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Super Guru

Part 1: Installation and Setup

Part 2: Classifying Images with ImageNet

34725-jetsontx1top.jpg

34726-jetsontx1logo.png

Use This Project: https://github.com/dusty-nv/jetson-inference

This will create a C++ executable to run Classification for ImageNet with TensorRT.

Shell Call Example

root@tegra-ubuntu:/media/nvidia/96ed93f9-7c40-4999-85ba-3eb24262d0a5/jetson-inference-master/build/aarch64/bin# ./runclassify.sh
imagenet-console
  args (3):  0 [/media/nvidia/96ed93f9-7c40-4999-85ba-3eb24262d0a5/jetson-inference-master/build/aarch64/bin/imagenet-console]  1 [backupimages/granny_smith_1.jpg]  2 [images/output_0.jpg]




imageNet -- loading classification network model from:
         -- prototxt     networks/googlenet.prototxt
         -- model        networks/bvlc_googlenet.caffemodel
         -- class_labels networks/ilsvrc12_synset_words.txt
         -- input_blob   'data'
         -- output_blob  'prob'
         -- batch_size   2


[GIE]  attempting to open cache file networks/bvlc_googlenet.caffemodel.2.tensorcache
[GIE]  loading network profile from cache... networks/bvlc_googlenet.caffemodel.2.tensorcache
[GIE]  platform has FP16 support.
[GIE]  networks/bvlc_googlenet.caffemodel loaded
[GIE]  CUDA engine context initialized with 2 bindings
[GIE]  networks/bvlc_googlenet.caffemodel input  binding index:  0
[GIE]  networks/bvlc_googlenet.caffemodel input  dims (b=2 c=3 h=224 w=224) size=1204224
[cuda]  cudaAllocMapped 1204224 bytes, CPU 0x100ce0000 GPU 0x100ce0000
[GIE]  networks/bvlc_googlenet.caffemodel output 0 prob  binding index:  1
[GIE]  networks/bvlc_googlenet.caffemodel output 0 prob  dims (b=2 c=1000 h=1 w=1) size=8000
[cuda]  cudaAllocMapped 8000 bytes, CPU 0x100e20000 GPU 0x100e20000
networks/bvlc_googlenet.caffemodel initialized.
[GIE]  networks/bvlc_googlenet.caffemodel loaded
imageNet -- loaded 1000 class info entries
networks/bvlc_googlenet.caffemodel initialized.
loaded image  backupimages/granny_smith_1.jpg  (1000 x 1000)  16000000 bytes
[cuda]  cudaAllocMapped 16000000 bytes, CPU 0x100f20000 GPU 0x100f20000
[GIE]  layer conv1/7x7_s2 + conv1/relu_7x7 input reformatter 0 - 1.207813 ms
[GIE]  layer conv1/7x7_s2 + conv1/relu_7x7 - 6.144531 ms
[GIE]  layer pool1/3x3_s2 - 1.301354 ms
[GIE]  layer pool1/norm1 - 0.412240 ms
[GIE]  layer conv2/3x3_reduce + conv2/relu_3x3_reduce - 0.737552 ms
[GIE]  layer conv2/3x3 + conv2/relu_3x3 - 11.184843 ms
[GIE]  layer conv2/norm2 - 1.052657 ms
[GIE]  layer pool2/3x3_s2 - 0.946510 ms
[GIE]  layer inception_3a/1x1 + inception_3a/relu_1x1 || inception_3a/3x3_reduce + inception_3a/relu_3x3_reduce || inception_3a/5x5_reduce + inception_3a/relu_5x5_reduce - 1.299844 ms
[GIE]  layer inception_3a/3x3 + inception_3a/relu_3x3 - 3.431562 ms
[GIE]  layer inception_3a/5x5 + inception_3a/relu_5x5 - 0.697657 ms
[GIE]  layer inception_3a/pool - 0.449479 ms
[GIE]  layer inception_3a/pool_proj + inception_3a/relu_pool_proj - 0.542916 ms
[GIE]  layer inception_3a/1x1 copy - 0.074375 ms
[GIE]  layer inception_3b/1x1 + inception_3b/relu_1x1 || inception_3b/3x3_reduce + inception_3b/relu_3x3_reduce || inception_3b/5x5_reduce + inception_3b/relu_5x5_reduce - 2.582917 ms
[GIE]  layer inception_3b/3x3 + inception_3b/relu_3x3 - 6.324167 ms
[GIE]  layer inception_3b/5x5 + inception_3b/relu_5x5 - 3.262968 ms
[GIE]  layer inception_3b/pool - 0.586719 ms
[GIE]  layer inception_3b/pool_proj + inception_3b/relu_pool_proj - 0.657552 ms
[GIE]  layer inception_3b/1x1 copy - 0.111511 ms
[GIE]  layer pool3/3x3_s2 - 0.608333 ms
[GIE]  layer inception_4a/1x1 + inception_4a/relu_1x1 || inception_4a/3x3_reduce + inception_4a/relu_3x3_reduce || inception_4a/5x5_reduce + inception_4a/relu_5x5_reduce - 1.589531 ms
[GIE]  layer inception_4a/3x3 + inception_4a/relu_3x3 - 1.027396 ms
[GIE]  layer inception_4a/5x5 + inception_4a/relu_5x5 - 0.420052 ms
[GIE]  layer inception_4a/pool - 0.306563 ms
[GIE]  layer inception_4a/pool_proj + inception_4a/relu_pool_proj - 0.464583 ms
[GIE]  layer inception_4a/1x1 copy - 0.060417 ms
[GIE]  layer inception_4b/1x1 + inception_4b/relu_1x1 || inception_4b/3x3_reduce + inception_4b/relu_3x3_reduce || inception_4b/5x5_reduce + inception_4b/relu_5x5_reduce - 1.416875 ms
[GIE]  layer inception_4b/3x3 + inception_4b/relu_3x3 - 1.157135 ms
[GIE]  layer inception_4b/5x5 + inception_4b/relu_5x5 - 0.555886 ms
[GIE]  layer inception_4b/pool - 0.331354 ms
[GIE]  layer inception_4b/pool_proj + inception_4b/relu_pool_proj - 0.485677 ms
[GIE]  layer inception_4b/1x1 copy - 0.056041 ms
[GIE]  layer inception_4c/1x1 + inception_4c/relu_1x1 || inception_4c/3x3_reduce + inception_4c/relu_3x3_reduce || inception_4c/5x5_reduce + inception_4c/relu_5x5_reduce - 1.454011 ms
[GIE]  layer inception_4c/3x3 + inception_4c/relu_3x3 - 2.771198 ms
[GIE]  layer inception_4c/5x5 + inception_4c/relu_5x5 - 0.554844 ms
[GIE]  layer inception_4c/pool - 0.502604 ms
[GIE]  layer inception_4c/pool_proj + inception_4c/relu_pool_proj - 0.486198 ms
[GIE]  layer inception_4c/1x1 copy - 0.050833 ms
[GIE]  layer inception_4d/1x1 + inception_4d/relu_1x1 || inception_4d/3x3_reduce + inception_4d/relu_3x3_reduce || inception_4d/5x5_reduce + inception_4d/relu_5x5_reduce - 1.419271 ms
[GIE]  layer inception_4d/3x3 + inception_4d/relu_3x3 - 1.781406 ms
[GIE]  layer inception_4d/5x5 + inception_4d/relu_5x5 - 0.680052 ms
[GIE]  layer inception_4d/pool - 0.333542 ms
[GIE]  layer inception_4d/pool_proj + inception_4d/relu_pool_proj - 0.483854 ms
[GIE]  layer inception_4d/1x1 copy - 0.048229 ms
[GIE]  layer inception_4e/1x1 + inception_4e/relu_1x1 || inception_4e/3x3_reduce + inception_4e/relu_3x3_reduce || inception_4e/5x5_reduce + inception_4e/relu_5x5_reduce - 2.225573 ms
[GIE]  layer inception_4e/3x3 + inception_4e/relu_3x3 - 4.142656 ms
[GIE]  layer inception_4e/5x5 + inception_4e/relu_5x5 - 0.954427 ms
[GIE]  layer inception_4e/pool - 0.332917 ms
[GIE]  layer inception_4e/pool_proj + inception_4e/relu_pool_proj - 0.667344 ms
[GIE]  layer inception_4e/1x1 copy - 0.071666 ms
[GIE]  layer pool4/3x3_s2 - 0.275625 ms
[GIE]  layer inception_5a/1x1 + inception_5a/relu_1x1 || inception_5a/3x3_reduce + inception_5a/relu_3x3_reduce || inception_5a/5x5_reduce + inception_5a/relu_5x5_reduce - 1.685417 ms
[GIE]  layer inception_5a/3x3 + inception_5a/relu_3x3 - 2.085990 ms
[GIE]  layer inception_5a/5x5 + inception_5a/relu_5x5 - 0.391198 ms
[GIE]  layer inception_5a/pool - 0.187552 ms
[GIE]  layer inception_5a/pool_proj + inception_5a/relu_pool_proj - 0.964791 ms
[GIE]  layer inception_5a/1x1 copy - 0.041094 ms
[GIE]  layer inception_5b/1x1 + inception_5b/relu_1x1 || inception_5b/3x3_reduce + inception_5b/relu_3x3_reduce || inception_5b/5x5_reduce + inception_5b/relu_5x5_reduce - 2.327656 ms
[GIE]  layer inception_5b/3x3 + inception_5b/relu_3x3 - 1.884532 ms
[GIE]  layer inception_5b/5x5 + inception_5b/relu_5x5 - 1.364895 ms
[GIE]  layer inception_5b/pool - 0.189219 ms
[GIE]  layer inception_5b/pool_proj + inception_5b/relu_pool_proj - 0.453490 ms
[GIE]  layer inception_5b/1x1 copy - 0.045781 ms
[GIE]  layer pool5/7x7_s1 - 0.743281 ms
[GIE]  layer loss3/classifier input reformatter 0 - 0.042552 ms
[GIE]  layer loss3/classifier - 0.848386 ms
[GIE]  layer loss3/classifier output reformatter 0 - 0.042969 ms
[GIE]  layer prob - 0.092343 ms
[GIE]  layer prob output reformatter 0 - 0.042552 ms
[GIE]  layer network time - 84.158958 ms
class 0948 - 1.000000  (Granny Smith)
imagenet-console:  'backupimages/granny_smith_1.jpg' -> 100.00000% class #948 (Granny Smith)
loaded image  fontmapA.png  (256 x 512)  2097152 bytes
[cuda]  cudaAllocMapped 2097152 bytes, CPU 0x101fa0000 GPU 0x101fa0000
[cuda]  cudaAllocMapped 8192 bytes, CPU 0x100e22000 GPU 0x100e22000
imagenet-console:  attempting to save output image to 'images/output_0.jpg'
imagenet-console:  completed saving 'images/output_0.jpg'


shutting down...


Input File

34766-tim.jpg

Output File

34765-output-tim-1.jpg

The output image contains the highest probability of what's in the image. This one says sunscreen which is a bit weird, I am guessing because my original image is very sunny.

Source Code

https://github.com/tspannhw/jetsontx1-TensorRT

Resources

  1. http://www.jetsonhacks.com/2017/01/28/install-samsung-ssd-on-nvidia-jetson-tx1/
  2. https://github.com/PhilipChicco/pedestrianSys
  3. https://github.com/jetsonhacks?tab=repositories
  4. https://github.com/Netzeband/JetsonTX1_im2txt
  5. https://github.com/DJTobias/Cherry-Autonomous-Racecar
  6. https://github.com/jetsonhacks/postFlashTX1
  7. https://github.com/jetsonhacks/installTensorFlowTX1
  8. http://www.jetsonhacks.com/2016/12/21/jetson-tx1-swap-file-and-development-preparation/
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Version history
Revision #:
2 of 2
Last update:
‎08-17-2019 11:08 AM
Updated by:
 
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