# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import tempfile import unittest import unittest.mock from contextlib import ExitStack from pathlib import Path import torch from parameterized import parameterized from pytorchvideo.data.dataset_manifest_utils import VideoClipInfo, VideoDatasetType from pytorchvideo.data.domsev import ( _get_overlap_for_time_range_pair, _seconds_to_frame_index, DomsevVideoDataset, LabelData, ) from pytorchvideo.data.utils import save_dataclass_objs_to_headered_csv from utils import ( get_encoded_video_infos, get_flat_video_frames, MOCK_VIDEO_IDS, MOCK_VIDEO_INFOS, ) class TestDomsevVideoDataset(unittest.TestCase): # video_id: str # start_time: float # Start time of the label, in seconds # stop_time: float # Stop time of the label, in seconds # start_frame: int # 0-indexed ID of the start frame (inclusive) # stop_frame: int # 0-index ID of the stop frame (inclusive) # label_id: int # label_name: str LABELS_DATA = { MOCK_VIDEO_IDS[0]: [ LabelData( MOCK_VIDEO_IDS[0], 0.0, 6.0, 1, 181, 1, "walking", ), LabelData( MOCK_VIDEO_IDS[0], 6.0333333, 10.0, 182, 301, 2, "running", ), LabelData( MOCK_VIDEO_IDS[0], 10.033333, 20.0, 302, 601, 0, "none", ), ], MOCK_VIDEO_IDS[1]: [ LabelData( MOCK_VIDEO_IDS[1], 3.0, 5.0, 181, 301, 7, "cooking", ), ], MOCK_VIDEO_IDS[2]: [ LabelData( MOCK_VIDEO_IDS[2], 100.0, 200.0, 3001, 6001, 9, "observing", ), ], MOCK_VIDEO_IDS[3]: [ LabelData( MOCK_VIDEO_IDS[3], 10.0, 20.0, 901, 1801, 5, "driving", ), ], } def setUp(self): pass def test_seconds_to_frame_index(self): self.assertEqual(_seconds_to_frame_index(10.56, 1, zero_indexed=True), 10) self.assertEqual(_seconds_to_frame_index(10.56, 1, zero_indexed=False), 11) self.assertEqual(_seconds_to_frame_index(9.99, 1, zero_indexed=True), 9) self.assertEqual(_seconds_to_frame_index(9.99, 1, zero_indexed=False), 10) self.assertEqual(_seconds_to_frame_index(1.01, 10, zero_indexed=True), 10) self.assertEqual(_seconds_to_frame_index(1.01, 10, zero_indexed=False), 11) def test_get_overlap_for_time_range_pair(self): self.assertEqual(_get_overlap_for_time_range_pair(0, 1, 0.1, 0.2), (0.1, 0.2)) self.assertEqual(_get_overlap_for_time_range_pair(0.1, 0.2, 0, 1), (0.1, 0.2)) self.assertEqual(_get_overlap_for_time_range_pair(0, 1, 0.9, 1.1), (0.9, 1.0)) self.assertEqual(_get_overlap_for_time_range_pair(0, 0.2, 0.1, 1), (0.1, 0.2)) @parameterized.expand([(VideoDatasetType.Frame,), (VideoDatasetType.EncodedVideo,)]) def test__len__(self, dataset_type): with tempfile.TemporaryDirectory(prefix=f"{TestDomsevVideoDataset}") as tempdir: tempdir = Path(tempdir) video_info_file = tempdir / "test_video_info.csv" save_dataclass_objs_to_headered_csv( list(MOCK_VIDEO_INFOS.values()), video_info_file ) label_file = tempdir / "activity_video_info.csv" labels = [] for label_list in self.LABELS_DATA.values(): for label_data in label_list: labels.append(label_data) save_dataclass_objs_to_headered_csv(labels, label_file) video_data_manifest_file_path = ( tempdir / "video_data_manifest_file_path.json" ) with ExitStack() as stack: if dataset_type == VideoDatasetType.Frame: video_data_dict = get_flat_video_frames(tempdir, "jpg") elif dataset_type == VideoDatasetType.EncodedVideo: video_data_dict = get_encoded_video_infos(tempdir, stack) save_dataclass_objs_to_headered_csv( list(video_data_dict.values()), video_data_manifest_file_path ) video_ids = list(self.LABELS_DATA) dataset = DomsevVideoDataset( video_data_manifest_file_path=str(video_data_manifest_file_path), video_info_file_path=str(video_info_file), labels_file_path=str(label_file), dataset_type=dataset_type, clip_sampler=lambda x, y: [ VideoClipInfo(video_ids[i // 2], i * 2.0, i * 2.0 + 0.9) for i in range(0, 7) ], ) self.assertEqual(len(dataset._videos), 4) total_labels = [ label_data for video_labels in list(dataset._labels_per_video.values()) for label_data in video_labels ] self.assertEqual(len(total_labels), 6) self.assertEqual(len(dataset), 7) # Num clips @parameterized.expand([(VideoDatasetType.Frame,), (VideoDatasetType.EncodedVideo,)]) def test__getitem__(self, dataset_type): with tempfile.TemporaryDirectory(prefix=f"{TestDomsevVideoDataset}") as tempdir: tempdir = Path(tempdir) video_info_file = tempdir / "test_video_info.csv" save_dataclass_objs_to_headered_csv( list(MOCK_VIDEO_INFOS.values()), video_info_file ) label_file = tempdir / "activity_video_info.csv" labels = [] for label_list in self.LABELS_DATA.values(): for label_data in label_list: labels.append(label_data) save_dataclass_objs_to_headered_csv(labels, label_file) video_data_manifest_file_path = ( tempdir / "video_data_manifest_file_path.json" ) with ExitStack() as stack: if dataset_type == VideoDatasetType.Frame: video_data_dict = get_flat_video_frames(tempdir, "jpg") elif dataset_type == VideoDatasetType.EncodedVideo: video_data_dict = get_encoded_video_infos(tempdir, stack) save_dataclass_objs_to_headered_csv( list(video_data_dict.values()), video_data_manifest_file_path ) video_ids = list(self.LABELS_DATA) dataset = DomsevVideoDataset( video_data_manifest_file_path=str(video_data_manifest_file_path), video_info_file_path=str(video_info_file), labels_file_path=str(label_file), dataset_type=dataset_type, clip_sampler=lambda x, y: [ VideoClipInfo(video_ids[i // 2], i * 2.0, i * 2.0 + 0.9) for i in range(0, 7) ], ) get_clip_string = ( "pytorchvideo.data.frame_video.FrameVideo.get_clip" if dataset_type == VideoDatasetType.Frame else "pytorchvideo.data.encoded_video.EncodedVideo.get_clip" ) with unittest.mock.patch( get_clip_string, return_value=({"video": torch.rand(3, 5, 10, 20), "audio": []}), ) as _: clip_1 = dataset.__getitem__(1) for i, a in enumerate(clip_1["labels"]): self.assertEqual(a, self.LABELS_DATA[video_ids[0]][i]) self.assertEqual(clip_1["start_time"], 2.0) self.assertEqual(clip_1["stop_time"], 2.9) self.assertEqual(clip_1["video_id"], MOCK_VIDEO_IDS[0]) clip_2 = dataset.__getitem__(2) for i, a in enumerate(clip_2["labels"]): self.assertEqual(a, self.LABELS_DATA[video_ids[1]][i]) self.assertEqual(clip_2["start_time"], 4.0) self.assertEqual(clip_2["stop_time"], 4.9) self.assertEqual(clip_2["video_id"], MOCK_VIDEO_IDS[1])