OpenPose VGG作为Backbone的模型结构

flyfish

取VGG的序号0到22

  (23): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (24): ReLU(inplace=True)
  (25): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (26): ReLU(inplace=True)

23到26层是新增的作为model0

完整结构如下

VGGOpenPose(
  (model0): OpenPose_Feature(
    (model): Sequential(
      (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): ReLU(inplace=True)
      (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): ReLU(inplace=True)
      (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (6): ReLU(inplace=True)
      (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (8): ReLU(inplace=True)
      (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (11): ReLU(inplace=True)
      (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (13): ReLU(inplace=True)
      (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (15): ReLU(inplace=True)
      (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (17): ReLU(inplace=True)
      (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (20): ReLU(inplace=True)
      (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (22): ReLU(inplace=True)
      (23): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (24): ReLU(inplace=True)
      (25): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (26): ReLU(inplace=True)
    )
  )
  (model1_1): Sequential(
    (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
    (7): ReLU(inplace=True)
    (8): Conv2d(512, 38, kernel_size=(1, 1), stride=(1, 1))
  )
  (model2_1): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 38, kernel_size=(1, 1), stride=(1, 1))
  )
  (model3_1): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 38, kernel_size=(1, 1), stride=(1, 1))
  )
  (model4_1): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 38, kernel_size=(1, 1), stride=(1, 1))
  )
  (model5_1): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 38, kernel_size=(1, 1), stride=(1, 1))
  )
  (model6_1): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 38, kernel_size=(1, 1), stride=(1, 1))
  )
  (model1_2): Sequential(
    (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
    (7): ReLU(inplace=True)
    (8): Conv2d(512, 19, kernel_size=(1, 1), stride=(1, 1))
  )
  (model2_2): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 19, kernel_size=(1, 1), stride=(1, 1))
  )
  (model3_2): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 19, kernel_size=(1, 1), stride=(1, 1))
  )
  (model4_2): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 19, kernel_size=(1, 1), stride=(1, 1))
  )
  (model5_2): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 19, kernel_size=(1, 1), stride=(1, 1))
  )
  (model6_2): Sequential(
    (0): Conv2d(185, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (1): ReLU(inplace=True)
    (2): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (3): ReLU(inplace=True)
    (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (5): ReLU(inplace=True)
    (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (7): ReLU(inplace=True)
    (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
    (9): ReLU(inplace=True)
    (10): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(128, 19, kernel_size=(1, 1), stride=(1, 1))
  )
)

整个结构及计算量是，图中的Backbone就是VGG
在这里插入图片描述

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