def _make_layers(self, cfg):
layers = []
in_channels = 3
for layer_index, x in enumerate(cfg):
if x == 'M':
# append max pooling layer
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
# append conv->(bn/gn)->activation layer
# determine activation function: we use tanh for last layer if self.use_tanh is set to True
is_last_layer = True
for xx in cfg[layer_index + 1:]:
if xx != 'M':
is_last_layer = False
if self.use_tanh and is_last_layer:
activation = nn.Tanh()
else:
activation = nn.ReLU(inplace=True)
layers += [
nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
normalization(self.normalization_type, x), activation
]
in_channels = x
# imagenet vgg13 does not do that
# layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
def normalization16(normalization_type, planes):
return normalization(normalization_type, planes, group_size=16)