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----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 112, 112] 864
BatchNorm2d-2 [-1, 32, 112, 112] 64
Sigmoid-3 [-1, 32, 112, 112] 0
Swish-4 [-1, 32, 112, 112] 0
Conv2d-5 [-1, 32, 112, 112] 288
BatchNorm2d-6 [-1, 32, 112, 112] 64
ReLU6-7 [-1, 32, 112, 112] 0
Conv2d-8 [-1, 16, 112, 112] 512
BatchNorm2d-9 [-1, 16, 112, 112] 32
LinearBottleneck-10 [-1, 16, 112, 112] 0
Conv2d-11 [-1, 96, 112, 112] 1,536
BatchNorm2d-12 [-1, 96, 112, 112] 192
Sigmoid-13 [-1, 96, 112, 112] 0
Swish-14 [-1, 96, 112, 112] 0
Conv2d-15 [-1, 96, 56, 56] 864
BatchNorm2d-16 [-1, 96, 56, 56] 192
ReLU6-17 [-1, 96, 56, 56] 0
Conv2d-18 [-1, 24, 56, 56] 2,304
BatchNorm2d-19 [-1, 24, 56, 56] 48
LinearBottleneck-20 [-1, 24, 56, 56] 0
Conv2d-21 [-1, 144, 56, 56] 3,456
BatchNorm2d-22 [-1, 144, 56, 56] 288
Sigmoid-23 [-1, 144, 56, 56] 0
Swish-24 [-1, 144, 56, 56] 0
Conv2d-25 [-1, 144, 56, 56] 1,296
BatchNorm2d-26 [-1, 144, 56, 56] 288
ReLU6-27 [-1, 144, 56, 56] 0
Conv2d-28 [-1, 32, 56, 56] 4,608
BatchNorm2d-29 [-1, 32, 56, 56] 64
LinearBottleneck-30 [-1, 32, 56, 56] 0
Conv2d-31 [-1, 192, 56, 56] 6,144
BatchNorm2d-32 [-1, 192, 56, 56] 384
Sigmoid-33 [-1, 192, 56, 56] 0
Swish-34 [-1, 192, 56, 56] 0
Conv2d-35 [-1, 192, 28, 28] 1,728
BatchNorm2d-36 [-1, 192, 28, 28] 384
AdaptiveAvgPool2d-37 [-1, 192, 1, 1] 0
Conv2d-38 [-1, 16, 1, 1] 3,088
BatchNorm2d-39 [-1, 16, 1, 1] 32
ReLU-40 [-1, 16, 1, 1] 0
Conv2d-41 [-1, 192, 1, 1] 3,264
Sigmoid-42 [-1, 192, 1, 1] 0
SE-43 [-1, 192, 28, 28] 0
ReLU6-44 [-1, 192, 28, 28] 0
Conv2d-45 [-1, 40, 28, 28] 7,680
BatchNorm2d-46 [-1, 40, 28, 28] 80
LinearBottleneck-47 [-1, 40, 28, 28] 0
Conv2d-48 [-1, 240, 28, 28] 9,600
BatchNorm2d-49 [-1, 240, 28, 28] 480
Sigmoid-50 [-1, 240, 28, 28] 0
Swish-51 [-1, 240, 28, 28] 0
Conv2d-52 [-1, 240, 28, 28] 2,160
BatchNorm2d-53 [-1, 240, 28, 28] 480
AdaptiveAvgPool2d-54 [-1, 240, 1, 1] 0
Conv2d-55 [-1, 20, 1, 1] 4,820
BatchNorm2d-56 [-1, 20, 1, 1] 40
ReLU-57 [-1, 20, 1, 1] 0
Conv2d-58 [-1, 240, 1, 1] 5,040
Sigmoid-59 [-1, 240, 1, 1] 0
SE-60 [-1, 240, 28, 28] 0
ReLU6-61 [-1, 240, 28, 28] 0
Conv2d-62 [-1, 48, 28, 28] 11,520
BatchNorm2d-63 [-1, 48, 28, 28] 96
LinearBottleneck-64 [-1, 48, 28, 28] 0
Conv2d-65 [-1, 288, 28, 28] 13,824
BatchNorm2d-66 [-1, 288, 28, 28] 576
Sigmoid-67 [-1, 288, 28, 28] 0
Swish-68 [-1, 288, 28, 28] 0
Conv2d-69 [-1, 288, 14, 14] 2,592
BatchNorm2d-70 [-1, 288, 14, 14] 576
AdaptiveAvgPool2d-71 [-1, 288, 1, 1] 0
Conv2d-72 [-1, 24, 1, 1] 6,936
BatchNorm2d-73 [-1, 24, 1, 1] 48
ReLU-74 [-1, 24, 1, 1] 0
Conv2d-75 [-1, 288, 1, 1] 7,200
Sigmoid-76 [-1, 288, 1, 1] 0
SE-77 [-1, 288, 14, 14] 0
ReLU6-78 [-1, 288, 14, 14] 0
Conv2d-79 [-1, 55, 14, 14] 15,840
BatchNorm2d-80 [-1, 55, 14, 14] 110
LinearBottleneck-81 [-1, 55, 14, 14] 0
Conv2d-82 [-1, 330, 14, 14] 18,150
BatchNorm2d-83 [-1, 330, 14, 14] 660
Sigmoid-84 [-1, 330, 14, 14] 0
Swish-85 [-1, 330, 14, 14] 0
Conv2d-86 [-1, 330, 14, 14] 2,970
BatchNorm2d-87 [-1, 330, 14, 14] 660
AdaptiveAvgPool2d-88 [-1, 330, 1, 1] 0
Conv2d-89 [-1, 27, 1, 1] 8,937
BatchNorm2d-90 [-1, 27, 1, 1] 54
ReLU-91 [-1, 27, 1, 1] 0
Conv2d-92 [-1, 330, 1, 1] 9,240
Sigmoid-93 [-1, 330, 1, 1] 0
SE-94 [-1, 330, 14, 14] 0
ReLU6-95 [-1, 330, 14, 14] 0
Conv2d-96 [-1, 63, 14, 14] 20,790
BatchNorm2d-97 [-1, 63, 14, 14] 126
LinearBottleneck-98 [-1, 63, 14, 14] 0
Conv2d-99 [-1, 378, 14, 14] 23,814
BatchNorm2d-100 [-1, 378, 14, 14] 756
Sigmoid-101 [-1, 378, 14, 14] 0
Swish-102 [-1, 378, 14, 14] 0
Conv2d-103 [-1, 378, 14, 14] 3,402
BatchNorm2d-104 [-1, 378, 14, 14] 756
AdaptiveAvgPool2d-105 [-1, 378, 1, 1] 0
Conv2d-106 [-1, 31, 1, 1] 11,749
BatchNorm2d-107 [-1, 31, 1, 1] 62
ReLU-108 [-1, 31, 1, 1] 0
Conv2d-109 [-1, 378, 1, 1] 12,096
Sigmoid-110 [-1, 378, 1, 1] 0
SE-111 [-1, 378, 14, 14] 0
ReLU6-112 [-1, 378, 14, 14] 0
Conv2d-113 [-1, 71, 14, 14] 26,838
BatchNorm2d-114 [-1, 71, 14, 14] 142
LinearBottleneck-115 [-1, 71, 14, 14] 0
Conv2d-116 [-1, 426, 14, 14] 30,246
BatchNorm2d-117 [-1, 426, 14, 14] 852
Sigmoid-118 [-1, 426, 14, 14] 0
Swish-119 [-1, 426, 14, 14] 0
Conv2d-120 [-1, 426, 14, 14] 3,834
BatchNorm2d-121 [-1, 426, 14, 14] 852
AdaptiveAvgPool2d-122 [-1, 426, 1, 1] 0
Conv2d-123 [-1, 35, 1, 1] 14,945
BatchNorm2d-124 [-1, 35, 1, 1] 70
ReLU-125 [-1, 35, 1, 1] 0
Conv2d-126 [-1, 426, 1, 1] 15,336
Sigmoid-127 [-1, 426, 1, 1] 0
SE-128 [-1, 426, 14, 14] 0
ReLU6-129 [-1, 426, 14, 14] 0
Conv2d-130 [-1, 79, 14, 14] 33,654
BatchNorm2d-131 [-1, 79, 14, 14] 158
LinearBottleneck-132 [-1, 79, 14, 14] 0
Conv2d-133 [-1, 474, 14, 14] 37,446
BatchNorm2d-134 [-1, 474, 14, 14] 948
Sigmoid-135 [-1, 474, 14, 14] 0
Swish-136 [-1, 474, 14, 14] 0
Conv2d-137 [-1, 474, 14, 14] 4,266
BatchNorm2d-138 [-1, 474, 14, 14] 948
AdaptiveAvgPool2d-139 [-1, 474, 1, 1] 0
Conv2d-140 [-1, 39, 1, 1] 18,525
BatchNorm2d-141 [-1, 39, 1, 1] 78
ReLU-142 [-1, 39, 1, 1] 0
Conv2d-143 [-1, 474, 1, 1] 18,960
Sigmoid-144 [-1, 474, 1, 1] 0
SE-145 [-1, 474, 14, 14] 0
ReLU6-146 [-1, 474, 14, 14] 0
Conv2d-147 [-1, 87, 14, 14] 41,238
BatchNorm2d-148 [-1, 87, 14, 14] 174
LinearBottleneck-149 [-1, 87, 14, 14] 0
Conv2d-150 [-1, 522, 14, 14] 45,414
BatchNorm2d-151 [-1, 522, 14, 14] 1,044
Sigmoid-152 [-1, 522, 14, 14] 0
Swish-153 [-1, 522, 14, 14] 0
Conv2d-154 [-1, 522, 14, 14] 4,698
BatchNorm2d-155 [-1, 522, 14, 14] 1,044
AdaptiveAvgPool2d-156 [-1, 522, 1, 1] 0
Conv2d-157 [-1, 43, 1, 1] 22,489
BatchNorm2d-158 [-1, 43, 1, 1] 86
ReLU-159 [-1, 43, 1, 1] 0
Conv2d-160 [-1, 522, 1, 1] 22,968
Sigmoid-161 [-1, 522, 1, 1] 0
SE-162 [-1, 522, 14, 14] 0
ReLU6-163 [-1, 522, 14, 14] 0
Conv2d-164 [-1, 95, 14, 14] 49,590
BatchNorm2d-165 [-1, 95, 14, 14] 190
LinearBottleneck-166 [-1, 95, 14, 14] 0
Conv2d-167 [-1, 570, 14, 14] 54,150
BatchNorm2d-168 [-1, 570, 14, 14] 1,140
Sigmoid-169 [-1, 570, 14, 14] 0
Swish-170 [-1, 570, 14, 14] 0
Conv2d-171 [-1, 570, 7, 7] 5,130
BatchNorm2d-172 [-1, 570, 7, 7] 1,140
AdaptiveAvgPool2d-173 [-1, 570, 1, 1] 0
Conv2d-174 [-1, 47, 1, 1] 26,837
BatchNorm2d-175 [-1, 47, 1, 1] 94
ReLU-176 [-1, 47, 1, 1] 0
Conv2d-177 [-1, 570, 1, 1] 27,360
Sigmoid-178 [-1, 570, 1, 1] 0
SE-179 [-1, 570, 7, 7] 0
ReLU6-180 [-1, 570, 7, 7] 0
Conv2d-181 [-1, 103, 7, 7] 58,710
BatchNorm2d-182 [-1, 103, 7, 7] 206
LinearBottleneck-183 [-1, 103, 7, 7] 0
Conv2d-184 [-1, 618, 7, 7] 63,654
BatchNorm2d-185 [-1, 618, 7, 7] 1,236
Sigmoid-186 [-1, 618, 7, 7] 0
Swish-187 [-1, 618, 7, 7] 0
Conv2d-188 [-1, 618, 7, 7] 5,562
BatchNorm2d-189 [-1, 618, 7, 7] 1,236
AdaptiveAvgPool2d-190 [-1, 618, 1, 1] 0
Conv2d-191 [-1, 51, 1, 1] 31,569
BatchNorm2d-192 [-1, 51, 1, 1] 102
ReLU-193 [-1, 51, 1, 1] 0
Conv2d-194 [-1, 618, 1, 1] 32,136
Sigmoid-195 [-1, 618, 1, 1] 0
SE-196 [-1, 618, 7, 7] 0
ReLU6-197 [-1, 618, 7, 7] 0
Conv2d-198 [-1, 110, 7, 7] 67,980
BatchNorm2d-199 [-1, 110, 7, 7] 220
LinearBottleneck-200 [-1, 110, 7, 7] 0
Conv2d-201 [-1, 660, 7, 7] 72,600
BatchNorm2d-202 [-1, 660, 7, 7] 1,320
Sigmoid-203 [-1, 660, 7, 7] 0
Swish-204 [-1, 660, 7, 7] 0
Conv2d-205 [-1, 660, 7, 7] 5,940
BatchNorm2d-206 [-1, 660, 7, 7] 1,320
AdaptiveAvgPool2d-207 [-1, 660, 1, 1] 0
Conv2d-208 [-1, 55, 1, 1] 36,355
BatchNorm2d-209 [-1, 55, 1, 1] 110
ReLU-210 [-1, 55, 1, 1] 0
Conv2d-211 [-1, 660, 1, 1] 36,960
Sigmoid-212 [-1, 660, 1, 1] 0
SE-213 [-1, 660, 7, 7] 0
ReLU6-214 [-1, 660, 7, 7] 0
Conv2d-215 [-1, 118, 7, 7] 77,880
BatchNorm2d-216 [-1, 118, 7, 7] 236
LinearBottleneck-217 [-1, 118, 7, 7] 0
Conv2d-218 [-1, 708, 7, 7] 83,544
BatchNorm2d-219 [-1, 708, 7, 7] 1,416
Sigmoid-220 [-1, 708, 7, 7] 0
Swish-221 [-1, 708, 7, 7] 0
Conv2d-222 [-1, 708, 7, 7] 6,372
BatchNorm2d-223 [-1, 708, 7, 7] 1,416
AdaptiveAvgPool2d-224 [-1, 708, 1, 1] 0
Conv2d-225 [-1, 59, 1, 1] 41,831
BatchNorm2d-226 [-1, 59, 1, 1] 118
ReLU-227 [-1, 59, 1, 1] 0
Conv2d-228 [-1, 708, 1, 1] 42,480
Sigmoid-229 [-1, 708, 1, 1] 0
SE-230 [-1, 708, 7, 7] 0
ReLU6-231 [-1, 708, 7, 7] 0
Conv2d-232 [-1, 126, 7, 7] 89,208
BatchNorm2d-233 [-1, 126, 7, 7] 252
LinearBottleneck-234 [-1, 126, 7, 7] 0
Conv2d-235 [-1, 756, 7, 7] 95,256
BatchNorm2d-236 [-1, 756, 7, 7] 1,512
Sigmoid-237 [-1, 756, 7, 7] 0
Swish-238 [-1, 756, 7, 7] 0
Conv2d-239 [-1, 756, 7, 7] 6,804
BatchNorm2d-240 [-1, 756, 7, 7] 1,512
AdaptiveAvgPool2d-241 [-1, 756, 1, 1] 0
Conv2d-242 [-1, 63, 1, 1] 47,691
BatchNorm2d-243 [-1, 63, 1, 1] 126
ReLU-244 [-1, 63, 1, 1] 0
Conv2d-245 [-1, 756, 1, 1] 48,384
Sigmoid-246 [-1, 756, 1, 1] 0
SE-247 [-1, 756, 7, 7] 0
ReLU6-248 [-1, 756, 7, 7] 0
Conv2d-249 [-1, 134, 7, 7] 101,304
BatchNorm2d-250 [-1, 134, 7, 7] 268
LinearBottleneck-251 [-1, 134, 7, 7] 0
Conv2d-252 [-1, 896, 7, 7] 120,064
BatchNorm2d-253 [-1, 896, 7, 7] 1,792
Sigmoid-254 [-1, 896, 7, 7] 0
Swish-255 [-1, 896, 7, 7] 0
AdaptiveAvgPool2d-256 [-1, 896, 1, 1] 0
Dropout-257 [-1, 896, 1, 1] 0
Conv2d-258 [-1, 4, 1, 1] 3,588
================================================================
Total params: 1,939,058
Trainable params: 1,939,058
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 184.59
Params size (MB): 7.40
Estimated Total Size (MB): 192.56
----------------------------------------------------------------
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