SYMBOL INDEX (341 symbols across 34 files)

FILE: datasets/__init__.py
  function get_coco_api_from_dataset (line 16) | def get_coco_api_from_dataset(dataset):
  function build_dataset (line 26) | def build_dataset(image_set, args):

FILE: datasets/coco.py
  class CocoDetection (line 26) | class CocoDetection(TvCocoDetection):
    method __init__ (line 27) | def __init__(self, img_folder, ann_file, transforms, return_masks, cac...
    method __getitem__ (line 33) | def __getitem__(self, idx):
  function convert_coco_poly_to_mask (line 43) | def convert_coco_poly_to_mask(segmentations, height, width):
  class ConvertCocoPolysToMask (line 60) | class ConvertCocoPolysToMask(object):
    method __init__ (line 61) | def __init__(self, return_masks=False):
    method __call__ (line 64) | def __call__(self, image, target):
  function make_coco_transforms (line 125) | def make_coco_transforms(image_set):
  function build (line 157) | def build(image_set, args):

FILE: datasets/coco_eval.py
  class CocoEvaluator (line 30) | class CocoEvaluator(object):
    method __init__ (line 31) | def __init__(self, coco_gt, iou_types):
    method update (line 44) | def update(self, predictions):
    method synchronize_between_processes (line 63) | def synchronize_between_processes(self):
    method accumulate (line 68) | def accumulate(self):
    method summarize (line 72) | def summarize(self):
    method prepare (line 77) | def prepare(self, predictions, iou_type):
    method prepare_for_coco_detection (line 87) | def prepare_for_coco_detection(self, predictions):
    method prepare_for_coco_segmentation (line 111) | def prepare_for_coco_segmentation(self, predictions):
    method prepare_for_coco_keypoint (line 146) | def prepare_for_coco_keypoint(self, predictions):
  function convert_to_xywh (line 173) | def convert_to_xywh(boxes):
  function merge (line 178) | def merge(img_ids, eval_imgs):
  function create_common_coco_eval (line 200) | def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
  function evaluate (line 216) | def evaluate(self):

FILE: datasets/coco_panoptic.py
  class CocoPanoptic (line 23) | class CocoPanoptic:
    method __init__ (line 24) | def __init__(self, img_folder, ann_folder, ann_file, transforms=None, ...
    method __getitem__ (line 42) | def __getitem__(self, idx):
    method __len__ (line 78) | def __len__(self):
    method get_height_and_width (line 81) | def get_height_and_width(self, idx):
  function build (line 88) | def build(image_set, args):

FILE: datasets/data_prefetcher.py
  function to_cuda (line 9) | def to_cuda(samples, targets, device):
  class data_prefetcher (line 14) | class data_prefetcher():
    method __init__ (line 15) | def __init__(self, loader, device, prefetch=True):
    method preload (line 23) | def preload(self):
    method next (line 51) | def next(self):

FILE: datasets/panoptic_eval.py
  class PanopticEvaluator (line 21) | class PanopticEvaluator(object):
    method __init__ (line 22) | def __init__(self, ann_file, ann_folder, output_dir="panoptic_eval"):
    method update (line 31) | def update(self, predictions):
    method synchronize_between_processes (line 38) | def synchronize_between_processes(self):
    method summarize (line 45) | def summarize(self):

FILE: datasets/samplers.py
  class DistributedSampler (line 16) | class DistributedSampler(Sampler):
    method __init__ (line 31) | def __init__(self, dataset, num_replicas=None, rank=None, local_rank=N...
    method __iter__ (line 48) | def __iter__(self):
    method __len__ (line 68) | def __len__(self):
    method set_epoch (line 71) | def set_epoch(self, epoch):
  class NodeDistributedSampler (line 75) | class NodeDistributedSampler(Sampler):
    method __init__ (line 90) | def __init__(self, dataset, num_replicas=None, rank=None, local_rank=N...
    method __iter__ (line 115) | def __iter__(self):
    method __len__ (line 135) | def __len__(self):
    method set_epoch (line 138) | def set_epoch(self, epoch):

FILE: datasets/torchvision_datasets/coco.py
  class CocoDetection (line 20) | class CocoDetection(VisionDataset):
    method __init__ (line 33) | def __init__(self, root, annFile, transform=None, target_transform=Non...
    method cache_images (line 46) | def cache_images(self):
    method get_image (line 55) | def get_image(self, path):
    method __getitem__ (line 63) | def __getitem__(self, index):
    method __len__ (line 83) | def __len__(self):

FILE: datasets/transforms.py
  function crop (line 24) | def crop(image, target, region):
  function hflip (line 67) | def hflip(image, target):
  function resize (line 84) | def resize(image, target, size, max_size=None):
  function pad (line 143) | def pad(image, target, padding):
  class RandomCrop (line 156) | class RandomCrop(object):
    method __init__ (line 157) | def __init__(self, size):
    method __call__ (line 160) | def __call__(self, img, target):
  class RandomSizeCrop (line 165) | class RandomSizeCrop(object):
    method __init__ (line 166) | def __init__(self, min_size: int, max_size: int):
    method __call__ (line 170) | def __call__(self, img: PIL.Image.Image, target: dict):
  class CenterCrop (line 177) | class CenterCrop(object):
    method __init__ (line 178) | def __init__(self, size):
    method __call__ (line 181) | def __call__(self, img, target):
  class RandomHorizontalFlip (line 189) | class RandomHorizontalFlip(object):
    method __init__ (line 190) | def __init__(self, p=0.5):
    method __call__ (line 193) | def __call__(self, img, target):
  class RandomResize (line 199) | class RandomResize(object):
    method __init__ (line 200) | def __init__(self, sizes, max_size=None):
    method __call__ (line 205) | def __call__(self, img, target=None):
  class RandomPad (line 210) | class RandomPad(object):
    method __init__ (line 211) | def __init__(self, max_pad):
    method __call__ (line 214) | def __call__(self, img, target):
  class RandomSelect (line 220) | class RandomSelect(object):
    method __init__ (line 225) | def __init__(self, transforms1, transforms2, p=0.5):
    method __call__ (line 230) | def __call__(self, img, target):
  class ToTensor (line 236) | class ToTensor(object):
    method __call__ (line 237) | def __call__(self, img, target):
  class RandomErasing (line 241) | class RandomErasing(object):
    method __init__ (line 243) | def __init__(self, *args, **kwargs):
    method __call__ (line 246) | def __call__(self, img, target):
  class Normalize (line 250) | class Normalize(object):
    method __init__ (line 251) | def __init__(self, mean, std):
    method __call__ (line 255) | def __call__(self, image, target=None):
  class Compose (line 269) | class Compose(object):
    method __init__ (line 270) | def __init__(self, transforms):
    method __call__ (line 273) | def __call__(self, image, target):
    method __repr__ (line 278) | def __repr__(self):

FILE: engine.py
  function train_one_epoch (line 31) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 107) | def evaluate(model, criterion, postprocessors, data_loader, base_ds, dev...

FILE: main.py
  function get_args_parser (line 37) | def get_args_parser():
  function main (line 156) | def main(args):
  function print_final_result_on_master (line 403) | def print_final_result_on_master(model, dataset_val, args, test_stats, s...

FILE: models/__init__.py
  function build_model (line 17) | def build_model(args):

FILE: models/backbone.py
  class FrozenBatchNorm2d (line 32) | class FrozenBatchNorm2d(torch.nn.Module):
    method __init__ (line 41) | def __init__(self, n, eps=1e-5):
    method _load_from_state_dict (line 49) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st...
    method forward (line 59) | def forward(self, x):
  class BackboneBase (line 72) | class BackboneBase(nn.Module):
    method __init__ (line 74) | def __init__(self, backbone: nn.Module, train_backbone: bool, return_i...
    method get_return_layers (line 120) | def get_return_layers(name: str, layer_ids):
    method forward (line 123) | def forward(self, tensor_list: NestedTensor):
  class DummyBackbone (line 134) | class DummyBackbone(torch.nn.Module):
    method __init__ (line 135) | def __init__(self):
  class Backbone (line 140) | class Backbone(BackboneBase):
    method __init__ (line 142) | def __init__(self, name: str,
  class Joiner (line 202) | class Joiner(nn.Sequential):
    method __init__ (line 203) | def __init__(self, backbone, position_embedding):
    method forward (line 208) | def forward(self, tensor_list: NestedTensor):
  function test_backbone (line 222) | def test_backbone(backbone):
  function build_backbone (line 231) | def build_backbone(args):

FILE: models/deformable_detr.py
  function _get_clones (line 36) | def _get_clones(module, N):
  class DeformableDETR (line 40) | class DeformableDETR(nn.Module):
    method __init__ (line 42) | def __init__(self, backbone, transformer, num_classes, num_queries, nu...
    method forward (line 144) | def forward(self, samples: NestedTensor):
    method _set_aux_loss (line 270) | def _set_aux_loss(self, outputs_class, outputs_coord):
  class SetCriterion (line 278) | class SetCriterion(nn.Module):
    method __init__ (line 284) | def __init__(self, num_classes, matcher, weight_dict, losses, args):
    method loss_labels (line 302) | def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
    method loss_cardinality (line 330) | def loss_cardinality(self, outputs, targets, indices, num_boxes):
    method loss_boxes (line 343) | def loss_boxes(self, outputs, targets, indices, num_boxes):
    method loss_masks (line 364) | def loss_masks(self, outputs, targets, indices, num_boxes):
    method loss_mask_prediction (line 393) | def loss_mask_prediction(self, outputs, targets, indices, num_boxes, l...
    method corr (line 430) | def corr(self, outputs, targets, indices, num_boxes):
    method _get_src_permutation_idx (line 457) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 463) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 469) | def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
    method forward (line 481) | def forward(self, outputs, targets):
  class PostProcess (line 579) | class PostProcess(nn.Module):
    method forward (line 583) | def forward(self, outputs, target_sizes):
  class MLP (line 614) | class MLP(nn.Module):
    method __init__ (line 617) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 623) | def forward(self, x):
  function build (line 629) | def build(args):

FILE: models/deformable_transformer.py
  class DeformableTransformer (line 27) | class DeformableTransformer(nn.Module):
    method __init__ (line 28) | def __init__(self, d_model=256, nhead=8,
    method _log_args (line 78) | def _log_args(self, *names):
    method _reset_parameters (line 83) | def _reset_parameters(self):
    method get_proposal_pos_embed (line 95) | def get_proposal_pos_embed(self, proposals):
    method gen_encoder_output_proposals (line 110) | def gen_encoder_output_proposals(self, memory, memory_padding_mask, sp...
    method get_valid_ratio (line 160) | def get_valid_ratio(self, mask):
    method forward (line 169) | def forward(self, srcs, masks, pos_embeds, query_embed=None):
  class DeformableTransformerEncoderLayer (line 313) | class DeformableTransformerEncoderLayer(nn.Module):
    method __init__ (line 314) | def __init__(self,
    method with_pos_embed (line 334) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 337) | def forward_ffn(self, src):
    method forward (line 343) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
  class DeformableTransformerEncoder (line 372) | class DeformableTransformerEncoder(nn.Module):
    method __init__ (line 373) | def __init__(self, encoder_layer, num_layers, mask_predictor_dim=256):
    method get_reference_points (line 383) | def get_reference_points(spatial_shapes, valid_ratios, device):
    method forward (line 405) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios,
  class DeformableTransformerDecoderLayer (line 473) | class DeformableTransformerDecoderLayer(nn.Module):
    method __init__ (line 474) | def __init__(self, d_model=256, d_ffn=1024, dropout=0.1, activation="r...
    method with_pos_embed (line 497) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 500) | def forward_ffn(self, tgt):
    method forward (line 506) | def forward(self, tgt, query_pos, reference_points, src, src_spatial_s...
  class DeformableTransformerDecoder (line 530) | class DeformableTransformerDecoder(nn.Module):
    method __init__ (line 531) | def __init__(self, decoder_layer, num_layers, return_intermediate=False):
    method forward (line 542) | def forward(self, tgt, reference_points, src, src_spatial_shapes, src_...
  class MaskPredictor (line 618) | class MaskPredictor(nn.Module):
    method __init__ (line 619) | def __init__(self, in_dim, h_dim):
    method forward (line 635) | def forward(self, x):
  function _get_clones (line 644) | def _get_clones(module, N):
  function _get_activation_fn (line 648) | def _get_activation_fn(activation):
  function build_deforamble_transformer (line 659) | def build_deforamble_transformer(args):

FILE: models/matcher.py
  class HungarianMatcher (line 24) | class HungarianMatcher(nn.Module):
    method __init__ (line 32) | def __init__(self,
    method forward (line 49) | def forward(self, outputs, targets):
  function build_matcher (line 104) | def build_matcher(args):

FILE: models/ops/functions/ms_deform_attn_func.py
  class MSDeformAttnFunction (line 21) | class MSDeformAttnFunction(Function):
    method forward (line 23) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
    method backward (line 32) | def backward(ctx, grad_output):
  function ms_deform_attn_core_pytorch (line 41) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...

FILE: models/ops/modules/ms_deform_attn.py
  function _is_power_of_2 (line 27) | def _is_power_of_2(n):
  class MSDeformAttn (line 33) | class MSDeformAttn(nn.Module):
    method __init__ (line 34) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
    method _reset_parameters (line 66) | def _reset_parameters(self):
    method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...
  function ms_deform_attn_core_pytorch (line 125) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...

FILE: models/ops/setup.py
  function get_extensions (line 23) | def get_extensions():

FILE: models/ops/src/cpu/ms_deform_attn_cpu.cpp
  function ms_deform_attn_cpu_forward (line 17) | at::Tensor
  function ms_deform_attn_cpu_backward (line 29) | std::vector<at::Tensor>

FILE: models/ops/src/ms_deform_attn.h
  function im2col_step (line 27) | int im2col_step)

FILE: models/ops/src/vision.cpp
  function PYBIND11_MODULE (line 13) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: models/ops/test.py
  function check_forward_equal_with_pytorch_double (line 32) | def check_forward_equal_with_pytorch_double():
  function check_forward_equal_with_pytorch_float (line 48) | def check_forward_equal_with_pytorch_float():
  function check_gradient_numerical (line 63) | def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_...

FILE: models/position_encoding.py
  class PositionEmbeddingSine (line 24) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 29) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 40) | def forward(self, tensor_list: NestedTensor):
  class PositionEmbeddingLearned (line 63) | class PositionEmbeddingLearned(nn.Module):
    method __init__ (line 67) | def __init__(self, num_pos_feats=256):
    method reset_parameters (line 73) | def reset_parameters(self):
    method forward (line 77) | def forward(self, tensor_list: NestedTensor):
  function build_position_encoding (line 91) | def build_position_encoding(args):

FILE: models/segmentation.py
  class DETRsegm (line 34) | class DETRsegm(nn.Module):
    method __init__ (line 35) | def __init__(self, detr, freeze_detr=False):
    method forward (line 47) | def forward(self, samples: NestedTensor):
  class MaskHeadSmallConv (line 76) | class MaskHeadSmallConv(nn.Module):
    method __init__ (line 82) | def __init__(self, dim, fpn_dims, context_dim):
    method forward (line 109) | def forward(self, x, bbox_mask, fpns):
  class MHAttentionMap (line 150) | class MHAttentionMap(nn.Module):
    method __init__ (line 153) | def __init__(self, query_dim, hidden_dim, num_heads, dropout=0, bias=T...
    method forward (line 168) | def forward(self, q, k, mask=None):
  function dice_loss (line 182) | def dice_loss(inputs, targets, num_boxes):
  function sigmoid_focal_loss (line 200) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...
  class PostProcessSegm (line 229) | class PostProcessSegm(nn.Module):
    method __init__ (line 230) | def __init__(self, threshold=0.5):
    method forward (line 235) | def forward(self, results, outputs, orig_target_sizes, max_target_sizes):
  class PostProcessPanoptic (line 252) | class PostProcessPanoptic(nn.Module):
    method __init__ (line 256) | def __init__(self, is_thing_map, threshold=0.85):
    method forward (line 267) | def forward(self, outputs, processed_sizes, target_sizes=None):

FILE: models/swin_transformer/build.py
  function build_model (line 31) | def build_model(name, out_indices, frozen_stages, pretrained):
  function _update_dict (line 67) | def _update_dict(tar, src):
  function load_config_yaml (line 77) | def load_config_yaml(cfg_file, config=None):

FILE: models/swin_transformer/config.py
  class Config (line 18) | class Config(SimpleNamespace):
    method __init__ (line 48) | def __init__(self, _dict=None, **kwargs):
    method _set_with_nested_dict (line 55) | def _set_with_nested_dict(self, _dict):
    method freezed (line 64) | def freezed(self):
    method from_yaml (line 68) | def from_yaml(cls, yaml_file):
    method __repr__ (line 73) | def __repr__(self):
    method __getitem__ (line 76) | def __getitem__(self, item):
    method __getattr__ (line 79) | def __getattr__(self, item):
    method __setattr__ (line 91) | def __setattr__(self, item, value):
    method __bool__ (line 96) | def __bool__(self):
    method __len__ (line 100) | def __len__(self):
    method __getstate__ (line 103) | def __getstate__(self):
    method __setstate__ (line 106) | def __setstate__(self, state):
    method __contains__ (line 109) | def __contains__(self, item):
    method __deepcopy__ (line 112) | def __deepcopy__(self, memodict={}):
    method __iter__ (line 115) | def __iter__(self):
    method pformat (line 119) | def pformat(self):
    method pprint (line 123) | def pprint(self):
    method freeze (line 126) | def freeze(self):
    method defrost (line 134) | def defrost(self):
    method get (line 141) | def get(self, *args, **kwargs):
    method keys (line 144) | def keys(self):
    method values (line 147) | def values(self):
    method items (line 150) | def items(self):
    method clone (line 153) | def clone(self):
    method update (line 156) | def update(self, dict_, delimiter='/'):
    method _update (line 160) | def _update(self, key, value, delimiter='/'):
    method to_dict (line 167) | def to_dict(self):

FILE: models/swin_transformer/swin_transformer.py
  class Mlp (line 21) | class Mlp(nn.Module):
    method __init__ (line 22) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 31) | def forward(self, x):
  function window_partition (line 40) | def window_partition(x, window_size):
  function window_reverse (line 55) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 72) | class WindowAttention(nn.Module):
    method __init__ (line 85) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 119) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 153) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 170) | def __init__(self, dim, num_heads, window_size=7, shift_size=0,
    method forward (line 194) | def forward(self, x, mask_matrix):
  class PatchMerging (line 253) | class PatchMerging(nn.Module):
    method __init__ (line 259) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 265) | def forward(self, x, H, W):
  class BasicLayer (line 294) | class BasicLayer(nn.Module):
    method __init__ (line 312) | def __init__(self,
    method forward (line 354) | def forward(self, x, H, W):
  class PatchEmbed (line 396) | class PatchEmbed(nn.Module):
    method __init__ (line 405) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 419) | def forward(self, x):
  class SwinTransformer (line 438) | class SwinTransformer(nn.Module):
    method __init__ (line 466) | def __init__(self,
    method _freeze_stages (line 546) | def _freeze_stages(self):
    method _init_weights (line 563) | def _init_weights(self, m):
    method forward (line 572) | def forward(self, x):

FILE: tools/launch.py
  function parse_args (line 116) | def parse_args():
  function main (line 159) | def main():

FILE: util/benchmark.py
  function measure_average_inference_time (line 17) | def measure_average_inference_time(model, inputs, num_iters=100, warm_it...
  function python_ops_mode_for_deform_attn (line 31) | def python_ops_mode_for_deform_attn(model, ops_mode):
  function compute_fps (line 39) | def compute_fps(model, dataset, num_iters=300, warm_iters=5, batch_size=4):
  function compute_gflops (line 54) | def compute_gflops(model, dataset, approximated=True):
  function flop_count_without_warnings (line 93) | def flop_count_without_warnings(

FILE: util/box_ops.py
  function box_cxcywh_to_xyxy (line 17) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 24) | def box_xyxy_to_cxcywh(x):
  function box_iou (line 32) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 48) | def generalized_box_iou(boxes1, boxes2):
  function masks_to_boxes (line 72) | def masks_to_boxes(masks):

FILE: util/dam.py
  function idx_to_flat_grid (line 21) | def idx_to_flat_grid(spatial_shapes, idx):
  function attn_map_to_flat_grid (line 29) | def attn_map_to_flat_grid(spatial_shapes, level_start_index, sampling_lo...
  function compute_corr (line 70) | def compute_corr(flat_grid_topk, flat_grid_attn_map, spatial_shapes):

FILE: util/misc.py
  function _check_size_scale_factor (line 39) | def _check_size_scale_factor(dim, size, scale_factor):
  function _output_size (line 50) | def _output_size(dim, input, size, scale_factor):
  class SmoothedValue (line 68) | class SmoothedValue(object):
    method __init__ (line 73) | def __init__(self, window_size=20, fmt=None):
    method update (line 81) | def update(self, value, n=1):
    method synchronize_between_processes (line 86) | def synchronize_between_processes(self):
    method median (line 100) | def median(self):
    method avg (line 105) | def avg(self):
    method global_avg (line 110) | def global_avg(self):
    method max (line 114) | def max(self):
    method value (line 118) | def value(self):
    method __str__ (line 121) | def __str__(self):
  function unwrap (line 130) | def unwrap(wrapped_module):
  function check_unused_parameters (line 138) | def check_unused_parameters(model, loss_dict, weight_dict):
  function all_gather (line 152) | def all_gather(data):
  function reduce_dict (line 195) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 222) | class MetricLogger(object):
    method __init__ (line 223) | def __init__(self, delimiter="\t"):
    method update (line 227) | def update(self, **kwargs):
    method __getattr__ (line 234) | def __getattr__(self, attr):
    method __str__ (line 242) | def __str__(self):
    method synchronize_between_processes (line 250) | def synchronize_between_processes(self):
    method add_meter (line 254) | def add_meter(self, name, meter):
    method log_every (line 257) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 312) | def get_sha():
  function collate_fn (line 332) | def collate_fn(batch):
  function _max_by_axis (line 338) | def _max_by_axis(the_list):
  function nested_tensor_from_tensor_list (line 347) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
  class NestedTensor (line 367) | class NestedTensor(object):
    method __init__ (line 368) | def __init__(self, tensors, mask: Optional[Tensor]):
    method to (line 372) | def to(self, device, non_blocking=False):
    method record_stream (line 383) | def record_stream(self, *args, **kwargs):
    method decompose (line 388) | def decompose(self):
    method __repr__ (line 391) | def __repr__(self):
  function setup_for_distributed (line 395) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 410) | def is_dist_avail_and_initialized():
  function get_world_size (line 418) | def get_world_size():
  function get_rank (line 424) | def get_rank():
  function get_local_size (line 430) | def get_local_size():
  function get_local_rank (line 436) | def get_local_rank():
  function is_main_process (line 442) | def is_main_process():
  function save_on_master (line 446) | def save_on_master(*args, **kwargs):
  function _check_if_valid_ip (line 450) | def _check_if_valid_ip(ip):
  function _maybe_gethostbyname (line 460) | def _maybe_gethostbyname(addr):
  function init_distributed_mode (line 483) | def init_distributed_mode(args):
  function accuracy (line 527) | def accuracy(output, target, topk=(1,)):
  function interpolate (line 545) | def interpolate(input, size=None, scale_factor=None, mode="nearest", ali...
  function get_total_grad_norm (line 567) | def get_total_grad_norm(parameters, norm_type=2):
  function inverse_sigmoid (line 576) | def inverse_sigmoid(x, eps=1e-5):
  function scale_learning_rate (line 583) | def scale_learning_rate(args):

FILE: util/plot_utils.py
  function plot_logs (line 21) | def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'),...
  function plot_precision_recall (line 77) | def plot_precision_recall(files, naming_scheme='iter'):