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pytorch-image-models/timm/models/_pruned/efficientnet_b3_pruned.txt

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conv_stem.weight:[40, 3, 3, 3]***bn1.weight:[40]***bn1.bias:[40]***bn1.running_mean:[40]***bn1.running_var:[40]***bn1.num_batches_tracked:[]***blocks.0.0.conv_dw.weight:[40, 1, 3, 3]***blocks.0.0.bn1.weight:[40]***blocks.0.0.bn1.bias:[40]***blocks.0.0.bn1.running_mean:[40]***blocks.0.0.bn1.running_var:[40]***blocks.0.0.bn1.num_batches_tracked:[]***blocks.0.0.se.conv_reduce.weight:[10, 40, 1, 1]***blocks.0.0.se.conv_reduce.bias:[10]***blocks.0.0.se.conv_expand.weight:[40, 10, 1, 1]***blocks.0.0.se.conv_expand.bias:[40]***blocks.0.0.conv_pw.weight:[24, 40, 1, 1]***blocks.0.0.bn2.weight:[24]***blocks.0.0.bn2.bias:[24]***blocks.0.0.bn2.running_mean:[24]***blocks.0.0.bn2.running_var:[24]***blocks.0.0.bn2.num_batches_tracked:[]***blocks.0.1.conv_dw.weight:[24, 1, 3, 3]***blocks.0.1.bn1.weight:[24]***blocks.0.1.bn1.bias:[24]***blocks.0.1.bn1.running_mean:[24]***blocks.0.1.bn1.running_var:[24]***blocks.0.1.bn1.num_batches_tracked:[]***blocks.0.1.se.conv_reduce.weight:[6, 24, 1, 1]***blocks.0.1.se.conv_reduce.bias:[6]***blocks.0.1.se.conv_expand.weight:[24, 6, 1, 1]***blocks.0.1.se.conv_expand.bias:[24]***blocks.0.1.conv_pw.weight:[24, 24, 1, 1]***blocks.0.1.bn2.weight:[24]***blocks.0.1.bn2.bias:[24]***blocks.0.1.bn2.running_mean:[24]***blocks.0.1.bn2.running_var:[24]***blocks.0.1.bn2.num_batches_tracked:[]***blocks.1.0.conv_pw.weight:[27, 24, 1, 1]***blocks.1.0.bn1.weight:[27]***blocks.1.0.bn1.bias:[27]***blocks.1.0.bn1.running_mean:[27]***blocks.1.0.bn1.running_var:[27]***blocks.1.0.bn1.num_batches_tracked:[]***blocks.1.0.conv_dw.weight:[27, 1, 3, 3]***blocks.1.0.bn2.weight:[27]***blocks.1.0.bn2.bias:[27]***blocks.1.0.bn2.running_mean:[27]***blocks.1.0.bn2.running_var:[27]***blocks.1.0.bn2.num_batches_tracked:[]***blocks.1.0.se.conv_reduce.weight:[6, 27, 1, 1]***blocks.1.0.se.conv_reduce.bias:[6]***blocks.1.0.se.conv_expand.weight:[27, 6, 1, 1]***blocks.1.0.se.conv_expand.bias:[27]***blocks.1.0.conv_pwl.weight:[12, 27, 1, 1]***blocks.1.0.bn3.weight:[12]***blocks.1.0.bn3.bias:[12]***blocks.1.0.bn3.running_mean:[12]***blocks.1.0.bn3.running_var:[12]***blocks.1.0.bn3.num_batches_tracked:[]***blocks.1.1.conv_pw.weight:[49, 12, 1, 1]***blocks.1.1.bn1.weight:[49]***blocks.1.1.bn1.bias:[49]***blocks.1.1.bn1.running_mean:[49]***blocks.1.1.bn1.running_var:[49]***blocks.1.1.bn1.num_batches_tracked:[]***blocks.1.1.conv_dw.weight:[49, 1, 3, 3]***blocks.1.1.bn2.weight:[49]***blocks.1.1.bn2.bias:[49]***blocks.1.1.bn2.running_mean:[49]***blocks.1.1.bn2.running_var:[49]***blocks.1.1.bn2.num_batches_tracked:[]***blocks.1.1.se.conv_reduce.weight:[8, 49, 1, 1]***blocks.1.1.se.conv_reduce.bias:[8]***blocks.1.1.se.conv_expand.weight:[49, 8, 1, 1]***blocks.1.1.se.conv_expand.bias:[49]***blocks.1.1.conv_pwl.weight:[12, 49, 1, 1]***blocks.1.1.bn3.weight:[12]***blocks.1.1.bn3.bias:[12]***blocks.1.1.bn3.running_mean:[12]***blocks.1.1.bn3.running_var:[12]***blocks.1.1.bn3.num_batches_tracked:[]***blocks.1.2.conv_pw.weight:[48, 12, 1, 1]***blocks.1.2.bn1.weight:[48]***blocks.1.2.bn1.bias:[48]***blocks.1.2.bn1.running_mean:[48]***blocks.1.2.bn1.running_var:[48]***blocks.1.2.bn1.num_batches_tracked:[]***blocks.1.2.conv_dw.weight:[48, 1, 3, 3]***blocks.1.2.bn2.weight:[48]***blocks.1.2.bn2.bias:[48]***blocks.1.2.bn2.running_mean:[48]***blocks.1.2.bn2.running_var:[48]***blocks.1.2.bn2.num_batches_tracked:[]***blocks.1.2.se.conv_reduce.weight:[8, 48, 1, 1]***blocks.1.2.se.conv_reduce.bias:[8]***blocks.1.2.se.conv_expand.weight:[48, 8, 1, 1]***blocks.1.2.se.conv_expand.bias:[48]***blocks.1.2.conv_pwl.weight:[12, 48, 1, 1]***blocks.1.2.bn3.weight:[12]***blocks.1.2.bn3.bias:[12]***blocks.1.2.bn3.running_mean:[12]***blocks.1.2.bn3.running_var:[12]***blocks.1.2.bn3.num_batches_tracked:[]***blocks.2.0.conv_pw.weight:[83, 12, 1, 1]***blocks.2.0.bn1.weight:[83]***blocks.2.0.bn1.bias:[83]***blocks.2.0.bn1.running_mean:[83]***blocks.2.0.bn1.running_var:[83]***blocks.2.0.bn1.num_batches_tracked:[]***blocks.2.0.conv_dw.weight:[83, 1, 5, 5]***blocks.2.0.bn2.weight:[83]***blocks.2.0.bn2.bias:[83]***blocks.2.0.bn2.running_mean:[83]***blocks.2.0.bn2.running_var:[83]***blocks.