Micaela Verucchi
55 lines
3.5 KiB
Markdown
55 lines
3.5 KiB
Markdown
# Monocular depth estimation with tkDNN
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Currently tkDNN supports only Monodepth2 as monocular depth esitmation network.
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## Run the demo
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To run the depth estimation demo follow these steps (example with monodepth2):
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```
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rm monodepth2_fp32.rt # be sure to delete(or move) old tensorRT files
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./test_monodepth2 # run the yolo test (is slow)
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./demoDepth monodepth2_fp32.rt ../demo/yolo_test.mp4
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```
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In general the demo program takes the following parameters:
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```
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./demoDepth <network-rt-file> <path-to-video> <show-flag> <save-flag>
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```
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where
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* ```<network-rt-file>``` is the rt file generated by a test
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* ```<<path-to-video>``` is the path to a video file or a camera input
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* ```<show-flag>``` if set to 0 the demo will not show the visualization, it will otherwise (default=1)
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* ```<save-flag>``` if set to 1 the demo will save the video into result.mp4, it won't otherwise (default=1)
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NB) By default it is used FP32 inference
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<!-- ## FPS Results
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Inference FPS of shelfnet with tkDNN, average of 1200 images on:
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* RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
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* Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
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| Platform | Test | Phase | FP32, ms | FP32, FPS | FP16, ms | FP16, FPS | INT8, ms | INT8, FPS |
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| :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
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| RTX 2080Ti | shelfnet 1024x1024 (B=1) | pre | 6.11863 | 163.435 | 5.81465 | 171.979 | 5.88699 | 169.866 |
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| RTX 2080Ti | shelfnet 1024x1024 (B=1) | inf | 11.5464 | 86.6074 | 7.35396 | 135.981 | 6.37623 | 156.832 |
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| RTX 2080Ti | shelfnet 1024x1024 (B=1) | post | 4.09058 | 244.464 | 3.91961 | 255.128 | 4.07343 | 245.493 |
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| RTX 2080Ti | shelfnet 1024x1024 (B=1) | tot | 21.7556 | 45.9652 | 17.0882 | 58.5199 | 16.3366 | 61.2121 |
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| RTX 2080Ti | shelfnet 2048x2048 (B=4) | pre | 25.435 | 39.3158 | 25.2953 | 39.5331 | 25.9303 | 38.565 |
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| RTX 2080Ti | shelfnet 2048x2048 (B=4) | inf | 36.5015 | 27.3961 | 17.0534 | 58.6395 | 15.6061 | 64.0773 |
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| RTX 2080Ti | shelfnet 2048x2048 (B=4) | post | 17.3917 | 57.4985 | 17.1649 | 58.2583 | 17.5539 | 56.9675 |
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| RTX 2080Ti | shelfnet 2048x2048 (B=4) | tot | 79.3283 | 12.6058 | 59.5136 | 16.8029 | 59.0903 | 16.9233 |
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| AGX Xavier | shelfnet 1024x1024 (B=1) | pre | 8.0174 | 124.729 | 7.5117 | 133.126 | 7.47333 | 133.809 |
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| AGX Xavier | shelfnet 1024x1024 (B=1) | inf | 72.4173 | 13.8089 | 37.505 | 26.6631 | 31.3286 | 31.9197 |
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| AGX Xavier | shelfnet 1024x1024 (B=1) | post | 8.89958 | 112.365 | 8.83576 | 113.176 | 9.42655 | 106.083 |
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| AGX Xavier | shelfnet 1024x1024 (B=1) | tot | 89.3342 | 11.1939 | 53.8525 | 18.5692 | 48.2285 | 20.7346 |
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| AGX Xavier | shelfnet 2048x2048 (B=4) | pre | 47.1454 | 21.211 | 21.6475 | 46.1947 | 21.4201 | 46.6851 |
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| AGX Xavier | shelfnet 2048x2048 (B=4) | inf | 266.537 | 3.75183 | 128.321 | 7.79293 | 107.621 | 9.29185 |
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| AGX Xavier | shelfnet 2048x2048 (B=4) | post | 44.0711 | 22.6906 | 40.1732 | 24.8922 | 39.873 | 25.0796 |
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| AGX Xavier | shelfnet 2048x2048 (B=4) | tot | 357.753 | 2.79522 | 190.142 | 5.25922 | 168.914 | 5.92016 | -->
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