Add ImageNet_ResNet

lib/models/ImageNet_ResNet.py

@@ -0,0 +1,172 @@
+# Deep Residual Learning for Image Recognition, CVPR 2016
+import torch.nn as nn
+from .initialization import initialize_resnet
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1):
+  return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+  return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+  expansion = 1
+
+  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64):
+    super(BasicBlock, self).__init__()
+    if groups != 1 or base_width != 64:
+      raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+    # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+    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):
+    identity = 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:
+      identity = self.downsample(x)
+
+    out += identity
+    out = self.relu(out)
+
+    return out
+
+
+class Bottleneck(nn.Module):
+  expansion = 4
+
+  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64):
+    super(Bottleneck, self).__init__()
+    width = int(planes * (base_width / 64.)) * groups
+    # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+    self.conv1 = conv1x1(inplanes, width)
+    self.bn1   = nn.BatchNorm2d(width)
+    self.conv2 = conv3x3(width, width, stride, groups)
+    self.bn2   = nn.BatchNorm2d(width)
+    self.conv3 = conv1x1(width, planes * self.expansion)
+    self.bn3   = nn.BatchNorm2d(planes * self.expansion)
+    self.relu  = nn.ReLU(inplace=True)
+    self.downsample = downsample
+    self.stride = stride
+
+  def forward(self, x):
+    identity = 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:
+      identity = self.downsample(x)
+
+    out += identity
+    out = self.relu(out)
+
+    return out
+
+
+class ResNet(nn.Module):
+
+  def __init__(self, block_name, layers, deep_stem, num_classes, zero_init_residual, groups, width_per_group):
+    super(ResNet, self).__init__()
+
+    #planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
+    if block_name == 'BasicBlock'  : block= BasicBlock
+    elif block_name == 'Bottleneck': block= Bottleneck
+    else                           : raise ValueError('invalid block-name : {:}'.format(block_name))
+
+    if not deep_stem:
+      self.conv = nn.Sequential(
+                   nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
+                   nn.BatchNorm2d(64), nn.ReLU(inplace=True))
+    else:
+      self.conv = nn.Sequential(
+                   nn.Conv2d(           3, 32, kernel_size=3, stride=2, padding=1, bias=False),
+                   nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+                   nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
+                   nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+                   nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
+                   nn.BatchNorm2d(64), nn.ReLU(inplace=True))
+    self.inplanes = 64
+    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+    self.layer1 = self._make_layer(block, 64 , layers[0], stride=1, groups=groups, base_width=width_per_group)
+    self.layer2 = self._make_layer(block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group)
+    self.layer3 = self._make_layer(block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group)
+    self.layer4 = self._make_layer(block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group)
+    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+    self.fc      = nn.Linear(512 * block.expansion, num_classes)
+    self.message = 'block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}'.format(block, layers, deep_stem, num_classes)
+
+    self.apply( initialize_resnet )
+
+    # Zero-initialize the last BN in each residual branch,
+    # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+    # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, Bottleneck):
+          nn.init.constant_(m.bn3.weight, 0)
+        elif isinstance(m, BasicBlock):
+          nn.init.constant_(m.bn2.weight, 0)
+
+  def _make_layer(self, block, planes, blocks, stride, groups, base_width):
+    downsample = None
+    if stride != 1 or self.inplanes != planes * block.expansion:
+      if stride == 2:
+        downsample = nn.Sequential(
+          nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+          conv1x1(self.inplanes, planes * block.expansion, 1),
+          nn.BatchNorm2d(planes * block.expansion),
+        )
+      elif stride == 1:
+        downsample = nn.Sequential(
+          conv1x1(self.inplanes, planes * block.expansion, stride),
+          nn.BatchNorm2d(planes * block.expansion),
+        )
+      else: raise ValueError('invalid stride [{:}] for downsample'.format(stride))
+
+    layers = []
+    layers.append(block(self.inplanes, planes, stride, downsample, groups, base_width))
+    self.inplanes = planes * block.expansion
+    for _ in range(1, blocks):
+      layers.append(block(self.inplanes, planes, 1, None, groups, base_width))
+
+    return nn.Sequential(*layers)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, x):
+    x = self.conv(x)
+    x = self.maxpool(x)
+
+    x = self.layer1(x)
+    x = self.layer2(x)
+    x = self.layer3(x)
+    x = self.layer4(x)
+
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.fc(features)
+
+    return features, logits

lib/models/__init__.py

@@ -109,7 +109,7 @@ def get_cifar_models(config, extra_path=None):
 def get_imagenet_models(config):
   super_type = getattr(config, 'super_type', 'basic')
   if super_type == 'basic':
-    from .ImagenetResNet import ResNet
+    from .ImageNet_ResNet import ResNet
     from .ImageNet_MobileNetV2 import MobileNetV2
     if config.arch == 'resnet':
       return ResNet(config.block_name, config.layers, config.deep_stem, config.class_num, config.zero_init_residual, config.groups, config.width_per_group)