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pix_network_1.py
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import torch
import torch.nn as nn
import math
import torchvision.transforms as transforms
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.conv1 = 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(7)
self.fc = nn.Linear(512 * block.expansion, num_classes)
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.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)
return x
def conditioning_network(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
class adaptation_network(nn.Module):
def __init__(self, in_size, out_size, kernel_size=1):
super(adaptation_network, self).__init__()
self.conv1 = nn.Conv2d(in_size, 512, kernel_size, bias=False)
self.conv2 = nn.Conv2d(512, 256, kernel_size, bias=False)
self.conv3 = nn.Conv2d(256, out_size, kernel_size, bias=False)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
out = self.conv3(out)
return out
class PixColor1(nn.Module):
def __init__(self, downsize):
super(PixColor1, self).__init__()
self.conditioning_network = conditioning_network()
self.adaptation_network = adaptation_network(1025, 2)
self.pixelCNN =
'''transform = transforms.Compose([
#transforms.ToPILImage(),
transforms.Scale(downsize, interpolation=2),
transforms.ToTensor()
])'''
self.resize = transforms.Scale(downsize, interpolation=2)
def forward(self, x):
x_conditioned = self.conditioning_network(x) # 64 x 1024 x 4 X 4 (4 if input imsize is 64)
# print(x_conditioned.size())
x_resized = self.resize(x) # 64 x 1 x 4 x 4
x_concat = torch.cat((x_resized, x_conditioned), 1) # 64 x 1025 x 4 x 4
out = self.adaptation_network(x_concat)
out = self.pixelCNN
return out