Comments (6)
pyt1234
commented on June 6, 2024
4
@he-y I was wondering how did you prune the last conv in the residual block but not prune the downsample conv? how did you make the summation? For example:
name of layer: layer1.0.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.0.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.0.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.0.downsample.0.weight
*** small model torch.Size([256, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.0.downsample.1.weight
*** small model torch.Size([256])
*** big model torch.Size([256])
name of layer: layer1.0.downsample.1.bias
*** small model torch.Size([256])
*** big model torch.Size([256])
180 vs 256 channels? Also, ref #38
from filter-pruning-geometric-median.
he-y
commented on June 6, 2024
Please take a look at the dimension of the original and pruned model (Part of ResNet-50).
name of layer: conv1.weight
*** small model torch.Size([45, 3, 7, 7])
*** big model torch.Size([64, 3, 7, 7])
name of layer: bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.conv1.weight
*** small model torch.Size([45, 45, 1, 1])
*** big model torch.Size([64, 64, 1, 1])
name of layer: layer1.0.bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.conv2.weight
*** small model torch.Size([45, 45, 3, 3])
*** big model torch.Size([64, 64, 3, 3])
name of layer: layer1.0.bn2.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.bn2.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.0.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.0.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.0.downsample.0.weight
*** small model torch.Size([256, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.0.downsample.1.weight
*** small model torch.Size([256])
*** big model torch.Size([256])
name of layer: layer1.0.downsample.1.bias
*** small model torch.Size([256])
*** big model torch.Size([256])
name of layer: layer1.1.conv1.weight
*** small model torch.Size([45, 256, 1, 1])
*** big model torch.Size([64, 256, 1, 1])
name of layer: layer1.1.bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.conv2.weight
*** small model torch.Size([45, 45, 3, 3])
*** big model torch.Size([64, 64, 3, 3])
name of layer: layer1.1.bn2.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.bn2.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.1.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.1.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.2.conv1.weight
*** small model torch.Size([45, 256, 1, 1])
*** big model torch.Size([64, 256, 1, 1])
name of layer: layer1.2.bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.conv2.weight
*** small model torch.Size([45, 45, 3, 3])
*** big model torch.Size([64, 64, 3, 3])
name of layer: layer1.2.bn2.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.bn2.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.2.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.2.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
from filter-pruning-geometric-median.
Yuting-Gao
commented on June 6, 2024
Do you have resnet20 model on cifar10？------------------ 原始邮件 ------------------
发件人: "Yang He"<[email protected]>
发送时间: 2019年8月5日(星期一) 下午3:23
收件人: "he-y/filter-pruning-geometric-median"<[email protected]>;
抄送: "Yuting-Gao"<[email protected]>;"Author"<[email protected]>;
主题: Re: [he-y/filter-pruning-geometric-median] residual network pruning(#23)
Please take a look at the dimension of the original and pruned model (Part of ResNet-50).
name of layer: conv1.weight
*** small model torch.Size([45, 3, 7, 7])
*** big model torch.Size([64, 3, 7, 7])
name of layer: bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.conv1.weight
*** small model torch.Size([45, 45, 1, 1])
*** big model torch.Size([64, 64, 1, 1])
name of layer: layer1.0.bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.conv2.weight
*** small model torch.Size([45, 45, 3, 3])
*** big model torch.Size([64, 64, 3, 3])
name of layer: layer1.0.bn2.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.bn2.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.0.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.0.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.0.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.0.downsample.0.weight
*** small model torch.Size([256, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.0.downsample.1.weight
*** small model torch.Size([256])
*** big model torch.Size([256])
name of layer: layer1.0.downsample.1.bias
*** small model torch.Size([256])
*** big model torch.Size([256])
name of layer: layer1.1.conv1.weight
*** small model torch.Size([45, 256, 1, 1])
*** big model torch.Size([64, 256, 1, 1])
name of layer: layer1.1.bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.conv2.weight
*** small model torch.Size([45, 45, 3, 3])
*** big model torch.Size([64, 64, 3, 3])
name of layer: layer1.1.bn2.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.bn2.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.1.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.1.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.1.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.2.conv1.weight
*** small model torch.Size([45, 256, 1, 1])
*** big model torch.Size([64, 256, 1, 1])
name of layer: layer1.2.bn1.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.bn1.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.conv2.weight
*** small model torch.Size([45, 45, 3, 3])
*** big model torch.Size([64, 64, 3, 3])
name of layer: layer1.2.bn2.weight
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.bn2.bias
*** small model torch.Size([45])
*** big model torch.Size([64])
name of layer: layer1.2.conv3.weight
*** small model torch.Size([180, 45, 1, 1])
*** big model torch.Size([256, 64, 1, 1])
name of layer: layer1.2.bn3.weight
*** small model torch.Size([180])
*** big model torch.Size([256])
name of layer: layer1.2.bn3.bias
*** small model torch.Size([180])
*** big model torch.Size([256])
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he-y
commented on June 6, 2024
Please take a look at this implementation.
from filter-pruning-geometric-median.
Yuting-Gao
commented on June 6, 2024
The results in geometric median use the soft filter pruning strategy, right?
from filter-pruning-geometric-median.
he-y
commented on June 6, 2024
The final structures of these two methods are the same.
The differences are the pruning criterion and performance.
from filter-pruning-geometric-median.
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