Create model.py

model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import antialiased_cnns
+
+
+def drop_path(x, drop_prob=0.0, training=False):
+    """Drop paths (Stochastic Depth) per sample."""
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class BasicBlock(nn.Module):
+    """Original ResNet Basic Block with Stochastic Depth"""
+    expansion = 1
+
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None, drop_prob=0.0, use_blurpool=False):
+        super().__init__()
+        self.use_blurpool = use_blurpool
+        self.stride = stride
+
+        # Modify conv1 based on stride and use_blurpool
+        if self.use_blurpool and self.stride == 2:
+            self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
+                                   stride=1, padding=1, bias=False)
+            self.blurpool = antialiased_cnns.BlurPool(out_channels, stride=2)
+        else:
+            self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
+                                   stride=stride, padding=1, bias=False)
+
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.downsample = downsample
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = F.relu(out, inplace=True)
+
+        # Apply blurpool after conv1 if downsampling with blurpool
+        if self.use_blurpool and self.stride == 2:
+             out = self.blurpool(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = drop_path(out, self.drop_prob, self.training)
+        out += identity
+        out = F.relu(out, inplace=True)
+
+        return out
+
+
+class BottleneckBlock(nn.Module):
+    """Original ResNet Bottleneck Block with Stochastic Depth"""
+    expansion = 4
+
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None, drop_prob=0.0, use_blurpool=False):
+        super().__init__()
+        self.use_blurpool = use_blurpool
+        self.stride = stride
+
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+
+        # Modify conv2 based on stride and use_blurpool
+        if self.use_blurpool and self.stride == 2:
+             self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+             self.blurpool = antialiased_cnns.BlurPool(out_channels, stride=2)
+        else:
+            self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
+                               stride=stride, padding=1, bias=False)
+
+        self.bn2 = nn.BatchNorm2d(out_channels)
+
+        self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion,
+                               kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
+
+        self.downsample = downsample
+        self.drop_prob = drop_prob
+
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = F.relu(out, inplace=True)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = F.relu(out, inplace=True)
+
+        # Apply blurpool after conv2 if downsampling with blurpool
+        if self.use_blurpool and self.stride == 2:
+             out = self.blurpool(out)
+
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = drop_path(out, self.drop_prob, self.training)
+        out += identity
+        out = F.relu(out, inplace=True)
+
+        return out
+
+
+class ResNet(nn.Module):
+    def __init__(self, block, layers, num_classes=1000, drop_path_rate=0.2, use_blurpool=False):
+        super().__init__()
+        self.in_channels = 64
+        self.use_blurpool = use_blurpool
+
+        # Initial conv layer
+        # Apply blurpool if use_blurpool is True and stride is 2
+        if self.use_blurpool:
+            self.conv1 = nn.Sequential(
+                nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3, bias=False),
+                nn.BatchNorm2d(64),
+                nn.ReLU(inplace=True),
+                antialiased_cnns.BlurPool(64, stride=2)
+            )
+        else:
+            self.conv1 = nn.Sequential(
+                nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
+                nn.BatchNorm2d(64),
+                nn.ReLU(inplace=True)
+            )
+
+        # Initial pooling layer (always MaxPool2d stride 2) replace maxpool by MaxBlurPool if use_blurpool is True
+        if self.use_blurpool:
+            self.maxpool_or_blurpool = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=1), antialiased_cnns.BlurPool(64, stride=2))
+        else:
+            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        
+
+
+        # Calculate total number of blocks
+        total_blocks = sum(layers)
+        # Linear drop path rate schedule
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_blocks)]
+
+        # Track current block index
+        block_idx = 0
+
+        self.layer1 = self._make_layer(block, 64, layers[0], stride=1,
+                                       drop_probs=dpr[block_idx:block_idx+layers[0]], use_blurpool=use_blurpool)
+        block_idx += layers[0]
+
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
+                                       drop_probs=dpr[block_idx:block_idx+layers[1]], use_blurpool=use_blurpool)
+        block_idx += layers[1]
+
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
+                                       drop_probs=dpr[block_idx:block_idx+layers[2]], use_blurpool=use_blurpool)
+        block_idx += layers[2]
+
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
+                                       drop_probs=dpr[block_idx:block_idx+layers[3]], use_blurpool=use_blurpool)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Conv2d(512 * block.expansion, num_classes, kernel_size=1)
+
+    def _make_layer(self, block, out_channels, blocks, stride, drop_probs, use_blurpool):
+        downsample = None
+        if stride != 1 or self.in_channels != out_channels * block.expansion:
+            # Downsample path
+            # If use_blurpool is True and stride is 2, replace strided conv with conv stride 1 + blurpool stride 2
+            if use_blurpool and stride == 2:
+                 downsample = nn.Sequential(
+                    nn.Conv2d(self.in_channels, out_channels * block.expansion,
+                            kernel_size=1, stride=1, bias=False), # Conv stride 1
+                    nn.BatchNorm2d(out_channels * block.expansion),
+                    antialiased_cnns.BlurPool(out_channels * block.expansion, stride=2) # BlurPool stride 2
+                )
+            else:
+                downsample = nn.Sequential(
+                    nn.Conv2d(self.in_channels, out_channels * block.expansion,
+                            kernel_size=1, stride=stride, bias=False),
+                    nn.BatchNorm2d(out_channels * block.expansion)
+                )
+
+
+        layers = []
+        # First block in the layer handles downsampling
+        layers.append(block(self.in_channels, out_channels, stride, downsample, drop_probs[0], use_blurpool=use_blurpool))
+        self.in_channels = out_channels * block.expansion
+
+        # Subsequent blocks have stride 1
+        for i in range(1, blocks):
+            layers.append(block(self.in_channels, out_channels, stride=1, drop_prob=drop_probs[i], use_blurpool=use_blurpool))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        # The original ResNet has maxpool after conv1 replace maxpool by MaxBlurPool if use_blurpool is True
+        if self.use_blurpool:
+            x = self.maxpool_or_blurpool(x)
+        else:
+            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 = self.fc(x)
+        x = torch.flatten(x, 1)
+
+        return x