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创建于 差异永不过期
33 删除
80 行
33 添加
80 行
Original SqueezeNet model
Pruned squeezenet model
ModifiedSqueezeNetModel(
ModifiedSqueezeNetModel(
Text moved with changes from lines 74-79 (98.7% similarity)
(classifier): Sequential(
(0): Dropout(p=0.5)
(1): Conv2d(30, 2, kernel_size=(1, 1), stride=(1, 1))
(2): ReLU(inplace)
(3): AvgPool2d(kernel_size=13, stride=1, padding=0)
)
(features): Sequential(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))
(0): Conv2d(3, 28, kernel_size=(3, 3), stride=(2, 2))
(1): ReLU(inplace)
(1): ReLU(inplace)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
(3): Fire(
(3): Fire(
(squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(28, 16, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(16, 43, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(16, 41, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(4): Fire(
(4): Fire(
(squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(84, 16, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(16, 38, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(16, 29, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
(6): Fire(
(6): Fire(
(squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(67, 32, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(32, 79, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(32, 65, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(7): Fire(
(7): Fire(
(squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(32, 80, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(32, 53, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
(8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
(9): Fire(
(9): Fire(
(squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(133, 48, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(48, 84, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(48, 83, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(10): Fire(
(10): Fire(
(squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(167, 48, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(48, 82, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(48, 81, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(11): Fire(
(11): Fire(
(squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(163, 64, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(64, 76, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(64, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
(12): Fire(
(12): Fire(
(squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(squeeze): Conv2d(144, 64, kernel_size=(1, 1), stride=(1, 1))
(squeeze_activation): ReLU(inplace)
(squeeze_activation): ReLU(inplace)
(expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
(expand1x1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
(expand1x1_activation): ReLU(inplace)
(expand1x1_activation): ReLU(inplace)
(expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3): Conv2d(64, 14, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(expand3x3_activation): ReLU(inplace)
(expand3x3_activation): ReLU(inplace)
)
)
)
)
Text moved with changes to lines 3-8 (98.7% similarity)
(classifier): Sequential(
(0): Dropout(p=0.5)
(1): Conv2d(512, 2, kernel_size=(1, 1), stride=(1, 1))
(2): ReLU(inplace)
(3): AvgPool2d(kernel_size=13, stride=1, padding=0)
)
)
)
已保存差异