add: fine tune using alexnet and resnet

README.md

@@ -228,10 +228,9 @@ _ _ _
 
 迁移学习领域有一些公开的数据集，用来对比算法的表现。
 
-- [这里](https://github.com/jindongwang/transferlearning/blob/master/doc/dataset.md)整理了常用的公开数据集
-- [这里](https://github.com/jindongwang/transferlearning/blob/master/doc/benchmark.md)整理汇总了一些已发表的文章在这些数据集上的实验结果。
+[这里](https://github.com/jindongwang/transferlearning/blob/master/data)整理了常用的公开数据集和一些已发表的文章在这些数据集上的实验结果。
 
-Please see [HERE](https://github.com/jindongwang/transferlearning/blob/master/doc/dataset.md) for the popular transfer learning datasets and [HERE](https://github.com/jindongwang/transferlearning/blob/master/doc/benchmark.md) for some benchmark results.
+Please see [HERE](https://github.com/jindongwang/transferlearning/blob/master/data) for the popular transfer learning datasets and certain benchmark results.
 
 - - -
 

code/AlexNet_ResNet/AlexNet.py

@@ -0,0 +1,80 @@
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+import torch
+
+__all__ = ['AlexNet', 'alexnet']
+
+model_urls = {
+    'alexnet': 'http://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
+}
+
+
+class AlexNet(nn.Module):
+
+    def __init__(self, num_classes=1000):
+        super(AlexNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(64, 192, kernel_size=5, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(256 * 6 * 6, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(4096, 4096),
+            nn.ReLU(inplace=True),
+            nn.Linear(4096, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), 256 * 6 * 6)
+        x = self.classifier(x)
+        return x
+
+
+class AlexNetFc(nn.Module):
+    def __init__(self, pretrained=False, num_classes=1000):
+        super(AlexNetFc, self).__init__()
+        model_alexnet = alexnet(pretrained=pretrained)
+        self.features = model_alexnet.features
+        self.classifier = nn.Sequential()
+        for i in range(6):
+            self.classifier.add_module("classifier" + str(i), model_alexnet.classifier[i])
+        self.__in_features = model_alexnet.classifier[6].in_features
+        self.nfc = nn.Linear(4096, num_classes)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), 256 * 6 * 6)
+        x = self.classifier(x)
+        x = self.nfc(x)
+        return x
+
+    def output_num(self):
+        return self.__in_features
+
+
+def alexnet(pretrained=False, **kwargs):
+    r"""AlexNet model architecture from the
+    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = AlexNet(**kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load('alexnet-owt-4df8aa71.pth'))
+    return model

code/AlexNet_ResNet/ResNet.py

@@ -0,0 +1,237 @@
+import torch.nn as nn
+import math
+import torch.utils.model_zoo as model_zoo
+import torch
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+           'resnet152']
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000):
+        self.inplanes = 64
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        self.in_feature = 512 * block.expansion
+
+        # for m in self.modules():
+        #     if isinstance(m, nn.Conv2d):
+        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+        #         m.weight.data.normal_(0, math.sqrt(2. / n))
+        #     elif isinstance(m, nn.BatchNorm2d):
+        #         m.weight.data.fill_(1)
+        #         m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+
+def resnet18(pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
+    return model
+
+
+def resnet34(pretrained=False, **kwargs):
+    """Constructs a ResNet-34 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
+    return model
+
+
+def resnet50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
+    return model
+
+
+def resnet101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
+    return model
+
+
+def resnet152(pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
+    return model
+
+
+class myresnet(nn.Module):
+    def __init__(self, pretrained=True, num_classes=1000):
+        super(myresnet,self).__init__()
+        model_resnet = resnet50(pretrained=pretrained)
+        self.features = nn.Sequential(
+            model_resnet.conv1,
+            model_resnet.bn1,
+            model_resnet.relu,
+            model_resnet.maxpool,
+            model_resnet.layer1,
+            model_resnet.layer2,
+            model_resnet.layer3,
+            model_resnet.layer4,
+            model_resnet.avgpool
+        )
+        self.nfc = nn.Linear(model_resnet.in_feature,num_classes)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.nfc(x)
+        return x

code/AlexNet_ResNet/data/readme.txt

@@ -0,0 +1,12 @@
+Download the Office-31 dataset (raw images) and extract it into this directory.
+This directory should look like:
+data
+--OFFICE31
+----amazon
+------class1
+------class2
+...
+----webcam
+------(same as amazon)
+----dslr
+------(same as amazon)

code/AlexNet_ResNet/data_loader.py

@@ -0,0 +1,27 @@
+from torchvision import datasets, transforms
+import torch
+
+def load_training(root_path, dir, batch_size):
+    transform = transforms.Compose(
+        [transforms.Resize([256,256]),
+         transforms.RandomResizedCrop(224),
+         transforms.RandomHorizontalFlip(),
+         transforms.ToTensor(),
+         transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                              std=[0.229, 0.224, 0.225]),
+         ])
+    data = datasets.ImageFolder(root=root_path + dir, transform=transform)
+    train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=True,num_workers=4)
+    return train_loader
+
+def load_testing(root_path, dir, batch_size):
+    transform = transforms.Compose(
+        [transforms.Resize([256, 256]),
+         transforms.RandomResizedCrop(224),
+         transforms.ToTensor(),
+         transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                              std=[0.229, 0.224, 0.225]),
+         ])
+    data = datasets.ImageFolder(root=root_path + dir, transform=transform)
+    test_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=False,num_workers=4)
+    return test_loader