@capitaso Thank you for implementing NxN conv. However, in your implementation, there is a serious error where could not run "least_square_sklearn" because of 4 dimension inputs.

Thanks,

@li-yun Thanks for reporting the error. Can you share the error massage? and what model did you try to prune?

The 4-dimensional inputs are reshaped into 2-dimensional matrix when using least_squares_sklearn, so that should not happen, but I may have done something wrong with it.

@capitaso Sure. I tried to prune a pre-trained VGG16. I also added the following error message.

Traceback (most recent call last):
File "amc_search.py", line 233, in
train(args.train_episode, agent, env, args.output)
File "amc_search.py", line 132, in train
observation2, reward, done, info = env.step(action)
File "/home/liyun/model_compression/amc/env/channel_pruning_env.py", line 99, in step
action, d_prime, preserve_idx = self.prune_kernel(self.prunable_idx[self.cur_ind], action, preserve_idx)
File "/home/liyun/model_compression/amc/env/channel_pruning_env.py", line 284, in prune_kernel
rec_weight = least_square_sklearn(X=masked_X, Y=Y)
File "/home/liyun/model_compression/amc/lib/utils.py", line 130, in least_square_sklearn
reg.fit(X, Y)
File "/home/liyun/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py", line 505, in fit
X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],
File "/home/liyun/anaconda3/lib/python3.8/site-packages/sklearn/base.py", line 432, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/home/liyun/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py", line 72, in inner_f
return f(**kwargs)
File "/home/liyun/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py", line 795, in check_X_y
X = check_array(X, accept_sparse=accept_sparse,
File "/home/liyun/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py", line 72, in inner_f
return f(**kwargs)
File "/home/liyun/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py", line 640, in check_array
raise ValueError("Found array with dim %d. %s expected <= 2."
ValueError: Found array with dim 4. Estimator expected <= 2.
liyun@ferrari:~/model_compression/amc$

Thank you for replying to my message.

@li-yun Thanks for the additional info. I quickly checked, and at least, I put something like below and it did not cause error.

python amc_search.py --job=train --model=vgg16 --ckpt_path=checkpoints/vgg16.pth --dataset=imagenet --data_root=../../datasets/ILSVRC2012 --preserve_ratio=0.5 --lbound=0.2 --rbound=1 --reward=acc_reward --n_calibration_batches 15 --seed 2018

Then, can you tell me, in what phase do you have the error? "strategy search" (--job=train) or "export" (--job=export)?

@li-yun Although I am not sure this is the cause of the error you have, I found a bug related to the first FC layer that happens in the exporting phase. Actually, I used AMC to prune the convolution layers only, and did not check about FC layers carefully. If you want to prune only convolution layers, the following change (in "env/channel_pruning_env.py" at line 24) may solve the problem. The bug will be fixed in the next few weeks.

•    self.prunable_layer_types = [torch.nn.modules.conv.Conv2d, torch.nn.modules.linear.Linear]
  

•    self.prunable_layer_types = [torch.nn.modules.conv.Conv2d]
  

@capitaso The error was in the search phase. My plan is also going to prune the convolution layers.

I believe the error is in the line "rec_weight = least_square_sklearn(X=masked_X, Y=Y)" in "env/channel_pruning_env.py". masked_X and Y are 4 dimension inputs where are [3000, 16, 32, 32] and [3000, 64, 32, 32] in my case. Because their size is great than 2, the linear regression function in sklearn fails to perform linear regression. Do you have any thoughts?

Another thing is that I am planning to prune the VGG16 model that is trained on CIFAR10 rather than Imagenet. I doubt the command you provided is working for me.

Thanks

@li-yun And what is the kernel width and height in that layer? If its 1 x 1, there might be a problem at line 229. I will fix it, but please clarify the kernel width/height.

I used a 3 by 3 kernel in that layer. Yeah. I agree with that.

@li-yun Then, the problem is something else... Can you share the whole network architecture? Pasting the output of "print(model)" will help.

@capitaso Sorry to reply to the message later. Sure. The following is the network architecture.

vgg(
(feature): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): ReLU(inplace=True)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(3): ReLU(inplace=True)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(6): ReLU(inplace=True)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): ReLU(inplace=True)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): ReLU(inplace=True)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(15): ReLU(inplace=True)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(18): ReLU(inplace=True)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(20): ReLU(inplace=True)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(22): ReLU(inplace=True)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(25): ReLU(inplace=True)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(27): ReLU(inplace=True)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(29): ReLU(inplace=True)
)
(classifier): Linear(in_features=512, out_features=10, bias=True)
)

Thanks

@li-yun Sorry, I'm late. I fixed a bit and committed. Can you try the new one? I think it should work now. If it still does not work, probably I need your source code to debug more.

@capitaso Thank you so much!! I will try the new one.

@capitaso I tried the new one, but I got a different error, which is IndexError: boolean index did not match indexed array along dimension 1; dimension is 65536 but corresponding boolean dimension is 64.

I guess the problem is in these lines.

231 k_size = int(X.shape[1] / weight.shape[1])
232 XX = X.reshape((X.shape[0],-1,k_size))
233 masked_X = XX[:, mask, :]
234 masked_X = masked_X.reshape((masked_X.shape[0],-1))

The shape of X and weight is [3000, 64, 32, 32] and [64, 64, 3, 3], respectively.

@capitaso please skip the previous message. The code is working.

Thank you for your work on this, and I implement the code. It works. But have you fix the accuracy problem of vgg model? I have the same problem you’ve mentioned.

@Beeeam Do you mean this problem? No, I could not fix it. After some struggling, I gave it up.

@capitaso Thanks for your relpy. Besides I am also curious about the parameter 'n_points_eachlayer'. I used a larger one(from 10 to 20), but got a worse results.

@Beeeam I think i did that too, but changing the hyper-parameters did not work at all. And I have no idea what is the remaining problem...

But, my implementation is maybe ok. I fixed the pruning rate (did not use amc) and confirmed it worked well.

@capitaso The hyper-parameters I found really importan is warmup...

Also, I am thinking that using filter pruning will help?

@Beeeam What do you exactly mean by filter pruning? Something like Fig. 1 (a) of this? If so, I have not tried it, but to my understanding filter pruning is equivalent to channel pruning in the previous layer.

@capitaso filter_pruned_num = int(weight_torch.size()[0] * (1 - compress_rate)) select weight by this dimension. It seems finer-grained than channel pruning.

@Beeeam Sorry for my late reply. I did not find such code in env/channel_pruning_env.py. Can you please specify the lines you are mentioning?