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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
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