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def cityscapes_classes():
return ['person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle']
|
def oid_challenge_classes():
return ['Footwear', 'Jeans', 'House', 'Tree', 'Woman', 'Man', 'Land vehicle', 'Person', 'Wheel', 'Bus', 'Human face', 'Bird', 'Dress', 'Girl', 'Vehicle', 'Building', 'Cat', 'Car', 'Belt', 'Elephant', 'Dessert', 'Butterfly', 'Train', 'Guitar', 'Poster', 'Book', 'Boy', 'Bee', 'Flower', ... |
def oid_v6_classes():
return ['Tortoise', 'Container', 'Magpie', 'Sea turtle', 'Football', 'Ambulance', 'Ladder', 'Toothbrush', 'Syringe', 'Sink', 'Toy', 'Organ (Musical Instrument)', 'Cassette deck', 'Apple', 'Human eye', 'Cosmetics', 'Paddle', 'Snowman', 'Beer', 'Chopsticks', 'Human beard', 'Bird', 'Parking met... |
def get_classes(dataset):
'Get class names of a dataset.'
alias2name = {}
for (name, aliases) in dataset_aliases.items():
for alias in aliases:
alias2name[alias] = name
if mmcv.is_str(dataset):
if (dataset in alias2name):
labels = eval((alias2name[dataset] + '_c... |
def _calc_dynamic_intervals(start_interval, dynamic_interval_list):
assert mmcv.is_list_of(dynamic_interval_list, tuple)
dynamic_milestones = [0]
dynamic_milestones.extend([dynamic_interval[0] for dynamic_interval in dynamic_interval_list])
dynamic_intervals = [start_interval]
dynamic_intervals.ex... |
class EvalHook(BaseEvalHook):
def __init__(self, *args, dynamic_intervals=None, **kwargs):
super(EvalHook, self).__init__(*args, **kwargs)
self.use_dynamic_intervals = (dynamic_intervals is not None)
if self.use_dynamic_intervals:
(self.dynamic_milestones, self.dynamic_interva... |
class DistEvalHook(BaseDistEvalHook):
def __init__(self, *args, dynamic_intervals=None, **kwargs):
super(DistEvalHook, self).__init__(*args, **kwargs)
self.use_dynamic_intervals = (dynamic_intervals is not None)
if self.use_dynamic_intervals:
(self.dynamic_milestones, self.dyn... |
def generate_inputs_and_wrap_model(config_path, checkpoint_path, input_config, cfg_options=None):
"Prepare sample input and wrap model for ONNX export.\n\n The ONNX export API only accept args, and all inputs should be\n torch.Tensor or corresponding types (such as tuple of tensor).\n So we should call t... |
def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None):
'Build a model from config and load the given checkpoint.\n\n Args:\n config_path (str): the OpenMMLab config for the model we want to\n export to ONNX\n checkpoint_path (str): Path to the corresponding checkpoin... |
def preprocess_example_input(input_config):
"Prepare an example input image for ``generate_inputs_and_wrap_model``.\n\n Args:\n input_config (dict): customized config describing the example input.\n\n Returns:\n tuple: (one_img, one_meta), tensor of the example input image and meta... |
@HOOKS.register_module()
class CheckInvalidLossHook(Hook):
'Check invalid loss hook.\n\n This hook will regularly check whether the loss is valid\n during training.\n\n Args:\n interval (int): Checking interval (every k iterations).\n Default: 50.\n '
def __init__(self, interval... |
class BaseEMAHook(Hook):
"Exponential Moving Average Hook.\n\n Use Exponential Moving Average on all parameters of model in training\n process. All parameters have a ema backup, which update by the formula\n as below. EMAHook takes priority over EvalHook and CheckpointHook. Note,\n the original model ... |
@HOOKS.register_module()
class ExpMomentumEMAHook(BaseEMAHook):
'EMAHook using exponential momentum strategy.\n\n Args:\n total_iter (int): The total number of iterations of EMA momentum.\n Defaults to 2000.\n '
def __init__(self, total_iter=2000, **kwargs):
super(ExpMomentumEM... |
@HOOKS.register_module()
class LinearMomentumEMAHook(BaseEMAHook):
'EMAHook using linear momentum strategy.\n\n Args:\n warm_up (int): During first warm_up steps, we may use smaller decay\n to update ema parameters more slowly. Defaults to 100.\n '
def __init__(self, warm_up=100, **kw... |
@HOOKS.register_module()
class SetEpochInfoHook(Hook):
"Set runner's epoch information to the model."
def before_train_epoch(self, runner):
epoch = runner.epoch
model = runner.model
if is_module_wrapper(model):
model = model.module
model.set_epoch(epoch)
|
def get_norm_states(module):
async_norm_states = OrderedDict()
for (name, child) in module.named_modules():
if isinstance(child, nn.modules.batchnorm._NormBase):
for (k, v) in child.state_dict().items():
async_norm_states['.'.join([name, k])] = v
return async_norm_state... |
@HOOKS.register_module()
class SyncNormHook(Hook):
'Synchronize Norm states after training epoch, currently used in YOLOX.\n\n Args:\n num_last_epochs (int): The number of latter epochs in the end of the\n training to switch to synchronizing norm interval. Default: 15.\n interval (int)... |
@HOOKS.register_module()
class SyncRandomSizeHook(Hook):
"Change and synchronize the random image size across ranks.\n SyncRandomSizeHook is deprecated, please use Resize pipeline to achieve\n similar functions. Such as `dict(type='Resize', img_scale=[(448, 448),\n (832, 832)], multiscale_mode='range', k... |
@HOOKS.register_module()
class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
'YOLOX learning rate scheme.\n\n There are two main differences between YOLOXLrUpdaterHook\n and CosineAnnealingLrUpdaterHook.\n\n 1. When the current running epoch is greater than\n `max_epoch-last_epoch`, a fi... |
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"Switch the mode of YOLOX during training.\n\n This hook turns off the mosaic and mixup data augmentation and switches\n to use L1 loss in bbox_head.\n\n Args:\n num_last_epochs (int): The number of latter epochs in the end of the\n ... |
def mask_matrix_nms(masks, labels, scores, filter_thr=(- 1), nms_pre=(- 1), max_num=(- 1), kernel='gaussian', sigma=2.0, mask_area=None):
"Matrix NMS for multi-class masks.\n\n Args:\n masks (Tensor): Has shape (num_instances, h, w)\n labels (Tensor): Labels of corresponding masks,\n h... |
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=(- 1)):
if (bucket_size_mb > 0):
bucket_size_bytes = ((bucket_size_mb * 1024) * 1024)
buckets = _take_tensors(tensors, bucket_size_bytes)
else:
buckets = OrderedDict()
for tensor in tensors:
tp = tensor.ty... |
def allreduce_grads(params, coalesce=True, bucket_size_mb=(- 1)):
'Allreduce gradients.\n\n Args:\n params (list[torch.Parameters]): List of parameters of a model\n coalesce (bool, optional): Whether allreduce parameters as a whole.\n Defaults to True.\n bucket_size_mb (int, opt... |
class DistOptimizerHook(OptimizerHook):
'Deprecated optimizer hook for distributed training.'
def __init__(self, *args, **kwargs):
warnings.warn('"DistOptimizerHook" is deprecated, please switch to"mmcv.runner.OptimizerHook".')
super().__init__(*args, **kwargs)
|
def reduce_mean(tensor):
'"Obtain the mean of tensor on different GPUs.'
if (not (dist.is_available() and dist.is_initialized())):
return tensor
tensor = tensor.clone()
dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
return tensor
|
def obj2tensor(pyobj, device='cuda'):
'Serialize picklable python object to tensor.'
storage = torch.ByteStorage.from_buffer(pickle.dumps(pyobj))
return torch.ByteTensor(storage).to(device=device)
|
def tensor2obj(tensor):
'Deserialize tensor to picklable python object.'
return pickle.loads(tensor.cpu().numpy().tobytes())
|
@functools.lru_cache()
def _get_global_gloo_group():
'Return a process group based on gloo backend, containing all the ranks\n The result is cached.'
if (dist.get_backend() == 'nccl'):
return dist.new_group(backend='gloo')
else:
return dist.group.WORLD
|
def all_reduce_dict(py_dict, op='sum', group=None, to_float=True):
"Apply all reduce function for python dict object.\n\n The code is modified from https://github.com/Megvii-\n BaseDetection/YOLOX/blob/main/yolox/utils/allreduce_norm.py.\n\n NOTE: make sure that py_dict in different ranks has the same ke... |
def palette_val(palette):
'Convert palette to matplotlib palette.\n\n Args:\n palette List[tuple]: A list of color tuples.\n\n Returns:\n List[tuple[float]]: A list of RGB matplotlib color tuples.\n '
new_palette = []
for color in palette:
color = [(c / 255) for c in color]
... |
def get_palette(palette, num_classes):
'Get palette from various inputs.\n\n Args:\n palette (list[tuple] | str | tuple | :obj:`Color`): palette inputs.\n num_classes (int): the number of classes.\n\n Returns:\n list[tuple[int]]: A list of color tuples.\n '
assert isinstance(num_... |
class COCO(_COCO):
'This class is almost the same as official pycocotools package.\n\n It implements some snake case function aliases. So that the COCO class has\n the same interface as LVIS class.\n '
def __init__(self, annotation_file=None):
if (getattr(pycocotools, '__version__', '0') >= ... |
def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, categories, file_client=None):
'The single core function to evaluate the metric of Panoptic\n Segmentation.\n\n Same as the function with the same name in `panopticapi`. Only the function\n to load the images is changed to use th... |
def pq_compute_multi_core(matched_annotations_list, gt_folder, pred_folder, categories, file_client=None):
'Evaluate the metrics of Panoptic Segmentation with multithreading.\n\n Same as the function with the same name in `panopticapi`.\n\n Args:\n matched_annotations_list (list): The matched annotat... |
def _concat_dataset(cfg, default_args=None):
from .dataset_wrappers import ConcatDataset
ann_files = cfg['ann_file']
img_prefixes = cfg.get('img_prefix', None)
seg_prefixes = cfg.get('seg_prefix', None)
proposal_files = cfg.get('proposal_file', None)
separate_eval = cfg.get('separate_eval', Tr... |
def build_dataset(cfg, default_args=None):
from .dataset_wrappers import ClassBalancedDataset, ConcatDataset, MultiImageMixDataset, RepeatDataset
if isinstance(cfg, (list, tuple)):
dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
elif (cfg['type'] == 'ConcatDataset'):
... |
def build_dataloader(dataset, samples_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, seed=None, runner_type='EpochBasedRunner', persistent_workers=False, **kwargs):
'Build PyTorch DataLoader.\n\n In distributed training, each GPU/process has a dataloader.\n In non-distributed training, there... |
def worker_init_fn(worker_id, num_workers, rank, seed):
worker_seed = (((num_workers * rank) + worker_id) + seed)
np.random.seed(worker_seed)
random.seed(worker_seed)
|
@DATASETS.register_module()
class CityscapesDataset(CocoDataset):
CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle')
PALETTE = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32)]
def _filter_imgs(self, min_size=... |
@DATASETS.register_module()
class CustomDataset(Dataset):
"Custom dataset for detection.\n\n The annotation format is shown as follows. The `ann` field is optional for\n testing.\n\n .. code-block:: none\n\n [\n {\n 'filename': 'a.jpg',\n 'width': 1280,\n ... |
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
'A wrapper of concatenated dataset.\n\n Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but\n concat the group flag for image aspect ratio.\n\n Args:\n datasets (list[:obj:`Dataset`]): A list of datasets.\n separate_eva... |
@DATASETS.register_module()
class RepeatDataset():
'A wrapper of repeated dataset.\n\n The length of repeated dataset will be `times` larger than the original\n dataset. This is useful when the data loading time is long but the dataset\n is small. Using RepeatDataset can reduce the data loading time betw... |
@DATASETS.register_module()
class ClassBalancedDataset():
'A wrapper of repeated dataset with repeat factor.\n\n Suitable for training on class imbalanced datasets like LVIS. Following\n the sampling strategy in the `paper <https://arxiv.org/abs/1908.03195>`_,\n in each epoch, an image may appear multipl... |
@DATASETS.register_module()
class MultiImageMixDataset():
'A wrapper of multiple images mixed dataset.\n\n Suitable for training on multiple images mixed data augmentation like\n mosaic and mixup. For the augmentation pipeline of mixed image data,\n the `get_indexes` method needs to be provided to obtain... |
@DATASETS.register_module()
class DeepFashionDataset(CocoDataset):
CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag', 'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair', 'skin', 'face')
PALETTE = [(0, 192, 64), (0, 64, 96), (128, 192, 192), (0, 64, 64), (0, 192, 224), (0, 192... |
@PIPELINES.register_module()
class Compose():
'Compose multiple transforms sequentially.\n\n Args:\n transforms (Sequence[dict | callable]): Sequence of transform object or\n config dict to be composed.\n '
def __init__(self, transforms):
assert isinstance(transforms, collecti... |
def to_tensor(data):
'Convert objects of various python types to :obj:`torch.Tensor`.\n\n Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,\n :class:`Sequence`, :class:`int` and :class:`float`.\n\n Args:\n data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to\n ... |
@PIPELINES.register_module()
class ToTensor():
'Convert some results to :obj:`torch.Tensor` by given keys.\n\n Args:\n keys (Sequence[str]): Keys that need to be converted to Tensor.\n '
def __init__(self, keys):
self.keys = keys
def __call__(self, results):
'Call function t... |
@PIPELINES.register_module()
class ImageToTensor():
'Convert image to :obj:`torch.Tensor` by given keys.\n\n The dimension order of input image is (H, W, C). The pipeline will convert\n it to (C, H, W). If only 2 dimension (H, W) is given, the output would be\n (1, H, W).\n\n Args:\n keys (Sequ... |
@PIPELINES.register_module()
class Transpose():
'Transpose some results by given keys.\n\n Args:\n keys (Sequence[str]): Keys of results to be transposed.\n order (Sequence[int]): Order of transpose.\n '
def __init__(self, keys, order):
self.keys = keys
self.order = order
... |
@PIPELINES.register_module()
class ToDataContainer():
"Convert results to :obj:`mmcv.DataContainer` by given fields.\n\n Args:\n fields (Sequence[dict]): Each field is a dict like\n ``dict(key='xxx', **kwargs)``. The ``key`` in result will\n be converted to :obj:`mmcv.DataContainer... |
@PIPELINES.register_module()
class DefaultFormatBundle():
'Default formatting bundle.\n\n It simplifies the pipeline of formatting common fields, including "img",\n "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".\n These fields are formatted as follows.\n\n - img: (1)transpose... |
@PIPELINES.register_module()
class Collect():
'Collect data from the loader relevant to the specific task.\n\n This is usually the last stage of the data loader pipeline. Typically keys\n is set to some subset of "img", "proposals", "gt_bboxes",\n "gt_bboxes_ignore", "gt_labels", and/or "gt_masks".\n\n ... |
@PIPELINES.register_module()
class WrapFieldsToLists():
"Wrap fields of the data dictionary into lists for evaluation.\n\n This class can be used as a last step of a test or validation\n pipeline for single image evaluation or inference.\n\n Example:\n >>> test_pipeline = [\n >>> dict(ty... |
@PIPELINES.register_module()
class InstaBoost():
'Data augmentation method in `InstaBoost: Boosting Instance\n Segmentation Via Probability Map Guided Copy-Pasting\n <https://arxiv.org/abs/1908.07801>`_.\n\n Refer to https://github.com/GothicAi/Instaboost for implementation details.\n\n Args:\n ... |
@PIPELINES.register_module()
class MultiScaleFlipAug():
'Test-time augmentation with multiple scales and flipping.\n\n An example configuration is as followed:\n\n .. code-block::\n\n img_scale=[(1333, 400), (1333, 800)],\n flip=True,\n transforms=[\n dict(type=\'Resize\', ke... |
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, seed=0):
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
self.seed = (seed if (seed is not None) else 0)
def __iter__(self):
if self... |
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):
assert hasattr(dataset, 'flag')
self.dataset = dataset
self.samples_per_gpu = samples_per_gpu
self.flag = dataset.flag.astype(np.int64)
self.group_sizes = np.bincount(self.flag)
self.num_s... |
class DistributedGroupSampler(Sampler):
'Sampler that restricts data loading to a subset of the dataset.\n\n It is especially useful in conjunction with\n :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each\n process can pass a DistributedSampler instance as a DataLoader sampler,\n ... |
class InfiniteGroupBatchSampler(Sampler):
'Similar to `BatchSampler` warping a `GroupSampler. It is designed for\n iteration-based runners like `IterBasedRunner` and yields a mini-batch\n indices each time, all indices in a batch should be in the same group.\n\n The implementation logic is referred to\n ... |
class InfiniteBatchSampler(Sampler):
'Similar to `BatchSampler` warping a `DistributedSampler. It is designed\n iteration-based runners like `IterBasedRunner` and yields a mini-batch\n indices each time.\n\n The implementation logic is referred to\n https://github.com/facebookresearch/detectron2/blob/... |
def replace_ImageToTensor(pipelines):
"Replace the ImageToTensor transform in a data pipeline to\n DefaultFormatBundle, which is normally useful in batch inference.\n\n Args:\n pipelines (list[dict]): Data pipeline configs.\n\n Returns:\n list: The new pipeline list with all ImageToTensor r... |
def get_loading_pipeline(pipeline):
"Only keep loading image and annotations related configuration.\n\n Args:\n pipeline (list[dict]): Data pipeline configs.\n\n Returns:\n list[dict]: The new pipeline list with only keep\n loading image and annotations related configuration.\n\n ... |
@HOOKS.register_module()
class NumClassCheckHook(Hook):
def _check_head(self, runner):
'Check whether the `num_classes` in head matches the length of\n `CLASSES` in `dataset`.\n\n Args:\n runner (obj:`EpochBasedRunner`): Epoch based Runner.\n '
model = runner.model... |
@DATASETS.register_module()
class VOCDataset(XMLDataset):
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
PALETTE = [(106, 0, 228), (119, 11, 32), (165,... |
@DATASETS.register_module()
class WIDERFaceDataset(XMLDataset):
'Reader for the WIDER Face dataset in PASCAL VOC format.\n\n Conversion scripts can be found in\n https://github.com/sovrasov/wider-face-pascal-voc-annotations\n '
CLASSES = ('face',)
PALETTE = [(0, 255, 0)]
def __init__(self, *... |
@DATASETS.register_module()
class XMLDataset(CustomDataset):
'XML dataset for detection.\n\n Args:\n min_size (int | float, optional): The minimum size of bounding\n boxes in the images. If the size of a bounding box is less than\n ``min_size``, it would be add to ignored field.\n ... |
class ResBlock(BaseModule):
"The basic residual block used in Darknet. Each ResBlock consists of two\n ConvModules and the input is added to the final output. Each ConvModule is\n composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer\n has half of the number of the filters as much as ... |
@BACKBONES.register_module()
class Darknet(BaseModule):
"Darknet backbone.\n\n Args:\n depth (int): Depth of Darknet. Currently only support 53.\n out_indices (Sequence[int]): Output from which stages.\n frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n -... |
class Bottleneck(_Bottleneck):
'Bottleneck for the ResNet backbone in `DetectoRS\n <https://arxiv.org/pdf/2006.02334.pdf>`_.\n\n This bottleneck allows the users to specify whether to use\n SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid).\n\n Args:\n inplanes (int): The... |
class ResLayer(Sequential):
"ResLayer to build ResNet style backbone for RPF in detectoRS.\n\n The difference between this module and base class is that we pass\n ``rfp_inplanes`` to the first block.\n\n Args:\n block (nn.Module): block used to build ResLayer.\n inplanes (int): inplanes of ... |
@BACKBONES.register_module()
class DetectoRS_ResNet(ResNet):
'ResNet backbone for DetectoRS.\n\n Args:\n sac (dict, optional): Dictionary to construct SAC (Switchable Atrous\n Convolution). Default: None.\n stage_with_sac (list): Which stage to use sac. Default: (False, False,\n ... |
class Bottleneck(_Bottleneck):
expansion = 4
def __init__(self, inplanes, planes, groups=1, base_width=4, base_channels=64, **kwargs):
'Bottleneck block for ResNeXt.\n\n If style is "pytorch", the stride-two layer is the 3x3 conv layer, if\n it is "caffe", the stride-two layer is the fi... |
@BACKBONES.register_module()
class DetectoRS_ResNeXt(DetectoRS_ResNet):
'ResNeXt backbone for DetectoRS.\n\n Args:\n groups (int): The number of groups in ResNeXt.\n base_width (int): The base width of ResNeXt.\n '
arch_settings = {50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 2... |
class HourglassModule(BaseModule):
"Hourglass Module for HourglassNet backbone.\n\n Generate module recursively and use BasicBlock as the base unit.\n\n Args:\n depth (int): Depth of current HourglassModule.\n stage_channels (list[int]): Feature channels of sub-modules in current\n ... |
@BACKBONES.register_module()
class HourglassNet(BaseModule):
'HourglassNet backbone.\n\n Stacked Hourglass Networks for Human Pose Estimation.\n More details can be found in the `paper\n <https://arxiv.org/abs/1603.06937>`_ .\n\n Args:\n downsample_times (int): Downsample times in a HourglassMo... |
class HRModule(BaseModule):
'High-Resolution Module for HRNet.\n\n In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange\n is in this module.\n '
def __init__(self, num_branches, blocks, num_blocks, in_channels, num_channels, multiscale_output=True, with_cp=False, conv_cfg=None... |
@BACKBONES.register_module()
class HRNet(BaseModule):
"HRNet backbone.\n\n `High-Resolution Representations for Labeling Pixels and Regions\n arXiv: <https://arxiv.org/abs/1904.04514>`_.\n\n Args:\n extra (dict): Detailed configuration for each stage of HRNet.\n There must be 4 stages, ... |
@BACKBONES.register_module()
class MobileNetV2(BaseModule):
"MobileNetV2 backbone.\n\n Args:\n widen_factor (float): Width multiplier, multiply number of\n channels in each layer by this amount. Default: 1.0.\n out_indices (Sequence[int], optional): Output from which stages.\n ... |
@BACKBONES.register_module()
class RegNet(ResNet):
'RegNet backbone.\n\n More details can be found in `paper <https://arxiv.org/abs/2003.13678>`_ .\n\n Args:\n arch (dict): The parameter of RegNets.\n\n - w0 (int): initial width\n - wa (float): slope of width\n - wm (... |
class Bottle2neck(_Bottleneck):
expansion = 4
def __init__(self, inplanes, planes, scales=4, base_width=26, base_channels=64, stage_type='normal', **kwargs):
'Bottle2neck block for Res2Net.\n\n If style is "pytorch", the stride-two layer is the 3x3 conv layer, if\n it is "caffe", the st... |
class Res2Layer(Sequential):
"Res2Layer to build Res2Net style backbone.\n\n Args:\n block (nn.Module): block used to build ResLayer.\n inplanes (int): inplanes of block.\n planes (int): planes of block.\n num_blocks (int): number of blocks.\n stride (int): stride of the firs... |
@BACKBONES.register_module()
class Res2Net(ResNet):
'Res2Net backbone.\n\n Args:\n scales (int): Scales used in Res2Net. Default: 4\n base_width (int): Basic width of each scale. Default: 26\n depth (int): Depth of res2net, from {50, 101, 152}.\n in_channels (int): Number of input i... |
class RSoftmax(nn.Module):
'Radix Softmax module in ``SplitAttentionConv2d``.\n\n Args:\n radix (int): Radix of input.\n groups (int): Groups of input.\n '
def __init__(self, radix, groups):
super().__init__()
self.radix = radix
self.groups = groups
def forwar... |
class SplitAttentionConv2d(BaseModule):
'Split-Attention Conv2d in ResNeSt.\n\n Args:\n in_channels (int): Number of channels in the input feature map.\n channels (int): Number of intermediate channels.\n kernel_size (int | tuple[int]): Size of the convolution kernel.\n stride (int ... |
class Bottleneck(_Bottleneck):
'Bottleneck block for ResNeSt.\n\n Args:\n inplane (int): Input planes of this block.\n planes (int): Middle planes of this block.\n groups (int): Groups of conv2.\n base_width (int): Base of width in terms of base channels. Default: 4.\n base_c... |
@BACKBONES.register_module()
class ResNeSt(ResNetV1d):
'ResNeSt backbone.\n\n Args:\n groups (int): Number of groups of Bottleneck. Default: 1\n base_width (int): Base width of Bottleneck. Default: 4\n radix (int): Radix of SplitAttentionConv2d. Default: 2\n reduction_factor (int): ... |
class BasicBlock(BaseModule):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), dcn=None, plugins=None, init_cfg=None):
super(BasicBlock, self).__init__(init_cfg)
assert (dcn is No... |
class Bottleneck(BaseModule):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), dcn=None, plugins=None, init_cfg=None):
'Bottleneck block for ResNet.\n\n If style is "pytorch", the ... |
@BACKBONES.register_module()
class ResNet(BaseModule):
'ResNet backbone.\n\n Args:\n depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.\n stem_channels (int | None): Number of stem channels. If not specified,\n it will be the same as `base_channels`. Default: None.\n bas... |
@BACKBONES.register_module()
class ResNetV1d(ResNet):
'ResNetV1d variant described in `Bag of Tricks\n <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in\n the input stem with three 3x3 convs. And in the downsampling block, a 2x2\n ... |
class Bottleneck(_Bottleneck):
expansion = 4
def __init__(self, inplanes, planes, groups=1, base_width=4, base_channels=64, **kwargs):
'Bottleneck block for ResNeXt.\n\n If style is "pytorch", the stride-two layer is the 3x3 conv layer, if\n it is "caffe", the stride-two layer is the fi... |
@BACKBONES.register_module()
class ResNeXt(ResNet):
'ResNeXt backbone.\n\n Args:\n depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.\n in_channels (int): Number of input image channels. Default: 3.\n num_stages (int): Resnet stages. Default: 4.\n groups (int): Group of resn... |
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
'VGG Backbone network for single-shot-detection.\n\n Args:\n depth (int): Depth of vgg, from {11, 13, 16, 19}.\n with_last_pool (bool): Whether to add a pooling layer at the last\n of the model\n ceil_mode (bool): When ... |
class L2Norm(ssd_neck.L2Norm):
def __init__(self, **kwargs):
super(L2Norm, self).__init__(**kwargs)
warnings.warn('DeprecationWarning: L2Norm in ssd_vgg.py is deprecated, please use L2Norm in mmdet/models/necks/ssd_neck.py instead')
|
class TridentConv(BaseModule):
'Trident Convolution Module.\n\n Args:\n in_channels (int): Number of channels in input.\n out_channels (int): Number of channels in output.\n kernel_size (int): Size of convolution kernel.\n stride (int, optional): Convolution stride. Default: 1.\n ... |
class TridentBottleneck(Bottleneck):
'BottleBlock for TridentResNet.\n\n Args:\n trident_dilations (tuple[int, int, int]): Dilations of different\n trident branch.\n test_branch_idx (int): In inference, all 3 branches will be used\n if `test_branch_idx==-1`, otherwise only b... |
def make_trident_res_layer(block, inplanes, planes, num_blocks, stride=1, trident_dilations=(1, 2, 3), style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), dcn=None, plugins=None, test_branch_idx=(- 1)):
'Build Trident Res Layers.'
downsample = None
if ((stride != 1) or (inplanes != (p... |
@BACKBONES.register_module()
class TridentResNet(ResNet):
'The stem layer, stage 1 and stage 2 in Trident ResNet are identical to\n ResNet, while in stage 3, Trident BottleBlock is utilized to replace the\n normal BottleBlock to yield trident output. Different branch shares the\n convolution weight but u... |
def build_backbone(cfg):
'Build backbone.'
return BACKBONES.build(cfg)
|
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