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Examples (a2q): adding links for pretrained models #707

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Examples (a2q): adding links for pretrained models #707

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31 changes: 19 additions & 12 deletions src/brevitas_examples/super_resolution/README.md
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# Integer-Quantized Super Resolution Experiments with Brevitas

This directory contains scripts demonstrating how to train integer-quantized super resolution models using [Brevitas](https://github.com/Xilinx/brevitas).
This directory contains scripts demonstrating how to train integer-quantized super resolution models using Brevitas.
Code is also provided to demonstrate accumulator-aware quantization (A2Q) as proposed in our ICCV 2023 paper "[A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance](https://arxiv.org/abs/2308.13504)".

## Experiments
Expand All @@ -12,25 +12,32 @@ During inference center cropping is applied.
Inputs are then downscaled by 2x and then used to train the model directly in the RGB space.
Note that this is a difference from many academic works that train only on the Y-channel in YCbCr format.

| Model Name | Upscale Factor | Weight quantization | Activation quantization | Peak Signal-to-Noise Ratio |
| Model Name | Upscale Factor | Weight quantization | Activation quantization | Peak Signal-to-Noise Ratio |
|-----------------------------|----------------|---------------------|-------------------------|----------------------------|
| bicubic_interp | x2 | N/A | N/A | 28.71 |
| [float_espcn_x2]() | x2 | float32 | float32 | 31.03 |
| bicubic_interp | x2 | N/A | N/A | 28.71 |
| [float_espcn_x2](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/float_espcn_x2-2f85a454.pth) | x2 | float32 | float32 | 31.03 |
||
| [quant_espcn_x2_w8a8_base]() | x2 | int8 | (u)int8 | 30.96 |
| [quant_espcn_x2_w8a8_a2q_32b]() | x2 | int8 | (u)int8 | 30.79 |
| [quant_espcn_x2_w8a8_a2q_16b]() | x2 | int8 | (u)int8 | 30.56 |
| [quant_espcn_x2_w8a8_base](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w8a8_base-f761e4a1.pth) | x2 | int8 | (u)int8 | 30.96 |
| [quant_espcn_x2_w8a8_a2q_32b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w8a8_a2q_32b-85470d9b.pth) | x2 | int8 | (u)int8 | 30.79 |
| [quant_espcn_x2_w8a8_a2q_16b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w8a8_a2q_16b-f9e1da66.pth) | x2 | int8 | (u)int8 | 30.56 |
||
| [quant_espcn_x2_w4a4_base]() | x2 | int4 | (u)int4 | 30.30 |
| [quant_espcn_x2_w4a4_a2q_32b]() | x2 | int4 | (u)int4 | 30.27 |
| [quant_espcn_x2_w4a4_a2q_13b]() | x2 | int4 | (u)int4 | 30.24 |
| [quant_espcn_x2_w4a4_base](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w4a4_base-80658e6d.pth) | x2 | int4 | (u)int4 | 30.30 |
| [quant_espcn_x2_w4a4_a2q_32b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w4a4_a2q_32b-8702a412.pth) | x2 | int4 | (u)int4 | 30.27 |
| [quant_espcn_x2_w4a4_a2q_13b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w4a4_a2q_13b-9fff234e.pth) | x2 | int4 | (u)int4 | 30.24 |


## Train

To start training a model from scratch (*e.g.*, `quant_espcn_x2_w8a8_a2q_32b`) run:
All models are trained from scratch as follows:
```bash
python train_model.py --data_root=data --model=quant_espcn_x2_w8a8_a2q_32b
python train_model.py^
--data_root=./data^
--model=quant_espcn_x2_w8a8_a2q_32b^
--batch_size=8^
--learning_rate=0.001^
--weight_decay=0.00001^
--gamma=0.999^
--step_size=1
```

## Evaluate
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13 changes: 8 additions & 5 deletions src/brevitas_examples/super_resolution/models/__init__.py
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Expand Up @@ -45,13 +45,16 @@
act_bit_width=4,
acc_bit_width=13)}

root_url = 'https://github.com/Xilinx/brevitas/releases/download/super_res-r0'
root_url = 'https://github.com/Xilinx/brevitas/releases/download/super_res_r1'

model_url = {
'float_espcn_x2': f'{root_url}/float_espcn_x2-2f3821e3.pth',
'quant_espcn_x2_w8a8_base': f'{root_url}/quant_espcn_x2_w8a8_base-7d54e29c.pth',
'quant_espcn_x2_w8a8_a2q_32b': f'{root_url}/quant_espcn_x2_w8a8_a2q_32b-0b1f361d.pth',
'quant_espcn_x2_w8a8_a2q_16b': f'{root_url}/quant_espcn_x2_w8a8_a2q_16b-3c4acd35.pth'}
'float_espcn_x2': f'{root_url}/float_espcn_x2-2f85a454.pth',
'quant_espcn_x2_w4a4_a2q_13b': f'{root_url}/quant_espcn_x2_w4a4_a2q_13b-9fff234e.pth',
'quant_espcn_x2_w4a4_a2q_32b': f'{root_url}/quant_espcn_x2_w4a4_a2q_32b-8702a412.pth',
'quant_espcn_x2_w4a4_base': f'{root_url}/quant_espcn_x2_w4a4_base-80658e6d.pth',
'quant_espcn_x2_w8a8_a2q_16b': f'{root_url}/quant_espcn_x2_w8a8_a2q_16b-f9e1da66.pth',
'quant_espcn_x2_w8a8_a2q_32b': f'{root_url}/quant_espcn_x2_w8a8_a2q_32b-85470d9b.pth',
'quant_espcn_x2_w8a8_base': f'{root_url}/quant_espcn_x2_w8a8_base-f761e4a1.pth'}


def get_model_by_name(name: str, pretrained: bool = False) -> Union[FloatESPCN, QuantESPCN]:
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