SYMBOL INDEX (256 symbols across 24 files)

FILE: build_net.py
  function make_model (line 29) | def make_model(args):

FILE: dataset.py
  class Dataset (line 22) | class Dataset(Dataset):
    method __init__ (line 23) | def __init__(self, root=None, transform=None, target_transform=None, t...
    method __len__ (line 38) | def __len__(self):
    method __getitem__ (line 41) | def __getitem__(self, index):
  class TestDataset (line 62) | class TestDataset(Dataset):
    method __init__ (line 63) | def __init__(self, root=None, transform=None, target_transform=None, t...
    method __len__ (line 79) | def __len__(self):
    method __getitem__ (line 82) | def __getitem__(self, index):
  class resizeNormalize (line 104) | class resizeNormalize(object):
    method __init__ (line 106) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 111) | def __call__(self, img):

FILE: models/Res.py
  function conv3x3 (line 30) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 36) | def conv1x1(in_planes, out_planes, stride=1):
  class BasicBlock (line 41) | class BasicBlock(nn.Module):
    method __init__ (line 44) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 62) | def forward(self, x):
  class Bottleneck (line 81) | class Bottleneck(nn.Module):
    method __init__ (line 84) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 101) | def forward(self, x):
  class ResNet (line 124) | class ResNet(nn.Module):
    method __init__ (line 126) | def __init__(self, block, layers, num_classes=1000, zero_init_residual...
    method _make_layer (line 177) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method forward (line 201) | def forward(self, x):
  function _resnet (line 219) | def _resnet(arch, block, layers, pretrained, progress, **kwargs):
  function resnet18 (line 228) | def resnet18(pretrained=False, progress=True, **kwargs):
  function resnet34 (line 238) | def resnet34(pretrained=False, progress=True, **kwargs):
  function resnet50 (line 248) | def resnet50(pretrained=False, progress=True, **kwargs):
  function resnet101 (line 258) | def resnet101(pretrained=False, progress=True, **kwargs):
  function resnet152 (line 268) | def resnet152(pretrained=False, progress=True, **kwargs):
  function resnext50_32x4d (line 278) | def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
  function resnext101_32x8d (line 290) | def resnext101_32x8d(pretrained=False, progress=True, **kwargs):

FILE: models/resnet_cbam.py
  function conv3x3 (line 32) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 38) | def conv1x1(in_planes, out_planes, stride=1):
  class ChannelAttention (line 44) | class ChannelAttention(nn.Module):
    method __init__ (line 45) | def __init__(self, in_planes, ratio=16):
    method forward (line 56) | def forward(self, x):
  class SpatialAttention (line 62) | class SpatialAttention(nn.Module):
    method __init__ (line 63) | def __init__(self, kernel_size=7):
    method forward (line 72) | def forward(self, x):
  class BasicBlock (line 80) | class BasicBlock(nn.Module):
    method __init__ (line 83) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 102) | def forward(self, x):
  class Bottleneck (line 121) | class Bottleneck(nn.Module):
    method __init__ (line 124) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 142) | def forward(self, x):
  class ResNet (line 165) | class ResNet(nn.Module):
    method __init__ (line 167) | def __init__(self, block, layers, num_classes=1000, zero_init_residual...
    method _make_layer (line 222) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method forward (line 246) | def forward(self, x):
  function _resnet (line 266) | def _resnet(arch, block, layers, pretrained, progress, **kwargs):
  function resnet18 (line 278) | def resnet18(pretrained=False, progress=True, **kwargs):
  function resnet34 (line 288) | def resnet34(pretrained=False, progress=True, **kwargs):
  function resnet50 (line 298) | def resnet50(pretrained=False, progress=True, **kwargs):
  function resnet101 (line 308) | def resnet101(pretrained=False, progress=True, **kwargs):
  function resnet152 (line 318) | def resnet152(pretrained=False, progress=True, **kwargs):
  function resnext50_32x4d (line 328) | def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
  function resnext101_32x8d (line 340) | def resnext101_32x8d(pretrained=False, progress=True, **kwargs):

FILE: models/resnetxt_wsl.py
  function _resnext (line 39) | def _resnext(arch, block, layers, pretrained, progress, **kwargs):
  function resnext101_32x8d_wsl (line 48) | def resnext101_32x8d_wsl(progress=True, **kwargs):
  function resnext101_32x16d_wsl (line 60) | def resnext101_32x16d_wsl(progress=True, **kwargs):
  function resnext101_32x32d_wsl (line 72) | def resnext101_32x32d_wsl(progress=True, **kwargs):
  function resnext101_32x48d_wsl (line 84) | def resnext101_32x48d_wsl(progress=True, **kwargs):

FILE: predict/Res.py
  function conv3x3 (line 30) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 36) | def conv1x1(in_planes, out_planes, stride=1):
  class BasicBlock (line 41) | class BasicBlock(nn.Module):
    method __init__ (line 44) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 62) | def forward(self, x):
  class Bottleneck (line 81) | class Bottleneck(nn.Module):
    method __init__ (line 84) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 101) | def forward(self, x):
  class ResNet (line 124) | class ResNet(nn.Module):
    method __init__ (line 126) | def __init__(self, block, layers, num_classes=1000, zero_init_residual...
    method _make_layer (line 177) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method forward (line 201) | def forward(self, x):
  function _resnet (line 219) | def _resnet(arch, block, layers, pretrained, progress, **kwargs):
  function resnet18 (line 228) | def resnet18(pretrained=False, progress=True, **kwargs):
  function resnet34 (line 238) | def resnet34(pretrained=False, progress=True, **kwargs):
  function resnet50 (line 248) | def resnet50(pretrained=False, progress=True, **kwargs):
  function resnet101 (line 258) | def resnet101(pretrained=False, progress=True, **kwargs):
  function resnet152 (line 268) | def resnet152(pretrained=False, progress=True, **kwargs):
  function resnext50_32x4d (line 278) | def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
  function resnext101_32x8d (line 290) | def resnext101_32x8d(pretrained=False, progress=True, **kwargs):

FILE: predict/customize_service.py
  class classfication_service (line 33) | class classfication_service(PTServingBaseService):
    method __init__ (line 34) | def __init__(self, model_name, model_path):
    method build_model (line 88) | def build_model(self, model_path):
    method preprocess_img (line 97) | def preprocess_img(self):
    method preprocess_img1 (line 106) | def preprocess_img1(self):
    method _preprocess (line 115) | def _preprocess(self, data):
    method _inference (line 124) | def _inference(self, data):
    method _postprocess (line 138) | def _postprocess(self, data):
  class Resize (line 142) | class Resize(object):
    method __init__ (line 143) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 147) | def __call__(self, img):

FILE: predict/predict.py
  class classfication_service (line 28) | class classfication_service():
    method __init__ (line 29) | def __init__(self, model_path):
    method build_model (line 81) | def build_model(self, model_path):
    method preprocess_img (line 101) | def preprocess_img(self):
    method _preprocess (line 110) | def _preprocess(self, data):
    method _inference (line 119) | def _inference(self, data):
    method _postprocess (line 138) | def _postprocess(self, data):
  class Resize (line 142) | class Resize(object):
    method __init__ (line 143) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 147) | def __call__(self, img):

FILE: predict/resnet_cbam.py
  function conv3x3 (line 32) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 38) | def conv1x1(in_planes, out_planes, stride=1):
  class ChannelAttention (line 44) | class ChannelAttention(nn.Module):
    method __init__ (line 45) | def __init__(self, in_planes, ratio=16):
    method forward (line 56) | def forward(self, x):
  class SpatialAttention (line 62) | class SpatialAttention(nn.Module):
    method __init__ (line 63) | def __init__(self, kernel_size=7):
    method forward (line 72) | def forward(self, x):
  class BasicBlock (line 80) | class BasicBlock(nn.Module):
    method __init__ (line 83) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 102) | def forward(self, x):
  class Bottleneck (line 121) | class Bottleneck(nn.Module):
    method __init__ (line 124) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 142) | def forward(self, x):
  class ResNet (line 165) | class ResNet(nn.Module):
    method __init__ (line 167) | def __init__(self, block, layers, num_classes=1000, zero_init_residual...
    method _make_layer (line 222) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method forward (line 246) | def forward(self, x):
  function _resnet (line 266) | def _resnet(arch, block, layers, pretrained, progress, **kwargs):
  function resnet18 (line 278) | def resnet18(pretrained=False, progress=True, **kwargs):
  function resnet34 (line 288) | def resnet34(pretrained=False, progress=True, **kwargs):
  function resnet50 (line 298) | def resnet50(pretrained=False, progress=True, **kwargs):
  function resnet101 (line 308) | def resnet101(pretrained=False, progress=True, **kwargs):
  function resnet152 (line 318) | def resnet152(pretrained=False, progress=True, **kwargs):
  function resnext50_32x4d (line 328) | def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
  function resnext101_32x8d (line 340) | def resnext101_32x8d(pretrained=False, progress=True, **kwargs):

FILE: predict/resnetxt_wsl.py
  function _resnext (line 40) | def _resnext(arch, block, layers, pretrained, progress, **kwargs):
  function resnext101_32x8d_wsl (line 50) | def resnext101_32x8d_wsl(pretrained=True, progress=True, **kwargs):
  function resnext101_32x16d_wsl (line 62) | def resnext101_32x16d_wsl(pretrained=True, progress=True, **kwargs):
  function resnext101_32x32d_wsl (line 74) | def resnext101_32x32d_wsl(pretrained=True, progress=True, **kwargs):
  function resnext101_32x48d_wsl (line 86) | def resnext101_32x48d_wsl(pretrained=True, progress=True, **kwargs):

FILE: train.py
  function main (line 47) | def main():
  function train (line 150) | def train(train_loader, model, criterion, optimizer, epoch, use_cuda):
  function test (line 205) | def test(val_loader, model, criterion, epoch, use_cuda):

FILE: transform.py
  class Resize (line 18) | class Resize(object):
    method __init__ (line 19) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 23) | def __call__(self, img):
  class RandomRotate (line 40) | class RandomRotate(object):
    method __init__ (line 41) | def __init__(self, degree, p=0.5):
    method __call__ (line 45) | def __call__(self, img):
  class RandomGaussianBlur (line 51) | class RandomGaussianBlur(object):
    method __init__ (line 52) | def __init__(self, p=0.5):
    method __call__ (line 54) | def __call__(self, img):
  function get_train_transform (line 60) | def get_train_transform(mean, std, size):
  function get_test_transform (line 73) | def get_test_transform(mean, std, size):
  function get_transforms (line 81) | def get_transforms(input_size=224, test_size=224, backbone=None):

FILE: utils/eval.py
  function accuracy (line 5) | def accuracy(output, target, topk=(1,)):
  function precision (line 20) | def precision(output, target):

FILE: utils/logger.py
  function savefig (line 11) | def savefig(fname, dpi=None):
  function plot_overlap (line 15) | def plot_overlap(logger, names=None):
  class Logger (line 23) | class Logger(object):
    method __init__ (line 25) | def __init__(self, fpath, title=None, resume=False):
    method set_names (line 47) | def set_names(self, names):
    method append (line 61) | def append(self, numbers):
    method plot (line 70) | def plot(self, names=None):
    method close (line 79) | def close(self):
  class LoggerMonitor (line 83) | class LoggerMonitor(object):
    method __init__ (line 85) | def __init__ (self, paths):
    method plot (line 92) | def plot(self, names=None):

FILE: utils/misc.py
  function get_mean_and_std (line 26) | def get_mean_and_std(dataset):
  function init_params (line 41) | def init_params(net):
  function mkdir_p (line 56) | def mkdir_p(path):
  class AverageMeter (line 66) | class AverageMeter(object):
    method __init__ (line 70) | def __init__(self):
    method reset (line 73) | def reset(self):
    method update (line 79) | def update(self, val, n=1):
  function get_optimizer (line 85) | def get_optimizer(model, args):
  function save_checkpoint (line 123) | def save_checkpoint(state, is_best, single=True, checkpoint='checkpoint'...
  function save_checkpoint2 (line 141) | def save_checkpoint2(state, is_best, checkpoint='checkpoint', filename='...

FILE: utils/progress/progress/__init__.py
  class Infinite (line 27) | class Infinite(object):
    method __init__ (line 31) | def __init__(self, *args, **kwargs):
    method __getitem__ (line 40) | def __getitem__(self, key):
    method elapsed (line 46) | def elapsed(self):
    method elapsed_td (line 50) | def elapsed_td(self):
    method update_avg (line 53) | def update_avg(self, n, dt):
    method update (line 58) | def update(self):
    method start (line 61) | def start(self):
    method finish (line 64) | def finish(self):
    method next (line 67) | def next(self, n=1):
    method iter (line 75) | def iter(self, it):
  class Progress (line 84) | class Progress(Infinite):
    method __init__ (line 85) | def __init__(self, *args, **kwargs):
    method eta (line 90) | def eta(self):
    method eta_td (line 94) | def eta_td(self):
    method percent (line 98) | def percent(self):
    method progress (line 102) | def progress(self):
    method remaining (line 106) | def remaining(self):
    method start (line 109) | def start(self):
    method goto (line 112) | def goto(self, index):
    method iter (line 116) | def iter(self, it):

FILE: utils/progress/progress/bar.py
  class Bar (line 22) | class Bar(WritelnMixin, Progress):
    method update (line 32) | def update(self):
  class ChargingBar (line 45) | class ChargingBar(Bar):
  class FillingSquaresBar (line 53) | class FillingSquaresBar(ChargingBar):
  class FillingCirclesBar (line 58) | class FillingCirclesBar(ChargingBar):
  class IncrementalBar (line 63) | class IncrementalBar(Bar):
    method update (line 66) | def update(self):
  class PixelBar (line 83) | class PixelBar(IncrementalBar):
  class ShadyBar (line 87) | class ShadyBar(IncrementalBar):

FILE: utils/progress/progress/counter.py
  class Counter (line 22) | class Counter(WriteMixin, Infinite):
    method update (line 26) | def update(self):
  class Countdown (line 30) | class Countdown(WriteMixin, Progress):
    method update (line 33) | def update(self):
  class Stack (line 37) | class Stack(WriteMixin, Progress):
    method update (line 41) | def update(self):
  class Pie (line 47) | class Pie(Stack):

FILE: utils/progress/progress/helpers.py
  class WriteMixin (line 22) | class WriteMixin(object):
    method __init__ (line 25) | def __init__(self, message=None, **kwargs):
    method write (line 37) | def write(self, s):
    method finish (line 45) | def finish(self):
  class WritelnMixin (line 50) | class WritelnMixin(object):
    method __init__ (line 53) | def __init__(self, message=None, **kwargs):
    method clearln (line 61) | def clearln(self):
    method writeln (line 65) | def writeln(self, line):
    method finish (line 71) | def finish(self):
  class SigIntMixin (line 82) | class SigIntMixin(object):
    method __init__ (line 85) | def __init__(self, *args, **kwargs):
    method _sigint_handler (line 89) | def _sigint_handler(self, signum, frame):

FILE: utils/progress/progress/spinner.py
  class Spinner (line 22) | class Spinner(WriteMixin, Infinite):
    method update (line 27) | def update(self):
  class PieSpinner (line 32) | class PieSpinner(Spinner):
  class MoonSpinner (line 36) | class MoonSpinner(Spinner):
  class LineSpinner (line 40) | class LineSpinner(Spinner):
  class PixelSpinner (line 43) | class PixelSpinner(Spinner):

FILE: utils/progress/test_progress.py
  function sleep (line 16) | def sleep():

FILE: utils/radam.py
  class RAdam (line 14) | class RAdam(Optimizer):
    method __init__ (line 16) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 21) | def __setstate__(self, state):
    method step (line 24) | def step(self, closure=None):
  class PlainRAdam (line 92) | class PlainRAdam(Optimizer):
    method __init__ (line 94) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 99) | def __setstate__(self, state):
    method step (line 102) | def step(self, closure=None):
  class AdamW (line 159) | class AdamW(Optimizer):
    method __init__ (line 161) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 166) | def __setstate__(self, state):
    method step (line 169) | def step(self, closure=None):

FILE: utils/utils.py
  function GetEncoder (line 22) | def GetEncoder(model):
  function GetPreTrainedModel (line 31) | def GetPreTrainedModel(model,n_Output,n_ZeroChild,n_ZeroLayer=None):
  class StackNet2 (line 48) | class StackNet2(nn.Module):
    method __init__ (line 49) | def __init__(self,models,n_Target):
    method forward (line 73) | def forward(self,x):
  function _download_file_from_google_drive (line 81) | def _download_file_from_google_drive(fid, dest):
  function _load_url (line 104) | def _load_url(url, dest):
  function load_pretrained (line 112) | def load_pretrained(m, meta, dest, pretrained=False):
  function l2_norm (line 124) | def l2_norm(input,axis=1):

FILE: utils/visualize.py
  function make_image (line 12) | def make_image(img, mean=(0,0,0), std=(1,1,1)):
  function gauss (line 18) | def gauss(x,a,b,c):
  function colorize (line 21) | def colorize(x):
  function show_batch (line 38) | def show_batch(images, Mean=(2, 2, 2), Std=(0.5,0.5,0.5)):
  function show_mask_single (line 44) | def show_mask_single(images, mask, Mean=(2, 2, 2), Std=(0.5,0.5,0.5)):
  function show_mask (line 73) | def show_mask(images, masklist, Mean=(2, 2, 2), Std=(0.5,0.5,0.5)):