******** Examples ******** We ship three end-to-end examples within the tidl-api package to demonstrate three categories of deep learning networks. The first two examples can run on AM57x SoCs with either DLA or DSP. The last example requires AM57x SoCs with both DLA and DSP. The performance numbers that we present here were obtained on an AM5729 EVM, which includes 2 ARM A15 cores running at 1.5GHz, 4 DLA cores at 535MHz, and 2 DSP cores at 750MHz. Imagenet -------- The imagenet example takes an image as input and outputs 1000 probabilities. Each probability corresponds to one object in the 1000 objects that the network is pre-trained with. Our example outputs top 5 probabilities as the most likely objects that the input image can be. The following figure and tables shows an input image, top 5 predicted objects as output, and the processing time on either DLA or DSP. .. image:: ../../examples/test/testvecs/input/objects/cat-pet-animal-domestic-104827.jpeg :width: 600 .. table:: ==== ============== ============ Rank Object Classes Probability ==== ============== ============ 1 tabby 0.996 2 Egyptian_cat 0.977 3 tiger_cat 0.973 4 lynx 0.941 5 Persian_cat 0.922 ==== ============== ============ .. table:: ====================== ==================== ============ Device Processing Time Host Processing Time API Overhead ====================== ==================== ============ DLA: 123.1 ms 124.7 ms 1.34 % **OR** DSP: 117.9 ms 119.3 ms 1.14 % ====================== ==================== ============ The particular network that we ran in this category, jacintonet11v2, has 14 layers. User can specify whether to run the network on DLA or DSP for acceleration. We can see that DLA time is slightly higher than DSP time. Host time includes the OpenCL runtime overhead and the time to copy user input data into padded TIDL buffers. We can see that the overall overhead is less than 1.5%. Segmentation ------------ The segmentation example takes an image as input and performs pixel-level classification according to pre-trained categories. The following figures show a street scene as input and the scene overlaid with pixel-level classifications as output: road in green, pedestrians in red, vehicles in blue and background in gray. .. image:: ../../examples/test/testvecs/input/roads/pexels-photo-972355.jpeg :width: 600 .. image:: images/pexels-photo-972355-seg.jpg :width: 600 The network we ran in this category is jsegnet21v2, which has 26 layers. From the reported time in the following table, we can see that this network runs significantly faster on DLA than on DSP. .. table:: ====================== ==================== ============ Device Processing Time Host Processing Time API Overhead ====================== ==================== ============ DLA: 296.5 ms 303.3 ms 2.26 % **OR** DSP: 812.0 ms 818.4 ms 0.79 % ====================== ==================== ============ .. _ssd-example: SSD --- SSD is the abbreviation for Single Shot multi-box Detector. The ssd_multibox example takes an image as input and detects multiple objects with bounding boxes according to pre-trained categories. The following figures show another street scene as input and the scene with recognized objects boxed as output: pedestrians in red, vehicles in blue and road signs in yellow. .. image:: ../../examples/test/testvecs/input/roads/pexels-photo-378570.jpeg :width: 600 .. image:: images/pexels-photo-378570-ssd.jpg :width: 600 The network can be run entirely on either DLA or DSP. But the best performance comes with running the first 30 layers on DLA and the next 13 layers on DSP, for this particular jdetnet_ssd network. Note the **AND** in the following table for the reported time. Our end-to-end example shows how easy it is to assign a layers group id to an *Executor* and how easy it is to connect the output from one *ExecutionObject* to the input to another *ExecutionObject*. .. table:: ====================== ==================== ============ Device Processing Time Host Processing Time API Overhead ====================== ==================== ============ DLA: 175.2 ms 179.1 ms 2.14 % **AND** DSP: 21.1 ms 22.3 ms 5.62 % ====================== ==================== ============ Running Examples ---------------- The examples are located in ``/usr/share/ti/tidl-api/examples`` on the EVM file system. Each example needs to be run its own directory. Running an example with ``-h`` will show help message with option set. The following code section shows how to run the examples, and the test program that tests all supported TIDL network configs. .. code:: shell root@am57xx-evm:~# cd /usr/share/ti/tidl-api/examples/imagenet/ root@am57xx-evm:/usr/share/ti/tidl-api/examples/imagenet# make -j4 root@am57xx-evm:/usr/share/ti/tidl-api/examples/imagenet# ./imagenet -t d Input: ../test/testvecs/input/objects/cat-pet-animal-domestic-104827.jpeg frame[0]: Time on device: 117.9ms, host: 119.3ms API overhead: 1.17 % 1: tabby, prob = 0.996 2: Egyptian_cat, prob = 0.977 3: tiger_cat, prob = 0.973 4: lynx, prob = 0.941 5: Persian_cat, prob = 0.922 imagenet PASSED root@am57xx-evm:/usr/share/ti/tidl-api/examples/imagenet# cd ../segmentation/; make -j4 root@am57xx-evm:/usr/share/ti/tidl-api/examples/segmentation# ./segmentation -i ../test/testvecs/input/roads/pexels-photo-972355.jpeg Input: ../test/testvecs/input/roads/pexels-photo-972355.jpeg frame[0]: Time on device: 296.5ms, host: 303.2ms API overhead: 2.21 % Saving frame 0 overlayed with segmentation to: overlay_0.png segmentation PASSED root@am57xx-evm:/usr/share/ti/tidl-api/examples/segmentation# cd ../ssd_multibox/; make -j4 root@am57xx-evm:/usr/share/ti/tidl-api/examples/ssd_multibox# ./ssd_multibox -i ../test/testvecs/input/roads/pexels-photo-378570.jpeg Input: ../test/testvecs/input/roads/pexels-photo-378570.jpeg frame[0]: Time on DLA: 175.2ms, host: 179ms API overhead: 2.1 % frame[0]: Time on DSP: 21.06ms, host: 22.43ms API overhead: 6.08 % Saving frame 0 with SSD multiboxes to: multibox_0.png Loop total time (including read/write/print/etc): 423.8ms ssd_multibox PASSED root@am57xx-evm:/usr/share/ti/tidl-api/examples/ssd_multibox# cd ../test; make -j4 root@am57xx-evm:/usr/share/ti/tidl-api/examples/test# ./test_tidl API Version: 01.00.00.d91e442 Running dense_1x1 on 2 devices, type EVE frame[0]: Time on device: 134.3ms, host: 135.6ms API overhead: 0.994 % dense_1x1 : PASSED Running j11_bn on 2 devices, type EVE frame[0]: Time on device: 176.2ms, host: 177.7ms API overhead: 0.835 % j11_bn : PASSED Running j11_cifar on 2 devices, type EVE frame[0]: Time on device: 53.86ms, host: 54.88ms API overhead: 1.85 % j11_cifar : PASSED Running j11_controlLayers on 2 devices, type EVE frame[0]: Time on device: 122.9ms, host: 123.9ms API overhead: 0.821 % j11_controlLayers : PASSED Running j11_prelu on 2 devices, type EVE frame[0]: Time on device: 300.8ms, host: 302.1ms API overhead: 0.437 % j11_prelu : PASSED Running j11_v2 on 2 devices, type EVE frame[0]: Time on device: 124.1ms, host: 125.6ms API overhead: 1.18 % j11_v2 : PASSED Running jseg21 on 2 devices, type EVE frame[0]: Time on device: 367ms, host: 374ms API overhead: 1.88 % jseg21 : PASSED Running jseg21_tiscapes on 2 devices, type EVE frame[0]: Time on device: 302.2ms, host: 308.5ms API overhead: 2.02 % frame[1]: Time on device: 301.9ms, host: 312.5ms API overhead: 3.38 % frame[2]: Time on device: 302.7ms, host: 305.9ms API overhead: 1.04 % frame[3]: Time on device: 301.9ms, host: 305ms API overhead: 1.01 % frame[4]: Time on device: 302.7ms, host: 305.9ms API overhead: 1.05 % frame[5]: Time on device: 301.9ms, host: 305.5ms API overhead: 1.17 % frame[6]: Time on device: 302.7ms, host: 305.9ms API overhead: 1.06 % frame[7]: Time on device: 301.9ms, host: 305ms API overhead: 1.02 % frame[8]: Time on device: 297ms, host: 300.3ms API overhead: 1.09 % Comparing frame: 0 jseg21_tiscapes : PASSED Running smallRoi on 2 devices, type EVE frame[0]: Time on device: 2.548ms, host: 3.637ms API overhead: 29.9 % smallRoi : PASSED Running squeeze1_1 on 2 devices, type EVE frame[0]: Time on device: 292.9ms, host: 294.6ms API overhead: 0.552 % squeeze1_1 : PASSED Multiple Executor... Running network tidl_config_j11_v2.txt on EVEs: 1 in thread 0 Running network tidl_config_j11_cifar.txt on EVEs: 0 in thread 1 Multiple executors: PASSED Running j11_bn on 2 devices, type DSP frame[0]: Time on device: 170.5ms, host: 171.5ms API overhead: 0.568 % j11_bn : PASSED Running j11_controlLayers on 2 devices, type DSP frame[0]: Time on device: 416.4ms, host: 417.1ms API overhead: 0.176 % j11_controlLayers : PASSED Running j11_v2 on 2 devices, type DSP frame[0]: Time on device: 118ms, host: 119.2ms API overhead: 1.01 % j11_v2 : PASSED Running jseg21 on 2 devices, type DSP frame[0]: Time on device: 1123ms, host: 1128ms API overhead: 0.443 % jseg21 : PASSED Running jseg21_tiscapes on 2 devices, type DSP frame[0]: Time on device: 812.3ms, host: 817.3ms API overhead: 0.614 % frame[1]: Time on device: 812.6ms, host: 818.6ms API overhead: 0.738 % frame[2]: Time on device: 812.3ms, host: 815.1ms API overhead: 0.343 % frame[3]: Time on device: 812.7ms, host: 815.2ms API overhead: 0.312 % frame[4]: Time on device: 812.3ms, host: 815.1ms API overhead: 0.353 % frame[5]: Time on device: 812.6ms, host: 815.1ms API overhead: 0.302 % frame[6]: Time on device: 812.2ms, host: 815.1ms API overhead: 0.357 % frame[7]: Time on device: 812.6ms, host: 815.2ms API overhead: 0.315 % frame[8]: Time on device: 812ms, host: 815ms API overhead: 0.367 % Comparing frame: 0 jseg21_tiscapes : PASSED Running smallRoi on 2 devices, type DSP frame[0]: Time on device: 14.21ms, host: 14.94ms API overhead: 4.89 % smallRoi : PASSED Running squeeze1_1 on 2 devices, type DSP frame[0]: Time on device: 960ms, host: 961.1ms API overhead: 0.116 % squeeze1_1 : PASSED tidl PASSED