Resnet-101结构的改进问题
Resnet-101结构的改进问题
提问于 2022-05-27 08:56:21
最近，我正在使用pytorch实现ME，但是我遇到了一些问题.在ResNet论文中(如所附的图1：https://i.stack.imgur.com/1uKRc.png)，顶部部分使用ResNet-101提取特征(使用ImageNet数据集上经过预先训练的ResNet-101权重作为初始权重)，其核心是算术均值。
我使用(256,256,11)作为图像输入，但不确定0~ stage 1属于ResNet-101中的哪个部分，无法在每个阶段找到与论文对应的输出特征映射大小。
我的网纸：
基于前哨2A数据提取红树林的深卷积神经网络
https://www.mdpi.com/2072-4292/13/7/1
from torchvision import models
res101 = models.resnet101(pretrained=True)
res101.conv1=nn.Conv2d(11, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
res101.cuda()
summary(res101, (11,256,256)res101概述：
Layer (type) Output Shape Param #
Conv2d-1 [-1, 64, 128, 128] 34,496
BatchNorm2d-2 [-1, 64, 128, 128] 128
ReLU-3 [-1, 64, 128, 128] 0
MaxPool2d-4 [-1, 64, 64, 64] 0
Conv2d-5 [-1, 64, 64, 64] 4,096
BatchNorm2d-6 [-1, 64, 64, 64] 128
ReLU-7 [-1, 64, 64, 64] 0
Conv2d-8 [-1, 64, 64, 64] 36,864
BatchNorm2d-9 [-1, 64, 64, 64] 128
ReLU-10 [-1, 64, 64, 64] 0
Conv2d-11 [-1, 256, 64, 64] 16,384
BatchNorm2d-12 [-1, 256, 64, 64] 512
Conv2d-13 [-1, 256, 64, 64] 16,384
BatchNorm2d-14 [-1, 256, 64, 64] 512
ReLU-15 [-1, 256, 64, 64] 0
Bottleneck-16 [-1, 256, 64, 64] 0
Conv2d-17 [-1, 64, 64, 64] 16,384
BatchNorm2d-18 [-1, 64, 64, 64] 128
ReLU-19 [-1, 64, 64, 64] 0
Conv2d-20 [-1, 64, 64, 64] 36,864
BatchNorm2d-21 [-1, 64, 64, 64] 128
ReLU-22 [-1, 64, 64, 64] 0
Conv2d-23 [-1, 256, 64, 64] 16,384
BatchNorm2d-24 [-1, 256, 64, 64] 512
ReLU-25 [-1, 256, 64, 64] 0
Bottleneck-26 [-1, 256, 64, 64] 0
Conv2d-27 [-1, 64, 64, 64] 16,384
BatchNorm2d-28 [-1, 64, 64, 64] 128
ReLU-29 [-1, 64, 64, 64] 0
Conv2d-30 [-1, 64, 64, 64] 36,864
BatchNorm2d-31 [-1, 64, 64, 64] 128
ReLU-32 [-1, 64, 64, 64] 0
Conv2d-33 [-1, 256, 64, 64] 16,384
BatchNorm2d-34 [-1, 256, 64, 64] 512
ReLU-35 [-1, 256, 64, 64] 0
Bottleneck-36 [-1, 256, 64, 64] 0
Conv2d-37 [-1, 128, 64, 64] 32,768
BatchNorm2d-38 [-1, 128, 64, 64] 256
ReLU-39 [-1, 128, 64, 64] 0
Conv2d-40 [-1, 128, 32, 32] 147,456
BatchNorm2d-41 [-1, 128, 32, 32] 256
ReLU-42 [-1, 128, 32, 32] 0
Conv2d-43 [-1, 512, 32, 32] 65,536
BatchNorm2d-44 [-1, 512, 32, 32] 1,024
Conv2d-45 [-1, 512, 32, 32] 131,072
BatchNorm2d-46 [-1, 512, 32, 32] 1,024
ReLU-47 [-1, 512, 32, 32] 0
Bottleneck-48 [-1, 512, 32, 32] 0
Conv2d-49 [-1, 128, 32, 32] 65,536
BatchNorm2d-50 [-1, 128, 32, 32] 256
ReLU-51 [-1, 128, 32, 32] 0
Conv2d-52 [-1, 128, 32, 32] 147,456
BatchNorm2d-53 [-1, 128, 32, 32] 256
ReLU-54 [-1, 128, 32, 32] 0
Conv2d-55 [-1, 512, 32, 32] 65,536
BatchNorm2d-56 [-1, 512, 32, 32] 1,024
ReLU-57 [-1, 512, 32, 32] 0
Bottleneck-58 [-1, 512, 32, 32] 0
Conv2d-59 [-1, 128, 32, 32] 65,536
BatchNorm2d-60 [-1, 128, 32, 32] 256
ReLU-61 [-1, 128, 32, 32] 0
Conv2d-62 [-1, 128, 32, 32] 147,456
BatchNorm2d-63 [-1, 128, 32, 32] 256
ReLU-64 [-1, 128, 32, 32] 0
Conv2d-65 [-1, 512, 32, 32] 65,536
BatchNorm2d-66 [-1, 512, 32, 32] 1,024
ReLU-67 [-1, 512, 32, 32] 0
Bottleneck-68 [-1, 512, 32, 32] 0
Conv2d-69 [-1, 128, 32, 32] 65,536
BatchNorm2d-70 [-1, 128, 32, 32] 256
ReLU-71 [-1, 128, 32, 32] 0
Conv2d-72 [-1, 128, 32, 32] 147,456
BatchNorm2d-73 [-1, 128, 32, 32] 256
ReLU-74 [-1, 128, 32, 32] 0
Conv2d-75 [-1, 512, 32, 32] 65,536
BatchNorm2d-76 [-1, 512, 32, 32] 1,024
ReLU-77 [-1, 512, 32, 32] 0
Bottleneck-78 [-1, 512, 32, 32] 0
Conv2d-79 [-1, 256, 32, 32] 131,072
BatchNorm2d-80 [-1, 256, 32, 32] 512
ReLU-81 [-1, 256, 32, 32] 0
Conv2d-82 [-1, 256, 16, 16] 589,824
BatchNorm2d-83 [-1, 256, 16, 16] 512
ReLU-84 [-1, 256, 16, 16] 0
Conv2d-85 [-1, 1024, 16, 16] 262,144
BatchNorm2d-86 [-1, 1024, 16, 16] 2,048
Conv2d-87 [-1, 1024, 16, 16] 524,288
BatchNorm2d-88 [-1, 1024, 16, 16] 2,048
ReLU-89 [-1, 1024, 16, 16] 0
Bottleneck-90 [-1, 1024, 16, 16] 0
Conv2d-91 [-1, 256, 16, 16] 262,144
BatchNorm2d-92 [-1, 256, 16, 16] 512
ReLU-93 [-1, 256, 16, 16] 0
Conv2d-94 [-1, 256, 16, 16] 589,824
BatchNorm2d-95 [-1, 256, 16, 16] 512
ReLU-96 [-1, 256, 16, 16] 0
Conv2d-97 [-1, 1024, 16, 16] 262,144
BatchNorm2d-98 [-1, 1024, 16, 16] 2,048
ReLU-99 [-1, 1024, 16, 16] 0
Bottleneck-100 [-1, 1024, 16, 16] 0
Conv2d-101 [-1, 256, 16, 16] 262,144
BatchNorm2d-102 [-1, 256, 16, 16] 512
ReLU-103 [-1, 256, 16, 16] 0
Conv2d-104 [-1, 256, 16, 16] 589,824
BatchNorm2d-105 [-1, 256, 16, 16] 512
ReLU-106 [-1, 256, 16, 16] 0
Conv2d-107 [-1, 1024, 16, 16] 262,144
BatchNorm2d-108 [-1, 1024, 16, 16] 2,048
ReLU-109 [-1, 1024, 16, 16] 0
Bottleneck-110 [-1, 1024, 16, 16] 0
Conv2d-111 [-1, 256, 16, 16] 262,144
BatchNorm2d-112 [-1, 256, 16, 16] 512
ReLU-113 [-1, 256, 16, 16] 0
Conv2d-114 [-1, 256, 16, 16] 589,824
BatchNorm2d-115 [-1, 256, 16, 16] 512
ReLU-116 [-1, 256, 16, 16] 0
Conv2d-117 [-1, 1024, 16, 16] 262,144
BatchNorm2d-118 [-1, 1024, 16, 16] 2,048
ReLU-119 [-1, 1024, 16, 16] 0
Bottleneck-120 [-1, 1024, 16, 16] 0
Conv2d-121 [-1, 256, 16, 16] 262,144
BatchNorm2d-122 [-1, 256, 16, 16] 512
ReLU-123 [-1, 256, 16, 16] 0
Conv2d-124 [-1, 256, 16, 16] 589,824
BatchNorm2d-125 [-1, 256, 16, 16] 512
ReLU-126 [-1, 256, 16, 16] 0
Conv2d-127 [-1, 1024, 16, 16] 262,144
BatchNorm2d-128 [-1, 1024, 16, 16] 2,048
ReLU-129 [-1, 1024, 16, 16] 0
Bottleneck-130 [-1, 1024, 16, 16] 0
Conv2d-131 [-1, 256, 16, 16] 262,144
BatchNorm2d-132 [-1, 256, 16, 16] 512
ReLU-133 [-1, 256, 16, 16] 0
Conv2d-134 [-1, 256, 16, 16] 589,824
BatchNorm2d-135 [-1, 256, 16, 16] 512
ReLU-136 [-1, 256, 16, 16] 0
Conv2d-137 [-1, 1024, 16, 16] 262,144
BatchNorm2d-138 [-1, 1024, 16, 16] 2,048
ReLU-139 [-1, 1024, 16, 16] 0
Bottleneck-140 [-1, 1024, 16, 16] 0
Conv2d-141 [-1, 256, 16, 16] 262,144
BatchNorm2d-142 [-1, 256, 16, 16] 512
ReLU-143 [-1, 256, 16, 16] 0
Conv2d-144 [-1, 256, 16, 16] 589,824
BatchNorm2d-145 [-1, 256, 16, 16] 512
ReLU-146 [-1, 256, 16, 16] 0
Conv2d-147 [-1, 1024, 16, 16] 262,144
BatchNorm2d-148 [-1, 1024, 16, 16] 2,048
ReLU-149 [-1, 1024, 16, 16] 0
Bottleneck-150 [-1, 1024, 16, 16] 0
Conv2d-151 [-1, 256, 16, 16] 262,144
BatchNorm2d-152 [-1, 256, 16, 16] 512
ReLU-153 [-1, 256, 16, 16] 0
Conv2d-154 [-1, 256, 16, 16] 589,824
BatchNorm2d-155 [-1, 256, 16, 16] 512
ReLU-156 [-1, 256, 16, 16] 0
Conv2d-157 [-1, 1024, 16, 16] 262,144
BatchNorm2d-158 [-1, 1024, 16, 16] 2,048
ReLU-159 [-1, 1024, 16, 16] 0
Bottleneck-160 [-1, 1024, 16, 16] 0
Conv2d-161 [-1, 256, 16, 16] 262,144
BatchNorm2d-162 [-1, 256, 16, 16] 512
ReLU-163 [-1, 256, 16, 16] 0
Conv2d-164 [-1, 256, 16, 16] 589,824
BatchNorm2d-165 [-1, 256, 16, 16] 512
ReLU-166 [-1, 256, 16, 16] 0
Conv2d-167 [-1, 1024, 16, 16] 262,144
BatchNorm2d-168 [-1, 1024, 16, 16] 2,048
ReLU-169 [-1, 1024, 16, 16] 0
Bottleneck-170 [-1, 1024, 16, 16] 0
Conv2d-171 [-1, 256, 16, 16] 262,144
BatchNorm2d-172 [-1, 256, 16, 16] 512
ReLU-173 [-1, 256, 16, 16] 0
Conv2d-174 [-1, 256, 16, 16] 589,824
BatchNorm2d-175 [-1, 256, 16, 16] 512
ReLU-176 [-1, 256, 16, 16] 0
Conv2d-177 [-1, 1024, 16, 16] 262,144
BatchNorm2d-178 [-1, 1024, 16, 16] 2,048
ReLU-179 [-1, 1024, 16, 16] 0
Bottleneck-180 [-1, 1024, 16, 16] 0
Conv2d-181 [-1, 256, 16, 16] 262,144
BatchNorm2d-182 [-1, 256, 16, 16] 512
ReLU-183 [-1, 256, 16, 16] 0
Conv2d-184 [-1, 256, 16, 16] 589,824
BatchNorm2d-185 [-1, 256, 16, 16] 512
ReLU-186 [-1, 256, 16, 16] 0
Conv2d-187 [-1, 1024, 16, 16] 262,144
BatchNorm2d-188 [-1, 1024, 16, 16] 2,048
ReLU-189 [-1, 1024, 16, 16] 0
Bottleneck-190 [-1, 1024, 16, 16] 0
Conv2d-191 [-1, 256, 16, 16] 262,144
BatchNorm2d-192 [-1, 256, 16, 16] 512
ReLU-193 [-1, 256, 16, 16] 0
Conv2d-194 [-1, 256, 16, 16] 589,824
BatchNorm2d-195 [-1, 256, 16, 16] 512
ReLU-196 [-1, 256, 16, 16] 0
Conv2d-197 [-1, 1024, 16, 16] 262,144
BatchNorm2d-198 [-1, 1024, 16, 16] 2,048
ReLU-199 [-1, 1024, 16, 16] 0
Bottleneck-200 [-1, 1024, 16, 16] 0
Conv2d-201 [-1, 256, 16, 16] 262,144
BatchNorm2d-202 [-1, 256, 16, 16] 512
ReLU-203 [-1, 256, 16, 16] 0
Conv2d-204 [-1, 256, 16, 16] 589,824
BatchNorm2d-205 [-1, 256, 16, 16] 512
ReLU-206 [-1, 256, 16, 16] 0
Conv2d-207 [-1, 1024, 16, 16] 262,144
BatchNorm2d-208 [-1, 1024, 16, 16] 2,048
ReLU-209 [-1, 1024, 16, 16] 0
Bottleneck-210 [-1, 1024, 16, 16] 0
Conv2d-211 [-1, 256, 16, 16] 262,144
BatchNorm2d-212 [-1, 256, 16, 16] 512
ReLU-213 [-1, 256, 16, 16] 0
Conv2d-214 [-1, 256, 16, 16] 589,824
BatchNorm2d-215 [-1, 256, 16, 16] 512
ReLU-216 [-1, 256, 16, 16] 0
Conv2d-217 [-1, 1024, 16, 16] 262,144
BatchNorm2d-218 [-1, 1024, 16, 16] 2,048
ReLU-219 [-1, 1024, 16, 16] 0
Bottleneck-220 [-1, 1024, 16, 16] 0
Conv2d-221 [-1, 256, 16, 16] 262,144
BatchNorm2d-222 [-1, 256, 16, 16] 512
ReLU-223 [-1, 256, 16, 16] 0
Conv2d-224 [-1, 256, 16, 16] 589,824
BatchNorm2d-225 [-1, 256, 16, 16] 512
ReLU-226 [-1, 256, 16, 16] 0
Conv2d-227 [-1, 1024, 16, 16] 262,144
BatchNorm2d-228 [-1, 1024, 16, 16] 2,048
ReLU-229 [-1, 1024, 16, 16] 0
Bottleneck-230 [-1, 1024, 16, 16] 0
Conv2d-231 [-1, 256, 16, 16] 262,144
BatchNorm2d-232 [-1, 256, 16, 16] 512
ReLU-233 [-1, 256, 16, 16] 0
Conv2d-234 [-1, 256, 16, 16] 589,824
BatchNorm2d-235 [-1, 256, 16, 16] 512
ReLU-236 [-1, 256, 16, 16] 0
Conv2d-237 [-1, 1024, 16, 16] 262,144
BatchNorm2d-238 [-1, 1024, 16, 16] 2,048
ReLU-239 [-1, 1024, 16, 16] 0
Bottleneck-240 [-1, 1024, 16, 16] 0
Conv2d-241 [-1, 256, 16, 16] 262,144
BatchNorm2d-242 [-1, 256, 16, 16] 512
ReLU-243 [-1, 256, 16, 16] 0
Conv2d-244 [-1, 256, 16, 16] 589,824
BatchNorm2d-245 [-1, 256, 16, 16] 512
ReLU-246 [-1, 256, 16, 16] 0
Conv2d-247 [-1, 1024, 16, 16] 262,144
BatchNorm2d-248 [-1, 1024, 16, 16] 2,048
ReLU-249 [-1, 1024, 16, 16] 0
Bottleneck-250 [-1, 1024, 16, 16] 0
Conv2d-251 [-1, 256, 16, 16] 262,144
BatchNorm2d-252 [-1, 256, 16, 16] 512
ReLU-253 [-1, 256, 16, 16] 0
Conv2d-254 [-1, 256, 16, 16] 589,824
BatchNorm2d-255 [-1, 256, 16, 16] 512
ReLU-256 [-1, 256, 16, 16] 0
Conv2d-257 [-1, 1024, 16, 16] 262,144
BatchNorm2d-258 [-1, 1024, 16, 16] 2,048
ReLU-259 [-1, 1024, 16, 16] 0
Bottleneck-260 [-1, 1024, 16, 16] 0
Conv2d-261 [-1, 256, 16, 16] 262,144
BatchNorm2d-262 [-1, 256, 16, 16] 512
ReLU-263 [-1, 256, 16, 16] 0
Conv2d-264 [-1, 256, 16, 16] 589,824
BatchNorm2d-265 [-1, 256, 16, 16] 512
ReLU-266 [-1, 256, 16, 16] 0
Conv2d-267 [-1, 1024, 16, 16] 262,144
BatchNorm2d-268 [-1, 1024, 16, 16] 2,048
ReLU-269 [-1, 1024, 16, 16] 0
Bottleneck-270 [-1, 1024, 16, 16] 0
Conv2d-271 [-1, 256, 16, 16] 262,144
BatchNorm2d-272 [-1, 256, 16, 16] 512
ReLU-273 [-1, 256, 16, 16] 0
Conv2d-274 [-1, 256, 16, 16] 589,824
BatchNorm2d-275 [-1, 256, 16, 16] 512
ReLU-276 [-1, 256, 16, 16] 0
Conv2d-277 [-1, 1024, 16, 16] 262,144
BatchNorm2d-278 [-1, 1024, 16, 16] 2,048
ReLU-279 [-1, 1024, 16, 16] 0
Bottleneck-280 [-1, 1024, 16, 16] 0
Conv2d-281 [-1, 256, 16, 16] 262,144
BatchNorm2d-282 [-1, 256, 16, 16] 512
ReLU-283 [-1, 256, 16, 16] 0
Conv2d-284 [-1, 256, 16, 16] 589,824
BatchNorm2d-285 [-1, 256, 16, 16] 512
ReLU-286 [-1, 256, 16, 16] 0
Conv2d-287 [-1, 1024, 16, 16] 262,144
BatchNorm2d-288 [-1, 1024, 16, 16] 2,048
ReLU-289 [-1, 1024, 16, 16] 0
Bottleneck-290 [-1, 1024, 16, 16] 0
Conv2d-291 [-1, 256, 16, 16] 262,144
BatchNorm2d-292 [-1, 256, 16, 16] 512
ReLU-293 [-1, 256, 16, 16] 0
Conv2d-294 [-1, 256, 16, 16] 589,824
BatchNorm2d-295 [-1, 256, 16, 16] 512
ReLU-296 [-1, 256, 16, 16] 0
Conv2d-297 [-1, 1024, 16, 16] 262,144
BatchNorm2d-298 [-1, 1024, 16, 16] 2,048
ReLU-299 [-1, 1024, 16, 16] 0
Bottleneck-300 [-1, 1024, 16, 16] 0
Conv2d-301 [-1, 256, 16, 16] 262,144
BatchNorm2d-302 [-1, 256, 16, 16] 512
ReLU-303 [-1, 256, 16, 16] 0
Conv2d-304 [-1, 256, 16, 16] 589,824
BatchNorm2d-305 [-1, 256, 16, 16] 512
ReLU-306 [-1, 256, 16, 16] 0
Conv2d-307 [-1, 1024, 16, 16] 262,144
BatchNorm2d-308 [-1, 1024, 16, 16] 2,048
ReLU-309 [-1, 1024, 16, 16] 0
Bottleneck-310 [-1, 1024, 16, 16] 0
Conv2d-311 [-1, 512, 16, 16] 524,288
BatchNorm2d-312 [-1, 512, 16, 16] 1,024
ReLU-313 [-1, 512, 16, 16] 0
Conv2d-314 [-1, 512, 8, 8] 2,359,296
BatchNorm2d-315 [-1, 512, 8, 8] 1,024
ReLU-316 [-1, 512, 8, 8] 0
Conv2d-317 [-1, 2048, 8, 8] 1,048,576
BatchNorm2d-318 [-1, 2048, 8, 8] 4,096
Conv2d-319 [-1, 2048, 8, 8] 2,097,152
BatchNorm2d-320 [-1, 2048, 8, 8] 4,096
ReLU-321 [-1, 2048, 8, 8] 0
Bottleneck-322 [-1, 2048, 8, 8] 0
Conv2d-323 [-1, 512, 8, 8] 1,048,576
BatchNorm2d-324 [-1, 512, 8, 8] 1,024
ReLU-325 [-1, 512, 8, 8] 0
Conv2d-326 [-1, 512, 8, 8] 2,359,296
BatchNorm2d-327 [-1, 512, 8, 8] 1,024
ReLU-328 [-1, 512, 8, 8] 0
Conv2d-329 [-1, 2048, 8, 8] 1,048,576
BatchNorm2d-330 [-1, 2048, 8, 8] 4,096
ReLU-331 [-1, 2048, 8, 8] 0
Bottleneck-332 [-1, 2048, 8, 8] 0
Conv2d-333 [-1, 512, 8, 8] 1,048,576
BatchNorm2d-334 [-1, 512, 8, 8] 1,024
ReLU-335 [-1, 512, 8, 8] 0
Conv2d-336 [-1, 512, 8, 8] 2,359,296
BatchNorm2d-337 [-1, 512, 8, 8] 1,024
ReLU-338 [-1, 512, 8, 8] 0
Conv2d-339 [-1, 2048, 8, 8] 1,048,576
BatchNorm2d-340 [-1, 2048, 8, 8] 4,096
ReLU-341 [-1, 2048, 8, 8] 0
Bottleneck-342 [-1, 2048, 8, 8] 0
AdaptiveAvgPool2d-343 [-1, 2048, 1, 1] 0
Linear-344 [-1, 1000] 2,049,000我认为stage0中的输出特征映射是层:ReLU-3；stage1中的输出特征映射是层:ReLU-32；stage2中的输出特征映射是层:ReLU-81；stage3中的输出特征映射是层:ReLU-313；无法在stage4和stage5中找到大小为(8,8,1024)和(4,4,2048)的输出特征映射。
-更新如下
要获取第0~第5阶段中的特征映射，我使用以下代码：
stage5 = nn.Sequential(*list(res101.children())[:-2])
stage4 = nn.Sequential(*list(res101.children())[:-3])
stage3 = nn.Sequential(*list(res101.children())[:-4])
stage2 = nn.Sequential(*list(res101.children())[:-5])
stage1 = nn.Sequential(*list(res101.children())[:-6])
stage0 = nn.Sequential(*list(res101.children())[:-7])然而，阶段0~5似乎仅仅代表了体系结构。
我怎样才能得到他们的特征地图？
回答 1
Stack Overflow用户
回答已采纳
发布于 2022-05-27 22:03:19
更容易查看PyTorch的源代码，看看像这样的麻烦，参见这里。
查看ResNet类(它用于通过类似工厂的函数创建不同的变体)寻找线索。
在这种情况下，相应的层将是：
conv1(stage0)conv1(stage1)的最大集合输出，此输出由形状假定layer1(stage2)layer2layer3layer4(stage5)
此外，您还可以使用print(res101)查看所有块，以便更容易地进行调试，因为它具有分层结构。
获取特征
为此，您应该使用PyTorch FX (参见这里)。
在你的例子中，它将是这样的东西：
from torchvision.models.feature_extractor import create_feature_extractor
extractor = create_feature_extractor(
model,
return_nodes=[
”conv1”,
“maxpool”,
“layer1”,
“layer2”,
“layer3”,
“layer4”,
]
)
features = extractor(inputs)特性应该是一个dict，其中keys是上面指定的层的名称，values是最终的tensors。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/72402905
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