SYMBOL INDEX (27 symbols across 4 files)

FILE: dataset.py
  class VideoDataset (line 9) | class VideoDataset(Dataset):
    method __init__ (line 21) | def __init__(self, directory, mode='train', clip_len=8):
    method __getitem__ (line 44) | def __getitem__(self, index):
    method loadvideo (line 54) | def loadvideo(self, fname):
    method crop (line 87) | def crop(self, buffer, clip_len, crop_size):
    method normalize (line 103) | def normalize(self, buffer):
    method __len__ (line 111) | def __len__(self):
  class VideoDataset1M (line 115) | class VideoDataset1M(VideoDataset):
    method __init__ (line 124) | def __init__(self, directory, mode='train', clip_len=8):
    method __getitem__ (line 128) | def __getitem__(self, index):
    method __len__ (line 143) | def __len__(self):

FILE: module.py
  class SpatioTemporalConv (line 7) | class SpatioTemporalConv(nn.Module):
    method __init__ (line 22) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, p...
    method forward (line 62) | def forward(self, x):

FILE: network.py
  class SpatioTemporalResBlock (line 7) | class SpatioTemporalResBlock(nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels, out_channels, kernel_size, downsample=...
    method forward (line 48) | def forward(self, x):
  class SpatioTemporalResLayer (line 58) | class SpatioTemporalResLayer(nn.Module):
    method __init__ (line 71) | def __init__(self, in_channels, out_channels, kernel_size, layer_size,...
    method forward (line 84) | def forward(self, x):
  class R2Plus1DNet (line 92) | class R2Plus1DNet(nn.Module):
    method __init__ (line 101) | def __init__(self, layer_sizes, block_type=SpatioTemporalResBlock):
    method forward (line 117) | def forward(self, x):
  class R2Plus1DClassifier (line 128) | class R2Plus1DClassifier(nn.Module):
    method __init__ (line 139) | def __init__(self, num_classes, layer_sizes, block_type=SpatioTemporal...
    method forward (line 145) | def forward(self, x):

FILE: trainer.py
  function train_model (line 17) | def train_model(num_classes, directory, layer_sizes=[2, 2, 2, 2], num_ep...