To generate an optimized TensorRT engine, the MLRecogNet .onnx file, which is generated using tao export, is
taken as an input to tao-deploy. Currently, MLRecogNet supports FP32, FP16 and INT8 data types.
For more information about training an MLRecogNet model, refer to the MLRecogNet training documentation.
Here is an example spec $TRT_GEN_SPEC for generating TensorRT engine from the exported MLRecogNet onnx model.
trt_config
The trt_config parameter provides options related to TensorRT generation.
results_dir: /path/to/results/dir
dataset:
val_dataset:
reference: /path/to/reference/set
query: /path/to/query/set
pixel_mean: [0.485, 0.456, 0.406]
pixel_std: [0.226, 0.226, 0.226]
model:
input_channel: 3
input_width: 224
input_height: 224
gen_trt_engine:
gpu_id: 0
onnx_file: /path/to/exported/onnx/file
trt_engine: /path/to/trt/engine/to/generate
tensorrt:
data_type: int8
workspace_size: 1024
min_batch_size: 1
opt_batch_size: 10
max_batch_size: 10
calibration:
cal_cache_file: /path/to/calibration/cache/file/to/generate
cal_batch_size: 16
cal_batches: 100
cal_image_dir:
- /path/to/calibration/image/folder
Parameter |
Datatype |
Default |
Description |
Supported Values |
|
string |
FP32 |
The precision to be used for the TensorRT engine |
FP32/FP16/INT8 |
|
unsigned int |
1024 |
The maximum workspace size for the TensorRT engine |
>1024 |
|
unsigned int |
1 |
The minimum batch size for optimization profile shape |
>0 |
|
unsigned int |
1 |
The optimal batch size for optimization profile shape |
>0 |
|
unsigned int |
1 |
The maximum batch size for optimization profile shape |
>0 |
|
dict config |
None |
The configuration for the INT8 calibration |
Calibration Config
Parameter |
Datatype |
Default |
Description |
Supported Values |
|
string |
None |
The path to calibration cache file. If there’s no calibration cache file at this path, a cache file is generated based on the the other |
|
|
unsigned int |
1 |
the batch size of calibration dataset |
>0 |
|
unsigned int |
1 |
The number of batches used for calibration. In total, there are |
>0 |
|
string |
None |
The directory containing the calibration images |
Use the following command to run MLRecogNet engine generation:
tao deploy ml_recog gen_trt_engine -e /path/to/spec.yaml \
gen_trt_engine.onnx_file=/path/to/onnx/file \
gen_trt_engine.trt_engine=/path/to/engine/file \
gen_trt_engine.tensorrt.data_type=<data_type>
Required Arguments
-e, --experiment_spec: The experiment spec file to set up the TensorRT engine generation. This should be the same as the export specification file.gen_trt_engine.onnx_file: The.onnxmodel to be converted.gen_trt_engine.trt_engine: The path where the generated engine will be stored.gen_trt_engine.tensorrt.data_type: MLRecogNet supports FP32, FP16 and INT8 TensorRT engine generation. When using INT8, you must provide the calibration dataset or calibration cache file.
Sample Usage
Here’s an example of using the gen_trt_engine command to generate an FP16 TensorRT engine:
tao model metric_learning_recognition gen_trt_engine -e $TRT_GEN_SPEC
gen_trt_engine.onnx_file=$ONNX_FILE \
gen_trt_engine.trt_engine=$ENGINE_FILE \
gen_trt_engine.tensorrt.data_type=FP16
Here’s an example of output $RESULTS_DIR/status.json:
{"date": "6/22/2023", "time": "18:17:11", "status": "STARTED", "verbosity": "INFO", "message": "Starting ml_recog gen_trt_engine."}
{"date": "6/22/2023", "time": "18:17:30", "status": "SUCCESS", "verbosity": "INFO", "message": "Gen_trt_engine finished successfully."}
The output log example is shown below:
Starting ml_recog gen_trt_engine.
[06/22/2023-18:17:12] [TRT] [I] [MemUsageChange] Init CUDA: CPU +318, GPU +0, now: CPU 356, GPU 1003 (MiB)
[06/22/2023-18:17:14] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +443, GPU +116, now: CPU 853, GPU 1119 (MiB)
[06/22/2023-18:17:14] [TRT] [W] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
Parsing ONNX model
[06/22/2023-18:17:14] [TRT] [W] The NetworkDefinitionCreationFlag::kEXPLICIT_PRECISION flag has been deprecated and has no effect. Please do not use this flag when creating the network.
[06/22/2023-18:17:15] [TRT] [W] onnx2trt_utils.cpp:377: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
Network Description
Input 'input' with shape (-1, 3, 224, 224) and dtype DataType.FLOAT
Output 'fc_pred' with shape (-1, 256) and dtype DataType.FLOAT
dynamic batch size handling
TensorRT engine build configurations:
OptimizationProfile:
"input": (1, 3, 224, 224), (10, 3, 224, 224), (10, 3, 224, 224)
BuilderFlag.TF32
Note: max representabile value is 2,147,483,648 bytes or 2GB.
MemoryPoolType.WORKSPACE = 1073741824 bytes
MemoryPoolType.DLA_MANAGED_SRAM = 0 bytes
MemoryPoolType.DLA_LOCAL_DRAM = 1073741824 bytes
MemoryPoolType.DLA_GLOBAL_DRAM = 536870912 bytes
Tactic Sources = 31
[06/22/2023-18:17:17] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +854, GPU +362, now: CPU 1800, GPU 1481 (MiB)
[06/22/2023-18:17:17] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +126, GPU +58, now: CPU 1926, GPU 1539 (MiB)
[06/22/2023-18:17:17] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
[06/22/2023-18:17:22] [TRT] [I] Some tactics do not have sufficient workspace memory to run. Increasing workspace size will enable more tactics, please check verbose output for requested sizes.
[06/22/2023-18:17:30] [TRT] [I] Total Activation Memory: 1565556736
[06/22/2023-18:17:30] [TRT] [I] Detected 1 inputs and 1 output network tensors.
[06/22/2023-18:17:30] [TRT] [I] Total Host Persistent Memory: 132192
[06/22/2023-18:17:30] [TRT] [I] Total Device Persistent Memory: 140288
[06/22/2023-18:17:30] [TRT] [I] Total Scratch Memory: 134217728
[06/22/2023-18:17:30] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 9 MiB, GPU 658 MiB
[06/22/2023-18:17:30] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 91 steps to complete.
[06/22/2023-18:17:30] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 1.66392ms to assign 5 blocks to 91 nodes requiring 184394240 bytes.
[06/22/2023-18:17:30] [TRT] [I] Total Activation Memory: 184394240
[06/22/2023-18:17:30] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2491, GPU 1889 (MiB)
[06/22/2023-18:17:30] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +0, GPU +101, now: CPU 0, GPU 101 (MiB)
Export finished successfully.
Gen_trt_engine finished successfully.
Same spec file as TAO evaluation spec file. The following is a sample spec file $EVAL_SPEC:
results_dir: /path/to/output_dir
evaluate:
trt_engine: /path/to/generated/trt_engine
batch_size: 8
topk: 5
dataset:
val_dataset:
reference: /path/to/reference/set
query: /path/to/query/set
Use the following command to run Deformable DETR engine evaluation:
tao deploy ml_recog evaluate -e /path/to/spec.yaml \
evaluate.trt_engine=/path/to/engine/file \
results_dir=/path/to/outputs
Required Arguments
-e, --experiment_spec: The experiment spec file for evaluation. This should be the same as thetao evaluatespecification file.evaluate.trt_engine: The engine file to run evaluationresults_dir: The directory where evaluation results will be stored. If not provided, the results will be stored inevaluate.results_dir. Thus at least one of the of results_dir is needed.
Sample Usage
In the following example, the evaluate command is used to run evaluation with the TensorRT engine:
tao deploy ml_recog evaluate -e $EVAL_SPEC
evaluate.trt_engine=$ENGINE_FILE \
results_dir=$RESULTS_DIR
Here’s an example of output $RESULTS_DIR/status.json:
{"date": "3/30/2023", "time": "6:7:14", "status": "STARTED", "verbosity": "INFO", "message": "Starting ml_recog evaluation."}
{"date": "3/30/2023", "time": "6:7:24", "status": "SUCCESS", "verbosity": "INFO", "message": "Evaluation finished successfully."}
The output log example is shown below:
Starting ml_recog evaluation.
[06/22/2023-20:41:53] [TRT] [W] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
[06/22/2023-20:41:53] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[06/22/2023-20:41:53] [TRT] [W] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
Loading gallery dataset...
...
Top 1 scores: 0.9958333333333333
Top 5 scores: 1.0
Confusion Matrix
[[ 34 0 0 0 0]
[ 0 106 0 0 0]
[ 0 0 29 0 0]
[ 0 0 0 31 0]
[ 0 0 0 1 47]]
Classification Report
precision recall f1-score support
c000001 1.00 1.00 1.00 34
c000002 1.00 1.00 1.00 106
c000003 1.00 1.00 1.00 29
c000004 0.97 1.00 0.98 31
c000005 1.00 0.98 0.99 48
accuracy 1.00 248
macro avg 0.99 1.00 0.99 248
weighted avg 1.00 1.00 1.00 248
Finished evaluation.
Evaluation finished successfully.
Same spec file as TAO inference spec file. Sample spec file $INFERENCE_SPEC:
results_dir: "/path/to/output_dir"
model:
input_channels: 3
input_width: 224
input_height: 224
inference:
trt_engine: "/path/to/generated/trt_engine"
batch_size: 10
inference_input_type: classification_folder
topk: 5
dataset:
val_dataset:
reference: "/path/to/reference/set"
query: ""
Use the following command to run MLRecogNet engine inference:
tao deploy ml_recog inference -e /path/to/spec.yaml \
inference.trt_engine=/path/to/engine/file \
results_dir=/path/to/outputs
Required Arguments
-e, --experiment_spec: The experiment spec file for inference. This should be the same as thetao inferencespecification file.inference.trt_engine: The engine file to run inference.results_dir: The directory where inference results will be stored.
Sample Usage
In the following example, the inference command is used to run inference with the TensorRT engine:
tao deploy ml_recog inference -e $INFERENCE_SPEC
inference.trt_engine=$ENGINE_FILE \
results_dir=$RESULTS_DIR
The JSON format results will be stored under $RESULTS_DIR/trt_inference.
Here’s an example of output $RESULTS_DIR/status.json:
{"date": "6/22/2023", "time": "20:46:38", "status": "STARTED", "verbosity": "INFO", "message": "Starting ml_recog inference."}
{"date": "6/22/2023", "time": "20:46:53", "status": "SUCCESS", "verbosity": "INFO", "message": "Inference finished successfully."}
The output log example is shown below:
Starting ml_recog inference.
[06/22/2023-20:46:39] [TRT] [W] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
[06/22/2023-20:46:39] [TRT] [W] The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[06/22/2023-20:46:39] [TRT] [W] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
Loading gallery dataset...
...
Finished inference.
Inference finished successfully.