Add ResNet, AlexNet, and VGG model definitions and model zoo

torchvision/models/__init__.py

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+from .resnet import resnet18, resnet34, resnet50, resnet101, resnet152, ResNet
+from .alexnet import alexnet, AlexNet
+from .vgg import vgg11, vgg13, vgg16, vgg19

torchvision/models/alexnet.py

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+import torch.nn as nn
+from . import model_zoo
+
+
+class AlexNet(nn.Container):
+    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
+
+
+def alexnet(pretrained=False):
+    r"""AlexNet model architecture from the "One weird trick" paper.
+    https://arxiv.org/abs/1404.5997
+    """
+    model = AlexNet()
+    if pretrained:
+        model.load_state_dict(model_zoo.load('alexnet'))
+    return model

torchvision/models/model_zoo.py

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+import torch
+
+import hashlib
+import os
+import re
+import shutil
+import sys
+import tempfile
+if sys.version_info[0] == 2:
+    from urlparse import urlparse
+    from urllib2 import urlopen
+else:
+    from urllib.request import urlopen
+    from urllib.parse import urlparse
+try:
+    from tqdm import tqdm
+except ImportError:
+    tqdm = None  # defined below
+
+
+DEFAULT_MODEL_DIR = os.path.expanduser('~/.torch/models')
+
+models = {
+    'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
+    'alexnet': 'https://s3.amazonaws.com/pytorch/models/alexnet-owt-4df8aa71.pth',
+}
+
+# matches bfd8deac from resnet18-bfd8deac.pth
+HASH_REGEX = re.compile(r'-([a-f0-9]*)\.')
+
+
+def load(model_name):
+    r"""Returns the state_dict for the given model name"""
+    return load_url(models[model_name])
+
+
+def load_url(url, model_dir=None):
+    if model_dir is None:
+        model_dir = os.getenv('TORCH_MODEL_ZOO', DEFAULT_MODEL_DIR)
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        hash_prefix = HASH_REGEX.search(filename).group(1)
+        download_url_to_file(url, cached_file, hash_prefix)
+    return torch.load(cached_file)
+
+
+def download_url_to_file(url, filename, hash_prefix):
+    u = urlopen(url)
+    meta = u.info()
+    if hasattr(meta, 'getheaders'):
+        file_size = int(meta.getheaders("Content-Length")[0])
+    else:
+        file_size = int(meta.get_all("Content-Length")[0])
+
+    with tempfile.NamedTemporaryFile(delete=False) as f, tqdm(total=file_size) as pbar:
+        while True:
+            buffer = u.read(8192)
+            if len(buffer) == 0:
+                break
+            f.write(buffer)
+            pbar.update(len(buffer))
+
+        f.seek(0)
+        sha256 = hashlib.sha256(f.read()).hexdigest()
+        f.close()
+        if sha256[:len(hash_prefix)] == hash_prefix:
+            shutil.move(f.name, filename)
+        else:
+            raise RuntimeError('invalid hash value (expected "{}", got "{}")'
+                               .format(hash_prefix, sha256))
+
+
+if tqdm is None:
+    # fake tqdm if it's not installed
+    class tqdm(object):
+        def __init__(self, total):
+            self.total = total
+            self.n = 0
+
+        def update(self, n):
+            self.n += n
+            sys.stderr.write("\r{0:.1f}%".format(100 * self.n / float(self.total)))
+            sys.stderr.flush()
+
+        def __enter__(self):
+            return self
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            sys.stderr.write('\n')

torchvision/models/resnet.py

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+import torch.nn as nn
+import math
+from . import model_zoo
+
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+           'resnet152']
+
+
+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.Container):
+    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.Container):
+    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.Container):
+    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)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        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):
+    model = ResNet(BasicBlock, [2, 2, 2, 2])
+    if pretrained:
+        model.load_state_dict(model_zoo.load('resnet18'))
+    return model
+
+
+def resnet34():
+    return ResNet(BasicBlock, [3, 4, 6, 3])
+
+
+def resnet50():
+    return ResNet(Bottleneck, [3, 4, 6, 3])
+
+
+def resnet101():
+    return ResNet(Bottleneck, [3, 4, 23, 3])
+
+
+def resnet152():
+    return ResNet(Bottleneck, [3, 8, 36, 3])

torchvision/models/vgg.py

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+import torch.nn as nn
+from . import model_zoo
+
+
+class VGG(nn.Container):
+    def __init__(self, features):
+        super(VGG, self).__init__()
+        self.features = features
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(512 * 7 * 7, 4096),
+            nn.ReLU(True),
+            nn.Dropout(),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Linear(4096, 1000),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+
+def make_layers(cfg, batch_norm=False):
+    layers = []
+    in_channels = 3
+    for v in cfg:
+        if v == 'M':
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            if batch_norm:
+                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
+            else:
+                layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+cfg = {
+    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
+    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
+}
+
+
+def vgg11():
+    return VGG(make_layers(cfg['A']))
+
+
+def vgg11_bn():
+    return VGG(make_layers(cfg['A'], batch_norm=True))
+
+
+def vgg13():
+    return VGG(make_layers(cfg['B']))
+
+
+def vgg13_bn():
+    return VGG(make_layers(cfg['B'], batch_norm=True))
+
+
+def vgg16():
+    return VGG(make_layers(cfg['D']))
+
+
+def vgg16_bn():
+    return VGG(make_layers(cfg['D'], batch_norm=True))
+
+
+def vgg19():
+    return VGG(make_layers(cfg['E']))
+
+
+def vgg19_bn():
+    return VGG(make_layers(cfg['E'], batch_norm=True))