-- coding: utf-8 --
‘’‘ResNet in PyTorch.
For Pre-activation ResNet, see ‘preact_resnet.py’.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
‘’’
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
    super(BasicBlock, self).__init__()
    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)

    self.shortcut = nn.Sequential()
    if stride != 1 or in_planes != self.expansion*planes:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(self.expansion*planes)
        )

def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = self.bn2(self.conv2(out))
    out += self.shortcut(x)
    out = F.relu(out)
    return out

class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(self.expansion*planes)

    self.shortcut = nn.Sequential()
    if stride != 1 or in_planes != self.expansion*planes:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(self.expansion*planes)
        )

def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = F.relu(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    out += self.shortcut(x)
    out = F.relu(out)
    return out

class ResNet(nn.Module):
def init(self, block, num_blocks, num_classes=15):
super(ResNet, self).init()
self.in_planes = 64
    self.conv1 = nn.Conv2d(3, 64, kernel_size=5, stride=2, padding=2, bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.MaxPool2d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
    self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
    self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
    self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
    self.linear = nn.Linear(512*block.expansion, num_classes)

def _make_layer(self, block, planes, num_blocks, stride):
    strides = [stride] + [1]*(num_blocks-1)
    layers = []
    for stride in strides:
        layers.append(block(self.in_planes, planes, stride))
        self.in_planes = planes * block.expansion
    return nn.Sequential(*layers)

def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))#32x32
    out = self.MaxPool2d(out)#16x16
    out = self.layer1(out)
    out = self.layer2(out)
    out = self.layer3(out)
    out = self.layer4(out)
    out = F.avg_pool2d(out, 8)
    out = self.linear(out)
    return out

 in train_model(model, criterion, optimizer, optimizer_name, learning_rate, num_epochs)
33                     with torch.set_grad_enabled(True):
34 #                         print(input1.dtype, input2.dtype)
—> 35                         outputs = model(input1, input2)
36     #                     dummy_img = outputs
37                         _, preds = torch.max(outputs, 1)
~/anaconda3/envs/pytorch1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
475             result = self._slow_forward(*input, **kwargs)
476         else:
–> 477             result = self.forward(*input, **kwargs)
478         for hook in self._forward_hooks.values():
479             hook_result = hook(self, input, result)
 in forward(self, x1, x2)
27     def forward(self,x1,x2):
28         print(x1.shape, x2.shape)
—> 29         feat1 = self.pretrain_place.forward(x1)
30 #         print(‘feat1’, feat1.shape)
31         x2 = self.branch1(x2)
~/Research/128size_modelpy/ResNet64.py in forward(self, x)
95         out = self.layer4(out)
96         out = F.avg_pool2d(out, 8)
—> 97         out = self.linear(out)
98         return out
99
TypeError: avg_pool2d(): argument ‘input’ (position 1) must be Tensor, not MaxPool2d