import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+-- 8 lines: if stride != 1 or in_planes != self.expansion*planes:-------------------------------------------------------------------------------------------------------------------------
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
return out
class PreActResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(PreActResNet, self).__init__()
self.in_planes = 64
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512*block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
+-- 11 lines: def forward(self, x):---------------------------------------------------------------------------------------------------------------------------------------------------------
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import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1, **kwargs):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+-- 8 lines: if stride != 1 or in_planes != self.expansion*planes:-------------------------------------------------------------------------------------------------------------------------
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
return out
class PreActResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, init_channels=64):
super(PreActResNet, self).__init__()
self.in_planes = init_channels
c = init_channels
self.conv1 = nn.Conv2d(3, c, kernel_size=3, stride=1, padding=1, bias=False)
self.layer1 = self._make_layer(block, c, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 2*c, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 4*c, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 8*c, num_blocks[3], stride=2)
self.linear = nn.Linear(8*c*block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
+-- 11 lines: def forward(self, x):---------------------------------------------------------------------------------------------------------------------------------------------------------
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