# 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 with 
the following parameters:

| Parameter                | value  |
| ------------------------ | ------ |
| `--batch_size`           | `32`   |
| `--epochs`               | `90`   |
| `--lr`                   | `0.1`  |
| `--momentum`             | `0.9`  |
| `--wd`, `--weight-decay` | `1e-4` |
| `--lr-step-size`         | `30`   |
| `--lr-gamma`             | `0.1`  |

### AlexNet and VGG

Since `AlexNet` and the original `VGG` architectures do not include batch 
normalization, the default initial learning rate `--lr 0.1` is too high.

```
torchrun --nproc_per_node=8 train.py\
    --model $MODEL --lr 1e-2
```

Here `$MODEL` is one of `alexnet`, `vgg11`, `vgg13`, `vgg16` or `vgg19`. Note
that `vgg11_bn`, `vgg13_bn`, `vgg16_bn`, and `vgg19_bn` include batch
normalization and thus are trained with the default parameters.

### GoogLeNet

The weights of the GoogLeNet model are ported from the original paper rather than trained from scratch.

### Inception V3

The weights of the Inception V3 model are ported from the original paper rather than trained from scratch.

Since it expects tensors with a size of N x 3 x 299 x 299, to validate the model use the following command:

```
torchrun --nproc_per_node=8 train.py --model inception_v3\
      --val-resize-size 342 --val-crop-size 299 --train-crop-size 299 --test-only --pretrained
```

### ResNet
```
torchrun --nproc_per_node=8 train.py --model $MODEL
```

Here `$MODEL` is one of `resnet18`, `resnet34`, `resnet50`, `resnet101` or `resnet152`.

### ResNext
```
torchrun --nproc_per_node=8 train.py\
    --model $MODEL --epochs 100
```

Here `$MODEL` is one of `resnext50_32x4d` or `resnext101_32x8d`.
Note that the above command corresponds to a single node with 8 GPUs. If you use
a different number of GPUs and/or a different batch size, then the learning rate
should be scaled accordingly. For example, the pretrained model provided by
`torchvision` was trained on 8 nodes, each with 8 GPUs (for a total of 64 GPUs),
with `--batch_size 16` and `--lr 0.4`, instead of the current defaults
which are respectively batch_size=32 and lr=0.1

### MobileNetV2
```
torchrun --nproc_per_node=8 train.py\
     --model mobilenet_v2 --epochs 300 --lr 0.045 --wd 0.00004\
     --lr-step-size 1 --lr-gamma 0.98
```


### MobileNetV3 Large & Small
```
torchrun --nproc_per_node=8 train.py\
     --model $MODEL --epochs 600 --opt rmsprop --batch-size 128 --lr 0.064\ 
     --wd 0.00001 --lr-step-size 2 --lr-gamma 0.973 --auto-augment imagenet --random-erase 0.2
```

Here `$MODEL` is one of `mobilenet_v3_large` or `mobilenet_v3_small`.

Then we averaged the parameters of the last 3 checkpoints that improved the Acc@1. See [#3182](https://github.com/pytorch/vision/pull/3182) 
and [#3354](https://github.com/pytorch/vision/pull/3354) for details.


### EfficientNet

The weights of the B0-B4 variants are ported from Ross Wightman's [timm repo](https://github.com/rwightman/pytorch-image-models/blob/01cb46a9a50e3ba4be167965b5764e9702f09b30/timm/models/efficientnet.py#L95-L108).

The weights of the B5-B7 variants are ported from Luke Melas' [EfficientNet-PyTorch repo](https://github.com/lukemelas/EfficientNet-PyTorch/blob/1039e009545d9329ea026c9f7541341439712b96/efficientnet_pytorch/utils.py#L562-L564).

All models were trained using Bicubic interpolation and each have custom crop and resize sizes. To validate the models use the following commands:
```
torchrun --nproc_per_node=8 train.py --model efficientnet_b0 --interpolation bicubic\
     --val-resize-size 256 --val-crop-size 224 --train-crop-size 224 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b1 --interpolation bicubic\
      --val-resize-size 256 --val-crop-size 240 --train-crop-size 240 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b2 --interpolation bicubic\
      --val-resize-size 288 --val-crop-size 288 --train-crop-size 288 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b3 --interpolation bicubic\
      --val-resize-size 320 --val-crop-size 300 --train-crop-size 300 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b4 --interpolation bicubic\
      --val-resize-size 384 --val-crop-size 380 --train-crop-size 380 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b5 --interpolation bicubic\
      --val-resize-size 456 --val-crop-size 456 --train-crop-size 456 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b6 --interpolation bicubic\
      --val-resize-size 528 --val-crop-size 528 --train-crop-size 528 --test-only --pretrained
torchrun --nproc_per_node=8 train.py --model efficientnet_b7 --interpolation bicubic\
      --val-resize-size 600 --val-crop-size 600 --train-crop-size 600 --test-only --pretrained
```

### RegNet

#### Small models
```
torchrun --nproc_per_node=8 train.py\
     --model $MODEL --epochs 100 --batch-size 128 --wd 0.00005 --lr=0.8\
     --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
     --lr-warmup-epochs=5 --lr-warmup-decay=0.1
```
Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper)(https://arxiv.org/abs/2003.13678).

#### Medium models
```
torchrun --nproc_per_node=8 train.py\
     --model $MODEL --epochs 100 --batch-size 64 --wd 0.00005 --lr=0.4\
     --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
     --lr-warmup-epochs=5 --lr-warmup-decay=0.1
```
Here `$MODEL` is one of `regnet_x_3_2gf`, `regnet_x_8gf`, `regnet_x_16gf`, `regnet_y_3_2gf` and `regnet_y_8gf`.

#### Large models
```
torchrun --nproc_per_node=8 train.py\
     --model $MODEL --epochs 100 --batch-size 32 --wd 0.00005 --lr=0.2\
     --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
     --lr-warmup-epochs=5 --lr-warmup-decay=0.1
```
Here `$MODEL` is one of `regnet_x_32gf`, `regnet_y_16gf` and `regnet_y_32gf`.

### Vision Transformer

#### Base models
```
torchrun --nproc_per_node=8 train.py\
    --model $MODEL --epochs 300 --batch-size 64 --opt adamw --lr 0.003 --wd 0.3\
    --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\
    --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra\
    --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema
```
Here `$MODEL` is one of `vit_b_16` and `vit_b_32`.

#### Large models
```
torchrun --nproc_per_node=8 train.py\
    --model $MODEL --epochs 300 --batch-size 16 --opt adamw --lr 0.003 --wd 0.3\
    --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\
    --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra\
    --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema
```
Here `$MODEL` is one of `vit_l_16` and `vit_l_32`.

## Mixed precision training
Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [torch.cuda.amp](https://pytorch.org/docs/stable/amp.html?highlight=amp#module-torch.cuda.amp).

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 `--amp=True`.

```
torchrun --nproc_per_node=8 train.py\
    --model resnext50_32x4d --epochs 100 --amp
```

## Quantized

### Post training quantized models

For all post training quantized models, the settings are:

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

```
python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' --model='$MODEL'
```
Here `$MODEL` is one of `googlenet`, `inception_v3`, `resnet18`, `resnet50`, `resnext101_32x8d`, `shufflenet_v2_x0_5` and `shufflenet_v2_x1_0`.

### QAT MobileNetV2

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
12. weight-decay: 0.0001

```
torchrun --nproc_per_node=8 train_quantization.py --model='mobilenet_v2'
```

Training converges at about 10 epochs.

### QAT MobileNetV3

For Mobilenet-v3 Large, 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: 0.001
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
12. weight-decay: 0.00001

```
torchrun --nproc_per_node=8 train_quantization.py --model='mobilenet_v3_large' \
    --wd 0.00001 --lr 0.001
```

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:

```
python train_quantization.py --device='cpu' --test-only --backend='<backend>' --model='<model_name>'
```

For inception_v3 you need to pass the following extra parameters:
```
--val-resize-size 342 --val-crop-size 299 --train-crop-size 299
```