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[ShuoLiu-Max]

import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url

all = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']

model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}

def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)

def conv1x1(in_planes, out_planes, stride=1):#in_planes决定了1x1卷积核的通道数，out_planes确定了这样的卷积核的个数
"""1x1 convolution"""
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, dilation=1, norm_layer=None):
    super(BasicBlock, self).__init__()
    if norm_layer is None:
        norm_layer = nn.BatchNorm2d
    if groups != 1 or base_width != 64:
        raise ValueError('BasicBlock only supports groups=1 and base_width=64')
    if dilation > 1:
        raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
    # Both self.conv1 and self.downsample layers downsample the input when stride != 1
    self.conv1 = conv3x3(inplanes, planes, stride)
    self.bn1 = norm_layer(planes)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, planes)
    self.bn2 = norm_layer(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):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

expansion = 4

def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
             base_width=64, dilation=1, norm_layer=None):
            #dowmsample确定是否下采样
			#inplanes为上一个block的输出维度，即当前block的输入通道数
			#planes为该block的第一层卷积的输出通道数
			#base_width为整个网络的基通道数即64
    super(Bottleneck, self).__init__()
    if norm_layer is None:
        norm_layer = nn.BatchNorm2d
    width = int(planes * (base_width / 64.)) * groups  ##当前block的第一层卷积层的输出通道数
    # Both self.conv2 and self.downsample layers downsample the input when stride != 1
    self.conv1 = conv1x1(inplanes, width)
    self.bn1 = norm_layer(width)
    self.conv2 = conv3x3(width, width, stride, groups, dilation)
    self.bn2 = norm_layer(width)
    self.conv3 = conv1x1(width, planes * self.expansion)
    self.bn3 = norm_layer(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, layers, num_classes=1000, zero_init_residual=False,
             groups=1, width_per_group=64, replace_stride_with_dilation=None,
             norm_layer=None):
    super(ResNet, self).__init__()
    if norm_layer is None:
        norm_layer = nn.BatchNorm2d
    self._norm_layer = norm_layer

    self.inplanes = 64
    self.dilation = 1
    if replace_stride_with_dilation is None:
        # each element in the tuple indicates if we should replace
        # the 2x2 stride with a dilated convolution instead
        replace_stride_with_dilation = [False, False, False]
    if len(replace_stride_with_dilation) != 3:
        raise ValueError("replace_stride_with_dilation should be None "
                         "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
    self.groups = groups
    self.base_width = width_per_group
    self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = norm_layer(self.inplanes)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.layer1 = self._make_layer(block, 64, layers[0])
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                   dilate=replace_stride_with_dilation[0])
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                   dilate=replace_stride_with_dilation[1])
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                   dilate=replace_stride_with_dilation[2])
    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  
    self.fc = nn.Linear(512 * block.expansion, num_classes)

    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
            nn.init.constant_(m.weight, 1)
            nn.init.constant_(m.bias, 0)

    # 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=1, dilate=False): #planes为每个stage第一层卷积层输出对应的通道数，blocks为每个block对应的bottleneck的个数
    norm_layer = self._norm_layer
    downsample = None
    previous_dilation = self.dilation
    if dilate:
        self.dilation *= stride
        stride = 1
    if stride != 1 or self.inplanes != planes * block.expansion:
        downsample = nn.Sequential(
            conv1x1(self.inplanes, planes * block.expansion, stride),
            norm_layer(planes * block.expansion),
        )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                        self.base_width, previous_dilation, norm_layer))
    self.inplanes = planes * block.expansion
    for _ in range(1, blocks):
        layers.append(block(self.inplanes, planes, groups=self.groups,
                            base_width=self.base_width, dilation=self.dilation,
                            norm_layer=norm_layer))

    return nn.Sequential(*layers)

def _forward_impl(self, x):
    # See note [TorchScript super()]
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    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 = torch.flatten(x, 1)
    x = self.fc(x)

    return x

def forward(self, x):
    return self._forward_impl(x)

def _resnet(arch, block, layers, pretrained, progress, **kwargs):
model = ResNet(block, layers, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
model.load_state_dict(state_dict)
return model

def resnet18(pretrained=False, progress=True, **kwargs):
r"""ResNet-18 model from
"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
               **kwargs)

def resnet34(pretrained=False, progress=True, **kwargs):
r"""ResNet-34 model from
"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
               **kwargs)

def resnet50(pretrained=False, progress=True, **kwargs):
r"""ResNet-50 model from
"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
               **kwargs)

def resnet101(pretrained=False, progress=True, **kwargs):
r"""ResNet-101 model from
"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
               **kwargs)

def resnet152(pretrained=False, progress=True, **kwargs):
r"""ResNet-152 model from
"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
               **kwargs)

def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-50 32x4d model from
"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 4
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
               pretrained, progress, **kwargs)

def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-101 32x8d model from
"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>_

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
               pretrained, progress, **kwargs)

def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-50-2 model from
"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>_

The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
               pretrained, progress, **kwargs)

def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-101-2 model from
"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>_

The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
    progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
               pretrained, progress, **kwargs)

import cv2
from torchvision import transforms
import cv2
import math
from matplotlib import image
from matplotlib import pyplot as plt
import numpy as np
img = cv2.imread("img/1.jpg") # 读取输入网络的图片224*224
img = transforms.ToTensor()(img)#把读入的图片数据转化为torch.tensor类型的数据,[C,H,W]
img = img.unsqueeze(0) # 图片扩展多一维,因为输入到保存的模型中是4维的[batch_size,通道,长，宽]，而普通图片只有三维，[通道,长，宽]

if name == 'main':
net50 = resnet50()
print(net50)
x=net50.forward(img)#调用类的函数forward()，把img送入函数，通过前面新建的网络实例，得到1000类的输出
print(x,'-->',type(x),'-->',x.shape,'-->',x.dtype)