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models_with_cam.py
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models_with_cam.py
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import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
import torch
from torch.autograd import Variable
__all__ = ['ResNet', 'resnet18']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.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, 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
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 * 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):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.upsampleby4 = nn.Upsample(scale_factor=4,mode = "nearest")
self.upsample = nn.Upsample(scale_factor=8,mode = "bilinear")
self.inputMask = (torch.FloatTensor(16,3,224,224).zero_()+1.0).cuda()
self.conv11 = nn.Conv2d(1, 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(8, stride=1)
self.fc_6outputs = nn.Linear(512 * block.expansion, num_classes)
self.convCAMlike = nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0)
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):
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):
x = self.upsampleby4(x);
x = self.conv11(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)
x1 = self.layer4(x)
# print("x1 size = {}".format(x1.shape))
x2= self.avgpool(x1)
# print("x2 size = {}".format(x2.shape))
x3 = x2.view(x2.size(0), -1)
x3 = self.fc_6outputs(x3)
outsm = F.softmax(x3)
w = torch.mm(outsm, Variable(self.fc_6outputs.weight.data) )
# print("w size = {}".format(w.shape))
# print("x1 size = {}".format(x1.shape))
cam = torch.mul(x1,w.unsqueeze(2).unsqueeze(3))
# print("cam size = {}".format(cam.shape))
# cam = cam.sum(1) # sum over all channels
cam = cam.sum(1).unsqueeze(1) # sum over all channels and make: batchSize x height x width --> batchSize x 1 x height x width
# print("cam size = {}".format(cam.shape))
# print("cam size = {}".format(cam.unsqueeze(1).shape))
# print("upscaling")
# print(self.upsample(cam ).shape) # make 16 x height x width --> 16 x 1 x height x width
return outsm , self.upsample(cam )
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']))
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']),strict=False) # strict argument allows us to load models that have subset.superset
return model