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v-iashin
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Jan 23, 2024
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Thank you for your pull request. However, it should be significantly revised before merging.
I'd be more positive about it if:
- one pull request wouldn't implement two features: one for the ten crop, the other for the changes in disk writing
- I don't like that each crop is treated as a separate video (now each video creates 10/5 times more files. can't we implement crop as a batch dimension?
- the augs are applied only to RGB stream, not both.
- the augs are implemented for i3d but not other models.
- i am not happy that the classes/functions that are common for many models are being changed without reflecting on how logic for other features that depend on them changes
i appreciate the efforts but i am not convinced that it is enough
| self.i3d_transforms = { | ||
| 'rgb': torchvision.transforms.Compose([ | ||
| TensorCenterCrop(self.central_crop_size), | ||
| aug_transform, |
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any reason why we can't do it for the flow?
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| if self.aug_type is not None: | ||
| feats_dict = {stream: [[] for _ in range(self.num_crop)] for stream in self.streams} |
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Why treat each crop as a separate tensor instead of a batch dimension: B, Crops, D --> B*Crops, D?
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| def __call__(self, tensor: torch.FloatTensor) -> torch.FloatTensor: | ||
| def __call__(self, tensor): | ||
| if isinstance(tensor, tuple): |
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| def __call__(self, tensor: torch.FloatTensor) -> torch.FloatTensor: | ||
| def __call__(self, tensor): | ||
| if isinstance(tensor, tuple): |
| print() | ||
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| def make_path(output_root, video_path, output_key, ext): | ||
| def make_path(output_root, video_path, output_key, ext, idx=None): |
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we shouldn't resort to this. it became incredibly redundant. we need to save all features in one file
| return | ||
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| for key, value in feats_dict.items(): | ||
| if self.save_option == 'rgb_only': |
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what's wrong with the streams argument in i3d?
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Add ten/five crop augmentation when extract the I3D features. Solve issue #92 #72 . And add save_option to the i3d.yaml file to save only reg features. Because I think that fps and timestamp features are really redundant. The shape of the rgb features imply timestamp.
Thank you for your great work. 🚀🚀