In [86]:

    

import torch
from torch import nn 
import numpy as np
torch.manual_seed(0)
torch.backends.cudnn.deterministic = True


    

In [2]:

    

def conv3x3(in_channels, out_channels, stride=1):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3,stride=stride, padding=1, bias=False)


    

In [105]:

    

class BasicBlock(nn.Module):
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        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


    

In [106]:

    

class ResNet(nn.Module):

    def __init__(self, BasicBlock, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.layer1 = self._make_layer(BasicBlock, 64, layers[0])
        self.layer2 = self._make_layer(BasicBlock, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(BasicBlock, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(BasicBlock, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 , num_classes)
        self.fc = nn.Linear(512 , num_classes)
        
    def _make_layer(self, BasicBlock, planes, num_blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(nn.Conv2d(self.inplanes, planes,kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes))
        layers = []
        layers.append(BasicBlock(self.inplanes, planes, stride, downsample))
        self.inplanes = planes
        for i in range(1, num_blocks):
            layers.append(BasicBlock(self.inplanes, planes))

        return nn.Sequential(*layers)
    def forward(self, x):
        x = self.conv1(x)    # 224x224
        x = self.bn1(x)     
        x = self.relu(x)
        x = self.maxpool(x)  # 112x112

        x = self.layer1(x)   # 56x56
        x = self.layer2(x)   # 28x28
        x = self.layer3(x)   # 14x14
        x = self.layer4(x)   # 7x7

        x = self.avgpool(x)  # 1x1
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


    

In [107]:

    

def resnet34(**kwargs):
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    return model


    

In [108]:

    

resnet = resnet34()


    

In [109]:

    

dummy_image = torch.Tensor(np.zeros([2,3,224,224]))


    

In [110]:

    

resnet(dummy_image).shape


    

    
Out[110]:





torch.Size([2, 1000])

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