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作成日 8 年前差分は期限切れになりません

33 削除

行
合計
削除

文字
合計
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80 行

33 追加

行
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追加

文字
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80 行

Original SqueezeNet model

Pruned squeezenet model

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(

(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)

(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)

(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)

(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)

(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)

)

(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)

(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)

(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)

)

(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)

(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)

(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)

(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)

)

(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)

(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)

(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)

)

(8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)

(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)

(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)

(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)

)

(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)

(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)

(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)

)

(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)

(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)

(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)

)

(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)

(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)

(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)

)

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)

)

保存された差分

原文

ファイルを開く

Original SqueezeNet model
ModifiedSqueezeNetModel(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))
    (1): ReLU(inplace)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
    (3): Fire(
      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (4): Fire(
      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
    (6): Fire(
      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (7): Fire(
      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
    (9): Fire(
      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (10): Fire(
      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (11): Fire(
      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (12): Fire(
      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
  )
  (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)
  )
)

変更されたテキスト

ファイルを開く

Pruned squeezenet model
ModifiedSqueezeNetModel(
  (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(
    (0): Conv2d(3, 28, kernel_size=(3, 3), stride=(2, 2))
    (1): ReLU(inplace)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
    (3): Fire(
      (squeeze): Conv2d(28, 16, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(16, 43, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(16, 41, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (4): Fire(
      (squeeze): Conv2d(84, 16, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(16, 38, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(16, 29, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
    (6): Fire(
      (squeeze): Conv2d(67, 32, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(32, 79, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(32, 65, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (7): Fire(
      (squeeze): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(32, 80, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(32, 53, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
    (9): Fire(
      (squeeze): Conv2d(133, 48, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(48, 84, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(48, 83, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (10): Fire(
      (squeeze): Conv2d(167, 48, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(48, 82, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(48, 81, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (11): Fire(
      (squeeze): Conv2d(163, 64, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(64, 76, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(64, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (12): Fire(
      (squeeze): Conv2d(144, 64, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d(64, 14, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
  )
)