return process_data, record.label, record.metadata

[fengfan028]

What is the record.metadata here? in dataset.py line 184
return process_data, record.label, record.metadata

[fengfan028]

i use ucf101, not epic-kitchens

[ekazakos]

Hi,
record.metadata is the narration_id for EPIC-KITCHENS-100 data, and is used for model evaluation / submission in the action recognition challenge. It is defined here:

temporal-binding-network/video_records/epickitchens100_record.py

Lines 40 to 42 in 9e4e108

	@property
	def metadata(self):
	return {'narration_id': self._series['narration_id'] if 'narration_id' in self._series else self._index}

For UCF101, first you need to create a ucf101_record.py in video_records/, see video_records/epickitchens100_record.py as an example. Also have a look in the readme under Train/evaluate with other datasets for more info.

Then, if there are any useful metadata in UCF101, that you would like to store along with the predictions of the model for evaluation purposes, you can also store them in the UCF101 video record as shown above.

[fengfan028]

Hi,
record.metadata is the narration_id for EPIC-KITCHENS-100 data, and is used for model evaluation / submission in the action recognition challenge. It is defined here:

temporal-binding-network/video_records/epickitchens100_record.py

Lines 40 to 42 in 9e4e108

	@property
	def metadata(self):
	return {'narration_id': self._series['narration_id'] if 'narration_id' in self._series else self._index}

For UCF101, first you need to create a ucf101_record.py in video_records/, see video_records/epickitchens100_record.py as an example. Also have a look in the readme under Train/evaluate with other datasets for more info.

Then, if there are any useful metadata in UCF101, that you would like to store along with the predictions of the model for evaluation purposes, you can also store them in the UCF101 video record as shown above.

Thanks very much for your reply! I have a another question:
I use pytorch version 1.8.1, when running train.py, it has the following error:

it seems def forward(self, input_tensor): and def backward(self, grad_output): in class SegmentConsensus(torch.autograd.Function): should use @staticmethod, but how to modify them?

[ekazakos]

Ah thanks for spotting that! There are 2 valid options:

1. You can just use static methods without any further modifications as shown here: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function
2. You can just delete backward, PyTorch is doing the backprop automatically so it is not needed.

I will update the code soon to be compatible with PyTorch 1.8.1 by implementing option 2.

[fengfan028]

Ah thanks for spotting that! There are 2 valid options:

1. You can just use static methods without any further modifications as shown here: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function
2. You can just delete backward, PyTorch is doing the backprop automatically so it is not needed.

I will update the code soon to be compatible with PyTorch 1.8.1 by implementing option 2.

thans a lot! bug has been fixed.