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Running
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Zero
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import torch.nn as nn
import torch.nn.functional as F
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution without padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
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):
def __init__(self, in_planes, planes, stride=1):
super().__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.conv2 = conv3x3(planes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
if stride == 1:
self.downsample = None
else:
self.downsample = nn.Sequential(
conv1x1(in_planes, planes, stride=stride),
nn.BatchNorm2d(planes)
)
def forward(self, x):
y = x
y = self.relu(self.bn1(self.conv1(y)))
y = self.bn2(self.conv2(y))
if self.downsample is not None:
x = self.downsample(x)
return self.relu(x+y)
class ResNet_8_2(nn.Module):
"""
ResNet, output resolution are 1/8 and 1/2.
Each block has 2 layers.
"""
def __init__(self, config):
super().__init__()
# Config
block = BasicBlock
initial_dim = config['initial_dim']
block_dims = config['block_dims']
# Class Variable
self.in_planes = initial_dim
# Networks
self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(initial_dim)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2
self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4
self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8
self.layer3_outconv = conv1x1(block_dims[2], block_dims[2])
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.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, dim, stride=1):
layer1 = block(self.in_planes, dim, stride=stride)
layer2 = block(dim, dim, stride=1)
layers = (layer1, layer2)
self.in_planes = dim
return nn.Sequential(*layers)
def forward(self, x):
# ResNet Backbone
x0 = self.relu(self.bn1(self.conv1(x)))
x1 = self.layer1(x0) # 1/2
x2 = self.layer2(x1) # 1/4
x3 = self.layer3(x2) # 1/8
x3_out = self.layer3_outconv(x3)
return x3_out, x2, x1
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