Selected framework: Tensorflow
2019-05-29 16:59:48.336944: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2019-05-29 16:59:48.799442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties:
name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
pciBusID: 0000:d9:00.0
totalMemory: 15.75GiB freeMemory: 15.34GiB
2019-05-29 16:59:48.799528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0
2019-05-29 16:59:49.216287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-29 16:59:49.216360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0
2019-05-29 16:59:49.216371: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N
2019-05-29 16:59:49.216689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14517 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:d9:00.0, compute capability: 7.0)

CNN model has been built and initialized
Architecture used: vgg

Number of videos: 12
Storage directory: test/
CPU cores: 1
Batch size: 32

Feature Extraction Process

0%| | 0/12 [00:00<?, ?video/s]('get Feature Time:', 1.6686129570007324)
8%|8 | 1/12 [00:02<00:22, 2.01s/video, video=2451831]('get Feature Time:', 0.2662198543548584)
8%|8 | 1/12 [00:02<00:22, 2.01s/video, video=1457058]('get Feature Time:', 0.28127503395080566)
8%|8 | 1/12 [00:02<00:22, 2.01s/video, video=2354797]('get Feature Time:', 0.020145893096923828)
8%|8 | 1/12 [00:02<00:22, 2.01s/video, video=1526389]('get Feature Time:', 0.22216320037841797)
42%|####1 | 5/12 [00:03<00:10, 1.48s/video, video=1180428]('get Feature Time:', 0.26467204093933105)
42%|####1 | 5/12 [00:03<00:10, 1.48s/video, video=1774533]('get Feature Time:', 0.01830887794494629)
42%|####1 | 5/12 [00:03<00:10, 1.48s/video, video=1451504]('get Feature Time:', 0.024552106857299805)
67%|######6 | 8/12 [00:04<00:04, 1.14s/video, video=1466764]('get Feature Time:', 0.023871183395385742)
67%|######6 | 8/12 [00:05<00:04, 1.14s/video, video=1957997]('get Feature Time:', 0.018220186233520508)
83%|########3 | 10/12 [00:05<00:01, 1.00video/s, video=1191604]('get Feature Time:', 0.023998022079467773)
83%|########3 | 10/12 [00:05<00:01, 1.00video/s, video=1469600]('get Feature Time:', 0.024385929107666016)
100%|##########| 12/12 [00:06<00:00, 1.15video/s, video=2163757]
('costTime:', 6.613268136978149)

the first video cost most time,but now i only need process one video and it's must in a fast time,i don't know why,hope to receive your reply,thank u.

Hi,

I was trying to extract features with the inception_v4 model, but I found that the nets/inception_utils.py is missing.

Would it be possible to provide such file?

Thanks

I found there are some special case in CCWEBVideos whose fps is inf.
So the load_video only return the first frame.
I think this line can be replaced by

if not fps or fps != fps or fps == np.inf: