Integrate Audiovisual SlowFast Networks into the repo #219
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Hi, There is a problem with the visualization of video. Audio are not provided so the next error is printed. Traceback (most recent call last): |
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Hey @fanyix Thanks for committing the code into PySlowFast, could you remind me if you have trained the av slowfast model in PySlowFast codebase? Could you remind me what is the performance you got? Thanks, |
Hi Haoqi, I haven't trained from scratch using pySlowFast, however I did try converting caffe2 models and finetune it with a short schedule in pySlowFast. I got something around 0.5 gap to the number achieved in the caffe2 codebase. It's possible my finetuning schedule is not optimized so it would be great if you can try it on your end (or even better train from scratch). |
| self.t_relu = nn.ReLU(inplace=self._inplace_relu) | ||
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| # 1x1x3, BN, ReLU. | ||
| self.f = nn.Conv3d( |
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Hi, I am trying to re-implement AVSlowfast, and feel confused here.
In the paper (Table 1), the dim_inner should be the same as the planes, but the code here shows that the dim_inner is 2 * planes. i.e., for res2: [3×1, 1×3], 32 should be [3×1, 1×3], 64.
| dim_inner // cfg.SLOWFAST.AU_BETA_INV | ||
| ], | ||
| temp_kernel_sizes=temp_kernel[1], | ||
| stride=[1] * 3, |
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Besides, here the stride means the stride of res1 of the audio pathway is 1. However in the paper Downsampling in time-frequency space is performed by stride 2^2 convolution in the center (“bottleneck”) filter of the first residual block in each stage from res2 to res5. In the code, it seems that res2 is not included.
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Please see projects/avslowfast/README.md for a starter for training and evaluation with an AVSlowFast 4x16 R50 model.