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Copyright (c) Microsoft

Licensed under the MIT License.

Create by Bin Xiao (Bin.Xiao@microsoft.com)

Modified by Tianheng Cheng(tianhengcheng@gmail.com), Yang Zhao

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from future import absolute_import from future import division from future import print_function

import os import logging

import torch import torch.nn as nn import torch.nn.functional as F

BatchNorm2d = nn.BatchNorm2d BN_MOMENTUM = 0.01 logger = logging.getLogger(name)

def conv3x3_dw(in_planes, out_planes, stride=1): '''3x3 深度可分离卷积 with padding''' return nn.Sequential( nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False), BatchNorm2d(in_planes, momentum=BN_MOMENTUM), nn.ReLU(inplace=True), nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False), BatchNorm2d(out_planes, momentum=BN_MOMENTUM), nn.ReLU(inplace=True) )

class BasicBlock(nn.Module): expansion = 1

def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(BasicBlock, self).__init__()
    self.conv1 = conv3x3_dw(inplanes, planes, stride)
    self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3_dw(planes, planes)
    self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
    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.Module): 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 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
    self.conv2 = conv3x3_dw(planes, planes, stride)  # 使用深度可分离卷积替换
    self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
    self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
                           bias=False)
    self.bn3 = BatchNorm2d(planes * self.expansion,
                           momentum=BN_MOMENTUM)
    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

... (HighResolutionModule, blocks_dict, HighResolutionNet, get_face_alignment_net 等代码保持不变)