slowfast 代码解析 输出检测数据
Posted WinstonYF
tags:
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目录
- 前言
- 代码跟踪
- 1，tools/run_net.py | demo(cfg)
- 2，tools/demo_net.py | demo(cfg)
- 3，/slowfast/visualization/demo_loader.py | VideoManager(cfg)
- 4，tools/demo_net.py | run_demo()
- 5，/slowfast/visualization/demo_loader.py | display()
- 6，/slowfast/visualization/video_visualizer.py | VideoVisualizer()
- 7，/slowfast/visualization/async_predictor.py | class AsyncVis
- 8，/slowfast/visualization/predictor.py | class ActionPredictor
- 9，/slowfast/visualization/utils.py | class TaskInfo
- 检测结果输出
前言
slowfast可以做很好的视频的行为检测，但是，最后呈现出来的结果只是人的视觉层面看到。我们要拿到检测的数据，检测的人在那个位置，在做什么，这一时刻，其他人都在做什么，我们都要拿到这些数据，然后对其进行数据分析。
代码跟踪
在之前的博客写了slowfast的复现过程
【SlowFast复现】SlowFast Networks for Video Recognition复现代码 使用自己的视频进行demo检测
下面这个就是代码的运行命令：
python tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50.yaml
1，tools/run_net.py | demo(cfg)
可以看到是先从 tools/run_net.py 开始执行的
2，tools/demo_net.py | demo(cfg)
接下来，代码跟踪到了demo_net.py中
1，将输入的视频或者摄像头送入代码中，进行处理。这里提到了摄像头，那么slowfast的检测可以用摄像头进行检测，如果做出来，那岂不是很棒。
2， if cfg.DETECTION.ENABLE and cfg.DEMO.PREDS_BOXES != “”:这里是不会进去的
3，else:会进入到这里的内容
4，if cfg.DEMO.THREAD_ENABLE:不会进入这里
5，else:会进入到这里的内容
6，frame_provider = VideoManager(cfg) 调用VideoManager这个函数，这个函数位于：slowfast/visualization/video_visualizer.py中
7，run_demo(cfg, frame_provider) 调用run_demo()函数，这个函数就是在tools/demo_net.py中，这个函数就是将检测视频行为的核心函数
8， tqdm.tqdm(run_demo(cfg, frame_provider))，tqdm就是显示出处理的进度，一般用于处理时间较长的函数，方便用户看到程序运行的进度。
9，frame_provider.display(task)，这就是一个显示的处理结果的函数，但是并不会显示，具体原因要看：/slowfast/visualization/demo_loader.py
10，frame_provider.join()这里就是将一张张处理好的图片合并为视频
3，/slowfast/visualization/demo_loader.py | VideoManager(cfg)
1，这个函数比较单纯，就是获取视频里的帧，还有视频的基本信息，比如视频的宽和长
4，tools/demo_net.py | run_demo()
1，run_demo着函数，就是视频行为检测的核心函数，处理过程和结果都在这里
1,/slowfast/visualization/video_visualizer.py中的class VideoVisualizer:，这其实就是一个显示的检测视频的函数，比如在一些帧上面画框，框上的动作显示之类的。
2，/slowfast/visualization/async_predictor.py中的class AsyncVis:，是对async_vis = AsyncVis(video_vis, n_workers=cfg.DEMO.NUM_VIS_INSTANCES)的解释
3，一般来说，1块GPU用来跑demo，if cfg.NUM_GPUS <= 1:
4，/slowfast/visualization/predictor.py中class ActionPredictor:，是对model = ActionPredictor(cfg=cfg, async_vis=async_vis)的解释。
1，上面用红色圈出来的，是输出视频的动作检测的数据，这一部分是这篇博客最核心的地方，也是我们最终的目的。
task的后缀有哪些，要参考/slowfast/visualization/utils.py中的class TaskInfo:
class TaskInfo:
def __init__(self):
self.frames = None
self.id = -1
self.bboxes = None
self.action_preds = None
self.num_buffer_frames = 0
self.img_height = -1
self.img_width = -1
self.crop_size = -1
self.clip_vis_size = -1
打印输出结果在这篇博客最下面：检测结果输出部分1
5，/slowfast/visualization/demo_loader.py | display()
1，这个display函数是显示处理后的图片
2，if self.output_file is None: 这个判断不会进入，因此图片也不会显示
3，下面的代码是将处理好的图片导出，以便后续的处理。如果要看到处理的图片，可以添加下面一行的代码
self.output_file.write(frame)
#cv2.imwrite('/home/lxn/0yangfan/Slowfast2/SlowFast-master/videoInAndOut/img/'+str(time.time())+'.png',frame)
6，/slowfast/visualization/video_visualizer.py | VideoVisualizer()
这个类还是挺重要的，具体大家自己看
7，/slowfast/visualization/async_predictor.py | class AsyncVis
8，/slowfast/visualization/predictor.py | class ActionPredictor
9，/slowfast/visualization/utils.py | class TaskInfo
检测结果输出
----task.action_preds------
tensor([[3.3068e-01, 6.3935e-01, 8.7209e-04, 1.8545e-02, 1.8673e-04, 2.9848e-05,
1.7888e-04],
[3.0377e-01, 6.6413e-01, 2.4549e-03, 6.3565e-01, 2.7237e-05, 1.4275e-04,
1.2613e-04],
[9.1854e-01, 6.8788e-02, 5.2226e-04, 2.1917e-01, 1.4145e-03, 4.3371e-04,
1.5717e-03],
[9.0379e-01, 8.5319e-02, 7.5482e-02, 1.8499e-01, 1.6704e-02, 5.9800e-03,
1.3741e-02],
[9.6596e-01, 3.5600e-02, 3.8787e-03, 6.3085e-01, 7.1388e-04, 1.4794e-03,
4.7487e-03],
[9.4797e-01, 4.9253e-02, 3.1182e-02, 1.4432e-01, 9.5782e-03, 3.5563e-03,
9.1131e-03],
[8.8620e-01, 9.8895e-02, 2.9716e-02, 1.6854e-01, 1.3244e-02, 4.6380e-03,
1.0545e-02],
[9.3523e-01, 6.0757e-02, 4.4252e-02, 1.0551e-01, 1.7112e-02, 4.0246e-03,
9.5690e-03],
[9.7697e-01, 2.6850e-02, 1.5782e-02, 3.9446e-01, 3.2984e-03, 3.3874e-03,
8.2793e-03],
[9.3359e-01, 6.3807e-02, 4.7994e-02, 2.2294e-01, 1.1378e-02, 9.0503e-03,
9.2492e-03],
[9.7168e-01, 2.9566e-02, 9.4610e-03, 3.0385e-01, 5.3192e-03, 3.0461e-03,
1.1144e-02],
[9.6288e-01, 3.6090e-02, 1.1582e-02, 1.9594e-01, 1.1243e-02, 2.8585e-03,
8.1071e-03],
[9.7794e-01, 2.1070e-02, 5.5183e-02, 8.9149e-02, 2.9202e-02, 4.1249e-03,
1.0422e-02],
[9.6687e-01, 3.7683e-02, 7.1257e-03, 5.1293e-01, 4.3076e-03, 2.3472e-03,
6.8290e-03],
[9.5520e-01, 4.0954e-02, 1.6906e-02, 2.4505e-01, 1.2851e-02, 5.2805e-03,
1.2938e-02],
[9.2210e-01, 7.5841e-02, 4.6801e-02, 1.9025e-01, 1.0466e-02, 3.1112e-03,
8.3077e-03],
[9.7855e-01, 2.3353e-02, 2.2226e-03, 4.7899e-01, 1.1018e-03, 1.7147e-03,
4.8098e-03],
[9.6118e-01, 3.7354e-02, 9.3433e-03, 2.2742e-01, 1.0877e-02, 3.3945e-03,
1.0026e-02],
[9.7981e-01, 2.0757e-02, 5.9553e-03, 1.6640e-01, 4.9369e-03, 2.2642e-03,
6.9605e-03],
[9.7422e-01, 2.7297e-02, 4.9212e-03, 2.5729e-01, 3.4356e-03, 2.3249e-03,
9.3547e-03],
[9.5745e-01, 4.2154e-02, 2.9013e-02, 9.5461e-02, 1.9649e-02, 3.8468e-03,
1.0328e-02],
[9.6437e-01, 3.4423e-02, 6.0313e-02, 9.0543e-02, 2.4570e-02, 3.8957e-03,
9.6738e-03],
[9.8304e-01, 1.7515e-02, 2.7738e-02, 1.6363e-01, 2.1263e-02, 8.6561e-03,
1.1963e-02],
[9.7785e-01, 2.1792e-02, 1.6125e-02, 1.9974e-01, 1.5624e-02, 4.7472e-03,
1.2880e-02],
[9.8032e-01, 2.0065e-02, 2.1097e-02, 3.0616e-01, 1.3230e-02, 7.1698e-03,
1.2716e-02]])
----task.id------
0
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[205.1555, 113.1897, 296.6750, 253.1612],
[ 23.0507, 175.7125, 101.2261, 252.6055],
[101.3232, 150.2620, 184.9991, 229.7745],
[428.3237, 94.3240, 456.3572, 155.4007],
[353.3588, 45.7152, 374.1545, 77.9063],
[292.6136, 118.3143, 349.4595, 175.5049],
[ 87.0510, 103.1097, 143.5463, 166.1544],
[188.8826, 60.1561, 220.3122, 112.5003],
[396.6401, 59.5460, 430.9417, 98.4192],
[ 20.2244, 120.2954, 59.6826, 186.0771],
[277.1523, 73.6372, 317.3305, 119.8548],
[347.8768, 107.9334, 383.4142, 153.9511],
[134.5424, 30.9124, 156.4270, 56.3248],
[369.4414, 63.1888, 397.3295, 134.8474],
[ 80.5445, 80.3836, 118.0698, 132.6446],
[226.4645, 84.3846, 269.7121, 128.8023],
[306.8896, 34.6551, 329.3459, 63.2425],
[212.9748, 47.6803, 238.3157, 79.3350],
[164.2714, 23.0587, 188.0774, 59.4795],
[226.6040, 20.9245, 245.5300, 47.5975],
[172.4699, 50.2295, 199.5882, 83.9325],
[147.1099, 43.6574, 177.3349, 73.8317],
[ 27.8072, 44.9736, 55.1582, 74.7165],
[ 66.3227, 65.6132, 96.0068, 102.2397],
[ 32.8406, 72.5305, 62.9094, 121.2337]])
----task.action_preds------
tensor([[4.3605e-01, 4.8477e-01, 1.6338e-03, 3.7873e-02, 4.1979e-04, 4.6885e-04,
6.5983e-04],
[4.6038e-01, 5.2889e-01, 3.3501e-04, 3.0178e-01, 3.1818e-05, 1.2990e-05,
9.2566e-05],
[9.2793e-01, 6.6854e-02, 3.5693e-02, 1.7233e-01, 1.3026e-02, 5.3476e-03,
1.1763e-02],
[2.3888e-01, 7.1164e-01, 2.3822e-03, 7.8848e-01, 1.4555e-05, 2.4186e-04,
2.5829e-04],
[9.6750e-01, 3.4163e-02, 2.8533e-03, 7.0046e-01, 5.8987e-04, 1.3488e-03,
4.8957e-03],
[9.6181e-01, 3.7302e-02, 7.0551e-03, 2.0271e-01, 7.3945e-03, 2.6559e-03,
7.2714e-03],
[9.4706e-01, 5.0823e-02, 1.4878e-02, 2.1074e-01, 6.1639e-03, 3.3783e-03,
7.7952e-03],
[9.7428e-01, 2.9350e-02, 1.4919e-02, 3.9166e-01, 2.8036e-03, 3.2878e-03,
7.1645e-03],
[8.9968e-01, 9.5331e-02, 6.6866e-02, 2.2519e-01, 8.9781e-03, 9.5469e-03,
7.4013e-03],
[9.2288e-01, 7.0904e-02, 8.1936e-02, 8.4682e-02, 2.5138e-02, 4.8544e-03,
9.6883e-03],
[9.6412e-01, 4.0552e-02, 7.2078e-03, 5.5007e-01, 4.1379e-03, 2.2172e-03,
6.4968e-03],
[9.5038e-01, 5.0112e-02, 4.8171e-03, 6.8202e-01, 9.6021e-04, 1.6828e-03,
5.9852e-03],
[9.5077e-01, 4.5994e-02, 1.0184e-01, 1.1110e-01, 2.2919e-02, 6.2548e-03,
1.3127e-02],
[9.7344e-01, 2.7692e-02, 9.6438e-03, 3.2124e-01, 6.1552e-03, 3.4201e-03,
1.0837e-02],
[9.7666e-01, 2.1955e-02, 7.0712e-02, 8.9474e-02, 3.0718e-02, 4.0404e-03,
1.0878e-02],
[8.9380e-01, 9.2756e-02, 8.4892e-02, 1.6392e-01, 1.8788e-02, 6.4404e-03,
1.2502e-02],
[9.3745e-01, 6.0565e-02, 5.4128e-02, 1.0674e-01, 1.6694e-02, 3.3858e-03,
9.1532e-03],
[9.6867e-01, 3.3012e-02, 3.1971e-03, 4.8835e-01, 8.8847e-04, 1.4866e-03,
4.5618e-03],
[9.6292e-01, 3.3970e-02, 1.3123e-02, 2.5425e-01, 1.3070e-02, 5.5062e-03,
1.2703e-02],
[9.7588e-01, 2.4046e-02, 1.2018e-02, 2.2605e-01, 1.3528e-02, 4.4318e-03,
1.1277e-02],
[9.7770e-01, 2.3029e-02, 5.5852e-03, 2.6520e-01, 3.8638e-03, 2.2240e-03,
9.0063e-03],
[9.7159e-01, 2.8558e-02, 6.8279e-03, 2.2590e-01, 4.6177e-03, 3.1039e-03,
8.2523e-03],
[9.7785e-01, 2.2620e-02, 8.1550e-03, 1.7335e-01, 5.9154e-03, 2.4702e-03,
7.6095e-03],
[9.3873e-01, 5.7228e-02, 1.2560e-02, 2.0949e-01, 1.1707e-02, 3.4055e-03,
9.0173e-03],
[9.7709e-01, 2.3037e-02, 1.1898e-02, 1.8053e-01, 1.0103e-02, 3.4541e-03,
9.3942e-03],
[9.8409e-01, 1.6662e-02, 2.7481e-02, 1.5502e-01, 2.0940e-02, 8.7810e-03,
1.1501e-02]])
----task.id------
1
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[226.0951, 116.2387, 306.2776, 254.1625],
[106.8367, 134.8050, 190.4715, 249.5462],
[ 82.6477, 102.9846, 136.4340, 175.5090],
[ 23.0995, 181.3977, 106.1833, 251.6965],
[352.5997, 46.4732, 376.6266, 77.8960],
[345.1780, 109.6968, 378.5544, 155.3690],
[294.1235, 118.1768, 349.6223, 170.5221],
[396.4695, 58.9945, 432.6089, 99.1782],
[ 17.1888, 119.8782, 69.1491, 187.0942],
[189.5729, 60.7763, 220.0245, 110.7694],
[369.4751, 62.9287, 397.0657, 138.6149],
[312.3970, 52.3937, 345.8136, 108.2510],
[224.8733, 86.1372, 261.8926, 178.0486],
[277.5289, 74.5879, 317.2148, 119.6560],
[134.7164, 30.6019, 157.3040, 56.4409],
[428.0922, 96.7356, 456.2453, 158.8804],
[162.1169, 48.5523, 200.4313, 83.7726],
[310.4715, 35.5477, 334.0857, 65.5065],
[ 81.2878, 80.9881, 113.7099, 130.2715],
[ 67.0207, 66.2691, 95.4131, 102.4701],
[225.4909, 20.4182, 244.4581, 52.1969],
[232.8325, 62.1350, 270.0650, 99.7619],
[163.0569, 22.7164, 187.9710, 56.9684],
[211.5023, 48.5311, 238.7073, 82.2666],
[187.4972, 35.5649, 210.0278, 64.0337],
[ 28.8317, 44.9023, 53.0700, 73.2665]])
----task.action_preds------
tensor([[6.8728e-01, 3.0441e-01, 1.4194e-03, 7.7762e-02, 5.4746e-04, 1.0592e-04,
4.4724e-04],
[8.6910e-01, 1.1072e-01, 5.4875e-04, 2.5608e-01, 6.0982e-04, 2.9624e-04,
1.3029e-03],
[2.4822e-01, 6.9673e-01, 3.7150e-03, 7.9882e-01, 1.8824e-05, 3.2245e-04,
2.7456e-04],
[9.6418e-01, 3.6943e-02, 2.3240e-03, 7.0943e-01, 6.7086e-04, 1.4433e-03,
5.0543e-03],
[9.7854e-01, 2.4330e-02, 1.7736e-02, 3.8468e-01, 3.4365e-03, 4.0875e-03,
8.0698e-03],
[9.0337e-01, 8.5157e-02, 7.7825e-02, 1.7628e-01, 1.9336e-02, 7.2187e-03,
1.3356e-02],
[9.4789e-01, 4.9250e-02, 1.4258e-02, 2.4548e-01, 8.6035e-03, 5.1466e-03,
1.2377e-02],
[9.5703e-01, 4.1966e-02, 7.8532e-03, 1.8006e-01, 5.8515e-03, 2.5529e-03,
6.8900e-03],
[9.3205e-01, 6.4642e-02, 6.5065e-02, 2.1805e-01, 1.0627e-02, 9.3370e-03,
7.6932e-03],
[9.7663e-01, 2.4086e-02, 5.9378e-03, 3.4992e-01, 5.7146e-03, 2.7622e-03,
9.5630e-03],
[9.5259e-01, 4.7989e-02, 3.8875e-03, 6.8412e-01, 8.3365e-04, 1.4945e-03,
5.5031e-03],
[9.7599e-01, 2.4706e-02, 3.2082e-02, 1.5698e-01, 1.6634e-02, 5.5304e-03,
1.2910e-02],
[9.4474e-01, 5.1621e-02, 3.1013e-02, 1.1708e-01, 1.0480e-02, 2.9943e-03,
8.6745e-03],
[9.6576e-01, 3.8516e-02, 6.8659e-03, 5.8390e-01, 3.7340e-03, 2.3982e-03,
6.7272e-03],
[9.6570e-01, 3.2488e-02, 1.2057e-02, 2.9686e-01, 9.7645e-03, 4.6607e-03,
1.2923e-02],
[9.7716e-01, 2.1254e-02, 8.8430e-02, 8.9455e-02, 3.3727e-02, 4.3542e-03,
1.1587e-02],
[9.7713e-01, 2.2993e-02, 1.3052e-02, 2.1793e-01, 1.2843e-02, 4.1645e-03,
1.1752e-02],
[9.4861e-01, 4.6891e-02, 8.4006e-02, 8.4897e-02, 2.6547e-02, 4.6588e-03,
1.2143e-02],
[9.6113e-01, 3.6407e-02, 2.3661e-02, 1.6106e-01, 1.8040e-02, 4.5373e-03,
1.2529e-02],
[9.6099e-01, 3.6044e-02, 1.3536e-01, 8.2377e-02, 3.4776e-02, 4.6967e-03,
1.1921e-02],
[9.8389e-01, 1.6404e-02, 2.6581e-02, 1.6186e-01, 2.0770e-02, 9.0128e-03,
1.2174e-02],
[9.7599e-01, 2.5049e-02, 5.3640e-03, 2.5803e-01, 3.9689e-03, 2.2921e-03,
8.9128e-03],
[9.7855e-01, 2.1785e-02, 8.7614e-03, 1.9643e-01, 9.0948e-03, 3.6757e-03,
9.3401e-03],
[9.7591e-01, 2.4005e-02, 1.0252e-02, 1.5498e-01, 7.1591e-03, 2.6404e-03,
8.1021e-03],
[9.6467e-01, 3.4233e-02, 1.8971e-02, 1.6432e-01, 1.1821e-02, 3.8630e-03,
1.0560e-02]])
----task.id------
2
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[165.9606, 82.9424, 250.7872, 250.0935],
[ 98.8055, 130.7034, 189.0764, 234.4252],
[ 21.9483, 176.7925, 103.8454, 254.5630],
[352.6056, 46.4173, 375.9865, 77.8118],
[400.9052, 58.7003, 434.4618, 95.9393],
[428.0079, 95.8639, 456.5182, 159.6947],
[ 83.3208, 102.0855, 123.8917, 176.9720],
[334.6016, 109.5730, 378.4851, 158.3314],
[ 18.7214, 118.7763, 64.2552, 184.3866],
[276.8261, 74.8295, 318.2937, 122.2291],
[311.7992, 50.9767, 345.1997, 109.1536],
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----task.action_preds------
tensor([[9.6700e-01, 2.8579e-02, 1.8417e-02, 1.1434e-01, 1.4482e-02, 4.0023e-03,
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1.3194e-02],
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1.1865e-02]])
----task.id------
3
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[162.3714, 45.7713, 212.0648, 205.8224],
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----task.action_preds------
tensor([[9.6549e-01, 3.0950e-02, 3.6776e-02, 9.1158e-02, 1.5527e-02, 5.3081e-03,
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----task.id------
4
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[157.1728, 44.3564, 207.3708, 200.4943],
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----task.action_preds------
tensor([[6.8677e-02, 9.0522e-01, 3.9602e-04, 5.8021e-01, 1.0085e-05, 2.2161e-05,
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----task.id------
5
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[ 28.2521, 179.1980, 106.4258, 252.4232],
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----task.action_preds------
tensor([[9.4863e-01, 4.2951e-02, 4.2287e-02, 1.3848e-01, 7.7026e-03, 5.0305e-03,
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----task.id------
6
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[106.8900, 28.0527, 152.9553, 149.5411],
[ 29.2393, 177.9328, 107.3877, 252.6000],
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[ 17.8943, 118.9166, 69.0416, 184.9833],
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[341.4632, 109.0987, 379.8153, 157.2321],
[292.9057, 117.4388, 345.9398, 175.5046],
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[ 58.2168, 26.1458, 93.3750, 87.4859],
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1it [00:07, 7.38s/it]----task.action_preds------
tensor([[3.3068e-01, 6.3935e-01, 8.7209e-04, 1.8545e-02, 1.8673e-04, 2.9848e-05,
1.7888e-04],
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1.2613e-04],
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1.5717e-03],
[9.0379e-01, 8.5319e-02, 7.5482e-02, 1.8499e-01, 1.6704e-02, 5.9800e-03,
1.3741e-02],
[9.6596e-01, 3.5600e-02, 3.8787e-03, 6.3085e-01, 7.1388e-04, 1.4794e-03,
4.7487e-03],
[9.4797e-01, 4.9253e-02, 3.1182e-02, 1.4432e-01, 9.5782e-03, 3.5563e-03,
9.1131e-03],
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1.0545e-02],
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9.5690e-03],
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8.2793e-03],
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9.2492e-03],
[9.7168e-01, 2.9566e-02, 9.4610e-03, 3.0385e-01, 5.3192e-03, 3.0461e-03,
1.1144e-02],
[9.6288e-01, 3.6090e-02, 1.1582e-02, 1.9594e-01, 1.1243e-02, 2.8585e-03,
8.1071e-03],
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1.0422e-02],
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6.8290e-03],
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1.2938e-02],
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8.3077e-03],
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4.8098e-03],
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1.0026e-02],
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6.9605e-03],
[9.7422e-01, 2.7297e-02, 4.9212e-03, 2.5729e-01, 3.4356e-03, 2.3249e-03,
9.3547e-03],
[9.5745e-01, 4.2154e-02, 2.9013e-02, 9.5461e-02, 1.9649e-02, 3.8468e-03,
1.0328e-02],
[9.6437e-01, 3.4423e-02, 6.0313e-02, 9.0543e-02, 2.4570e-02, 3.8957e-03,
9.6738e-03],
[9.8304e-01, 1.7515e-02, 2.7738e-02, 1.6363e-01, 2.1263e-02, 8.6561e-03,
1.1963e-02],
[9.7785e-01, 2.1792e-02, 1.6125e-02, 1.9974e-01, 1.5624e-02, 4.7472e-03,
1.2880e-02],
[9.8032e-01, 2.0065e-02, 2.1097e-02, 3.0616e-01, 1.3230e-02, 7.1698e-03,
1.2716e-02]])
----task.id------
0
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[205.1555, 113.1897, 296.6750, 253.1612],
[ 23.0507, 175.7125, 101.2261, 252.6055],
[101.3232, 150.2620, 184.9991, 229.7745],
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[ 87.0510, 103.1097, 143.5463, 166.1544],
[188.8826, 60.1561, 220.3122, 112.5003],
[396.6401, 59.5460, 430.9417, 98.4192],
[ 20.2244, 120.2954, 59.6826, 186.0771],
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[ 32.8406, 72.5305, 62.9094, 121.2337]])
2it [00:08, 3.44s/it]----task.action_preds------
tensor([[4.3605e-01, 4.8477e-01, 1.6338e-03, 3.7873e-02, 4.1979e-04, 4.6885e-04,
6.5983e-04],
[4.6038e-01, 5.2889e-01, 3.3501e-04, 3.0178e-01, 3.1818e-05, 1.2990e-05,
9.2566e-05],
[9.2793e-01, 6.6854e-02, 3.5693e-02, 1.7233e-01, 1.3026e-02, 5.3476e-03,
1.1763e-02],
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2.5829e-04],
[9.6750e-01, 3.4163e-02, 2.8533e-03, 7.0046e-01, 5.8987e-04, 1.3488e-03,
4.8957e-03],
[9.6181e-01, 3.7302e-02, 7.0551e-03, 2.0271e-01, 7.3945e-03, 2.6559e-03,
7.2714e-03],
[9.4706e-01, 5.0823e-02, 1.4878e-02, 2.1074e-01, 6.1639e-03, 3.3783e-03,
7.7952e-03],
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7.1645e-03],
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7.4013e-03],
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9.6883e-03],
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6.4968e-03],
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5.9852e-03],
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1.3127e-02],
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1.0837e-02],
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1.0878e-02],
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9.1532e-03],
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1.2703e-02],
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1.1277e-02],
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9.0063e-03],
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8.2523e-03],
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7.6095e-03],
[9.3873e-01, 5.7228e-02, 1.2560e-02, 2.0949e-01, 1.1707e-02, 3.4055e-03,
9.0173e-03],
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9.3942e-03],
[9.8409e-01, 1.6662e-02, 2.7481e-02, 1.5502e-01, 2.0940e-02, 8.7810e-03,
1.1501e-02]])
----task.id------
1
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[226.0951, 116.2387, 306.2776, 254.1625],
[106.8367, 134.8050, 190.4715, 249.5462],
[ 82.6477, 102.9846, 136.4340, 175.5090],
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[163.0569, 22.7164, 187.9710, 56.9684],
[211.5023, 48.5311, 238.7073, 82.2666],
[187.4972, 35.5649, 210.0278, 64.0337],
[ 28.8317, 44.9023, 53.0700, 73.2665]])
----task.action_preds------
tensor([[2.4259e-01, 6.7330e-01, 1.3514e-03, 5.5906e-01, 4.1080e-05, 1.7601e-04,
2.7325e-04],
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1.0325e-02],
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7.4751e-03],
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4.3116e-03],
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9.0219e-03],
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1.3106e-02],
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8.2680e-03],
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1.6997e-03],
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9.3660e-03],
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9.6860e-03],
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1.0875e-02],
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4.7126e-03],
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8.1041e-03],
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7.7210e-03],
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1.1761e-02],
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1.2655e-02],
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7.0964e-03],
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1.0567e-02],
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8.8441e-03],
[9.7950e-01, 2.2188e-02, 2.1504e-03, 2.8611e-01, 2.2996e-03, 1.4384e-03,
6.1573e-03]])
----task.id------
9
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[ 24.2481, 175.2021, 100.1377, 253.1301],
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[ 18.5611, 120.0067, 68.1094, 184.1929],
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[ 76.6527, 86.1585, 114.1236, 140.5480],
[ 24.0002, 96.2128, 60.8464, 142.3790],
[312.2249, 52.7448, 344.8302, 110.9484],
[ 29.7520, 71.6654, 67.2908, 112.9142],
[240.2513, 63.2977, 273.9822, 101.1070],
[226.1520, 20.4335, 246.9137, 46.5517]])
----task.action_preds------
tensor([[2.6150e-01, 6.5994e-01, 5.7528e-04, 6.2036e-01, 3.3023e-05, 1.4922e-04,
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[9.6883e-01, 3.4542e-02, 7.8717e-04, 6.2701e-01, 2.8711e-04, 6.7355e-04,
2.8694e-03]])
----task.id------
10
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[ 26.4119, 178.4839, 100.1098, 253.5490],
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----task.action_preds------
tensor([[9.3891e-01, 5.3203e-02, 3.6799e-03, 1.6344e-01, 4.3274e-03, 9.6568e-04,
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----task.id------
11
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[148.1673, 48.7096, 215.4821, 228.5038],
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----task.action_preds------
tensor([[6.4108e-01, 3.3970e-01, 1.7734e-03, 4.6523e-02, 3.7280e-04, 3.5876e-05,
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----task.id------
12
----task.num_buffer_frames------
0
----task.bboxes------
tensor([[178.8035, 76.3236, 253.4669, 252.0955],
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3it [00:10, 2.94s/it]----task.action_preds------
tensor([[6.8728e-01, 3.0441e-01, 1.4194e-03, 7.7762e-02, 5.4746e-04, 1.0592e-04,
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