def build_classifier()

in iep/models/module_net.py [0:0]

• 28 lines of code
• 9 McCabe index (conditional complexity)

def build_classifier(module_C, module_H, module_W, num_answers,
                     fc_dims=[], proj_dim=None, downsample='maxpool2',
                     with_batchnorm=True, dropout=0):
  layers = []
  prev_dim = module_C * module_H * module_W
  if proj_dim is not None and proj_dim > 0:
    layers.append(nn.Conv2d(module_C, proj_dim, kernel_size=1))
    if with_batchnorm:
      layers.append(nn.BatchNorm2d(proj_dim))
    layers.append(nn.ReLU(inplace=True))
    prev_dim = proj_dim * module_H * module_W
  if downsample == 'maxpool2':
    layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
    prev_dim //= 4
  elif downsample == 'maxpool4':
    layers.append(nn.MaxPool2d(kernel_size=4, stride=4))
    prev_dim //= 16
  layers.append(Flatten())
  for next_dim in fc_dims:
    layers.append(nn.Linear(prev_dim, next_dim))
    if with_batchnorm:
      layers.append(nn.BatchNorm1d(next_dim))
    layers.append(nn.ReLU(inplace=True))
    if dropout > 0:
      layers.append(nn.Dropout(p=dropout))
    prev_dim = next_dim
  layers.append(nn.Linear(prev_dim, num_answers))
  return nn.Sequential(*layers)