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from unittest.mock import MagicMock
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import torch
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from mmaction.registry import MODELS
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from mmaction.structures import ActionDataSample
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from mmaction.testing import get_recognizer_cfg
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from mmaction.utils import register_all_modules
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def test_omni_resnet():
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register_all_modules()
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config = get_recognizer_cfg(
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'omnisource/slowonly_r50_8xb16-8x8x1-256e_imagenet-kinetics400-rgb.py')
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recognizer = MODELS.build(config.model)
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video_sample = {
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'inputs': [
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torch.randint(0, 255, (1, 3, 8, 224, 224)),
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torch.randint(0, 255, (1, 3, 8, 224, 224))
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],
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'data_samples': [
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ActionDataSample().set_gt_label(2),
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ActionDataSample().set_gt_label(2)
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]
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}
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image_sample = {
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'inputs': [
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torch.randint(0, 255, (1, 3, 224, 224)),
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torch.randint(0, 255, (1, 3, 224, 224))
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],
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'data_samples': [
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ActionDataSample().set_gt_label(2),
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ActionDataSample().set_gt_label(2)
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]
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}
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optim_wrapper = MagicMock()
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loss_vars = recognizer.train_step([video_sample, image_sample],
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optim_wrapper)
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assert 'loss_cls_0' in loss_vars
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assert 'loss_cls_1' in loss_vars
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loss_vars = recognizer.train_step([image_sample, video_sample],
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optim_wrapper)
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assert 'loss_cls_0' in loss_vars
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assert 'loss_cls_1' in loss_vars
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with torch.no_grad():
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predictions = recognizer.test_step(video_sample)
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score = predictions[0].pred_score
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assert len(predictions) == 2
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assert torch.min(score) >= 0
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assert torch.max(score) <= 1
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