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import time, pdb, argparse, subprocess |
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import glob |
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import os |
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from tqdm import tqdm |
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from SyncNetInstance_calc_scores import * |
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parser = argparse.ArgumentParser(description = "SyncNet"); |
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parser.add_argument('--initial_model', type=str, default="data/syncnet_v2.model", help=''); |
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parser.add_argument('--batch_size', type=int, default='20', help=''); |
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parser.add_argument('--vshift', type=int, default='15', help=''); |
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parser.add_argument('--data_root', type=str, required=True, help=''); |
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parser.add_argument('--tmp_dir', type=str, default="data/work/pytmp", help=''); |
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parser.add_argument('--reference', type=str, default="demo", help=''); |
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opt = parser.parse_args(); |
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s = SyncNetInstance(); |
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s.loadParameters(opt.initial_model); |
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path = os.path.join(opt.data_root, "*.mp4") |
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all_videos = glob.glob(path) |
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prog_bar = tqdm(range(len(all_videos))) |
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avg_confidence = 0. |
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avg_min_distance = 0. |
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for videofile_idx in prog_bar: |
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videofile = all_videos[videofile_idx] |
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offset, confidence, min_distance = s.evaluate(opt, videofile=videofile) |
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avg_confidence += confidence |
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avg_min_distance += min_distance |
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prog_bar.set_description('Avg Confidence: {}, Avg Minimum Dist: {}'.format(round(avg_confidence / (videofile_idx + 1), 3), round(avg_min_distance / (videofile_idx + 1), 3))) |
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prog_bar.refresh() |
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print ('Average Confidence: {}'.format(avg_confidence/len(all_videos))) |
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print ('Average Minimum Distance: {}'.format(avg_min_distance/len(all_videos))) |
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