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View Code? Open in Web Editor NEW(CVPR 2021, Oral) Dynamic Slimmable Network
(CVPR 2021, Oral) Dynamic Slimmable Network
请问大佬下面这个问题是为什么
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
/root/anaconda3/envs/0108/lib/python3.6/site-packages/torchvision/io/image.py:11: UserWarning: Failed to load image Python extension: /root/anaconda3/envs/0108/lib/python3.6/site-packages/torchvision/image.so: undefined symbol: _ZNK3c106IValue23reportToTensorTypeErrorEv
warn(f"Failed to load image Python extension: {e}")
/root/anaconda3/envs/0108/lib/python3.6/site-packages/torchvision/io/image.py:11: UserWarning: Failed to load image Python extension: /root/anaconda3/envs/0108/lib/python3.6/site-packages/torchvision/image.so: undefined symbol: _ZNK3c106IValue23reportToTensorTypeErrorEv
warn(f"Failed to load image Python extension: {e}")
01/21 05:42:18 AM Added key: store_based_barrier_key:1 to store for rank: 1
01/21 05:42:18 AM Added key: store_based_barrier_key:1 to store for rank: 0
01/21 05:42:18 AM Training in distributed mode with multiple processes, 1 GPU per process. Process 0, total 2.
01/21 05:42:18 AM Training in distributed mode with multiple processes, 1 GPU per process. Process 1, total 2.
01/21 05:42:20 AM Model slimmable_mbnet_v1_bn_uniform created, param count: 7676204
01/21 05:42:20 AM Data processing configuration for current model + dataset:
01/21 05:42:20 AM input_size: (3, 224, 224)
01/21 05:42:20 AM interpolation: bicubic
01/21 05:42:20 AM mean: (0.485, 0.456, 0.406)
01/21 05:42:20 AM std: (0.229, 0.224, 0.225)
01/21 05:42:20 AM crop_pct: 0.875
01/21 05:42:20 AM NVIDIA APEX not installed. AMP off.
01/21 05:42:21 AM Using torch DistributedDataParallel. Install NVIDIA Apex for Apex DDP.
01/21 05:42:21 AM Scheduled epochs: 40
01/21 05:42:21 AM Training folder does not exist at: images/train
01/21 05:42:21 AM Training folder does not exist at: images/train
Killing subprocess 239
Killing subprocess 240
Traceback (most recent call last):
File "/root/anaconda3/envs/0108/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/root/anaconda3/envs/0108/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/root/anaconda3/envs/0108/lib/python3.6/site-packages/torch/distributed/launch.py", line 340, in
main()
File "/root/anaconda3/envs/0108/lib/python3.6/site-packages/torch/distributed/launch.py", line 326, in main
sigkill_handler(signal.SIGTERM, None) # not coming back
File "/root/anaconda3/envs/0108/lib/python3.6/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/root/anaconda3/envs/0108/bin/python', '-u', 'train.py', '--local_rank=1', 'images', '-c', './configs/mobilenetv1_bn_uniform_reset_bn.yml']' returned non-zero exit status 1.
It is not easy to deploy gate operator with some other backends, like TensorRT.
So my question is can we futher improve autoalim without the dynamic gate when inference?Any ongoing work are doing this?
Hi,
Could you provide the approximate date for releasing the Stage II training code?
Why you set num_choice in mobilenetv1_bn_uniform_reset_bn.yml as 4, but set this parameter as 14 in the other two yml file?
老哥,如果你也是**人,咱们还是用中文交流吧,我英语水平比较感人。。。
I'm glad to see such a perfect work. I want to ask how the gradient passes through slim and how to set the parameter( ) in the optimizer.
Hi, is it possible to release some of the pertained models? Thank you!
I follow your suggestion to set the num_choice
in mobilenetv1_bn_uniform_reset_bn.yml to 14, but get an expected error when I use python -m torch.distributed.launch --nproc_per_node=8 train.py /PATH/TO/ImageNet -c ./configs/mobilenetv1_bn_uniform_reset_bn.yml.
08/25 10:15:57 AM Recalibrating BatchNorm statistics...
08/25 10:16:10 AM Finish recalibrating BatchNorm statistics.
08/25 10:16:19 AM Finish recalibrating BatchNorm statistics.
08/25 10:16:21 AM Test: [ 0/0] Mode: 0 Time: 0.344 (0.344) Loss: 6.9204 (6.9204) Prec@1: 0.0000 ( 0.0000) Prec@5: 0.0000 ( 0.0000) Flops: 132890408 (132890408)
08/25 10:16:22 AM Test: [ 0/0] Mode: 1 Time: 0.406 (0.406) Loss: 6.9189 (6.9189) Prec@1: 0.0000 ( 0.0000) Prec@5: 0.0000 ( 0.0000) Flops: 152917440 (152917440)
08/25 10:16:22 AM Test: [ 0/0] Mode: 2 Time: 0.381 (0.381) Loss: 6.9187 (6.9187) Prec@1: 0.0000 ( 0.0000) Prec@5: 0.0000 ( 0.0000) Flops: 175152224 (175152224)
08/25 10:16:23 AM Test: [ 0/0] Mode: 3 Time: 0.389 (0.389) Loss: 6.9134 (6.9134) Prec@1: 0.0000 ( 0.0000) Prec@5: 0.0000 ( 0.0000) Flops: 199594752 (199594752)
Traceback (most recent call last):
File "train.py", line 658, in
main()
File "train.py", line 635, in main
eval_metrics.append(validate_slim(model,
File "/home/chauncey/PycharmProjects/DS-Net-main/dyn_slim/apis/train_slim.py", line 215, in validate_slim
output = model(input)
File "/home/chauncey/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/chauncey/PycharmProjects/DS-Net-main/dyn_slim/models/dyn_slim_net.py", line 191, in forward
x = self.forward_features(x)
File "/home/chauncey/PycharmProjects/DS-Net-main/dyn_slim/models/dyn_slim_net.py", line 178, in forward_features
x = stage(x)
File "/home/chauncey/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/chauncey/PycharmProjects/DS-Net-main/dyn_slim/models/dyn_slim_stages.py", line 48, in forward
x = self.first_block(x)
File "/home/chauncey/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/chauncey/PycharmProjects/DS-Net-main/dyn_slim/models/dyn_slim_blocks.py", line 240, in forward
x = self.conv_pw(x)
File "/home/chauncey/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/chauncey/PycharmProjects/DS-Net-main/dyn_slim/models/dyn_slim_ops.py", line 94, in forward
self.running_outc = self.out_channels_list[self.channel_choice]
IndexError: list index out of range
It looks like we should make some adjustment in other py files.
Thank you for your very nice work,I want to know that the effect of gumble softmax,because I think the network can be trained without gumble softmax.
Is the gumbel softmax just aimed to increase the randomness of channel choice?
Hi authors,
Thanks for releasing the code and pre-trained model.
Could you provide a small script or some instructions to perform inference in a dynamic mode?
I am more interested in observing how each sample activates respective paths.
Thanks in advance!
can you please show us the code with objection detection?thank you!
Hi. Thanks for your work. I am reading your paper and trying to reimplement, and I feel confused about some details.
You mentioned in your paper that the slimming ratio ρ∈[0.35 : 0.05 : 1.25], which have 18 paths.
However, in your code, there are only 14 paths ρ∈[0.35 : 0.05 : 1] as mentioned in
DS-Net/dyn_slim/models/dyn_slim_net.py
Line 36 in 15cd303
DS-Net/dyn_slim/models/dyn_slim_blocks.py
Line 204 in 15cd303
Hello. Thanks for your work. I noticed that you also conducted some experiments in object detection. I wonder whether or when you will release the code
Hi,could you provide the environment for the project?I try to train the network with python=3.8 pytorch=1.7.1,cuda=10.2.Shortly after starting training,there's a RuntimeError: CUDA error: device-side assert triggered happened,and some other environment also lead to this error.I'm not sure whether the problem is caused by the difference of environment.
Hi,
I found that ensemble_ib is set to False for both slim training and gate training from the configs, but from paper it would boost the performance when set toTrue.
Any idea?
Dear authors, thanks for this nice work.
I wonder why the calculation of the SGS loss is using the softmaxed data rather than the logits, considering the PyTorch CrossEntropyLoss already contains a softmax inside.
DS-Net/dyn_slim/apis/train_slim_gate.py
Line 98 in 15cd303
DS-Net/dyn_slim/models/dyn_slim_blocks.py
Lines 324 to 355 in 15cd303
Thank you for your great work! I have a question about the latency. Could the method achieve actual acceleration on Resnet?
Hi Changlin, your work is excellent. I have a question about the calculation of MAdds, in README.md the MAdds of Subnetwork 13
is 565M, but I think the MAdds of Subnetwork 13
should be 821M observed in my experiments, because the channel number of Subnetwork 13
is larger than the original MobileNetV1, and the original MobileNetV1 1.0's MAdds should be 565M. Looking forward to your reply.
Why I get an warning:
/home/chauncey/.local/lib/python3.8/site-packages/torchvision/transforms/functional.py:364: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. warnings.warn(
when I use
python3 -m torch.distributed.launch --nproc_per_node=1 train.py ./imagenet -c ./configs/mobilenetv1_bn_uniform.yml
Thank you for your great work, and I have a question about how to calculate MAdds in your paper.
The dynamic network has different widths and MAdds for each instance, but you denoted MAdds for your networks.
Are they the average MAdds for the whole dataset?
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