各种卷积网络的实现
VGG
整体结构如下所示
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----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1,180,160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2,359,808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2,359,808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2,359,808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
AdaptiveAvgPool2d-32 [-1, 512, 7, 7] 0
Linear-33 [-1, 4096] 102,764,544
ReLU-34 [-1, 4096] 0
Dropout-35 [-1, 4096] 0
Linear-36 [-1, 4096] 16,781,312
ReLU-37 [-1, 4096] 0
Dropout-38 [-1, 4096] 0
Linear-39 [-1, 1000] 4,097,000
================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.78
Params size (MB): 527.79
Estimated Total Size (MB): 747.15
----------------------------------------------------------------
ResNet
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----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 112, 112] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
Conv2d-5 [-1, 64, 56, 56] 4,096
BatchNorm2d-6 [-1, 64, 56, 56] 128
ReLU-7 [-1, 64, 56, 56] 0
Conv2d-8 [-1, 64, 56, 56] 36,864
BatchNorm2d-9 [-1, 64, 56, 56] 128
ReLU-10 [-1, 64, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 16,384
BatchNorm2d-12 [-1, 256, 56, 56] 512
Conv2d-13 [-1, 256, 56, 56] 16,384
BatchNorm2d-14 [-1, 256, 56, 56] 512
ReLU-15 [-1, 256, 56, 56] 0
Bottleneck-16 [-1, 256, 56, 56] 0
Conv2d-17 [-1, 64, 56, 56] 16,384
BatchNorm2d-18 [-1, 64, 56, 56] 128
ReLU-19 [-1, 64, 56, 56] 0
Conv2d-20 [-1, 64, 56, 56] 36,864
BatchNorm2d-21 [-1, 64, 56, 56] 128
ReLU-22 [-1, 64, 56, 56] 0
Conv2d-23 [-1, 256, 56, 56] 16,384
BatchNorm2d-24 [-1, 256, 56, 56] 512
ReLU-25 [-1, 256, 56, 56] 0
Bottleneck-26 [-1, 256, 56, 56] 0
Conv2d-27 [-1, 64, 56, 56] 16,384
BatchNorm2d-28 [-1, 64, 56, 56] 128
ReLU-29 [-1, 64, 56, 56] 0
Conv2d-30 [-1, 64, 56, 56] 36,864
BatchNorm2d-31 [-1, 64, 56, 56] 128
ReLU-32 [-1, 64, 56, 56] 0
Conv2d-33 [-1, 256, 56, 56] 16,384
BatchNorm2d-34 [-1, 256, 56, 56] 512
ReLU-35 [-1, 256, 56, 56] 0
Bottleneck-36 [-1, 256, 56, 56] 0
Conv2d-37 [-1, 128, 56, 56] 32,768
BatchNorm2d-38 [-1, 128, 56, 56] 256
ReLU-39 [-1, 128, 56, 56] 0
Conv2d-40 [-1, 128, 28, 28] 147,456
BatchNorm2d-41 [-1, 128, 28, 28] 256
ReLU-42 [-1, 128, 28, 28] 0
Conv2d-43 [-1, 512, 28, 28] 65,536
BatchNorm2d-44 [-1, 512, 28, 28] 1,024
Conv2d-45 [-1, 512, 28, 28] 131,072
BatchNorm2d-46 [-1, 512, 28, 28] 1,024
ReLU-47 [-1, 512, 28, 28] 0
Bottleneck-48 [-1, 512, 28, 28] 0
Conv2d-49 [-1, 128, 28, 28] 65,536
BatchNorm2d-50 [-1, 128, 28, 28] 256
ReLU-51 [-1, 128, 28, 28] 0
Conv2d-52 [-1, 128, 28, 28] 147,456
BatchNorm2d-53 [-1, 128, 28, 28] 256
ReLU-54 [-1, 128, 28, 28] 0
Conv2d-55 [-1, 512, 28, 28] 65,536
BatchNorm2d-56 [-1, 512, 28, 28] 1,024
ReLU-57 [-1, 512, 28, 28] 0
Bottleneck-58 [-1, 512, 28, 28] 0
Conv2d-59 [-1, 128, 28, 28] 65,536
BatchNorm2d-60 [-1, 128, 28, 28] 256
ReLU-61 [-1, 128, 28, 28] 0
Conv2d-62 [-1, 128, 28, 28] 147,456
BatchNorm2d-63 [-1, 128, 28, 28] 256
ReLU-64 [-1, 128, 28, 28] 0
Conv2d-65 [-1, 512, 28, 28] 65,536
BatchNorm2d-66 [-1, 512, 28, 28] 1,024
ReLU-67 [-1, 512, 28, 28] 0
Bottleneck-68 [-1, 512, 28, 28] 0
Conv2d-69 [-1, 128, 28, 28] 65,536
BatchNorm2d-70 [-1, 128, 28, 28] 256
ReLU-71 [-1, 128, 28, 28] 0
Conv2d-72 [-1, 128, 28, 28] 147,456
BatchNorm2d-73 [-1, 128, 28, 28] 256
ReLU-74 [-1, 128, 28, 28] 0
Conv2d-75 [-1, 512, 28, 28] 65,536
BatchNorm2d-76 [-1, 512, 28, 28] 1,024
ReLU-77 [-1, 512, 28, 28] 0
Bottleneck-78 [-1, 512, 28, 28] 0
Conv2d-79 [-1, 256, 28, 28] 131,072
BatchNorm2d-80 [-1, 256, 28, 28] 512
ReLU-81 [-1, 256, 28, 28] 0
Conv2d-82 [-1, 256, 14, 14] 589,824
BatchNorm2d-83 [-1, 256, 14, 14] 512
ReLU-84 [-1, 256, 14, 14] 0
Conv2d-85 [-1, 1024, 14, 14] 262,144
BatchNorm2d-86 [-1, 1024, 14, 14] 2,048
Conv2d-87 [-1, 1024, 14, 14] 524,288
BatchNorm2d-88 [-1, 1024, 14, 14] 2,048
ReLU-89 [-1, 1024, 14, 14] 0
Bottleneck-90 [-1, 1024, 14, 14] 0
Conv2d-91 [-1, 256, 14, 14] 262,144
BatchNorm2d-92 [-1, 256, 14, 14] 512
ReLU-93 [-1, 256, 14, 14] 0
Conv2d-94 [-1, 256, 14, 14] 589,824
BatchNorm2d-95 [-1, 256, 14, 14] 512
ReLU-96 [-1, 256, 14, 14] 0
Conv2d-97 [-1, 1024, 14, 14] 262,144
BatchNorm2d-98 [-1, 1024, 14, 14] 2,048
ReLU-99 [-1, 1024, 14, 14] 0
Bottleneck-100 [-1, 1024, 14, 14] 0
Conv2d-101 [-1, 256, 14, 14] 262,144
BatchNorm2d-102 [-1, 256, 14, 14] 512
ReLU-103 [-1, 256, 14, 14] 0
Conv2d-104 [-1, 256, 14, 14] 589,824
BatchNorm2d-105 [-1, 256, 14, 14] 512
ReLU-106 [-1, 256, 14, 14] 0
Conv2d-107 [-1, 1024, 14, 14] 262,144
BatchNorm2d-108 [-1, 1024, 14, 14] 2,048
ReLU-109 [-1, 1024, 14, 14] 0
Bottleneck-110 [-1, 1024, 14, 14] 0
Conv2d-111 [-1, 256, 14, 14] 262,144
BatchNorm2d-112 [-1, 256, 14, 14] 512
ReLU-113 [-1, 256, 14, 14] 0
Conv2d-114 [-1, 256, 14, 14] 589,824
BatchNorm2d-115 [-1, 256, 14, 14] 512
ReLU-116 [-1, 256, 14, 14] 0
Conv2d-117 [-1, 1024, 14, 14] 262,144
BatchNorm2d-118 [-1, 1024, 14, 14] 2,048
ReLU-119 [-1, 1024, 14, 14] 0
Bottleneck-120 [-1, 1024, 14, 14] 0
Conv2d-121 [-1, 256, 14, 14] 262,144
BatchNorm2d-122 [-1, 256, 14, 14] 512
ReLU-123 [-1, 256, 14, 14] 0
Conv2d-124 [-1, 256, 14, 14] 589,824
BatchNorm2d-125 [-1, 256, 14, 14] 512
ReLU-126 [-1, 256, 14, 14] 0
Conv2d-127 [-1, 1024, 14, 14] 262,144
BatchNorm2d-128 [-1, 1024, 14, 14] 2,048
ReLU-129 [-1, 1024, 14, 14] 0
Bottleneck-130 [-1, 1024, 14, 14] 0
Conv2d-131 [-1, 256, 14, 14] 262,144
BatchNorm2d-132 [-1, 256, 14, 14] 512
ReLU-133 [-1, 256, 14, 14] 0
Conv2d-134 [-1, 256, 14, 14] 589,824
BatchNorm2d-135 [-1, 256, 14, 14] 512
ReLU-136 [-1, 256, 14, 14] 0
Conv2d-137 [-1, 1024, 14, 14] 262,144
BatchNorm2d-138 [-1, 1024, 14, 14] 2,048
ReLU-139 [-1, 1024, 14, 14] 0
Bottleneck-140 [-1, 1024, 14, 14] 0
Conv2d-141 [-1, 512, 14, 14] 524,288
BatchNorm2d-142 [-1, 512, 14, 14] 1,024
ReLU-143 [-1, 512, 14, 14] 0
Conv2d-144 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-145 [-1, 512, 7, 7] 1,024
ReLU-146 [-1, 512, 7, 7] 0
Conv2d-147 [-1, 2048, 7, 7] 1,048,576
BatchNorm2d-148 [-1, 2048, 7, 7] 4,096
Conv2d-149 [-1, 2048, 7, 7] 2,097,152
BatchNorm2d-150 [-1, 2048, 7, 7] 4,096
ReLU-151 [-1, 2048, 7, 7] 0
Bottleneck-152 [-1, 2048, 7, 7] 0
Conv2d-153 [-1, 512, 7, 7] 1,048,576
BatchNorm2d-154 [-1, 512, 7, 7] 1,024
ReLU-155 [-1, 512, 7, 7] 0
Conv2d-156 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-157 [-1, 512, 7, 7] 1,024
ReLU-158 [-1, 512, 7, 7] 0
Conv2d-159 [-1, 2048, 7, 7] 1,048,576
BatchNorm2d-160 [-1, 2048, 7, 7] 4,096
ReLU-161 [-1, 2048, 7, 7] 0
Bottleneck-162 [-1, 2048, 7, 7] 0
Conv2d-163 [-1, 512, 7, 7] 1,048,576
BatchNorm2d-164 [-1, 512, 7, 7] 1,024
ReLU-165 [-1, 512, 7, 7] 0
Conv2d-166 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-167 [-1, 512, 7, 7] 1,024
ReLU-168 [-1, 512, 7, 7] 0
Conv2d-169 [-1, 2048, 7, 7] 1,048,576
BatchNorm2d-170 [-1, 2048, 7, 7] 4,096
ReLU-171 [-1, 2048, 7, 7] 0
Bottleneck-172 [-1, 2048, 7, 7] 0
AdaptiveAvgPool2d-173 [-1, 2048, 1, 1] 0
Linear-174 [-1, 1000] 2,049,000
================================================================
Total params: 25,557,032
Trainable params: 25,557,032
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 286.56
Params size (MB): 97.49
Estimated Total Size (MB): 384.62
----------------------------------------------------------------
MobileNet
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----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 112, 112] 864
BatchNorm2d-2 [-1, 32, 112, 112] 64
ReLU6-3 [-1, 32, 112, 112] 0
Conv2d-4 [-1, 32, 112, 112] 288
BatchNorm2d-5 [-1, 32, 112, 112] 64
ReLU6-6 [-1, 32, 112, 112] 0
Conv2d-7 [-1, 16, 112, 112] 512
BatchNorm2d-8 [-1, 16, 112, 112] 32
InvertedResidual-9 [-1, 16, 112, 112] 0
Conv2d-10 [-1, 96, 112, 112] 1,536
BatchNorm2d-11 [-1, 96, 112, 112] 192
ReLU6-12 [-1, 96, 112, 112] 0
Conv2d-13 [-1, 96, 56, 56] 864
BatchNorm2d-14 [-1, 96, 56, 56] 192
ReLU6-15 [-1, 96, 56, 56] 0
Conv2d-16 [-1, 24, 56, 56] 2,304
BatchNorm2d-17 [-1, 24, 56, 56] 48
InvertedResidual-18 [-1, 24, 56, 56] 0
Conv2d-19 [-1, 144, 56, 56] 3,456
BatchNorm2d-20 [-1, 144, 56, 56] 288
ReLU6-21 [-1, 144, 56, 56] 0
Conv2d-22 [-1, 144, 56, 56] 1,296
BatchNorm2d-23 [-1, 144, 56, 56] 288
ReLU6-24 [-1, 144, 56, 56] 0
Conv2d-25 [-1, 24, 56, 56] 3,456
BatchNorm2d-26 [-1, 24, 56, 56] 48
InvertedResidual-27 [-1, 24, 56, 56] 0
Conv2d-28 [-1, 144, 56, 56] 3,456
BatchNorm2d-29 [-1, 144, 56, 56] 288
ReLU6-30 [-1, 144, 56, 56] 0
Conv2d-31 [-1, 144, 28, 28] 1,296
BatchNorm2d-32 [-1, 144, 28, 28] 288
ReLU6-33 [-1, 144, 28, 28] 0
Conv2d-34 [-1, 32, 28, 28] 4,608
BatchNorm2d-35 [-1, 32, 28, 28] 64
InvertedResidual-36 [-1, 32, 28, 28] 0
Conv2d-37 [-1, 192, 28, 28] 6,144
BatchNorm2d-38 [-1, 192, 28, 28] 384
ReLU6-39 [-1, 192, 28, 28] 0
Conv2d-40 [-1, 192, 28, 28] 1,728
BatchNorm2d-41 [-1, 192, 28, 28] 384
ReLU6-42 [-1, 192, 28, 28] 0
Conv2d-43 [-1, 32, 28, 28] 6,144
BatchNorm2d-44 [-1, 32, 28, 28] 64
InvertedResidual-45 [-1, 32, 28, 28] 0
Conv2d-46 [-1, 192, 28, 28] 6,144
BatchNorm2d-47 [-1, 192, 28, 28] 384
ReLU6-48 [-1, 192, 28, 28] 0
Conv2d-49 [-1, 192, 28, 28] 1,728
BatchNorm2d-50 [-1, 192, 28, 28] 384
ReLU6-51 [-1, 192, 28, 28] 0
Conv2d-52 [-1, 32, 28, 28] 6,144
BatchNorm2d-53 [-1, 32, 28, 28] 64
InvertedResidual-54 [-1, 32, 28, 28] 0
Conv2d-55 [-1, 192, 28, 28] 6,144
BatchNorm2d-56 [-1, 192, 28, 28] 384
ReLU6-57 [-1, 192, 28, 28] 0
Conv2d-58 [-1, 192, 14, 14] 1,728
BatchNorm2d-59 [-1, 192, 14, 14] 384
ReLU6-60 [-1, 192, 14, 14] 0
Conv2d-61 [-1, 64, 14, 14] 12,288
BatchNorm2d-62 [-1, 64, 14, 14] 128
InvertedResidual-63 [-1, 64, 14, 14] 0
Conv2d-64 [-1, 384, 14, 14] 24,576
BatchNorm2d-65 [-1, 384, 14, 14] 768
ReLU6-66 [-1, 384, 14, 14] 0
Conv2d-67 [-1, 384, 14, 14] 3,456
BatchNorm2d-68 [-1, 384, 14, 14] 768
ReLU6-69 [-1, 384, 14, 14] 0
Conv2d-70 [-1, 64, 14, 14] 24,576
BatchNorm2d-71 [-1, 64, 14, 14] 128
InvertedResidual-72 [-1, 64, 14, 14] 0
Conv2d-73 [-1, 384, 14, 14] 24,576
BatchNorm2d-74 [-1, 384, 14, 14] 768
ReLU6-75 [-1, 384, 14, 14] 0
Conv2d-76 [-1, 384, 14, 14] 3,456
BatchNorm2d-77 [-1, 384, 14, 14] 768
ReLU6-78 [-1, 384, 14, 14] 0
Conv2d-79 [-1, 64, 14, 14] 24,576
BatchNorm2d-80 [-1, 64, 14, 14] 128
InvertedResidual-81 [-1, 64, 14, 14] 0
Conv2d-82 [-1, 384, 14, 14] 24,576
BatchNorm2d-83 [-1, 384, 14, 14] 768
ReLU6-84 [-1, 384, 14, 14] 0
Conv2d-85 [-1, 384, 14, 14] 3,456
BatchNorm2d-86 [-1, 384, 14, 14] 768
ReLU6-87 [-1, 384, 14, 14] 0
Conv2d-88 [-1, 64, 14, 14] 24,576
BatchNorm2d-89 [-1, 64, 14, 14] 128
InvertedResidual-90 [-1, 64, 14, 14] 0
Conv2d-91 [-1, 384, 14, 14] 24,576
BatchNorm2d-92 [-1, 384, 14, 14] 768
ReLU6-93 [-1, 384, 14, 14] 0
Conv2d-94 [-1, 384, 14, 14] 3,456
BatchNorm2d-95 [-1, 384, 14, 14] 768
ReLU6-96 [-1, 384, 14, 14] 0
Conv2d-97 [-1, 96, 14, 14] 36,864
BatchNorm2d-98 [-1, 96, 14, 14] 192
InvertedResidual-99 [-1, 96, 14, 14] 0
Conv2d-100 [-1, 576, 14, 14] 55,296
BatchNorm2d-101 [-1, 576, 14, 14] 1,152
ReLU6-102 [-1, 576, 14, 14] 0
Conv2d-103 [-1, 576, 14, 14] 5,184
BatchNorm2d-104 [-1, 576, 14, 14] 1,152
ReLU6-105 [-1, 576, 14, 14] 0
Conv2d-106 [-1, 96, 14, 14] 55,296
BatchNorm2d-107 [-1, 96, 14, 14] 192
InvertedResidual-108 [-1, 96, 14, 14] 0
Conv2d-109 [-1, 576, 14, 14] 55,296
BatchNorm2d-110 [-1, 576, 14, 14] 1,152
ReLU6-111 [-1, 576, 14, 14] 0
Conv2d-112 [-1, 576, 14, 14] 5,184
BatchNorm2d-113 [-1, 576, 14, 14] 1,152
ReLU6-114 [-1, 576, 14, 14] 0
Conv2d-115 [-1, 96, 14, 14] 55,296
BatchNorm2d-116 [-1, 96, 14, 14] 192
InvertedResidual-117 [-1, 96, 14, 14] 0
Conv2d-118 [-1, 576, 14, 14] 55,296
BatchNorm2d-119 [-1, 576, 14, 14] 1,152
ReLU6-120 [-1, 576, 14, 14] 0
Conv2d-121 [-1, 576, 7, 7] 5,184
BatchNorm2d-122 [-1, 576, 7, 7] 1,152
ReLU6-123 [-1, 576, 7, 7] 0
Conv2d-124 [-1, 160, 7, 7] 92,160
BatchNorm2d-125 [-1, 160, 7, 7] 320
InvertedResidual-126 [-1, 160, 7, 7] 0
Conv2d-127 [-1, 960, 7, 7] 153,600
BatchNorm2d-128 [-1, 960, 7, 7] 1,920
ReLU6-129 [-1, 960, 7, 7] 0
Conv2d-130 [-1, 960, 7, 7] 8,640
BatchNorm2d-131 [-1, 960, 7, 7] 1,920
ReLU6-132 [-1, 960, 7, 7] 0
Conv2d-133 [-1, 160, 7, 7] 153,600
BatchNorm2d-134 [-1, 160, 7, 7] 320
InvertedResidual-135 [-1, 160, 7, 7] 0
Conv2d-136 [-1, 960, 7, 7] 153,600
BatchNorm2d-137 [-1, 960, 7, 7] 1,920
ReLU6-138 [-1, 960, 7, 7] 0
Conv2d-139 [-1, 960, 7, 7] 8,640
BatchNorm2d-140 [-1, 960, 7, 7] 1,920
ReLU6-141 [-1, 960, 7, 7] 0
Conv2d-142 [-1, 160, 7, 7] 153,600
BatchNorm2d-143 [-1, 160, 7, 7] 320
InvertedResidual-144 [-1, 160, 7, 7] 0
Conv2d-145 [-1, 960, 7, 7] 153,600
BatchNorm2d-146 [-1, 960, 7, 7] 1,920
ReLU6-147 [-1, 960, 7, 7] 0
Conv2d-148 [-1, 960, 7, 7] 8,640
BatchNorm2d-149 [-1, 960, 7, 7] 1,920
ReLU6-150 [-1, 960, 7, 7] 0
Conv2d-151 [-1, 320, 7, 7] 307,200
BatchNorm2d-152 [-1, 320, 7, 7] 640
InvertedResidual-153 [-1, 320, 7, 7] 0
Conv2d-154 [-1, 1280, 7, 7] 409,600
BatchNorm2d-155 [-1, 1280, 7, 7] 2,560
ReLU6-156 [-1, 1280, 7, 7] 0
Dropout-157 [-1, 1280] 0
Linear-158 [-1, 1000] 1,281,000
================================================================
Total params: 3,504,872
Trainable params: 3,504,872
Non-trainable params: 0
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Input size (MB): 0.57
Forward/backward pass size (MB): 152.87
Params size (MB): 13.37
Estimated Total Size (MB): 166.81
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