Update reparam.py

reparam.py

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+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+from typing import Union, Tuple
+
+import copy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+__all__ = ["MobileOneBlock", "reparameterize_model"]
+
+
+class SEBlock(nn.Module):
+    """Squeeze and Excite module.
+
+    Pytorch implementation of `Squeeze-and-Excitation Networks` -
+    https://arxiv.org/pdf/1709.01507.pdf
+    """
+
+    def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None:
+        """Construct a Squeeze and Excite Module.
+
+        Args:
+            in_channels: Number of input channels.
+            rd_ratio: Input channel reduction ratio.
+        """
+        super(SEBlock, self).__init__()
+        self.reduce = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=int(in_channels * rd_ratio),
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+        self.expand = nn.Conv2d(
+            in_channels=int(in_channels * rd_ratio),
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        b, c, h, w = inputs.size()
+        x = F.avg_pool2d(inputs, kernel_size=[h, w])
+        x = self.reduce(x)
+        x = F.relu(x)
+        x = self.expand(x)
+        x = torch.sigmoid(x)
+        x = x.view(-1, c, 1, 1)
+        return inputs * x
+
+
+class MobileOneBlock(nn.Module):
+    """MobileOne building block.
+
+    This block has a multi-branched architecture at train-time
+    and plain-CNN style architecture at inference time
+    For more details, please refer to our paper:
+    `An Improved One millisecond Mobile Backbone` -
+    https://arxiv.org/pdf/2206.04040.pdf
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        use_act: bool = True,
+        use_scale_branch: bool = True,
+        num_conv_branches: int = 1,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a MobileOneBlock module.
+
+        Args:
+            in_channels: Number of channels in the input.
+            out_channels: Number of channels produced by the block.
+            kernel_size: Size of the convolution kernel.
+            stride: Stride size.
+            padding: Zero-padding size.
+            dilation: Kernel dilation factor.
+            groups: Group number.
+            inference_mode: If True, instantiates model in inference mode.
+            use_se: Whether to use SE-ReLU activations.
+            use_act: Whether to use activation. Default: ``True``
+            use_scale_branch: Whether to use scale branch. Default: ``True``
+            num_conv_branches: Number of linear conv branches.
+        """
+        super(MobileOneBlock, self).__init__()
+        self.inference_mode = inference_mode
+        self.groups = groups
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.kernel_size = kernel_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_conv_branches = num_conv_branches
+
+        # Check if SE-ReLU is requested
+        if use_se:
+            self.se = SEBlock(out_channels)
+        else:
+            self.se = nn.Identity()
+
+        if use_act:
+            self.activation = activation
+        else:
+            self.activation = nn.Identity()
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            # Re-parameterizable skip connection
+            self.rbr_skip = (
+                nn.BatchNorm2d(num_features=in_channels)
+                if out_channels == in_channels and stride == 1
+                else None
+            )
+
+            # Re-parameterizable conv branches
+            if num_conv_branches > 0:
+                rbr_conv = list()
+                for _ in range(self.num_conv_branches):
+                    rbr_conv.append(
+                        self._conv_bn(kernel_size=kernel_size, padding=padding)
+                    )
+                self.rbr_conv = nn.ModuleList(rbr_conv)
+            else:
+                self.rbr_conv = None
+
+            # Re-parameterizable scale branch
+            self.rbr_scale = None
+            if not isinstance(kernel_size, int):
+                kernel_size = kernel_size[0]
+            if (kernel_size > 1) and use_scale_branch:
+                self.rbr_scale = self._conv_bn(kernel_size=1, padding=0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        # Inference mode forward pass.
+        if self.inference_mode:
+            return self.activation(self.se(self.reparam_conv(x)))
+
+        # Multi-branched train-time forward pass.
+        # Skip branch output
+        identity_out = 0
+        if self.rbr_skip is not None:
+            identity_out = self.rbr_skip(x)
+
+        # Scale branch output
+        scale_out = 0
+        if self.rbr_scale is not None:
+            scale_out = self.rbr_scale(x)
+
+        # Other branches
+        out = scale_out + identity_out
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                out += self.rbr_conv[ix](x)
+
+        return self.activation(self.se(out))
+
+    def reparameterize(self):
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
+        https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
+        architecture used at training time to obtain a plain CNN-like structure
+        for inference.
+        """
+        if self.inference_mode:
+            return
+        kernel, bias = self._get_kernel_bias()
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = kernel
+        self.reparam_conv.bias.data = bias
+
+        # Delete un-used branches
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("rbr_conv")
+        self.__delattr__("rbr_scale")
+        if hasattr(self, "rbr_skip"):
+            self.__delattr__("rbr_skip")
+
+        self.inference_mode = True
+
+    def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias.
+        Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83
+
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        # get weights and bias of scale branch
+        kernel_scale = 0
+        bias_scale = 0
+        if self.rbr_scale is not None:
+            kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
+            # Pad scale branch kernel to match conv branch kernel size.
+            pad = self.kernel_size // 2
+            kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
+
+        # get weights and bias of skip branch
+        kernel_identity = 0
+        bias_identity = 0
+        if self.rbr_skip is not None:
+            kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
+
+        # get weights and bias of conv branches
+        kernel_conv = 0
+        bias_conv = 0
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
+                kernel_conv += _kernel
+                bias_conv += _bias
+
+        kernel_final = kernel_conv + kernel_scale + kernel_identity
+        bias_final = bias_conv + bias_scale + bias_identity
+        return kernel_final, bias_final
+
+    def _fuse_bn_tensor(
+        self, branch: Union[nn.Sequential, nn.BatchNorm2d]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with preceeding conv layer.
+        Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
+
+        Args:
+            branch: Sequence of ops to be fused.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        if isinstance(branch, nn.Sequential):
+            kernel = branch.conv.weight
+            running_mean = branch.bn.running_mean
+            running_var = branch.bn.running_var
+            gamma = branch.bn.weight
+            beta = branch.bn.bias
+            eps = branch.bn.eps
+        else:
+            assert isinstance(branch, nn.BatchNorm2d)
+            if not hasattr(self, "id_tensor"):
+                input_dim = self.in_channels // self.groups
+
+                kernel_size = self.kernel_size
+                if isinstance(self.kernel_size, int):
+                    kernel_size = (self.kernel_size, self.kernel_size)
+
+                kernel_value = torch.zeros(
+                    (self.in_channels, input_dim, kernel_size[0], kernel_size[1]),
+                    dtype=branch.weight.dtype,
+                    device=branch.weight.device,
+                )
+                for i in range(self.in_channels):
+                    kernel_value[
+                        i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2
+                    ] = 1
+                self.id_tensor = kernel_value
+            kernel = self.id_tensor
+            running_mean = branch.running_mean
+            running_var = branch.running_var
+            gamma = branch.weight
+            beta = branch.bias
+            eps = branch.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+
+        Returns:
+            Conv-BN module.
+        """
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels))
+        return mod_list
+
+
+def reparameterize_model(model: torch.nn.Module) -> nn.Module:
+    """Method returns a model where a multi-branched structure
+        used in training is re-parameterized into a single branch
+        for inference.
+
+    Args:
+        model: MobileOne model in train mode.
+
+    Returns:
+        MobileOne model in inference mode.
+    """
+    # Avoid editing original graph
+    model = copy.deepcopy(model)
+    for module in model.modules():
+        if hasattr(module, "reparameterize"):
+            module.reparameterize()
+    return model