# InternImage for Image Classification This folder contains the implementation of the InternImage for image classification. - [Installation](#installation) - [Data Preparation](#data-preparation) - [Released Models](#released-models) - [Evaluation](#evaluation) - [Training from Scratch on ImageNet-1K](#training-from-scratch-on-imagenet-1k) - [Manage Jobs with Slurm](#manage-jobs-with-slurm) - [Training with Deepspeed](#training-with-deepspeed) - [Extracting Intermediate Features](#extracting-intermediate-features) - [Export](#export) ## Installation - Clone this repository: ```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=3.9 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 # Please use a version of numpy lower than 2.0 pip install numpy==1.26.4 pip install pydantic==1.10.13 ``` - Compiling CUDA operators Before compiling, please use the `nvcc -V` command to check whether your `nvcc` version matches the CUDA version of PyTorch. ```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 precompiled `.whl` files [DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files) ## Data Preparation We provide the following ways to prepare data:
Standard ImageNet-1K We use standard ImageNet dataset, you can download it from http://image-net.org/. - 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 │ └── ... └── ... ```
Zipped ImageNet-1K - 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 ```
ImageNet-22K - 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 └── ... ```
## Released Models
Open-Source Visual Pretrained Models
| name | pretrain | pre-training resolution | #param | download | | :------------: | :----------: | :----------------------: | :----: | :---------------------------------------------------------------------------------------------------: | | InternImage-L | ImageNet-22K | 384x384 | 223M | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth) | | InternImage-XL | ImageNet-22K | 384x384 | 335M | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22k_192to384.pth) | | InternImage-H | Joint 427M | 384x384 | 1.08B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_jointto22k_384.pth) | | InternImage-G | - | 384x384 | 3B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_pretrainto22k_384.pth) |
ImageNet-1K Image Classification
| name | pretrain | resolution | acc@1 | #param | FLOPs | download | | :------------: | :----------: | :--------: | :---: | :----: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | | InternImage-T | ImageNet-1K | 224x224 | 83.5 | 30M | 5G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_t_1k_224.pth) \| [cfg](configs/without_lr_decay/internimage_t_1k_224.yaml) | | InternImage-S | ImageNet-1K | 224x224 | 84.2 | 50M | 8G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_s_1k_224.pth) \| [cfg](configs/without_lr_decay/internimage_s_1k_224.yaml) | | InternImage-B | ImageNet-1K | 224x224 | 84.9 | 97M | 16G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_b_1k_224.pth) \| [cfg](configs/without_lr_decay/internimage_b_1k_224.yaml) | | InternImage-L | ImageNet-22K | 384x384 | 87.7 | 223M | 108G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22kto1k_384.pth) \| [cfg](configs/without_lr_decay/internimage_l_22kto1k_384.yaml) | | InternImage-XL | ImageNet-22K | 384x384 | 88.0 | 335M | 163G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22kto1k_384.pth) \| [cfg](configs/without_lr_decay/internimage_xl_22kto1k_384.yaml) | | InternImage-H | Joint 427M | 640x640 | 89.6 | 1.08B | 1478G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_22kto1k_640.pth) \| [cfg](configs/without_lr_decay/internimage_h_22kto1k_640.yaml) | | InternImage-G | - | 512x512 | 90.1 | 3B | 2700G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_22kto1k_512.pth) \| [cfg](configs/without_lr_decay/internimage_g_22kto1k_512.yaml) |
## Evaluation To evaluate a pretrained `InternImage` on ImageNet val, run: ```bash python -m torch.distributed.launch --nproc_per_node --master_port 12345 main.py --eval \ --cfg --resume --data-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 ``` ## 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 --master_port 12345 main.py \ --cfg --data-path [--batch-size --output --tag ] ``` ## Manage Jobs with Slurm For example, to train or evaluate `InternImage` with 8 GPU on a single node, run: `InternImage-T`: ```bash # Train for 300 epochs GPUS=8 sh train_in1k.sh configs/internimage_t_1k_224.yaml # Evaluate on ImageNet-1K GPUS=8 sh train_in1k.sh configs/internimage_t_1k_224.yaml --resume pretrained/internimage_t_1k_224.pth --eval ``` `InternImage-S`: ```bash # Train for 300 epochs GPUS=8 sh train_in1k.sh configs/internimage_s_1k_224.yaml # Evaluate on ImageNet-1K GPUS=8 sh train_in1k.sh configs/internimage_s_1k_224.yaml --resume pretrained/internimage_s_1k_224.pth --eval ``` `InternImage-XL`: ```bash # Train for 300 epochs GPUS=8 sh train_in1k.sh configs/internimage_xl_22kto1k_384.pth # Evaluate on ImageNet-1K GPUS=8 sh train_in1k.sh configs/internimage_xl_22kto1k_384.pth --resume pretrained/internimage_xl_22kto1k_384.pth --eval ``` ## 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==0.8.3 ``` Then you could launch the training in a slurm system with 8 GPUs as follows (tiny and huge as examples). The default zero stage is 1 and it could config via command line args `--zero-stage`. ``` GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh 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 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 configs/internimage_h_22kto1k_640.yaml --batch-size 16 --accumulation-steps 4 --pretrained pretrained/internimage_h_jointto22k_384.pth GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh configs/internimage_h_22kto1k_640.yaml --batch-size 16 --accumulation-steps 4 --pretrained pretrained/internimage_h_jointto22k_384.pth --zero-stage 3 ``` 🤗 **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==0.18.0 ``` ```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 data/imagenet --batch-size 16 --pretrained pretrained/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 data/imagenet --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 data/imagenet --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 | Zero Stage | 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. ## Extracting Intermediate Features To extract the features of an intermediate layer, you could use `extract_feature.py`. For example, extract features of `b.png` from layers `patch_embed` and `levels.0.downsample` and save them to 'b.pth'. ```bash python extract_feature.py --cfg configs/internimage_t_1k_224.yaml --img b.png --keys patch_embed levels.0.downsample --save --resume internimage_t_1k_224.pth ``` ## 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 ```