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Half channel sizes in cifar_resnet

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This commit is contained in:
James Betker 2021-06-09 17:06:37 -06:00
parent aea12e1b9c
commit 220f11a5e4
1 changed files with 10 additions and 10 deletions
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20
codes/models/classifiers/cifar_resnet.py
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@ -85,20 +85,20 @@ class ResNet(nn.Module):
def __init__(self, block, num_block, num_classes=100): def __init__(self, block, num_block, num_classes=100):
super().__init__() super().__init__()
self.in_channels = 64 self.in_channels = 32
self.conv1 = nn.Sequential( self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64), nn.BatchNorm2d(32),
nn.ReLU(inplace=True)) nn.ReLU(inplace=True))
#we use a different inputsize than the original paper #we use a different inputsize than the original paper
#so conv2_x's stride is 1 #so conv2_x's stride is 1
self.conv2_x = self._make_layer(block, 64, num_block[0], 1) self.conv2_x = self._make_layer(block, 32, num_block[0], 1)
self.conv3_x = self._make_layer(block, 128, num_block[1], 2) self.conv3_x = self._make_layer(block, 64, num_block[1], 2)
self.conv4_x = self._make_layer(block, 256, num_block[2], 2) self.conv4_x = self._make_layer(block, 128, num_block[2], 2)
self.conv5_x = self._make_layer(block, 512, num_block[3], 2) self.conv5_x = self._make_layer(block, 256, num_block[3], 2)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes) self.fc = nn.Linear(256 * block.expansion, num_classes)
def _make_layer(self, block, out_channels, num_blocks, stride): def _make_layer(self, block, out_channels, num_blocks, stride):
"""make resnet layers(by layer i didnt mean this 'layer' was the """make resnet layers(by layer i didnt mean this 'layer' was the
@ -131,7 +131,7 @@ class ResNet(nn.Module):
output = self.conv3_x(output) output = self.conv3_x(output)
output = self.conv4_x(output) output = self.conv4_x(output)
output = self.conv5_x(output) output = self.conv5_x(output)
output = self.avg_pool(output) output = self.avgpool(output)
output = output.view(output.size(0), -1) output = output.view(output.size(0), -1)
output = self.fc(output) output = self.fc(output)