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analysis_resnet.txt
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analysis_resnet.txt
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Loading data
Loading training data
Files already downloaded and verified
Took 1.0200386047363281
Loading validation data
Files already downloaded and verified
Creating data loaders
layer1.0.conv1.lut torch.Size([64, 64, 16])
layer1.0.conv1.lut 1024.0
layer1.0.conv1.thresholds torch.Size([960])
layer1.0.conv2.lut torch.Size([64, 64, 16])
layer1.0.conv2.lut 1024.0
layer1.0.conv2.thresholds torch.Size([960])
layer1.1.conv1.lut torch.Size([64, 64, 16])
layer1.1.conv1.lut 1024.0
layer1.1.conv1.thresholds torch.Size([960])
layer1.1.conv2.lut torch.Size([64, 64, 16])
layer1.1.conv2.lut 1024.0
layer1.1.conv2.thresholds torch.Size([960])
layer2.0.conv1.lut torch.Size([128, 64, 16])
layer2.0.conv1.lut 2048.0
layer2.0.conv1.thresholds torch.Size([960])
layer2.0.conv2.lut torch.Size([128, 64, 16])
layer2.0.conv2.lut 2048.0
layer2.0.conv2.thresholds torch.Size([960])
layer2.0.downsample.0.lut torch.Size([128, 64, 16])
layer2.0.downsample.0.lut 2048.0
layer2.0.downsample.0.thresholds torch.Size([960])
layer2.1.conv1.lut torch.Size([128, 64, 16])
layer2.1.conv1.lut 2048.0
layer2.1.conv1.thresholds torch.Size([960])
layer2.1.conv2.lut torch.Size([128, 64, 16])
layer2.1.conv2.lut 2048.0
layer2.1.conv2.thresholds torch.Size([960])
layer3.0.conv1.lut torch.Size([256, 64, 16])
layer3.0.conv1.lut 4096.0
layer3.0.conv1.thresholds torch.Size([960])
layer3.0.conv2.lut torch.Size([256, 64, 16])
layer3.0.conv2.lut 4096.0
layer3.0.conv2.thresholds torch.Size([960])
layer3.0.downsample.0.lut torch.Size([256, 64, 16])
layer3.0.downsample.0.lut 4096.0
layer3.0.downsample.0.thresholds torch.Size([960])
layer3.1.conv1.lut torch.Size([256, 64, 16])
layer3.1.conv1.lut 4096.0
layer3.1.conv1.thresholds torch.Size([960])
layer3.1.conv2.lut torch.Size([256, 64, 16])
layer3.1.conv2.lut 4096.0
layer3.1.conv2.thresholds torch.Size([960])
layer4.0.conv1.lut torch.Size([512, 64, 16])
layer4.0.conv1.lut 8192.0
layer4.0.conv1.thresholds torch.Size([960])
layer4.0.conv2.lut torch.Size([512, 64, 16])
layer4.0.conv2.lut 8192.0
layer4.0.conv2.thresholds torch.Size([960])
layer4.0.downsample.0.lut torch.Size([512, 64, 16])
layer4.0.downsample.0.lut 8192.0
layer4.0.downsample.0.thresholds torch.Size([960])
layer4.1.conv1.lut torch.Size([512, 64, 16])
layer4.1.conv1.lut 8192.0
layer4.1.conv1.thresholds torch.Size([960])
layer4.1.conv2.lut torch.Size([512, 64, 16])
layer4.1.conv2.lut 8192.0
layer4.1.conv2.thresholds torch.Size([960])
fc.lut torch.Size([1])
fc.thresholds torch.Size([1])
total params 4867906
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
ResNet [1, 10] --
├─Conv2d: 1-1 [1, 64, 32, 32] 1,728
├─BatchNorm2d: 1-2 [1, 64, 32, 32] 128
├─ReLU: 1-3 [1, 64, 32, 32] --
├─Identity: 1-4 [1, 64, 32, 32] --
├─Sequential: 1-5 [1, 64, 32, 32] --
│ └─BasicBlock: 2-1 [1, 64, 32, 32] --
│ │ └─HalutConv2d: 3-1 [1, 64, 32, 32] 1,335,241
│ │ └─BatchNorm2d: 3-2 [1, 64, 32, 32] 128
│ │ └─ReLU: 3-3 [1, 64, 32, 32] --
│ │ └─HalutConv2d: 3-4 [1, 64, 32, 32] 1,335,241
│ │ └─BatchNorm2d: 3-5 [1, 64, 32, 32] 128
│ │ └─ReLU: 3-6 [1, 64, 32, 32] --
│ └─BasicBlock: 2-2 [1, 64, 32, 32] --
│ │ └─HalutConv2d: 3-7 [1, 64, 32, 32] 1,335,241
│ │ └─BatchNorm2d: 3-8 [1, 64, 32, 32] 128
│ │ └─ReLU: 3-9 [1, 64, 32, 32] --
│ │ └─HalutConv2d: 3-10 [1, 64, 32, 32] 1,335,241
│ │ └─BatchNorm2d: 3-11 [1, 64, 32, 32] 128
│ │ └─ReLU: 3-12 [1, 64, 32, 32] --
├─Sequential: 1-6 [1, 128, 16, 16] --
│ └─BasicBlock: 2-3 [1, 128, 16, 16] --
│ │ └─HalutConv2d: 3-13 [1, 128, 16, 16] 1,437,641
│ │ └─BatchNorm2d: 3-14 [1, 128, 16, 16] 256
│ │ └─ReLU: 3-15 [1, 128, 16, 16] --
│ │ └─HalutConv2d: 3-16 [1, 128, 16, 16] 1,511,369
│ │ └─BatchNorm2d: 3-17 [1, 128, 16, 16] 256
│ │ └─Sequential: 3-18 [1, 128, 16, 16] --
│ │ │ └─HalutConv2d: 4-1 [1, 128, 16, 16] 1,372,105
│ │ │ └─BatchNorm2d: 4-2 [1, 128, 16, 16] 256
│ │ └─ReLU: 3-19 [1, 128, 16, 16] --
│ └─BasicBlock: 2-4 [1, 128, 16, 16] --
│ │ └─HalutConv2d: 3-20 [1, 128, 16, 16] 1,511,369
│ │ └─BatchNorm2d: 3-21 [1, 128, 16, 16] 256
│ │ └─ReLU: 3-22 [1, 128, 16, 16] --
│ │ └─HalutConv2d: 3-23 [1, 128, 16, 16] 1,511,369
│ │ └─BatchNorm2d: 3-24 [1, 128, 16, 16] 256
│ │ └─ReLU: 3-25 [1, 128, 16, 16] --
├─Sequential: 1-7 [1, 256, 8, 8] --
│ └─BasicBlock: 2-5 [1, 256, 8, 8] --
│ │ └─HalutConv2d: 3-26 [1, 256, 8, 8] 1,789,897
│ │ └─BatchNorm2d: 3-27 [1, 256, 8, 8] 512
│ │ └─ReLU: 3-28 [1, 256, 8, 8] --
│ │ └─HalutConv2d: 3-29 [1, 256, 8, 8] 2,084,809
│ │ └─BatchNorm2d: 3-30 [1, 256, 8, 8] 512
│ │ └─Sequential: 3-31 [1, 256, 8, 8] --
│ │ │ └─HalutConv2d: 4-3 [1, 256, 8, 8] 1,527,753
│ │ │ └─BatchNorm2d: 4-4 [1, 256, 8, 8] 512
│ │ └─ReLU: 3-32 [1, 256, 8, 8] --
│ └─BasicBlock: 2-6 [1, 256, 8, 8] --
│ │ └─HalutConv2d: 3-33 [1, 256, 8, 8] 2,084,809
│ │ └─BatchNorm2d: 3-34 [1, 256, 8, 8] 512
│ │ └─ReLU: 3-35 [1, 256, 8, 8] --
│ │ └─HalutConv2d: 3-36 [1, 256, 8, 8] 2,084,809
│ │ └─BatchNorm2d: 3-37 [1, 256, 8, 8] 512
│ │ └─ReLU: 3-38 [1, 256, 8, 8] --
├─Sequential: 1-8 [1, 512, 4, 4] --
│ └─BasicBlock: 2-7 [1, 512, 4, 4] --
│ │ └─HalutConv2d: 3-39 [1, 512, 4, 4] 2,936,777
│ │ └─BatchNorm2d: 3-40 [1, 512, 4, 4] 1,024
│ │ └─ReLU: 3-41 [1, 512, 4, 4] --
│ │ └─HalutConv2d: 3-42 [1, 512, 4, 4] 4,116,425
│ │ └─BatchNorm2d: 3-43 [1, 512, 4, 4] 1,024
│ │ └─Sequential: 3-44 [1, 512, 4, 4] --
│ │ │ └─HalutConv2d: 4-5 [1, 512, 4, 4] 1,888,201
│ │ │ └─BatchNorm2d: 4-6 [1, 512, 4, 4] 1,024
│ │ └─ReLU: 3-45 [1, 512, 4, 4] --
│ └─BasicBlock: 2-8 [1, 512, 4, 4] --
│ │ └─HalutConv2d: 3-46 [1, 512, 4, 4] 4,116,425
│ │ └─BatchNorm2d: 3-47 [1, 512, 4, 4] 1,024
│ │ └─ReLU: 3-48 [1, 512, 4, 4] --
│ │ └─HalutConv2d: 3-49 [1, 512, 4, 4] 4,116,425
│ │ └─BatchNorm2d: 3-50 [1, 512, 4, 4] 1,024
│ │ └─ReLU: 3-51 [1, 512, 4, 4] --
├─AdaptiveAvgPool2d: 1-9 [1, 512, 1, 1] --
├─HalutLinear: 1-10 [1, 10] 5,145
==========================================================================================
Total params: 39,447,620
Trainable params: 16,041,866
Non-trainable params: 23,405,754
Total mult-adds (M): 555.43
==========================================================================================
Input size (MB): 0.01
Forward/backward pass size (MB): 9.83
Params size (MB): 157.81
Estimated Total Size (MB): 167.65
==========================================================================================