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fiqa-model-summary.txt
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fiqa-model-summary.txt
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----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 112, 112] 1,728
BatchNorm2d-2 [-1, 64, 112, 112] 128
PReLU-3 [-1, 64, 112, 112] 64
MaxPool2d-4 [-1, 64, 56, 56] 0
BatchNorm2d-5 [-1, 64, 112, 112] 128
Conv2d-6 [-1, 64, 112, 112] 36,864
PReLU-7 [-1, 64, 112, 112] 64
Conv2d-8 [-1, 64, 56, 56] 36,864
BatchNorm2d-9 [-1, 64, 56, 56] 128
bottleneck_IR-10 [-1, 64, 56, 56] 0
MaxPool2d-11 [-1, 64, 56, 56] 0
BatchNorm2d-12 [-1, 64, 56, 56] 128
Conv2d-13 [-1, 64, 56, 56] 36,864
PReLU-14 [-1, 64, 56, 56] 64
Conv2d-15 [-1, 64, 56, 56] 36,864
BatchNorm2d-16 [-1, 64, 56, 56] 128
bottleneck_IR-17 [-1, 64, 56, 56] 0
MaxPool2d-18 [-1, 64, 56, 56] 0
BatchNorm2d-19 [-1, 64, 56, 56] 128
Conv2d-20 [-1, 64, 56, 56] 36,864
PReLU-21 [-1, 64, 56, 56] 64
Conv2d-22 [-1, 64, 56, 56] 36,864
BatchNorm2d-23 [-1, 64, 56, 56] 128
bottleneck_IR-24 [-1, 64, 56, 56] 0
Conv2d-25 [-1, 128, 28, 28] 8,192
BatchNorm2d-26 [-1, 128, 28, 28] 256
BatchNorm2d-27 [-1, 64, 56, 56] 128
Conv2d-28 [-1, 128, 56, 56] 73,728
PReLU-29 [-1, 128, 56, 56] 128
Conv2d-30 [-1, 128, 28, 28] 147,456
BatchNorm2d-31 [-1, 128, 28, 28] 256
bottleneck_IR-32 [-1, 128, 28, 28] 0
MaxPool2d-33 [-1, 128, 28, 28] 0
BatchNorm2d-34 [-1, 128, 28, 28] 256
Conv2d-35 [-1, 128, 28, 28] 147,456
PReLU-36 [-1, 128, 28, 28] 128
Conv2d-37 [-1, 128, 28, 28] 147,456
BatchNorm2d-38 [-1, 128, 28, 28] 256
bottleneck_IR-39 [-1, 128, 28, 28] 0
MaxPool2d-40 [-1, 128, 28, 28] 0
BatchNorm2d-41 [-1, 128, 28, 28] 256
Conv2d-42 [-1, 128, 28, 28] 147,456
PReLU-43 [-1, 128, 28, 28] 128
Conv2d-44 [-1, 128, 28, 28] 147,456
BatchNorm2d-45 [-1, 128, 28, 28] 256
bottleneck_IR-46 [-1, 128, 28, 28] 0
MaxPool2d-47 [-1, 128, 28, 28] 0
BatchNorm2d-48 [-1, 128, 28, 28] 256
Conv2d-49 [-1, 128, 28, 28] 147,456
PReLU-50 [-1, 128, 28, 28] 128
Conv2d-51 [-1, 128, 28, 28] 147,456
BatchNorm2d-52 [-1, 128, 28, 28] 256
bottleneck_IR-53 [-1, 128, 28, 28] 0
Conv2d-54 [-1, 256, 14, 14] 32,768
BatchNorm2d-55 [-1, 256, 14, 14] 512
BatchNorm2d-56 [-1, 128, 28, 28] 256
Conv2d-57 [-1, 256, 28, 28] 294,912
PReLU-58 [-1, 256, 28, 28] 256
Conv2d-59 [-1, 256, 14, 14] 589,824
BatchNorm2d-60 [-1, 256, 14, 14] 512
bottleneck_IR-61 [-1, 256, 14, 14] 0
MaxPool2d-62 [-1, 256, 14, 14] 0
BatchNorm2d-63 [-1, 256, 14, 14] 512
Conv2d-64 [-1, 256, 14, 14] 589,824
PReLU-65 [-1, 256, 14, 14] 256
Conv2d-66 [-1, 256, 14, 14] 589,824
BatchNorm2d-67 [-1, 256, 14, 14] 512
bottleneck_IR-68 [-1, 256, 14, 14] 0
MaxPool2d-69 [-1, 256, 14, 14] 0
BatchNorm2d-70 [-1, 256, 14, 14] 512
Conv2d-71 [-1, 256, 14, 14] 589,824
PReLU-72 [-1, 256, 14, 14] 256
Conv2d-73 [-1, 256, 14, 14] 589,824
BatchNorm2d-74 [-1, 256, 14, 14] 512
bottleneck_IR-75 [-1, 256, 14, 14] 0
MaxPool2d-76 [-1, 256, 14, 14] 0
BatchNorm2d-77 [-1, 256, 14, 14] 512
Conv2d-78 [-1, 256, 14, 14] 589,824
PReLU-79 [-1, 256, 14, 14] 256
Conv2d-80 [-1, 256, 14, 14] 589,824
BatchNorm2d-81 [-1, 256, 14, 14] 512
bottleneck_IR-82 [-1, 256, 14, 14] 0
MaxPool2d-83 [-1, 256, 14, 14] 0
BatchNorm2d-84 [-1, 256, 14, 14] 512
Conv2d-85 [-1, 256, 14, 14] 589,824
PReLU-86 [-1, 256, 14, 14] 256
Conv2d-87 [-1, 256, 14, 14] 589,824
BatchNorm2d-88 [-1, 256, 14, 14] 512
bottleneck_IR-89 [-1, 256, 14, 14] 0
MaxPool2d-90 [-1, 256, 14, 14] 0
BatchNorm2d-91 [-1, 256, 14, 14] 512
Conv2d-92 [-1, 256, 14, 14] 589,824
PReLU-93 [-1, 256, 14, 14] 256
Conv2d-94 [-1, 256, 14, 14] 589,824
BatchNorm2d-95 [-1, 256, 14, 14] 512
bottleneck_IR-96 [-1, 256, 14, 14] 0
MaxPool2d-97 [-1, 256, 14, 14] 0
BatchNorm2d-98 [-1, 256, 14, 14] 512
Conv2d-99 [-1, 256, 14, 14] 589,824
PReLU-100 [-1, 256, 14, 14] 256
Conv2d-101 [-1, 256, 14, 14] 589,824
BatchNorm2d-102 [-1, 256, 14, 14] 512
bottleneck_IR-103 [-1, 256, 14, 14] 0
MaxPool2d-104 [-1, 256, 14, 14] 0
BatchNorm2d-105 [-1, 256, 14, 14] 512
Conv2d-106 [-1, 256, 14, 14] 589,824
PReLU-107 [-1, 256, 14, 14] 256
Conv2d-108 [-1, 256, 14, 14] 589,824
BatchNorm2d-109 [-1, 256, 14, 14] 512
bottleneck_IR-110 [-1, 256, 14, 14] 0
MaxPool2d-111 [-1, 256, 14, 14] 0
BatchNorm2d-112 [-1, 256, 14, 14] 512
Conv2d-113 [-1, 256, 14, 14] 589,824
PReLU-114 [-1, 256, 14, 14] 256
Conv2d-115 [-1, 256, 14, 14] 589,824
BatchNorm2d-116 [-1, 256, 14, 14] 512
bottleneck_IR-117 [-1, 256, 14, 14] 0
MaxPool2d-118 [-1, 256, 14, 14] 0
BatchNorm2d-119 [-1, 256, 14, 14] 512
Conv2d-120 [-1, 256, 14, 14] 589,824
PReLU-121 [-1, 256, 14, 14] 256
Conv2d-122 [-1, 256, 14, 14] 589,824
BatchNorm2d-123 [-1, 256, 14, 14] 512
bottleneck_IR-124 [-1, 256, 14, 14] 0
MaxPool2d-125 [-1, 256, 14, 14] 0
BatchNorm2d-126 [-1, 256, 14, 14] 512
Conv2d-127 [-1, 256, 14, 14] 589,824
PReLU-128 [-1, 256, 14, 14] 256
Conv2d-129 [-1, 256, 14, 14] 589,824
BatchNorm2d-130 [-1, 256, 14, 14] 512
bottleneck_IR-131 [-1, 256, 14, 14] 0
MaxPool2d-132 [-1, 256, 14, 14] 0
BatchNorm2d-133 [-1, 256, 14, 14] 512
Conv2d-134 [-1, 256, 14, 14] 589,824
PReLU-135 [-1, 256, 14, 14] 256
Conv2d-136 [-1, 256, 14, 14] 589,824
BatchNorm2d-137 [-1, 256, 14, 14] 512
bottleneck_IR-138 [-1, 256, 14, 14] 0
MaxPool2d-139 [-1, 256, 14, 14] 0
BatchNorm2d-140 [-1, 256, 14, 14] 512
Conv2d-141 [-1, 256, 14, 14] 589,824
PReLU-142 [-1, 256, 14, 14] 256
Conv2d-143 [-1, 256, 14, 14] 589,824
BatchNorm2d-144 [-1, 256, 14, 14] 512
bottleneck_IR-145 [-1, 256, 14, 14] 0
MaxPool2d-146 [-1, 256, 14, 14] 0
BatchNorm2d-147 [-1, 256, 14, 14] 512
Conv2d-148 [-1, 256, 14, 14] 589,824
PReLU-149 [-1, 256, 14, 14] 256
Conv2d-150 [-1, 256, 14, 14] 589,824
BatchNorm2d-151 [-1, 256, 14, 14] 512
bottleneck_IR-152 [-1, 256, 14, 14] 0
Conv2d-153 [-1, 512, 7, 7] 131,072
BatchNorm2d-154 [-1, 512, 7, 7] 1,024
BatchNorm2d-155 [-1, 256, 14, 14] 512
Conv2d-156 [-1, 512, 14, 14] 1,179,648
PReLU-157 [-1, 512, 14, 14] 512
Conv2d-158 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-159 [-1, 512, 7, 7] 1,024
bottleneck_IR-160 [-1, 512, 7, 7] 0
MaxPool2d-161 [-1, 512, 7, 7] 0
BatchNorm2d-162 [-1, 512, 7, 7] 1,024
Conv2d-163 [-1, 512, 7, 7] 2,359,296
PReLU-164 [-1, 512, 7, 7] 512
Conv2d-165 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-166 [-1, 512, 7, 7] 1,024
bottleneck_IR-167 [-1, 512, 7, 7] 0
MaxPool2d-168 [-1, 512, 7, 7] 0
BatchNorm2d-169 [-1, 512, 7, 7] 1,024
Conv2d-170 [-1, 512, 7, 7] 2,359,296
PReLU-171 [-1, 512, 7, 7] 512
Conv2d-172 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-173 [-1, 512, 7, 7] 1,024
bottleneck_IR-174 [-1, 512, 7, 7] 0
Flatten-175 [-1, 25088] 0
PReLU-176 [-1, 25088] 25,088
Dropout-177 [-1, 25088] 0
Linear-178 [-1, 1] 25,089
================================================================
Total params: 30,777,537
Trainable params: 30,777,537
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.14
Forward/backward pass size (MB): 138.58
Params size (MB): 117.41
Estimated Total Size (MB): 256.13
----------------------------------------------------------------