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resnet.py
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import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
# Original file: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
# Updated to add shape annotations (BasicBlock and ResNet modules)
import sys
from tsalib import dim_vars, get_dim_vars
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
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',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes: 'Ci', planes: 'Co', 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):
#x: 'b,ci,h,w'
residual: 'b,ci,h1,w1' = x
out: 'b,co,h1,w1' = self.conv1(x)
out: 'b,co,h1,w1' = self.bn1(out)
out: 'b,co,h1,w1' = self.relu(out)
out: 'b,co,h1,w1' = self.conv2(out)
out: 'b,co,h1,w1' = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
#assert residual.size() == out.size()
out: 'b,co,h1,w1' = out + residual
out: 'b,co,h1,w1' = 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 = 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, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
#print (f'block expansion {block.expansion}')
self.inplanes = 64
N = dim_vars(f'N(nc):{num_classes}')
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(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=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.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x): #H = W = 224
x: 'b,3,h,w'
x: 'b,64,h//2,w//2' = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x: 'b,64,h//4,w//4' = self.maxpool(x)
x: 'b,64*e,h//4,w//4' = self.layer1(x)
x: 'b,128*e,h//8,w//8' = self.layer2(x)
x: 'b,256*e,h//16,w//16' = self.layer3(x)
x: 'b,512*e,h//32,w//32' = self.layer4(x)
x: 'b,512*e,1,1' = self.avgpool(x)
B, Ex = get_dim_vars('b e')
x: 'b,512*e' = x.view(B, 512*Ex)
x: 'b,nc' = self.fc(x)
return x
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
def test_basic_block():
bb = BasicBlock(64, 64)
x = torch.ones(10, 64, 256, 256)
out = bb.forward(x)
print (out.size())
def test_resnet ():
# declare dim vars: required for checking
B, C, Ci, Co = dim_vars('Batch(b):10 Channels(c):3 ChannelsIn(ci) ChannelsOut(co)')
H, W, Ex, NC = dim_vars('Height(h):224 Width(w):224 BlockExpansion(e):1 NumClasses(nc):1000')
rs18 = resnet18()
x: 'bchw' = torch.ones(10, 3, 224, 224)
from tsanley.dynamic import init_analyzer
#init_analyzer(trace_func_names=['ResNet.forward', 'BasicBlock.forward'])
init_analyzer(trace_func_names=['ResNet.forward'])
out = rs18.forward(x)
print (out.size())
if __name__ == '__main__':
test_resnet()