|
- from torch import nn
-
-
- class ConvModel(nn.Module):
- def __init__(self, channel, n_random_features, net_width = 64, net_depth = 3, net_act = 'relu',
- net_norm = 'batchnorm', net_pooling = 'avgpooling', im_size = (32,32)):
- super().__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, n_random_features)
-
- def forward(self, x):
- out = self.features(x)
- out = out.reshape(out.size(0), -1)
- out = self.classifier(out)
- 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 == 'gelu':
- return nn.SiLU()
- else:
- raise Exception('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:
- raise Exception('unknown net_pooling: %s'%net_pooling)
-
- def _get_normlayer(self, net_norm, shape_feat):
- 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:
- raise Exception('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
- 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='same')]
-
- 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
|
