Use summary to view network parameters
If you need to view the specific parameters of the network, use the use summary
from torchsummary import summary
summary(model, (3, 448, 448))
Show results
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 9,408
BatchNorm2d-2 [-1, 64, 224, 224] 128
ReLU-3 [-1, 64, 224, 224] 0
MaxPool2d-4 [-1, 64, 112, 112] 0
Conv2d-5 [-1, 64, 112, 112] 4,096
BatchNorm2d-6 [-1, 64, 112, 112] 128
ReLU-7 [-1, 64, 112, 112] 0
Conv2d-8 [-1, 64, 112, 112] 36,864
BatchNorm2d-9 [-1, 64, 112, 112] 128
ReLU-10 [-1, 64, 112, 112] 0
Conv2d-11 [-1, 256, 112, 112] 16,384
BatchNorm2d-12 [-1, 256, 112, 112] 512
Conv2d-13 [-1, 256, 112, 112] 16,384
BatchNorm2d-14 [-1, 256, 112, 112] 512
ReLU-15 [-1, 256, 112, 112] 0
Bottleneck-16 [-1, 256, 112, 112] 0
Conv2d-17 [-1, 64, 112, 112] 16,384
BatchNorm2d-18 [-1, 64, 112, 112] 128
ReLU-19 [-1, 64, 112, 112] 0
Conv2d-20 [-1, 64, 112, 112] 36,864
BatchNorm2d-21 [-1, 64, 112, 112] 128
ReLU-22 [-1, 64, 112, 112] 0
Conv2d-23 [-1, 256, 112, 112] 16,384
BatchNorm2d-24 [-1, 256, 112, 112] 512
ReLU-25 [-1, 256, 112, 112] 0
Bottleneck-26 [-1, 256, 112, 112] 0
Conv2d-27 [-1, 64, 112, 112] 16,384
BatchNorm2d-28 [-1, 64, 112, 112] 128
ReLU-29 [-1, 64, 112, 112] 0
Conv2d-30 [-1, 64, 112, 112] 36,864
BatchNorm2d-31 [-1, 64, 112, 112] 128
ReLU-32 [-1, 64, 112, 112] 0
Conv2d-33 [-1, 256, 112, 112] 16,384
BatchNorm2d-34 [-1, 256, 112, 112] 512
ReLU-35 [-1, 256, 112, 112] 0
Bottleneck-36 [-1, 256, 112, 112] 0
Conv2d-37 [-1, 128, 112, 112] 32,768
BatchNorm2d-38 [-1, 128, 112, 112] 256
ReLU-39 [-1, 128, 112, 112] 0
Conv2d-40 [-1, 128, 56, 56] 147,456
BatchNorm2d-41 [-1, 128, 56, 56] 256
ReLU-42 [-1, 128, 56, 56] 0
Conv2d-43 [-1, 512, 56, 56] 65,536
BatchNorm2d-44 [-1, 512, 56, 56] 1,024
Conv2d-45 [-1, 512, 56, 56] 131,072
BatchNorm2d-46 [-1, 512, 56, 56] 1,024
ReLU-47 [-1, 512, 56, 56] 0
Bottleneck-48 [-1, 512, 56, 56] 0
Conv2d-49 [-1, 128, 56, 56] 65,536
BatchNorm2d-50 [-1, 128, 56, 56] 256
ReLU-51 [-1, 128, 56, 56] 0
Conv2d-52 [-1, 128, 56, 56] 147,456
BatchNorm2d-53 [-1, 128, 56, 56] 256
ReLU-54 [-1, 128, 56, 56] 0
Conv2d-55 [-1, 512, 56, 56] 65,536
BatchNorm2d-56 [-1, 512, 56, 56] 1,024
ReLU-57 [-1, 512, 56, 56] 0
Bottleneck-58 [-1, 512, 56, 56] 0
Conv2d-59 [-1, 128, 56, 56] 65,536
BatchNorm2d-60 [-1, 128, 56, 56] 256
ReLU-61 [-1, 128, 56, 56] 0
Conv2d-62 [-1, 128, 56, 56] 147,456
BatchNorm2d-63 [-1, 128, 56, 56] 256
ReLU-64 [-1, 128, 56, 56] 0
Conv2d-65 [-1, 512, 56, 56] 65,536
BatchNorm2d-66 [-1, 512, 56, 56] 1,024
ReLU-67 [-1, 512, 56, 56] 0
Bottleneck-68 [-1, 512, 56, 56] 0
Conv2d-69 [-1, 128, 56, 56] 65,536
BatchNorm2d-70 [-1, 128, 56, 56] 256
ReLU-71 [-1, 128, 56, 56] 0
Conv2d-72 [-1, 128, 56, 56] 147,456
BatchNorm2d-73 [-1, 128, 56, 56] 256
ReLU-74 [-1, 128, 56, 56] 0
Conv2d-75 [-1, 512, 56, 56] 65,536
BatchNorm2d-76 [-1, 512, 56, 56] 1,024
ReLU-77 [-1, 512, 56, 56] 0
Bottleneck-78 [-1, 512, 56, 56] 0
Conv2d-79 [-1, 256, 56, 56] 131,072
BatchNorm2d-80 [-1, 256, 56, 56] 512
ReLU-81 [-1, 256, 56, 56] 0
Conv2d-82 [-1, 256, 28, 28] 589,824
BatchNorm2d-83 [-1, 256, 28, 28] 512
ReLU-84 [-1, 256, 28, 28] 0
Conv2d-85 [-1, 1024, 28, 28] 262,144
BatchNorm2d-86 [-1, 1024, 28, 28] 2,048
Conv2d-87 [-1, 1024, 28, 28] 524,288
BatchNorm2d-88 [-1, 1024, 28, 28] 2,048
ReLU-89 [-1, 1024, 28, 28] 0
Bottleneck-90 [-1, 1024, 28, 28] 0
Conv2d-91 [-1, 256, 28, 28] 262,144
BatchNorm2d-92 [-1, 256, 28, 28] 512
ReLU-93 [-1, 256, 28, 28] 0
Conv2d-94 [-1, 256, 28, 28] 589,824
BatchNorm2d-95 [-1, 256, 28, 28] 512
ReLU-96 [-1, 256, 28, 28] 0
Conv2d-97 [-1, 1024, 28, 28] 262,144
BatchNorm2d-98 [-1, 1024, 28, 28] 2,048
ReLU-99 [-1, 1024, 28, 28] 0
Bottleneck-100 [-1, 1024, 28, 28] 0
Conv2d-101 [-1, 256, 28, 28] 262,144
BatchNorm2d-102 [-1, 256, 28, 28] 512
ReLU-103 [-1, 256, 28, 28] 0
Conv2d-104 [-1, 256, 28, 28] 589,824
BatchNorm2d-105 [-1, 256, 28, 28] 512
ReLU-106 [-1, 256, 28, 28] 0
Conv2d-107 [-1, 1024, 28, 28] 262,144
BatchNorm2d-108 [-1, 1024, 28, 28] 2,048
ReLU-109 [-1, 1024, 28, 28] 0
Bottleneck-110 [-1, 1024, 28, 28] 0
Conv2d-111 [-1, 256, 28, 28] 262,144
BatchNorm2d-112 [-1, 256, 28, 28] 512
ReLU-113 [-1, 256, 28, 28] 0
Conv2d-114 [-1, 256, 28, 28] 589,824
BatchNorm2d-115 [-1, 256, 28, 28] 512
ReLU-116 [-1, 256, 28, 28] 0
Conv2d-117 [-1, 1024, 28, 28] 262,144
BatchNorm2d-118 [-1, 1024, 28, 28] 2,048
ReLU-119 [-1, 1024, 28, 28] 0
Bottleneck-120 [-1, 1024, 28, 28] 0
Conv2d-121 [-1, 256, 28, 28] 262,144
BatchNorm2d-122 [-1, 256, 28, 28] 512
ReLU-123 [-1, 256, 28, 28] 0
Conv2d-124 [-1, 256, 28, 28] 589,824
BatchNorm2d-125 [-1, 256, 28, 28] 512
ReLU-126 [-1, 256, 28, 28] 0
Conv2d-127 [-1, 1024, 28, 28] 262,144
BatchNorm2d-128 [-1, 1024, 28, 28] 2,048
ReLU-129 [-1, 1024, 28, 28] 0
Bottleneck-130 [-1, 1024, 28, 28] 0
Conv2d-131 [-1, 256, 28, 28] 262,144
BatchNorm2d-132 [-1, 256, 28, 28] 512
ReLU-133 [-1, 256, 28, 28] 0
Conv2d-134 [-1, 256, 28, 28] 589,824
BatchNorm2d-135 [-1, 256, 28, 28] 512
ReLU-136 [-1, 256, 28, 28] 0
Conv2d-137 [-1, 1024, 28, 28] 262,144
BatchNorm2d-138 [-1, 1024, 28, 28] 2,048
ReLU-139 [-1, 1024, 28, 28] 0
Bottleneck-140 [-1, 1024, 28, 28] 0
Conv2d-141 [-1, 512, 28, 28] 524,288
BatchNorm2d-142 [-1, 512, 28, 28] 1,024
ReLU-143 [-1, 512, 28, 28] 0
Conv2d-144 [-1, 512, 14, 14] 2,359,296
BatchNorm2d-145 [-1, 512, 14, 14] 1,024
ReLU-146 [-1, 512, 14, 14] 0
Conv2d-147 [-1, 2048, 14, 14] 1,048,576
BatchNorm2d-148 [-1, 2048, 14, 14] 4,096
Conv2d-149 [-1, 2048, 14, 14] 2,097,152
BatchNorm2d-150 [-1, 2048, 14, 14] 4,096
ReLU-151 [-1, 2048, 14, 14] 0
Bottleneck-152 [-1, 2048, 14, 14] 0
Conv2d-153 [-1, 512, 14, 14] 1,048,576
BatchNorm2d-154 [-1, 512, 14, 14] 1,024
ReLU-155 [-1, 512, 14, 14] 0
Conv2d-156 [-1, 512, 14, 14] 2,359,296
BatchNorm2d-157 [-1, 512, 14, 14] 1,024
ReLU-158 [-1, 512, 14, 14] 0
Conv2d-159 [-1, 2048, 14, 14] 1,048,576
BatchNorm2d-160 [-1, 2048, 14, 14] 4,096
ReLU-161 [-1, 2048, 14, 14] 0
Bottleneck-162 [-1, 2048, 14, 14] 0
Conv2d-163 [-1, 512, 14, 14] 1,048,576
BatchNorm2d-164 [-1, 512, 14, 14] 1,024
ReLU-165 [-1, 512, 14, 14] 0
Conv2d-166 [-1, 512, 14, 14] 2,359,296
BatchNorm2d-167 [-1, 512, 14, 14] 1,024
ReLU-168 [-1, 512, 14, 14] 0
Conv2d-169 [-1, 2048, 14, 14] 1,048,576
BatchNorm2d-170 [-1, 2048, 14, 14] 4,096
ReLU-171 [-1, 2048, 14, 14] 0
Bottleneck-172 [-1, 2048, 14, 14] 0
Conv2d-173 [-1, 256, 14, 14] 524,288
BatchNorm2d-174 [-1, 256, 14, 14] 512
Conv2d-175 [-1, 256, 14, 14] 589,824
BatchNorm2d-176 [-1, 256, 14, 14] 512
Conv2d-177 [-1, 256, 14, 14] 65,536
BatchNorm2d-178 [-1, 256, 14, 14] 512
Conv2d-179 [-1, 256, 14, 14] 524,288
BatchNorm2d-180 [-1, 256, 14, 14] 512
detnet_bottleneck-181 [-1, 256, 14, 14] 0
Conv2d-182 [-1, 256, 14, 14] 65,536
BatchNorm2d-183 [-1, 256, 14, 14] 512
Conv2d-184 [-1, 256, 14, 14] 589,824
BatchNorm2d-185 [-1, 256, 14, 14] 512
BatchNorm2d-197 [-1, 30, 14, 14] 60
================================================================
Error reported:
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
Run the model in the graphics card:
from torchsummary import summary
summary(net.cuda(), (3, 448, 448))Read More:
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