SYMBOL INDEX (248 symbols across 35 files)

FILE: examples/test.py
  function get_data (line 23) | def get_data(name, data_dir, height, width, batch_size, workers):
  function main (line 45) | def main():
  function main_worker (line 57) | def main_worker(args):

FILE: examples/train.py
  function get_data (line 36) | def get_data(name, data_dir):
  function get_train_loader (line 42) | def get_train_loader(args, dataset, height, width, batch_size, workers,
  function get_test_loader (line 72) | def get_test_loader(dataset, height, width, batch_size, workers, testset...
  function create_model (line 93) | def create_model(args):
  function main (line 110) | def main():
  function main_worker (line 122) | def main_worker(args):

FILE: hhcl/datasets/__init__.py
  function names (line 20) | def names():
  function create (line 24) | def create(name, root, *args, **kwargs):
  function get_dataset (line 47) | def get_dataset(name, root, *args, **kwargs):

FILE: hhcl/datasets/celebreid.py
  class CelebReID (line 8) | class CelebReID(BaseImageDataset):
    method __init__ (line 14) | def __init__(self, root, verbose=True, **kwargs):
    method _check_before_run (line 39) | def _check_before_run(self):
    method _process_dir (line 50) | def _process_dir(self, dir_path, relabel=False):

FILE: hhcl/datasets/dukemtmcreid.py
  function process_dir (line 7) | def process_dir(dir_path, relabel=False):
  class DukeMTMCreID (line 37) | class DukeMTMCreID(BaseImageDataset):
    method __init__ (line 55) | def __init__(self, root, verbose=True):
    method _check_before_run (line 76) | def _check_before_run(self):

FILE: hhcl/datasets/market1501.py
  class Market1501 (line 8) | class Market1501(BaseImageDataset):
    method __init__ (line 21) | def __init__(self, root, verbose=True, **kwargs):
    method _check_before_run (line 46) | def _check_before_run(self):
    method _process_dir (line 57) | def _process_dir(self, dir_path, relabel=False):

FILE: hhcl/datasets/msmt17.py
  function _process_dir (line 9) | def _process_dir(dir_path, relabel=False):
  class MSMT17 (line 34) | class MSMT17(BaseImageDataset):
    method __init__ (line 37) | def __init__(self, root, verbose=True, **kwargs):
    method _check_before_run (line 62) | def _check_before_run(self):

FILE: hhcl/datasets/personx.py
  class PersonX (line 9) | class PersonX(BaseImageDataset):
    method __init__ (line 21) | def __init__(self, root, verbose=True, **kwargs):
    method _check_before_run (line 46) | def _check_before_run(self):
    method _process_dir (line 57) | def _process_dir(self, dir_path, relabel=False):

FILE: hhcl/evaluation_metrics/classification.py
  function accuracy (line 7) | def accuracy(output, target, topk=(1,)):

FILE: hhcl/evaluation_metrics/ranking.py
  function _unique_sample (line 10) | def _unique_sample(ids_dict, num):
  function cmc (line 18) | def cmc(distmat, query_ids=None, gallery_ids=None,
  function mean_ap (line 82) | def mean_ap(distmat, query_ids=None, gallery_ids=None,

FILE: hhcl/evaluators.py
  function extract_cnn_feature (line 16) | def extract_cnn_feature(model, inputs):
  function extract_features (line 23) | def extract_features(model, data_loader, print_freq=50):
  function pairwise_distance (line 55) | def pairwise_distance(features, query=None, gallery=None):
  function evaluate_all (line 75) | def evaluate_all(query_features, gallery_features, distmat, query=None, ...
  class Evaluator (line 109) | class Evaluator(object):
    method __init__ (line 110) | def __init__(self, model):
    method evaluate (line 114) | def evaluate(self, data_loader, query, gallery, cmc_flag=False, rerank...

FILE: hhcl/models/__init__.py
  function names (line 17) | def names():
  function create (line 21) | def create(name, *args, **kwargs):

FILE: hhcl/models/cm.py
  class CM (line 10) | class CM(autograd.Function):
    method forward (line 13) | def forward(ctx, inputs, targets, features, momentum):
    method backward (line 22) | def backward(ctx, grad_outputs):
  function cm (line 36) | def cm(inputs, indexes, features, momentum=0.5):
  class CM_Hard (line 40) | class CM_Hard(autograd.Function):
    method forward (line 43) | def forward(ctx, inputs, targets, features, momentum):
    method backward (line 52) | def backward(ctx, grad_outputs):
  function cm_hard (line 75) | def cm_hard(inputs, indexes, features, momentum=0.5):
  class CM_Hybrid (line 79) | class CM_Hybrid(autograd.Function):
    method forward (line 82) | def forward(ctx, inputs, targets, features, momentum):
    method backward (line 91) | def backward(ctx, grad_outputs):
  function cm_hybrid (line 119) | def cm_hybrid(inputs, indexes, features, momentum=0.5):
  class CM_Hybrid_v2 (line 123) | class CM_Hybrid_v2(autograd.Function):
    method forward (line 126) | def forward(ctx, inputs, targets, features, momentum, num_instances):
    method backward (line 136) | def backward(ctx, grad_outputs):
  function cm_hybrid_v2 (line 162) | def cm_hybrid_v2(inputs, indexes, features, momentum=0.5, num_instances=...
  class ClusterMemory (line 166) | class ClusterMemory(nn.Module, ABC):
    method __init__ (line 173) | def __init__(self, num_features, num_samples, temp=0.05, momentum=0.2,...
    method forward (line 201) | def forward(self, inputs, targets):

FILE: hhcl/models/dsbn.py
  class DSBN2d (line 6) | class DSBN2d(nn.Module):
    method __init__ (line 7) | def __init__(self, planes):
    method forward (line 13) | def forward(self, x):
  class DSBN1d (line 25) | class DSBN1d(nn.Module):
    method __init__ (line 26) | def __init__(self, planes):
    method forward (line 32) | def forward(self, x):
  function convert_dsbn (line 44) | def convert_dsbn(model):
  function convert_bn (line 60) | def convert_bn(model, use_target=True):

FILE: hhcl/models/kmeans.py
  function label_generator_kmeans (line 14) | def label_generator_kmeans(features, num_classes=500, cuda=True):

FILE: hhcl/models/losses.py
  class CrossEntropyLabelSmooth (line 7) | class CrossEntropyLabelSmooth(nn.Module):
    method __init__ (line 17) | def __init__(self, num_classes=0, epsilon=0.1, topk_smoothing=False):
    method forward (line 24) | def forward(self, inputs, targets):
  class SoftEntropy (line 42) | class SoftEntropy(nn.Module):
    method __init__ (line 43) | def __init__(self, input_prob=False):
    method forward (line 48) | def forward(self, inputs, targets):
  class SoftEntropySmooth (line 57) | class SoftEntropySmooth(nn.Module):
    method __init__ (line 58) | def __init__(self, epsilon=0.1):
    method forward (line 63) | def forward(self, inputs, soft_targets, targets):
  class Softmax (line 72) | class Softmax(nn.Module):
    method __init__ (line 74) | def __init__(self, feat_dim, num_class, temp=0.05):
    method forward (line 80) | def forward(self, feats, labels):
  class CircleLoss (line 89) | class CircleLoss(nn.Module):
    method __init__ (line 93) | def __init__(self, feat_dim, num_class, margin=0.25, gamma=256):
    method forward (line 105) | def forward(self, feats, labels):
  class CosFace (line 131) | class CosFace(nn.Module):
    method __init__ (line 140) | def __init__(self, feat_dim, num_class, s = 64.0, m = 0.35):
    method forward (line 150) | def forward(self, input, label):
    method __repr__ (line 165) | def __repr__(self):
  class InstanceLoss (line 175) | class InstanceLoss(nn.Module):
    method __init__ (line 176) | def __init__(self, batch_size, temperature, device):
    method mask_correlated_samples (line 185) | def mask_correlated_samples(self, batch_size):
    method forward (line 195) | def forward(self, z_i, z_j):
  class ClusterLoss (line 214) | class ClusterLoss(nn.Module):
    method __init__ (line 215) | def __init__(self, class_num, temperature, device):
    method mask_correlated_clusters (line 225) | def mask_correlated_clusters(self, class_num):
    method forward (line 235) | def forward(self, c_i, c_j):
  class FocalLoss (line 264) | class FocalLoss(nn.Module):
    method __init__ (line 265) | def __init__(self, gamma=2, alpha=0.25):
    method forward (line 271) | def forward(self, input, target):
  class LabelRefineLoss (line 288) | class LabelRefineLoss(nn.Module):
    method __init__ (line 289) | def __init__(self, lambda1=0.0):
    method forward (line 294) | def forward(self, input, target):
  class FocalTopLoss (line 311) | class FocalTopLoss(nn.Module):
    method __init__ (line 312) | def __init__(self, top_percent=0.7):
    method masked_softmax_multi_focal (line 316) | def masked_softmax_multi_focal(self, vec, targets=None, dim=1):
    method forward (line 345) | def forward(self, input, target):

FILE: hhcl/models/pooling.py
  class GeneralizedMeanPoolingList (line 19) | class GeneralizedMeanPoolingList(nn.Module, ABC):
    method __init__ (line 34) | def __init__(self, output_size=1, eps=1e-6):
    method forward (line 39) | def forward(self, x_list):
    method __repr__ (line 47) | def __repr__(self):
  class GeneralizedMeanPooling (line 57) | class GeneralizedMeanPooling(nn.Module, ABC):
    method __init__ (line 72) | def __init__(self, norm, output_size=1, eps=1e-6):
    method forward (line 79) | def forward(self, x):
    method __repr__ (line 85) | def __repr__(self):
  class GeneralizedMeanPoolingP (line 97) | class GeneralizedMeanPoolingP(GeneralizedMeanPooling, ABC):
    method __init__ (line 101) | def __init__(self, norm=3, output_size=1, eps=1e-6):
  class GeneralizedMeanPoolingFpn (line 106) | class GeneralizedMeanPoolingFpn(nn.Module, ABC):
    method __init__ (line 121) | def __init__(self, norm, output_size=1, eps=1e-6):
    method forward (line 128) | def forward(self, x_lists):
    method __repr__ (line 138) | def __repr__(self):
  class GeneralizedMeanPoolingPFpn (line 150) | class GeneralizedMeanPoolingPFpn(GeneralizedMeanPoolingFpn, ABC):
    method __init__ (line 154) | def __init__(self, norm=3, output_size=1, eps=1e-6):
  class AdaptiveAvgMaxPool2d (line 159) | class AdaptiveAvgMaxPool2d(nn.Module, ABC):
    method __init__ (line 160) | def __init__(self):
    method forward (line 164) | def forward(self, x):
  class FastGlobalAvgPool2d (line 171) | class FastGlobalAvgPool2d(nn.Module, ABC):
    method __init__ (line 172) | def __init__(self, flatten=False):
    method forward (line 176) | def forward(self, x):
  function avg_pooling (line 188) | def avg_pooling():
  function max_pooling (line 193) | def max_pooling():
  class Flatten (line 197) | class Flatten(nn.Module):
    method forward (line 198) | def forward(self, input):
  function pooling_names (line 212) | def pooling_names():
  function build_pooling_layer (line 216) | def build_pooling_layer(name):

FILE: hhcl/models/resnet.py
  class ResNet (line 15) | class ResNet(nn.Module):
    method __init__ (line 24) | def __init__(self, depth, pretrained=True, cut_at_pooling=False,
    method forward (line 75) | def forward(self, x):
    method reset_params (line 109) | def reset_params(self):
  function resnet18 (line 127) | def resnet18(**kwargs):
  function resnet34 (line 131) | def resnet34(**kwargs):
  function resnet50 (line 135) | def resnet50(**kwargs):
  function resnet101 (line 139) | def resnet101(**kwargs):
  function resnet152 (line 143) | def resnet152(**kwargs):

FILE: hhcl/models/resnet_ibn.py
  class ResNetIBN (line 16) | class ResNetIBN(nn.Module):
    method __init__ (line 22) | def __init__(self, depth, pretrained=True, cut_at_pooling=False,
    method forward (line 74) | def forward(self, x):
    method reset_params (line 107) | def reset_params(self):
  function resnet_ibn50a (line 125) | def resnet_ibn50a(**kwargs):
  function resnet_ibn101a (line 129) | def resnet_ibn101a(**kwargs):

FILE: hhcl/models/resnet_ibn_a.py
  function conv3x3 (line 16) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 22) | class BasicBlock(nn.Module):
    method __init__ (line 25) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 35) | def forward(self, x):
  class IBN (line 54) | class IBN(nn.Module):
    method __init__ (line 55) | def __init__(self, planes):
    method forward (line 63) | def forward(self, x):
  class Bottleneck (line 70) | class Bottleneck(nn.Module):
    method __init__ (line 73) | def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=N...
    method forward (line 89) | def forward(self, x):
  class ResNet (line 112) | class ResNet(nn.Module):
    method __init__ (line 114) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 141) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 161) | def forward(self, x):
  function resnet50_ibn_a (line 179) | def resnet50_ibn_a(pretrained=False, **kwargs):
  function resnet101_ibn_a (line 192) | def resnet101_ibn_a(pretrained=False, **kwargs):
  function remove_module_key (line 205) | def remove_module_key(state_dict):

FILE: hhcl/models/triplet.py
  function euclidean_dist (line 8) | def euclidean_dist(x, y):
  function cosine_dist (line 17) | def cosine_dist(x, y):
  function _batch_hard (line 25) | def _batch_hard(mat_distance, mat_similarity, indice=False):
  class TripletLoss (line 36) | class TripletLoss(nn.Module):
    method __init__ (line 42) | def __init__(self, margin, normalize_feature=False):
    method forward (line 48) | def forward(self, emb, label):
  class SoftTripletLoss (line 65) | class SoftTripletLoss(nn.Module):
    method __init__ (line 67) | def __init__(self, margin=None, normalize_feature=False):
    method forward (line 72) | def forward(self, emb1, emb2, label):

FILE: hhcl/trainers.py
  class Trainer (line 8) | class Trainer(object):
    method __init__ (line 9) | def __init__(self, encoder, memory=None):
    method train (line 14) | def train(self, epoch, data_loader, optimizer, print_freq=10, train_it...
    method _parse_data (line 56) | def _parse_data(self, inputs):
    method _forward (line 60) | def _forward(self, inputs):

FILE: hhcl/utils/__init__.py
  function to_numpy (line 6) | def to_numpy(tensor):
  function to_torch (line 15) | def to_torch(ndarray):

FILE: hhcl/utils/data/__init__.py
  class IterLoader (line 7) | class IterLoader:
    method __init__ (line 8) | def __init__(self, loader, length=None):
    method __len__ (line 13) | def __len__(self):
    method new_epoch (line 19) | def new_epoch(self):
    method next (line 22) | def next(self):

FILE: hhcl/utils/data/base_dataset.py
  class BaseDataset (line 5) | class BaseDataset(object):
    method get_imagedata_info (line 10) | def get_imagedata_info(self, data):
    method print_dataset_statistics (line 22) | def print_dataset_statistics(self):
    method images_dir (line 26) | def images_dir(self):
  class BaseImageDataset (line 30) | class BaseImageDataset(BaseDataset):
    method print_dataset_statistics (line 35) | def print_dataset_statistics(self, train, query, gallery):

FILE: hhcl/utils/data/preprocessor.py
  class Preprocessor (line 11) | class Preprocessor(Dataset):
    method __init__ (line 12) | def __init__(self, dataset, root=None, transform=None, mutual=False):
    method __len__ (line 19) | def __len__(self):
    method __getitem__ (line 22) | def __getitem__(self, indices):
    method _get_single_item (line 28) | def _get_single_item(self, index):
    method _get_mutual_item (line 41) | def _get_mutual_item(self, index):

FILE: hhcl/utils/data/sampler.py
  function No_index (line 14) | def No_index(a, b):
  class RandomIdentitySampler (line 19) | class RandomIdentitySampler(Sampler):
    method __init__ (line 20) | def __init__(self, data_source, num_instances):
    method __len__ (line 29) | def __len__(self):
    method __iter__ (line 32) | def __iter__(self):
  class RandomMultipleGallerySampler (line 46) | class RandomMultipleGallerySampler(Sampler):
    method __init__ (line 47) | def __init__(self, data_source, num_instances=4):
    method __len__ (line 65) | def __len__(self):
    method __iter__ (line 68) | def __iter__(self):

FILE: hhcl/utils/data/transforms.py
  class RectScale (line 9) | class RectScale(object):
    method __init__ (line 10) | def __init__(self, height, width, interpolation=Image.BILINEAR):
    method __call__ (line 15) | def __call__(self, img):
  class RandomSizedRectCrop (line 22) | class RandomSizedRectCrop(object):
    method __init__ (line 23) | def __init__(self, height, width, interpolation=Image.BILINEAR):
    method __call__ (line 28) | def __call__(self, img):
  class RandomErasing (line 52) | class RandomErasing(object):
    method __init__ (line 64) | def __init__(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=(0.4...
    method __call__ (line 71) | def __call__(self, img):

FILE: hhcl/utils/faiss_rerank.py
  function k_reciprocal_neigh (line 23) | def k_reciprocal_neigh(initial_rank, i, k1):
  function compute_jaccard_distance (line 30) | def compute_jaccard_distance(target_features, k1=20, k2=6, print_flag=Tr...

FILE: hhcl/utils/faiss_utils.py
  function swig_ptr_from_FloatTensor (line 6) | def swig_ptr_from_FloatTensor(x):
  function swig_ptr_from_LongTensor (line 12) | def swig_ptr_from_LongTensor(x):
  function search_index_pytorch (line 19) | def search_index_pytorch(index, x, k, D=None, I=None):
  function search_raw_array_pytorch (line 44) | def search_raw_array_pytorch(res, xb, xq, k, D=None, I=None,
  function index_init_gpu (line 92) | def index_init_gpu(ngpus, feat_dim):
  function index_init_cpu (line 108) | def index_init_cpu(feat_dim):

FILE: hhcl/utils/logging.py
  class Logger (line 8) | class Logger(object):
    method __init__ (line 9) | def __init__(self, fpath=None):
    method __del__ (line 16) | def __del__(self):
    method __enter__ (line 19) | def __enter__(self):
    method __exit__ (line 22) | def __exit__(self, *args):
    method write (line 25) | def write(self, msg):
    method flush (line 30) | def flush(self):
    method close (line 36) | def close(self):

FILE: hhcl/utils/meters.py
  class AverageMeter (line 4) | class AverageMeter(object):
    method __init__ (line 7) | def __init__(self):
    method reset (line 13) | def reset(self):
    method update (line 19) | def update(self, val, n=1):

FILE: hhcl/utils/osutils.py
  function mkdir_if_missing (line 6) | def mkdir_if_missing(dir_path):

FILE: hhcl/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: hhcl/utils/serialization.py
  function read_json (line 12) | def read_json(fpath):
  function write_json (line 18) | def write_json(obj, fpath):
  function save_checkpoint (line 24) | def save_checkpoint(state, is_best, fpath='checkpoint.pth.tar'):
  function load_checkpoint (line 31) | def load_checkpoint(fpath):
  function copy_state_dict (line 41) | def copy_state_dict(state_dict, model, strip=None):