mit-han-lab / amc Goto Github PK

Hello,
I already evaluated amc code using mobiilenet with imagenet and cifar10 dataset.
It works well.

I would like to explore other networks such as mobilenet_v2, resnet50, inception_v3 etc.
According to the paper, some of these models are already implemented.
When these models are available?

the code logic in the #371 line of channel_pruning_env.py is missing one line code: self.shared_idx.append(share_group) should be added after the for loop.

MobileNetV2 pls！

Hi, thanks for your great work!

However, I find that it always validate the original model under "train" mode.
In env.step(), it validates the model(line 123) to get the reward after prune_kernel() operation(line 97).
But the model will be pruned only under the "export" mode as line 249 to line 261.

So what's the problem?

Hello, thanks for your work.

I modified your code to prune 3x3 conv layers using im2col in order to prune VGG16. The result is, about 40% accuracy loss from baseline before re-training, and 3.7% loss after re-training. This is much worse than your result. I used the experimental settings written in the paper as far as possible, and for some settings that could not be found in the paper, I used the default settings. I also tried some other settings (--use_new_input, --n_calibration_batches=XX, and so on), all failed.

I realized that I pruned lots of channels in Conv5-1 and -2 layers (they are not very redundant and should not be pruned, according to your previous paper about channel pruning.) Therefore, one possibility for improvement is manually excluding those layers from the pruning targets. However, I think AMC should be able to do it automatically.

Do you have any idea how to reproduce the paper's result? For the codes of pruning 3x3 conv, please see the pull request. Is the implementation correct?

I had tried to compress mobilenet v1 model /w visual wake word dataset, the compression ratio can not too high, e.g. 7 times (or 14% of original model size), the accuracy drop is obvious.

However, I compress the model by two stage, i.e., 1st 25% and 2nd 56%, the accuracy can be kept as high as original model and the total compression ratio could be 14%.

Does it mean that amc can't work well in high compression ratio? Or I misunderstand your algorithm? Do you have test high compression ratio case?

Hi all,
I use the im2col to implement the conv3*3 linearRegression, but the prune speed is slow, it takes about 2 days to finish 800 steps train. The reward is converage, but the acc reward is low (prune 50%) , the best acc is about 8%。 the resnet-18 is better acc highest reward is 23%. Is this phenomenon normal ?

Hi, thanks for your great job.
I wonder to know how can I get the right imagenet dataset you used for strategy search on mobilenet?

How to search&export resnet and mobilenet v2? Can you give me some advice? Thank you very much.

I'm trying to compress ProxlessNAS but met the following error.
where the model comes from
model = torch.hub.load('mit-han-lab/ProxylessNAS', "proxyless_cpu", pretrained=True)

Could you please give me comments to fix the problem?

Error message:
File "amc_search.py", line 262, in
train(args.train_episode, agent, env, args.output)
File "amc_search.py", line 142, in train
observation2, reward, done, info = env.step(action)
File "/home/work/amc_vww_proxless/env/channel_pruning_env.py", line 97, in step
action, d_prime, preserve_idx = self.prune_kernel(self.prunable_idx[self.cur_ind], action, preserve_idx)
File "/home/work/amc_vww_proxless/env/channel_pruning_env.py", line 228, in prune_kernel
masked_X = X[:, mask]
IndexError: boolean index did not match indexed array along dimension 1; dimension is 80 but corresponding boolean dimension is 40

Hey. I notice that on the imagenet task, the accuracy of a model after pruning is used to calculate reward. In object detection scenarios, what do you use to calculate that? mAP, loss?

Thank you for sharing the code.

1. In the env/channel_pruning_env.py, line 282 and 285, found conflicts between code and comments:

if i == self.cur_ind - 1:  # TODO: add other member in the set
  this_comp += flop * self.strategy_dict[idx][0]
  # add buffer (but not influenced by ratio)
  other_comp += buffer_flop * self.strategy_dict[idx][0]
elif i == self.cur_ind:
  this_comp += flop * self.strategy_dict[idx][1]
  # also add buffer here (influenced by ratio)
  this_comp += buffer_flop

2. Why you only consider input reduction of buffer flops?

Hello,
In the AMC paper, I see that the author also compressed Plain-20 and ResNet on CIFAR-10 dataset. Currently, I want to compress Alexnet on CIFAR-10. So, can I base on this code to compress Alexnet? And if it's possible, so what portion of code or variables are needed to modified, etc

Hello,
I wonder that when the mobilenet v2 is supported.
If that's hard to do for now, could you please explain the details?
For example, what portion of code or variables are needed to modified, etc.

Thank you

Hi, thanks for your great work.

I want to compare the inference time after compressing MobileNet. I measured with the batch size set to 1 with the code snippet below, is this the correct way? (eval_mobilenet.py)

start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
start.record()
outputs = net(inputs) # forward
end.record()
torch.cuda.synchronize()
curr_time = start.elapsed_time(end)

When measured, the inference time of MobileNet before and after compression was about 2.9 ms. This is average of curr_time. The value is much larger than the inference time of the paper(0.4 ms), so I want to know how the inference time was measured.

thank you.

Can the backbone pruned from this code be used for target detection tasks? Or does it mean that this method can compress the task of target detection? @tonylins

Thank you for sharing the code. In the /checkpoints，I didn't find download.sh, Can you give me some information? And, when i run ./scripts/search_mobilenetv2_0.7flops.sh, a mistake was encountered, as follows:
~/AMC$ ./scripts/search_mobilenetv2_0.7flops.sh
=> Preparing data: imagenet...
=> Conv layers to share channels: [[4, 6], [8, 10, 12], [14, 16, 18, 20], [22, 24, 26]]
=> Prunable layer idx: [3, 11, 15, 21, 25, 31, 35, 41, 45, 51, 55, 61, 65, 71, 75, 81, 85, 91, 95, 101, 105, 111, 115, 121, 125, 131, 135, 141, 145, 151, 155, 161, 165, 171, 174, 179]
=> Buffer layer idx: [8, 18, 28, 38, 48, 58, 68, 78, 88, 98, 108, 118, 128, 138, 148, 158, 168]
=> Initial min strategy dict: {3: [1, 0.2], 11: [0.2, 0.2], 15: [0.2, 0.2], 21: [0.2, 0.2], 25: [0.2, 0.2], 31: [0.2, 0.2], 35: [0.2, 0.2], 41: [0.2, 0.2], 45: [0.2, 0.2], 51: [0.2, 0.2], 55: [0.2, 0.2], 61: [0.2, 0.2], 65: [0.2, 0.2], 71: [0.2, 0.2], 75: [0.2, 0.2], 81: [0.2, 0.2], 85: [0.2, 0.2], 91: [0.2, 0.2], 95: [0.2, 0.2], 101: [0.2, 0.2], 105: [0.2, 0.2], 111: [0.2, 0.2], 115: [0.2, 0.2], 121: [0.2, 0.2], 125: [0.2, 0.2], 131: [0.2, 0.2], 135: [0.2, 0.2], 141: [0.2, 0.2], 145: [0.2, 0.2], 151: [0.2, 0.2], 155: [0.2, 0.2], 161: [0.2, 0.2], 165: [0.2, 0.2], 171: [0.2, 0.2], 174: [0.2, 0.2], 179: [0.2, 1]}
=> Extracting information...
=> shape of embedding (n_layer * n_dim): (36, 10)
=> original acc: 97.067%
=> original weight size: 3.4708 M param
=> FLOPs:
[10.838016, 3.612672, 6.422528, 19.267584, 2.709504, 7.225344, 10.838016, 4.064256, 10.838016, 10.838016, 1.016064, 3.612672, 4.816896, 1.354752, 4.816896, 4.816896, 1.354752, 4.816896, 4.816896, 0.338688, 2.408448, 4.816896, 0.677376, 4.816896, 4.816896, 0.677376, 4.816896, 4.816896, 0.677376, 4.816896, 4.816896, 0.677376, 7.225344, 10.838016, 1.016064, 10.838016, 10.838016, 1.016064, 10.838016, 10.838016, 0.254016, 4.51584, 7.5264, 0.42336, 7.5264, 7.5264, 0.42336, 7.5264, 7.5264, 0.42336, 15.0528, 20.0704, 1.281]
=> original FLOPs: 300.7753 M
=> Saving logs to ./logs/mobilenetv2_imagenet_r0.7_search-run1
=> Output path: ./logs/mobilenetv2_imagenet_r0.7_search-run1...
** Actual replay buffer size: 3600
Traceback (most recent call last):
File "amc_search.py", line 242, in
train(args.train_episode, agent, env, args.output)
File "amc_search.py", line 122, in train
observation2, reward, done, info = env.step(action)
File "/home/wangzhaoming/AMC/env/channel_pruning_env.py", line 152, in step
self.layer_embedding[self.cur_ind][-2] = sum(self.flops_list[self.cur_ind + 1:]) * 1. / self.org_flops # rest
AttributeError: 'ChannelPruningEnv' object has no attribute 'flops_list'
Can it be solved? Thank you very much.

Frist, thank you for your brilliant work!
I am wondering that after 2400 episodes of training, how could I reuse the RL model? The --resume argument doesn't work. The load_weights function in agent.py seems have no apperant effect on the accuracy in the begining of training. Is the well-trained RL model useless to other pruning task?

I use the pretrained model, but the result is too bad

Hi, could you please explain how to organize the data folder structure for imagenet?

@zhijian-liu Hi, have you compressed ResNet on ImageNet using AMC, Could you share your result?

Hi, thanks for your gread jod.
I just wonder can the Mobilenet V2 work now? or maybe I sould modify some code to make it work?