category {} AP: \n".format("segm") + table)
+
+ results.update({"AP-" + name: ap for name, ap in results_per_category})
+ return results
+
+
+def instances_to_coco_json_video(inputs, outputs):
+ """
+ Dump an "Instances" object to a COCO-format json that's used for evaluation.
+
+ Args:
+ instances (Instances):
+ video_id (int): the image id
+
+ Returns:
+ list[dict]: list of json annotations in COCO format.
+ """
+ assert len(inputs) == 1, "More than one inputs are loaded for inference!"
+
+ video_id = inputs[0]["video_id"]
+ video_length = inputs[0]["length"]
+
+ scores = outputs["pred_scores"]
+ labels = outputs["pred_labels"]
+ masks = outputs["pred_masks"]
+
+ ytvis_results = []
+ for instance_id, (s, l, m) in enumerate(zip(scores, labels, masks)):
+ segms = [
+ mask_util.encode(np.array(_mask[:, :, None], order="F", dtype="uint8"))[0]
+ for _mask in m
+ ]
+ for rle in segms:
+ rle["counts"] = rle["counts"].decode("utf-8")
+
+ res = {
+ "video_id": video_id,
+ "score": s,
+ "category_id": l,
+ "segmentations": segms,
+ }
+ ytvis_results.append(res)
+
+ return ytvis_results
+
+
+def _evaluate_predictions_on_coco(
+ coco_gt,
+ coco_results,
+ img_ids=None,
+):
+ """
+ Evaluate the coco results using COCOEval API.
+ """
+ assert len(coco_results) > 0
+
+ coco_results = copy.deepcopy(coco_results)
+ # When evaluating mask AP, if the results contain bbox, cocoapi will
+ # use the box area as the area of the instance, instead of the mask area.
+ # This leads to a different definition of small/medium/large.
+ # We remove the bbox field to let mask AP use mask area.
+ for c in coco_results:
+ c.pop("bbox", None)
+
+ coco_dt = coco_gt.loadRes(coco_results)
+ coco_eval = YTVOSeval(coco_gt, coco_dt)
+ # For COCO, the default max_dets_per_image is [1, 10, 100].
+ max_dets_per_image = [1, 10, 100] # Default from COCOEval
+ coco_eval.params.maxDets = max_dets_per_image
+
+ if img_ids is not None:
+ coco_eval.params.imgIds = img_ids
+
+ coco_eval.evaluate()
+ coco_eval.accumulate()
+ coco_eval.summarize()
+
+ return coco_eval
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/__init__.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cc855fb05ad6ad02824f6f307539845a3cea192
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .transformer_decoder.video_mask2former_transformer_decoder import VideoMultiScaleMaskedTransformerDecoder
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/criterion.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/criterion.py
new file mode 100644
index 0000000000000000000000000000000000000000..176a55c09eef5a8db7dd87bf187404f2cbe98489
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/criterion.py
@@ -0,0 +1,259 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py
+"""
+MaskFormer criterion.
+"""
+import logging
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from detectron2.utils.comm import get_world_size
+from detectron2.projects.point_rend.point_features import (
+ get_uncertain_point_coords_with_randomness,
+ point_sample,
+)
+
+from mask2former.utils.misc import is_dist_avail_and_initialized
+
+
+def dice_loss(
+ inputs: torch.Tensor,
+ targets: torch.Tensor,
+ num_masks: float,
+ ):
+ """
+ Compute the DICE loss, similar to generalized IOU for masks
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ """
+ inputs = inputs.sigmoid()
+ inputs = inputs.flatten(1)
+ numerator = 2 * (inputs * targets).sum(-1)
+ denominator = inputs.sum(-1) + targets.sum(-1)
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss.sum() / num_masks
+
+
+dice_loss_jit = torch.jit.script(
+ dice_loss
+) # type: torch.jit.ScriptModule
+
+
+def sigmoid_ce_loss(
+ inputs: torch.Tensor,
+ targets: torch.Tensor,
+ num_masks: float,
+ ):
+ """
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ Returns:
+ Loss tensor
+ """
+ loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+
+ return loss.mean(1).sum() / num_masks
+
+
+sigmoid_ce_loss_jit = torch.jit.script(
+ sigmoid_ce_loss
+) # type: torch.jit.ScriptModule
+
+
+def calculate_uncertainty(logits):
+ """
+ We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
+ foreground class in `classes`.
+ Args:
+ logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or
+ class-agnostic, where R is the total number of predicted masks in all images and C is
+ the number of foreground classes. The values are logits.
+ Returns:
+ scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
+ the most uncertain locations having the highest uncertainty score.
+ """
+ assert logits.shape[1] == 1
+ gt_class_logits = logits.clone()
+ return -(torch.abs(gt_class_logits))
+
+
+class VideoSetCriterion(nn.Module):
+ """This class computes the loss for DETR.
+ The process happens in two steps:
+ 1) we compute hungarian assignment between ground truth boxes and the outputs of the model
+ 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
+ """
+
+ def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,
+ num_points, oversample_ratio, importance_sample_ratio):
+ """Create the criterion.
+ Parameters:
+ num_classes: number of object categories, omitting the special no-object category
+ matcher: module able to compute a matching between targets and proposals
+ weight_dict: dict containing as key the names of the losses and as values their relative weight.
+ eos_coef: relative classification weight applied to the no-object category
+ losses: list of all the losses to be applied. See get_loss for list of available losses.
+ """
+ super().__init__()
+ self.num_classes = num_classes
+ self.matcher = matcher
+ self.weight_dict = weight_dict
+ self.eos_coef = eos_coef
+ self.losses = losses
+ empty_weight = torch.ones(self.num_classes + 1)
+ empty_weight[-1] = self.eos_coef
+ self.register_buffer("empty_weight", empty_weight)
+
+ # pointwise mask loss parameters
+ self.num_points = num_points
+ self.oversample_ratio = oversample_ratio
+ self.importance_sample_ratio = importance_sample_ratio
+
+ def loss_labels(self, outputs, targets, indices, num_masks):
+ """Classification loss (NLL)
+ targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
+ """
+ assert "pred_logits" in outputs
+ src_logits = outputs["pred_logits"].float()
+
+ idx = self._get_src_permutation_idx(indices)
+ target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
+ target_classes = torch.full(
+ src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
+ )
+ target_classes[idx] = target_classes_o
+
+ loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)
+ losses = {"loss_ce": loss_ce}
+ return losses
+
+ def loss_masks(self, outputs, targets, indices, num_masks):
+ """Compute the losses related to the masks: the focal loss and the dice loss.
+ targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
+ """
+ assert "pred_masks" in outputs
+
+ src_idx = self._get_src_permutation_idx(indices)
+ src_masks = outputs["pred_masks"]
+ src_masks = src_masks[src_idx]
+ # Modified to handle video
+ target_masks = torch.cat([t['masks'][i] for t, (_, i) in zip(targets, indices)]).to(src_masks)
+
+ # No need to upsample predictions as we are using normalized coordinates :)
+ # NT x 1 x H x W
+ src_masks = src_masks.flatten(0, 1)[:, None]
+ target_masks = target_masks.flatten(0, 1)[:, None]
+
+ with torch.no_grad():
+ # sample point_coords
+ point_coords = get_uncertain_point_coords_with_randomness(
+ src_masks,
+ lambda logits: calculate_uncertainty(logits),
+ self.num_points,
+ self.oversample_ratio,
+ self.importance_sample_ratio,
+ )
+ # get gt labels
+ point_labels = point_sample(
+ target_masks,
+ point_coords,
+ align_corners=False,
+ ).squeeze(1)
+
+ point_logits = point_sample(
+ src_masks,
+ point_coords,
+ align_corners=False,
+ ).squeeze(1)
+
+ losses = {
+ "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),
+ "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks),
+ }
+
+ del src_masks
+ del target_masks
+ return losses
+
+ def _get_src_permutation_idx(self, indices):
+ # permute predictions following indices
+ batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+ src_idx = torch.cat([src for (src, _) in indices])
+ return batch_idx, src_idx
+
+ def _get_tgt_permutation_idx(self, indices):
+ # permute targets following indices
+ batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+ tgt_idx = torch.cat([tgt for (_, tgt) in indices])
+ return batch_idx, tgt_idx
+
+ def get_loss(self, loss, outputs, targets, indices, num_masks):
+ loss_map = {
+ 'labels': self.loss_labels,
+ 'masks': self.loss_masks,
+ }
+ assert loss in loss_map, f"do you really want to compute {loss} loss?"
+ return loss_map[loss](outputs, targets, indices, num_masks)
+
+ def forward(self, outputs, targets):
+ """This performs the loss computation.
+ Parameters:
+ outputs: dict of tensors, see the output specification of the model for the format
+ targets: list of dicts, such that len(targets) == batch_size.
+ The expected keys in each dict depends on the losses applied, see each loss' doc
+ """
+ outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"}
+
+ # Retrieve the matching between the outputs of the last layer and the targets
+ indices = self.matcher(outputs_without_aux, targets)
+
+ # Compute the average number of target boxes accross all nodes, for normalization purposes
+ num_masks = sum(len(t["labels"]) for t in targets)
+ num_masks = torch.as_tensor(
+ [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device
+ )
+ if is_dist_avail_and_initialized():
+ torch.distributed.all_reduce(num_masks)
+ num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
+
+ # Compute all the requested losses
+ losses = {}
+ for loss in self.losses:
+ losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))
+
+ # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+ if "aux_outputs" in outputs:
+ for i, aux_outputs in enumerate(outputs["aux_outputs"]):
+ indices = self.matcher(aux_outputs, targets)
+ for loss in self.losses:
+ l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)
+ l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
+ losses.update(l_dict)
+
+ return losses
+
+ def __repr__(self):
+ head = "Criterion " + self.__class__.__name__
+ body = [
+ "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)),
+ "losses: {}".format(self.losses),
+ "weight_dict: {}".format(self.weight_dict),
+ "num_classes: {}".format(self.num_classes),
+ "eos_coef: {}".format(self.eos_coef),
+ "num_points: {}".format(self.num_points),
+ "oversample_ratio: {}".format(self.oversample_ratio),
+ "importance_sample_ratio: {}".format(self.importance_sample_ratio),
+ ]
+ _repr_indent = 4
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/matcher.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..642f36022fae7c380d316ae4897220b741904db2
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/matcher.py
@@ -0,0 +1,189 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py
+"""
+Modules to compute the matching cost and solve the corresponding LSAP.
+"""
+import torch
+import torch.nn.functional as F
+from scipy.optimize import linear_sum_assignment
+from torch import nn
+from torch.cuda.amp import autocast
+
+from detectron2.projects.point_rend.point_features import point_sample
+
+
+def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):
+ """
+ Compute the DICE loss, similar to generalized IOU for masks
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ """
+ inputs = inputs.sigmoid()
+ inputs = inputs.flatten(1)
+ numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets)
+ denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss
+
+
+batch_dice_loss_jit = torch.jit.script(
+ batch_dice_loss
+) # type: torch.jit.ScriptModule
+
+
+def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
+ """
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ Returns:
+ Loss tensor
+ """
+ hw = inputs.shape[1]
+
+ pos = F.binary_cross_entropy_with_logits(
+ inputs, torch.ones_like(inputs), reduction="none"
+ )
+ neg = F.binary_cross_entropy_with_logits(
+ inputs, torch.zeros_like(inputs), reduction="none"
+ )
+
+ loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum(
+ "nc,mc->nm", neg, (1 - targets)
+ )
+
+ return loss / hw
+
+
+batch_sigmoid_ce_loss_jit = torch.jit.script(
+ batch_sigmoid_ce_loss
+) # type: torch.jit.ScriptModule
+
+
+class VideoHungarianMatcher(nn.Module):
+ """This class computes an assignment between the targets and the predictions of the network
+
+ For efficiency reasons, the targets don't include the no_object. Because of this, in general,
+ there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
+ while the others are un-matched (and thus treated as non-objects).
+ """
+
+ def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0):
+ """Creates the matcher
+
+ Params:
+ cost_class: This is the relative weight of the classification error in the matching cost
+ cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost
+ cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost
+ """
+ super().__init__()
+ self.cost_class = cost_class
+ self.cost_mask = cost_mask
+ self.cost_dice = cost_dice
+
+ assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0"
+
+ self.num_points = num_points
+
+ @torch.no_grad()
+ def memory_efficient_forward(self, outputs, targets):
+ """More memory-friendly matching"""
+ bs, num_queries = outputs["pred_logits"].shape[:2]
+
+ indices = []
+
+ # Iterate through batch size
+ for b in range(bs):
+
+ out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes]
+ tgt_ids = targets[b]["labels"]
+
+ # Compute the classification cost. Contrary to the loss, we don't use the NLL,
+ # but approximate it in 1 - proba[target class].
+ # The 1 is a constant that doesn't change the matching, it can be ommitted.
+ cost_class = -out_prob[:, tgt_ids]
+
+ out_mask = outputs["pred_masks"][b] # [num_queries, T, H_pred, W_pred]
+ # gt masks are already padded when preparing target
+ tgt_mask = targets[b]["masks"].to(out_mask) # [num_gts, T, H_pred, W_pred]
+
+ # out_mask = out_mask[:, None]
+ # tgt_mask = tgt_mask[:, None]
+ # all masks share the same set of points for efficient matching!
+ point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device)
+ # get gt labels
+ tgt_mask = point_sample(
+ tgt_mask,
+ point_coords.repeat(tgt_mask.shape[0], 1, 1),
+ align_corners=False,
+ ).flatten(1)
+
+ out_mask = point_sample(
+ out_mask,
+ point_coords.repeat(out_mask.shape[0], 1, 1),
+ align_corners=False,
+ ).flatten(1)
+
+ with autocast(enabled=False):
+ out_mask = out_mask.float()
+ tgt_mask = tgt_mask.float()
+ # Compute the focal loss between masks
+ cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
+
+ # Compute the dice loss betwen masks
+ cost_dice = batch_dice_loss_jit(out_mask, tgt_mask)
+
+ # Final cost matrix
+ C = (
+ self.cost_mask * cost_mask
+ + self.cost_class * cost_class
+ + self.cost_dice * cost_dice
+ )
+ C = C.reshape(num_queries, -1).cpu()
+
+ indices.append(linear_sum_assignment(C))
+
+ return [
+ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
+ for i, j in indices
+ ]
+
+ @torch.no_grad()
+ def forward(self, outputs, targets):
+ """Performs the matching
+
+ Params:
+ outputs: This is a dict that contains at least these entries:
+ "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
+ "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks
+
+ targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
+ "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
+ objects in the target) containing the class labels
+ "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks
+
+ Returns:
+ A list of size batch_size, containing tuples of (index_i, index_j) where:
+ - index_i is the indices of the selected predictions (in order)
+ - index_j is the indices of the corresponding selected targets (in order)
+ For each batch element, it holds:
+ len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
+ """
+ return self.memory_efficient_forward(outputs, targets)
+
+ def __repr__(self, _repr_indent=4):
+ head = "Matcher " + self.__class__.__name__
+ body = [
+ "cost_class: {}".format(self.cost_class),
+ "cost_mask: {}".format(self.cost_mask),
+ "cost_dice: {}".format(self.cost_dice),
+ ]
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/__init__.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1971bbb1e4216f43e28a511f166a31ba68b3fbd
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .video_mask2former_transformer_decoder import VideoMultiScaleMaskedTransformerDecoder
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/position_encoding.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/position_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0ebc769de4ed6a86069fcbc9b1e819d5f6b4bc8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/position_encoding.py
@@ -0,0 +1,57 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine3D(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one
+ used by the Attention is all you need paper, generalized to work on images.
+ """
+
+ def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, x, mask=None):
+ # b, t, c, h, w
+ assert x.dim() == 5, f"{x.shape} should be a 5-dimensional Tensor, got {x.dim()}-dimensional Tensor instead"
+ if mask is None:
+ mask = torch.zeros((x.size(0), x.size(1), x.size(3), x.size(4)), device=x.device, dtype=torch.bool)
+ not_mask = ~mask
+ z_embed = not_mask.cumsum(1, dtype=torch.float32)
+ y_embed = not_mask.cumsum(2, dtype=torch.float32)
+ x_embed = not_mask.cumsum(3, dtype=torch.float32)
+ if self.normalize:
+ eps = 1e-6
+ z_embed = z_embed / (z_embed[:, -1:, :, :] + eps) * self.scale
+ y_embed = y_embed / (y_embed[:, :, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ dim_t_z = torch.arange((self.num_pos_feats * 2), dtype=torch.float32, device=x.device)
+ dim_t_z = self.temperature ** (2 * (dim_t_z // 2) / (self.num_pos_feats * 2))
+
+ pos_x = x_embed[:, :, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, :, None] / dim_t
+ pos_z = z_embed[:, :, :, :, None] / dim_t_z
+ pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
+ pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
+ pos_z = torch.stack((pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
+ pos = (torch.cat((pos_y, pos_x), dim=4) + pos_z).permute(0, 1, 4, 2, 3) # b, t, c, h, w
+ return pos
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..06d4e8511e99faa3b104b8588cba6163f548135e
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py
@@ -0,0 +1,474 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
+import logging
+import fvcore.nn.weight_init as weight_init
+from typing import Optional
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d
+
+from mask2former.modeling.transformer_decoder.maskformer_transformer_decoder import TRANSFORMER_DECODER_REGISTRY
+
+from .position_encoding import PositionEmbeddingSine3D
+
+
+class SelfAttentionLayer(nn.Module):
+
+ def __init__(self, d_model, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ q = k = self.with_pos_embed(tgt, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+
+ return tgt
+
+ def forward_pre(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ tgt2 = self.norm(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+
+ return tgt
+
+ def forward(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ if self.normalize_before:
+ return self.forward_pre(tgt, tgt_mask,
+ tgt_key_padding_mask, query_pos)
+ return self.forward_post(tgt, tgt_mask,
+ tgt_key_padding_mask, query_pos)
+
+
+class CrossAttentionLayer(nn.Module):
+
+ def __init__(self, d_model, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt, memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory, attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+
+ return tgt
+
+ def forward_pre(self, tgt, memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ tgt2 = self.norm(tgt)
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory, attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+
+ return tgt
+
+ def forward(self, tgt, memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ if self.normalize_before:
+ return self.forward_pre(tgt, memory, memory_mask,
+ memory_key_padding_mask, pos, query_pos)
+ return self.forward_post(tgt, memory, memory_mask,
+ memory_key_padding_mask, pos, query_pos)
+
+
+class FFNLayer(nn.Module):
+
+ def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm = nn.LayerNorm(d_model)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt):
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+ return tgt
+
+ def forward_pre(self, tgt):
+ tgt2 = self.norm(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout(tgt2)
+ return tgt
+
+ def forward(self, tgt):
+ if self.normalize_before:
+ return self.forward_pre(tgt)
+ return self.forward_post(tgt)
+
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class MLP(nn.Module):
+ """ Very simple multi-layer perceptron (also called FFN)"""
+
+ def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+
+@TRANSFORMER_DECODER_REGISTRY.register()
+class VideoMultiScaleMaskedTransformerDecoder(nn.Module):
+
+ _version = 2
+
+ def _load_from_state_dict(
+ self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ ):
+ version = local_metadata.get("version", None)
+ if version is None or version < 2:
+ # Do not warn if train from scratch
+ scratch = True
+ logger = logging.getLogger(__name__)
+ for k in list(state_dict.keys()):
+ newk = k
+ if "static_query" in k:
+ newk = k.replace("static_query", "query_feat")
+ if newk != k:
+ state_dict[newk] = state_dict[k]
+ del state_dict[k]
+ scratch = False
+
+ if not scratch:
+ logger.warning(
+ f"Weight format of {self.__class__.__name__} have changed! "
+ "Please upgrade your models. Applying automatic conversion now ..."
+ )
+
+ @configurable
+ def __init__(
+ self,
+ in_channels,
+ mask_classification=True,
+ *,
+ num_classes: int,
+ hidden_dim: int,
+ num_queries: int,
+ nheads: int,
+ dim_feedforward: int,
+ dec_layers: int,
+ pre_norm: bool,
+ mask_dim: int,
+ enforce_input_project: bool,
+ # video related
+ num_frames,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ in_channels: channels of the input features
+ mask_classification: whether to add mask classifier or not
+ num_classes: number of classes
+ hidden_dim: Transformer feature dimension
+ num_queries: number of queries
+ nheads: number of heads
+ dim_feedforward: feature dimension in feedforward network
+ enc_layers: number of Transformer encoder layers
+ dec_layers: number of Transformer decoder layers
+ pre_norm: whether to use pre-LayerNorm or not
+ mask_dim: mask feature dimension
+ enforce_input_project: add input project 1x1 conv even if input
+ channels and hidden dim is identical
+ """
+ super().__init__()
+
+ assert mask_classification, "Only support mask classification model"
+ self.mask_classification = mask_classification
+
+ self.num_frames = num_frames
+
+ # positional encoding
+ N_steps = hidden_dim // 2
+ self.pe_layer = PositionEmbeddingSine3D(N_steps, normalize=True)
+
+ # define Transformer decoder here
+ self.num_heads = nheads
+ self.num_layers = dec_layers
+ self.transformer_self_attention_layers = nn.ModuleList()
+ self.transformer_cross_attention_layers = nn.ModuleList()
+ self.transformer_ffn_layers = nn.ModuleList()
+
+ for _ in range(self.num_layers):
+ self.transformer_self_attention_layers.append(
+ SelfAttentionLayer(
+ d_model=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.transformer_cross_attention_layers.append(
+ CrossAttentionLayer(
+ d_model=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.transformer_ffn_layers.append(
+ FFNLayer(
+ d_model=hidden_dim,
+ dim_feedforward=dim_feedforward,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.decoder_norm = nn.LayerNorm(hidden_dim)
+
+ self.num_queries = num_queries
+ # learnable query features
+ self.query_feat = nn.Embedding(num_queries, hidden_dim)
+ # learnable query p.e.
+ self.query_embed = nn.Embedding(num_queries, hidden_dim)
+
+ # level embedding (we always use 3 scales)
+ self.num_feature_levels = 3
+ self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
+ self.input_proj = nn.ModuleList()
+ for _ in range(self.num_feature_levels):
+ if in_channels != hidden_dim or enforce_input_project:
+ self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))
+ weight_init.c2_xavier_fill(self.input_proj[-1])
+ else:
+ self.input_proj.append(nn.Sequential())
+
+ # output FFNs
+ if self.mask_classification:
+ self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
+ self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+ @classmethod
+ def from_config(cls, cfg, in_channels, mask_classification):
+ ret = {}
+ ret["in_channels"] = in_channels
+ ret["mask_classification"] = mask_classification
+
+ ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
+ ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM
+ ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
+ # Transformer parameters:
+ ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
+ ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
+
+ # NOTE: because we add learnable query features which requires supervision,
+ # we add minus 1 to decoder layers to be consistent with our loss
+ # implementation: that is, number of auxiliary losses is always
+ # equal to number of decoder layers. With learnable query features, the number of
+ # auxiliary losses equals number of decoders plus 1.
+ assert cfg.MODEL.MASK_FORMER.DEC_LAYERS >= 1
+ ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1
+ ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
+ ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ
+
+ ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+
+ ret["num_frames"] = cfg.INPUT.SAMPLING_FRAME_NUM
+
+ return ret
+
+ def forward(self, x, mask_features, mask = None):
+ bt, c_m, h_m, w_m = mask_features.shape
+ bs = bt // self.num_frames if self.training else 1
+ t = bt // bs
+ mask_features = mask_features.view(bs, t, c_m, h_m, w_m)
+
+ # x is a list of multi-scale feature
+ assert len(x) == self.num_feature_levels
+ src = []
+ pos = []
+ size_list = []
+
+ # disable mask, it does not affect performance
+ del mask
+
+ for i in range(self.num_feature_levels):
+ size_list.append(x[i].shape[-2:])
+ pos.append(self.pe_layer(x[i].view(bs, t, -1, size_list[-1][0], size_list[-1][1]), None).flatten(3))
+ src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])
+
+ # NTxCxHW => NxTxCxHW => (TxHW)xNxC
+ _, c, hw = src[-1].shape
+ pos[-1] = pos[-1].view(bs, t, c, hw).permute(1, 3, 0, 2).flatten(0, 1)
+ src[-1] = src[-1].view(bs, t, c, hw).permute(1, 3, 0, 2).flatten(0, 1)
+
+ # QxNxC
+ query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)
+ output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1)
+
+ predictions_class = []
+ predictions_mask = []
+
+ # prediction heads on learnable query features
+ outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0])
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+
+ for i in range(self.num_layers):
+ level_index = i % self.num_feature_levels
+ attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+ # attention: cross-attention first
+ output = self.transformer_cross_attention_layers[i](
+ output, src[level_index],
+ memory_mask=attn_mask,
+ memory_key_padding_mask=None, # here we do not apply masking on padded region
+ pos=pos[level_index], query_pos=query_embed
+ )
+
+ output = self.transformer_self_attention_layers[i](
+ output, tgt_mask=None,
+ tgt_key_padding_mask=None,
+ query_pos=query_embed
+ )
+
+ # FFN
+ output = self.transformer_ffn_layers[i](
+ output
+ )
+
+ 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])
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+
+ assert len(predictions_class) == self.num_layers + 1
+
+ out = {
+ 'pred_logits': predictions_class[-1],
+ 'pred_masks': predictions_mask[-1],
+ 'aux_outputs': self._set_aux_loss(
+ predictions_class if self.mask_classification else None, predictions_mask
+ )
+ }
+ return out
+
+ def forward_prediction_heads(self, output, mask_features, attn_mask_target_size):
+ decoder_output = self.decoder_norm(output)
+ decoder_output = decoder_output.transpose(0, 1)
+ outputs_class = self.class_embed(decoder_output)
+ mask_embed = self.mask_embed(decoder_output)
+ outputs_mask = torch.einsum("bqc,btchw->bqthw", mask_embed, mask_features)
+ b, q, t, _, _ = outputs_mask.shape
+
+ # NOTE: prediction is of higher-resolution
+ # [B, Q, T, H, W] -> [B, Q, T*H*W] -> [B, h, Q, T*H*W] -> [B*h, Q, T*HW]
+ attn_mask = F.interpolate(outputs_mask.flatten(0, 1), size=attn_mask_target_size, mode="bilinear", align_corners=False).view(
+ b, q, t, attn_mask_target_size[0], attn_mask_target_size[1])
+ # must use bool type
+ # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.
+ attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()
+ attn_mask = attn_mask.detach()
+
+ return outputs_class, outputs_mask, attn_mask
+
+ @torch.jit.unused
+ def _set_aux_loss(self, outputs_class, outputs_seg_masks):
+ # this is a workaround to make torchscript happy, as torchscript
+ # doesn't support dictionary with non-homogeneous values, such
+ # as a dict having both a Tensor and a list.
+ if self.mask_classification:
+ return [
+ {"pred_logits": a, "pred_masks": b}
+ for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])
+ ]
+ else:
+ return [{"pred_masks": b} for b in outputs_seg_masks[:-1]]
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/utils/__init__.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/utils/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/utils/memory.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/utils/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ee5f15d279f85f1738f16e1a5a613d79e26f89a
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/utils/memory.py
@@ -0,0 +1,80 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import logging
+from contextlib import contextmanager
+from functools import wraps
+import torch
+from torch.cuda.amp import autocast
+
+__all__ = ["retry_if_cuda_oom"]
+
+
+@contextmanager
+def _ignore_torch_cuda_oom():
+ """
+ A context which ignores CUDA OOM exception from pytorch.
+ """
+ try:
+ yield
+ except RuntimeError as e:
+ # NOTE: the string may change?
+ if "CUDA out of memory. " in str(e):
+ pass
+ else:
+ raise
+
+
+def retry_if_cuda_oom(func):
+ """
+ Makes a function retry itself after encountering
+ pytorch's CUDA OOM error.
+ It will first retry after calling `torch.cuda.empty_cache()`.
+ If that still fails, it will then retry by trying to convert inputs to CPUs.
+ In this case, it expects the function to dispatch to CPU implementation.
+ The return values may become CPU tensors as well and it's user's
+ responsibility to convert it back to CUDA tensor if needed.
+ Args:
+ func: a stateless callable that takes tensor-like objects as arguments
+ Returns:
+ a callable which retries `func` if OOM is encountered.
+ Examples:
+ ::
+ output = retry_if_cuda_oom(some_torch_function)(input1, input2)
+ # output may be on CPU even if inputs are on GPU
+ Note:
+ 1. When converting inputs to CPU, it will only look at each argument and check
+ if it has `.device` and `.to` for conversion. Nested structures of tensors
+ are not supported.
+ 2. Since the function might be called more than once, it has to be
+ stateless.
+ """
+
+ def maybe_to_cpu(x):
+ try:
+ like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to")
+ except AttributeError:
+ like_gpu_tensor = False
+ if like_gpu_tensor:
+ return x.to(device="cpu").to(torch.float32)
+ else:
+ return x
+
+ @wraps(func)
+ def wrapped(*args, **kwargs):
+ with _ignore_torch_cuda_oom():
+ return func(*args, **kwargs)
+
+ # Clear cache and retry
+ torch.cuda.empty_cache()
+ with _ignore_torch_cuda_oom():
+ return func(*args, **kwargs)
+
+ # Try on CPU. This slows down the code significantly, therefore print a notice.
+ logger = logging.getLogger(__name__)
+ logger.info("Attempting to copy inputs to CPU due to CUDA OOM")
+ new_args = (maybe_to_cpu(x) for x in args)
+ new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}
+ with autocast(enabled=False):
+ return func(*new_args, **new_kwargs)
+
+ return wrapped
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/video_maskformer_model.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/video_maskformer_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f62ca0c053cf59496e58170d9b64e39804ed71a0
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/mask2former_video/video_maskformer_model.py
@@ -0,0 +1,287 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+import math
+from typing import Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import MetadataCatalog
+from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
+from detectron2.modeling.backbone import Backbone
+from detectron2.modeling.postprocessing import sem_seg_postprocess
+from detectron2.structures import Boxes, ImageList, Instances, BitMasks
+
+from .modeling.criterion import VideoSetCriterion
+from .modeling.matcher import VideoHungarianMatcher
+from .utils.memory import retry_if_cuda_oom
+
+logger = logging.getLogger(__name__)
+
+
+@META_ARCH_REGISTRY.register()
+class VideoMaskFormer(nn.Module):
+ """
+ Main class for mask classification semantic segmentation architectures.
+ """
+
+ @configurable
+ def __init__(
+ self,
+ *,
+ backbone: Backbone,
+ sem_seg_head: nn.Module,
+ criterion: nn.Module,
+ num_queries: int,
+ object_mask_threshold: float,
+ overlap_threshold: float,
+ metadata,
+ size_divisibility: int,
+ sem_seg_postprocess_before_inference: bool,
+ pixel_mean: Tuple[float],
+ pixel_std: Tuple[float],
+ # video
+ num_frames,
+ ):
+ """
+ Args:
+ backbone: a backbone module, must follow detectron2's backbone interface
+ sem_seg_head: a module that predicts semantic segmentation from backbone features
+ criterion: a module that defines the loss
+ num_queries: int, number of queries
+ object_mask_threshold: float, threshold to filter query based on classification score
+ for panoptic segmentation inference
+ overlap_threshold: overlap threshold used in general inference for panoptic segmentation
+ metadata: dataset meta, get `thing` and `stuff` category names for panoptic
+ segmentation inference
+ size_divisibility: Some backbones require the input height and width to be divisible by a
+ specific integer. We can use this to override such requirement.
+ sem_seg_postprocess_before_inference: whether to resize the prediction back
+ to original input size before semantic segmentation inference or after.
+ For high-resolution dataset like Mapillary, resizing predictions before
+ inference will cause OOM error.
+ pixel_mean, pixel_std: list or tuple with #channels element, representing
+ the per-channel mean and std to be used to normalize the input image
+ semantic_on: bool, whether to output semantic segmentation prediction
+ instance_on: bool, whether to output instance segmentation prediction
+ panoptic_on: bool, whether to output panoptic segmentation prediction
+ test_topk_per_image: int, instance segmentation parameter, keep topk instances per image
+ """
+ super().__init__()
+ self.backbone = backbone
+ self.sem_seg_head = sem_seg_head
+ self.criterion = criterion
+ self.num_queries = num_queries
+ self.overlap_threshold = overlap_threshold
+ self.object_mask_threshold = object_mask_threshold
+ self.metadata = metadata
+ if size_divisibility < 0:
+ # use backbone size_divisibility if not set
+ size_divisibility = self.backbone.size_divisibility
+ self.size_divisibility = size_divisibility
+ self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference
+ self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+ self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+ self.num_frames = num_frames
+
+ @classmethod
+ def from_config(cls, cfg):
+ backbone = build_backbone(cfg)
+ sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
+
+ # Loss parameters:
+ deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
+ no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT
+
+ # loss weights
+ class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT
+ dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT
+ mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT
+
+ # building criterion
+ matcher = VideoHungarianMatcher(
+ cost_class=class_weight,
+ cost_mask=mask_weight,
+ cost_dice=dice_weight,
+ num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS,
+ )
+
+ weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight}
+
+ if deep_supervision:
+ dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS
+ aux_weight_dict = {}
+ for i in range(dec_layers - 1):
+ aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
+ weight_dict.update(aux_weight_dict)
+
+ losses = ["labels", "masks"]
+
+ criterion = VideoSetCriterion(
+ sem_seg_head.num_classes,
+ matcher=matcher,
+ weight_dict=weight_dict,
+ eos_coef=no_object_weight,
+ losses=losses,
+ num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS,
+ oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO,
+ importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO,
+ )
+
+ return {
+ "backbone": backbone,
+ "sem_seg_head": sem_seg_head,
+ "criterion": criterion,
+ "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES,
+ "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD,
+ "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD,
+ "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
+ "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY,
+ "sem_seg_postprocess_before_inference": True,
+ "pixel_mean": cfg.MODEL.PIXEL_MEAN,
+ "pixel_std": cfg.MODEL.PIXEL_STD,
+ # video
+ "num_frames": cfg.INPUT.SAMPLING_FRAME_NUM,
+ }
+
+ @property
+ def device(self):
+ return self.pixel_mean.device
+
+ def forward(self, batched_inputs):
+ """
+ Args:
+ batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
+ Each item in the list contains the inputs for one image.
+ For now, each item in the list is a dict that contains:
+ * "image": Tensor, image in (C, H, W) format.
+ * "instances": per-region ground truth
+ * Other information that's included in the original dicts, such as:
+ "height", "width" (int): the output resolution of the model (may be different
+ from input resolution), used in inference.
+ Returns:
+ list[dict]:
+ each dict has the results for one image. The dict contains the following keys:
+
+ * "sem_seg":
+ A Tensor that represents the
+ per-pixel segmentation prediced by the head.
+ The prediction has shape KxHxW that represents the logits of
+ each class for each pixel.
+ * "panoptic_seg":
+ A tuple that represent panoptic output
+ panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
+ segments_info (list[dict]): Describe each segment in `panoptic_seg`.
+ Each dict contains keys "id", "category_id", "isthing".
+ """
+ images = []
+ for video in batched_inputs:
+ for frame in video["image"]:
+ images.append(frame.to(self.device))
+ images = [(x - self.pixel_mean) / self.pixel_std for x in images]
+ images = ImageList.from_tensors(images, self.size_divisibility)
+
+ features = self.backbone(images.tensor)
+ outputs = self.sem_seg_head(features)
+
+ if self.training:
+ # mask classification target
+ targets = self.prepare_targets(batched_inputs, images)
+
+ # bipartite matching-based loss
+ losses = self.criterion(outputs, targets)
+
+ for k in list(losses.keys()):
+ if k in self.criterion.weight_dict:
+ losses[k] *= self.criterion.weight_dict[k]
+ else:
+ # remove this loss if not specified in `weight_dict`
+ losses.pop(k)
+ return losses
+ else:
+ mask_cls_results = outputs["pred_logits"]
+ mask_pred_results = outputs["pred_masks"]
+
+ mask_cls_result = mask_cls_results[0]
+ # upsample masks
+ mask_pred_result = retry_if_cuda_oom(F.interpolate)(
+ mask_pred_results[0],
+ size=(images.tensor.shape[-2], images.tensor.shape[-1]),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ del outputs
+
+ input_per_image = batched_inputs[0]
+ image_size = images.image_sizes[0] # image size without padding after data augmentation
+
+ height = input_per_image.get("height", image_size[0]) # raw image size before data augmentation
+ width = input_per_image.get("width", image_size[1])
+
+ return retry_if_cuda_oom(self.inference_video)(mask_cls_result, mask_pred_result, image_size, height, width)
+
+ def prepare_targets(self, targets, images):
+ h_pad, w_pad = images.tensor.shape[-2:]
+ gt_instances = []
+ for targets_per_video in targets:
+ _num_instance = len(targets_per_video["instances"][0])
+ mask_shape = [_num_instance, self.num_frames, h_pad, w_pad]
+ gt_masks_per_video = torch.zeros(mask_shape, dtype=torch.bool, device=self.device)
+
+ gt_ids_per_video = []
+ for f_i, targets_per_frame in enumerate(targets_per_video["instances"]):
+ targets_per_frame = targets_per_frame.to(self.device)
+ h, w = targets_per_frame.image_size
+
+ gt_ids_per_video.append(targets_per_frame.gt_ids[:, None])
+ gt_masks_per_video[:, f_i, :h, :w] = targets_per_frame.gt_masks.tensor
+
+ gt_ids_per_video = torch.cat(gt_ids_per_video, dim=1)
+ valid_idx = (gt_ids_per_video != -1).any(dim=-1)
+
+ gt_classes_per_video = targets_per_frame.gt_classes[valid_idx] # N,
+ gt_ids_per_video = gt_ids_per_video[valid_idx] # N, num_frames
+
+ gt_instances.append({"labels": gt_classes_per_video, "ids": gt_ids_per_video})
+ gt_masks_per_video = gt_masks_per_video[valid_idx].float() # N, num_frames, H, W
+ gt_instances[-1].update({"masks": gt_masks_per_video})
+
+ return gt_instances
+
+ def inference_video(self, pred_cls, pred_masks, img_size, output_height, output_width):
+ if len(pred_cls) > 0:
+ scores = F.softmax(pred_cls, dim=-1)[:, :-1]
+ labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)
+ # keep top-10 predictions
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(10, sorted=False)
+ labels_per_image = labels[topk_indices]
+ topk_indices = topk_indices // self.sem_seg_head.num_classes
+ pred_masks = pred_masks[topk_indices]
+
+ pred_masks = pred_masks[:, :, : img_size[0], : img_size[1]]
+ pred_masks = F.interpolate(
+ pred_masks, size=(output_height, output_width), mode="bilinear", align_corners=False
+ )
+
+ masks = pred_masks > 0.
+
+ out_scores = scores_per_image.tolist()
+ out_labels = labels_per_image.tolist()
+ out_masks = [m for m in masks.cpu()]
+ else:
+ out_scores = []
+ out_labels = []
+ out_masks = []
+
+ video_output = {
+ "image_size": (output_height, output_width),
+ "pred_scores": out_scores,
+ "pred_labels": out_labels,
+ "pred_masks": out_masks,
+ }
+
+ return video_output
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/predict.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..453479c2429b65aa37bb8f8cdad70101ea7f6988
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/predict.py
@@ -0,0 +1,54 @@
+import sys
+sys.path.insert(0, "Mask2Former")
+import tempfile
+from pathlib import Path
+import numpy as np
+import cv2
+import cog
+
+# import some common detectron2 utilities
+from detectron2.config import CfgNode as CN
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer, ColorMode
+from detectron2.data import MetadataCatalog
+from detectron2.projects.deeplab import add_deeplab_config
+
+# import Mask2Former project
+from mask2former import add_maskformer2_config
+
+
+class Predictor(cog.Predictor):
+ def setup(self):
+ cfg = get_cfg()
+ add_deeplab_config(cfg)
+ add_maskformer2_config(cfg)
+ cfg.merge_from_file("Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml")
+ cfg.MODEL.WEIGHTS = 'model_final_f07440.pkl'
+ cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
+ cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True
+ cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True
+ self.predictor = DefaultPredictor(cfg)
+ self.coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic")
+
+
+ @cog.input(
+ "image",
+ type=Path,
+ help="Input image for segmentation. Output will be the concatenation of Panoptic segmentation (top), "
+ "instance segmentation (middle), and semantic segmentation (bottom).",
+ )
+ def predict(self, image):
+ im = cv2.imread(str(image))
+ outputs = self.predictor(im)
+ v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
+ panoptic_result = v.draw_panoptic_seg(outputs["panoptic_seg"][0].to("cpu"),
+ outputs["panoptic_seg"][1]).get_image()
+ v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
+ instance_result = v.draw_instance_predictions(outputs["instances"].to("cpu")).get_image()
+ v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
+ semantic_result = v.draw_sem_seg(outputs["sem_seg"].argmax(0).to("cpu")).get_image()
+ result = np.concatenate((panoptic_result, instance_result, semantic_result), axis=0)[:, :, ::-1]
+ out_path = Path(tempfile.mkdtemp()) / "out.png"
+ cv2.imwrite(str(out_path), result)
+ return out_path
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/requirements.txt b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cceed66a846f6afe22cdd69d86c17b231e5d8b72
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/requirements.txt
@@ -0,0 +1,7 @@
+cython
+scipy
+shapely
+timm
+h5py
+submitit
+scikit-image
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/setup.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..399dfbb3be7810956a62be85e6c2dd8df39711d5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/setup.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import glob
+import os
+from os import path
+from setuptools import find_packages, setup
+import torch
+from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
+
+torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
+assert torch_ver >= [1, 8], "Requires PyTorch >= 1.8"
+
+
+def get_version():
+ init_py_path = path.join(path.abspath(path.dirname(__file__)), "mask2former", "__init__.py")
+ init_py = open(init_py_path, "r").readlines()
+ version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
+ version = version_line.split("=")[-1].strip().strip("'\"")
+
+ return version
+
+
+# Copied from Detectron2
+def get_extensions():
+ # skip building
+ if not (os.environ.get("FORCE_CUDA") or torch.cuda.is_available()) or CUDA_HOME is None:
+ return []
+
+ this_dir = os.path.dirname(os.path.abspath(__file__))
+ extensions_dir = os.path.join(this_dir, "mask2former/modeling/pixel_decoder/ops/src")
+
+ main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
+ source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
+ source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
+
+ sources = main_file + source_cpu
+ extension = CppExtension
+ extra_compile_args = {"cxx": []}
+ define_macros = []
+
+ # Force cuda since torch ask for a device, not if cuda is in fact available.
+ if (os.environ.get("FORCE_CUDA") or torch.cuda.is_available()) and CUDA_HOME is not None:
+ extension = CUDAExtension
+ sources += source_cuda
+ define_macros += [("WITH_CUDA", None)]
+ extra_compile_args["nvcc"] = [
+ "-DCUDA_HAS_FP16=1",
+ "-D__CUDA_NO_HALF_OPERATORS__",
+ "-D__CUDA_NO_HALF_CONVERSIONS__",
+ "-D__CUDA_NO_HALF2_OPERATORS__",
+ ]
+ else:
+ if CUDA_HOME is None:
+ raise NotImplementedError(
+ "CUDA_HOME is None. Please set environment variable CUDA_HOME."
+ )
+ else:
+ raise NotImplementedError(
+ "No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available()." # noqa
+ )
+
+ sources = [os.path.join(extensions_dir, s) for s in sources]
+ include_dirs = [extensions_dir]
+ ext_modules = [
+ extension(
+ "MultiScaleDeformableAttention",
+ sources,
+ include_dirs=include_dirs,
+ define_macros=define_macros,
+ extra_compile_args=extra_compile_args,
+ )
+ ]
+ return ext_modules
+
+
+setup(
+ name="mask2former",
+ version=get_version(),
+ author="Bowen Cheng", # Thanks Bowen!
+ url="https://github.com/facebook/mask2former",
+ description="A pip installable version of mask2former",
+ packages=find_packages(exclude=("configs", "tests*")),
+ python_requires=">=3.6",
+ install_requires=[
+ "detectron2 @ https://github.com/facebookresearch/detectron2/archive/v0.6.zip",
+ "scipy>=1.7.3",
+ "boto3>=1.21.25",
+ "hydra-core==1.1.1",
+ # there is BC breaking in omegaconf 2.2.1
+ # see: https://github.com/omry/omegaconf/issues/939
+ "omegaconf==2.1.1",
+ "panopticapi @ https://github.com/cocodataset/panopticapi/archive/master.zip",
+ "lvis @ https://github.com/lvis-dataset/lvis-api/archive/master.zip",
+ ],
+ ext_modules=get_extensions(),
+ cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/README.md b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..309f767b8f3ae10208107e72032f64d18f429a8c
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/README.md
@@ -0,0 +1,79 @@
+This directory contains few tools for MaskFormer.
+
+* `convert-torchvision-to-d2.py`
+
+Tool to convert torchvision pre-trained weights for D2.
+
+```
+wget https://download.pytorch.org/models/resnet101-63fe2227.pth
+python tools/convert-torchvision-to-d2.py resnet101-63fe2227.pth R-101.pkl
+```
+
+* `convert-pretrained-swin-model-to-d2.py`
+
+Tool to convert Swin Transformer pre-trained weights for D2.
+
+```
+pip install timm
+
+wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
+python tools/convert-pretrained-swin-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl
+
+wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth
+python tools/convert-pretrained-swin-model-to-d2.py swin_small_patch4_window7_224.pth swin_small_patch4_window7_224.pkl
+
+wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth
+python tools/convert-pretrained-swin-model-to-d2.py swin_base_patch4_window12_384_22k.pth swin_base_patch4_window12_384_22k.pkl
+
+wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth
+python tools/convert-pretrained-swin-model-to-d2.py swin_large_patch4_window12_384_22k.pth swin_large_patch4_window12_384_22k.pkl
+```
+
+* `evaluate_pq_for_semantic_segmentation.py`
+
+Tool to evaluate PQ (PQ-stuff) for semantic segmentation predictions.
+
+Usage:
+
+```
+python tools/evaluate_pq_for_semantic_segmentation.py --dataset-name ade20k_sem_seg_val --json-file OUTPUT_DIR/inference/sem_seg_predictions.json
+```
+
+where `OUTPUT_DIR` is set in the config file.
+
+* `evaluate_coco_boundary_ap.py`
+
+Tool to evaluate Boundary AP for instance segmentation predictions.
+
+Usage:
+
+```
+python tools/coco_instance_evaluation.py --gt-json-file COCO_GT_JSON --dt-json-file COCO_DT_JSON
+```
+
+To install Boundary IoU API, run:
+
+```
+pip install git+https://github.com/bowenc0221/boundary-iou-api.git
+```
+
+* `analyze_model.py`
+
+Tool to analyze model parameters and flops.
+
+Usage for semantic segmentation (ADE20K only, use with caution!):
+
+```
+python tools/analyze_model.py --num-inputs 1 --tasks flop --use-fixed-input-size --config-file CONFIG_FILE
+```
+
+Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to `cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]`.
+Please do not use `--use-fixed-input-size` for calculating FLOPs on other datasets like Cityscapes!
+
+Usage for panoptic and instance segmentation:
+
+```
+python tools/analyze_model.py --num-inputs 100 --tasks flop --config-file CONFIG_FILE
+```
+
+Note that, for panoptic and instance segmentation, we compute the average flops over 100 real validation images.
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/analyze_model.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/analyze_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..a92227e2a530bbdcbdf6d72bc9ed93886f24a7bb
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/analyze_model.py
@@ -0,0 +1,177 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detectron2/blob/main/tools/analyze_model.py
+
+import logging
+import numpy as np
+from collections import Counter
+import tqdm
+from fvcore.nn import flop_count_table # can also try flop_count_str
+
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
+from detectron2.data import build_detection_test_loader
+from detectron2.engine import default_argument_parser
+from detectron2.modeling import build_model
+from detectron2.projects.deeplab import add_deeplab_config
+from detectron2.utils.analysis import (
+ FlopCountAnalysis,
+ activation_count_operators,
+ parameter_count_table,
+)
+from detectron2.utils.logger import setup_logger
+
+# fmt: off
+import os
+import sys
+sys.path.insert(1, os.path.join(sys.path[0], '..'))
+# fmt: on
+
+from mask2former import add_maskformer2_config
+
+logger = logging.getLogger("detectron2")
+
+
+def setup(args):
+ if args.config_file.endswith(".yaml"):
+ cfg = get_cfg()
+ add_deeplab_config(cfg)
+ add_maskformer2_config(cfg)
+ cfg.merge_from_file(args.config_file)
+ cfg.DATALOADER.NUM_WORKERS = 0
+ cfg.merge_from_list(args.opts)
+ cfg.freeze()
+ else:
+ cfg = LazyConfig.load(args.config_file)
+ cfg = LazyConfig.apply_overrides(cfg, args.opts)
+ setup_logger(name="fvcore")
+ setup_logger()
+ return cfg
+
+
+def do_flop(cfg):
+ if isinstance(cfg, CfgNode):
+ data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
+ model = build_model(cfg)
+ DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
+ else:
+ data_loader = instantiate(cfg.dataloader.test)
+ model = instantiate(cfg.model)
+ model.to(cfg.train.device)
+ DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
+ model.eval()
+
+ counts = Counter()
+ total_flops = []
+ for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa
+ if args.use_fixed_input_size and isinstance(cfg, CfgNode):
+ import torch
+ crop_size = cfg.INPUT.CROP.SIZE[0]
+ data[0]["image"] = torch.zeros((3, crop_size, crop_size))
+ flops = FlopCountAnalysis(model, data)
+ if idx > 0:
+ flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False)
+ counts += flops.by_operator()
+ total_flops.append(flops.total())
+
+ logger.info("Flops table computed from only one input sample:\n" + flop_count_table(flops))
+ logger.info(
+ "Average GFlops for each type of operators:\n"
+ + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()])
+ )
+ logger.info(
+ "Total GFlops: {:.1f}±{:.1f}".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9)
+ )
+
+
+def do_activation(cfg):
+ if isinstance(cfg, CfgNode):
+ data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
+ model = build_model(cfg)
+ DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
+ else:
+ data_loader = instantiate(cfg.dataloader.test)
+ model = instantiate(cfg.model)
+ model.to(cfg.train.device)
+ DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
+ model.eval()
+
+ counts = Counter()
+ total_activations = []
+ for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa
+ count = activation_count_operators(model, data)
+ counts += count
+ total_activations.append(sum(count.values()))
+ logger.info(
+ "(Million) Activations for Each Type of Operators:\n"
+ + str([(k, v / idx) for k, v in counts.items()])
+ )
+ logger.info(
+ "Total (Million) Activations: {}±{}".format(
+ np.mean(total_activations), np.std(total_activations)
+ )
+ )
+
+
+def do_parameter(cfg):
+ if isinstance(cfg, CfgNode):
+ model = build_model(cfg)
+ else:
+ model = instantiate(cfg.model)
+ logger.info("Parameter Count:\n" + parameter_count_table(model, max_depth=5))
+
+
+def do_structure(cfg):
+ if isinstance(cfg, CfgNode):
+ model = build_model(cfg)
+ else:
+ model = instantiate(cfg.model)
+ logger.info("Model Structure:\n" + str(model))
+
+
+if __name__ == "__main__":
+ parser = default_argument_parser(
+ epilog="""
+Examples:
+To show parameters of a model:
+$ ./analyze_model.py --tasks parameter \\
+ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
+Flops and activations are data-dependent, therefore inputs and model weights
+are needed to count them:
+$ ./analyze_model.py --num-inputs 100 --tasks flop \\
+ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\
+ MODEL.WEIGHTS /path/to/model.pkl
+"""
+ )
+ parser.add_argument(
+ "--tasks",
+ choices=["flop", "activation", "parameter", "structure"],
+ required=True,
+ nargs="+",
+ )
+ parser.add_argument(
+ "-n",
+ "--num-inputs",
+ default=100,
+ type=int,
+ help="number of inputs used to compute statistics for flops/activations, "
+ "both are data dependent.",
+ )
+ parser.add_argument(
+ "--use-fixed-input-size",
+ action="store_true",
+ help="use fixed input size when calculating flops",
+ )
+ args = parser.parse_args()
+ assert not args.eval_only
+ assert args.num_gpus == 1
+
+ cfg = setup(args)
+
+ for task in args.tasks:
+ {
+ "flop": do_flop,
+ "activation": do_activation,
+ "parameter": do_parameter,
+ "structure": do_structure,
+ }[task](cfg)
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/convert-pretrained-swin-model-to-d2.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/convert-pretrained-swin-model-to-d2.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbaeab990743e60bfe9f6197cbf3bf8585abcb6
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/convert-pretrained-swin-model-to-d2.py
@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+import pickle as pkl
+import sys
+
+import torch
+
+"""
+Usage:
+ # download pretrained swin model:
+ wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
+ # run the conversion
+ ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl
+ # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config:
+MODEL:
+ WEIGHTS: "/path/to/swin_tiny_patch4_window7_224.pkl"
+INPUT:
+ FORMAT: "RGB"
+"""
+
+if __name__ == "__main__":
+ input = sys.argv[1]
+
+ obj = torch.load(input, map_location="cpu")["model"]
+
+ res = {"model": obj, "__author__": "third_party", "matching_heuristics": True}
+
+ with open(sys.argv[2], "wb") as f:
+ pkl.dump(res, f)
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/convert-torchvision-to-d2.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/convert-torchvision-to-d2.py
new file mode 100644
index 0000000000000000000000000000000000000000..060390a2c26297d7d9ee97ceb601453c9372f1d5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/convert-torchvision-to-d2.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import pickle as pkl
+import sys
+
+import torch
+
+"""
+Usage:
+ # download one of the ResNet{18,34,50,101,152} models from torchvision:
+ wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
+ # run the conversion
+ ./convert-torchvision-to-d2.py r50.pth r50.pkl
+ # Then, use r50.pkl with the following changes in config:
+MODEL:
+ WEIGHTS: "/path/to/r50.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ RESNETS:
+ DEPTH: 50
+ STRIDE_IN_1X1: False
+INPUT:
+ FORMAT: "RGB"
+"""
+
+if __name__ == "__main__":
+ input = sys.argv[1]
+
+ obj = torch.load(input, map_location="cpu")
+
+ newmodel = {}
+ for k in list(obj.keys()):
+ old_k = k
+ if "layer" not in k:
+ k = "stem." + k
+ for t in [1, 2, 3, 4]:
+ k = k.replace("layer{}".format(t), "res{}".format(t + 1))
+ for t in [1, 2, 3]:
+ k = k.replace("bn{}".format(t), "conv{}.norm".format(t))
+ k = k.replace("downsample.0", "shortcut")
+ k = k.replace("downsample.1", "shortcut.norm")
+ print(old_k, "->", k)
+ newmodel[k] = obj.pop(old_k).detach().numpy()
+
+ res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True}
+
+ with open(sys.argv[2], "wb") as f:
+ pkl.dump(res, f)
+ if obj:
+ print("Unconverted keys:", obj.keys())
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/evaluate_coco_boundary_ap.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/evaluate_coco_boundary_ap.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e96b5d2f7d3f0987098904e8f5d97854906d58a
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/evaluate_coco_boundary_ap.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Modified by Bowen Cheng from: https://github.com/bowenc0221/boundary-iou-api/blob/master/tools/coco_instance_evaluation.py
+
+"""
+Evaluation for COCO val2017:
+python ./tools/coco_instance_evaluation.py \
+ --gt-json-file COCO_GT_JSON \
+ --dt-json-file COCO_DT_JSON
+"""
+import argparse
+import json
+
+from boundary_iou.coco_instance_api.coco import COCO
+from boundary_iou.coco_instance_api.cocoeval import COCOeval
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--gt-json-file", default="")
+ parser.add_argument("--dt-json-file", default="")
+ parser.add_argument("--iou-type", default="boundary")
+ parser.add_argument("--dilation-ratio", default="0.020", type=float)
+ args = parser.parse_args()
+ print(args)
+
+ annFile = args.gt_json_file
+ resFile = args.dt_json_file
+ dilation_ratio = args.dilation_ratio
+ if args.iou_type == "boundary":
+ get_boundary = True
+ else:
+ get_boundary = False
+ cocoGt = COCO(annFile, get_boundary=get_boundary, dilation_ratio=dilation_ratio)
+
+ # remove box predictions
+ resFile = json.load(open(resFile))
+ for c in resFile:
+ c.pop("bbox", None)
+
+ cocoDt = cocoGt.loadRes(resFile)
+ cocoEval = COCOeval(cocoGt, cocoDt, iouType=args.iou_type, dilation_ratio=dilation_ratio)
+ cocoEval.evaluate()
+ cocoEval.accumulate()
+ cocoEval.summarize()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/evaluate_pq_for_semantic_segmentation.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/evaluate_pq_for_semantic_segmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..d22d735b632d885bb213de7804dad68b6068a8d0
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/tools/evaluate_pq_for_semantic_segmentation.py
@@ -0,0 +1,245 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import argparse
+import json
+import os
+from collections import defaultdict
+from tqdm import tqdm
+
+import numpy as np
+import torch
+
+from detectron2.data import MetadataCatalog
+from detectron2.data.detection_utils import read_image
+from detectron2.utils.file_io import PathManager
+from pycocotools import mask as maskUtils
+
+from panopticapi.evaluation import PQStat
+
+
+def default_argument_parser():
+ """
+ Creates a parser with some common arguments used by analysis tools.
+ Returns:
+ argparse.ArgumentParser:
+ """
+ parser = argparse.ArgumentParser(description="Evaluate PQ metric for semantic segmentation.")
+ # NOTE: currently does not support Cityscapes, you need to convert
+ # Cityscapes prediction format to Detectron2 prediction format.
+ parser.add_argument(
+ "--dataset-name",
+ default="ade20k_sem_seg_val",
+ choices=["ade20k_sem_seg_val", "coco_2017_test_stuff_10k_sem_seg", "ade20k_full_sem_seg_val"],
+ help="dataset name you want to evaluate")
+ parser.add_argument("--json-file", default="", help="path to detection json file")
+
+ return parser
+
+
+# Modified from the official panoptic api: https://github.com/cocodataset/panopticapi/blob/master/panopticapi/evaluation.py
+def pq_compute_single_image(segm_gt, segm_dt, categories, ignore_label):
+ pq_stat = PQStat()
+ VOID = ignore_label
+ OFFSET = 256 * 256 * 256
+
+ pan_gt = segm_gt
+ pan_pred = segm_dt
+
+ gt_ann = {'segments_info': []}
+ labels, labels_cnt = np.unique(segm_gt, return_counts=True)
+ for cat_id, cnt in zip(labels, labels_cnt):
+ if cat_id == VOID:
+ continue
+ gt_ann['segments_info'].append(
+ {"id": cat_id, "category_id": cat_id, "area": cnt, "iscrowd": 0}
+ )
+
+ pred_ann = {'segments_info': []}
+ for cat_id in np.unique(segm_dt):
+ pred_ann['segments_info'].append({"id": cat_id, "category_id": cat_id})
+
+ gt_segms = {el['id']: el for el in gt_ann['segments_info']}
+ pred_segms = {el['id']: el for el in pred_ann['segments_info']}
+
+ # predicted segments area calculation + prediction sanity checks
+ pred_labels_set = set(el['id'] for el in pred_ann['segments_info'])
+ labels, labels_cnt = np.unique(pan_pred, return_counts=True)
+ for label, label_cnt in zip(labels, labels_cnt):
+ if label not in pred_segms:
+ if label == VOID:
+ continue
+ raise KeyError('In the image with ID {} segment with ID {} is presented in PNG and not presented in JSON.'.format(image_id, label))
+ pred_segms[label]['area'] = label_cnt
+ pred_labels_set.remove(label)
+ if pred_segms[label]['category_id'] not in categories:
+ raise KeyError('In the image with ID {} segment with ID {} has unknown category_id {}.'.format(image_id, label, pred_segms[label]['category_id']))
+ if len(pred_labels_set) != 0:
+ raise KeyError('In the image with ID {} the following segment IDs {} are presented in JSON and not presented in PNG.'.format(image_id, list(pred_labels_set)))
+
+ # confusion matrix calculation
+ pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(np.uint64)
+ gt_pred_map = {}
+ labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True)
+ for label, intersection in zip(labels, labels_cnt):
+ gt_id = label // OFFSET
+ pred_id = label % OFFSET
+ gt_pred_map[(gt_id, pred_id)] = intersection
+
+ # count all matched pairs
+ gt_matched = set()
+ pred_matched = set()
+ for label_tuple, intersection in gt_pred_map.items():
+ gt_label, pred_label = label_tuple
+ if gt_label not in gt_segms:
+ continue
+ if pred_label not in pred_segms:
+ continue
+ if gt_segms[gt_label]['iscrowd'] == 1:
+ continue
+ if gt_segms[gt_label]['category_id'] != pred_segms[pred_label]['category_id']:
+ continue
+
+ union = pred_segms[pred_label]['area'] + gt_segms[gt_label]['area'] - intersection - gt_pred_map.get((VOID, pred_label), 0)
+ iou = intersection / union
+ if iou > 0.5:
+ pq_stat[gt_segms[gt_label]['category_id']].tp += 1
+ pq_stat[gt_segms[gt_label]['category_id']].iou += iou
+ gt_matched.add(gt_label)
+ pred_matched.add(pred_label)
+
+ # count false positives
+ crowd_labels_dict = {}
+ for gt_label, gt_info in gt_segms.items():
+ if gt_label in gt_matched:
+ continue
+ # crowd segments are ignored
+ if gt_info['iscrowd'] == 1:
+ crowd_labels_dict[gt_info['category_id']] = gt_label
+ continue
+ pq_stat[gt_info['category_id']].fn += 1
+
+ # count false positives
+ for pred_label, pred_info in pred_segms.items():
+ if pred_label in pred_matched:
+ continue
+ # intersection of the segment with VOID
+ intersection = gt_pred_map.get((VOID, pred_label), 0)
+ # plus intersection with corresponding CROWD region if it exists
+ if pred_info['category_id'] in crowd_labels_dict:
+ intersection += gt_pred_map.get((crowd_labels_dict[pred_info['category_id']], pred_label), 0)
+ # predicted segment is ignored if more than half of the segment correspond to VOID and CROWD regions
+ if intersection / pred_info['area'] > 0.5:
+ continue
+ pq_stat[pred_info['category_id']].fp += 1
+
+ return pq_stat
+
+
+def main():
+ parser = default_argument_parser()
+ args = parser.parse_args()
+
+ _root = os.getenv("DETECTRON2_DATASETS", "datasets")
+ json_file = args.json_file
+
+ with open(json_file) as f:
+ predictions = json.load(f)
+
+ imgToAnns = defaultdict(list)
+ for pred in predictions:
+ image_id = os.path.basename(pred["file_name"]).split(".")[0]
+ imgToAnns[image_id].append(
+ {"category_id" : pred["category_id"], "segmentation" : pred["segmentation"]}
+ )
+
+ image_ids = list(imgToAnns.keys())
+
+ meta = MetadataCatalog.get(args.dataset_name)
+ class_names = meta.stuff_classes
+ num_classes = len(meta.stuff_classes)
+ ignore_label = meta.ignore_label
+ conf_matrix = np.zeros((num_classes + 1, num_classes + 1), dtype=np.int64)
+
+ categories = {}
+ for i in range(num_classes):
+ categories[i] = {"id": i, "name": class_names[i], "isthing": 0}
+
+ pq_stat = PQStat()
+
+ for image_id in tqdm(image_ids):
+ if args.dataset_name == "ade20k_sem_seg_val":
+ gt_dir = os.path.join(_root, "ADEChallengeData2016", "annotations_detectron2", "validation")
+ segm_gt = read_image(os.path.join(gt_dir, image_id + ".png")).copy().astype(np.int64)
+ elif args.dataset_name == "coco_2017_test_stuff_10k_sem_seg":
+ gt_dir = os.path.join(_root, "coco", "coco_stuff_10k", "annotations_detectron2", "test")
+ segm_gt = read_image(os.path.join(gt_dir, image_id + ".png")).copy().astype(np.int64)
+ elif args.dataset_name == "ade20k_full_sem_seg_val":
+ gt_dir = os.path.join(_root, "ADE20K_2021_17_01", "annotations_detectron2", "validation")
+ segm_gt = read_image(os.path.join(gt_dir, image_id + ".tif")).copy().astype(np.int64)
+ else:
+ raise ValueError(f"Unsupported dataset {args.dataset_name}")
+
+ # get predictions
+ segm_dt = np.zeros_like(segm_gt)
+ anns = imgToAnns[image_id]
+ for ann in anns:
+ # map back category_id
+ if hasattr(meta, "stuff_dataset_id_to_contiguous_id"):
+ if ann["category_id"] in meta.stuff_dataset_id_to_contiguous_id:
+ category_id = meta.stuff_dataset_id_to_contiguous_id[ann["category_id"]]
+ else:
+ category_id = ann["category_id"]
+ mask = maskUtils.decode(ann["segmentation"])
+ segm_dt[mask > 0] = category_id
+
+ # miou
+ gt = segm_gt.copy()
+ pred = segm_dt.copy()
+ gt[gt == ignore_label] = num_classes
+ conf_matrix += np.bincount(
+ (num_classes + 1) * pred.reshape(-1) + gt.reshape(-1),
+ minlength=conf_matrix.size,
+ ).reshape(conf_matrix.shape)
+
+ # pq
+ pq_stat_single = pq_compute_single_image(segm_gt, segm_dt, categories, meta.ignore_label)
+ pq_stat += pq_stat_single
+
+ metrics = [("All", None), ("Stuff", False)]
+ results = {}
+ for name, isthing in metrics:
+ results[name], per_class_results = pq_stat.pq_average(categories, isthing=isthing)
+ if name == 'All':
+ results['per_class'] = per_class_results
+ print("{:10s}| {:>5s} {:>5s} {:>5s} {:>5s}".format("", "PQ", "SQ", "RQ", "N"))
+ print("-" * (10 + 7 * 4))
+
+ for name, _isthing in metrics:
+ print("{:10s}| {:5.1f} {:5.1f} {:5.1f} {:5d}".format(
+ name,
+ 100 * results[name]['pq'],
+ 100 * results[name]['sq'],
+ 100 * results[name]['rq'],
+ results[name]['n'])
+ )
+
+ # calculate miou
+ acc = np.full(num_classes, np.nan, dtype=np.float64)
+ iou = np.full(num_classes, np.nan, dtype=np.float64)
+ tp = conf_matrix.diagonal()[:-1].astype(np.float64)
+ pos_gt = np.sum(conf_matrix[:-1, :-1], axis=0).astype(np.float64)
+ pos_pred = np.sum(conf_matrix[:-1, :-1], axis=1).astype(np.float64)
+ acc_valid = pos_gt > 0
+ acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
+ iou_valid = (pos_gt + pos_pred) > 0
+ union = pos_gt + pos_pred - tp
+ iou[acc_valid] = tp[acc_valid] / union[acc_valid]
+ miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
+
+ print("")
+ print(f"mIoU: {miou}")
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/train_net.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/train_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c0af983fccd0b1042e67d1771bee922567d419c
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/train_net.py
@@ -0,0 +1,328 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+MaskFormer Training Script.
+
+This script is a simplified version of the training script in detectron2/tools.
+"""
+try:
+ # ignore ShapelyDeprecationWarning from fvcore
+ from shapely.errors import ShapelyDeprecationWarning
+ import warnings
+ warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
+except:
+ pass
+
+import copy
+import itertools
+import logging
+import os
+
+from collections import OrderedDict
+from typing import Any, Dict, List, Set
+
+import torch
+
+import detectron2.utils.comm as comm
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.config import get_cfg
+from detectron2.data import MetadataCatalog, build_detection_train_loader
+from detectron2.engine import (
+ DefaultTrainer,
+ default_argument_parser,
+ default_setup,
+ launch,
+)
+from detectron2.evaluation import (
+ CityscapesInstanceEvaluator,
+ CityscapesSemSegEvaluator,
+ COCOEvaluator,
+ COCOPanopticEvaluator,
+ DatasetEvaluators,
+ LVISEvaluator,
+ SemSegEvaluator,
+ verify_results,
+)
+from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
+from detectron2.solver.build import maybe_add_gradient_clipping
+from detectron2.utils.logger import setup_logger
+
+# MaskFormer
+from mask2former import (
+ COCOInstanceNewBaselineDatasetMapper,
+ COCOPanopticNewBaselineDatasetMapper,
+ InstanceSegEvaluator,
+ MaskFormerInstanceDatasetMapper,
+ MaskFormerPanopticDatasetMapper,
+ MaskFormerSemanticDatasetMapper,
+ SemanticSegmentorWithTTA,
+ add_maskformer2_config,
+)
+
+
+class Trainer(DefaultTrainer):
+ """
+ Extension of the Trainer class adapted to MaskFormer.
+ """
+
+ @classmethod
+ def build_evaluator(cls, cfg, dataset_name, output_folder=None):
+ """
+ Create evaluator(s) for a given dataset.
+ This uses the special metadata "evaluator_type" associated with each
+ builtin dataset. For your own dataset, you can simply create an
+ evaluator manually in your script and do not have to worry about the
+ hacky if-else logic here.
+ """
+ if output_folder is None:
+ output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
+ evaluator_list = []
+ evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
+ # semantic segmentation
+ if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
+ evaluator_list.append(
+ SemSegEvaluator(
+ dataset_name,
+ distributed=True,
+ output_dir=output_folder,
+ )
+ )
+ # instance segmentation
+ if evaluator_type == "coco":
+ evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
+ # panoptic segmentation
+ if evaluator_type in [
+ "coco_panoptic_seg",
+ "ade20k_panoptic_seg",
+ "cityscapes_panoptic_seg",
+ "mapillary_vistas_panoptic_seg",
+ ]:
+ if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
+ evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
+ # COCO
+ if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
+ evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
+ if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
+ evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
+ # Mapillary Vistas
+ if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
+ evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
+ if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
+ evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
+ # Cityscapes
+ if evaluator_type == "cityscapes_instance":
+ assert (
+ torch.cuda.device_count() > comm.get_rank()
+ ), "CityscapesEvaluator currently do not work with multiple machines."
+ return CityscapesInstanceEvaluator(dataset_name)
+ if evaluator_type == "cityscapes_sem_seg":
+ assert (
+ torch.cuda.device_count() > comm.get_rank()
+ ), "CityscapesEvaluator currently do not work with multiple machines."
+ return CityscapesSemSegEvaluator(dataset_name)
+ if evaluator_type == "cityscapes_panoptic_seg":
+ if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
+ assert (
+ torch.cuda.device_count() > comm.get_rank()
+ ), "CityscapesEvaluator currently do not work with multiple machines."
+ evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
+ if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
+ assert (
+ torch.cuda.device_count() > comm.get_rank()
+ ), "CityscapesEvaluator currently do not work with multiple machines."
+ evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
+ # ADE20K
+ if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
+ evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
+ # LVIS
+ if evaluator_type == "lvis":
+ return LVISEvaluator(dataset_name, output_dir=output_folder)
+ if len(evaluator_list) == 0:
+ raise NotImplementedError(
+ "no Evaluator for the dataset {} with the type {}".format(
+ dataset_name, evaluator_type
+ )
+ )
+ elif len(evaluator_list) == 1:
+ return evaluator_list[0]
+ return DatasetEvaluators(evaluator_list)
+
+ @classmethod
+ def build_train_loader(cls, cfg):
+ # Semantic segmentation dataset mapper
+ if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
+ mapper = MaskFormerSemanticDatasetMapper(cfg, True)
+ return build_detection_train_loader(cfg, mapper=mapper)
+ # Panoptic segmentation dataset mapper
+ elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
+ mapper = MaskFormerPanopticDatasetMapper(cfg, True)
+ return build_detection_train_loader(cfg, mapper=mapper)
+ # Instance segmentation dataset mapper
+ elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance":
+ mapper = MaskFormerInstanceDatasetMapper(cfg, True)
+ return build_detection_train_loader(cfg, mapper=mapper)
+ # coco instance segmentation lsj new baseline
+ elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj":
+ mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)
+ return build_detection_train_loader(cfg, mapper=mapper)
+ # coco panoptic segmentation lsj new baseline
+ elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj":
+ mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)
+ return build_detection_train_loader(cfg, mapper=mapper)
+ else:
+ mapper = None
+ return build_detection_train_loader(cfg, mapper=mapper)
+
+ @classmethod
+ def build_lr_scheduler(cls, cfg, optimizer):
+ """
+ It now calls :func:`detectron2.solver.build_lr_scheduler`.
+ Overwrite it if you'd like a different scheduler.
+ """
+ return build_lr_scheduler(cfg, optimizer)
+
+ @classmethod
+ def build_optimizer(cls, cfg, model):
+ weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
+ weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED
+
+ defaults = {}
+ defaults["lr"] = cfg.SOLVER.BASE_LR
+ defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY
+
+ norm_module_types = (
+ torch.nn.BatchNorm1d,
+ torch.nn.BatchNorm2d,
+ torch.nn.BatchNorm3d,
+ torch.nn.SyncBatchNorm,
+ # NaiveSyncBatchNorm inherits from BatchNorm2d
+ torch.nn.GroupNorm,
+ torch.nn.InstanceNorm1d,
+ torch.nn.InstanceNorm2d,
+ torch.nn.InstanceNorm3d,
+ torch.nn.LayerNorm,
+ torch.nn.LocalResponseNorm,
+ )
+
+ params: List[Dict[str, Any]] = []
+ memo: Set[torch.nn.parameter.Parameter] = set()
+ for module_name, module in model.named_modules():
+ for module_param_name, value in module.named_parameters(recurse=False):
+ if not value.requires_grad:
+ continue
+ # Avoid duplicating parameters
+ if value in memo:
+ continue
+ memo.add(value)
+
+ hyperparams = copy.copy(defaults)
+ if "backbone" in module_name:
+ hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
+ if (
+ "relative_position_bias_table" in module_param_name
+ or "absolute_pos_embed" in module_param_name
+ ):
+ print(module_param_name)
+ hyperparams["weight_decay"] = 0.0
+ if isinstance(module, norm_module_types):
+ hyperparams["weight_decay"] = weight_decay_norm
+ if isinstance(module, torch.nn.Embedding):
+ hyperparams["weight_decay"] = weight_decay_embed
+ params.append({"params": [value], **hyperparams})
+
+ def maybe_add_full_model_gradient_clipping(optim):
+ # detectron2 doesn't have full model gradient clipping now
+ clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
+ enable = (
+ cfg.SOLVER.CLIP_GRADIENTS.ENABLED
+ and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
+ and clip_norm_val > 0.0
+ )
+
+ class FullModelGradientClippingOptimizer(optim):
+ def step(self, closure=None):
+ all_params = itertools.chain(*[x["params"] for x in self.param_groups])
+ torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
+ super().step(closure=closure)
+
+ return FullModelGradientClippingOptimizer if enable else optim
+
+ optimizer_type = cfg.SOLVER.OPTIMIZER
+ if optimizer_type == "SGD":
+ optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
+ params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
+ )
+ elif optimizer_type == "ADAMW":
+ optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
+ params, cfg.SOLVER.BASE_LR
+ )
+ else:
+ raise NotImplementedError(f"no optimizer type {optimizer_type}")
+ if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
+ optimizer = maybe_add_gradient_clipping(cfg, optimizer)
+ return optimizer
+
+ @classmethod
+ def test_with_TTA(cls, cfg, model):
+ logger = logging.getLogger("detectron2.trainer")
+ # In the end of training, run an evaluation with TTA.
+ logger.info("Running inference with test-time augmentation ...")
+ model = SemanticSegmentorWithTTA(cfg, model)
+ evaluators = [
+ cls.build_evaluator(
+ cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
+ )
+ for name in cfg.DATASETS.TEST
+ ]
+ res = cls.test(cfg, model, evaluators)
+ res = OrderedDict({k + "_TTA": v for k, v in res.items()})
+ return res
+
+
+def setup(args):
+ """
+ Create configs and perform basic setups.
+ """
+ cfg = get_cfg()
+ # for poly lr schedule
+ add_deeplab_config(cfg)
+ add_maskformer2_config(cfg)
+ cfg.merge_from_file(args.config_file)
+ cfg.merge_from_list(args.opts)
+ cfg.freeze()
+ default_setup(cfg, args)
+ # Setup logger for "mask_former" module
+ setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask2former")
+ return cfg
+
+
+def main(args):
+ cfg = setup(args)
+
+ if args.eval_only:
+ model = Trainer.build_model(cfg)
+ DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
+ cfg.MODEL.WEIGHTS, resume=args.resume
+ )
+ res = Trainer.test(cfg, model)
+ if cfg.TEST.AUG.ENABLED:
+ res.update(Trainer.test_with_TTA(cfg, model))
+ if comm.is_main_process():
+ verify_results(cfg, res)
+ return res
+
+ trainer = Trainer(cfg)
+ trainer.resume_or_load(resume=args.resume)
+ return trainer.train()
+
+
+if __name__ == "__main__":
+ args = default_argument_parser().parse_args()
+ print("Command Line Args:", args)
+ launch(
+ main,
+ args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ args=(args,),
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/Mask2Former/train_net_video.py b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/train_net_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d22345ed9659acfe70ed8d536b9775c748623f0
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/Mask2Former/train_net_video.py
@@ -0,0 +1,290 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+MaskFormer Training Script.
+
+This script is a simplified version of the training script in detectron2/tools.
+"""
+try:
+ # ignore ShapelyDeprecationWarning from fvcore
+ from shapely.errors import ShapelyDeprecationWarning
+ import warnings
+ warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
+except:
+ pass
+
+import copy
+import itertools
+import logging
+import os
+
+from collections import OrderedDict
+from typing import Any, Dict, List, Set
+
+import torch
+
+import detectron2.utils.comm as comm
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.config import get_cfg
+from detectron2.data import MetadataCatalog
+from detectron2.engine import (
+ DefaultTrainer,
+ default_argument_parser,
+ default_setup,
+ launch,
+)
+from detectron2.evaluation import (
+ DatasetEvaluator,
+ inference_on_dataset,
+ print_csv_format,
+ verify_results,
+)
+from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
+from detectron2.solver.build import maybe_add_gradient_clipping
+from detectron2.utils.logger import setup_logger
+
+# MaskFormer
+from mask2former import add_maskformer2_config
+from mask2former_video import (
+ YTVISDatasetMapper,
+ YTVISEvaluator,
+ add_maskformer2_video_config,
+ build_detection_train_loader,
+ build_detection_test_loader,
+ get_detection_dataset_dicts,
+)
+
+
+class Trainer(DefaultTrainer):
+ """
+ Extension of the Trainer class adapted to MaskFormer.
+ """
+
+ @classmethod
+ def build_evaluator(cls, cfg, dataset_name, output_folder=None):
+ """
+ Create evaluator(s) for a given dataset.
+ This uses the special metadata "evaluator_type" associated with each builtin dataset.
+ For your own dataset, you can simply create an evaluator manually in your
+ script and do not have to worry about the hacky if-else logic here.
+ """
+ if output_folder is None:
+ output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
+ os.makedirs(output_folder, exist_ok=True)
+
+ return YTVISEvaluator(dataset_name, cfg, True, output_folder)
+
+ @classmethod
+ def build_train_loader(cls, cfg):
+ dataset_name = cfg.DATASETS.TRAIN[0]
+ mapper = YTVISDatasetMapper(cfg, is_train=True)
+
+ dataset_dict = get_detection_dataset_dicts(
+ dataset_name,
+ filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
+ proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
+ )
+
+ return build_detection_train_loader(cfg, mapper=mapper, dataset=dataset_dict)
+
+ @classmethod
+ def build_test_loader(cls, cfg, dataset_name):
+ dataset_name = cfg.DATASETS.TEST[0]
+ mapper = YTVISDatasetMapper(cfg, is_train=False)
+ return build_detection_test_loader(cfg, dataset_name, mapper=mapper)
+
+ @classmethod
+ def build_lr_scheduler(cls, cfg, optimizer):
+ """
+ It now calls :func:`detectron2.solver.build_lr_scheduler`.
+ Overwrite it if you'd like a different scheduler.
+ """
+ return build_lr_scheduler(cfg, optimizer)
+
+ @classmethod
+ def build_optimizer(cls, cfg, model):
+ weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
+ weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED
+
+ defaults = {}
+ defaults["lr"] = cfg.SOLVER.BASE_LR
+ defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY
+
+ norm_module_types = (
+ torch.nn.BatchNorm1d,
+ torch.nn.BatchNorm2d,
+ torch.nn.BatchNorm3d,
+ torch.nn.SyncBatchNorm,
+ # NaiveSyncBatchNorm inherits from BatchNorm2d
+ torch.nn.GroupNorm,
+ torch.nn.InstanceNorm1d,
+ torch.nn.InstanceNorm2d,
+ torch.nn.InstanceNorm3d,
+ torch.nn.LayerNorm,
+ torch.nn.LocalResponseNorm,
+ )
+
+ params: List[Dict[str, Any]] = []
+ memo: Set[torch.nn.parameter.Parameter] = set()
+ for module_name, module in model.named_modules():
+ for module_param_name, value in module.named_parameters(recurse=False):
+ if not value.requires_grad:
+ continue
+ # Avoid duplicating parameters
+ if value in memo:
+ continue
+ memo.add(value)
+
+ hyperparams = copy.copy(defaults)
+ if "backbone" in module_name:
+ hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
+ if (
+ "relative_position_bias_table" in module_param_name
+ or "absolute_pos_embed" in module_param_name
+ ):
+ print(module_param_name)
+ hyperparams["weight_decay"] = 0.0
+ if isinstance(module, norm_module_types):
+ hyperparams["weight_decay"] = weight_decay_norm
+ if isinstance(module, torch.nn.Embedding):
+ hyperparams["weight_decay"] = weight_decay_embed
+ params.append({"params": [value], **hyperparams})
+
+ def maybe_add_full_model_gradient_clipping(optim):
+ # detectron2 doesn't have full model gradient clipping now
+ clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
+ enable = (
+ cfg.SOLVER.CLIP_GRADIENTS.ENABLED
+ and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
+ and clip_norm_val > 0.0
+ )
+
+ class FullModelGradientClippingOptimizer(optim):
+ def step(self, closure=None):
+ all_params = itertools.chain(*[x["params"] for x in self.param_groups])
+ torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
+ super().step(closure=closure)
+
+ return FullModelGradientClippingOptimizer if enable else optim
+
+ optimizer_type = cfg.SOLVER.OPTIMIZER
+ if optimizer_type == "SGD":
+ optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
+ params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
+ )
+ elif optimizer_type == "ADAMW":
+ optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
+ params, cfg.SOLVER.BASE_LR
+ )
+ else:
+ raise NotImplementedError(f"no optimizer type {optimizer_type}")
+ if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
+ optimizer = maybe_add_gradient_clipping(cfg, optimizer)
+ return optimizer
+
+ @classmethod
+ def test(cls, cfg, model, evaluators=None):
+ """
+ Evaluate the given model. The given model is expected to already contain
+ weights to evaluate.
+ Args:
+ cfg (CfgNode):
+ model (nn.Module):
+ evaluators (list[DatasetEvaluator] or None): if None, will call
+ :meth:`build_evaluator`. Otherwise, must have the same length as
+ ``cfg.DATASETS.TEST``.
+ Returns:
+ dict: a dict of result metrics
+ """
+ from torch.cuda.amp import autocast
+ logger = logging.getLogger(__name__)
+ if isinstance(evaluators, DatasetEvaluator):
+ evaluators = [evaluators]
+ if evaluators is not None:
+ assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
+ len(cfg.DATASETS.TEST), len(evaluators)
+ )
+
+ results = OrderedDict()
+ for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
+ data_loader = cls.build_test_loader(cfg, dataset_name)
+ # When evaluators are passed in as arguments,
+ # implicitly assume that evaluators can be created before data_loader.
+ if evaluators is not None:
+ evaluator = evaluators[idx]
+ else:
+ try:
+ evaluator = cls.build_evaluator(cfg, dataset_name)
+ except NotImplementedError:
+ logger.warn(
+ "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
+ "or implement its `build_evaluator` method."
+ )
+ results[dataset_name] = {}
+ continue
+ with autocast():
+ results_i = inference_on_dataset(model, data_loader, evaluator)
+ results[dataset_name] = results_i
+ if comm.is_main_process():
+ assert isinstance(
+ results_i, dict
+ ), "Evaluator must return a dict on the main process. Got {} instead.".format(
+ results_i
+ )
+ logger.info("Evaluation results for {} in csv format:".format(dataset_name))
+ print_csv_format(results_i)
+
+ if len(results) == 1:
+ results = list(results.values())[0]
+ return results
+
+
+def setup(args):
+ """
+ Create configs and perform basic setups.
+ """
+ cfg = get_cfg()
+ # for poly lr schedule
+ add_deeplab_config(cfg)
+ add_maskformer2_config(cfg)
+ add_maskformer2_video_config(cfg)
+ cfg.merge_from_file(args.config_file)
+ cfg.merge_from_list(args.opts)
+ cfg.freeze()
+ default_setup(cfg, args)
+ # Setup logger for "mask_former" module
+ setup_logger(name="mask2former")
+ setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask2former_video")
+ return cfg
+
+
+def main(args):
+ cfg = setup(args)
+
+ if args.eval_only:
+ model = Trainer.build_model(cfg)
+ DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
+ cfg.MODEL.WEIGHTS, resume=args.resume
+ )
+ res = Trainer.test(cfg, model)
+ if cfg.TEST.AUG.ENABLED:
+ raise NotImplementedError
+ if comm.is_main_process():
+ verify_results(cfg, res)
+ return res
+
+ trainer = Trainer(cfg)
+ trainer.resume_or_load(resume=args.resume)
+ return trainer.train()
+
+
+if __name__ == "__main__":
+ args = default_argument_parser().parse_args()
+ print("Command Line Args:", args)
+ launch(
+ main,
+ args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ args=(args,),
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/GETTING_STARTED.md b/Code/Baselines/GeoAware-SC/third_party/ODISE/GETTING_STARTED.md
new file mode 100644
index 0000000000000000000000000000000000000000..7eb67e283b741fe4514220f8f04fe6b75c090eb8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/GETTING_STARTED.md
@@ -0,0 +1,87 @@
+## Getting Started with ODISE
+
+This document provides a brief introduction on how to infer with and train ODISE.
+
+For further reading, please refer to [Getting Started with Detectron2](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md).
+
+**Important Note**: ODISE's `demo/demo.py` and `tools/train_net.py` scripts link to the original pre-trained models for [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt) and [CLIP](https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt). When you run them for the very first time, these scripts will automatically download the pre-trained models for Stable Diffuson and CLIP, from their original sources, to your local directories `$HOME/.torch/` and `$HOME/.cache/clip`, respectively. Their use is subject to the original license terms defined at [https://github.com/CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and [https://github.com/openai/CLIP](https://github.com/openai/CLIP), respectively.
+
+
+### Inference Demo with Pre-trained ODISE Models
+
+1. Choose a model for ODISE and its corresponding configuration file from
+ [model zoo](README.md#model-zoo),
+ for example, `configs/Panoptic/odise_label_coco_50e.py`.
+ In `demo/demo.py` we also provide a default inbuilt configuration.
+2. Run the `demo/demo.py` with:
+```
+python demo/demo.py --config-file configs/Panoptic/odise_label_coco_50e.py \
+ --input input1.jpg input2.jpg \
+ --init-from /path/to/checkpoint_file
+ [--other-options]
+```
+This command will run ODISE's inference and show visualizations in an OpenCV window.
+
+For details of the command line arguments, see `demo/demo.py -h` or look at its source code
+to understand its behavior. Some common arguments are:
+* To run __with a customized vocabulary__, use `--vocab` to specify additional vocabulary names.
+* To run __with a caption__, use `--caption` to specify a caption.
+* To run __on your webcam__, replace `--input files` with `--webcam`.
+* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
+* To run __on the cpu__, add `train.device=cpu` at the end.
+* To save outputs to a directory (for images) or a file (for webcam or video), use the `--output` option.
+
+The default behavior is to append the user-provided extra vocabulary to the labels from COCO, ADE20K and LVIS.
+To use **only** the user-provided vocabulary use `--label ""`.
+
+```
+python demo/demo.py --input demo/examples/purse.jpeg --output demo/purse_pred.jpg --label "" --vocab "purse"
+```
+
+or
+
+```
+python demo/demo.py --input demo/examples/purse.jpeg --output demo/purse_pred.jpg --label "" --caption "there is a black purse"
+```
+
+### Command line-based Training & Evaluation
+
+We provide a script `tools/train_net.py` that trains all configurations of ODISE.
+
+To train a model with `tools/train_net.py`, first prepare the datasets following the instructions in
+[datasets/README.md](./datasets/README.md) and then run, for single-node (8-GPUs) NVIDIA AMP-based training:
+```bash
+(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --num-gpus 8 --amp
+```
+For 4-node (32-GPUs) AMP-based training, run:
+```bash
+(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 0 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
+(node1)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 1 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
+(node2)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 2 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
+(node3)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 3 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
+```
+
+Note that our default training configurations are designed for 32 GPUs.
+Since we use the AdamW optimizer, it is not clear as to how to scale the learning rate with batch size.
+However, we provide the ability to automatically scale the learning rate and the batch size for any number of GPUs used for training by passing in the`--ref $REFERENCE_WORLD_SIZE` argument.
+For example, if you set `$REFERENCE_WORLD_SIZE=32` while training on 8 GPUs, the batch size and learning rate will be set to 8/32 = 0.25 of the original ones.
+
+```bash
+(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --num-gpus 8 --amp --ref 32
+```
+
+ODISE trains in 6 days on 32 NVIDIA V100 GPUs.
+
+To evaluate a trained ODISE model's performance, run on single node
+```
+(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --num-gpus 8 --eval-only --init-from /path/to/checkpoint
+```
+or for multi-node inference:
+```bash
+(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 0 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
+(node1)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 1 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
+(node2)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 2 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
+(node3)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 3 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
+```
+
+To use the our provided ODISE [model zoo](README.md#model-zoo), you can pass in the arguments `--config-file configs/Panoptic/odise_label_coco_50e.py --init-from odise://Panoptic/odise_label_coco_50e` or `--config-file configs/Panoptic/odise_label_coco_50e.py --init-from odise://Panoptic/odise_caption_coco_50e` to `./tools/train_net.py`, respectively.
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/LICENSE b/Code/Baselines/GeoAware-SC/third_party/ODISE/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..6aad82f5191018b15fb883b5f2a7f715d5be4356
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/LICENSE
@@ -0,0 +1,84 @@
+Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+NVIDIA Source Code License for ODISE: Open-Vocabulary Panoptic
+Segmentation with Text-to-Image Diffusion Models
+
+=======================================================================
+
+1. Definitions
+
+“Licensor” means any person or entity that distributes its Work.
+
+“Work” means (a) the original work of authorship made available under
+this license, which may include software, documentation, or other files,
+and (b) any additions to or derivative works thereof that are made
+available under this license.
+
+The terms “reproduce,” “reproduction,” “derivative works,” and “distribution”
+have the meaning as provided under U.S. copyright law; provided, however,
+that for the purposes of this license, derivative works shall not include works
+that remain separable from, or merely link (or bind by name) to the
+interfaces of, the Work.
+
+Works are “made available” under this license by including in or with the Work
+either (a) a copyright notice referencing the applicability of
+this license to the Work, or (b) a copy of this license.
+
+2. License Grant
+
+2.1 Copyright Grant. Subject to the terms and conditions of this license, each
+Licensor grants to you a perpetual, worldwide, non-exclusive, royalty-free,
+copyright license to use, reproduce, prepare derivative works of, publicly display,
+publicly perform, sublicense and distribute its Work and any resulting derivative
+works in any form.
+
+3. Limitations
+
+3.1 Redistribution. You may reproduce or distribute the Work only if (a) you do so under
+this license, (b) you include a complete copy of this license with your distribution,
+and (c) you retain without modification any copyright, patent, trademark, or
+attribution notices that are present in the Work.
+
+3.2 Derivative Works. You may specify that additional or different terms apply to the use,
+reproduction, and distribution of your derivative works of the Work (“Your Terms”) only
+if (a) Your Terms provide that the use limitation in Section 3.3 applies to your derivative
+works, and (b) you identify the specific derivative works that are subject to Your Terms.
+Notwithstanding Your Terms, this license (including the redistribution requirements in
+Section 3.1) will continue to apply to the Work itself.
+
+3.3 Use Limitation. The Work and any derivative works thereof only may be used or
+intended for use non-commercially. Notwithstanding the foregoing, NVIDIA Corporation
+and its affiliates may use the Work and any derivative works commercially.
+As used herein, “non-commercially” means for research or evaluation purposes only.
+
+3.4 Patent Claims. If you bring or threaten to bring a patent claim against any Licensor
+(including any claim, cross-claim or counterclaim in a lawsuit) to enforce any patents that
+you allege are infringed by any Work, then your rights under this license from
+such Licensor (including the grant in Section 2.1) will terminate immediately.
+
+3.5 Trademarks. This license does not grant any rights to use any Licensor’s or its
+affiliates’ names, logos, or trademarks, except as necessary to reproduce
+the notices described in this license.
+
+3.6 Termination. If you violate any term of this license, then your rights under
+this license (including the grant in Section 2.1) will terminate immediately.
+
+4. Disclaimer of Warranty.
+
+THE WORK IS PROVIDED “AS IS” WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
+EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT.
+YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE.
+
+5. Limitation of Liability.
+
+EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY,
+WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR
+BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL,
+OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR
+INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS
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+=======================================================================
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/MANIFEST.in b/Code/Baselines/GeoAware-SC/third_party/ODISE/MANIFEST.in
new file mode 100644
index 0000000000000000000000000000000000000000..626c0aa7d76319bcf4f9bae3f1570aa4352855d1
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/MANIFEST.in
@@ -0,0 +1 @@
+include odise/data/datasets/openseg_labels/*.txt
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/README.md b/Code/Baselines/GeoAware-SC/third_party/ODISE/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1d0983554d604abe65c62ef404498382b7d65915
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/README.md
@@ -0,0 +1,182 @@
+# ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
+
+**ODISE**: **O**pen-vocabulary **DI**ffusion-based panoptic **SE**gmentation exploits pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation.
+It leverages the frozen representation of both these models to perform panoptic segmentation of any category in the wild.
+
+This repository is the official implementation of ODISE introduced in the paper:
+
+[**Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models**](https://arxiv.org/abs/2303.04803)
+[*Jiarui Xu*](https://jerryxu.net),
+[*Sifei Liu**](https://research.nvidia.com/person/sifei-liu),
+[*Arash Vahdat**](http://latentspace.cc/),
+[*Wonmin Byeon*](https://wonmin-byeon.github.io/),
+[*Xiaolong Wang*](https://xiaolonw.github.io/),
+[*Shalini De Mello*](https://research.nvidia.com/person/shalini-de-mello)
+CVPR 2023 Highlight. (*equal contribution)
+
+For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/).
+
+
+
+## Visual Results
+
+
+
+
+
+## Links
+* [Jiarui Xu's Project Page](https://jerryxu.net/ODISE/) (with additional visual results)
+* [HuggingFace 🤗 Demo](https://huggingface.co/spaces/xvjiarui/ODISE)
+* [arXiv Page](https://arxiv.org/abs/2303.04803)
+
+## Citation
+
+If you find our work useful in your research, please cite:
+
+```BiBTeX
+@article{xu2023odise,
+ title={{Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models}},
+ author={Xu, Jiarui and Liu, Sifei and Vahdat, Arash and Byeon, Wonmin and Wang, Xiaolong and De Mello, Shalini},
+ journal={arXiv preprint arXiv:2303.04803},
+ year={2023}
+}
+```
+
+## Environment Setup
+
+Install dependencies by running:
+
+```bash
+conda create -n odise python=3.9
+conda activate odise
+conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.6 -c pytorch -c nvidia
+conda install -c "nvidia/label/cuda-11.6.1" libcusolver-dev
+git clone git@github.com:NVlabs/ODISE.git
+cd ODISE
+pip install -e .
+```
+
+(Optional) install [xformers](https://github.com/facebookresearch/xformers) for efficient transformer implementation:
+One could either install the pre-built version
+
+```
+pip install xformers==0.0.16
+```
+
+or build from latest source
+
+```bash
+# (Optional) Makes the build much faster
+pip install ninja
+# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
+pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
+# (this can take dozens of minutes)
+```
+
+## Model Zoo
+
+We provide two pre-trained models for ODISE trained with label or caption
+supervision on [COCO's](https://cocodataset.org/#home) entire training set.
+ODISE's pre-trained models are subject to the [Creative Commons — Attribution-NonCommercial-ShareAlike 4.0 International — CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) terms.
+Each model contains 28.1M trainable parameters.
+The download links for these models are provided in the table below.
+When you run the `demo/demo.py` or inference script for the very first time, it will also automatically download ODISE's pre-trained model to your local folder `$HOME/.torch/iopath_cache/NVlabs/ODISE/releases/download/v1.0.0/`.
+
+
+
+
+ |
+ ADE20K(A-150) |
+ COCO |
+ ADE20K-Full
(A-847) |
+ Pascal Context 59
(PC-59) |
+ Pascal Context 459
(PC-459) |
+ Pascal VOC 21
(PAS-21) |
+ download |
+
+
+
+
+ |
+ PQ |
+ mAP |
+ mIoU |
+ PQ |
+ mAP |
+ mIoU |
+ mIoU |
+ mIoU |
+ mIoU |
+ mIoU |
+
+
+ | ODISE (label) |
+ 22.6 |
+ 14.4 |
+ 29.9 |
+ 55.4 |
+ 46.0 |
+ 65.2 |
+ 11.1 |
+ 57.3 |
+ 14.5 |
+ 84.6 |
+ checkpoint |
+
+
+ | ODISE (caption) |
+ 23.4 |
+ 13.9 |
+ 28.7 |
+ 45.6 |
+ 38.4 |
+ 52.4 |
+ 11.0 |
+ 55.3 |
+ 13.8 |
+ 82.7 |
+ checkpoint |
+
+
+
+
+## Get Started
+See [Preparing Datasets for ODISE](./datasets/README.md).
+
+See [Getting Started with ODISE](./GETTING_STARTED.md) for detailed instructions on training and inference with ODISE.
+## Demo
+
+* Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the web demo: [](https://huggingface.co/spaces/xvjiarui/ODISE)
+
+* Run the demo on Google Colab: [](https://colab.research.google.com/github/NVlabs/ODISE/blob/master/demo/demo.ipynb)
+
+
+**Important Note**: When you run the `demo/demo.py` script for the very first time, besides ODISE's pre-trained models, it will also automaticlaly download the pre-trained models for [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt) and [CLIP](https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt), from their original sources, to your local directories `$HOME/.torch/` and `$HOME/.cache/clip`, respectively.
+The pre-trained models for Stable Diffusion and CLIP are subject to their original license terms from [Stable Diffusion](https://github.com/CompVis/stable-diffusion) and [CLIP](https://github.com/openai/CLIP), respectively.
+
+* To run ODISE's demo from the command line:
+
+ ```shell
+ python demo/demo.py --input demo/examples/coco.jpg --output demo/coco_pred.jpg --vocab "black pickup truck, pickup truck; blue sky, sky"
+ ```
+ The output is saved in `demo/coco_pred.jpg`. For more detailed options for `demo/demo.py` see [Getting Started with ODISE](GETTING_STARTED.md).
+
+
+* To run the [Gradio](https://github.com/gradio-app/gradio) demo locally:
+ ```shell
+ python demo/app.py
+ ```
+
+## Acknowledgement
+
+Code is largely based on [Detectron2](https://github.com/facebookresearch/detectron2), [Stable Diffusion](https://github.com/CompVis/stable-diffusion), [Mask2Former](https://github.com/facebookresearch/Mask2Former), [OpenCLIP](https://github.com/mlfoundations/open_clip) and [GLIDE](https://github.com/openai/glide-text2im).
+
+Thank you, all, for the great open-source projects!
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/Panoptic/odise_caption_coco_50e.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/Panoptic/odise_caption_coco_50e.py
new file mode 100644
index 0000000000000000000000000000000000000000..52696f4978d68afb07f629f96886477467f12515
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/Panoptic/odise_caption_coco_50e.py
@@ -0,0 +1,59 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import LazyCall as L
+from detectron2.solver import WarmupParamScheduler
+from fvcore.common.param_scheduler import MultiStepParamScheduler
+
+from ..common.models.odise_with_caption import model
+from ..common.data.coco_panoptic_semseg import dataloader
+from ..common.train import train
+from ..common.optim import AdamW as optimizer
+from ..common.data.pano_open_d2_eval import (
+ ade150_open_eval as _ade150_eval,
+ ctx59_open_eval as _ctx59_eval,
+ ade847_open_eval as _ade847_eval,
+ ctx459_open_eval as _ctx459_eval,
+ pas21_open_eval as _pas21_eval,
+)
+
+train.max_iter = 92_188
+train.grad_clip = 0.01
+train.checkpointer.period = 4500
+
+lr_multiplier = L(WarmupParamScheduler)(
+ scheduler=L(MultiStepParamScheduler)(
+ values=[1.0, 0.1, 0.01],
+ # assume 100e with batch-size 64 as original LSJ
+ # Equivalent to 100 epochs.
+ # 100 ep = 184375 iters * 64 images/iter / 118000 images/ep
+ milestones=[163889, 177546],
+ num_updates=184375,
+ ),
+ # for warmup length we adopted COCO LSJ setting
+ warmup_length=500 / 184375,
+ warmup_factor=0.067,
+)
+
+optimizer.lr = 1e-4
+optimizer.weight_decay = 0.05
+
+dataloader.train.dataset.names = "coco_2017_train_panoptic_caption_with_sem_seg"
+
+_ade847_eval.final_iter_only = True
+_ctx459_eval.final_iter_only = True
+
+dataloader.extra_task = dict(
+ eval_ade150=_ade150_eval,
+ eval_ctx59=_ctx59_eval,
+ eval_ade847=_ade847_eval,
+ eval_ctx459=_ctx459_eval,
+ eval_pas21=_pas21_eval,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/Panoptic/odise_label_coco_50e.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/Panoptic/odise_label_coco_50e.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1e4bd703c042da8c54818a9d4a19ef4f5e0ac9a
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/Panoptic/odise_label_coco_50e.py
@@ -0,0 +1,57 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import LazyCall as L
+from detectron2.solver import WarmupParamScheduler
+from fvcore.common.param_scheduler import MultiStepParamScheduler
+
+from ..common.models.odise_with_label import model
+from ..common.data.coco_panoptic_semseg import dataloader
+from ..common.train import train
+from ..common.optim import AdamW as optimizer
+from ..common.data.pano_open_d2_eval import (
+ ade150_open_eval as _ade150_eval,
+ ctx59_open_eval as _ctx59_eval,
+ ade847_open_eval as _ade847_eval,
+ ctx459_open_eval as _ctx459_eval,
+ pas21_open_eval as _pas21_eval,
+)
+
+train.max_iter = 92_188
+train.grad_clip = 0.01
+train.checkpointer.period = 4500
+
+lr_multiplier = L(WarmupParamScheduler)(
+ scheduler=L(MultiStepParamScheduler)(
+ values=[1.0, 0.1, 0.01],
+ # assume 100e with batch-size 64 as original LSJ
+ # Equivalent to 100 epochs.
+ # 100 ep = 184375 iters * 64 images/iter / 118000 images/ep
+ milestones=[163889, 177546],
+ num_updates=184375,
+ ),
+ # for warmup length we adopted COCO LSJ setting
+ warmup_length=500 / 184375,
+ warmup_factor=0.067,
+)
+
+optimizer.lr = 1e-4
+optimizer.weight_decay = 0.05
+
+_ade847_eval.final_iter_only = True
+_ctx459_eval.final_iter_only = True
+
+dataloader.extra_task = dict(
+ eval_ade150=_ade150_eval,
+ eval_ctx59=_ctx59_eval,
+ eval_ade847=_ade847_eval,
+ eval_ctx459=_ctx459_eval,
+ eval_pas21=_pas21_eval,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/data/coco_panoptic_semseg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/data/coco_panoptic_semseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6fa24b2e28c4f65bbe468c9829d4c037622a839
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/data/coco_panoptic_semseg.py
@@ -0,0 +1,95 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from omegaconf import OmegaConf
+
+import detectron2.data.transforms as T
+from detectron2.config import LazyCall as L
+from detectron2.data import get_detection_dataset_dicts
+from detectron2.data import DatasetMapper
+
+from odise.data import (
+ COCOPanopticDatasetMapper,
+ build_d2_test_dataloader,
+ build_d2_train_dataloader,
+ get_openseg_labels,
+)
+from odise.evaluation.d2_evaluator import (
+ COCOEvaluator,
+ COCOPanopticEvaluator,
+ SemSegEvaluator,
+)
+from odise.modeling.wrapper.pano_wrapper import OpenPanopticInference
+from detectron2.data import MetadataCatalog
+
+dataloader = OmegaConf.create()
+
+dataloader.train = L(build_d2_train_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(
+ names="coco_2017_train_panoptic_with_sem_seg", filter_empty=True
+ ),
+ mapper=L(COCOPanopticDatasetMapper)(
+ is_train=True,
+ # COCO LSJ aug
+ augmentations=[
+ L(T.RandomFlip)(horizontal=True),
+ L(T.ResizeScale)(
+ min_scale=0.1,
+ max_scale=2.0,
+ target_height=1024,
+ target_width=1024,
+ ),
+ L(T.FixedSizeCrop)(crop_size=(1024, 1024)),
+ ],
+ image_format="RGB",
+ ),
+ total_batch_size=64,
+ num_workers=4,
+)
+
+dataloader.test = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(
+ names="coco_2017_val_panoptic_with_sem_seg",
+ filter_empty=False,
+ ),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(short_edge_length=1024, sample_style="choice", max_size=2560),
+ ],
+ image_format="${...train.mapper.image_format}",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+dataloader.evaluator = [
+ L(COCOEvaluator)(
+ dataset_name="${...test.dataset.names}",
+ tasks=("segm",),
+ ),
+ L(SemSegEvaluator)(
+ dataset_name="${...test.dataset.names}",
+ ),
+ L(COCOPanopticEvaluator)(
+ dataset_name="${...test.dataset.names}",
+ ),
+]
+
+dataloader.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="coco_panoptic", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name="${...test.dataset.names}"),
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/data/pano_open_d2_eval.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/data/pano_open_d2_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..0651e19606a4fba05f2a089084456704951f84ee
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/data/pano_open_d2_eval.py
@@ -0,0 +1,235 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from omegaconf import OmegaConf
+
+import detectron2.data.transforms as T
+from detectron2.config import LazyCall as L
+from detectron2.data import get_detection_dataset_dicts
+from detectron2.data import MetadataCatalog
+from detectron2.data import DatasetMapper
+
+from odise.modeling.wrapper.pano_wrapper import OpenPanopticInference
+from odise.data import build_d2_test_dataloader, get_openseg_labels
+
+from odise.evaluation.d2_evaluator import (
+ COCOEvaluator,
+ COCOPanopticEvaluator,
+ SemSegEvaluator,
+ InstanceSegEvaluator,
+)
+
+coco133_open_eval = OmegaConf.create()
+coco133_open_eval.loader = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(
+ names="coco_2017_val_panoptic_with_sem_seg", filter_empty=False
+ ),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(
+ short_edge_length=1024,
+ max_size=2560,
+ sample_style="choice",
+ ),
+ ],
+ image_format="RGB",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+coco133_open_eval.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="coco_panoptic", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name=coco133_open_eval.loader.dataset.names),
+)
+
+coco133_open_eval.evaluator = [
+ L(COCOEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ tasks=("segm",),
+ ),
+ L(SemSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+ L(COCOPanopticEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+]
+
+ade150_open_eval = OmegaConf.create()
+ade150_open_eval.loader = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(names="ade20k_panoptic_val", filter_empty=False),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(
+ short_edge_length=1024,
+ max_size=2560,
+ sample_style="choice",
+ ),
+ ],
+ image_format="RGB",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+ade150_open_eval.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="ade20k_150", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name=ade150_open_eval.loader.dataset.names),
+)
+
+ade150_open_eval.evaluator = [
+ L(InstanceSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ tasks=("segm",),
+ ),
+ L(SemSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+ L(COCOPanopticEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+]
+
+ade847_open_eval = OmegaConf.create()
+ade847_open_eval.loader = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(names="ade20k_full_sem_seg_val", filter_empty=False),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(
+ short_edge_length=1024,
+ max_size=2560,
+ sample_style="choice",
+ ),
+ ],
+ image_format="RGB",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+ade847_open_eval.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="ade20k_847", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name=ade847_open_eval.loader.dataset.names),
+ semantic_on=True,
+ instance_on=False,
+ panoptic_on=False,
+)
+
+ade847_open_eval.evaluator = [
+ L(SemSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+]
+
+ctx59_open_eval = OmegaConf.create()
+ctx59_open_eval.loader = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(names="ctx59_sem_seg_val", filter_empty=False),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(
+ short_edge_length=1024,
+ max_size=2560,
+ sample_style="choice",
+ ),
+ ],
+ image_format="RGB",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+ctx59_open_eval.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="pascal_context_59", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name=ctx59_open_eval.loader.dataset.names),
+ semantic_on=True,
+ instance_on=False,
+ panoptic_on=False,
+)
+
+ctx59_open_eval.evaluator = [
+ L(SemSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+]
+
+ctx459_open_eval = OmegaConf.create()
+ctx459_open_eval.loader = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(names="ctx459_sem_seg_val", filter_empty=False),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(
+ short_edge_length=1024,
+ max_size=2560,
+ sample_style="choice",
+ ),
+ ],
+ image_format="RGB",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+ctx459_open_eval.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="pascal_context_459", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name=ctx459_open_eval.loader.dataset.names),
+ semantic_on=True,
+ instance_on=False,
+ panoptic_on=False,
+)
+
+ctx459_open_eval.evaluator = [
+ L(SemSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+]
+
+pas21_open_eval = OmegaConf.create()
+pas21_open_eval.loader = L(build_d2_test_dataloader)(
+ dataset=L(get_detection_dataset_dicts)(names="pascal21_sem_seg_val", filter_empty=False),
+ mapper=L(DatasetMapper)(
+ is_train=False,
+ augmentations=[
+ L(T.ResizeShortestEdge)(
+ short_edge_length=1024,
+ max_size=2560,
+ sample_style="choice",
+ ),
+ ],
+ image_format="RGB",
+ ),
+ local_batch_size=1,
+ num_workers=1,
+)
+
+pas21_open_eval.wrapper = L(OpenPanopticInference)(
+ labels=L(get_openseg_labels)(dataset="pascal_voc_21", prompt_engineered=True),
+ metadata=L(MetadataCatalog.get)(name=pas21_open_eval.loader.dataset.names),
+ semantic_on=True,
+ instance_on=False,
+ panoptic_on=False,
+)
+
+pas21_open_eval.evaluator = [
+ L(SemSegEvaluator)(
+ dataset_name="${...loader.dataset.names}",
+ ),
+]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/mask_generator_with_caption.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/mask_generator_with_caption.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9150735b69232bb79e4385984e0157233c0fee9
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/mask_generator_with_caption.py
@@ -0,0 +1,105 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import LazyCall as L
+from detectron2.data import MetadataCatalog
+
+from odise.modeling.meta_arch.odise import (
+ CaptionODISE,
+ ODISEMultiScaleMaskedTransformerDecoder,
+ PooledMaskEmbed,
+ MaskGroundingCriterion,
+ WordEmbed,
+ PoolingCLIPHead,
+)
+from mask2former.modeling.meta_arch.mask_former_head import MaskFormerHead
+from mask2former.modeling.criterion import SetCriterion
+from mask2former.modeling.matcher import HungarianMatcher
+from mask2former.modeling.pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder
+
+model = L(CaptionODISE)(
+ sem_seg_head=L(MaskFormerHead)(
+ ignore_value=255,
+ num_classes=1,
+ pixel_decoder=L(MSDeformAttnPixelDecoder)(
+ conv_dim=256,
+ mask_dim=256,
+ norm="GN",
+ transformer_dropout=0.0,
+ transformer_nheads=8,
+ transformer_dim_feedforward=1024,
+ transformer_enc_layers=6,
+ transformer_in_features=["res3", "res4", "res5"],
+ common_stride=4,
+ ),
+ loss_weight=1.0,
+ transformer_in_feature="multi_scale_pixel_decoder",
+ transformer_predictor=L(ODISEMultiScaleMaskedTransformerDecoder)(
+ hidden_dim=256,
+ post_mask_embed=L(PooledMaskEmbed)(
+ hidden_dim="${..hidden_dim}",
+ mask_dim="${..mask_dim}",
+ projection_dim="${..mask_dim}",
+ ),
+ in_channels="${..pixel_decoder.conv_dim}",
+ mask_classification=True,
+ num_classes="${..num_classes}",
+ num_queries="${...num_queries}",
+ nheads=8,
+ dim_feedforward=2048,
+ # 9 decoder layers, add one for the loss on learnable query
+ dec_layers=9,
+ pre_norm=False,
+ enforce_input_project=False,
+ mask_dim=256,
+ ),
+ ),
+ criterion=L(SetCriterion)(
+ num_layers="${model.sem_seg_head.transformer_predictor.dec_layers}",
+ class_weight=2.0,
+ mask_weight=5.0,
+ dice_weight=5.0,
+ num_classes="${model.sem_seg_head.num_classes}",
+ matcher=L(HungarianMatcher)(
+ cost_class="${..class_weight}",
+ cost_mask="${..mask_weight}",
+ cost_dice="${..dice_weight}",
+ num_points="${..num_points}",
+ ),
+ eos_coef=0.1,
+ losses=["labels", "masks"],
+ num_points=12544,
+ oversample_ratio=3.0,
+ importance_sample_ratio=0.75,
+ ),
+ grounding_criterion=L(MaskGroundingCriterion)(collect_mode="diff"),
+ word_head=L(WordEmbed)(
+ clip_model_name="ViT-L-14-336",
+ projection_dim="${model.sem_seg_head.transformer_predictor.post_mask_embed.projection_dim}",
+ word_tags="noun_phrase",
+ word_dropout=0.0,
+ prompt="photo",
+ ),
+ clip_head=L(PoolingCLIPHead)(),
+ num_queries=100,
+ object_mask_threshold=0.0,
+ overlap_threshold=0.8,
+ metadata=L(MetadataCatalog.get)(name="coco_2017_train_panoptic_with_sem_seg"),
+ size_divisibility=64,
+ sem_seg_postprocess_before_inference=True,
+ # normalize to [0, 1]
+ pixel_mean=[0.0, 0.0, 0.0],
+ pixel_std=[255.0, 255.0, 255.0],
+ # inference
+ semantic_on=True,
+ instance_on=True,
+ panoptic_on=True,
+ test_topk_per_image=100,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/mask_generator_with_label.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/mask_generator_with_label.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3e1f9b073f77c33fe0504cfe3cee94aba515be9
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/mask_generator_with_label.py
@@ -0,0 +1,104 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import LazyCall as L
+from detectron2.data import MetadataCatalog
+
+from odise.data.build import get_openseg_labels
+from odise.modeling.meta_arch.odise import (
+ CategoryODISE,
+ ODISEMultiScaleMaskedTransformerDecoder,
+ PooledMaskEmbed,
+ CategoryEmbed,
+ PseudoClassEmbed,
+ PoolingCLIPHead,
+)
+from mask2former.modeling.meta_arch.mask_former_head import MaskFormerHead
+from mask2former.modeling.criterion import SetCriterion
+from mask2former.modeling.matcher import HungarianMatcher
+from mask2former.modeling.pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder
+
+model = L(CategoryODISE)(
+ sem_seg_head=L(MaskFormerHead)(
+ ignore_value=255,
+ num_classes=133,
+ pixel_decoder=L(MSDeformAttnPixelDecoder)(
+ conv_dim=256,
+ mask_dim=256,
+ norm="GN",
+ transformer_dropout=0.0,
+ transformer_nheads=8,
+ transformer_dim_feedforward=1024,
+ transformer_enc_layers=6,
+ transformer_in_features=["s3", "s4", "s5"],
+ common_stride=4,
+ ),
+ loss_weight=1.0,
+ transformer_in_feature="multi_scale_pixel_decoder",
+ transformer_predictor=L(ODISEMultiScaleMaskedTransformerDecoder)(
+ class_embed=L(PseudoClassEmbed)(num_classes="${..num_classes}"),
+ hidden_dim=256,
+ post_mask_embed=L(PooledMaskEmbed)(
+ hidden_dim="${..hidden_dim}",
+ mask_dim="${..mask_dim}",
+ projection_dim="${..mask_dim}",
+ ),
+ in_channels="${..pixel_decoder.conv_dim}",
+ mask_classification=True,
+ num_classes="${..num_classes}",
+ num_queries="${...num_queries}",
+ nheads=8,
+ dim_feedforward=2048,
+ # 9 decoder layers, add one for the loss on learnable query
+ dec_layers=9,
+ pre_norm=False,
+ enforce_input_project=False,
+ mask_dim=256,
+ ),
+ ),
+ criterion=L(SetCriterion)(
+ num_layers="${..sem_seg_head.transformer_predictor.dec_layers}",
+ class_weight=2.0,
+ mask_weight=5.0,
+ dice_weight=5.0,
+ num_classes="${..sem_seg_head.num_classes}",
+ matcher=L(HungarianMatcher)(
+ cost_class="${..class_weight}",
+ cost_mask="${..mask_weight}",
+ cost_dice="${..dice_weight}",
+ num_points="${..num_points}",
+ ),
+ eos_coef=0.1,
+ losses=["labels", "masks"],
+ num_points=12544,
+ oversample_ratio=3.0,
+ importance_sample_ratio=0.75,
+ ),
+ category_head=L(CategoryEmbed)(
+ clip_model_name="ViT-L-14-336",
+ labels=L(get_openseg_labels)(dataset="coco_panoptic", prompt_engineered=True),
+ projection_dim="${..sem_seg_head.transformer_predictor.post_mask_embed.projection_dim}",
+ ),
+ clip_head=L(PoolingCLIPHead)(),
+ num_queries=100,
+ object_mask_threshold=0.0,
+ overlap_threshold=0.8,
+ metadata=L(MetadataCatalog.get)(name="coco_2017_train_panoptic_with_sem_seg"),
+ size_divisibility=64,
+ sem_seg_postprocess_before_inference=True,
+ # normalize to [0, 1]
+ pixel_mean=[0.0, 0.0, 0.0],
+ pixel_std=[255.0, 255.0, 255.0],
+ # inference
+ semantic_on=True,
+ instance_on=True,
+ panoptic_on=True,
+ test_topk_per_image=100,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/odise_with_caption.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/odise_with_caption.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2862cbaa41959d33acf4ca1f9becaa867077810
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/odise_with_caption.py
@@ -0,0 +1,32 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import LazyCall as L
+from odise.modeling.meta_arch.ldm import LdmImplicitCaptionerExtractor
+from odise.modeling.backbone.feature_extractor import FeatureExtractorBackbone
+from .mask_generator_with_caption import model
+
+model.backbone = L(FeatureExtractorBackbone)(
+ feature_extractor=L(LdmImplicitCaptionerExtractor)(
+ encoder_block_indices=(5, 7),
+ unet_block_indices=(2, 5, 8, 11),
+ decoder_block_indices=(2, 5),
+ steps=(0,),
+ learnable_time_embed=True,
+ num_timesteps=1,
+ clip_model_name="ViT-L-14-336",
+ ),
+ out_features=["s2", "s3", "s4", "s5"],
+ use_checkpoint=True,
+ slide_training=True,
+)
+model.sem_seg_head.pixel_decoder.transformer_in_features = ["s3", "s4", "s5"]
+model.clip_head.alpha = 0.35
+model.clip_head.beta = 0.65
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/odise_with_label.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/odise_with_label.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cc6ee89705aaece1201baa538e1c33f304e20ec
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/models/odise_with_label.py
@@ -0,0 +1,32 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import LazyCall as L
+from odise.modeling.meta_arch.ldm import LdmImplicitCaptionerExtractor
+from odise.modeling.backbone.feature_extractor import FeatureExtractorBackbone
+from .mask_generator_with_label import model
+
+model.backbone = L(FeatureExtractorBackbone)(
+ feature_extractor=L(LdmImplicitCaptionerExtractor)(
+ encoder_block_indices=(5, 7),
+ unet_block_indices=(2, 5, 8, 11),
+ decoder_block_indices=(2, 5),
+ steps=(0,),
+ learnable_time_embed=True,
+ num_timesteps=1,
+ clip_model_name="ViT-L-14-336",
+ ),
+ out_features=["s2", "s3", "s4", "s5"],
+ use_checkpoint=True,
+ slide_training=True,
+)
+model.sem_seg_head.pixel_decoder.transformer_in_features = ["s3", "s4", "s5"]
+model.clip_head.alpha = 0.3
+model.clip_head.beta = 0.7
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/optim.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/optim.py
new file mode 100644
index 0000000000000000000000000000000000000000..45fb5deefc05eb48df4861e2074ec4849a21cc27
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/optim.py
@@ -0,0 +1,32 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import torch
+
+from detectron2.config import LazyCall as L
+from detectron2.solver.build import get_default_optimizer_params
+
+
+AdamW = L(torch.optim.AdamW)(
+ params=L(get_default_optimizer_params)(
+ # params.model is meant to be set to the model object, before instantiating
+ # the optimizer.
+ weight_decay_norm=0.0,
+ weight_decay_bias=0.0,
+ ),
+ lr="???",
+ weight_decay="???",
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/schedule.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/schedule.py
new file mode 100644
index 0000000000000000000000000000000000000000..3979ff69e87f6d6d8ff6c0ef60cd2806ef44b407
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/schedule.py
@@ -0,0 +1,27 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from fvcore.common.param_scheduler import CosineParamScheduler
+
+from detectron2.config import LazyCall as L
+from detectron2.solver import WarmupParamScheduler
+
+cosine_lr_multiplier = L(WarmupParamScheduler)(
+ scheduler=L(CosineParamScheduler)(start_value=1.0, end_value=0.01),
+ warmup_length="???",
+ warmup_method="linear",
+ warmup_factor=0.001,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/train.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..8382d137c244218ad7c2e9a655175600a852589f
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/configs/common/train.py
@@ -0,0 +1,49 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+# Common training-related configs that are designed for "tools/lazyconfig_train_net.py"
+# You can use your own instead, together with your own train_net.py
+
+train = dict(
+ output_dir="./output",
+ init_checkpoint="",
+ max_iter="???",
+ amp=dict(
+ enabled=False,
+ opt_level=None,
+ ), # options for Automatic Mixed Precision
+ grad_clip=None,
+ ddp=dict( # options for DistributedDataParallel
+ broadcast_buffers=False,
+ find_unused_parameters=False,
+ fp16_compression=False,
+ ),
+ checkpointer=dict(period=5000, max_to_keep=2), # options for PeriodicCheckpointer
+ eval_period="${train.checkpointer.period}",
+ log_period=50,
+ device="cuda",
+ seed=42,
+ # ...
+ wandb=dict(
+ enable_writer=False,
+ resume=False,
+ project="ODISE",
+ ),
+ cfg_name="",
+ run_name="",
+ run_tag="",
+ reference_world_size=0,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/README.md b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..cdb0a352872fff2b19776f0b502defe5fb8ee844
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/README.md
@@ -0,0 +1,223 @@
+# Prepare Datasets for ODISE
+
+Dataset preparation for ODISE follows [Detectron2](https://github.com/facebookresearch/detectron2/blob/main/datasets/README.md) and [Mask2Former](https://github.com/facebookresearch/Mask2Former/blob/main/datasets/README.md).
+
+A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
+for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
+This document explains how to setup the builtin datasets so they can be used by the above APIs.
+[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
+and how to add new datasets to them.
+
+ODISE has builtin support for a few datasets.
+The datasets are assumed to exist in a directory specified by the environment variable `DETECTRON2_DATASETS`.
+Under this directory, detectron2 will look for datasets in the structure described below, if needed.
+```bash
+$DETECTRON2_DATASETS/
+ ade/
+ coco/
+ VOCdevkit/
+ pascal_ctx_d2/
+ pascal_voc_d2/
+```
+
+You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
+If left unset, the default is `./datasets` under the ODISE project directory.
+
+The [model zoo](../README.md#model-zoo)
+contains configs and models that use these builtin datasets.
+
+
+## Expected dataset structure for [COCO](https://cocodataset.org/#download):
+
+```bash
+coco/
+ annotations/
+ instances_{train,val}2017.json
+ panoptic_{train,val}2017.json
+ captions_{train,val}2017.json
+ # below are prepare_coco_caption.py
+ panoptic_caption_{train,val}2017.json
+ {train,val}2017/
+ panoptic_{train,val}2017/
+ # below are generated by prepare_coco_semantic_annos_from_panoptic_annos.py
+ panoptic_semseg_{train,val}2017/ #
+```
+
+Download the dataset from http://cocodataset.org/#download:
+```bash
+cd $DETECTRON2_DATASETS
+wget http://images.cocodataset.org/zips/train2017.zip
+unzip train2017.zip -d coco/
+wget http://images.cocodataset.org/zips/val2017.zip
+unzip val2017.zip -d coco/
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+unzip annotations_trainval2017.zip -d coco/
+wget http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip
+unzip panoptic_annotations_trainval2017.zip -d coco/
+unzip coco/annotations/panoptic_train2017.zip -d coco/
+unzip coco/annotations/panoptic_val2017.zip -d coco/
+```
+
+Install the panopticapi (also automatically done by installing ODISE) by:
+```bash
+pip install git+https://github.com/cocodataset/panopticapi.git
+```
+
+Generate the semantic segmentation annotations `coco/panoptic_semseg_{train,val}2017/` by running:
+```bash
+python datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
+```
+to extract the semantic annotations from the panoptic ones (only used for evaluation).
+
+Generate the panoptic annotations with COCO captions `panoptic_caption_{train,val}2017.json` by running:
+```bash
+python datasets/prepare_coco_caption.py
+```
+
+
+## Expected dataset structure for [ADE20k (A-150)](http://sceneparsing.csail.mit.edu/) and [ADE20k-Full (A-847)](https://groups.csail.mit.edu/vision/datasets/ADE20K/):
+```bash
+ade/
+ ADEChallengeData2016/
+ images/
+ annotations/
+ objectInfo150.txt
+ # downloaded instance annotation
+ annotations_instance/
+ # generated by prepare_ade20k_sem_seg.py
+ annotations_detectron2/
+ # generated by prepare_ade20k_ins_seg.py
+ ade20k_instance_{train,val}.json
+ # generated by prepare_ade20k_pan_seg.py
+ ade20k_panoptic_{train,val}.json
+ ade20k_panoptic_{train,val}/
+
+ ADE20K_2021_17_01/
+ images/
+ index_ade20k.pkl
+ objects.txt
+ # generated by prepare_ade20k_full_sem_seg.py
+ images_detectron2/
+ annotations_detectron2/
+
+```
+
+### ADE20k(A-150)
+
+Download the dataset from http://sceneparsing.csail.mit.edu/:
+```bash
+cd $DETECTRON2_DATASETS
+wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
+# generate folder ade/ADEChallengeData2016/
+unzip ADEChallengeData2016.zip -d ade/
+```
+
+Download the instance annotations from http://sceneparsing.csail.mit.edu/:
+```bash
+cd $DETECTRON2_DATASETS
+wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar
+# generate folder ade/ADEChallengeData2016/annotations_instance/
+tar -xvf annotations_instance.tar -C ade/ADEChallengeData2016/
+```
+
+Generate the directory `ade/ADEChallengeData2016/annotations_detectron2` by running:
+```bash
+python datasets/prepare_ade20k_sem_seg.py
+```
+
+Generate instance annotations `ade/ADEChallengeData2016/ade20k_instance_{train,val}.json` by running:
+```bash
+python datasets/prepare_ade20k_ins_seg.py
+```
+
+Generate panoptic annotations `ade/ADEChallengeData2016/ade20k_panoptic_{train,val}.json` and `ade/ADEChallengeData2016/ade20k_panoptic_{train,val}` by running:
+```bash
+python datasets/prepare_ade20k_pan_seg.py
+```
+
+### ADE20k-Full(A-847)
+
+Register and download the dataset from https://groups.csail.mit.edu/vision/datasets/ADE20K/:
+```bash
+cd $DETECTRON2_DATASETS
+wget your/personal/download/link/{username}_{hash}.zip
+unzip {username}_{hash}.zip -d ade/
+```
+
+Generate the directories `ade/ADE20K_2021_17_01/images_detectron2` and `ade/ADE20K_2021_17_01/annotations_detectron2` by running:
+```bash
+python datasets/prepare_ade20k_full_sem_seg.py
+```
+
+## Expected dataset structure for [PASCAL Context (PC-59)](https://www.cs.stanford.edu/~roozbeh/pascal-context/), [PASCAL Context Full (PC-459)](https://www.cs.stanford.edu/~roozbeh/pascal-context/) and [PASCAL VOC (PAS-21)](http://host.robots.ox.ac.uk/pascal/VOC/):
+
+```bash
+VOCdevkit/
+ VOC2012/
+ Annotations/
+ JPEGImages/
+ ImageSets/
+ Segmentation/
+ VOC2010/
+ JPEGImages/
+ trainval/
+ trainval_merged.json
+# generated by prepare_pascal_voc_sem_seg.py
+pascal_voc_d2/
+ images/
+ annotations_pascal21/
+# generated by prepare_pascal_ctx_sem_seg.py
+pascal_ctx_d2/
+ images/
+ annotations_ctx59/
+ # generated by prepare_pascal_ctx_full_sem_seg.py
+ annotations_ctx459/
+
+```
+### PASCAL VOC (PAS-21)
+
+Download the dataset from http://host.robots.ox.ac.uk/pascal/VOC/:
+```bash
+cd $DETECTRON2_DATASETS
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+# generate folder VOCdevkit/VOC2012
+tar -xvf VOCtrainval_11-May-2012.tar
+```
+
+Generate directory `pascal_voc_d2` running:
+```bash
+python datasets/prepare_pascal_voc_sem_seg.py
+```
+
+### PASCAL Context (PC-59)
+
+Download the dataset from http://host.robots.ox.ac.uk/pascal/VOC/ and annotation from https://www.cs.stanford.edu/~roozbeh/pascal-context/:
+```bash
+cd $DETECTRON2_DATASETS
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
+# generate folder VOCdevkit/VOC2010
+tar -xvf VOCtrainval_03-May-2010.tar
+wget https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz
+# generate folder VOCdevkit/VOC2010/trainval
+tar -xvzf trainval.tar.gz -C VOCdevkit/VOC2010
+wget https://codalabuser.blob.core.windows.net/public/trainval_merged.json -P VOCdevkit/VOC2010/
+```
+
+Install [Detail API](https://github.com/zhanghang1989/detail-api) by:
+```bash
+git clone https://github.com/zhanghang1989/detail-api.git
+rm detail-api/PythonAPI/detail/_mask.c
+pip install -e detail-api/PythonAPI/
+```
+
+Generate directory `pascal_ctx_d2/images` and `pascal_ctx_d2/annotations_ctx59` running:
+```bash
+python datasets/prepare_pascal_ctx_sem_seg.py
+```
+
+### PASCAL Context Full (PC-459)
+
+Generate directory `pascal_ctx_d2/annotations_ctx459` running:
+```bash
+python datasets/prepare_pascal_ctx_full_sem_seg.py
+```
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/ade20k_instance_catid_mapping.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/ade20k_instance_catid_mapping.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ba388d09cc00fa92a733d2a605f7068dc2271f82
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/ade20k_instance_catid_mapping.txt
@@ -0,0 +1,104 @@
+Instacne100 SceneParse150 FullADE20K
+1 8 165
+2 9 3055
+3 11 350
+4 13 1831
+5 15 774
+5 15 783
+6 16 2684
+7 19 687
+8 20 471
+9 21 401
+10 23 1735
+11 24 2473
+12 25 2329
+13 28 1564
+14 31 57
+15 32 2272
+16 33 907
+17 34 724
+18 36 2985
+18 36 533
+19 37 1395
+20 38 155
+21 39 2053
+22 40 689
+23 42 266
+24 43 581
+25 44 2380
+26 45 491
+27 46 627
+28 48 2388
+29 50 943
+30 51 2096
+31 54 2530
+32 56 420
+33 57 1948
+34 58 1869
+35 59 2251
+36 63 239
+37 65 571
+38 66 2793
+39 67 978
+40 68 236
+41 70 181
+42 71 629
+43 72 2598
+44 73 1744
+45 74 1374
+46 75 591
+47 76 2679
+48 77 223
+49 79 47
+50 81 327
+51 82 2821
+52 83 1451
+53 84 2880
+54 86 480
+55 87 77
+56 88 2616
+57 89 246
+57 89 247
+58 90 2733
+59 91 14
+60 93 38
+61 94 1936
+62 96 120
+63 98 1702
+64 99 249
+65 103 2928
+66 104 2337
+67 105 1023
+68 108 2989
+69 109 1930
+70 111 2586
+71 112 131
+72 113 146
+73 116 95
+74 117 1563
+75 119 1708
+76 120 103
+77 121 1002
+78 122 2569
+79 124 2833
+80 125 1551
+81 126 1981
+82 127 29
+83 128 187
+84 130 747
+85 131 2254
+86 133 2262
+87 134 1260
+88 135 2243
+89 136 2932
+90 137 2836
+91 138 2850
+92 139 64
+93 140 894
+94 143 1919
+95 144 1583
+96 145 318
+97 147 2046
+98 148 1098
+99 149 530
+100 150 954
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/ade20k_instance_imgCatIds.json b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/ade20k_instance_imgCatIds.json
new file mode 100644
index 0000000000000000000000000000000000000000..d142da440f6987d7be6d28a436f4322ef0d51ae5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/ade20k_instance_imgCatIds.json
@@ -0,0 +1 @@
+{"images": [{"file_name": "ADE_train_00000001.jpg", "width": 683, "id": 0, "height": 512}, {"file_name": "ADE_train_00000002.jpg", "width": 711, "id": 1, "height": 512}, {"file_name": "ADE_train_00000003.jpg", "width": 683, "id": 2, "height": 512}, {"file_name": "ADE_train_00000004.jpg", "width": 384, "id": 3, "height": 251}, {"file_name": "ADE_train_00000005.jpg", "width": 683, "id": 4, "height": 512}, {"file_name": "ADE_train_00000006.jpg", "width": 512, "id": 5, "height": 973}, {"file_name": "ADE_train_00000007.jpg", "width": 512, "id": 6, "height": 735}, {"file_name": "ADE_train_00000008.jpg", "width": 538, "id": 7, "height": 512}, {"file_name": "ADE_train_00000009.jpg", "width": 683, "id": 8, "height": 512}, {"file_name": "ADE_train_00000010.jpg", "width": 512, "id": 9, "height": 764}, {"file_name": "ADE_train_00000011.jpg", "width": 356, "id": 10, "height": 247}, {"file_name": "ADE_train_00000012.jpg", "width": 512, "id": 11, "height": 683}, {"file_name": "ADE_train_00000013.jpg", "width": 683, "id": 12, "height": 512}, {"file_name": "ADE_train_00000014.jpg", "width": 683, "id": 13, "height": 512}, {"file_name": "ADE_train_00000015.jpg", "width": 637, "id": 14, "height": 512}, {"file_name": "ADE_train_00000016.jpg", "width": 683, "id": 15, "height": 512}, {"file_name": "ADE_train_00000017.jpg", "width": 512, "id": 16, "height": 683}, {"file_name": "ADE_train_00000018.jpg", "width": 770, "id": 17, "height": 512}, {"file_name": "ADE_train_00000019.jpg", "width": 640, "id": 18, "height": 431}, {"file_name": "ADE_train_00000020.jpg", "width": 512, "id": 19, "height": 517}, {"file_name": "ADE_train_00000021.jpg", "width": 233, "id": 20, "height": 311}, {"file_name": "ADE_train_00000022.jpg", "width": 200, "id": 21, "height": 303}, {"file_name": "ADE_train_00000023.jpg", "width": 512, "id": 22, "height": 708}, {"file_name": "ADE_train_00000024.jpg", "width": 512, "id": 23, "height": 753}, {"file_name": "ADE_train_00000025.jpg", "width": 512, "id": 24, "height": 769}, {"file_name": "ADE_train_00000026.jpg", "width": 657, "id": 25, "height": 512}, {"file_name": "ADE_train_00000027.jpg", "width": 512, "id": 26, "height": 683}, {"file_name": "ADE_train_00000028.jpg", "width": 512, "id": 27, "height": 683}, {"file_name": "ADE_train_00000029.jpg", "width": 512, "id": 28, "height": 683}, {"file_name": "ADE_train_00000030.jpg", "width": 512, "id": 29, "height": 683}, {"file_name": "ADE_train_00000031.jpg", "width": 512, "id": 30, "height": 589}, {"file_name": "ADE_train_00000032.jpg", "width": 512, "id": 31, "height": 717}, {"file_name": "ADE_train_00000033.jpg", "width": 683, "id": 32, "height": 512}, {"file_name": "ADE_train_00000034.jpg", "width": 683, "id": 33, "height": 512}, {"file_name": "ADE_train_00000035.jpg", "width": 512, "id": 34, "height": 683}, {"file_name": "ADE_train_00000036.jpg", "width": 336, "id": 35, "height": 225}, {"file_name": "ADE_train_00000037.jpg", "width": 546, "id": 36, "height": 512}, {"file_name": "ADE_train_00000038.jpg", "width": 640, "id": 37, "height": 480}, {"file_name": "ADE_train_00000039.jpg", "width": 640, "id": 38, "height": 512}, {"file_name": "ADE_train_00000040.jpg", "width": 512, "id": 39, "height": 683}, {"file_name": "ADE_train_00000041.jpg", "width": 512, "id": 40, "height": 683}, {"file_name": "ADE_train_00000042.jpg", "width": 402, "id": 41, "height": 536}, {"file_name": "ADE_train_00000043.jpg", "width": 512, "id": 42, "height": 683}, {"file_name": "ADE_train_00000044.jpg", "width": 400, "id": 43, "height": 510}, {"file_name": "ADE_train_00000045.jpg", "width": 500, "id": 44, "height": 327}, {"file_name": "ADE_train_00000046.jpg", "width": 768, "id": 45, "height": 512}, {"file_name": "ADE_train_00000047.jpg", "width": 512, "id": 46, "height": 683}, {"file_name": "ADE_train_00000048.jpg", "width": 512, "id": 47, "height": 690}, {"file_name": "ADE_train_00000049.jpg", "width": 512, "id": 48, "height": 681}, {"file_name": "ADE_train_00000050.jpg", "width": 1024, "id": 49, "height": 1024}, {"file_name": "ADE_train_00000051.jpg", "width": 653, "id": 50, "height": 512}, {"file_name": "ADE_train_00000052.jpg", "width": 512, "id": 51, "height": 683}, {"file_name": "ADE_train_00000053.jpg", "width": 512, "id": 52, "height": 683}, {"file_name": "ADE_train_00000054.jpg", "width": 683, "id": 53, "height": 512}, {"file_name": "ADE_train_00000055.jpg", "width": 683, "id": 54, "height": 512}, {"file_name": "ADE_train_00000056.jpg", "width": 768, "id": 55, "height": 512}, {"file_name": "ADE_train_00000057.jpg", "width": 766, "id": 56, "height": 512}, {"file_name": "ADE_train_00000058.jpg", "width": 735, "id": 57, "height": 512}, {"file_name": "ADE_train_00000059.jpg", "width": 683, "id": 58, "height": 512}, {"file_name": "ADE_train_00000060.jpg", "width": 738, "id": 59, "height": 512}, {"file_name": "ADE_train_00000061.jpg", "width": 768, "id": 60, "height": 512}, {"file_name": "ADE_train_00000062.jpg", "width": 1000, "id": 61, "height": 1000}, {"file_name": "ADE_train_00000063.jpg", "width": 512, "id": 62, "height": 683}, {"file_name": "ADE_train_00000064.jpg", "width": 512, "id": 63, "height": 672}, {"file_name": "ADE_train_00000065.jpg", "width": 624, "id": 64, "height": 512}, {"file_name": "ADE_train_00000066.jpg", "width": 768, "id": 65, "height": 512}, {"file_name": "ADE_train_00000067.jpg", "width": 512, "id": 66, "height": 683}, {"file_name": "ADE_train_00000068.jpg", "width": 683, "id": 67, "height": 512}, {"file_name": "ADE_train_00000069.jpg", "width": 683, "id": 68, "height": 512}, {"file_name": "ADE_train_00000070.jpg", "width": 745, "id": 69, "height": 512}, {"file_name": "ADE_train_00000071.jpg", "width": 770, "id": 70, "height": 512}, {"file_name": "ADE_train_00000072.jpg", "width": 761, "id": 71, "height": 512}, {"file_name": "ADE_train_00000073.jpg", "width": 716, "id": 72, "height": 512}, {"file_name": "ADE_train_00000074.jpg", "width": 683, "id": 73, "height": 512}, {"file_name": "ADE_train_00000075.jpg", "width": 648, "id": 74, "height": 512}, {"file_name": "ADE_train_00000076.jpg", "width": 512, "id": 75, "height": 683}, {"file_name": "ADE_train_00000077.jpg", "width": 683, "id": 76, "height": 512}, {"file_name": "ADE_train_00000078.jpg", "width": 512, "id": 77, "height": 683}, {"file_name": "ADE_train_00000079.jpg", "width": 512, "id": 78, "height": 768}, {"file_name": "ADE_train_00000080.jpg", "width": 516, "id": 79, "height": 512}, {"file_name": "ADE_train_00000081.jpg", "width": 512, "id": 80, "height": 768}, {"file_name": "ADE_train_00000082.jpg", "width": 979, "id": 81, "height": 979}, {"file_name": "ADE_train_00000083.jpg", "width": 680, "id": 82, "height": 512}, {"file_name": "ADE_train_00000084.jpg", "width": 683, "id": 83, "height": 512}, {"file_name": "ADE_train_00000085.jpg", "width": 683, "id": 84, "height": 512}, {"file_name": "ADE_train_00000086.jpg", "width": 683, "id": 85, "height": 512}, {"file_name": "ADE_train_00000087.jpg", "width": 640, "id": 86, "height": 480}, {"file_name": "ADE_train_00000088.jpg", "width": 512, "id": 87, "height": 768}, {"file_name": "ADE_train_00000089.jpg", "width": 683, "id": 88, "height": 512}, {"file_name": "ADE_train_00000090.jpg", "width": 512, "id": 89, "height": 683}, {"file_name": "ADE_train_00000091.jpg", "width": 639, "id": 90, "height": 512}, {"file_name": "ADE_train_00000092.jpg", "width": 701, "id": 91, "height": 512}, {"file_name": "ADE_train_00000093.jpg", "width": 512, "id": 92, "height": 682}, {"file_name": "ADE_train_00000094.jpg", "width": 512, "id": 93, "height": 683}, {"file_name": "ADE_train_00000095.jpg", "width": 768, "id": 94, "height": 512}, {"file_name": "ADE_train_00000096.jpg", "width": 512, "id": 95, "height": 683}, {"file_name": "ADE_train_00000097.jpg", "width": 512, "id": 96, "height": 683}, {"file_name": "ADE_train_00000098.jpg", "width": 683, "id": 97, "height": 512}, {"file_name": "ADE_train_00000099.jpg", "width": 300, "id": 98, "height": 300}, {"file_name": "ADE_train_00000100.jpg", 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"ADE_test_00003383.jpg", "width": 695, "id": 25455, "height": 512}, {"file_name": "ADE_test_00003384.jpg", "width": 852, "id": 25456, "height": 480}, {"file_name": "ADE_test_00003385.jpg", "width": 683, "id": 25457, "height": 512}, {"file_name": "ADE_test_00003386.jpg", "width": 358, "id": 25458, "height": 478}, {"file_name": "ADE_test_00003387.jpg", "width": 768, "id": 25459, "height": 512}, {"file_name": "ADE_test_00003388.jpg", "width": 768, "id": 25460, "height": 512}, {"file_name": "ADE_test_00003389.jpg", "width": 683, "id": 25461, "height": 512}, {"file_name": "ADE_test_00003390.jpg", "width": 683, "id": 25462, "height": 512}, {"file_name": "ADE_test_00003391.jpg", "width": 768, "id": 25463, "height": 512}, {"file_name": "ADE_test_00003392.jpg", "width": 640, "id": 25464, "height": 480}, {"file_name": "ADE_test_00003393.jpg", "width": 650, "id": 25465, "height": 366}, {"file_name": "ADE_test_00003394.jpg", "width": 620, "id": 25466, "height": 349}, {"file_name": "ADE_test_00003395.jpg", "width": 783, "id": 25467, "height": 512}, {"file_name": "ADE_test_00003396.jpg", "width": 769, "id": 25468, "height": 512}, {"file_name": "ADE_test_00003397.jpg", "width": 768, "id": 25469, "height": 512}, {"file_name": "ADE_test_00003398.jpg", "width": 683, "id": 25470, "height": 512}, {"file_name": "ADE_test_00003399.jpg", "width": 683, "id": 25471, "height": 512}, {"file_name": "ADE_test_00003400.jpg", "width": 768, "id": 25472, "height": 512}, {"file_name": "ADE_test_00003401.jpg", "width": 773, "id": 25473, "height": 512}, {"file_name": "ADE_test_00003402.jpg", "width": 512, "id": 25474, "height": 683}, {"file_name": "ADE_test_00003403.jpg", "width": 750, "id": 25475, "height": 512}, {"file_name": "ADE_test_00003404.jpg", "width": 768, "id": 25476, "height": 512}, {"file_name": "ADE_test_00003405.jpg", "width": 1024, "id": 25477, "height": 512}, {"file_name": "ADE_test_00003406.jpg", "width": 640, "id": 25478, "height": 480}, {"file_name": "ADE_test_00003407.jpg", "width": 512, "id": 25479, "height": 684}, {"file_name": "ADE_test_00003408.jpg", "width": 683, "id": 25480, "height": 512}, {"file_name": "ADE_test_00003409.jpg", "width": 683, "id": 25481, "height": 512}, {"file_name": "ADE_test_00003410.jpg", "width": 678, "id": 25482, "height": 512}, {"file_name": "ADE_test_00003411.jpg", "width": 512, "id": 25483, "height": 640}, {"file_name": "ADE_test_00003412.jpg", "width": 764, "id": 25484, "height": 512}, {"file_name": "ADE_test_00003413.jpg", "width": 683, "id": 25485, "height": 512}, {"file_name": "ADE_test_00003414.jpg", "width": 640, "id": 25486, "height": 480}, {"file_name": "ADE_test_00003415.jpg", "width": 797, "id": 25487, "height": 512}, {"file_name": "ADE_test_00003416.jpg", "width": 768, "id": 25488, "height": 512}, {"file_name": "ADE_test_00003417.jpg", "width": 765, "id": 25489, "height": 512}, {"file_name": "ADE_test_00003418.jpg", "width": 910, "id": 25490, "height": 512}, {"file_name": "ADE_test_00003419.jpg", "width": 765, "id": 25491, "height": 512}, {"file_name": "ADE_test_00003420.jpg", "width": 768, "id": 25492, "height": 512}, {"file_name": "ADE_test_00003421.jpg", "width": 768, "id": 25493, "height": 512}, {"file_name": "ADE_test_00003422.jpg", "width": 910, "id": 25494, "height": 512}, {"file_name": "ADE_test_00003423.jpg", "width": 771, "id": 25495, "height": 512}, {"file_name": "ADE_test_00003424.jpg", "width": 1024, "id": 25496, "height": 495}, {"file_name": "ADE_test_00003425.jpg", "width": 762, "id": 25497, "height": 512}, {"file_name": "ADE_test_00003426.jpg", "width": 768, "id": 25498, "height": 512}, {"file_name": "ADE_test_00003427.jpg", "width": 768, "id": 25499, "height": 512}, {"file_name": "ADE_test_00003428.jpg", "width": 768, "id": 25500, "height": 512}, {"file_name": "ADE_test_00003429.jpg", "width": 768, "id": 25501, "height": 512}, {"file_name": "ADE_test_00003430.jpg", "width": 910, "id": 25502, "height": 512}, {"file_name": "ADE_test_00003431.jpg", "width": 910, "id": 25503, "height": 512}, {"file_name": "ADE_test_00003432.jpg", "width": 520, "id": 25504, "height": 390}, {"file_name": "ADE_test_00003433.jpg", "width": 740, "id": 25505, "height": 320}, {"file_name": "ADE_test_00003434.jpg", "width": 819, "id": 25506, "height": 512}, {"file_name": "ADE_test_00003435.jpg", "width": 683, "id": 25507, "height": 512}, {"file_name": "ADE_test_00003436.jpg", "width": 768, "id": 25508, "height": 512}, {"file_name": "ADE_test_00003437.jpg", "width": 787, "id": 25509, "height": 512}, {"file_name": "ADE_test_00003438.jpg", "width": 910, "id": 25510, "height": 512}, {"file_name": "ADE_test_00003439.jpg", "width": 554, "id": 25511, "height": 405}, {"file_name": "ADE_test_00003440.jpg", "width": 821, "id": 25512, "height": 512}, {"file_name": "ADE_test_00003441.jpg", "width": 512, "id": 25513, "height": 716}, {"file_name": "ADE_test_00003442.jpg", "width": 512, "id": 25514, "height": 685}, {"file_name": "ADE_test_00003443.jpg", "width": 512, "id": 25515, "height": 772}, {"file_name": "ADE_test_00003444.jpg", "width": 512, "id": 25516, "height": 681}, {"file_name": "ADE_test_00003445.jpg", "width": 512, "id": 25517, "height": 683}, {"file_name": "ADE_test_00003446.jpg", "width": 512, "id": 25518, "height": 910}, {"file_name": "ADE_test_00003447.jpg", "width": 512, "id": 25519, "height": 683}, {"file_name": "ADE_test_00003448.jpg", "width": 512, "id": 25520, "height": 683}, {"file_name": "ADE_test_00003449.jpg", "width": 640, "id": 25521, "height": 480}, {"file_name": "ADE_test_00003450.jpg", "width": 512, "id": 25522, "height": 683}, {"file_name": "ADE_test_00003451.jpg", "width": 768, "id": 25523, "height": 512}, {"file_name": "ADE_test_00003452.jpg", "width": 512, "id": 25524, "height": 702}, {"file_name": "ADE_test_00003453.jpg", "width": 512, "id": 25525, "height": 921}, {"file_name": "ADE_test_00003454.jpg", "width": 512, "id": 25526, "height": 683}, {"file_name": "ADE_test_00003455.jpg", "width": 512, "id": 25527, "height": 683}, {"file_name": "ADE_test_00003456.jpg", "width": 512, "id": 25528, "height": 683}, {"file_name": "ADE_test_00003457.jpg", "width": 512, "id": 25529, "height": 770}, {"file_name": "ADE_test_00003458.jpg", "width": 512, "id": 25530, "height": 685}, {"file_name": "ADE_test_00003459.jpg", "width": 512, "id": 25531, "height": 683}, {"file_name": "ADE_test_00003460.jpg", "width": 512, "id": 25532, "height": 683}, {"file_name": "ADE_test_00003461.jpg", "width": 512, "id": 25533, "height": 978}, {"file_name": "ADE_test_00003462.jpg", "width": 768, "id": 25534, "height": 512}, {"file_name": "ADE_test_00003463.jpg", "width": 512, "id": 25535, "height": 770}, {"file_name": "ADE_test_00003464.jpg", "width": 1024, "id": 25536, "height": 1024}, {"file_name": "ADE_test_00003465.jpg", "width": 512, "id": 25537, "height": 719}, {"file_name": "ADE_test_00003466.jpg", "width": 512, "id": 25538, "height": 777}, {"file_name": "ADE_test_00003467.jpg", "width": 512, "id": 25539, "height": 771}, {"file_name": "ADE_test_00003468.jpg", "width": 512, "id": 25540, "height": 765}, {"file_name": "ADE_test_00003469.jpg", "width": 512, "id": 25541, "height": 768}, {"file_name": "ADE_test_00003470.jpg", "width": 512, "id": 25542, "height": 768}, {"file_name": "ADE_test_00003471.jpg", "width": 512, "id": 25543, "height": 909}, {"file_name": "ADE_test_00003472.jpg", "width": 768, "id": 25544, "height": 512}, {"file_name": "ADE_test_00003473.jpg", "width": 512, "id": 25545, "height": 685}, {"file_name": "ADE_test_00003474.jpg", "width": 512, "id": 25546, "height": 910}, {"file_name": "ADE_test_00003475.jpg", "width": 512, "id": 25547, "height": 640}, {"file_name": "ADE_test_00003476.jpg", "width": 772, "id": 25548, "height": 512}, {"file_name": "ADE_test_00003477.jpg", "width": 771, "id": 25549, "height": 512}, {"file_name": "ADE_test_00003478.jpg", "width": 512, "id": 25550, "height": 765}, {"file_name": "ADE_test_00003479.jpg", "width": 512, "id": 25551, "height": 715}, {"file_name": "ADE_test_00003480.jpg", "width": 512, "id": 25552, "height": 768}, {"file_name": "ADE_test_00003481.jpg", "width": 683, "id": 25553, "height": 512}, {"file_name": "ADE_test_00003482.jpg", "width": 480, "id": 25554, "height": 640}, {"file_name": "ADE_test_00003483.jpg", "width": 770, "id": 25555, "height": 512}, {"file_name": "ADE_test_00003484.jpg", "width": 683, "id": 25556, "height": 512}, {"file_name": "ADE_test_00003485.jpg", "width": 512, "id": 25557, "height": 770}, {"file_name": "ADE_test_00003486.jpg", "width": 512, "id": 25558, "height": 765}, {"file_name": "ADE_test_00003487.jpg", "width": 512, "id": 25559, "height": 683}, {"file_name": "ADE_test_00003488.jpg", "width": 512, "id": 25560, "height": 716}, {"file_name": "ADE_test_00003489.jpg", "width": 512, "id": 25561, "height": 750}], "categories": [{"id": 1, "name": "bed"}, {"id": 2, "name": "windowpane"}, {"id": 3, "name": "cabinet"}, {"id": 4, "name": "person"}, {"id": 5, "name": "door"}, {"id": 6, "name": "table"}, {"id": 7, "name": "curtain"}, {"id": 8, "name": "chair"}, {"id": 9, "name": "car"}, {"id": 10, "name": "painting"}, {"id": 11, "name": "sofa"}, {"id": 12, "name": "shelf"}, {"id": 13, "name": "mirror"}, {"id": 14, "name": "armchair"}, {"id": 15, "name": "seat"}, {"id": 16, "name": "fence"}, {"id": 17, "name": "desk"}, {"id": 18, "name": "wardrobe"}, {"id": 19, "name": "lamp"}, {"id": 20, "name": "bathtub"}, {"id": 21, "name": "railing"}, {"id": 22, "name": "cushion"}, {"id": 23, "name": "box"}, {"id": 24, "name": "column"}, {"id": 25, "name": "signboard"}, {"id": 26, "name": "chest of drawers"}, {"id": 27, "name": "counter"}, {"id": 28, "name": "sink"}, {"id": 29, "name": "fireplace"}, {"id": 30, "name": "refrigerator"}, {"id": 31, "name": "stairs"}, {"id": 32, "name": "case"}, {"id": 33, "name": "pool table"}, {"id": 34, "name": "pillow"}, {"id": 35, "name": "screen door"}, {"id": 36, "name": "bookcase"}, {"id": 37, "name": "coffee table"}, {"id": 38, "name": "toilet"}, {"id": 39, "name": "flower"}, {"id": 40, "name": "book"}, {"id": 41, "name": "bench"}, {"id": 42, "name": "countertop"}, {"id": 43, "name": "stove"}, {"id": 44, "name": "palm"}, {"id": 45, "name": "kitchen island"}, {"id": 46, "name": "computer"}, {"id": 47, "name": "swivel chair"}, {"id": 48, "name": "boat"}, {"id": 49, "name": "arcade machine"}, {"id": 50, "name": "bus"}, {"id": 51, "name": "towel"}, {"id": 52, "name": "light"}, {"id": 53, "name": "truck"}, {"id": 54, "name": "chandelier"}, {"id": 55, "name": "awning"}, {"id": 56, "name": "streetlight"}, {"id": 57, "name": "booth"}, {"id": 58, "name": "television receiver"}, {"id": 59, "name": "airplane"}, {"id": 60, "name": "apparel"}, {"id": 61, "name": "pole"}, {"id": 62, "name": "bannister"}, {"id": 63, "name": "ottoman"}, {"id": 64, "name": "bottle"}, {"id": 65, "name": "van"}, {"id": 66, "name": "ship"}, {"id": 67, "name": "fountain"}, {"id": 68, "name": "washer"}, {"id": 69, "name": "plaything"}, {"id": 70, "name": "stool"}, {"id": 71, "name": "barrel"}, {"id": 72, "name": "basket"}, {"id": 73, "name": "bag"}, {"id": 74, "name": "minibike"}, {"id": 75, "name": "oven"}, {"id": 76, "name": "ball"}, {"id": 77, "name": "food"}, {"id": 78, "name": "step"}, {"id": 79, "name": "trade name"}, {"id": 80, "name": "microwave"}, {"id": 81, "name": "pot"}, {"id": 82, "name": "animal"}, {"id": 83, "name": "bicycle"}, {"id": 84, "name": "dishwasher"}, {"id": 85, "name": "screen"}, {"id": 86, "name": "sculpture"}, {"id": 87, "name": "hood"}, {"id": 88, "name": "sconce"}, {"id": 89, "name": "vase"}, {"id": 90, "name": "traffic light"}, {"id": 91, "name": "tray"}, {"id": 92, "name": "ashcan"}, {"id": 93, "name": "fan"}, {"id": 94, "name": "plate"}, {"id": 95, "name": "monitor"}, {"id": 96, "name": "bulletin board"}, {"id": 97, "name": "radiator"}, {"id": 98, "name": "glass"}, {"id": 99, "name": "clock"}, {"id": 100, "name": "flag"}]}
\ No newline at end of file
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_full_sem_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_full_sem_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..34bda54398faaeb329950603aad118661155fdaa
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_full_sem_seg.py
@@ -0,0 +1,103 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+import pickle as pkl
+from pathlib import Path
+
+import cv2
+import numpy as np
+import tqdm
+from PIL import Image
+from mask2former.data.datasets.register_ade20k_full import ADE20K_SEM_SEG_FULL_CATEGORIES
+
+
+def loadAde20K(file):
+ fileseg = file.replace(".jpg", "_seg.png")
+ with Image.open(fileseg) as io:
+ seg = np.array(io)
+
+ R = seg[:, :, 0]
+ G = seg[:, :, 1]
+ ObjectClassMasks = (R / 10).astype(np.int32) * 256 + (G.astype(np.int32))
+
+ return {"img_name": file, "segm_name": fileseg, "class_mask": ObjectClassMasks}
+
+
+if __name__ == "__main__":
+ dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets"))
+ index_file = dataset_dir / "ade/ADE20K_2021_17_01" / "index_ade20k.pkl"
+ with open(index_file, "rb") as f:
+ index_ade20k = pkl.load(f)
+
+ id_map = {}
+ for cat in ADE20K_SEM_SEG_FULL_CATEGORIES:
+ id_map[cat["id"]] = cat["trainId"]
+
+ # make output dir
+ for name in ["training", "validation"]:
+ image_dir = dataset_dir / "ade/ADE20K_2021_17_01" / "images_detectron2" / name
+ image_dir.mkdir(parents=True, exist_ok=True)
+ annotation_dir = dataset_dir / "ade/ADE20K_2021_17_01" / "annotations_detectron2" / name
+ annotation_dir.mkdir(parents=True, exist_ok=True)
+
+ # process image and gt
+ for folder_name, file_name in tqdm.tqdm(
+ zip(index_ade20k["folder"], index_ade20k["filename"]),
+ total=len(index_ade20k["filename"]),
+ ):
+ split = "validation" if file_name.split("_")[1] == "val" else "training"
+ info = loadAde20K(str(dataset_dir / "ade" / folder_name / file_name))
+
+ # resize image and label
+ img = np.asarray(Image.open(info["img_name"]))
+ lab = np.asarray(info["class_mask"])
+
+ h, w = img.shape[0], img.shape[1]
+ max_size = 512
+ resize = True
+ if w >= h > max_size:
+ h_new, w_new = max_size, round(w / float(h) * max_size)
+ elif h >= w > max_size:
+ h_new, w_new = round(h / float(w) * max_size), max_size
+ else:
+ resize = False
+
+ if resize:
+ img = cv2.resize(img, (w_new, h_new), interpolation=cv2.INTER_LINEAR)
+ lab = cv2.resize(lab, (w_new, h_new), interpolation=cv2.INTER_NEAREST)
+
+ assert img.dtype == np.uint8
+ assert lab.dtype == np.int32
+
+ # apply label conversion and save into uint16 images
+ output = np.zeros_like(lab, dtype=np.uint16) + 65535
+ for obj_id in np.unique(lab):
+ if obj_id in id_map:
+ output[lab == obj_id] = id_map[obj_id]
+
+ output_img = dataset_dir / "ade/ADE20K_2021_17_01" / "images_detectron2" / split / file_name
+ output_lab = (
+ dataset_dir
+ / "ade/ADE20K_2021_17_01"
+ / "annotations_detectron2"
+ / split
+ / file_name.replace(".jpg", ".tif")
+ )
+ Image.fromarray(img).save(output_img)
+
+ assert output.dtype == np.uint16
+ Image.fromarray(output).save(output_lab)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_ins_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_ins_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..933d09d8c2797a691faacc5e00c0319f113231ba
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_ins_seg.py
@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+
+import glob
+import json
+import os
+
+import numpy as np
+import tqdm
+from panopticapi.utils import save_json
+from PIL import Image
+import pycocotools.mask as mask_util
+
+
+if __name__ == "__main__":
+ dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets")
+
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(dataset_dir, f"ade/ADEChallengeData2016/images/{dirname}/")
+ instance_dir = os.path.join(
+ dataset_dir, f"ade/ADEChallengeData2016/annotations_instance/{dirname}/"
+ )
+
+ # img_id = 0
+ ann_id = 1
+
+ # json
+ out_file = os.path.join(
+ dataset_dir, f"ade/ADEChallengeData2016/ade20k_instance_{name}.json"
+ )
+
+ # json config
+ instance_config_file = "datasets/ade20k_instance_imgCatIds.json"
+ with open(instance_config_file) as f:
+ category_dict = json.load(f)["categories"]
+
+ # load catid mapping
+ # it is important to share category id for both instance and panoptic annotations
+ mapping_file = "datasets/ade20k_instance_catid_mapping.txt"
+ with open(mapping_file) as f:
+ map_id = {}
+ for i, line in enumerate(f.readlines()):
+ if i == 0:
+ continue
+ ins_id, sem_id, _ = line.strip().split()
+ # shift id by 1 because we want it to start from 0!
+ # ignore_label becomes 255
+ map_id[int(ins_id)] = int(sem_id) - 1
+
+ for cat in category_dict:
+ cat["id"] = map_id[cat["id"]]
+
+ filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg")))
+
+ ann_dict = {}
+ images = []
+ annotations = []
+
+ for idx, filename in enumerate(tqdm.tqdm(filenames)):
+ image = {}
+ image_id = os.path.basename(filename).split(".")[0]
+
+ image["id"] = image_id
+ image["file_name"] = os.path.basename(filename)
+
+ original_format = np.array(Image.open(filename))
+ image["width"] = original_format.shape[1]
+ image["height"] = original_format.shape[0]
+
+ images.append(image)
+
+ filename_instance = os.path.join(instance_dir, image_id + ".png")
+ ins_seg = np.asarray(Image.open(filename_instance))
+ assert ins_seg.dtype == np.uint8
+
+ instance_cat_ids = ins_seg[..., 0]
+ # instance id starts from 1!
+ # because 0 is reserved as VOID label
+ instance_ins_ids = ins_seg[..., 1]
+
+ # process things
+ for thing_id in np.unique(instance_ins_ids):
+ if thing_id == 0:
+ continue
+ mask = instance_ins_ids == thing_id
+ instance_cat_id = np.unique(instance_cat_ids[mask])
+ assert len(instance_cat_id) == 1
+
+ anno = {}
+ anno["id"] = ann_id
+ ann_id += 1
+ anno["image_id"] = image["id"]
+ anno["iscrowd"] = int(0)
+ anno["category_id"] = int(map_id[instance_cat_id[0]])
+
+ inds = np.nonzero(mask)
+ ymin, ymax = inds[0].min(), inds[0].max()
+ xmin, xmax = inds[1].min(), inds[1].max()
+ anno["bbox"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)]
+ # if xmax <= xmin or ymax <= ymin:
+ # continue
+ rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
+ rle["counts"] = rle["counts"].decode("utf-8")
+ anno["segmentation"] = rle
+ anno["area"] = int(mask_util.area(rle))
+ annotations.append(anno)
+
+ # save this
+ ann_dict["images"] = images
+ ann_dict["categories"] = category_dict
+ ann_dict["annotations"] = annotations
+
+ save_json(ann_dict, out_file)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_pan_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_pan_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..06f00f8bb9075f517c5cf9835d5539ef609e5f4a
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_pan_seg.py
@@ -0,0 +1,211 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import glob
+import json
+import os
+
+import numpy as np
+import tqdm
+from panopticapi.utils import IdGenerator, save_json
+from PIL import Image
+from mask2former.data.datasets.register_ade20k_panoptic import ADE20K_150_CATEGORIES
+
+ADE20K_SEM_SEG_CATEGORIES = [c["name"] for c in ADE20K_150_CATEGORIES]
+PALETTE = [c["color"] for c in ADE20K_150_CATEGORIES]
+
+if __name__ == "__main__":
+ dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets")
+
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(dataset_dir, f"ade/ADEChallengeData2016/images/{dirname}/")
+ semantic_dir = os.path.join(dataset_dir, f"ade/ADEChallengeData2016/annotations/{dirname}/")
+ instance_dir = os.path.join(
+ dataset_dir, f"ade/ADEChallengeData2016/annotations_instance/{dirname}/"
+ )
+
+ # folder to store panoptic PNGs
+ out_folder = os.path.join(dataset_dir, f"ade/ADEChallengeData2016/ade20k_panoptic_{name}/")
+ # json with segmentations information
+ out_file = os.path.join(
+ dataset_dir, f"ade/ADEChallengeData2016/ade20k_panoptic_{name}.json"
+ )
+
+ if not os.path.isdir(out_folder):
+ print("Creating folder {} for panoptic segmentation PNGs".format(out_folder))
+ os.mkdir(out_folder)
+
+ # json config
+ config_file = "datasets/ade20k_instance_imgCatIds.json"
+ with open(config_file) as f:
+ config = json.load(f)
+
+ # load catid mapping
+ mapping_file = "datasets/ade20k_instance_catid_mapping.txt"
+ with open(mapping_file) as f:
+ map_id = {}
+ for i, line in enumerate(f.readlines()):
+ if i == 0:
+ continue
+ ins_id, sem_id, _ = line.strip().split()
+ # shift id by 1 because we want it to start from 0!
+ # ignore_label becomes 255
+ map_id[int(ins_id) - 1] = int(sem_id) - 1
+
+ ADE20K_150_CATEGORIES = []
+ for cat_id, cat_name in enumerate(ADE20K_SEM_SEG_CATEGORIES):
+ ADE20K_150_CATEGORIES.append(
+ {
+ "name": cat_name,
+ "id": cat_id,
+ "isthing": int(cat_id in map_id.values()),
+ "color": PALETTE[cat_id],
+ }
+ )
+ categories_dict = {cat["id"]: cat for cat in ADE20K_150_CATEGORIES}
+
+ panoptic_json_categories = ADE20K_150_CATEGORIES[:]
+ panoptic_json_images = []
+ panoptic_json_annotations = []
+
+ filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg")))
+ for filename in tqdm.tqdm(filenames):
+ panoptic_json_image = {}
+ panoptic_json_annotation = {}
+
+ image_id = os.path.basename(filename).split(".")[0]
+
+ panoptic_json_image["id"] = image_id
+ panoptic_json_image["file_name"] = os.path.basename(filename)
+
+ original_format = np.array(Image.open(filename))
+ panoptic_json_image["width"] = original_format.shape[1]
+ panoptic_json_image["height"] = original_format.shape[0]
+
+ pan_seg = np.zeros(
+ (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8
+ )
+ id_generator = IdGenerator(categories_dict)
+
+ filename_semantic = os.path.join(semantic_dir, image_id + ".png")
+ filename_instance = os.path.join(instance_dir, image_id + ".png")
+
+ sem_seg = np.asarray(Image.open(filename_semantic))
+ ins_seg = np.asarray(Image.open(filename_instance))
+
+ assert sem_seg.dtype == np.uint8
+ assert ins_seg.dtype == np.uint8
+
+ semantic_cat_ids = sem_seg - 1
+ instance_cat_ids = ins_seg[..., 0] - 1
+ # instance id starts from 1!
+ # because 0 is reserved as VOID label
+ instance_ins_ids = ins_seg[..., 1]
+
+ segm_info = []
+
+ # NOTE: there is some overlap between semantic and instance annotation
+ # thus we paste stuffs first
+
+ # process stuffs
+ for semantic_cat_id in np.unique(semantic_cat_ids):
+ if semantic_cat_id == 255:
+ continue
+ if categories_dict[semantic_cat_id]["isthing"]:
+ continue
+ mask = semantic_cat_ids == semantic_cat_id
+ # should not have any overlap
+ assert pan_seg[mask].sum() == 0
+
+ segment_id, color = id_generator.get_id_and_color(semantic_cat_id)
+ pan_seg[mask] = color
+
+ area = np.sum(mask) # segment area computation
+ # bbox computation for a segment
+ hor = np.sum(mask, axis=0)
+ hor_idx = np.nonzero(hor)[0]
+ x = hor_idx[0]
+ width = hor_idx[-1] - x + 1
+ vert = np.sum(mask, axis=1)
+ vert_idx = np.nonzero(vert)[0]
+ y = vert_idx[0]
+ height = vert_idx[-1] - y + 1
+ bbox = [int(x), int(y), int(width), int(height)]
+
+ segm_info.append(
+ {
+ "id": int(segment_id),
+ "category_id": int(semantic_cat_id),
+ "area": int(area),
+ "bbox": bbox,
+ "iscrowd": 0,
+ }
+ )
+
+ # process things
+ for thing_id in np.unique(instance_ins_ids):
+ if thing_id == 0:
+ continue
+ mask = instance_ins_ids == thing_id
+ instance_cat_id = np.unique(instance_cat_ids[mask])
+ assert len(instance_cat_id) == 1
+
+ semantic_cat_id = map_id[instance_cat_id[0]]
+
+ segment_id, color = id_generator.get_id_and_color(semantic_cat_id)
+ pan_seg[mask] = color
+
+ area = np.sum(mask) # segment area computation
+ # bbox computation for a segment
+ hor = np.sum(mask, axis=0)
+ hor_idx = np.nonzero(hor)[0]
+ x = hor_idx[0]
+ width = hor_idx[-1] - x + 1
+ vert = np.sum(mask, axis=1)
+ vert_idx = np.nonzero(vert)[0]
+ y = vert_idx[0]
+ height = vert_idx[-1] - y + 1
+ bbox = [int(x), int(y), int(width), int(height)]
+
+ segm_info.append(
+ {
+ "id": int(segment_id),
+ "category_id": int(semantic_cat_id),
+ "area": int(area),
+ "bbox": bbox,
+ "iscrowd": 0,
+ }
+ )
+
+ panoptic_json_annotation = {
+ "image_id": image_id,
+ "file_name": image_id + ".png",
+ "segments_info": segm_info,
+ }
+
+ Image.fromarray(pan_seg).save(os.path.join(out_folder, image_id + ".png"))
+
+ panoptic_json_images.append(panoptic_json_image)
+ panoptic_json_annotations.append(panoptic_json_annotation)
+
+ # save this
+ d = {
+ "images": panoptic_json_images,
+ "annotations": panoptic_json_annotations,
+ "categories": panoptic_json_categories,
+ }
+
+ save_json(d, out_file)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_sem_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_sem_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa2823bad584e081eeb069da265298ed5067a741
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_ade20k_sem_seg.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+
+import os
+from pathlib import Path
+
+import numpy as np
+import tqdm
+from PIL import Image
+
+
+def convert(input, output):
+ img = np.asarray(Image.open(input))
+ assert img.dtype == np.uint8
+ img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1
+ Image.fromarray(img).save(output)
+
+
+if __name__ == "__main__":
+ dataset_dir = (
+ Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ade" / "ADEChallengeData2016"
+ )
+ for name in ["training", "validation"]:
+ annotation_dir = dataset_dir / "annotations" / name
+ output_dir = dataset_dir / "annotations_detectron2" / name
+ output_dir.mkdir(parents=True, exist_ok=True)
+ for file in tqdm.tqdm(list(annotation_dir.iterdir())):
+ output_file = output_dir / file.name
+ convert(file, output_file)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_coco_caption.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_coco_caption.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb1f96e8b5e9bd261f0754588d8d3543facf607b
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_coco_caption.py
@@ -0,0 +1,58 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+# Convert adding COCO captions into annotation json
+
+import json
+import os
+from collections import defaultdict
+
+
+def load_coco_caption():
+ id2caption = defaultdict(list)
+ dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
+ for json_file in ["captions_train2017.json", "captions_val2017.json"]:
+ with open(os.path.join(dataset_dir, "annotations", json_file)) as f:
+ obj = json.load(f)
+ for ann in obj["annotations"]:
+ id2caption[int(ann["image_id"])].append(ann["caption"])
+
+ return id2caption
+
+
+def create_annotation_with_caption(input_json, output_json):
+ id2coco_caption = load_coco_caption()
+
+ with open(input_json) as f:
+ obj = json.load(f)
+
+ coco_count = 0
+
+ print(f"Starting to add captions to {input_json} ...")
+ print(f"Total images: {len(obj['annotations'])}")
+ for ann in obj["annotations"]:
+ image_id = int(ann["image_id"])
+ if image_id in id2coco_caption:
+ ann["coco_captions"] = id2coco_caption[image_id]
+ coco_count += 1
+ print(f"Found {coco_count} captions from COCO ")
+
+ print(f"Start writing to {output_json} ...")
+ with open(output_json, "w") as f:
+ json.dump(obj, f)
+
+
+if __name__ == "__main__":
+ dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
+ for s in ["val2017", "val2017_100", "train2017"]:
+ create_annotation_with_caption(
+ os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)),
+ os.path.join(dataset_dir, "annotations/panoptic_caption_{}.json".format(s)),
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c7f77affd8cf7a5928bd37cf50d33f1d52723a8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
@@ -0,0 +1,124 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import functools
+import json
+import multiprocessing as mp
+import numpy as np
+import os
+import time
+from fvcore.common.download import download
+from panopticapi.utils import rgb2id
+from PIL import Image
+
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+
+
+def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):
+ panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)
+ panoptic = rgb2id(panoptic)
+ output = np.zeros_like(panoptic, dtype=np.uint8) + 255
+ for seg in segments:
+ cat_id = seg["category_id"]
+ new_cat_id = id_map[cat_id]
+ output[panoptic == seg["id"]] = new_cat_id
+ Image.fromarray(output).save(output_semantic)
+
+
+def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):
+ """
+ Create semantic segmentation annotations from panoptic segmentation
+ annotations, to be used by PanopticFPN.
+ It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.
+ It maps all stuff categories to contiguous ids starting from 1.
+ Args:
+ panoptic_json (str): path to the panoptic json file, in COCO's format.
+ panoptic_root (str): a directory with panoptic annotation files, in COCO's format.
+ sem_seg_root (str): a directory to output semantic annotation files
+ categories (list[dict]): category metadata. Each dict needs to have:
+ "id": corresponds to the "category_id" in the json annotations
+ "isthing": 0 or 1
+ """
+ os.makedirs(sem_seg_root, exist_ok=True)
+
+ id_map = {} # map from category id to id in the output semantic annotation
+ assert len(categories) <= 254
+ for i, k in enumerate(categories):
+ id_map[k["id"]] = i
+ # what is id = 0?
+ # id_map[0] = 255
+ print(id_map)
+
+ with open(panoptic_json) as f:
+ obj = json.load(f)
+
+ pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))
+
+ def iter_annotations():
+ for anno in obj["annotations"]:
+ file_name = anno["file_name"]
+ segments = anno["segments_info"]
+ input = os.path.join(panoptic_root, file_name)
+ output = os.path.join(sem_seg_root, file_name)
+ yield input, output, segments
+
+ print("Start writing to {} ...".format(sem_seg_root))
+ start = time.time()
+ pool.starmap(
+ functools.partial(_process_panoptic_to_semantic, id_map=id_map),
+ iter_annotations(),
+ chunksize=100,
+ )
+ print("Finished. time: {:.2f}s".format(time.time() - start))
+
+
+if __name__ == "__main__":
+ dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
+ for s in ["val2017", "train2017"]:
+ separate_coco_semantic_from_panoptic(
+ os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)),
+ os.path.join(dataset_dir, "panoptic_{}".format(s)),
+ os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)),
+ COCO_CATEGORIES,
+ )
+
+ # Prepare val2017_100 for quick testing:
+
+ dest_dir = os.path.join(dataset_dir, "annotations/")
+ URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
+ download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json", dest_dir)
+ with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f:
+ obj = json.load(f)
+
+ def link_val100(dir_full, dir_100):
+ print("Creating " + dir_100 + " ...")
+ os.makedirs(dir_100, exist_ok=True)
+ for img in obj["images"]:
+ basename = os.path.splitext(img["file_name"])[0]
+ src = os.path.join(dir_full, basename + ".png")
+ dst = os.path.join(dir_100, basename + ".png")
+ src = os.path.relpath(src, start=dir_100)
+ os.symlink(src, dst)
+
+ link_val100(
+ os.path.join(dataset_dir, "panoptic_val2017"),
+ os.path.join(dataset_dir, "panoptic_val2017_100"),
+ )
+
+ link_val100(
+ os.path.join(dataset_dir, "panoptic_semseg_val2017"),
+ os.path.join(dataset_dir, "panoptic_semseg_val2017_100"),
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_lvis_openseg_labels.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_lvis_openseg_labels.py
new file mode 100644
index 0000000000000000000000000000000000000000..45b273c85ed52466ffbf26b015e2c37e516b72bf
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_lvis_openseg_labels.py
@@ -0,0 +1,53 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import json
+import os
+
+if __name__ == "__main__":
+ dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
+ ann = os.path.join(dataset_dir, "annotations/lvis_v1_val.json")
+ print("Loading", ann)
+ data = json.load(open(ann, "r"))
+ cat_names = [x["name"] for x in sorted(data["categories"], key=lambda x: x["id"])]
+ nonrare_names = [
+ x["name"]
+ for x in sorted(data["categories"], key=lambda x: x["id"])
+ if x["frequency"] != "r"
+ ]
+
+ synonyms = [x["synonyms"] for x in sorted(data["categories"], key=lambda x: x["id"])]
+ nonrare_synonyms = [
+ x["synonyms"]
+ for x in sorted(data["categories"], key=lambda x: x["id"])
+ if x["frequency"] != "r"
+ ]
+
+ with open("datasets/openseg/lvis_1203.txt", "w") as f:
+ for idx, cat in enumerate(cat_names):
+ cat = cat.replace("_", " ")
+ f.write(f"{idx+1}:{cat}\n")
+
+ with open("datasets/openseg/lvis_1203_with_prompt_eng.txt", "w") as f:
+ for idx, syns in enumerate(synonyms):
+ cat = ",".join(syns)
+ cat = cat.replace("_", " ")
+ f.write(f"{idx+1}:{cat}\n")
+
+ with open("datasets/openseg/lvis_nonrare_866.txt", "w") as f:
+ for idx, cat in enumerate(nonrare_names):
+ cat = cat.replace("_", " ")
+ f.write(f"{idx+1}:{cat}\n")
+
+ with open("datasets/openseg/lvis_nonrare_866_with_prompt_eng.txt", "w") as f:
+ for idx, syns in enumerate(nonrare_synonyms):
+ cat = ",".join(syns)
+ cat = cat.replace("_", " ")
+ f.write(f"{idx+1}:{cat}\n")
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_ctx_full_sem_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_ctx_full_sem_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..80fa9c96fb21f109ccbe4c43e5cb8f799ab11cfc
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_ctx_full_sem_seg.py
@@ -0,0 +1,48 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+import numpy as np
+from pathlib import Path
+from PIL import Image
+import scipy.io as sio
+
+import tqdm
+
+
+def generate_labels(mat_file, out_dir):
+
+ mat = sio.loadmat(mat_file)
+ label_map = mat["LabelMap"]
+ assert label_map.dtype == np.uint16
+ label_map[label_map == 0] = 65535
+ label_map = label_map - 1
+ label_map[label_map == 65534] = 65535
+
+ out_file = out_dir / Path(mat_file.name).with_suffix(".tif")
+ Image.fromarray(label_map).save(out_file)
+
+
+if __name__ == "__main__":
+ dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "pascal_ctx_d2"
+ voc_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "VOCdevkit/VOC2010"
+ mat_dir = voc_dir / "trainval"
+ for split in ["training", "validation"]:
+ file_names = list((dataset_dir / "images" / split).glob("*.jpg"))
+ output_img_dir = dataset_dir / "images" / split
+ output_ann_dir = dataset_dir / "annotations_ctx459" / split
+
+ output_img_dir.mkdir(parents=True, exist_ok=True)
+ output_ann_dir.mkdir(parents=True, exist_ok=True)
+
+ for file_name in tqdm.tqdm(file_names):
+ mat_file_path = mat_dir / f"{file_name.stem}.mat"
+
+ generate_labels(mat_file_path, output_ann_dir)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_ctx_sem_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_ctx_sem_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..8649a5fc47a6430f29965b79e90f9a83e29bac42
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_ctx_sem_seg.py
@@ -0,0 +1,84 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+from pathlib import Path
+import shutil
+
+import numpy as np
+import tqdm
+from PIL import Image
+import multiprocessing as mp
+import functools
+from detail import Detail
+
+# fmt: off
+_mapping = np.sort(
+ np.array([
+ 0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22, 23, 397, 25, 284,
+ 158, 159, 416, 33, 162, 420, 454, 295, 296, 427, 44, 45, 46, 308, 59,
+ 440, 445, 31, 232, 65, 354, 424, 68, 326, 72, 458, 34, 207, 80, 355,
+ 85, 347, 220, 349, 360, 98, 187, 104, 105, 366, 189, 368, 113, 115
+ ]))
+# fmt: on
+_key = np.array(range(len(_mapping))).astype("uint8")
+
+
+def generate_labels(img_info, detail_api, out_dir):
+ def _class_to_index(mask, _mapping, _key):
+ # assert the values
+ values = np.unique(mask)
+ for i in range(len(values)):
+ assert values[i] in _mapping
+ index = np.digitize(mask.ravel(), _mapping, right=True)
+ return _key[index].reshape(mask.shape)
+
+ sem_seg = _class_to_index(detail_api.getMask(img_info), _mapping=_mapping, _key=_key)
+ sem_seg = sem_seg - 1 # 0 (ignore) becomes 255. others are shifted by 1
+ filename = img_info["file_name"]
+
+ Image.fromarray(sem_seg).save(out_dir / filename.replace("jpg", "png"))
+
+
+def copy_images(img_info, img_dir, out_dir):
+ filename = img_info["file_name"]
+ shutil.copy2(img_dir / filename, out_dir / filename)
+
+
+if __name__ == "__main__":
+ dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "pascal_ctx_d2"
+ voc_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "VOCdevkit/VOC2010"
+ for split in ["training", "validation"]:
+ img_dir = voc_dir / "JPEGImages"
+ if split == "training":
+ detail_api = Detail(voc_dir / "trainval_merged.json", img_dir, "train")
+ else:
+ detail_api = Detail(voc_dir / "trainval_merged.json", img_dir, "val")
+ img_infos = detail_api.getImgs()
+
+ output_img_dir = dataset_dir / "images" / split
+ output_ann_dir = dataset_dir / "annotations_ctx59" / split
+
+ output_img_dir.mkdir(parents=True, exist_ok=True)
+ output_ann_dir.mkdir(parents=True, exist_ok=True)
+
+ pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))
+
+ pool.map(
+ functools.partial(copy_images, img_dir=img_dir, out_dir=output_img_dir),
+ tqdm.tqdm(img_infos, desc=f"Writing {split} images to {output_img_dir} ..."),
+ chunksize=100,
+ )
+
+ pool.map(
+ functools.partial(generate_labels, detail_api=detail_api, out_dir=output_ann_dir),
+ tqdm.tqdm(img_infos, desc=f"Writing {split} images to {output_ann_dir} ..."),
+ chunksize=100,
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_voc_sem_seg.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_voc_sem_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d5acaf4f4d478560afa40e164ed71cd6883c585
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/datasets/prepare_pascal_voc_sem_seg.py
@@ -0,0 +1,53 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+from pathlib import Path
+import shutil
+
+import numpy as np
+import tqdm
+from PIL import Image
+
+
+def convert(input, output):
+ img = np.asarray(Image.open(input))
+ assert img.dtype == np.uint8
+ # do nothing
+ Image.fromarray(img).save(output)
+
+
+if __name__ == "__main__":
+ dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "pascal_voc_d2"
+ voc_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "VOCdevkit/VOC2012"
+ for split in ["training", "validation"]:
+ if split == "training":
+ img_name_path = voc_dir / "ImageSets/Segmentation/train.txt"
+ else:
+ img_name_path = voc_dir / "ImageSets/Segmentation/val.txt"
+ img_dir = voc_dir / "JPEGImages"
+ ann_dir = voc_dir / "SegmentationClass"
+
+ output_img_dir = dataset_dir / "images" / split
+ output_ann_dir = dataset_dir / "annotations_pascal21" / split
+
+ output_img_dir.mkdir(parents=True, exist_ok=True)
+ output_ann_dir.mkdir(parents=True, exist_ok=True)
+
+ with open(img_name_path) as f:
+ for line in tqdm.tqdm(f.readlines()):
+ img_name = line.strip()
+ img_path = img_dir / f"{img_name}.jpg"
+ ann_path = ann_dir / f"{img_name}.png"
+
+ # print(f'copy2 {output_img_dir}')
+ shutil.copy2(img_path, output_img_dir)
+ # print(f"convert {ann_dir} to {output_ann_dir / f'{img_name}.png'}")
+ convert(ann_path, output_ann_dir / f"{img_name}.png")
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/app.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7eaea4c96dff5e3acab54a935ea1bac448139ac
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/app.py
@@ -0,0 +1,332 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import itertools
+import json
+from contextlib import ExitStack
+import gradio as gr
+import torch
+from detectron2.config import instantiate
+from detectron2.data import MetadataCatalog
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+from detectron2.evaluation import inference_context
+from detectron2.utils.env import seed_all_rng
+from detectron2.utils.logger import setup_logger
+from detectron2.utils.visualizer import ColorMode, Visualizer, random_color
+from mask2former.data.datasets.register_ade20k_panoptic import ADE20K_150_CATEGORIES
+from PIL import Image
+from torch.cuda.amp import autocast
+
+from odise import model_zoo
+from odise.checkpoint import ODISECheckpointer
+from odise.config import instantiate_odise
+from odise.data import get_openseg_labels
+from odise.modeling.wrapper import OpenPanopticInference
+
+setup_logger()
+logger = setup_logger(name="odise")
+
+COCO_THING_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("coco_panoptic", True))
+ if COCO_CATEGORIES[idx]["isthing"] == 1
+]
+COCO_THING_COLORS = [c["color"] for c in COCO_CATEGORIES if c["isthing"] == 1]
+COCO_STUFF_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("coco_panoptic", True))
+ if COCO_CATEGORIES[idx]["isthing"] == 0
+]
+COCO_STUFF_COLORS = [c["color"] for c in COCO_CATEGORIES if c["isthing"] == 0]
+
+ADE_THING_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("ade20k_150", True))
+ if ADE20K_150_CATEGORIES[idx]["isthing"] == 1
+]
+ADE_THING_COLORS = [c["color"] for c in ADE20K_150_CATEGORIES if c["isthing"] == 1]
+ADE_STUFF_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("ade20k_150", True))
+ if ADE20K_150_CATEGORIES[idx]["isthing"] == 0
+]
+ADE_STUFF_COLORS = [c["color"] for c in ADE20K_150_CATEGORIES if c["isthing"] == 0]
+
+LVIS_CLASSES = get_openseg_labels("lvis_1203", True)
+# use beautiful coco colors
+LVIS_COLORS = list(
+ itertools.islice(itertools.cycle([c["color"] for c in COCO_CATEGORIES]), len(LVIS_CLASSES))
+)
+
+
+class VisualizationDemo(object):
+ def __init__(self, model, metadata, aug, instance_mode=ColorMode.IMAGE):
+ """
+ Args:
+ model (nn.Module):
+ metadata (MetadataCatalog): image metadata.
+ instance_mode (ColorMode):
+ parallel (bool): whether to run the model in different processes from visualization.
+ Useful since the visualization logic can be slow.
+ """
+ self.model = model
+ self.metadata = metadata
+ self.aug = aug
+ self.cpu_device = torch.device("cpu")
+ self.instance_mode = instance_mode
+
+ def predict(self, original_image):
+ """
+ Args:
+ original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+
+ Returns:
+ predictions (dict):
+ the output of the model for one image only.
+ See :doc:`/tutorials/models` for details about the format.
+ """
+ height, width = original_image.shape[:2]
+ aug_input = T.AugInput(original_image, sem_seg=None)
+ self.aug(aug_input)
+ image = aug_input.image
+ image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+
+ inputs = {"image": image, "height": height, "width": width}
+ logger.info("forwarding")
+ with autocast():
+ predictions = self.model([inputs])[0]
+ logger.info("done")
+ return predictions
+
+ def run_on_image(self, image):
+ """
+ Args:
+ image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+ This is the format used by OpenCV.
+ Returns:
+ predictions (dict): the output of the model.
+ vis_output (VisImage): the visualized image output.
+ """
+ vis_output = None
+ predictions = self.predict(image)
+ visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
+ if "panoptic_seg" in predictions:
+ panoptic_seg, segments_info = predictions["panoptic_seg"]
+ vis_output = visualizer.draw_panoptic_seg(
+ panoptic_seg.to(self.cpu_device), segments_info
+ )
+ else:
+ if "sem_seg" in predictions:
+ vis_output = visualizer.draw_sem_seg(
+ predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
+ )
+ if "instances" in predictions:
+ instances = predictions["instances"].to(self.cpu_device)
+ vis_output = visualizer.draw_instance_predictions(predictions=instances)
+
+ return predictions, vis_output
+
+
+models = {}
+for model_name, cfg_name in zip(
+ ["ODISE(Label)", "ODISE(Caption)"],
+ ["Panoptic/odise_label_coco_50e.py", "Panoptic/odise_caption_coco_50e.py"],
+):
+
+ cfg = model_zoo.get_config(cfg_name, trained=True)
+
+ cfg.model.overlap_threshold = 0
+ cfg.model.clip_head.alpha = 0.35
+ cfg.model.clip_head.beta = 0.65
+ cfg.train.device = "cuda" if torch.cuda.is_available() else "cpu"
+ seed_all_rng(42)
+
+ dataset_cfg = cfg.dataloader.test
+ wrapper_cfg = cfg.dataloader.wrapper
+
+ aug = instantiate(dataset_cfg.mapper).augmentations
+
+ model = instantiate_odise(cfg.model)
+ model.to(torch.float16)
+ model.to(cfg.train.device)
+ ODISECheckpointer(model).load(cfg.train.init_checkpoint)
+ models[model_name] = model
+
+
+title = "ODISE"
+description = """
+Gradio demo for ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models. \n
+You may click on of the examples or upload your own image. \n
+
+ODISE could perform open vocabulary segmentation, you may input more classes (separate by comma).
+The expected format is 'a1,a2;b1,b2', where a1,a2 are synonyms vocabularies for the first class.
+The first word will be displayed as the class name.
+""" # noqa
+
+article = """
+Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models | Github Repo
+""" # noqa
+
+examples = [
+ [
+ "demo/examples/coco.jpg",
+ "black pickup truck, pickup truck; blue sky, sky",
+ ["COCO (133 categories)", "ADE (150 categories)", "LVIS (1203 categories)"],
+ ],
+ [
+ "demo/examples/ade.jpg",
+ "luggage, suitcase, baggage;handbag",
+ ["ADE (150 categories)"],
+ ],
+ [
+ "demo/examples/ego4d.jpg",
+ "faucet, tap; kitchen paper, paper towels",
+ ["COCO (133 categories)"],
+ ],
+]
+
+
+def build_demo_classes_and_metadata(vocab, label_list):
+ extra_classes = []
+
+ if vocab:
+ for words in vocab.split(";"):
+ extra_classes.append([word.strip() for word in words.split(",")])
+ extra_colors = [random_color(rgb=True, maximum=1) for _ in range(len(extra_classes))]
+
+ demo_thing_classes = extra_classes
+ demo_stuff_classes = []
+ demo_thing_colors = extra_colors
+ demo_stuff_colors = []
+
+ if any("COCO" in label for label in label_list):
+ demo_thing_classes += COCO_THING_CLASSES
+ demo_stuff_classes += COCO_STUFF_CLASSES
+ demo_thing_colors += COCO_THING_COLORS
+ demo_stuff_colors += COCO_STUFF_COLORS
+ if any("ADE" in label for label in label_list):
+ demo_thing_classes += ADE_THING_CLASSES
+ demo_stuff_classes += ADE_STUFF_CLASSES
+ demo_thing_colors += ADE_THING_COLORS
+ demo_stuff_colors += ADE_STUFF_COLORS
+ if any("LVIS" in label for label in label_list):
+ demo_thing_classes += LVIS_CLASSES
+ demo_thing_colors += LVIS_COLORS
+
+ MetadataCatalog.pop("odise_demo_metadata", None)
+ demo_metadata = MetadataCatalog.get("odise_demo_metadata")
+ demo_metadata.thing_classes = [c[0] for c in demo_thing_classes]
+ demo_metadata.stuff_classes = [
+ *demo_metadata.thing_classes,
+ *[c[0] for c in demo_stuff_classes],
+ ]
+ demo_metadata.thing_colors = demo_thing_colors
+ demo_metadata.stuff_colors = demo_thing_colors + demo_stuff_colors
+ demo_metadata.stuff_dataset_id_to_contiguous_id = {
+ idx: idx for idx in range(len(demo_metadata.stuff_classes))
+ }
+ demo_metadata.thing_dataset_id_to_contiguous_id = {
+ idx: idx for idx in range(len(demo_metadata.thing_classes))
+ }
+
+ demo_classes = demo_thing_classes + demo_stuff_classes
+
+ return demo_classes, demo_metadata
+
+
+def inference(image_path, vocab, label_list, model_name):
+
+ logger.info("building class names")
+ demo_classes, demo_metadata = build_demo_classes_and_metadata(vocab, label_list)
+ if model_name is None:
+ model_name = "ODISE(Label)"
+ with ExitStack() as stack:
+ logger.info(f"loading model {model_name}")
+ inference_model = OpenPanopticInference(
+ model=models[model_name],
+ labels=demo_classes,
+ metadata=demo_metadata,
+ semantic_on=False,
+ instance_on=False,
+ panoptic_on=True,
+ )
+ stack.enter_context(inference_context(inference_model))
+ stack.enter_context(torch.no_grad())
+
+ demo = VisualizationDemo(inference_model, demo_metadata, aug)
+ img = utils.read_image(image_path, format="RGB")
+ _, visualized_output = demo.run_on_image(img)
+ return Image.fromarray(visualized_output.get_image())
+
+
+with gr.Blocks(title=title) as demo:
+ gr.Markdown("" + title + "
")
+ gr.Markdown(description)
+ input_components = []
+ output_components = []
+
+ with gr.Row():
+ output_image_gr = gr.outputs.Image(label="Panoptic Segmentation", type="pil")
+ output_components.append(output_image_gr)
+
+ with gr.Row().style(equal_height=True, mobile_collapse=True):
+ with gr.Column(scale=3, variant="panel") as input_component_column:
+ input_image_gr = gr.inputs.Image(type="filepath")
+ model_name_gr = gr.inputs.Dropdown(
+ label="Model", choices=["ODISE(Label)", "ODISE(Caption)"], default="ODISE(Label)"
+ )
+ extra_vocab_gr = gr.inputs.Textbox(default="", label="Extra Vocabulary")
+ category_list_gr = gr.inputs.CheckboxGroup(
+ choices=["COCO (133 categories)", "ADE (150 categories)", "LVIS (1203 categories)"],
+ default=["COCO (133 categories)", "ADE (150 categories)", "LVIS (1203 categories)"],
+ label="Category to use",
+ )
+ input_components.extend([input_image_gr, extra_vocab_gr, category_list_gr])
+
+ with gr.Column(scale=2):
+ examples_handler = gr.Examples(
+ examples=examples,
+ inputs=[c for c in input_components if not isinstance(c, gr.State)],
+ outputs=[c for c in output_components if not isinstance(c, gr.State)],
+ fn=inference,
+ cache_examples=torch.cuda.is_available(),
+ examples_per_page=5,
+ )
+ with gr.Row():
+ clear_btn = gr.Button("Clear")
+ submit_btn = gr.Button("Submit", variant="primary")
+
+ gr.Markdown(article)
+
+ submit_btn.click(
+ inference,
+ input_components + [model_name_gr],
+ output_components,
+ api_name="predict",
+ scroll_to_output=True,
+ )
+
+ clear_btn.click(
+ None,
+ [],
+ (input_components + output_components + [input_component_column]),
+ _js=f"""() => {json.dumps(
+ [component.cleared_value if hasattr(component, "cleared_value") else None
+ for component in input_components + output_components] + (
+ [gr.Column.update(visible=True)]
+ )
+ + ([gr.Column.update(visible=False)])
+ )}
+ """,
+ )
+
+demo.launch()
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/demo.ipynb b/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..78e5e07037411bacdbefd96208331d4b3bf2ed8c
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/demo.ipynb
@@ -0,0 +1,538 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/NVlabs/ODISE/blob/master/demo/demo.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Install and setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Uncomment following if you are running this notebook on Google Colab\n",
+ "# !pip uninstall torchtext -y\n",
+ "# !pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116\n",
+ "# !pip install git+https://@github.com/NVlabs/ODISE.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/junyi/miniconda3/envs/sd_dino/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests\n",
+ "import itertools\n",
+ "from contextlib import ExitStack\n",
+ "import torch\n",
+ "from mask2former.data.datasets.register_ade20k_panoptic import ADE20K_150_CATEGORIES\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "from detectron2.config import instantiate\n",
+ "from detectron2.data import MetadataCatalog\n",
+ "from detectron2.data import detection_utils as utils\n",
+ "from detectron2.data import transforms as T\n",
+ "from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES\n",
+ "from detectron2.evaluation import inference_context\n",
+ "from detectron2.utils.env import seed_all_rng\n",
+ "from detectron2.utils.logger import setup_logger\n",
+ "from detectron2.utils.visualizer import ColorMode, Visualizer, random_color\n",
+ "\n",
+ "from odise import model_zoo\n",
+ "from odise.checkpoint import ODISECheckpointer\n",
+ "from odise.config import instantiate_odise\n",
+ "from odise.data import get_openseg_labels\n",
+ "from odise.modeling.wrapper import OpenPanopticInference\n",
+ "\n",
+ "setup_logger()\n",
+ "logger = setup_logger(name=\"odise\")\n",
+ "\n",
+ "COCO_THING_CLASSES = [\n",
+ " label\n",
+ " for idx, label in enumerate(get_openseg_labels(\"coco_panoptic\", True))\n",
+ " if COCO_CATEGORIES[idx][\"isthing\"] == 1\n",
+ "]\n",
+ "COCO_THING_COLORS = [c[\"color\"] for c in COCO_CATEGORIES if c[\"isthing\"] == 1]\n",
+ "COCO_STUFF_CLASSES = [\n",
+ " label\n",
+ " for idx, label in enumerate(get_openseg_labels(\"coco_panoptic\", True))\n",
+ " if COCO_CATEGORIES[idx][\"isthing\"] == 0\n",
+ "]\n",
+ "COCO_STUFF_COLORS = [c[\"color\"] for c in COCO_CATEGORIES if c[\"isthing\"] == 0]\n",
+ "\n",
+ "ADE_THING_CLASSES = [\n",
+ " label\n",
+ " for idx, label in enumerate(get_openseg_labels(\"ade20k_150\", True))\n",
+ " if ADE20K_150_CATEGORIES[idx][\"isthing\"] == 1\n",
+ "]\n",
+ "ADE_THING_COLORS = [c[\"color\"] for c in ADE20K_150_CATEGORIES if c[\"isthing\"] == 1]\n",
+ "ADE_STUFF_CLASSES = [\n",
+ " label\n",
+ " for idx, label in enumerate(get_openseg_labels(\"ade20k_150\", True))\n",
+ " if ADE20K_150_CATEGORIES[idx][\"isthing\"] == 0\n",
+ "]\n",
+ "ADE_STUFF_COLORS = [c[\"color\"] for c in ADE20K_150_CATEGORIES if c[\"isthing\"] == 0]\n",
+ "\n",
+ "LVIS_CLASSES = get_openseg_labels(\"lvis_1203\", True)\n",
+ "# use beautiful coco colors\n",
+ "LVIS_COLORS = list(\n",
+ " itertools.islice(itertools.cycle([c[\"color\"] for c in COCO_CATEGORIES]), len(LVIS_CLASSES))\n",
+ ")\n",
+ "\n",
+ "\n",
+ "class VisualizationDemo(object):\n",
+ " def __init__(self, model, metadata, aug, instance_mode=ColorMode.IMAGE):\n",
+ " \"\"\"\n",
+ " Args:\n",
+ " model (nn.Module):\n",
+ " metadata (MetadataCatalog): image metadata.\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.model = model\n",
+ " self.metadata = metadata\n",
+ " self.aug = aug\n",
+ " self.cpu_device = torch.device(\"cpu\")\n",
+ " self.instance_mode = instance_mode\n",
+ "\n",
+ " def predict(self, original_image):\n",
+ " \"\"\"\n",
+ " Args:\n",
+ " original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n",
+ "\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",
+ " height, width = original_image.shape[:2]\n",
+ " aug_input = T.AugInput(original_image, sem_seg=None)\n",
+ " self.aug(aug_input)\n",
+ " image = aug_input.image\n",
+ " image = torch.as_tensor(image.astype(\"float32\").transpose(2, 0, 1))\n",
+ "\n",
+ " inputs = {\"image\": image, \"height\": height, \"width\": width}\n",
+ " predictions = self.model([inputs])[0]\n",
+ " return predictions\n",
+ "\n",
+ " def run_on_image(self, image):\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",
+ " predictions = self.predict(image)\n",
+ " visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)\n",
+ " if \"panoptic_seg\" in predictions:\n",
+ " panoptic_seg, segments_info = predictions[\"panoptic_seg\"]\n",
+ " vis_output = visualizer.draw_panoptic_seg(\n",
+ " panoptic_seg.to(self.cpu_device), segments_info\n",
+ " )\n",
+ " else:\n",
+ " if \"sem_seg\" in predictions:\n",
+ " vis_output = visualizer.draw_sem_seg(\n",
+ " predictions[\"sem_seg\"].argmax(dim=0).to(self.cpu_device)\n",
+ " )\n",
+ " if \"instances\" in predictions:\n",
+ " instances = predictions[\"instances\"].to(self.cpu_device)\n",
+ " vis_output = visualizer.draw_instance_predictions(predictions=instances)\n",
+ "\n",
+ " return predictions, vis_output\n",
+ "\n",
+ "def build_demo_classes_and_metadata(vocab, label_list):\n",
+ " extra_classes = []\n",
+ "\n",
+ " if vocab:\n",
+ " for words in vocab.split(\";\"):\n",
+ " extra_classes.append([word.strip() for word in words.split(\",\")])\n",
+ " extra_colors = [random_color(rgb=True, maximum=1) for _ in range(len(extra_classes))]\n",
+ "\n",
+ " demo_thing_classes = extra_classes\n",
+ " demo_stuff_classes = []\n",
+ " demo_thing_colors = extra_colors\n",
+ " demo_stuff_colors = []\n",
+ "\n",
+ " if \"COCO\" in label_list:\n",
+ " demo_thing_classes += COCO_THING_CLASSES\n",
+ " demo_stuff_classes += COCO_STUFF_CLASSES\n",
+ " demo_thing_colors += COCO_THING_COLORS\n",
+ " demo_stuff_colors += COCO_STUFF_COLORS\n",
+ " if \"ADE\" in label_list:\n",
+ " demo_thing_classes += ADE_THING_CLASSES\n",
+ " demo_stuff_classes += ADE_STUFF_CLASSES\n",
+ " demo_thing_colors += ADE_THING_COLORS\n",
+ " demo_stuff_colors += ADE_STUFF_COLORS\n",
+ " if \"LVIS\" in label_list:\n",
+ " demo_thing_classes += LVIS_CLASSES\n",
+ " demo_thing_colors += LVIS_COLORS\n",
+ "\n",
+ " MetadataCatalog.pop(\"odise_demo_metadata\", None)\n",
+ " demo_metadata = MetadataCatalog.get(\"odise_demo_metadata\")\n",
+ " demo_metadata.thing_classes = [c[0] for c in demo_thing_classes]\n",
+ " demo_metadata.stuff_classes = [\n",
+ " *demo_metadata.thing_classes,\n",
+ " *[c[0] for c in demo_stuff_classes],\n",
+ " ]\n",
+ " demo_metadata.thing_colors = demo_thing_colors\n",
+ " demo_metadata.stuff_colors = demo_thing_colors + demo_stuff_colors\n",
+ " demo_metadata.stuff_dataset_id_to_contiguous_id = {\n",
+ " idx: idx for idx in range(len(demo_metadata.stuff_classes))\n",
+ " }\n",
+ " demo_metadata.thing_dataset_id_to_contiguous_id = {\n",
+ " idx: idx for idx in range(len(demo_metadata.thing_classes))\n",
+ " }\n",
+ "\n",
+ " demo_classes = demo_thing_classes + demo_stuff_classes\n",
+ "\n",
+ " return demo_classes, demo_metadata\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[06/08 17:20:45 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(1024, 1024), max_size=2560, sample_style='choice')]\n",
+ "LatentDiffusion: Running in eps-prediction mode\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 8 heads.\n",
+ "Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 768 and using 8 heads.\n",
+ "DiffusionWrapper has 859.52 M params.\n",
+ "making attention of type 'vanilla-xformers' with 512 in_channels\n",
+ "building MemoryEfficientAttnBlock with 512 in_channels...\n",
+ "Working with z of shape (1, 4, 32, 32) = 4096 dimensions.\n",
+ "making attention of type 'vanilla-xformers' with 512 in_channels\n",
+ "building MemoryEfficientAttnBlock with 512 in_channels...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The checkpoint state_dict contains keys that are not used by the model:\n",
+ " model_ema.{decay, num_updates}\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[06/08 17:22:43 odise.modeling.backbone.feature_extractor]: backbone_in_size: (512, 512), slide_training: True, \n",
+ "slide_inference: True, \n",
+ "min_stride: 4, max_stride: 32, \n",
+ "projection_dim: 512, \n",
+ "out_feature_channels: {'s2': 512, 's3': 512, 's4': 512, 's5': 512}\n",
+ "out_feature_strides: {'s2': 4, 's3': 8, 's4': 16, 's5': 32}\n",
+ "use_checkpoint: True\n",
+ "\n",
+ "[06/08 17:23:11 odise.modeling.meta_arch.clip]: Using provided CLIP model\n",
+ "[06/08 17:23:24 odise.modeling.meta_arch.clip]: Built text_embed of shape torch.Size([254, 768]) for 133 labels: (('person', 'child', 'girl', 'boy', 'woman', 'man', 'people', 'childeren', 'girls', 'boys', 'women', 'men', 'lady', 'guy', 'ladies', 'guys', 'clothes'), ('bicycle', 'bicycles', 'bike', 'bikes'), ('car', 'cars'), ('motorcycle', 'motorcycles'), ('airplane', 'airplanes'), ('bus', 'buses'), ('train', 'trains', 'locomotive', 'locomotives', 'freight train'), ('truck', 'trucks'), ('boat', 'boats'), ('traffic light',), ('fire hydrant',), ('stop sign',), ('parking meter',), ('bench', 'benches'), ('bird', 'birds'), ('cat', 'cats', 'kitties', 'kitty'), ('dog', 'dogs', 'puppy', 'puppies'), ('horse', 'horses', 'foal'), ('sheep',), ('cow', 'cows', 'calf'), ('elephant', 'elephants'), ('bear', 'bears'), ('zebra', 'zebras'), ('giraffe', 'giraffes'), ('backpack', 'backpacks'), ('umbrella', 'umbrellas'), ('handbag', 'handbags'), ('tie',), ('suitcase', 'suitcases'), ('frisbee',), ('skis',), ('snowboard',), ('sports ball',), ('kite', 'kites'), ('baseball bat',), ('baseball glove',), ('skateboard',), ('surfboard',), ('tennis racket',), ('bottle', 'bottles', 'water bottle'), ('wine glass', 'wine glasses', 'wineglass'), ('cup', 'cups', 'water cup', 'water glass'), ('fork', 'forks'), ('knife', 'knives'), ('spoon', 'spoons'), ('bowl', 'bowls'), ('banana', 'bananas'), ('apple', 'apples', 'apple fruit'), ('sandwich', 'sandwiches'), ('orange fruit',), ('broccoli',), ('carrot', 'carrots'), ('hot dog',), ('pizza',), ('donut', 'donuts'), ('cake', 'cakes'), ('chair', 'chairs'), ('couch', 'sofa', 'sofas'), ('potted plant', 'potted plants', 'pottedplant', 'pottedplants', 'planter', 'planters'), ('bed', 'beds'), ('dining table', 'dining tables', 'diningtable', 'diningtables', 'plate', 'plates', 'diningtable tablecloth'), ('toilet',), ('tv',), ('laptop',), ('mouse',), ('tv remote', 'remote control'), ('keyboard',), ('cell phone', 'mobile'), ('microwave',), ('oven', 'ovens'), ('toaster',), ('sink', 'sinks'), ('refrigerator', 'fridge'), ('book', 'books'), ('clock',), ('vase', 'vases'), ('scissor', 'scissors'), ('teddy bear', 'teddy bears'), ('hair drier',), ('toothbrush', 'toothbrushes'), ('banner', 'banners'), ('blanket', 'blankets'), ('bridge',), ('cardboard',), ('counter',), ('curtain', 'curtains'), ('door', 'doors'), ('wood floor',), ('flower', 'flowers'), ('fruit', 'fruits'), ('gravel',), ('house',), ('lamp', 'bulb', 'lamps', 'bulbs'), ('mirror',), ('tennis net',), ('pillow', 'pillows'), ('platform',), ('playingfield', 'tennis court', 'baseball feild', 'soccer field', 'tennis field'), ('railroad',), ('river',), ('road',), ('roof',), ('sand',), ('sea', 'sea wave', 'wave', 'waves'), ('shelf',), ('snow',), ('stairs',), ('tent',), ('towel',), ('brick wall',), ('stone wall',), ('tile wall',), ('wood wall',), ('water',), ('window blind',), ('window',), ('tree', 'trees', 'palm tree', 'bushes'), ('fence', 'fences'), ('ceiling',), ('sky', 'clouds'), ('cabinet', 'cabinets'), ('table',), ('floor', 'flooring', 'tile floor'), ('pavement',), ('mountain', 'mountains'), ('grass',), ('dirt',), ('paper',), ('food',), ('building', 'buildings'), ('rock',), ('wall', 'walls'), ('rug',))\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:fvcore.common.checkpoint:Keys with prefix are removed from state_dict:\n",
+ "backbone.feature_extractor.,category_head.clip.clip.,clip_head.clip.clip.\n",
+ "WARNING:fvcore.common.checkpoint:Removed 2483 ignored_state_dict keys from missing_keys\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'finished loading model'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "cfg = model_zoo.get_config(\"Panoptic/odise_label_coco_50e.py\", trained=True)\n",
+ "\n",
+ "cfg.model.overlap_threshold = 0\n",
+ "seed_all_rng(42)\n",
+ "\n",
+ "dataset_cfg = cfg.dataloader.test\n",
+ "wrapper_cfg = cfg.dataloader.wrapper\n",
+ "\n",
+ "aug = instantiate(dataset_cfg.mapper).augmentations\n",
+ "\n",
+ "model = instantiate_odise(cfg.model)\n",
+ "model.to(cfg.train.device)\n",
+ "ODISECheckpointer(model).load(cfg.train.init_checkpoint)\n",
+ "\"finished loading model\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model predict function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def inference(image, vocab, label_list):\n",
+ "\n",
+ " demo_classes, demo_metadata = build_demo_classes_and_metadata(vocab, label_list)\n",
+ " with ExitStack() as stack:\n",
+ " inference_model = OpenPanopticInference(\n",
+ " model=model,\n",
+ " labels=demo_classes,\n",
+ " metadata=demo_metadata,\n",
+ " semantic_on=False,\n",
+ " instance_on=False,\n",
+ " panoptic_on=True,\n",
+ " )\n",
+ " stack.enter_context(inference_context(inference_model))\n",
+ " stack.enter_context(torch.no_grad())\n",
+ "\n",
+ " demo = VisualizationDemo(inference_model, demo_metadata, aug)\n",
+ " _, visualized_output = demo.run_on_image(np.array(image))\n",
+ " return Image.fromarray(visualized_output.get_image())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let try a COCO image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "input_image = Image.open(requests.get(\"http://images.cocodataset.org/val2017/000000467848.jpg\", stream=True).raw)\n",
+ "input_image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/junyi/miniconda3/envs/sd_dino/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/TensorShape.cpp:3190.)\n",
+ " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[06/08 17:25:55 odise.modeling.meta_arch.clip]: Using provided CLIP model\n",
+ "[06/08 17:25:58 odise.modeling.meta_arch.clip]: Built text_embed of shape torch.Size([2486, 768]) for 1488 labels: (('black pickup truck', 'pickup truck'), ('blue sky', 'sky'), ('person', 'child', 'girl', 'boy', 'woman', 'man', 'people', 'childeren', 'girls', 'boys', 'women', 'men', 'lady', 'guy', 'ladies', 'guys', 'clothes'), ('bicycle', 'bicycles', 'bike', 'bikes'), ('car', 'cars'), ('motorcycle', 'motorcycles'), ('airplane', 'airplanes'), ('bus', 'buses'), ('train', 'trains', 'locomotive', 'locomotives', 'freight train'), ('truck', 'trucks'), ('boat', 'boats'), ('traffic light',), ('fire hydrant',), ('stop sign',), ('parking meter',), ('bench', 'benches'), ('bird', 'birds'), ('cat', 'cats', 'kitties', 'kitty'), ('dog', 'dogs', 'puppy', 'puppies'), ('horse', 'horses', 'foal'), ('sheep',), ('cow', 'cows', 'calf'), ('elephant', 'elephants'), ('bear', 'bears'), ('zebra', 'zebras'), ('giraffe', 'giraffes'), ('backpack', 'backpacks'), ('umbrella', 'umbrellas'), ('handbag', 'handbags'), ('tie',), ('suitcase', 'suitcases'), ('frisbee',), ('skis',), ('snowboard',), ('sports ball',), ('kite', 'kites'), ('baseball bat',), ('baseball glove',), ('skateboard',), ('surfboard',), ('tennis racket',), ('bottle', 'bottles', 'water bottle'), ('wine glass', 'wine glasses', 'wineglass'), ('cup', 'cups', 'water cup', 'water glass'), ('fork', 'forks'), ('knife', 'knives'), ('spoon', 'spoons'), ('bowl', 'bowls'), ('banana', 'bananas'), ('apple', 'apples', 'apple fruit'), ('sandwich', 'sandwiches'), ('orange fruit',), ('broccoli',), ('carrot', 'carrots'), ('hot dog',), ('pizza',), ('donut', 'donuts'), ('cake', 'cakes'), ('chair', 'chairs'), ('couch', 'sofa', 'sofas'), ('potted plant', 'potted plants', 'pottedplant', 'pottedplants', 'planter', 'planters'), ('bed', 'beds'), ('dining table', 'dining tables', 'diningtable', 'diningtables', 'plate', 'plates', 'diningtable tablecloth'), ('toilet',), ('tv',), ('laptop',), ('mouse',), ('tv remote', 'remote control'), ('keyboard',), ('cell phone', 'mobile'), ('microwave',), ('oven', 'ovens'), ('toaster',), ('sink', 'sinks'), ('refrigerator', 'fridge'), ('book', 'books'), ('clock',), ('vase', 'vases'), ('scissor', 'scissors'), ('teddy bear', 'teddy bears'), ('hair drier',), ('toothbrush', 'toothbrushes'), ('bed', 'beds'), ('windowpane', 'window', 'windows'), ('cabinet', 'cabinets', 'wall mounted cabine'), ('person', 'child', 'girl', 'boy', 'woman', 'man', 'people', 'children', 'girls', 'boys', 'women', 'men'), ('door', 'double door', 'doors'), ('table', 'tables', 'tablecloth'), ('curtain', 'drape', 'drapery', 'mantle', 'pall'), ('chair', 'chairs'), ('car', 'automobile', 'cars'), ('painting', 'picture', 'paintings', 'pictures', 'wallart', 'framed canvas'), ('sofa', 'couch', 'sofas', 'couches'), ('shelf', 'shelves'), ('mirror', 'mirrors'), ('armchair', 'armchairs'), ('seat', 'seats'), ('fence', 'fencing'), ('desk', 'desks'), ('wardrobe', 'closet', 'press', 'wardrobes', 'closets'), ('lamp', 'lamps'), ('bathtub', 'bathing tub', 'bath', 'tub'), ('railing', 'rail'), ('cushion', 'cushions'), ('box', 'boxes'), ('column', 'pillar'), ('signboard', 'sign', 'signboards', 'signs'), ('chest of drawers', 'chest', 'bureau', 'dresser'), ('counter',), ('sink',), ('fireplace', 'hearth', 'open fireplace'), ('refrigerator', 'icebox'), ('stairs', 'steps'), ('case', 'display case', 'showcase', 'vitrine'), ('pool table', 'billiard table', 'snooker table'), ('pillow', 'pillows'), ('screen door', 'shower door'), ('bookcase',), ('coffee table', 'cocktail table'), ('toilet', 'commode', 'crapper', 'potty'), ('flower', 'flowers'), ('book', 'books'), ('bench', 'benches'), ('countertop', 'counter top', 'worktop'), ('stove', 'kitchen stove', 'kitchen range', 'kitchen range', 'cooking stove'), ('palm tree', 'palm trees'), ('kitchen island',), ('computer', 'computing machine', 'computing device', 'data processor', 'electronic computer', 'information processing system'), ('swivel chair',), ('boat',), ('arcade machine', 'arcade machines'), ('bus', 'autobus', 'double-decker', 'jitney', 'motorbus', 'motorcoach', 'omnibus', 'passenger vehicle'), ('towel',), ('light bulb', 'lightbulb', 'bulb', 'incandescent lamp', 'electric light', 'electric-light bulb'), ('truck', 'motortruck'), ('chandelier', 'pendant', 'pendent'), ('awning', 'sunshade', 'sunblind'), ('streetlight', 'street lamp'), ('booth', 'cubicle', 'stall', 'kiosk'), ('television receiver', 'television', 'television set', 'tv', 'tv set'), ('airplane', 'aeroplane', 'airplanes', 'aeroplanes'), ('apparel', 'wearing apparel', 'dress', 'clothes'), ('pole',), ('bannister', 'banister', 'balustrade', 'balusters', 'handrail'), ('ottoman', 'pouf', 'pouffe', 'puff', 'hassock'), ('bottle', 'bottles', 'water bottle'), ('van',), ('ship',), ('fountain',), ('washer', 'automatic washer', 'washing machine'), ('plaything', 'toy', 'toys'), ('stool', 'stools'), ('barrel', 'cask', 'barrels', 'casks'), ('basket', 'handbasket'), ('bag', 'bags', 'gift bag', 'paper bag'), ('minibike', 'motorbike'), ('oven',), ('ball', 'balls'), ('food', 'solid food'), ('step', 'stair'), ('trade name', 'brand name', 'brand', 'marque'), ('microwave', 'microwave oven'), ('plant pots', 'plant pot', 'flower pot', 'flowerpot', 'planter'), ('animal', 'animate being', 'dog', 'cat', 'horse', 'cow', 'sheep', 'zebra', 'girraffe', 'bird'), ('bicycle', 'bike'), ('dishwasher', 'dish washer', 'dishwashing machine'), ('projection screen',), ('sculpture', 'sculptures'), ('exhaust hood',), ('sconce', 'sconce lamp', 'sconce light'), ('vase', 'vases'), ('traffic light', 'traffic signal', 'traffic lights'), ('tray', 'trays'), ('ashcan', 'trash can', 'garbage can', 'wastebin', 'ash bin', 'ash-bin', 'ashbin', 'dustbin', 'trash barrel', 'trash bin'), ('ceiling fan', 'floor fan'), ('plate', 'plates'), ('monitor', 'monitoring device', 'monitors'), ('bulletin board', 'notice board'), ('radiator',), ('cup', 'cups', 'drinking glass', 'drinking glasses'), ('clock',), ('flag', 'flags'), ('aerosol can', 'spray can'), ('air conditioner',), ('airplane', 'aeroplane'), ('alarm clock',), ('alcohol', 'alcoholic beverage'), ('alligator', 'gator'), ('almond',), ('ambulance',), ('amplifier',), ('anklet', 'ankle bracelet'), ('antenna', 'aerial', 'transmitting aerial'), ('apple',), ('applesauce',), ('apricot',), ('apron',), ('aquarium', 'fish tank'), ('arctic (type of shoe)', 'galosh', 'golosh', 'rubber (type of shoe)', 'gumshoe'), ('armband',), ('armchair',), ('armoire',), ('armor', 'armour'), ('artichoke',), ('trash can', 'garbage can', 'wastebin', 'dustbin', 'trash barrel', 'trash bin'), ('ashtray',), ('asparagus',), ('atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'), ('avocado',), ('award', 'accolade'), ('awning',), ('ax', 'axe'), ('baboon',), ('baby buggy', 'baby carriage', 'perambulator', 'pram', 'stroller'), ('basketball backboard',), ('backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'), ('handbag', 'purse', 'pocketbook'), ('suitcase', 'baggage', 'luggage'), ('bagel', 'beigel'), ('bagpipe',), ('baguet', 'baguette'), ('bait', 'lure'), ('ball',), ('ballet skirt', 'tutu'), ('balloon',), ('bamboo',), ('banana',), ('Band Aid',), ('bandage',), ('bandanna', 'bandana'), ('banjo',), ('banner', 'streamer'), ('barbell',), ('barge',), ('barrel', 'cask'), ('barrette',), ('barrow', 'garden cart', 'lawn cart', 'wheelbarrow'), ('baseball base',), ('baseball',), ('baseball bat',), ('baseball cap', 'jockey cap', 'golf cap'), ('baseball glove', 'baseball mitt'), ('basket', 'handbasket'), ('basketball',), ('bass horn', 'sousaphone', 'tuba'), ('bat (animal)',), ('bath mat',), ('bath towel',), ('bathrobe',), ('bathtub', 'bathing tub'), ('batter (food)',), ('battery',), ('beachball',), ('bead',), ('bean curd', 'tofu'), ('beanbag',), ('beanie', 'beany'), ('bear',), ('bed',), ('bedpan',), ('bedspread', 'bedcover', 'bed covering', 'counterpane', 'spread'), ('cow',), ('beef (food)', 'boeuf (food)'), ('beeper', 'pager'), ('beer bottle',), ('beer can',), ('beetle',), ('bell',), ('bell pepper', 'capsicum'), ('belt',), ('belt buckle',), ('bench',), ('beret',), ('bib',), ('Bible',), ('bicycle', 'bike (bicycle)'), ('visor', 'vizor'), ('billboard',), ('binder', 'ring-binder'), ('binoculars', 'field glasses', 'opera glasses'), ('bird',), ('birdfeeder',), ('birdbath',), ('birdcage',), ('birdhouse',), ('birthday cake',), ('birthday card',), ('pirate flag',), ('black sheep',), ('blackberry',), ('blackboard', 'chalkboard'), ('blanket',), ('blazer', 'sport jacket', 'sport coat', 'sports jacket', 'sports coat'), ('blender', 'liquidizer', 'liquidiser'), ('blimp',), ('blinker', 'flasher'), ('blouse',), ('blueberry',), ('gameboard',), ('boat', 'ship (boat)'), ('bob', 'bobber', 'bobfloat'), ('bobbin', 'spool', 'reel'), ('bobby pin', 'hairgrip'), ('boiled egg', 'coddled egg'), ('bolo tie', 'bolo', 'bola tie', 'bola'), ('deadbolt',), ('bolt',), ('bonnet',), ('book',), ('bookcase',), ('booklet', 'brochure', 'leaflet', 'pamphlet'), ('bookmark', 'bookmarker'), ('boom microphone', 'microphone boom'), ('boot',), ('bottle',), ('bottle opener',), ('bouquet',), ('bow (weapon)',), ('bow (decorative ribbons)',), ('bow-tie', 'bowtie'), ('bowl',), ('pipe bowl',), ('bowler hat', 'bowler', 'derby hat', 'derby', 'plug hat'), ('bowling ball',), ('box',), ('boxing glove',), ('suspenders',), ('bracelet', 'bangle'), ('brass plaque',), ('brassiere', 'bra', 'bandeau'), ('bread-bin', 'breadbox'), ('bread',), ('breechcloth', 'breechclout', 'loincloth'), ('bridal gown', 'wedding gown', 'wedding dress'), ('briefcase',), ('broccoli',), ('broach',), ('broom',), ('brownie',), ('brussels sprouts',), ('bubble gum',), ('bucket', 'pail'), ('horse buggy',), ('horned cow',), ('bulldog',), ('bulldozer', 'dozer'), ('bullet train',), ('bulletin board', 'notice board'), ('bulletproof vest',), ('bullhorn', 'megaphone'), ('bun', 'roll'), ('bunk bed',), ('buoy',), ('burrito',), ('bus (vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'), ('business card',), ('butter',), ('butterfly',), ('button',), ('cab (taxi)', 'taxi', 'taxicab'), ('cabana',), ('cabin car', 'caboose'), ('cabinet',), ('locker', 'storage locker'), ('cake',), ('calculator',), ('calendar',), ('calf',), ('camcorder',), ('camel',), ('camera',), ('camera lens',), ('camper (vehicle)', 'camping bus', 'motor home'), ('can', 'tin can'), ('can opener', 'tin opener'), ('candle', 'candlestick'), ('candle holder',), ('candy bar',), ('candy cane',), ('walking cane',), ('canister', 'cannister'), ('canoe',), ('cantaloup', 'cantaloupe'), ('canteen',), ('cap (headwear)',), ('bottle cap', 'cap (container lid)'), ('cape',), ('cappuccino', 'coffee cappuccino'), ('car (automobile)', 'auto (automobile)', 'automobile'), ('railcar (part of a train)', 'railway car (part of a train)', 'railroad car (part of a train)'), ('elevator car',), ('car battery', 'automobile battery'), ('identity card',), ('card',), ('cardigan',), ('cargo ship', 'cargo vessel'), ('carnation',), ('horse carriage',), ('carrot',), ('tote bag',), ('cart',), ('carton',), ('cash register', 'register (for cash transactions)'), ('casserole',), ('cassette',), ('cast', 'plaster cast', 'plaster bandage'), ('cat',), ('cauliflower',), ('cayenne (spice)', 'cayenne pepper (spice)', 'red pepper (spice)'), ('CD player',), ('celery',), ('cellular telephone', 'cellular phone', 'cellphone', 'mobile phone', 'smart phone'), ('chain mail', 'ring mail', 'chain armor', 'chain armour', 'ring armor', 'ring armour'), ('chair',), ('chaise longue', 'chaise', 'daybed'), ('chalice',), ('chandelier',), ('chap',), ('checkbook', 'chequebook'), ('checkerboard',), ('cherry',), ('chessboard',), ('chicken (animal)',), ('chickpea', 'garbanzo'), ('chili (vegetable)', 'chili pepper (vegetable)', 'chilli (vegetable)', 'chilly (vegetable)', 'chile (vegetable)'), ('chime', 'gong'), ('chinaware',), ('crisp (potato chip)', 'potato chip'), ('poker chip',), ('chocolate bar',), ('chocolate cake',), ('chocolate milk',), ('chocolate mousse',), ('choker', 'collar', 'neckband'), ('chopping board', 'cutting board', 'chopping block'), ('chopstick',), ('Christmas tree',), ('slide',), ('cider', 'cyder'), ('cigar box',), ('cigarette',), ('cigarette case', 'cigarette pack'), ('cistern', 'water tank'), ('clarinet',), ('clasp',), ('cleansing agent', 'cleanser', 'cleaner'), ('cleat (for securing rope)',), ('clementine',), ('clip',), ('clipboard',), ('clippers (for plants)',), ('cloak',), ('clock', 'timepiece', 'timekeeper'), ('clock tower',), ('clothes hamper', 'laundry basket', 'clothes basket'), ('clothespin', 'clothes peg'), ('clutch bag',), ('coaster',), ('coat',), ('coat hanger', 'clothes hanger', 'dress hanger'), ('coatrack', 'hatrack'), ('cock', 'rooster'), ('cockroach',), ('cocoa (beverage)', 'hot chocolate (beverage)', 'drinking chocolate'), ('coconut', 'cocoanut'), ('coffee maker', 'coffee machine'), ('coffee table', 'cocktail table'), ('coffeepot',), ('coil',), ('coin',), ('colander', 'cullender'), ('coleslaw', 'slaw'), ('coloring material', 'colouring material'), ('combination lock',), ('pacifier', 'teething ring'), ('comic book',), ('compass',), ('computer keyboard', 'keyboard (computer)'), ('condiment',), ('cone', 'traffic cone'), ('control', 'controller'), ('convertible (automobile)',), ('sofa bed',), ('cooker',), ('cookie', 'cooky', 'biscuit (cookie)'), ('cooking utensil',), ('cooler (for food)', 'ice chest'), ('cork (bottle plug)', 'bottle cork'), ('corkboard',), ('corkscrew', 'bottle screw'), ('edible corn', 'corn', 'maize'), ('cornbread',), ('cornet', 'horn', 'trumpet'), ('cornice', 'valance', 'valance board', 'pelmet'), ('cornmeal',), ('corset', 'girdle'), ('costume',), ('cougar', 'puma', 'catamount', 'mountain lion', 'panther'), ('coverall',), ('cowbell',), ('cowboy hat', 'ten-gallon hat'), ('crab (animal)',), ('crabmeat',), ('cracker',), ('crape', 'crepe', 'French pancake'), ('crate',), ('crayon', 'wax crayon'), ('cream pitcher',), ('crescent roll', 'croissant'), ('crib', 'cot'), ('crock pot', 'earthenware jar'), ('crossbar',), ('crouton',), ('crow',), ('crowbar', 'wrecking bar', 'pry bar'), ('crown',), ('crucifix',), ('cruise ship', 'cruise liner'), ('police cruiser', 'patrol car', 'police car', 'squad car'), ('crumb',), ('crutch',), ('cub (animal)',), ('cube', 'square block'), ('cucumber', 'cuke'), ('cufflink',), ('cup',), ('trophy cup',), ('cupboard', 'closet'), ('cupcake',), ('hair curler', 'hair roller', 'hair crimper'), ('curling iron',), ('curtain', 'drapery'), ('cushion',), ('cylinder',), ('cymbal',), ('dagger',), ('dalmatian',), ('dartboard',), ('date (fruit)',), ('deck chair', 'beach chair'), ('deer', 'cervid'), ('dental floss', 'floss'), ('desk',), ('detergent',), ('diaper',), ('diary', 'journal'), ('die', 'dice'), ('dinghy', 'dory', 'rowboat'), ('dining table',), ('tux', 'tuxedo'), ('dish',), ('dish antenna',), ('dishrag', 'dishcloth'), ('dishtowel', 'tea towel'), ('dishwasher', 'dishwashing machine'), ('dishwasher detergent', 'dishwashing detergent', 'dishwashing liquid', 'dishsoap'), ('dispenser',), ('diving board',), ('Dixie cup', 'paper cup'), ('dog',), ('dog collar',), ('doll',), ('dollar', 'dollar bill', 'one dollar bill'), ('dollhouse', \"doll's house\"), ('dolphin',), ('domestic ass', 'donkey'), ('doorknob', 'doorhandle'), ('doormat', 'welcome mat'), ('doughnut', 'donut'), ('dove',), ('dragonfly',), ('drawer',), ('underdrawers', 'boxers', 'boxershorts'), ('dress', 'frock'), ('dress hat', 'high hat', 'opera hat', 'silk hat', 'top hat'), ('dress suit',), ('dresser',), ('drill',), ('drone',), ('dropper', 'eye dropper'), ('drum (musical instrument)',), ('drumstick',), ('duck',), ('duckling',), ('duct tape',), ('duffel bag', 'duffle bag', 'duffel', 'duffle'), ('dumbbell',), ('dumpster',), ('dustpan',), ('eagle',), ('earphone', 'earpiece', 'headphone'), ('earplug',), ('earring',), ('easel',), ('eclair',), ('eel',), ('egg', 'eggs'), ('egg roll', 'spring roll'), ('egg yolk', 'yolk (egg)'), ('eggbeater', 'eggwhisk'), ('eggplant', 'aubergine'), ('electric chair',), ('refrigerator',), ('elephant',), ('elk', 'moose'), ('envelope',), ('eraser',), ('escargot',), ('eyepatch',), ('falcon',), ('fan',), ('faucet', 'spigot', 'tap'), ('fedora',), ('ferret',), ('Ferris wheel',), ('ferry', 'ferryboat'), ('fig (fruit)',), ('fighter jet', 'fighter aircraft', 'attack aircraft'), ('figurine',), ('file cabinet', 'filing cabinet'), ('file (tool)',), ('fire alarm', 'smoke alarm'), ('fire engine', 'fire truck'), ('fire extinguisher', 'extinguisher'), ('fire hose',), ('fireplace',), ('fireplug', 'fire hydrant', 'hydrant'), ('first-aid kit',), ('fish',), ('fish (food)',), ('fishbowl', 'goldfish bowl'), ('fishing rod', 'fishing pole'), ('flag',), ('flagpole', 'flagstaff'), ('flamingo',), ('flannel',), ('flap',), ('flash', 'flashbulb'), ('flashlight', 'torch'), ('fleece',), ('flip-flop (sandal)',), ('flipper (footwear)', 'fin (footwear)'), ('flower arrangement', 'floral arrangement'), ('flute glass', 'champagne flute'), ('foal',), ('folding chair',), ('food processor',), ('football (American)',), ('football helmet',), ('footstool', 'footrest'), ('fork',), ('forklift',), ('freight car',), ('French toast',), ('freshener', 'air freshener'), ('frisbee',), ('frog', 'toad', 'toad frog'), ('fruit juice',), ('frying pan', 'frypan', 'skillet'), ('fudge',), ('funnel',), ('futon',), ('gag', 'muzzle'), ('garbage',), ('garbage truck',), ('garden hose',), ('gargle', 'mouthwash'), ('gargoyle',), ('garlic', 'ail'), ('gasmask', 'respirator', 'gas helmet'), ('gazelle',), ('gelatin', 'jelly'), ('gemstone',), ('generator',), ('giant panda', 'panda', 'panda bear'), ('gift wrap',), ('ginger', 'gingerroot'), ('giraffe',), ('cincture', 'sash', 'waistband', 'waistcloth'), ('glass (drink container)', 'drinking glass'), ('globe',), ('glove',), ('goat',), ('goggles',), ('goldfish',), ('golf club', 'golf-club'), ('golfcart',), ('gondola (boat)',), ('goose',), ('gorilla',), ('gourd',), ('grape',), ('grater',), ('gravestone', 'headstone', 'tombstone'), ('gravy boat', 'gravy holder'), ('green bean',), ('green onion', 'spring onion', 'scallion'), ('griddle',), ('grill', 'grille', 'grillwork', 'radiator grille'), ('grits', 'hominy grits'), ('grizzly', 'grizzly bear'), ('grocery bag',), ('guitar',), ('gull', 'seagull'), ('gun',), ('hairbrush',), ('hairnet',), ('hairpin',), ('halter top',), ('ham', 'jambon', 'gammon'), ('hamburger', 'beefburger', 'burger'), ('hammer',), ('hammock',), ('hamper',), ('hamster',), ('hair dryer',), ('hand glass', 'hand mirror'), ('hand towel', 'face towel'), ('handcart', 'pushcart', 'hand truck'), ('handcuff',), ('handkerchief',), ('handle', 'grip', 'handgrip'), ('handsaw', \"carpenter's saw\"), ('hardback book', 'hardcover book'), ('harmonium', 'organ (musical instrument)', 'reed organ (musical instrument)'), ('hat',), ('hatbox',), ('veil',), ('headband',), ('headboard',), ('headlight', 'headlamp'), ('headscarf',), ('headset',), ('headstall (for horses)', 'headpiece (for horses)'), ('heart',), ('heater', 'warmer'), ('helicopter',), ('helmet',), ('heron',), ('highchair', 'feeding chair'), ('hinge',), ('hippopotamus',), ('hockey stick',), ('hog', 'pig'), ('home plate (baseball)', 'home base (baseball)'), ('honey',), ('fume hood', 'exhaust hood'), ('hook',), ('hookah', 'narghile', 'nargileh', 'sheesha', 'shisha', 'water pipe'), ('hornet',), ('horse',), ('hose', 'hosepipe'), ('hot-air balloon',), ('hotplate',), ('hot sauce',), ('hourglass',), ('houseboat',), ('hummingbird',), ('hummus', 'humus', 'hommos', 'hoummos', 'humous'), ('polar bear',), ('icecream',), ('popsicle',), ('ice maker',), ('ice pack', 'ice bag'), ('ice skate',), ('igniter', 'ignitor', 'lighter'), ('inhaler', 'inhalator'), ('iPod',), ('iron (for clothing)', 'smoothing iron (for clothing)'), ('ironing board',), ('jacket',), ('jam',), ('jar',), ('jean', 'blue jean', 'denim'), ('jeep', 'landrover'), ('jelly bean', 'jelly egg'), ('jersey', 'T-shirt', 'tee shirt'), ('jet plane', 'jet-propelled plane'), ('jewel', 'gem', 'precious stone'), ('jewelry', 'jewellery'), ('joystick',), ('jumpsuit',), ('kayak',), ('keg',), ('kennel', 'doghouse'), ('kettle', 'boiler'), ('key',), ('keycard',), ('kilt',), ('kimono',), ('kitchen sink',), ('kitchen table',), ('kite',), ('kitten', 'kitty'), ('kiwi fruit',), ('knee pad',), ('knife',), ('knitting needle',), ('knob',), ('knocker (on a door)', 'doorknocker'), ('koala', 'koala bear'), ('lab coat', 'laboratory coat'), ('ladder',), ('ladle',), ('ladybug', 'ladybeetle', 'ladybird beetle'), ('lamb (animal)',), ('lamb-chop', 'lambchop'), ('lamp',), ('lamppost',), ('lampshade',), ('lantern',), ('lanyard', 'laniard'), ('laptop computer', 'notebook computer'), ('lasagna', 'lasagne'), ('latch',), ('lawn mower',), ('leather',), ('legging (clothing)', 'leging (clothing)', 'leg covering'), ('Lego', 'Lego set'), ('legume',), ('lemon',), ('lemonade',), ('lettuce',), ('license plate', 'numberplate'), ('life buoy', 'lifesaver', 'life belt', 'life ring'), ('life jacket', 'life vest'), ('lightbulb',), ('lightning rod', 'lightning conductor'), ('lime',), ('limousine',), ('lion',), ('lip balm',), ('liquor', 'spirits', 'hard liquor', 'liqueur', 'cordial'), ('lizard',), ('log',), ('lollipop',), ('speaker (stero equipment)',), ('loveseat',), ('machine gun',), ('magazine',), ('magnet',), ('mail slot',), ('mailbox (at home)', 'letter box (at home)'), ('mallard',), ('mallet',), ('mammoth',), ('manatee',), ('mandarin orange',), ('manger', 'trough'), ('manhole',), ('map',), ('marker',), ('martini',), ('mascot',), ('mashed potato',), ('masher',), ('mask', 'facemask'), ('mast',), ('mat (gym equipment)', 'gym mat'), ('matchbox',), ('mattress',), ('measuring cup',), ('measuring stick', 'ruler (measuring stick)', 'measuring rod'), ('meatball',), ('medicine',), ('melon',), ('microphone',), ('microscope',), ('microwave oven',), ('milestone', 'milepost'), ('milk',), ('milk can',), ('milkshake',), ('minivan',), ('mint candy',), ('mirror',), ('mitten',), ('mixer (kitchen tool)', 'stand mixer'), ('money',), ('monitor (computer equipment) computer monitor',), ('monkey',), ('motor',), ('motor scooter', 'scooter'), ('motor vehicle', 'automotive vehicle'), ('motorcycle',), ('mound (baseball)', \"pitcher's mound\"), ('mouse (computer equipment)', 'computer mouse'), ('mousepad',), ('muffin',), ('mug',), ('mushroom',), ('music stool', 'piano stool'), ('musical instrument', 'instrument (musical)'), ('nailfile',), ('napkin', 'table napkin', 'serviette'), ('neckerchief',), ('necklace',), ('necktie', 'tie (necktie)'), ('needle',), ('nest',), ('newspaper', 'paper (newspaper)'), ('newsstand',), ('nightshirt', 'nightwear', 'sleepwear', 'nightclothes'), ('nosebag (for animals)', 'feedbag'), ('noseband (for animals)', 'nosepiece (for animals)'), ('notebook',), ('notepad',), ('nut',), ('nutcracker',), ('oar',), ('octopus (food)',), ('octopus (animal)',), ('oil lamp', 'kerosene lamp', 'kerosine lamp'), ('olive oil',), ('omelet', 'omelette'), ('onion',), ('orange (fruit)',), ('orange juice',), ('ostrich',), ('ottoman', 'pouf', 'pouffe', 'hassock'), ('oven',), ('overalls (clothing)',), ('owl',), ('packet',), ('inkpad', 'inking pad', 'stamp pad'), ('pad',), ('paddle', 'boat paddle'), ('padlock',), ('paintbrush',), ('painting',), ('pajamas', 'pyjamas'), ('palette', 'pallet'), ('pan (for cooking)', 'cooking pan'), ('pan (metal container)',), ('pancake',), ('pantyhose',), ('papaya',), ('paper plate',), ('paper towel',), ('paperback book', 'paper-back book', 'softback book', 'soft-cover book'), ('paperweight',), ('parachute',), ('parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'), ('parasail (sports)',), ('parasol', 'sunshade'), ('parchment',), ('parka', 'anorak'), ('parking meter',), ('parrot',), ('passenger car (part of a train)', 'coach (part of a train)'), ('passenger ship',), ('passport',), ('pastry',), ('patty (food)',), ('pea (food)',), ('peach',), ('peanut butter',), ('pear',), ('peeler (tool for fruit and vegetables)',), ('wooden leg', 'pegleg'), ('pegboard',), ('pelican',), ('pen',), ('pencil',), ('pencil box', 'pencil case'), ('pencil sharpener',), ('pendulum',), ('penguin',), ('pennant',), ('penny (coin)',), ('pepper', 'peppercorn'), ('pepper mill', 'pepper grinder'), ('perfume',), ('persimmon',), ('person', 'baby', 'child', 'boy', 'girl', 'man', 'woman', 'human'), ('pet',), ('pew (church bench)', 'church bench'), ('phonebook', 'telephone book', 'telephone directory'), ('phonograph record', 'phonograph recording', 'record (phonograph recording)'), ('piano',), ('pickle',), ('pickup truck',), ('pie',), ('pigeon',), ('piggy bank', 'penny bank'), ('pillow',), ('pin (non jewelry)',), ('pineapple',), ('pinecone',), ('ping-pong ball',), ('pinwheel',), ('tobacco pipe',), ('pipe', 'piping'), ('pistol', 'handgun'), ('pita (bread)', 'pocket bread'), ('pitcher (vessel for liquid)', 'ewer'), ('pitchfork',), ('pizza',), ('place mat',), ('plate',), ('platter',), ('playpen',), ('pliers', 'plyers'), ('plow (farm equipment)', 'plough (farm equipment)'), ('plume',), ('pocket watch',), ('pocketknife',), ('poker (fire stirring tool)', 'stove poker', 'fire hook'), ('pole', 'post'), ('polo shirt', 'sport shirt'), ('poncho',), ('pony',), ('pool table', 'billiard table', 'snooker table'), ('pop (soda)', 'soda (pop)', 'tonic', 'soft drink'), ('postbox (public)', 'mailbox (public)'), ('postcard', 'postal card', 'mailing-card'), ('poster', 'placard'), ('pot',), ('flowerpot',), ('potato',), ('potholder',), ('pottery', 'clayware'), ('pouch',), ('power shovel', 'excavator', 'digger'), ('prawn', 'shrimp'), ('pretzel',), ('printer', 'printing machine'), ('projectile (weapon)', 'missile'), ('projector',), ('propeller', 'propellor'), ('prune',), ('pudding',), ('puffer (fish)', 'pufferfish', 'blowfish', 'globefish'), ('puffin',), ('pug-dog',), ('pumpkin',), ('puncher',), ('puppet', 'marionette'), ('puppy',), ('quesadilla',), ('quiche',), ('quilt', 'comforter'), ('rabbit',), ('race car', 'racing car'), ('racket', 'racquet'), ('radar',), ('radiator',), ('radio receiver', 'radio set', 'radio', 'tuner (radio)'), ('radish', 'daikon'), ('raft',), ('rag doll',), ('raincoat', 'waterproof jacket'), ('ram (animal)',), ('raspberry',), ('rat',), ('razorblade',), ('reamer (juicer)', 'juicer', 'juice reamer'), ('rearview mirror',), ('receipt',), ('recliner', 'reclining chair', 'lounger (chair)'), ('record player', 'phonograph (record player)', 'turntable'), ('reflector',), ('remote control',), ('rhinoceros',), ('rib (food)',), ('rifle',), ('ring',), ('river boat',), ('road map',), ('robe',), ('rocking chair',), ('rodent',), ('roller skate',), ('Rollerblade',), ('rolling pin',), ('root beer',), ('router (computer equipment)',), ('rubber band', 'elastic band'), ('runner (carpet)',), ('plastic bag', 'paper bag'), ('saddle (on an animal)',), ('saddle blanket', 'saddlecloth', 'horse blanket'), ('saddlebag',), ('safety pin',), ('sail',), ('salad',), ('salad plate', 'salad bowl'), ('salami',), ('salmon (fish)',), ('salmon (food)',), ('salsa',), ('saltshaker',), ('sandal (type of shoe)',), ('sandwich',), ('satchel',), ('saucepan',), ('saucer',), ('sausage',), ('sawhorse', 'sawbuck'), ('saxophone',), ('scale (measuring instrument)',), ('scarecrow', 'strawman'), ('scarf',), ('school bus',), ('scissors',), ('scoreboard',), ('scraper',), ('screwdriver',), ('scrubbing brush',), ('sculpture',), ('seabird', 'seafowl'), ('seahorse',), ('seaplane', 'hydroplane'), ('seashell',), ('sewing machine',), ('shaker',), ('shampoo',), ('shark',), ('sharpener',), ('Sharpie',), ('shaver (electric)', 'electric shaver', 'electric razor'), ('shaving cream', 'shaving soap'), ('shawl',), ('shears',), ('sheep',), ('shepherd dog', 'sheepdog'), ('sherbert', 'sherbet'), ('shield',), ('shirt',), ('shoe', 'sneaker (type of shoe)', 'tennis shoe'), ('shopping bag',), ('shopping cart',), ('short pants', 'shorts (clothing)', 'trunks (clothing)'), ('shot glass',), ('shoulder bag',), ('shovel',), ('shower head',), ('shower cap',), ('shower curtain',), ('shredder (for paper)',), ('signboard',), ('silo',), ('sink',), ('skateboard',), ('skewer',), ('ski',), ('ski boot',), ('ski parka', 'ski jacket'), ('ski pole',), ('skirt',), ('skullcap',), ('sled', 'sledge', 'sleigh'), ('sleeping bag',), ('sling (bandage)', 'triangular bandage'), ('slipper (footwear)', 'carpet slipper (footwear)'), ('smoothie',), ('snake', 'serpent'), ('snowboard',), ('snowman',), ('snowmobile',), ('soap',), ('soccer ball',), ('sock',), ('sofa', 'couch', 'lounge'), ('softball',), ('solar array', 'solar battery', 'solar panel'), ('sombrero',), ('soup',), ('soup bowl',), ('soupspoon',), ('sour cream', 'soured cream'), ('soya milk', 'soybean milk', 'soymilk'), ('space shuttle',), ('sparkler (fireworks)',), ('spatula',), ('spear', 'lance'), ('spectacles', 'specs', 'eyeglasses', 'glasses'), ('spice rack',), ('spider',), ('crawfish', 'crayfish'), ('sponge',), ('spoon',), ('sportswear', 'athletic wear', 'activewear'), ('spotlight',), ('squid (food)', 'calamari', 'calamary'), ('squirrel',), ('stagecoach',), ('stapler (stapling machine)',), ('starfish', 'sea star'), ('statue (sculpture)',), ('steak (food)',), ('steak knife',), ('steering wheel',), ('stepladder',), ('step stool',), ('stereo (sound system)',), ('stew',), ('stirrer',), ('stirrup',), ('stool',), ('stop sign',), ('brake light',), ('stove', 'kitchen stove', 'range (kitchen appliance)', 'kitchen range', 'cooking stove'), ('strainer',), ('strap',), ('straw (for drinking)', 'drinking straw'), ('strawberry',), ('street sign',), ('streetlight', 'street lamp'), ('string cheese',), ('stylus',), ('subwoofer',), ('sugar bowl',), ('sugarcane (plant)',), ('suit (clothing)',), ('sunflower',), ('sunglasses',), ('sunhat',), ('surfboard',), ('sushi',), ('mop',), ('sweat pants',), ('sweatband',), ('sweater',), ('sweatshirt',), ('sweet potato',), ('swimsuit', 'swimwear', 'bathing suit', 'swimming costume', 'bathing costume', 'swimming trunks', 'bathing trunks'), ('sword',), ('syringe',), ('Tabasco sauce',), ('table-tennis table', 'ping-pong table'), ('table',), ('table lamp',), ('tablecloth',), ('tachometer',), ('taco',), ('tag',), ('taillight', 'rear light'), ('tambourine',), ('army tank', 'armored combat vehicle', 'armoured combat vehicle'), ('tank (storage vessel)', 'storage tank'), ('tank top (clothing)',), ('tape (sticky cloth or paper)',), ('tape measure', 'measuring tape'), ('tapestry',), ('tarp',), ('tartan', 'plaid'), ('tassel',), ('tea bag',), ('teacup',), ('teakettle',), ('teapot',), ('teddy bear',), ('telephone', 'phone', 'telephone set'), ('telephone booth', 'phone booth', 'call box', 'telephone box', 'telephone kiosk'), ('telephone pole', 'telegraph pole', 'telegraph post'), ('telephoto lens', 'zoom lens'), ('television camera', 'tv camera'), ('television set', 'tv', 'tv set'), ('tennis ball',), ('tennis racket',), ('tequila',), ('thermometer',), ('thermos bottle',), ('thermostat',), ('thimble',), ('thread', 'yarn'), ('thumbtack', 'drawing pin', 'pushpin'), ('tiara',), ('tiger',), ('tights (clothing)', 'leotards'), ('timer', 'stopwatch'), ('tinfoil',), ('tinsel',), ('tissue paper',), ('toast (food)',), ('toaster',), ('toaster oven',), ('toilet',), ('toilet tissue', 'toilet paper', 'bathroom tissue'), ('tomato',), ('tongs',), ('toolbox',), ('toothbrush',), ('toothpaste',), ('toothpick',), ('cover',), ('tortilla',), ('tow truck',), ('towel',), ('towel rack', 'towel rail', 'towel bar'), ('toy',), ('tractor (farm equipment)',), ('traffic light',), ('dirt bike',), ('trailer truck', 'tractor trailer', 'trucking rig', 'articulated lorry', 'semi truck'), ('train (railroad vehicle)', 'railroad train'), ('trampoline',), ('tray',), ('trench coat',), ('triangle (musical instrument)',), ('tricycle',), ('tripod',), ('trousers', 'pants (clothing)'), ('truck',), ('truffle (chocolate)', 'chocolate truffle'), ('trunk',), ('vat',), ('turban',), ('turkey (food)',), ('turnip',), ('turtle',), ('turtleneck (clothing)', 'polo-neck'), ('typewriter',), ('umbrella',), ('underwear', 'underclothes', 'underclothing', 'underpants'), ('unicycle',), ('urinal',), ('urn',), ('vacuum cleaner',), ('vase',), ('vending machine',), ('vent', 'blowhole', 'air vent'), ('vest', 'waistcoat'), ('videotape',), ('vinegar',), ('violin', 'fiddle'), ('vodka',), ('volleyball',), ('vulture',), ('waffle',), ('waffle iron',), ('wagon',), ('wagon wheel',), ('walking stick',), ('wall clock',), ('wall socket', 'wall plug', 'electric outlet', 'electrical outlet', 'outlet', 'electric receptacle'), ('wallet', 'billfold'), ('walrus',), ('wardrobe',), ('washbasin', 'basin (for washing)', 'washbowl', 'washstand', 'handbasin'), ('automatic washer', 'washing machine'), ('watch', 'wristwatch'), ('water bottle',), ('water cooler',), ('water faucet', 'water tap', 'tap (water faucet)'), ('water heater', 'hot-water heater'), ('water jug',), ('water gun', 'squirt gun'), ('water scooter', 'sea scooter', 'jet ski'), ('water ski',), ('water tower',), ('watering can',), ('watermelon',), ('weathervane', 'vane (weathervane)', 'wind vane'), ('webcam',), ('wedding cake', 'bridecake'), ('wedding ring', 'wedding band'), ('wet suit',), ('wheel',), ('wheelchair',), ('whipped cream',), ('whistle',), ('wig',), ('wind chime',), ('windmill',), ('window box (for plants)',), ('windshield wiper', 'windscreen wiper', 'wiper (for windshield/screen)'), ('windsock', 'air sock', 'air-sleeve', 'wind sleeve', 'wind cone'), ('wine bottle',), ('wine bucket', 'wine cooler'), ('wineglass',), ('blinder (for horses)',), ('wok',), ('wolf',), ('wooden spoon',), ('wreath',), ('wrench', 'spanner'), ('wristband',), ('wristlet', 'wrist band'), ('yacht',), ('yogurt', 'yoghurt', 'yoghourt'), ('yoke (animal equipment)',), ('zebra',), ('zucchini', 'courgette'), ('banner', 'banners'), ('blanket', 'blankets'), ('bridge',), ('cardboard',), ('counter',), ('curtain', 'curtains'), ('door', 'doors'), ('wood floor',), ('flower', 'flowers'), ('fruit', 'fruits'), ('gravel',), ('house',), ('lamp', 'bulb', 'lamps', 'bulbs'), ('mirror',), ('tennis net',), ('pillow', 'pillows'), ('platform',), ('playingfield', 'tennis court', 'baseball feild', 'soccer field', 'tennis field'), ('railroad',), ('river',), ('road',), ('roof',), ('sand',), ('sea', 'sea wave', 'wave', 'waves'), ('shelf',), ('snow',), ('stairs',), ('tent',), ('towel',), ('brick wall',), ('stone wall',), ('tile wall',), ('wood wall',), ('water',), ('window blind',), ('window',), ('tree', 'trees', 'palm tree', 'bushes'), ('fence', 'fences'), ('ceiling',), ('sky', 'clouds'), ('cabinet', 'cabinets'), ('table',), ('floor', 'flooring', 'tile floor'), ('pavement',), ('mountain', 'mountains'), ('grass',), ('dirt',), ('paper',), ('food',), ('building', 'buildings'), ('rock',), ('wall', 'walls'), ('rug',), ('wall', 'walls', 'brick wall', 'stone wall', 'interior wall'), ('building', 'buildings', 'edifice', 'edifices'), ('sky', 'clouds'), ('floor', 'flooring'), ('tree', 'trees'), ('ceiling',), ('road', 'route', 'street', 'roads', 'streets', 'routes'), ('grass', 'grass field'), ('sidewalk', 'pavement'), ('earth', 'ground'), ('mountain', 'mount', 'mountains'), ('plant', 'flora', 'plant life', 'plants', 'bushes'), ('water',), ('house exterior',), ('sea', 'ocean'), ('rug', 'carpet', 'carpeting'), ('field',), ('rock', 'stone', 'rocks', 'stones'), ('pedestal',), ('sand',), ('skyscraper', 'skyscrapers'), ('grandstand', 'covered stand'), ('path',), ('runway',), ('stairway', 'staircase'), ('river',), ('bridge', 'span'), ('window screen', 'door screen'), ('hill',), ('bar',), ('hovel', 'hut', 'hutch', 'shack', 'shanty'), ('tower', 'towers'), ('dirt track',), ('land', 'soil'), ('escalator', 'moving staircase', 'moving stairway'), ('buffet', 'sideboard'), ('poster', 'posting', 'placard', 'notice', 'bill', 'card'), ('stage',), ('conveyer belt', 'conveyor belt', 'conveyer', 'conveyor', 'transporter'), ('canopy',), ('swimming pool', 'swimming bath'), ('waterfall', 'falls'), ('tent', 'collapsible shelter'), ('cradle',), ('tank', 'storage tank'), ('lake',), ('blanket', 'cover'), ('pier', 'wharf', 'wharfage', 'dock'), ('crt screen',), ('shower',))\n",
+ "[06/08 17:25:58 odise.modeling.meta_arch.clip]: Using provided CLIP model\n",
+ "[06/08 17:26:01 odise.modeling.meta_arch.clip]: Built text_embed of shape torch.Size([2486, 768]) for 1488 labels: (('a photo of a black pickup truck.', 'a photo of a pickup truck.'), ('a photo of a blue sky.', 'a photo of a sky.'), ('a photo of a person.', 'a photo of a child.', 'a photo of a girl.', 'a photo of a boy.', 'a photo of a woman.', 'a photo of a man.', 'a photo of a people.', 'a photo of a childeren.', 'a photo of a girls.', 'a photo of a boys.', 'a photo of a women.', 'a photo of a men.', 'a photo of a lady.', 'a photo of a guy.', 'a photo of a ladies.', 'a photo of a guys.', 'a photo of a clothes.'), ('a photo of a bicycle.', 'a photo of a bicycles.', 'a photo of a bike.', 'a photo of a bikes.'), ('a photo of a car.', 'a photo of a cars.'), ('a photo of a motorcycle.', 'a photo of a motorcycles.'), ('a photo of a airplane.', 'a photo of a airplanes.'), ('a photo of a bus.', 'a photo of a buses.'), ('a photo of a train.', 'a photo of a trains.', 'a photo of a locomotive.', 'a photo of a locomotives.', 'a photo of a freight train.'), ('a photo of a truck.', 'a photo of a trucks.'), ('a photo of a boat.', 'a photo of a boats.'), ('a photo of a traffic light.',), ('a photo of a fire hydrant.',), ('a photo of a stop sign.',), ('a photo of a parking meter.',), ('a photo of a bench.', 'a photo of a benches.'), ('a photo of a bird.', 'a photo of a birds.'), ('a photo of a cat.', 'a photo of a cats.', 'a photo of a kitties.', 'a photo of a kitty.'), ('a photo of a dog.', 'a photo of a dogs.', 'a photo of a puppy.', 'a photo of a puppies.'), ('a photo of a horse.', 'a photo of a horses.', 'a photo of a foal.'), ('a photo of a sheep.',), ('a photo of a cow.', 'a photo of a cows.', 'a photo of a calf.'), ('a photo of a elephant.', 'a photo of a elephants.'), ('a photo of a bear.', 'a photo of a bears.'), ('a photo of a zebra.', 'a photo of a zebras.'), ('a photo of a giraffe.', 'a photo of a giraffes.'), ('a photo of a backpack.', 'a photo of a backpacks.'), ('a photo of a umbrella.', 'a photo of a umbrellas.'), ('a photo of a handbag.', 'a photo of a handbags.'), ('a photo of a tie.',), ('a photo of a suitcase.', 'a photo of a suitcases.'), ('a photo of a frisbee.',), ('a photo of a skis.',), ('a photo of a snowboard.',), ('a photo of a sports ball.',), ('a photo of a kite.', 'a photo of a kites.'), ('a photo of a baseball bat.',), ('a photo of a baseball glove.',), ('a photo of a skateboard.',), ('a photo of a surfboard.',), ('a photo of a tennis racket.',), ('a photo of a bottle.', 'a photo of a bottles.', 'a photo of a water bottle.'), ('a photo of a wine glass.', 'a photo of a wine glasses.', 'a photo of a wineglass.'), ('a photo of a cup.', 'a photo of a cups.', 'a photo of a water cup.', 'a photo of a water glass.'), ('a photo of a fork.', 'a photo of a forks.'), ('a photo of a knife.', 'a photo of a knives.'), ('a photo of a spoon.', 'a photo of a spoons.'), ('a photo of a bowl.', 'a photo of a bowls.'), ('a photo of a banana.', 'a photo of a bananas.'), ('a photo of a apple.', 'a photo of a apples.', 'a photo of a apple fruit.'), ('a photo of a sandwich.', 'a photo of a sandwiches.'), ('a photo of a orange fruit.',), ('a photo of a broccoli.',), ('a photo of a carrot.', 'a photo of a carrots.'), ('a photo of a hot dog.',), ('a photo of a pizza.',), ('a photo of a donut.', 'a photo of a donuts.'), ('a photo of a cake.', 'a photo of a cakes.'), ('a photo of a chair.', 'a photo of a chairs.'), ('a photo of a couch.', 'a photo of a sofa.', 'a photo of a sofas.'), ('a photo of a potted plant.', 'a photo of a potted plants.', 'a photo of a pottedplant.', 'a photo of a pottedplants.', 'a photo of a planter.', 'a photo of a planters.'), ('a photo of a bed.', 'a photo of a beds.'), ('a photo of a dining table.', 'a photo of a dining tables.', 'a photo of a diningtable.', 'a photo of a diningtables.', 'a photo of a plate.', 'a photo of a plates.', 'a photo of a diningtable tablecloth.'), ('a photo of a toilet.',), ('a photo of a tv.',), ('a photo of a laptop.',), ('a photo of a mouse.',), ('a photo of a tv remote.', 'a photo of a remote control.'), ('a photo of a keyboard.',), ('a photo of a cell phone.', 'a photo of a mobile.'), ('a photo of a microwave.',), ('a photo of a oven.', 'a photo of a ovens.'), ('a photo of a toaster.',), ('a photo of a sink.', 'a photo of a sinks.'), ('a photo of a refrigerator.', 'a photo of a fridge.'), ('a photo of a book.', 'a photo of a books.'), ('a photo of a clock.',), ('a photo of a vase.', 'a photo of a vases.'), ('a photo of a scissor.', 'a photo of a scissors.'), ('a photo of a teddy bear.', 'a photo of a teddy bears.'), ('a photo of a hair drier.',), ('a photo of a toothbrush.', 'a photo of a toothbrushes.'), ('a photo of a bed.', 'a photo of a beds.'), ('a photo of a windowpane.', 'a photo of a window.', 'a photo of a windows.'), ('a photo of a cabinet.', 'a photo of a cabinets.', 'a photo of a wall mounted cabine.'), ('a photo of a person.', 'a photo of a child.', 'a photo of a girl.', 'a photo of a boy.', 'a photo of a woman.', 'a photo of a man.', 'a photo of a people.', 'a photo of a children.', 'a photo of a girls.', 'a photo of a boys.', 'a photo of a women.', 'a photo of a men.'), ('a photo of a door.', 'a photo of a double door.', 'a photo of a doors.'), ('a photo of a table.', 'a photo of a tables.', 'a photo of a tablecloth.'), ('a photo of a curtain.', 'a photo of a drape.', 'a photo of a drapery.', 'a photo of a mantle.', 'a photo of a pall.'), ('a photo of a chair.', 'a photo of a chairs.'), ('a photo of a car.', 'a photo of a automobile.', 'a photo of a cars.'), ('a photo of a painting.', 'a photo of a picture.', 'a photo of a paintings.', 'a photo of a pictures.', 'a photo of a wallart.', 'a photo of a framed canvas.'), ('a photo of a sofa.', 'a photo of a couch.', 'a photo of a sofas.', 'a photo of a couches.'), ('a photo of a shelf.', 'a photo of a shelves.'), ('a photo of a mirror.', 'a photo of a mirrors.'), ('a photo of a armchair.', 'a photo of a armchairs.'), ('a photo of a seat.', 'a photo of a seats.'), ('a photo of a fence.', 'a photo of a fencing.'), ('a photo of a desk.', 'a photo of a desks.'), ('a photo of a wardrobe.', 'a photo of a closet.', 'a photo of a press.', 'a photo of a wardrobes.', 'a photo of a closets.'), ('a photo of a lamp.', 'a photo of a lamps.'), ('a photo of a bathtub.', 'a photo of a bathing tub.', 'a photo of a bath.', 'a photo of a tub.'), ('a photo of a railing.', 'a photo of a rail.'), ('a photo of a cushion.', 'a photo of a cushions.'), ('a photo of a box.', 'a photo of a boxes.'), ('a photo of a column.', 'a photo of a pillar.'), ('a photo of a signboard.', 'a photo of a sign.', 'a photo of a signboards.', 'a photo of a signs.'), ('a photo of a chest of drawers.', 'a photo of a chest.', 'a photo of a bureau.', 'a photo of a dresser.'), ('a photo of a counter.',), ('a photo of a sink.',), ('a photo of a fireplace.', 'a photo of a hearth.', 'a photo of a open fireplace.'), ('a photo of a refrigerator.', 'a photo of a icebox.'), ('a photo of a stairs.', 'a photo of a steps.'), ('a photo of a case.', 'a photo of a display case.', 'a photo of a showcase.', 'a photo of a vitrine.'), ('a photo of a pool table.', 'a photo of a billiard table.', 'a photo of a snooker table.'), ('a photo of a pillow.', 'a photo of a pillows.'), ('a photo of a screen door.', 'a photo of a shower door.'), ('a photo of a bookcase.',), ('a photo of a coffee table.', 'a photo of a cocktail table.'), ('a photo of a toilet.', 'a photo of a commode.', 'a photo of a crapper.', 'a photo of a potty.'), ('a photo of a flower.', 'a photo of a flowers.'), ('a photo of a book.', 'a photo of a books.'), ('a photo of a bench.', 'a photo of a benches.'), ('a photo of a countertop.', 'a photo of a counter top.', 'a photo of a worktop.'), ('a photo of a stove.', 'a photo of a kitchen stove.', 'a photo of a kitchen range.', 'a photo of a kitchen range.', 'a photo of a cooking stove.'), ('a photo of a palm tree.', 'a photo of a palm trees.'), ('a photo of a kitchen island.',), ('a photo of a computer.', 'a photo of a computing machine.', 'a photo of a computing device.', 'a photo of a data processor.', 'a photo of a electronic computer.', 'a photo of a information processing system.'), ('a photo of a swivel chair.',), ('a photo of a boat.',), ('a photo of a arcade machine.', 'a photo of a arcade machines.'), ('a photo of a bus.', 'a photo of a autobus.', 'a photo of a double-decker.', 'a photo of a jitney.', 'a photo of a motorbus.', 'a photo of a motorcoach.', 'a photo of a omnibus.', 'a photo of a passenger vehicle.'), ('a photo of a towel.',), ('a photo of a light bulb.', 'a photo of a lightbulb.', 'a photo of a bulb.', 'a photo of a incandescent lamp.', 'a photo of a electric light.', 'a photo of a electric-light bulb.'), ('a photo of a truck.', 'a photo of a motortruck.'), ('a photo of a chandelier.', 'a photo of a pendant.', 'a photo of a pendent.'), ('a photo of a awning.', 'a photo of a sunshade.', 'a photo of a sunblind.'), ('a photo of a streetlight.', 'a photo of a street lamp.'), ('a photo of a booth.', 'a photo of a cubicle.', 'a photo of a stall.', 'a photo of a kiosk.'), ('a photo of a television receiver.', 'a photo of a television.', 'a photo of a television set.', 'a photo of a tv.', 'a photo of a tv set.'), ('a photo of a airplane.', 'a photo of a aeroplane.', 'a photo of a airplanes.', 'a photo of a aeroplanes.'), ('a photo of a apparel.', 'a photo of a wearing apparel.', 'a photo of a dress.', 'a photo of a clothes.'), ('a photo of a pole.',), ('a photo of a bannister.', 'a photo of a banister.', 'a photo of a balustrade.', 'a photo of a balusters.', 'a photo of a handrail.'), ('a photo of a ottoman.', 'a photo of a pouf.', 'a photo of a pouffe.', 'a photo of a puff.', 'a photo of a hassock.'), ('a photo of a bottle.', 'a photo of a bottles.', 'a photo of a water bottle.'), ('a photo of a van.',), ('a photo of a ship.',), ('a photo of a fountain.',), ('a photo of a washer.', 'a photo of a automatic washer.', 'a photo of a washing machine.'), ('a photo of a plaything.', 'a photo of a toy.', 'a photo of a toys.'), ('a photo of a stool.', 'a photo of a stools.'), ('a photo of a barrel.', 'a photo of a cask.', 'a photo of a barrels.', 'a photo of a casks.'), ('a photo of a basket.', 'a photo of a handbasket.'), ('a photo of a bag.', 'a photo of a bags.', 'a photo of a gift bag.', 'a photo of a paper bag.'), ('a photo of a minibike.', 'a photo of a motorbike.'), ('a photo of a oven.',), ('a photo of a ball.', 'a photo of a balls.'), ('a photo of a food.', 'a photo of a solid food.'), ('a photo of a step.', 'a photo of a stair.'), ('a photo of a trade name.', 'a photo of a brand name.', 'a photo of a brand.', 'a photo of a marque.'), ('a photo of a microwave.', 'a photo of a microwave oven.'), ('a photo of a plant pots.', 'a photo of a plant pot.', 'a photo of a flower pot.', 'a photo of a flowerpot.', 'a photo of a planter.'), ('a photo of a animal.', 'a photo of a animate being.', 'a photo of a dog.', 'a photo of a cat.', 'a photo of a horse.', 'a photo of a cow.', 'a photo of a sheep.', 'a photo of a zebra.', 'a photo of a girraffe.', 'a photo of a bird.'), ('a photo of a bicycle.', 'a photo of a bike.'), ('a photo of a dishwasher.', 'a photo of a dish washer.', 'a photo of a dishwashing machine.'), ('a photo of a projection screen.',), ('a photo of a sculpture.', 'a photo of a sculptures.'), ('a photo of a exhaust hood.',), ('a photo of a sconce.', 'a photo of a sconce lamp.', 'a photo of a sconce light.'), ('a photo of a vase.', 'a photo of a vases.'), ('a photo of a traffic light.', 'a photo of a traffic signal.', 'a photo of a traffic lights.'), ('a photo of a tray.', 'a photo of a trays.'), ('a photo of a ashcan.', 'a photo of a trash can.', 'a photo of a garbage can.', 'a photo of a wastebin.', 'a photo of a ash bin.', 'a photo of a ash-bin.', 'a photo of a ashbin.', 'a photo of a dustbin.', 'a photo of a trash barrel.', 'a photo of a trash bin.'), ('a photo of a ceiling fan.', 'a photo of a floor fan.'), ('a photo of a plate.', 'a photo of a plates.'), ('a photo of a monitor.', 'a photo of a monitoring device.', 'a photo of a monitors.'), ('a photo of a bulletin board.', 'a photo of a notice board.'), ('a photo of a radiator.',), ('a photo of a cup.', 'a photo of a cups.', 'a photo of a drinking glass.', 'a photo of a drinking glasses.'), ('a photo of a clock.',), ('a photo of a flag.', 'a photo of a flags.'), ('a photo of a aerosol can.', 'a photo of a spray can.'), ('a photo of a air conditioner.',), ('a photo of a airplane.', 'a photo of a aeroplane.'), ('a photo of a alarm clock.',), ('a photo of a alcohol.', 'a photo of a alcoholic beverage.'), ('a photo of a alligator.', 'a photo of a gator.'), ('a photo of a almond.',), ('a photo of a ambulance.',), ('a photo of a amplifier.',), ('a photo of a anklet.', 'a photo of a ankle bracelet.'), ('a photo of a antenna.', 'a photo of a aerial.', 'a photo of a transmitting aerial.'), ('a photo of a apple.',), ('a photo of a applesauce.',), ('a photo of a apricot.',), ('a photo of a apron.',), ('a photo of a aquarium.', 'a photo of a fish tank.'), ('a photo of a arctic (type of shoe).', 'a photo of a galosh.', 'a photo of a golosh.', 'a photo of a rubber (type of shoe).', 'a photo of a gumshoe.'), ('a photo of a armband.',), ('a photo of a armchair.',), ('a photo of a armoire.',), ('a photo of a armor.', 'a photo of a armour.'), ('a photo of a artichoke.',), ('a photo of a trash can.', 'a photo of a garbage can.', 'a photo of a wastebin.', 'a photo of a dustbin.', 'a photo of a trash barrel.', 'a photo of a trash bin.'), ('a photo of a ashtray.',), ('a photo of a asparagus.',), ('a photo of a atomizer.', 'a photo of a atomiser.', 'a photo of a spray.', 'a photo of a sprayer.', 'a photo of a nebulizer.', 'a photo of a nebuliser.'), ('a photo of a avocado.',), ('a photo of a award.', 'a photo of a accolade.'), ('a photo of a awning.',), ('a photo of a ax.', 'a photo of a axe.'), ('a photo of a baboon.',), ('a photo of a baby buggy.', 'a photo of a baby carriage.', 'a photo of a perambulator.', 'a photo of a pram.', 'a photo of a stroller.'), ('a photo of a basketball backboard.',), ('a photo of a backpack.', 'a photo of a knapsack.', 'a photo of a packsack.', 'a photo of a rucksack.', 'a photo of a haversack.'), ('a photo of a handbag.', 'a photo of a purse.', 'a photo of a pocketbook.'), ('a photo of a suitcase.', 'a photo of a baggage.', 'a photo of a luggage.'), ('a photo of a bagel.', 'a photo of a beigel.'), ('a photo of a bagpipe.',), ('a photo of a baguet.', 'a photo of a baguette.'), ('a photo of a bait.', 'a photo of a lure.'), ('a photo of a ball.',), ('a photo of a ballet skirt.', 'a photo of a tutu.'), ('a photo of a balloon.',), ('a photo of a bamboo.',), ('a photo of a banana.',), ('a photo of a Band Aid.',), ('a photo of a bandage.',), ('a photo of a bandanna.', 'a photo of a bandana.'), ('a photo of a banjo.',), ('a photo of a banner.', 'a photo of a streamer.'), ('a photo of a barbell.',), ('a photo of a barge.',), ('a photo of a barrel.', 'a photo of a cask.'), ('a photo of a barrette.',), ('a photo of a barrow.', 'a photo of a garden cart.', 'a photo of a lawn cart.', 'a photo of a wheelbarrow.'), ('a photo of a baseball base.',), ('a photo of a baseball.',), ('a photo of a baseball bat.',), ('a photo of a baseball cap.', 'a photo of a jockey cap.', 'a photo of a golf cap.'), ('a photo of a baseball glove.', 'a photo of a baseball mitt.'), ('a photo of a basket.', 'a photo of a handbasket.'), ('a photo of a basketball.',), ('a photo of a bass horn.', 'a photo of a sousaphone.', 'a photo of a tuba.'), ('a photo of a bat (animal).',), ('a photo of a bath mat.',), ('a photo of a bath towel.',), ('a photo of a bathrobe.',), ('a photo of a bathtub.', 'a photo of a bathing tub.'), ('a photo of a batter (food).',), ('a photo of a battery.',), ('a photo of a beachball.',), ('a photo of a bead.',), ('a photo of a bean curd.', 'a photo of a tofu.'), ('a photo of a beanbag.',), ('a photo of a beanie.', 'a photo of a beany.'), ('a photo of a bear.',), ('a photo of a bed.',), ('a photo of a bedpan.',), ('a photo of a bedspread.', 'a photo of a bedcover.', 'a photo of a bed covering.', 'a photo of a counterpane.', 'a photo of a spread.'), ('a photo of a cow.',), ('a photo of a beef (food).', 'a photo of a boeuf (food).'), ('a photo of a beeper.', 'a photo of a pager.'), ('a photo of a beer bottle.',), ('a photo of a beer can.',), ('a photo of a beetle.',), ('a photo of a bell.',), ('a photo of a bell pepper.', 'a photo of a capsicum.'), ('a photo of a belt.',), ('a photo of a belt buckle.',), ('a photo of a bench.',), ('a photo of a beret.',), ('a photo of a bib.',), ('a photo of a Bible.',), ('a photo of a bicycle.', 'a photo of a bike (bicycle).'), ('a photo of a visor.', 'a photo of a vizor.'), ('a photo of a billboard.',), ('a photo of a binder.', 'a photo of a ring-binder.'), ('a photo of a binoculars.', 'a photo of a field glasses.', 'a photo of a opera glasses.'), ('a photo of a bird.',), ('a photo of a birdfeeder.',), ('a photo of a birdbath.',), ('a photo of a birdcage.',), ('a photo of a birdhouse.',), ('a photo of a birthday cake.',), ('a photo of a birthday card.',), ('a photo of a pirate flag.',), ('a photo of a black sheep.',), ('a photo of a blackberry.',), ('a photo of a blackboard.', 'a photo of a chalkboard.'), ('a photo of a blanket.',), ('a photo of a blazer.', 'a photo of a sport jacket.', 'a photo of a sport coat.', 'a photo of a sports jacket.', 'a photo of a sports coat.'), ('a photo of a blender.', 'a photo of a liquidizer.', 'a photo of a liquidiser.'), ('a photo of a blimp.',), ('a photo of a blinker.', 'a photo of a flasher.'), ('a photo of a blouse.',), ('a photo of a blueberry.',), ('a photo of a gameboard.',), ('a photo of a boat.', 'a photo of a ship (boat).'), ('a photo of a bob.', 'a photo of a bobber.', 'a photo of a bobfloat.'), ('a photo of a bobbin.', 'a photo of a spool.', 'a photo of a reel.'), ('a photo of a bobby pin.', 'a photo of a hairgrip.'), ('a photo of a boiled egg.', 'a photo of a coddled egg.'), ('a photo of a bolo tie.', 'a photo of a bolo.', 'a photo of a bola tie.', 'a photo of a bola.'), ('a photo of a deadbolt.',), ('a photo of a bolt.',), ('a photo of a bonnet.',), ('a photo of a book.',), ('a photo of a bookcase.',), ('a photo of a booklet.', 'a photo of a brochure.', 'a photo of a leaflet.', 'a photo of a pamphlet.'), ('a photo of a bookmark.', 'a photo of a bookmarker.'), ('a photo of a boom microphone.', 'a photo of a microphone boom.'), ('a photo of a boot.',), ('a photo of a bottle.',), ('a photo of a bottle opener.',), ('a photo of a bouquet.',), ('a photo of a bow (weapon).',), ('a photo of a bow (decorative ribbons).',), ('a photo of a bow-tie.', 'a photo of a bowtie.'), ('a photo of a bowl.',), ('a photo of a pipe bowl.',), ('a photo of a bowler hat.', 'a photo of a bowler.', 'a photo of a derby hat.', 'a photo of a derby.', 'a photo of a plug hat.'), ('a photo of a bowling ball.',), ('a photo of a box.',), ('a photo of a boxing glove.',), ('a photo of a suspenders.',), ('a photo of a bracelet.', 'a photo of a bangle.'), ('a photo of a brass plaque.',), ('a photo of a brassiere.', 'a photo of a bra.', 'a photo of a bandeau.'), ('a photo of a bread-bin.', 'a photo of a breadbox.'), ('a photo of a bread.',), ('a photo of a breechcloth.', 'a photo of a breechclout.', 'a photo of a loincloth.'), ('a photo of a bridal gown.', 'a photo of a wedding gown.', 'a photo of a wedding dress.'), ('a photo of a briefcase.',), ('a photo of a broccoli.',), ('a photo of a broach.',), ('a photo of a broom.',), ('a photo of a brownie.',), ('a photo of a brussels sprouts.',), ('a photo of a bubble gum.',), ('a photo of a bucket.', 'a photo of a pail.'), ('a photo of a horse buggy.',), ('a photo of a horned cow.',), ('a photo of a bulldog.',), ('a photo of a bulldozer.', 'a photo of a dozer.'), ('a photo of a bullet train.',), ('a photo of a bulletin board.', 'a photo of a notice board.'), ('a photo of a bulletproof vest.',), ('a photo of a bullhorn.', 'a photo of a megaphone.'), ('a photo of a bun.', 'a photo of a roll.'), ('a photo of a bunk bed.',), ('a photo of a buoy.',), ('a photo of a burrito.',), ('a photo of a bus (vehicle).', 'a photo of a autobus.', 'a photo of a charabanc.', 'a photo of a double-decker.', 'a photo of a motorbus.', 'a photo of a motorcoach.'), ('a photo of a business card.',), ('a photo of a butter.',), ('a photo of a butterfly.',), ('a photo of a button.',), ('a photo of a cab (taxi).', 'a photo of a taxi.', 'a photo of a taxicab.'), ('a photo of a cabana.',), ('a photo of a cabin car.', 'a photo of a caboose.'), ('a photo of a cabinet.',), ('a photo of a locker.', 'a photo of a storage locker.'), ('a photo of a cake.',), ('a photo of a calculator.',), ('a photo of a calendar.',), ('a photo of a calf.',), ('a photo of a camcorder.',), ('a photo of a camel.',), ('a photo of a camera.',), ('a photo of a camera lens.',), ('a photo of a camper (vehicle).', 'a photo of a camping bus.', 'a photo of a motor home.'), ('a photo of a can.', 'a photo of a tin can.'), ('a photo of a can opener.', 'a photo of a tin opener.'), ('a photo of a candle.', 'a photo of a candlestick.'), ('a photo of a candle holder.',), ('a photo of a candy bar.',), ('a photo of a candy cane.',), ('a photo of a walking cane.',), ('a photo of a canister.', 'a photo of a cannister.'), ('a photo of a canoe.',), ('a photo of a cantaloup.', 'a photo of a cantaloupe.'), ('a photo of a canteen.',), ('a photo of a cap (headwear).',), ('a photo of a bottle cap.', 'a photo of a cap (container lid).'), ('a photo of a cape.',), ('a photo of a cappuccino.', 'a photo of a coffee cappuccino.'), ('a photo of a car (automobile).', 'a photo of a auto (automobile).', 'a photo of a automobile.'), ('a photo of a railcar (part of a train).', 'a photo of a railway car (part of a train).', 'a photo of a railroad car (part of a train).'), ('a photo of a elevator car.',), ('a photo of a car battery.', 'a photo of a automobile battery.'), ('a photo of a identity card.',), ('a photo of a card.',), ('a photo of a cardigan.',), ('a photo of a cargo ship.', 'a photo of a cargo vessel.'), ('a photo of a carnation.',), ('a photo of a horse carriage.',), ('a photo of a carrot.',), ('a photo of a tote bag.',), ('a photo of a cart.',), ('a photo of a carton.',), ('a photo of a cash register.', 'a photo of a register (for cash transactions).'), ('a photo of a casserole.',), ('a photo of a cassette.',), ('a photo of a cast.', 'a photo of a plaster cast.', 'a photo of a plaster bandage.'), ('a photo of a cat.',), ('a photo of a cauliflower.',), ('a photo of a cayenne (spice).', 'a photo of a cayenne pepper (spice).', 'a photo of a red pepper (spice).'), ('a photo of a CD player.',), ('a photo of a celery.',), ('a photo of a cellular telephone.', 'a photo of a cellular phone.', 'a photo of a cellphone.', 'a photo of a mobile phone.', 'a photo of a smart phone.'), ('a photo of a chain mail.', 'a photo of a ring mail.', 'a photo of a chain armor.', 'a photo of a chain armour.', 'a photo of a ring armor.', 'a photo of a ring armour.'), ('a photo of a chair.',), ('a photo of a chaise longue.', 'a photo of a chaise.', 'a photo of a daybed.'), ('a photo of a chalice.',), ('a photo of a chandelier.',), ('a photo of a chap.',), ('a photo of a checkbook.', 'a photo of a chequebook.'), ('a photo of a checkerboard.',), ('a photo of a cherry.',), ('a photo of a chessboard.',), ('a photo of a chicken (animal).',), ('a photo of a chickpea.', 'a photo of a garbanzo.'), ('a photo of a chili (vegetable).', 'a photo of a chili pepper (vegetable).', 'a photo of a chilli (vegetable).', 'a photo of a chilly (vegetable).', 'a photo of a chile (vegetable).'), ('a photo of a chime.', 'a photo of a gong.'), ('a photo of a chinaware.',), ('a photo of a crisp (potato chip).', 'a photo of a potato chip.'), ('a photo of a poker chip.',), ('a photo of a chocolate bar.',), ('a photo of a chocolate cake.',), ('a photo of a chocolate milk.',), ('a photo of a chocolate mousse.',), ('a photo of a choker.', 'a photo of a collar.', 'a photo of a neckband.'), ('a photo of a chopping board.', 'a photo of a cutting board.', 'a photo of a chopping block.'), ('a photo of a chopstick.',), ('a photo of a Christmas tree.',), ('a photo of a slide.',), ('a photo of a cider.', 'a photo of a cyder.'), ('a photo of a cigar box.',), ('a photo of a cigarette.',), ('a photo of a cigarette case.', 'a photo of a cigarette pack.'), ('a photo of a cistern.', 'a photo of a water tank.'), ('a photo of a clarinet.',), ('a photo of a clasp.',), ('a photo of a cleansing agent.', 'a photo of a cleanser.', 'a photo of a cleaner.'), ('a photo of a cleat (for securing rope).',), ('a photo of a clementine.',), ('a photo of a clip.',), ('a photo of a clipboard.',), ('a photo of a clippers (for plants).',), ('a photo of a cloak.',), ('a photo of a clock.', 'a photo of a timepiece.', 'a photo of a timekeeper.'), ('a photo of a clock tower.',), ('a photo of a clothes hamper.', 'a photo of a laundry basket.', 'a photo of a clothes basket.'), ('a photo of a clothespin.', 'a photo of a clothes peg.'), ('a photo of a clutch bag.',), ('a photo of a coaster.',), ('a photo of a coat.',), ('a photo of a coat hanger.', 'a photo of a clothes hanger.', 'a photo of a dress hanger.'), ('a photo of a coatrack.', 'a photo of a hatrack.'), ('a photo of a cock.', 'a photo of a rooster.'), ('a photo of a cockroach.',), ('a photo of a cocoa (beverage).', 'a photo of a hot chocolate (beverage).', 'a photo of a drinking chocolate.'), ('a photo of a coconut.', 'a photo of a cocoanut.'), ('a photo of a coffee maker.', 'a photo of a coffee machine.'), ('a photo of a coffee table.', 'a photo of a cocktail table.'), ('a photo of a coffeepot.',), ('a photo of a coil.',), ('a photo of a coin.',), ('a photo of a colander.', 'a photo of a cullender.'), ('a photo of a coleslaw.', 'a photo of a slaw.'), ('a photo of a coloring material.', 'a photo of a colouring material.'), ('a photo of a combination lock.',), ('a photo of a pacifier.', 'a photo of a teething ring.'), ('a photo of a comic book.',), ('a photo of a compass.',), ('a photo of a computer keyboard.', 'a photo of a keyboard (computer).'), ('a photo of a condiment.',), ('a photo of a cone.', 'a photo of a traffic cone.'), ('a photo of a control.', 'a photo of a controller.'), ('a photo of a convertible (automobile).',), ('a photo of a sofa bed.',), ('a photo of a cooker.',), ('a photo of a cookie.', 'a photo of a cooky.', 'a photo of a biscuit (cookie).'), ('a photo of a cooking utensil.',), ('a photo of a cooler (for food).', 'a photo of a ice chest.'), ('a photo of a cork (bottle plug).', 'a photo of a bottle cork.'), ('a photo of a corkboard.',), ('a photo of a corkscrew.', 'a photo of a bottle screw.'), ('a photo of a edible corn.', 'a photo of a corn.', 'a photo of a maize.'), ('a photo of a cornbread.',), ('a photo of a cornet.', 'a photo of a horn.', 'a photo of a trumpet.'), ('a photo of a cornice.', 'a photo of a valance.', 'a photo of a valance board.', 'a photo of a pelmet.'), ('a photo of a cornmeal.',), ('a photo of a corset.', 'a photo of a girdle.'), ('a photo of a costume.',), ('a photo of a cougar.', 'a photo of a puma.', 'a photo of a catamount.', 'a photo of a mountain lion.', 'a photo of a panther.'), ('a photo of a coverall.',), ('a photo of a cowbell.',), ('a photo of a cowboy hat.', 'a photo of a ten-gallon hat.'), ('a photo of a crab (animal).',), ('a photo of a crabmeat.',), ('a photo of a cracker.',), ('a photo of a crape.', 'a photo of a crepe.', 'a photo of a French pancake.'), ('a photo of a crate.',), ('a photo of a crayon.', 'a photo of a wax crayon.'), ('a photo of a cream pitcher.',), ('a photo of a crescent roll.', 'a photo of a croissant.'), ('a photo of a crib.', 'a photo of a cot.'), ('a photo of a crock pot.', 'a photo of a earthenware jar.'), ('a photo of a crossbar.',), ('a photo of a crouton.',), ('a photo of a crow.',), ('a photo of a crowbar.', 'a photo of a wrecking bar.', 'a photo of a pry bar.'), ('a photo of a crown.',), ('a photo of a crucifix.',), ('a photo of a cruise ship.', 'a photo of a cruise liner.'), ('a photo of a police cruiser.', 'a photo of a patrol car.', 'a photo of a police car.', 'a photo of a squad car.'), ('a photo of a crumb.',), ('a photo of a crutch.',), ('a photo of a cub (animal).',), ('a photo of a cube.', 'a photo of a square block.'), ('a photo of a cucumber.', 'a photo of a cuke.'), ('a photo of a cufflink.',), ('a photo of a cup.',), ('a photo of a trophy cup.',), ('a photo of a cupboard.', 'a photo of a closet.'), ('a photo of a cupcake.',), ('a photo of a hair curler.', 'a photo of a hair roller.', 'a photo of a hair crimper.'), ('a photo of a curling iron.',), ('a photo of a curtain.', 'a photo of a drapery.'), ('a photo of a cushion.',), ('a photo of a cylinder.',), ('a photo of a cymbal.',), ('a photo of a dagger.',), ('a photo of a dalmatian.',), ('a photo of a dartboard.',), ('a photo of a date (fruit).',), ('a photo of a deck chair.', 'a photo of a beach chair.'), ('a photo of a deer.', 'a photo of a cervid.'), ('a photo of a dental floss.', 'a photo of a floss.'), ('a photo of a desk.',), ('a photo of a detergent.',), ('a photo of a diaper.',), ('a photo of a diary.', 'a photo of a journal.'), ('a photo of a die.', 'a photo of a dice.'), ('a photo of a dinghy.', 'a photo of a dory.', 'a photo of a rowboat.'), ('a photo of a dining table.',), ('a photo of a tux.', 'a photo of a tuxedo.'), ('a photo of a dish.',), ('a photo of a dish antenna.',), ('a photo of a dishrag.', 'a photo of a dishcloth.'), ('a photo of a dishtowel.', 'a photo of a tea towel.'), ('a photo of a dishwasher.', 'a photo of a dishwashing machine.'), ('a photo of a dishwasher detergent.', 'a photo of a dishwashing detergent.', 'a photo of a dishwashing liquid.', 'a photo of a dishsoap.'), ('a photo of a dispenser.',), ('a photo of a diving board.',), ('a photo of a Dixie cup.', 'a photo of a paper cup.'), ('a photo of a dog.',), ('a photo of a dog collar.',), ('a photo of a doll.',), ('a photo of a dollar.', 'a photo of a dollar bill.', 'a photo of a one dollar bill.'), ('a photo of a dollhouse.', \"a photo of a doll's house.\"), ('a photo of a dolphin.',), ('a photo of a domestic ass.', 'a photo of a donkey.'), ('a photo of a doorknob.', 'a photo of a doorhandle.'), ('a photo of a doormat.', 'a photo of a welcome mat.'), ('a photo of a doughnut.', 'a photo of a donut.'), ('a photo of a dove.',), ('a photo of a dragonfly.',), ('a photo of a drawer.',), ('a photo of a underdrawers.', 'a photo of a boxers.', 'a photo of a boxershorts.'), ('a photo of a dress.', 'a photo of a frock.'), ('a photo of a dress hat.', 'a photo of a high hat.', 'a photo of a opera hat.', 'a photo of a silk hat.', 'a photo of a top hat.'), ('a photo of a dress suit.',), ('a photo of a dresser.',), ('a photo of a drill.',), ('a photo of a drone.',), ('a photo of a dropper.', 'a photo of a eye dropper.'), ('a photo of a drum (musical instrument).',), ('a photo of a drumstick.',), ('a photo of a duck.',), ('a photo of a duckling.',), ('a photo of a duct tape.',), ('a photo of a duffel bag.', 'a photo of a duffle bag.', 'a photo of a duffel.', 'a photo of a duffle.'), ('a photo of a dumbbell.',), ('a photo of a dumpster.',), ('a photo of a dustpan.',), ('a photo of a eagle.',), ('a photo of a earphone.', 'a photo of a earpiece.', 'a photo of a headphone.'), ('a photo of a earplug.',), ('a photo of a earring.',), ('a photo of a easel.',), ('a photo of a eclair.',), ('a photo of a eel.',), ('a photo of a egg.', 'a photo of a eggs.'), ('a photo of a egg roll.', 'a photo of a spring roll.'), ('a photo of a egg yolk.', 'a photo of a yolk (egg).'), ('a photo of a eggbeater.', 'a photo of a eggwhisk.'), ('a photo of a eggplant.', 'a photo of a aubergine.'), ('a photo of a electric chair.',), ('a photo of a refrigerator.',), ('a photo of a elephant.',), ('a photo of a elk.', 'a photo of a moose.'), ('a photo of a envelope.',), ('a photo of a eraser.',), ('a photo of a escargot.',), ('a photo of a eyepatch.',), ('a photo of a falcon.',), ('a photo of a fan.',), ('a photo of a faucet.', 'a photo of a spigot.', 'a photo of a tap.'), ('a photo of a fedora.',), ('a photo of a ferret.',), ('a photo of a Ferris wheel.',), ('a photo of a ferry.', 'a photo of a ferryboat.'), ('a photo of a fig (fruit).',), ('a photo of a fighter jet.', 'a photo of a fighter aircraft.', 'a photo of a attack aircraft.'), ('a photo of a figurine.',), ('a photo of a file cabinet.', 'a photo of a filing cabinet.'), ('a photo of a file (tool).',), ('a photo of a fire alarm.', 'a photo of a smoke alarm.'), ('a photo of a fire engine.', 'a photo of a fire truck.'), ('a photo of a fire extinguisher.', 'a photo of a extinguisher.'), ('a photo of a fire hose.',), ('a photo of a fireplace.',), ('a photo of a fireplug.', 'a photo of a fire hydrant.', 'a photo of a hydrant.'), ('a photo of a first-aid kit.',), ('a photo of a fish.',), ('a photo of a fish (food).',), ('a photo of a fishbowl.', 'a photo of a goldfish bowl.'), ('a photo of a fishing rod.', 'a photo of a fishing pole.'), ('a photo of a flag.',), ('a photo of a flagpole.', 'a photo of a flagstaff.'), ('a photo of a flamingo.',), ('a photo of a flannel.',), ('a photo of a flap.',), ('a photo of a flash.', 'a photo of a flashbulb.'), ('a photo of a flashlight.', 'a photo of a torch.'), ('a photo of a fleece.',), ('a photo of a flip-flop (sandal).',), ('a photo of a flipper (footwear).', 'a photo of a fin (footwear).'), ('a photo of a flower arrangement.', 'a photo of a floral arrangement.'), ('a photo of a flute glass.', 'a photo of a champagne flute.'), ('a photo of a foal.',), ('a photo of a folding chair.',), ('a photo of a food processor.',), ('a photo of a football (American).',), ('a photo of a football helmet.',), ('a photo of a footstool.', 'a photo of a footrest.'), ('a photo of a fork.',), ('a photo of a forklift.',), ('a photo of a freight car.',), ('a photo of a French toast.',), ('a photo of a freshener.', 'a photo of a air freshener.'), ('a photo of a frisbee.',), ('a photo of a frog.', 'a photo of a toad.', 'a photo of a toad frog.'), ('a photo of a fruit juice.',), ('a photo of a frying pan.', 'a photo of a frypan.', 'a photo of a skillet.'), ('a photo of a fudge.',), ('a photo of a funnel.',), ('a photo of a futon.',), ('a photo of a gag.', 'a photo of a muzzle.'), ('a photo of a garbage.',), ('a photo of a garbage truck.',), ('a photo of a garden hose.',), ('a photo of a gargle.', 'a photo of a mouthwash.'), ('a photo of a gargoyle.',), ('a photo of a garlic.', 'a photo of a ail.'), ('a photo of a gasmask.', 'a photo of a respirator.', 'a photo of a gas helmet.'), ('a photo of a gazelle.',), ('a photo of a gelatin.', 'a photo of a jelly.'), ('a photo of a gemstone.',), ('a photo of a generator.',), ('a photo of a giant panda.', 'a photo of a panda.', 'a photo of a panda bear.'), ('a photo of a gift wrap.',), ('a photo of a ginger.', 'a photo of a gingerroot.'), ('a photo of a giraffe.',), ('a photo of a cincture.', 'a photo of a sash.', 'a photo of a waistband.', 'a photo of a waistcloth.'), ('a photo of a glass (drink container).', 'a photo of a drinking glass.'), ('a photo of a globe.',), ('a photo of a glove.',), ('a photo of a goat.',), ('a photo of a goggles.',), ('a photo of a goldfish.',), ('a photo of a golf club.', 'a photo of a golf-club.'), ('a photo of a golfcart.',), ('a photo of a gondola (boat).',), ('a photo of a goose.',), ('a photo of a gorilla.',), ('a photo of a gourd.',), ('a photo of a grape.',), ('a photo of a grater.',), ('a photo of a gravestone.', 'a photo of a headstone.', 'a photo of a tombstone.'), ('a photo of a gravy boat.', 'a photo of a gravy holder.'), ('a photo of a green bean.',), ('a photo of a green onion.', 'a photo of a spring onion.', 'a photo of a scallion.'), ('a photo of a griddle.',), ('a photo of a grill.', 'a photo of a grille.', 'a photo of a grillwork.', 'a photo of a radiator grille.'), ('a photo of a grits.', 'a photo of a hominy grits.'), ('a photo of a grizzly.', 'a photo of a grizzly bear.'), ('a photo of a grocery bag.',), ('a photo of a guitar.',), ('a photo of a gull.', 'a photo of a seagull.'), ('a photo of a gun.',), ('a photo of a hairbrush.',), ('a photo of a hairnet.',), ('a photo of a hairpin.',), ('a photo of a halter top.',), ('a photo of a ham.', 'a photo of a jambon.', 'a photo of a gammon.'), ('a photo of a hamburger.', 'a photo of a beefburger.', 'a photo of a burger.'), ('a photo of a hammer.',), ('a photo of a hammock.',), ('a photo of a hamper.',), ('a photo of a hamster.',), ('a photo of a hair dryer.',), ('a photo of a hand glass.', 'a photo of a hand mirror.'), ('a photo of a hand towel.', 'a photo of a face towel.'), ('a photo of a handcart.', 'a photo of a pushcart.', 'a photo of a hand truck.'), ('a photo of a handcuff.',), ('a photo of a handkerchief.',), ('a photo of a handle.', 'a photo of a grip.', 'a photo of a handgrip.'), ('a photo of a handsaw.', \"a photo of a carpenter's saw.\"), ('a photo of a hardback book.', 'a photo of a hardcover book.'), ('a photo of a harmonium.', 'a photo of a organ (musical instrument).', 'a photo of a reed organ (musical instrument).'), ('a photo of a hat.',), ('a photo of a hatbox.',), ('a photo of a veil.',), ('a photo of a headband.',), ('a photo of a headboard.',), ('a photo of a headlight.', 'a photo of a headlamp.'), ('a photo of a headscarf.',), ('a photo of a headset.',), ('a photo of a headstall (for horses).', 'a photo of a headpiece (for horses).'), ('a photo of a heart.',), ('a photo of a heater.', 'a photo of a warmer.'), ('a photo of a helicopter.',), ('a photo of a helmet.',), ('a photo of a heron.',), ('a photo of a highchair.', 'a photo of a feeding chair.'), ('a photo of a hinge.',), ('a photo of a hippopotamus.',), ('a photo of a hockey stick.',), ('a photo of a hog.', 'a photo of a pig.'), ('a photo of a home plate (baseball).', 'a photo of a home base (baseball).'), ('a photo of a honey.',), ('a photo of a fume hood.', 'a photo of a exhaust hood.'), ('a photo of a hook.',), ('a photo of a hookah.', 'a photo of a narghile.', 'a photo of a nargileh.', 'a photo of a sheesha.', 'a photo of a shisha.', 'a photo of a water pipe.'), ('a photo of a hornet.',), ('a photo of a horse.',), ('a photo of a hose.', 'a photo of a hosepipe.'), ('a photo of a hot-air balloon.',), ('a photo of a hotplate.',), ('a photo of a hot sauce.',), ('a photo of a hourglass.',), ('a photo of a houseboat.',), ('a photo of a hummingbird.',), ('a photo of a hummus.', 'a photo of a humus.', 'a photo of a hommos.', 'a photo of a hoummos.', 'a photo of a humous.'), ('a photo of a polar bear.',), ('a photo of a icecream.',), ('a photo of a popsicle.',), ('a photo of a ice maker.',), ('a photo of a ice pack.', 'a photo of a ice bag.'), ('a photo of a ice skate.',), ('a photo of a igniter.', 'a photo of a ignitor.', 'a photo of a lighter.'), ('a photo of a inhaler.', 'a photo of a inhalator.'), ('a photo of a iPod.',), ('a photo of a iron (for clothing).', 'a photo of a smoothing iron (for clothing).'), ('a photo of a ironing board.',), ('a photo of a jacket.',), ('a photo of a jam.',), ('a photo of a jar.',), ('a photo of a jean.', 'a photo of a blue jean.', 'a photo of a denim.'), ('a photo of a jeep.', 'a photo of a landrover.'), ('a photo of a jelly bean.', 'a photo of a jelly egg.'), ('a photo of a jersey.', 'a photo of a T-shirt.', 'a photo of a tee shirt.'), ('a photo of a jet plane.', 'a photo of a jet-propelled plane.'), ('a photo of a jewel.', 'a photo of a gem.', 'a photo of a precious stone.'), ('a photo of a jewelry.', 'a photo of a jewellery.'), ('a photo of a joystick.',), ('a photo of a jumpsuit.',), ('a photo of a kayak.',), ('a photo of a keg.',), ('a photo of a kennel.', 'a photo of a doghouse.'), ('a photo of a kettle.', 'a photo of a boiler.'), ('a photo of a key.',), ('a photo of a keycard.',), ('a photo of a kilt.',), ('a photo of a kimono.',), ('a photo of a kitchen sink.',), ('a photo of a kitchen table.',), ('a photo of a kite.',), ('a photo of a kitten.', 'a photo of a kitty.'), ('a photo of a kiwi fruit.',), ('a photo of a knee pad.',), ('a photo of a knife.',), ('a photo of a knitting needle.',), ('a photo of a knob.',), ('a photo of a knocker (on a door).', 'a photo of a doorknocker.'), ('a photo of a koala.', 'a photo of a koala bear.'), ('a photo of a lab coat.', 'a photo of a laboratory coat.'), ('a photo of a ladder.',), ('a photo of a ladle.',), ('a photo of a ladybug.', 'a photo of a ladybeetle.', 'a photo of a ladybird beetle.'), ('a photo of a lamb (animal).',), ('a photo of a lamb-chop.', 'a photo of a lambchop.'), ('a photo of a lamp.',), ('a photo of a lamppost.',), ('a photo of a lampshade.',), ('a photo of a lantern.',), ('a photo of a lanyard.', 'a photo of a laniard.'), ('a photo of a laptop computer.', 'a photo of a notebook computer.'), ('a photo of a lasagna.', 'a photo of a lasagne.'), ('a photo of a latch.',), ('a photo of a lawn mower.',), ('a photo of a leather.',), ('a photo of a legging (clothing).', 'a photo of a leging (clothing).', 'a photo of a leg covering.'), ('a photo of a Lego.', 'a photo of a Lego set.'), ('a photo of a legume.',), ('a photo of a lemon.',), ('a photo of a lemonade.',), ('a photo of a lettuce.',), ('a photo of a license plate.', 'a photo of a numberplate.'), ('a photo of a life buoy.', 'a photo of a lifesaver.', 'a photo of a life belt.', 'a photo of a life ring.'), ('a photo of a life jacket.', 'a photo of a life vest.'), ('a photo of a lightbulb.',), ('a photo of a lightning rod.', 'a photo of a lightning conductor.'), ('a photo of a lime.',), ('a photo of a limousine.',), ('a photo of a lion.',), ('a photo of a lip balm.',), ('a photo of a liquor.', 'a photo of a spirits.', 'a photo of a hard liquor.', 'a photo of a liqueur.', 'a photo of a cordial.'), ('a photo of a lizard.',), ('a photo of a log.',), ('a photo of a lollipop.',), ('a photo of a speaker (stero equipment).',), ('a photo of a loveseat.',), ('a photo of a machine gun.',), ('a photo of a magazine.',), ('a photo of a magnet.',), ('a photo of a mail slot.',), ('a photo of a mailbox (at home).', 'a photo of a letter box (at home).'), ('a photo of a mallard.',), ('a photo of a mallet.',), ('a photo of a mammoth.',), ('a photo of a manatee.',), ('a photo of a mandarin orange.',), ('a photo of a manger.', 'a photo of a trough.'), ('a photo of a manhole.',), ('a photo of a map.',), ('a photo of a marker.',), ('a photo of a martini.',), ('a photo of a mascot.',), ('a photo of a mashed potato.',), ('a photo of a masher.',), ('a photo of a mask.', 'a photo of a facemask.'), ('a photo of a mast.',), ('a photo of a mat (gym equipment).', 'a photo of a gym mat.'), ('a photo of a matchbox.',), ('a photo of a mattress.',), ('a photo of a measuring cup.',), ('a photo of a measuring stick.', 'a photo of a ruler (measuring stick).', 'a photo of a measuring rod.'), ('a photo of a meatball.',), ('a photo of a medicine.',), ('a photo of a melon.',), ('a photo of a microphone.',), ('a photo of a microscope.',), ('a photo of a microwave oven.',), ('a photo of a milestone.', 'a photo of a milepost.'), ('a photo of a milk.',), ('a photo of a milk can.',), ('a photo of a milkshake.',), ('a photo of a minivan.',), ('a photo of a mint candy.',), ('a photo of a mirror.',), ('a photo of a mitten.',), ('a photo of a mixer (kitchen tool).', 'a photo of a stand mixer.'), ('a photo of a money.',), ('a photo of a monitor (computer equipment) computer monitor.',), ('a photo of a monkey.',), ('a photo of a motor.',), ('a photo of a motor scooter.', 'a photo of a scooter.'), ('a photo of a motor vehicle.', 'a photo of a automotive vehicle.'), ('a photo of a motorcycle.',), ('a photo of a mound (baseball).', \"a photo of a pitcher's mound.\"), ('a photo of a mouse (computer equipment).', 'a photo of a computer mouse.'), ('a photo of a mousepad.',), ('a photo of a muffin.',), ('a photo of a mug.',), ('a photo of a mushroom.',), ('a photo of a music stool.', 'a photo of a piano stool.'), ('a photo of a musical instrument.', 'a photo of a instrument (musical).'), ('a photo of a nailfile.',), ('a photo of a napkin.', 'a photo of a table napkin.', 'a photo of a serviette.'), ('a photo of a neckerchief.',), ('a photo of a necklace.',), ('a photo of a necktie.', 'a photo of a tie (necktie).'), ('a photo of a needle.',), ('a photo of a nest.',), ('a photo of a newspaper.', 'a photo of a paper (newspaper).'), ('a photo of a newsstand.',), ('a photo of a nightshirt.', 'a photo of a nightwear.', 'a photo of a sleepwear.', 'a photo of a nightclothes.'), ('a photo of a nosebag (for animals).', 'a photo of a feedbag.'), ('a photo of a noseband (for animals).', 'a photo of a nosepiece (for animals).'), ('a photo of a notebook.',), ('a photo of a notepad.',), ('a photo of a nut.',), ('a photo of a nutcracker.',), ('a photo of a oar.',), ('a photo of a octopus (food).',), ('a photo of a octopus (animal).',), ('a photo of a oil lamp.', 'a photo of a kerosene lamp.', 'a photo of a kerosine lamp.'), ('a photo of a olive oil.',), ('a photo of a omelet.', 'a photo of a omelette.'), ('a photo of a onion.',), ('a photo of a orange (fruit).',), ('a photo of a orange juice.',), ('a photo of a ostrich.',), ('a photo of a ottoman.', 'a photo of a pouf.', 'a photo of a pouffe.', 'a photo of a hassock.'), ('a photo of a oven.',), ('a photo of a overalls (clothing).',), ('a photo of a owl.',), ('a photo of a packet.',), ('a photo of a inkpad.', 'a photo of a inking pad.', 'a photo of a stamp pad.'), ('a photo of a pad.',), ('a photo of a paddle.', 'a photo of a boat paddle.'), ('a photo of a padlock.',), ('a photo of a paintbrush.',), ('a photo of a painting.',), ('a photo of a pajamas.', 'a photo of a pyjamas.'), ('a photo of a palette.', 'a photo of a pallet.'), ('a photo of a pan (for cooking).', 'a photo of a cooking pan.'), ('a photo of a pan (metal container).',), ('a photo of a pancake.',), ('a photo of a pantyhose.',), ('a photo of a papaya.',), ('a photo of a paper plate.',), ('a photo of a paper towel.',), ('a photo of a paperback book.', 'a photo of a paper-back book.', 'a photo of a softback book.', 'a photo of a soft-cover book.'), ('a photo of a paperweight.',), ('a photo of a parachute.',), ('a photo of a parakeet.', 'a photo of a parrakeet.', 'a photo of a parroket.', 'a photo of a paraquet.', 'a photo of a paroquet.', 'a photo of a parroquet.'), ('a photo of a parasail (sports).',), ('a photo of a parasol.', 'a photo of a sunshade.'), ('a photo of a parchment.',), ('a photo of a parka.', 'a photo of a anorak.'), ('a photo of a parking meter.',), ('a photo of a parrot.',), ('a photo of a passenger car (part of a train).', 'a photo of a coach (part of a train).'), ('a photo of a passenger ship.',), ('a photo of a passport.',), ('a photo of a pastry.',), ('a photo of a patty (food).',), ('a photo of a pea (food).',), ('a photo of a peach.',), ('a photo of a peanut butter.',), ('a photo of a pear.',), ('a photo of a peeler (tool for fruit and vegetables).',), ('a photo of a wooden leg.', 'a photo of a pegleg.'), ('a photo of a pegboard.',), ('a photo of a pelican.',), ('a photo of a pen.',), ('a photo of a pencil.',), ('a photo of a pencil box.', 'a photo of a pencil case.'), ('a photo of a pencil sharpener.',), ('a photo of a pendulum.',), ('a photo of a penguin.',), ('a photo of a pennant.',), ('a photo of a penny (coin).',), ('a photo of a pepper.', 'a photo of a peppercorn.'), ('a photo of a pepper mill.', 'a photo of a pepper grinder.'), ('a photo of a perfume.',), ('a photo of a persimmon.',), ('a photo of a person.', 'a photo of a baby.', 'a photo of a child.', 'a photo of a boy.', 'a photo of a girl.', 'a photo of a man.', 'a photo of a woman.', 'a photo of a human.'), ('a photo of a pet.',), ('a photo of a pew (church bench).', 'a photo of a church bench.'), ('a photo of a phonebook.', 'a photo of a telephone book.', 'a photo of a telephone directory.'), ('a photo of a phonograph record.', 'a photo of a phonograph recording.', 'a photo of a record (phonograph recording).'), ('a photo of a piano.',), ('a photo of a pickle.',), ('a photo of a pickup truck.',), ('a photo of a pie.',), ('a photo of a pigeon.',), ('a photo of a piggy bank.', 'a photo of a penny bank.'), ('a photo of a pillow.',), ('a photo of a pin (non jewelry).',), ('a photo of a pineapple.',), ('a photo of a pinecone.',), ('a photo of a ping-pong ball.',), ('a photo of a pinwheel.',), ('a photo of a tobacco pipe.',), ('a photo of a pipe.', 'a photo of a piping.'), ('a photo of a pistol.', 'a photo of a handgun.'), ('a photo of a pita (bread).', 'a photo of a pocket bread.'), ('a photo of a pitcher (vessel for liquid).', 'a photo of a ewer.'), ('a photo of a pitchfork.',), ('a photo of a pizza.',), ('a photo of a place mat.',), ('a photo of a plate.',), ('a photo of a platter.',), ('a photo of a playpen.',), ('a photo of a pliers.', 'a photo of a plyers.'), ('a photo of a plow (farm equipment).', 'a photo of a plough (farm equipment).'), ('a photo of a plume.',), ('a photo of a pocket watch.',), ('a photo of a pocketknife.',), ('a photo of a poker (fire stirring tool).', 'a photo of a stove poker.', 'a photo of a fire hook.'), ('a photo of a pole.', 'a photo of a post.'), ('a photo of a polo shirt.', 'a photo of a sport shirt.'), ('a photo of a poncho.',), ('a photo of a pony.',), ('a photo of a pool table.', 'a photo of a billiard table.', 'a photo of a snooker table.'), ('a photo of a pop (soda).', 'a photo of a soda (pop).', 'a photo of a tonic.', 'a photo of a soft drink.'), ('a photo of a postbox (public).', 'a photo of a mailbox (public).'), ('a photo of a postcard.', 'a photo of a postal card.', 'a photo of a mailing-card.'), ('a photo of a poster.', 'a photo of a placard.'), ('a photo of a pot.',), ('a photo of a flowerpot.',), ('a photo of a potato.',), ('a photo of a potholder.',), ('a photo of a pottery.', 'a photo of a clayware.'), ('a photo of a pouch.',), ('a photo of a power shovel.', 'a photo of a excavator.', 'a photo of a digger.'), ('a photo of a prawn.', 'a photo of a shrimp.'), ('a photo of a pretzel.',), ('a photo of a printer.', 'a photo of a printing machine.'), ('a photo of a projectile (weapon).', 'a photo of a missile.'), ('a photo of a projector.',), ('a photo of a propeller.', 'a photo of a propellor.'), ('a photo of a prune.',), ('a photo of a pudding.',), ('a photo of a puffer (fish).', 'a photo of a pufferfish.', 'a photo of a blowfish.', 'a photo of a globefish.'), ('a photo of a puffin.',), ('a photo of a pug-dog.',), ('a photo of a pumpkin.',), ('a photo of a puncher.',), ('a photo of a puppet.', 'a photo of a marionette.'), ('a photo of a puppy.',), ('a photo of a quesadilla.',), ('a photo of a quiche.',), ('a photo of a quilt.', 'a photo of a comforter.'), ('a photo of a rabbit.',), ('a photo of a race car.', 'a photo of a racing car.'), ('a photo of a racket.', 'a photo of a racquet.'), ('a photo of a radar.',), ('a photo of a radiator.',), ('a photo of a radio receiver.', 'a photo of a radio set.', 'a photo of a radio.', 'a photo of a tuner (radio).'), ('a photo of a radish.', 'a photo of a daikon.'), ('a photo of a raft.',), ('a photo of a rag doll.',), ('a photo of a raincoat.', 'a photo of a waterproof jacket.'), ('a photo of a ram (animal).',), ('a photo of a raspberry.',), ('a photo of a rat.',), ('a photo of a razorblade.',), ('a photo of a reamer (juicer).', 'a photo of a juicer.', 'a photo of a juice reamer.'), ('a photo of a rearview mirror.',), ('a photo of a receipt.',), ('a photo of a recliner.', 'a photo of a reclining chair.', 'a photo of a lounger (chair).'), ('a photo of a record player.', 'a photo of a phonograph (record player).', 'a photo of a turntable.'), ('a photo of a reflector.',), ('a photo of a remote control.',), ('a photo of a rhinoceros.',), ('a photo of a rib (food).',), ('a photo of a rifle.',), ('a photo of a ring.',), ('a photo of a river boat.',), ('a photo of a road map.',), ('a photo of a robe.',), ('a photo of a rocking chair.',), ('a photo of a rodent.',), ('a photo of a roller skate.',), ('a photo of a Rollerblade.',), ('a photo of a rolling pin.',), ('a photo of a root beer.',), ('a photo of a router (computer equipment).',), ('a photo of a rubber band.', 'a photo of a elastic band.'), ('a photo of a runner (carpet).',), ('a photo of a plastic bag.', 'a photo of a paper bag.'), ('a photo of a saddle (on an animal).',), ('a photo of a saddle blanket.', 'a photo of a saddlecloth.', 'a photo of a horse blanket.'), ('a photo of a saddlebag.',), ('a photo of a safety pin.',), ('a photo of a sail.',), ('a photo of a salad.',), ('a photo of a salad plate.', 'a photo of a salad bowl.'), ('a photo of a salami.',), ('a photo of a salmon (fish).',), ('a photo of a salmon (food).',), ('a photo of a salsa.',), ('a photo of a saltshaker.',), ('a photo of a sandal (type of shoe).',), ('a photo of a sandwich.',), ('a photo of a satchel.',), ('a photo of a saucepan.',), ('a photo of a saucer.',), ('a photo of a sausage.',), ('a photo of a sawhorse.', 'a photo of a sawbuck.'), ('a photo of a saxophone.',), ('a photo of a scale (measuring instrument).',), ('a photo of a scarecrow.', 'a photo of a strawman.'), ('a photo of a scarf.',), ('a photo of a school bus.',), ('a photo of a scissors.',), ('a photo of a scoreboard.',), ('a photo of a scraper.',), ('a photo of a screwdriver.',), ('a photo of a scrubbing brush.',), ('a photo of a sculpture.',), ('a photo of a seabird.', 'a photo of a seafowl.'), ('a photo of a seahorse.',), ('a photo of a seaplane.', 'a photo of a hydroplane.'), ('a photo of a seashell.',), ('a photo of a sewing machine.',), ('a photo of a shaker.',), ('a photo of a shampoo.',), ('a photo of a shark.',), ('a photo of a sharpener.',), ('a photo of a Sharpie.',), ('a photo of a shaver (electric).', 'a photo of a electric shaver.', 'a photo of a electric razor.'), ('a photo of a shaving cream.', 'a photo of a shaving soap.'), ('a photo of a shawl.',), ('a photo of a shears.',), ('a photo of a sheep.',), ('a photo of a shepherd dog.', 'a photo of a sheepdog.'), ('a photo of a sherbert.', 'a photo of a sherbet.'), ('a photo of a shield.',), ('a photo of a shirt.',), ('a photo of a shoe.', 'a photo of a sneaker (type of shoe).', 'a photo of a tennis shoe.'), ('a photo of a shopping bag.',), ('a photo of a shopping cart.',), ('a photo of a short pants.', 'a photo of a shorts (clothing).', 'a photo of a trunks (clothing).'), ('a photo of a shot glass.',), ('a photo of a shoulder bag.',), ('a photo of a shovel.',), ('a photo of a shower head.',), ('a photo of a shower cap.',), ('a photo of a shower curtain.',), ('a photo of a shredder (for paper).',), ('a photo of a signboard.',), ('a photo of a silo.',), ('a photo of a sink.',), ('a photo of a skateboard.',), ('a photo of a skewer.',), ('a photo of a ski.',), ('a photo of a ski boot.',), ('a photo of a ski parka.', 'a photo of a ski jacket.'), ('a photo of a ski pole.',), ('a photo of a skirt.',), ('a photo of a skullcap.',), ('a photo of a sled.', 'a photo of a sledge.', 'a photo of a sleigh.'), ('a photo of a sleeping bag.',), ('a photo of a sling (bandage).', 'a photo of a triangular bandage.'), ('a photo of a slipper (footwear).', 'a photo of a carpet slipper (footwear).'), ('a photo of a smoothie.',), ('a photo of a snake.', 'a photo of a serpent.'), ('a photo of a snowboard.',), ('a photo of a snowman.',), ('a photo of a snowmobile.',), ('a photo of a soap.',), ('a photo of a soccer ball.',), ('a photo of a sock.',), ('a photo of a sofa.', 'a photo of a couch.', 'a photo of a lounge.'), ('a photo of a softball.',), ('a photo of a solar array.', 'a photo of a solar battery.', 'a photo of a solar panel.'), ('a photo of a sombrero.',), ('a photo of a soup.',), ('a photo of a soup bowl.',), ('a photo of a soupspoon.',), ('a photo of a sour cream.', 'a photo of a soured cream.'), ('a photo of a soya milk.', 'a photo of a soybean milk.', 'a photo of a soymilk.'), ('a photo of a space shuttle.',), ('a photo of a sparkler (fireworks).',), ('a photo of a spatula.',), ('a photo of a spear.', 'a photo of a lance.'), ('a photo of a spectacles.', 'a photo of a specs.', 'a photo of a eyeglasses.', 'a photo of a glasses.'), ('a photo of a spice rack.',), ('a photo of a spider.',), ('a photo of a crawfish.', 'a photo of a crayfish.'), ('a photo of a sponge.',), ('a photo of a spoon.',), ('a photo of a sportswear.', 'a photo of a athletic wear.', 'a photo of a activewear.'), ('a photo of a spotlight.',), ('a photo of a squid (food).', 'a photo of a calamari.', 'a photo of a calamary.'), ('a photo of a squirrel.',), ('a photo of a stagecoach.',), ('a photo of a stapler (stapling machine).',), ('a photo of a starfish.', 'a photo of a sea star.'), ('a photo of a statue (sculpture).',), ('a photo of a steak (food).',), ('a photo of a steak knife.',), ('a photo of a steering wheel.',), ('a photo of a stepladder.',), ('a photo of a step stool.',), ('a photo of a stereo (sound system).',), ('a photo of a stew.',), ('a photo of a stirrer.',), ('a photo of a stirrup.',), ('a photo of a stool.',), ('a photo of a stop sign.',), ('a photo of a brake light.',), ('a photo of a stove.', 'a photo of a kitchen stove.', 'a photo of a range (kitchen appliance).', 'a photo of a kitchen range.', 'a photo of a cooking stove.'), ('a photo of a strainer.',), ('a photo of a strap.',), ('a photo of a straw (for drinking).', 'a photo of a drinking straw.'), ('a photo of a strawberry.',), ('a photo of a street sign.',), ('a photo of a streetlight.', 'a photo of a street lamp.'), ('a photo of a string cheese.',), ('a photo of a stylus.',), ('a photo of a subwoofer.',), ('a photo of a sugar bowl.',), ('a photo of a sugarcane (plant).',), ('a photo of a suit (clothing).',), ('a photo of a sunflower.',), ('a photo of a sunglasses.',), ('a photo of a sunhat.',), ('a photo of a surfboard.',), ('a photo of a sushi.',), ('a photo of a mop.',), ('a photo of a sweat pants.',), ('a photo of a sweatband.',), ('a photo of a sweater.',), ('a photo of a sweatshirt.',), ('a photo of a sweet potato.',), ('a photo of a swimsuit.', 'a photo of a swimwear.', 'a photo of a bathing suit.', 'a photo of a swimming costume.', 'a photo of a bathing costume.', 'a photo of a swimming trunks.', 'a photo of a bathing trunks.'), ('a photo of a sword.',), ('a photo of a syringe.',), ('a photo of a Tabasco sauce.',), ('a photo of a table-tennis table.', 'a photo of a ping-pong table.'), ('a photo of a table.',), ('a photo of a table lamp.',), ('a photo of a tablecloth.',), ('a photo of a tachometer.',), ('a photo of a taco.',), ('a photo of a tag.',), ('a photo of a taillight.', 'a photo of a rear light.'), ('a photo of a tambourine.',), ('a photo of a army tank.', 'a photo of a armored combat vehicle.', 'a photo of a armoured combat vehicle.'), ('a photo of a tank (storage vessel).', 'a photo of a storage tank.'), ('a photo of a tank top (clothing).',), ('a photo of a tape (sticky cloth or paper).',), ('a photo of a tape measure.', 'a photo of a measuring tape.'), ('a photo of a tapestry.',), ('a photo of a tarp.',), ('a photo of a tartan.', 'a photo of a plaid.'), ('a photo of a tassel.',), ('a photo of a tea bag.',), ('a photo of a teacup.',), ('a photo of a teakettle.',), ('a photo of a teapot.',), ('a photo of a teddy bear.',), ('a photo of a telephone.', 'a photo of a phone.', 'a photo of a telephone set.'), ('a photo of a telephone booth.', 'a photo of a phone booth.', 'a photo of a call box.', 'a photo of a telephone box.', 'a photo of a telephone kiosk.'), ('a photo of a telephone pole.', 'a photo of a telegraph pole.', 'a photo of a telegraph post.'), ('a photo of a telephoto lens.', 'a photo of a zoom lens.'), ('a photo of a television camera.', 'a photo of a tv camera.'), ('a photo of a television set.', 'a photo of a tv.', 'a photo of a tv set.'), ('a photo of a tennis ball.',), ('a photo of a tennis racket.',), ('a photo of a tequila.',), ('a photo of a thermometer.',), ('a photo of a thermos bottle.',), ('a photo of a thermostat.',), ('a photo of a thimble.',), ('a photo of a thread.', 'a photo of a yarn.'), ('a photo of a thumbtack.', 'a photo of a drawing pin.', 'a photo of a pushpin.'), ('a photo of a tiara.',), ('a photo of a tiger.',), ('a photo of a tights (clothing).', 'a photo of a leotards.'), ('a photo of a timer.', 'a photo of a stopwatch.'), ('a photo of a tinfoil.',), ('a photo of a tinsel.',), ('a photo of a tissue paper.',), ('a photo of a toast (food).',), ('a photo of a toaster.',), ('a photo of a toaster oven.',), ('a photo of a toilet.',), ('a photo of a toilet tissue.', 'a photo of a toilet paper.', 'a photo of a bathroom tissue.'), ('a photo of a tomato.',), ('a photo of a tongs.',), ('a photo of a toolbox.',), ('a photo of a toothbrush.',), ('a photo of a toothpaste.',), ('a photo of a toothpick.',), ('a photo of a cover.',), ('a photo of a tortilla.',), ('a photo of a tow truck.',), ('a photo of a towel.',), ('a photo of a towel rack.', 'a photo of a towel rail.', 'a photo of a towel bar.'), ('a photo of a toy.',), ('a photo of a tractor (farm equipment).',), ('a photo of a traffic light.',), ('a photo of a dirt bike.',), ('a photo of a trailer truck.', 'a photo of a tractor trailer.', 'a photo of a trucking rig.', 'a photo of a articulated lorry.', 'a photo of a semi truck.'), ('a photo of a train (railroad vehicle).', 'a photo of a railroad train.'), ('a photo of a trampoline.',), ('a photo of a tray.',), ('a photo of a trench coat.',), ('a photo of a triangle (musical instrument).',), ('a photo of a tricycle.',), ('a photo of a tripod.',), ('a photo of a trousers.', 'a photo of a pants (clothing).'), ('a photo of a truck.',), ('a photo of a truffle (chocolate).', 'a photo of a chocolate truffle.'), ('a photo of a trunk.',), ('a photo of a vat.',), ('a photo of a turban.',), ('a photo of a turkey (food).',), ('a photo of a turnip.',), ('a photo of a turtle.',), ('a photo of a turtleneck (clothing).', 'a photo of a polo-neck.'), ('a photo of a typewriter.',), ('a photo of a umbrella.',), ('a photo of a underwear.', 'a photo of a underclothes.', 'a photo of a underclothing.', 'a photo of a underpants.'), ('a photo of a unicycle.',), ('a photo of a urinal.',), ('a photo of a urn.',), ('a photo of a vacuum cleaner.',), ('a photo of a vase.',), ('a photo of a vending machine.',), ('a photo of a vent.', 'a photo of a blowhole.', 'a photo of a air vent.'), ('a photo of a vest.', 'a photo of a waistcoat.'), ('a photo of a videotape.',), ('a photo of a vinegar.',), ('a photo of a violin.', 'a photo of a fiddle.'), ('a photo of a vodka.',), ('a photo of a volleyball.',), ('a photo of a vulture.',), ('a photo of a waffle.',), ('a photo of a waffle iron.',), ('a photo of a wagon.',), ('a photo of a wagon wheel.',), ('a photo of a walking stick.',), ('a photo of a wall clock.',), ('a photo of a wall socket.', 'a photo of a wall plug.', 'a photo of a electric outlet.', 'a photo of a electrical outlet.', 'a photo of a outlet.', 'a photo of a electric receptacle.'), ('a photo of a wallet.', 'a photo of a billfold.'), ('a photo of a walrus.',), ('a photo of a wardrobe.',), ('a photo of a washbasin.', 'a photo of a basin (for washing).', 'a photo of a washbowl.', 'a photo of a washstand.', 'a photo of a handbasin.'), ('a photo of a automatic washer.', 'a photo of a washing machine.'), ('a photo of a watch.', 'a photo of a wristwatch.'), ('a photo of a water bottle.',), ('a photo of a water cooler.',), ('a photo of a water faucet.', 'a photo of a water tap.', 'a photo of a tap (water faucet).'), ('a photo of a water heater.', 'a photo of a hot-water heater.'), ('a photo of a water jug.',), ('a photo of a water gun.', 'a photo of a squirt gun.'), ('a photo of a water scooter.', 'a photo of a sea scooter.', 'a photo of a jet ski.'), ('a photo of a water ski.',), ('a photo of a water tower.',), ('a photo of a watering can.',), ('a photo of a watermelon.',), ('a photo of a weathervane.', 'a photo of a vane (weathervane).', 'a photo of a wind vane.'), ('a photo of a webcam.',), ('a photo of a wedding cake.', 'a photo of a bridecake.'), ('a photo of a wedding ring.', 'a photo of a wedding band.'), ('a photo of a wet suit.',), ('a photo of a wheel.',), ('a photo of a wheelchair.',), ('a photo of a whipped cream.',), ('a photo of a whistle.',), ('a photo of a wig.',), ('a photo of a wind chime.',), ('a photo of a windmill.',), ('a photo of a window box (for plants).',), ('a photo of a windshield wiper.', 'a photo of a windscreen wiper.', 'a photo of a wiper (for windshield/screen).'), ('a photo of a windsock.', 'a photo of a air sock.', 'a photo of a air-sleeve.', 'a photo of a wind sleeve.', 'a photo of a wind cone.'), ('a photo of a wine bottle.',), ('a photo of a wine bucket.', 'a photo of a wine cooler.'), ('a photo of a wineglass.',), ('a photo of a blinder (for horses).',), ('a photo of a wok.',), ('a photo of a wolf.',), ('a photo of a wooden spoon.',), ('a photo of a wreath.',), ('a photo of a wrench.', 'a photo of a spanner.'), ('a photo of a wristband.',), ('a photo of a wristlet.', 'a photo of a wrist band.'), ('a photo of a yacht.',), ('a photo of a yogurt.', 'a photo of a yoghurt.', 'a photo of a yoghourt.'), ('a photo of a yoke (animal equipment).',), ('a photo of a zebra.',), ('a photo of a zucchini.', 'a photo of a courgette.'), ('a photo of a banner.', 'a photo of a banners.'), ('a photo of a blanket.', 'a photo of a blankets.'), ('a photo of a bridge.',), ('a photo of a cardboard.',), ('a photo of a counter.',), ('a photo of a curtain.', 'a photo of a curtains.'), ('a photo of a door.', 'a photo of a doors.'), ('a photo of a wood floor.',), ('a photo of a flower.', 'a photo of a flowers.'), ('a photo of a fruit.', 'a photo of a fruits.'), ('a photo of a gravel.',), ('a photo of a house.',), ('a photo of a lamp.', 'a photo of a bulb.', 'a photo of a lamps.', 'a photo of a bulbs.'), ('a photo of a mirror.',), ('a photo of a tennis net.',), ('a photo of a pillow.', 'a photo of a pillows.'), ('a photo of a platform.',), ('a photo of a playingfield.', 'a photo of a tennis court.', 'a photo of a baseball feild.', 'a photo of a soccer field.', 'a photo of a tennis field.'), ('a photo of a railroad.',), ('a photo of a river.',), ('a photo of a road.',), ('a photo of a roof.',), ('a photo of a sand.',), ('a photo of a sea.', 'a photo of a sea wave.', 'a photo of a wave.', 'a photo of a waves.'), ('a photo of a shelf.',), ('a photo of a snow.',), ('a photo of a stairs.',), ('a photo of a tent.',), ('a photo of a towel.',), ('a photo of a brick wall.',), ('a photo of a stone wall.',), ('a photo of a tile wall.',), ('a photo of a wood wall.',), ('a photo of a water.',), ('a photo of a window blind.',), ('a photo of a window.',), ('a photo of a tree.', 'a photo of a trees.', 'a photo of a palm tree.', 'a photo of a bushes.'), ('a photo of a fence.', 'a photo of a fences.'), ('a photo of a ceiling.',), ('a photo of a sky.', 'a photo of a clouds.'), ('a photo of a cabinet.', 'a photo of a cabinets.'), ('a photo of a table.',), ('a photo of a floor.', 'a photo of a flooring.', 'a photo of a tile floor.'), ('a photo of a pavement.',), ('a photo of a mountain.', 'a photo of a mountains.'), ('a photo of a grass.',), ('a photo of a dirt.',), ('a photo of a paper.',), ('a photo of a food.',), ('a photo of a building.', 'a photo of a buildings.'), ('a photo of a rock.',), ('a photo of a wall.', 'a photo of a walls.'), ('a photo of a rug.',), ('a photo of a wall.', 'a photo of a walls.', 'a photo of a brick wall.', 'a photo of a stone wall.', 'a photo of a interior wall.'), ('a photo of a building.', 'a photo of a buildings.', 'a photo of a edifice.', 'a photo of a edifices.'), ('a photo of a sky.', 'a photo of a clouds.'), ('a photo of a floor.', 'a photo of a flooring.'), ('a photo of a tree.', 'a photo of a trees.'), ('a photo of a ceiling.',), ('a photo of a road.', 'a photo of a route.', 'a photo of a street.', 'a photo of a roads.', 'a photo of a streets.', 'a photo of a routes.'), ('a photo of a grass.', 'a photo of a grass field.'), ('a photo of a sidewalk.', 'a photo of a pavement.'), ('a photo of a earth.', 'a photo of a ground.'), ('a photo of a mountain.', 'a photo of a mount.', 'a photo of a mountains.'), ('a photo of a plant.', 'a photo of a flora.', 'a photo of a plant life.', 'a photo of a plants.', 'a photo of a bushes.'), ('a photo of a water.',), ('a photo of a house exterior.',), ('a photo of a sea.', 'a photo of a ocean.'), ('a photo of a rug.', 'a photo of a carpet.', 'a photo of a carpeting.'), ('a photo of a field.',), ('a photo of a rock.', 'a photo of a stone.', 'a photo of a rocks.', 'a photo of a stones.'), ('a photo of a pedestal.',), ('a photo of a sand.',), ('a photo of a skyscraper.', 'a photo of a skyscrapers.'), ('a photo of a grandstand.', 'a photo of a covered stand.'), ('a photo of a path.',), ('a photo of a runway.',), ('a photo of a stairway.', 'a photo of a staircase.'), ('a photo of a river.',), ('a photo of a bridge.', 'a photo of a span.'), ('a photo of a window screen.', 'a photo of a door screen.'), ('a photo of a hill.',), ('a photo of a bar.',), ('a photo of a hovel.', 'a photo of a hut.', 'a photo of a hutch.', 'a photo of a shack.', 'a photo of a shanty.'), ('a photo of a tower.', 'a photo of a towers.'), ('a photo of a dirt track.',), ('a photo of a land.', 'a photo of a soil.'), ('a photo of a escalator.', 'a photo of a moving staircase.', 'a photo of a moving stairway.'), ('a photo of a buffet.', 'a photo of a sideboard.'), ('a photo of a poster.', 'a photo of a posting.', 'a photo of a placard.', 'a photo of a notice.', 'a photo of a bill.', 'a photo of a card.'), ('a photo of a stage.',), ('a photo of a conveyer belt.', 'a photo of a conveyor belt.', 'a photo of a conveyer.', 'a photo of a conveyor.', 'a photo of a transporter.'), ('a photo of a canopy.',), ('a photo of a swimming pool.', 'a photo of a swimming bath.'), ('a photo of a waterfall.', 'a photo of a falls.'), ('a photo of a tent.', 'a photo of a collapsible shelter.'), ('a photo of a cradle.',), ('a photo of a tank.', 'a photo of a storage tank.'), ('a photo of a lake.',), ('a photo of a blanket.', 'a photo of a cover.'), ('a photo of a pier.', 'a photo of a wharf.', 'a photo of a wharfage.', 'a photo of a dock.'), ('a photo of a crt screen.',), ('a photo of a shower.',))\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab = \"black pickup truck, pickup truck; blue sky, sky\"\n",
+ "label_list = [\"COCO\", \"ADE\", \"LVIS\"]\n",
+ "inference(input_image, vocab, label_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's also try non-COCO image, [messy desk image](https://web.eecs.umich.edu/~fouhey/fun/desk/desk.jpg) (image credit [David Fouhey](https://web.eecs.umich.edu/~fouhey))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "input_image = Image.open(requests.get(\"https://web.eecs.umich.edu/~fouhey/fun/desk/desk.jpg\", stream=True).raw)\n",
+ "input_image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[06/08 17:26:19 odise.modeling.meta_arch.clip]: Using provided CLIP model\n",
+ "[06/08 17:26:22 odise.modeling.meta_arch.clip]: Built text_embed of shape torch.Size([2482, 768]) for 1486 labels: (('person', 'child', 'girl', 'boy', 'woman', 'man', 'people', 'childeren', 'girls', 'boys', 'women', 'men', 'lady', 'guy', 'ladies', 'guys', 'clothes'), ('bicycle', 'bicycles', 'bike', 'bikes'), ('car', 'cars'), ('motorcycle', 'motorcycles'), ('airplane', 'airplanes'), ('bus', 'buses'), ('train', 'trains', 'locomotive', 'locomotives', 'freight train'), ('truck', 'trucks'), ('boat', 'boats'), ('traffic light',), ('fire hydrant',), ('stop sign',), ('parking meter',), ('bench', 'benches'), ('bird', 'birds'), ('cat', 'cats', 'kitties', 'kitty'), ('dog', 'dogs', 'puppy', 'puppies'), ('horse', 'horses', 'foal'), ('sheep',), ('cow', 'cows', 'calf'), ('elephant', 'elephants'), ('bear', 'bears'), ('zebra', 'zebras'), ('giraffe', 'giraffes'), ('backpack', 'backpacks'), ('umbrella', 'umbrellas'), ('handbag', 'handbags'), ('tie',), ('suitcase', 'suitcases'), ('frisbee',), ('skis',), ('snowboard',), ('sports ball',), ('kite', 'kites'), ('baseball bat',), ('baseball glove',), ('skateboard',), ('surfboard',), ('tennis racket',), ('bottle', 'bottles', 'water bottle'), ('wine glass', 'wine glasses', 'wineglass'), ('cup', 'cups', 'water cup', 'water glass'), ('fork', 'forks'), ('knife', 'knives'), ('spoon', 'spoons'), ('bowl', 'bowls'), ('banana', 'bananas'), ('apple', 'apples', 'apple fruit'), ('sandwich', 'sandwiches'), ('orange fruit',), ('broccoli',), ('carrot', 'carrots'), ('hot dog',), ('pizza',), ('donut', 'donuts'), ('cake', 'cakes'), ('chair', 'chairs'), ('couch', 'sofa', 'sofas'), ('potted plant', 'potted plants', 'pottedplant', 'pottedplants', 'planter', 'planters'), ('bed', 'beds'), ('dining table', 'dining tables', 'diningtable', 'diningtables', 'plate', 'plates', 'diningtable tablecloth'), ('toilet',), ('tv',), ('laptop',), ('mouse',), ('tv remote', 'remote control'), ('keyboard',), ('cell phone', 'mobile'), ('microwave',), ('oven', 'ovens'), ('toaster',), ('sink', 'sinks'), ('refrigerator', 'fridge'), ('book', 'books'), ('clock',), ('vase', 'vases'), ('scissor', 'scissors'), ('teddy bear', 'teddy bears'), ('hair drier',), ('toothbrush', 'toothbrushes'), ('bed', 'beds'), ('windowpane', 'window', 'windows'), ('cabinet', 'cabinets', 'wall mounted cabine'), ('person', 'child', 'girl', 'boy', 'woman', 'man', 'people', 'children', 'girls', 'boys', 'women', 'men'), ('door', 'double door', 'doors'), ('table', 'tables', 'tablecloth'), ('curtain', 'drape', 'drapery', 'mantle', 'pall'), ('chair', 'chairs'), ('car', 'automobile', 'cars'), ('painting', 'picture', 'paintings', 'pictures', 'wallart', 'framed canvas'), ('sofa', 'couch', 'sofas', 'couches'), ('shelf', 'shelves'), ('mirror', 'mirrors'), ('armchair', 'armchairs'), ('seat', 'seats'), ('fence', 'fencing'), ('desk', 'desks'), ('wardrobe', 'closet', 'press', 'wardrobes', 'closets'), ('lamp', 'lamps'), ('bathtub', 'bathing tub', 'bath', 'tub'), ('railing', 'rail'), ('cushion', 'cushions'), ('box', 'boxes'), ('column', 'pillar'), ('signboard', 'sign', 'signboards', 'signs'), ('chest of drawers', 'chest', 'bureau', 'dresser'), ('counter',), ('sink',), ('fireplace', 'hearth', 'open fireplace'), ('refrigerator', 'icebox'), ('stairs', 'steps'), ('case', 'display case', 'showcase', 'vitrine'), ('pool table', 'billiard table', 'snooker table'), ('pillow', 'pillows'), ('screen door', 'shower door'), ('bookcase',), ('coffee table', 'cocktail table'), ('toilet', 'commode', 'crapper', 'potty'), ('flower', 'flowers'), ('book', 'books'), ('bench', 'benches'), ('countertop', 'counter top', 'worktop'), ('stove', 'kitchen stove', 'kitchen range', 'kitchen range', 'cooking stove'), ('palm tree', 'palm trees'), ('kitchen island',), ('computer', 'computing machine', 'computing device', 'data processor', 'electronic computer', 'information processing system'), ('swivel chair',), ('boat',), ('arcade machine', 'arcade machines'), ('bus', 'autobus', 'double-decker', 'jitney', 'motorbus', 'motorcoach', 'omnibus', 'passenger vehicle'), ('towel',), ('light bulb', 'lightbulb', 'bulb', 'incandescent lamp', 'electric light', 'electric-light bulb'), ('truck', 'motortruck'), ('chandelier', 'pendant', 'pendent'), ('awning', 'sunshade', 'sunblind'), ('streetlight', 'street lamp'), ('booth', 'cubicle', 'stall', 'kiosk'), ('television receiver', 'television', 'television set', 'tv', 'tv set'), ('airplane', 'aeroplane', 'airplanes', 'aeroplanes'), ('apparel', 'wearing apparel', 'dress', 'clothes'), ('pole',), ('bannister', 'banister', 'balustrade', 'balusters', 'handrail'), ('ottoman', 'pouf', 'pouffe', 'puff', 'hassock'), ('bottle', 'bottles', 'water bottle'), ('van',), ('ship',), ('fountain',), ('washer', 'automatic washer', 'washing machine'), ('plaything', 'toy', 'toys'), ('stool', 'stools'), ('barrel', 'cask', 'barrels', 'casks'), ('basket', 'handbasket'), ('bag', 'bags', 'gift bag', 'paper bag'), ('minibike', 'motorbike'), ('oven',), ('ball', 'balls'), ('food', 'solid food'), ('step', 'stair'), ('trade name', 'brand name', 'brand', 'marque'), ('microwave', 'microwave oven'), ('plant pots', 'plant pot', 'flower pot', 'flowerpot', 'planter'), ('animal', 'animate being', 'dog', 'cat', 'horse', 'cow', 'sheep', 'zebra', 'girraffe', 'bird'), ('bicycle', 'bike'), ('dishwasher', 'dish washer', 'dishwashing machine'), ('projection screen',), ('sculpture', 'sculptures'), ('exhaust hood',), ('sconce', 'sconce lamp', 'sconce light'), ('vase', 'vases'), ('traffic light', 'traffic signal', 'traffic lights'), ('tray', 'trays'), ('ashcan', 'trash can', 'garbage can', 'wastebin', 'ash bin', 'ash-bin', 'ashbin', 'dustbin', 'trash barrel', 'trash bin'), ('ceiling fan', 'floor fan'), ('plate', 'plates'), ('monitor', 'monitoring device', 'monitors'), ('bulletin board', 'notice board'), ('radiator',), ('cup', 'cups', 'drinking glass', 'drinking glasses'), ('clock',), ('flag', 'flags'), ('aerosol can', 'spray can'), ('air conditioner',), ('airplane', 'aeroplane'), ('alarm clock',), ('alcohol', 'alcoholic beverage'), ('alligator', 'gator'), ('almond',), ('ambulance',), ('amplifier',), ('anklet', 'ankle bracelet'), ('antenna', 'aerial', 'transmitting aerial'), ('apple',), ('applesauce',), ('apricot',), ('apron',), ('aquarium', 'fish tank'), ('arctic (type of shoe)', 'galosh', 'golosh', 'rubber (type of shoe)', 'gumshoe'), ('armband',), ('armchair',), ('armoire',), ('armor', 'armour'), ('artichoke',), ('trash can', 'garbage can', 'wastebin', 'dustbin', 'trash barrel', 'trash bin'), ('ashtray',), ('asparagus',), ('atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'), ('avocado',), ('award', 'accolade'), ('awning',), ('ax', 'axe'), ('baboon',), ('baby buggy', 'baby carriage', 'perambulator', 'pram', 'stroller'), ('basketball backboard',), ('backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'), ('handbag', 'purse', 'pocketbook'), ('suitcase', 'baggage', 'luggage'), ('bagel', 'beigel'), ('bagpipe',), ('baguet', 'baguette'), ('bait', 'lure'), ('ball',), ('ballet skirt', 'tutu'), ('balloon',), ('bamboo',), ('banana',), ('Band Aid',), ('bandage',), ('bandanna', 'bandana'), ('banjo',), ('banner', 'streamer'), ('barbell',), ('barge',), ('barrel', 'cask'), ('barrette',), ('barrow', 'garden cart', 'lawn cart', 'wheelbarrow'), ('baseball base',), ('baseball',), ('baseball bat',), ('baseball cap', 'jockey cap', 'golf cap'), ('baseball glove', 'baseball mitt'), ('basket', 'handbasket'), ('basketball',), ('bass horn', 'sousaphone', 'tuba'), ('bat (animal)',), ('bath mat',), ('bath towel',), ('bathrobe',), ('bathtub', 'bathing tub'), ('batter (food)',), ('battery',), ('beachball',), ('bead',), ('bean curd', 'tofu'), ('beanbag',), ('beanie', 'beany'), ('bear',), ('bed',), ('bedpan',), ('bedspread', 'bedcover', 'bed covering', 'counterpane', 'spread'), ('cow',), ('beef (food)', 'boeuf (food)'), ('beeper', 'pager'), ('beer bottle',), ('beer can',), ('beetle',), ('bell',), ('bell pepper', 'capsicum'), ('belt',), ('belt buckle',), ('bench',), ('beret',), ('bib',), ('Bible',), ('bicycle', 'bike (bicycle)'), ('visor', 'vizor'), ('billboard',), ('binder', 'ring-binder'), ('binoculars', 'field glasses', 'opera glasses'), ('bird',), ('birdfeeder',), ('birdbath',), ('birdcage',), ('birdhouse',), ('birthday cake',), ('birthday card',), ('pirate flag',), ('black sheep',), ('blackberry',), ('blackboard', 'chalkboard'), ('blanket',), ('blazer', 'sport jacket', 'sport coat', 'sports jacket', 'sports coat'), ('blender', 'liquidizer', 'liquidiser'), ('blimp',), ('blinker', 'flasher'), ('blouse',), ('blueberry',), ('gameboard',), ('boat', 'ship (boat)'), ('bob', 'bobber', 'bobfloat'), ('bobbin', 'spool', 'reel'), ('bobby pin', 'hairgrip'), ('boiled egg', 'coddled egg'), ('bolo tie', 'bolo', 'bola tie', 'bola'), ('deadbolt',), ('bolt',), ('bonnet',), ('book',), ('bookcase',), ('booklet', 'brochure', 'leaflet', 'pamphlet'), ('bookmark', 'bookmarker'), ('boom microphone', 'microphone boom'), ('boot',), ('bottle',), ('bottle opener',), ('bouquet',), ('bow (weapon)',), ('bow (decorative ribbons)',), ('bow-tie', 'bowtie'), ('bowl',), ('pipe bowl',), ('bowler hat', 'bowler', 'derby hat', 'derby', 'plug hat'), ('bowling ball',), ('box',), ('boxing glove',), ('suspenders',), ('bracelet', 'bangle'), ('brass plaque',), ('brassiere', 'bra', 'bandeau'), ('bread-bin', 'breadbox'), ('bread',), ('breechcloth', 'breechclout', 'loincloth'), ('bridal gown', 'wedding gown', 'wedding dress'), ('briefcase',), ('broccoli',), ('broach',), ('broom',), ('brownie',), ('brussels sprouts',), ('bubble gum',), ('bucket', 'pail'), ('horse buggy',), ('horned cow',), ('bulldog',), ('bulldozer', 'dozer'), ('bullet train',), ('bulletin board', 'notice board'), ('bulletproof vest',), ('bullhorn', 'megaphone'), ('bun', 'roll'), ('bunk bed',), ('buoy',), ('burrito',), ('bus (vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'), ('business card',), ('butter',), ('butterfly',), ('button',), ('cab (taxi)', 'taxi', 'taxicab'), ('cabana',), ('cabin car', 'caboose'), ('cabinet',), ('locker', 'storage locker'), ('cake',), ('calculator',), ('calendar',), ('calf',), ('camcorder',), ('camel',), ('camera',), ('camera lens',), ('camper (vehicle)', 'camping bus', 'motor home'), ('can', 'tin can'), ('can opener', 'tin opener'), ('candle', 'candlestick'), ('candle holder',), ('candy bar',), ('candy cane',), ('walking cane',), ('canister', 'cannister'), ('canoe',), ('cantaloup', 'cantaloupe'), ('canteen',), ('cap (headwear)',), ('bottle cap', 'cap (container lid)'), ('cape',), ('cappuccino', 'coffee cappuccino'), ('car (automobile)', 'auto (automobile)', 'automobile'), ('railcar (part of a train)', 'railway car (part of a train)', 'railroad car (part of a train)'), ('elevator car',), ('car battery', 'automobile battery'), ('identity card',), ('card',), ('cardigan',), ('cargo ship', 'cargo vessel'), ('carnation',), ('horse carriage',), ('carrot',), ('tote bag',), ('cart',), ('carton',), ('cash register', 'register (for cash transactions)'), ('casserole',), ('cassette',), ('cast', 'plaster cast', 'plaster bandage'), ('cat',), ('cauliflower',), ('cayenne (spice)', 'cayenne pepper (spice)', 'red pepper (spice)'), ('CD player',), ('celery',), ('cellular telephone', 'cellular phone', 'cellphone', 'mobile phone', 'smart phone'), ('chain mail', 'ring mail', 'chain armor', 'chain armour', 'ring armor', 'ring armour'), ('chair',), ('chaise longue', 'chaise', 'daybed'), ('chalice',), ('chandelier',), ('chap',), ('checkbook', 'chequebook'), ('checkerboard',), ('cherry',), ('chessboard',), ('chicken (animal)',), ('chickpea', 'garbanzo'), ('chili (vegetable)', 'chili pepper (vegetable)', 'chilli (vegetable)', 'chilly (vegetable)', 'chile (vegetable)'), ('chime', 'gong'), ('chinaware',), ('crisp (potato chip)', 'potato chip'), ('poker chip',), ('chocolate bar',), ('chocolate cake',), ('chocolate milk',), ('chocolate mousse',), ('choker', 'collar', 'neckband'), ('chopping board', 'cutting board', 'chopping block'), ('chopstick',), ('Christmas tree',), ('slide',), ('cider', 'cyder'), ('cigar box',), ('cigarette',), ('cigarette case', 'cigarette pack'), ('cistern', 'water tank'), ('clarinet',), ('clasp',), ('cleansing agent', 'cleanser', 'cleaner'), ('cleat (for securing rope)',), ('clementine',), ('clip',), ('clipboard',), ('clippers (for plants)',), ('cloak',), ('clock', 'timepiece', 'timekeeper'), ('clock tower',), ('clothes hamper', 'laundry basket', 'clothes basket'), ('clothespin', 'clothes peg'), ('clutch bag',), ('coaster',), ('coat',), ('coat hanger', 'clothes hanger', 'dress hanger'), ('coatrack', 'hatrack'), ('cock', 'rooster'), ('cockroach',), ('cocoa (beverage)', 'hot chocolate (beverage)', 'drinking chocolate'), ('coconut', 'cocoanut'), ('coffee maker', 'coffee machine'), ('coffee table', 'cocktail table'), ('coffeepot',), ('coil',), ('coin',), ('colander', 'cullender'), ('coleslaw', 'slaw'), ('coloring material', 'colouring material'), ('combination lock',), ('pacifier', 'teething ring'), ('comic book',), ('compass',), ('computer keyboard', 'keyboard (computer)'), ('condiment',), ('cone', 'traffic cone'), ('control', 'controller'), ('convertible (automobile)',), ('sofa bed',), ('cooker',), ('cookie', 'cooky', 'biscuit (cookie)'), ('cooking utensil',), ('cooler (for food)', 'ice chest'), ('cork (bottle plug)', 'bottle cork'), ('corkboard',), ('corkscrew', 'bottle screw'), ('edible corn', 'corn', 'maize'), ('cornbread',), ('cornet', 'horn', 'trumpet'), ('cornice', 'valance', 'valance board', 'pelmet'), ('cornmeal',), ('corset', 'girdle'), ('costume',), ('cougar', 'puma', 'catamount', 'mountain lion', 'panther'), ('coverall',), ('cowbell',), ('cowboy hat', 'ten-gallon hat'), ('crab (animal)',), ('crabmeat',), ('cracker',), ('crape', 'crepe', 'French pancake'), ('crate',), ('crayon', 'wax crayon'), ('cream pitcher',), ('crescent roll', 'croissant'), ('crib', 'cot'), ('crock pot', 'earthenware jar'), ('crossbar',), ('crouton',), ('crow',), ('crowbar', 'wrecking bar', 'pry bar'), ('crown',), ('crucifix',), ('cruise ship', 'cruise liner'), ('police cruiser', 'patrol car', 'police car', 'squad car'), ('crumb',), ('crutch',), ('cub (animal)',), ('cube', 'square block'), ('cucumber', 'cuke'), ('cufflink',), ('cup',), ('trophy cup',), ('cupboard', 'closet'), ('cupcake',), ('hair curler', 'hair roller', 'hair crimper'), ('curling iron',), ('curtain', 'drapery'), ('cushion',), ('cylinder',), ('cymbal',), ('dagger',), ('dalmatian',), ('dartboard',), ('date (fruit)',), ('deck chair', 'beach chair'), ('deer', 'cervid'), ('dental floss', 'floss'), ('desk',), ('detergent',), ('diaper',), ('diary', 'journal'), ('die', 'dice'), ('dinghy', 'dory', 'rowboat'), ('dining table',), ('tux', 'tuxedo'), ('dish',), ('dish antenna',), ('dishrag', 'dishcloth'), ('dishtowel', 'tea towel'), ('dishwasher', 'dishwashing machine'), ('dishwasher detergent', 'dishwashing detergent', 'dishwashing liquid', 'dishsoap'), ('dispenser',), ('diving board',), ('Dixie cup', 'paper cup'), ('dog',), ('dog collar',), ('doll',), ('dollar', 'dollar bill', 'one dollar bill'), ('dollhouse', \"doll's house\"), ('dolphin',), ('domestic ass', 'donkey'), ('doorknob', 'doorhandle'), ('doormat', 'welcome mat'), ('doughnut', 'donut'), ('dove',), ('dragonfly',), ('drawer',), ('underdrawers', 'boxers', 'boxershorts'), ('dress', 'frock'), ('dress hat', 'high hat', 'opera hat', 'silk hat', 'top hat'), ('dress suit',), ('dresser',), ('drill',), ('drone',), ('dropper', 'eye dropper'), ('drum (musical instrument)',), ('drumstick',), ('duck',), ('duckling',), ('duct tape',), ('duffel bag', 'duffle bag', 'duffel', 'duffle'), ('dumbbell',), ('dumpster',), ('dustpan',), ('eagle',), ('earphone', 'earpiece', 'headphone'), ('earplug',), ('earring',), ('easel',), ('eclair',), ('eel',), ('egg', 'eggs'), ('egg roll', 'spring roll'), ('egg yolk', 'yolk (egg)'), ('eggbeater', 'eggwhisk'), ('eggplant', 'aubergine'), ('electric chair',), ('refrigerator',), ('elephant',), ('elk', 'moose'), ('envelope',), ('eraser',), ('escargot',), ('eyepatch',), ('falcon',), ('fan',), ('faucet', 'spigot', 'tap'), ('fedora',), ('ferret',), ('Ferris wheel',), ('ferry', 'ferryboat'), ('fig (fruit)',), ('fighter jet', 'fighter aircraft', 'attack aircraft'), ('figurine',), ('file cabinet', 'filing cabinet'), ('file (tool)',), ('fire alarm', 'smoke alarm'), ('fire engine', 'fire truck'), ('fire extinguisher', 'extinguisher'), ('fire hose',), ('fireplace',), ('fireplug', 'fire hydrant', 'hydrant'), ('first-aid kit',), ('fish',), ('fish (food)',), ('fishbowl', 'goldfish bowl'), ('fishing rod', 'fishing pole'), ('flag',), ('flagpole', 'flagstaff'), ('flamingo',), ('flannel',), ('flap',), ('flash', 'flashbulb'), ('flashlight', 'torch'), ('fleece',), ('flip-flop (sandal)',), ('flipper (footwear)', 'fin (footwear)'), ('flower arrangement', 'floral arrangement'), ('flute glass', 'champagne flute'), ('foal',), ('folding chair',), ('food processor',), ('football (American)',), ('football helmet',), ('footstool', 'footrest'), ('fork',), ('forklift',), ('freight car',), ('French toast',), ('freshener', 'air freshener'), ('frisbee',), ('frog', 'toad', 'toad frog'), ('fruit juice',), ('frying pan', 'frypan', 'skillet'), ('fudge',), ('funnel',), ('futon',), ('gag', 'muzzle'), ('garbage',), ('garbage truck',), ('garden hose',), ('gargle', 'mouthwash'), ('gargoyle',), ('garlic', 'ail'), ('gasmask', 'respirator', 'gas helmet'), ('gazelle',), ('gelatin', 'jelly'), ('gemstone',), ('generator',), ('giant panda', 'panda', 'panda bear'), ('gift wrap',), ('ginger', 'gingerroot'), ('giraffe',), ('cincture', 'sash', 'waistband', 'waistcloth'), ('glass (drink container)', 'drinking glass'), ('globe',), ('glove',), ('goat',), ('goggles',), ('goldfish',), ('golf club', 'golf-club'), ('golfcart',), ('gondola (boat)',), ('goose',), ('gorilla',), ('gourd',), ('grape',), ('grater',), ('gravestone', 'headstone', 'tombstone'), ('gravy boat', 'gravy holder'), ('green bean',), ('green onion', 'spring onion', 'scallion'), ('griddle',), ('grill', 'grille', 'grillwork', 'radiator grille'), ('grits', 'hominy grits'), ('grizzly', 'grizzly bear'), ('grocery bag',), ('guitar',), ('gull', 'seagull'), ('gun',), ('hairbrush',), ('hairnet',), ('hairpin',), ('halter top',), ('ham', 'jambon', 'gammon'), ('hamburger', 'beefburger', 'burger'), ('hammer',), ('hammock',), ('hamper',), ('hamster',), ('hair dryer',), ('hand glass', 'hand mirror'), ('hand towel', 'face towel'), ('handcart', 'pushcart', 'hand truck'), ('handcuff',), ('handkerchief',), ('handle', 'grip', 'handgrip'), ('handsaw', \"carpenter's saw\"), ('hardback book', 'hardcover book'), ('harmonium', 'organ (musical instrument)', 'reed organ (musical instrument)'), ('hat',), ('hatbox',), ('veil',), ('headband',), ('headboard',), ('headlight', 'headlamp'), ('headscarf',), ('headset',), ('headstall (for horses)', 'headpiece (for horses)'), ('heart',), ('heater', 'warmer'), ('helicopter',), ('helmet',), ('heron',), ('highchair', 'feeding chair'), ('hinge',), ('hippopotamus',), ('hockey stick',), ('hog', 'pig'), ('home plate (baseball)', 'home base (baseball)'), ('honey',), ('fume hood', 'exhaust hood'), ('hook',), ('hookah', 'narghile', 'nargileh', 'sheesha', 'shisha', 'water pipe'), ('hornet',), ('horse',), ('hose', 'hosepipe'), ('hot-air balloon',), ('hotplate',), ('hot sauce',), ('hourglass',), ('houseboat',), ('hummingbird',), ('hummus', 'humus', 'hommos', 'hoummos', 'humous'), ('polar bear',), ('icecream',), ('popsicle',), ('ice maker',), ('ice pack', 'ice bag'), ('ice skate',), ('igniter', 'ignitor', 'lighter'), ('inhaler', 'inhalator'), ('iPod',), ('iron (for clothing)', 'smoothing iron (for clothing)'), ('ironing board',), ('jacket',), ('jam',), ('jar',), ('jean', 'blue jean', 'denim'), ('jeep', 'landrover'), ('jelly bean', 'jelly egg'), ('jersey', 'T-shirt', 'tee shirt'), ('jet plane', 'jet-propelled plane'), ('jewel', 'gem', 'precious stone'), ('jewelry', 'jewellery'), ('joystick',), ('jumpsuit',), ('kayak',), ('keg',), ('kennel', 'doghouse'), ('kettle', 'boiler'), ('key',), ('keycard',), ('kilt',), ('kimono',), ('kitchen sink',), ('kitchen table',), ('kite',), ('kitten', 'kitty'), ('kiwi fruit',), ('knee pad',), ('knife',), ('knitting needle',), ('knob',), ('knocker (on a door)', 'doorknocker'), ('koala', 'koala bear'), ('lab coat', 'laboratory coat'), ('ladder',), ('ladle',), ('ladybug', 'ladybeetle', 'ladybird beetle'), ('lamb (animal)',), ('lamb-chop', 'lambchop'), ('lamp',), ('lamppost',), ('lampshade',), ('lantern',), ('lanyard', 'laniard'), ('laptop computer', 'notebook computer'), ('lasagna', 'lasagne'), ('latch',), ('lawn mower',), ('leather',), ('legging (clothing)', 'leging (clothing)', 'leg covering'), ('Lego', 'Lego set'), ('legume',), ('lemon',), ('lemonade',), ('lettuce',), ('license plate', 'numberplate'), ('life buoy', 'lifesaver', 'life belt', 'life ring'), ('life jacket', 'life vest'), ('lightbulb',), ('lightning rod', 'lightning conductor'), ('lime',), ('limousine',), ('lion',), ('lip balm',), ('liquor', 'spirits', 'hard liquor', 'liqueur', 'cordial'), ('lizard',), ('log',), ('lollipop',), ('speaker (stero equipment)',), ('loveseat',), ('machine gun',), ('magazine',), ('magnet',), ('mail slot',), ('mailbox (at home)', 'letter box (at home)'), ('mallard',), ('mallet',), ('mammoth',), ('manatee',), ('mandarin orange',), ('manger', 'trough'), ('manhole',), ('map',), ('marker',), ('martini',), ('mascot',), ('mashed potato',), ('masher',), ('mask', 'facemask'), ('mast',), ('mat (gym equipment)', 'gym mat'), ('matchbox',), ('mattress',), ('measuring cup',), ('measuring stick', 'ruler (measuring stick)', 'measuring rod'), ('meatball',), ('medicine',), ('melon',), ('microphone',), ('microscope',), ('microwave oven',), ('milestone', 'milepost'), ('milk',), ('milk can',), ('milkshake',), ('minivan',), ('mint candy',), ('mirror',), ('mitten',), ('mixer (kitchen tool)', 'stand mixer'), ('money',), ('monitor (computer equipment) computer monitor',), ('monkey',), ('motor',), ('motor scooter', 'scooter'), ('motor vehicle', 'automotive vehicle'), ('motorcycle',), ('mound (baseball)', \"pitcher's mound\"), ('mouse (computer equipment)', 'computer mouse'), ('mousepad',), ('muffin',), ('mug',), ('mushroom',), ('music stool', 'piano stool'), ('musical instrument', 'instrument (musical)'), ('nailfile',), ('napkin', 'table napkin', 'serviette'), ('neckerchief',), ('necklace',), ('necktie', 'tie (necktie)'), ('needle',), ('nest',), ('newspaper', 'paper (newspaper)'), ('newsstand',), ('nightshirt', 'nightwear', 'sleepwear', 'nightclothes'), ('nosebag (for animals)', 'feedbag'), ('noseband (for animals)', 'nosepiece (for animals)'), ('notebook',), ('notepad',), ('nut',), ('nutcracker',), ('oar',), ('octopus (food)',), ('octopus (animal)',), ('oil lamp', 'kerosene lamp', 'kerosine lamp'), ('olive oil',), ('omelet', 'omelette'), ('onion',), ('orange (fruit)',), ('orange juice',), ('ostrich',), ('ottoman', 'pouf', 'pouffe', 'hassock'), ('oven',), ('overalls (clothing)',), ('owl',), ('packet',), ('inkpad', 'inking pad', 'stamp pad'), ('pad',), ('paddle', 'boat paddle'), ('padlock',), ('paintbrush',), ('painting',), ('pajamas', 'pyjamas'), ('palette', 'pallet'), ('pan (for cooking)', 'cooking pan'), ('pan (metal container)',), ('pancake',), ('pantyhose',), ('papaya',), ('paper plate',), ('paper towel',), ('paperback book', 'paper-back book', 'softback book', 'soft-cover book'), ('paperweight',), ('parachute',), ('parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'), ('parasail (sports)',), ('parasol', 'sunshade'), ('parchment',), ('parka', 'anorak'), ('parking meter',), ('parrot',), ('passenger car (part of a train)', 'coach (part of a train)'), ('passenger ship',), ('passport',), ('pastry',), ('patty (food)',), ('pea (food)',), ('peach',), ('peanut butter',), ('pear',), ('peeler (tool for fruit and vegetables)',), ('wooden leg', 'pegleg'), ('pegboard',), ('pelican',), ('pen',), ('pencil',), ('pencil box', 'pencil case'), ('pencil sharpener',), ('pendulum',), ('penguin',), ('pennant',), ('penny (coin)',), ('pepper', 'peppercorn'), ('pepper mill', 'pepper grinder'), ('perfume',), ('persimmon',), ('person', 'baby', 'child', 'boy', 'girl', 'man', 'woman', 'human'), ('pet',), ('pew (church bench)', 'church bench'), ('phonebook', 'telephone book', 'telephone directory'), ('phonograph record', 'phonograph recording', 'record (phonograph recording)'), ('piano',), ('pickle',), ('pickup truck',), ('pie',), ('pigeon',), ('piggy bank', 'penny bank'), ('pillow',), ('pin (non jewelry)',), ('pineapple',), ('pinecone',), ('ping-pong ball',), ('pinwheel',), ('tobacco pipe',), ('pipe', 'piping'), ('pistol', 'handgun'), ('pita (bread)', 'pocket bread'), ('pitcher (vessel for liquid)', 'ewer'), ('pitchfork',), ('pizza',), ('place mat',), ('plate',), ('platter',), ('playpen',), ('pliers', 'plyers'), ('plow (farm equipment)', 'plough (farm equipment)'), ('plume',), ('pocket watch',), ('pocketknife',), ('poker (fire stirring tool)', 'stove poker', 'fire hook'), ('pole', 'post'), ('polo shirt', 'sport shirt'), ('poncho',), ('pony',), ('pool table', 'billiard table', 'snooker table'), ('pop (soda)', 'soda (pop)', 'tonic', 'soft drink'), ('postbox (public)', 'mailbox (public)'), ('postcard', 'postal card', 'mailing-card'), ('poster', 'placard'), ('pot',), ('flowerpot',), ('potato',), ('potholder',), ('pottery', 'clayware'), ('pouch',), ('power shovel', 'excavator', 'digger'), ('prawn', 'shrimp'), ('pretzel',), ('printer', 'printing machine'), ('projectile (weapon)', 'missile'), ('projector',), ('propeller', 'propellor'), ('prune',), ('pudding',), ('puffer (fish)', 'pufferfish', 'blowfish', 'globefish'), ('puffin',), ('pug-dog',), ('pumpkin',), ('puncher',), ('puppet', 'marionette'), ('puppy',), ('quesadilla',), ('quiche',), ('quilt', 'comforter'), ('rabbit',), ('race car', 'racing car'), ('racket', 'racquet'), ('radar',), ('radiator',), ('radio receiver', 'radio set', 'radio', 'tuner (radio)'), ('radish', 'daikon'), ('raft',), ('rag doll',), ('raincoat', 'waterproof jacket'), ('ram (animal)',), ('raspberry',), ('rat',), ('razorblade',), ('reamer (juicer)', 'juicer', 'juice reamer'), ('rearview mirror',), ('receipt',), ('recliner', 'reclining chair', 'lounger (chair)'), ('record player', 'phonograph (record player)', 'turntable'), ('reflector',), ('remote control',), ('rhinoceros',), ('rib (food)',), ('rifle',), ('ring',), ('river boat',), ('road map',), ('robe',), ('rocking chair',), ('rodent',), ('roller skate',), ('Rollerblade',), ('rolling pin',), ('root beer',), ('router (computer equipment)',), ('rubber band', 'elastic band'), ('runner (carpet)',), ('plastic bag', 'paper bag'), ('saddle (on an animal)',), ('saddle blanket', 'saddlecloth', 'horse blanket'), ('saddlebag',), ('safety pin',), ('sail',), ('salad',), ('salad plate', 'salad bowl'), ('salami',), ('salmon (fish)',), ('salmon (food)',), ('salsa',), ('saltshaker',), ('sandal (type of shoe)',), ('sandwich',), ('satchel',), ('saucepan',), ('saucer',), ('sausage',), ('sawhorse', 'sawbuck'), ('saxophone',), ('scale (measuring instrument)',), ('scarecrow', 'strawman'), ('scarf',), ('school bus',), ('scissors',), ('scoreboard',), ('scraper',), ('screwdriver',), ('scrubbing brush',), ('sculpture',), ('seabird', 'seafowl'), ('seahorse',), ('seaplane', 'hydroplane'), ('seashell',), ('sewing machine',), ('shaker',), ('shampoo',), ('shark',), ('sharpener',), ('Sharpie',), ('shaver (electric)', 'electric shaver', 'electric razor'), ('shaving cream', 'shaving soap'), ('shawl',), ('shears',), ('sheep',), ('shepherd dog', 'sheepdog'), ('sherbert', 'sherbet'), ('shield',), ('shirt',), ('shoe', 'sneaker (type of shoe)', 'tennis shoe'), ('shopping bag',), ('shopping cart',), ('short pants', 'shorts (clothing)', 'trunks (clothing)'), ('shot glass',), ('shoulder bag',), ('shovel',), ('shower head',), ('shower cap',), ('shower curtain',), ('shredder (for paper)',), ('signboard',), ('silo',), ('sink',), ('skateboard',), ('skewer',), ('ski',), ('ski boot',), ('ski parka', 'ski jacket'), ('ski pole',), ('skirt',), ('skullcap',), ('sled', 'sledge', 'sleigh'), ('sleeping bag',), ('sling (bandage)', 'triangular bandage'), ('slipper (footwear)', 'carpet slipper (footwear)'), ('smoothie',), ('snake', 'serpent'), ('snowboard',), ('snowman',), ('snowmobile',), ('soap',), ('soccer ball',), ('sock',), ('sofa', 'couch', 'lounge'), ('softball',), ('solar array', 'solar battery', 'solar panel'), ('sombrero',), ('soup',), ('soup bowl',), ('soupspoon',), ('sour cream', 'soured cream'), ('soya milk', 'soybean milk', 'soymilk'), ('space shuttle',), ('sparkler (fireworks)',), ('spatula',), ('spear', 'lance'), ('spectacles', 'specs', 'eyeglasses', 'glasses'), ('spice rack',), ('spider',), ('crawfish', 'crayfish'), ('sponge',), ('spoon',), ('sportswear', 'athletic wear', 'activewear'), ('spotlight',), ('squid (food)', 'calamari', 'calamary'), ('squirrel',), ('stagecoach',), ('stapler (stapling machine)',), ('starfish', 'sea star'), ('statue (sculpture)',), ('steak (food)',), ('steak knife',), ('steering wheel',), ('stepladder',), ('step stool',), ('stereo (sound system)',), ('stew',), ('stirrer',), ('stirrup',), ('stool',), ('stop sign',), ('brake light',), ('stove', 'kitchen stove', 'range (kitchen appliance)', 'kitchen range', 'cooking stove'), ('strainer',), ('strap',), ('straw (for drinking)', 'drinking straw'), ('strawberry',), ('street sign',), ('streetlight', 'street lamp'), ('string cheese',), ('stylus',), ('subwoofer',), ('sugar bowl',), ('sugarcane (plant)',), ('suit (clothing)',), ('sunflower',), ('sunglasses',), ('sunhat',), ('surfboard',), ('sushi',), ('mop',), ('sweat pants',), ('sweatband',), ('sweater',), ('sweatshirt',), ('sweet potato',), ('swimsuit', 'swimwear', 'bathing suit', 'swimming costume', 'bathing costume', 'swimming trunks', 'bathing trunks'), ('sword',), ('syringe',), ('Tabasco sauce',), ('table-tennis table', 'ping-pong table'), ('table',), ('table lamp',), ('tablecloth',), ('tachometer',), ('taco',), ('tag',), ('taillight', 'rear light'), ('tambourine',), ('army tank', 'armored combat vehicle', 'armoured combat vehicle'), ('tank (storage vessel)', 'storage tank'), ('tank top (clothing)',), ('tape (sticky cloth or paper)',), ('tape measure', 'measuring tape'), ('tapestry',), ('tarp',), ('tartan', 'plaid'), ('tassel',), ('tea bag',), ('teacup',), ('teakettle',), ('teapot',), ('teddy bear',), ('telephone', 'phone', 'telephone set'), ('telephone booth', 'phone booth', 'call box', 'telephone box', 'telephone kiosk'), ('telephone pole', 'telegraph pole', 'telegraph post'), ('telephoto lens', 'zoom lens'), ('television camera', 'tv camera'), ('television set', 'tv', 'tv set'), ('tennis ball',), ('tennis racket',), ('tequila',), ('thermometer',), ('thermos bottle',), ('thermostat',), ('thimble',), ('thread', 'yarn'), ('thumbtack', 'drawing pin', 'pushpin'), ('tiara',), ('tiger',), ('tights (clothing)', 'leotards'), ('timer', 'stopwatch'), ('tinfoil',), ('tinsel',), ('tissue paper',), ('toast (food)',), ('toaster',), ('toaster oven',), ('toilet',), ('toilet tissue', 'toilet paper', 'bathroom tissue'), ('tomato',), ('tongs',), ('toolbox',), ('toothbrush',), ('toothpaste',), ('toothpick',), ('cover',), ('tortilla',), ('tow truck',), ('towel',), ('towel rack', 'towel rail', 'towel bar'), ('toy',), ('tractor (farm equipment)',), ('traffic light',), ('dirt bike',), ('trailer truck', 'tractor trailer', 'trucking rig', 'articulated lorry', 'semi truck'), ('train (railroad vehicle)', 'railroad train'), ('trampoline',), ('tray',), ('trench coat',), ('triangle (musical instrument)',), ('tricycle',), ('tripod',), ('trousers', 'pants (clothing)'), ('truck',), ('truffle (chocolate)', 'chocolate truffle'), ('trunk',), ('vat',), ('turban',), ('turkey (food)',), ('turnip',), ('turtle',), ('turtleneck (clothing)', 'polo-neck'), ('typewriter',), ('umbrella',), ('underwear', 'underclothes', 'underclothing', 'underpants'), ('unicycle',), ('urinal',), ('urn',), ('vacuum cleaner',), ('vase',), ('vending machine',), ('vent', 'blowhole', 'air vent'), ('vest', 'waistcoat'), ('videotape',), ('vinegar',), ('violin', 'fiddle'), ('vodka',), ('volleyball',), ('vulture',), ('waffle',), ('waffle iron',), ('wagon',), ('wagon wheel',), ('walking stick',), ('wall clock',), ('wall socket', 'wall plug', 'electric outlet', 'electrical outlet', 'outlet', 'electric receptacle'), ('wallet', 'billfold'), ('walrus',), ('wardrobe',), ('washbasin', 'basin (for washing)', 'washbowl', 'washstand', 'handbasin'), ('automatic washer', 'washing machine'), ('watch', 'wristwatch'), ('water bottle',), ('water cooler',), ('water faucet', 'water tap', 'tap (water faucet)'), ('water heater', 'hot-water heater'), ('water jug',), ('water gun', 'squirt gun'), ('water scooter', 'sea scooter', 'jet ski'), ('water ski',), ('water tower',), ('watering can',), ('watermelon',), ('weathervane', 'vane (weathervane)', 'wind vane'), ('webcam',), ('wedding cake', 'bridecake'), ('wedding ring', 'wedding band'), ('wet suit',), ('wheel',), ('wheelchair',), ('whipped cream',), ('whistle',), ('wig',), ('wind chime',), ('windmill',), ('window box (for plants)',), ('windshield wiper', 'windscreen wiper', 'wiper (for windshield/screen)'), ('windsock', 'air sock', 'air-sleeve', 'wind sleeve', 'wind cone'), ('wine bottle',), ('wine bucket', 'wine cooler'), ('wineglass',), ('blinder (for horses)',), ('wok',), ('wolf',), ('wooden spoon',), ('wreath',), ('wrench', 'spanner'), ('wristband',), ('wristlet', 'wrist band'), ('yacht',), ('yogurt', 'yoghurt', 'yoghourt'), ('yoke (animal equipment)',), ('zebra',), ('zucchini', 'courgette'), ('banner', 'banners'), ('blanket', 'blankets'), ('bridge',), ('cardboard',), ('counter',), ('curtain', 'curtains'), ('door', 'doors'), ('wood floor',), ('flower', 'flowers'), ('fruit', 'fruits'), ('gravel',), ('house',), ('lamp', 'bulb', 'lamps', 'bulbs'), ('mirror',), ('tennis net',), ('pillow', 'pillows'), ('platform',), ('playingfield', 'tennis court', 'baseball feild', 'soccer field', 'tennis field'), ('railroad',), ('river',), ('road',), ('roof',), ('sand',), ('sea', 'sea wave', 'wave', 'waves'), ('shelf',), ('snow',), ('stairs',), ('tent',), ('towel',), ('brick wall',), ('stone wall',), ('tile wall',), ('wood wall',), ('water',), ('window blind',), ('window',), ('tree', 'trees', 'palm tree', 'bushes'), ('fence', 'fences'), ('ceiling',), ('sky', 'clouds'), ('cabinet', 'cabinets'), ('table',), ('floor', 'flooring', 'tile floor'), ('pavement',), ('mountain', 'mountains'), ('grass',), ('dirt',), ('paper',), ('food',), ('building', 'buildings'), ('rock',), ('wall', 'walls'), ('rug',), ('wall', 'walls', 'brick wall', 'stone wall', 'interior wall'), ('building', 'buildings', 'edifice', 'edifices'), ('sky', 'clouds'), ('floor', 'flooring'), ('tree', 'trees'), ('ceiling',), ('road', 'route', 'street', 'roads', 'streets', 'routes'), ('grass', 'grass field'), ('sidewalk', 'pavement'), ('earth', 'ground'), ('mountain', 'mount', 'mountains'), ('plant', 'flora', 'plant life', 'plants', 'bushes'), ('water',), ('house exterior',), ('sea', 'ocean'), ('rug', 'carpet', 'carpeting'), ('field',), ('rock', 'stone', 'rocks', 'stones'), ('pedestal',), ('sand',), ('skyscraper', 'skyscrapers'), ('grandstand', 'covered stand'), ('path',), ('runway',), ('stairway', 'staircase'), ('river',), ('bridge', 'span'), ('window screen', 'door screen'), ('hill',), ('bar',), ('hovel', 'hut', 'hutch', 'shack', 'shanty'), ('tower', 'towers'), ('dirt track',), ('land', 'soil'), ('escalator', 'moving staircase', 'moving stairway'), ('buffet', 'sideboard'), ('poster', 'posting', 'placard', 'notice', 'bill', 'card'), ('stage',), ('conveyer belt', 'conveyor belt', 'conveyer', 'conveyor', 'transporter'), ('canopy',), ('swimming pool', 'swimming bath'), ('waterfall', 'falls'), ('tent', 'collapsible shelter'), ('cradle',), ('tank', 'storage tank'), ('lake',), ('blanket', 'cover'), ('pier', 'wharf', 'wharfage', 'dock'), ('crt screen',), ('shower',))\n",
+ "[06/08 17:26:22 odise.modeling.meta_arch.clip]: Using provided CLIP model\n",
+ "[06/08 17:26:25 odise.modeling.meta_arch.clip]: Built text_embed of shape torch.Size([2482, 768]) for 1486 labels: (('a photo of a person.', 'a photo of a child.', 'a photo of a girl.', 'a photo of a boy.', 'a photo of a woman.', 'a photo of a man.', 'a photo of a people.', 'a photo of a childeren.', 'a photo of a girls.', 'a photo of a boys.', 'a photo of a women.', 'a photo of a men.', 'a photo of a lady.', 'a photo of a guy.', 'a photo of a ladies.', 'a photo of a guys.', 'a photo of a clothes.'), ('a photo of a bicycle.', 'a photo of a bicycles.', 'a photo of a bike.', 'a photo of a bikes.'), ('a photo of a car.', 'a photo of a cars.'), ('a photo of a motorcycle.', 'a photo of a motorcycles.'), ('a photo of a airplane.', 'a photo of a airplanes.'), ('a photo of a bus.', 'a photo of a buses.'), ('a photo of a train.', 'a photo of a trains.', 'a photo of a locomotive.', 'a photo of a locomotives.', 'a photo of a freight train.'), ('a photo of a truck.', 'a photo of a trucks.'), ('a photo of a boat.', 'a photo of a boats.'), ('a photo of a traffic light.',), ('a photo of a fire hydrant.',), ('a photo of a stop sign.',), ('a photo of a parking meter.',), ('a photo of a bench.', 'a photo of a benches.'), ('a photo of a bird.', 'a photo of a birds.'), ('a photo of a cat.', 'a photo of a cats.', 'a photo of a kitties.', 'a photo of a kitty.'), ('a photo of a dog.', 'a photo of a dogs.', 'a photo of a puppy.', 'a photo of a puppies.'), ('a photo of a horse.', 'a photo of a horses.', 'a photo of a foal.'), ('a photo of a sheep.',), ('a photo of a cow.', 'a photo of a cows.', 'a photo of a calf.'), ('a photo of a elephant.', 'a photo of a elephants.'), ('a photo of a bear.', 'a photo of a bears.'), ('a photo of a zebra.', 'a photo of a zebras.'), ('a photo of a giraffe.', 'a photo of a giraffes.'), ('a photo of a backpack.', 'a photo of a backpacks.'), ('a photo of a umbrella.', 'a photo of a umbrellas.'), ('a photo of a handbag.', 'a photo of a handbags.'), ('a photo of a tie.',), ('a photo of a suitcase.', 'a photo of a suitcases.'), ('a photo of a frisbee.',), ('a photo of a skis.',), ('a photo of a snowboard.',), ('a photo of a sports ball.',), ('a photo of a kite.', 'a photo of a kites.'), ('a photo of a baseball bat.',), ('a photo of a baseball glove.',), ('a photo of a skateboard.',), ('a photo of a surfboard.',), ('a photo of a tennis racket.',), ('a photo of a bottle.', 'a photo of a bottles.', 'a photo of a water bottle.'), ('a photo of a wine glass.', 'a photo of a wine glasses.', 'a photo of a wineglass.'), ('a photo of a cup.', 'a photo of a cups.', 'a photo of a water cup.', 'a photo of a water glass.'), ('a photo of a fork.', 'a photo of a forks.'), ('a photo of a knife.', 'a photo of a knives.'), ('a photo of a spoon.', 'a photo of a spoons.'), ('a photo of a bowl.', 'a photo of a bowls.'), ('a photo of a banana.', 'a photo of a bananas.'), ('a photo of a apple.', 'a photo of a apples.', 'a photo of a apple fruit.'), ('a photo of a sandwich.', 'a photo of a sandwiches.'), ('a photo of a orange fruit.',), ('a photo of a broccoli.',), ('a photo of a carrot.', 'a photo of a carrots.'), ('a photo of a hot dog.',), ('a photo of a pizza.',), ('a photo of a donut.', 'a photo of a donuts.'), ('a photo of a cake.', 'a photo of a cakes.'), ('a photo of a chair.', 'a photo of a chairs.'), ('a photo of a couch.', 'a photo of a sofa.', 'a photo of a sofas.'), ('a photo of a potted plant.', 'a photo of a potted plants.', 'a photo of a pottedplant.', 'a photo of a pottedplants.', 'a photo of a planter.', 'a photo of a planters.'), ('a photo of a bed.', 'a photo of a beds.'), ('a photo of a dining table.', 'a photo of a dining tables.', 'a photo of a diningtable.', 'a photo of a diningtables.', 'a photo of a plate.', 'a photo of a plates.', 'a photo of a diningtable tablecloth.'), ('a photo of a toilet.',), ('a photo of a tv.',), ('a photo of a laptop.',), ('a photo of a mouse.',), ('a photo of a tv remote.', 'a photo of a remote control.'), ('a photo of a keyboard.',), ('a photo of a cell phone.', 'a photo of a mobile.'), ('a photo of a microwave.',), ('a photo of a oven.', 'a photo of a ovens.'), ('a photo of a toaster.',), ('a photo of a sink.', 'a photo of a sinks.'), ('a photo of a refrigerator.', 'a photo of a fridge.'), ('a photo of a book.', 'a photo of a books.'), ('a photo of a clock.',), ('a photo of a vase.', 'a photo of a vases.'), ('a photo of a scissor.', 'a photo of a scissors.'), ('a photo of a teddy bear.', 'a photo of a teddy bears.'), ('a photo of a hair drier.',), ('a photo of a toothbrush.', 'a photo of a toothbrushes.'), ('a photo of a bed.', 'a photo of a beds.'), ('a photo of a windowpane.', 'a photo of a window.', 'a photo of a windows.'), ('a photo of a cabinet.', 'a photo of a cabinets.', 'a photo of a wall mounted cabine.'), ('a photo of a person.', 'a photo of a child.', 'a photo of a girl.', 'a photo of a boy.', 'a photo of a woman.', 'a photo of a man.', 'a photo of a people.', 'a photo of a children.', 'a photo of a girls.', 'a photo of a boys.', 'a photo of a women.', 'a photo of a men.'), ('a photo of a door.', 'a photo of a double door.', 'a photo of a doors.'), ('a photo of a table.', 'a photo of a tables.', 'a photo of a tablecloth.'), ('a photo of a curtain.', 'a photo of a drape.', 'a photo of a drapery.', 'a photo of a mantle.', 'a photo of a pall.'), ('a photo of a chair.', 'a photo of a chairs.'), ('a photo of a car.', 'a photo of a automobile.', 'a photo of a cars.'), ('a photo of a painting.', 'a photo of a picture.', 'a photo of a paintings.', 'a photo of a pictures.', 'a photo of a wallart.', 'a photo of a framed canvas.'), ('a photo of a sofa.', 'a photo of a couch.', 'a photo of a sofas.', 'a photo of a couches.'), ('a photo of a shelf.', 'a photo of a shelves.'), ('a photo of a mirror.', 'a photo of a mirrors.'), ('a photo of a armchair.', 'a photo of a armchairs.'), ('a photo of a seat.', 'a photo of a seats.'), ('a photo of a fence.', 'a photo of a fencing.'), ('a photo of a desk.', 'a photo of a desks.'), ('a photo of a wardrobe.', 'a photo of a closet.', 'a photo of a press.', 'a photo of a wardrobes.', 'a photo of a closets.'), ('a photo of a lamp.', 'a photo of a lamps.'), ('a photo of a bathtub.', 'a photo of a bathing tub.', 'a photo of a bath.', 'a photo of a tub.'), ('a photo of a railing.', 'a photo of a rail.'), ('a photo of a cushion.', 'a photo of a cushions.'), ('a photo of a box.', 'a photo of a boxes.'), ('a photo of a column.', 'a photo of a pillar.'), ('a photo of a signboard.', 'a photo of a sign.', 'a photo of a signboards.', 'a photo of a signs.'), ('a photo of a chest of drawers.', 'a photo of a chest.', 'a photo of a bureau.', 'a photo of a dresser.'), ('a photo of a counter.',), ('a photo of a sink.',), ('a photo of a fireplace.', 'a photo of a hearth.', 'a photo of a open fireplace.'), ('a photo of a refrigerator.', 'a photo of a icebox.'), ('a photo of a stairs.', 'a photo of a steps.'), ('a photo of a case.', 'a photo of a display case.', 'a photo of a showcase.', 'a photo of a vitrine.'), ('a photo of a pool table.', 'a photo of a billiard table.', 'a photo of a snooker table.'), ('a photo of a pillow.', 'a photo of a pillows.'), ('a photo of a screen door.', 'a photo of a shower door.'), ('a photo of a bookcase.',), ('a photo of a coffee table.', 'a photo of a cocktail table.'), ('a photo of a toilet.', 'a photo of a commode.', 'a photo of a crapper.', 'a photo of a potty.'), ('a photo of a flower.', 'a photo of a flowers.'), ('a photo of a book.', 'a photo of a books.'), ('a photo of a bench.', 'a photo of a benches.'), ('a photo of a countertop.', 'a photo of a counter top.', 'a photo of a worktop.'), ('a photo of a stove.', 'a photo of a kitchen stove.', 'a photo of a kitchen range.', 'a photo of a kitchen range.', 'a photo of a cooking stove.'), ('a photo of a palm tree.', 'a photo of a palm trees.'), ('a photo of a kitchen island.',), ('a photo of a computer.', 'a photo of a computing machine.', 'a photo of a computing device.', 'a photo of a data processor.', 'a photo of a electronic computer.', 'a photo of a information processing system.'), ('a photo of a swivel chair.',), ('a photo of a boat.',), ('a photo of a arcade machine.', 'a photo of a arcade machines.'), ('a photo of a bus.', 'a photo of a autobus.', 'a photo of a double-decker.', 'a photo of a jitney.', 'a photo of a motorbus.', 'a photo of a motorcoach.', 'a photo of a omnibus.', 'a photo of a passenger vehicle.'), ('a photo of a towel.',), ('a photo of a light bulb.', 'a photo of a lightbulb.', 'a photo of a bulb.', 'a photo of a incandescent lamp.', 'a photo of a electric light.', 'a photo of a electric-light bulb.'), ('a photo of a truck.', 'a photo of a motortruck.'), ('a photo of a chandelier.', 'a photo of a pendant.', 'a photo of a pendent.'), ('a photo of a awning.', 'a photo of a sunshade.', 'a photo of a sunblind.'), ('a photo of a streetlight.', 'a photo of a street lamp.'), ('a photo of a booth.', 'a photo of a cubicle.', 'a photo of a stall.', 'a photo of a kiosk.'), ('a photo of a television receiver.', 'a photo of a television.', 'a photo of a television set.', 'a photo of a tv.', 'a photo of a tv set.'), ('a photo of a airplane.', 'a photo of a aeroplane.', 'a photo of a airplanes.', 'a photo of a aeroplanes.'), ('a photo of a apparel.', 'a photo of a wearing apparel.', 'a photo of a dress.', 'a photo of a clothes.'), ('a photo of a pole.',), ('a photo of a bannister.', 'a photo of a banister.', 'a photo of a balustrade.', 'a photo of a balusters.', 'a photo of a handrail.'), ('a photo of a ottoman.', 'a photo of a pouf.', 'a photo of a pouffe.', 'a photo of a puff.', 'a photo of a hassock.'), ('a photo of a bottle.', 'a photo of a bottles.', 'a photo of a water bottle.'), ('a photo of a van.',), ('a photo of a ship.',), ('a photo of a fountain.',), ('a photo of a washer.', 'a photo of a automatic washer.', 'a photo of a washing machine.'), ('a photo of a plaything.', 'a photo of a toy.', 'a photo of a toys.'), ('a photo of a stool.', 'a photo of a stools.'), ('a photo of a barrel.', 'a photo of a cask.', 'a photo of a barrels.', 'a photo of a casks.'), ('a photo of a basket.', 'a photo of a handbasket.'), ('a photo of a bag.', 'a photo of a bags.', 'a photo of a gift bag.', 'a photo of a paper bag.'), ('a photo of a minibike.', 'a photo of a motorbike.'), ('a photo of a oven.',), ('a photo of a ball.', 'a photo of a balls.'), ('a photo of a food.', 'a photo of a solid food.'), ('a photo of a step.', 'a photo of a stair.'), ('a photo of a trade name.', 'a photo of a brand name.', 'a photo of a brand.', 'a photo of a marque.'), ('a photo of a microwave.', 'a photo of a microwave oven.'), ('a photo of a plant pots.', 'a photo of a plant pot.', 'a photo of a flower pot.', 'a photo of a flowerpot.', 'a photo of a planter.'), ('a photo of a animal.', 'a photo of a animate being.', 'a photo of a dog.', 'a photo of a cat.', 'a photo of a horse.', 'a photo of a cow.', 'a photo of a sheep.', 'a photo of a zebra.', 'a photo of a girraffe.', 'a photo of a bird.'), ('a photo of a bicycle.', 'a photo of a bike.'), ('a photo of a dishwasher.', 'a photo of a dish washer.', 'a photo of a dishwashing machine.'), ('a photo of a projection screen.',), ('a photo of a sculpture.', 'a photo of a sculptures.'), ('a photo of a exhaust hood.',), ('a photo of a sconce.', 'a photo of a sconce lamp.', 'a photo of a sconce light.'), ('a photo of a vase.', 'a photo of a vases.'), ('a photo of a traffic light.', 'a photo of a traffic signal.', 'a photo of a traffic lights.'), ('a photo of a tray.', 'a photo of a trays.'), ('a photo of a ashcan.', 'a photo of a trash can.', 'a photo of a garbage can.', 'a photo of a wastebin.', 'a photo of a ash bin.', 'a photo of a ash-bin.', 'a photo of a ashbin.', 'a photo of a dustbin.', 'a photo of a trash barrel.', 'a photo of a trash bin.'), ('a photo of a ceiling fan.', 'a photo of a floor fan.'), ('a photo of a plate.', 'a photo of a plates.'), ('a photo of a monitor.', 'a photo of a monitoring device.', 'a photo of a monitors.'), ('a photo of a bulletin board.', 'a photo of a notice board.'), ('a photo of a radiator.',), ('a photo of a cup.', 'a photo of a cups.', 'a photo of a drinking glass.', 'a photo of a drinking glasses.'), ('a photo of a clock.',), ('a photo of a flag.', 'a photo of a flags.'), ('a photo of a aerosol can.', 'a photo of a spray can.'), ('a photo of a air conditioner.',), ('a photo of a airplane.', 'a photo of a aeroplane.'), ('a photo of a alarm clock.',), ('a photo of a alcohol.', 'a photo of a alcoholic beverage.'), ('a photo of a alligator.', 'a photo of a gator.'), ('a photo of a almond.',), ('a photo of a ambulance.',), ('a photo of a amplifier.',), ('a photo of a anklet.', 'a photo of a ankle bracelet.'), ('a photo of a antenna.', 'a photo of a aerial.', 'a photo of a transmitting aerial.'), ('a photo of a apple.',), ('a photo of a applesauce.',), ('a photo of a apricot.',), ('a photo of a apron.',), ('a photo of a aquarium.', 'a photo of a fish tank.'), ('a photo of a arctic (type of shoe).', 'a photo of a galosh.', 'a photo of a golosh.', 'a photo of a rubber (type of shoe).', 'a photo of a gumshoe.'), ('a photo of a armband.',), ('a photo of a armchair.',), ('a photo of a armoire.',), ('a photo of a armor.', 'a photo of a armour.'), ('a photo of a artichoke.',), ('a photo of a trash can.', 'a photo of a garbage can.', 'a photo of a wastebin.', 'a photo of a dustbin.', 'a photo of a trash barrel.', 'a photo of a trash bin.'), ('a photo of a ashtray.',), ('a photo of a asparagus.',), ('a photo of a atomizer.', 'a photo of a atomiser.', 'a photo of a spray.', 'a photo of a sprayer.', 'a photo of a nebulizer.', 'a photo of a nebuliser.'), ('a photo of a avocado.',), ('a photo of a award.', 'a photo of a accolade.'), ('a photo of a awning.',), ('a photo of a ax.', 'a photo of a axe.'), ('a photo of a baboon.',), ('a photo of a baby buggy.', 'a photo of a baby carriage.', 'a photo of a perambulator.', 'a photo of a pram.', 'a photo of a stroller.'), ('a photo of a basketball backboard.',), ('a photo of a backpack.', 'a photo of a knapsack.', 'a photo of a packsack.', 'a photo of a rucksack.', 'a photo of a haversack.'), ('a photo of a handbag.', 'a photo of a purse.', 'a photo of a pocketbook.'), ('a photo of a suitcase.', 'a photo of a baggage.', 'a photo of a luggage.'), ('a photo of a bagel.', 'a photo of a beigel.'), ('a photo of a bagpipe.',), ('a photo of a baguet.', 'a photo of a baguette.'), ('a photo of a bait.', 'a photo of a lure.'), ('a photo of a ball.',), ('a photo of a ballet skirt.', 'a photo of a tutu.'), ('a photo of a balloon.',), ('a photo of a bamboo.',), ('a photo of a banana.',), ('a photo of a Band Aid.',), ('a photo of a bandage.',), ('a photo of a bandanna.', 'a photo of a bandana.'), ('a photo of a banjo.',), ('a photo of a banner.', 'a photo of a streamer.'), ('a photo of a barbell.',), ('a photo of a barge.',), ('a photo of a barrel.', 'a photo of a cask.'), ('a photo of a barrette.',), ('a photo of a barrow.', 'a photo of a garden cart.', 'a photo of a lawn cart.', 'a photo of a wheelbarrow.'), ('a photo of a baseball base.',), ('a photo of a baseball.',), ('a photo of a baseball bat.',), ('a photo of a baseball cap.', 'a photo of a jockey cap.', 'a photo of a golf cap.'), ('a photo of a baseball glove.', 'a photo of a baseball mitt.'), ('a photo of a basket.', 'a photo of a handbasket.'), ('a photo of a basketball.',), ('a photo of a bass horn.', 'a photo of a sousaphone.', 'a photo of a tuba.'), ('a photo of a bat (animal).',), ('a photo of a bath mat.',), ('a photo of a bath towel.',), ('a photo of a bathrobe.',), ('a photo of a bathtub.', 'a photo of a bathing tub.'), ('a photo of a batter (food).',), ('a photo of a battery.',), ('a photo of a beachball.',), ('a photo of a bead.',), ('a photo of a bean curd.', 'a photo of a tofu.'), ('a photo of a beanbag.',), ('a photo of a beanie.', 'a photo of a beany.'), ('a photo of a bear.',), ('a photo of a bed.',), ('a photo of a bedpan.',), ('a photo of a bedspread.', 'a photo of a bedcover.', 'a photo of a bed covering.', 'a photo of a counterpane.', 'a photo of a spread.'), ('a photo of a cow.',), ('a photo of a beef (food).', 'a photo of a boeuf (food).'), ('a photo of a beeper.', 'a photo of a pager.'), ('a photo of a beer bottle.',), ('a photo of a beer can.',), ('a photo of a beetle.',), ('a photo of a bell.',), ('a photo of a bell pepper.', 'a photo of a capsicum.'), ('a photo of a belt.',), ('a photo of a belt buckle.',), ('a photo of a bench.',), ('a photo of a beret.',), ('a photo of a bib.',), ('a photo of a Bible.',), ('a photo of a bicycle.', 'a photo of a bike (bicycle).'), ('a photo of a visor.', 'a photo of a vizor.'), ('a photo of a billboard.',), ('a photo of a binder.', 'a photo of a ring-binder.'), ('a photo of a binoculars.', 'a photo of a field glasses.', 'a photo of a opera glasses.'), ('a photo of a bird.',), ('a photo of a birdfeeder.',), ('a photo of a birdbath.',), ('a photo of a birdcage.',), ('a photo of a birdhouse.',), ('a photo of a birthday cake.',), ('a photo of a birthday card.',), ('a photo of a pirate flag.',), ('a photo of a black sheep.',), ('a photo of a blackberry.',), ('a photo of a blackboard.', 'a photo of a chalkboard.'), ('a photo of a blanket.',), ('a photo of a blazer.', 'a photo of a sport jacket.', 'a photo of a sport coat.', 'a photo of a sports jacket.', 'a photo of a sports coat.'), ('a photo of a blender.', 'a photo of a liquidizer.', 'a photo of a liquidiser.'), ('a photo of a blimp.',), ('a photo of a blinker.', 'a photo of a flasher.'), ('a photo of a blouse.',), ('a photo of a blueberry.',), ('a photo of a gameboard.',), ('a photo of a boat.', 'a photo of a ship (boat).'), ('a photo of a bob.', 'a photo of a bobber.', 'a photo of a bobfloat.'), ('a photo of a bobbin.', 'a photo of a spool.', 'a photo of a reel.'), ('a photo of a bobby pin.', 'a photo of a hairgrip.'), ('a photo of a boiled egg.', 'a photo of a coddled egg.'), ('a photo of a bolo tie.', 'a photo of a bolo.', 'a photo of a bola tie.', 'a photo of a bola.'), ('a photo of a deadbolt.',), ('a photo of a bolt.',), ('a photo of a bonnet.',), ('a photo of a book.',), ('a photo of a bookcase.',), ('a photo of a booklet.', 'a photo of a brochure.', 'a photo of a leaflet.', 'a photo of a pamphlet.'), ('a photo of a bookmark.', 'a photo of a bookmarker.'), ('a photo of a boom microphone.', 'a photo of a microphone boom.'), ('a photo of a boot.',), ('a photo of a bottle.',), ('a photo of a bottle opener.',), ('a photo of a bouquet.',), ('a photo of a bow (weapon).',), ('a photo of a bow (decorative ribbons).',), ('a photo of a bow-tie.', 'a photo of a bowtie.'), ('a photo of a bowl.',), ('a photo of a pipe bowl.',), ('a photo of a bowler hat.', 'a photo of a bowler.', 'a photo of a derby hat.', 'a photo of a derby.', 'a photo of a plug hat.'), ('a photo of a bowling ball.',), ('a photo of a box.',), ('a photo of a boxing glove.',), ('a photo of a suspenders.',), ('a photo of a bracelet.', 'a photo of a bangle.'), ('a photo of a brass plaque.',), ('a photo of a brassiere.', 'a photo of a bra.', 'a photo of a bandeau.'), ('a photo of a bread-bin.', 'a photo of a breadbox.'), ('a photo of a bread.',), ('a photo of a breechcloth.', 'a photo of a breechclout.', 'a photo of a loincloth.'), ('a photo of a bridal gown.', 'a photo of a wedding gown.', 'a photo of a wedding dress.'), ('a photo of a briefcase.',), ('a photo of a broccoli.',), ('a photo of a broach.',), ('a photo of a broom.',), ('a photo of a brownie.',), ('a photo of a brussels sprouts.',), ('a photo of a bubble gum.',), ('a photo of a bucket.', 'a photo of a pail.'), ('a photo of a horse buggy.',), ('a photo of a horned cow.',), ('a photo of a bulldog.',), ('a photo of a bulldozer.', 'a photo of a dozer.'), ('a photo of a bullet train.',), ('a photo of a bulletin board.', 'a photo of a notice board.'), ('a photo of a bulletproof vest.',), ('a photo of a bullhorn.', 'a photo of a megaphone.'), ('a photo of a bun.', 'a photo of a roll.'), ('a photo of a bunk bed.',), ('a photo of a buoy.',), ('a photo of a burrito.',), ('a photo of a bus (vehicle).', 'a photo of a autobus.', 'a photo of a charabanc.', 'a photo of a double-decker.', 'a photo of a motorbus.', 'a photo of a motorcoach.'), ('a photo of a business card.',), ('a photo of a butter.',), ('a photo of a butterfly.',), ('a photo of a button.',), ('a photo of a cab (taxi).', 'a photo of a taxi.', 'a photo of a taxicab.'), ('a photo of a cabana.',), ('a photo of a cabin car.', 'a photo of a caboose.'), ('a photo of a cabinet.',), ('a photo of a locker.', 'a photo of a storage locker.'), ('a photo of a cake.',), ('a photo of a calculator.',), ('a photo of a calendar.',), ('a photo of a calf.',), ('a photo of a camcorder.',), ('a photo of a camel.',), ('a photo of a camera.',), ('a photo of a camera lens.',), ('a photo of a camper (vehicle).', 'a photo of a camping bus.', 'a photo of a motor home.'), ('a photo of a can.', 'a photo of a tin can.'), ('a photo of a can opener.', 'a photo of a tin opener.'), ('a photo of a candle.', 'a photo of a candlestick.'), ('a photo of a candle holder.',), ('a photo of a candy bar.',), ('a photo of a candy cane.',), ('a photo of a walking cane.',), ('a photo of a canister.', 'a photo of a cannister.'), ('a photo of a canoe.',), ('a photo of a cantaloup.', 'a photo of a cantaloupe.'), ('a photo of a canteen.',), ('a photo of a cap (headwear).',), ('a photo of a bottle cap.', 'a photo of a cap (container lid).'), ('a photo of a cape.',), ('a photo of a cappuccino.', 'a photo of a coffee cappuccino.'), ('a photo of a car (automobile).', 'a photo of a auto (automobile).', 'a photo of a automobile.'), ('a photo of a railcar (part of a train).', 'a photo of a railway car (part of a train).', 'a photo of a railroad car (part of a train).'), ('a photo of a elevator car.',), ('a photo of a car battery.', 'a photo of a automobile battery.'), ('a photo of a identity card.',), ('a photo of a card.',), ('a photo of a cardigan.',), ('a photo of a cargo ship.', 'a photo of a cargo vessel.'), ('a photo of a carnation.',), ('a photo of a horse carriage.',), ('a photo of a carrot.',), ('a photo of a tote bag.',), ('a photo of a cart.',), ('a photo of a carton.',), ('a photo of a cash register.', 'a photo of a register (for cash transactions).'), ('a photo of a casserole.',), ('a photo of a cassette.',), ('a photo of a cast.', 'a photo of a plaster cast.', 'a photo of a plaster bandage.'), ('a photo of a cat.',), ('a photo of a cauliflower.',), ('a photo of a cayenne (spice).', 'a photo of a cayenne pepper (spice).', 'a photo of a red pepper (spice).'), ('a photo of a CD player.',), ('a photo of a celery.',), ('a photo of a cellular telephone.', 'a photo of a cellular phone.', 'a photo of a cellphone.', 'a photo of a mobile phone.', 'a photo of a smart phone.'), ('a photo of a chain mail.', 'a photo of a ring mail.', 'a photo of a chain armor.', 'a photo of a chain armour.', 'a photo of a ring armor.', 'a photo of a ring armour.'), ('a photo of a chair.',), ('a photo of a chaise longue.', 'a photo of a chaise.', 'a photo of a daybed.'), ('a photo of a chalice.',), ('a photo of a chandelier.',), ('a photo of a chap.',), ('a photo of a checkbook.', 'a photo of a chequebook.'), ('a photo of a checkerboard.',), ('a photo of a cherry.',), ('a photo of a chessboard.',), ('a photo of a chicken (animal).',), ('a photo of a chickpea.', 'a photo of a garbanzo.'), ('a photo of a chili (vegetable).', 'a photo of a chili pepper (vegetable).', 'a photo of a chilli (vegetable).', 'a photo of a chilly (vegetable).', 'a photo of a chile (vegetable).'), ('a photo of a chime.', 'a photo of a gong.'), ('a photo of a chinaware.',), ('a photo of a crisp (potato chip).', 'a photo of a potato chip.'), ('a photo of a poker chip.',), ('a photo of a chocolate bar.',), ('a photo of a chocolate cake.',), ('a photo of a chocolate milk.',), ('a photo of a chocolate mousse.',), ('a photo of a choker.', 'a photo of a collar.', 'a photo of a neckband.'), ('a photo of a chopping board.', 'a photo of a cutting board.', 'a photo of a chopping block.'), ('a photo of a chopstick.',), ('a photo of a Christmas tree.',), ('a photo of a slide.',), ('a photo of a cider.', 'a photo of a cyder.'), ('a photo of a cigar box.',), ('a photo of a cigarette.',), ('a photo of a cigarette case.', 'a photo of a cigarette pack.'), ('a photo of a cistern.', 'a photo of a water tank.'), ('a photo of a clarinet.',), ('a photo of a clasp.',), ('a photo of a cleansing agent.', 'a photo of a cleanser.', 'a photo of a cleaner.'), ('a photo of a cleat (for securing rope).',), ('a photo of a clementine.',), ('a photo of a clip.',), ('a photo of a clipboard.',), ('a photo of a clippers (for plants).',), ('a photo of a cloak.',), ('a photo of a clock.', 'a photo of a timepiece.', 'a photo of a timekeeper.'), ('a photo of a clock tower.',), ('a photo of a clothes hamper.', 'a photo of a laundry basket.', 'a photo of a clothes basket.'), ('a photo of a clothespin.', 'a photo of a clothes peg.'), ('a photo of a clutch bag.',), ('a photo of a coaster.',), ('a photo of a coat.',), ('a photo of a coat hanger.', 'a photo of a clothes hanger.', 'a photo of a dress hanger.'), ('a photo of a coatrack.', 'a photo of a hatrack.'), ('a photo of a cock.', 'a photo of a rooster.'), ('a photo of a cockroach.',), ('a photo of a cocoa (beverage).', 'a photo of a hot chocolate (beverage).', 'a photo of a drinking chocolate.'), ('a photo of a coconut.', 'a photo of a cocoanut.'), ('a photo of a coffee maker.', 'a photo of a coffee machine.'), ('a photo of a coffee table.', 'a photo of a cocktail table.'), ('a photo of a coffeepot.',), ('a photo of a coil.',), ('a photo of a coin.',), ('a photo of a colander.', 'a photo of a cullender.'), ('a photo of a coleslaw.', 'a photo of a slaw.'), ('a photo of a coloring material.', 'a photo of a colouring material.'), ('a photo of a combination lock.',), ('a photo of a pacifier.', 'a photo of a teething ring.'), ('a photo of a comic book.',), ('a photo of a compass.',), ('a photo of a computer keyboard.', 'a photo of a keyboard (computer).'), ('a photo of a condiment.',), ('a photo of a cone.', 'a photo of a traffic cone.'), ('a photo of a control.', 'a photo of a controller.'), ('a photo of a convertible (automobile).',), ('a photo of a sofa bed.',), ('a photo of a cooker.',), ('a photo of a cookie.', 'a photo of a cooky.', 'a photo of a biscuit (cookie).'), ('a photo of a cooking utensil.',), ('a photo of a cooler (for food).', 'a photo of a ice chest.'), ('a photo of a cork (bottle plug).', 'a photo of a bottle cork.'), ('a photo of a corkboard.',), ('a photo of a corkscrew.', 'a photo of a bottle screw.'), ('a photo of a edible corn.', 'a photo of a corn.', 'a photo of a maize.'), ('a photo of a cornbread.',), ('a photo of a cornet.', 'a photo of a horn.', 'a photo of a trumpet.'), ('a photo of a cornice.', 'a photo of a valance.', 'a photo of a valance board.', 'a photo of a pelmet.'), ('a photo of a cornmeal.',), ('a photo of a corset.', 'a photo of a girdle.'), ('a photo of a costume.',), ('a photo of a cougar.', 'a photo of a puma.', 'a photo of a catamount.', 'a photo of a mountain lion.', 'a photo of a panther.'), ('a photo of a coverall.',), ('a photo of a cowbell.',), ('a photo of a cowboy hat.', 'a photo of a ten-gallon hat.'), ('a photo of a crab (animal).',), ('a photo of a crabmeat.',), ('a photo of a cracker.',), ('a photo of a crape.', 'a photo of a crepe.', 'a photo of a French pancake.'), ('a photo of a crate.',), ('a photo of a crayon.', 'a photo of a wax crayon.'), ('a photo of a cream pitcher.',), ('a photo of a crescent roll.', 'a photo of a croissant.'), ('a photo of a crib.', 'a photo of a cot.'), ('a photo of a crock pot.', 'a photo of a earthenware jar.'), ('a photo of a crossbar.',), ('a photo of a crouton.',), ('a photo of a crow.',), ('a photo of a crowbar.', 'a photo of a wrecking bar.', 'a photo of a pry bar.'), ('a photo of a crown.',), ('a photo of a crucifix.',), ('a photo of a cruise ship.', 'a photo of a cruise liner.'), ('a photo of a police cruiser.', 'a photo of a patrol car.', 'a photo of a police car.', 'a photo of a squad car.'), ('a photo of a crumb.',), ('a photo of a crutch.',), ('a photo of a cub (animal).',), ('a photo of a cube.', 'a photo of a square block.'), ('a photo of a cucumber.', 'a photo of a cuke.'), ('a photo of a cufflink.',), ('a photo of a cup.',), ('a photo of a trophy cup.',), ('a photo of a cupboard.', 'a photo of a closet.'), ('a photo of a cupcake.',), ('a photo of a hair curler.', 'a photo of a hair roller.', 'a photo of a hair crimper.'), ('a photo of a curling iron.',), ('a photo of a curtain.', 'a photo of a drapery.'), ('a photo of a cushion.',), ('a photo of a cylinder.',), ('a photo of a cymbal.',), ('a photo of a dagger.',), ('a photo of a dalmatian.',), ('a photo of a dartboard.',), ('a photo of a date (fruit).',), ('a photo of a deck chair.', 'a photo of a beach chair.'), ('a photo of a deer.', 'a photo of a cervid.'), ('a photo of a dental floss.', 'a photo of a floss.'), ('a photo of a desk.',), ('a photo of a detergent.',), ('a photo of a diaper.',), ('a photo of a diary.', 'a photo of a journal.'), ('a photo of a die.', 'a photo of a dice.'), ('a photo of a dinghy.', 'a photo of a dory.', 'a photo of a rowboat.'), ('a photo of a dining table.',), ('a photo of a tux.', 'a photo of a tuxedo.'), ('a photo of a dish.',), ('a photo of a dish antenna.',), ('a photo of a dishrag.', 'a photo of a dishcloth.'), ('a photo of a dishtowel.', 'a photo of a tea towel.'), ('a photo of a dishwasher.', 'a photo of a dishwashing machine.'), ('a photo of a dishwasher detergent.', 'a photo of a dishwashing detergent.', 'a photo of a dishwashing liquid.', 'a photo of a dishsoap.'), ('a photo of a dispenser.',), ('a photo of a diving board.',), ('a photo of a Dixie cup.', 'a photo of a paper cup.'), ('a photo of a dog.',), ('a photo of a dog collar.',), ('a photo of a doll.',), ('a photo of a dollar.', 'a photo of a dollar bill.', 'a photo of a one dollar bill.'), ('a photo of a dollhouse.', \"a photo of a doll's house.\"), ('a photo of a dolphin.',), ('a photo of a domestic ass.', 'a photo of a donkey.'), ('a photo of a doorknob.', 'a photo of a doorhandle.'), ('a photo of a doormat.', 'a photo of a welcome mat.'), ('a photo of a doughnut.', 'a photo of a donut.'), ('a photo of a dove.',), ('a photo of a dragonfly.',), ('a photo of a drawer.',), ('a photo of a underdrawers.', 'a photo of a boxers.', 'a photo of a boxershorts.'), ('a photo of a dress.', 'a photo of a frock.'), ('a photo of a dress hat.', 'a photo of a high hat.', 'a photo of a opera hat.', 'a photo of a silk hat.', 'a photo of a top hat.'), ('a photo of a dress suit.',), ('a photo of a dresser.',), ('a photo of a drill.',), ('a photo of a drone.',), ('a photo of a dropper.', 'a photo of a eye dropper.'), ('a photo of a drum (musical instrument).',), ('a photo of a drumstick.',), ('a photo of a duck.',), ('a photo of a duckling.',), ('a photo of a duct tape.',), ('a photo of a duffel bag.', 'a photo of a duffle bag.', 'a photo of a duffel.', 'a photo of a duffle.'), ('a photo of a dumbbell.',), ('a photo of a dumpster.',), ('a photo of a dustpan.',), ('a photo of a eagle.',), ('a photo of a earphone.', 'a photo of a earpiece.', 'a photo of a headphone.'), ('a photo of a earplug.',), ('a photo of a earring.',), ('a photo of a easel.',), ('a photo of a eclair.',), ('a photo of a eel.',), ('a photo of a egg.', 'a photo of a eggs.'), ('a photo of a egg roll.', 'a photo of a spring roll.'), ('a photo of a egg yolk.', 'a photo of a yolk (egg).'), ('a photo of a eggbeater.', 'a photo of a eggwhisk.'), ('a photo of a eggplant.', 'a photo of a aubergine.'), ('a photo of a electric chair.',), ('a photo of a refrigerator.',), ('a photo of a elephant.',), ('a photo of a elk.', 'a photo of a moose.'), ('a photo of a envelope.',), ('a photo of a eraser.',), ('a photo of a escargot.',), ('a photo of a eyepatch.',), ('a photo of a falcon.',), ('a photo of a fan.',), ('a photo of a faucet.', 'a photo of a spigot.', 'a photo of a tap.'), ('a photo of a fedora.',), ('a photo of a ferret.',), ('a photo of a Ferris wheel.',), ('a photo of a ferry.', 'a photo of a ferryboat.'), ('a photo of a fig (fruit).',), ('a photo of a fighter jet.', 'a photo of a fighter aircraft.', 'a photo of a attack aircraft.'), ('a photo of a figurine.',), ('a photo of a file cabinet.', 'a photo of a filing cabinet.'), ('a photo of a file (tool).',), ('a photo of a fire alarm.', 'a photo of a smoke alarm.'), ('a photo of a fire engine.', 'a photo of a fire truck.'), ('a photo of a fire extinguisher.', 'a photo of a extinguisher.'), ('a photo of a fire hose.',), ('a photo of a fireplace.',), ('a photo of a fireplug.', 'a photo of a fire hydrant.', 'a photo of a hydrant.'), ('a photo of a first-aid kit.',), ('a photo of a fish.',), ('a photo of a fish (food).',), ('a photo of a fishbowl.', 'a photo of a goldfish bowl.'), ('a photo of a fishing rod.', 'a photo of a fishing pole.'), ('a photo of a flag.',), ('a photo of a flagpole.', 'a photo of a flagstaff.'), ('a photo of a flamingo.',), ('a photo of a flannel.',), ('a photo of a flap.',), ('a photo of a flash.', 'a photo of a flashbulb.'), ('a photo of a flashlight.', 'a photo of a torch.'), ('a photo of a fleece.',), ('a photo of a flip-flop (sandal).',), ('a photo of a flipper (footwear).', 'a photo of a fin (footwear).'), ('a photo of a flower arrangement.', 'a photo of a floral arrangement.'), ('a photo of a flute glass.', 'a photo of a champagne flute.'), ('a photo of a foal.',), ('a photo of a folding chair.',), ('a photo of a food processor.',), ('a photo of a football (American).',), ('a photo of a football helmet.',), ('a photo of a footstool.', 'a photo of a footrest.'), ('a photo of a fork.',), ('a photo of a forklift.',), ('a photo of a freight car.',), ('a photo of a French toast.',), ('a photo of a freshener.', 'a photo of a air freshener.'), ('a photo of a frisbee.',), ('a photo of a frog.', 'a photo of a toad.', 'a photo of a toad frog.'), ('a photo of a fruit juice.',), ('a photo of a frying pan.', 'a photo of a frypan.', 'a photo of a skillet.'), ('a photo of a fudge.',), ('a photo of a funnel.',), ('a photo of a futon.',), ('a photo of a gag.', 'a photo of a muzzle.'), ('a photo of a garbage.',), ('a photo of a garbage truck.',), ('a photo of a garden hose.',), ('a photo of a gargle.', 'a photo of a mouthwash.'), ('a photo of a gargoyle.',), ('a photo of a garlic.', 'a photo of a ail.'), ('a photo of a gasmask.', 'a photo of a respirator.', 'a photo of a gas helmet.'), ('a photo of a gazelle.',), ('a photo of a gelatin.', 'a photo of a jelly.'), ('a photo of a gemstone.',), ('a photo of a generator.',), ('a photo of a giant panda.', 'a photo of a panda.', 'a photo of a panda bear.'), ('a photo of a gift wrap.',), ('a photo of a ginger.', 'a photo of a gingerroot.'), ('a photo of a giraffe.',), ('a photo of a cincture.', 'a photo of a sash.', 'a photo of a waistband.', 'a photo of a waistcloth.'), ('a photo of a glass (drink container).', 'a photo of a drinking glass.'), ('a photo of a globe.',), ('a photo of a glove.',), ('a photo of a goat.',), ('a photo of a goggles.',), ('a photo of a goldfish.',), ('a photo of a golf club.', 'a photo of a golf-club.'), ('a photo of a golfcart.',), ('a photo of a gondola (boat).',), ('a photo of a goose.',), ('a photo of a gorilla.',), ('a photo of a gourd.',), ('a photo of a grape.',), ('a photo of a grater.',), ('a photo of a gravestone.', 'a photo of a headstone.', 'a photo of a tombstone.'), ('a photo of a gravy boat.', 'a photo of a gravy holder.'), ('a photo of a green bean.',), ('a photo of a green onion.', 'a photo of a spring onion.', 'a photo of a scallion.'), ('a photo of a griddle.',), ('a photo of a grill.', 'a photo of a grille.', 'a photo of a grillwork.', 'a photo of a radiator grille.'), ('a photo of a grits.', 'a photo of a hominy grits.'), ('a photo of a grizzly.', 'a photo of a grizzly bear.'), ('a photo of a grocery bag.',), ('a photo of a guitar.',), ('a photo of a gull.', 'a photo of a seagull.'), ('a photo of a gun.',), ('a photo of a hairbrush.',), ('a photo of a hairnet.',), ('a photo of a hairpin.',), ('a photo of a halter top.',), ('a photo of a ham.', 'a photo of a jambon.', 'a photo of a gammon.'), ('a photo of a hamburger.', 'a photo of a beefburger.', 'a photo of a burger.'), ('a photo of a hammer.',), ('a photo of a hammock.',), ('a photo of a hamper.',), ('a photo of a hamster.',), ('a photo of a hair dryer.',), ('a photo of a hand glass.', 'a photo of a hand mirror.'), ('a photo of a hand towel.', 'a photo of a face towel.'), ('a photo of a handcart.', 'a photo of a pushcart.', 'a photo of a hand truck.'), ('a photo of a handcuff.',), ('a photo of a handkerchief.',), ('a photo of a handle.', 'a photo of a grip.', 'a photo of a handgrip.'), ('a photo of a handsaw.', \"a photo of a carpenter's saw.\"), ('a photo of a hardback book.', 'a photo of a hardcover book.'), ('a photo of a harmonium.', 'a photo of a organ (musical instrument).', 'a photo of a reed organ (musical instrument).'), ('a photo of a hat.',), ('a photo of a hatbox.',), ('a photo of a veil.',), ('a photo of a headband.',), ('a photo of a headboard.',), ('a photo of a headlight.', 'a photo of a headlamp.'), ('a photo of a headscarf.',), ('a photo of a headset.',), ('a photo of a headstall (for horses).', 'a photo of a headpiece (for horses).'), ('a photo of a heart.',), ('a photo of a heater.', 'a photo of a warmer.'), ('a photo of a helicopter.',), ('a photo of a helmet.',), ('a photo of a heron.',), ('a photo of a highchair.', 'a photo of a feeding chair.'), ('a photo of a hinge.',), ('a photo of a hippopotamus.',), ('a photo of a hockey stick.',), ('a photo of a hog.', 'a photo of a pig.'), ('a photo of a home plate (baseball).', 'a photo of a home base (baseball).'), ('a photo of a honey.',), ('a photo of a fume hood.', 'a photo of a exhaust hood.'), ('a photo of a hook.',), ('a photo of a hookah.', 'a photo of a narghile.', 'a photo of a nargileh.', 'a photo of a sheesha.', 'a photo of a shisha.', 'a photo of a water pipe.'), ('a photo of a hornet.',), ('a photo of a horse.',), ('a photo of a hose.', 'a photo of a hosepipe.'), ('a photo of a hot-air balloon.',), ('a photo of a hotplate.',), ('a photo of a hot sauce.',), ('a photo of a hourglass.',), ('a photo of a houseboat.',), ('a photo of a hummingbird.',), ('a photo of a hummus.', 'a photo of a humus.', 'a photo of a hommos.', 'a photo of a hoummos.', 'a photo of a humous.'), ('a photo of a polar bear.',), ('a photo of a icecream.',), ('a photo of a popsicle.',), ('a photo of a ice maker.',), ('a photo of a ice pack.', 'a photo of a ice bag.'), ('a photo of a ice skate.',), ('a photo of a igniter.', 'a photo of a ignitor.', 'a photo of a lighter.'), ('a photo of a inhaler.', 'a photo of a inhalator.'), ('a photo of a iPod.',), ('a photo of a iron (for clothing).', 'a photo of a smoothing iron (for clothing).'), ('a photo of a ironing board.',), ('a photo of a jacket.',), ('a photo of a jam.',), ('a photo of a jar.',), ('a photo of a jean.', 'a photo of a blue jean.', 'a photo of a denim.'), ('a photo of a jeep.', 'a photo of a landrover.'), ('a photo of a jelly bean.', 'a photo of a jelly egg.'), ('a photo of a jersey.', 'a photo of a T-shirt.', 'a photo of a tee shirt.'), ('a photo of a jet plane.', 'a photo of a jet-propelled plane.'), ('a photo of a jewel.', 'a photo of a gem.', 'a photo of a precious stone.'), ('a photo of a jewelry.', 'a photo of a jewellery.'), ('a photo of a joystick.',), ('a photo of a jumpsuit.',), ('a photo of a kayak.',), ('a photo of a keg.',), ('a photo of a kennel.', 'a photo of a doghouse.'), ('a photo of a kettle.', 'a photo of a boiler.'), ('a photo of a key.',), ('a photo of a keycard.',), ('a photo of a kilt.',), ('a photo of a kimono.',), ('a photo of a kitchen sink.',), ('a photo of a kitchen table.',), ('a photo of a kite.',), ('a photo of a kitten.', 'a photo of a kitty.'), ('a photo of a kiwi fruit.',), ('a photo of a knee pad.',), ('a photo of a knife.',), ('a photo of a knitting needle.',), ('a photo of a knob.',), ('a photo of a knocker (on a door).', 'a photo of a doorknocker.'), ('a photo of a koala.', 'a photo of a koala bear.'), ('a photo of a lab coat.', 'a photo of a laboratory coat.'), ('a photo of a ladder.',), ('a photo of a ladle.',), ('a photo of a ladybug.', 'a photo of a ladybeetle.', 'a photo of a ladybird beetle.'), ('a photo of a lamb (animal).',), ('a photo of a lamb-chop.', 'a photo of a lambchop.'), ('a photo of a lamp.',), ('a photo of a lamppost.',), ('a photo of a lampshade.',), ('a photo of a lantern.',), ('a photo of a lanyard.', 'a photo of a laniard.'), ('a photo of a laptop computer.', 'a photo of a notebook computer.'), ('a photo of a lasagna.', 'a photo of a lasagne.'), ('a photo of a latch.',), ('a photo of a lawn mower.',), ('a photo of a leather.',), ('a photo of a legging (clothing).', 'a photo of a leging (clothing).', 'a photo of a leg covering.'), ('a photo of a Lego.', 'a photo of a Lego set.'), ('a photo of a legume.',), ('a photo of a lemon.',), ('a photo of a lemonade.',), ('a photo of a lettuce.',), ('a photo of a license plate.', 'a photo of a numberplate.'), ('a photo of a life buoy.', 'a photo of a lifesaver.', 'a photo of a life belt.', 'a photo of a life ring.'), ('a photo of a life jacket.', 'a photo of a life vest.'), ('a photo of a lightbulb.',), ('a photo of a lightning rod.', 'a photo of a lightning conductor.'), ('a photo of a lime.',), ('a photo of a limousine.',), ('a photo of a lion.',), ('a photo of a lip balm.',), ('a photo of a liquor.', 'a photo of a spirits.', 'a photo of a hard liquor.', 'a photo of a liqueur.', 'a photo of a cordial.'), ('a photo of a lizard.',), ('a photo of a log.',), ('a photo of a lollipop.',), ('a photo of a speaker (stero equipment).',), ('a photo of a loveseat.',), ('a photo of a machine gun.',), ('a photo of a magazine.',), ('a photo of a magnet.',), ('a photo of a mail slot.',), ('a photo of a mailbox (at home).', 'a photo of a letter box (at home).'), ('a photo of a mallard.',), ('a photo of a mallet.',), ('a photo of a mammoth.',), ('a photo of a manatee.',), ('a photo of a mandarin orange.',), ('a photo of a manger.', 'a photo of a trough.'), ('a photo of a manhole.',), ('a photo of a map.',), ('a photo of a marker.',), ('a photo of a martini.',), ('a photo of a mascot.',), ('a photo of a mashed potato.',), ('a photo of a masher.',), ('a photo of a mask.', 'a photo of a facemask.'), ('a photo of a mast.',), ('a photo of a mat (gym equipment).', 'a photo of a gym mat.'), ('a photo of a matchbox.',), ('a photo of a mattress.',), ('a photo of a measuring cup.',), ('a photo of a measuring stick.', 'a photo of a ruler (measuring stick).', 'a photo of a measuring rod.'), ('a photo of a meatball.',), ('a photo of a medicine.',), ('a photo of a melon.',), ('a photo of a microphone.',), ('a photo of a microscope.',), ('a photo of a microwave oven.',), ('a photo of a milestone.', 'a photo of a milepost.'), ('a photo of a milk.',), ('a photo of a milk can.',), ('a photo of a milkshake.',), ('a photo of a minivan.',), ('a photo of a mint candy.',), ('a photo of a mirror.',), ('a photo of a mitten.',), ('a photo of a mixer (kitchen tool).', 'a photo of a stand mixer.'), ('a photo of a money.',), ('a photo of a monitor (computer equipment) computer monitor.',), ('a photo of a monkey.',), ('a photo of a motor.',), ('a photo of a motor scooter.', 'a photo of a scooter.'), ('a photo of a motor vehicle.', 'a photo of a automotive vehicle.'), ('a photo of a motorcycle.',), ('a photo of a mound (baseball).', \"a photo of a pitcher's mound.\"), ('a photo of a mouse (computer equipment).', 'a photo of a computer mouse.'), ('a photo of a mousepad.',), ('a photo of a muffin.',), ('a photo of a mug.',), ('a photo of a mushroom.',), ('a photo of a music stool.', 'a photo of a piano stool.'), ('a photo of a musical instrument.', 'a photo of a instrument (musical).'), ('a photo of a nailfile.',), ('a photo of a napkin.', 'a photo of a table napkin.', 'a photo of a serviette.'), ('a photo of a neckerchief.',), ('a photo of a necklace.',), ('a photo of a necktie.', 'a photo of a tie (necktie).'), ('a photo of a needle.',), ('a photo of a nest.',), ('a photo of a newspaper.', 'a photo of a paper (newspaper).'), ('a photo of a newsstand.',), ('a photo of a nightshirt.', 'a photo of a nightwear.', 'a photo of a sleepwear.', 'a photo of a nightclothes.'), ('a photo of a nosebag (for animals).', 'a photo of a feedbag.'), ('a photo of a noseband (for animals).', 'a photo of a nosepiece (for animals).'), ('a photo of a notebook.',), ('a photo of a notepad.',), ('a photo of a nut.',), ('a photo of a nutcracker.',), ('a photo of a oar.',), ('a photo of a octopus (food).',), ('a photo of a octopus (animal).',), ('a photo of a oil lamp.', 'a photo of a kerosene lamp.', 'a photo of a kerosine lamp.'), ('a photo of a olive oil.',), ('a photo of a omelet.', 'a photo of a omelette.'), ('a photo of a onion.',), ('a photo of a orange (fruit).',), ('a photo of a orange juice.',), ('a photo of a ostrich.',), ('a photo of a ottoman.', 'a photo of a pouf.', 'a photo of a pouffe.', 'a photo of a hassock.'), ('a photo of a oven.',), ('a photo of a overalls (clothing).',), ('a photo of a owl.',), ('a photo of a packet.',), ('a photo of a inkpad.', 'a photo of a inking pad.', 'a photo of a stamp pad.'), ('a photo of a pad.',), ('a photo of a paddle.', 'a photo of a boat paddle.'), ('a photo of a padlock.',), ('a photo of a paintbrush.',), ('a photo of a painting.',), ('a photo of a pajamas.', 'a photo of a pyjamas.'), ('a photo of a palette.', 'a photo of a pallet.'), ('a photo of a pan (for cooking).', 'a photo of a cooking pan.'), ('a photo of a pan (metal container).',), ('a photo of a pancake.',), ('a photo of a pantyhose.',), ('a photo of a papaya.',), ('a photo of a paper plate.',), ('a photo of a paper towel.',), ('a photo of a paperback book.', 'a photo of a paper-back book.', 'a photo of a softback book.', 'a photo of a soft-cover book.'), ('a photo of a paperweight.',), ('a photo of a parachute.',), ('a photo of a parakeet.', 'a photo of a parrakeet.', 'a photo of a parroket.', 'a photo of a paraquet.', 'a photo of a paroquet.', 'a photo of a parroquet.'), ('a photo of a parasail (sports).',), ('a photo of a parasol.', 'a photo of a sunshade.'), ('a photo of a parchment.',), ('a photo of a parka.', 'a photo of a anorak.'), ('a photo of a parking meter.',), ('a photo of a parrot.',), ('a photo of a passenger car (part of a train).', 'a photo of a coach (part of a train).'), ('a photo of a passenger ship.',), ('a photo of a passport.',), ('a photo of a pastry.',), ('a photo of a patty (food).',), ('a photo of a pea (food).',), ('a photo of a peach.',), ('a photo of a peanut butter.',), ('a photo of a pear.',), ('a photo of a peeler (tool for fruit and vegetables).',), ('a photo of a wooden leg.', 'a photo of a pegleg.'), ('a photo of a pegboard.',), ('a photo of a pelican.',), ('a photo of a pen.',), ('a photo of a pencil.',), ('a photo of a pencil box.', 'a photo of a pencil case.'), ('a photo of a pencil sharpener.',), ('a photo of a pendulum.',), ('a photo of a penguin.',), ('a photo of a pennant.',), ('a photo of a penny (coin).',), ('a photo of a pepper.', 'a photo of a peppercorn.'), ('a photo of a pepper mill.', 'a photo of a pepper grinder.'), ('a photo of a perfume.',), ('a photo of a persimmon.',), ('a photo of a person.', 'a photo of a baby.', 'a photo of a child.', 'a photo of a boy.', 'a photo of a girl.', 'a photo of a man.', 'a photo of a woman.', 'a photo of a human.'), ('a photo of a pet.',), ('a photo of a pew (church bench).', 'a photo of a church bench.'), ('a photo of a phonebook.', 'a photo of a telephone book.', 'a photo of a telephone directory.'), ('a photo of a phonograph record.', 'a photo of a phonograph recording.', 'a photo of a record (phonograph recording).'), ('a photo of a piano.',), ('a photo of a pickle.',), ('a photo of a pickup truck.',), ('a photo of a pie.',), ('a photo of a pigeon.',), ('a photo of a piggy bank.', 'a photo of a penny bank.'), ('a photo of a pillow.',), ('a photo of a pin (non jewelry).',), ('a photo of a pineapple.',), ('a photo of a pinecone.',), ('a photo of a ping-pong ball.',), ('a photo of a pinwheel.',), ('a photo of a tobacco pipe.',), ('a photo of a pipe.', 'a photo of a piping.'), ('a photo of a pistol.', 'a photo of a handgun.'), ('a photo of a pita (bread).', 'a photo of a pocket bread.'), ('a photo of a pitcher (vessel for liquid).', 'a photo of a ewer.'), ('a photo of a pitchfork.',), ('a photo of a pizza.',), ('a photo of a place mat.',), ('a photo of a plate.',), ('a photo of a platter.',), ('a photo of a playpen.',), ('a photo of a pliers.', 'a photo of a plyers.'), ('a photo of a plow (farm equipment).', 'a photo of a plough (farm equipment).'), ('a photo of a plume.',), ('a photo of a pocket watch.',), ('a photo of a pocketknife.',), ('a photo of a poker (fire stirring tool).', 'a photo of a stove poker.', 'a photo of a fire hook.'), ('a photo of a pole.', 'a photo of a post.'), ('a photo of a polo shirt.', 'a photo of a sport shirt.'), ('a photo of a poncho.',), ('a photo of a pony.',), ('a photo of a pool table.', 'a photo of a billiard table.', 'a photo of a snooker table.'), ('a photo of a pop (soda).', 'a photo of a soda (pop).', 'a photo of a tonic.', 'a photo of a soft drink.'), ('a photo of a postbox (public).', 'a photo of a mailbox (public).'), ('a photo of a postcard.', 'a photo of a postal card.', 'a photo of a mailing-card.'), ('a photo of a poster.', 'a photo of a placard.'), ('a photo of a pot.',), ('a photo of a flowerpot.',), ('a photo of a potato.',), ('a photo of a potholder.',), ('a photo of a pottery.', 'a photo of a clayware.'), ('a photo of a pouch.',), ('a photo of a power shovel.', 'a photo of a excavator.', 'a photo of a digger.'), ('a photo of a prawn.', 'a photo of a shrimp.'), ('a photo of a pretzel.',), ('a photo of a printer.', 'a photo of a printing machine.'), ('a photo of a projectile (weapon).', 'a photo of a missile.'), ('a photo of a projector.',), ('a photo of a propeller.', 'a photo of a propellor.'), ('a photo of a prune.',), ('a photo of a pudding.',), ('a photo of a puffer (fish).', 'a photo of a pufferfish.', 'a photo of a blowfish.', 'a photo of a globefish.'), ('a photo of a puffin.',), ('a photo of a pug-dog.',), ('a photo of a pumpkin.',), ('a photo of a puncher.',), ('a photo of a puppet.', 'a photo of a marionette.'), ('a photo of a puppy.',), ('a photo of a quesadilla.',), ('a photo of a quiche.',), ('a photo of a quilt.', 'a photo of a comforter.'), ('a photo of a rabbit.',), ('a photo of a race car.', 'a photo of a racing car.'), ('a photo of a racket.', 'a photo of a racquet.'), ('a photo of a radar.',), ('a photo of a radiator.',), ('a photo of a radio receiver.', 'a photo of a radio set.', 'a photo of a radio.', 'a photo of a tuner (radio).'), ('a photo of a radish.', 'a photo of a daikon.'), ('a photo of a raft.',), ('a photo of a rag doll.',), ('a photo of a raincoat.', 'a photo of a waterproof jacket.'), ('a photo of a ram (animal).',), ('a photo of a raspberry.',), ('a photo of a rat.',), ('a photo of a razorblade.',), ('a photo of a reamer (juicer).', 'a photo of a juicer.', 'a photo of a juice reamer.'), ('a photo of a rearview mirror.',), ('a photo of a receipt.',), ('a photo of a recliner.', 'a photo of a reclining chair.', 'a photo of a lounger (chair).'), ('a photo of a record player.', 'a photo of a phonograph (record player).', 'a photo of a turntable.'), ('a photo of a reflector.',), ('a photo of a remote control.',), ('a photo of a rhinoceros.',), ('a photo of a rib (food).',), ('a photo of a rifle.',), ('a photo of a ring.',), ('a photo of a river boat.',), ('a photo of a road map.',), ('a photo of a robe.',), ('a photo of a rocking chair.',), ('a photo of a rodent.',), ('a photo of a roller skate.',), ('a photo of a Rollerblade.',), ('a photo of a rolling pin.',), ('a photo of a root beer.',), ('a photo of a router (computer equipment).',), ('a photo of a rubber band.', 'a photo of a elastic band.'), ('a photo of a runner (carpet).',), ('a photo of a plastic bag.', 'a photo of a paper bag.'), ('a photo of a saddle (on an animal).',), ('a photo of a saddle blanket.', 'a photo of a saddlecloth.', 'a photo of a horse blanket.'), ('a photo of a saddlebag.',), ('a photo of a safety pin.',), ('a photo of a sail.',), ('a photo of a salad.',), ('a photo of a salad plate.', 'a photo of a salad bowl.'), ('a photo of a salami.',), ('a photo of a salmon (fish).',), ('a photo of a salmon (food).',), ('a photo of a salsa.',), ('a photo of a saltshaker.',), ('a photo of a sandal (type of shoe).',), ('a photo of a sandwich.',), ('a photo of a satchel.',), ('a photo of a saucepan.',), ('a photo of a saucer.',), ('a photo of a sausage.',), ('a photo of a sawhorse.', 'a photo of a sawbuck.'), ('a photo of a saxophone.',), ('a photo of a scale (measuring instrument).',), ('a photo of a scarecrow.', 'a photo of a strawman.'), ('a photo of a scarf.',), ('a photo of a school bus.',), ('a photo of a scissors.',), ('a photo of a scoreboard.',), ('a photo of a scraper.',), ('a photo of a screwdriver.',), ('a photo of a scrubbing brush.',), ('a photo of a sculpture.',), ('a photo of a seabird.', 'a photo of a seafowl.'), ('a photo of a seahorse.',), ('a photo of a seaplane.', 'a photo of a hydroplane.'), ('a photo of a seashell.',), ('a photo of a sewing machine.',), ('a photo of a shaker.',), ('a photo of a shampoo.',), ('a photo of a shark.',), ('a photo of a sharpener.',), ('a photo of a Sharpie.',), ('a photo of a shaver (electric).', 'a photo of a electric shaver.', 'a photo of a electric razor.'), ('a photo of a shaving cream.', 'a photo of a shaving soap.'), ('a photo of a shawl.',), ('a photo of a shears.',), ('a photo of a sheep.',), ('a photo of a shepherd dog.', 'a photo of a sheepdog.'), ('a photo of a sherbert.', 'a photo of a sherbet.'), ('a photo of a shield.',), ('a photo of a shirt.',), ('a photo of a shoe.', 'a photo of a sneaker (type of shoe).', 'a photo of a tennis shoe.'), ('a photo of a shopping bag.',), ('a photo of a shopping cart.',), ('a photo of a short pants.', 'a photo of a shorts (clothing).', 'a photo of a trunks (clothing).'), ('a photo of a shot glass.',), ('a photo of a shoulder bag.',), ('a photo of a shovel.',), ('a photo of a shower head.',), ('a photo of a shower cap.',), ('a photo of a shower curtain.',), ('a photo of a shredder (for paper).',), ('a photo of a signboard.',), ('a photo of a silo.',), ('a photo of a sink.',), ('a photo of a skateboard.',), ('a photo of a skewer.',), ('a photo of a ski.',), ('a photo of a ski boot.',), ('a photo of a ski parka.', 'a photo of a ski jacket.'), ('a photo of a ski pole.',), ('a photo of a skirt.',), ('a photo of a skullcap.',), ('a photo of a sled.', 'a photo of a sledge.', 'a photo of a sleigh.'), ('a photo of a sleeping bag.',), ('a photo of a sling (bandage).', 'a photo of a triangular bandage.'), ('a photo of a slipper (footwear).', 'a photo of a carpet slipper (footwear).'), ('a photo of a smoothie.',), ('a photo of a snake.', 'a photo of a serpent.'), ('a photo of a snowboard.',), ('a photo of a snowman.',), ('a photo of a snowmobile.',), ('a photo of a soap.',), ('a photo of a soccer ball.',), ('a photo of a sock.',), ('a photo of a sofa.', 'a photo of a couch.', 'a photo of a lounge.'), ('a photo of a softball.',), ('a photo of a solar array.', 'a photo of a solar battery.', 'a photo of a solar panel.'), ('a photo of a sombrero.',), ('a photo of a soup.',), ('a photo of a soup bowl.',), ('a photo of a soupspoon.',), ('a photo of a sour cream.', 'a photo of a soured cream.'), ('a photo of a soya milk.', 'a photo of a soybean milk.', 'a photo of a soymilk.'), ('a photo of a space shuttle.',), ('a photo of a sparkler (fireworks).',), ('a photo of a spatula.',), ('a photo of a spear.', 'a photo of a lance.'), ('a photo of a spectacles.', 'a photo of a specs.', 'a photo of a eyeglasses.', 'a photo of a glasses.'), ('a photo of a spice rack.',), ('a photo of a spider.',), ('a photo of a crawfish.', 'a photo of a crayfish.'), ('a photo of a sponge.',), ('a photo of a spoon.',), ('a photo of a sportswear.', 'a photo of a athletic wear.', 'a photo of a activewear.'), ('a photo of a spotlight.',), ('a photo of a squid (food).', 'a photo of a calamari.', 'a photo of a calamary.'), ('a photo of a squirrel.',), ('a photo of a stagecoach.',), ('a photo of a stapler (stapling machine).',), ('a photo of a starfish.', 'a photo of a sea star.'), ('a photo of a statue (sculpture).',), ('a photo of a steak (food).',), ('a photo of a steak knife.',), ('a photo of a steering wheel.',), ('a photo of a stepladder.',), ('a photo of a step stool.',), ('a photo of a stereo (sound system).',), ('a photo of a stew.',), ('a photo of a stirrer.',), ('a photo of a stirrup.',), ('a photo of a stool.',), ('a photo of a stop sign.',), ('a photo of a brake light.',), ('a photo of a stove.', 'a photo of a kitchen stove.', 'a photo of a range (kitchen appliance).', 'a photo of a kitchen range.', 'a photo of a cooking stove.'), ('a photo of a strainer.',), ('a photo of a strap.',), ('a photo of a straw (for drinking).', 'a photo of a drinking straw.'), ('a photo of a strawberry.',), ('a photo of a street sign.',), ('a photo of a streetlight.', 'a photo of a street lamp.'), ('a photo of a string cheese.',), ('a photo of a stylus.',), ('a photo of a subwoofer.',), ('a photo of a sugar bowl.',), ('a photo of a sugarcane (plant).',), ('a photo of a suit (clothing).',), ('a photo of a sunflower.',), ('a photo of a sunglasses.',), ('a photo of a sunhat.',), ('a photo of a surfboard.',), ('a photo of a sushi.',), ('a photo of a mop.',), ('a photo of a sweat pants.',), ('a photo of a sweatband.',), ('a photo of a sweater.',), ('a photo of a sweatshirt.',), ('a photo of a sweet potato.',), ('a photo of a swimsuit.', 'a photo of a swimwear.', 'a photo of a bathing suit.', 'a photo of a swimming costume.', 'a photo of a bathing costume.', 'a photo of a swimming trunks.', 'a photo of a bathing trunks.'), ('a photo of a sword.',), ('a photo of a syringe.',), ('a photo of a Tabasco sauce.',), ('a photo of a table-tennis table.', 'a photo of a ping-pong table.'), ('a photo of a table.',), ('a photo of a table lamp.',), ('a photo of a tablecloth.',), ('a photo of a tachometer.',), ('a photo of a taco.',), ('a photo of a tag.',), ('a photo of a taillight.', 'a photo of a rear light.'), ('a photo of a tambourine.',), ('a photo of a army tank.', 'a photo of a armored combat vehicle.', 'a photo of a armoured combat vehicle.'), ('a photo of a tank (storage vessel).', 'a photo of a storage tank.'), ('a photo of a tank top (clothing).',), ('a photo of a tape (sticky cloth or paper).',), ('a photo of a tape measure.', 'a photo of a measuring tape.'), ('a photo of a tapestry.',), ('a photo of a tarp.',), ('a photo of a tartan.', 'a photo of a plaid.'), ('a photo of a tassel.',), ('a photo of a tea bag.',), ('a photo of a teacup.',), ('a photo of a teakettle.',), ('a photo of a teapot.',), ('a photo of a teddy bear.',), ('a photo of a telephone.', 'a photo of a phone.', 'a photo of a telephone set.'), ('a photo of a telephone booth.', 'a photo of a phone booth.', 'a photo of a call box.', 'a photo of a telephone box.', 'a photo of a telephone kiosk.'), ('a photo of a telephone pole.', 'a photo of a telegraph pole.', 'a photo of a telegraph post.'), ('a photo of a telephoto lens.', 'a photo of a zoom lens.'), ('a photo of a television camera.', 'a photo of a tv camera.'), ('a photo of a television set.', 'a photo of a tv.', 'a photo of a tv set.'), ('a photo of a tennis ball.',), ('a photo of a tennis racket.',), ('a photo of a tequila.',), ('a photo of a thermometer.',), ('a photo of a thermos bottle.',), ('a photo of a thermostat.',), ('a photo of a thimble.',), ('a photo of a thread.', 'a photo of a yarn.'), ('a photo of a thumbtack.', 'a photo of a drawing pin.', 'a photo of a pushpin.'), ('a photo of a tiara.',), ('a photo of a tiger.',), ('a photo of a tights (clothing).', 'a photo of a leotards.'), ('a photo of a timer.', 'a photo of a stopwatch.'), ('a photo of a tinfoil.',), ('a photo of a tinsel.',), ('a photo of a tissue paper.',), ('a photo of a toast (food).',), ('a photo of a toaster.',), ('a photo of a toaster oven.',), ('a photo of a toilet.',), ('a photo of a toilet tissue.', 'a photo of a toilet paper.', 'a photo of a bathroom tissue.'), ('a photo of a tomato.',), ('a photo of a tongs.',), ('a photo of a toolbox.',), ('a photo of a toothbrush.',), ('a photo of a toothpaste.',), ('a photo of a toothpick.',), ('a photo of a cover.',), ('a photo of a tortilla.',), ('a photo of a tow truck.',), ('a photo of a towel.',), ('a photo of a towel rack.', 'a photo of a towel rail.', 'a photo of a towel bar.'), ('a photo of a toy.',), ('a photo of a tractor (farm equipment).',), ('a photo of a traffic light.',), ('a photo of a dirt bike.',), ('a photo of a trailer truck.', 'a photo of a tractor trailer.', 'a photo of a trucking rig.', 'a photo of a articulated lorry.', 'a photo of a semi truck.'), ('a photo of a train (railroad vehicle).', 'a photo of a railroad train.'), ('a photo of a trampoline.',), ('a photo of a tray.',), ('a photo of a trench coat.',), ('a photo of a triangle (musical instrument).',), ('a photo of a tricycle.',), ('a photo of a tripod.',), ('a photo of a trousers.', 'a photo of a pants (clothing).'), ('a photo of a truck.',), ('a photo of a truffle (chocolate).', 'a photo of a chocolate truffle.'), ('a photo of a trunk.',), ('a photo of a vat.',), ('a photo of a turban.',), ('a photo of a turkey (food).',), ('a photo of a turnip.',), ('a photo of a turtle.',), ('a photo of a turtleneck (clothing).', 'a photo of a polo-neck.'), ('a photo of a typewriter.',), ('a photo of a umbrella.',), ('a photo of a underwear.', 'a photo of a underclothes.', 'a photo of a underclothing.', 'a photo of a underpants.'), ('a photo of a unicycle.',), ('a photo of a urinal.',), ('a photo of a urn.',), ('a photo of a vacuum cleaner.',), ('a photo of a vase.',), ('a photo of a vending machine.',), ('a photo of a vent.', 'a photo of a blowhole.', 'a photo of a air vent.'), ('a photo of a vest.', 'a photo of a waistcoat.'), ('a photo of a videotape.',), ('a photo of a vinegar.',), ('a photo of a violin.', 'a photo of a fiddle.'), ('a photo of a vodka.',), ('a photo of a volleyball.',), ('a photo of a vulture.',), ('a photo of a waffle.',), ('a photo of a waffle iron.',), ('a photo of a wagon.',), ('a photo of a wagon wheel.',), ('a photo of a walking stick.',), ('a photo of a wall clock.',), ('a photo of a wall socket.', 'a photo of a wall plug.', 'a photo of a electric outlet.', 'a photo of a electrical outlet.', 'a photo of a outlet.', 'a photo of a electric receptacle.'), ('a photo of a wallet.', 'a photo of a billfold.'), ('a photo of a walrus.',), ('a photo of a wardrobe.',), ('a photo of a washbasin.', 'a photo of a basin (for washing).', 'a photo of a washbowl.', 'a photo of a washstand.', 'a photo of a handbasin.'), ('a photo of a automatic washer.', 'a photo of a washing machine.'), ('a photo of a watch.', 'a photo of a wristwatch.'), ('a photo of a water bottle.',), ('a photo of a water cooler.',), ('a photo of a water faucet.', 'a photo of a water tap.', 'a photo of a tap (water faucet).'), ('a photo of a water heater.', 'a photo of a hot-water heater.'), ('a photo of a water jug.',), ('a photo of a water gun.', 'a photo of a squirt gun.'), ('a photo of a water scooter.', 'a photo of a sea scooter.', 'a photo of a jet ski.'), ('a photo of a water ski.',), ('a photo of a water tower.',), ('a photo of a watering can.',), ('a photo of a watermelon.',), ('a photo of a weathervane.', 'a photo of a vane (weathervane).', 'a photo of a wind vane.'), ('a photo of a webcam.',), ('a photo of a wedding cake.', 'a photo of a bridecake.'), ('a photo of a wedding ring.', 'a photo of a wedding band.'), ('a photo of a wet suit.',), ('a photo of a wheel.',), ('a photo of a wheelchair.',), ('a photo of a whipped cream.',), ('a photo of a whistle.',), ('a photo of a wig.',), ('a photo of a wind chime.',), ('a photo of a windmill.',), ('a photo of a window box (for plants).',), ('a photo of a windshield wiper.', 'a photo of a windscreen wiper.', 'a photo of a wiper (for windshield/screen).'), ('a photo of a windsock.', 'a photo of a air sock.', 'a photo of a air-sleeve.', 'a photo of a wind sleeve.', 'a photo of a wind cone.'), ('a photo of a wine bottle.',), ('a photo of a wine bucket.', 'a photo of a wine cooler.'), ('a photo of a wineglass.',), ('a photo of a blinder (for horses).',), ('a photo of a wok.',), ('a photo of a wolf.',), ('a photo of a wooden spoon.',), ('a photo of a wreath.',), ('a photo of a wrench.', 'a photo of a spanner.'), ('a photo of a wristband.',), ('a photo of a wristlet.', 'a photo of a wrist band.'), ('a photo of a yacht.',), ('a photo of a yogurt.', 'a photo of a yoghurt.', 'a photo of a yoghourt.'), ('a photo of a yoke (animal equipment).',), ('a photo of a zebra.',), ('a photo of a zucchini.', 'a photo of a courgette.'), ('a photo of a banner.', 'a photo of a banners.'), ('a photo of a blanket.', 'a photo of a blankets.'), ('a photo of a bridge.',), ('a photo of a cardboard.',), ('a photo of a counter.',), ('a photo of a curtain.', 'a photo of a curtains.'), ('a photo of a door.', 'a photo of a doors.'), ('a photo of a wood floor.',), ('a photo of a flower.', 'a photo of a flowers.'), ('a photo of a fruit.', 'a photo of a fruits.'), ('a photo of a gravel.',), ('a photo of a house.',), ('a photo of a lamp.', 'a photo of a bulb.', 'a photo of a lamps.', 'a photo of a bulbs.'), ('a photo of a mirror.',), ('a photo of a tennis net.',), ('a photo of a pillow.', 'a photo of a pillows.'), ('a photo of a platform.',), ('a photo of a playingfield.', 'a photo of a tennis court.', 'a photo of a baseball feild.', 'a photo of a soccer field.', 'a photo of a tennis field.'), ('a photo of a railroad.',), ('a photo of a river.',), ('a photo of a road.',), ('a photo of a roof.',), ('a photo of a sand.',), ('a photo of a sea.', 'a photo of a sea wave.', 'a photo of a wave.', 'a photo of a waves.'), ('a photo of a shelf.',), ('a photo of a snow.',), ('a photo of a stairs.',), ('a photo of a tent.',), ('a photo of a towel.',), ('a photo of a brick wall.',), ('a photo of a stone wall.',), ('a photo of a tile wall.',), ('a photo of a wood wall.',), ('a photo of a water.',), ('a photo of a window blind.',), ('a photo of a window.',), ('a photo of a tree.', 'a photo of a trees.', 'a photo of a palm tree.', 'a photo of a bushes.'), ('a photo of a fence.', 'a photo of a fences.'), ('a photo of a ceiling.',), ('a photo of a sky.', 'a photo of a clouds.'), ('a photo of a cabinet.', 'a photo of a cabinets.'), ('a photo of a table.',), ('a photo of a floor.', 'a photo of a flooring.', 'a photo of a tile floor.'), ('a photo of a pavement.',), ('a photo of a mountain.', 'a photo of a mountains.'), ('a photo of a grass.',), ('a photo of a dirt.',), ('a photo of a paper.',), ('a photo of a food.',), ('a photo of a building.', 'a photo of a buildings.'), ('a photo of a rock.',), ('a photo of a wall.', 'a photo of a walls.'), ('a photo of a rug.',), ('a photo of a wall.', 'a photo of a walls.', 'a photo of a brick wall.', 'a photo of a stone wall.', 'a photo of a interior wall.'), ('a photo of a building.', 'a photo of a buildings.', 'a photo of a edifice.', 'a photo of a edifices.'), ('a photo of a sky.', 'a photo of a clouds.'), ('a photo of a floor.', 'a photo of a flooring.'), ('a photo of a tree.', 'a photo of a trees.'), ('a photo of a ceiling.',), ('a photo of a road.', 'a photo of a route.', 'a photo of a street.', 'a photo of a roads.', 'a photo of a streets.', 'a photo of a routes.'), ('a photo of a grass.', 'a photo of a grass field.'), ('a photo of a sidewalk.', 'a photo of a pavement.'), ('a photo of a earth.', 'a photo of a ground.'), ('a photo of a mountain.', 'a photo of a mount.', 'a photo of a mountains.'), ('a photo of a plant.', 'a photo of a flora.', 'a photo of a plant life.', 'a photo of a plants.', 'a photo of a bushes.'), ('a photo of a water.',), ('a photo of a house exterior.',), ('a photo of a sea.', 'a photo of a ocean.'), ('a photo of a rug.', 'a photo of a carpet.', 'a photo of a carpeting.'), ('a photo of a field.',), ('a photo of a rock.', 'a photo of a stone.', 'a photo of a rocks.', 'a photo of a stones.'), ('a photo of a pedestal.',), ('a photo of a sand.',), ('a photo of a skyscraper.', 'a photo of a skyscrapers.'), ('a photo of a grandstand.', 'a photo of a covered stand.'), ('a photo of a path.',), ('a photo of a runway.',), ('a photo of a stairway.', 'a photo of a staircase.'), ('a photo of a river.',), ('a photo of a bridge.', 'a photo of a span.'), ('a photo of a window screen.', 'a photo of a door screen.'), ('a photo of a hill.',), ('a photo of a bar.',), ('a photo of a hovel.', 'a photo of a hut.', 'a photo of a hutch.', 'a photo of a shack.', 'a photo of a shanty.'), ('a photo of a tower.', 'a photo of a towers.'), ('a photo of a dirt track.',), ('a photo of a land.', 'a photo of a soil.'), ('a photo of a escalator.', 'a photo of a moving staircase.', 'a photo of a moving stairway.'), ('a photo of a buffet.', 'a photo of a sideboard.'), ('a photo of a poster.', 'a photo of a posting.', 'a photo of a placard.', 'a photo of a notice.', 'a photo of a bill.', 'a photo of a card.'), ('a photo of a stage.',), ('a photo of a conveyer belt.', 'a photo of a conveyor belt.', 'a photo of a conveyer.', 'a photo of a conveyor.', 'a photo of a transporter.'), ('a photo of a canopy.',), ('a photo of a swimming pool.', 'a photo of a swimming bath.'), ('a photo of a waterfall.', 'a photo of a falls.'), ('a photo of a tent.', 'a photo of a collapsible shelter.'), ('a photo of a cradle.',), ('a photo of a tank.', 'a photo of a storage tank.'), ('a photo of a lake.',), ('a photo of a blanket.', 'a photo of a cover.'), ('a photo of a pier.', 'a photo of a wharf.', 'a photo of a wharfage.', 'a photo of a dock.'), ('a photo of a crt screen.',), ('a photo of a shower.',))\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details."
+ ]
+ }
+ ],
+ "source": [
+ "vocab = \"\"\n",
+ "label_list = [\"COCO\", \"ADE\", \"LVIS\"]\n",
+ "inference(input_image, vocab, label_list)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.15 ('odise')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "28ed3b998f5e77364eb443e6a07ff8c559c1427ace11964088e2f67d6ba1f461"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/demo.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c8af51c2e95a49a3140fb21d971395581c1ebff
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/demo/demo.py
@@ -0,0 +1,483 @@
+#!/usr/bin/env python
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from: https://github.com/facebookresearch/detectron2/blob/master/demo/demo.py
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import argparse
+import glob
+import itertools
+import numpy as np
+import os
+import tempfile
+import time
+import warnings
+from contextlib import ExitStack
+import cv2
+import nltk
+import torch
+import tqdm
+from detectron2.config import LazyConfig, instantiate
+from detectron2.data import MetadataCatalog
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+from detectron2.engine import create_ddp_model
+from detectron2.evaluation import inference_context
+from detectron2.utils.env import seed_all_rng
+from detectron2.utils.logger import setup_logger
+from detectron2.utils.video_visualizer import VideoVisualizer
+from detectron2.utils.visualizer import ColorMode, Visualizer, random_color
+from mask2former.data.datasets.register_ade20k_panoptic import ADE20K_150_CATEGORIES
+from torch import nn
+
+from odise.checkpoint import ODISECheckpointer
+from odise.config import instantiate_odise
+from odise.data import get_openseg_labels
+from odise.engine.defaults import get_model_from_module
+
+nltk.download("popular", quiet=True)
+nltk.download("universal_tagset", quiet=True)
+
+# constants
+WINDOW_NAME = "ODISE demo"
+
+COCO_THING_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("coco_panoptic", True))
+ if COCO_CATEGORIES[idx]["isthing"] == 1
+]
+COCO_THING_COLORS = [c["color"] for c in COCO_CATEGORIES if c["isthing"] == 1]
+COCO_STUFF_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("coco_panoptic", True))
+ if COCO_CATEGORIES[idx]["isthing"] == 0
+]
+COCO_STUFF_COLORS = [c["color"] for c in COCO_CATEGORIES if c["isthing"] == 0]
+
+ADE_THING_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("ade20k_150", True))
+ if ADE20K_150_CATEGORIES[idx]["isthing"] == 1
+]
+ADE_THING_COLORS = [c["color"] for c in ADE20K_150_CATEGORIES if c["isthing"] == 1]
+ADE_STUFF_CLASSES = [
+ label
+ for idx, label in enumerate(get_openseg_labels("ade20k_150", True))
+ if ADE20K_150_CATEGORIES[idx]["isthing"] == 0
+]
+ADE_STUFF_COLORS = [c["color"] for c in ADE20K_150_CATEGORIES if c["isthing"] == 0]
+
+LVIS_CLASSES = get_openseg_labels("lvis_1203", True)
+# use beautiful coco colors
+LVIS_COLORS = list(
+ itertools.islice(itertools.cycle([c["color"] for c in COCO_CATEGORIES]), len(LVIS_CLASSES))
+)
+
+
+def get_nouns(caption, with_preposition):
+ if with_preposition:
+ # Taken from Su Nam Kim Paper...
+ grammar = r"""
+ NBAR:
+ {*} # Nouns and Adjectives, terminated with Nouns
+
+ NP:
+ {} # Above, connected with in/of/etc...
+ {} # If pattern is not found, just a single NBAR is ok
+ """
+ else:
+ # Taken from Su Nam Kim Paper...
+ grammar = r"""
+ NBAR:
+ {*} # Nouns and Adjectives, terminated with Nouns
+
+ NP:
+ {} # If pattern is not found, just a single NBAR is ok
+ """
+ tokenized = nltk.word_tokenize(caption)
+ chunker = nltk.RegexpParser(grammar)
+
+ chunked = chunker.parse(nltk.pos_tag(tokenized))
+ continuous_chunk = []
+ current_chunk = []
+
+ for subtree in chunked:
+ if isinstance(subtree, nltk.Tree):
+ current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
+ elif current_chunk:
+ named_entity = " ".join(current_chunk)
+ if named_entity not in continuous_chunk:
+ continuous_chunk.append(named_entity)
+ current_chunk = []
+ else:
+ continue
+
+ if current_chunk:
+ named_entity = " ".join(current_chunk)
+ if named_entity not in continuous_chunk:
+ continuous_chunk.append(named_entity)
+
+ return continuous_chunk
+
+
+class VisualizationDemo(object):
+ def __init__(self, model, metadata, aug, instance_mode=ColorMode.IMAGE):
+ """
+ Args:
+ model (nn.Module):
+ metadata (MetadataCatalog): image metadata.
+ instance_mode (ColorMode):
+ parallel (bool): whether to run the model in different processes from visualization.
+ Useful since the visualization logic can be slow.
+ """
+ self.model = model
+ self.metadata = metadata
+ self.aug = aug
+ self.cpu_device = torch.device("cpu")
+ self.instance_mode = instance_mode
+
+ def predict(self, original_image):
+ """
+ Args:
+ original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+
+ Returns:
+ predictions (dict):
+ the output of the model for one image only.
+ See :doc:`/tutorials/models` for details about the format.
+ """
+ height, width = original_image.shape[:2]
+ aug_input = T.AugInput(original_image, sem_seg=None)
+ self.aug(aug_input)
+ image = aug_input.image
+ image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+
+ inputs = {"image": image, "height": height, "width": width}
+ predictions = self.model([inputs])[0]
+ return predictions
+
+ def run_on_image(self, image):
+ """
+ Args:
+ image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+ This is the format used by OpenCV.
+ Returns:
+ predictions (dict): the output of the model.
+ vis_output (VisImage): the visualized image output.
+ """
+ vis_output = None
+ predictions = self.predict(image)
+ visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
+ if "panoptic_seg" in predictions:
+ panoptic_seg, segments_info = predictions["panoptic_seg"]
+ vis_output = visualizer.draw_panoptic_seg(
+ panoptic_seg.to(self.cpu_device), segments_info
+ )
+ else:
+ if "sem_seg" in predictions:
+ vis_output = visualizer.draw_sem_seg(
+ predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
+ )
+ if "instances" in predictions:
+ instances = predictions["instances"].to(self.cpu_device)
+ vis_output = visualizer.draw_instance_predictions(predictions=instances)
+
+ return predictions, vis_output
+
+ def _frame_from_video(self, video):
+ while video.isOpened():
+ success, frame = video.read()
+ if success:
+ yield frame
+ else:
+ break
+
+ def run_on_video(self, video):
+ """
+ Visualizes predictions on frames of the input video.
+
+ Args:
+ video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be
+ either a webcam or a video file.
+
+ Yields:
+ ndarray: BGR visualizations of each video frame.
+ """
+ video_visualizer = VideoVisualizer(self.metadata, self.instance_mode)
+
+ def process_predictions(frame, predictions):
+ if "panoptic_seg" in predictions:
+ panoptic_seg, segments_info = predictions["panoptic_seg"]
+ vis_frame = video_visualizer.draw_panoptic_seg_predictions(
+ frame, panoptic_seg.to(self.cpu_device), segments_info
+ )
+ elif "instances" in predictions:
+ predictions = predictions["instances"].to(self.cpu_device)
+ vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
+ elif "sem_seg" in predictions:
+ vis_frame = video_visualizer.draw_sem_seg(
+ frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
+ )
+
+ # Converts Matplotlib RGB format to OpenCV BGR format
+ vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
+ return vis_frame
+
+ frame_gen = self._frame_from_video(video)
+ for frame in frame_gen:
+ frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+ yield process_predictions(frame, self.predict(frame))
+
+
+def test_opencv_video_format(codec, file_ext):
+ with tempfile.TemporaryDirectory(prefix="video_format_test") as dir:
+ filename = os.path.join(dir, "test_file" + file_ext)
+ writer = cv2.VideoWriter(
+ filename=filename,
+ fourcc=cv2.VideoWriter_fourcc(*codec),
+ fps=float(30),
+ frameSize=(10, 10),
+ isColor=True,
+ )
+ [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)]
+ writer.release()
+ if os.path.isfile(filename):
+ return True
+ return False
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="ODISE demo.")
+ parser.add_argument(
+ "--config-file",
+ default="configs/Panoptic/odise_label_coco_50e.py",
+ type=str,
+ help="path to config file",
+ )
+ parser.add_argument(
+ "--init-from",
+ type=str,
+ help="init from the given checkpoint",
+ default="odise://Panoptic/odise_label_coco_50e",
+ )
+ parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
+ parser.add_argument("--video-input", help="Path to video file.")
+ parser.add_argument(
+ "--input",
+ nargs="+",
+ help="A list of space separated input images; "
+ "or a single glob pattern such as 'directory/*.jpg'",
+ )
+ parser.add_argument(
+ "--output",
+ help="A file or directory to save output visualizations. "
+ "If not given, will show output in an OpenCV window.",
+ )
+ parser.add_argument(
+ "--vocab",
+ help="extra vocabulary, in format 'a1,a2;b1,b2',"
+ "where a1,a2 are synonyms vocabularies for the first class"
+ "first word will be displayed as the class name",
+ )
+ parser.add_argument(
+ "--label",
+ help="label set to use, could be multiple options from 'COCO', 'ADE' and 'LVIS'.",
+ choices=["COCO", "ADE", "LVIS", ""],
+ nargs="+",
+ default=["COCO", "ADE", "LVIS"],
+ )
+ parser.add_argument("--caption", help="caption contains nouns (noun phrases) to be segmented")
+ parser.add_argument(
+ "opts",
+ help="Modify config options at the end of the command, " "in 'path.key=value' format",
+ default=None,
+ nargs=argparse.REMAINDER,
+ )
+ args = parser.parse_args()
+
+ setup_logger()
+ logger = setup_logger(name="odise")
+
+ cfg = LazyConfig.load(args.config_file)
+ cfg.model.overlap_threshold = 0
+ cfg.model.clip_head.alpha = 0.35
+ cfg.model.clip_head.beta = 0.65
+ seed_all_rng(42)
+
+ dataset_cfg = cfg.dataloader.test
+ wrapper_cfg = cfg.dataloader.wrapper
+
+ extra_classes = []
+
+ if args.vocab:
+ for words in args.vocab.split(";"):
+ extra_classes.append([word.strip() for word in words.split(",")])
+
+ if args.caption:
+ caption_words = []
+ caption_words.extend(get_nouns(args.caption, True))
+ caption_words.extend(get_nouns(args.caption, False))
+ for word in list(set(caption_words)):
+ extra_classes.append([word.strip()])
+
+ logger.info(f"extra classes: {extra_classes}")
+ extra_colors = [random_color(rgb=True, maximum=1) for _ in range(len(extra_classes))]
+
+ # demo_thing_classes = extra_classes + COCO_THING_CLASSES + ADE_THING_CLASSES + LVIS_CLASSES
+ # demo_stuff_classes = COCO_STUFF_CLASSES + ADE_STUFF_CLASSES
+ # demo_thing_colors = extra_colors + COCO_THING_COLORS + ADE_THING_COLORS + LVIS_COLORS
+ # demo_stuff_colors = COCO_STUFF_COLORS + ADE_STUFF_COLORS
+
+ demo_thing_classes = extra_classes
+ demo_stuff_classes = []
+ demo_thing_colors = extra_colors
+ demo_stuff_colors = []
+
+ if "COCO" in args.label:
+ demo_thing_classes += COCO_THING_CLASSES
+ demo_stuff_classes += COCO_STUFF_CLASSES
+ demo_thing_colors += COCO_THING_COLORS
+ demo_stuff_colors = COCO_STUFF_COLORS
+ if "ADE" in args.label:
+ demo_thing_classes += ADE_THING_CLASSES
+ demo_stuff_classes += ADE_STUFF_CLASSES
+ demo_thing_colors += ADE_THING_COLORS
+ demo_stuff_colors += ADE_STUFF_COLORS
+ if "LVIS" in args.label:
+ demo_thing_classes += LVIS_CLASSES
+ demo_thing_colors += LVIS_COLORS
+
+ demo_metadata = MetadataCatalog.get("odise_demo_metadata")
+ demo_metadata.thing_classes = [c[0] for c in demo_thing_classes]
+ demo_metadata.stuff_classes = [
+ *demo_metadata.thing_classes,
+ *[c[0] for c in demo_stuff_classes],
+ ]
+ demo_metadata.thing_colors = demo_thing_colors
+ demo_metadata.stuff_colors = demo_thing_colors + demo_stuff_colors
+ demo_metadata.stuff_dataset_id_to_contiguous_id = {
+ idx: idx for idx in range(len(demo_metadata.stuff_classes))
+ }
+ demo_metadata.thing_dataset_id_to_contiguous_id = {
+ idx: idx for idx in range(len(demo_metadata.thing_classes))
+ }
+
+ wrapper_cfg.labels = demo_thing_classes + demo_stuff_classes
+ wrapper_cfg.metadata = demo_metadata
+
+ aug = instantiate(dataset_cfg.mapper).augmentations
+
+ model = instantiate_odise(cfg.model)
+ model.to(cfg.train.device)
+ ODISECheckpointer(model).load(args.init_from)
+ # look for the last wrapper
+ while "model" in wrapper_cfg:
+ wrapper_cfg = wrapper_cfg.model
+ wrapper_cfg.model = get_model_from_module(model)
+
+ inference_model = create_ddp_model(instantiate(cfg.dataloader.wrapper))
+ with ExitStack() as stack:
+ if isinstance(inference_model, nn.Module):
+ stack.enter_context(inference_context(inference_model))
+ stack.enter_context(torch.no_grad())
+ demo = VisualizationDemo(inference_model, demo_metadata, aug)
+
+ if args.input:
+ if len(args.input) == 1:
+ args.input = glob.glob(os.path.expanduser(args.input[0]))
+ assert args.input, "The input path(s) was not found"
+ for path in tqdm.tqdm(args.input, disable=not args.output):
+ # use PIL, to be consistent with evaluation
+ img = utils.read_image(path, format="RGB")
+ start_time = time.time()
+ predictions, visualized_output = demo.run_on_image(img)
+ logger.info(
+ "{}: {} in {:.2f}s".format(
+ path,
+ "detected {} instances".format(len(predictions["instances"]))
+ if "instances" in predictions
+ else "finished",
+ time.time() - start_time,
+ )
+ )
+
+ if args.output:
+ if os.path.isdir(args.output):
+ assert os.path.isdir(args.output), args.output
+ out_filename = os.path.join(args.output, os.path.basename(path))
+ else:
+ assert len(args.input) == 1, "Please specify a directory with args.output"
+ out_filename = args.output
+ visualized_output.save(out_filename)
+ else:
+ cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
+ cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
+ if cv2.waitKey(0) == 27:
+ break # esc to quit
+ elif args.webcam:
+ assert args.input is None, "Cannot have both --input and --webcam!"
+ assert args.output is None, "output not yet supported with --webcam!"
+ cam = cv2.VideoCapture(0)
+ for vis in tqdm.tqdm(demo.run_on_video(cam)):
+ cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
+ cv2.imshow(WINDOW_NAME, vis)
+ if cv2.waitKey(1) == 27:
+ break # esc to quit
+ cam.release()
+ cv2.destroyAllWindows()
+ elif args.video_input:
+ video = cv2.VideoCapture(args.video_input)
+ width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+ height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ frames_per_second = video.get(cv2.CAP_PROP_FPS)
+ num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+ basename = os.path.basename(args.video_input)
+ codec, file_ext = (
+ ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4")
+ )
+ if codec == ".mp4v":
+ warnings.warn("x264 codec not available, switching to mp4v")
+ if args.output:
+ if os.path.isdir(args.output):
+ output_fname = os.path.join(args.output, basename)
+ output_fname = os.path.splitext(output_fname)[0] + file_ext
+ else:
+ output_fname = args.output
+ assert not os.path.isfile(output_fname), output_fname
+ output_file = cv2.VideoWriter(
+ filename=output_fname,
+ # some installation of opencv may not support x264 (due to its license),
+ # you can try other format (e.g. MPEG)
+ fourcc=cv2.VideoWriter_fourcc(*codec),
+ fps=float(frames_per_second),
+ frameSize=(width, height),
+ isColor=True,
+ )
+ assert os.path.isfile(args.video_input)
+ for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames):
+ if args.output:
+ output_file.write(vis_frame)
+ else:
+ cv2.namedWindow(basename, cv2.WINDOW_NORMAL)
+ cv2.imshow(basename, vis_frame)
+ if cv2.waitKey(1) == 27:
+ break # esc to quit
+ video.release()
+ if args.output:
+ output_file.release()
+ else:
+ cv2.destroyAllWindows()
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/docker/Dockerfile b/Code/Baselines/GeoAware-SC/third_party/ODISE/docker/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..0ede023a72c662f3d2f705e7622e5cf231ec45c8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/docker/Dockerfile
@@ -0,0 +1,83 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel
+
+WORKDIR /workspace
+
+ARG DEBIAN_FRONTEND=noninteractive
+ENV TZ=US/Pacific
+
+RUN apt-get update && apt-get install -y \
+ build-essential \
+ cmake \
+ curl \
+ g++ \
+ wget \
+ bzip2 \
+ git \
+ vim \
+ tmux \
+ htop \
+ git \
+ zip \
+ unzip \
+ ca-certificates \
+ libosmesa6-dev \
+ libgl1-mesa-glx \
+ libglfw3 \
+ patchelf \
+ libglu1-mesa \
+ libxext6 \
+ libxtst6 \
+ libxrender1 \
+ libxi6 \
+ libjpeg-dev \
+ libpng-dev \
+ libopenblas-dev \
+ libopencv-dev \
+ libyaml-dev \
+ libavformat-dev \
+ libavcodec-dev \
+ libswscale-dev \
+ libavutil-dev \
+ libavfilter-dev \
+ libavdevice-dev \
+ libswresample-dev \
+ less \
+ groff \
+ mpich
+
+RUN apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Install git lfs
+RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash
+RUN apt-get install -y git-lfs
+RUN git lfs install
+
+
+RUN curl https://rclone.org/install.sh | bash
+
+# Set timezone
+RUN ln -sf /usr/share/zoneinfo/US/Pacific /etc/localtime
+
+# Set CUDA_ROOT
+RUN export CUDA_HOME="/usr/local/cuda"
+
+# Install pytorch
+#RUN conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c conda-forge -y
+
+# Install zsh
+RUN sh -c "$(wget -O- https://github.com/deluan/zsh-in-docker/releases/download/v1.1.1/zsh-in-docker.sh)" -t robbyrussell -p git
+
+# Set a fixed model cache directory.
+ENV FVCORE_CACHE="/tmp"
+
+ENV HOME /workspace
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/PKG-INFO b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..945566688ffd698931325a27fed800ece46829fe
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/PKG-INFO
@@ -0,0 +1,31 @@
+Metadata-Version: 2.4
+Name: odise
+Version: 0.1
+Summary: Open-vocabulary DIffusion-based Panoptic Segmentation
+Home-page: https://github.com/NVlabs/ODISE
+Author: Jiarui Xu
+Requires-Python: >=3.8
+License-File: LICENSE
+Requires-Dist: timm==0.6.11
+Requires-Dist: opencv-python==4.6.0.66
+Requires-Dist: diffdist==0.1
+Requires-Dist: nltk>=3.6.2
+Requires-Dist: einops>=0.3.0
+Requires-Dist: wandb>=0.12.11
+Requires-Dist: omegaconf==2.1.1
+Requires-Dist: open-clip-torch==2.0.2
+Requires-Dist: stable-diffusion-sdkit==2.1.3
+Provides-Extra: dev
+Requires-Dist: flake8==3.8.1; extra == "dev"
+Requires-Dist: isort==4.3.21; extra == "dev"
+Requires-Dist: flake8-bugbear; extra == "dev"
+Requires-Dist: flake8-comprehensions; extra == "dev"
+Requires-Dist: click==8.0.4; extra == "dev"
+Requires-Dist: importlib-metadata==4.11.3; extra == "dev"
+Dynamic: author
+Dynamic: home-page
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/SOURCES.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9e976b61e9e8b6d8000e42cb04bd7048e2f7e2d5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/SOURCES.txt
@@ -0,0 +1,77 @@
+LICENSE
+MANIFEST.in
+README.md
+setup.cfg
+setup.py
+odise/__init__.py
+odise.egg-info/PKG-INFO
+odise.egg-info/SOURCES.txt
+odise.egg-info/dependency_links.txt
+odise.egg-info/requires.txt
+odise.egg-info/top_level.txt
+odise/checkpoint/__init__.py
+odise/checkpoint/odise_checkpointer.py
+odise/config/__init__.py
+odise/config/instantiate.py
+odise/config/utils.py
+odise/data/__init__.py
+odise/data/build.py
+odise/data/dataset_mapper.py
+odise/data/datasets/__init__.py
+odise/data/datasets/register_coco_caption.py
+odise/data/datasets/register_pascal.py
+odise/data/datasets/openseg_labels/ade20k_150.txt
+odise/data/datasets/openseg_labels/ade20k_150_with_prompt_eng.txt
+odise/data/datasets/openseg_labels/ade20k_847.txt
+odise/data/datasets/openseg_labels/ade20k_847_with_prompt_eng.txt
+odise/data/datasets/openseg_labels/coco_panoptic.txt
+odise/data/datasets/openseg_labels/coco_panoptic_with_prompt_eng.txt
+odise/data/datasets/openseg_labels/lvis_1203.txt
+odise/data/datasets/openseg_labels/lvis_1203_with_prompt_eng.txt
+odise/data/datasets/openseg_labels/pascal_context_459.txt
+odise/data/datasets/openseg_labels/pascal_context_459_with_prompt_eng.txt
+odise/data/datasets/openseg_labels/pascal_context_59.txt
+odise/data/datasets/openseg_labels/pascal_context_59_with_prompt_eng.txt
+odise/data/datasets/openseg_labels/pascal_voc_21.txt
+odise/data/datasets/openseg_labels/pascal_voc_21_with_prompt_eng.txt
+odise/engine/__init__.py
+odise/engine/defaults.py
+odise/engine/hooks.py
+odise/engine/train_loop.py
+odise/evaluation/__init__.py
+odise/evaluation/d2_evaluator.py
+odise/evaluation/evaluator.py
+odise/model_zoo/__init__.py
+odise/model_zoo/model_zoo.py
+odise/model_zoo/configs/Panoptic/odise_caption_coco_50e.py
+odise/model_zoo/configs/Panoptic/odise_label_coco_50e.py
+odise/model_zoo/configs/common/optim.py
+odise/model_zoo/configs/common/schedule.py
+odise/model_zoo/configs/common/train.py
+odise/model_zoo/configs/common/data/coco_panoptic_semseg.py
+odise/model_zoo/configs/common/data/pano_open_d2_eval.py
+odise/model_zoo/configs/common/models/mask_generator_with_caption.py
+odise/model_zoo/configs/common/models/mask_generator_with_label.py
+odise/model_zoo/configs/common/models/odise_with_caption.py
+odise/model_zoo/configs/common/models/odise_with_label.py
+odise/modeling/__init__.py
+odise/modeling/preprocess.py
+odise/modeling/backbone/__init__.py
+odise/modeling/backbone/feature_extractor.py
+odise/modeling/diffusion/__init__.py
+odise/modeling/diffusion/diffusion_builder.py
+odise/modeling/diffusion/gaussian_diffusion.py
+odise/modeling/diffusion/resample.py
+odise/modeling/diffusion/respace.py
+odise/modeling/meta_arch/__init__.py
+odise/modeling/meta_arch/clip.py
+odise/modeling/meta_arch/helper.py
+odise/modeling/meta_arch/ldm.py
+odise/modeling/meta_arch/odise.py
+odise/modeling/wrapper/__init__.py
+odise/modeling/wrapper/pano_wrapper.py
+odise/utils/__init__.py
+odise/utils/collect_env.py
+odise/utils/events.py
+odise/utils/file_io.py
+odise/utils/parameter_count.py
\ No newline at end of file
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/dependency_links.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/requires.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1b234eadbfed84f1c8a44801c5b1f0cd90d8a989
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/requires.txt
@@ -0,0 +1,17 @@
+timm==0.6.11
+opencv-python==4.6.0.66
+diffdist==0.1
+nltk>=3.6.2
+einops>=0.3.0
+wandb>=0.12.11
+omegaconf==2.1.1
+open-clip-torch==2.0.2
+stable-diffusion-sdkit==2.1.3
+
+[dev]
+flake8==3.8.1
+isort==4.3.21
+flake8-bugbear
+flake8-comprehensions
+click==8.0.4
+importlib-metadata==4.11.3
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/top_level.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2123a48e36f80fdd469e84a06721cd9c21d7b77c
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise.egg-info/top_level.txt
@@ -0,0 +1 @@
+odise
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b01ac4488a8c39cff0a0f5c0c83345e19769de25
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+# This line will be programatically read/write by setup.py.
+# Leave them at the bottom of this file and don't touch them.
+__version__ = "0.1"
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3271688d31e17402ff6ca002a8b6e4238802e32d
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .odise_checkpointer import ODISECheckpointer
+
+__all__ = ["ODISECheckpointer"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__pycache__/odise_checkpointer.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/__pycache__/odise_checkpointer.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/odise_checkpointer.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/odise_checkpointer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb281c98f6de79e52efd294970633423c73a35b5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/checkpoint/odise_checkpointer.py
@@ -0,0 +1,140 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os.path as osp
+from collections import defaultdict
+from typing import List
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.checkpoint.c2_model_loading import align_and_update_state_dicts
+from fvcore.common.checkpoint import Checkpointer
+
+from odise.utils.file_io import PathManager
+
+
+def _longest_common_prefix(names: List[str]) -> str:
+ """
+ ["abc.zfg", "abc.zef"] -> "abc."
+ """
+ names = [n.split(".") for n in names]
+ m1, m2 = min(names), max(names)
+ ret = []
+ for a, b in zip(m1, m2):
+ if a == b:
+ ret.append(a)
+ else:
+ # break for the first non-matching element
+ # Fixing BUG in detectron2
+ break
+ ret = ".".join(ret) + "." if len(ret) else ""
+ return ret
+
+
+def group_by_prefix(names):
+ grouped_names = defaultdict(list)
+
+ for name in names:
+ grouped_names[name.split(".")[0]].append(name)
+
+ return grouped_names
+
+
+class ODISECheckpointer(DetectionCheckpointer):
+ def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
+ super().__init__(
+ model=model, save_dir=save_dir, save_to_disk=save_to_disk, **checkpointables
+ )
+ self.path_manager = PathManager
+
+ def _load_model(self, checkpoint):
+
+ if hasattr(self.model, "preprocess_state_dict"):
+ self.logger.info("Preprocessing model state_dict")
+ checkpoint["model"] = self.model.preprocess_state_dict(checkpoint["model"])
+ if checkpoint.get("matching_heuristics", False):
+ self._convert_ndarray_to_tensor(checkpoint["model"])
+ # convert weights by name-matching heuristics
+ checkpoint["model"] = align_and_update_state_dicts(
+ self.model.state_dict(),
+ checkpoint["model"],
+ c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
+ )
+
+ # for non-caffe2 models, use standard ways to load it
+ incompatible = super(DetectionCheckpointer, self)._load_model(checkpoint)
+
+ model_buffers = dict(self.model.named_buffers(recurse=False))
+ for k in ["pixel_mean", "pixel_std"]:
+ # Ignore missing key message about pixel_mean/std.
+ # Though they may be missing in old checkpoints, they will be correctly
+ # initialized from config anyway.
+ if k in model_buffers:
+ try:
+ incompatible.missing_keys.remove(k)
+ except ValueError:
+ pass
+ for k in incompatible.unexpected_keys[:]:
+ # Ignore unexpected keys about cell anchors. They exist in old checkpoints
+ # but now they are non-persistent buffers and will not be in new checkpoints.
+ if "anchor_generator.cell_anchors" in k:
+ incompatible.unexpected_keys.remove(k)
+
+ removed_keys = []
+ for k in incompatible.missing_keys[:]:
+ if hasattr(self.model, "ignored_state_dict"):
+ ignored_keys = set(self.model.ignored_state_dict().keys())
+ else:
+ ignored_keys = set()
+ # Ignore clip.clip since it's fixed in DALLE2 decoder
+ # Ignore text_encoder.encoder since it's fixed in Imagen
+ if k in ignored_keys:
+ incompatible.missing_keys.remove(k)
+ removed_keys.append(k)
+ if len(removed_keys) > 0:
+ prefix_list = [
+ _longest_common_prefix(grouped_names)
+ for grouped_names in group_by_prefix(removed_keys).values()
+ ]
+ self.logger.warn(
+ "Keys with prefix are removed from state_dict:\n" + ",".join(prefix_list)
+ )
+
+ self.logger.warn(
+ f"Removed {len(removed_keys)} ignored_state_dict keys from missing_keys"
+ )
+
+ return incompatible
+
+ @staticmethod
+ def has_checkpoint_in_dir(save_dir) -> bool:
+ """
+ Returns:
+ bool: whether a checkpoint exists in the target directory.
+ """
+ save_file = osp.join(save_dir, "last_checkpoint")
+ return osp.exists(save_file)
+
+
+class LdmCheckpointer(Checkpointer):
+ def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
+ super().__init__(
+ model=model, save_dir=save_dir, save_to_disk=save_to_disk, **checkpointables
+ )
+ self.path_manager = PathManager
+
+ def _load_model(self, checkpoint):
+ # rename the keys in checkpoint
+ checkpoint["model"] = checkpoint.pop("state_dict")
+ return super()._load_model(checkpoint)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..613caa594632681bea519d566041b36ffa9f8535
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/__init__.py
@@ -0,0 +1,17 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .instantiate import instantiate_odise
+from .utils import auto_scale_workers
+
+__all__ = [
+ "instantiate_odise",
+ "auto_scale_workers",
+]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/instantiate.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/instantiate.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf8d2b2199befd72d86876310d9c05766cd10b3
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/instantiate.py
@@ -0,0 +1,21 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from detectron2.config import instantiate
+
+
+def instantiate_odise(cfg):
+ backbone = instantiate(cfg.backbone)
+ cfg.sem_seg_head.input_shape = backbone.output_shape()
+ cfg.sem_seg_head.pixel_decoder.input_shape = backbone.output_shape()
+ cfg.backbone = backbone
+ model = instantiate(cfg)
+
+ return model
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/utils.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f803fd44756e156191943640cb37369e235ea1e
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/config/utils.py
@@ -0,0 +1,82 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from copy import deepcopy
+
+
+def auto_scale_workers(cfg, num_workers: int):
+ """
+ When the config is defined for certain number of workers (according to
+ ``cfg.train.reference_world_size``) that's different from the number of
+ workers currently in use, returns a new cfg where the total batch size
+ is scaled so that the per-GPU batch size stays the same as the
+ original ``total_batch_size // reference_world_size``.
+
+ Other config options are also scaled accordingly:
+ * training steps and warmup steps are scaled inverse proportionally.
+ * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.
+
+ For example, with the original config like the following:
+
+ .. code-block:: yaml
+
+ dataloader.train.total_batch_size: 16
+ optimizer.lr: 0.1
+ train.reference_world_size: 8
+ train.max_iter: 5000
+ train.checkpointer.period: 1000
+
+ When this config is used on 16 GPUs instead of the reference number 8,
+ calling this method will return a new config with:
+
+ .. code-block:: yaml
+
+ dataloader.train.total_batch_size: 32
+ optimizer.lr: 0.2
+ train.reference_world_size: 16
+ train.max_iter: 2500
+ train.checkpointer.period: 500
+
+ Note that both the original config and this new config can be trained on 16 GPUs.
+ It's up to user whether to enable this feature (by setting ``reference_world_size``).
+
+ Returns:
+ CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.
+ """
+ old_world_size = cfg.train.reference_world_size
+ if old_world_size == 0 or old_world_size == num_workers:
+ print("No need to scale the config.")
+ return cfg
+ cfg = deepcopy(cfg)
+
+ assert cfg.dataloader.train.total_batch_size % old_world_size == 0, (
+ f"Invalid reference_world_size in config! "
+ f"{cfg.dataloader.train.total_batch_size} % {old_world_size} != 0"
+ )
+ scale = num_workers / old_world_size
+ bs = cfg.dataloader.train.total_batch_size = int(
+ round(cfg.dataloader.train.total_batch_size * scale)
+ )
+ lr = cfg.optimizer.lr = cfg.optimizer.lr * scale
+ max_iter = cfg.train.max_iter = int(round(cfg.train.max_iter / scale))
+ cfg.train.eval_period = int(round(cfg.train.eval_period / scale))
+ cfg.train.checkpointer.period = int(round(cfg.train.checkpointer.period / scale))
+ cfg.train.reference_world_size = num_workers # maintain invariant
+ print(
+ f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, " f"max_iter={max_iter}."
+ )
+
+ return cfg
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..16cf5b66bb51a12839203a1d8b602d26c188f9d6
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/__init__.py
@@ -0,0 +1,30 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+
+from .build import get_openseg_labels, build_d2_train_dataloader, build_d2_test_dataloader
+from .dataset_mapper import COCOPanopticDatasetMapper
+from .datasets import (
+ register_all_ctx59,
+ register_all_pascal21,
+ register_all_ctx459,
+ register_all_coco_panoptic_annos_sem_seg_caption,
+)
+
+__all__ = [
+ "COCOPanopticDatasetMapper",
+ "get_openseg_labels",
+ "build_d2_train_dataloader",
+ "build_d2_test_dataloader",
+ "register_all_ctx59",
+ "register_all_pascal21",
+ "register_all_ctx459",
+ "register_all_coco_panoptic_annos_sem_seg_caption",
+]
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/build.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..47ff778ae17e35b0a3a6c7dc1ecbb016931e662f
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/build.py
@@ -0,0 +1,151 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import os.path as osp
+from detectron2.data import build_detection_test_loader, build_detection_train_loader
+from detectron2.utils import comm
+
+
+def get_openseg_labels(dataset, prompt_engineered=False):
+ """get the labels in double list format,
+ e.g. [[background, bag, bed, ...], ["aeroplane"], ...]
+ """
+
+ invalid_name = "invalid_class_id"
+ assert dataset in [
+ "ade20k_150",
+ "ade20k_847",
+ "coco_panoptic",
+ "pascal_context_59",
+ "pascal_context_459",
+ "pascal_voc_21",
+ "lvis_1203",
+ ]
+
+ label_path = osp.join(
+ osp.dirname(osp.abspath(__file__)),
+ "datasets/openseg_labels",
+ f"{dataset}_with_prompt_eng.txt" if prompt_engineered else f"{dataset}.txt",
+ )
+
+ # read text in id:name format
+ with open(label_path, "r") as f:
+ lines = f.read().splitlines()
+
+ categories = []
+ for line in lines:
+ id, name = line.split(":")
+ if name == invalid_name:
+ continue
+ categories.append({"id": int(id), "name": name})
+
+ return [dic["name"].split(",") for dic in categories]
+
+
+def prompt_labels(labels, prompt):
+ if prompt is None:
+ return labels
+ labels = copy.deepcopy(labels)
+ assert prompt in ["a", "photo", "scene"]
+ if prompt == "a":
+ for i in range(len(labels)):
+ labels[i] = [f"a {l}" for l in labels[i]]
+ elif prompt == "photo":
+ for i in range(len(labels)):
+ labels[i] = [f"a photo of a {l}." for l in labels[i]]
+ elif prompt == "scene":
+ for i in range(len(labels)):
+ labels[i] = [f"a photo of a {l} in the scene." for l in labels[i]]
+ else:
+ raise NotImplementedError
+
+ return labels
+
+
+def build_d2_train_dataloader(
+ dataset,
+ mapper=None,
+ total_batch_size=None,
+ local_batch_size=None,
+ num_workers=0,
+ sampler=None,
+):
+
+ assert (total_batch_size is None) != (
+ local_batch_size is None
+ ), "Either total_batch_size or local_batch_size must be specified"
+
+ world_size = comm.get_world_size()
+
+ if total_batch_size is not None:
+ assert (
+ total_batch_size > 0 and total_batch_size % world_size == 0
+ ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
+ total_batch_size, world_size
+ )
+ batch_size = total_batch_size // world_size
+
+ if local_batch_size is not None:
+ batch_size = local_batch_size
+
+ total_batch_size = batch_size * world_size
+
+ return build_detection_train_loader(
+ dataset=dataset,
+ mapper=mapper,
+ sampler=sampler,
+ total_batch_size=total_batch_size,
+ aspect_ratio_grouping=True,
+ num_workers=num_workers,
+ collate_fn=None,
+ )
+
+
+def build_d2_test_dataloader(
+ dataset,
+ mapper=None,
+ total_batch_size=None,
+ local_batch_size=None,
+ num_workers=0,
+):
+
+ assert (total_batch_size is None) != (
+ local_batch_size is None
+ ), "Either total_batch_size or local_batch_size must be specified"
+
+ world_size = comm.get_world_size()
+
+ if total_batch_size is not None:
+ assert (
+ total_batch_size > 0 and total_batch_size % world_size == 0
+ ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
+ total_batch_size, world_size
+ )
+ batch_size = total_batch_size // world_size
+
+ if local_batch_size is not None:
+ batch_size = local_batch_size
+
+ logger = logging.getLogger(__name__)
+ if batch_size != 1:
+ logger.warning(
+ "When testing, batch size is set to 1. "
+ "This is the only mode that is supported for d2."
+ )
+
+ return build_detection_test_loader(
+ dataset=dataset,
+ mapper=mapper,
+ sampler=None,
+ num_workers=num_workers,
+ collate_fn=None,
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/dataset_mapper.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..185ec88a24ec5f1fd3ca79a468fd52b3d9066700
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/dataset_mapper.py
@@ -0,0 +1,149 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/Mask2Former/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Modified by Jiarui Xu
+# from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py # noqa
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import numpy as np
+from typing import List, Union
+import torch
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.structures import BitMasks, Boxes, Instances
+from panopticapi.utils import rgb2id
+
+
+class COCOPanopticDatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by MaskFormer.
+
+ This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies geometric transforms to the image and annotation
+ 3. Find and applies suitable cropping to the image and annotation
+ 4. Prepare image and annotation to Tensors
+ """
+
+ def __init__(
+ self,
+ is_train: bool = True,
+ *,
+ augmentations: List[Union[T.Augmentation, T.Transform]],
+ image_format: str,
+ segmentation_format: str = "L",
+ caption_key: str = "coco_captions",
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ is_train: for training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ crop_gen: crop augmentation
+ tfm_gens: data augmentation
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ """
+ self.augmentations = T.AugmentationList(augmentations)
+ logging.getLogger(__name__).info(
+ f"[{self.__class__.__name__}] Full TransformGens used in training: {self.augmentations}"
+ )
+
+ self.img_format = image_format
+ self.seg_format = segmentation_format
+ self.cap_key = caption_key
+ self.is_train = is_train
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+ utils.check_image_size(dataset_dict, image)
+
+ # USER: Remove if you don't do semantic/panoptic segmentation.
+ if "sem_seg_file_name" in dataset_dict:
+ sem_seg_gt = utils.read_image(
+ dataset_dict.pop("sem_seg_file_name"), format=self.seg_format
+ )
+ if self.seg_format == "L":
+ sem_seg_gt = sem_seg_gt.squeeze(2)
+ else:
+ sem_seg_gt = None
+
+ # image, transforms = T.apply_augmentations(self.augmentations, image)
+ aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+ transforms = self.augmentations(aug_input)
+ image, sem_seg_gt = aug_input.image, aug_input.sem_seg
+
+ image_shape = image.shape[:2] # h, w
+
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+ if sem_seg_gt is not None:
+ dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
+
+ # if not self.is_train:
+ # # USER: Modify this if you want to keep them for some reason.
+ # dataset_dict.pop("annotations", None)
+ # return dataset_dict
+
+ pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+ segments_info = dataset_dict["segments_info"]
+
+ # apply the same transformation to panoptic segmentation
+ pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+ pan_seg_gt = rgb2id(pan_seg_gt)
+ dataset_dict["pan_seg_gt"] = torch.from_numpy(np.ascontiguousarray(pan_seg_gt))
+
+ instances = Instances(image_shape)
+ classes = []
+ masks = []
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if not segment_info["iscrowd"]:
+ classes.append(class_id)
+ masks.append(pan_seg_gt == segment_info["id"])
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_boxes = Boxes(torch.zeros((0, 4)))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ instances.gt_boxes = masks.get_bounding_boxes()
+
+ if self.cap_key in dataset_dict:
+ dataset_dict["captions"] = dataset_dict.pop(self.cap_key)
+
+ dataset_dict["instances"] = instances
+
+ return dataset_dict
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8dadf25b6d239c18009bbc107248e01ff647aaa
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/__init__.py
@@ -0,0 +1,19 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .register_pascal import register_all_ctx59, register_all_pascal21, register_all_ctx459
+from .register_coco_caption import register_all_coco_panoptic_annos_sem_seg_caption
+
+__all__ = [
+ "register_all_ctx59",
+ "register_all_pascal21",
+ "register_all_ctx459",
+ "register_all_coco_panoptic_annos_sem_seg_caption",
+]
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/README.md b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d5ac2a7ade82b4c6161c72d95a52624472801522
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/README.md
@@ -0,0 +1,17 @@
+# Acknowledgement
+
+We thank Golnaz Ghiasi for providing the [OpenSeg](https://arxiv.org/abs/2112.12143) labels for evaluation.
+
+
+## Citation
+
+```BiBTeX
+@inproceedings{ghiasi2022scaling,
+ title={Scaling open-vocabulary image segmentation with image-level labels},
+ author={Ghiasi, Golnaz and Gu, Xiuye and Cui, Yin and Lin, Tsung-Yi},
+ booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXXVI},
+ pages={540--557},
+ year={2022},
+ organization={Springer}
+}
+```
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_150.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_150.txt
new file mode 100644
index 0000000000000000000000000000000000000000..67f108d95b83ea7365e84bbce5a34a4d07bcef27
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_150.txt
@@ -0,0 +1,151 @@
+0:invalid_class_id
+1:wall
+2:building,edifice
+3:sky
+4:floor,flooring
+5:tree
+6:ceiling
+7:road,route
+8:bed
+9:windowpane,window
+10:grass
+11:cabinet
+12:sidewalk,pavement
+13:person,individual,someone,somebody,mortal,soul
+14:earth,ground
+15:door,double door
+16:table
+17:mountain,mount
+18:plant,flora,plant life
+19:curtain,drape,drapery,mantle,pall
+20:chair
+21:car,auto,automobile,machine,motorcar
+22:water
+23:painting,picture
+24:sofa,couch,lounge
+25:shelf
+26:house
+27:sea
+28:mirror
+29:rug,carpet,carpeting
+30:field
+31:armchair
+32:seat
+33:fence,fencing
+34:desk
+35:rock,stone
+36:wardrobe,closet,press
+37:lamp
+38:bathtub,bathing tub,bath,tub
+39:railing,rail
+40:cushion
+41:base,pedestal,stand
+42:box
+43:column,pillar
+44:signboard,sign
+45:chest of drawers,chest,bureau,dresser
+46:counter
+47:sand
+48:sink
+49:skyscraper
+50:fireplace,hearth,open fireplace
+51:refrigerator,icebox
+52:grandstand,covered stand
+53:path
+54:stairs,steps
+55:runway
+56:case,display case,showcase,vitrine
+57:pool table,billiard table,snooker table
+58:pillow
+59:screen door,screen
+60:stairway,staircase
+61:river
+62:bridge,span
+63:bookcase
+64:blind,screen
+65:coffee table,cocktail table
+66:toilet,can,commode,crapper,pot,potty,stool,throne
+67:flower
+68:book
+69:hill
+70:bench
+71:countertop
+72:stove,kitchen stove,range,kitchen range,cooking stove
+73:palm,palm tree
+74:kitchen island
+75:computer,computing machine,computing device,data processor,electronic computer,information processing system
+76:swivel chair
+77:boat
+78:bar
+79:arcade machine
+80:hovel,hut,hutch,shack,shanty
+81:bus,autobus,coach,charabanc,double-decker,jitney,motorbus,motorcoach,omnibus,passenger vehicle
+82:towel
+83:light,light source
+84:truck,motortruck
+85:tower
+86:chandelier,pendant,pendent
+87:awning,sunshade,sunblind
+88:streetlight,street lamp
+89:booth,cubicle,stall,kiosk
+90:television receiver,television,television set,tv,tv set,idiot box,boob tube,telly,goggle box
+91:airplane,aeroplane,plane
+92:dirt track
+93:apparel,wearing apparel,dress,clothes
+94:pole
+95:land,ground,soil
+96:bannister,banister,balustrade,balusters,handrail
+97:escalator,moving staircase,moving stairway
+98:ottoman,pouf,pouffe,puff,hassock
+99:bottle
+100:buffet,counter,sideboard
+101:poster,posting,placard,notice,bill,card
+102:stage
+103:van
+104:ship
+105:fountain
+106:conveyer belt,conveyor belt,conveyer,conveyor,transporter
+107:canopy
+108:washer,automatic washer,washing machine
+109:plaything,toy
+110:swimming pool,swimming bath,natatorium
+111:stool
+112:barrel,cask
+113:basket,handbasket
+114:waterfall,falls
+115:tent,collapsible shelter
+116:bag
+117:minibike,motorbike
+118:cradle
+119:oven
+120:ball
+121:food,solid food
+122:step,stair
+123:tank,storage tank
+124:trade name,brand name,brand,marque
+125:microwave,microwave oven
+126:pot,flowerpot
+127:animal,animate being,beast,brute,creature,fauna
+128:bicycle,bike,wheel,cycle
+129:lake
+130:dishwasher,dish washer,dishwashing machine
+131:screen,silver screen,projection screen
+132:blanket,cover
+133:sculpture
+134:hood,exhaust hood
+135:sconce
+136:vase
+137:traffic light,traffic signal,stoplight
+138:tray
+139:ashcan,trash can,garbage can,wastebin,ash bin,ash-bin,ashbin,dustbin,trash barrel,trash bin
+140:fan
+141:pier,wharf,wharfage,dock
+142:crt screen
+143:plate
+144:monitor,monitoring device
+145:bulletin board,notice board
+146:shower
+147:radiator
+148:glass,drinking glass
+149:clock
+150:flag
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_150_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_150_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cd894827f4952f1dc88ef46d7cd5c160910dbbaf
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_150_with_prompt_eng.txt
@@ -0,0 +1,151 @@
+0:invalid_class_id
+1:wall,walls,brick wall,stone wall,interior wall
+2:building,buildings,edifice,edifices
+3:sky,clouds
+4:floor,flooring
+5:tree,trees
+6:ceiling
+7:road,route,street,roads,streets,routes
+8:bed,beds
+9:windowpane,window,windows
+10:grass,grass field
+11:cabinet,cabinets,wall mounted cabine
+12:sidewalk,pavement
+13:person,child,girl,boy,woman,man,people,children,girls,boys,women,men
+14:earth,ground
+15:door,double door,doors
+16:table,tables,tablecloth
+17:mountain,mount,mountains
+18:plant,flora,plant life,plants,bushes
+19:curtain,drape,drapery,mantle,pall
+20:chair,chairs
+21:car,automobile,cars
+22:water
+23:painting,picture,paintings,pictures,wallart,framed canvas
+24:sofa,couch,sofas,couches
+25:shelf,shelves
+26:house exterior
+27:sea,ocean
+28:mirror,mirrors
+29:rug,carpet,carpeting
+30:field
+31:armchair,armchairs
+32:seat,seats
+33:fence,fencing
+34:desk,desks
+35:rock,stone,rocks,stones
+36:wardrobe,closet,press,wardrobes,closets
+37:lamp,lamps
+38:bathtub,bathing tub,bath,tub
+39:railing,rail
+40:cushion,cushions
+41:pedestal
+42:box,boxes
+43:column,pillar
+44:signboard,sign,signboards,signs
+45:chest of drawers,chest,bureau,dresser
+46:counter
+47:sand
+48:sink
+49:skyscraper,skyscrapers
+50:fireplace,hearth,open fireplace
+51:refrigerator,icebox
+52:grandstand,covered stand
+53:path
+54:stairs,steps
+55:runway
+56:case,display case,showcase,vitrine
+57:pool table,billiard table,snooker table
+58:pillow,pillows
+59:screen door,shower door
+60:stairway,staircase
+61:river
+62:bridge,span
+63:bookcase
+64:window screen,door screen
+65:coffee table,cocktail table
+66:toilet,commode,crapper,potty
+67:flower,flowers
+68:book,books
+69:hill
+70:bench,benches
+71:countertop,counter top,worktop
+72:stove,kitchen stove,kitchen range,kitchen range,cooking stove
+73:palm tree,palm trees
+74:kitchen island
+75:computer,computing machine,computing device,data processor,electronic computer,information processing system
+76:swivel chair
+77:boat
+78:bar
+79:arcade machine,arcade machines
+80:hovel,hut,hutch,shack,shanty
+81:bus,autobus,double-decker,jitney,motorbus,motorcoach,omnibus,passenger vehicle
+82:towel
+83:light bulb,lightbulb,bulb,incandescent lamp,electric light,electric-light bulb
+84:truck,motortruck
+85:tower,towers
+86:chandelier,pendant,pendent
+87:awning,sunshade,sunblind
+88:streetlight,street lamp
+89:booth,cubicle,stall,kiosk
+90:television receiver,television,television set,tv,tv set
+91:airplane,aeroplane,airplanes,aeroplanes
+92:dirt track
+93:apparel,wearing apparel,dress,clothes
+94:pole
+95:land,soil
+96:bannister,banister,balustrade,balusters,handrail
+97:escalator,moving staircase,moving stairway
+98:ottoman,pouf,pouffe,puff,hassock
+99:bottle,bottles,water bottle
+100:buffet,sideboard
+101:poster,posting,placard,notice,bill,card
+102:stage
+103:van
+104:ship
+105:fountain
+106:conveyer belt,conveyor belt,conveyer,conveyor,transporter
+107:canopy
+108:washer,automatic washer,washing machine
+109:plaything,toy,toys
+110:swimming pool,swimming bath
+111:stool,stools
+112:barrel,cask,barrels,casks
+113:basket,handbasket
+114:waterfall,falls
+115:tent,collapsible shelter
+116:bag,bags,gift bag,paper bag
+117:minibike,motorbike
+118:cradle
+119:oven
+120:ball,balls
+121:food,solid food
+122:step,stair
+123:tank,storage tank
+124:trade name,brand name,brand,marque
+125:microwave,microwave oven
+126:plant pots,plant pot,flower pot,flowerpot,planter
+127:animal,animate being,dog,cat,horse,cow,sheep,zebra,girraffe,bird
+128:bicycle,bike
+129:lake
+130:dishwasher,dish washer,dishwashing machine
+131:projection screen
+132:blanket,cover
+133:sculpture,sculptures
+134:exhaust hood
+135:sconce,sconce lamp,sconce light
+136:vase,vases
+137:traffic light,traffic signal,traffic lights
+138:tray,trays
+139:ashcan,trash can,garbage can,wastebin,ash bin,ash-bin,ashbin,dustbin,trash barrel,trash bin
+140:ceiling fan,floor fan
+141:pier,wharf,wharfage,dock
+142:crt screen
+143:plate,plates
+144:monitor,monitoring device,monitors
+145:bulletin board,notice board
+146:shower
+147:radiator
+148:cup,cups,drinking glass,drinking glasses
+149:clock
+150:flag,flags
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_847.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_847.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7e9250924138f18edea60cf11ba35cb4edbcf1c
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_847.txt
@@ -0,0 +1,848 @@
+0:invalid_class_id
+1:wall
+2:building,edifice
+3:sky
+4:tree
+5:road,route
+6:floor,flooring
+7:ceiling
+8:bed
+9:sidewalk,pavement
+10:earth,ground
+11:cabinet
+12:person,individual,someone,somebody,mortal,soul
+13:grass
+14:windowpane,window
+15:car,auto,automobile,machine,motorcar
+16:mountain,mount
+17:plant,flora,plant life
+18:table
+19:chair
+20:curtain,drape,drapery,mantle,pall
+21:door
+22:sofa,couch,lounge
+23:sea
+24:painting,picture
+25:water
+26:mirror
+27:house
+28:rug,carpet,carpeting
+29:shelf
+30:armchair
+31:fence,fencing
+32:field
+33:lamp
+34:rock,stone
+35:seat
+36:river
+37:desk
+38:bathtub,bathing tub,bath,tub
+39:railing,rail
+40:signboard,sign
+41:cushion
+42:path
+43:work surface
+44:stairs,steps
+45:column,pillar
+46:sink
+47:wardrobe,closet,press
+48:snow
+49:refrigerator,icebox
+50:base,pedestal,stand
+51:bridge,span
+52:blind,screen
+53:runway
+54:cliff,drop,drop-off
+55:sand
+56:fireplace,hearth,open fireplace
+57:pillow
+58:screen door,screen
+59:toilet,can,commode,crapper,pot,potty,stool,throne
+60:skyscraper
+61:grandstand,covered stand
+62:box
+63:pool table,billiard table,snooker table
+64:palm,palm tree
+65:double door
+66:coffee table,cocktail table
+67:counter
+68:countertop
+69:chest of drawers,chest,bureau,dresser
+70:kitchen island
+71:boat
+72:waterfall,falls
+73:stove,kitchen stove,range,kitchen range,cooking stove
+74:flower
+75:bookcase
+76:controls
+77:book
+78:stairway,staircase
+79:streetlight,street lamp
+80:computer,computing machine,computing device,data processor,electronic computer,information processing system
+81:bus,autobus,coach,charabanc,double-decker,jitney,motorbus,motorcoach,omnibus,passenger vehicle
+82:swivel chair
+83:light,light source
+84:bench
+85:case,display case,showcase,vitrine
+86:towel
+87:fountain
+88:embankment
+89:television receiver,television,television set,tv,tv set,idiot box,boob tube,telly,goggle box
+90:van
+91:hill
+92:awning,sunshade,sunblind
+93:poster,posting,placard,notice,bill,card
+94:truck,motortruck
+95:airplane,aeroplane,plane
+96:pole
+97:tower
+98:court
+99:ball
+100:aircraft carrier,carrier,flattop,attack aircraft carrier
+101:buffet,counter,sideboard
+102:hovel,hut,hutch,shack,shanty
+103:apparel,wearing apparel,dress,clothes
+104:minibike,motorbike
+105:animal,animate being,beast,brute,creature,fauna
+106:chandelier,pendant,pendent
+107:step,stair
+108:booth,cubicle,stall,kiosk
+109:bicycle,bike,wheel,cycle
+110:doorframe,doorcase
+111:sconce
+112:pond
+113:trade name,brand name,brand,marque
+114:bannister,banister,balustrade,balusters,handrail
+115:bag
+116:traffic light,traffic signal,stoplight
+117:gazebo
+118:escalator,moving staircase,moving stairway
+119:land,ground,soil
+120:board,plank
+121:arcade machine
+122:eiderdown,duvet,continental quilt
+123:bar
+124:stall,stand,sales booth
+125:playground
+126:ship
+127:ottoman,pouf,pouffe,puff,hassock
+128:ashcan,trash can,garbage can,wastebin,ash bin,ash-bin,ashbin,dustbin,trash barrel,trash bin
+129:bottle
+130:cradle
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+132:conveyer belt,conveyor belt,conveyer,conveyor,transporter
+133:train,railroad train
+134:stool
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+137:ice,water ice
+138:basket,handbasket
+139:manhole
+140:tent,collapsible shelter
+141:canopy
+142:microwave,microwave oven
+143:barrel,cask
+144:dirt track
+145:beam
+146:dishwasher,dish washer,dishwashing machine
+147:plate
+148:screen,crt screen
+149:ruins
+150:washer,automatic washer,washing machine
+151:blanket,cover
+152:plaything,toy
+153:food,solid food
+154:screen,silver screen,projection screen
+155:oven
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+158:umbrella
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+163:hood,exhaust hood
+164:curb,curbing,kerb
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+166:horse,equus caballus
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+168:glass,drinking glass
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+176:fan
+177:inflatable bounce game
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+182:helicopter
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+193:bulletin board,notice board
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+195:trunk,tree trunk,bole
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+198:laptop,laptop computer
+199:instrument panel
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+214:sand dune
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+220:fluorescent,fluorescent fixture
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+223:exhibitor
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+229:water tower
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+233:rubbish,trash,scrap
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+264:ad,advertisement,advertizement,advertising,advertizing,advert
+265:amphitheater,amphitheatre,coliseum
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+270:windmill,aerogenerator,wind generator
+271:pool
+272:elevator,lift
+273:decoration,ornament,ornamentation
+274:labyrinth
+275:text,textual matter
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+294:figurine,statuette
+295:recycling bin
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+306:candlestick,candle holder
+307:shower stall,shower bath
+308:television stand
+309:wall socket,wall plug,electric outlet,electrical outlet,outlet,electric receptacle
+310:skeleton
+311:grand piano,grand
+312:candy,confect
+313:grille door
+314:pedestal,plinth,footstall
+315:jersey,t-shirt,tee shirt
+316:shoe
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+318:shanty
+319:structure
+320:rocking chair,rocker
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+326:lockers
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+335:streetcar,tram,tramcar,trolley,trolley car
+336:napkin,table napkin,serviette
+337:dummy
+338:booklet,brochure,folder,leaflet,pamphlet
+339:sand trap
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+351:shirt
+352:merchandise,ware,product
+353:railway
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+355:chimney
+356:can,tin,tin can
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+358:fabric,cloth,material,textile
+359:alga,algae
+360:system
+361:map
+362:greenhouse
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+365:trailer
+366:toilet tissue,toilet paper,bathroom tissue
+367:organ
+368:dishrag,dishcloth
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+373:steering wheel,wheel
+374:pitcher,ewer
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+393:switch,electric switch,electrical switch
+394:baseboard,mopboard,skirting board
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+397:faucet,spigot
+398:bulldozer
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+403:gear
+404:candelabrum,candelabra
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+408:wire,conducting wire
+409:kettle,boiler
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+411:baby buggy,baby carriage,carriage,perambulator,pram,stroller,go-cart,pushchair,pusher
+412:music stand
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+414:cup
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+416:ice hockey rink
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+425:towel rack,towel horse
+426:roundabout
+427:canister,cannister,tin
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+431:canvas
+432:box office,ticket office,ticket booth
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+435:workbench
+436:valley,vale
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+439:podium
+440:ramp
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+442:fireplug,fire hydrant,plug
+443:gym shoe,sneaker,tennis shoe
+444:lab bench
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+460:blast furnace
+461:scale,weighing machine
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+469:alembic
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+479:light bulb,lightbulb,bulb,incandescent lamp,electric light,electric-light bulb
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+497:leaf,leafage,foliage
+498:remote control,remote
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+502:cue,cue stick,pool cue,pool stick
+503:vent,venthole,vent-hole,blowhole
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+800:bonfire
+801:dog dish
+802:belt
+803:dumbbells
+804:videocassette recorder,vcr
+805:hook
+806:envelopes
+807:shower faucet
+808:watch
+809:padlock
+810:swimming pool ladder
+811:spanners
+812:gravy boat
+813:notice board
+814:trash bags
+815:fire alarm
+816:ladle
+817:stethoscope
+818:rocket
+819:funnel
+820:bowling pins
+821:valve
+822:thermometer
+823:cups
+824:spice jar
+825:night light
+826:soaps
+827:games table
+828:slotted spoon
+829:reel
+830:scourer
+831:sleeping robe
+832:desk mat
+833:dumbbell
+834:hammer
+835:tie
+836:typewriter
+837:shaker
+838:cheese dish
+839:sea star
+840:racquet
+841:butane gas cylinder
+842:paper weight
+843:shaving brush
+844:sunglasses
+845:gear shift
+846:towel rail
+847:adding machine,totalizer,totaliser
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_847_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_847_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..49c43e6812dd0086c5168aef3fededc49829ab40
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/ade20k_847_with_prompt_eng.txt
@@ -0,0 +1,848 @@
+0:invalid_class_id
+1:wall,walls,interior wall,brick wall,stone wall
+2:building,buildings,edifice,edifices
+3:sky,clouds
+4:tree,trees
+5:road,route,street,roads,streets,routes
+6:floor,flooring
+7:ceiling
+8:bed,beds
+9:sidewalk,pavement
+10:earth,ground
+11:cabinet,cabinets,wall mounted cabine
+12:person,child,girl,boy,woman,man,people,children,girls,boys,women,men
+13:grass,grass field
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+15:car,automobile,cars
+16:mountain,mount,mountains
+17:plant,flora,plant life,plants,bushes
+18:table,tables,tablecloth
+19:chair,chairs
+20:curtain,drape,drapery,mantle,pall
+21:door,double door,doors
+22:sofa,couch,sofas,couches
+23:sea,ocean
+24:painting,picture,paintings,pictures,wallart,framed canvas
+25:water
+26:mirror,mirrors
+27:house exterior
+28:rug,carpet,carpeting
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+30:armchair,armchairs
+31:fence,fencing
+32:field
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+34:rock,stone,rocks,stones
+35:seat,seats
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+38:bathtub,bathing tub,bath,tub
+39:railing,rail
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+48:snow
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+828:slotted spoon
+829:reel
+830:scourer
+831:sleeping robe
+832:desk mat
+833:dumbbell
+834:hammer
+835:tie
+836:typewriter
+837:shaker
+838:cheese dish
+839:sea star
+840:racquet
+841:butane gas cylinder
+842:paper weight
+843:shaving brush
+844:sunglasses
+845:gear shift
+846:towel rail
+847:adding machine,totalizer,totaliser
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/coco_panoptic.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/coco_panoptic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..69a6b6250bb8ca0119867e34f49c71e3a05d61a3
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/coco_panoptic.txt
@@ -0,0 +1,201 @@
+0:invalid_class_id
+1:person
+2:bicycle
+3:car
+4:motorcycle
+5:airplane
+6:bus
+7:train
+8:truck
+9:boat
+10:traffic light
+11:fire hydrant
+12:invalid_class_id
+13:stop sign
+14:parking meter
+15:bench
+16:bird
+17:cat
+18:dog
+19:horse
+20:sheep
+21:cow
+22:elephant
+23:bear
+24:zebra
+25:giraffe
+26:invalid_class_id
+27:backpack
+28:umbrella
+29:invalid_class_id
+30:invalid_class_id
+31:handbag
+32:tie
+33:suitcase
+34:frisbee
+35:skis
+36:snowboard
+37:sports ball
+38:kite
+39:baseball bat
+40:baseball glove
+41:skateboard
+42:surfboard
+43:tennis racket
+44:bottle
+45:invalid_class_id
+46:wine glass
+47:cup
+48:fork
+49:knife
+50:spoon
+51:bowl
+52:banana
+53:apple
+54:sandwich
+55:orange
+56:broccoli
+57:carrot
+58:hot dog
+59:pizza
+60:donut
+61:cake
+62:chair
+63:couch
+64:potted plant
+65:bed
+66:invalid_class_id
+67:dining table
+68:invalid_class_id
+69:invalid_class_id
+70:toilet
+71:invalid_class_id
+72:tv
+73:laptop
+74:mouse
+75:remote
+76:keyboard
+77:cell phone
+78:microwave
+79:oven
+80:toaster
+81:sink
+82:refrigerator
+83:invalid_class_id
+84:book
+85:clock
+86:vase
+87:scissors
+88:teddy bear
+89:hair drier
+90:toothbrush
+91:invalid_class_id
+92:banner
+93:blanket
+94:invalid_class_id
+95:bridge
+96:invalid_class_id
+97:invalid_class_id
+98:invalid_class_id
+99:invalid_class_id
+100:cardboard
+101:invalid_class_id
+102:invalid_class_id
+103:invalid_class_id
+104:invalid_class_id
+105:invalid_class_id
+106:invalid_class_id
+107:counter
+108:invalid_class_id
+109:curtain
+110:invalid_class_id
+111:invalid_class_id
+112:door
+113:invalid_class_id
+114:invalid_class_id
+115:invalid_class_id
+116:invalid_class_id
+117:invalid_class_id
+118:wood floor
+119:flower
+120:invalid_class_id
+121:invalid_class_id
+122:fruit
+123:invalid_class_id
+124:invalid_class_id
+125:gravel
+126:invalid_class_id
+127:invalid_class_id
+128:house
+129:invalid_class_id
+130:light
+131:invalid_class_id
+132:invalid_class_id
+133:mirror-stuff
+134:invalid_class_id
+135:invalid_class_id
+136:invalid_class_id
+137:invalid_class_id
+138:net
+139:invalid_class_id
+140:invalid_class_id
+141:pillow
+142:invalid_class_id
+143:invalid_class_id
+144:platform
+145:playingfield
+146:invalid_class_id
+147:railroad
+148:river
+149:road
+150:invalid_class_id
+151:roof
+152:invalid_class_id
+153:invalid_class_id
+154:sand
+155:sea
+156:shelf
+157:invalid_class_id
+158:invalid_class_id
+159:snow
+160:invalid_class_id
+161:stairs
+162:invalid_class_id
+163:invalid_class_id
+164:invalid_class_id
+165:invalid_class_id
+166:tent
+167:invalid_class_id
+168:towel
+169:invalid_class_id
+170:invalid_class_id
+171:brick wall
+172:invalid_class_id
+173:invalid_class_id
+174:invalid_class_id
+175:stone wall
+176:tile wall
+177:wood wall
+178:water
+179:invalid_class_id
+180:window blind
+181:window
+182:invalid_class_id
+183:invalid_class_id
+184:tree
+185:fence
+186:ceiling
+187:sky
+188:cabinet
+189:table
+190:floor
+191:pavement
+192:mountain
+193:grass
+194:dirt
+195:paper
+196:food
+197:building
+198:rock
+199:wall
+200:rug
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/coco_panoptic_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/coco_panoptic_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..996d5040a92a15f3f16019f039f25eecaa0ad0f5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/coco_panoptic_with_prompt_eng.txt
@@ -0,0 +1,201 @@
+0:invalid_class_id
+1:person,child,girl,boy,woman,man,people,childeren,girls,boys,women,men,lady,guy,ladies,guys,clothes
+2:bicycle,bicycles,bike,bikes
+3:car,cars
+4:motorcycle,motorcycles
+5:airplane,airplanes
+6:bus,buses
+7:train,trains,locomotive,locomotives,freight train
+8:truck,trucks
+9:boat,boats
+10:traffic light
+11:fire hydrant
+12:invalid_class_id
+13:stop sign
+14:parking meter
+15:bench,benches
+16:bird,birds
+17:cat,cats,kitties,kitty
+18:dog,dogs,puppy,puppies
+19:horse,horses,foal
+20:sheep
+21:cow,cows,calf
+22:elephant,elephants
+23:bear,bears
+24:zebra,zebras
+25:giraffe,giraffes
+26:invalid_class_id
+27:backpack,backpacks
+28:umbrella,umbrellas
+29:invalid_class_id
+30:invalid_class_id
+31:handbag,handbags
+32:tie
+33:suitcase,suitcases
+34:frisbee
+35:skis
+36:snowboard
+37:sports ball
+38:kite,kites
+39:baseball bat
+40:baseball glove
+41:skateboard
+42:surfboard
+43:tennis racket
+44:bottle,bottles,water bottle
+45:invalid_class_id
+46:wine glass,wine glasses,wineglass
+47:cup,cups,water cup,water glass
+48:fork,forks
+49:knife,knives
+50:spoon,spoons
+51:bowl,bowls
+52:banana,bananas
+53:apple,apples,apple fruit
+54:sandwich,sandwiches
+55:orange fruit
+56:broccoli
+57:carrot,carrots
+58:hot dog
+59:pizza
+60:donut,donuts
+61:cake,cakes
+62:chair,chairs
+63:couch,sofa,sofas
+64:potted plant,potted plants,pottedplant,pottedplants,planter,planters
+65:bed,beds
+66:invalid_class_id
+67:dining table,dining tables,diningtable,diningtables,plate,plates,diningtable tablecloth
+68:invalid_class_id
+69:invalid_class_id
+70:toilet
+71:invalid_class_id
+72:tv
+73:laptop
+74:mouse
+75:tv remote,remote control
+76:keyboard
+77:cell phone,mobile
+78:microwave
+79:oven,ovens
+80:toaster
+81:sink,sinks
+82:refrigerator,fridge
+83:invalid_class_id
+84:book,books
+85:clock
+86:vase,vases
+87:scissor,scissors
+88:teddy bear,teddy bears
+89:hair drier
+90:toothbrush,toothbrushes
+91:invalid_class_id
+92:banner,banners
+93:blanket,blankets
+94:invalid_class_id
+95:bridge
+96:invalid_class_id
+97:invalid_class_id
+98:invalid_class_id
+99:invalid_class_id
+100:cardboard
+101:invalid_class_id
+102:invalid_class_id
+103:invalid_class_id
+104:invalid_class_id
+105:invalid_class_id
+106:invalid_class_id
+107:counter
+108:invalid_class_id
+109:curtain,curtains
+110:invalid_class_id
+111:invalid_class_id
+112:door,doors
+113:invalid_class_id
+114:invalid_class_id
+115:invalid_class_id
+116:invalid_class_id
+117:invalid_class_id
+118:wood floor
+119:flower,flowers
+120:invalid_class_id
+121:invalid_class_id
+122:fruit,fruits
+123:invalid_class_id
+124:invalid_class_id
+125:gravel
+126:invalid_class_id
+127:invalid_class_id
+128:house
+129:invalid_class_id
+130:lamp,bulb,lamps,bulbs
+131:invalid_class_id
+132:invalid_class_id
+133:mirror
+134:invalid_class_id
+135:invalid_class_id
+136:invalid_class_id
+137:invalid_class_id
+138:tennis net
+139:invalid_class_id
+140:invalid_class_id
+141:pillow,pillows
+142:invalid_class_id
+143:invalid_class_id
+144:platform
+145:playingfield,tennis court,baseball feild,soccer field,tennis field
+146:invalid_class_id
+147:railroad
+148:river
+149:road
+150:invalid_class_id
+151:roof
+152:invalid_class_id
+153:invalid_class_id
+154:sand
+155:sea,sea wave,wave,waves
+156:shelf
+157:invalid_class_id
+158:invalid_class_id
+159:snow
+160:invalid_class_id
+161:stairs
+162:invalid_class_id
+163:invalid_class_id
+164:invalid_class_id
+165:invalid_class_id
+166:tent
+167:invalid_class_id
+168:towel
+169:invalid_class_id
+170:invalid_class_id
+171:brick wall
+172:invalid_class_id
+173:invalid_class_id
+174:invalid_class_id
+175:stone wall
+176:tile wall
+177:wood wall
+178:water
+179:invalid_class_id
+180:window blind
+181:window
+182:invalid_class_id
+183:invalid_class_id
+184:tree,trees,palm tree,bushes
+185:fence,fences
+186:ceiling
+187:sky,clouds
+188:cabinet,cabinets
+189:table
+190:floor,flooring,tile floor
+191:pavement
+192:mountain,mountains
+193:grass
+194:dirt
+195:paper
+196:food
+197:building,buildings
+198:rock
+199:wall,walls
+200:rug
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/lvis_1203.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/lvis_1203.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3e4c786a85802761311a49b62d7b0a9c206aa54
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/lvis_1203.txt
@@ -0,0 +1,1203 @@
+1:aerosol can
+2:air conditioner
+3:airplane
+4:alarm clock
+5:alcohol
+6:alligator
+7:almond
+8:ambulance
+9:amplifier
+10:anklet
+11:antenna
+12:apple
+13:applesauce
+14:apricot
+15:apron
+16:aquarium
+17:arctic (type of shoe)
+18:armband
+19:armchair
+20:armoire
+21:armor
+22:artichoke
+23:trash can
+24:ashtray
+25:asparagus
+26:atomizer
+27:avocado
+28:award
+29:awning
+30:ax
+31:baboon
+32:baby buggy
+33:basketball backboard
+34:backpack
+35:handbag
+36:suitcase
+37:bagel
+38:bagpipe
+39:baguet
+40:bait
+41:ball
+42:ballet skirt
+43:balloon
+44:bamboo
+45:banana
+46:Band Aid
+47:bandage
+48:bandanna
+49:banjo
+50:banner
+51:barbell
+52:barge
+53:barrel
+54:barrette
+55:barrow
+56:baseball base
+57:baseball
+58:baseball bat
+59:baseball cap
+60:baseball glove
+61:basket
+62:basketball
+63:bass horn
+64:bat (animal)
+65:bath mat
+66:bath towel
+67:bathrobe
+68:bathtub
+69:batter (food)
+70:battery
+71:beachball
+72:bead
+73:bean curd
+74:beanbag
+75:beanie
+76:bear
+77:bed
+78:bedpan
+79:bedspread
+80:cow
+81:beef (food)
+82:beeper
+83:beer bottle
+84:beer can
+85:beetle
+86:bell
+87:bell pepper
+88:belt
+89:belt buckle
+90:bench
+91:beret
+92:bib
+93:Bible
+94:bicycle
+95:visor
+96:billboard
+97:binder
+98:binoculars
+99:bird
+100:birdfeeder
+101:birdbath
+102:birdcage
+103:birdhouse
+104:birthday cake
+105:birthday card
+106:pirate flag
+107:black sheep
+108:blackberry
+109:blackboard
+110:blanket
+111:blazer
+112:blender
+113:blimp
+114:blinker
+115:blouse
+116:blueberry
+117:gameboard
+118:boat
+119:bob
+120:bobbin
+121:bobby pin
+122:boiled egg
+123:bolo tie
+124:deadbolt
+125:bolt
+126:bonnet
+127:book
+128:bookcase
+129:booklet
+130:bookmark
+131:boom microphone
+132:boot
+133:bottle
+134:bottle opener
+135:bouquet
+136:bow (weapon)
+137:bow (decorative ribbons)
+138:bow-tie
+139:bowl
+140:pipe bowl
+141:bowler hat
+142:bowling ball
+143:box
+144:boxing glove
+145:suspenders
+146:bracelet
+147:brass plaque
+148:brassiere
+149:bread-bin
+150:bread
+151:breechcloth
+152:bridal gown
+153:briefcase
+154:broccoli
+155:broach
+156:broom
+157:brownie
+158:brussels sprouts
+159:bubble gum
+160:bucket
+161:horse buggy
+162:bull
+163:bulldog
+164:bulldozer
+165:bullet train
+166:bulletin board
+167:bulletproof vest
+168:bullhorn
+169:bun
+170:bunk bed
+171:buoy
+172:burrito
+173:bus (vehicle)
+174:business card
+175:butter
+176:butterfly
+177:button
+178:cab (taxi)
+179:cabana
+180:cabin car
+181:cabinet
+182:locker
+183:cake
+184:calculator
+185:calendar
+186:calf
+187:camcorder
+188:camel
+189:camera
+190:camera lens
+191:camper (vehicle)
+192:can
+193:can opener
+194:candle
+195:candle holder
+196:candy bar
+197:candy cane
+198:walking cane
+199:canister
+200:canoe
+201:cantaloup
+202:canteen
+203:cap (headwear)
+204:bottle cap
+205:cape
+206:cappuccino
+207:car (automobile)
+208:railcar (part of a train)
+209:elevator car
+210:car battery
+211:identity card
+212:card
+213:cardigan
+214:cargo ship
+215:carnation
+216:horse carriage
+217:carrot
+218:tote bag
+219:cart
+220:carton
+221:cash register
+222:casserole
+223:cassette
+224:cast
+225:cat
+226:cauliflower
+227:cayenne (spice)
+228:CD player
+229:celery
+230:cellular telephone
+231:chain mail
+232:chair
+233:chaise longue
+234:chalice
+235:chandelier
+236:chap
+237:checkbook
+238:checkerboard
+239:cherry
+240:chessboard
+241:chicken (animal)
+242:chickpea
+243:chili (vegetable)
+244:chime
+245:chinaware
+246:crisp (potato chip)
+247:poker chip
+248:chocolate bar
+249:chocolate cake
+250:chocolate milk
+251:chocolate mousse
+252:choker
+253:chopping board
+254:chopstick
+255:Christmas tree
+256:slide
+257:cider
+258:cigar box
+259:cigarette
+260:cigarette case
+261:cistern
+262:clarinet
+263:clasp
+264:cleansing agent
+265:cleat (for securing rope)
+266:clementine
+267:clip
+268:clipboard
+269:clippers (for plants)
+270:cloak
+271:clock
+272:clock tower
+273:clothes hamper
+274:clothespin
+275:clutch bag
+276:coaster
+277:coat
+278:coat hanger
+279:coatrack
+280:cock
+281:cockroach
+282:cocoa (beverage)
+283:coconut
+284:coffee maker
+285:coffee table
+286:coffeepot
+287:coil
+288:coin
+289:colander
+290:coleslaw
+291:coloring material
+292:combination lock
+293:pacifier
+294:comic book
+295:compass
+296:computer keyboard
+297:condiment
+298:cone
+299:control
+300:convertible (automobile)
+301:sofa bed
+302:cooker
+303:cookie
+304:cooking utensil
+305:cooler (for food)
+306:cork (bottle plug)
+307:corkboard
+308:corkscrew
+309:edible corn
+310:cornbread
+311:cornet
+312:cornice
+313:cornmeal
+314:corset
+315:costume
+316:cougar
+317:coverall
+318:cowbell
+319:cowboy hat
+320:crab (animal)
+321:crabmeat
+322:cracker
+323:crape
+324:crate
+325:crayon
+326:cream pitcher
+327:crescent roll
+328:crib
+329:crock pot
+330:crossbar
+331:crouton
+332:crow
+333:crowbar
+334:crown
+335:crucifix
+336:cruise ship
+337:police cruiser
+338:crumb
+339:crutch
+340:cub (animal)
+341:cube
+342:cucumber
+343:cufflink
+344:cup
+345:trophy cup
+346:cupboard
+347:cupcake
+348:hair curler
+349:curling iron
+350:curtain
+351:cushion
+352:cylinder
+353:cymbal
+354:dagger
+355:dalmatian
+356:dartboard
+357:date (fruit)
+358:deck chair
+359:deer
+360:dental floss
+361:desk
+362:detergent
+363:diaper
+364:diary
+365:die
+366:dinghy
+367:dining table
+368:tux
+369:dish
+370:dish antenna
+371:dishrag
+372:dishtowel
+373:dishwasher
+374:dishwasher detergent
+375:dispenser
+376:diving board
+377:Dixie cup
+378:dog
+379:dog collar
+380:doll
+381:dollar
+382:dollhouse
+383:dolphin
+384:domestic ass
+385:doorknob
+386:doormat
+387:doughnut
+388:dove
+389:dragonfly
+390:drawer
+391:underdrawers
+392:dress
+393:dress hat
+394:dress suit
+395:dresser
+396:drill
+397:drone
+398:dropper
+399:drum (musical instrument)
+400:drumstick
+401:duck
+402:duckling
+403:duct tape
+404:duffel bag
+405:dumbbell
+406:dumpster
+407:dustpan
+408:eagle
+409:earphone
+410:earplug
+411:earring
+412:easel
+413:eclair
+414:eel
+415:egg
+416:egg roll
+417:egg yolk
+418:eggbeater
+419:eggplant
+420:electric chair
+421:refrigerator
+422:elephant
+423:elk
+424:envelope
+425:eraser
+426:escargot
+427:eyepatch
+428:falcon
+429:fan
+430:faucet
+431:fedora
+432:ferret
+433:Ferris wheel
+434:ferry
+435:fig (fruit)
+436:fighter jet
+437:figurine
+438:file cabinet
+439:file (tool)
+440:fire alarm
+441:fire engine
+442:fire extinguisher
+443:fire hose
+444:fireplace
+445:fireplug
+446:first-aid kit
+447:fish
+448:fish (food)
+449:fishbowl
+450:fishing rod
+451:flag
+452:flagpole
+453:flamingo
+454:flannel
+455:flap
+456:flash
+457:flashlight
+458:fleece
+459:flip-flop (sandal)
+460:flipper (footwear)
+461:flower arrangement
+462:flute glass
+463:foal
+464:folding chair
+465:food processor
+466:football (American)
+467:football helmet
+468:footstool
+469:fork
+470:forklift
+471:freight car
+472:French toast
+473:freshener
+474:frisbee
+475:frog
+476:fruit juice
+477:frying pan
+478:fudge
+479:funnel
+480:futon
+481:gag
+482:garbage
+483:garbage truck
+484:garden hose
+485:gargle
+486:gargoyle
+487:garlic
+488:gasmask
+489:gazelle
+490:gelatin
+491:gemstone
+492:generator
+493:giant panda
+494:gift wrap
+495:ginger
+496:giraffe
+497:cincture
+498:glass (drink container)
+499:globe
+500:glove
+501:goat
+502:goggles
+503:goldfish
+504:golf club
+505:golfcart
+506:gondola (boat)
+507:goose
+508:gorilla
+509:gourd
+510:grape
+511:grater
+512:gravestone
+513:gravy boat
+514:green bean
+515:green onion
+516:griddle
+517:grill
+518:grits
+519:grizzly
+520:grocery bag
+521:guitar
+522:gull
+523:gun
+524:hairbrush
+525:hairnet
+526:hairpin
+527:halter top
+528:ham
+529:hamburger
+530:hammer
+531:hammock
+532:hamper
+533:hamster
+534:hair dryer
+535:hand glass
+536:hand towel
+537:handcart
+538:handcuff
+539:handkerchief
+540:handle
+541:handsaw
+542:hardback book
+543:harmonium
+544:hat
+545:hatbox
+546:veil
+547:headband
+548:headboard
+549:headlight
+550:headscarf
+551:headset
+552:headstall (for horses)
+553:heart
+554:heater
+555:helicopter
+556:helmet
+557:heron
+558:highchair
+559:hinge
+560:hippopotamus
+561:hockey stick
+562:hog
+563:home plate (baseball)
+564:honey
+565:fume hood
+566:hook
+567:hookah
+568:hornet
+569:horse
+570:hose
+571:hot-air balloon
+572:hotplate
+573:hot sauce
+574:hourglass
+575:houseboat
+576:hummingbird
+577:hummus
+578:polar bear
+579:icecream
+580:popsicle
+581:ice maker
+582:ice pack
+583:ice skate
+584:igniter
+585:inhaler
+586:iPod
+587:iron (for clothing)
+588:ironing board
+589:jacket
+590:jam
+591:jar
+592:jean
+593:jeep
+594:jelly bean
+595:jersey
+596:jet plane
+597:jewel
+598:jewelry
+599:joystick
+600:jumpsuit
+601:kayak
+602:keg
+603:kennel
+604:kettle
+605:key
+606:keycard
+607:kilt
+608:kimono
+609:kitchen sink
+610:kitchen table
+611:kite
+612:kitten
+613:kiwi fruit
+614:knee pad
+615:knife
+616:knitting needle
+617:knob
+618:knocker (on a door)
+619:koala
+620:lab coat
+621:ladder
+622:ladle
+623:ladybug
+624:lamb (animal)
+625:lamb-chop
+626:lamp
+627:lamppost
+628:lampshade
+629:lantern
+630:lanyard
+631:laptop computer
+632:lasagna
+633:latch
+634:lawn mower
+635:leather
+636:legging (clothing)
+637:Lego
+638:legume
+639:lemon
+640:lemonade
+641:lettuce
+642:license plate
+643:life buoy
+644:life jacket
+645:lightbulb
+646:lightning rod
+647:lime
+648:limousine
+649:lion
+650:lip balm
+651:liquor
+652:lizard
+653:log
+654:lollipop
+655:speaker (stero equipment)
+656:loveseat
+657:machine gun
+658:magazine
+659:magnet
+660:mail slot
+661:mailbox (at home)
+662:mallard
+663:mallet
+664:mammoth
+665:manatee
+666:mandarin orange
+667:manger
+668:manhole
+669:map
+670:marker
+671:martini
+672:mascot
+673:mashed potato
+674:masher
+675:mask
+676:mast
+677:mat (gym equipment)
+678:matchbox
+679:mattress
+680:measuring cup
+681:measuring stick
+682:meatball
+683:medicine
+684:melon
+685:microphone
+686:microscope
+687:microwave oven
+688:milestone
+689:milk
+690:milk can
+691:milkshake
+692:minivan
+693:mint candy
+694:mirror
+695:mitten
+696:mixer (kitchen tool)
+697:money
+698:monitor (computer equipment) computer monitor
+699:monkey
+700:motor
+701:motor scooter
+702:motor vehicle
+703:motorcycle
+704:mound (baseball)
+705:mouse (computer equipment)
+706:mousepad
+707:muffin
+708:mug
+709:mushroom
+710:music stool
+711:musical instrument
+712:nailfile
+713:napkin
+714:neckerchief
+715:necklace
+716:necktie
+717:needle
+718:nest
+719:newspaper
+720:newsstand
+721:nightshirt
+722:nosebag (for animals)
+723:noseband (for animals)
+724:notebook
+725:notepad
+726:nut
+727:nutcracker
+728:oar
+729:octopus (food)
+730:octopus (animal)
+731:oil lamp
+732:olive oil
+733:omelet
+734:onion
+735:orange (fruit)
+736:orange juice
+737:ostrich
+738:ottoman
+739:oven
+740:overalls (clothing)
+741:owl
+742:packet
+743:inkpad
+744:pad
+745:paddle
+746:padlock
+747:paintbrush
+748:painting
+749:pajamas
+750:palette
+751:pan (for cooking)
+752:pan (metal container)
+753:pancake
+754:pantyhose
+755:papaya
+756:paper plate
+757:paper towel
+758:paperback book
+759:paperweight
+760:parachute
+761:parakeet
+762:parasail (sports)
+763:parasol
+764:parchment
+765:parka
+766:parking meter
+767:parrot
+768:passenger car (part of a train)
+769:passenger ship
+770:passport
+771:pastry
+772:patty (food)
+773:pea (food)
+774:peach
+775:peanut butter
+776:pear
+777:peeler (tool for fruit and vegetables)
+778:wooden leg
+779:pegboard
+780:pelican
+781:pen
+782:pencil
+783:pencil box
+784:pencil sharpener
+785:pendulum
+786:penguin
+787:pennant
+788:penny (coin)
+789:pepper
+790:pepper mill
+791:perfume
+792:persimmon
+793:person
+794:pet
+795:pew (church bench)
+796:phonebook
+797:phonograph record
+798:piano
+799:pickle
+800:pickup truck
+801:pie
+802:pigeon
+803:piggy bank
+804:pillow
+805:pin (non jewelry)
+806:pineapple
+807:pinecone
+808:ping-pong ball
+809:pinwheel
+810:tobacco pipe
+811:pipe
+812:pistol
+813:pita (bread)
+814:pitcher (vessel for liquid)
+815:pitchfork
+816:pizza
+817:place mat
+818:plate
+819:platter
+820:playpen
+821:pliers
+822:plow (farm equipment)
+823:plume
+824:pocket watch
+825:pocketknife
+826:poker (fire stirring tool)
+827:pole
+828:polo shirt
+829:poncho
+830:pony
+831:pool table
+832:pop (soda)
+833:postbox (public)
+834:postcard
+835:poster
+836:pot
+837:flowerpot
+838:potato
+839:potholder
+840:pottery
+841:pouch
+842:power shovel
+843:prawn
+844:pretzel
+845:printer
+846:projectile (weapon)
+847:projector
+848:propeller
+849:prune
+850:pudding
+851:puffer (fish)
+852:puffin
+853:pug-dog
+854:pumpkin
+855:puncher
+856:puppet
+857:puppy
+858:quesadilla
+859:quiche
+860:quilt
+861:rabbit
+862:race car
+863:racket
+864:radar
+865:radiator
+866:radio receiver
+867:radish
+868:raft
+869:rag doll
+870:raincoat
+871:ram (animal)
+872:raspberry
+873:rat
+874:razorblade
+875:reamer (juicer)
+876:rearview mirror
+877:receipt
+878:recliner
+879:record player
+880:reflector
+881:remote control
+882:rhinoceros
+883:rib (food)
+884:rifle
+885:ring
+886:river boat
+887:road map
+888:robe
+889:rocking chair
+890:rodent
+891:roller skate
+892:Rollerblade
+893:rolling pin
+894:root beer
+895:router (computer equipment)
+896:rubber band
+897:runner (carpet)
+898:plastic bag
+899:saddle (on an animal)
+900:saddle blanket
+901:saddlebag
+902:safety pin
+903:sail
+904:salad
+905:salad plate
+906:salami
+907:salmon (fish)
+908:salmon (food)
+909:salsa
+910:saltshaker
+911:sandal (type of shoe)
+912:sandwich
+913:satchel
+914:saucepan
+915:saucer
+916:sausage
+917:sawhorse
+918:saxophone
+919:scale (measuring instrument)
+920:scarecrow
+921:scarf
+922:school bus
+923:scissors
+924:scoreboard
+925:scraper
+926:screwdriver
+927:scrubbing brush
+928:sculpture
+929:seabird
+930:seahorse
+931:seaplane
+932:seashell
+933:sewing machine
+934:shaker
+935:shampoo
+936:shark
+937:sharpener
+938:Sharpie
+939:shaver (electric)
+940:shaving cream
+941:shawl
+942:shears
+943:sheep
+944:shepherd dog
+945:sherbert
+946:shield
+947:shirt
+948:shoe
+949:shopping bag
+950:shopping cart
+951:short pants
+952:shot glass
+953:shoulder bag
+954:shovel
+955:shower head
+956:shower cap
+957:shower curtain
+958:shredder (for paper)
+959:signboard
+960:silo
+961:sink
+962:skateboard
+963:skewer
+964:ski
+965:ski boot
+966:ski parka
+967:ski pole
+968:skirt
+969:skullcap
+970:sled
+971:sleeping bag
+972:sling (bandage)
+973:slipper (footwear)
+974:smoothie
+975:snake
+976:snowboard
+977:snowman
+978:snowmobile
+979:soap
+980:soccer ball
+981:sock
+982:sofa
+983:softball
+984:solar array
+985:sombrero
+986:soup
+987:soup bowl
+988:soupspoon
+989:sour cream
+990:soya milk
+991:space shuttle
+992:sparkler (fireworks)
+993:spatula
+994:spear
+995:spectacles
+996:spice rack
+997:spider
+998:crawfish
+999:sponge
+1000:spoon
+1001:sportswear
+1002:spotlight
+1003:squid (food)
+1004:squirrel
+1005:stagecoach
+1006:stapler (stapling machine)
+1007:starfish
+1008:statue (sculpture)
+1009:steak (food)
+1010:steak knife
+1011:steering wheel
+1012:stepladder
+1013:step stool
+1014:stereo (sound system)
+1015:stew
+1016:stirrer
+1017:stirrup
+1018:stool
+1019:stop sign
+1020:brake light
+1021:stove
+1022:strainer
+1023:strap
+1024:straw (for drinking)
+1025:strawberry
+1026:street sign
+1027:streetlight
+1028:string cheese
+1029:stylus
+1030:subwoofer
+1031:sugar bowl
+1032:sugarcane (plant)
+1033:suit (clothing)
+1034:sunflower
+1035:sunglasses
+1036:sunhat
+1037:surfboard
+1038:sushi
+1039:mop
+1040:sweat pants
+1041:sweatband
+1042:sweater
+1043:sweatshirt
+1044:sweet potato
+1045:swimsuit
+1046:sword
+1047:syringe
+1048:Tabasco sauce
+1049:table-tennis table
+1050:table
+1051:table lamp
+1052:tablecloth
+1053:tachometer
+1054:taco
+1055:tag
+1056:taillight
+1057:tambourine
+1058:army tank
+1059:tank (storage vessel)
+1060:tank top (clothing)
+1061:tape (sticky cloth or paper)
+1062:tape measure
+1063:tapestry
+1064:tarp
+1065:tartan
+1066:tassel
+1067:tea bag
+1068:teacup
+1069:teakettle
+1070:teapot
+1071:teddy bear
+1072:telephone
+1073:telephone booth
+1074:telephone pole
+1075:telephoto lens
+1076:television camera
+1077:television set
+1078:tennis ball
+1079:tennis racket
+1080:tequila
+1081:thermometer
+1082:thermos bottle
+1083:thermostat
+1084:thimble
+1085:thread
+1086:thumbtack
+1087:tiara
+1088:tiger
+1089:tights (clothing)
+1090:timer
+1091:tinfoil
+1092:tinsel
+1093:tissue paper
+1094:toast (food)
+1095:toaster
+1096:toaster oven
+1097:toilet
+1098:toilet tissue
+1099:tomato
+1100:tongs
+1101:toolbox
+1102:toothbrush
+1103:toothpaste
+1104:toothpick
+1105:cover
+1106:tortilla
+1107:tow truck
+1108:towel
+1109:towel rack
+1110:toy
+1111:tractor (farm equipment)
+1112:traffic light
+1113:dirt bike
+1114:trailer truck
+1115:train (railroad vehicle)
+1116:trampoline
+1117:tray
+1118:trench coat
+1119:triangle (musical instrument)
+1120:tricycle
+1121:tripod
+1122:trousers
+1123:truck
+1124:truffle (chocolate)
+1125:trunk
+1126:vat
+1127:turban
+1128:turkey (food)
+1129:turnip
+1130:turtle
+1131:turtleneck (clothing)
+1132:typewriter
+1133:umbrella
+1134:underwear
+1135:unicycle
+1136:urinal
+1137:urn
+1138:vacuum cleaner
+1139:vase
+1140:vending machine
+1141:vent
+1142:vest
+1143:videotape
+1144:vinegar
+1145:violin
+1146:vodka
+1147:volleyball
+1148:vulture
+1149:waffle
+1150:waffle iron
+1151:wagon
+1152:wagon wheel
+1153:walking stick
+1154:wall clock
+1155:wall socket
+1156:wallet
+1157:walrus
+1158:wardrobe
+1159:washbasin
+1160:automatic washer
+1161:watch
+1162:water bottle
+1163:water cooler
+1164:water faucet
+1165:water heater
+1166:water jug
+1167:water gun
+1168:water scooter
+1169:water ski
+1170:water tower
+1171:watering can
+1172:watermelon
+1173:weathervane
+1174:webcam
+1175:wedding cake
+1176:wedding ring
+1177:wet suit
+1178:wheel
+1179:wheelchair
+1180:whipped cream
+1181:whistle
+1182:wig
+1183:wind chime
+1184:windmill
+1185:window box (for plants)
+1186:windshield wiper
+1187:windsock
+1188:wine bottle
+1189:wine bucket
+1190:wineglass
+1191:blinder (for horses)
+1192:wok
+1193:wolf
+1194:wooden spoon
+1195:wreath
+1196:wrench
+1197:wristband
+1198:wristlet
+1199:yacht
+1200:yogurt
+1201:yoke (animal equipment)
+1202:zebra
+1203:zucchini
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/lvis_1203_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/lvis_1203_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..40269203e3775b5dc5834253e465471f57e5fe8b
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/lvis_1203_with_prompt_eng.txt
@@ -0,0 +1,1203 @@
+1:aerosol can,spray can
+2:air conditioner
+3:airplane,aeroplane
+4:alarm clock
+5:alcohol,alcoholic beverage
+6:alligator,gator
+7:almond
+8:ambulance
+9:amplifier
+10:anklet,ankle bracelet
+11:antenna,aerial,transmitting aerial
+12:apple
+13:applesauce
+14:apricot
+15:apron
+16:aquarium,fish tank
+17:arctic (type of shoe),galosh,golosh,rubber (type of shoe),gumshoe
+18:armband
+19:armchair
+20:armoire
+21:armor,armour
+22:artichoke
+23:trash can,garbage can,wastebin,dustbin,trash barrel,trash bin
+24:ashtray
+25:asparagus
+26:atomizer,atomiser,spray,sprayer,nebulizer,nebuliser
+27:avocado
+28:award,accolade
+29:awning
+30:ax,axe
+31:baboon
+32:baby buggy,baby carriage,perambulator,pram,stroller
+33:basketball backboard
+34:backpack,knapsack,packsack,rucksack,haversack
+35:handbag,purse,pocketbook
+36:suitcase,baggage,luggage
+37:bagel,beigel
+38:bagpipe
+39:baguet,baguette
+40:bait,lure
+41:ball
+42:ballet skirt,tutu
+43:balloon
+44:bamboo
+45:banana
+46:Band Aid
+47:bandage
+48:bandanna,bandana
+49:banjo
+50:banner,streamer
+51:barbell
+52:barge
+53:barrel,cask
+54:barrette
+55:barrow,garden cart,lawn cart,wheelbarrow
+56:baseball base
+57:baseball
+58:baseball bat
+59:baseball cap,jockey cap,golf cap
+60:baseball glove,baseball mitt
+61:basket,handbasket
+62:basketball
+63:bass horn,sousaphone,tuba
+64:bat (animal)
+65:bath mat
+66:bath towel
+67:bathrobe
+68:bathtub,bathing tub
+69:batter (food)
+70:battery
+71:beachball
+72:bead
+73:bean curd,tofu
+74:beanbag
+75:beanie,beany
+76:bear
+77:bed
+78:bedpan
+79:bedspread,bedcover,bed covering,counterpane,spread
+80:cow
+81:beef (food),boeuf (food)
+82:beeper,pager
+83:beer bottle
+84:beer can
+85:beetle
+86:bell
+87:bell pepper,capsicum
+88:belt
+89:belt buckle
+90:bench
+91:beret
+92:bib
+93:Bible
+94:bicycle,bike (bicycle)
+95:visor,vizor
+96:billboard
+97:binder,ring-binder
+98:binoculars,field glasses,opera glasses
+99:bird
+100:birdfeeder
+101:birdbath
+102:birdcage
+103:birdhouse
+104:birthday cake
+105:birthday card
+106:pirate flag
+107:black sheep
+108:blackberry
+109:blackboard,chalkboard
+110:blanket
+111:blazer,sport jacket,sport coat,sports jacket,sports coat
+112:blender,liquidizer,liquidiser
+113:blimp
+114:blinker,flasher
+115:blouse
+116:blueberry
+117:gameboard
+118:boat,ship (boat)
+119:bob,bobber,bobfloat
+120:bobbin,spool,reel
+121:bobby pin,hairgrip
+122:boiled egg,coddled egg
+123:bolo tie,bolo,bola tie,bola
+124:deadbolt
+125:bolt
+126:bonnet
+127:book
+128:bookcase
+129:booklet,brochure,leaflet,pamphlet
+130:bookmark,bookmarker
+131:boom microphone,microphone boom
+132:boot
+133:bottle
+134:bottle opener
+135:bouquet
+136:bow (weapon)
+137:bow (decorative ribbons)
+138:bow-tie,bowtie
+139:bowl
+140:pipe bowl
+141:bowler hat,bowler,derby hat,derby,plug hat
+142:bowling ball
+143:box
+144:boxing glove
+145:suspenders
+146:bracelet,bangle
+147:brass plaque
+148:brassiere,bra,bandeau
+149:bread-bin,breadbox
+150:bread
+151:breechcloth,breechclout,loincloth
+152:bridal gown,wedding gown,wedding dress
+153:briefcase
+154:broccoli
+155:broach
+156:broom
+157:brownie
+158:brussels sprouts
+159:bubble gum
+160:bucket,pail
+161:horse buggy
+162:horned cow
+163:bulldog
+164:bulldozer,dozer
+165:bullet train
+166:bulletin board,notice board
+167:bulletproof vest
+168:bullhorn,megaphone
+169:bun,roll
+170:bunk bed
+171:buoy
+172:burrito
+173:bus (vehicle),autobus,charabanc,double-decker,motorbus,motorcoach
+174:business card
+175:butter
+176:butterfly
+177:button
+178:cab (taxi),taxi,taxicab
+179:cabana
+180:cabin car,caboose
+181:cabinet
+182:locker,storage locker
+183:cake
+184:calculator
+185:calendar
+186:calf
+187:camcorder
+188:camel
+189:camera
+190:camera lens
+191:camper (vehicle),camping bus,motor home
+192:can,tin can
+193:can opener,tin opener
+194:candle,candlestick
+195:candle holder
+196:candy bar
+197:candy cane
+198:walking cane
+199:canister,cannister
+200:canoe
+201:cantaloup,cantaloupe
+202:canteen
+203:cap (headwear)
+204:bottle cap,cap (container lid)
+205:cape
+206:cappuccino,coffee cappuccino
+207:car (automobile),auto (automobile),automobile
+208:railcar (part of a train),railway car (part of a train),railroad car (part of a train)
+209:elevator car
+210:car battery,automobile battery
+211:identity card
+212:card
+213:cardigan
+214:cargo ship,cargo vessel
+215:carnation
+216:horse carriage
+217:carrot
+218:tote bag
+219:cart
+220:carton
+221:cash register,register (for cash transactions)
+222:casserole
+223:cassette
+224:cast,plaster cast,plaster bandage
+225:cat
+226:cauliflower
+227:cayenne (spice),cayenne pepper (spice),red pepper (spice)
+228:CD player
+229:celery
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+1027:streetlight,street lamp
+1028:string cheese
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+1032:sugarcane (plant)
+1033:suit (clothing)
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+1035:sunglasses
+1036:sunhat
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+1041:sweatband
+1042:sweater
+1043:sweatshirt
+1044:sweet potato
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+1047:syringe
+1048:Tabasco sauce
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+1051:table lamp
+1052:tablecloth
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+1054:taco
+1055:tag
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+1057:tambourine
+1058:army tank,armored combat vehicle,armoured combat vehicle
+1059:tank (storage vessel),storage tank
+1060:tank top (clothing)
+1061:tape (sticky cloth or paper)
+1062:tape measure,measuring tape
+1063:tapestry
+1064:tarp
+1065:tartan,plaid
+1066:tassel
+1067:tea bag
+1068:teacup
+1069:teakettle
+1070:teapot
+1071:teddy bear
+1072:telephone,phone,telephone set
+1073:telephone booth,phone booth,call box,telephone box,telephone kiosk
+1074:telephone pole,telegraph pole,telegraph post
+1075:telephoto lens,zoom lens
+1076:television camera,tv camera
+1077:television set,tv,tv set
+1078:tennis ball
+1079:tennis racket
+1080:tequila
+1081:thermometer
+1082:thermos bottle
+1083:thermostat
+1084:thimble
+1085:thread,yarn
+1086:thumbtack,drawing pin,pushpin
+1087:tiara
+1088:tiger
+1089:tights (clothing),leotards
+1090:timer,stopwatch
+1091:tinfoil
+1092:tinsel
+1093:tissue paper
+1094:toast (food)
+1095:toaster
+1096:toaster oven
+1097:toilet
+1098:toilet tissue,toilet paper,bathroom tissue
+1099:tomato
+1100:tongs
+1101:toolbox
+1102:toothbrush
+1103:toothpaste
+1104:toothpick
+1105:cover
+1106:tortilla
+1107:tow truck
+1108:towel
+1109:towel rack,towel rail,towel bar
+1110:toy
+1111:tractor (farm equipment)
+1112:traffic light
+1113:dirt bike
+1114:trailer truck,tractor trailer,trucking rig,articulated lorry,semi truck
+1115:train (railroad vehicle),railroad train
+1116:trampoline
+1117:tray
+1118:trench coat
+1119:triangle (musical instrument)
+1120:tricycle
+1121:tripod
+1122:trousers,pants (clothing)
+1123:truck
+1124:truffle (chocolate),chocolate truffle
+1125:trunk
+1126:vat
+1127:turban
+1128:turkey (food)
+1129:turnip
+1130:turtle
+1131:turtleneck (clothing),polo-neck
+1132:typewriter
+1133:umbrella
+1134:underwear,underclothes,underclothing,underpants
+1135:unicycle
+1136:urinal
+1137:urn
+1138:vacuum cleaner
+1139:vase
+1140:vending machine
+1141:vent,blowhole,air vent
+1142:vest,waistcoat
+1143:videotape
+1144:vinegar
+1145:violin,fiddle
+1146:vodka
+1147:volleyball
+1148:vulture
+1149:waffle
+1150:waffle iron
+1151:wagon
+1152:wagon wheel
+1153:walking stick
+1154:wall clock
+1155:wall socket,wall plug,electric outlet,electrical outlet,outlet,electric receptacle
+1156:wallet,billfold
+1157:walrus
+1158:wardrobe
+1159:washbasin,basin (for washing),washbowl,washstand,handbasin
+1160:automatic washer,washing machine
+1161:watch,wristwatch
+1162:water bottle
+1163:water cooler
+1164:water faucet,water tap,tap (water faucet)
+1165:water heater,hot-water heater
+1166:water jug
+1167:water gun,squirt gun
+1168:water scooter,sea scooter,jet ski
+1169:water ski
+1170:water tower
+1171:watering can
+1172:watermelon
+1173:weathervane,vane (weathervane),wind vane
+1174:webcam
+1175:wedding cake,bridecake
+1176:wedding ring,wedding band
+1177:wet suit
+1178:wheel
+1179:wheelchair
+1180:whipped cream
+1181:whistle
+1182:wig
+1183:wind chime
+1184:windmill
+1185:window box (for plants)
+1186:windshield wiper,windscreen wiper,wiper (for windshield/screen)
+1187:windsock,air sock,air-sleeve,wind sleeve,wind cone
+1188:wine bottle
+1189:wine bucket,wine cooler
+1190:wineglass
+1191:blinder (for horses)
+1192:wok
+1193:wolf
+1194:wooden spoon
+1195:wreath
+1196:wrench,spanner
+1197:wristband
+1198:wristlet,wrist band
+1199:yacht
+1200:yogurt,yoghurt,yoghourt
+1201:yoke (animal equipment)
+1202:zebra
+1203:zucchini,courgette
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_459.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_459.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0a605e162f03fdeaf41009124104cb09ca718477
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_459.txt
@@ -0,0 +1,460 @@
+0:invalid_class_id
+1:accordion
+2:aeroplane
+3:air conditioner
+4:antenna
+5:artillery
+6:ashtray
+7:atrium
+8:baby carriage
+9:bag
+10:ball
+11:balloon
+12:bamboo weaving
+13:barrel
+14:baseball bat
+15:basket
+16:basketball backboard
+17:bathtub
+18:bed
+19:bedclothes
+20:beer
+21:bell
+22:bench
+23:bicycle
+24:binoculars
+25:bird
+26:bird cage
+27:bird feeder
+28:bird nest
+29:blackboard
+30:board
+31:boat
+32:bone
+33:book
+34:bottle
+35:bottle opener
+36:bowl
+37:box
+38:bracelet
+39:brick
+40:bridge
+41:broom
+42:brush
+43:bucket
+44:building
+45:bus
+46:cabinet
+47:cabinet door
+48:cage
+49:cake
+50:calculator
+51:calendar
+52:camel
+53:camera
+54:camera lens
+55:can
+56:candle
+57:candle holder
+58:cap
+59:car
+60:card
+61:cart
+62:case
+63:casette recorder
+64:cash register
+65:cat
+66:cd
+67:cd player
+68:ceiling
+69:cell phone
+70:cello
+71:chain
+72:chair
+73:chessboard
+74:chicken
+75:chopstick
+76:clip
+77:clippers
+78:clock
+79:closet
+80:cloth
+81:clothes tree
+82:coffee
+83:coffee machine
+84:comb
+85:computer
+86:concrete
+87:cone
+88:container
+89:control booth
+90:controller
+91:cooker
+92:copying machine
+93:coral
+94:cork
+95:corkscrew
+96:counter
+97:court
+98:cow
+99:crabstick
+100:crane
+101:crate
+102:cross
+103:crutch
+104:cup
+105:curtain
+106:cushion
+107:cutting board
+108:dais
+109:disc
+110:disc case
+111:dishwasher
+112:dock
+113:dog
+114:dolphin
+115:door
+116:drainer
+117:dray
+118:drink dispenser
+119:drinking machine
+120:drop
+121:drug
+122:drum
+123:drum kit
+124:duck
+125:dumbbell
+126:earphone
+127:earrings
+128:egg
+129:electric fan
+130:electric iron
+131:electric pot
+132:electric saw
+133:electronic keyboard
+134:engine
+135:envelope
+136:equipment
+137:escalator
+138:exhibition booth
+139:extinguisher
+140:eyeglass
+141:fan
+142:faucet
+143:fax machine
+144:fence
+145:ferris wheel
+146:fire extinguisher
+147:fire hydrant
+148:fire place
+149:fish
+150:fish tank
+151:fishbowl
+152:fishing net
+153:fishing pole
+154:flag
+155:flagstaff
+156:flame
+157:flashlight
+158:floor
+159:flower
+160:fly
+161:foam
+162:food
+163:footbridge
+164:forceps
+165:fork
+166:forklift
+167:fountain
+168:fox
+169:frame
+170:fridge
+171:frog
+172:fruit
+173:funnel
+174:furnace
+175:game controller
+176:game machine
+177:gas cylinder
+178:gas hood
+179:gas stove
+180:gift box
+181:glass
+182:glass marble
+183:globe
+184:glove
+185:goal
+186:grandstand
+187:grass
+188:gravestone
+189:ground
+190:guardrail
+191:guitar
+192:gun
+193:hammer
+194:hand cart
+195:handle
+196:handrail
+197:hanger
+198:hard disk drive
+199:hat
+200:hay
+201:headphone
+202:heater
+203:helicopter
+204:helmet
+205:holder
+206:hook
+207:horse
+208:horse-drawn carriage
+209:hot-air balloon
+210:hydrovalve
+211:ice
+212:inflator pump
+213:ipod
+214:iron
+215:ironing board
+216:jar
+217:kart
+218:kettle
+219:key
+220:keyboard
+221:kitchen range
+222:kite
+223:knife
+224:knife block
+225:ladder
+226:ladder truck
+227:ladle
+228:laptop
+229:leaves
+230:lid
+231:life buoy
+232:light
+233:light bulb
+234:lighter
+235:line
+236:lion
+237:lobster
+238:lock
+239:machine
+240:mailbox
+241:mannequin
+242:map
+243:mask
+244:mat
+245:match book
+246:mattress
+247:menu
+248:metal
+249:meter box
+250:microphone
+251:microwave
+252:mirror
+253:missile
+254:model
+255:money
+256:monkey
+257:mop
+258:motorbike
+259:mountain
+260:mouse
+261:mouse pad
+262:musical instrument
+263:napkin
+264:net
+265:newspaper
+266:oar
+267:ornament
+268:outlet
+269:oven
+270:oxygen bottle
+271:pack
+272:pan
+273:paper
+274:paper box
+275:paper cutter
+276:parachute
+277:parasol
+278:parterre
+279:patio
+280:pelage
+281:pen
+282:pen container
+283:pencil
+284:person
+285:photo
+286:piano
+287:picture
+288:pig
+289:pillar
+290:pillow
+291:pipe
+292:pitcher
+293:plant
+294:plastic
+295:plate
+296:platform
+297:player
+298:playground
+299:pliers
+300:plume
+301:poker
+302:poker chip
+303:pole
+304:pool table
+305:postcard
+306:poster
+307:pot
+308:pottedplant
+309:printer
+310:projector
+311:pumpkin
+312:rabbit
+313:racket
+314:radiator
+315:radio
+316:rail
+317:rake
+318:ramp
+319:range hood
+320:receiver
+321:recorder
+322:recreational machines
+323:remote control
+324:road
+325:robot
+326:rock
+327:rocket
+328:rocking horse
+329:rope
+330:rug
+331:ruler
+332:runway
+333:saddle
+334:sand
+335:saw
+336:scale
+337:scanner
+338:scissors
+339:scoop
+340:screen
+341:screwdriver
+342:sculpture
+343:scythe
+344:sewer
+345:sewing machine
+346:shed
+347:sheep
+348:shell
+349:shelves
+350:shoe
+351:shopping cart
+352:shovel
+353:sidecar
+354:sidewalk
+355:sign
+356:signal light
+357:sink
+358:skateboard
+359:ski
+360:sky
+361:sled
+362:slippers
+363:smoke
+364:snail
+365:snake
+366:snow
+367:snowmobiles
+368:sofa
+369:spanner
+370:spatula
+371:speaker
+372:speed bump
+373:spice container
+374:spoon
+375:sprayer
+376:squirrel
+377:stage
+378:stair
+379:stapler
+380:stick
+381:sticky note
+382:stone
+383:stool
+384:stove
+385:straw
+386:stretcher
+387:sun
+388:sunglass
+389:sunshade
+390:surveillance camera
+391:swan
+392:sweeper
+393:swim ring
+394:swimming pool
+395:swing
+396:switch
+397:table
+398:tableware
+399:tank
+400:tap
+401:tape
+402:tarp
+403:telephone
+404:telephone booth
+405:tent
+406:tire
+407:toaster
+408:toilet
+409:tong
+410:tool
+411:toothbrush
+412:towel
+413:toy
+414:toy car
+415:track
+416:train
+417:trampoline
+418:trash bin
+419:tray
+420:tree
+421:tricycle
+422:tripod
+423:trophy
+424:truck
+425:tube
+426:turtle
+427:tvmonitor
+428:tweezers
+429:typewriter
+430:umbrella
+431:unknown
+432:vacuum cleaner
+433:vending machine
+434:video camera
+435:video game console
+436:video player
+437:video tape
+438:violin
+439:wakeboard
+440:wall
+441:wallet
+442:wardrobe
+443:washing machine
+444:watch
+445:water
+446:water dispenser
+447:water pipe
+448:water skate board
+449:watermelon
+450:whale
+451:wharf
+452:wheel
+453:wheelchair
+454:window
+455:window blinds
+456:wineglass
+457:wire
+458:wood
+459:wool
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_459_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_459_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57454eddffc53cd2fb7d5c361f0a3d62af1e21af
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_459_with_prompt_eng.txt
@@ -0,0 +1,460 @@
+0:invalid_class_id
+1:accordion
+2:aeroplane,aeroplanes,airplanes,airplane
+3:air conditioner
+4:antenna
+5:artillery
+6:ashtray
+7:atrium
+8:baby carriage
+9:bag,bags
+10:ball
+11:balloon
+12:bamboo weaving
+13:barrel
+14:baseball bat
+15:basket
+16:basketball backboard
+17:bathtub
+18:bed,beds
+19:bedclothes
+20:beer
+21:bell
+22:bench,benches
+23:bicycle,bicycles
+24:binoculars
+25:bird,birds
+26:bird cage
+27:bird feeder
+28:bird nest
+29:blackboard
+30:board
+31:boat,boats
+32:bone
+33:book,books
+34:bottle,bottles,water bottle
+35:bottle opener
+36:bowl
+37:box
+38:bracelet
+39:brick
+40:bridge
+41:broom
+42:brush
+43:bucket
+44:building,buildings
+45:bus,buses
+46:cabinet,cabinets,drawer,drawers
+47:cabinet door
+48:cage
+49:cake
+50:calculator
+51:calendar
+52:camel
+53:camera
+54:camera lens
+55:can
+56:candle
+57:candle holder
+58:cap
+59:car,cars
+60:card
+61:cart
+62:computer case
+63:casette recorder
+64:cash register
+65:cat,cats,kitties,kitty
+66:cd
+67:cd player
+68:ceiling
+69:cell phone
+70:cello
+71:chain
+72:chair,chairs
+73:chessboard
+74:chicken
+75:chopstick
+76:clip
+77:clippers
+78:clock
+79:closet
+80:cloth,clothes
+81:clothes tree
+82:coffee
+83:coffee machine
+84:comb
+85:computer
+86:concrete
+87:cone
+88:container
+89:control booth
+90:controller
+91:cooker
+92:copying machine
+93:coral
+94:cork
+95:corkscrew
+96:counter
+97:court
+98:cow,cows
+99:crabstick
+100:crane
+101:crate
+102:cross
+103:crutch
+104:cup,cups
+105:curtain,curtains
+106:cushion
+107:cutting board
+108:dais
+109:disc
+110:disc case
+111:dishwasher
+112:dock
+113:dog,dogs,puppy,puppies
+114:dolphin
+115:door,doors
+116:drainer
+117:dray
+118:drink dispenser
+119:drinking machine
+120:drop
+121:drug
+122:drum
+123:drum kit
+124:duck
+125:dumbbell
+126:earphone
+127:earrings
+128:egg
+129:electric fan
+130:electric iron
+131:electric pot
+132:electric saw
+133:electronic keyboard
+134:engine
+135:envelope
+136:equipment
+137:escalator
+138:exhibition booth
+139:extinguisher
+140:eyeglass
+141:fan
+142:faucet
+143:fax machine
+144:fence,fences
+145:ferris wheel
+146:fire extinguisher
+147:fire hydrant
+148:fire place
+149:fish
+150:fish tank
+151:fishbowl
+152:fishing net
+153:fishing pole
+154:flag
+155:flagstaff
+156:flame
+157:flashlight
+158:floor,tile ground,carpet,rug,flooring
+159:flower,flowers
+160:fly
+161:foam
+162:food
+163:footbridge
+164:forceps
+165:fork
+166:forklift
+167:fountain
+168:fox
+169:frame
+170:fridge
+171:frog
+172:fruit
+173:funnel
+174:furnace
+175:game controller
+176:game machine
+177:gas cylinder
+178:gas hood
+179:gas stove
+180:gift box
+181:glass
+182:glass marble
+183:globe
+184:glove
+185:goal
+186:grandstand
+187:grass,grasses,lawn,turf
+188:gravestone
+189:ground,soil,soil ground,dirt ground
+190:guardrail
+191:guitar
+192:gun
+193:hammer
+194:hand cart
+195:handle
+196:handrail
+197:hanger
+198:hard disk drive
+199:hat
+200:hay
+201:headphone
+202:heater
+203:helicopter
+204:helmet
+205:holder
+206:hook
+207:horse,horses,foal
+208:horse-drawn carriage
+209:hot-air balloon
+210:hydrovalve
+211:ice
+212:inflator pump
+213:ipod
+214:iron
+215:ironing board
+216:jar
+217:kart
+218:kettle
+219:key
+220:keyboard,keyboards
+221:kitchen range
+222:kite
+223:knife
+224:knife block
+225:ladder
+226:ladder truck
+227:ladle
+228:laptop
+229:leaves
+230:lid
+231:life buoy
+232:lamp,lamps
+233:light bulb
+234:lighter
+235:line
+236:lion
+237:lobster
+238:lock
+239:machine
+240:mailbox
+241:mannequin
+242:map
+243:mask
+244:mat
+245:match book
+246:mattress
+247:menu
+248:metal
+249:meter box
+250:microphone
+251:microwave
+252:mirror
+253:missile
+254:model
+255:money
+256:monkey
+257:mop
+258:motorbike,motorcycle,motorbikes,motorcycles
+259:mountain,mountains
+260:mouse
+261:mouse pad
+262:musical instrument
+263:napkin
+264:net
+265:newspaper
+266:oar
+267:ornament
+268:outlet
+269:oven
+270:oxygen bottle
+271:pack
+272:pan
+273:paper
+274:paper box
+275:paper cutter
+276:parachute
+277:parasol
+278:parterre
+279:patio
+280:pelage
+281:pen
+282:pen container
+283:pencil
+284:person,child,girl,boy,woman,man,people,childeren,girls,boys,women,men,lady,guy,ladies,guys
+285:photo
+286:piano
+287:picture
+288:pig
+289:pillar
+290:pillow
+291:pipe
+292:pitcher
+293:plant
+294:plastic
+295:plate,plates
+296:platform,platforms
+297:player
+298:playground
+299:pliers
+300:plume
+301:poker
+302:poker chip
+303:pole
+304:pool table
+305:postcard
+306:poster
+307:pot
+308:pottedplant,pottedplants,plant pot,plant pots,planter,planters
+309:printer
+310:projector
+311:pumpkin
+312:rabbit
+313:racket
+314:radiator
+315:radio
+316:rail
+317:rake
+318:ramp
+319:range hood
+320:receiver
+321:recorder
+322:recreational machines
+323:remote control
+324:road,street,streets
+325:robot
+326:rock,rocks,stone,stones
+327:rocket
+328:rocking horse
+329:rope
+330:rug
+331:ruler
+332:runway
+333:saddle
+334:sand
+335:saw
+336:scale
+337:scanner
+338:scissors
+339:scoop
+340:screen
+341:screwdriver
+342:sculpture
+343:scythe
+344:sewer
+345:sewing machine
+346:shed
+347:sheep
+348:shell
+349:shelves,shelf
+350:shoe
+351:shopping cart
+352:shovel
+353:sidecar
+354:sidewalk
+355:sign,signs
+356:signal light
+357:sink
+358:skateboard
+359:ski
+360:sky,clouds
+361:sled
+362:slippers
+363:smoke
+364:snail
+365:snake
+366:snow
+367:snowmobiles
+368:sofa
+369:spanner
+370:spatula
+371:speaker
+372:speed bump
+373:spice container
+374:spoon
+375:sprayer
+376:squirrel
+377:stage
+378:stair
+379:stapler
+380:stick
+381:sticky note
+382:stone
+383:stool
+384:stove
+385:straw
+386:stretcher
+387:sun
+388:sunglass
+389:sunshade
+390:surveillance camera
+391:swan
+392:sweeper
+393:swim ring
+394:swimming pool
+395:swing
+396:switch
+397:table,diningtable,diningtables,tables,desk,desks,side table,side tables,coffee table
+398:tableware
+399:tank
+400:tap
+401:tape
+402:tarp
+403:telephone
+404:telephone booth
+405:tent
+406:tire
+407:toaster
+408:toilet
+409:tong
+410:tool
+411:toothbrush
+412:towel
+413:toy
+414:toy car
+415:track,train track,railroad
+416:train,trains,locomotive,locomotives,freight train
+417:trampoline
+418:trash bin
+419:tray
+420:tree,trees
+421:tricycle
+422:tripod
+423:trophy
+424:truck,trucks
+425:tube
+426:turtle
+427:tvmonitor,monitor,tv
+428:tweezers
+429:typewriter
+430:umbrella
+431:unknown
+432:vacuum cleaner
+433:vending machine
+434:video camera
+435:video game console
+436:video player
+437:video tape
+438:violin
+439:wakeboard
+440:wall,walls
+441:wallet
+442:wardrobe
+443:washing machine
+444:watch
+445:water
+446:water dispenser
+447:water pipe
+448:water skate board
+449:watermelon
+450:whale
+451:wharf
+452:wheel
+453:wheelchair
+454:window,windows
+455:window blinds
+456:wineglass
+457:wire
+458:wood piece
+459:wool
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_59.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_59.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9b7e3b554fd24e1d8643a26d10671513240fcc3
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_59.txt
@@ -0,0 +1,60 @@
+0:invalid_class_id
+1:aeroplane
+2:bag
+3:bed
+4:bedclothes
+5:bench
+6:bicycle
+7:bird
+8:boat
+9:book
+10:bottle
+11:building
+12:bus
+13:cabinet
+14:car
+15:cat
+16:ceiling
+17:chair
+18:cloth
+19:computer
+20:cow
+21:cup
+22:curtain
+23:dog
+24:door
+25:fence
+26:floor
+27:flower
+28:food
+29:grass
+30:ground
+31:horse
+32:keyboard
+33:light
+34:motorbike
+35:mountain
+36:mouse
+37:person
+38:plate
+39:platform
+40:pottedplant
+41:road
+42:rock
+43:sheep
+44:shelves
+45:sidewalk
+46:sign
+47:sky
+48:snow
+49:sofa
+50:diningtable
+51:track
+52:train
+53:tree
+54:truck
+55:tvmonitor
+56:wall
+57:water
+58:window
+59:wood
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_59_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_59_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e1d86aaa1e8590c49aa52322dcd1cd640525f7d5
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_context_59_with_prompt_eng.txt
@@ -0,0 +1,60 @@
+0:invalid_class_id
+1:aeroplane,aeroplanes,airplanes,airplane
+2:bag,bags
+3:bed,beds
+4:bedclothes
+5:bench,benches
+6:bicycle,bicycles
+7:bird,birds
+8:boat,boats
+9:book,books
+10:bottle,bottles,water bottle
+11:building,buildings
+12:bus,buses
+13:cabinet,cabinets,drawer,drawers
+14:car,cars
+15:cat,cats,kitties,kitty
+16:ceiling
+17:chair,chairs
+18:cloth,clothes
+19:computer case
+20:cow,cows
+21:cup,cups
+22:curtain,curtains
+23:dog,dogs,puppy,puppies
+24:door,doors
+25:fence,fences
+26:floor,tile ground,carpet,rug,flooring
+27:flower,flowers
+28:food
+29:grass,grasses,lawn,turf
+30:ground,soil,soil ground,dirt ground
+31:horse,horses,foal
+32:keyboard,keyboards
+33:lamp,lamps,bulb,bulbs
+34:motorbike,motorcycle,motorbikes,motorcycles
+35:mountain,mountains
+36:mouse
+37:person,child,girl,boy,woman,man,people,childeren,girls,boys,women,men,lady,guy,ladies,guys
+38:plate,plates
+39:platform,platforms
+40:pottedplant,pottedplants,plant pot,plant pots,planter,planters
+41:street,streets
+42:rock,rocks,stone,stones
+43:sheep
+44:shelves,shelf
+45:sidewalk
+46:sign,signs
+47:sky,clouds
+48:snow
+49:sofa
+50:diningtable,diningtables,table,tables,desk,desks,side table,side tables,coffee table
+51:track,train track,railroad
+52:train,trains,locomotive,locomotives,freight train
+53:tree,trees
+54:truck,trucks
+55:tvmonitor,monitor,tv
+56:wall,walls
+57:water
+58:window,windows
+59:wood piece
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_voc_21.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_voc_21.txt
new file mode 100644
index 0000000000000000000000000000000000000000..713a2dbd7eb5b790efc662c4be065fef22ffc928
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_voc_21.txt
@@ -0,0 +1,21 @@
+0:background,bag,bed,bench,book,building,cabinet,ceiling,cloth,computer,cup,door,fence,floor,flower,food,grass,ground,keyboard,light,mountain,mouse,curtain,platform,sign,plate,road,rock,shelves,sidewalk,sky,snow,bedclothes,track,tree,truck,wall,water,window,wood
+1:aeroplane
+2:bicycle
+3:bird
+4:boat
+5:bottle
+6:bus
+7:car
+8:cat
+9:chair
+10:cow
+11:diningtable
+12:dog
+13:horse
+14:motorbike
+15:person
+16:pottedplant
+17:sheep
+18:sofa
+19:train
+20:tvmonitor
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_voc_21_with_prompt_eng.txt b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_voc_21_with_prompt_eng.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b037b315a41fda59ad1b4651925fe42ae0b9dca9
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/openseg_labels/pascal_voc_21_with_prompt_eng.txt
@@ -0,0 +1,21 @@
+0:background,crops,bush,shrub,tiles,pavement,rug,carpet,box,boxes,speaker,storage,painting,board,panel,poster,clock,cage,drinking glass,park,plaything,toy,fireplace,bag,bag,bed,bench,book,books,building,buildings,cabinet,drawer,ceiling,computer,computer case,cup,cups,door,fence,floor,flower,grass,lawn,turf,ground,soil,dirt,tiles,keyboard,lamp,mountain,hills,mouse,curtain,platform,sign,street,rock,stone,shelf,sidewalk,sky,clouds,snow,track,train track,tree,trees,wall,water,window,wood,woods
+1:aeroplane,airplane,aeroplanes,airplanes
+2:bicycle,bicycles,bike,bikes
+3:bird,birds
+4:boat,boats
+5:bottle,bottles,water bottle
+6:bus,buses
+7:car,cars
+8:cat,cats,kitties,kitty
+9:chair,chairs
+10:cow,cows,calf
+11:diningtable,dining table,diningtables,dining tables,plate,plates
+12:dog,dogs,puppy,puppies
+13:horse,horses,foal
+14:motorbike,motorcycle,motorbikes,motorcycles
+15:person,child,girl,boy,woman,man,people,childeren,girls,boys,women,men,lady,guy,ladies,guys,clothes
+16:pottedplant,pottedplants,plant pot,plant pots,planter,planters
+17:sheep
+18:sofa,sofas
+19:train,trains,locomotive,locomotives,freight train
+20:tvmonitor,monitor,tv
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/register_coco_caption.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/register_coco_caption.py
new file mode 100644
index 0000000000000000000000000000000000000000..b835d9a0dbbeb2e4dace912b5eb61e0063f7204b
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/register_coco_caption.py
@@ -0,0 +1,67 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+from detectron2.data import MetadataCatalog
+from mask2former.data.datasets.register_coco_panoptic_annos_semseg import (
+ get_metadata,
+ register_coco_panoptic_annos_sem_seg,
+)
+
+_PREDEFINED_SPLITS_COCO_PANOPTIC_CAPTION = {
+ "coco_2017_train_panoptic_caption": (
+ # This is the original panoptic annotation directory
+ "coco/panoptic_train2017",
+ "coco/annotations/panoptic_caption_train2017.json",
+ # This directory contains semantic annotations that are
+ # converted from panoptic annotations.
+ # It is used by PanopticFPN.
+ # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
+ # to create these directories.
+ "coco/panoptic_semseg_train2017",
+ ),
+ "coco_2017_val_panoptic_caption": (
+ "coco/panoptic_val2017",
+ "coco/annotations/panoptic_caption_val2017.json",
+ "coco/panoptic_semseg_val2017",
+ ),
+ "coco_2017_val_100_panoptic_caption": (
+ "coco/panoptic_val2017_100",
+ "coco/annotations/panoptic_caption_val2017_100.json",
+ "coco/panoptic_semseg_val2017_100",
+ ),
+}
+
+
+# NOTE: the name is "coco_2017_train_panoptic_caption_with_sem_seg" and "coco_2017_val_panoptic_caption_with_sem_seg" # noqa
+def register_all_coco_panoptic_annos_sem_seg_caption(root):
+ for (
+ prefix,
+ (panoptic_root, panoptic_json, semantic_root),
+ ) in _PREDEFINED_SPLITS_COCO_PANOPTIC_CAPTION.items():
+ if prefix.endswith("_panoptic_caption"):
+ prefix_instances = prefix[: -len("_panoptic_caption")]
+ else:
+ raise ValueError("Unknown prefix: {}".format(prefix))
+ instances_meta = MetadataCatalog.get(prefix_instances)
+ image_root, instances_json = instances_meta.image_root, instances_meta.json_file
+
+ register_coco_panoptic_annos_sem_seg(
+ prefix,
+ get_metadata(),
+ image_root,
+ os.path.join(root, panoptic_root),
+ os.path.join(root, panoptic_json),
+ os.path.join(root, semantic_root),
+ instances_json,
+ )
+
+
+register_all_coco_panoptic_annos_sem_seg_caption(os.getenv("DETECTRON2_DATASETS", "datasets"))
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/register_pascal.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/register_pascal.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1d4f2899e82bd117cc584a23f2f619a34051bca
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/data/datasets/register_pascal.py
@@ -0,0 +1,687 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+
+PASCAL_CTX_59_CATEGORIES = [
+ {"color": [180, 120, 120], "id": 0, "isthing": 0, "name": "aeroplane"},
+ {"color": [6, 230, 230], "id": 1, "isthing": 0, "name": "bag"},
+ {"color": [80, 50, 50], "id": 2, "isthing": 0, "name": "bed"},
+ {"color": [4, 200, 3], "id": 3, "isthing": 0, "name": "bedclothes"},
+ {"color": [120, 120, 80], "id": 4, "isthing": 0, "name": "bench"},
+ {"color": [140, 140, 140], "id": 5, "isthing": 0, "name": "bicycle"},
+ {"color": [204, 5, 255], "id": 6, "isthing": 0, "name": "bird"},
+ {"color": [230, 230, 230], "id": 7, "isthing": 0, "name": "boat"},
+ {"color": [4, 250, 7], "id": 8, "isthing": 0, "name": "book"},
+ {"color": [224, 5, 255], "id": 9, "isthing": 0, "name": "bottle"},
+ {"color": [235, 255, 7], "id": 10, "isthing": 0, "name": "building"},
+ {"color": [150, 5, 61], "id": 11, "isthing": 0, "name": "bus"},
+ {"color": [120, 120, 70], "id": 12, "isthing": 0, "name": "cabinet"},
+ {"color": [8, 255, 51], "id": 13, "isthing": 0, "name": "car"},
+ {"color": [255, 6, 82], "id": 14, "isthing": 0, "name": "cat"},
+ {"color": [143, 255, 140], "id": 15, "isthing": 0, "name": "ceiling"},
+ {"color": [204, 255, 4], "id": 16, "isthing": 0, "name": "chair"},
+ {"color": [255, 51, 7], "id": 17, "isthing": 0, "name": "cloth"},
+ {"color": [204, 70, 3], "id": 18, "isthing": 0, "name": "computer"},
+ {"color": [0, 102, 200], "id": 19, "isthing": 0, "name": "cow"},
+ {"color": [61, 230, 250], "id": 20, "isthing": 0, "name": "cup"},
+ {"color": [255, 6, 51], "id": 21, "isthing": 0, "name": "curtain"},
+ {"color": [11, 102, 255], "id": 22, "isthing": 0, "name": "dog"},
+ {"color": [255, 7, 71], "id": 23, "isthing": 0, "name": "door"},
+ {"color": [255, 9, 224], "id": 24, "isthing": 0, "name": "fence"},
+ {"color": [9, 7, 230], "id": 25, "isthing": 0, "name": "floor"},
+ {"color": [220, 220, 220], "id": 26, "isthing": 0, "name": "flower"},
+ {"color": [255, 9, 92], "id": 27, "isthing": 0, "name": "food"},
+ {"color": [112, 9, 255], "id": 28, "isthing": 0, "name": "grass"},
+ {"color": [8, 255, 214], "id": 29, "isthing": 0, "name": "ground"},
+ {"color": [7, 255, 224], "id": 30, "isthing": 0, "name": "horse"},
+ {"color": [255, 184, 6], "id": 31, "isthing": 0, "name": "keyboard"},
+ {"color": [10, 255, 71], "id": 32, "isthing": 0, "name": "light"},
+ {"color": [255, 41, 10], "id": 33, "isthing": 0, "name": "motorbike"},
+ {"color": [7, 255, 255], "id": 34, "isthing": 0, "name": "mountain"},
+ {"color": [224, 255, 8], "id": 35, "isthing": 0, "name": "mouse"},
+ {"color": [102, 8, 255], "id": 36, "isthing": 0, "name": "person"},
+ {"color": [255, 61, 6], "id": 37, "isthing": 0, "name": "plate"},
+ {"color": [255, 194, 7], "id": 38, "isthing": 0, "name": "platform"},
+ {"color": [255, 122, 8], "id": 39, "isthing": 0, "name": "pottedplant"},
+ {"color": [0, 255, 20], "id": 40, "isthing": 0, "name": "road"},
+ {"color": [255, 8, 41], "id": 41, "isthing": 0, "name": "rock"},
+ {"color": [255, 5, 153], "id": 42, "isthing": 0, "name": "sheep"},
+ {"color": [6, 51, 255], "id": 43, "isthing": 0, "name": "shelves"},
+ {"color": [235, 12, 255], "id": 44, "isthing": 0, "name": "sidewalk"},
+ {"color": [160, 150, 20], "id": 45, "isthing": 0, "name": "sign"},
+ {"color": [0, 163, 255], "id": 46, "isthing": 0, "name": "sky"},
+ {"color": [140, 140, 140], "id": 47, "isthing": 0, "name": "snow"},
+ {"color": [250, 10, 15], "id": 48, "isthing": 0, "name": "sofa"},
+ {"color": [20, 255, 0], "id": 49, "isthing": 0, "name": "diningtable"},
+ {"color": [31, 255, 0], "id": 50, "isthing": 0, "name": "track"},
+ {"color": [255, 31, 0], "id": 51, "isthing": 0, "name": "train"},
+ {"color": [255, 224, 0], "id": 52, "isthing": 0, "name": "tree"},
+ {"color": [153, 255, 0], "id": 53, "isthing": 0, "name": "truck"},
+ {"color": [0, 0, 255], "id": 54, "isthing": 0, "name": "tvmonitor"},
+ {"color": [255, 71, 0], "id": 55, "isthing": 0, "name": "wall"},
+ {"color": [0, 235, 255], "id": 56, "isthing": 0, "name": "water"},
+ {"color": [0, 173, 255], "id": 57, "isthing": 0, "name": "window"},
+ {"color": [31, 0, 255], "id": 58, "isthing": 0, "name": "wood"},
+]
+
+PASCAL_CTX_459_CATEGORIES = [
+ {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "accordion"},
+ {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "aeroplane"},
+ {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "air conditioner"},
+ {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "antenna"},
+ {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "artillery"},
+ {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ashtray"},
+ {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "atrium"},
+ {"color": [204, 5, 255], "id": 7, "isthing": 0, "name": "baby carriage"},
+ {"color": [230, 230, 230], "id": 8, "isthing": 0, "name": "bag"},
+ {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "ball"},
+ {"color": [224, 5, 255], "id": 10, "isthing": 0, "name": "balloon"},
+ {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "bamboo weaving"},
+ {"color": [150, 5, 61], "id": 12, "isthing": 0, "name": "barrel"},
+ {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "baseball bat"},
+ {"color": [8, 255, 51], "id": 14, "isthing": 0, "name": "basket"},
+ {"color": [255, 6, 82], "id": 15, "isthing": 0, "name": "basketball backboard"},
+ {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "bathtub"},
+ {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "bed"},
+ {"color": [255, 51, 7], "id": 18, "isthing": 0, "name": "bedclothes"},
+ {"color": [204, 70, 3], "id": 19, "isthing": 0, "name": "beer"},
+ {"color": [0, 102, 200], "id": 20, "isthing": 0, "name": "bell"},
+ {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "bench"},
+ {"color": [255, 6, 51], "id": 22, "isthing": 0, "name": "bicycle"},
+ {"color": [11, 102, 255], "id": 23, "isthing": 0, "name": "binoculars"},
+ {"color": [255, 7, 71], "id": 24, "isthing": 0, "name": "bird"},
+ {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "bird cage"},
+ {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "bird feeder"},
+ {"color": [220, 220, 220], "id": 27, "isthing": 0, "name": "bird nest"},
+ {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "blackboard"},
+ {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "board"},
+ {"color": [8, 255, 214], "id": 30, "isthing": 0, "name": "boat"},
+ {"color": [7, 255, 224], "id": 31, "isthing": 0, "name": "bone"},
+ {"color": [255, 184, 6], "id": 32, "isthing": 0, "name": "book"},
+ {"color": [10, 255, 71], "id": 33, "isthing": 0, "name": "bottle"},
+ {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "bottle opener"},
+ {"color": [7, 255, 255], "id": 35, "isthing": 0, "name": "bowl"},
+ {"color": [224, 255, 8], "id": 36, "isthing": 0, "name": "box"},
+ {"color": [102, 8, 255], "id": 37, "isthing": 0, "name": "bracelet"},
+ {"color": [255, 61, 6], "id": 38, "isthing": 0, "name": "brick"},
+ {"color": [255, 194, 7], "id": 39, "isthing": 0, "name": "bridge"},
+ {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "broom"},
+ {"color": [0, 255, 20], "id": 41, "isthing": 0, "name": "brush"},
+ {"color": [255, 8, 41], "id": 42, "isthing": 0, "name": "bucket"},
+ {"color": [255, 5, 153], "id": 43, "isthing": 0, "name": "building"},
+ {"color": [6, 51, 255], "id": 44, "isthing": 0, "name": "bus"},
+ {"color": [235, 12, 255], "id": 45, "isthing": 0, "name": "cabinet"},
+ {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "cabinet door"},
+ {"color": [0, 163, 255], "id": 47, "isthing": 0, "name": "cage"},
+ {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "cake"},
+ {"color": [250, 10, 15], "id": 49, "isthing": 0, "name": "calculator"},
+ {"color": [20, 255, 0], "id": 50, "isthing": 0, "name": "calendar"},
+ {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "camel"},
+ {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "camera"},
+ {"color": [255, 224, 0], "id": 53, "isthing": 0, "name": "camera lens"},
+ {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "can"},
+ {"color": [0, 0, 255], "id": 55, "isthing": 0, "name": "candle"},
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+ {"color": [204, 255, 4], "id": 317, "isthing": 0, "name": "ramp"},
+ {"color": [255, 51, 7], "id": 318, "isthing": 0, "name": "range hood"},
+ {"color": [204, 70, 3], "id": 319, "isthing": 0, "name": "receiver"},
+ {"color": [0, 102, 200], "id": 320, "isthing": 0, "name": "recorder"},
+ {"color": [61, 230, 250], "id": 321, "isthing": 0, "name": "recreational machines"},
+ {"color": [255, 6, 51], "id": 322, "isthing": 0, "name": "remote control"},
+ {"color": [11, 102, 255], "id": 323, "isthing": 0, "name": "road"},
+ {"color": [255, 7, 71], "id": 324, "isthing": 0, "name": "robot"},
+ {"color": [255, 9, 224], "id": 325, "isthing": 0, "name": "rock"},
+ {"color": [9, 7, 230], "id": 326, "isthing": 0, "name": "rocket"},
+ {"color": [220, 220, 220], "id": 327, "isthing": 0, "name": "rocking horse"},
+ {"color": [255, 9, 92], "id": 328, "isthing": 0, "name": "rope"},
+ {"color": [112, 9, 255], "id": 329, "isthing": 0, "name": "rug"},
+ {"color": [8, 255, 214], "id": 330, "isthing": 0, "name": "ruler"},
+ {"color": [7, 255, 224], "id": 331, "isthing": 0, "name": "runway"},
+ {"color": [255, 184, 6], "id": 332, "isthing": 0, "name": "saddle"},
+ {"color": [10, 255, 71], "id": 333, "isthing": 0, "name": "sand"},
+ {"color": [255, 41, 10], "id": 334, "isthing": 0, "name": "saw"},
+ {"color": [7, 255, 255], "id": 335, "isthing": 0, "name": "scale"},
+ {"color": [224, 255, 8], "id": 336, "isthing": 0, "name": "scanner"},
+ {"color": [102, 8, 255], "id": 337, "isthing": 0, "name": "scissors"},
+ {"color": [255, 61, 6], "id": 338, "isthing": 0, "name": "scoop"},
+ {"color": [255, 194, 7], "id": 339, "isthing": 0, "name": "screen"},
+ {"color": [255, 122, 8], "id": 340, "isthing": 0, "name": "screwdriver"},
+ {"color": [0, 255, 20], "id": 341, "isthing": 0, "name": "sculpture"},
+ {"color": [255, 8, 41], "id": 342, "isthing": 0, "name": "scythe"},
+ {"color": [255, 5, 153], "id": 343, "isthing": 0, "name": "sewer"},
+ {"color": [6, 51, 255], "id": 344, "isthing": 0, "name": "sewing machine"},
+ {"color": [235, 12, 255], "id": 345, "isthing": 0, "name": "shed"},
+ {"color": [160, 150, 20], "id": 346, "isthing": 0, "name": "sheep"},
+ {"color": [0, 163, 255], "id": 347, "isthing": 0, "name": "shell"},
+ {"color": [140, 140, 140], "id": 348, "isthing": 0, "name": "shelves"},
+ {"color": [250, 10, 15], "id": 349, "isthing": 0, "name": "shoe"},
+ {"color": [20, 255, 0], "id": 350, "isthing": 0, "name": "shopping cart"},
+ {"color": [31, 255, 0], "id": 351, "isthing": 0, "name": "shovel"},
+ {"color": [255, 31, 0], "id": 352, "isthing": 0, "name": "sidecar"},
+ {"color": [255, 224, 0], "id": 353, "isthing": 0, "name": "sidewalk"},
+ {"color": [153, 255, 0], "id": 354, "isthing": 0, "name": "sign"},
+ {"color": [0, 0, 255], "id": 355, "isthing": 0, "name": "signal light"},
+ {"color": [255, 71, 0], "id": 356, "isthing": 0, "name": "sink"},
+ {"color": [0, 235, 255], "id": 357, "isthing": 0, "name": "skateboard"},
+ {"color": [0, 173, 255], "id": 358, "isthing": 0, "name": "ski"},
+ {"color": [31, 0, 255], "id": 359, "isthing": 0, "name": "sky"},
+ {"color": [120, 120, 120], "id": 360, "isthing": 0, "name": "sled"},
+ {"color": [180, 120, 120], "id": 361, "isthing": 0, "name": "slippers"},
+ {"color": [6, 230, 230], "id": 362, "isthing": 0, "name": "smoke"},
+ {"color": [80, 50, 50], "id": 363, "isthing": 0, "name": "snail"},
+ {"color": [4, 200, 3], "id": 364, "isthing": 0, "name": "snake"},
+ {"color": [120, 120, 80], "id": 365, "isthing": 0, "name": "snow"},
+ {"color": [140, 140, 140], "id": 366, "isthing": 0, "name": "snowmobiles"},
+ {"color": [204, 5, 255], "id": 367, "isthing": 0, "name": "sofa"},
+ {"color": [230, 230, 230], "id": 368, "isthing": 0, "name": "spanner"},
+ {"color": [4, 250, 7], "id": 369, "isthing": 0, "name": "spatula"},
+ {"color": [224, 5, 255], "id": 370, "isthing": 0, "name": "speaker"},
+ {"color": [235, 255, 7], "id": 371, "isthing": 0, "name": "speed bump"},
+ {"color": [150, 5, 61], "id": 372, "isthing": 0, "name": "spice container"},
+ {"color": [120, 120, 70], "id": 373, "isthing": 0, "name": "spoon"},
+ {"color": [8, 255, 51], "id": 374, "isthing": 0, "name": "sprayer"},
+ {"color": [255, 6, 82], "id": 375, "isthing": 0, "name": "squirrel"},
+ {"color": [143, 255, 140], "id": 376, "isthing": 0, "name": "stage"},
+ {"color": [204, 255, 4], "id": 377, "isthing": 0, "name": "stair"},
+ {"color": [255, 51, 7], "id": 378, "isthing": 0, "name": "stapler"},
+ {"color": [204, 70, 3], "id": 379, "isthing": 0, "name": "stick"},
+ {"color": [0, 102, 200], "id": 380, "isthing": 0, "name": "sticky note"},
+ {"color": [61, 230, 250], "id": 381, "isthing": 0, "name": "stone"},
+ {"color": [255, 6, 51], "id": 382, "isthing": 0, "name": "stool"},
+ {"color": [11, 102, 255], "id": 383, "isthing": 0, "name": "stove"},
+ {"color": [255, 7, 71], "id": 384, "isthing": 0, "name": "straw"},
+ {"color": [255, 9, 224], "id": 385, "isthing": 0, "name": "stretcher"},
+ {"color": [9, 7, 230], "id": 386, "isthing": 0, "name": "sun"},
+ {"color": [220, 220, 220], "id": 387, "isthing": 0, "name": "sunglass"},
+ {"color": [255, 9, 92], "id": 388, "isthing": 0, "name": "sunshade"},
+ {"color": [112, 9, 255], "id": 389, "isthing": 0, "name": "surveillance camera"},
+ {"color": [8, 255, 214], "id": 390, "isthing": 0, "name": "swan"},
+ {"color": [7, 255, 224], "id": 391, "isthing": 0, "name": "sweeper"},
+ {"color": [255, 184, 6], "id": 392, "isthing": 0, "name": "swim ring"},
+ {"color": [10, 255, 71], "id": 393, "isthing": 0, "name": "swimming pool"},
+ {"color": [255, 41, 10], "id": 394, "isthing": 0, "name": "swing"},
+ {"color": [7, 255, 255], "id": 395, "isthing": 0, "name": "switch"},
+ {"color": [224, 255, 8], "id": 396, "isthing": 0, "name": "table"},
+ {"color": [102, 8, 255], "id": 397, "isthing": 0, "name": "tableware"},
+ {"color": [255, 61, 6], "id": 398, "isthing": 0, "name": "tank"},
+ {"color": [255, 194, 7], "id": 399, "isthing": 0, "name": "tap"},
+ {"color": [255, 122, 8], "id": 400, "isthing": 0, "name": "tape"},
+ {"color": [0, 255, 20], "id": 401, "isthing": 0, "name": "tarp"},
+ {"color": [255, 8, 41], "id": 402, "isthing": 0, "name": "telephone"},
+ {"color": [255, 5, 153], "id": 403, "isthing": 0, "name": "telephone booth"},
+ {"color": [6, 51, 255], "id": 404, "isthing": 0, "name": "tent"},
+ {"color": [235, 12, 255], "id": 405, "isthing": 0, "name": "tire"},
+ {"color": [160, 150, 20], "id": 406, "isthing": 0, "name": "toaster"},
+ {"color": [0, 163, 255], "id": 407, "isthing": 0, "name": "toilet"},
+ {"color": [140, 140, 140], "id": 408, "isthing": 0, "name": "tong"},
+ {"color": [250, 10, 15], "id": 409, "isthing": 0, "name": "tool"},
+ {"color": [20, 255, 0], "id": 410, "isthing": 0, "name": "toothbrush"},
+ {"color": [31, 255, 0], "id": 411, "isthing": 0, "name": "towel"},
+ {"color": [255, 31, 0], "id": 412, "isthing": 0, "name": "toy"},
+ {"color": [255, 224, 0], "id": 413, "isthing": 0, "name": "toy car"},
+ {"color": [153, 255, 0], "id": 414, "isthing": 0, "name": "track"},
+ {"color": [0, 0, 255], "id": 415, "isthing": 0, "name": "train"},
+ {"color": [255, 71, 0], "id": 416, "isthing": 0, "name": "trampoline"},
+ {"color": [0, 235, 255], "id": 417, "isthing": 0, "name": "trash bin"},
+ {"color": [0, 173, 255], "id": 418, "isthing": 0, "name": "tray"},
+ {"color": [31, 0, 255], "id": 419, "isthing": 0, "name": "tree"},
+ {"color": [120, 120, 120], "id": 420, "isthing": 0, "name": "tricycle"},
+ {"color": [180, 120, 120], "id": 421, "isthing": 0, "name": "tripod"},
+ {"color": [6, 230, 230], "id": 422, "isthing": 0, "name": "trophy"},
+ {"color": [80, 50, 50], "id": 423, "isthing": 0, "name": "truck"},
+ {"color": [4, 200, 3], "id": 424, "isthing": 0, "name": "tube"},
+ {"color": [120, 120, 80], "id": 425, "isthing": 0, "name": "turtle"},
+ {"color": [140, 140, 140], "id": 426, "isthing": 0, "name": "tvmonitor"},
+ {"color": [204, 5, 255], "id": 427, "isthing": 0, "name": "tweezers"},
+ {"color": [230, 230, 230], "id": 428, "isthing": 0, "name": "typewriter"},
+ {"color": [4, 250, 7], "id": 429, "isthing": 0, "name": "umbrella"},
+ {"color": [224, 5, 255], "id": 430, "isthing": 0, "name": "unknown"},
+ {"color": [235, 255, 7], "id": 431, "isthing": 0, "name": "vacuum cleaner"},
+ {"color": [150, 5, 61], "id": 432, "isthing": 0, "name": "vending machine"},
+ {"color": [120, 120, 70], "id": 433, "isthing": 0, "name": "video camera"},
+ {"color": [8, 255, 51], "id": 434, "isthing": 0, "name": "video game console"},
+ {"color": [255, 6, 82], "id": 435, "isthing": 0, "name": "video player"},
+ {"color": [143, 255, 140], "id": 436, "isthing": 0, "name": "video tape"},
+ {"color": [204, 255, 4], "id": 437, "isthing": 0, "name": "violin"},
+ {"color": [255, 51, 7], "id": 438, "isthing": 0, "name": "wakeboard"},
+ {"color": [204, 70, 3], "id": 439, "isthing": 0, "name": "wall"},
+ {"color": [0, 102, 200], "id": 440, "isthing": 0, "name": "wallet"},
+ {"color": [61, 230, 250], "id": 441, "isthing": 0, "name": "wardrobe"},
+ {"color": [255, 6, 51], "id": 442, "isthing": 0, "name": "washing machine"},
+ {"color": [11, 102, 255], "id": 443, "isthing": 0, "name": "watch"},
+ {"color": [255, 7, 71], "id": 444, "isthing": 0, "name": "water"},
+ {"color": [255, 9, 224], "id": 445, "isthing": 0, "name": "water dispenser"},
+ {"color": [9, 7, 230], "id": 446, "isthing": 0, "name": "water pipe"},
+ {"color": [220, 220, 220], "id": 447, "isthing": 0, "name": "water skate board"},
+ {"color": [255, 9, 92], "id": 448, "isthing": 0, "name": "watermelon"},
+ {"color": [112, 9, 255], "id": 449, "isthing": 0, "name": "whale"},
+ {"color": [8, 255, 214], "id": 450, "isthing": 0, "name": "wharf"},
+ {"color": [7, 255, 224], "id": 451, "isthing": 0, "name": "wheel"},
+ {"color": [255, 184, 6], "id": 452, "isthing": 0, "name": "wheelchair"},
+ {"color": [10, 255, 71], "id": 453, "isthing": 0, "name": "window"},
+ {"color": [255, 41, 10], "id": 454, "isthing": 0, "name": "window blinds"},
+ {"color": [7, 255, 255], "id": 455, "isthing": 0, "name": "wineglass"},
+ {"color": [224, 255, 8], "id": 456, "isthing": 0, "name": "wire"},
+ {"color": [102, 8, 255], "id": 457, "isthing": 0, "name": "wood"},
+ {"color": [255, 61, 6], "id": 458, "isthing": 0, "name": "wool"},
+]
+
+PASCAL_VOC_21_CATEGORIES = [
+ {"color": [0, 0, 0], "id": 0, "isthing": 1, "name": "background"},
+ {"color": [128, 0, 0], "id": 1, "isthing": 1, "name": "aeroplane"},
+ {"color": [0, 128, 0], "id": 2, "isthing": 1, "name": "bicycle"},
+ {"color": [128, 128, 0], "id": 3, "isthing": 1, "name": "bird"},
+ {"color": [0, 0, 128], "id": 4, "isthing": 1, "name": "boat"},
+ {"color": [128, 0, 128], "id": 5, "isthing": 1, "name": "bottle"},
+ {"color": [0, 128, 128], "id": 6, "isthing": 1, "name": "bus"},
+ {"color": [128, 128, 128], "id": 7, "isthing": 1, "name": "car"},
+ {"color": [64, 0, 0], "id": 8, "isthing": 1, "name": "cat"},
+ {"color": [192, 0, 0], "id": 9, "isthing": 1, "name": "chair"},
+ {"color": [64, 128, 0], "id": 10, "isthing": 1, "name": "cow"},
+ {"color": [192, 128, 0], "id": 11, "isthing": 1, "name": "diningtable"},
+ {"color": [64, 0, 128], "id": 12, "isthing": 1, "name": "dog"},
+ {"color": [192, 0, 128], "id": 13, "isthing": 1, "name": "horse"},
+ {"color": [64, 128, 128], "id": 14, "isthing": 1, "name": "motorbike"},
+ {"color": [192, 128, 128], "id": 15, "isthing": 1, "name": "person"},
+ {"color": [0, 64, 0], "id": 16, "isthing": 1, "name": "pottedplant"},
+ {"color": [128, 64, 0], "id": 17, "isthing": 1, "name": "sheep"},
+ {"color": [0, 192, 0], "id": 18, "isthing": 1, "name": "sofa"},
+ {"color": [128, 192, 0], "id": 19, "isthing": 1, "name": "train"},
+ {"color": [0, 64, 128], "id": 20, "isthing": 1, "name": "tvmonitor"},
+]
+
+
+def _get_ctx59_meta():
+ # Id 0 is reserved for ignore_label, we change ignore_label for 0
+ # to 255 in our pre-processing, so all ids are shifted by 1.
+ stuff_ids = [k["id"] for k in PASCAL_CTX_59_CATEGORIES]
+ assert len(stuff_ids) == 59, len(stuff_ids)
+
+ # For semantic segmentation, this mapping maps from contiguous stuff id
+ # (in [0, 91], used in models) to ids in the dataset (used for processing results)
+ stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
+ stuff_classes = [k["name"] for k in PASCAL_CTX_59_CATEGORIES]
+
+ ret = {
+ "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
+ "stuff_classes": stuff_classes,
+ }
+ return ret
+
+
+def register_all_ctx59(root):
+ root = os.path.join(root, "pascal_ctx_d2")
+ meta = _get_ctx59_meta()
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(root, "images", dirname)
+ gt_dir = os.path.join(root, "annotations_ctx59", dirname)
+ name = f"ctx59_sem_seg_{name}"
+ DatasetCatalog.register(
+ name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
+ )
+ MetadataCatalog.get(name).set(
+ stuff_classes=meta["stuff_classes"][:],
+ thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy
+ stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"],
+ image_root=image_dir,
+ sem_seg_root=gt_dir,
+ evaluator_type="sem_seg",
+ ignore_label=255,
+ )
+
+
+register_all_ctx59(os.getenv("DETECTRON2_DATASETS", "datasets"))
+
+
+def _get_pascal21_meta():
+ # Id 0 is reserved for ignore_label, we change ignore_label for 0
+ # to 255 in our pre-processing, so all ids are shifted by 1.
+ stuff_ids = [k["id"] for k in PASCAL_VOC_21_CATEGORIES]
+ assert len(stuff_ids) == 21, len(stuff_ids)
+
+ # For semantic segmentation, this mapping maps from contiguous stuff id
+ # (in [0, 91], used in models) to ids in the dataset (used for processing results)
+ stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
+ stuff_classes = [k["name"] for k in PASCAL_VOC_21_CATEGORIES]
+
+ ret = {
+ "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
+ "stuff_classes": stuff_classes,
+ }
+ return ret
+
+
+def register_all_pascal21(root):
+ root = os.path.join(root, "pascal_voc_d2")
+ meta = _get_pascal21_meta()
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(root, "images", dirname)
+ gt_dir = os.path.join(root, "annotations_pascal21", dirname)
+ name = f"pascal21_sem_seg_{name}"
+ DatasetCatalog.register(
+ name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
+ )
+ MetadataCatalog.get(name).set(
+ stuff_classes=meta["stuff_classes"][:],
+ thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy
+ stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"],
+ image_root=image_dir,
+ sem_seg_root=gt_dir,
+ evaluator_type="sem_seg",
+ ignore_label=255,
+ )
+
+
+register_all_pascal21(os.getenv("DETECTRON2_DATASETS", "datasets"))
+
+
+def _get_ctx459_meta():
+ # Id 0 is reserved for ignore_label, we change ignore_label for 0
+ # to 255 in our pre-processing, so all ids are shifted by 1.
+ stuff_ids = [k["id"] for k in PASCAL_CTX_459_CATEGORIES]
+ assert len(stuff_ids) == 459, len(stuff_ids)
+
+ # For semantic segmentation, this mapping maps from contiguous stuff id
+ # (in [0, 91], used in models) to ids in the dataset (used for processing results)
+ stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
+ stuff_classes = [k["name"] for k in PASCAL_CTX_459_CATEGORIES]
+
+ ret = {
+ "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
+ "stuff_classes": stuff_classes,
+ }
+ return ret
+
+
+def register_all_ctx459(root):
+ root = os.path.join(root, "pascal_ctx_d2")
+ meta = _get_ctx459_meta()
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(root, "images", dirname)
+ gt_dir = os.path.join(root, "annotations_ctx459", dirname)
+ name = f"ctx459_sem_seg_{name}"
+ DatasetCatalog.register(
+ name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg")
+ )
+ MetadataCatalog.get(name).set(
+ stuff_classes=meta["stuff_classes"][:],
+ thing_dataset_id_to_contiguous_id={}, # to make Mask2Former happy
+ stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"],
+ image_root=image_dir,
+ sem_seg_root=gt_dir,
+ evaluator_type="sem_seg",
+ ignore_label=65535, # NOTE: gt is saved in 16-bit TIFF images
+ )
+
+
+register_all_ctx459(os.getenv("DETECTRON2_DATASETS", "datasets"))
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bf84068a6a069ca9d04d80266e1dcfc633ccb4b
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .train_loop import SimpleTrainer, AMPTrainer
+
+__all__ = ["SimpleTrainer", "AMPTrainer"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/defaults.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2f906eb9dd6664bc2776ac0232aa40f402f78e8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/defaults.py
@@ -0,0 +1,100 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import os
+import torch
+import torch.utils.data as torchdata
+from detectron2.config import CfgNode, LazyConfig
+from detectron2.engine.defaults import _highlight, _try_get_key
+from detectron2.utils import comm
+from detectron2.utils.env import seed_all_rng
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import setup_logger
+
+from odise.utils.collect_env import collect_env_info
+
+
+def get_model_from_module(model):
+ if hasattr(model, "module"):
+ return model.module
+ else:
+ return model
+
+
+def get_dataset_from_loader(loader):
+ if isinstance(loader, torchdata.DataLoader):
+ return loader.dataset
+ else:
+ return loader
+
+
+def default_setup(cfg, args):
+ """
+ Perform some basic common setups at the beginning of a job, including:
+
+ 1. Set up the detectron2 logger
+ 2. Log basic information about environment, cmdline arguments, and config
+ 3. Backup the config to the output directory
+
+ Args:
+ cfg (CfgNode or omegaconf.DictConfig): the full config to be used
+ args (argparse.NameSpace): the command line arguments to be logged
+ """
+ output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir", "train.output_dir")
+ if comm.is_main_process() and output_dir:
+ PathManager.mkdirs(output_dir)
+
+ rank = comm.get_rank()
+ log_dir = _try_get_key(cfg, "train.log_dir", "output_dir", "train.output_dir")
+ if comm.is_main_process() and log_dir:
+ PathManager.mkdirs(log_dir)
+ setup_logger(log_dir, distributed_rank=rank, name="fvcore")
+ logger = setup_logger(log_dir, distributed_rank=rank)
+
+ logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size()))
+ logger.info("Environment info:\n" + collect_env_info())
+
+ logger.info("Command line arguments: " + str(args))
+ if hasattr(args, "config_file") and args.config_file != "":
+ logger.info(
+ "Contents of args.config_file={}:\n{}".format(
+ args.config_file,
+ _highlight(PathManager.open(args.config_file, "r").read(), args.config_file),
+ )
+ )
+
+ if comm.is_main_process() and log_dir:
+ # Note: some of our scripts may expect the existence of
+ # config.yaml in output directory
+ path = os.path.join(log_dir, "config.yaml")
+ if isinstance(cfg, CfgNode):
+ logger.info("Running with full config:\n{}".format(_highlight(cfg.dump(), ".yaml")))
+ with PathManager.open(path, "w") as f:
+ f.write(cfg.dump())
+ else:
+ LazyConfig.save(cfg, path)
+ logger.info("Full config saved to {}".format(path))
+
+ # make sure each worker has a different, yet deterministic seed if specified
+ seed = _try_get_key(cfg, "SEED", "train.seed", default=-1)
+ seed_all_rng(None if seed < 0 else seed + rank)
+
+ # cudnn benchmark has large overhead. It shouldn't be used considering the small size of
+ # typical validation set.
+ if not (hasattr(args, "eval_only") and args.eval_only):
+ torch.backends.cudnn.benchmark = _try_get_key(
+ cfg, "CUDNN_BENCHMARK", "train.cudnn_benchmark", default=False
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/hooks.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..abe1459e86173bfb64b21651e5295ce7ca60a2f8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/hooks.py
@@ -0,0 +1,68 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import inspect
+import detectron2.utils.comm as comm
+from detectron2.engine import EvalHook as _EvalHook
+from detectron2.evaluation.testing import flatten_results_dict
+
+
+class EvalHook(_EvalHook):
+ def __init__(self, eval_period, eval_function):
+ super().__init__(eval_period, eval_function)
+ func_args = inspect.getfullargspec(eval_function).args
+ assert {"final_iter", "next_iter"}.issubset(set(func_args)), (
+ f"Eval function must have either 'final_iter' or 'next_iter' as an argument."
+ f"Got {func_args} instead."
+ )
+
+ def _do_eval(self, final_iter=False, next_iter=0):
+ results = self._func(final_iter=final_iter, next_iter=next_iter)
+
+ if results:
+ assert isinstance(
+ results, dict
+ ), "Eval function must return a dict. Got {} instead.".format(results)
+
+ flattened_results = flatten_results_dict(results)
+ for k, v in flattened_results.items():
+ try:
+ v = float(v)
+ except Exception as e:
+ raise ValueError(
+ "[EvalHook] eval_function should return a nested dict of float. "
+ "Got '{}: {}' instead.".format(k, v)
+ ) from e
+ self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False)
+
+ # Evaluation may take different time among workers.
+ # A barrier make them start the next iteration together.
+ comm.synchronize()
+
+ def after_step(self):
+ next_iter = self.trainer.iter + 1
+ if self._period > 0 and next_iter % self._period == 0:
+ # do the last eval in after_train
+ if next_iter != self.trainer.max_iter:
+ self._do_eval(next_iter=next_iter)
+
+ def after_train(self):
+ # This condition is to prevent the eval from running after a failed training
+ if self.trainer.iter + 1 >= self.trainer.max_iter:
+ self._do_eval(final_iter=True)
+ # func is likely a closure that holds reference to the trainer
+ # therefore we clean it to avoid circular reference in the end
+ del self._func
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/train_loop.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/train_loop.py
new file mode 100644
index 0000000000000000000000000000000000000000..a147a02cb29a5bf9298026d183a9bb5e8837e136
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/engine/train_loop.py
@@ -0,0 +1,323 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import itertools
+import logging
+import numpy as np
+import time
+from typing import Iterable, Mapping, Union
+import detectron2.utils.comm as comm
+import torch
+from detectron2.engine import SimpleTrainer as _SimpleTrainer
+from detectron2.utils.events import get_event_storage
+from torch._six import inf
+from torch.nn.parallel import DataParallel, DistributedDataParallel
+
+from odise.utils.parameter_count import parameter_count_table
+
+logger = logging.getLogger(__name__)
+
+_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
+
+
+def get_grad_norm(parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor:
+ r"""get gradient norm of an iterable of parameters.
+
+ The norm is computed over all gradients together, as if they were
+ concatenated into a single vector.
+
+ Args:
+ parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+ single Tensor that will have gradients normalized
+ norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+ infinity norm.
+
+ Returns:
+ Total norm of the parameters (viewed as a single vector).
+ """
+ if isinstance(parameters, torch.Tensor):
+ parameters = [parameters]
+ parameters = [p for p in parameters if p.grad is not None]
+ norm_type = float(norm_type)
+ if len(parameters) == 0:
+ return torch.tensor(0.0)
+ device = parameters[0].grad.device
+ if norm_type == inf:
+ norms = [p.grad.detach().abs().max().to(device) for p in parameters]
+ total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
+ else:
+ total_norm = torch.norm(
+ torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
+ norm_type,
+ )
+
+ return total_norm
+
+
+class SimpleTrainer(_SimpleTrainer):
+ def __init__(self, model, data_loader, optimizer, *, grad_clip=None):
+ super().__init__(model, data_loader, optimizer)
+ self.grad_clip = grad_clip
+ logger.info(f"Trainer: {self.__class__.__name__}")
+ logger.info(f"grad_clip: {grad_clip}")
+ logger.info("All parameters: \n" + parameter_count_table(model))
+
+ # print trainable parameters
+ logger.info("Trainable parameters: \n" + parameter_count_table(model, trainable_only=True))
+
+ def raise_loss_nan(self, losses):
+ losses = losses.detach().cpu()
+ loss_nan = (~torch.isfinite(losses)).any()
+ all_loss_nan = comm.all_gather(loss_nan)
+ all_loss_nan = [l.item() for l in all_loss_nan]
+ if any(all_loss_nan):
+ raise FloatingPointError(
+ f"Loss became infinite or NaN for rank: {np.where(all_loss_nan)[0].tolist()} "
+ f"at iteration={self.storage.iter}!\n"
+ )
+
+ def run_step(self):
+ """
+ Implement the standard training logic described above.
+ """
+ assert self.model.training, "[SimpleTrainer] model was changed to eval mode!"
+ start = time.perf_counter()
+ """
+ If you want to do something with the data, you can wrap the dataloader.
+ """
+ data = next(self._data_loader_iter)
+ data_time = time.perf_counter() - start
+
+ """
+ If you want to do something with the losses, you can wrap the model.
+ """
+ # NOTE: added "runner_meta" if input is dict, by Jiarui
+ if isinstance(data, dict):
+ data["runner_meta"] = dict()
+ data["runner_meta"]["iter"] = self.iter
+ data["runner_meta"]["max_iter"] = self.max_iter
+ loss_dict = self.model(data)
+ if isinstance(loss_dict, torch.Tensor):
+ losses = loss_dict
+ loss_dict = {"total_loss": loss_dict}
+ else:
+ losses = sum(loss_dict.values())
+
+ """
+ If you need to accumulate gradients or do something similar, you can
+ wrap the optimizer with your custom `zero_grad()` method.
+ """
+ self.optimizer.zero_grad()
+ losses.backward()
+
+ # NOTE: added "grad_norm" by Jiarui
+ if self.grad_clip is not None:
+ grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip)
+ else:
+ grad_norm = get_grad_norm(self.model.parameters())
+ self.storage.put_scalar("grad_norm", grad_norm)
+
+ # disable to see if it is necessary
+ # NaN check before write metric, exit all process
+ # self.raise_loss_nan(losses)
+
+ self._write_metrics(loss_dict, data_time)
+
+ """
+ If you need gradient clipping/scaling or other processing, you can
+ wrap the optimizer with your custom `step()` method. But it is
+ suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4
+ """
+ self.optimizer.step()
+
+ # Almost the same as parent, except for log all `metric_dict` instead of
+ # len(metric_dict) > 1 in orginal detectron2, maybe a bug
+ def _write_metrics(
+ self,
+ loss_dict: Mapping[str, torch.Tensor],
+ data_time: float,
+ prefix: str = "",
+ ) -> None:
+ """
+ Args:
+ loss_dict (dict): dict of scalar losses
+ data_time (float): time taken by the dataloader iteration
+ prefix (str): prefix for logging keys
+ """
+ metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}
+ metrics_dict["data_time"] = data_time
+
+ # Gather metrics among all workers for logging
+ # This assumes we do DDP-style training, which is currently the only
+ # supported method in detectron2.
+ all_metrics_dict = comm.gather(metrics_dict)
+
+ if comm.is_main_process():
+ storage = get_event_storage()
+
+ # data_time among workers can have high variance. The actual latency
+ # caused by data_time is the maximum among workers.
+ data_time = np.max([x.pop("data_time") for x in all_metrics_dict])
+ storage.put_scalar("data_time", data_time)
+
+ # average the rest metrics
+ metrics_dict = {
+ k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()
+ }
+ total_losses_reduced = sum(metrics_dict.values())
+ if not np.isfinite(total_losses_reduced):
+ raise FloatingPointError(
+ f"Loss became infinite or NaN at iteration={storage.iter}!\n"
+ f"loss_dict = {metrics_dict}"
+ )
+
+ storage.put_scalar("{}total_loss".format(prefix), total_losses_reduced)
+ if len(metrics_dict):
+ storage.put_scalars(**metrics_dict)
+
+
+class NativeScalerWithGradNormCount:
+ """
+ Reference: https://github.com/microsoft/Swin-Transformer/blob/afeb877fba1139dfbc186276983af2abb02c2196/main.py#L194
+ """ # noqa
+
+ state_dict_key = "amp_scaler"
+
+ def __init__(self):
+ from torch.cuda.amp import GradScaler
+
+ self._scaler = GradScaler()
+
+ def __call__(
+ self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True
+ ):
+ self._scaler.scale(loss).backward(create_graph=create_graph)
+ if update_grad:
+ if clip_grad is not None:
+ assert parameters is not None
+ self._scaler.unscale_(
+ optimizer
+ ) # unscale the gradients of optimizer's assigned params in-place
+ norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
+ else:
+ self._scaler.unscale_(optimizer)
+ norm = get_grad_norm(parameters)
+
+ self._scaler.step(optimizer)
+ self._scaler.update()
+ else:
+ norm = None
+ return norm
+
+ def state_dict(self):
+ return self._scaler.state_dict()
+
+ def load_state_dict(self, state_dict):
+ self._scaler.load_state_dict(state_dict)
+
+
+def get_optimizer_parameters(optimizer):
+ return itertools.chain(*[x["params"] for x in optimizer.param_groups])
+
+
+class AMPTrainer(SimpleTrainer):
+ """
+ Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision
+ in the training loop.
+ """
+
+ def __init__(self, model, data_loader, optimizer, grad_clip=None):
+ """
+ Args:
+ model, data_loader, optimizer: same as in :class:`SimpleTrainer`.
+ grad_scaler: torch GradScaler to automatically scale gradients.
+ """
+ unsupported = "AMPTrainer does not support single-process multi-device training!"
+ if isinstance(model, DistributedDataParallel):
+ assert not (model.device_ids and len(model.device_ids) > 1), unsupported
+ # NOTE: Jiarui relaxed this check, because it is not necessary for AMP
+ elif isinstance(model, DataParallel):
+ assert not len(model.device_ids) > 1, unsupported
+ # assert not isinstance(model, DataParallel), unsupported
+
+ super().__init__(model, data_loader, optimizer, grad_clip=grad_clip)
+
+ self.grad_scaler = NativeScalerWithGradNormCount()
+
+ def run_step(self):
+ """
+ Implement the AMP training logic.
+ """
+ assert self.model.training, "[AMPTrainer] model was changed to eval mode!"
+ assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!"
+ from torch.cuda.amp import autocast
+
+ start = time.perf_counter()
+ data = next(self._data_loader_iter)
+ data_time = time.perf_counter() - start
+
+ # detectron2 use trivial collate function, so data is just a list of dict
+ # we don't add meta info for them
+ # for webdataset, data is a dict of tensors
+ if isinstance(data, dict):
+ # NOTE: added "runner_meta" by Jiarui
+ data["runner_meta"] = dict()
+ data["runner_meta"]["iter"] = self.iter
+ data["runner_meta"]["max_iter"] = self.max_iter
+ with autocast():
+ loss_dict = self.model(data)
+ if isinstance(loss_dict, torch.Tensor):
+ losses = loss_dict
+ loss_dict = {"total_loss": loss_dict}
+ else:
+ losses = sum(loss_dict.values())
+
+ # instead of using self.optimizer.zero_grad(),
+ # we zero out the gradients for all parameters in the model
+ self.model.zero_grad()
+
+ # NOTE: the following operations are wrapped
+ # in __call__() of NativeScalerWithGradNormCount():
+ # self.grad_scaler.scale(losses).backward()
+ # self.grad_scaler.step(self.optimizer)
+ # self.grad_scaler.update()
+ grad_norm = self.grad_scaler(
+ losses,
+ self.optimizer,
+ clip_grad=self.grad_clip,
+ # instead of using self.model.parameters(), we use parameters in the optimizer
+ # so we don't clip the parameters that are not in the optimizer
+ parameters=get_optimizer_parameters(self.optimizer),
+ update_grad=True,
+ )
+ self.storage.put_scalar("grad_norm", grad_norm)
+ self.storage.put_scalar(
+ "clipped_grad_norm", get_grad_norm(get_optimizer_parameters(self.optimizer))
+ )
+
+ loss_scale_value = self.grad_scaler.state_dict()["scale"]
+ self.storage.put_scalar("loss_scale", loss_scale_value)
+
+ self._write_metrics(loss_dict, data_time)
+
+ def state_dict(self):
+ ret = super().state_dict()
+ ret["grad_scaler"] = self.grad_scaler.state_dict()
+ return ret
+
+ def load_state_dict(self, state_dict):
+ super().load_state_dict(state_dict)
+ self.grad_scaler.load_state_dict(state_dict["grad_scaler"])
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..11da14dddceeac19861ce19d81391840501c52b2
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/__init__.py
@@ -0,0 +1,25 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .evaluator import inference_on_dataset
+from .d2_evaluator import (
+ COCOPanopticEvaluator,
+ InstanceSegEvaluator,
+ SemSegEvaluator,
+ COCOEvaluator,
+)
+
+__all__ = [
+ "inference_on_dataset",
+ "COCOPanopticEvaluator",
+ "InstanceSegEvaluator",
+ "SemSegEvaluator",
+ "COCOEvaluator",
+]
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/d2_evaluator.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/d2_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2492b325519038a8f773c9b8067fe8518f26d1c7
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/d2_evaluator.py
@@ -0,0 +1,181 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import itertools
+import json
+import os
+from collections import OrderedDict
+from detectron2.evaluation import COCOEvaluator as _COCOEvaluator
+from detectron2.evaluation import COCOPanopticEvaluator as _COCOPanopticEvaluator
+from detectron2.evaluation import SemSegEvaluator as _SemSegEvaluator
+from detectron2.evaluation.coco_evaluation import _evaluate_predictions_on_coco
+from detectron2.utils.file_io import PathManager
+from tabulate import tabulate
+
+
+class COCOEvaluator(_COCOEvaluator):
+ def __init__(self, *, dataset_name, **kwargs):
+ super().__init__(dataset_name=dataset_name, **kwargs)
+ self.dataset_name = dataset_name
+
+ def evaluate(self, img_ids=None):
+ """
+ Args:
+ img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
+ """
+
+ results = super().evaluate(img_ids)
+ prefix_results = OrderedDict()
+ for k, v in results.items():
+ prefix_results[f"{self.dataset_name}/{k}"] = v
+
+ return prefix_results
+
+
+class COCOPanopticEvaluator(_COCOPanopticEvaluator):
+ def __init__(self, *, dataset_name, **kwargs):
+ super().__init__(dataset_name=dataset_name, **kwargs)
+ self.dataset_name = dataset_name
+
+ def evaluate(self):
+ results = super().evaluate()
+ if results is None:
+ return
+ prefix_results = OrderedDict()
+ for k, v in results.items():
+ prefix_results[f"{self.dataset_name}/{k}"] = v
+
+ return prefix_results
+
+
+class SemSegEvaluator(_SemSegEvaluator):
+ def __init__(self, *, dataset_name, prefix="", **kwargs):
+ super().__init__(dataset_name=dataset_name, **kwargs)
+ self.dataset_name = dataset_name
+ if len(prefix) and not prefix.endswith("_"):
+ prefix += "_"
+ self.prefix = prefix
+
+ def evaluate(self):
+ results = super().evaluate()
+ if results is None:
+ return
+ results_per_category = []
+ for name in self._class_names:
+ # area range index 0: all area ranges
+ # max dets index -1: typically 100 per image
+ results_per_category.append((str(name), float(results["sem_seg"][f"IoU-{name}"])))
+
+ # tabulate it
+ N_COLS = min(6, len(results_per_category) * 2)
+ results_flatten = list(itertools.chain(*results_per_category))
+ results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
+ table = tabulate(
+ results_2d,
+ tablefmt="pipe",
+ floatfmt=".3f",
+ headers=["category", "IoU"] * (N_COLS // 2),
+ numalign="left",
+ )
+ self._logger.info("Per-category IoU: \n" + table)
+
+ prefix_results = OrderedDict()
+ for k, v in results.items():
+ prefix_results[f"{self.dataset_name}/{self.prefix}{k}"] = v
+
+ return prefix_results
+
+
+# Copied from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py # noqa
+# modified from COCOEvaluator for instance segmentation
+class InstanceSegEvaluator(COCOEvaluator):
+ """
+ Evaluate AR for object proposals, AP for instance detection/segmentation, AP
+ for keypoint detection outputs using COCO's metrics.
+ See http://cocodataset.org/#detection-eval and
+ http://cocodataset.org/#keypoints-eval to understand its metrics.
+ The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
+ the metric cannot be computed (e.g. due to no predictions made).
+
+ In addition to COCO, this evaluator is able to support any bounding box detection,
+ instance segmentation, or keypoint detection dataset.
+ """
+
+ def _eval_predictions(self, predictions, img_ids=None):
+ """
+ Evaluate predictions. Fill self._results with the metrics of the tasks.
+ """
+ self._logger.info("Preparing results for COCO format ...")
+ coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
+ tasks = self._tasks or self._tasks_from_predictions(coco_results)
+
+ # unmap the category ids for COCO
+ if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
+ dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
+ # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
+ # num_classes = len(all_contiguous_ids)
+ # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
+
+ reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
+ for result in coco_results:
+ category_id = result["category_id"]
+ # assert category_id < num_classes, (
+ # f"A prediction has class={category_id}, "
+ # f"but the dataset only has {num_classes} classes and "
+ # f"predicted class id should be in [0, {num_classes - 1}]."
+ # )
+ assert category_id in reverse_id_mapping, (
+ f"A prediction has class={category_id}, "
+ f"but the dataset only has class ids in {dataset_id_to_contiguous_id}."
+ )
+ result["category_id"] = reverse_id_mapping[category_id]
+
+ if self._output_dir:
+ file_path = os.path.join(self._output_dir, "coco_instances_results.json")
+ self._logger.info("Saving results to {}".format(file_path))
+ with PathManager.open(file_path, "w") as f:
+ f.write(json.dumps(coco_results))
+ f.flush()
+
+ if not self._do_evaluation:
+ self._logger.info("Annotations are not available for evaluation.")
+ return
+
+ self._logger.info(
+ "Evaluating predictions with {} COCO API...".format(
+ "unofficial" if self._use_fast_impl else "official"
+ )
+ )
+ for task in sorted(tasks):
+ assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!"
+ coco_eval = (
+ _evaluate_predictions_on_coco(
+ self._coco_api,
+ coco_results,
+ task,
+ kpt_oks_sigmas=self._kpt_oks_sigmas,
+ use_fast_impl=self._use_fast_impl,
+ img_ids=img_ids,
+ max_dets_per_image=self._max_dets_per_image,
+ )
+ if len(coco_results) > 0
+ else None # cocoapi does not handle empty results very well
+ )
+
+ res = self._derive_coco_results(
+ coco_eval, task, class_names=self._metadata.get("thing_classes")
+ )
+ self._results[task] = res
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/evaluator.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..c89aa406d7b8081e39348d0792fbd00c37d12829
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/evaluation/evaluator.py
@@ -0,0 +1,149 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import datetime
+import logging
+import time
+from collections import abc
+from contextlib import ExitStack, nullcontext
+from typing import List, Union
+import torch
+from detectron2.evaluation import DatasetEvaluator, DatasetEvaluators, inference_context
+from detectron2.utils.comm import get_world_size
+from detectron2.utils.logger import log_every_n_seconds
+from torch import nn
+
+
+def inference_on_dataset(
+ model,
+ data_loader,
+ evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None],
+ use_amp=False,
+):
+ """
+ Run model on the data_loader and evaluate the metrics with evaluator.
+ Also benchmark the inference speed of `model.__call__` accurately.
+ The model will be used in eval mode.
+
+ Args:
+ model (callable): a callable which takes an object from
+ `data_loader` and returns some outputs.
+
+ If it's an nn.Module, it will be temporarily set to `eval` mode.
+ If you wish to evaluate a model in `training` mode instead, you can
+ wrap the given model and override its behavior of `.eval()` and `.train()`.
+ data_loader: an iterable object with a length.
+ The elements it generates will be the inputs to the model.
+ evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,
+ but don't want to do any evaluation.
+
+ Returns:
+ The return value of `evaluator.evaluate()`
+ """
+ num_devices = get_world_size()
+ logger = logging.getLogger(__name__)
+ logger.info("Start inference on {} batches".format(len(data_loader)))
+
+ total = len(data_loader) # inference data loader must have a fixed length
+ if evaluator is None:
+ # create a no-op evaluator
+ evaluator = DatasetEvaluators([])
+ if isinstance(evaluator, abc.MutableSequence):
+ evaluator = DatasetEvaluators(evaluator)
+ evaluator.reset()
+
+ num_warmup = min(5, total - 1)
+ start_time = time.perf_counter()
+ total_data_time = 0
+ total_compute_time = 0
+ total_eval_time = 0
+
+ if use_amp and torch.cuda.is_available():
+ from torch.cuda.amp import autocast
+ else:
+ # Use ExitStack as placeholder
+ autocast = nullcontext
+
+ with ExitStack() as stack:
+ if isinstance(model, nn.Module):
+ stack.enter_context(inference_context(model))
+ stack.enter_context(torch.no_grad())
+
+ start_data_time = time.perf_counter()
+
+ for idx, inputs in enumerate(data_loader):
+ total_data_time += time.perf_counter() - start_data_time
+ if idx == num_warmup:
+ start_time = time.perf_counter()
+ total_data_time = 0
+ total_compute_time = 0
+ total_eval_time = 0
+
+ start_compute_time = time.perf_counter()
+ with autocast():
+ outputs = model(inputs)
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+ total_compute_time += time.perf_counter() - start_compute_time
+
+ start_eval_time = time.perf_counter()
+
+ evaluator.process(inputs, outputs)
+ total_eval_time += time.perf_counter() - start_eval_time
+
+ iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
+ data_seconds_per_iter = total_data_time / iters_after_start
+ compute_seconds_per_iter = total_compute_time / iters_after_start
+ eval_seconds_per_iter = total_eval_time / iters_after_start
+ total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start
+ if idx >= num_warmup * 2 or compute_seconds_per_iter > 5:
+ eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))
+ log_every_n_seconds(
+ logging.INFO,
+ (
+ f"Inference done {idx + 1}/{total}. "
+ f"Dataloading: {data_seconds_per_iter:.4f} s/iter. "
+ f"Inference: {compute_seconds_per_iter:.4f} s/iter. "
+ f"Eval: {eval_seconds_per_iter:.4f} s/iter. "
+ f"Total: {total_seconds_per_iter:.4f} s/iter. "
+ f"ETA={eta}"
+ ),
+ n=5,
+ )
+ start_data_time = time.perf_counter()
+
+ # Measure the time only for this worker (before the synchronization barrier)
+ total_time = time.perf_counter() - start_time
+ total_time_str = str(datetime.timedelta(seconds=total_time))
+ # NOTE this format is parsed by grep
+ logger.info(
+ "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format(
+ total_time_str, total_time / (total - num_warmup), num_devices
+ )
+ )
+ total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))
+ logger.info(
+ "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format(
+ total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
+ )
+ )
+
+ results = evaluator.evaluate()
+ # An evaluator may return None when not in main process.
+ # Replace it by an empty dict instead to make it easier for downstream code to handle
+ if results is None:
+ results = {}
+ return results
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4ac4fc27deacc6d1f020b9aedbcc04d9a5b4e6f
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/__init__.py
@@ -0,0 +1,25 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+"""
+Model Zoo API for ODISE: a collection of functions to create common model architectures
+listed in `MODEL_ZOO.md `_,
+and optionally load their pre-trained weights.
+"""
+
+from .model_zoo import get, get_config_file, get_checkpoint_url, get_config
+
+__all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/model_zoo.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/model_zoo.py
new file mode 100644
index 0000000000000000000000000000000000000000..32c2edd04c4733b81d07f699477fe17590ccb86e
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/model_zoo/model_zoo.py
@@ -0,0 +1,152 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import logging
+import os
+from typing import Optional
+import pkg_resources
+import torch
+from detectron2.config import LazyConfig
+
+from odise.checkpoint import ODISECheckpointer
+from odise.config import instantiate_odise
+
+
+class _ModelZooUrls(object):
+ """
+ Mapping from names to officially released ODISE pre-trained models.
+ """
+
+ PREFIX = "https://github.com/NVlabs/ODISE/releases/download/v1.0.0/"
+
+ # format: {config_path.yaml} -> model_{sha256sum}.pth
+ CONFIG_PATH_TO_URL_SUFFIX = {
+ "Panoptic/odise_caption_coco_50e": "odise_caption_coco_50e-853cc971.pth",
+ "Panoptic/odise_label_coco_50e": "odise_label_coco_50e-b67d2efc.pth",
+ }
+
+ @staticmethod
+ def query(config_path: str) -> Optional[str]:
+ """
+ Args:
+ config_path: relative config filename
+ """
+ name = config_path.replace(".yaml", "").replace(".py", "")
+ if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX:
+ suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name]
+ path = os.path.join(_ModelZooUrls.PREFIX, suffix)
+ local_model_zoo = os.environ.get("ODISE_MODEL_ZOO", "")
+ if local_model_zoo:
+ local_path = os.path.join(local_model_zoo, os.path.basename(path))
+ if os.path.exists(local_path):
+ logging.getLogger(__name__).info(f"Using local model zoo: {local_path}.")
+ path = local_path
+ return path
+ return None
+
+
+def get_checkpoint_url(config_path):
+ """
+ Returns the URL to the model trained using the given config
+
+ Args:
+ config_path (str): config file name relative to ODISE's "configs/"
+ directory, e.g., "Panoptic/odise_label_coco_50e.py"
+
+ Returns:
+ str: a URL to the model
+ """
+ url = _ModelZooUrls.query(config_path)
+ if url is None:
+ raise RuntimeError("Pretrained model for {} is not available!".format(config_path))
+ return url
+
+
+def get_config_file(config_path):
+ """
+ Returns path to a builtin config file.
+
+ Args:
+ config_path (str): config file name relative to ODISE's "configs/"
+ directory, e.g., "Panoptic/odise_label_coco_50e.py"
+
+ Returns:
+ str: the real path to the config file.
+ """
+ cfg_file = pkg_resources.resource_filename(
+ "odise.model_zoo", os.path.join("configs", config_path)
+ )
+ if not os.path.exists(cfg_file):
+ raise RuntimeError("{} not available in Model Zoo!".format(config_path))
+ return cfg_file
+
+
+def get_config(config_path, trained: bool = False):
+ """
+ Returns a config object for a model in model zoo.
+
+ Args:
+ config_path (str): config file name relative to ODISE's "configs/"
+ directory, e.g., "Panoptic/odise_label_coco_50e.py"
+ trained (bool): If True, will set ``train.init_checkpoint`` to trained model zoo weights.
+ If False, the checkpoint specified in the config file's ``train.init_checkpoint``
+ is used instead.
+
+ Returns:
+ CfgNode or omegaconf.DictConfig: a config object
+ """
+ cfg_file = get_config_file(config_path)
+ assert cfg_file.endswith(".py")
+ cfg = LazyConfig.load(cfg_file)
+ if trained:
+ url = get_checkpoint_url(config_path)
+ if "train" in cfg and "init_checkpoint" in cfg.train:
+ cfg.train.init_checkpoint = url
+ else:
+ raise NotImplementedError
+ return cfg
+
+
+def get(config_path, trained: bool = False, device: Optional[str] = None):
+ """
+ Get a model specified by relative path under ODISE's official ``configs/`` directory.
+
+ Args:
+ config_path (str): config file name relative to ODISE's "configs/"
+ directory, e.g., "Panoptic/odise_label_coco_50e.py"
+ trained (bool): see :func:`get_config`.
+ device (str or None): overwrite the device in config, if given.
+
+ Returns:
+ nn.Module: a odise model. Will be in training mode.
+
+ Example:
+ ::
+ from odise import model_zoo
+ model = model_zoo.get("Panoptic/odise_label_coco_50e.py", trained=True)
+ """
+ cfg = get_config(config_path, trained)
+ if device is None and not torch.cuda.is_available():
+ device = "cpu"
+ if device is not None:
+ cfg.train.device = device
+
+ model = instantiate_odise(cfg.model)
+ if device is not None:
+ model = model.to(device)
+ if "train" in cfg and "init_checkpoint" in cfg.train:
+ ODISECheckpointer(model).load(cfg.train.init_checkpoint)
+ return model
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c09be65fbf553da4b4c6d323cd1c0ed7fc063d
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .backbone import FeatureExtractorBackbone
+
+__all__ = ["FeatureExtractorBackbone"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b57215f30d29594f9832268f5714ad7b52bea4e3
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .feature_extractor import FeatureExtractorBackbone
+
+__all__ = ["FeatureExtractorBackbone"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/feature_extractor.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..533d933fe47aca8543b896095b80cc038907c9b9
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/backbone/feature_extractor.py
@@ -0,0 +1,270 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import logging
+import math
+from collections import OrderedDict, defaultdict
+from typing import List, Tuple, Union
+import torch
+import torch.utils.checkpoint as checkpoint
+import torchvision.transforms as T
+from detectron2.modeling.backbone import Backbone
+from detectron2.modeling.backbone.resnet import BottleneckBlock, ResNet
+from detectron2.structures import ImageList
+from torch import nn
+from torch.nn import functional as F
+
+from ..meta_arch.helper import FeatureExtractor
+
+logger = logging.getLogger(__name__)
+
+
+class FeatureExtractorBackbone(Backbone):
+ """Backbone implement following for FeatureExtractor
+
+ 1. Project same group features into the one single feature map
+ 2. Sort the features by area, from large to small
+ 3. Get the stride of each feature map
+ """
+
+ def __init__(
+ self,
+ feature_extractor: FeatureExtractor,
+ out_features: List[str],
+ backbone_in_size: Union[int, Tuple[int]] = (512, 512),
+ min_stride: int = 4,
+ max_stride: int = 32,
+ projection_dim: int = 512,
+ num_res_blocks: int = 1,
+ use_checkpoint: bool = False,
+ slide_training: bool = False,
+ ):
+ super().__init__()
+ self.feature_extractor = feature_extractor
+ self.use_checkpoint = use_checkpoint
+ self.feature_projections = nn.ModuleList()
+ for feature_dim in self.feature_extractor.feature_dims:
+ self.feature_projections.append(
+ nn.Sequential(
+ *ResNet.make_stage(
+ BottleneckBlock,
+ num_blocks=num_res_blocks,
+ in_channels=feature_dim,
+ bottleneck_channels=projection_dim // 4,
+ out_channels=projection_dim,
+ norm="GN",
+ )
+ )
+ )
+ self.channel_scale = [dim/512 for dim in self.feature_extractor.feature_dims[2:6]]
+ if isinstance(backbone_in_size, int):
+ self.image_preprocess = T.Resize(
+ size=backbone_in_size, max_size=1280, interpolation=T.InterpolationMode.BICUBIC
+ )
+ self.backbone_in_size = (backbone_in_size, backbone_in_size)
+ self._slide_inference = False
+ else:
+ self.image_preprocess = T.Resize(
+ size=tuple(backbone_in_size), interpolation=T.InterpolationMode.BICUBIC
+ )
+ self.backbone_in_size = tuple(backbone_in_size)
+ self._slide_inference = True
+
+ self._slide_training = slide_training
+ if self._slide_training:
+ assert self._slide_inference, "slide training must be used with slide inference"
+
+ self.min_stride = min_stride
+ self.max_stride = max_stride
+
+ idx_to_stride = {}
+ stride_to_indices = defaultdict(list)
+ for indices in self.feature_extractor.grouped_indices:
+ for idx in indices:
+ stride = self.feature_extractor.feature_strides[idx]
+ stride = min(max(stride, self.min_stride), self.max_stride)
+ idx_to_stride[idx] = stride
+ stride_to_indices[stride].append(idx)
+
+ self._sorted_grouped_indices = []
+ for stride in sorted(stride_to_indices.keys()):
+ self._sorted_grouped_indices.append(stride_to_indices[stride])
+
+ self._out_feature_channels = {}
+ self._out_feature_strides = {}
+
+ for indices in self._sorted_grouped_indices: # some bugs here, cannot adjust layers of different stride
+ stride = idx_to_stride[indices[0]]
+ name = f"s{int(math.log2(stride))}"
+ if name not in out_features:
+ continue
+ assert name not in self._out_feature_strides, f"Duplicate feature name {name}"
+ self._out_feature_strides[name] = stride # self._out_feature_strides need to be modified
+ self._out_feature_channels[name] = projection_dim
+ self._out_features = list(self._out_feature_strides.keys())
+
+ logger.info(
+ f"backbone_in_size: {backbone_in_size}, "
+ f"slide_training: {self._slide_training}, \n"
+ f"slide_inference: {self._slide_inference}, \n"
+ f"min_stride: {min_stride}, "
+ f"max_stride: {max_stride}, \n"
+ f"projection_dim: {projection_dim}, \n"
+ f"out_feature_channels: {self._out_feature_channels}\n"
+ f"out_feature_strides: {self._out_feature_strides}\n"
+ f"use_checkpoint: {use_checkpoint}\n"
+ )
+
+ @property
+ def size_divisibility(self) -> int:
+ return 64
+
+ def ignored_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ destination._metadata = OrderedDict()
+ for name, module in self._modules.items():
+ if module is not None and hasattr(module, "ignored_state_dict"):
+ module.ignored_state_dict(destination, prefix + name + ".")
+ return destination
+
+ def single_forward(self, img, caption=None, raw=False):
+
+ # save memory
+ input_image_size = img.shape[-2:]
+ # print("input_image_size:", img.shape)
+ img = self.image_preprocess(img)
+ # print("processed_image_size:", img.shape)
+ img = ImageList.from_tensors(list(img), self.size_divisibility).tensor
+ # print("padded size:", img.shape)
+
+ # print device
+ # print("img.device:", img.device)
+ # self.feature_extractor.to(img.device)
+ # print("feature_extractor.device:", self.feature_extractor.device)
+ if caption is not None:
+ features = self.feature_extractor(dict(img=img, caption=caption))
+ else:
+ features = self.feature_extractor(dict(img=img))
+ if raw:
+ out_features = {}
+ out_features['s5']=features[2]
+ out_features['s4']=features[3]
+ out_features['s3']=features[4]
+ out_features['s2']=features[5]
+ return out_features
+ if self.use_checkpoint:
+ return checkpoint.checkpoint(
+ self.forward_features, features, input_image_size, use_reentrant=False
+ )
+ else:
+ return self.forward_features(features, input_image_size)
+
+ def forward_features(self, features, input_image_size):
+ output_features = {}
+ for name, indices in zip(self._out_features, self._sorted_grouped_indices):
+ output_feature = None
+ stride = self._out_feature_strides[name]
+ for idx in indices:
+ # print("before restore", name, idx, features[idx].shape, stride)
+ # restore aspect ratio
+ restored_feature = F.interpolate(
+ features[idx],
+ size=(input_image_size[-2] // stride, input_image_size[-1] // stride),
+ )
+ projected_feature = self.feature_projections[idx](restored_feature)
+ if output_feature is None:
+ output_feature = projected_feature
+ else:
+ output_feature = output_feature + projected_feature
+ output_features[name] = output_feature
+
+ # for k in output_features:
+ # print(k, output_features[k].shape)
+
+ return output_features
+
+ def slide_forward(self, img, caption=None, raw=False):
+ scale = 2 if raw else 1
+ channel_scale = {'s5': self.channel_scale[0], 's4': self.channel_scale[1], 's3': self.channel_scale[2], 's2': self.channel_scale[3]} if raw else {'s5': 1, 's4': 1, 's3': 1, 's2': 1} #todo
+ batch_size, _, h_img, w_img = img.shape
+ # output_features = {k: torch.zeros_like(v) for k, v in self.single_forward(img).items()}
+ output_features = {}
+ for k in self._out_features:
+ stride = self._out_feature_strides[k] * scale
+ channel = int(self._out_feature_channels[k] * channel_scale[k])
+ output_features[k] = torch.zeros(
+ (batch_size, channel, h_img // stride, w_img // stride),
+ dtype=img.dtype,
+ device=img.device,
+ )
+
+ count_mats = {k: torch.zeros_like(v) for k, v in output_features.items()}
+
+ if self._slide_training:
+ short_side = min(min(self.backbone_in_size), min(img.shape[-2:]))
+ else:
+ # if not slide training then use the shorter side to crop
+ short_side = min(img.shape[-2:])
+
+ # h_img, w_img = img.shape[-2:]
+
+ h_crop = w_crop = short_side
+
+ h_stride = w_stride = short_side
+
+ h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
+ w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
+
+ # print("img.shape:", img.shape)
+ # for k in output_features:
+ # print(k, output_features[k].shape)
+ # print("h_grids:", h_grids, "w_grids:", w_grids)
+ for h_idx in range(h_grids):
+ for w_idx in range(w_grids):
+ y1 = h_idx * h_stride
+ x1 = w_idx * w_stride
+ y2 = min(y1 + h_crop, h_img)
+ x2 = min(x1 + w_crop, w_img)
+ y1 = max(y2 - h_crop, 0)
+ x1 = max(x2 - w_crop, 0)
+ crop_img = img[:, :, y1:y2, x1:x2] #(1,3,512,512)
+ assert crop_img.shape[-2:] == (h_crop, w_crop), f"{crop_img.shape} from {img.shape}"
+ # print("crop_img.shape:", crop_img.shape)
+ crop_features = self.single_forward(crop_img,caption=caption,raw=raw)
+ for k in crop_features:
+ k_x1 = x1 // (self._out_feature_strides[k] * scale)
+ k_x2 = x2 // (self._out_feature_strides[k] * scale)
+ k_y1 = y1 // (self._out_feature_strides[k] * scale)
+ k_y2 = y2 // (self._out_feature_strides[k] * scale)
+ # output_features[k] += F.pad(
+ # crop_features[k],
+ # (
+ # k_x1,
+ # output_features[k].shape[-1] - k_x1 - crop_features[k].shape[-1],
+ # k_y1,
+ # output_features[k].shape[-2] - k_y1 - crop_features[k].shape[-2],
+ # ),
+ # )
+ # this version should save some memory
+ output_features[k][:, :, k_y1:k_y2, k_x1:k_x2] += crop_features[k]
+ count_mats[k][..., k_y1:k_y2, k_x1:k_x2] += 1
+ assert all((count_mats[k] == 0).sum() == 0 for k in count_mats)
+
+ for k in output_features:
+ output_features[k] /= count_mats[k]
+
+ return output_features
+
+ def forward(self, img, caption=None, raw=False):
+ if (self.training and not self._slide_training) or not self._slide_inference:
+ return self.single_forward(img,caption,raw)
+ else:
+ return self.slide_forward(img,caption,raw)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8da4bb83eef52290fb6019193d927d5b0c2b6758
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__init__.py
@@ -0,0 +1,14 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .diffusion_builder import create_gaussian_diffusion
+from .gaussian_diffusion import GaussianDiffusion
+
+__all__ = ["create_gaussian_diffusion", "GaussianDiffusion"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__pycache__/respace.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/__pycache__/respace.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/diffusion_builder.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/diffusion_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f787d1f3a5166a41d72b2f288b882ddf777c8d8
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/diffusion_builder.py
@@ -0,0 +1,55 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2021 OpenAI
+# To view a copy of this license, visit
+# https://github.com/openai/glide-text2im/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from . import gaussian_diffusion as gd
+from .respace import SpacedDiffusion, space_timesteps
+
+
+def create_gaussian_diffusion(
+ *,
+ steps=1000,
+ learn_sigma=False,
+ sigma_small=False,
+ noise_schedule="linear",
+ use_kl=False,
+ predict_xstart=False,
+ rescale_timesteps=False,
+ rescale_learned_sigmas=False,
+ timestep_respacing="",
+):
+ betas = gd.get_named_beta_schedule(noise_schedule, steps)
+ if use_kl:
+ loss_type = gd.LossType.RESCALED_KL
+ elif rescale_learned_sigmas:
+ loss_type = gd.LossType.RESCALED_MSE
+ else:
+ loss_type = gd.LossType.MSE
+ if not timestep_respacing:
+ timestep_respacing = [steps]
+ return SpacedDiffusion(
+ use_timesteps=space_timesteps(steps, timestep_respacing),
+ betas=betas,
+ model_mean_type=(
+ gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
+ ),
+ model_var_type=(
+ (gd.ModelVarType.FIXED_LARGE if not sigma_small else gd.ModelVarType.FIXED_SMALL)
+ if not learn_sigma
+ else gd.ModelVarType.LEARNED_RANGE
+ ),
+ loss_type=loss_type,
+ rescale_timesteps=rescale_timesteps,
+ )
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/gaussian_diffusion.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/gaussian_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd04c7ffd02c7048d94927ab10dce238bd137371
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/gaussian_diffusion.py
@@ -0,0 +1,1051 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2021 OpenAI
+# To view a copy of this license, visit
+# https://github.com/openai/glide-text2im/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+"""
+This code started out as a PyTorch port of Ho et al's diffusion models:
+https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
+
+Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
+"""
+
+import enum
+import math
+import numpy as np
+import torch as th
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ Compute the KL divergence between two gaussians.
+
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, th.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for th.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor) for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + th.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
+ )
+
+
+def approx_standard_normal_cdf(x):
+ """
+ A fast approximation of the cumulative distribution function of the
+ standard normal.
+ """
+ return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
+
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+ """
+ Compute the log-likelihood of a Gaussian distribution discretizing to a
+ given image.
+
+ :param x: the target images. It is assumed that this was uint8 values,
+ rescaled to the range [-1, 1].
+ :param means: the Gaussian mean Tensor.
+ :param log_scales: the Gaussian log stddev Tensor.
+ :return: a tensor like x of log probabilities (in nats).
+ """
+ assert x.shape == means.shape == log_scales.shape
+ centered_x = x - means
+ inv_stdv = th.exp(-log_scales)
+ plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
+ cdf_plus = approx_standard_normal_cdf(plus_in)
+ min_in = inv_stdv * (centered_x - 1.0 / 255.0)
+ cdf_min = approx_standard_normal_cdf(min_in)
+ log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
+ log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
+ cdf_delta = cdf_plus - cdf_min
+ log_probs = th.where(
+ x < -0.999,
+ log_cdf_plus,
+ th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
+ )
+ assert log_probs.shape == x.shape
+ return log_probs
+
+
+def mean_flat(tensor):
+ """
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+ """
+ Get a pre-defined beta schedule for the given name.
+
+ The beta schedule library consists of beta schedules which remain similar
+ in the limit of num_diffusion_timesteps.
+ Beta schedules may be added, but should not be removed or changed once
+ they are committed to maintain backwards compatibility.
+ """
+ if schedule_name == "linear":
+ # Linear schedule from Ho et al, extended to work for any number of
+ # diffusion steps.
+ scale = 1000 / num_diffusion_timesteps
+ beta_start = scale * 0.0001
+ beta_end = scale * 0.02
+ return np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
+ elif schedule_name == "cosine":
+ return betas_for_alpha_bar(
+ num_diffusion_timesteps,
+ lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+ )
+ elif schedule_name == "ldm_linear":
+ # ref: https://github.com/CompVis/stable-diffusion/blob/d39f5b51a8d607fd855425a0d546b9f871034c3d/ldm/modules/diffusionmodules/util.py#L22 # noqa
+ scale = 1000 / num_diffusion_timesteps
+ beta_start = scale * 0.00085
+ beta_end = scale * 0.012
+ return (
+ np.linspace(
+ beta_start**0.5, beta_end**0.5, num_diffusion_timesteps, dtype=np.float64
+ )
+ ** 2
+ )
+ else:
+ raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+ """
+ Create a beta schedule that discretizes the given alpha_t_bar function,
+ which defines the cumulative product of (1-beta) over time from t = [0,1].
+
+ :param num_diffusion_timesteps: the number of betas to produce.
+ :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+ produces the cumulative product of (1-beta) up to that
+ part of the diffusion process.
+ :param max_beta: the maximum beta to use; use values lower than 1 to
+ prevent singularities.
+ """
+ betas = []
+ for i in range(num_diffusion_timesteps):
+ t1 = i / num_diffusion_timesteps
+ t2 = (i + 1) / num_diffusion_timesteps
+ betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+ return np.array(betas)
+
+
+class ModelMeanType(enum.Enum):
+ """
+ Which type of output the model predicts.
+ """
+
+ PREVIOUS_X = enum.auto() # the model predicts x_{t-1}
+ START_X = enum.auto() # the model predicts x_0
+ EPSILON = enum.auto() # the model predicts epsilon
+
+
+class ModelVarType(enum.Enum):
+ """
+ What is used as the model's output variance.
+
+ The LEARNED_RANGE option has been added to allow the model to predict
+ values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+ """
+
+ LEARNED = enum.auto()
+ FIXED_SMALL = enum.auto()
+ FIXED_LARGE = enum.auto()
+ LEARNED_RANGE = enum.auto()
+
+
+class LossType(enum.Enum):
+ MSE = enum.auto() # use raw MSE loss (and KL when learning variances)
+ RESCALED_MSE = enum.auto() # use raw MSE loss (with RESCALED_KL when learning variances)
+ KL = enum.auto() # use the variational lower-bound
+ RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB
+
+ def is_vb(self):
+ return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+class GaussianDiffusion:
+ """
+ Utilities for training and sampling diffusion models.
+
+ Ported directly from here, and then adapted over time to further experimentation.
+ https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+
+ :param betas: a 1-D numpy array of betas for each diffusion timestep,
+ starting at T and going to 1.
+ :param model_mean_type: a ModelMeanType determining what the model outputs.
+ :param model_var_type: a ModelVarType determining how variance is output.
+ :param loss_type: a LossType determining the loss function to use.
+ :param rescale_timesteps: if True, pass floating point timesteps into the
+ model so that they are always scaled like in the
+ original paper (0 to 1000).
+ """
+
+ def __init__(
+ self,
+ *,
+ betas,
+ model_mean_type,
+ model_var_type,
+ loss_type,
+ rescale_timesteps=False,
+ ):
+ self.model_mean_type = model_mean_type
+ self.model_var_type = model_var_type
+ self.loss_type = loss_type
+ self.rescale_timesteps = rescale_timesteps
+
+ # Use float64 for accuracy.
+ betas = np.array(betas, dtype=np.float64)
+ self.betas = betas
+ assert len(betas.shape) == 1, "betas must be 1-D"
+ assert (betas > 0).all() and (betas <= 1).all()
+
+ self.num_timesteps = int(betas.shape[0])
+
+ alphas = 1.0 - betas
+ self.alphas_cumprod = np.cumprod(alphas, axis=0)
+ self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+ self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+ assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+ self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+ self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+ self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+ self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ self.posterior_variance = (
+ betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+ )
+ # log calculation clipped because the posterior variance is 0 at the
+ # beginning of the diffusion chain.
+ self.posterior_log_variance_clipped = np.log(
+ np.append(self.posterior_variance[1], self.posterior_variance[1:])
+ )
+ self.posterior_mean_coef1 = (
+ betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+ )
+ self.posterior_mean_coef2 = (
+ (1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
+ )
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def q_sample(self, x_start, t, noise=None):
+ """
+ Diffuse the data for a given number of diffusion steps.
+
+ In other words, sample from q(x_t | x_0).
+
+ :param x_start: the initial data batch.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :param noise: if specified, the split-out normal noise.
+ :return: A noisy version of x_start.
+ """
+ if noise is None:
+ noise = th.randn_like(x_start)
+ assert noise.shape == x_start.shape
+ return (
+ _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+ )
+
+ def q_posterior_mean_variance(self, x_start, x_t, t):
+ """
+ Compute the mean and variance of the diffusion posterior:
+
+ q(x_{t-1} | x_t, x_0)
+
+ """
+ assert x_start.shape == x_t.shape
+ posterior_mean = (
+ _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = _extract_into_tensor(
+ self.posterior_log_variance_clipped, t, x_t.shape
+ )
+ assert (
+ posterior_mean.shape[0]
+ == posterior_variance.shape[0]
+ == posterior_log_variance_clipped.shape[0]
+ == x_start.shape[0]
+ )
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
+ """
+ Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+ the initial x, x_0.
+
+ :param model: the model, which takes a signal and a batch of timesteps
+ as input.
+ :param x: the [N x C x ...] tensor at time t.
+ :param t: a 1-D Tensor of timesteps.
+ :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+ :param denoised_fn: if not None, a function which applies to the
+ x_start prediction before it is used to sample. Applies before
+ clip_denoised.
+ :param model_kwargs: if not None, a dict of extra keyword arguments to
+ pass to the model. This can be used for conditioning.
+ :return: a dict with the following keys:
+ - 'mean': the model mean output.
+ - 'variance': the model variance output.
+ - 'log_variance': the log of 'variance'.
+ - 'pred_xstart': the prediction for x_0.
+ """
+ if model_kwargs is None:
+ model_kwargs = {}
+
+ B, C = x.shape[:2]
+ assert t.shape == (B,)
+ model_output = model(x, self._scale_timesteps(t), **model_kwargs)
+
+ if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
+ assert model_output.shape == (B, C * 2, *x.shape[2:])
+ model_output, model_var_values = th.split(model_output, C, dim=1)
+ if self.model_var_type == ModelVarType.LEARNED:
+ model_log_variance = model_var_values
+ model_variance = th.exp(model_log_variance)
+ else:
+ min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
+ max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+ # The model_var_values is [-1, 1] for [min_var, max_var].
+ frac = (model_var_values + 1) / 2
+ model_log_variance = frac * max_log + (1 - frac) * min_log
+ model_variance = th.exp(model_log_variance)
+ else:
+ model_variance, model_log_variance = {
+ # for fixedlarge, we set the initial (log-)variance like so
+ # to get a better decoder log likelihood.
+ ModelVarType.FIXED_LARGE: (
+ np.append(self.posterior_variance[1], self.betas[1:]),
+ np.log(np.append(self.posterior_variance[1], self.betas[1:])),
+ ),
+ ModelVarType.FIXED_SMALL: (
+ self.posterior_variance,
+ self.posterior_log_variance_clipped,
+ ),
+ }[self.model_var_type]
+ model_variance = _extract_into_tensor(model_variance, t, x.shape)
+ model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
+
+ def process_xstart(x):
+ if denoised_fn is not None:
+ x = denoised_fn(x)
+ if clip_denoised:
+ return x.clamp(-1, 1)
+ return x
+
+ if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+ pred_xstart = process_xstart(
+ self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
+ )
+ model_mean = model_output
+ elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
+ if self.model_mean_type == ModelMeanType.START_X:
+ pred_xstart = process_xstart(model_output)
+ else:
+ pred_xstart = process_xstart(
+ self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+ )
+ model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
+ else:
+ raise NotImplementedError(self.model_mean_type)
+
+ assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+ return {
+ "mean": model_mean,
+ "variance": model_variance,
+ "log_variance": model_log_variance,
+ "pred_xstart": pred_xstart,
+ }
+
+ def _predict_xstart_from_eps(self, x_t, t, eps):
+ assert x_t.shape == eps.shape
+ return (
+ _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+ - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+ )
+
+ def _predict_xstart_from_xprev(self, x_t, t, xprev):
+ assert x_t.shape == xprev.shape
+ return ( # (xprev - coef2*x_t) / coef1
+ _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
+ - _extract_into_tensor(
+ self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
+ )
+ * x_t
+ )
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (
+ _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
+ ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _scale_timesteps(self, t):
+ if self.rescale_timesteps:
+ return t.float() * (1000.0 / self.num_timesteps)
+ return t
+
+ def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+ """
+ Compute the mean for the previous step, given a function cond_fn that
+ computes the gradient of a conditional log probability with respect to
+ x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+ condition on y.
+
+ This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+ """
+ gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
+ new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+ return new_mean
+
+ def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+ """
+ Compute what the p_mean_variance output would have been, should the
+ model's score function be conditioned by cond_fn.
+
+ See condition_mean() for details on cond_fn.
+
+ Unlike condition_mean(), this instead uses the conditioning strategy
+ from Song et al (2020).
+ """
+ alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+ eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+ eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, self._scale_timesteps(t), **model_kwargs)
+
+ out = p_mean_var.copy()
+ out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+ out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
+ return out
+
+ def p_sample(
+ self,
+ model,
+ x,
+ t,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ ):
+ """
+ Sample x_{t-1} from the model at the given timestep.
+
+ :param model: the model to sample from.
+ :param x: the current tensor at x_{t-1}.
+ :param t: the value of t, starting at 0 for the first diffusion step.
+ :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+ :param denoised_fn: if not None, a function which applies to the
+ x_start prediction before it is used to sample.
+ :param cond_fn: if not None, this is a gradient function that acts
+ similarly to the model.
+ :param model_kwargs: if not None, a dict of extra keyword arguments to
+ pass to the model. This can be used for conditioning.
+ :return: a dict containing the following keys:
+ - 'sample': a random sample from the model.
+ - 'pred_xstart': a prediction of x_0.
+ """
+ out = self.p_mean_variance(
+ model,
+ x,
+ t,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ model_kwargs=model_kwargs,
+ )
+ noise = th.randn_like(x)
+ nonzero_mask = (
+ (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+ ) # no noise when t == 0
+ if cond_fn is not None:
+ out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
+ sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
+ return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+ def p_sample_loop(
+ self,
+ model,
+ shape,
+ noise=None,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ device=None,
+ progress=False,
+ ):
+ """
+ Generate samples from the model.
+
+ :param model: the model module.
+ :param shape: the shape of the samples, (N, C, H, W).
+ :param noise: if specified, the noise from the encoder to sample.
+ Should be of the same shape as `shape`.
+ :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+ :param denoised_fn: if not None, a function which applies to the
+ x_start prediction before it is used to sample.
+ :param cond_fn: if not None, this is a gradient function that acts
+ similarly to the model.
+ :param model_kwargs: if not None, a dict of extra keyword arguments to
+ pass to the model. This can be used for conditioning.
+ :param device: if specified, the device to create the samples on.
+ If not specified, use a model parameter's device.
+ :param progress: if True, show a tqdm progress bar.
+ :return: a non-differentiable batch of samples.
+ """
+ final = None
+ for sample in self.p_sample_loop_progressive(
+ model,
+ shape,
+ noise=noise,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ cond_fn=cond_fn,
+ model_kwargs=model_kwargs,
+ device=device,
+ progress=progress,
+ ):
+ final = sample
+ return final["sample"]
+
+ def p_sample_loop_progressive(
+ self,
+ model,
+ shape,
+ noise=None,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ device=None,
+ progress=False,
+ ):
+ """
+ Generate samples from the model and yield intermediate samples from
+ each timestep of diffusion.
+
+ Arguments are the same as p_sample_loop().
+ Returns a generator over dicts, where each dict is the return value of
+ p_sample().
+ """
+ if device is None:
+ device = next(model.parameters()).device
+ assert isinstance(shape, (tuple, list))
+ if noise is not None:
+ img = noise
+ else:
+ img = th.randn(*shape, device=device)
+ indices = list(range(self.num_timesteps))[::-1]
+
+ if progress:
+ # Lazy import so that we don't depend on tqdm.
+ from tqdm.auto import tqdm
+
+ indices = tqdm(indices)
+
+ for i in indices:
+ t = th.tensor([i] * shape[0], device=device)
+ with th.no_grad():
+ out = self.p_sample(
+ model,
+ img,
+ t,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ cond_fn=cond_fn,
+ model_kwargs=model_kwargs,
+ )
+ yield out
+ img = out["sample"]
+
+ def ddim_sample(
+ self,
+ model,
+ x,
+ t,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ eta=0.0,
+ ):
+ """
+ Sample x_{t-1} from the model using DDIM.
+
+ Same usage as p_sample().
+ """
+ out = self.p_mean_variance(
+ model,
+ x,
+ t,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ model_kwargs=model_kwargs,
+ )
+ if cond_fn is not None:
+ out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+ # Usually our model outputs epsilon, but we re-derive it
+ # in case we used x_start or x_prev prediction.
+ eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+ alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+ alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+ sigma = (
+ eta
+ * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+ * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+ )
+ # Equation 12.
+ noise = th.randn_like(x)
+ mean_pred = (
+ out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+ + th.sqrt(1 - alpha_bar_prev - sigma**2) * eps
+ )
+ nonzero_mask = (
+ (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+ ) # no noise when t == 0
+ sample = mean_pred + nonzero_mask * sigma * noise
+ return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+ def ddim_reverse_sample(
+ self,
+ model,
+ x,
+ t,
+ clip_denoised=True,
+ denoised_fn=None,
+ model_kwargs=None,
+ eta=0.0,
+ ):
+ """
+ Sample x_{t+1} from the model using DDIM reverse ODE.
+ """
+ assert eta == 0.0, "Reverse ODE only for deterministic path"
+ out = self.p_mean_variance(
+ model,
+ x,
+ t,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ model_kwargs=model_kwargs,
+ )
+ # Usually our model outputs epsilon, but we re-derive it
+ # in case we used x_start or x_prev prediction.
+ eps = (
+ _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+ - out["pred_xstart"]
+ ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+ alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+ # Equation 12. reversed
+ mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
+
+ return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+ def ddim_sample_loop(
+ self,
+ model,
+ shape,
+ noise=None,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ device=None,
+ progress=False,
+ eta=0.0,
+ ):
+ """
+ Generate samples from the model using DDIM.
+
+ Same usage as p_sample_loop().
+ """
+ final = None
+ for sample in self.ddim_sample_loop_progressive(
+ model,
+ shape,
+ noise=noise,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ cond_fn=cond_fn,
+ model_kwargs=model_kwargs,
+ device=device,
+ progress=progress,
+ eta=eta,
+ ):
+ final = sample
+ return final["sample"]
+
+ def ddim_sample_loop_progressive(
+ self,
+ model,
+ shape,
+ noise=None,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ device=None,
+ progress=False,
+ eta=0.0,
+ ):
+ """
+ Use DDIM to sample from the model and yield intermediate samples from
+ each timestep of DDIM.
+
+ Same usage as p_sample_loop_progressive().
+ """
+ if device is None:
+ device = next(model.parameters()).device
+ assert isinstance(shape, (tuple, list))
+ if noise is not None:
+ img = noise
+ else:
+ img = th.randn(*shape, device=device)
+ indices = list(range(self.num_timesteps))[::-1]
+
+ if progress:
+ # Lazy import so that we don't depend on tqdm.
+ from tqdm.auto import tqdm
+
+ indices = tqdm(indices)
+
+ for i in indices:
+ t = th.tensor([i] * shape[0], device=device)
+ with th.no_grad():
+ out = self.ddim_sample(
+ model,
+ img,
+ t,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ cond_fn=cond_fn,
+ model_kwargs=model_kwargs,
+ eta=eta,
+ )
+ yield out
+ img = out["sample"]
+
+ def ddim_reverse_sample_loop(
+ self,
+ model,
+ shape,
+ noise,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ device=None,
+ progress=False,
+ eta=0.0,
+ ):
+ """
+ Generate noise from the model using DDIM reverse.
+ The input noise is actually x_{0}
+
+ Same usage as p_sample_loop().
+ """
+ final = None
+ for sample in self.ddim_reverse_sample_loop_progressive(
+ model,
+ shape,
+ noise=noise,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ cond_fn=cond_fn,
+ model_kwargs=model_kwargs,
+ device=device,
+ progress=progress,
+ eta=eta,
+ ):
+ final = sample
+ return final["sample"]
+
+ def ddim_reverse_sample_loop_progressive(
+ self,
+ model,
+ shape,
+ noise,
+ clip_denoised=True,
+ denoised_fn=None,
+ cond_fn=None,
+ model_kwargs=None,
+ device=None,
+ progress=False,
+ eta=0.0,
+ ):
+ """
+ Use DDIM to sample from the model and yield intermediate samples from
+ each timestep of DDIM reverse.
+
+ Same usage as p_sample_loop_progressive().
+ """
+ if device is None:
+ device = next(model.parameters()).device
+ assert isinstance(shape, (tuple, list))
+ assert noise is not None, "input image has to be provided as noise"
+ img = noise
+ # NOTE: we need to add noise starting from step 0 to num_timesteps - 1
+ indices = list(range(self.num_timesteps))
+
+ if progress:
+ # Lazy import so that we don't depend on tqdm.
+ from tqdm.auto import tqdm
+
+ indices = tqdm(indices)
+
+ for i in indices:
+ t = th.tensor([i] * shape[0], device=device)
+ with th.no_grad():
+ out = self.ddim_reverse_sample(
+ model,
+ img,
+ t,
+ clip_denoised=clip_denoised,
+ denoised_fn=denoised_fn,
+ model_kwargs=model_kwargs,
+ eta=eta,
+ )
+ yield out
+ img = out["sample"]
+
+ def _vb_terms_bpd(self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None):
+ """
+ Get a term for the variational lower-bound.
+
+ The resulting units are bits (rather than nats, as one might expect).
+ This allows for comparison to other papers.
+
+ :return: a dict with the following keys:
+ - 'output': a shape [N] tensor of NLLs or KLs.
+ - 'pred_xstart': the x_0 predictions.
+ """
+ true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+ x_start=x_start, x_t=x_t, t=t
+ )
+ out = self.p_mean_variance(
+ model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
+ )
+ kl = normal_kl(true_mean, true_log_variance_clipped, out["mean"], out["log_variance"])
+ kl = mean_flat(kl) / np.log(2.0)
+
+ decoder_nll = -discretized_gaussian_log_likelihood(
+ x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
+ )
+ assert decoder_nll.shape == x_start.shape
+ decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+ # At the first timestep return the decoder NLL,
+ # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+ output = th.where((t == 0), decoder_nll, kl)
+ return {"output": output, "pred_xstart": out["pred_xstart"]}
+
+ def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
+ """
+ Compute training losses for a single timestep.
+
+ :param model: the model to evaluate loss on.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :param t: a batch of timestep indices.
+ :param model_kwargs: if not None, a dict of extra keyword arguments to
+ pass to the model. This can be used for conditioning.
+ :param noise: if specified, the specific Gaussian noise to try to remove.
+ :return: a dict with the key "loss" containing a tensor of shape [N].
+ Some mean or variance settings may also have other keys.
+ """
+ if model_kwargs is None:
+ model_kwargs = {}
+ if noise is None:
+ noise = th.randn_like(x_start)
+ x_t = self.q_sample(x_start, t, noise=noise)
+
+ terms = {}
+
+ if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+ terms["loss"] = self._vb_terms_bpd(
+ model=model,
+ x_start=x_start,
+ x_t=x_t,
+ t=t,
+ clip_denoised=False,
+ model_kwargs=model_kwargs,
+ )["output"]
+ if self.loss_type == LossType.RESCALED_KL:
+ terms["loss"] *= self.num_timesteps
+ elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+ model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
+
+ if self.model_var_type in [
+ ModelVarType.LEARNED,
+ ModelVarType.LEARNED_RANGE,
+ ]:
+ B, C = x_t.shape[:2]
+ assert model_output.shape == (B, C * 2, *x_t.shape[2:])
+ model_output, model_var_values = th.split(model_output, C, dim=1)
+ # Learn the variance using the variational bound, but don't let
+ # it affect our mean prediction.
+ frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
+ terms["vb"] = self._vb_terms_bpd(
+ model=lambda *args, r=frozen_out: r,
+ x_start=x_start,
+ x_t=x_t,
+ t=t,
+ clip_denoised=False,
+ )["output"]
+ if self.loss_type == LossType.RESCALED_MSE:
+ # Divide by 1000 for equivalence with initial implementation.
+ # Without a factor of 1/1000, the VB term hurts the MSE term.
+ terms["vb"] *= self.num_timesteps / 1000.0
+
+ target = {
+ ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
+ x_start=x_start, x_t=x_t, t=t
+ )[0],
+ ModelMeanType.START_X: x_start,
+ ModelMeanType.EPSILON: noise,
+ }[self.model_mean_type]
+ assert model_output.shape == target.shape == x_start.shape
+ terms["mse"] = mean_flat((target - model_output) ** 2)
+ if "vb" in terms:
+ terms["loss"] = terms["mse"] + terms["vb"]
+ else:
+ terms["loss"] = terms["mse"]
+ else:
+ raise NotImplementedError(self.loss_type)
+
+ return terms
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+
+ This term can't be optimized, as it only depends on the encoder.
+
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
+ """
+ Compute the entire variational lower-bound, measured in bits-per-dim,
+ as well as other related quantities.
+
+ :param model: the model to evaluate loss on.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :param clip_denoised: if True, clip denoised samples.
+ :param model_kwargs: if not None, a dict of extra keyword arguments to
+ pass to the model. This can be used for conditioning.
+
+ :return: a dict containing the following keys:
+ - total_bpd: the total variational lower-bound, per batch element.
+ - prior_bpd: the prior term in the lower-bound.
+ - vb: an [N x T] tensor of terms in the lower-bound.
+ - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+ - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+ """
+ device = x_start.device
+ batch_size = x_start.shape[0]
+
+ vb = []
+ xstart_mse = []
+ mse = []
+ for t in list(range(self.num_timesteps))[::-1]:
+ t_batch = th.tensor([t] * batch_size, device=device)
+ noise = th.randn_like(x_start)
+ x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+ # Calculate VLB term at the current timestep
+ with th.no_grad():
+ out = self._vb_terms_bpd(
+ model,
+ x_start=x_start,
+ x_t=x_t,
+ t=t_batch,
+ clip_denoised=clip_denoised,
+ model_kwargs=model_kwargs,
+ )
+ vb.append(out["output"])
+ xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
+ eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
+ mse.append(mean_flat((eps - noise) ** 2))
+
+ vb = th.stack(vb, dim=1)
+ xstart_mse = th.stack(xstart_mse, dim=1)
+ mse = th.stack(mse, dim=1)
+
+ prior_bpd = self._prior_bpd(x_start)
+ total_bpd = vb.sum(dim=1) + prior_bpd
+ return {
+ "total_bpd": total_bpd,
+ "prior_bpd": prior_bpd,
+ "vb": vb,
+ "xstart_mse": xstart_mse,
+ "mse": mse,
+ }
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+ """
+ Extract values from a 1-D numpy array for a batch of indices.
+
+ :param arr: the 1-D numpy array.
+ :param timesteps: a tensor of indices into the array to extract.
+ :param broadcast_shape: a larger shape of K dimensions with the batch
+ dimension equal to the length of timesteps.
+ :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+ """
+ res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+ while len(res.shape) < len(broadcast_shape):
+ res = res[..., None]
+ return res.expand(broadcast_shape)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/resample.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d86f928932376f193025e965e7b5e58a5424e2a
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/resample.py
@@ -0,0 +1,165 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2021 OpenAI
+# To view a copy of this license, visit
+# https://github.com/openai/glide-text2im/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import numpy as np
+from abc import ABC, abstractmethod
+import torch as th
+import torch.distributed as dist
+
+
+def create_named_schedule_sampler(name, diffusion):
+ """
+ Create a ScheduleSampler from a library of pre-defined samplers.
+
+ :param name: the name of the sampler.
+ :param diffusion: the diffusion object to sample for.
+ """
+ if name == "uniform":
+ return UniformSampler(diffusion)
+ elif name == "loss-second-moment":
+ return LossSecondMomentResampler(diffusion)
+ else:
+ raise NotImplementedError(f"unknown schedule sampler: {name}")
+
+
+class ScheduleSampler(ABC):
+ """
+ A distribution over timesteps in the diffusion process, intended to reduce
+ variance of the objective.
+
+ By default, samplers perform unbiased importance sampling, in which the
+ objective's mean is unchanged.
+ However, subclasses may override sample() to change how the resampled
+ terms are reweighted, allowing for actual changes in the objective.
+ """
+
+ @abstractmethod
+ def weights(self):
+ """
+ Get a numpy array of weights, one per diffusion step.
+
+ The weights needn't be normalized, but must be positive.
+ """
+
+ def sample(self, batch_size, device):
+ """
+ Importance-sample timesteps for a batch.
+
+ :param batch_size: the number of timesteps.
+ :param device: the torch device to save to.
+ :return: a tuple (timesteps, weights):
+ - timesteps: a tensor of timestep indices.
+ - weights: a tensor of weights to scale the resulting losses.
+ """
+ w = self.weights()
+ p = w / np.sum(w)
+ indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
+ indices = th.from_numpy(indices_np).long().to(device)
+ weights_np = 1 / (len(p) * p[indices_np])
+ weights = th.from_numpy(weights_np).float().to(device)
+ return indices, weights
+
+
+class UniformSampler(ScheduleSampler):
+ def __init__(self, diffusion):
+ self.diffusion = diffusion
+ self._weights = np.ones([diffusion.num_timesteps])
+
+ def weights(self):
+ return self._weights
+
+
+class LossAwareSampler(ScheduleSampler):
+ def update_with_local_losses(self, local_ts, local_losses):
+ """
+ Update the reweighting using losses from a model.
+
+ Call this method from each rank with a batch of timesteps and the
+ corresponding losses for each of those timesteps.
+ This method will perform synchronization to make sure all of the ranks
+ maintain the exact same reweighting.
+
+ :param local_ts: an integer Tensor of timesteps.
+ :param local_losses: a 1D Tensor of losses.
+ """
+ batch_sizes = [
+ th.tensor([0], dtype=th.int32, device=local_ts.device)
+ for _ in range(dist.get_world_size())
+ ]
+ dist.all_gather(
+ batch_sizes,
+ th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
+ )
+
+ # Pad all_gather batches to be the maximum batch size.
+ batch_sizes = [x.item() for x in batch_sizes]
+ max_bs = max(batch_sizes)
+
+ timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
+ loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
+ dist.all_gather(timestep_batches, local_ts)
+ dist.all_gather(loss_batches, local_losses)
+ timesteps = [x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]]
+ losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
+ self.update_with_all_losses(timesteps, losses)
+
+ @abstractmethod
+ def update_with_all_losses(self, ts, losses):
+ """
+ Update the reweighting using losses from a model.
+
+ Sub-classes should override this method to update the reweighting
+ using losses from the model.
+
+ This method directly updates the reweighting without synchronizing
+ between workers. It is called by update_with_local_losses from all
+ ranks with identical arguments. Thus, it should have deterministic
+ behavior to maintain state across workers.
+
+ :param ts: a list of int timesteps.
+ :param losses: a list of float losses, one per timestep.
+ """
+
+
+class LossSecondMomentResampler(LossAwareSampler):
+ def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
+ self.diffusion = diffusion
+ self.history_per_term = history_per_term
+ self.uniform_prob = uniform_prob
+ self._loss_history = np.zeros([diffusion.num_timesteps, history_per_term], dtype=np.float64)
+ self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
+
+ def weights(self):
+ if not self._warmed_up():
+ return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
+ weights = np.sqrt(np.mean(self._loss_history**2, axis=-1))
+ weights /= np.sum(weights)
+ weights *= 1 - self.uniform_prob
+ weights += self.uniform_prob / len(weights)
+ return weights
+
+ def update_with_all_losses(self, ts, losses):
+ for t, loss in zip(ts, losses):
+ if self._loss_counts[t] == self.history_per_term:
+ # Shift out the oldest loss term.
+ self._loss_history[t, :-1] = self._loss_history[t, 1:]
+ self._loss_history[t, -1] = loss
+ else:
+ self._loss_history[t, self._loss_counts[t]] = loss
+ self._loss_counts[t] += 1
+
+ def _warmed_up(self):
+ return (self._loss_counts == self.history_per_term).all()
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/respace.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/respace.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e91eaa395f206c3aedd6cf2f468476721399186
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/diffusion/respace.py
@@ -0,0 +1,150 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2021 OpenAI
+# To view a copy of this license, visit
+# https://github.com/openai/glide-text2im/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import numpy as np
+import torch as th
+
+from .gaussian_diffusion import GaussianDiffusion
+
+
+def space_timesteps(num_timesteps, section_counts):
+ """
+ Create a list of timesteps to use from an original diffusion process,
+ given the number of timesteps we want to take from equally-sized portions
+ of the original process.
+
+ For example, if there's 300 timesteps and the section counts are [10,15,20]
+ then the first 100 timesteps are strided to be 10 timesteps, the second 100
+ are strided to be 15 timesteps, and the final 100 are strided to be 20.
+
+ If the stride is a string starting with "ddim", then the fixed striding
+ from the DDIM paper is used, and only one section is allowed.
+
+ :param num_timesteps: the number of diffusion steps in the original
+ process to divide up.
+ :param section_counts: either a list of numbers, or a string containing
+ comma-separated numbers, indicating the step count
+ per section. As a special case, use "ddimN" where N
+ is a number of steps to use the striding from the
+ DDIM paper.
+ :return: a set of diffusion steps from the original process to use.
+ """
+ if isinstance(section_counts, str):
+ if section_counts.startswith("ddim"):
+ desired_count = int(section_counts[len("ddim") :])
+ for i in range(1, num_timesteps):
+ if len(range(0, num_timesteps, i)) == desired_count:
+ return set(range(0, num_timesteps, i))
+ raise ValueError(f"cannot create exactly {num_timesteps} steps with an integer stride")
+ elif section_counts.startswith("ldm_ddim"):
+ desired_count = int(section_counts[len("ldm_ddim") :])
+ # compared to ddim, we add one to get the final alpha values right (the ones from first scale to data during sampling) # noqa
+ # ref: https://github.com/CompVis/stable-diffusion/blob/d39f5b51a8d607fd855425a0d546b9f871034c3d/ldm/modules/diffusionmodules/util.py#L56 # noqa
+ for i in range(1, num_timesteps):
+ if len(range(0, num_timesteps, i)) == desired_count:
+ return set(range(1, num_timesteps + 1, i))
+ raise ValueError(f"cannot create exactly {num_timesteps} steps with an integer stride")
+ elif section_counts == "fast27":
+ steps = space_timesteps(num_timesteps, "10,10,3,2,2")
+ # Help reduce DDIM artifacts from noisiest timesteps.
+ steps.remove(num_timesteps - 1)
+ steps.add(num_timesteps - 3)
+ return steps
+ section_counts = [int(x) for x in section_counts.split(",")]
+ size_per = num_timesteps // len(section_counts)
+ extra = num_timesteps % len(section_counts)
+ start_idx = 0
+ all_steps = []
+ for i, section_count in enumerate(section_counts):
+ size = size_per + (1 if i < extra else 0)
+ if size < section_count:
+ raise ValueError(f"cannot divide section of {size} steps into {section_count}")
+ if section_count <= 1:
+ frac_stride = 1
+ else:
+ frac_stride = (size - 1) / (section_count - 1)
+ cur_idx = 0.0
+ taken_steps = []
+ for _ in range(section_count):
+ taken_steps.append(start_idx + round(cur_idx))
+ cur_idx += frac_stride
+ all_steps += taken_steps
+ start_idx += size
+ return set(all_steps)
+
+
+class SpacedDiffusion(GaussianDiffusion):
+ """
+ A diffusion process which can skip steps in a base diffusion process.
+
+ :param use_timesteps: a collection (sequence or set) of timesteps from the
+ original diffusion process to retain.
+ :param kwargs: the kwargs to create the base diffusion process.
+ """
+
+ def __init__(self, use_timesteps, **kwargs):
+ self.use_timesteps = set(use_timesteps)
+ self.timestep_map = []
+ self.original_num_steps = len(kwargs["betas"])
+
+ base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa
+ last_alpha_cumprod = 1.0
+ new_betas = []
+ for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+ if i in self.use_timesteps:
+ new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+ last_alpha_cumprod = alpha_cumprod
+ self.timestep_map.append(i)
+ kwargs["betas"] = np.array(new_betas)
+ super().__init__(**kwargs)
+
+ def p_mean_variance(self, model, *args, **kwargs): # pylint: disable=signature-differs
+ return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+ def training_losses(self, model, *args, **kwargs): # pylint: disable=signature-differs
+ return super().training_losses(self._wrap_model(model), *args, **kwargs)
+
+ def condition_mean(self, cond_fn, *args, **kwargs):
+ return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+ def condition_score(self, cond_fn, *args, **kwargs):
+ return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+ def _wrap_model(self, model):
+ if isinstance(model, _WrappedModel):
+ return model
+ return _WrappedModel(
+ model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
+ )
+
+ def _scale_timesteps(self, t):
+ # Scaling is done by the wrapped model.
+ return t
+
+
+class _WrappedModel:
+ def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
+ self.model = model
+ self.timestep_map = timestep_map
+ self.rescale_timesteps = rescale_timesteps
+ self.original_num_steps = original_num_steps
+
+ def __call__(self, x, ts, **kwargs):
+ map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+ new_ts = map_tensor[ts]
+ if self.rescale_timesteps:
+ new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+ return self.model(x, new_ts, **kwargs)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b83bd9877eaf5fc8995ff6d24ff2fec3f885698
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/__init__.py
@@ -0,0 +1,15 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+from .odise import CategoryODISE, CaptionODISE
+
+__all__ = [
+ "CategoryODISE",
+ "CaptionODISE",
+]
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/clip.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..06dfa172d9576323cbd9d5935cd4267afda23347
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/clip.py
@@ -0,0 +1,361 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import logging
+from collections import OrderedDict, namedtuple
+import open_clip
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+from detectron2.utils import comm
+from einops import rearrange
+
+from .helper import ensemble_logits_with_labels
+
+logger = logging.getLogger(__name__)
+
+EmbeddedText = namedtuple("EmbedTextReturn", ["text_embed", "text_encodings", "text_mask"])
+EmbeddedImage = namedtuple("EmbedImageReturn", ["image_embed", "image_encodings"])
+
+
+def build_clip_text_embed(clip_model_name, labels, device="cuda", verbose=True):
+ if isinstance(clip_model_name, str):
+ clip, _, _ = open_clip.create_model_and_transforms(
+ model_name=clip_model_name,
+ pretrained="openai",
+ device=device if torch.cuda.is_available() else "cpu",
+ )
+ if verbose:
+ logger.info(f"Loading CLIP model {clip_model_name}")
+ else:
+ clip = clip_model_name
+ if verbose:
+ logger.info("Using provided CLIP model")
+ clip_device = next(clip.parameters()).device
+ if isinstance(labels, str):
+ labels = [labels]
+ if isinstance(labels[0], str):
+ labels = [[t] for t in labels]
+
+ labels = tuple(tuple(t) for t in labels)
+
+ # check if is ensemble
+ assert isinstance(
+ labels[0], (list, tuple)
+ ), f"labels should be a list of list of str, but got {type(labels[0])}"
+
+ # unravel list of list of str
+ flatten_text = [t for sublist in labels for t in sublist]
+
+ text_embed_list = []
+
+ local_batch_size = 256
+
+ for i in range(0, len(flatten_text), local_batch_size):
+ cur_text = flatten_text[i : i + local_batch_size]
+ text_embed = clip.encode_text(open_clip.tokenize(cur_text).to(clip_device))
+ text_embed_list.extend(list(text_embed))
+
+ out_text_embed = torch.stack(text_embed_list)
+ if verbose:
+ logger.info(
+ f"Built text_embed of shape {out_text_embed.shape} for {len(labels)} labels: {labels}" # noqa
+ )
+
+ return out_text_embed
+
+
+# Modified from https://github.com/lucidrains/DALLE2-pytorch/blob/350a3d60456693a8ecdccc820e97dbb6b0c81866/dalle2_pytorch/dalle2_pytorch.py#L238 # noqa
+class ClipAdapter(nn.Module):
+ def __init__(self, name="ViT-B-32", normalize=True):
+
+ # download on local rank 0 first
+ if comm.get_local_rank() == 0:
+ open_clip.create_model_and_transforms(name, pretrained="openai")
+ comm.synchronize()
+
+ # checked, the same as openai original CLIP
+ openai_clip, _, preprocess = open_clip.create_model_and_transforms(
+ name, pretrained="openai"
+ )
+ super().__init__()
+ self.clip = openai_clip
+
+ # self.clip_normalize = preprocess.transforms[-1]
+ # the first two are Resize and Crop, the last one is normalization
+ self.clip_preprocess = T.Compose([*preprocess.transforms[:2], preprocess.transforms[-1]])
+ self._freeze()
+ self.name = name
+ self.normalize = normalize
+
+ def extra_repr(self) -> str:
+ return f"name={self.name}, normalize={self.normalize}"
+
+ def _freeze(self):
+ self.clip.eval()
+ for p in self.clip.parameters():
+ p.requires_grad = False
+
+ def ignored_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ destination._metadata = OrderedDict()
+ for name, module in self._modules.items():
+ if module is not None and hasattr(module, "ignored_state_dict"):
+ module.ignored_state_dict(destination, prefix + name + ".")
+ return super().state_dict(destination=destination, prefix=prefix)
+
+ @property
+ def device(self):
+ return next(self.parameters()).device
+
+ # don't save clip model
+ def state_dict(self, destination=None, prefix="", keep_vars=False):
+ return OrderedDict()
+
+ def train(self, mode: bool = True):
+ super().train(mode)
+ self._freeze()
+ return self
+
+ @property
+ def dim_latent(self):
+ return self.clip.text_projection.shape[-1]
+
+ @property
+ def image_size(self):
+ if isinstance(self.clip.visual.image_size, tuple):
+ return self.clip.visual.image_size
+ else:
+ return (self.clip.visual.image_size, self.clip.visual.image_size)
+
+ @property
+ def image_channels(self):
+ return 3
+
+ @property
+ def max_text_len(self):
+ return self.clip.context_length
+
+ def _encode_text(self, text):
+ x = self.clip.token_embedding(text) # [batch_size, n_ctx, d_model]
+ x = x + self.clip.positional_embedding
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.clip.transformer(x, attn_mask=self.clip.attn_mask)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.clip.ln_final(x)
+ text_encodings = x
+
+ # x.shape = [batch_size, n_ctx, transformer.width]
+ # take features from the eot embedding (eot_token is the highest number in each sequence)
+ text_embed = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.clip.text_projection
+
+ return text_embed, text_encodings
+
+ @torch.no_grad()
+ def embed_text(self, captions):
+ text = open_clip.tokenize(captions).to(next(self.parameters()).device)
+ text = text[..., : self.max_text_len]
+ text_mask = (text != 0).long()
+
+ text_embed, text_encodings = self._encode_text(text)
+ if self.normalize:
+ return EmbeddedText(
+ F.normalize(text_embed.float(), dim=-1), text_encodings.float(), text_mask
+ )
+ else:
+ return EmbeddedText(text_embed.float(), text_encodings.float(), text_mask)
+
+ def _encode_image(self, image):
+ if hasattr(self.clip.visual, "positional_embedding"):
+ x = self.clip.visual.conv1(image) # shape = [*, width, grid, grid]
+ x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
+ x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
+ x = torch.cat(
+ [
+ self.clip.visual.class_embedding.to(x.dtype)
+ + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+ x,
+ ],
+ dim=1,
+ ) # shape = [*, grid ** 2 + 1, width]
+ x = x + self.clip.visual.positional_embedding.to(x.dtype)
+ x = self.clip.visual.ln_pre(x)
+
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.clip.visual.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+
+ # [batch_size, num_patches+1, transformer.width]
+ x = self.clip.visual.ln_post(x)
+ batch_size, num_tokens, _ = x.shape
+
+ if self.clip.visual.proj is not None:
+ x = rearrange(x, "b n c -> (b n) c", b=batch_size, n=num_tokens)
+ x = x @ self.clip.visual.proj
+ x = rearrange(x, "(b n) c -> b n c", b=batch_size, n=num_tokens)
+
+ image_embed = x[:, 0, :]
+ image_encodings = x[:, 1:, :]
+
+ width = height = int(image_encodings.shape[1] ** 0.5)
+
+ image_encodings = rearrange(image_encodings, "b (h w) c -> b c h w", h=height, w=width)
+
+ image_encodings = F.interpolate(
+ image_encodings,
+ size=(image.shape[2] // 16, image.shape[3] // 16),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ return image_embed, image_encodings
+ else:
+ image_embed = self.clip.encode_image(image)
+ return image_embed, None
+
+ @torch.no_grad()
+ def embed_image(self, image):
+ image_embed, image_encodings = self._encode_image(self.clip_preprocess(image))
+ if self.normalize:
+ return EmbeddedImage(F.normalize(image_embed.float(), dim=-1), image_encodings)
+ else:
+ return EmbeddedImage(image_embed.float(), image_encodings)
+
+ @torch.no_grad()
+ def build_text_embed(self, labels):
+ return build_clip_text_embed(self.clip, labels)
+
+
+# Thanks Zheng Ding for sharing the nice implementation, we modified based on that.
+class MaskCLIP(ClipAdapter):
+ """
+ Ref: https://arxiv.org/abs/2208.08984
+ """
+
+ def __init__(self, name="ViT-L-14-336"):
+ super().__init__(name=name, normalize=False)
+
+ @property
+ def logit_scale(self):
+ logit_scale = torch.clamp(self.clip.logit_scale.exp(), max=100)
+ return logit_scale
+
+ def _mask_clip_forward(self, x: torch.Tensor, attn_mask: torch.Tensor, num_mask_tokens: int):
+ x = self.clip.visual.conv1(x) # shape = [*, width, grid, grid]
+ x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
+ x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
+ x = torch.cat(
+ [
+ self.clip.visual.class_embedding.to(x.dtype)
+ + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
+ x,
+ ],
+ dim=1,
+ ) # shape = [*, grid ** 2 + 1, width]
+ x = x + self.clip.visual.positional_embedding.to(x.dtype)
+ x = self.clip.visual.ln_pre(x)
+
+ x = x.permute(1, 0, 2) # NLD -> LND
+ cls_embed = x[0:1]
+ cls_embed = cls_embed.expand(num_mask_tokens, -1, -1)
+ x = torch.cat([cls_embed, x], dim=0)
+ x = self.clip.visual.transformer(x, attn_mask)
+ x = x.permute(1, 0, 2) # LND -> NLD
+
+ # [N, L, D]
+ x = self.clip.visual.ln_post(x[:, :num_mask_tokens, :])
+
+ if self.clip.visual.proj is not None:
+ x = torch.einsum("nld,dc->nlc", x, self.clip.visual.proj)
+
+ return x
+
+ def encode_image_with_mask(self, image, mask):
+ assert hasattr(self.clip.visual, "positional_embedding")
+ image = self.clip_preprocess(image)
+ batch_size = image.shape[0]
+ assert batch_size == mask.shape[0]
+ num_queries = mask.shape[1]
+
+ # [B, Q, H, W], Q is the number of quries, H and W are the height and width of the image
+ mask = mask.sigmoid()
+ # [B, Q, H//P, W//P]
+ patch_mask = F.max_pool2d(
+ mask,
+ kernel_size=self.clip.visual.conv1.kernel_size,
+ stride=self.clip.visual.conv1.stride,
+ )
+ # 0 means not masked out, 1 mean masked out
+ # so if 1 pixel > 0.5, it is not masked out
+ # aka if all pixels (max pixel) < 0.5, it is masked out
+ mask_token_attn_mask = patch_mask < 0.5
+ # [B, Q, H//P x W//P]
+ mask_token_attn_mask = mask_token_attn_mask.reshape(batch_size, num_queries, -1)
+
+ num_mask_token = num_queries
+ num_image_cls_token = self.clip.visual.positional_embedding.shape[0]
+ num_image_token = num_image_cls_token - 1
+ num_all_token = num_mask_token + num_image_cls_token
+
+ # we start with no mask out
+ attn_mask = torch.zeros(
+ (num_all_token, num_all_token), dtype=torch.bool, device=image.device
+ )
+
+ # mask+cls+image token to mask token attention is masked out
+ attn_mask[:, :num_mask_token] = True
+
+ attn_mask = attn_mask.unsqueeze(0).repeat_interleave(batch_size, dim=0)
+ attn_mask[:, :num_mask_token, -num_image_token:] = mask_token_attn_mask
+ num_heads = self.clip.visual.conv1.out_channels // 64 # head width 64
+ attn_mask = attn_mask.unsqueeze(1).expand(-1, num_heads, -1, -1)
+ attn_mask = attn_mask.reshape(batch_size * num_heads, num_all_token, num_all_token)
+
+ return self._mask_clip_forward(image, attn_mask, num_mask_token)
+
+ def get_mask_embed(self, image, mask):
+
+ image = F.interpolate(
+ image,
+ size=self.image_size,
+ mode="bilinear",
+ align_corners=False,
+ )
+ mask = F.interpolate(mask, size=image.shape[-2:], mode="bilinear", align_corners=False)
+
+ # [B, Q, C]
+ mask_embed = self.encode_image_with_mask(image, mask)
+
+ return mask_embed
+
+ def pred_logits(self, mask_embed, text_embed, labels):
+ logit_per_mask = (
+ torch.einsum(
+ "bqc,nc->bqn", F.normalize(mask_embed, dim=-1), F.normalize(text_embed, dim=-1)
+ )
+ * self.logit_scale
+ )
+
+ logit_per_mask = ensemble_logits_with_labels(logit_per_mask, labels)
+
+ return logit_per_mask
+
+ def forward(self, image, mask, text_embed, labels):
+
+ mask_embed = self.get_mask_embed(image, mask)
+ output = {"mask_embed": mask_embed}
+
+ if text_embed is not None and labels is not None:
+
+ output["mask_pred_open_logits"] = self.pred_logits(mask_embed, text_embed, labels)
+
+ return output
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/helper.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b10d839b028f6b5dabf80f3d9aa263639713f85
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/helper.py
@@ -0,0 +1,114 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+from typing import List
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LayerNorm2d(nn.LayerNorm):
+ """LayerNorm for channels of '2D' spatial NCHW tensors"""
+
+ def __init__(self, num_channels, eps=1e-6, affine=True):
+ super().__init__(num_channels, eps=eps, elementwise_affine=affine)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return F.layer_norm(
+ x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps
+ ).permute(0, 3, 1, 2)
+
+
+class FeatureExtractor(nn.Module, metaclass=ABCMeta):
+ def __init__(self):
+ super().__init__()
+
+ def ignored_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ destination._metadata = OrderedDict()
+ for name, module in self._modules.items():
+ if module is not None and hasattr(module, "ignored_state_dict"):
+ module.ignored_state_dict(destination, prefix + name + ".")
+ return super().state_dict(destination=destination, prefix=prefix)
+
+ # don't save DDPM model
+ def state_dict(self, destination=None, prefix="", keep_vars=False):
+ return OrderedDict()
+
+ def train(self, mode: bool = True):
+ super().train(mode)
+ self._freeze()
+ return self
+
+ def _freeze(self):
+ super().train(mode=False)
+ for p in self.parameters():
+ p.requires_grad = False
+
+ @property
+ @abstractmethod
+ def feature_dims(self) -> List[int]:
+ pass
+
+ @property
+ @abstractmethod
+ def feature_size(self) -> int:
+ pass
+
+ @property
+ @abstractmethod
+ def num_groups(self) -> int:
+ pass
+
+ @property
+ @abstractmethod
+ def grouped_indices(self, features):
+ pass
+
+
+def ensemble_logits_with_labels(
+ logits: torch.Tensor, labels: List[List[str]], ensemble_method: str = "max"
+):
+ """Ensemble logits.
+ Args:
+ logits (torch.Tensor): logits of each model. The last dim is probability.
+ labels (list[list[str]]): list of list of labels.
+ ensemble_method (str): ensemble method. Options are 'mean' and 'max'.
+ Returns:
+ torch.Tensor: logits of ensemble model.
+ """
+ len_list = [len(l) for l in labels]
+ assert logits.shape[-1] == sum(len_list), f"{logits.shape[-1]} != {sum(len_list)}"
+ assert ensemble_method in ["mean", "max"]
+ ensemble_logits = torch.zeros(
+ *logits.shape[:-1], len(labels), dtype=logits.dtype, device=logits.device
+ )
+ if ensemble_method == "max":
+ for i in range(len(labels)):
+ ensemble_logits[..., i] = (
+ logits[..., sum(len_list[:i]) : sum(len_list[: i + 1])].max(dim=-1).values
+ )
+ elif ensemble_method == "mean":
+ for i in range(len(labels)):
+ ensemble_logits[..., i] = logits[..., sum(len_list[:i]) : sum(len_list[: i + 1])].mean(
+ dim=-1
+ )
+ else:
+ raise ValueError(f"Unknown ensemble method: {ensemble_method}")
+
+ return ensemble_logits
+
+
+# Ref:https://stackoverflow.com/questions/27049998/convert-a-mixed-nested-list-to-a-nested-tuple
+def to_tuple(lst):
+ return tuple(to_tuple(i) if isinstance(i, list) else i for i in lst)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/ldm.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/ldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..97c0307989ca5e4c860e8076c46a583f8c8d9e9b
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/ldm.py
@@ -0,0 +1,1028 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import logging
+from typing import List, Optional, Tuple
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+from ldm.models.diffusion.ddpm import LatentDiffusion as _LatentDiffusion
+from ldm.modules.diffusionmodules.openaimodel import timestep_embedding
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+from ldm.util import instantiate_from_config
+from omegaconf import OmegaConf
+from timm.models.layers import trunc_normal_
+
+from odise.checkpoint.odise_checkpointer import LdmCheckpointer
+from odise.modeling.meta_arch.clip import ClipAdapter
+from odise.utils.file_io import PathManager
+
+from ..diffusion import GaussianDiffusion, create_gaussian_diffusion
+from ..preprocess import batched_input_to_device
+from .helper import FeatureExtractor
+
+from inspect import isfunction
+import abc
+from einops import rearrange, repeat
+from torch import nn, einsum, fft
+
+def Fourier_filter(x, threshold, scale):
+ # FFT
+ x_freq = fft.fftn(x, dim=(-2, -1))
+ x_freq = fft.fftshift(x_freq, dim=(-2, -1))
+
+ B, C, H, W = x_freq.shape
+ # mask = torch.ones((B, C, H, W)).cuda()
+ mask = torch.ones((B, C, H, W), device=x_freq.device)
+
+
+ crow, ccol = H // 2, W //2
+ mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
+ # mask.to(x_freq.device)
+
+ # print("mask device:", mask.device)
+ # print("x_freq device:", x_freq.device)
+ # print("scale device:", scale.device)
+
+ x_freq = x_freq * mask
+
+ # IFFT
+ x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
+ x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
+
+ return x_filtered
+
+def exists(val):
+ return val is not None
+
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+def register_attention_control(model, controller):
+ def ca_forward(self, place_in_unet):
+ def forward(x, context=None, mask=None):
+ h = self.heads
+
+ q = self.to_q(x)
+ is_cross = context is not None
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+ if exists(mask):
+ mask = rearrange(mask, 'b ... -> b (...)')
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ sim.masked_fill_(~mask, max_neg_value)
+
+ # attention, what we cannot get enough of
+ attn = sim.softmax(dim=-1)
+
+ attn2 = rearrange(attn, '(b h) k c -> h b k c', h=h).mean(0)
+ controller(attn2, is_cross, place_in_unet)
+
+ out = einsum('b i j, b j d -> b i d', attn, v)
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+ return forward
+
+ class DummyController:
+ def __call__(self, *args):
+ return args[0]
+
+ def __init__(self):
+ self.num_att_layers = 0
+
+ if controller is None:
+ controller = DummyController()
+
+ def register_recr(net_, count, place_in_unet):
+ if net_.__class__.__name__ == 'CrossAttention':
+ net_.forward = ca_forward(net_, place_in_unet)
+ return count + 1
+ elif hasattr(net_, 'children'):
+ for net__ in net_.children():
+ count = register_recr(net__, count, place_in_unet)
+ return count
+
+ cross_att_count = 0
+ sub_nets = model.diffusion_model.named_children()
+
+ for net in sub_nets:
+ if "input_blocks" in net[0]:
+ cross_att_count += register_recr(net[1], 0, "down")
+ elif "output_blocks" in net[0]:
+ cross_att_count += register_recr(net[1], 0, "up")
+ elif "middle_block" in net[0]:
+ cross_att_count += register_recr(net[1], 0, "mid")
+
+ controller.num_att_layers = cross_att_count
+
+
+class AttentionControl(abc.ABC):
+
+ def step_callback(self, x_t):
+ return x_t
+
+ def between_steps(self):
+ return
+
+ @property
+ def num_uncond_att_layers(self):
+ return 0
+
+ @abc.abstractmethod
+ def forward (self, attn, is_cross: bool, place_in_unet: str):
+ raise NotImplementedError
+
+ def __call__(self, attn, is_cross: bool, place_in_unet: str):
+ attn = self.forward(attn, is_cross, place_in_unet)
+ return attn
+
+ def reset(self):
+ self.cur_step = 0
+ self.cur_att_layer = 0
+
+ def __init__(self):
+ self.cur_step = 0
+ self.num_att_layers = -1
+ self.cur_att_layer = 0
+
+
+class AttentionStore(AttentionControl):
+ @staticmethod
+ def get_empty_store():
+ return {"down_cross": [], "mid_cross": [], "up_cross": [],
+ "down_self": [], "mid_self": [], "up_self": []}
+
+ def forward(self, attn, is_cross: bool, place_in_unet: str):
+ key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
+ if attn.shape[1] <= (self.max_size) ** 2: # avoid memory overhead
+ self.step_store[key].append(attn)
+ return attn
+
+ def between_steps(self):
+ if len(self.attention_store) == 0:
+ self.attention_store = self.step_store
+ else:
+ for key in self.attention_store:
+ for i in range(len(self.attention_store[key])):
+ self.attention_store[key][i] += self.step_store[key][i]
+ self.step_store = self.get_empty_store()
+
+ def get_average_attention(self):
+ average_attention = {key: [item for item in self.step_store[key]] for key in self.step_store}
+ return average_attention
+
+ def reset(self):
+ super(AttentionStore, self).reset()
+ self.step_store = self.get_empty_store()
+ self.attention_store = {}
+
+ def __init__(self, base_size=64, max_size=None):
+ super(AttentionStore, self).__init__()
+ self.step_store = self.get_empty_store()
+ self.attention_store = {}
+ self.base_size = base_size
+ if max_size is None:
+ self.max_size = self.base_size // 2
+ else:
+ self.max_size = max_size
+
+def build_ldm_from_cfg(cfg_name) -> _LatentDiffusion:
+
+ if cfg_name.startswith("v1"):
+ url_prefix = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/" # noqa
+ elif cfg_name.startswith("v2"):
+ url_prefix = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/" # noqa
+
+ logging.getLogger(__name__).info(f"Loading LDM config from {cfg_name}")
+ config = OmegaConf.load(PathManager.open(url_prefix + cfg_name))
+ return instantiate_from_config(config.model)
+
+
+class DisableLogger:
+ "Disable HF logger"
+
+ def __enter__(self):
+ logging.disable(logging.CRITICAL)
+
+ def __exit__(self, exit_type, exit_value, exit_traceback):
+ logging.disable(logging.NOTSET)
+
+
+def add_device_property(cls):
+ class TempClass(cls):
+ pass
+
+ TempClass.device = property(lambda m: next(m.parameters()).device)
+
+ return TempClass
+
+
+class LatentDiffusion(nn.Module):
+
+ LDM_CONFIGS = {
+ "sd://v1-3": ("v1-inference.yaml", (512, 512), (64, 64)),
+ "sd://v1-4": ("v1-inference.yaml", (512, 512), (64, 64)),
+ "sd://v1-5": ("v1-inference.yaml", (512, 512), (64, 64)),
+ "sd://v2-0-base": ("v2-inference.yaml", (512, 512), (64, 64)),
+ "sd://v2-0-v": ("v2-inference.yaml", (768, 768), (96, 96)),
+ "sd://v2-1-base": ("v2-inference.yaml", (512, 512), (64, 64)),
+ "sd://v2-1-v": ("v2-inference.yaml", (768, 768), (96, 96)),
+ }
+
+ def __init__(
+ self,
+ diffusion: Optional[GaussianDiffusion] = None,
+ guidance_scale: float = 7.5,
+ pixel_mean: Tuple[float] = (0.5, 0.5, 0.5),
+ pixel_std: Tuple[float] = (0.5, 0.5, 0.5),
+ init_checkpoint="sd://v1-3",
+ ):
+
+ super().__init__()
+ self._logger = logging.getLogger(__name__)
+
+ ldm_cfg, image_size, latent_image_size = self.LDM_CONFIGS[init_checkpoint]
+
+ with DisableLogger():
+ self.ldm: _LatentDiffusion = build_ldm_from_cfg(ldm_cfg)
+ # hack to replace device property
+ self.ldm.cond_stage_model.__class__ = add_device_property(
+ self.ldm.cond_stage_model.__class__
+ )
+
+ self.init_checkpoint = init_checkpoint
+ self.load_pretrain()
+
+ self.image_size = image_size
+ self.latent_image_size = latent_image_size
+
+ self.latent_dim = self.ldm.channels
+ assert self.latent_dim == self.ldm.first_stage_model.embed_dim
+ if diffusion is None:
+ diffusion = create_gaussian_diffusion(
+ steps=1000,
+ learn_sigma=False,
+ noise_schedule="ldm_linear",
+ # timestep_respacing="ldm_ddim50",
+ )
+ self.diffusion = diffusion
+
+ self.guidance_scale = guidance_scale
+
+ self.register_buffer("uncond_inputs", self.embed_text([""]))
+
+ self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False)
+ self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False)
+
+ def load_pretrain(self):
+ LdmCheckpointer(self.ldm).load(self.init_checkpoint)
+
+ @property
+ def device(self):
+ return self.pixel_mean.device
+
+ def forward(self, batched_inputs):
+
+ batched_inputs = batched_input_to_device(batched_inputs, next(self.parameters()).device)
+
+ if self.training:
+ return self.forward_train(batched_inputs)
+ else:
+ return self.forward_test(batched_inputs)
+
+ def forward_train(self, batched_inputs):
+ raise NotImplementedError
+
+ def apply_model_with_guidence(self, x_noisy, t, cond):
+ # reference: https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+ half = x_noisy[: len(x_noisy) // 2]
+ combined = torch.cat([half, half], dim=0)
+ model_out = self.ldm.apply_model(combined, t, cond)
+ eps, rest = model_out[:, : self.latent_dim], model_out[:, self.latent_dim :]
+ cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+ half_eps = uncond_eps + self.guidance_scale * (cond_eps - uncond_eps)
+ eps = torch.cat([half_eps, half_eps], dim=0)
+ return torch.cat([eps, rest], dim=1)
+
+ def embed_text(self, text):
+ return self.ldm.get_learned_conditioning(text)
+
+ @property
+ def encoder(self):
+ return self.ldm.first_stage_model.encoder
+
+ @property
+ def unet(self):
+ return self.ldm.model.diffusion_model
+
+ @property
+ def decoder(self):
+ return self.ldm.first_stage_model.decoder
+
+ @torch.no_grad()
+ def encode_to_latent(self, input_image):
+ encoder_posterior = self.ldm.encode_first_stage(input_image)
+ # NOTE: make encode process deterministic, we use mean instead of sample from posterior
+ latent_image = self.ldm.get_first_stage_encoding(encoder_posterior.mean)
+
+ return latent_image
+
+ @torch.no_grad()
+ def decode_from_latent(self, latent_image):
+ return self.ldm.decode_first_stage(latent_image)
+
+ def forward_test(self, batched_inputs):
+
+ caption = batched_inputs["caption"]
+ batch_size = len(caption)
+
+ cond_inputs = self.embed_text(caption)
+
+ if self.guidance_scale != 1.0:
+ uncond_inputs = self.uncond_inputs.expand_as(cond_inputs)
+ else:
+ uncond_inputs = None
+
+ # For debugging checking inference result equivalence
+ # rng = torch.Generator(device=self.device).manual_seed(42)
+ # noise = torch.randn(
+ # (batch_size, self.latent_dim, *self.latent_image_size),
+ # device=self.device,
+ # generator=rng,
+ # )
+ # sampler = DDIMSampler(self.ldm)
+ # latent_samples, _ = sampler.sample(
+ # S=50,
+ # conditioning=cond_inputs,
+ # batch_size=batch_size,
+ # shape=(self.latent_dim, *self.latent_image_size),
+ # verbose=False,
+ # unconditional_guidance_scale=self.guidance_scale,
+ # unconditional_conditioning=uncond_inputs,
+ # eta=0,
+ # x_T=noise,
+ # )
+
+ if uncond_inputs is None:
+ latent_samples = self.diffusion.ddim_sample_loop(
+ model=self.ldm.apply_model,
+ shape=(batch_size, self.latent_dim, *self.latent_image_size),
+ device=self.device,
+ clip_denoised=False,
+ model_kwargs={"cond": cond_inputs},
+ )
+ else:
+ # noise = torch.cat([noise] * 2, dim=0)
+ latent_samples = self.diffusion.ddim_sample_loop(
+ model=self.apply_model_with_guidence,
+ shape=(batch_size * 2, self.latent_dim, *self.latent_image_size),
+ # noise=noise,
+ device=self.device,
+ clip_denoised=False, # no clip_denoised for LDM inference
+ model_kwargs={"cond": torch.cat([cond_inputs, uncond_inputs], dim=0)},
+ )[:batch_size]
+
+ decoded_samples = self.ldm.decode_first_stage(latent_samples)
+ out_samples = decoded_samples * self.pixel_std + self.pixel_mean
+ out_samples = out_samples.clamp(0.0, 1.0)
+
+ return out_samples
+
+
+class LdmExtractor(FeatureExtractor):
+ def __init__(
+ self,
+ ldm: Optional[LatentDiffusion] = None,
+ encoder_block_indices: Tuple[int, ...] = (5, 7),
+ unet_block_indices: Tuple[int, ...] = (2, 5, 8, 11),
+ decoder_block_indices: Tuple[int, ...] = (2, 5),
+ steps=(0,),
+ share_noise: bool = True,
+ enable_resize: bool = False,
+ init_checkpoint = "sd://v1-3",
+ decoder_only=False,
+ encoder_only=False,
+ resblock_only=False,
+ ):
+
+ super().__init__()
+ self.encoder_block_indices = encoder_block_indices
+ self.unet_block_indices = unet_block_indices
+ self.decoder_block_indices = decoder_block_indices
+ self.decoder_only = decoder_only
+ self.encoder_only = encoder_only
+ self.resblock_only = resblock_only
+
+ self.steps = steps
+ self.b1 = 1.2
+ self.b2 = 1.4
+ self.s1 = 0.9
+ self.s2 = 0.2
+ self.freeu = True
+
+ if ldm is not None:
+ self.ldm = ldm
+ else:
+ self.ldm = LatentDiffusion(init_checkpoint=init_checkpoint)
+ if enable_resize:
+ self.image_preprocess = T.Resize(
+ size=self.ldm.image_size, interpolation=T.InterpolationMode.BICUBIC
+ )
+ else:
+ self.image_preprocess = None
+
+ # self.hooks = []
+
+ # self._feature_dims, self._feature_strides = self.get_dim_stride()
+
+ if share_noise:
+ # use seed 42 for now
+ rng = torch.Generator().manual_seed(42)
+ self.register_buffer(
+ "shared_noise",
+ torch.randn(1, self.ldm.latent_dim, *self.ldm.latent_image_size, generator=rng),
+ )
+ else:
+ self.shared_noise = None
+
+ self.reset_dim_stride()
+ self._freeze()
+
+ def reset_dim_stride(self):
+ """Besides return dim and stride, this function also reset `self.encoder_blocks`,
+ `self.unet_blocks`, `self.decoder_blocks` for feature extractor
+
+ Returns:
+ feature_dims: list of feature dimensions
+ feature_strides: list of feature strides
+ """
+
+ # Encoder
+ all_encoder_blocks = []
+ for i_level in range(self.ldm.encoder.num_resolutions):
+ for i_block in range(self.ldm.encoder.num_res_blocks):
+ all_encoder_blocks.append(self.ldm.encoder.down[i_level].block[i_block])
+ # encoder_blocks.extend([self.ldm.encoder.mid.block_1, self.ldm.encoder.mid.block_2])
+
+ encoder_dims = []
+ encoder_strides = []
+ encoder_blocks = []
+ for idx in self.encoder_block_indices:
+ encoder_dims.append(all_encoder_blocks[idx].in_channels)
+ group_size = 2
+ encoder_strides.append(2 ** ((idx + group_size) // group_size - 1))
+ encoder_blocks.append(all_encoder_blocks[idx])
+
+ # UNet
+ assert set(self.unet_block_indices).issubset(set(range(len(self.ldm.unet.output_blocks))))
+ unet_dims = []
+ unet_strides = []
+ unet_blocks = []
+ for idx, block in enumerate(self.ldm.unet.output_blocks):
+ if idx in self.unet_block_indices:
+ # The first block of TimestepEmbedSequential
+ if self.decoder_only:
+ if idx == 6 or idx == 9:
+ unet_dims.append(block[0].channels-block[0].out_channels)
+ else:
+ unet_dims.append(block[0].out_channels)
+ elif self.encoder_only:
+ if idx == 6 or idx == 9:
+ unet_dims.append(block[0].out_channels)
+ else:
+ unet_dims.append(block[0].channels-block[0].out_channels)
+ elif self.resblock_only:
+ unet_dims.append(block[0].out_channels)
+ else:
+ unet_dims.append(block[0].channels)
+
+ group_size = 3
+ unet_strides.append(64 // (2 ** ((idx + group_size) // group_size - 1)))
+ unet_blocks.append(block)
+ # Decoder
+ all_decoder_blocks = []
+ # decoder_blocks.extend(self.ldm.decoder.mid.block_1, self.ldm.decoder.mid.block_2)
+ for i_level in reversed(range(self.ldm.decoder.num_resolutions)):
+ for i_block in range(self.ldm.decoder.num_res_blocks + 1):
+ all_decoder_blocks.append(self.ldm.decoder.up[i_level].block[i_block])
+
+ decoder_dims = []
+ decoder_strides = []
+ decoder_blocks = []
+ for idx in self.decoder_block_indices:
+ decoder_dims.append(all_decoder_blocks[idx].in_channels)
+ group_size = 3
+ decoder_strides.append(8 // (2 ** ((idx + group_size) // group_size - 1)))
+ decoder_blocks.append(all_decoder_blocks[idx])
+
+ feature_dims = encoder_dims + unet_dims * len(self.steps) + decoder_dims
+ feature_strides = encoder_strides + unet_strides * len(self.steps) + decoder_strides
+
+ self.encoder_blocks = encoder_blocks
+ self.unet_blocks = unet_blocks
+ self.decoder_blocks = decoder_blocks
+
+ return feature_dims, feature_strides
+
+ @property
+ def feature_size(self):
+ return self.ldm.image_size
+
+ @property
+ def feature_dims(self):
+ return self.reset_dim_stride()[0]
+
+ @property
+ def feature_strides(self):
+ return self.reset_dim_stride()[1]
+
+ @property
+ def num_groups(self) -> int:
+
+ num_groups = len(self.encoder_block_indices)
+ num_groups += len(self.unet_block_indices)
+ num_groups += len(self.decoder_block_indices)
+ return num_groups
+
+ @property
+ def grouped_indices(self):
+
+ ret = []
+
+ for i in range(len(self.encoder_block_indices)):
+ ret.append([i])
+
+ offset = len(self.encoder_block_indices)
+
+ for i in range(len(self.unet_block_indices)):
+ cur_indices = []
+ for t in range(len(self.steps)):
+ cur_indices.append(i + t * len(self.unet_block_indices) + offset)
+ ret.append(cur_indices)
+
+ offset += len(self.steps) * len(self.unet_block_indices)
+
+ for i in range(len(self.decoder_block_indices)):
+ ret.append([i + offset])
+ return ret
+
+ @property
+ def pixel_mean(self):
+ return self.ldm.pixel_mean
+
+ @property
+ def pixel_std(self):
+ return self.ldm.pixel_std
+
+ @property
+ def device(self):
+ return self.ldm.device
+
+ @torch.no_grad()
+ def build_text_embed(self, text: List[List[str]], batch_size=64, flatten=True):
+ if isinstance(text, str):
+ text = [text]
+ if isinstance(text[0], str):
+ text = [[t] for t in text]
+
+ # check if is ensemble
+ assert isinstance(text[0], list)
+
+ # unravel list of list of str
+ flatten_text = [t for sublist in text for t in sublist]
+
+ text_embed_list = []
+
+ for i in range(0, len(flatten_text), batch_size):
+ cur_text = flatten_text[i : i + batch_size]
+ text_embed = self.ldm.embed_text(cur_text)
+ text_embed_list.append(text_embed)
+
+ return torch.concat(text_embed_list, dim=0)
+
+ def encoder_forward(self, x):
+ encoder = self.ldm.encoder
+ ret_features = []
+
+ # timestep embedding
+ temb = None
+
+ # downsampling
+ hs = [encoder.conv_in(x)]
+ for i_level in range(encoder.num_resolutions):
+ for i_block in range(encoder.num_res_blocks):
+
+ # add return features
+ if encoder.down[i_level].block[i_block] in self.encoder_blocks:
+ ret_features.append(hs[-1].contiguous())
+
+ h = encoder.down[i_level].block[i_block](hs[-1], temb)
+ if len(encoder.down[i_level].attn) > 0:
+ h = encoder.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != encoder.num_resolutions - 1:
+ hs.append(encoder.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = encoder.mid.block_1(h, temb)
+ h = encoder.mid.attn_1(h)
+ h = encoder.mid.block_2(h, temb)
+
+ # end
+ h = encoder.norm_out(h)
+ h = h * torch.sigmoid(h)
+ h = encoder.conv_out(h)
+ return h, ret_features
+
+ def encode_to_latent(self, image: torch.Tensor):
+ h, ret_features = self.encoder_forward(image)
+ moments = self.ldm.ldm.first_stage_model.quant_conv(h)
+ posterior = DiagonalGaussianDistribution(moments)
+
+ # NOTE: make encode process deterministic, we use mean instead of sample from posterior
+ latent_image = self.ldm.ldm.scale_factor * posterior.mean
+
+ return latent_image, ret_features
+
+ def unet_forward(self, x, timesteps, context, cond_emb=None):
+ unet = self.ldm.unet
+ ret_features = []
+
+ hs = []
+ t_emb = timestep_embedding(timesteps, unet.model_channels, repeat_only=False)
+ emb = unet.time_embed(t_emb)
+ if cond_emb is not None:
+ emb += cond_emb
+
+ # h = x.type(self.dtype)
+ h = x
+
+ if self.decoder_only:
+ for module in unet.input_blocks: # down sample
+ h = module(h, emb, context)
+ hs.append(h)
+ h = unet.middle_block(h, emb, context) # middle
+ for module in unet.output_blocks: # up sample
+ if module in self.unet_blocks:
+ ret_features.append(h.contiguous())
+ h_out = hs.pop()
+ if self.freeu:
+ # --------------- FreeU code -----------------------
+ # Only operate on the first two stages
+ if h.shape[1] == 1280:
+ h[:,:640] = h[:,:640] * self.b1
+ h_out = Fourier_filter(h_out, threshold=1, scale=self.s1)
+ if h.shape[1] == 640:
+ h[:,:320] = h[:,:320] * self.b2
+ h_out = Fourier_filter(h_out, threshold=1, scale=self.s2)
+ # ---------------------------------------------------------
+ h = torch.cat([h, h_out], dim=1)
+ h = module(h, emb, context)
+
+ elif self.encoder_only:
+ for module in unet.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = unet.middle_block(h, emb, context)
+ for module in unet.output_blocks:
+ h_out = hs.pop()
+ if module in self.unet_blocks:
+ ret_features.append(h_out.contiguous())
+ h = torch.cat([h, h_out], dim=1)
+ h = module(h, emb, context)
+
+ elif self.resblock_only:
+ for module in unet.input_blocks: # down sample
+ h = module(h, emb, context)
+ hs.append(h)
+ h = unet.middle_block(h, emb, context) # middle
+ for module in unet.output_blocks: # up sample
+ h = torch.cat([h, hs.pop()], dim=1)
+ if module in self.unet_blocks:
+ with torch.no_grad():
+ h_res = module[0](h, emb)-module[0].skip_connection(h)
+ ret_features.append(h_res.contiguous())
+ h = module(h, emb, context)
+ else:
+ for module in unet.input_blocks: # down sample
+ h = module(h, emb, context)
+ hs.append(h)
+ h = unet.middle_block(h, emb, context) # middle
+ for module in unet.output_blocks: # up sample
+ h_out = hs.pop()
+ if self.freeu:
+ # --------------- FreeU code -----------------------
+ # Only operate on the first two stages
+ if h.shape[1] == 1280:
+ h[:,:640] = h[:,:640] * self.b1
+ h_out = Fourier_filter(h_out, threshold=1, scale=self.s1)
+ if h.shape[1] == 640:
+ h[:,:320] = h[:,:320] * self.b2
+ h_out = Fourier_filter(h_out, threshold=1, scale=self.s2)
+ # ---------------------------------------------------------
+ h = torch.cat([h, h_out], dim=1)
+ if module in self.unet_blocks:
+ ret_features.append(h.contiguous())
+ h = module(h, emb, context)
+ # h = h.type(x.dtype)
+ return unet.out(h), ret_features
+
+ def unet_forward_crossattn(self, x, timesteps, context, cond_emb=None):
+
+
+ unet = self.unet
+ attention_store = AttentionStore(base_size=512 // 8, max_size=None)
+ register_attention_control(unet, attention_store)
+ ret_features = []
+
+ hs = []
+ t_emb = timestep_embedding(timesteps, unet.model_channels, repeat_only=False)
+ emb = unet.time_embed(t_emb)
+ if cond_emb is not None:
+ emb += cond_emb
+
+ h = x
+ for module in unet.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = unet.middle_block(h, emb, context)
+
+
+ attention_store.reset()
+
+ for module in unet.output_blocks:
+ h = torch.cat([h, hs.pop()], dim=1)
+ if module in self.unet_blocks:
+
+ avg_attn = attention_store.get_average_attention()
+ attn16, attn32, attn64 = avg_attn
+ if attn16 is not None:
+ h = torch.cat([h, attn16], dim=1)
+ elif attn32 is not None:
+ h = torch.cat([h, attn32], dim=1)
+ elif attn64 is not None:
+ h = torch.cat([h, attn64], dim=1)
+
+ ret_features.append(h.contiguous())
+ h = module(h, emb, context)
+
+ return unet.out(h), ret_features
+
+ def decoder_forward(self, z):
+ decoder = self.ldm.decoder
+ ret_features = []
+
+ decoder.last_z_shape = z.shape
+
+ # timestep embedding
+ temb = None
+
+ # z to block_in
+ h = decoder.conv_in(z)
+
+ # middle
+ h = decoder.mid.block_1(h, temb)
+ h = decoder.mid.attn_1(h)
+ h = decoder.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(decoder.num_resolutions)):
+ for i_block in range(decoder.num_res_blocks + 1):
+
+ # add return features
+ if decoder.up[i_level].block[i_block] in self.decoder_blocks:
+ ret_features.append(h.contiguous())
+
+ h = decoder.up[i_level].block[i_block](h, temb)
+ if len(decoder.up[i_level].attn) > 0:
+ h = decoder.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = decoder.up[i_level].upsample(h)
+
+ # end
+ if decoder.give_pre_end:
+ return h
+
+ h = decoder.norm_out(h)
+ h = h * torch.sigmoid(h)
+ h = decoder.conv_out(h)
+ if decoder.tanh_out:
+ h = torch.tanh(h)
+ return h, ret_features
+
+ def decode_to_image(self, z):
+ z = 1.0 / self.ldm.ldm.scale_factor * z
+
+ z = self.ldm.ldm.first_stage_model.post_quant_conv(z)
+ dec, ret_features = self.decoder_forward(z)
+
+ return dec, ret_features
+
+ def forward(self, batched_inputs):
+ """
+ Args:
+ batched_inputs (dict): expected keys: "img", Optional["caption"]
+
+ """
+
+ features = []
+
+ image = batched_inputs["img"]
+ batch_size = image.shape[0]
+
+ if self.image_preprocess is None:
+ normalized_image = (image - self.pixel_mean) / self.pixel_std
+ else:
+ normalized_image = self.image_preprocess((image - self.pixel_mean) / self.pixel_std)
+
+ if "caption" in batched_inputs:
+ captions = batched_inputs["caption"]
+ # print(captions)
+ else:
+ captions = [""] * batch_size
+
+ # latent_image = self.ldm.encode_to_latent(normalized_image)
+ latent_image, encoder_features = self.encode_to_latent(normalized_image)
+ cond_inputs = batched_inputs.get("cond_inputs", self.ldm.embed_text(captions))
+ if "caption" in batched_inputs:
+ cond_inputs = self.ldm.embed_text(captions)
+ unet_features = []
+ for i, t in enumerate(self.steps):
+
+ if "cond_emb" in batched_inputs:
+ cond_emb = batched_inputs["cond_emb"][:, i]
+ else:
+ cond_emb = None
+
+ if t < 0:
+ noisy_latent_image = latent_image
+ # use 0 as no noise timestep
+ t = torch.tensor([0], device=self.device).expand(batch_size)
+ else:
+ t = torch.tensor([t], device=self.device).expand(batch_size)
+ if self.shared_noise is not None:
+ if self.shared_noise.shape[2:] != latent_image.shape[2:]:
+ assert self.image_preprocess is None
+ shared_noise = F.interpolate(
+ self.shared_noise,
+ size=latent_image.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+ else:
+ shared_noise = self.shared_noise
+ noise = shared_noise.expand_as(latent_image)
+ else:
+ noise = None
+
+ noisy_latent_image = self.ldm.diffusion.q_sample(latent_image, t, noise)
+ # self.ldm.ldm.apply_model(noisy_latent_image, t, cond_inputs)
+ _, cond_unet_features = self.unet_forward(
+ noisy_latent_image, t, cond_inputs, cond_emb=cond_emb
+ )
+ unet_features.extend(cond_unet_features)
+
+ # self.ldm.decode_from_latent(latent_image)
+ _, decoder_features = self.decode_to_image(latent_image)
+
+ features = [*encoder_features, *unet_features, *decoder_features]
+
+ assert len(features) == len(
+ self.feature_dims
+ ), f"{len(features)} != {len(self.feature_dims)}"
+
+ for indices in self.grouped_indices:
+ for idx in indices:
+ if self.image_preprocess is not None:
+ continue
+ assert image.shape[-2] // self.feature_strides[idx] == features[idx].shape[-2]
+ assert image.shape[-1] // self.feature_strides[idx] == features[idx].shape[-1]
+
+ return features
+
+
+class PositionalLinear(nn.Module):
+ def __init__(self, in_features, out_features, seq_len=77, bias=True):
+ super().__init__()
+ self.linear = nn.Linear(in_features, out_features, bias=bias)
+ self.positional_embedding = nn.Parameter(torch.zeros(1, seq_len, out_features))
+ trunc_normal_(self.positional_embedding, std=0.02)
+
+ def forward(self, x):
+ x = self.linear(x)
+ x = x.unsqueeze(1) + self.positional_embedding
+
+ return x
+
+
+class LdmImplicitCaptionerExtractor(nn.Module):
+ def __init__(
+ self,
+ learnable_time_embed=True,
+ num_timesteps=1,
+ clip_model_name="ViT-L-14",
+ **kwargs,
+ ):
+ super().__init__()
+ self.ldm_extractor = LdmExtractor(**kwargs)
+ num_timesteps=1
+ self.text_embed_shape = self.ldm_extractor.ldm.embed_text([""]).shape[1:]
+
+ self.clip = ClipAdapter(name=clip_model_name, normalize=False)
+
+ self.clip_project = PositionalLinear(
+ self.clip.dim_latent, self.text_embed_shape[1], self.text_embed_shape[0]
+ )
+ self.alpha_cond = nn.Parameter(torch.zeros_like(self.ldm_extractor.ldm.uncond_inputs))
+
+ self.learnable_time_embed = learnable_time_embed
+
+ if self.learnable_time_embed:
+ # self.ldm_extractor.ldm.unet.time_embed is nn.Sequential
+ self.time_embed_project = PositionalLinear(
+ self.clip.dim_latent,
+ self.ldm_extractor.ldm.unet.time_embed[-1].out_features,
+ num_timesteps,
+ )
+ self.alpha_cond_time_embed = nn.Parameter(
+ torch.zeros(self.ldm_extractor.ldm.unet.time_embed[-1].out_features)
+ )
+
+ @property
+ def feature_size(self):
+ return self.ldm_extractor.feature_size
+
+ @property
+ def feature_dims(self):
+ return self.ldm_extractor.feature_dims
+
+ @property
+ def feature_strides(self):
+ return self.ldm_extractor.feature_strides
+
+ @property
+ def num_groups(self) -> int:
+
+ return self.ldm_extractor.num_groups
+
+ @property
+ def grouped_indices(self):
+
+ return self.ldm_extractor.grouped_indices
+
+ def extra_repr(self):
+ return f"learnable_time_embed={self.learnable_time_embed}"
+
+ def forward(self, batched_inputs):
+ """
+ Args:
+ batched_inputs (dict): expected keys: "img", Optional["caption"]
+
+ """
+ image = batched_inputs["img"]
+
+ prefix = self.clip.embed_image(image).image_embed
+ prefix_embed = self.clip_project(prefix)
+
+ if "caption" not in batched_inputs or batched_inputs["caption"] is None:
+
+ batched_inputs["cond_inputs"] = (
+ self.ldm_extractor.ldm.uncond_inputs + torch.tanh(self.alpha_cond) * prefix_embed
+ )
+
+ if self.learnable_time_embed:
+ batched_inputs["cond_emb"] = torch.tanh(
+ self.alpha_cond_time_embed
+ ) * self.time_embed_project(prefix)
+
+ self.set_requires_grad(self.training)
+
+ return self.ldm_extractor(batched_inputs)
+
+ def set_requires_grad(self, requires_grad):
+ for p in self.ldm_extractor.ldm.ldm.model.parameters():
+ p.requires_grad = requires_grad
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/odise.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/odise.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a8bc0f0f11202de6f045267a88ffe3226efe704
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/meta_arch/odise.py
@@ -0,0 +1,1557 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import logging
+import numpy as np
+import operator
+from collections import OrderedDict
+from typing import Any, Mapping
+import diffdist.functional as diff_dist
+import torch
+from detectron2.modeling.postprocessing import sem_seg_postprocess
+from detectron2.structures import ImageList
+from detectron2.utils import comm
+from detectron2.utils.memory import retry_if_cuda_oom
+from mask2former.maskformer_model import MaskFormer
+from mask2former.modeling.transformer_decoder.mask2former_transformer_decoder import (
+ MLP,
+ MultiScaleMaskedTransformerDecoder,
+)
+from torch import nn
+from torch.nn import functional as F
+
+from odise.data.build import get_openseg_labels, prompt_labels
+
+from .clip import ClipAdapter, MaskCLIP, build_clip_text_embed
+from .helper import ensemble_logits_with_labels
+
+logger = logging.getLogger(__name__)
+
+
+# Ref:https://stackoverflow.com/questions/27049998/convert-a-mixed-nested-list-to-a-nested-tuple
+def to_tuple(lst):
+ return tuple(to_tuple(i) if isinstance(i, list) else i for i in lst)
+
+
+@torch.no_grad()
+def _concat_all_gather(tensor):
+ """
+ Performs all_gather operation on the provided tensors.
+ *** Warning ***: torch.distributed.all_gather has no gradient.
+ """
+ if comm.get_world_size() == 1:
+ return tensor
+ tensors_gather = [torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())]
+ torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+ output = torch.cat(tensors_gather, dim=0)
+ return output
+
+
+def get_world_batch_sizes(batch_size: int, device):
+ batch_size = torch.as_tensor([batch_size], dtype=torch.long, device=device)
+ global_batch_sizes = _concat_all_gather(batch_size)
+ return global_batch_sizes
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+ """
+ Performs all_gather operation on the provided tensors, with dynamic batch size.
+ *** Warning ***: torch.distributed.all_gather has no gradient.
+ """
+ if comm.get_world_size() == 1:
+ return tensor
+ global_batch_sizes = get_world_batch_sizes(tensor.shape[0], tensor.device)
+ max_batch_size = global_batch_sizes.max().item()
+ padded_tensor = torch.zeros(
+ max_batch_size, *tensor.shape[1:], device=tensor.device, dtype=tensor.dtype
+ )
+ padded_tensor[: tensor.shape[0]] = tensor
+
+ tensors_gather = [
+ torch.ones((max_batch_size, *tensor.shape[1:]), dtype=tensor.dtype, device=tensor.device)
+ for _ in range(comm.get_world_size())
+ ]
+ torch.distributed.all_gather(tensors_gather, padded_tensor, async_op=False)
+
+ results = []
+ for i, batch_size in enumerate(global_batch_sizes):
+ results.append(tensors_gather[i][:batch_size])
+
+ output = torch.cat(results, dim=0)
+ return output
+
+
+def dist_collect(tensor):
+ """
+ Performs all_gather operation on the provided tensors, with dynamic batch size.
+ Use diff_dist to get gradient
+ """
+ if comm.get_world_size() == 1:
+ return tensor
+ global_batch_sizes = get_world_batch_sizes(tensor.shape[0], tensor.device)
+ max_batch_size = global_batch_sizes.max().item()
+ padded_tensor = torch.zeros(
+ max_batch_size, *tensor.shape[1:], device=tensor.device, dtype=tensor.dtype
+ )
+ padded_tensor[: tensor.shape[0]] = tensor
+
+ tensors_gather = [
+ torch.ones((max_batch_size, *tensor.shape[1:]), dtype=tensor.dtype, device=tensor.device)
+ for _ in range(comm.get_world_size())
+ ]
+ tensors_gather = diff_dist.all_gather(tensors_gather, padded_tensor)
+
+ results = []
+ for i, batch_size in enumerate(global_batch_sizes):
+ results.append(tensors_gather[i][:batch_size])
+
+ output = torch.cat(results, dim=0)
+ return output
+
+
+class ODISE(MaskFormer):
+ def ignored_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ destination._metadata = OrderedDict()
+ for name, module in self.named_modules(remove_duplicate=True):
+ if module is self:
+ continue
+ if module is not None and hasattr(module, "ignored_state_dict"):
+ module.ignored_state_dict(destination, prefix + name + ".")
+ return destination
+
+ def _open_state_dict(self):
+ return {
+ "sem_seg_head.num_classes": self.sem_seg_head.num_classes,
+ "metadata": self.metadata,
+ "test_topk_per_image": self.test_topk_per_image,
+ "semantic_on": self.semantic_on,
+ "panoptic_on": self.panoptic_on,
+ "instance_on": self.instance_on,
+ }
+
+ def _save_open_state_dict(self, destination, prefix):
+ for k, v in self._open_state_dict().items():
+ destination[prefix + k] = v
+
+ def open_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ self._save_open_state_dict(destination, prefix)
+ for name, module in self.named_modules(remove_duplicate=True):
+ if module is self:
+ continue
+ if module is not None and hasattr(module, "open_state_dict"):
+ module.open_state_dict(destination, prefix + name + ".")
+ return destination
+
+ def load_open_state_dict(self, state_dict: Mapping[str, Any]):
+ for k, v in state_dict.items():
+ # handle nested modules
+ if len(k.rsplit(".", 1)) == 2:
+ prefix, suffix = k.rsplit(".", 1)
+ operator.attrgetter(prefix)(self).__setattr__(suffix, v)
+ else:
+ self.__setattr__(k, v)
+ assert operator.attrgetter(k)(self) == v, f"{k} is not loaded correctly"
+
+
+class CategoryODISE(ODISE):
+ def __init__(
+ self,
+ *,
+ category_head=None,
+ clip_head=None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.category_head = category_head
+ self.clip_head = clip_head
+ # self.size_divisibility = 16
+
+ def cal_pred_logits(self, outputs):
+ # [B, Q, C]
+ mask_embed = outputs["mask_embed"]
+ # [K, C]
+ text_embed = outputs["text_embed"]
+ # [1, C]
+ text_embed = outputs["text_embed"]
+ null_embed = outputs["null_embed"]
+
+ labels = outputs["labels"]
+
+ mask_embed = F.normalize(mask_embed, dim=-1)
+ text_embed = F.normalize(text_embed, dim=-1)
+ logit_scale = outputs["logit_scale"]
+
+ # [B, Q, K]
+ pred = logit_scale * (mask_embed @ text_embed.t())
+
+ pred = ensemble_logits_with_labels(pred, labels, ensemble_method="max")
+
+ null_embed = F.normalize(null_embed, dim=-1)
+ null_pred = logit_scale * (mask_embed @ null_embed.t())
+
+ # [B, Q, K+1]
+ pred = torch.cat([pred, null_pred], dim=-1)
+
+ return pred
+
+ def get_features(self, batched_inputs, caption=None, pca=None):
+ images = [x["image"].to(self.device) for x in batched_inputs]
+ images = [(x - self.pixel_mean) / self.pixel_std for x in images]
+ images = ImageList.from_tensors(images, self.size_divisibility)
+
+ # print('input device:', images.tensor.device)
+ # print('type backbone:', type(self.backbone))
+ self.backbone.to(images.tensor.device)
+ if caption is not None:
+ features = self.backbone(images.tensor, caption, raw=pca)
+ else:
+ features = self.backbone(images.tensor, raw=pca)
+ return features
+
+ def forward(self, batched_inputs):
+ """
+ Args:
+ batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
+ Each item in the list contains the inputs for one image.
+ For now, each item in the list is a dict that contains:
+ * "image": Tensor, image in (C, H, W) format.
+ * "instances": per-region ground truth
+ * Other information that's included in the original dicts, such as:
+ "height", "width" (int): the output resolution of the model (may be different
+ from input resolution), used in inference.
+ Returns:
+ list[dict]:
+ each dict has the results for one image. The dict contains the following keys:
+
+ * "sem_seg":
+ A Tensor that represents the
+ per-pixel segmentation prediced by the head.
+ The prediction has shape KxHxW that represents the logits of
+ each class for each pixel.
+ * "panoptic_seg":
+ A tuple that represent panoptic output
+ panoptic_seg (Tensor): of shape (height, width) where the
+ values are ids for each segment.
+ segments_info (list[dict]): Describe each segment in `panoptic_seg`.
+ Each dict contains keys "id", "category_id", "isthing".
+ """
+ images = [x["image"].to(self.device) for x in batched_inputs]
+ images = [(x - self.pixel_mean) / self.pixel_std for x in images]
+ images = ImageList.from_tensors(images, self.size_divisibility)
+
+ denormalized_images = ImageList.from_tensors(
+ [x["image"].to(self.device) / 255.0 for x in batched_inputs]
+ )
+
+ features = self.backbone(images.tensor)
+ outputs = self.sem_seg_head(features)
+ outputs["images"] = denormalized_images.tensor
+
+ if self.training:
+ # mask classification target
+ if "instances" in batched_inputs[0]:
+ gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
+ targets = self.prepare_targets(gt_instances, images)
+ else:
+ targets = None
+
+ if self.category_head is not None:
+ category_head_outputs = self.category_head(outputs, targets)
+ outputs.update(category_head_outputs)
+ # inplace change pred_logits
+ outputs["pred_logits"] = self.cal_pred_logits(outputs)
+ if "aux_outputs" in outputs:
+ for aux_outputs in outputs["aux_outputs"]:
+ aux_outputs.update(category_head_outputs)
+ # inplace change pred_logits
+ aux_outputs["pred_logits"] = self.cal_pred_logits(aux_outputs)
+
+ # CLIP head needs output to prepare targets
+ # disable for now
+ # targets = self.clip_head.prepare_targets(outputs, targets)
+
+ # bipartite matching-based loss
+ losses = self.criterion(outputs, targets)
+
+ for k in list(losses.keys()):
+ if k in self.criterion.weight_dict:
+ losses[k] *= self.criterion.weight_dict[k]
+ else:
+ # remove this loss if not specified in `weight_dict`
+ losses.pop(k)
+
+ return losses
+ else:
+
+ # get text_embeddings
+ outputs.update(self.category_head(outputs))
+
+ outputs["pred_logits"] = self.cal_pred_logits(outputs)
+
+ mask_pred_results = outputs["pred_masks"]
+ mask_cls_results = outputs["pred_logits"]
+
+ if self.clip_head is not None:
+ if self.clip_head.with_bg:
+ # [B, Q, K+1]
+ outputs["pred_open_logits"] = outputs["pred_logits"]
+ outputs.update(self.clip_head(outputs))
+ mask_cls_results = outputs["pred_open_logits"]
+ else:
+ # [B, Q, K]
+ outputs["pred_open_logits"] = outputs["pred_logits"][..., :-1]
+ outputs.update(self.clip_head(outputs))
+
+ # merge with bg scores
+ open_logits = outputs["pred_open_logits"]
+
+ # in case the prediction is not binary
+ binary_probs = torch.zeros(
+ (mask_cls_results.shape[0], mask_cls_results.shape[1], 2),
+ device=mask_cls_results.device,
+ dtype=mask_cls_results.dtype,
+ )
+ binary_probs[..., -1] = F.softmax(mask_cls_results, dim=-1)[..., -1]
+ binary_probs[..., 0] = 1 - binary_probs[..., -1]
+
+ masks_class_probs = F.softmax(open_logits, dim=-1)
+ # [B, Q, K+1]
+ mask_cls_results = torch.cat(
+ [masks_class_probs * binary_probs[..., 0:1], binary_probs[..., 1:2]], dim=-1
+ )
+ # NOTE: mask_cls_results is already multiplied with logit_scale,
+ # avoid double scale, which cause overflow in softmax
+ # mask_cls_results = torch.log(mask_cls_results + 1e-8) * outputs["logit_scale"]
+ mask_cls_results = torch.log(mask_cls_results + 1e-8)
+
+ # upsample masks
+ mask_pred_results = F.interpolate(
+ mask_pred_results,
+ size=(images.tensor.shape[-2], images.tensor.shape[-1]),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ del outputs
+
+ processed_results = []
+ for mask_cls_result, mask_pred_result, input_per_image, image_size in zip(
+ mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
+ ):
+ height = input_per_image.get("height", image_size[0])
+ width = input_per_image.get("width", image_size[1])
+ processed_results.append({})
+
+ if self.sem_seg_postprocess_before_inference:
+ mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
+ mask_pred_result, image_size, height, width
+ )
+ mask_cls_result = mask_cls_result.to(mask_pred_result)
+
+ # semantic segmentation inference
+ if self.semantic_on:
+ r = retry_if_cuda_oom(self.semantic_inference)(
+ mask_cls_result, mask_pred_result
+ )
+ if not self.sem_seg_postprocess_before_inference:
+ r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)
+ processed_results[-1]["sem_seg"] = r
+
+ # panoptic segmentation inference
+ if self.panoptic_on:
+ panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(
+ mask_cls_result, mask_pred_result
+ )
+ processed_results[-1]["panoptic_seg"] = panoptic_r
+
+ # instance segmentation inference
+ if self.instance_on:
+ instance_r = retry_if_cuda_oom(self.instance_inference)(
+ mask_cls_result, mask_pred_result
+ )
+ processed_results[-1]["instances"] = instance_r
+
+ return processed_results
+
+
+class CaptionODISE(ODISE):
+ def __init__(
+ self,
+ *,
+ word_head=None,
+ clip_head=None,
+ grounding_criterion=None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.word_head = word_head
+ self.clip_head = clip_head
+
+ self.grounding_criterion = grounding_criterion
+
+ def prepare_targets(self, targets, images):
+ h_pad, w_pad = images.tensor.shape[-2:]
+ new_targets = []
+ for targets_per_image in targets:
+ # pad gt
+ gt_masks = targets_per_image.gt_masks
+ padded_masks = torch.zeros(
+ (gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device
+ )
+ padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks
+ new_targets.append(
+ {
+ "labels": targets_per_image.gt_classes,
+ "masks": padded_masks,
+ }
+ )
+
+ if targets_per_image.has("original_gt_classes"):
+ # "labels" maybe binary, store original labels in as well
+ new_targets[-1]["original_labels"] = targets_per_image.original_gt_classes
+
+ return new_targets
+
+ def prepare_pseudo_targets(self, images):
+ h_pad, w_pad = images.tensor.shape[-2:]
+ new_targets = []
+ for _ in range(len(images)):
+ # pad gt
+ padded_masks = torch.zeros((0, h_pad, w_pad), dtype=torch.bool, device=images.device)
+ new_targets.append(
+ {
+ "labels": torch.zeros(0, dtype=torch.long, device=images.device),
+ "masks": padded_masks,
+ }
+ )
+
+ return new_targets
+
+ @property
+ def binary_classification(self):
+ return self.sem_seg_head.num_classes == 1
+
+ def cal_pred_open_logits(self, outputs):
+ # [B, Q, C]
+ mask_embed = outputs["mask_embed"]
+ # [K, C]
+ text_embed = outputs["text_embed"]
+
+ labels = outputs["labels"]
+
+ mask_embed = F.normalize(mask_embed, dim=-1)
+ text_embed = F.normalize(text_embed, dim=-1)
+ logit_scale = outputs["logit_scale"]
+
+ # [B, Q, K]
+ pred = logit_scale * (mask_embed @ text_embed.t())
+
+ pred = ensemble_logits_with_labels(pred, labels, ensemble_method="max")
+
+ return pred
+
+ def forward(self, batched_inputs):
+ """
+ Args:
+ batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
+ Each item in the list contains the inputs for one image.
+ For now, each item in the list is a dict that contains:
+ * "image": Tensor, image in (C, H, W) format.
+ * "instances": per-region ground truth
+ * Other information that's included in the original dicts, such as:
+ "height", "width" (int): the output resolution of the model (may be different
+ from input resolution), used in inference.
+ Returns:
+ list[dict]:
+ each dict has the results for one image. The dict contains the following keys:
+
+ * "sem_seg":
+ A Tensor that represents the
+ per-pixel segmentation prediced by the head.
+ The prediction has shape KxHxW that represents the logits of
+ each class for each pixel.
+ * "panoptic_seg":
+ A tuple that represent panoptic output
+ panoptic_seg (Tensor): of shape (height, width) where the
+ values are ids for each segment.
+ segments_info (list[dict]): Describe each segment in `panoptic_seg`.
+ Each dict contains keys "id", "category_id", "isthing".
+ """
+ images = [x["image"].to(self.device) for x in batched_inputs]
+ images = [(x - self.pixel_mean) / self.pixel_std for x in images]
+ images = ImageList.from_tensors(images, self.size_divisibility)
+
+ denormalized_images = ImageList.from_tensors(
+ [x["image"].to(self.device) / 255.0 for x in batched_inputs]
+ )
+
+ features = self.backbone(images.tensor)
+ outputs = self.sem_seg_head(features)
+ outputs["images"] = denormalized_images.tensor
+
+ if self.training:
+ # mask classification target
+ if "instances" in batched_inputs[0]:
+ gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
+
+ if self.binary_classification:
+ # NOTE: convert to binary classification target
+ for i in range(len(gt_instances)):
+ gt_instances[i].original_gt_classes = gt_instances[i].gt_classes.clone()
+ gt_instances[i].gt_classes = torch.zeros_like(gt_instances[i].gt_classes)
+
+ targets = self.prepare_targets(gt_instances, images)
+ has_anno = True
+ else:
+ targets = self.prepare_pseudo_targets(images)
+ has_anno = False
+
+ if "captions" in batched_inputs[0]:
+ gt_captions = [x["captions"] for x in batched_inputs]
+ targets = self.word_head.prepare_targets(gt_captions, targets)
+
+ if self.word_head is not None:
+ word_head_outputs = self.word_head(outputs, targets)
+ outputs.update(word_head_outputs)
+ if "aux_outputs" in outputs:
+ for aux_outputs in outputs["aux_outputs"]:
+ aux_outputs.update(word_head_outputs)
+
+ # CLIP head needs output to prepare targets
+ # disable for now
+ # targets = self.clip_head.prepare_targets(outputs, targets)
+
+ if self.criterion is not None:
+ # bipartite matching-based loss
+ losses = self.criterion(outputs, targets)
+
+ for k in list(losses.keys()):
+ if k in self.criterion.weight_dict:
+ losses[k] *= self.criterion.weight_dict[k]
+ else:
+ # remove this loss if not specified in `weight_dict`
+ losses.pop(k)
+
+ # multiple by 0 to avoid gradient but make sure the param is used
+ if not has_anno:
+ for k in list(losses.keys()):
+ losses[k] *= 0
+ else:
+ losses = {}
+
+ if self.grounding_criterion is not None:
+ grounding_losses = self.grounding_criterion(outputs, targets)
+ losses.update(grounding_losses)
+
+ return losses
+ else:
+
+ mask_cls_results = outputs["pred_logits"]
+ mask_pred_results = outputs["pred_masks"]
+
+ if "pred_open_logits" not in outputs:
+ if self.word_head is not None:
+ outputs.update(self.word_head(outputs))
+ outputs["pred_open_logits"] = self.cal_pred_open_logits(outputs)
+
+ if self.clip_head is not None:
+ outputs.update(self.clip_head(outputs))
+
+ assert mask_cls_results.shape[-1] == 2 and "pred_open_logits" in outputs
+
+ open_logits = outputs["pred_open_logits"]
+ binary_probs = F.softmax(mask_cls_results, dim=-1)
+ masks_class_probs = F.softmax(open_logits, dim=-1)
+ # [B, Q, K+1]
+ mask_cls_results = torch.cat(
+ [masks_class_probs * binary_probs[..., 0:1], binary_probs[..., 1:2]], dim=-1
+ )
+ # NOTE: mask_cls_results is already multiplied with logit_scale,
+ # avoid double scale, which cause overflow in softmax
+ # mask_cls_results = torch.log(mask_cls_results + 1e-8) * outputs["logit_scale"]
+ mask_cls_results = torch.log(mask_cls_results + 1e-8)
+
+ # upsample masks
+ mask_pred_results = F.interpolate(
+ mask_pred_results,
+ size=(images.tensor.shape[-2], images.tensor.shape[-1]),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ del outputs
+
+ processed_results = []
+ for mask_cls_result, mask_pred_result, input_per_image, image_size in zip(
+ mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
+ ):
+ height = input_per_image.get("height", image_size[0])
+ width = input_per_image.get("width", image_size[1])
+ processed_results.append({})
+
+ if self.sem_seg_postprocess_before_inference:
+ mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
+ mask_pred_result, image_size, height, width
+ )
+ mask_cls_result = mask_cls_result.to(mask_pred_result)
+
+ # semantic segmentation inference
+ if self.semantic_on:
+ r = retry_if_cuda_oom(self.semantic_inference)(
+ mask_cls_result, mask_pred_result
+ )
+ if not self.sem_seg_postprocess_before_inference:
+ r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)
+ processed_results[-1]["sem_seg"] = r
+
+ # panoptic segmentation inference
+ if self.panoptic_on:
+ panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(
+ mask_cls_result, mask_pred_result
+ )
+ processed_results[-1]["panoptic_seg"] = panoptic_r
+
+ # instance segmentation inference
+ if self.instance_on:
+ instance_r = retry_if_cuda_oom(self.instance_inference)(
+ mask_cls_result, mask_pred_result
+ )
+ processed_results[-1]["instances"] = instance_r
+
+ return processed_results
+
+
+class ODISEMultiScaleMaskedTransformerDecoder(MultiScaleMaskedTransformerDecoder):
+ def __init__(
+ self,
+ *,
+ class_embed=None,
+ mask_embed=None,
+ post_mask_embed=None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ assert self.mask_classification
+
+ if class_embed is not None:
+ self.class_embed = class_embed
+ if mask_embed is not None:
+ self.mask_embed = mask_embed
+ if post_mask_embed is not None:
+ assert mask_embed is None
+ self.post_mask_embed = post_mask_embed
+
+ def forward(self, x, mask_features, mask=None, *, inputs_dict=None):
+ # x is a list of multi-scale feature
+ assert len(x) == self.num_feature_levels
+ src = []
+ pos = []
+ size_list = []
+
+ # disable mask, it does not affect performance
+ del mask
+
+ for i in range(self.num_feature_levels):
+ size_list.append(x[i].shape[-2:])
+ pos.append(self.pe_layer(x[i], None).flatten(2))
+ src.append(
+ self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]
+ )
+
+ # flatten NxCxHxW to HWxNxC
+ pos[-1] = pos[-1].permute(2, 0, 1)
+ src[-1] = src[-1].permute(2, 0, 1)
+
+ _, bs, _ = src[0].shape
+
+ # QxNxC
+ query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)
+ output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1)
+
+ predictions_class = []
+ predictions_mask = []
+ predictions_extra_results = []
+
+ # prediction heads on learnable query features
+ outputs_class, outputs_mask, attn_mask, extra_results = self.forward_prediction_heads(
+ output, mask_features, attn_mask_target_size=size_list[0], inputs_dict=inputs_dict
+ )
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+ predictions_extra_results.append(extra_results)
+
+ for i in range(self.num_layers):
+ level_index = i % self.num_feature_levels
+ attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+ # attention: cross-attention first
+ output = self.transformer_cross_attention_layers[i](
+ output,
+ src[level_index],
+ memory_mask=attn_mask,
+ memory_key_padding_mask=None, # here we do not apply masking on padded region
+ pos=pos[level_index],
+ query_pos=query_embed,
+ )
+
+ output = self.transformer_self_attention_layers[i](
+ output, tgt_mask=None, tgt_key_padding_mask=None, query_pos=query_embed
+ )
+
+ # FFN
+ output = self.transformer_ffn_layers[i](output)
+
+ outputs_class, outputs_mask, attn_mask, extra_results = self.forward_prediction_heads(
+ output,
+ mask_features,
+ attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels],
+ inputs_dict=inputs_dict,
+ )
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+ predictions_extra_results.append(extra_results)
+
+ assert len(predictions_class) == self.num_layers + 1
+
+ out = {
+ "pred_logits": predictions_class[-1],
+ "pred_masks": predictions_mask[-1],
+ "aux_outputs": self._set_aux_loss(
+ predictions_class if self.mask_classification else None, predictions_mask
+ ),
+ }
+
+ # adding extra_results to out and out["aux_outputs"]
+ for k in predictions_extra_results[-1].keys():
+ out[k] = predictions_extra_results[-1][k]
+ for i in range(len(predictions_extra_results) - 1):
+ out["aux_outputs"][i][k] = predictions_extra_results[i][k]
+
+ return out
+
+ def forward_prediction_heads(
+ self, output, mask_features, attn_mask_target_size, *, inputs_dict=None
+ ):
+ decoder_output = self.decoder_norm(output)
+ decoder_output = decoder_output.transpose(0, 1)
+ outputs_class = self.class_embed(decoder_output)
+
+ extra_results = dict()
+
+ mask_embed_results = self.mask_embed(decoder_output)
+ if isinstance(mask_embed_results, dict):
+ mask_embed = mask_embed_results.pop("mask_embed")
+ extra_results.update(mask_embed_results)
+ # BC
+ else:
+ mask_embed = mask_embed_results
+
+ outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
+
+ if self.post_mask_embed is not None:
+ post_mask_embed_results = self.post_mask_embed(
+ decoder_output, mask_embed, mask_features, outputs_class, outputs_mask
+ )
+
+ if "outputs_mask" in post_mask_embed_results:
+ outputs_mask = post_mask_embed_results.pop("outputs_mask")
+
+ extra_results.update(post_mask_embed_results)
+
+ # NOTE: prediction is of higher-resolution
+ # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]
+ attn_mask = F.interpolate(
+ outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False
+ )
+ # must use bool type
+ # If a BoolTensor is provided, positions with ``True`` are not allowed to attend,
+ # while ``False`` values will be unchanged.
+ attn_mask = (
+ attn_mask.sigmoid()
+ .flatten(2)
+ .unsqueeze(1)
+ .repeat(1, self.num_heads, 1, 1)
+ .flatten(0, 1)
+ < 0.5
+ ).bool()
+ attn_mask = attn_mask.detach()
+
+ return outputs_class, outputs_mask, attn_mask, extra_results
+
+
+class MaskGroundingCriterion(nn.Module):
+ def __init__(
+ self,
+ collect_mode: str = "concat",
+ loss_weight=1.0,
+ ):
+ super().__init__()
+
+ self.collect_mode = collect_mode
+ self.loss_weight = loss_weight
+
+ if collect_mode == "diff":
+ self.collect_func = dist_collect
+ elif collect_mode == "concat":
+ self.collect_func = concat_all_gather
+ elif collect_mode is None:
+ self.collect_func = lambda x: x
+ else:
+ raise ValueError(f"collect_mode {collect_mode} is not supported")
+
+ def extra_repr(self) -> str:
+ return f"collect_mode={self.collect_mode}, \n" f"loss_weight={self.loss_weight} \n"
+
+ def forward(self, outputs, targets):
+
+ losses = {}
+ losses.update(self.get_loss(outputs, targets))
+
+ if "aux_outputs" in outputs:
+ for i, aux_outputs in enumerate(outputs["aux_outputs"]):
+ l_dict = self.get_loss(aux_outputs, targets)
+ l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
+ losses.update(l_dict)
+
+ return losses
+
+ def get_loss(self, outputs, targets):
+
+ logit_scale = outputs["logit_scale"]
+
+ rank = comm.get_rank() if self.collect_mode is not None else 0
+
+ # normalized embeds
+ # [B, Q, C]
+ mask_embed = outputs["mask_embed"]
+ # [B, K, C]
+ word_embed = outputs["word_embed"]
+ # [B, K]
+ word_valid_mask = torch.stack([t["word_valid_mask"] for t in targets], dim=0)
+
+ mask_embed = F.normalize(mask_embed, dim=-1)
+ word_embed = F.normalize(word_embed, dim=-1)
+
+ batch_size, num_queries, embed_dim = mask_embed.shape
+ assert batch_size == word_embed.shape[0], f"{batch_size} != {word_embed.shape[0]}"
+ assert embed_dim == word_embed.shape[2], f"{embed_dim} != {word_embed.shape[2]}"
+ num_words = word_embed.shape[1]
+
+ # [B*Q, C]
+ mask_embed = mask_embed.reshape(batch_size * num_queries, embed_dim)
+ # [B*K, C]
+ word_embed = word_embed.reshape(batch_size * num_words, embed_dim)
+
+ if self.collect_mode is not None and comm.get_world_size() > 1:
+ global_batch_sizes = get_world_batch_sizes(batch_size, device=mask_embed.device)
+ global_batch_size = global_batch_sizes.sum().item()
+ else:
+ global_batch_sizes = None
+ global_batch_size = batch_size
+
+ # [W*B*Q, B*K]
+ sim_global_mask_word = self.collect_func(mask_embed) @ word_embed.t() * logit_scale
+
+ # [W*B, Q, B, K]
+ sim_global_mask_word = sim_global_mask_word.view(
+ global_batch_size, num_queries, batch_size, num_words
+ )
+
+ # [W*B, B]
+ sim_global_img_txt = (
+ (sim_global_mask_word.softmax(dim=1) * sim_global_mask_word).sum(dim=1).mean(dim=-1)
+ )
+
+ # [B*Q, W*B*K]
+ sim_mask_global_word = mask_embed @ self.collect_func(word_embed).t() * logit_scale
+
+ # [B, Q, W*B, K]
+ sim_mask_global_word = sim_mask_global_word.view(
+ batch_size, num_queries, global_batch_size, num_words
+ )
+
+ # [B, W*B]
+ sim_img_global_txt = (
+ (sim_mask_global_word.softmax(dim=1) * sim_mask_global_word).sum(dim=1).mean(dim=-1)
+ )
+
+ if global_batch_sizes is None:
+ # get label globally
+ labels = (
+ torch.arange(batch_size, dtype=torch.long, device=mask_embed.device)
+ + batch_size * rank
+ )
+ else:
+ # get label globally and dynamically
+ labels = (
+ torch.arange(batch_size, dtype=torch.long, device=mask_embed.device)
+ + global_batch_sizes[:rank].sum()
+ )
+
+ # [B]
+ valid_mask = word_valid_mask.any(dim=-1)
+ # [W*B]
+ global_valid_mask = self.collect_func(valid_mask)
+
+ # [WxB, B] -> [B, WXB] -> [B]
+ loss_global_img_txt = F.cross_entropy(sim_global_img_txt.t(), labels, reduction="none")
+ loss_global_img_txt = (loss_global_img_txt * valid_mask).mean()
+
+ # [B, WXB] -> [B]
+ loss_img_global_txt = F.cross_entropy(
+ sim_img_global_txt, labels, weight=global_valid_mask.float()
+ )
+ if not torch.isfinite(loss_img_global_txt).all():
+ # TODO: find reason. Not using vaid mask if NaN as temporary solution
+ loss_img_global_txt = F.cross_entropy(sim_img_global_txt, labels)
+
+ loss = 0.5 * (loss_global_img_txt + loss_img_global_txt)
+
+ return {"loss_mask_word": loss * self.loss_weight}
+
+
+class PseudoClassEmbed(nn.Module):
+ def __init__(self, num_classes):
+ super().__init__()
+ self.num_classes = num_classes
+
+ def forward(self, x):
+ # predict as foreground only
+ fg_logits = torch.ones((*x.shape[:-1], self.num_classes), dtype=x.dtype, device=x.device)
+ bg_logits = torch.zeros((*x.shape[:-1], 1), dtype=x.dtype, device=x.device)
+ logits = torch.cat([fg_logits, bg_logits], dim=-1)
+ return logits
+
+
+class MaskPooling(nn.Module):
+ def __init__(
+ self,
+ hard_pooling=True,
+ mask_threshold=0.5,
+ ):
+ super().__init__()
+ # if the pooling is hard, it's not differentiable
+ self.hard_pooling = hard_pooling
+ self.mask_threshold = mask_threshold
+
+ def extra_repr(self) -> str:
+ return f"hard_pooling={self.hard_pooling}\n" f"mask_threshold={self.mask_threshold}\n"
+
+ def forward(self, x, mask):
+ """
+ Args:
+ x: [B, C, H, W]
+ mask: [B, Q, H, W]
+ """
+
+ assert x.shape[-2:] == mask.shape[-2:]
+
+ mask = mask.detach()
+
+ mask = mask.sigmoid()
+
+ if self.hard_pooling:
+ mask = (mask > self.mask_threshold).to(mask.dtype)
+
+ denorm = mask.sum(dim=(-1, -2), keepdim=True) + 1e-8
+
+ mask_pooled_x = torch.einsum(
+ "bchw,bqhw->bqc",
+ x,
+ mask / denorm,
+ )
+
+ output = {"mask_pooled_features": mask_pooled_x}
+
+ return output
+
+
+class PooledMaskEmbed(nn.Module):
+ def __init__(
+ self,
+ hidden_dim,
+ mask_dim,
+ projection_dim,
+ temperature=0.07,
+ ):
+ super().__init__()
+ self.pool_proj = nn.Sequential(nn.LayerNorm(hidden_dim), nn.Linear(hidden_dim, hidden_dim))
+ self.mask_embed = nn.Sequential(
+ nn.LayerNorm(mask_dim), MLP(mask_dim, hidden_dim, projection_dim, 3)
+ )
+
+ self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
+
+ self.mask_pooling = MaskPooling()
+
+ def forward(self, decoder_output, input_mask_embed, mask_features, pred_logits, pred_masks):
+ """
+ Args:
+ decoder_output: [B, Q, C]
+ input_mask_embed: [B, Q, C]
+ mask_features: [B, C, H, W]
+ pred_logits: [B, Q, K+1]
+ pred_masks: [B, Q, H, W]
+ """
+ mask_pooled_x = self.mask_pooling(mask_features, pred_masks)
+ mask_pooled_results = self.mask_pooling(mask_features, pred_masks)
+ mask_pooled_x = mask_pooled_results["mask_pooled_features"]
+ outputs_mask = mask_pooled_results.get("outputs_mask", None)
+
+ mask_pooled_x = self.pool_proj(mask_pooled_x)
+
+ mask_pooled_x += decoder_output
+
+ mask_embed = self.mask_embed(mask_pooled_x)
+
+ logit_scale = torch.clamp(self.logit_scale.exp(), max=100)
+
+ output = {
+ "mask_embed": mask_embed,
+ "mask_pooled_features": mask_pooled_x,
+ "logit_scale": logit_scale,
+ }
+
+ if outputs_mask is not None:
+ output["outputs_mask"] = outputs_mask
+
+ return output
+
+
+class WordEmbed(nn.Module):
+ def __init__(
+ self,
+ projection_dim,
+ clip_model_name="ViT-L-14",
+ word_dropout=0.0,
+ word_tags="noun_phrase",
+ num_words=8,
+ prompt="photo",
+ ):
+ super().__init__()
+
+ self.clip_model_name = clip_model_name
+ self.clip = ClipAdapter(name=self.clip_model_name, normalize=False)
+
+ if projection_dim < 0:
+ self.text_proj = nn.Identity()
+ else:
+ self.text_proj = nn.Linear(self.clip.dim_latent, projection_dim)
+
+ self.test_labels = None
+ self._test_text_embed_dict = OrderedDict()
+
+ import nltk
+
+ if comm.get_local_rank() == 0:
+ nltk.download("popular", quiet=True)
+ nltk.download("universal_tagset", quiet=True)
+ comm.synchronize()
+ self.nltk = nltk
+
+ self.word_dropout = word_dropout
+ self.word_tags = word_tags
+ self.num_words = num_words
+ self.prompt = prompt
+
+ def extra_repr(self) -> str:
+ return (
+ f"clip_model_name={self.clip_model_name},\n"
+ f"word_dropout={self.word_dropout},\n"
+ f"word_tags={self.word_tags},\n"
+ f"num_words={self.num_words}"
+ )
+
+ @property
+ def device(self):
+ return self.clip.device
+
+ def _open_state_dict(self):
+ return {"test_labels": self.test_labels}
+
+ def _save_open_state_dict(self, destination, prefix):
+ for k, v in self._open_state_dict().items():
+ destination[prefix + k] = v
+
+ def open_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ self._save_open_state_dict(destination, prefix)
+ for name, module in self.named_modules(remove_duplicate=True):
+ if module is self:
+ continue
+ if module is not None and hasattr(module, "open_state_dict"):
+ module.open_state_dict(destination, prefix + name + ".")
+ return destination
+
+ @torch.no_grad()
+ def build_text_embed(self, labels, verbose=False):
+ return build_clip_text_embed(
+ clip_model_name=self.clip.clip,
+ labels=labels,
+ verbose=verbose,
+ )
+
+ def get_and_cache_test_text_embed(self, labels):
+ labels = to_tuple(labels)
+ if labels not in self._test_text_embed_dict:
+ text_embed = self.build_text_embed(labels, verbose=True)
+ if len(self._test_text_embed_dict) > 3:
+ # pop the first element, only caching 3 elements
+ self._test_text_embed_dict.pop(list(self._test_text_embed_dict.keys())[0])
+ self._test_text_embed_dict[labels] = text_embed.cpu()
+ else:
+ text_embed = self._test_text_embed_dict[labels].to(self.device)
+ return text_embed
+
+ def get_tag(self, caption, tags):
+ if not isinstance(tags, (list, tuple)):
+ tags = [tags]
+ ret = []
+ for (word, pos) in self.nltk.pos_tag(self.nltk.word_tokenize(caption), tagset="universal"):
+ for tag in tags:
+ if pos == tag:
+ ret.append(word)
+ return ret
+
+ def _get_phrase(self, caption, with_preposition):
+ if with_preposition:
+ # Taken from Su Nam Kim Paper...
+ grammar = r"""
+ NBAR:
+ {*} # Nouns and Adjectives, terminated with Nouns
+
+ NP:
+ {} # Above, connected with in/of/etc...
+ {} # If pattern is not found, just a single NBAR is ok
+ """
+ else:
+ # Taken from Su Nam Kim Paper...
+ grammar = r"""
+ NBAR:
+ {*} # Nouns and Adjectives, terminated with Nouns
+
+ NP:
+ {} # If pattern is not found, just a single NBAR is ok
+ """
+ tokenized = self.nltk.word_tokenize(caption)
+ chunker = self.nltk.RegexpParser(grammar)
+
+ chunked = chunker.parse(self.nltk.pos_tag(tokenized))
+ continuous_chunk = []
+ current_chunk = []
+
+ for subtree in chunked:
+ if isinstance(subtree, self.nltk.Tree):
+ current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
+ elif current_chunk:
+ named_entity = " ".join(current_chunk)
+ if named_entity not in continuous_chunk:
+ continuous_chunk.append(named_entity)
+ current_chunk = []
+ else:
+ continue
+
+ return continuous_chunk
+
+ def get_noun_phrase(self, caption):
+ noun_phrase = []
+ noun_phrase.extend(self._get_phrase(caption, with_preposition=False))
+ noun_phrase.extend(self._get_phrase(caption, with_preposition=True))
+
+ return list(set(noun_phrase))
+
+ def prepare_targets(self, captions, targets):
+
+ if targets is None:
+ targets = [{} for _ in range(len(captions))]
+
+ for caption, target in zip(captions, targets):
+ caption = np.random.choice(caption)
+ if self.word_tags == "noun_phrase":
+ words = self.get_noun_phrase(caption)
+ elif "noun_phrase" in self.word_tags:
+ words = []
+ words.extend(self.get_noun_phrase(caption))
+ words.extend(self.get_tag(caption, tuple(set(self.word_tags) - set("noun_phrase"))))
+ words = list(set(words))
+ else:
+ words = self.get_tag(caption, self.word_tags)
+ if not len(words):
+ words = [""]
+ # drop with probability
+ words_after_drop = [w for w in words if np.random.rand() > self.word_dropout]
+ if len(words_after_drop) == 0:
+ # Fall back to no drop if all words are dropped
+ words_after_drop = words
+ words = np.random.choice(words_after_drop, size=self.num_words).tolist()
+ target["words"] = words
+
+ valid_mask = [len(w) > 0 for w in words]
+ valid_mask = torch.tensor(valid_mask, device=self.device, dtype=torch.bool)
+ target["word_valid_mask"] = valid_mask
+
+ return targets
+
+ def forward(self, outputs, targets=None):
+ if self.training:
+ words = [x["words"] for x in targets]
+
+ words = prompt_labels(words, self.prompt)
+
+ word_embed = self.build_text_embed(words)
+ # [B, K, C]
+ word_embed = torch.stack(word_embed.split([len(w) for w in words]), dim=0)
+
+ word_embed = self.text_proj(word_embed)
+
+ return {"word_embed": word_embed}
+ else:
+ assert targets is None
+ assert self.test_labels is not None
+ labels = self.test_labels
+
+ labels = prompt_labels(labels, self.prompt)
+
+ text_embed = self.get_and_cache_test_text_embed(labels)
+
+ text_embed = self.text_proj(text_embed)
+ return {"text_embed": text_embed, "labels": labels}
+
+
+class CategoryEmbed(nn.Module):
+ def __init__(
+ self,
+ labels,
+ projection_dim,
+ clip_model_name="ViT-L-14",
+ prompt=None,
+ ):
+ super().__init__()
+ self.labels = labels
+
+ self.clip_model_name = clip_model_name
+ self.clip = ClipAdapter(name=self.clip_model_name, normalize=False)
+
+ if projection_dim < 0:
+ self.text_proj = nn.Identity()
+ else:
+ self.text_proj = nn.Linear(self.clip.dim_latent, projection_dim)
+
+ self.register_buffer(
+ "text_embed", self.build_text_embed(prompt_labels(labels, prompt), verbose=True), False
+ )
+ self.null_embed = nn.Parameter(self.build_text_embed(""))
+
+ self.prompt = prompt
+
+ self.test_labels = None
+ self._test_text_embed_dict = dict()
+
+ def extra_repr(self) -> str:
+ return f"clip_model_name={self.clip_model_name},\n"
+
+ @property
+ def device(self):
+ return self.clip.device
+
+ def _open_state_dict(self):
+ return {"test_labels": self.test_labels}
+
+ def _save_open_state_dict(self, destination, prefix):
+ for k, v in self._open_state_dict().items():
+ destination[prefix + k] = v
+
+ def open_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ self._save_open_state_dict(destination, prefix)
+ for name, module in self.named_modules(remove_duplicate=True):
+ if module is self:
+ continue
+ if module is not None and hasattr(module, "open_state_dict"):
+ module.open_state_dict(destination, prefix + name + ".")
+ return destination
+
+ @torch.no_grad()
+ def build_text_embed(self, labels, verbose=False):
+ return build_clip_text_embed(
+ clip_model_name=self.clip.clip,
+ labels=labels,
+ verbose=verbose,
+ )
+
+ def get_and_cache_test_text_embed(self, labels):
+ labels = to_tuple(labels)
+ if labels not in self._test_text_embed_dict:
+ text_embed = self.build_text_embed(labels, verbose=True)
+ self._test_text_embed_dict[labels] = text_embed.cpu()
+ else:
+ text_embed = self._test_text_embed_dict[labels].to(self.device)
+ return text_embed
+
+ def forward(self, outputs, targets=None):
+ if self.training:
+
+ text_embed = self.text_proj(self.text_embed)
+ null_embed = self.text_proj(self.null_embed)
+
+ return {"text_embed": text_embed, "null_embed": null_embed, "labels": self.labels}
+
+ else:
+ assert targets is None
+ assert self.test_labels is not None
+ labels = self.test_labels
+ text_embed = self.get_and_cache_test_text_embed(prompt_labels(labels, self.prompt))
+
+ text_embed = self.text_proj(text_embed)
+ null_embed = self.text_proj(self.null_embed)
+
+ return {"text_embed": text_embed, "null_embed": null_embed, "labels": labels}
+
+
+class CLIPOpenClassEmbed(nn.Module):
+ def __init__(
+ self,
+ labels,
+ hidden_dim,
+ projection_modality="text",
+ clip_model_name="ViT-L-14",
+ with_null_embed=True,
+ temperature=0.07,
+ ensemble_method="max",
+ ):
+ super().__init__()
+ self.labels = labels
+ assert projection_modality in ["text", "image"]
+ self.projection_modality = projection_modality
+ self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
+
+ self.clip_model_name = clip_model_name
+ self.register_buffer("text_embed", self.build_text_embed(labels), False)
+ if with_null_embed:
+ self.null_embed = nn.Parameter(self.build_text_embed(""))
+ else:
+ self.null_embed = None
+
+ if self.projection_modality == "text":
+ self.embed_projection = nn.Linear(self.text_embed.shape[-1], hidden_dim, bias=False)
+ else:
+ self.embed_projection = nn.Linear(hidden_dim, self.text_embed.shape[-1], bias=False)
+
+ assert ensemble_method in [
+ "max",
+ "mean",
+ ], f"ensemble_method {ensemble_method} not supported"
+ self.ensemble_method = ensemble_method
+
+ self.test_labels = None
+ self._test_text_embed_dict = dict()
+
+ def _open_state_dict(self):
+ return {"test_labels": self.test_labels}
+
+ def _save_open_state_dict(self, destination, prefix):
+ for k, v in self._open_state_dict().items():
+ destination[prefix + k] = v
+
+ def open_state_dict(self, destination=None, prefix=""):
+ if destination is None:
+ destination = OrderedDict()
+ self._save_open_state_dict(destination, prefix)
+ for name, module in self.named_modules(remove_duplicate=True):
+ if module is self:
+ continue
+ if module is not None and hasattr(module, "open_state_dict"):
+ module.open_state_dict(destination, prefix + name + ".")
+ return destination
+
+ def extra_repr(self):
+ return (
+ f"clip_model_name={self.clip_model_name}, \n"
+ f"ensemble_method={self.ensemble_method}, \n"
+ f"projection_modality={self.projection_modality}, \n"
+ )
+
+ @torch.no_grad()
+ def build_text_embed(self, labels):
+ return build_clip_text_embed(
+ clip_model_name=self.clip_model_name,
+ labels=labels,
+ )
+
+ def get_and_cache_test_text_embed(self, labels):
+ labels = to_tuple(labels)
+ if labels not in self._test_text_embed_dict:
+ self._test_text_embed_dict[labels] = self.build_text_embed(labels)
+ return self._test_text_embed_dict[labels]
+
+ def forward(self, x):
+ if self.projection_modality == "image":
+ x = self.embed_projection(x)
+ x = F.normalize(x, dim=-1)
+
+ logit_scale = torch.clamp(self.logit_scale.exp(), max=100)
+
+ if self.test_labels is None:
+ labels = self.labels
+ text_embed = self.text_embed
+ else:
+ labels = self.test_labels
+ text_embed = self.get_and_cache_test_text_embed(labels).to(x.device)
+
+ if self.projection_modality == "text":
+ text_embed = self.embed_projection(text_embed)
+ text_embed = F.normalize(text_embed, dim=-1)
+
+ # [B, Q, K]
+ pred = logit_scale * (x @ text_embed.t())
+
+ pred = ensemble_logits_with_labels(pred, labels, ensemble_method=self.ensemble_method)
+
+ if self.null_embed is not None:
+ if self.projection_modality == "text":
+ null_embed = self.embed_projection(self.null_embed)
+ else:
+ null_embed = self.null_embed
+ null_embed = F.normalize(null_embed, dim=-1)
+ null_pred = logit_scale * (x @ null_embed.t())
+ # [B, Q, K+1]
+ pred = torch.cat([pred, null_pred], dim=-1)
+
+ return pred
+
+
+class PoolingCLIPHead(WordEmbed):
+ def __init__(
+ self,
+ clip_model_name="ViT-L-14-336",
+ alpha=0.35,
+ beta=0.65,
+ prompt="photo",
+ train_labels=None,
+ normalize_logits=True,
+ bg_labels=None,
+ ):
+ super(WordEmbed, self).__init__()
+ self.clip_model_name = clip_model_name
+ # For ViT CLIP, we found MaskCLIP yields slightly better performance
+ # than pooling on CLIP feature map
+ self.clip = MaskCLIP(name=self.clip_model_name)
+
+ self.alpha = alpha
+ self.beta = beta
+
+ self.test_labels = None
+ self._test_text_embed_dict = dict()
+
+ self.prompt = prompt
+ if train_labels is None:
+ self.train_labels = get_openseg_labels("coco_panoptic", prompt_engineered=True)
+ else:
+ self.train_labels = train_labels
+ self.bg_labels = bg_labels
+ self.normalize_logits = normalize_logits
+
+ def extra_repr(self) -> str:
+ return f"clip_model_name={self.clip_model_name},\n"
+
+ @property
+ def with_bg(self):
+ return self.bg_labels is not None
+
+ def prepare_targets(self, outputs, targets):
+
+ target_mask_embed = self.clip.get_mask_embed(outputs["images"], outputs["pred_masks"])
+
+ for idx in range(len(targets)):
+ targets[idx]["target_mask_embed"] = target_mask_embed[idx]
+
+ return targets
+
+ def forward(self, outputs, targets=None):
+ assert not self.training, "PoolingCLIPHead only supports inference"
+ assert targets is None
+ assert self.test_labels is not None
+ pred_open_logits = outputs.pop("pred_open_logits")
+
+ labels = prompt_labels(self.test_labels, self.prompt)
+ if self.with_bg and pred_open_logits.shape[-1] == len(self.test_labels) + 1:
+ labels.append(self.bg_labels)
+
+ category_overlapping_list = []
+
+ train_labels = {l for label in self.train_labels for l in label}
+
+ for test_label in self.test_labels:
+ category_overlapping_list.append(not set(train_labels).isdisjoint(set(test_label)))
+
+ if self.with_bg and pred_open_logits.shape[-1] == len(self.test_labels) + 1:
+ category_overlapping_list.append(False)
+
+ category_overlapping_mask = torch.tensor(
+ category_overlapping_list, device=outputs["images"].device, dtype=torch.long
+ )
+
+ text_embed = self.get_and_cache_test_text_embed(labels)
+
+ mask_pred_results = outputs["pred_masks"]
+
+ clip_results = self.clip(
+ outputs["images"],
+ mask_pred_results,
+ text_embed,
+ labels,
+ )
+
+ mask_pred_open_logits = clip_results["mask_pred_open_logits"]
+
+ if self.normalize_logits:
+ pred_open_prob = pred_open_logits.softmax(dim=-1)
+
+ mask_pred_open_prob = mask_pred_open_logits.softmax(dim=-1)
+
+ # NOTE: logits are multiplied with logit_scale,
+ # avoid double scale, which cause overflow in softmax
+ pred_open_logits_base = (
+ (pred_open_prob ** (1 - self.alpha) * mask_pred_open_prob**self.alpha).log()
+ # * outputs["logit_scale"]
+ * category_overlapping_mask
+ )
+
+ pred_open_logits_novel = (
+ (pred_open_prob ** (1 - self.beta) * mask_pred_open_prob**self.beta).log()
+ # * outputs["logit_scale"]
+ * (1 - category_overlapping_mask)
+ )
+ else:
+
+ # NOTE: this version ignore the scale difference during ensemble,
+
+ pred_open_logits_base = (
+ pred_open_logits * (1 - self.alpha)
+ + mask_pred_open_logits * self.alpha * category_overlapping_mask
+ )
+ pred_open_logits_novel = pred_open_logits * (
+ 1 - self.beta
+ ) + mask_pred_open_logits * self.beta * (1 - category_overlapping_mask)
+
+ pred_open_logits = pred_open_logits_base + pred_open_logits_novel
+
+ ret = {"pred_open_logits": pred_open_logits}
+ if "labels" in outputs:
+ ret["labels"] = labels
+
+ return ret
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/preprocess.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b19dc7b87f24f35a85b2e2ad0d87a3ce198cd35
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/preprocess.py
@@ -0,0 +1,37 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import collections.abc
+import torch
+
+
+def batched_input_to_device(batched_inputs, device, exclude=()):
+
+ if isinstance(exclude, str):
+ exclude = [exclude]
+
+ if isinstance(batched_inputs, torch.Tensor):
+ batch = batched_inputs.to(device, non_blocking=True)
+ return batch
+ elif isinstance(batched_inputs, collections.abc.Mapping):
+ batch = {}
+ for k in batched_inputs:
+ if k not in exclude:
+ batched_inputs[k] = batched_input_to_device(batched_inputs[k], device)
+ return batched_inputs
+
+ elif isinstance(batched_inputs, collections.abc.Sequence) and not isinstance(
+ batched_inputs, str
+ ):
+ return [batched_input_to_device(d, device) for d in batched_inputs]
+ elif isinstance(batched_inputs, str):
+ return batched_inputs
+ else:
+ raise TypeError(f"Unsupported type {type(batched_inputs)}")
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7344a6ef2fa428776b9dd28a584a573808c82c90
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from .pano_wrapper import OpenPanopticInference
+
+__all__ = ["OpenPanopticInference"]
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__pycache__/pano_wrapper.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/__pycache__/pano_wrapper.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..738a7db6d85348655c5e13de30828bfb494869cb
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/pano_wrapper.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/pano_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2f6677fc5d66c83d6f0acf151d31ed2ea7fd9fe
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/modeling/wrapper/pano_wrapper.py
@@ -0,0 +1,80 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+from collections import OrderedDict
+import torch.nn as nn
+
+
+class OpenPanopticInference(nn.Module):
+ def __init__(
+ self,
+ model,
+ labels,
+ metadata=None,
+ semantic_on=True,
+ instance_on=True,
+ panoptic_on=True,
+ test_topk_per_image=100,
+ ):
+ super().__init__()
+ self.model = model
+ self.labels = labels
+ self.metadata = metadata
+
+ self.semantic_on = semantic_on
+ self.instance_on = instance_on
+ self.panoptic_on = panoptic_on
+ self.test_topk_per_image = test_topk_per_image
+
+ self.open_state_dict = OrderedDict()
+
+ for k in self.model.open_state_dict():
+ if k.endswith("test_labels"):
+ self.open_state_dict[k] = self.labels
+ elif k.endswith("metadata"):
+ self.open_state_dict[k] = self.metadata
+ elif k.endswith("num_classes"):
+ self.open_state_dict[k] = self.num_classes
+ elif k.endswith("semantic_on"):
+ self.open_state_dict[k] = self.semantic_on
+ elif k.endswith("instance_on"):
+ self.open_state_dict[k] = self.instance_on
+ elif k.endswith("panoptic_on"):
+ self.open_state_dict[k] = self.panoptic_on
+ elif k.endswith("test_topk_per_image"):
+ self.open_state_dict[k] = self.test_topk_per_image
+
+ @property
+ def num_classes(self):
+ return len(self.labels)
+
+ def get_features(self, batched_inputs, caption=None, pca=None):
+ assert not self.training
+
+ _open_state_dict = self.model.open_state_dict()
+ self.model.load_open_state_dict(self.open_state_dict)
+ # print("model state", self.model)
+ # self.model.to('cuda:1')
+ # print('input device:', batched_inputs[0]['image'].device)
+ # print('inside OpenPanopticInference model device:', self.model.device) # device 1, passed successfully
+ results = self.model.get_features(batched_inputs, caption, pca=pca) if caption is not None else self.model.get_features(batched_inputs, pca=pca)
+ return results
+
+ def forward(self, batched_inputs):
+ assert not self.training
+
+ _open_state_dict = self.model.open_state_dict()
+ self.model.load_open_state_dict(self.open_state_dict)
+
+ results = self.model(batched_inputs)
+
+ self.model.load_open_state_dict(_open_state_dict)
+
+ return results
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/__init__.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/__init__.py
new file mode 100644
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/__pycache__/__init__.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/__pycache__/__init__.cpython-39.pyc
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/__pycache__/file_io.cpython-39.pyc b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/__pycache__/file_io.cpython-39.pyc
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index 0000000000000000000000000000000000000000..2c1d30130a83b33a421727254a0621585c703a98
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diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/collect_env.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/collect_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d391fd7bb6459199213141a204f74e958b81b65
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/collect_env.py
@@ -0,0 +1,251 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import importlib
+import numpy as np
+import os
+import subprocess
+import sys
+from collections import defaultdict
+import PIL
+import torch
+import torchvision
+from detectron2.utils.collect_env import (
+ collect_torch_env,
+ detect_compute_compatibility,
+ get_env_module,
+ test_nccl_ops,
+)
+from tabulate import tabulate
+
+__all__ = ["collect_env_info"]
+
+
+def _minimal_ext_cmd(cmd):
+ # construct minimal environment
+ env = {}
+ for k in ["SYSTEMROOT", "PATH", "HOME"]:
+ v = os.environ.get(k)
+ if v is not None:
+ env[k] = v
+ # LANGUAGE is used on win32
+ env["LANGUAGE"] = "C"
+ env["LANG"] = "C"
+ env["LC_ALL"] = "C"
+ out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
+ return out
+
+
+def get_git_hash(fallback="unknown", digits=None):
+ """Get the git hash of the current repo.
+
+ Args:
+ fallback (str, optional): The fallback string when git hash is
+ unavailable. Defaults to 'unknown'.
+ digits (int, optional): kept digits of the hash. Defaults to None,
+ meaning all digits are kept.
+
+ Returns:
+ str: Git commit hash.
+ """
+
+ if digits is not None and not isinstance(digits, int):
+ raise TypeError("digits must be None or an integer")
+
+ try:
+ out = _minimal_ext_cmd(["git", "rev-parse", "HEAD"])
+ sha = out.strip().decode("ascii")
+ if digits is not None:
+ sha = sha[:digits]
+ except OSError:
+ sha = fallback
+
+ return sha
+
+
+def collect_env_info():
+ has_gpu = torch.cuda.is_available() # true for both CUDA & ROCM
+ torch_version = torch.__version__
+
+ # NOTE that CUDA_HOME/ROCM_HOME could be None even when CUDA runtime libs are functional
+ from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME
+
+ has_rocm = False
+ if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None):
+ has_rocm = True
+ has_cuda = has_gpu and (not has_rocm)
+
+ data = []
+ data.append(("sys.platform", sys.platform)) # check-template.yml depends on it
+ data.append(("Python", sys.version.replace("\n", "")))
+ data.append(("numpy", np.__version__))
+
+ data.append(("ODISE git hash", get_git_hash()))
+
+ try:
+ import detectron2 # noqa
+
+ data.append(
+ ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__))
+ )
+ except ImportError:
+ data.append(("detectron2", "failed to import"))
+ except AttributeError:
+ data.append(("detectron2", "imported a wrong installation"))
+
+ try:
+ import detectron2._C as _C
+ except ImportError as e:
+ data.append(("detectron2._C", f"not built correctly: {e}"))
+
+ # print system compilers when extension fails to build
+ if sys.platform != "win32": # don't know what to do for windows
+ try:
+ # this is how torch/utils/cpp_extensions.py choose compiler
+ cxx = os.environ.get("CXX", "c++")
+ cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True)
+ cxx = cxx.decode("utf-8").strip().split("\n")[0]
+ except subprocess.SubprocessError:
+ cxx = "Not found"
+ data.append(("Compiler ($CXX)", cxx))
+
+ if has_cuda and CUDA_HOME is not None:
+ try:
+ nvcc = os.path.join(CUDA_HOME, "bin", "nvcc")
+ nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True)
+ nvcc = nvcc.decode("utf-8").strip().split("\n")[-1]
+ except subprocess.SubprocessError:
+ nvcc = "Not found"
+ data.append(("CUDA compiler", nvcc))
+ if has_cuda and sys.platform != "win32":
+ try:
+ so_file = importlib.util.find_spec("detectron2._C").origin
+ except (ImportError, AttributeError):
+ pass
+ else:
+ data.append(
+ ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file))
+ )
+ else:
+ # print compilers that are used to build extension
+ data.append(("Compiler", _C.get_compiler_version()))
+ data.append(("CUDA compiler", _C.get_cuda_version())) # cuda or hip
+ if has_cuda and getattr(_C, "has_cuda", lambda: True)():
+ data.append(
+ ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__))
+ )
+
+ data.append(get_env_module())
+ data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__)))
+ data.append(("PyTorch debug build", torch.version.debug))
+
+ if not has_gpu:
+ has_gpu_text = "No: torch.cuda.is_available() == False"
+ else:
+ has_gpu_text = "Yes"
+ data.append(("GPU available", has_gpu_text))
+ if has_gpu:
+ devices = defaultdict(list)
+ for k in range(torch.cuda.device_count()):
+ cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k)))
+ name = torch.cuda.get_device_name(k) + f" (arch={cap})"
+ devices[name].append(str(k))
+ for name, devids in devices.items():
+ data.append(("GPU " + ",".join(devids), name))
+
+ if has_rocm:
+ msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else ""
+ data.append(("ROCM_HOME", str(ROCM_HOME) + msg))
+ else:
+ try:
+ from torch.utils.collect_env import get_nvidia_driver_version, run as _run
+
+ data.append(("Driver version", get_nvidia_driver_version(_run)))
+ except Exception:
+ pass
+ msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else ""
+ data.append(("CUDA_HOME", str(CUDA_HOME) + msg))
+
+ cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)
+ if cuda_arch_list:
+ data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list))
+ data.append(("Pillow", PIL.__version__))
+
+ try:
+ data.append(
+ (
+ "torchvision",
+ str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__),
+ )
+ )
+ if has_cuda:
+ try:
+ torchvision_C = importlib.util.find_spec("torchvision._C").origin
+ msg = detect_compute_compatibility(CUDA_HOME, torchvision_C)
+ data.append(("torchvision arch flags", msg))
+ except (ImportError, AttributeError):
+ data.append(("torchvision._C", "Not found"))
+ except AttributeError:
+ data.append(("torchvision", "unknown"))
+
+ try:
+ import fvcore
+
+ data.append(("fvcore", fvcore.__version__))
+ except (ImportError, AttributeError):
+ pass
+
+ try:
+ import iopath
+
+ data.append(("iopath", iopath.__version__))
+ except (ImportError, AttributeError):
+ pass
+
+ try:
+ import cv2
+
+ data.append(("cv2", cv2.__version__))
+ except (ImportError, AttributeError):
+ data.append(("cv2", "Not found"))
+
+ env_str = tabulate(data) + "\n"
+ env_str += collect_torch_env()
+ return env_str
+
+
+if __name__ == "__main__":
+ try:
+ from odise.utils.collect_env import collect_env_info as f
+
+ print(f())
+ except ImportError:
+ print(collect_env_info())
+
+ if torch.cuda.is_available():
+ num_gpu = torch.cuda.device_count()
+ for k in range(num_gpu):
+ device = f"cuda:{k}"
+ try:
+ x = torch.tensor([1, 2.0], dtype=torch.float32)
+ x = x.to(device)
+ except Exception as e:
+ print(
+ f"Unable to copy tensor to device={device}: {e}. "
+ "Your CUDA environment is broken."
+ )
+ if num_gpu > 1:
+ test_nccl_ops()
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/events.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/events.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c5315f12c02747192fd7b3fc2a1cc5cc4228b5f
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/events.py
@@ -0,0 +1,193 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import json
+import logging
+import os
+import os.path as osp
+from typing import Dict, Optional, Union
+import torch
+from detectron2.config import CfgNode
+from detectron2.utils.events import CommonMetricPrinter as _CommonMetricPrinter
+from detectron2.utils.events import EventWriter, get_event_storage
+
+
+class WandbWriter(EventWriter):
+ """Write all scalars to a wandb tool.
+ based on https://github.com/facebookresearch/detectron2/pull/3716
+ """
+
+ def __init__(
+ self,
+ max_iter: int,
+ run_name: str,
+ output_dir: str,
+ project: str = "ODISE",
+ config: Union[Dict, CfgNode] = {}, # noqa: B006
+ resume: bool = False,
+ window_size: int = 20,
+ **kwargs,
+ ):
+ """
+ Args:
+ project (str): W&B Project name
+ config Union[Dict, CfgNode]: the project level configuration object
+ window_size (int): the scalars will be median-smoothed by this window size
+ kwargs: other arguments passed to `wandb.init(...)`
+ """
+ logger = logging.getLogger(__name__)
+ # this could avoid hanging when process failed before Trainer.train()
+ os.environ.setdefault("WANDB_START_METHOD", "thread")
+ logger.info(f"Setting WANDB_START_METHOD to '{os.environ['WANDB_START_METHOD']}'")
+
+ import wandb
+
+ self._window_size = window_size
+ self._run = (
+ wandb.init(
+ project=project,
+ name=run_name,
+ config=config,
+ dir=output_dir,
+ resume=resume,
+ **kwargs,
+ )
+ if not wandb.run
+ else wandb.run
+ )
+ self._run._label(repo="vision")
+ self._max_iter = max_iter
+
+ # manually write "wandb-resume.json" file
+ # it is automatically created by wandb.init() only resume=True
+ # so we manually create it when resume=False
+ # such that we could resume a run even if we passed resume=False previously
+ resume_file = osp.join(output_dir, "wandb/wandb-resume.json")
+ if not resume:
+ logger.warning("Manually create wandb-resume.json file")
+ with open(resume_file, "w") as f:
+ json.dump({"run_id": self._run.id}, f)
+
+ def write(self):
+ storage = get_event_storage()
+
+ log_dict = {}
+ for k, (v, _) in storage.latest_with_smoothing_hint(self._window_size).items():
+ log_dict[k] = v
+
+ log_dict["progress"] = storage.iter / self._max_iter
+
+ self._run.log(log_dict)
+
+ def close(self):
+ try:
+ storage = get_event_storage()
+ iteration = storage.iter
+ if iteration >= self._max_iter - 1:
+ # finish the run after reaching max_iter
+ # finish() will automatically remove the "wandb-resume.json" file
+ self._run.finish()
+ else:
+ # mark the run haven't finish yet
+ self._run.finish(1)
+ except AssertionError:
+ # no trainer/event_storage yet
+ # mark the run haven't finish yet
+ self._run.finish(1)
+
+
+class CommonMetricPrinter(_CommonMetricPrinter):
+ """
+ Print **common** metrics to the terminal, including
+ iteration time, ETA, memory, all losses, and the learning rate.
+ It also applies smoothing using a window of 20 elements.
+
+ It's meant to print common metrics in common ways.
+ To print something in more customized ways, please implement a similar printer by yourself.
+ """
+
+ def __init__(self, max_iter: Optional[int] = None, window_size: int = 20, run_name: str = ""):
+ super().__init__(max_iter=max_iter, window_size=window_size)
+ self.run_name = run_name
+
+ def write(self):
+ storage = get_event_storage()
+ iteration = storage.iter
+ if iteration == self._max_iter:
+ # This hook only reports training progress (loss, ETA, etc) but not other data,
+ # therefore do not write anything after training succeeds, even if this method
+ # is called.
+ return
+
+ try:
+ data_time = storage.history("data_time").avg(20)
+ except KeyError:
+ # they may not exist in the first few iterations (due to warmup)
+ # or when SimpleTrainer is not used
+ data_time = None
+ try:
+ iter_time = storage.history("time").global_avg()
+ except KeyError:
+ iter_time = None
+ try:
+ lr = "{:.5g}".format(storage.history("lr").latest())
+ except KeyError:
+ lr = "N/A"
+
+ eta_string = self._get_eta(storage)
+
+ storage_hide_keys = ["eta_seconds", "data_time", "time", "lr"]
+
+ if torch.cuda.is_available():
+ max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
+ else:
+ max_mem_mb = None
+
+ # NOTE: max_mem is parsed by grep in "dev/parse_results.sh"
+ self.logger.info(
+ "{run_name} {eta}iter: {iter}{max_iter} {losses} {time}{data_time}lr: {lr} {memory}".format( # noqa: E501,B950
+ run_name=self.run_name,
+ eta=f"eta: {eta_string} " if eta_string else "",
+ iter=iteration,
+ max_iter=f"/{self._max_iter}" if self._max_iter else "",
+ # NOTE: Jiarui makes losses include all the storage.histories() except for
+ # non-smoothing metrics. This hack will make writter log all the metrics in
+ # the storage, but excluding metrics from EvalHook since smoothing_hint=False.
+ losses=" ".join(
+ [
+ "{}: {:.4g}".format(k, v.median(self._window_size))
+ for k, v in storage.histories().items()
+ if k not in storage_hide_keys and storage.smoothing_hints()[k]
+ ]
+ ),
+ time="time: {:.4f} ".format(iter_time) if iter_time is not None else "",
+ data_time="data_time: {:.4f} ".format(data_time) if data_time is not None else "",
+ lr=lr,
+ memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "",
+ )
+ )
+
+
+class WriterStack:
+ def __init__(self, logger, writers=None):
+ self.logger = logger
+ self.writers = writers
+
+ def __enter__(self):
+ pass
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ if exc_type is not None:
+ self.logger.error("Error occurred in the writer", exc_info=(exc_type, exc_val, exc_tb))
+ self.logger.error("Closing all writers")
+ if self.writers is not None:
+ for writer in self.writers:
+ writer.close()
+ self.logger.error("All writers closed")
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/file_io.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/file_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3aa96c98c7cebd20317bb910852d31723382739
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/file_io.py
@@ -0,0 +1,96 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+import logging
+import os
+from detectron2.utils.file_io import PathManager
+from iopath.common.file_io import PathHandler
+
+
+class ODISEHandler(PathHandler):
+ """
+ Resolve anything that's hosted under ODISE's namespace.
+ """
+
+ PREFIX = "odise://"
+ URLS = {
+ "Panoptic/odise_caption_coco_50e": "https://github.com/NVlabs/ODISE/releases/download/v1.0.0/odise_caption_coco_50e-853cc971.pth", # noqa
+ "Panoptic/odise_label_coco_50e": "https://github.com/NVlabs/ODISE/releases/download/v1.0.0/odise_label_coco_50e-b67d2efc.pth", # noqa
+ }
+
+ def _get_supported_prefixes(self):
+ return [self.PREFIX]
+
+ @property
+ def local_model_zoo(self):
+ return os.environ.get("ODISE_MODEL_ZOO", "")
+
+ def _get_local_path(self, path, **kwargs):
+ name = path[len(self.PREFIX) :]
+ assert name in self.URLS, f"{name} is not a valid ODISE model from {self.URLS.keys()}!"
+ path = self.URLS[name]
+ if self.local_model_zoo:
+ local_path = os.path.join(self.local_model_zoo, os.path.basename(path))
+ if os.path.exists(local_path):
+ logging.getLogger(__name__).info(f"Using local model zoo: {local_path}.")
+ path = local_path
+ return PathManager.get_local_path(path, **kwargs)
+
+ def _open(self, path, mode="r", **kwargs):
+ return PathManager.open(self._get_local_path(path), mode, **kwargs)
+
+
+class StableDiffusionHandler(PathHandler):
+ """
+ Resolve anything that's hosted under ODISE's namespace.
+ """
+
+ PREFIX = "sd://"
+ URLS = {
+ "v1-3": "https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt", # noqa
+ # following is not used yet
+ # we are adding them here for generalization
+ "v1-4": "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt", # noqa
+ "v1-5": "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", # noqa
+ "v2-0-base": "https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt", # noqa
+ "v2-0-v": "https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt", # noqa
+ "v2-1-base": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt", # noqa
+ "v2-1-v": "https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt", # noqa
+ }
+
+ def _get_supported_prefixes(self):
+ return [self.PREFIX]
+
+ @property
+ def local_model_zoo(self):
+ return os.environ.get("ODISE_MODEL_ZOO", "")
+
+ def _get_local_path(self, path, **kwargs):
+ name = path[len(self.PREFIX) :]
+ assert name in self.URLS, f"{name} is not a valid ODISE model from {self.URLS.keys()}!"
+ path = self.URLS[name]
+ if self.local_model_zoo:
+ local_path = os.path.join(self.local_model_zoo, os.path.basename(path))
+ if os.path.exists(local_path):
+ logging.getLogger(__name__).info(f"Using local model zoo: {local_path}.")
+ path = local_path
+ return PathManager.get_local_path(path, **kwargs)
+
+ def _open(self, path, mode="r", **kwargs):
+ return PathManager.open(self._get_local_path(path), mode, **kwargs)
+
+
+PathManager.register_handler(ODISEHandler())
+PathManager.register_handler(StableDiffusionHandler())
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/parameter_count.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/parameter_count.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a738054d2f916a185fb13e762cb08aa804308b3
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/odise/utils/parameter_count.py
@@ -0,0 +1,136 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import typing
+from collections import defaultdict
+import tabulate
+from torch import nn
+
+
+def parameter_count(model: nn.Module, trainable_only: bool = False) -> typing.DefaultDict[str, int]:
+ """
+ Count parameters of a model and its submodules.
+
+ Args:
+ model: a torch module
+
+ Returns:
+ dict (str-> int): the key is either a parameter name or a module name.
+ The value is the number of elements in the parameter, or in all
+ parameters of the module. The key "" corresponds to the total
+ number of parameters of the model.
+ """
+ r = defaultdict(int)
+ for name, prm in model.named_parameters():
+ if trainable_only:
+ if not prm.requires_grad:
+ continue
+ size = prm.numel()
+ name = name.split(".")
+ for k in range(0, len(name) + 1):
+ prefix = ".".join(name[:k])
+ r[prefix] += size
+ return r
+
+
+def parameter_count_table(
+ model: nn.Module, max_depth: int = 3, trainable_only: bool = False
+) -> str:
+ """
+ Format the parameter count of the model (and its submodules or parameters)
+ in a nice table. It looks like this:
+
+ ::
+
+ | name | #elements or shape |
+ |:--------------------------------|:---------------------|
+ | model | 37.9M |
+ | backbone | 31.5M |
+ | backbone.fpn_lateral3 | 0.1M |
+ | backbone.fpn_lateral3.weight | (256, 512, 1, 1) |
+ | backbone.fpn_lateral3.bias | (256,) |
+ | backbone.fpn_output3 | 0.6M |
+ | backbone.fpn_output3.weight | (256, 256, 3, 3) |
+ | backbone.fpn_output3.bias | (256,) |
+ | backbone.fpn_lateral4 | 0.3M |
+ | backbone.fpn_lateral4.weight | (256, 1024, 1, 1) |
+ | backbone.fpn_lateral4.bias | (256,) |
+ | backbone.fpn_output4 | 0.6M |
+ | backbone.fpn_output4.weight | (256, 256, 3, 3) |
+ | backbone.fpn_output4.bias | (256,) |
+ | backbone.fpn_lateral5 | 0.5M |
+ | backbone.fpn_lateral5.weight | (256, 2048, 1, 1) |
+ | backbone.fpn_lateral5.bias | (256,) |
+ | backbone.fpn_output5 | 0.6M |
+ | backbone.fpn_output5.weight | (256, 256, 3, 3) |
+ | backbone.fpn_output5.bias | (256,) |
+ | backbone.top_block | 5.3M |
+ | backbone.top_block.p6 | 4.7M |
+ | backbone.top_block.p7 | 0.6M |
+ | backbone.bottom_up | 23.5M |
+ | backbone.bottom_up.stem | 9.4K |
+ | backbone.bottom_up.res2 | 0.2M |
+ | backbone.bottom_up.res3 | 1.2M |
+ | backbone.bottom_up.res4 | 7.1M |
+ | backbone.bottom_up.res5 | 14.9M |
+ | ...... | ..... |
+
+ Args:
+ model: a torch module
+ max_depth (int): maximum depth to recursively print submodules or
+ parameters
+
+ Returns:
+ str: the table to be printed
+ """
+ count: typing.DefaultDict[str, int] = parameter_count(model, trainable_only)
+ # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
+ param_shape: typing.Dict[str, typing.Tuple] = {
+ k: tuple(v.shape) for k, v in model.named_parameters()
+ }
+
+ # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
+ table: typing.List[typing.Tuple] = []
+
+ def format_size(x: int) -> str:
+ if x > 1e8:
+ return "{:.1f}G".format(x / 1e9)
+ if x > 1e5:
+ return "{:.1f}M".format(x / 1e6)
+ if x > 1e2:
+ return "{:.1f}K".format(x / 1e3)
+ return str(x)
+
+ def fill(lvl: int, prefix: str) -> None:
+ if lvl >= max_depth:
+ return
+ for name, v in count.items():
+ if name.count(".") == lvl and name.startswith(prefix):
+ indent = " " * (lvl + 1)
+ if name in param_shape:
+ table.append((indent + name, indent + str(param_shape[name])))
+ else:
+ table.append((indent + name, indent + format_size(v)))
+ fill(lvl + 1, name + ".")
+
+ table.append(("model", format_size(count.pop(""))))
+ fill(0, "")
+
+ old_ws = tabulate.PRESERVE_WHITESPACE
+ tabulate.PRESERVE_WHITESPACE = True
+ tab = tabulate.tabulate(table, headers=["name", "#elements or shape"], tablefmt="pipe")
+ tabulate.PRESERVE_WHITESPACE = old_ws
+ return tab
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/setup.cfg b/Code/Baselines/GeoAware-SC/third_party/ODISE/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..331479344c1f8df98179c45808a60cc21b99b48f
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/setup.cfg
@@ -0,0 +1,26 @@
+[isort]
+line_length=100
+multi_line_output=3
+include_trailing_comma=True
+known_standard_library=numpy,setuptools,mock
+skip=./datasets,docs,local_data,third_party
+skip_glob=*/__init__.py,**/configs/**,tests/config/**,vision/modeling/mask2former/**,output/**
+known_myself=odise
+known_third_party=fvcore,matplotlib,cv2,torch,torchvision,PIL,pycocotools,yacs,termcolor,cityscapesscripts,tabulate,tqdm,scipy,lvis,psutil,pkg_resources,caffe2,onnx,panopticapi,black,isort,av,iopath,omegaconf,hydra,yaml,pydoc,submitit,cloudpickle
+no_lines_before=STDLIB,THIRDPARTY
+sections=FUTURE,STDLIB,THIRDPARTY,myself,FIRSTPARTY,LOCALFOLDER
+default_section=FIRSTPARTY
+
+[mypy]
+python_version=3.6
+ignore_missing_imports = True
+warn_unused_configs = True
+disallow_untyped_defs = True
+check_untyped_defs = True
+warn_unused_ignores = True
+warn_redundant_casts = True
+show_column_numbers = True
+follow_imports = silent
+allow_redefinition = True
+; Require all functions to be annotated
+disallow_incomplete_defs = True
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/setup.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..521c62eb29c4334ab27ab5c581b00e37d1cc4d56
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/setup.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+
+import glob
+import os
+import shutil
+from os import path
+from setuptools import find_packages, setup
+from typing import List
+import torch
+
+torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
+assert torch_ver >= [1, 8], "Requires PyTorch >= 1.8"
+
+
+def get_version():
+ init_py_path = path.join(path.abspath(path.dirname(__file__)), "odise", "__init__.py")
+ init_py = open(init_py_path, "r").readlines()
+ version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
+ version = version_line.split("=")[-1].strip().strip("'\"")
+
+ return version
+
+
+def get_model_zoo_configs() -> List[str]:
+ """
+ Return a list of configs to include in package for model zoo. Copy over these configs inside
+ odise/model_zoo.
+ """
+
+ # Use absolute paths while symlinking.
+ source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs")
+ destination = path.join(path.dirname(path.realpath(__file__)), "odise", "model_zoo", "configs")
+ # Symlink the config directory inside package to have a cleaner pip install.
+
+ # Remove stale symlink/directory from a previous build.
+ if path.exists(source_configs_dir):
+ if path.islink(destination):
+ os.unlink(destination)
+ elif path.isdir(destination):
+ shutil.rmtree(destination)
+
+ if not path.exists(destination):
+ try:
+ os.symlink(source_configs_dir, destination)
+ except OSError:
+ # Fall back to copying if symlink fails: ex. on Windows.
+ shutil.copytree(source_configs_dir, destination)
+
+ config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob(
+ "configs/**/*.py", recursive=True
+ )
+ return config_paths
+
+
+setup(
+ name="odise",
+ version=get_version(),
+ author="Jiarui Xu",
+ url="https://github.com/NVlabs/ODISE",
+ description="Open-vocabulary DIffusion-based Panoptic Segmentation",
+ packages=find_packages(exclude=("configs", "tests*")),
+ package_data={"odise.model_zoo": get_model_zoo_configs()},
+ python_requires=">=3.8",
+ install_requires=[
+ "timm==0.6.11", # freeze timm version for stabliity
+ "opencv-python==4.6.0.66",
+ "diffdist==0.1",
+ "nltk>=3.6.2",
+ "einops>=0.3.0",
+ "wandb>=0.12.11",
+ # "transformers==4.20.1", # freeze transformers version for stabliity
+ # there is BC breaking in omegaconf 2.2.1
+ # see: https://github.com/omry/omegaconf/issues/939
+ "omegaconf==2.1.1",
+ "open-clip-torch==2.0.2",
+ "stable-diffusion-sdkit==2.1.3",
+ ],
+ extras_require={
+ # dev dependencies. Install them by `pip install 'odise[dev]'`
+ "dev": [
+ "flake8==3.8.1",
+ "isort==4.3.21",
+ "flake8-bugbear",
+ "flake8-comprehensions",
+ "click==8.0.4",
+ "importlib-metadata==4.11.3",
+ ],
+ },
+ include_package_data=True,
+)
diff --git a/Code/Baselines/GeoAware-SC/third_party/ODISE/tools/train_net.py b/Code/Baselines/GeoAware-SC/third_party/ODISE/tools/train_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..c19fecf43dfac8dc2500becd34e2d287d780735d
--- /dev/null
+++ b/Code/Baselines/GeoAware-SC/third_party/ODISE/tools/train_net.py
@@ -0,0 +1,399 @@
+#!/usr/bin/env python
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates.
+# To view a copy of this license, visit
+# https://github.com/facebookresearch/detectron2/blob/main/LICENSE
+# ------------------------------------------------------------------------------
+#
+# ------------------------------------------------------------------------------
+# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# This work is made available under the Nvidia Source Code License.
+# To view a copy of this license, visit
+# https://github.com/NVlabs/ODISE/blob/main/LICENSE
+#
+# Written by Jiarui Xu
+# ------------------------------------------------------------------------------
+"""
+Training script using the new "LazyConfig" python config files.
+
+This scripts reads a given python config file and runs the training or evaluation.
+It can be used to train any models or dataset as long as they can be
+instantiated by the recursive construction defined in the given config file.
+
+Besides lazy construction of models, dataloader, etc., this scripts expects a
+few common configuration parameters currently defined in "configs/common/train.py".
+To add more complicated training logic, you can easily add other configs
+in the config file and implement a new train_net.py to handle them.
+"""
+import argparse
+import logging
+import os.path as osp
+from contextlib import ExitStack
+from typing import MutableSequence
+from detectron2.config import LazyConfig, instantiate
+from detectron2.engine import create_ddp_model, default_argument_parser, hooks, launch
+from detectron2.evaluation import print_csv_format
+from detectron2.utils import comm
+from detectron2.utils.events import JSONWriter
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import setup_logger
+from iopath.common.s3 import S3PathHandler
+from omegaconf import OmegaConf
+
+from odise.checkpoint import ODISECheckpointer
+from odise.config import auto_scale_workers, instantiate_odise
+from odise.engine.defaults import default_setup, get_dataset_from_loader, get_model_from_module
+from odise.engine.hooks import EvalHook
+from odise.engine.train_loop import AMPTrainer, SimpleTrainer
+from odise.evaluation import inference_on_dataset
+from odise.utils.events import CommonMetricPrinter, WandbWriter, WriterStack
+
+PathManager.register_handler(S3PathHandler())
+
+logger = logging.getLogger("odise")
+
+
+def default_writers(cfg):
+ """
+ Build a list of :class:`EventWriter` to be used.
+ It now consists of a :class:`CommonMetricPrinter`,
+ :class:`TensorboardXWriter` and :class:`JSONWriter`.
+
+ Args:
+ output_dir: directory to store JSON metrics and tensorboard events
+ max_iter: the total number of iterations
+
+ Returns:
+ list[EventWriter]: a list of :class:`EventWriter` objects.
+ """
+ if "log_dir" in cfg.train:
+ log_dir = cfg.train.log_dir
+ else:
+ log_dir = cfg.train.output_dir
+ PathManager.mkdirs(log_dir)
+ ret = [
+ # It may not always print what you want to see, since it prints "common" metrics only.
+ CommonMetricPrinter(
+ cfg.train.max_iter, run_name=osp.join(cfg.train.run_name, cfg.train.run_tag)
+ ),
+ JSONWriter(osp.join(log_dir, "metrics.json")),
+ ]
+ if cfg.train.wandb.enable_writer:
+ ret.append(
+ WandbWriter(
+ max_iter=cfg.train.max_iter,
+ run_name=osp.join(cfg.train.run_name, cfg.train.run_tag),
+ output_dir=log_dir,
+ project=cfg.train.wandb.project,
+ config=OmegaConf.to_container(cfg, resolve=False),
+ resume=cfg.train.wandb.resume,
+ )
+ )
+
+ return ret
+
+
+class InferenceRunner:
+ def __init__(self, cfg, model):
+ self.cfg = cfg
+ self.model = model
+
+ def __call__(self, final_iter=False, next_iter=0):
+ return do_test(self.cfg, self.model, final_iter=final_iter, next_iter=next_iter)
+
+
+def do_test(cfg, model, *, final_iter=False, next_iter=0):
+ all_ret = dict()
+ # make a copy incase we modify it every time calling do_test
+ cfg = OmegaConf.create(cfg)
+
+ # BC for detectron
+ if "evaluator" in cfg.dataloader and "test" in cfg.dataloader:
+ task_final_iter_only = cfg.dataloader.get("final_iter_only", False)
+ task_eval_period = cfg.dataloader.get("eval_period", 1)
+ if not final_iter and (task_final_iter_only or next_iter % task_eval_period != 0):
+ logger.info(
+ f"Skip test set evaluation at iter {next_iter}, "
+ f"since task_final_iter_only={task_final_iter_only}, "
+ f"next_iter {next_iter} % task_eval_period {task_eval_period}"
+ f"={next_iter % task_eval_period} != 0"
+ )
+ else:
+ loader = instantiate(cfg.dataloader.test)
+
+ if "wrapper" in cfg.dataloader:
+ wrapper_cfg = cfg.dataloader.wrapper
+ # look for the last wrapper
+ while "model" in wrapper_cfg:
+ wrapper_cfg = wrapper_cfg.model
+ wrapper_cfg.model = get_model_from_module(model)
+ # poping _with_dataset_
+ if wrapper_cfg.pop("_with_dataset_", False):
+ wrapper_cfg.dataset = get_dataset_from_loader(loader)
+ inference_model = create_ddp_model(instantiate(cfg.dataloader.wrapper))
+ else:
+ inference_model = model
+
+ # poping _with_dataset_
+ if isinstance(cfg.dataloader.evaluator, MutableSequence):
+ for i in range(len(cfg.dataloader.evaluator)):
+ if cfg.dataloader.evaluator[i].pop("_with_dataset_", False):
+ cfg.dataloader.evaluator[i].dataset = get_dataset_from_loader(loader)
+ else:
+ if cfg.dataloader.evaluator.pop("_with_dataset_", False):
+ cfg.dataloader.evaluator.dataset = get_dataset_from_loader(loader)
+
+ ret = inference_on_dataset(
+ inference_model,
+ loader,
+ instantiate(cfg.dataloader.evaluator),
+ use_amp=cfg.train.amp.enabled,
+ )
+ print_csv_format(ret)
+ all_ret.update(ret)
+ if "extra_task" in cfg.dataloader:
+ for task in cfg.dataloader.extra_task:
+ task_final_iter_only = cfg.dataloader.extra_task[task].get("final_iter_only", False)
+ task_eval_period = cfg.dataloader.extra_task[task].get("eval_period", 1)
+ if not final_iter and (task_final_iter_only or next_iter % task_eval_period != 0):
+ logger.info(
+ f"Skip {task} evaluation at iter {next_iter}, "
+ f"since task_final_iter_only={task_final_iter_only}, "
+ f"next_iter {next_iter} % task_eval_period {task_eval_period}"
+ f"={next_iter % task_eval_period} != 0"
+ )
+ continue
+
+ logger.info(f"Evaluating extra task: {task}")
+ loader = instantiate(cfg.dataloader.extra_task[task].loader)
+
+ # poping _with_dataset_
+ if isinstance(cfg.dataloader.extra_task[task].evaluator, MutableSequence):
+ for i in range(len(cfg.dataloader.extra_task[task].evaluator)):
+ if cfg.dataloader.extra_task[task].evaluator[i].pop("_with_dataset_", False):
+ cfg.dataloader.extra_task[task].evaluator[
+ i
+ ].dataset = get_dataset_from_loader(loader)
+ else:
+ if cfg.dataloader.extra_task[task].evaluator.pop("_with_dataset_", False):
+ cfg.dataloader.extra_task[task].evaluator.dataset = get_dataset_from_loader(
+ loader
+ )
+
+ if "wrapper" in cfg.dataloader.extra_task[task]:
+ wrapper_cfg = cfg.dataloader.extra_task[task].wrapper
+ # look for the last wrapper
+ while "model" in wrapper_cfg:
+ wrapper_cfg = wrapper_cfg.model
+ wrapper_cfg.model = get_model_from_module(model)
+ # poping _with_dataset_
+ if wrapper_cfg.pop("_with_dataset_", False):
+ wrapper_cfg.dataset = get_dataset_from_loader(loader)
+ inference_model = create_ddp_model(
+ instantiate(cfg.dataloader.extra_task[task].wrapper)
+ )
+ else:
+ inference_model = model
+
+ ret = inference_on_dataset(
+ inference_model,
+ loader,
+ instantiate(cfg.dataloader.extra_task[task].evaluator),
+ use_amp=cfg.train.amp.enabled,
+ )
+ print_csv_format(ret)
+ all_ret.update(ret)
+ logger.info("Evaluation results for all tasks:")
+ print_csv_format(all_ret)
+ return all_ret
+
+
+def do_train(args, cfg):
+ """
+ Args:
+ cfg: an object with the following attributes:
+ model: instantiate to a module
+ dataloader.{train,test}: instantiate to dataloaders
+ dataloader.evaluator: instantiate to evaluator for test set
+ optimizer: instantaite to an optimizer
+ lr_multiplier: instantiate to a fvcore scheduler
+ train: other misc config defined in `configs/common/train.py`, including:
+ output_dir (str)
+ init_checkpoint (str)
+ amp.enabled (bool)
+ max_iter (int)
+ eval_period, log_period (int)
+ device (str)
+ checkpointer (dict)
+ ddp (dict)
+ """
+ logger = logging.getLogger("odise")
+ # set wandb resume before create writer
+ cfg.train.wandb.resume = args.resume and ODISECheckpointer.has_checkpoint_in_dir(
+ cfg.train.output_dir
+ )
+ # create writers at the beginning for W&B logging
+ if comm.is_main_process():
+ writers = default_writers(cfg)
+ comm.synchronize()
+
+ # not sure why d2 use ExitStack(), maybe easier for multiple context
+ with ExitStack() as stack:
+ stack.enter_context(
+ WriterStack(
+ logger=logger,
+ writers=writers if comm.is_main_process() else None,
+ )
+ )
+ logger.info(f"Wandb resume: {cfg.train.wandb.resume}")
+ # log config again for w&b
+ logger.info(f"Config:\n{LazyConfig.to_py(cfg)}")
+
+ model = instantiate_odise(cfg.model)
+ model.to(cfg.train.device)
+
+ cfg.optimizer.params.model = model
+ optim = instantiate(cfg.optimizer)
+
+ train_loader = instantiate(cfg.dataloader.train)
+
+ if cfg.train.amp.enabled:
+ model = create_ddp_model(model, **cfg.train.ddp)
+ trainer = AMPTrainer(model, train_loader, optim, grad_clip=cfg.train.grad_clip)
+ else:
+ model = create_ddp_model(model, **cfg.train.ddp)
+ trainer = SimpleTrainer(model, train_loader, optim, grad_clip=cfg.train.grad_clip)
+
+ checkpointer = ODISECheckpointer(
+ model,
+ cfg.train.output_dir,
+ trainer=trainer,
+ )
+
+ # set wandb resume before create writer
+ cfg.train.wandb.resume = args.resume and checkpointer.has_checkpoint()
+ logger.info(f"Wandb resume: {cfg.train.wandb.resume}")
+
+ trainer.register_hooks(
+ [
+ hooks.IterationTimer(),
+ hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)),
+ hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer)
+ if comm.is_main_process()
+ else None,
+ EvalHook(cfg.train.eval_period, InferenceRunner(cfg, model)),
+ hooks.BestCheckpointer(checkpointer=checkpointer, **cfg.train.best_checkpointer)
+ if comm.is_main_process() and "best_checkpointer" in cfg.train
+ else None,
+ hooks.PeriodicWriter(
+ writers=writers,
+ period=cfg.train.log_period,
+ )
+ if comm.is_main_process()
+ else None,
+ ]
+ )
+ comm.synchronize()
+
+ checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=args.resume)
+ if args.resume and checkpointer.has_checkpoint():
+ # The checkpoint stores the training iteration that just finished, thus we start
+ # at the next iteration
+ start_iter = trainer.iter + 1
+ else:
+ start_iter = 0
+ comm.synchronize()
+ # keep trainer.train() out of stack since it has try/catch handling
+ trainer.train(start_iter, cfg.train.max_iter)
+
+
+def main(args):
+ cfg = LazyConfig.load(args.config_file)
+ cfg.train.run_name = (
+ "${train.cfg_name}_bs${dataloader.train.total_batch_size}" + f"x{comm.get_world_size()}"
+ )
+ if hasattr(args, "reference_world_size") and args.reference_world_size:
+ cfg.train.reference_world_size = args.reference_world_size
+ cfg = auto_scale_workers(cfg, comm.get_world_size())
+ cfg.train.cfg_name = osp.splitext(osp.basename(args.config_file))[0]
+ if hasattr(args, "output") and args.output:
+ cfg.train.output_dir = args.output
+ else:
+ cfg.train.output_dir = osp.join("output", cfg.train.run_name)
+ if hasattr(args, "tag") and args.tag:
+ cfg.train.run_tag = args.tag
+ cfg.train.output_dir = osp.join(cfg.train.output_dir, cfg.train.run_tag)
+ if hasattr(args, "wandb") and args.wandb:
+ cfg.train.wandb.enable_writer = args.wandb
+ cfg.train.wandb.enable_visualizer = args.wandb
+ if hasattr(args, "amp") and args.amp:
+ cfg.train.amp.enabled = args.amp
+ if hasattr(args, "init_from") and args.init_from:
+ cfg.train.init_checkpoint = args.init_from
+ cfg.train.log_dir = cfg.train.output_dir
+ if hasattr(args, "log_tag") and args.log_tag:
+ cfg.train.log_dir = osp.join(cfg.train.log_dir, args.log_tag)
+ cfg = LazyConfig.apply_overrides(cfg, args.opts)
+ default_setup(cfg, args)
+ logger = setup_logger(cfg.train.log_dir, distributed_rank=comm.get_rank(), name="odise")
+
+ logger.info(f"Running with config:\n{LazyConfig.to_py(cfg)}")
+
+ if args.eval_only:
+ model = instantiate_odise(cfg.model)
+ model.to(cfg.train.device)
+ model = create_ddp_model(model)
+ ODISECheckpointer(model, cfg.train.output_dir).resume_or_load(
+ cfg.train.init_checkpoint, resume=args.resume
+ )
+ with ExitStack() as stack:
+ stack.enter_context(
+ WriterStack(
+ logger=logger,
+ writers=default_writers(cfg) if comm.is_main_process() else None,
+ )
+ )
+ logger.info(do_test(cfg, model, final_iter=True))
+ # Evaluation may take different time among workers.
+ # A barrier make them start the next iteration together.
+ comm.synchronize()
+ else:
+ do_train(args, cfg)
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(
+ "odise training and evaluation script",
+ parents=[default_argument_parser()],
+ add_help=False,
+ )
+
+ parser.add_argument(
+ "--output",
+ type=str,
+ help="root of output folder, " "the full path is 
+
+
+
+
+
+
+