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convnet.py
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155 lines (135 loc) · 5.85 KB
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import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self, channel, num_classes, net_width, net_depth, net_act, net_norm, net_pooling, im_size = (32,32)):
super(ConvNet, self).__init__()
self.features, shape_feat = self._make_layers(channel, net_width, net_depth, net_norm, net_act, net_pooling, im_size)
num_feat = shape_feat[0]*shape_feat[1]*shape_feat[2]
self.classifier = nn.Linear(num_feat, num_classes)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
out = self.classifier(x)
return out, x
def embed(self, x):
out = self.features(x)
out = out.view(out.size(0), -1)
return out
def _get_activation(self, net_act):
if net_act == 'sigmoid':
return nn.Sigmoid()
elif net_act == 'relu':
return nn.ReLU(inplace=True)
elif net_act == 'leakyrelu':
return nn.LeakyReLU(negative_slope=0.01)
elif net_act == 'swish':
return Swish()
else:
exit('unknown activation function: %s'%net_act)
def _get_pooling(self, net_pooling):
if net_pooling == 'maxpooling':
return nn.MaxPool2d(kernel_size=2, stride=2)
elif net_pooling == 'avgpooling':
return nn.AvgPool2d(kernel_size=2, stride=2)
elif net_pooling == 'none':
return None
else:
exit('unknown net_pooling: %s'%net_pooling)
def _get_normlayer(self, net_norm, shape_feat):
# shape_feat = (c*h*w)
if net_norm == 'batchnorm':
return nn.BatchNorm2d(shape_feat[0], affine=True)
elif net_norm == 'layernorm':
return nn.LayerNorm(shape_feat, elementwise_affine=True)
elif net_norm == 'instancenorm':
return nn.GroupNorm(shape_feat[0], shape_feat[0], affine=True)
elif net_norm == 'groupnorm':
return nn.GroupNorm(4, shape_feat[0], affine=True)
elif net_norm == 'none':
return None
else:
exit('unknown net_norm: %s'%net_norm)
def _make_layers(self, channel, net_width, net_depth, net_norm, net_act, net_pooling, im_size):
layers = []
in_channels = channel
if im_size[0] == 28:
im_size = (32, 32)
shape_feat = [in_channels, im_size[0], im_size[1]]
for d in range(net_depth):
layers += [nn.Conv2d(in_channels, net_width, kernel_size=3, padding=3 if channel == 1 and d == 0 else 1)]
shape_feat[0] = net_width
if net_norm != 'none':
layers += [self._get_normlayer(net_norm, shape_feat)]
layers += [self._get_activation(net_act)]
in_channels = net_width
if net_pooling != 'none':
layers += [self._get_pooling(net_pooling)]
shape_feat[1] //= 2
shape_feat[2] //= 2
return nn.Sequential(*layers), shape_feat
class ConvNet2(nn.Module):
def __init__(self, channel, num_classes, net_width, net_depth, net_act, net_norm, net_pooling, im_size = (32,32)):
super(ConvNet2, self).__init__()
self.features, shape_feat = self._make_layers(channel, net_width, net_depth, net_norm, net_act, net_pooling, im_size)
num_feat = shape_feat[0]*shape_feat[1]*shape_feat[2]
self.classifier = nn.Linear(num_feat, num_classes)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
out = self.classifier(x)
return out, x
def embed(self, x):
out = self.features(x)
out = out.view(out.size(0), -1)
return out
def _get_activation(self, net_act):
if net_act == 'sigmoid':
return nn.Sigmoid()
elif net_act == 'relu':
return nn.ReLU(inplace=True)
elif net_act == 'leakyrelu':
return nn.LeakyReLU(negative_slope=0.01)
elif net_act == 'swish':
return Swish()
else:
exit('unknown activation function: %s'%net_act)
def _get_pooling(self, net_pooling):
if net_pooling == 'maxpooling':
return nn.MaxPool2d(kernel_size=2, stride=2)
elif net_pooling == 'avgpooling':
return nn.AvgPool2d(kernel_size=2, stride=2)
elif net_pooling == 'none':
return None
else:
exit('unknown net_pooling: %s'%net_pooling)
def _get_normlayer(self, net_norm, shape_feat):
# shape_feat = (c*h*w)
if net_norm == 'batchnorm':
return nn.BatchNorm2d(shape_feat[0], affine=True)
elif net_norm == 'layernorm':
return nn.LayerNorm(shape_feat, elementwise_affine=True)
elif net_norm == 'instancenorm':
return nn.GroupNorm(shape_feat[0], shape_feat[0], affine=True)
elif net_norm == 'groupnorm':
return nn.GroupNorm(4, shape_feat[0], affine=True)
elif net_norm == 'none':
return None
else:
exit('unknown net_norm: %s'%net_norm)
def _make_layers(self, channel, net_width, net_depth, net_norm, net_act, net_pooling, im_size):
layers = []
in_channels = channel
if im_size[0] == 28:
im_size = (32, 32)
shape_feat = [in_channels, im_size[0], im_size[1]]
for d in range(net_depth):
layers += [nn.Conv2d(in_channels, net_width*(2**d), kernel_size=3, padding=3 if channel == 1 and d == 0 else 1)]
shape_feat[0] = net_width*(2**d)
if net_norm != 'none':
layers += [self._get_normlayer(net_norm, shape_feat)]
layers += [self._get_activation(net_act)]
in_channels = net_width*(2**d)
if net_pooling != 'none':
layers += [self._get_pooling(net_pooling)]
shape_feat[1] //= 2
shape_feat[2] //= 2
return nn.Sequential(*layers), shape_feat