from_tensors support for VideoClassification

[krshrimali]

What does this PR do?

Addresses #1356. A sample script to try it out:

import torch
from flash.video import VideoClassifier, VideoClassificationData
import flash

# 5 number of frames, 3 channels, height = 10 and width = 10
mock_tensors = torch.randint(size=(3, 5, 10, 10), low=0, high=255)
datamodule = VideoClassificationData.from_tensors(
    train_data=[mock_tensors, mock_tensors],  # can also stack: torch.stack((mock_tensors, mock_tensors))
    train_targets=["patient", "doctor"],
    predict_data=[mock_tensors],
    batch_size=1,
)

model = VideoClassifier(num_classes=datamodule.num_classes, pretrained=False, backbone="slow_r50", labels=datamodule.labels)
trainer = flash.Trainer(fast_dev_run=True)
trainer.finetune(model, datamodule=datamodule)

Output:

  | Name          | Type       | Params
---------------------------------------------
0 | train_metrics | ModuleDict | 0
1 | val_metrics   | ModuleDict | 0
2 | test_metrics  | ModuleDict | 0
3 | backbone      | Net        | 32.5 M
4 | head          | Sequential | 802
---------------------------------------------
32.5 M    Trainable params
0         Non-trainable params
32.5 M    Total params
129.820   Total estimated model params size (MB)
/home/krshrimali/anaconda3/envs/pl-17/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 24 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
/home/krshrimali/anaconda3/envs/pl-17/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1938: PossibleUserWarning: The number of training samples (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
  rank_zero_warn(
Epoch 0: 100%|█| 1/1.0 [00:01<00:00,  1.60s/it, loss=0.744, v_num=, train_accuracy_step=0.000, train_cross_entropy_step=0.744,

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[codecov]

Codecov Report

Merging #1389 (1cf3d75) into master (0253d71) will decrease coverage by 0.06%.
The diff coverage is 82.92%.

@@            Coverage Diff             @@
##           master    #1389      +/-   ##
==========================================
- Coverage   92.90%   92.83%   -0.07%     
==========================================
  Files         286      287       +1     
  Lines       12874    12969      +95     
==========================================
+ Hits        11960    12040      +80     
- Misses        914      929      +15     

Flag	Coverage Δ
unittests	92.83% <82.92%> (-0.07%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
flash/video/classification/input.py	90.37% <76.74%> (-6.48%)	⬇️
flash/video/classification/utils.py	87.87% <87.87%> (ø)
flash/video/classification/data.py	100.00% <100.00%> (ø)
flash/core/finetuning.py	88.23% <0.00%> (-2.47%)	⬇️
flash/core/utilities/imports.py	91.47% <0.00%> (+0.04%)	⬆️
flash/core/classification.py	95.75% <0.00%> (+0.60%)	⬆️
flash/core/serve/dag/task.py	97.88% <0.00%> (+1.05%)	⬆️

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[ethanwharris]

Looking good, few comments 😃

[ethanwharris]

LGTM, small suggestion

[ethanwharris]

Looks great, just a comment on the API

[ethanwharris]

I think it would be better to make the API consistent with what we have for image classification. E.g. like this:

            >>> datamodule = VideoClassificationData.from_tensors(
            ...     train_data=[input_video, input_video, input_video],
            ...     train_targets=[1, 2, 3],
            ...     predict_data=predict_data,
            ...     batch_size=1,
            ... )

[ethanwharris]

Just requesting changes so we don't accidentally merge it before agreeing on the API 😃

[ethanwharris]

LGTM 😃