# InternImage for Image Classification

This folder contains the implementation of the InternImage for image classification.

## Usage

### Install

- Clone this repo:

```bash
git clone https://github.com/OpenGVLab/InternImage.git
cd InternImage
```

- Create a conda virtual environment and activate it:

```bash
conda create -n internimage python=3.7 -y
conda activate internimage
```

- Install `CUDA>=10.2` with `cudnn>=7` following
  the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
- Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:

For examples, to install torch==1.11 with CUDA==11.3:
```bash
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html
```

- Install `timm==0.6.11` and `mmcv-full==1.5.0`:

```bash
pip install -U openmim
mim install mmcv-full==1.5.0
pip install timm==0.6.11 mmdet==2.28.1
```

- Install other requirements:

```bash
pip install opencv-python termcolor yacs pyyaml scipy
```

- Compiling CUDA operators
```bash
cd ./ops_dcnv3
sh ./make.sh
# unit test (should see all checking is True)
python test.py
```
- You can also install the operator using .whl files
[DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files)
### Data Preparation

We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to
load data:

- For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:
  ```bash
  $ tree data
  imagenet
  ├── train
  │   ├── class1
  │   │   ├── img1.jpeg
  │   │   ├── img2.jpeg
  │   │   └── ...
  │   ├── class2
  │   │   ├── img3.jpeg
  │   │   └── ...
  │   └── ...
  └── val
      ├── class1
      │   ├── img4.jpeg
      │   ├── img5.jpeg
      │   └── ...
      ├── class2
      │   ├── img6.jpeg
      │   └── ...
      └── ...
 
  ```
- To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes
  four files:
    - `train.zip`, `val.zip`: which store the zipped folder for train and validate splits.
    - `train.txt`, `val.txt`: which store the relative path in the corresponding zip file and ground truth
      label. Make sure the data folder looks like this:

  ```bash
  $ tree data
  data
  └── ImageNet-Zip
      ├── train_map.txt
      ├── train.zip
      ├── val_map.txt
      └── val.zip
  
  $ head -n 5 meta_data/val.txt
  ILSVRC2012_val_00000001.JPEG	65
  ILSVRC2012_val_00000002.JPEG	970
  ILSVRC2012_val_00000003.JPEG	230
  ILSVRC2012_val_00000004.JPEG	809
  ILSVRC2012_val_00000005.JPEG	516
  
  $ head -n 5 meta_data/train.txt
  n01440764/n01440764_10026.JPEG	0
  n01440764/n01440764_10027.JPEG	0
  n01440764/n01440764_10029.JPEG	0
  n01440764/n01440764_10040.JPEG	0
  n01440764/n01440764_10042.JPEG	0
  ```
- For ImageNet-22K dataset, make a folder named `fall11_whole` and move all images to labeled sub-folders in this
  folder. Then download the train-val split
  file ([ILSVRC2011fall_whole_map_train.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_train.txt)
  & [ILSVRC2011fall_whole_map_val.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_val.txt))
  , and put them in the parent directory of `fall11_whole`. The file structure should look like:

  ```bash
    $ tree imagenet22k/
    imagenet22k/
    └── fall11_whole
        ├── n00004475
        ├── n00005787
        ├── n00006024
        ├── n00006484
        └── ...
  ```

### Evaluation

To evaluate a pretrained `InternImage` on ImageNet val, run:

```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
--cfg <config-file> --resume <checkpoint> --data-path <imagenet-path> 
```

For example, to evaluate the `InternImage-B` with a single GPU:

```bash
python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --eval \
--cfg configs/internimage_b_1k_224.yaml --resume internimage_b_1k_224.pth --data-path <imagenet-path>
```

### Training from Scratch on ImageNet-1K

> The paper results were obtained from models trained with configs in `configs/without_lr_decay`.

To train an `InternImage` on ImageNet from scratch, run:

```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \ 
--cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
```

### Manage Jobs with Slurm.

For example, to train `InternImage` with 8 GPU on a single node for 300 epochs, run:

`InternImage-T`:

```bash
GPUS=8 sh train_in1k.sh <partition> <job-name> configs/internimage_t_1k_224.yaml --resume internimage_t_1k_224.pth --eval
```

`InternImage-S`:

```bash
GPUS=8 sh train_in1k.sh <partition> <job-name> configs/internimage_s_1k_224.yaml --resume internimage_s_1k_224.pth --eval
```

`InternImage-XL`:

```bash
GPUS=8 sh train_in1k.sh <partition> <job-name> configs/internimage_xl_22kto1k_384.pth --resume internimage_xl_22kto1k_384.pth --eval
```

<!-- 
### Test pretrained model on ImageNet-22K

For example, to evaluate the `InternImage-L-22k`:

```bash
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \ 
--cfg configs/internimage_xl_22k_192to384.yaml --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory>] \
--resume internimage_xl_22k_192to384.pth --eval
``` -->

<!-- ### Fine-tuning from a ImageNet-22K pretrained model

For example, to fine-tune a `InternImage-XL-22k` model pretrained on ImageNet-22K:

```bashs
GPUS=8 sh train_in1k.sh <partition> <job-name> configs/intern_image_.yaml --pretrained intern_image_b.pth --eval
python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345  main.py \
--cfg configs/.yaml --pretrained swin_base_patch4_window7_224_22k.pth \
--data-path <imagenet-path> --batch-size 64 --accumulation-steps 2 [--use-checkpoint]
``` -->

### Training with Deepspeed

We support utilizing [Deepspeed](https://github.com/microsoft/DeepSpeed) to reduce memory costs for training large-scale models, e.g. InternImage-H with over 1 billion parameters.
To use it, first install the requirements as

```bash
pip install deepspeed
```

Then you could launch the training in a slurm system with 8 GPUs as follows (tiny and huge as examples)

```
GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 
GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 --eval --resume ckpt.pth
GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 --eval --resume deepspeed_ckpt_dir
GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_h_22kto1k_640.yaml --batch-size 16 --accumulation-steps 4 --pretrained ckpt/internimage_h_jointto22k_384.pth
```


🤗 **Huggingface Accelerate Integration of Deepspeed**

Optionally, you could use our [Huggingface accelerate](https://github.com/huggingface/accelerate) integration to use deepspeed.

```bash
pip install accelerate
```

```bash
accelerate launch --config_file configs/accelerate/dist_8gpus_zero3_wo_loss_scale.yaml main_accelerate.py  --cfg configs/internimage_h_22kto1k_640.yaml --data-path /mnt/lustre/share/images --batch-size 16 --pretrained ckpt/internimage_h_jointto22k_384.pth --accumulation-steps 4
accelerate launch --config_file configs/accelerate/dist_8gpus_zero3_offload.yaml main_accelerate.py  --cfg configs/internimage_t_1k_224.yaml --data-path /mnt/lustre/share/images --batch-size 128 --accumulation-steps 4 --output output_zero3_offload
accelerate launch --config_file configs/accelerate/dist_8gpus_zero1.yaml main_accelerate.py  --cfg configs/internimage_t_1k_224.yaml --data-path /mnt/lustre/share/images --batch-size 128 --accumulation-steps 4
```

**Memory Costs**

Here is the reference GPU memory cost for InternImage-H with 8 GPUs.

- total batch size = 512, 16 batch size for each GPU, gradient accumulation steps = 4.

| Resolution | Deepspeed | Cpu offloading | Memory |
| --- | --- | --- | --- |
| 640 | zero1 | False | 22572 |
| 640 | zero3 | False | 20000 |
| 640 | zero3 | True | 19144 |
| 384 | zero1 | False | 16000 |
| 384 | zero3 | True | 11928 |

**Convert Checkpoints**

To convert deepspeed checkpoints to pytorch fp32 checkpoint, you could use the following snippet.

```python
from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, 'best.pth', tag='best')
```

Then, you could use `best.pth` as usual, e.g., `model.load_state_dict(torch.load('best.pth'))`

> Due to the lack of computational resources, the deepspeed training scripts are currently only verified for the first few epochs. Please fire an issue if you have problems for reproducing the whole training.

### Export

To export `InternImage-T` from PyTorch to ONNX, run:
```shell
python export.py --model_name internimage_t_1k_224 --ckpt_dir /path/to/ckpt/dir --onnx
```

To export `InternImage-T` from PyTorch to TensorRT, run:
```shell
python export.py --model_name internimage_t_1k_224 --ckpt_dir /path/to/ckpt/dir --trt
```
