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Simplify cifar resnet further for faster training

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This commit is contained in:
James Betker 2021-06-06 10:02:24 -06:00
parent 75567a9814
commit a0158ebc69
1 changed files with 12 additions and 12 deletions
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24
codes/models/classifiers/cifar_resnet_branched.py
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@ -85,11 +85,11 @@ class ResNetTail(nn.Module):
def __init__(self, block, num_block, num_classes=100):
super().__init__()
self.in_channels = 128
self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
self.in_channels = 64
self.conv4_x = self._make_layer(block, 128, num_block[2], 2)
self.conv5_x = self._make_layer(block, 256, num_block[3], 2)
self.avg_pool = 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):
strides = [stride] + [1] * (num_blocks - 1)
@ -111,19 +111,19 @@ class ResNetTail(nn.Module):
class ResNet(nn.Module):
def __init__(self, block, num_block, num_classes=100, num_tails=20):
def __init__(self, block, num_block, num_classes=100, num_tails=8):
super().__init__()
self.in_channels = 64
self.in_channels = 32
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True))
self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
self.conv2_x = self._make_layer(block, 32, num_block[0], 1)
self.conv3_x = self._make_layer(block, 64, num_block[1], 2)
self.tails = nn.ModuleList([ResNetTail(block, num_block, 256) for _ in range(num_tails)])
self.selector = ResNetTail(block, num_block, num_tails)
self.final_linear = nn.Linear(256, 100)
self.final_linear = nn.Linear(256, num_classes)
def _make_layer(self, block, out_channels, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
@ -181,5 +181,5 @@ def resnet152():
if __name__ == '__main__':
model = ResNet(BasicBlock, [2,2,2,2])
print(model(torch.randn(2,3,32,32), torch.LongTensor([4,19])).shape)
print(model(torch.randn(2,3,32,32), torch.LongTensor([4,7])).shape)