Quick Start ======================================================================================== This chapter uses dvs-mnist as an example to illustrate the usage of the BIDL project. .. _choose_config_file: Choose Configuration File -------------------------------------------------------------------------------------------------------------- The dvs-mnist dataset uses the *clif3fc3dm_itout-b16x1-dvsmnist.py* configuration file. Since dvs-mnist belongs to the classification of dvs data, create a *dvs_mnist* folder in the *applications/classification/dvs* directory. Based on its model structure, create a *clif3fc3dm* folder under the new *dvs_mnist* folder, and finally place the configuration file in this path. .. attention:: Configuration files are Python source code files, and their filenames have a .py suffix. Prepare Dataset -------------------------------------------------------------------------------------------------------- Check the dataset path set in the configuration file chosen in :ref:`choose_config_file`, and then create the corresponding dataset directory in the project's root directory. :: data = dict( samples_per_gpu=16, workers_per_gpu=2, train=dict( pipeline=train_pipeline ), val=dict( pipeline=test_pipeline, test_mode=True ), test=dict( pipeline=test_pipeline, test_mode=True ) ) .. _mxxl: Model Training -------------------------------------------------------------------------------- In the *tools* directory, execute the following command to start model training. :: python train.py --config clif3fc3dm_itout-b16x1-dvsmnist .. attention:: The parameter ``--config`` followed by ``clif3fc3dm_itout-b16x1-dvsmnist`` is the name of the configuration file selected in the :ref:`choose_config_file` section, without the .py. After training starts, the configuration information involved in this training will be printed in sequence: - Configuration information from the configuration file - The path information for saving checkpoints. By default, checkpoint files are saved in the project root directory */work_dirs/clif3fc3dm_itout-b16x1-dvsmnist/*, where a *latest.pth* file is generated for the last saved checkpoint of the epoch, and a *best.pth* file is created to save the checkpoint with the best accuracy on the validation set. The directory tree is as follows: :: clif3fc3dm_itout-b16x1-dvsmnist/ ├── 20240829_171552.log #All logs in this training process, such as loss, accuracy ├── best.pth #Checkpoint of the model with the highest accuracy on the validation set └── latest.pth #Checkpoint of the model from the last training round - The following contents are model training log information. Below is the log information output in the first two epochs: :: 2024-08-29 17:33:37,021 - cls - INFO - Epoch [1][100/452] lr: 2.060e-02 top-1: 22.7500 top-5: 62.5000 loss: 2.1440 2024-08-29 17:33:48,696 - cls - INFO - Epoch [1][200/452] lr: 4.040e-02 top-1: 73.8750 top-5: 96.8125 loss: 1.1264 2024-08-29 17:34:00,255 - cls - INFO - Epoch [1][300/452] lr: 6.020e-02 top-1: 89.1875 top-5: 98.6250 loss: 0.8457 2024-08-29 17:34:11,309 - cls - INFO - Epoch [1][400/452] lr: 8.000e-02 top-1: 90.6250 top-5: 99.1250 loss: 0.7871 2024-08-29 17:34:16,431 - cls - INFO - Saving checkpoint at 1 epochs 2024-08-29 17:34:21,611 - cls - INFO - Epoch(val) [1][20] top-1: 87.1275 top-5: 98.9238 2024-08-29 17:34:31,200 - cls - INFO - Epoch [2][100/452] lr: 9.980e-02 top-1: 94.3750 top-5: 99.6250 loss: 0.7186 2024-08-29 17:34:41,470 - cls - INFO - Epoch [2][200/452] lr: 9.980e-02 top-1: 94.6875 top-5: 99.6250 loss: 0.6894 2024-08-29 17:34:51,961 - cls - INFO - Epoch [2][300/452] lr: 9.980e-02 top-1: 97.3125 top-5: 99.8750 loss: 0.6128 2024-08-29 17:35:02,393 - cls - INFO - Epoch [2][400/452] lr: 9.980e-02 top-1: 97.3750 top-5: 99.9375 loss: 0.5926 2024-08-29 17:35:07,965 - cls - INFO - Saving checkpoint at 2 epochs 2024-08-29 17:35:14,549 - cls - INFO - Epoch(val) [2][20] top-1: 98.1374 top-5: 100.0000 After the training, copy the best weight file to the weight file path in the resource package: */weight_files/classification/clif3fc3dm/lif* Model Inference (GPU) -------------------------------------------------------------------------------- In the *tools* directory, run the following command for model inference. :: python test.py --config clif3fc3dm_itout-b16x1-dvsmnist --checkpoint latest.pth --use_lyngor 0 --use_legacy 0 The inference process is as follows: :: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2409/2409 [01:23<00:00, 28.85it/s] acc top-1: 99.21 top-5: 100.00 gpu test speed = 576.6303 fps gpu test speed (without pipeline) = 583.3422 fps Since the checkpoint files' save path is fixed by default during training, and the subdirectory is the same as the configuration file name, the parameter for ``--checkpoint`` only needs to specify the *pth* filename. Model Inference (Lynxi brain-inspired systems) -------------------------------------------------------------------------------- In the BIDL framework, a switch is used to toggle the backend execution environment. Specifically, when using *test.py*, configure as follows: +----------------+------------------------------------------------------------+ | Configuration | Description | +================+============================================================+ | \-\-use_lyngor | Used to specify whether to use Lyngor for compilation. | | | | | | [Data Type] ENUM | | | | | | [Value Range] | | | | | | - 1: Use Lyngor for compilation, executed with Lynxi | | | brain-inspired systems; | | | - 0: Do not use Lyngor for compilation (GPU execution). | +----------------+------------------------------------------------------------+ | \-\-use_legacy | Used to specify whether to directly load historical | | | compilation artifacts, skipping the compilation step. | | | | | | [Data Type] ENUM | | | | | | [Value Range] | | | | | | - 1: Load historical compilation artifacts, skip | | | compilation step; | | | - 0: Do not load historical compilation artifacts, do | | | not skip compilation step. | +----------------+------------------------------------------------------------+ Like this: :: python test.py --config clif3fc3dm_itout-b16x1-dvsmnist --checkpoint latest.pth --use_lyngor 1 --use_legacy 0 .. attention:: You need to copy the trained files (located in *work_dirs*) and the validation dataset to Lynxi brain-inspiredcomputing devices before executing this script.