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Sanster / pytorch-network-slimming

A package to make do Network Slimming a little easier
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AssertionError: len(in_keep_idxes): 2048, module.weight.shape[1]: 128 #11

Open GeneralJing opened 2 years ago

GeneralJing commented 2 years ago

Traceback (most recent call last): File "gen_schema.py", line 77, in pruner.run(0.6) File "/root/anaconda3/envs/slim/lib/python3.8/site-packages/pns-0.1.0-py3.8.egg/pns/pns.py", line 307, in run conv2d.prune( File "/root/anaconda3/envs/slim/lib/python3.8/site-packages/pns-0.1.0-py3.8.egg/pns/pns.py", line 50, in prune self.in_channels_keep_idxes, self.out_channels_keep_idxes = prune_conv2d( File "/root/anaconda3/envs/slim/lib/python3.8/site-packages/pns-0.1.0-py3.8.egg/pns/functional.py", line 75, in prune_conv2d assert ( AssertionError: len(in_keep_idxes): 2048, module.weight.shape[1]: 128 报错如上。 模型结构： model: Net( (backbone): ResNetWrapper( (model): ResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer2): Sequential( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer3): Sequential( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer4): Sequential( (0): Bottleneck( (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) ) (out): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) ) (xxx): xxx( (conv_d0): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_u0): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_r0): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_l0): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_d1): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_u1): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_r1): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_l1): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_d2): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_u2): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_r2): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_l2): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_d3): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_u3): Conv2d(128, 128, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (conv_r3): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (conv_l3): Conv2d(128, 128, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) ) (decoder): BUSD( (layers): ModuleList( (0): UpsamplerBlock( (conv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (follows): ModuleList( (0): non_bottleneck_1d( (conv3x1_1): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_1): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (conv3x1_2): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_2): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn2): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (dropout): Dropout2d(p=0, inplace=False) ) (1): non_bottleneck_1d( (conv3x1_1): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_1): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (conv3x1_2): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_2): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn2): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (dropout): Dropout2d(p=0, inplace=False) ) ) (interpolate_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (interpolate_bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) ) (1): UpsamplerBlock( (conv): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (follows): ModuleList( (0): non_bottleneck_1d( (conv3x1_1): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_1): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn1): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (conv3x1_2): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_2): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn2): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (dropout): Dropout2d(p=0, inplace=False) ) (1): non_bottleneck_1d( (conv3x1_1): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_1): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn1): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (conv3x1_2): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_2): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn2): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (dropout): Dropout2d(p=0, inplace=False) ) ) (interpolate_conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (interpolate_bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) ) (2): UpsamplerBlock( (conv): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (follows): ModuleList( (0): non_bottleneck_1d( (conv3x1_1): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_1): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn1): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (conv3x1_2): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_2): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn2): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (dropout): Dropout2d(p=0, inplace=False) ) (1): non_bottleneck_1d( (conv3x1_1): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_1): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn1): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (conv3x1_2): Conv2d(16, 16, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv1x3_2): Conv2d(16, 16, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (bn2): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (dropout): Dropout2d(p=0, inplace=False) ) ) (interpolate_conv): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (interpolate_bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) ) ) (output_conv): Conv2d(16, 6, kernel_size=(1, 1), stride=(1, 1), bias=False) ) (heads): ExistHead( (dropout): Dropout2d(p=0.1, inplace=False) (conv8): Conv2d(128, 6, kernel_size=(1, 1), stride=(1, 1)) (fc9): Linear(in_features=5280, out_features=128, bias=True) (fc10): Linear(in_features=128, out_features=5, bias=True) ) )