pytorch中resnet_利用pytorch实现resnet18?

import torch

import torch.nn as nn

# from .utils import load_state_dict_from_url

__all__ = ['ResNet', 'resnet18']

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):

"""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):

expansion = 4

def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,

base_width=64, dilation=1, norm_layer=None):

super(Bottleneck, self).__init__()

if norm_layer is None:

norm_layer = nn.BatchNorm2d

width = int(planes * (base_width / 64.)) * groups

# 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):

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(self, x):

print(x.shape)

x = self.conv1(x)

print(x.shape)

x = self.bn1(x)

x = self.relu(x)

x = self.maxpool(x)

x = self.layer1(x)

print(x.shape)

x = self.layer2(x)

print(x.shape)

x = self.layer3(x)

print(x.shape)

x = self.layer4(x)

print(x.shape)

x = self.avgpool(x)

print(x.shape)

x = torch.flatten(x, 1)

x = self.fc(x)

return 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" `_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""

return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,

**kwargs)

net=resnet18()

net(torch.zeros(2,3,128,128))

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