chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
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+## About
+This script presents a QAT end-to-end workflow (TF2-to-ONNX) for [Inception models](https://keras.io/api/applications/inceptionv3/) in `tf.keras.applications`.
+
+### Contents
+[Requirements](#requirements) • [Workflow](#workflow) • [Results](#results)  
+
+## Requirements
+Install base requirements and prepare data. Please refer to [examples' README](../README.md).
+
+## Workflow
+
+### Step 1: Model Quantization and Fine-tuning
+> Similar to [ResNet](../resnet): different model and different input pre-processing.
+
+Please run the following to quantize, fine-tune, and save the final graph in SavedModel format (checkpoints are also saved).
+
+```sh
+python run_qat_workflow.py
+```
+
+### Step 2: Conversion to ONNX
+Step 1 already does the conversion from SavedModel to ONNX automatically. For manual steps, please see step 3 in [EfficientNet's README](../efficientnet_b0/README.md).
+
+### Step 3: TensorRT Deployment
+Please refer to the [examples' README](../README.md).
+
+## Results
+Results obtained on NVIDIA's A100 GPU and TensorRT 8.4.2.4 (GA Update 1).
+
+### Inception-v3
+
+| Model    | TF (%) | TF latency (ms, bs=1) | TRT(%) | TRT latency (ms, bs=1) |
+|----------|--------|-----------------------|--------|------------------------|
+| Baseline | 77.86  | 9.01                  | 77.86  | 1.39                   |
+| PTQ      | -      | -                     | 77.73  | 0.82                   |
+| **QAT**  | 78.11  | 101.97                | 78.08  | 0.82                   |
+
+### Notes
+- Optimization: MaxPool needs to be quantized to trigger horizontal fusion in Concat layer.
+- QAT fine-tuning hyper-params:
+  - Optimizer: `piecewise_sgd`, `lr_schedule=[(1.0, 1), (0.1, 2), (0.01, 7)]` (default)
+  - Hyper-parameters: `bs=64, ep=10, lr=0.001, steps_per_epoch=500`
+- PTQ calibration: `bs=64`.