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deepcore/nets/vgg.py

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+import torch.nn as nn
+from torch import set_grad_enabled, flatten, Tensor
+from .nets_utils import EmbeddingRecorder
+from torchvision.models import vgg
+
+# Acknowledgement to
+# https://github.com/kuangliu/pytorch-cifar,
+# https://github.com/BIGBALLON/CIFAR-ZOO,
+
+cfg_vgg = {
+    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
+    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
+}
+
+
+class VGG_32x32(nn.Module):
+    def __init__(self, vgg_name, channel, num_classes, record_embedding=False, no_grad=False):
+        super(VGG_32x32, self).__init__()
+        self.channel = channel
+        self.features = self._make_layers(cfg_vgg[vgg_name])
+        self.classifier = nn.Linear(512 if vgg_name != 'VGGS' else 128, num_classes)
+
+        self.embedding_recorder = EmbeddingRecorder(record_embedding)
+        self.no_grad = no_grad
+
+    def forward(self, x):
+        with set_grad_enabled(not self.no_grad):
+            x = self.features(x)
+            x = x.view(x.size(0), -1)
+            x = self.embedding_recorder(x)
+            x = self.classifier(x)
+        return x
+
+    def get_last_layer(self):
+        return self.classifier
+
+    def _make_layers(self, cfg):
+        layers = []
+        in_channels = self.channel
+        for ic, x in enumerate(cfg):
+            if x == 'M':
+                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+            else:
+                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=3 if self.channel == 1 and ic == 0 else 1),
+                           nn.BatchNorm2d(x),
+                           nn.ReLU(inplace=True)]
+                in_channels = x
+        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
+        return nn.Sequential(*layers)
+
+
+class VGG_224x224(vgg.VGG):
+    def __init__(self, features: nn.Module, channel: int, num_classes: int, record_embedding: bool = False,
+                 no_grad: bool = False, **kwargs):
+        super(VGG_224x224, self).__init__(features, num_classes, **kwargs)
+        self.embedding_recorder = EmbeddingRecorder(record_embedding)
+        if channel != 3:
+            self.features[0] = nn.Conv2d(channel, 64, kernel_size=3, padding=1)
+        self.fc = self.classifier[-1]
+        self.classifier[-1] = self.embedding_recorder
+        self.classifier.add_module("fc", self.fc)
+
+        self.no_grad = no_grad
+
+    def get_last_layer(self):
+        return self.fc
+
+    def forward(self, x: Tensor) -> Tensor:
+        with set_grad_enabled(not self.no_grad):
+            x = self.features(x)
+            x = self.avgpool(x)
+            x = flatten(x, 1)
+            x = self.classifier(x)
+            return x
+
+
+def VGG(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
+        pretrained: bool = False):
+    arch = arch.lower()
+    if pretrained:
+        if im_size[0] != 224 or im_size[1] != 224:
+            raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224")
+        net = VGG_224x224(features=vgg.make_layers(cfg_vgg[arch], True), channel=3, num_classes=1000,
+                          record_embedding=record_embedding, no_grad=no_grad)
+
+        from torch.hub import load_state_dict_from_url
+        state_dict = load_state_dict_from_url(vgg.model_urls[arch], progress=True)
+        net.load_state_dict(state_dict)
+
+        if channel != 3:
+            net.features[0] = nn.Conv2d(channel, 64, kernel_size=3, padding=1)
+
+        if num_classes != 1000:
+            net.fc = nn.Linear(4096, num_classes)
+            net.classifier[-1] = net.fc
+
+    elif im_size[0] == 224 and im_size[1] == 224:
+        net = VGG_224x224(features=vgg.make_layers(cfg_vgg[arch], True), channel=channel, num_classes=num_classes,
+                          record_embedding=record_embedding, no_grad=no_grad)
+
+    elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or (
+            channel == 3 and im_size[0] == 32 and im_size[1] == 32):
+        net = VGG_32x32(arch, channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad)
+    else:
+        raise NotImplementedError("Network Architecture for current dataset has not been implemented.")
+    return net
+
+
+def VGG11(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
+          pretrained: bool = False):
+    return VGG("vgg11", channel, num_classes, im_size, record_embedding, no_grad, pretrained)
+
+
+def VGG13(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
+          pretrained: bool = False):
+    return VGG('vgg13', channel, num_classes, im_size, record_embedding, no_grad, pretrained)
+
+
+def VGG16(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
+          pretrained: bool = False):
+    return VGG('vgg16', channel, num_classes, im_size, record_embedding, no_grad, pretrained)
+
+
+def VGG19(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
+          pretrained: bool = False):
+    return VGG('vgg19', channel, num_classes, im_size, record_embedding, no_grad, pretrained)