tools/tensorflow-quantization/examples/resnet/README.md
This script presents a QAT end-to-end workflow (TF2-to-ONNX) for ResNet models in tf.keras.applications.
Requirements • Workflow • Results
Install base requirements and prepare data. Please refer to examples' README.
Please run the following to quantize, fine-tune, and save the final graph in SavedModel format (checkpoints are also saved).
python run_qat_workflow.py
Step 1 already does the conversion from SavedModel to ONNX automatically. For manual steps, please see step 3 in EfficientNet's README.
Please refer to the examples' README.
Results obtained on NVIDIA's A100 GPU and TensorRT 8.4 EA.
| Model | TF (%) | TF latency (ms, bs=1) | TRT(%) | TRT latency (ms, bs=1) |
|---|---|---|---|---|
| Baseline | 75.05 | 7.95 | 75.05 | 1.96 |
| PTQ | - | - | 74.96 | 0.46 |
| QAT | 75.11 (ep5) | - | 75.12 | 0.45 |
| Model | TF (%) | TF latency (ms, bs=1) | TRT(%) | TRT latency (ms, bs=1) |
|---|---|---|---|---|
| Baseline | 75.36 | 6.16 | 75.37 | 2.35 |
| PTQ | - | - | 75.48 | 0.57 |
| QAT | 75.59 (ep5) | - | 75.65 | 0.57 |
| Model | TF (%) | TF latency (ms, bs=1) | TRT(%) | TRT latency (ms, bs=1) |
|---|---|---|---|---|
| Baseline | 76.47 | 15.92 | 76.48 | 3.84 |
| PTQ | - | - | 76.32 | 0.84 |
| QAT | 76.33 (ep30) | - | 76.26 | 0.84 |
| Model | TF (%) | TF latency (ms, bs=1) | TRT(%) | TRT latency (ms, bs=1) |
|---|---|---|---|---|
| Baseline | 76.89 | 14.13 | 76.88 | 4.55 |
| PTQ | - | - | 76.94 | 1.05 |
| QAT | 77.20 | - | 77.15 | 1.05 |
QAT fine-tuning hyper-parameters for ResNet101-v2:
bs=32(bs=64was OOM).
piecewise_sgd, lr_schedule=[(1.0,1),(0.1,2),(0.01,7)] (default)bs=64, ep=10, lr=0.001.bs=64.