# Image classification reference training scripts

This folder contains reference training scripts for image classification.
They serve as a log of how to train specific models, as provide baseline
training and evaluation scripts to quickly bootstrap research.

Except otherwise noted, all models have been trained on 8x V100 GPUs.

### ResNext-50 32x4d
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
    --model resnext50_32x4d --epochs 100
```


### ResNext-101 32x8d

On 8 nodes, each with 8 GPUs (for a total of 64 GPUS)
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
    --model resnext101_32x8d --epochs 100
```


### MobileNetV2
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
     --model mobilenet_v2 --epochs 300 --lr 0.045 --wd 0.00004\
     --lr-step-size 1 --lr-gamma 0.98
```

## Mixed precision training
Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [NVIDIA Apex extension](https://github.com/NVIDIA/apex).

Mixed precision training makes use of both FP32 and FP16 precisions where appropriate. FP16 operations can leverage the Tensor cores on NVIDIA GPUs (Volta, Turing or newer architectures) for improved throughput, generally without loss in model accuracy. Mixed precision training also often allows larger batch sizes. GPU automatic mixed precision training for Pytorch Vision can be enabled via the flag value `--apex=True`.

```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
    --model resnext50_32x4d --epochs 100 --apex
```

## Quantized
### INT8 models
We add INT8 quantized models to follow the quantization support added in PyTorch 1.3. 

Obtaining a pre-trained quantized model can be obtained with a few lines of code:
```
model = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
model.eval()
# run the model with quantized inputs and weights
out = model(torch.rand(1, 3, 224, 224))
```
We provide pre-trained quantized weights for the following models:

|       Model       |  Acc@1 |  Acc@5 |
|:-----------------:|:------:|:------:|
|    MobileNet V2   | 71.658 | 90.150 |
|   ShuffleNet V2:  | 68.360 | 87.582 |
|     ResNet 18     | 69.494 | 88.882 |
|     ResNet 50     | 75.920 | 92.814 |
| ResNext 101 32x8d | 78.986 | 94.480 |
|    Inception V3   | 77.176 | 93.354 |
|     GoogleNet     | 69.826 | 89.404 |

### Parameters used for generating quantized models:

For all post training quantized models (All quantized models except mobilenet-v2), the settings are:

1. num_calibration_batches: 32
2. num_workers: 16
3. batch_size: 32
4. eval_batch_size: 128
5. backend: 'fbgemm'

For Mobilenet-v2, the model was trained with quantization aware training, the settings used are:
1. num_workers: 16
2. batch_size: 32
3. eval_batch_size: 128
4. backend: 'qnnpack'
5. learning-rate: 0.0001
6. num_epochs: 90
7. num_observer_update_epochs:4
8. num_batch_norm_update_epochs:3
9. momentum: 0.9
10. lr_step_size:30
11. lr_gamma: 0.1

Training converges at about 10 epochs.

For post training quant, device is set to CPU. For training, the device is set to CUDA

### Command to evaluate quantized models using the pre-trained weights:
For all quantized models except inception_v3:
```
python references/classification/train_quantization.py  --data-path='imagenet_full_size/' \
    --device='cpu' --test-only --backend='fbgemm' --model='<model_name>'
```

For inception_v3, since it expects tensors with a size of N x 3 x 299 x 299, before running above command,
need to change the input size of dataset_test in train.py to:
```
dataset_test = torchvision.datasets.ImageFolder(
    valdir,
    transforms.Compose([
        transforms.Resize(342),
        transforms.CenterCrop(299),
        transforms.ToTensor(),
        normalize,
    ]))
```

