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resnet.py
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resnet.py
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import math
import torch.nn as nn
from collections import OrderedDict
import torch.utils.model_zoo as model_zoo
from torchvision.models.resnet import model_urls
import torch
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, bias=False, dilation=dilation)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1, use_bn=True):
super(BasicBlock, self).__init__()
self.use_bn = use_bn
self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation)
if use_bn:
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, dilation=dilation)
if use_bn:
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
if self.use_bn:
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
if self.use_bn:
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
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=dilation, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
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):
""" ResNet network module. Allows extracting specific feature blocks."""
def __init__(self, block, layers, output_layers, sat_grd, inplanes=64, dilation_factor=1):
self.inplanes = inplanes
self.sat_grd = sat_grd
super(ResNet, self).__init__()
self.output_layers = output_layers
self.conv1 = nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
stride = [1 + (dilation_factor < l) for l in (8, 4, 2)]
self.layer1 = self._make_layer(block, inplanes, layers[0], dilation=max(dilation_factor//8, 1))
self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=stride[0], dilation=max(dilation_factor//4, 1))
self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=1, dilation=2)
out_feature_strides = {'conv1': 4, 'layer1': 4, 'layer2': 4*stride[0], 'layer3': 4*stride[0]*stride[1],
'layer4': 4*stride[0]*stride[1]*stride[2]}
# TODO better way?
if isinstance(self.layer1[0], BasicBlock):
out_feature_channels = {'conv1': inplanes, 'layer1': inplanes, 'layer2': inplanes*2, 'layer3': inplanes*4,
'layer4': inplanes*8}
elif isinstance(self.layer1[0], Bottleneck):
base_num_channels = 4 * inplanes
out_feature_channels = {'conv1': inplanes, 'layer1': base_num_channels, 'layer2': base_num_channels * 2,
'layer3': base_num_channels * 4, 'layer4': base_num_channels * 8}
else:
raise Exception('block not supported')
self._out_feature_strides = out_feature_strides
self._out_feature_channels = out_feature_channels
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=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, dilation=dilation))
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):
""" Forward pass with input x. The output_layers specify the feature blocks which must be returned """
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)
if self.sat_grd == 'sat':
x = F.adaptive_avg_pool2d(x, (32, 32))
return x
def resnet18(output_layers=None, pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
"""
if output_layers is None:
output_layers = ['default']
else:
for l in output_layers:
if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']:
raise ValueError('Unknown layer: {}'.format(l))
model = ResNet(BasicBlock, [2, 2, 2, 2], output_layers, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet50(sat_grd, output_layers=None, pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
if output_layers is None:
output_layers = ['default']
else:
for l in output_layers:
if l not in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']:
raise ValueError('Unknown layer: {}'.format(l))
model = ResNet(Bottleneck, [3, 4, 6, 3], output_layers, sat_grd, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict=False)
return model
if __name__ == '__main__':
model = resnet50('sat', output_layers=['layer3'], pretrained=True)
img = torch.randn(2, 3, 512, 512)
result = model(img)
print(result.shape)