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RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
torchsummary.summary( )中出现了上述错误，torchsummary是应用在pytorch中的一种结构表达方式。
如下图fishnet110的结构图
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 112, 112] 864
BatchNorm2d-2 [-1, 32, 112, 112] 64
ReLU-3 [-1, 32, 112, 112] 0
Conv2d-4 [-1, 32, 112, 112] 9,216
BatchNorm2d-5 [-1, 32, 112, 112] 64
ReLU-6 [-1, 32, 112, 112] 0
Conv2d-7 [-1, 64, 112, 112] 18,432
BatchNorm2d-8 [-1, 64, 112, 112] 128
ReLU-9 [-1, 64, 112, 112] 0
MaxPool2d-10 [-1, 64, 56, 56] 0
BatchNorm2d-11 [-1, 64, 56, 56] 128
ReLU-12 [-1, 64, 56, 56] 0
ReLU-13 [-1, 64, 56, 56] 0
Conv2d-14 [-1, 32, 56, 56] 2,048
BatchNorm2d-15 [-1, 32, 56, 56] 64
ReLU-16 [-1, 32, 56, 56] 0
ReLU-17 [-1, 32, 56, 56] 0
Conv2d-18 [-1, 32, 56, 56] 9,216
BatchNorm2d-19 [-1, 32, 56, 56] 64
ReLU-20 [-1, 32, 56, 56] 0
ReLU-21 [-1, 32, 56, 56] 0
Conv2d-22 [-1, 128, 56, 56] 4,096
BatchNorm2d-23 [-1, 64, 56, 56] 128
ReLU-24 [-1, 64, 56, 56] 0
ReLU-25 [-1, 64, 56, 56] 0
Conv2d-26 [-1, 128, 56, 56] 8,192
Bottleneck-27 [-1, 128, 56, 56] 0
BatchNorm2d-28 [-1, 128, 56, 56] 256
ReLU-29 [-1, 128, 56, 56] 0
Conv2d-30 [-1, 32, 56, 56] 4,096
BatchNorm2d-31 [-1, 32, 56, 56] 64
ReLU-32 [-1, 32, 56, 56] 0
Conv2d-33 [-1, 32, 56, 56] 9,216
BatchNorm2d-34 [-1, 32, 56, 56] 64
ReLU-35 [-1, 32, 56, 56] 0
Conv2d-36 [-1, 128, 56, 56] 4,096
Bottleneck-37 [-1, 128, 56, 56] 0
MaxPool2d-38 [-1, 128, 28, 28] 0
MaxPool2d-39 [-1, 128, 28, 28] 0
MaxPool2d-40 [-1, 128, 28, 28] 0
MaxPool2d-41 [-1, 128, 28, 28] 0
MaxPool2d-42 [-1, 128, 28, 28] 0
MaxPool2d-43 [-1, 128, 28, 28] 0
MaxPool2d-44 [-1, 128, 28, 28] 0
BatchNorm2d-45 [-1, 128, 28, 28] 256
ReLU-46 [-1, 128, 28, 28] 0
ReLU-47 [-1, 128, 28, 28] 0
Conv2d-48 [-1, 64, 28, 28] 8,192
BatchNorm2d-49 [-1, 64, 28, 28] 128
ReLU-50 [-1, 64, 28, 28] 0
ReLU-51 [-1, 64, 28, 28] 0
Conv2d-52 [-1, 64, 28, 28] 36,864
BatchNorm2d-53 [-1, 64, 28, 28] 128
ReLU-54 [-1, 64, 28, 28] 0
ReLU-55 [-1, 64, 28, 28] 0
Conv2d-56 [-1, 256, 28, 28] 16,384
BatchNorm2d-57 [-1, 128, 28, 28] 256
ReLU-58 [-1, 128, 28, 28] 0
ReLU-59 [-1, 128, 28, 28] 0
Conv2d-60 [-1, 256, 28, 28] 32,768
Bottleneck-61 [-1, 256, 28, 28] 0
BatchNorm2d-62 [-1, 256, 28, 28] 512
ReLU-63 [-1, 256, 28, 28] 0
Conv2d-64 [-1, 64, 28, 28] 16,384
BatchNorm2d-65 [-1, 64, 28, 28] 128
ReLU-66 [-1, 64, 28, 28] 0
Conv2d-67 [-1, 64, 28, 28] 36,864
BatchNorm2d-68 [-1, 64, 28, 28] 128
ReLU-69 [-1, 64, 28, 28] 0
Conv2d-70 [-1, 256, 28, 28] 16,384
Bottleneck-71 [-1, 256, 28, 28] 0
MaxPool2d-72 [-1, 256, 14, 14] 0
MaxPool2d-73 [-1, 256, 14, 14] 0
MaxPool2d-74 [-1, 256, 14, 14] 0
MaxPool2d-75 [-1, 256, 14, 14] 0
MaxPool2d-76 [-1, 256, 14, 14] 0
MaxPool2d-77 [-1, 256, 14, 14] 0
MaxPool2d-78 [-1, 256, 14, 14] 0
BatchNorm2d-79 [-1, 256, 14, 14] 512
ReLU-80 [-1, 256, 14, 14] 0
ReLU-81 [-1, 256, 14, 14] 0
Conv2d-82 [-1, 128, 14, 14] 32,768
BatchNorm2d-83 [-1, 128, 14, 14] 256
ReLU-84 [-1, 128, 14, 14] 0
ReLU-85 [-1, 128, 14, 14] 0
Conv2d-86 [-1, 128, 14, 14] 147,456
BatchNorm2d-87 [-1, 128, 14, 14] 256
ReLU-88 [-1, 128, 14, 14] 0
ReLU-89 [-1, 128, 14, 14] 0
Conv2d-90 [-1, 512, 14, 14] 65,536
BatchNorm2d-91 [-1, 256, 14, 14] 512
ReLU-92 [-1, 256, 14, 14] 0
ReLU-93 [-1, 256, 14, 14] 0
Conv2d-94 [-1, 512, 14, 14] 131,072
Bottleneck-95 [-1, 512, 14, 14] 0
BatchNorm2d-96 [-1, 512, 14, 14] 1,024
ReLU-97 [-1, 512, 14, 14] 0
Conv2d-98 [-1, 128, 14, 14] 65,536
BatchNorm2d-99 [-1, 128, 14, 14] 256
ReLU-100 [-1, 128, 14, 14] 0
Conv2d-101 [-1, 128, 14, 14] 147,456
BatchNorm2d-102 [-1, 128, 14, 14] 256
ReLU-103 [-1, 128, 14, 14] 0
Conv2d-104 [-1, 512, 14, 14] 65,536
Bottleneck-105 [-1, 512, 14, 14] 0
BatchNorm2d-106 [-1, 512, 14, 14] 1,024
ReLU-107 [-1, 512, 14, 14] 0
Conv2d-108 [-1, 128, 14, 14] 65,536
BatchNorm2d-109 [-1, 128, 14, 14] 256
ReLU-110 [-1, 128, 14, 14] 0
Conv2d-111 [-1, 128, 14, 14] 147,456
BatchNorm2d-112 [-1, 128, 14, 14] 256
ReLU-113 [-1, 128, 14, 14] 0
Conv2d-114 [-1, 512, 14, 14] 65,536
Bottleneck-115 [-1, 512, 14, 14] 0
BatchNorm2d-116 [-1, 512, 14, 14] 1,024
ReLU-117 [-1, 512, 14, 14] 0
Conv2d-118 [-1, 128, 14, 14] 65,536
BatchNorm2d-119 [-1, 128, 14, 14] 256
ReLU-120 [-1, 128, 14, 14] 0
Conv2d-121 [-1, 128, 14, 14] 147,456
BatchNorm2d-122 [-1, 128, 14, 14] 256
ReLU-123 [-1, 128, 14, 14] 0
Conv2d-124 [-1, 512, 14, 14] 65,536
Bottleneck-125 [-1, 512, 14, 14] 0
BatchNorm2d-126 [-1, 512, 14, 14] 1,024
ReLU-127 [-1, 512, 14, 14] 0
Conv2d-128 [-1, 128, 14, 14] 65,536
BatchNorm2d-129 [-1, 128, 14, 14] 256
ReLU-130 [-1, 128, 14, 14] 0
Conv2d-131 [-1, 128, 14, 14] 147,456
BatchNorm2d-132 [-1, 128, 14, 14] 256
ReLU-133 [-1, 128, 14, 14] 0
Conv2d-134 [-1, 512, 14, 14] 65,536
Bottleneck-135 [-1, 512, 14, 14] 0
BatchNorm2d-136 [-1, 512, 14, 14] 1,024
ReLU-137 [-1, 512, 14, 14] 0
Conv2d-138 [-1, 128, 14, 14] 65,536
BatchNorm2d-139 [-1, 128, 14, 14] 256
ReLU-140 [-1, 128, 14, 14] 0
Conv2d-141 [-1, 128, 14, 14] 147,456
BatchNorm2d-142 [-1, 128, 14, 14] 256
ReLU-143 [-1, 128, 14, 14] 0
Conv2d-144 [-1, 512, 14, 14] 65,536
Bottleneck-145 [-1, 512, 14, 14] 0
MaxPool2d-146 [-1, 512, 7, 7] 0
MaxPool2d-147 [-1, 512, 7, 7] 0
MaxPool2d-148 [-1, 512, 7, 7] 0
MaxPool2d-149 [-1, 512, 7, 7] 0
MaxPool2d-150 [-1, 512, 7, 7] 0
MaxPool2d-151 [-1, 512, 7, 7] 0
MaxPool2d-152 [-1, 512, 7, 7] 0
BatchNorm2d-153 [-1, 512, 7, 7] 1,024
ReLU-154 [-1, 512, 7, 7] 0
Conv2d-155 [-1, 256, 7, 7] 131,072
BatchNorm2d-156 [-1, 256, 7, 7] 512
ReLU-157 [-1, 256, 7, 7] 0
Conv2d-158 [-1, 1024, 7, 7] 263,168
BatchNorm2d-159 [-1, 1024, 7, 7] 2,048
ReLU-160 [-1, 1024, 7, 7] 0
AdaptiveAvgPool2d-161 [-1, 1024, 1, 1] 0
Conv2d-162 [-1, 32, 1, 1] 32,800
ReLU-163 [-1, 32, 1, 1] 0
Conv2d-164 [-1, 512, 1, 1] 16,896
Sigmoid-165 [-1, 512, 1, 1] 0
BatchNorm2d-166 [-1, 1024, 7, 7] 2,048
ReLU-167 [-1, 1024, 7, 7] 0
ReLU-168 [-1, 1024, 7, 7] 0
Conv2d-169 [-1, 128, 7, 7] 131,072
BatchNorm2d-170 [-1, 128, 7, 7] 256
ReLU-171 [-1, 128, 7, 7] 0
ReLU-172 [-1, 128, 7, 7] 0
Conv2d-173 [-1, 128, 7, 7] 147,456
BatchNorm2d-174 [-1, 128, 7, 7] 256
ReLU-175 [-1, 128, 7, 7] 0
ReLU-176 [-1, 128, 7, 7] 0
Conv2d-177 [-1, 512, 7, 7] 65,536
BatchNorm2d-178 [-1, 1024, 7, 7] 2,048
ReLU-179 [-1, 1024, 7, 7] 0
ReLU-180 [-1, 1024, 7, 7] 0
Conv2d-181 [-1, 512, 7, 7] 524,288
Bottleneck-182 [-1, 512, 7, 7] 0
BatchNorm2d-183 [-1, 512, 7, 7] 1,024
ReLU-184 [-1, 512, 7, 7] 0
Conv2d-185 [-1, 128, 7, 7] 65,536
BatchNorm2d-186 [-1, 128, 7, 7] 256
ReLU-187 [-1, 128, 7, 7] 0
Conv2d-188 [-1, 128, 7, 7] 147,456
BatchNorm2d-189 [-1, 128, 7, 7] 256
ReLU-190 [-1, 128, 7, 7] 0
Conv2d-191 [-1, 512, 7, 7] 65,536
Bottleneck-192 [-1, 512, 7, 7] 0
BatchNorm2d-193 [-1, 512, 7, 7] 1,024
ReLU-194 [-1, 512, 7, 7] 0
Conv2d-195 [-1, 128, 7, 7] 65,536
BatchNorm2d-196 [-1, 128, 7, 7] 256
ReLU-197 [-1, 128, 7, 7] 0
Conv2d-198 [-1, 128, 7, 7] 147,456
BatchNorm2d-199 [-1, 128, 7, 7] 256
ReLU-200 [-1, 128, 7, 7] 0
Conv2d-201 [-1, 512, 7, 7] 65,536
Bottleneck-202 [-1, 512, 7, 7] 0
Upsample-203 [-1, 512, 14, 14] 0
Upsample-204 [-1, 512, 14, 14] 0
Upsample-205 [-1, 512, 14, 14] 0
Upsample-206 [-1, 512, 14, 14] 0
BatchNorm2d-207 [-1, 256, 14, 14] 512
ReLU-208 [-1, 256, 14, 14] 0
Conv2d-209 [-1, 64, 14, 14] 16,384
BatchNorm2d-210 [-1, 64, 14, 14] 128
ReLU-211 [-1, 64, 14, 14] 0
Conv2d-212 [-1, 64, 14, 14] 36,864
BatchNorm2d-213 [-1, 64, 14, 14] 128
ReLU-214 [-1, 64, 14, 14] 0
Conv2d-215 [-1, 256, 14, 14] 16,384
Bottleneck-216 [-1, 256, 14, 14] 0
BatchNorm2d-217 [-1, 768, 14, 14] 1,536
ReLU-218 [-1, 768, 14, 14] 0
Conv2d-219 [-1, 96, 14, 14] 73,728
BatchNorm2d-220 [-1, 96, 14, 14] 192
ReLU-221 [-1, 96, 14, 14] 0
Conv2d-222 [-1, 96, 14, 14] 82,944
BatchNorm2d-223 [-1, 96, 14, 14] 192
ReLU-224 [-1, 96, 14, 14] 0
Conv2d-225 [-1, 384, 14, 14] 36,864
Bottleneck-226 [-1, 384, 14, 14] 0
Upsample-227 [-1, 384, 28, 28] 0
Upsample-228 [-1, 384, 28, 28] 0
Upsample-229 [-1, 384, 28, 28] 0
Upsample-230 [-1, 384, 28, 28] 0
BatchNorm2d-231 [-1, 128, 28, 28] 256
ReLU-232 [-1, 128, 28, 28] 0
Conv2d-233 [-1, 32, 28, 28] 4,096
BatchNorm2d-234 [-1, 32, 28, 28] 64
ReLU-235 [-1, 32, 28, 28] 0
Conv2d-236 [-1, 32, 28, 28] 9,216
BatchNorm2d-237 [-1, 32, 28, 28] 64
ReLU-238 [-1, 32, 28, 28] 0
Conv2d-239 [-1, 128, 28, 28] 4,096
Bottleneck-240 [-1, 128, 28, 28] 0
BatchNorm2d-241 [-1, 512, 28, 28] 1,024
ReLU-242 [-1, 512, 28, 28] 0
Conv2d-243 [-1, 64, 28, 28] 32,768
BatchNorm2d-244 [-1, 64, 28, 28] 128
ReLU-245 [-1, 64, 28, 28] 0
Conv2d-246 [-1, 64, 28, 28] 36,864
BatchNorm2d-247 [-1, 64, 28, 28] 128
ReLU-248 [-1, 64, 28, 28] 0
Conv2d-249 [-1, 256, 28, 28] 16,384
Bottleneck-250 [-1, 256, 28, 28] 0
Upsample-251 [-1, 256, 56, 56] 0
Upsample-252 [-1, 256, 56, 56] 0
Upsample-253 [-1, 256, 56, 56] 0
Upsample-254 [-1, 256, 56, 56] 0
BatchNorm2d-255 [-1, 64, 56, 56] 128
ReLU-256 [-1, 64, 56, 56] 0
Conv2d-257 [-1, 16, 56, 56] 1,024
BatchNorm2d-258 [-1, 16, 56, 56] 32
ReLU-259 [-1, 16, 56, 56] 0
Conv2d-260 [-1, 16, 56, 56] 2,304
BatchNorm2d-261 [-1, 16, 56, 56] 32
ReLU-262 [-1, 16, 56, 56] 0
Conv2d-263 [-1, 64, 56, 56] 1,024
Bottleneck-264 [-1, 64, 56, 56] 0
BatchNorm2d-265 [-1, 320, 56, 56] 640
ReLU-266 [-1, 320, 56, 56] 0
Conv2d-267 [-1, 80, 56, 56] 25,600
BatchNorm2d-268 [-1, 80, 56, 56] 160
ReLU-269 [-1, 80, 56, 56] 0
Conv2d-270 [-1, 80, 56, 56] 57,600
BatchNorm2d-271 [-1, 80, 56, 56] 160
ReLU-272 [-1, 80, 56, 56] 0
Conv2d-273 [-1, 320, 56, 56] 25,600
Bottleneck-274 [-1, 320, 56, 56] 0
MaxPool2d-275 [-1, 320, 28, 28] 0
MaxPool2d-276 [-1, 320, 28, 28] 0
MaxPool2d-277 [-1, 320, 28, 28] 0
MaxPool2d-278 [-1, 320, 28, 28] 0
MaxPool2d-279 [-1, 320, 28, 28] 0
MaxPool2d-280 [-1, 320, 28, 28] 0
MaxPool2d-281 [-1, 320, 28, 28] 0
BatchNorm2d-282 [-1, 512, 28, 28] 1,024
ReLU-283 [-1, 512, 28, 28] 0
Conv2d-284 [-1, 128, 28, 28] 65,536
BatchNorm2d-285 [-1, 128, 28, 28] 256
ReLU-286 [-1, 128, 28, 28] 0
Conv2d-287 [-1, 128, 28, 28] 147,456
BatchNorm2d-288 [-1, 128, 28, 28] 256
ReLU-289 [-1, 128, 28, 28] 0
Conv2d-290 [-1, 512, 28, 28] 65,536
Bottleneck-291 [-1, 512, 28, 28] 0
BatchNorm2d-292 [-1, 832, 28, 28] 1,664
ReLU-293 [-1, 832, 28, 28] 0
Conv2d-294 [-1, 208, 28, 28] 173,056
BatchNorm2d-295 [-1, 208, 28, 28] 416
ReLU-296 [-1, 208, 28, 28] 0
Conv2d-297 [-1, 208, 28, 28] 389,376
BatchNorm2d-298 [-1, 208, 28, 28] 416
ReLU-299 [-1, 208, 28, 28] 0
Conv2d-300 [-1, 832, 28, 28] 173,056
Bottleneck-301 [-1, 832, 28, 28] 0
BatchNorm2d-302 [-1, 832, 28, 28] 1,664
ReLU-303 [-1, 832, 28, 28] 0
Conv2d-304 [-1, 208, 28, 28] 173,056
BatchNorm2d-305 [-1, 208, 28, 28] 416
ReLU-306 [-1, 208, 28, 28] 0
Conv2d-307 [-1, 208, 28, 28] 389,376
BatchNorm2d-308 [-1, 208, 28, 28] 416
ReLU-309 [-1, 208, 28, 28] 0
Conv2d-310 [-1, 832, 28, 28] 173,056
Bottleneck-311 [-1, 832, 28, 28] 0
MaxPool2d-312 [-1, 832, 14, 14] 0
MaxPool2d-313 [-1, 832, 14, 14] 0
MaxPool2d-314 [-1, 832, 14, 14] 0
MaxPool2d-315 [-1, 832, 14, 14] 0
MaxPool2d-316 [-1, 832, 14, 14] 0
MaxPool2d-317 [-1, 832, 14, 14] 0
MaxPool2d-318 [-1, 832, 14, 14] 0
BatchNorm2d-319 [-1, 768, 14, 14] 1,536
ReLU-320 [-1, 768, 14, 14] 0
Conv2d-321 [-1, 192, 14, 14] 147,456
BatchNorm2d-322 [-1, 192, 14, 14] 384
ReLU-323 [-1, 192, 14, 14] 0
Conv2d-324 [-1, 192, 14, 14] 331,776
BatchNorm2d-325 [-1, 192, 14, 14] 384
ReLU-326 [-1, 192, 14, 14] 0
Conv2d-327 [-1, 768, 14, 14] 147,456
Bottleneck-328 [-1, 768, 14, 14] 0
BatchNorm2d-329 [-1, 1600, 14, 14] 3,200
ReLU-330 [-1, 1600, 14, 14] 0
Conv2d-331 [-1, 400, 14, 14] 640,000
BatchNorm2d-332 [-1, 400, 14, 14] 800
ReLU-333 [-1, 400, 14, 14] 0
Conv2d-334 [-1, 400, 14, 14] 1,440,000
BatchNorm2d-335 [-1, 400, 14, 14] 800
ReLU-336 [-1, 400, 14, 14] 0
Conv2d-337 [-1, 1600, 14, 14] 640,000
Bottleneck-338 [-1, 1600, 14, 14] 0
BatchNorm2d-339 [-1, 1600, 14, 14] 3,200
ReLU-340 [-1, 1600, 14, 14] 0
Conv2d-341 [-1, 400, 14, 14] 640,000
BatchNorm2d-342 [-1, 400, 14, 14] 800
ReLU-343 [-1, 400, 14, 14] 0
Conv2d-344 [-1, 400, 14, 14] 1,440,000
BatchNorm2d-345 [-1, 400, 14, 14] 800
ReLU-346 [-1, 400, 14, 14] 0
Conv2d-347 [-1, 1600, 14, 14] 640,000
Bottleneck-348 [-1, 1600, 14, 14] 0
MaxPool2d-349 [-1, 1600, 7, 7] 0
MaxPool2d-350 [-1, 1600, 7, 7] 0
MaxPool2d-351 [-1, 1600, 7, 7] 0
MaxPool2d-352 [-1, 1600, 7, 7] 0
MaxPool2d-353 [-1, 1600, 7, 7] 0
MaxPool2d-354 [-1, 1600, 7, 7] 0
MaxPool2d-355 [-1, 1600, 7, 7] 0
BatchNorm2d-356 [-1, 512, 7, 7] 1,024
ReLU-357 [-1, 512, 7, 7] 0
Conv2d-358 [-1, 128, 7, 7] 65,536
BatchNorm2d-359 [-1, 128, 7, 7] 256
ReLU-360 [-1, 128, 7, 7] 0
Conv2d-361 [-1, 128, 7, 7] 147,456
BatchNorm2d-362 [-1, 128, 7, 7] 256
ReLU-363 [-1, 128, 7, 7] 0
Conv2d-364 [-1, 512, 7, 7] 65,536
Bottleneck-365 [-1, 512, 7, 7] 0
BatchNorm2d-366 [-1, 512, 7, 7] 1,024
ReLU-367 [-1, 512, 7, 7] 0
Conv2d-368 [-1, 128, 7, 7] 65,536
BatchNorm2d-369 [-1, 128, 7, 7] 256
ReLU-370 [-1, 128, 7, 7] 0
Conv2d-371 [-1, 128, 7, 7] 147,456
BatchNorm2d-372 [-1, 128, 7, 7] 256
ReLU-373 [-1, 128, 7, 7] 0
Conv2d-374 [-1, 512, 7, 7] 65,536
Bottleneck-375 [-1, 512, 7, 7] 0
BatchNorm2d-376 [-1, 512, 7, 7] 1,024
ReLU-377 [-1, 512, 7, 7] 0
Conv2d-378 [-1, 128, 7, 7] 65,536
BatchNorm2d-379 [-1, 128, 7, 7] 256
ReLU-380 [-1, 128, 7, 7] 0
Conv2d-381 [-1, 128, 7, 7] 147,456
BatchNorm2d-382 [-1, 128, 7, 7] 256
ReLU-383 [-1, 128, 7, 7] 0
Conv2d-384 [-1, 512, 7, 7] 65,536
Bottleneck-385 [-1, 512, 7, 7] 0
BatchNorm2d-386 [-1, 512, 7, 7] 1,024
ReLU-387 [-1, 512, 7, 7] 0
Conv2d-388 [-1, 128, 7, 7] 65,536
BatchNorm2d-389 [-1, 128, 7, 7] 256
ReLU-390 [-1, 128, 7, 7] 0
Conv2d-391 [-1, 128, 7, 7] 147,456
BatchNorm2d-392 [-1, 128, 7, 7] 256
ReLU-393 [-1, 128, 7, 7] 0
Conv2d-394 [-1, 512, 7, 7] 65,536
Bottleneck-395 [-1, 512, 7, 7] 0
BatchNorm2d-396 [-1, 2112, 7, 7] 4,224
ReLU-397 [-1, 2112, 7, 7] 0
Conv2d-398 [-1, 1056, 7, 7] 2,230,272
BatchNorm2d-399 [-1, 1056, 7, 7] 2,112
ReLU-400 [-1, 1056, 7, 7] 0
AdaptiveAvgPool2d-401 [-1, 1056, 1, 1] 0
Conv2d-402 [-1, 1000, 1, 1] 1,057,000
Fish-403 [-1, 1000, 1, 1] 0
================================================================
Total params: 16,628,904
Trainable params: 16,628,904
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 390.95
Params size (MB): 63.43
Estimated Total Size (MB): 454.96
----------------------------------------------------------------
Process finished with exit code 0
有两种方式可以更改上述错误：
1.
if __name__ == '__main__':
model = fishnet99()
torchsummary.summary(model.cuda(), (3, 224, 224))2.
if __name__ == '__main__':
model = fishnet99()
torchsummary.summary(model, (3, 224, 224),device='cpu')这两种方式都可以避免上述错误
第一种就是 convert your network to cuda，第二种就是 call `torchsummary.summary` with `device='cpu'`