SYMBOL INDEX (1199 symbols across 111 files)

FILE: setup.py
  function readme (line 7) | def readme():
  function numpy_include (line 13) | def numpy_include():
  function get_requirements (line 30) | def get_requirements(filename='requirements.txt'):

FILE: torchreid/data/data_augmentation/random_occlusion.py
  function main (line 17) | def main():
  function load_occluders (line 39) | def load_occluders(
  function occlude_with_objects (line 98) | def occlude_with_objects(im, occluders, n=1, min_overlap=0.1, max_overla...
  function paste_over (line 118) | def paste_over(im_src, im_dst, center, is_mask=False):
  function resize_by_factor (line 160) | def resize_by_factor(im, factor):
  function list_filepaths (line 169) | def list_filepaths(dirpath):
  class RandomOcclusion (line 175) | class RandomOcclusion(DualTransform):
    method __init__ (line 176) | def __init__(self,
    method check_range (line 196) | def check_range(self, dimension):
    method apply (line 202) | def apply(self, image, occluders=(), centers=(), **params):
    method apply_to_mask (line 223) | def apply_to_mask(self, image, occluders=(), centers=(), **params):
    method get_params_dependent_on_targets (line 228) | def get_params_dependent_on_targets(self, params):
    method targets_as_params (line 250) | def targets_as_params(self):
    method get_transform_init_args_names (line 253) | def get_transform_init_args_names(self):

FILE: torchreid/data/datamanager.py
  class DataManager (line 10) | class DataManager(object):
    method __init__ (line 26) | def __init__(
    method num_train_pids (line 77) | def num_train_pids(self):
    method num_train_cams (line 82) | def num_train_cams(self):
    method fetch_test_loaders (line 86) | def fetch_test_loaders(self, name):
    method preprocess_pil_img (line 97) | def preprocess_pil_img(self, img):
  class ImageDataManager (line 102) | class ImageDataManager(DataManager):
    method __init__ (line 158) | def __init__(
  class VideoDataManager (line 374) | class VideoDataManager(DataManager):
    method __init__ (line 431) | def __init__(

FILE: torchreid/data/datasets/__init__.py
  function configure_dataset_class (line 63) | def configure_dataset_class(clazz, **ext_kwargs):
  function get_dataset_nickname (line 77) | def get_dataset_nickname(name):
  function get_image_dataset (line 81) | def get_image_dataset(name):
  function init_image_dataset (line 91) | def init_image_dataset(name, mode='train', **kwargs):
  function init_video_dataset (line 117) | def init_video_dataset(name, **kwargs):
  function register_image_dataset (line 128) | def register_image_dataset(name, dataset, nickname=None):
  function register_video_dataset (line 162) | def register_video_dataset(name, dataset):

FILE: torchreid/data/datasets/dataset.py
  class Dataset (line 14) | class Dataset(object):
    method gallery_filter (line 35) | def gallery_filter(self, q_pid, q_camid, q_ann, g_pids, g_camids, g_an...
    method infer_masks_path (line 42) | def infer_masks_path(self, img_path):
    method __init__ (line 46) | def __init__(
    method transforms (line 87) | def transforms(self, mode):
    method data (line 99) | def data(self, mode):
    method __getitem__ (line 118) | def __getitem__(self, index):
    method __len__ (line 121) | def __len__(self):  # kept for backward compatibility
    method len (line 124) | def len(self, mode):
    method __add__ (line 127) | def __add__(self, other):
    method __radd__ (line 172) | def __radd__(self, other):
    method parse_data (line 179) | def parse_data(self, data):
    method get_num_pids (line 193) | def get_num_pids(self, data):
    method get_num_cams (line 197) | def get_num_cams(self, data):
    method show_summary (line 201) | def show_summary(self):
    method combine_all (line 205) | def combine_all(self):
    method download_dataset (line 232) | def download_dataset(self, dataset_dir, dataset_url):
    method check_before_run (line 274) | def check_before_run(self, required_files):
    method __repr__ (line 287) | def __repr__(self):
  class ImageDataset (line 308) | class ImageDataset(Dataset):
    method __init__ (line 319) | def __init__(self, train, query, gallery, **kwargs):
    method __getitem__ (line 322) | def __getitem__(self, index):  # kept for backward compatibility
    method getitem (line 325) | def getitem(self, index, mode):
    method show_summary (line 344) | def show_summary(self):
  class VideoDataset (line 371) | class VideoDataset(Dataset):
    method __init__ (line 382) | def __init__(
    method getitem (line 398) | def getitem(self, index, mode):
    method show_summary (line 452) | def show_summary(self):

FILE: torchreid/data/datasets/image/cuhk01.py
  class CUHK01 (line 12) | class CUHK01(ImageDataset):
    method __init__ (line 29) | def __init__(self, root='', split_id=0, **kwargs):
    method extract_file (line 63) | def extract_file(self):
    method prepare_split (line 70) | def prepare_split(self):

FILE: torchreid/data/datasets/image/cuhk02.py
  class CUHK02 (line 8) | class CUHK02(ImageDataset):
    method __init__ (line 29) | def __init__(self, root='', **kwargs):
    method get_data_list (line 40) | def get_data_list(self):

FILE: torchreid/data/datasets/image/cuhk03.py
  class CUHK03 (line 9) | class CUHK03(ImageDataset):
    method __init__ (line 27) | def __init__(
    method preprocess_split (line 93) | def preprocess_split(self):

FILE: torchreid/data/datasets/image/dukemtmcreid.py
  class DukeMTMCreID (line 10) | class DukeMTMCreID(ImageDataset):
    method get_masks_config (line 35) | def get_masks_config(masks_dir):
    method __init__ (line 41) | def __init__(self, root='', masks_dir=None, **kwargs):
    method process_dir (line 65) | def process_dir(self, dir_path, relabel=False):

FILE: torchreid/data/datasets/image/grid.py
  class GRID (line 11) | class GRID(ImageDataset):
    method __init__ (line 27) | def __init__(self, root='', split_id=0, **kwargs):
    method prepare_split (line 71) | def prepare_split(self):

FILE: torchreid/data/datasets/image/ilids.py
  class iLIDS (line 13) | class iLIDS(ImageDataset):
    method __init__ (line 27) | def __init__(self, root='', split_id=0, **kwargs):
    method prepare_split (line 52) | def prepare_split(self):
    method get_pid2label (line 109) | def get_pid2label(self, img_names):
    method parse_img_names (line 117) | def parse_img_names(self, img_names, pid2label=None):
    method process_split (line 130) | def process_split(self, split):

FILE: torchreid/data/datasets/image/market1501.py
  class Market1501 (line 11) | class Market1501(ImageDataset):
    method get_masks_config (line 35) | def get_masks_config(masks_dir):
    method __init__ (line 41) | def __init__(self, root='', market1501_500k=False, masks_dir=None, **k...
    method process_dir (line 79) | def process_dir(self, dir_path, relabel=False):

FILE: torchreid/data/datasets/image/msmt17.py
  class MSMT17 (line 25) | class MSMT17(ImageDataset):
    method get_masks_config (line 46) | def get_masks_config(masks_dir):
    method __init__ (line 52) | def __init__(self, root='', masks_dir=None, **kwargs):
    method process_dir (line 101) | def process_dir(self, dir_path, list_path):

FILE: torchreid/data/datasets/image/occluded_dukemtmc.py
  class OccludedDuke (line 15) | class OccludedDuke(ImageDataset):
    method get_masks_config (line 27) | def get_masks_config(masks_dir):
    method __init__ (line 33) | def __init__(self, root='', masks_dir=None, **kwargs):
    method process_dir (line 56) | def process_dir(self, dir_path, relabel=False):

FILE: torchreid/data/datasets/image/occluded_reid.py
  class OccludedReID (line 16) | class OccludedReID(ImageDataset):
    method get_masks_config (line 27) | def get_masks_config(masks_dir):
    method infer_masks_path (line 33) | def infer_masks_path(self, img_path):
    method __init__ (line 37) | def __init__(self, root='', masks_dir=None, **kwargs):
    method process_dir (line 60) | def process_dir(self, dir_path, relabel=False, is_query=True):

FILE: torchreid/data/datasets/image/p_ETHZ.py
  class P_ETHZ (line 16) | class P_ETHZ(ImageDataset):
    method __init__ (line 19) | def __init__(self, root='', **kwargs):
    method process_dir (line 35) | def process_dir(self, dir_path, relabel=False, is_query=True):

FILE: torchreid/data/datasets/image/p_dukemtmc_reid.py
  class PDukemtmcReid (line 16) | class PDukemtmcReid(ImageDataset):
    method get_masks_config (line 27) | def get_masks_config(masks_dir):
    method infer_masks_path (line 33) | def infer_masks_path(self, img_path):
    method __init__ (line 43) | def __init__(self, root='', masks_dir=None, **kwargs):
    method process_train_dir (line 66) | def process_train_dir(self, dir_path, relabel=True):
    method process_dir (line 100) | def process_dir(self, dir_path, relabel=False, is_query=True):

FILE: torchreid/data/datasets/image/partial_ilids.py
  class Partial_iLIDS (line 15) | class Partial_iLIDS(ImageDataset):
    method __init__ (line 18) | def __init__(self, root='', **kwargs):
    method process_dir (line 42) | def process_dir(self, dir_path, is_query=True):

FILE: torchreid/data/datasets/image/partial_reid.py
  class Partial_REID (line 16) | class Partial_REID(ImageDataset):
    method __init__ (line 19) | def __init__(self, root='', **kwargs):
    method process_dir (line 43) | def process_dir(self, dir_path, relabel=False, is_query=True):

FILE: torchreid/data/datasets/image/prid.py
  class PRID (line 10) | class PRID(ImageDataset):
    method __init__ (line 28) | def __init__(self, root='', split_id=0, **kwargs):
    method prepare_split (line 58) | def prepare_split(self):
    method process_split (line 77) | def process_split(self, split):

FILE: torchreid/data/datasets/image/sensereid.py
  class SenseReID (line 9) | class SenseReID(ImageDataset):
    method __init__ (line 27) | def __init__(self, root='', **kwargs):
    method process_dir (line 60) | def process_dir(self, dir_path):

FILE: torchreid/data/datasets/image/viper.py
  class VIPeR (line 11) | class VIPeR(ImageDataset):
    method __init__ (line 27) | def __init__(self, root='', split_id=0, **kwargs):
    method prepare_split (line 61) | def prepare_split(self):

FILE: torchreid/data/datasets/video/dukemtmcvidreid.py
  class DukeMTMCVidReID (line 11) | class DukeMTMCVidReID(VideoDataset):
    method __init__ (line 29) | def __init__(self, root='', min_seq_len=0, **kwargs):
    method process_dir (line 67) | def process_dir(self, dir_path, json_path, relabel):

FILE: torchreid/data/datasets/video/ilidsvid.py
  class iLIDSVID (line 11) | class iLIDSVID(VideoDataset):
    method __init__ (line 27) | def __init__(self, root='', split_id=0, **kwargs):
    method prepare_split (line 65) | def prepare_split(self):
    method process_data (line 122) | def process_data(self, dirnames, cam1=True, cam2=True):

FILE: torchreid/data/datasets/video/mars.py
  class Mars (line 9) | class Mars(VideoDataset):
    method __init__ (line 25) | def __init__(self, root='', **kwargs):
    method get_names (line 76) | def get_names(self, fpath):
    method process_data (line 84) | def process_data(
    method combine_all (line 129) | def combine_all(self):

FILE: torchreid/data/datasets/video/prid2011.py
  class PRID2011 (line 10) | class PRID2011(VideoDataset):
    method __init__ (line 27) | def __init__(self, root='', split_id=0, **kwargs):
    method process_dir (line 59) | def process_dir(self, dirnames, cam1=True, cam2=True):

FILE: torchreid/data/masks_transforms/__init__.py
  function compute_parts_num_and_names (line 55) | def compute_parts_num_and_names(cfg):

FILE: torchreid/data/masks_transforms/coco_keypoints_transforms.py
  class CocoToSixBodyMasks (line 10) | class CocoToSixBodyMasks(MaskGroupingTransform):
    method __init__ (line 20) | def __init__(self):

FILE: torchreid/data/masks_transforms/mask_transform.py
  class MaskTransform (line 7) | class MaskTransform(DualTransform):
    method __init__ (line 8) | def __init__(self):
    method apply (line 11) | def apply(self, img, **params):
    method apply_to_bbox (line 14) | def apply_to_bbox(self, bbox, **params):
    method apply_to_keypoint (line 17) | def apply_to_keypoint(self, keypoint, **params):
  class MaskGroupingTransform (line 21) | class MaskGroupingTransform(MaskTransform):
    method __init__ (line 23) | def __init__(self, parts_grouping, parts_map, combine_mode='max'):
    method apply_to_mask (line 31) | def apply_to_mask(self, masks, **params):
  class PermuteMasksDim (line 41) | class PermuteMasksDim(MaskTransform):
    method apply_to_mask (line 42) | def apply_to_mask(self, masks, **params):
  class ResizeMasks (line 46) | class ResizeMasks(MaskTransform):
    method __init__ (line 47) | def __init__(self, height, width, mask_scale):
    method apply_to_mask (line 51) | def apply_to_mask(self, masks, **params):
  class RemoveBackgroundMask (line 55) | class RemoveBackgroundMask(MaskTransform):
    method apply_to_mask (line 56) | def apply_to_mask(self, masks, **params):
  class AddBackgroundMask (line 60) | class AddBackgroundMask(MaskTransform):
    method __init__ (line 61) | def __init__(self, background_computation_strategy='sum', softmax_weig...
    method apply_to_mask (line 67) | def apply_to_mask(self, masks, **params):
  class IdentityMask (line 88) | class IdentityMask(MaskTransform):
    method apply_to_mask (line 91) | def apply_to_mask(self, masks, **params):

FILE: torchreid/data/masks_transforms/pcb_transforms.py
  class PCBMasks (line 7) | class PCBMasks(MaskTransform):
    method apply_to_mask (line 8) | def apply_to_mask(self, masks, **params):
  class PCBMasks2 (line 21) | class PCBMasks2(PCBMasks):
  class PCBMasks3 (line 26) | class PCBMasks3(PCBMasks):
  class PCBMasks4 (line 31) | class PCBMasks4(PCBMasks):
  class PCBMasks5 (line 36) | class PCBMasks5(PCBMasks):
  class PCBMasks6 (line 41) | class PCBMasks6(PCBMasks):
  class PCBMasks7 (line 46) | class PCBMasks7(PCBMasks):
  class PCBMasks8 (line 51) | class PCBMasks8(PCBMasks):

FILE: torchreid/data/masks_transforms/pifpaf_mask_transform.py
  class CombinePifPafIntoFullBodyMask (line 22) | class CombinePifPafIntoFullBodyMask(MaskGroupingTransform):
    method __init__ (line 27) | def __init__(self):
  class AddFullBodyMaskToBaseMasks (line 31) | class AddFullBodyMaskToBaseMasks(MaskGroupingTransform):
    method __init__ (line 38) | def __init__(self):
  class AddFullBodyMaskAndFullBoundingBoxToBaseMasks (line 42) | class AddFullBodyMaskAndFullBoundingBoxToBaseMasks(MaskGroupingTransform):
    method apply_to_mask (line 46) | def apply_to_mask(self, masks, **params):
  class CombinePifPafIntoMultiScaleBodyMasks (line 57) | class CombinePifPafIntoMultiScaleBodyMasks(MaskGroupingTransform):
    method __init__ (line 82) | def __init__(self):
  class CombinePifPafIntoOneBodyMasks (line 86) | class CombinePifPafIntoOneBodyMasks(MaskGroupingTransform):
    method __init__ (line 91) | def __init__(self):
  class CombinePifPafIntoTwoBodyMasks (line 95) | class CombinePifPafIntoTwoBodyMasks(MaskGroupingTransform):
    method __init__ (line 111) | def __init__(self):
  class CombinePifPafIntoThreeBodyMasks (line 115) | class CombinePifPafIntoThreeBodyMasks(MaskGroupingTransform):
    method __init__ (line 132) | def __init__(self):
  class CombinePifPafIntoFourBodyMasks (line 136) | class CombinePifPafIntoFourBodyMasks(MaskGroupingTransform):
    method __init__ (line 153) | def __init__(self):
  class CombinePifPafIntoFourBodyMasksNoOverlap (line 157) | class CombinePifPafIntoFourBodyMasksNoOverlap(MaskGroupingTransform):
    method __init__ (line 174) | def __init__(self):
  class CombinePifPafIntoFourVerticalParts (line 178) | class CombinePifPafIntoFourVerticalParts(MaskGroupingTransform):
    method __init__ (line 195) | def __init__(self):
  class CombinePifPafIntoFourVerticalPartsPif (line 199) | class CombinePifPafIntoFourVerticalPartsPif(MaskGroupingTransform):
    method __init__ (line 207) | def __init__(self):
  class CombinePifPafIntoFiveVerticalParts (line 211) | class CombinePifPafIntoFiveVerticalParts(MaskGroupingTransform):
    method __init__ (line 231) | def __init__(self):
  class CombinePifPafIntoFiveBodyMasks (line 235) | class CombinePifPafIntoFiveBodyMasks(MaskGroupingTransform):
    method __init__ (line 257) | def __init__(self):
  class CombinePifPafIntoSixVerticalParts (line 261) | class CombinePifPafIntoSixVerticalParts(MaskGroupingTransform):
    method __init__ (line 279) | def __init__(self):
  class CombinePifPafIntoSixBodyMasks (line 283) | class CombinePifPafIntoSixBodyMasks(MaskGroupingTransform):
    method __init__ (line 302) | def __init__(self):
  class CombinePifPafIntoSixBodyMasksSum (line 306) | class CombinePifPafIntoSixBodyMasksSum(MaskGroupingTransform):
    method __init__ (line 325) | def __init__(self):
  class CombinePifPafIntoSixBodyMasksSimilarToEight (line 329) | class CombinePifPafIntoSixBodyMasksSimilarToEight(MaskGroupingTransform):
    method __init__ (line 348) | def __init__(self):
  class CombinePifPafIntoEightBodyMasks (line 352) | class CombinePifPafIntoEightBodyMasks(MaskGroupingTransform):
    method __init__ (line 373) | def __init__(self):
  class CombinePifPafIntoEightVerticalBodyMasks (line 378) | class CombinePifPafIntoEightVerticalBodyMasks(MaskGroupingTransform):
    method __init__ (line 399) | def __init__(self):
  class CombinePifPafIntoTenMSBodyMasks (line 403) | class CombinePifPafIntoTenMSBodyMasks(MaskGroupingTransform):
    method __init__ (line 442) | def __init__(self):
  class CombinePifPafIntoSevenVerticalBodyMasks (line 446) | class CombinePifPafIntoSevenVerticalBodyMasks(MaskGroupingTransform):
    method __init__ (line 460) | def __init__(self):
  class CombinePifPafIntoSevenBodyMasksSimilarToEight (line 464) | class CombinePifPafIntoSevenBodyMasksSimilarToEight(MaskGroupingTransform):
    method __init__ (line 483) | def __init__(self):
  class CombinePifPafIntoElevenBodyMasks (line 488) | class CombinePifPafIntoElevenBodyMasks(MaskGroupingTransform):
    method __init__ (line 509) | def __init__(self):
  class CombinePifPafIntoFourteenBodyMasks (line 513) | class CombinePifPafIntoFourteenBodyMasks(MaskGroupingTransform):
    method __init__ (line 533) | def __init__(self):

FILE: torchreid/data/sampler.py
  class RandomIdentitySampler (line 11) | class RandomIdentitySampler(Sampler):
    method __init__ (line 20) | def __init__(self, data_source, batch_size, num_instances):
    method __iter__ (line 45) | def __iter__(self):
    method __len__ (line 75) | def __len__(self):
  function build_train_sampler (line 79) | def build_train_sampler(

FILE: torchreid/data/transforms.py
  class NpToTensor (line 15) | class NpToTensor(object):
    method __call__ (line 16) | def __call__(self, masks):
    method __repr__ (line 20) | def __repr__(self):
  function build_transforms (line 24) | def build_transforms(

FILE: torchreid/engine/engine.py
  class Engine (line 19) | class Engine(object):
    method __init__ (line 31) | def __init__(self, config, datamanager, writer, engine_state, use_gpu=...
    method register_model (line 52) | def register_model(self, name='model', model=None, optim=None, sched=N...
    method get_model_names (line 72) | def get_model_names(self, names=None):
    method save_model (line 83) | def save_model(self, epoch, cmc, mAP, ssmd, save_dir, is_best=False):
    method set_model_mode (line 104) | def set_model_mode(self, mode='train', names=None):
    method get_current_lr (line 114) | def get_current_lr(self, names=None):
    method update_lr (line 119) | def update_lr(self, names=None):
    method run (line 127) | def run(
    method train (line 255) | def train(self, fixbase_epoch=0, open_layers=None):
    method forward_backward (line 279) | def forward_backward(self, data):
    method test (line 282) | def test(
    method _evaluate (line 389) | def _evaluate(
    method _feature_extraction (line 491) | def _feature_extraction(self, data_loader):
    method compute_loss (line 512) | def compute_loss(self, criterion, outputs, targets, **kwargs):
    method extract_features (line 519) | def extract_features(self, input):
    method parse_data_for_train (line 522) | def parse_data_for_train(self, data):
    method parse_data_for_eval (line 527) | def parse_data_for_eval(self, data):
    method two_stepped_transfer_learning (line 533) | def two_stepped_transfer_learning(
    method normalize (line 558) | def normalize(self, features):

FILE: torchreid/engine/image/part_based_engine.py
  class ImagePartBasedEngine (line 22) | class ImagePartBasedEngine(Engine):
    method __init__ (line 25) | def __init__(
    method forward_backward (line 77) | def forward_backward(self, data):
    method combine_losses (line 107) | def combine_losses(self, visibility_scores_dict, embeddings_dict, id_c...
    method _feature_extraction (line 132) | def _feature_extraction(self, data_loader):
    method _evaluate (line 169) | def _evaluate(
    method compute_pixels_cls_accuracy (line 297) | def compute_pixels_cls_accuracy(self, target_masks, pixels_cls_scores):
    method display_individual_parts_ranking_performances (line 308) | def display_individual_parts_ranking_performances(self, body_parts_dis...
    method parse_data_for_train (line 341) | def parse_data_for_train(self, data):
    method parse_data_for_eval (line 358) | def parse_data_for_eval(self, data):
    method extract_test_embeddings (line 365) | def extract_test_embeddings(self, model_output):

FILE: torchreid/engine/image/softmax.py
  class ImageSoftmaxEngine (line 9) | class ImageSoftmaxEngine(Engine):
    method __init__ (line 55) | def __init__(
    method forward_backward (line 78) | def forward_backward(self, data):

FILE: torchreid/engine/image/triplet.py
  class ImageTripletEngine (line 9) | class ImageTripletEngine(Engine):
    method __init__ (line 61) | def __init__(
    method forward_backward (line 91) | def forward_backward(self, data):

FILE: torchreid/engine/video/softmax.py
  class VideoSoftmaxEngine (line 7) | class VideoSoftmaxEngine(ImageSoftmaxEngine):
    method __init__ (line 59) | def __init__(
    method parse_data_for_train (line 83) | def parse_data_for_train(self, data):
    method extract_features (line 98) | def extract_features(self, input):

FILE: torchreid/engine/video/triplet.py
  class VideoTripletEngine (line 7) | class VideoTripletEngine(ImageTripletEngine, VideoSoftmaxEngine):
    method __init__ (line 66) | def __init__(

FILE: torchreid/hyperparameter/custom_hyperparameter_optimizer.py
  function build_datamanager (line 21) | def build_datamanager(cfg):
  function build_engine (line 28) | def build_engine(cfg, datamanager, model, optimizer, scheduler, writer):
  function reset_config (line 108) | def reset_config(cfg, args):
  function main (line 119) | def main():

FILE: torchreid/hyperparameter/hyperparameter_optimizer.py
  function job_complete_callback (line 8) | def job_complete_callback(

FILE: torchreid/losses/GiLt_loss.py
  class GiLtLoss (line 11) | class GiLtLoss(nn.Module):
    method __init__ (line 27) | def __init__(self,
    method forward (line 45) | def forward(self, embeddings_dict, visibility_scores_dict, id_cls_scor...
    method compute_triplet_loss (line 95) | def compute_triplet_loss(self, embeddings, visibility_scores, pids):
    method compute_id_cls_loss (line 105) | def compute_id_cls_loss(self, id_cls_scores, visibility_scores, pids):

FILE: torchreid/losses/__init__.py
  function init_part_based_triplet_loss (line 24) | def init_part_based_triplet_loss(name, **kwargs):
  function deep_supervision (line 35) | def deep_supervision(criterion, xs, y):

FILE: torchreid/losses/body_part_attention_loss.py
  class BodyPartAttentionLoss (line 11) | class BodyPartAttentionLoss(nn.Module):
    method __init__ (line 17) | def __init__(self, loss_type='cl', label_smoothing=0.1, use_gpu=False):
    method forward (line 31) | def forward(self, pixels_cls_scores, targets):
    method compute_pixels_cls_loss (line 45) | def compute_pixels_cls_loss(self, pixels_cls_scores, targets):

FILE: torchreid/losses/cross_entropy_loss.py
  class CrossEntropyLoss (line 6) | class CrossEntropyLoss(nn.Module):
    method __init__ (line 29) | def __init__(self, eps=0.1, label_smooth=True):
    method forward (line 34) | def forward(self, inputs, targets, weights=None):

FILE: torchreid/losses/hard_mine_triplet_loss.py
  class TripletLoss (line 6) | class TripletLoss(nn.Module):
    method __init__ (line 18) | def __init__(self, margin=0.3):
    method forward (line 23) | def forward(self, inputs, targets):
    method compute_hard_mine_triplet_loss (line 36) | def compute_hard_mine_triplet_loss(self, dist, inputs, targets):
    method compute_dist_matrix (line 49) | def compute_dist_matrix(self, inputs):

FILE: torchreid/losses/inter_parts_triplet_loss.py
  class InterPartsTripletLoss (line 6) | class InterPartsTripletLoss(PartAveragedTripletLoss):
    method __init__ (line 8) | def __init__(self, **kwargs):
    method forward (line 11) | def forward(self, body_parts_features, targets, n_iter=0, parts_visibi...
    method compute_mixed_body_parts_dist_matrices (line 16) | def compute_mixed_body_parts_dist_matrices(self, body_parts_features):
    method hard_mine_triplet_loss (line 21) | def hard_mine_triplet_loss(self, dist, targets): # TODO extract code f...

FILE: torchreid/losses/part_averaged_triplet_loss.py
  class PartAveragedTripletLoss (line 10) | class PartAveragedTripletLoss(nn.Module):
    method __init__ (line 26) | def __init__(self, margin=0.3, epsilon=1e-16, writer=None):
    method forward (line 35) | def forward(self, part_based_embeddings, labels, parts_visibility=None):
    method _combine_part_based_dist_matrices (line 67) | def _combine_part_based_dist_matrices(self, part_based_pairwise_dist, ...
    method _part_based_pairwise_distance_matrix (line 77) | def _part_based_pairwise_distance_matrix(self, embeddings, squared=Fal...
    method _hard_mine_triplet_loss (line 95) | def _hard_mine_triplet_loss(self, batch_pairwise_dist, labels, margin):
    method hard_margin_triplet_loss (line 175) | def hard_margin_triplet_loss(self, margin, valid_hardest_dist, valid_t...
    method soft_margin_triplet_loss (line 182) | def soft_margin_triplet_loss(self, margin, valid_hardest_dist, valid_t...
    method _get_anchor_positive_mask (line 198) | def _get_anchor_positive_mask(labels):
    method _get_anchor_negative_mask (line 215) | def _get_anchor_negative_mask(labels):

FILE: torchreid/losses/part_individual_triplet_loss.py
  class PartIndividualTripletLoss (line 7) | class PartIndividualTripletLoss(PartAveragedTripletLoss):
    method __init__ (line 20) | def __init__(self, **kwargs):
    method _combine_part_based_dist_matrices (line 23) | def _combine_part_based_dist_matrices(self, part_based_pairwise_dist, ...

FILE: torchreid/losses/part_max_min_triplet_loss.py
  class PartMaxMinTripletLoss (line 9) | class PartMaxMinTripletLoss(PartAveragedTripletLoss):
    method __init__ (line 11) | def __init__(self, **kwargs):
    method _combine_part_based_dist_matrices (line 14) | def _combine_part_based_dist_matrices(self, part_based_pairwise_dist, ...

FILE: torchreid/losses/part_max_triplet_loss.py
  class PartMaxTripletLoss (line 7) | class PartMaxTripletLoss(PartAveragedTripletLoss):
    method __init__ (line 9) | def __init__(self, **kwargs):
    method _combine_part_based_dist_matrices (line 12) | def _combine_part_based_dist_matrices(self, part_based_pairwise_dist, ...

FILE: torchreid/losses/part_min_triplet_loss.py
  class PartMinTripletLoss (line 9) | class PartMinTripletLoss(PartAveragedTripletLoss):
    method __init__ (line 11) | def __init__(self, **kwargs):
    method _combine_part_based_dist_matrices (line 14) | def _combine_part_based_dist_matrices(self, part_based_pairwise_dist, ...

FILE: torchreid/losses/part_random_max_min_triplet_loss.py
  class PartRandomMaxMinTripletLoss (line 9) | class PartRandomMaxMinTripletLoss(PartAveragedTripletLoss):
    method __init__ (line 11) | def __init__(self, **kwargs):
    method _combine_part_based_dist_matrices (line 14) | def _combine_part_based_dist_matrices(self, part_based_pairwise_dist, ...

FILE: torchreid/metrics/accuracy.py
  function accuracy (line 4) | def accuracy(output, target, topk=(1, )):

FILE: torchreid/metrics/distance.py
  function compute_distance_matrix (line 9) | def compute_distance_matrix(input1, input2, metric='euclidean'):
  function euclidean_squared_distance (line 52) | def euclidean_squared_distance(input1, input2):
  function cosine_distance (line 71) | def cosine_distance(input1, input2):
  function compute_distance_matrix_using_bp_features (line 87) | def compute_distance_matrix_using_bp_features(qf, gf, qf_parts_visibilit...
  function _compute_distance_matrix_using_bp_features (line 102) | def _compute_distance_matrix_using_bp_features(qf, gf, dist_combine_stra...
  function _compute_distance_matrix_using_bp_features_and_masks (line 131) | def _compute_distance_matrix_using_bp_features_and_masks(qf, gf, qf_part...
  function _compute_distance_matrix_using_bp_features_and_visibility_scores (line 181) | def _compute_distance_matrix_using_bp_features_and_visibility_scores(qf,...
  function _compute_body_parts_dist_matrices (line 222) | def _compute_body_parts_dist_matrices(qf, gf, metric='euclidean'):

FILE: torchreid/metrics/rank.py
  function eval_cuhk03 (line 17) | def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
  function eval_market1501 (line 97) | def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
  function evaluate_py (line 162) | def evaluate_py(
  function evaluate_rank (line 173) | def evaluate_rank(

FILE: torchreid/metrics/rank_cylib/setup.py
  function numpy_include (line 7) | def numpy_include():

FILE: torchreid/models/__init__.py
  function show_avai_models (line 92) | def show_avai_models():
  function build_model (line 102) | def build_model(

FILE: torchreid/models/bpbreid.py
  class BPBreID (line 15) | class BPBreID(nn.Module):
    method __init__ (line 18) | def __init__(self, num_classes, pretrained, loss, model_cfg, horizonta...
    method init_dim_reduce_layers (line 84) | def init_dim_reduce_layers(self, dim_reduce_mode, spatial_feature_size...
    method forward (line 116) | def forward(self, images, external_parts_masks=None):
    method parts_identity_classification (line 261) | def parts_identity_classification(self, D, N, parts_embeddings):
  class BeforePoolingDimReduceLayer (line 286) | class BeforePoolingDimReduceLayer(nn.Module):
    method __init__ (line 287) | def __init__(self, input_dim, output_dim):
    method forward (line 301) | def forward(self, x):
    method _init_params (line 304) | def _init_params(self):
  class AfterPoolingDimReduceLayer (line 324) | class AfterPoolingDimReduceLayer(nn.Module):
    method __init__ (line 325) | def __init__(self, input_dim, output_dim, dropout_p=None):
    method forward (line 342) | def forward(self, x):
    method _init_params (line 352) | def _init_params(self):
  class PixelToPartClassifier (line 376) | class PixelToPartClassifier(nn.Module):
    method __init__ (line 377) | def __init__(self, dim_reduce_output, parts_num):
    method forward (line 383) | def forward(self, x):
    method _init_params (line 387) | def _init_params(self):
  class BNClassifier (line 398) | class BNClassifier(nn.Module):
    method __init__ (line 400) | def __init__(self, in_dim, class_num):
    method forward (line 412) | def forward(self, x):
    method _init_params (line 417) | def _init_params(self):
  function init_part_attention_pooling_head (line 432) | def init_part_attention_pooling_head(normalization, pooling, dim_reduce_...
  class GlobalMaskWeightedPoolingHead (line 444) | class GlobalMaskWeightedPoolingHead(nn.Module):
    method __init__ (line 445) | def __init__(self, depth, normalization='identity'):
    method forward (line 458) | def forward(self, features, part_masks):
    method _init_params (line 470) | def _init_params(self):
  class GlobalMaxPoolingHead (line 481) | class GlobalMaxPoolingHead(GlobalMaskWeightedPoolingHead):
  class GlobalAveragePoolingHead (line 485) | class GlobalAveragePoolingHead(GlobalMaskWeightedPoolingHead):
  class GlobalWeightedAveragePoolingHead (line 489) | class GlobalWeightedAveragePoolingHead(GlobalMaskWeightedPoolingHead):
    method forward (line 490) | def forward(self, features, part_masks):
  function bpbreid (line 510) | def bpbreid(num_classes, loss='part_based', pretrained=True, config=None...
  function pcb (line 521) | def pcb(num_classes, loss='part_based', pretrained=True, config=None, **...
  function bot (line 535) | def bot(num_classes, loss='part_based', pretrained=True, config=None, **...

FILE: torchreid/models/compact_bilinear_pooling.py
  function CountSketchFn_forward (line 7) | def CountSketchFn_forward(h, s, output_size, x, force_cpu_scatter_add=Fa...
  function CountSketchFn_backward (line 30) | def CountSketchFn_backward(h, s, x_size, grad_output):
  class CountSketchFn (line 41) | class CountSketchFn(Function):
    method forward (line 44) | def forward(ctx, h, s, output_size, x, force_cpu_scatter_add=False):
    method backward (line 53) | def backward(ctx, grad_output):
  class CountSketch (line 60) | class CountSketch(nn.Module):
    method __init__ (line 86) | def __init__(self, input_size, output_size, h=None, s=None):
    method forward (line 110) | def forward(self, x):
  function ComplexMultiply_forward (line 118) | def ComplexMultiply_forward(X_re, X_im, Y_re, Y_im):
  function ComplexMultiply_backward (line 124) | def ComplexMultiply_backward(X_re, X_im, Y_re, Y_im, grad_Z_re, grad_Z_im):
  class ComplexMultiply (line 132) | class ComplexMultiply(torch.autograd.Function):
    method forward (line 135) | def forward(ctx, X_re, X_im, Y_re, Y_im):
    method backward (line 140) | def backward(ctx, grad_Z_re, grad_Z_im):
  class CompactBilinearPoolingFn (line 145) | class CompactBilinearPoolingFn(Function):
    method forward (line 148) | def forward(ctx, h1, s1, h2, s2, output_size, x, y, force_cpu_scatter_...
    method backward (line 180) | def backward(ctx, grad_output):
  class CompactBilinearPooling (line 229) | class CompactBilinearPooling(nn.Module):
    method __init__ (line 260) | def __init__(self, input1_size, input2_size, output_size, h1=None, s1=...
    method forward (line 268) | def forward(self, x, y=None):

FILE: torchreid/models/densenet.py
  class _DenseLayer (line 29) | class _DenseLayer(nn.Sequential):
    method __init__ (line 31) | def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
    method forward (line 60) | def forward(self, x):
  class _DenseBlock (line 69) | class _DenseBlock(nn.Sequential):
    method __init__ (line 71) | def __init__(
  class _Transition (line 83) | class _Transition(nn.Sequential):
    method __init__ (line 85) | def __init__(self, num_input_features, num_output_features):
  class DenseNet (line 102) | class DenseNet(nn.Module):
    method __init__ (line 116) | def __init__(
    method _construct_fc_layer (line 190) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 221) | def _init_params(self):
    method forward (line 240) | def forward(self, x):
  function init_pretrained_weights (line 262) | def init_pretrained_weights(model, model_url):
  function densenet121 (line 303) | def densenet121(num_classes, loss='softmax', pretrained=True, **kwargs):
  function densenet169 (line 319) | def densenet169(num_classes, loss='softmax', pretrained=True, **kwargs):
  function densenet201 (line 335) | def densenet201(num_classes, loss='softmax', pretrained=True, **kwargs):
  function densenet161 (line 351) | def densenet161(num_classes, loss='softmax', pretrained=True, **kwargs):
  function densenet121_fc512 (line 367) | def densenet121_fc512(num_classes, loss='softmax', pretrained=True, **kw...

FILE: torchreid/models/hacnn.py
  class ConvBlock (line 9) | class ConvBlock(nn.Module):
    method __init__ (line 22) | def __init__(self, in_c, out_c, k, s=1, p=0):
    method forward (line 27) | def forward(self, x):
  class InceptionA (line 31) | class InceptionA(nn.Module):
    method __init__ (line 33) | def __init__(self, in_channels, out_channels):
    method forward (line 54) | def forward(self, x):
  class InceptionB (line 63) | class InceptionB(nn.Module):
    method __init__ (line 65) | def __init__(self, in_channels, out_channels):
    method forward (line 83) | def forward(self, x):
  class SpatialAttn (line 91) | class SpatialAttn(nn.Module):
    method __init__ (line 94) | def __init__(self):
    method forward (line 99) | def forward(self, x):
  class ChannelAttn (line 115) | class ChannelAttn(nn.Module):
    method __init__ (line 118) | def __init__(self, in_channels, reduction_rate=16):
    method forward (line 124) | def forward(self, x):
  class SoftAttn (line 133) | class SoftAttn(nn.Module):
    method __init__ (line 141) | def __init__(self, in_channels):
    method forward (line 147) | def forward(self, x):
  class HardAttn (line 155) | class HardAttn(nn.Module):
    method __init__ (line 158) | def __init__(self, in_channels):
    method init_params (line 163) | def init_params(self):
    method forward (line 171) | def forward(self, x):
  class HarmAttn (line 180) | class HarmAttn(nn.Module):
    method __init__ (line 183) | def __init__(self, in_channels):
    method forward (line 188) | def forward(self, x):
  class HACNN (line 194) | class HACNN(nn.Module):
    method __init__ (line 210) | def __init__(
    method init_scale_factors (line 271) | def init_scale_factors(self):
    method stn (line 287) | def stn(self, x, theta):
    method transform_theta (line 297) | def transform_theta(self, theta_i, region_idx):
    method forward (line 307) | def forward(self, x):

FILE: torchreid/models/hrnet.py
  function get_hrnet_config (line 26) | def get_hrnet_config():
  function conv3x3 (line 61) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 67) | class BasicBlock(nn.Module):
    method __init__ (line 70) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 80) | def forward(self, x):
  class Bottleneck (line 99) | class Bottleneck(nn.Module):
    method __init__ (line 102) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 117) | def forward(self, x):
  class HighResolutionModule (line 140) | class HighResolutionModule(nn.Module):
    method __init__ (line 141) | def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
    method _check_branches (line 158) | def _check_branches(self, num_branches, blocks, num_blocks,
    method _make_one_branch (line 178) | def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
    method _make_branches (line 202) | def _make_branches(self, num_branches, block, num_blocks, num_channels):
    method _make_fuse_layers (line 211) | def _make_fuse_layers(self):
    method get_num_inchannels (line 259) | def get_num_inchannels(self):
    method forward (line 262) | def forward(self, x):
  class ConvBlock (line 288) | class ConvBlock(nn.Module):
    method __init__ (line 289) | def __init__(self, in_c, out_c, k, s=1, p=0):
    method forward (line 295) | def forward(self, x):
  function weights_init_kaiming (line 299) | def weights_init_kaiming(m):
  class HighResolutionNet (line 314) | class HighResolutionNet(nn.Module):
    method __init__ (line 316) | def __init__(self, cfg, enable_dim_reduction, dim_reduction_channels, ...
    method _make_incre_channel_nin (line 382) | def _make_incre_channel_nin(self):
    method _make_head (line 400) | def _make_head(self, pre_stage_channels):
    method _make_transition_layer (line 449) | def _make_transition_layer(
    method _make_layer (line 485) | def _make_layer(self, block, inplanes, planes, blocks, stride=1):
    method _make_stage (line 502) | def _make_stage(self, layer_config, num_inchannels,
    method forward (line 532) | def forward(self, x):
    method random_init (line 578) | def random_init(self):
    method load_param (line 588) | def load_param(self, pretrained_path):
  function init_pretrained_weights (line 605) | def init_pretrained_weights(model, pretrain_path, model_key):
  function hrnet32 (line 611) | def hrnet32(num_classes, loss='part_based', pretrained=True, enable_dim_...

FILE: torchreid/models/inceptionresnetv2.py
  class BasicConv2d (line 37) | class BasicConv2d(nn.Module):
    method __init__ (line 39) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 57) | def forward(self, x):
  class Mixed_5b (line 64) | class Mixed_5b(nn.Module):
    method __init__ (line 66) | def __init__(self):
    method forward (line 87) | def forward(self, x):
  class Block35 (line 96) | class Block35(nn.Module):
    method __init__ (line 98) | def __init__(self, scale=1.0):
    method forward (line 119) | def forward(self, x):
  class Mixed_6a (line 130) | class Mixed_6a(nn.Module):
    method __init__ (line 132) | def __init__(self):
    method forward (line 145) | def forward(self, x):
  class Block17 (line 153) | class Block17(nn.Module):
    method __init__ (line 155) | def __init__(self, scale=1.0):
    method forward (line 175) | def forward(self, x):
  class Mixed_7a (line 185) | class Mixed_7a(nn.Module):
    method __init__ (line 187) | def __init__(self):
    method forward (line 208) | def forward(self, x):
  class Block8 (line 217) | class Block8(nn.Module):
    method __init__ (line 219) | def __init__(self, scale=1.0, noReLU=False):
    method forward (line 241) | def forward(self, x):
  class InceptionResNetV2 (line 255) | class InceptionResNetV2(nn.Module):
    method __init__ (line 266) | def __init__(self, num_classes, loss='softmax', **kwargs):
    method load_imagenet_weights (line 309) | def load_imagenet_weights(self):
    method featuremaps (line 321) | def featuremaps(self, x):
    method forward (line 339) | def forward(self, x):
  function inceptionresnetv2 (line 357) | def inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kw...

FILE: torchreid/models/inceptionv4.py
  class BasicConv2d (line 37) | class BasicConv2d(nn.Module):
    method __init__ (line 39) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 57) | def forward(self, x):
  class Mixed_3a (line 64) | class Mixed_3a(nn.Module):
    method __init__ (line 66) | def __init__(self):
    method forward (line 71) | def forward(self, x):
  class Mixed_4a (line 78) | class Mixed_4a(nn.Module):
    method __init__ (line 80) | def __init__(self):
    method forward (line 95) | def forward(self, x):
  class Mixed_5a (line 102) | class Mixed_5a(nn.Module):
    method __init__ (line 104) | def __init__(self):
    method forward (line 109) | def forward(self, x):
  class Inception_A (line 116) | class Inception_A(nn.Module):
    method __init__ (line 118) | def __init__(self):
    method forward (line 138) | def forward(self, x):
  class Reduction_A (line 147) | class Reduction_A(nn.Module):
    method __init__ (line 149) | def __init__(self):
    method forward (line 161) | def forward(self, x):
  class Inception_B (line 169) | class Inception_B(nn.Module):
    method __init__ (line 171) | def __init__(self):
    method forward (line 206) | def forward(self, x):
  class Reduction_B (line 215) | class Reduction_B(nn.Module):
    method __init__ (line 217) | def __init__(self):
    method forward (line 237) | def forward(self, x):
  class Inception_C (line 245) | class Inception_C(nn.Module):
    method __init__ (line 247) | def __init__(self):
    method forward (line 279) | def forward(self, x):
  class InceptionV4 (line 300) | class InceptionV4(nn.Module):
    method __init__ (line 311) | def __init__(self, num_classes, loss, **kwargs):
    method forward (line 342) | def forward(self, x):
  function init_pretrained_weights (line 360) | def init_pretrained_weights(model, model_url):
  function inceptionv4 (line 376) | def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/mlfn.py
  class MLFNBlock (line 16) | class MLFNBlock(nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 64) | def forward(self, x):
  class MLFN (line 98) | class MLFN(nn.Module):
    method __init__ (line 109) | def __init__(
    method init_params (line 196) | def init_params(self):
    method forward (line 212) | def forward(self, x):
  function init_pretrained_weights (line 244) | def init_pretrained_weights(model, model_url):
  function mlfn (line 260) | def mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/mobilenetv2.py
  class ConvBlock (line 18) | class ConvBlock(nn.Module):
    method __init__ (line 33) | def __init__(self, in_c, out_c, k, s=1, p=0, g=1):
    method forward (line 40) | def forward(self, x):
  class Bottleneck (line 44) | class Bottleneck(nn.Module):
    method __init__ (line 46) | def __init__(self, in_channels, out_channels, expansion_factor, stride...
    method forward (line 59) | def forward(self, x):
  class MobileNetV2 (line 69) | class MobileNetV2(nn.Module):
    method __init__ (line 81) | def __init__(
    method _make_layer (line 128) | def _make_layer(self, block, t, c, n, s):
    method _construct_fc_layer (line 140) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 171) | def _init_params(self):
    method featuremaps (line 190) | def featuremaps(self, x):
    method forward (line 202) | def forward(self, x):
  function init_pretrained_weights (line 223) | def init_pretrained_weights(model, model_url):
  function mobilenetv2_x1_0 (line 239) | def mobilenetv2_x1_0(num_classes, loss, pretrained=True, **kwargs):
  function mobilenetv2_x1_4 (line 258) | def mobilenetv2_x1_4(num_classes, loss, pretrained=True, **kwargs):

FILE: torchreid/models/mudeep.py
  class ConvBlock (line 9) | class ConvBlock(nn.Module):
    method __init__ (line 22) | def __init__(self, in_c, out_c, k, s, p):
    method forward (line 27) | def forward(self, x):
  class ConvLayers (line 31) | class ConvLayers(nn.Module):
    method __init__ (line 34) | def __init__(self):
    method forward (line 40) | def forward(self, x):
  class MultiScaleA (line 47) | class MultiScaleA(nn.Module):
    method __init__ (line 50) | def __init__(self):
    method forward (line 67) | def forward(self, x):
  class Reduction (line 76) | class Reduction(nn.Module):
    method __init__ (line 79) | def __init__(self):
    method forward (line 89) | def forward(self, x):
  class MultiScaleB (line 97) | class MultiScaleB(nn.Module):
    method __init__ (line 100) | def __init__(self):
    method forward (line 120) | def forward(self, x):
  class Fusion (line 128) | class Fusion(nn.Module):
    method __init__ (line 131) | def __init__(self):
    method forward (line 142) | def forward(self, x1, x2, x3, x4):
  class MuDeep (line 151) | class MuDeep(nn.Module):
    method __init__ (line 162) | def __init__(self, num_classes, loss='softmax', **kwargs):
    method featuremaps (line 184) | def featuremaps(self, x):
    method forward (line 192) | def forward(self, x):

FILE: torchreid/models/nasnet.py
  class MaxPoolPad (line 52) | class MaxPoolPad(nn.Module):
    method __init__ (line 54) | def __init__(self):
    method forward (line 59) | def forward(self, x):
  class AvgPoolPad (line 66) | class AvgPoolPad(nn.Module):
    method __init__ (line 68) | def __init__(self, stride=2, padding=1):
    method forward (line 75) | def forward(self, x):
  class SeparableConv2d (line 82) | class SeparableConv2d(nn.Module):
    method __init__ (line 84) | def __init__(
    method forward (line 107) | def forward(self, x):
  class BranchSeparables (line 113) | class BranchSeparables(nn.Module):
    method __init__ (line 115) | def __init__(
    method forward (line 142) | def forward(self, x):
  class BranchSeparablesStem (line 157) | class BranchSeparablesStem(nn.Module):
    method __init__ (line 159) | def __init__(
    method forward (line 184) | def forward(self, x):
  class BranchSeparablesReduction (line 194) | class BranchSeparablesReduction(BranchSeparables):
    method __init__ (line 196) | def __init__(
    method forward (line 211) | def forward(self, x):
  class CellStem0 (line 223) | class CellStem0(nn.Module):
    method __init__ (line 225) | def __init__(self, stem_filters, num_filters=42):
    method forward (line 272) | def forward(self, x):
  class CellStem1 (line 300) | class CellStem1(nn.Module):
    method __init__ (line 302) | def __init__(self, stem_filters, num_filters):
    method forward (line 419) | def forward(self, x_conv0, x_stem_0):
  class FirstCell (line 458) | class FirstCell(nn.Module):
    method __init__ (line 460) | def __init__(
    method forward (line 536) | def forward(self, x, x_prev):
  class NormalCell (line 577) | class NormalCell(nn.Module):
    method __init__ (line 579) | def __init__(
    method forward (line 643) | def forward(self, x, x_prev):
  class ReductionCell0 (line 674) | class ReductionCell0(nn.Module):
    method __init__ (line 676) | def __init__(
    method forward (line 737) | def forward(self, x, x_prev):
  class ReductionCell1 (line 766) | class ReductionCell1(nn.Module):
    method __init__ (line 768) | def __init__(
    method forward (line 862) | def forward(self, x, x_prev):
  class NASNetAMobile (line 891) | class NASNetAMobile(nn.Module):
    method __init__ (line 902) | def __init__(
    method _init_params (line 1041) | def _init_params(self):
    method features (line 1060) | def features(self, input):
    method forward (line 1094) | def forward(self, input):
  function init_pretrained_weights (line 1110) | def init_pretrained_weights(model, model_url):
  function nasnetamobile (line 1126) | def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/osnet.py
  class ConvLayer (line 28) | class ConvLayer(nn.Module):
    method __init__ (line 31) | def __init__(
    method forward (line 57) | def forward(self, x):
  class Conv1x1 (line 64) | class Conv1x1(nn.Module):
    method __init__ (line 67) | def __init__(self, in_channels, out_channels, stride=1, groups=1):
    method forward (line 81) | def forward(self, x):
  class Conv1x1Linear (line 88) | class Conv1x1Linear(nn.Module):
    method __init__ (line 91) | def __init__(self, in_channels, out_channels, stride=1):
    method forward (line 98) | def forward(self, x):
  class Conv3x3 (line 104) | class Conv3x3(nn.Module):
    method __init__ (line 107) | def __init__(self, in_channels, out_channels, stride=1, groups=1):
    method forward (line 121) | def forward(self, x):
  class LightConv3x3 (line 128) | class LightConv3x3(nn.Module):
    method __init__ (line 134) | def __init__(self, in_channels, out_channels):
    method forward (line 151) | def forward(self, x):
  class ChannelGate (line 162) | class ChannelGate(nn.Module):
    method __init__ (line 165) | def __init__(
    method forward (line 208) | def forward(self, x):
  class OSBlock (line 223) | class OSBlock(nn.Module):
    method __init__ (line 226) | def __init__(
    method forward (line 262) | def forward(self, x):
  class OSNet (line 282) | class OSNet(nn.Module):
    method __init__ (line 291) | def __init__(
    method _make_layer (line 344) | def _make_layer(
    method _construct_fc_layer (line 369) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 390) | def _init_params(self):
    method featuremaps (line 412) | def featuremaps(self, x):
    method forward (line 421) | def forward(self, x, return_featuremaps=False):
  function init_pretrained_weights (line 440) | def init_pretrained_weights(model, key=''):
  function osnet_x1_0 (line 521) | def osnet_x1_0(num_classes=1000, pretrained=True, loss='softmax', **kwar...
  function osnet_x0_75 (line 536) | def osnet_x0_75(num_classes=1000, pretrained=True, loss='softmax', **kwa...
  function osnet_x0_5 (line 551) | def osnet_x0_5(num_classes=1000, pretrained=True, loss='softmax', **kwar...
  function osnet_x0_25 (line 566) | def osnet_x0_25(num_classes=1000, pretrained=True, loss='softmax', **kwa...
  function osnet_ibn_x1_0 (line 581) | def osnet_ibn_x1_0(

FILE: torchreid/models/osnet_ain.py
  class ConvLayer (line 18) | class ConvLayer(nn.Module):
    method __init__ (line 21) | def __init__(
    method forward (line 47) | def forward(self, x):
  class Conv1x1 (line 53) | class Conv1x1(nn.Module):
    method __init__ (line 56) | def __init__(self, in_channels, out_channels, stride=1, groups=1):
    method forward (line 70) | def forward(self, x):
  class Conv1x1Linear (line 76) | class Conv1x1Linear(nn.Module):
    method __init__ (line 79) | def __init__(self, in_channels, out_channels, stride=1, bn=True):
    method forward (line 88) | def forward(self, x):
  class Conv3x3 (line 95) | class Conv3x3(nn.Module):
    method __init__ (line 98) | def __init__(self, in_channels, out_channels, stride=1, groups=1):
    method forward (line 112) | def forward(self, x):
  class LightConv3x3 (line 118) | class LightConv3x3(nn.Module):
    method __init__ (line 124) | def __init__(self, in_channels, out_channels):
    method forward (line 141) | def forward(self, x):
  class LightConvStream (line 148) | class LightConvStream(nn.Module):
    method __init__ (line 151) | def __init__(self, in_channels, out_channels, depth):
    method forward (line 162) | def forward(self, x):
  class ChannelGate (line 169) | class ChannelGate(nn.Module):
    method __init__ (line 172) | def __init__(
    method forward (line 215) | def forward(self, x):
  class OSBlock (line 230) | class OSBlock(nn.Module):
    method __init__ (line 233) | def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwar...
    method forward (line 249) | def forward(self, x):
  class OSBlockINin (line 263) | class OSBlockINin(nn.Module):
    method __init__ (line 266) | def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwar...
    method forward (line 283) | def forward(self, x):
  class OSNet (line 301) | class OSNet(nn.Module):
    method __init__ (line 310) | def __init__(
    method _make_layer (line 359) | def _make_layer(self, blocks, layer, in_channels, out_channels):
    method _construct_fc_layer (line 366) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 387) | def _init_params(self):
    method featuremaps (line 413) | def featuremaps(self, x):
    method forward (line 424) | def forward(self, x, return_featuremaps=False):
  function init_pretrained_weights (line 443) | def init_pretrained_weights(model, key=''):
  function osnet_ain_x1_0 (line 524) | def osnet_ain_x1_0(

FILE: torchreid/models/pcb.py
  function conv3x3 (line 17) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 29) | class BasicBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 42) | def forward(self, x):
  class Bottleneck (line 61) | class Bottleneck(nn.Module):
    method __init__ (line 64) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 85) | def forward(self, x):
  class DimReduceLayer (line 108) | class DimReduceLayer(nn.Module):
    method __init__ (line 110) | def __init__(self, in_channels, out_channels, nonlinear):
    method forward (line 127) | def forward(self, x):
  class PCB (line 131) | class PCB(nn.Module):
    method __init__ (line 143) | def __init__(
    method _make_layer (line 188) | def _make_layer(self, block, planes, blocks, stride=1):
    method _init_params (line 210) | def _init_params(self):
    method featuremaps (line 229) | def featuremaps(self, x):
    method forward (line 240) | def forward(self, x):
  function init_pretrained_weights (line 267) | def init_pretrained_weights(model, model_url):
  function pcb_p6 (line 283) | def pcb_p6(num_classes, loss='softmax', pretrained=True, **kwargs):
  function pcb_p4 (line 300) | def pcb_p4(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/pvpm.py
  function conv3x3 (line 25) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 31) | class BasicBlock(nn.Module):
    method __init__ (line 34) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 44) | def forward(self, x):
  class Bottleneck (line 63) | class Bottleneck(nn.Module):
    method __init__ (line 66) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 79) | def forward(self, x):
  class DimReduceLayer (line 102) | class DimReduceLayer(nn.Module):
    method __init__ (line 104) | def __init__(self, in_channels, out_channels, nonlinear):
    method forward (line 117) | def forward(self, x):
  class PCB (line 121) | class PCB(nn.Module):
    method __init__ (line 133) | def __init__(self, num_classes, loss, block, layers,
    method _construct_em_layer (line 165) | def _construct_em_layer(self, fc_dims, input_dim, dropout_p=0.5): # TO...
    method _make_layer (line 190) | def _make_layer(self, block, planes, blocks, stride=1):
    method _init_params (line 207) | def _init_params(self):
    method featuremaps (line 224) | def featuremaps(self, x):
    method forward (line 235) | def forward(self, x):
  function init_pretrained_weights (line 268) | def init_pretrained_weights(model, model_url):
  function pcb_p6 (line 280) | def pcb_p6(num_classes, loss='softmax', pretrained=True, **kwargs):
  function pcb_p4 (line 297) | def pcb_p4(num_classes, loss='softmax', pretrained=True, **kwargs):
  class Conv1x1_att (line 314) | class Conv1x1_att(nn.Module):
    method __init__ (line 317) | def __init__(self, in_channels, out_channels, stride=1, groups=1):
    method forward (line 324) | def forward(self, x):
  class score_embedding (line 331) | class score_embedding(nn.Module):
    method __init__ (line 334) | def __init__(self, in_channels, out_channels):
    method forward (line 341) | def forward(self, x):
  class Pose_Subnet (line 350) | class Pose_Subnet(nn.Module): # TODO
    method __init__ (line 354) | def __init__(self, blocks, in_channels, channels, att_num=1, IN=False,...
    method _make_layer (line 371) | def _make_layer(self, block, layer, in_channels, out_channels, reduce_...
    method forward (line 386) | def forward(self, x):
    method _init_params (line 401) | def _init_params(self):
  class pose_guide_att_Resnet (line 422) | class pose_guide_att_Resnet(PCB):
    method __init__ (line 423) | def __init__(self, num_classes, loss, block, layers, last_stride=2, pa...
    method forward (line 435) | def forward(self, x, pose_map):
  function pose_resnet50_256_p4 (line 479) | def pose_resnet50_256_p4(num_classes, loss='softmax', pretrained=True, *...
  function pose_resnet50_256_p6 (line 495) | def pose_resnet50_256_p6(num_classes, loss='softmax', pretrained=True, *...
  function pose_resnet50_256_p6_pscore_reg (line 511) | def pose_resnet50_256_p6_pscore_reg(num_classes, loss='softmax', pretrai...
  function pose_resnet50_256_p4_pscore_reg (line 528) | def pose_resnet50_256_p4_pscore_reg(num_classes, loss='softmax', pretrai...

FILE: torchreid/models/resnet.py
  function conv3x3 (line 31) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 45) | def conv1x1(in_planes, out_planes, stride=1):
  class BasicBlock (line 52) | class BasicBlock(nn.Module):
    method __init__ (line 55) | def __init__(
    method forward (line 86) | def forward(self, x):
  class Bottleneck (line 105) | class Bottleneck(nn.Module):
    method __init__ (line 108) | def __init__(
    method forward (line 134) | def forward(self, x):
  class ResNet (line 157) | class ResNet(nn.Module):
    method __init__ (line 175) | def __init__(
    method _make_layer (line 257) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method _construct_fc_layer (line 292) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 323) | def _init_params(self):
    method featuremaps (line 342) | def featuremaps(self, x):
    method forward (line 354) | def forward(self, x):
  function init_pretrained_weights (line 379) | def init_pretrained_weights(model, model_url):
  function resnet18 (line 398) | def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
  function resnet34 (line 414) | def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
  function resnet50 (line 430) | def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
  function resnet101 (line 445) | def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
  function resnet152 (line 461) | def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
  function resnext50_32x4d (line 480) | def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwar...
  function resnext101_32x8d (line 498) | def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwa...
  function resnet50_fc512 (line 521) | def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/resnet_fastreid.py
  function get_norm (line 36) | def get_norm(norm, out_channels, **kwargs):
  class Non_local (line 60) | class Non_local(nn.Module):
    method __init__ (line 61) | def __init__(self, in_channels, bn_norm, reduc_ratio=2):
    method forward (line 84) | def forward(self, x):
  class IBN (line 108) | class IBN(nn.Module):
    method __init__ (line 109) | def __init__(self, planes, bn_norm, **kwargs):
    method forward (line 117) | def forward(self, x):
  class BatchNorm (line 125) | class BatchNorm(nn.BatchNorm2d):
    method __init__ (line 126) | def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freez...
  class SELayer (line 135) | class SELayer(nn.Module):
    method __init__ (line 136) | def __init__(self, channel, reduction=16):
    method forward (line 146) | def forward(self, x):
  class BasicBlock (line 153) | class BasicBlock(nn.Module):
    method __init__ (line 156) | def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=...
    method forward (line 174) | def forward(self, x):
  class Bottleneck (line 194) | class Bottleneck(nn.Module):
    method __init__ (line 197) | def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=...
    method forward (line 218) | def forward(self, x):
  class ResNet (line 242) | class ResNet(nn.Module):
    method __init__ (line 243) | def __init__(self, last_stride, bn_norm, with_ibn, with_se, with_nl, b...
    method _make_layer (line 265) | def _make_layer(self, block, planes, blocks, stride=1, bn_norm="BN", w...
    method _build_nonlocal (line 282) | def _build_nonlocal(self, layers, non_layers, bn_norm):
    method forward (line 296) | def forward(self, x):
    method random_init (line 347) | def random_init(self):
  function init_pretrained_weights (line 357) | def init_pretrained_weights(key):
  function fastreid_resnet (line 405) | def fastreid_resnet(pretrained=True, **kwargs):
  function fastreid_resnet_ibn (line 408) | def fastreid_resnet_ibn(pretrained=True, **kwargs):
  function fastreid_resnet_nl (line 411) | def fastreid_resnet_nl(pretrained=True, **kwargs):
  function fastreid_resnet_ibn_nl (line 414) | def fastreid_resnet_ibn_nl(pretrained=True, **kwargs):
  function build_resnet_backbone (line 417) | def build_resnet_backbone(pretrained=True, with_ibn=False, with_nl=False...

FILE: torchreid/models/resnet_ibn_a.py
  function conv3x3 (line 19) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 31) | class BasicBlock(nn.Module):
    method __init__ (line 34) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 44) | def forward(self, x):
  class IBN (line 63) | class IBN(nn.Module):
    method __init__ (line 65) | def __init__(self, planes):
    method forward (line 73) | def forward(self, x):
  class Bottleneck (line 81) | class Bottleneck(nn.Module):
    method __init__ (line 84) | def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=N...
    method forward (line 108) | def forward(self, x):
  class ResNet (line 131) | class ResNet(nn.Module):
    method __init__ (line 140) | def __init__(
    method _make_layer (line 183) | def _make_layer(self, block, planes, blocks, stride=1):
    method _construct_fc_layer (line 208) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method featuremaps (line 239) | def featuremaps(self, x):
    method forward (line 250) | def forward(self, x):
  function init_pretrained_weights (line 267) | def init_pretrained_weights(model, model_url):
  function resnet50_ibn_a (line 283) | def resnet50_ibn_a(num_classes, loss='softmax', pretrained=False, **kwar...

FILE: torchreid/models/resnet_ibn_b.py
  function conv3x3 (line 18) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 30) | class BasicBlock(nn.Module):
    method __init__ (line 33) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 43) | def forward(self, x):
  class Bottleneck (line 62) | class Bottleneck(nn.Module):
    method __init__ (line 65) | def __init__(self, inplanes, planes, stride=1, downsample=None, IN=Fal...
    method forward (line 89) | def forward(self, x):
  class ResNet (line 114) | class ResNet(nn.Module):
    method __init__ (line 123) | def __init__(
    method _make_layer (line 170) | def _make_layer(self, block, planes, blocks, stride=1, IN=False):
    method _construct_fc_layer (line 193) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method featuremaps (line 224) | def featuremaps(self, x):
    method forward (line 235) | def forward(self, x):
  function init_pretrained_weights (line 252) | def init_pretrained_weights(model, model_url):
  function resnet50_ibn_b (line 268) | def resnet50_ibn_b(num_classes, loss='softmax', pretrained=False, **kwar...

FILE: torchreid/models/resnetmid.py
  function conv3x3 (line 17) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 29) | class BasicBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 42) | def forward(self, x):
  class Bottleneck (line 61) | class Bottleneck(nn.Module):
    method __init__ (line 64) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 85) | def forward(self, x):
  class ResNetMid (line 108) | class ResNetMid(nn.Module):
    method __init__ (line 119) | def __init__(
    method _make_layer (line 158) | def _make_layer(self, block, planes, blocks, stride=1):
    method _construct_fc_layer (line 180) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 211) | def _init_params(self):
    method featuremaps (line 230) | def featuremaps(self, x):
    method forward (line 243) | def forward(self, x):
  function init_pretrained_weights (line 268) | def init_pretrained_weights(model, model_url):
  function resnet50mid (line 295) | def resnet50mid(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/senet.py
  class SEModule (line 91) | class SEModule(nn.Module):
    method __init__ (line 93) | def __init__(self, channels, reduction):
    method forward (line 105) | def forward(self, x):
  class Bottleneck (line 115) | class Bottleneck(nn.Module):
    method forward (line 120) | def forward(self, x):
  class SEBottleneck (line 143) | class SEBottleneck(Bottleneck):
    method __init__ (line 149) | def __init__(
  class SEResNetBottleneck (line 175) | class SEResNetBottleneck(Bottleneck):
    method __init__ (line 183) | def __init__(
  class SEResNeXtBottleneck (line 208) | class SEResNeXtBottleneck(Bottleneck):
    method __init__ (line 212) | def __init__(
  class SENet (line 246) | class SENet(nn.Module):
    method __init__ (line 262) | def __init__(
    method _make_layer (line 416) | def _make_layer(
    method _construct_fc_layer (line 453) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method featuremaps (line 485) | def featuremaps(self, x):
    method forward (line 493) | def forward(self, x):
  function init_pretrained_weights (line 514) | def init_pretrained_weights(model, model_url):
  function senet154 (line 530) | def senet154(num_classes, loss='softmax', pretrained=True, **kwargs):
  function se_resnet50 (line 549) | def se_resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
  function se_resnet50_fc512 (line 572) | def se_resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kw...
  function se_resnet101 (line 595) | def se_resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
  function se_resnet152 (line 618) | def se_resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
  function se_resnext50_32x4d (line 641) | def se_resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **k...
  function se_resnext101_32x4d (line 665) | def se_resnext101_32x4d(

FILE: torchreid/models/shufflenet.py
  class ChannelShuffle (line 16) | class ChannelShuffle(nn.Module):
    method __init__ (line 18) | def __init__(self, num_groups):
    method forward (line 22) | def forward(self, x):
  class Bottleneck (line 34) | class Bottleneck(nn.Module):
    method __init__ (line 36) | def __init__(
    method forward (line 78) | def forward(self, x):
  class ShuffleNet (line 101) | class ShuffleNet(nn.Module):
    method __init__ (line 112) | def __init__(self, num_classes, loss='softmax', num_groups=3, **kwargs):
    method forward (line 153) | def forward(self, x):
  function init_pretrained_weights (line 173) | def init_pretrained_weights(model, model_url):
  function shufflenet (line 189) | def shufflenet(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/shufflenetv2.py
  function channel_shuffle (line 24) | def channel_shuffle(x, groups):
  class InvertedResidual (line 39) | class InvertedResidual(nn.Module):
    method __init__ (line 41) | def __init__(self, inp, oup, stride):
    method depthwise_conv (line 101) | def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
    method forward (line 106) | def forward(self, x):
  class ShuffleNetV2 (line 118) | class ShuffleNetV2(nn.Module):
    method __init__ (line 131) | def __init__(
    method featuremaps (line 180) | def featuremaps(self, x):
    method forward (line 189) | def forward(self, x):
  function init_pretrained_weights (line 207) | def init_pretrained_weights(model, model_url):
  function shufflenet_v2_x0_5 (line 229) | def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **k...
  function shufflenet_v2_x1_0 (line 238) | def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **k...
  function shufflenet_v2_x1_5 (line 247) | def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **k...
  function shufflenet_v2_x2_0 (line 256) | def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **k...

FILE: torchreid/models/squeezenet.py
  class Fire (line 19) | class Fire(nn.Module):
    method __init__ (line 21) | def __init__(
    method forward (line 37) | def forward(self, x):
  class SqueezeNet (line 47) | class SqueezeNet(nn.Module):
    method __init__ (line 60) | def __init__(
    method _construct_fc_layer (line 118) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 149) | def _init_params(self):
    method forward (line 168) | def forward(self, x):
  function init_pretrained_weights (line 189) | def init_pretrained_weights(model, model_url):
  function squeezenet1_0 (line 205) | def squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
  function squeezenet1_0_fc512 (line 214) | def squeezenet1_0_fc512(
  function squeezenet1_1 (line 230) | def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/models/xception.py
  class SeparableConv2d (line 26) | class SeparableConv2d(nn.Module):
    method __init__ (line 28) | def __init__(
    method forward (line 54) | def forward(self, x):
  class Block (line 60) | class Block(nn.Module):
    method __init__ (line 62) | def __init__(
    method forward (line 132) | def forward(self, inp):
  class Xception (line 145) | class Xception(nn.Module):
    method __init__ (line 156) | def __init__(
    method _construct_fc_layer (line 221) | def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
    method _init_params (line 252) | def _init_params(self):
    method featuremaps (line 271) | def featuremaps(self, input):
    method forward (line 302) | def forward(self, x):
  function init_pretrained_weights (line 323) | def init_pretrained_weights(model, model_url):
  function xception (line 339) | def xception(num_classes, loss='softmax', pretrained=True, **kwargs):

FILE: torchreid/optim/lr_scheduler.py
  function build_lr_scheduler (line 8) | def build_lr_scheduler(
  class WarmupMultiStepLR (line 88) | class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    method __init__ (line 90) | def __init__(
    method get_lr (line 118) | def get_lr(self):

FILE: torchreid/optim/optimizer.py
  function build_optimizer (line 11) | def build_optimizer(

FILE: torchreid/optim/radam.py
  class RAdam (line 19) | class RAdam(Optimizer):
    method __init__ (line 21) | def __init__(
    method __setstate__ (line 48) | def __setstate__(self, state):
    method step (line 51) | def step(self, closure=None):
  class PlainRAdam (line 134) | class PlainRAdam(Optimizer):
    method __init__ (line 136) | def __init__(
    method __setstate__ (line 163) | def __setstate__(self, state):
    method step (line 166) | def step(self, closure=None):
  class AdamW (line 233) | class AdamW(Optimizer):
    method __init__ (line 235) | def __init__(
    method __setstate__ (line 266) | def __setstate__(self, state):
    method step (line 269) | def step(self, closure=None):

FILE: torchreid/scripts/default_config.py
  function get_default_config (line 11) | def get_default_config():
  function imagedata_kwargs (line 254) | def imagedata_kwargs(cfg):
  function videodata_kwargs (line 284) | def videodata_kwargs(cfg):
  function optimizer_kwargs (line 308) | def optimizer_kwargs(cfg):
  function lr_scheduler_kwargs (line 325) | def lr_scheduler_kwargs(cfg):
  function engine_run_kwargs (line 334) | def engine_run_kwargs(cfg):
  function display_config_diff (line 353) | def display_config_diff(cfg, default_cfg_copy):

FILE: torchreid/scripts/get_labels.py
  function build_config_maskrcnn (line 21) | def build_config_maskrcnn(model_config_name):
  function compare_arrays (line 29) | def compare_arrays(array1, array2):
  function get_image_paths (line 51) | def get_image_paths(source, path_format=False):
  function format_path (line 72) | def format_path(img_path, dataset_dir):
  function get_label_paths (line 91) | def get_label_paths(is_mask, img_paths, dataset_dir):
  function skip_existing (line 116) | def skip_existing(is_mask, imagery, dataset_dir):
  function save_files (line 136) | def save_files(files, files_path, verbose=True):
  class ImageDataset (line 152) | class ImageDataset(Dataset):
    method __init__ (line 163) | def __init__(self, imagery: List[Path]):
    method __getitem__ (line 166) | def __getitem__(self, index):
    method __len__ (line 169) | def __len__(self):
  class BatchPifPaf (line 173) | class BatchPifPaf:
    method __init__ (line 174) | def __init__(self, model_name: str = "shufflenetv2k16", batch_size: in...
    method __call__ (line 197) | def __call__(self, imagery: List[Path] or List[str], dataset_dir: List...
    method __get_pifpaf_conf (line 254) | def __get_pifpaf_conf(self, processed_image_batch: Instances):
  class BatchMask (line 282) | class BatchMask:
    method __init__ (line 283) | def __init__(self, cfg: CfgNode or str, batch_size: int = None, worker...
    method __collate (line 322) | def __collate(self, batch):
    method __call__ (line 348) | def __call__(self, imagery: List[Path] or List[str], dataset_dir: List...
    method __filter_pifpaf_with_mask (line 404) | def __filter_pifpaf_with_mask(self, batch,
  function main (line 487) | def main():

FILE: torchreid/scripts/main.py
  function build_datamanager (line 20) | def build_datamanager(cfg):
  function build_engine (line 27) | def build_engine(cfg, datamanager, model, optimizer, scheduler, writer, ...
  function reset_config (line 111) | def reset_config(cfg, args):
  function main (line 128) | def main():
  function build_config (line 192) | def build_config(args=None, config_file=None, config=None):
  function build_torchreid_model_engine (line 226) | def build_torchreid_model_engine(cfg):

FILE: torchreid/tools/compute_mean_std.py
  function main (line 19) | def main():

FILE: torchreid/tools/extract_part_based_features.py
  function extract_part_based_features (line 10) | def extract_part_based_features(extractor, image_list, batch_size=400):
  function extract_det_idx (line 44) | def extract_det_idx(img_path):
  function extract_reid_features (line 48) | def extract_reid_features(cfg, base_folder, out_path, model=None, model_...

FILE: torchreid/tools/feature_extractor.py
  class FeatureExtractor (line 14) | class FeatureExtractor(object):
    method __init__ (line 60) | def __init__(
    method __call__ (line 125) | def __call__(self, input, external_parts_masks=None):

FILE: torchreid/utils/avgmeter.py
  class AverageMeter (line 15) | class AverageMeter(object):
    method __init__ (line 25) | def __init__(self):
    method reset (line 28) | def reset(self):
    method update (line 34) | def update(self, val, n=1):
  class BatchMeter (line 41) | class BatchMeter(object):
    method __init__ (line 42) | def __init__(self, epoch_count, batch_count):
    method reset (line 47) | def reset(self):
    method update (line 51) | def update(self, epoch, batch, val):
    method total_for_epoch (line 55) | def total_for_epoch(self, epoch):
    method avg_for_epoch (line 58) | def avg_for_epoch(self, epoch):
    method batch_avg (line 61) | def batch_avg(self):
    method epoch_avg (line 64) | def epoch_avg(self):
    method total (line 67) | def total(self):
  class SingleMeter (line 71) | class SingleMeter(EngineStateListener):
    method __init__ (line 72) | def __init__(self, engine_state):
    method reset (line 78) | def reset(self):
    method update (line 82) | def update(self, val, total):
    method ratio (line 92) | def ratio(self):
  class EpochMeter (line 98) | class EpochMeter(EngineStateListener):
    method __init__ (line 100) | def __init__(self, engine_state):
    method update (line 113) | def update(self, val, total=1.):
    method epoch_completed (line 124) | def epoch_completed(self):
    method last_val (line 142) | def last_val(self):
    method epoch_ratio (line 145) | def epoch_ratio(self, epoch):
    method total_ratio (line 148) | def total_ratio(self):
  class EpochArrayMeter (line 152) | class EpochArrayMeter(EngineStateListener):
    method __init__ (line 154) | def __init__(self, engine_state, array_size):
    method update (line 167) | def update(self, val, total):
    method epoch_completed (line 183) | def epoch_completed(self):
    method epoch_ratio (line 200) | def epoch_ratio(self, epoch):
    method total_ratio (line 203) | def total_ratio(self):
  class TimeMeter (line 207) | class TimeMeter(AverageMeter):
    method __init__ (line 211) | def __init__(self, name):
    method _format_time (line 216) | def _format_time(self, time):
    method total_time (line 219) | def total_time(self):
    method average_time (line 222) | def average_time(self):
    method start (line 225) | def start(self):
    method stop (line 228) | def stop(self):
    method _current_time_ms (line 238) | def _current_time_ms():
  class TorchTimeMeter (line 242) | class TorchTimeMeter(TimeMeter):
    method __init__ (line 246) | def __init__(self, name, plot=True):
    method start (line 253) | def start(self):
    method _start_cuda (line 259) | def _start_cuda(self):
    method stop (line 264) | def stop(self):
    method _stop_cuda (line 270) | def _stop_cuda(self):
  class EpochMetricsMeter (line 284) | class EpochMetricsMeter(object):
    method __init__ (line 299) | def __init__(self, engine_state, delimiter='\t'):
    method update (line 304) | def update(self, input_dict):
    method summary (line 320) | def summary(self, epoch):
  class LossEpochMetricsMeter (line 328) | class LossEpochMetricsMeter(object):
    method __init__ (line 329) | def __init__(self, engine_state, delimiter='\t'):
    method update (line 334) | def update(self, input_dict):
    method summary (line 353) | def summary(self, epoch):
  class MetricMeter (line 366) | class MetricMeter(object):
    method __init__ (line 381) | def __init__(self, delimiter='\t'):
    method update (line 385) | def update(self, input_dict):
    method __str__ (line 399) | def __str__(self):

FILE: torchreid/utils/constants.py
  function get_test_embeddings_names (line 21) | def get_test_embeddings_names(parts_names, test_embeddings):

FILE: torchreid/utils/distribution.py
  function plot_body_parts_pairs_distance_distribution (line 10) | def plot_body_parts_pairs_distance_distribution(body_part_pairwise_dist,...
  function plot_pairs_distance_distribution (line 28) | def plot_pairs_distance_distribution(distmat, q_pids, g_pids, tag):
  function compute_distance_distribution (line 35) | def compute_distance_distribution(ax, distmat, q_pids, g_pids, title):
  function compute_ssmd (line 47) | def compute_ssmd(neg_p, pos_p):
  function plot_distributions (line 57) | def plot_distributions(ax, neg_p, pos_p, pos_p_mean, pos_p_std, neg_p_me...

FILE: torchreid/utils/engine_state.py
  class EngineStateListener (line 5) | class EngineStateListener:
    method batch_completed (line 6) | def batch_completed(self):
    method epoch_started (line 9) | def epoch_started(self):
    method epoch_completed (line 12) | def epoch_completed(self):
    method training_started (line 15) | def training_started(self):
    method training_completed (line 18) | def training_completed(self):
    method test_completed (line 21) | def test_completed(self):
    method run_completed (line 24) | def run_completed(self):
  class EngineState (line 28) | class EngineState(EngineStateListener):
    method current_engine_state (line 32) | def current_engine_state(cls):
    method __init__ (line 36) | def __init__(self, start_epoch, max_epoch):
    method add_listener (line 50) | def add_listener(self, listener, last=False):
    method batch_completed (line 57) | def batch_completed(self):
    method epoch_started (line 63) | def epoch_started(self):
    method epoch_completed (line 68) | def epoch_completed(self):
    method training_started (line 74) | def training_started(self):
    method training_completed (line 79) | def training_completed(self):
    method test_completed (line 84) | def test_completed(self):
    method run_completed (line 88) | def run_completed(self):
    method update_lr (line 92) | def update_lr(self, lr):

FILE: torchreid/utils/imagetools.py
  function gkern (line 5) | def gkern(kernlen=21, std=None):
  function build_gaussian_heatmaps (line 14) | def build_gaussian_heatmaps(kp_xyc, w, h, gaussian=None):

FILE: torchreid/utils/logging/deprecated_loggers.py
  class StdoutLogger (line 11) | class StdoutLogger(object):
    method __init__ (line 29) | def __init__(self, fpath=None):
    method __del__ (line 36) | def __del__(self):
    method __enter__ (line 39) | def __enter__(self):
    method __exit__ (line 42) | def __exit__(self, *args):
    method write (line 45) | def write(self, msg):
    method flush (line 50) | def flush(self):
    method close (line 56) | def close(self):
  class RankLogger (line 62) | class RankLogger(object):
    method __init__ (line 108) | def __init__(self, sources, targets):
    method write (line 126) | def write(self, name, epoch, rank1):
    method show_summary (line 137) | def show_summary(self):

FILE: torchreid/utils/logging/logger.py
  class Logger (line 10) | class Logger:
    method current_logger (line 17) | def current_logger(cls):
    method __init__ (line 21) | def __init__(self, cfg):
    method add_model (line 55) | def add_model(self, model):
    method add_text (line 59) | def add_text(self, tag, value):
    method add_scalar (line 63) | def add_scalar(self, tag, scalar_value, step):
    method add_figure (line 69) | def add_figure(self, tag, figure, step):
    method add_image (line 84) | def add_image(self, group, name, image, step):
    method add_embeddings (line 96) | def add_embeddings(self, tag, embeddings, labels, imgs, step):
    method close (line 105) | def close(self):

FILE: torchreid/utils/model_complexity.py
  function _ntuple (line 17) | def _ntuple(n):
  function hook_convNd (line 35) | def hook_convNd(m, x, y):
  function hook_maxpool1d (line 50) | def hook_maxpool1d(m, x, y):
  function hook_maxpool2d (line 56) | def hook_maxpool2d(m, x, y):
  function hook_maxpool3d (line 65) | def hook_maxpool3d(m, x, y):
  function hook_avgpool1d (line 73) | def hook_avgpool1d(m, x, y):
  function hook_avgpool2d (line 79) | def hook_avgpool2d(m, x, y):
  function hook_avgpool3d (line 87) | def hook_avgpool3d(m, x, y):
  function hook_adapmaxpool1d (line 95) | def hook_adapmaxpool1d(m, x, y):
  function hook_adapmaxpool2d (line 104) | def hook_adapmaxpool2d(m, x, y):
  function hook_adapmaxpool3d (line 114) | def hook_adapmaxpool3d(m, x, y):
  function hook_adapavgpool1d (line 124) | def hook_adapavgpool1d(m, x, y):
  function hook_adapavgpool2d (line 133) | def hook_adapavgpool2d(m, x, y):
  function hook_adapavgpool3d (line 143) | def hook_adapavgpool3d(m, x, y):
  function hook_relu (line 158) | def hook_relu(m, x, y):
  function hook_leakyrelu (line 164) | def hook_leakyrelu(m, x, y):
  function hook_batchnormNd (line 176) | def hook_batchnormNd(m, x, y):
  function hook_instancenormNd (line 184) | def hook_instancenormNd(m, x, y):
  function hook_groupnorm (line 188) | def hook_groupnorm(m, x, y):
  function hook_layernorm (line 192) | def hook_layernorm(m, x, y):
  function hook_linear (line 205) | def hook_linear(m, x, y):
  function _get_flops_counter (line 258) | def _get_flops_counter(only_conv_linear):
  function compute_model_complexity (line 264) | def compute_model_complexity(

FILE: torchreid/utils/reidtools.py
  function visualize_ranked_results (line 19) | def visualize_ranked_results(

FILE: torchreid/utils/rerank.py
  function re_ranking (line 31) | def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0...

FILE: torchreid/utils/tensortools.py
  function replace_values (line 3) | def replace_values(input, mask, value):
  function masked_mean (line 12) | def masked_mean(input, mask):

FILE: torchreid/utils/tools.py
  function mkdir_if_missing (line 23) | def mkdir_if_missing(dirname):
  function check_isfile (line 33) | def check_isfile(fpath):
  function read_json (line 48) | def read_json(fpath):
  function write_json (line 55) | def write_json(obj, fpath):
  function set_random_seed (line 62) | def set_random_seed(seed):
  function download_url (line 69) | def download_url(url, dst):
  function read_image (line 99) | def read_image(path):
  function read_masks (line 124) | def read_masks(masks_path):
  function collect_env_info (line 150) | def collect_env_info():
  function perc (line 161) | def perc(val, decimals=2):
  function extract_test_embeddings (line 164) | def extract_test_embeddings(model_output, test_embeddings):

FILE: torchreid/utils/torch_receptive_field/receptive_field.py
  function check_same (line 10) | def check_same(stride):
  function receptive_field (line 16) | def receptive_field(model, input_size, batch_size=-1, device="cuda"):
  function receptive_field_for_unit (line 154) | def receptive_field_for_unit(receptive_field_dict, layer, unit_position):

FILE: torchreid/utils/torchtools.py
  function save_checkpoint (line 24) | def save_checkpoint(
  function load_checkpoint (line 65) | def load_checkpoint(fpath):
  function resume_from_checkpoint (line 101) | def resume_from_checkpoint(fpath, model, optimizer=None, scheduler=None):
  function adjust_learning_rate (line 140) | def adjust_learning_rate(
  function set_bn_to_eval (line 166) | def set_bn_to_eval(m):
  function open_all_layers (line 175) | def open_all_layers(model):
  function open_specified_layers (line 187) | def open_specified_layers(model, open_layers):
  function count_num_param (line 228) | def count_num_param(model):
  function load_pretrained_weights (line 260) | def load_pretrained_weights(model, weight_path):
  function collate (line 323) | def collate(batch):

FILE: torchreid/utils/visualization/display_batch_triplets.py
  function show_triplet_grid (line 20) | def show_triplet_grid(triplets):
  function show_triplet (line 50) | def show_triplet(anc, pos, neg, pos_dist, neg_dist):
  function add_border (line 61) | def add_border(img, color):
  function show_instance (line 67) | def show_instance(ax, instance, dist, color):

FILE: torchreid/utils/visualization/embeddings_projection.py
  function visualize_embeddings (line 9) | def visualize_embeddings(qf, gf, q_pids, g_pids, test_loader, dataset_na...
  function extract_samples (line 26) | def extract_samples(features, dataset, sample_size):

FILE: torchreid/utils/visualization/feature_map_visualization.py
  function flatten (line 26) | def flatten(maps):
  function organize (line 31) | def organize(flattened, num_map, feat_dim, h, w):
  function feat_to_color (line 37) | def feat_to_color(maps):
  function normalize_01 (line 45) | def normalize_01(m):
  function mapwise_normalize (line 53) | def mapwise_normalize(map1, feats):
  function visualize_pca_multi (line 68) | def visualize_pca_multi(maps_all, feats, pids, tag):
  function display_feature_maps (line 128) | def display_feature_maps(embeddings_dict, spatial_features, body_part_ma...

FILE: torchreid/utils/visualization/visualize_query_gallery_rankings.py
  function visualize_ranking_grid (line 34) | def visualize_ranking_grid(distmat, body_parts_distmat, test_loader, dat...
  function show_ranking_grid (line 104) | def show_ranking_grid(query_sample, gallery_topk_samples, mAP, rank1, da...
  function insert_background_line (line 166) | def insert_background_line(grid_img, match_color, row, height, padding_t...
  function display_sample_on_row (line 178) | def display_sample_on_row(grid_img, sample, row, img_shape, mask_filteri...
  function mask_overlay (line 255) | def mask_overlay(img, mask, clip=True, interpolation=cv2.INTER_NEAREST):
  function align_top_text (line 268) | def align_top_text(img, text, pos, fontScale=1.0, thickness=1, padding=4):
  function align_top_multi_text (line 276) | def align_top_multi_text(img, text, pos, fontScale=1.0, thickness=1, pad...
  function align_bottom_text (line 296) | def align_bottom_text(img, text, pos, fontScale=1.0, thickness=1, paddin...
  function align_right_text (line 304) | def align_right_text(img, text, pos, fontScale=1.0, thickness=1, padding...
  function align_left_multitext (line 312) | def align_left_multitext(img, text, pos, fontScale=1.0, thickness=1, pad...
  function centered_text (line 332) | def centered_text(img, text, pos, fontScale=1, thickness=1):
  function insert_img_into_grid (line 340) | def insert_img_into_grid(grid_img, img, row, col):
  function make_border (line 351) | def make_border(img, border_color, bw):

FILE: torchreid/utils/writer.py
  class Writer (line 17) | class Writer(EngineStateListener):
    method current_writer (line 24) | def current_writer(cls):
    method __init__ (line 28) | def __init__(self, cfg):
    method init_engine_state (line 56) | def init_engine_state(self, engine_state, parts_num):
    method report_performance (line 75) | def report_performance(self, cmc, mAP, ssmd, pxl_acc_avg, name=""):
    method report_global_performance (line 84) | def report_global_performance(self,
    method intermediate_evaluate (line 97) | def intermediate_evaluate(self):
    method update_invalid_pairwise_distances_count (line 101) | def update_invalid_pairwise_distances_count(self, batch_pairwise_dist):
    method update_invalid_part_based_pairwise_distances_count (line 104) | def update_invalid_part_based_pairwise_distances_count(self, valid_bod...
    method used_parts_statistics (line 108) | def used_parts_statistics(self, M, body_part_id):
    method visualize_triplets (line 134) | def visualize_triplets(self, images, masks, mask, dist):
    method qg_pairwise_dist_statistics (line 172) | def qg_pairwise_dist_statistics(self, pairwise_dist, body_part_pairwis...
    method qg_body_part_distances_boxplot (line 188) | def qg_body_part_distances_boxplot(self, body_part_pairwise_dist):
    method qg_body_part_pairs_availability_barplot (line 202) | def qg_body_part_pairs_availability_barplot(self, body_part_pairwise_d...
    method qg_body_part_availability_barplot (line 233) | def qg_body_part_availability_barplot(self, qf_parts_visibility, gf_pa...
    method qg_distribution_of_body_part_availability_histogram (line 267) | def qg_distribution_of_body_part_availability_histogram(self, qf_parts...
    method visualize_embeddings (line 293) | def visualize_embeddings(self, qf, gf, q_pids, g_pids, test_loader, da...
    method visualize_rank (line 298) | def visualize_rank(self, test_loader, dataset_name, distmat, save_dir,...
    method training_started (line 320) | def training_started(self):
    method epoch_started (line 323) | def epoch_started(self):
    method epoch_completed (line 328) | def epoch_completed(self):
    method training_completed (line 358) | def training_completed(self):
    method test_completed (line 366) | def test_completed(self):
    method run_completed (line 373) | def run_completed(self):
    method display_used_body_parts (line 397) | def display_used_body_parts(self):