191 lines
5.9 KiB
Markdown
191 lines
5.9 KiB
Markdown
# FlashInternImage for Image Classification
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This folder contains the implementation of the FlashInternImage for image classification.
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## Usage
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### Install
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- Clone this repo:
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```bash
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git clone https://github.com/OpenGVLab/DCNv4.git
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cd DCNv4
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```
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- Create a conda virtual environment and activate it:
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```bash
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conda create -n internimage python=3.7 -y
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conda activate internimage
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```
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- Install `CUDA>=10.2` with `cudnn>=7` following
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the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
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- Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
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For examples, to install torch==1.11 with CUDA==11.3:
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```bash
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pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
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```
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- Install `timm==0.6.11` and `mmcv-full==1.5.0`:
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```bash
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pip install -U openmim
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mim install mmcv-full==1.5.0
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pip install timm==0.6.11 mmdet==2.28.1
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```
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- Install other requirements:
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```bash
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pip install opencv-python termcolor yacs pyyaml scipy
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```
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- Install DCNv4
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```bash
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pip install DCNv4==latest
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```
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### Data Preparation
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We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to
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load data:
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- For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:
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```bash
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$ tree data
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imagenet
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├── train
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│ ├── class1
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│ │ ├── img1.jpeg
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│ │ ├── img2.jpeg
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│ │ └── ...
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│ ├── class2
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│ │ ├── img3.jpeg
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│ │ └── ...
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│ └── ...
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└── val
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├── class1
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│ ├── img4.jpeg
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│ ├── img5.jpeg
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│ └── ...
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├── class2
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│ ├── img6.jpeg
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│ └── ...
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└── ...
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```
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- To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes
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four files:
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- `train.zip`, `val.zip`: which store the zipped folder for train and validate splits.
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- `train.txt`, `val.txt`: which store the relative path in the corresponding zip file and ground truth
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label. Make sure the data folder looks like this:
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```bash
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$ tree data
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data
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└── ImageNet-Zip
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├── train_map.txt
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├── train.zip
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├── val_map.txt
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└── val.zip
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$ head -n 5 meta_data/val.txt
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ILSVRC2012_val_00000001.JPEG 65
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ILSVRC2012_val_00000002.JPEG 970
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ILSVRC2012_val_00000003.JPEG 230
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ILSVRC2012_val_00000004.JPEG 809
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ILSVRC2012_val_00000005.JPEG 516
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$ head -n 5 meta_data/train.txt
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n01440764/n01440764_10026.JPEG 0
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n01440764/n01440764_10027.JPEG 0
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n01440764/n01440764_10029.JPEG 0
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n01440764/n01440764_10040.JPEG 0
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n01440764/n01440764_10042.JPEG 0
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```
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- For ImageNet-22K dataset, make a folder named `fall11_whole` and move all images to labeled sub-folders in this
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folder. Then download the train-val split
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file ([ILSVRC2011fall_whole_map_train.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_train.txt)
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& [ILSVRC2011fall_whole_map_val.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_val.txt))
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, and put them in the parent directory of `fall11_whole`. The file structure should look like:
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```bash
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$ tree imagenet22k/
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imagenet22k/
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└── fall11_whole
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├── n00004475
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├── n00005787
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├── n00006024
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├── n00006484
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└── ...
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```
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### Evaluation
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To evaluate a pretrained `FlashInternImage` on ImageNet val, run:
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```bash
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python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
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--cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>
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```
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For example, to evaluate the `FlashInternImage-B` with a single GPU:
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```bash
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python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --eval \
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--cfg configs/flash_intern_image_b_1k_224.yaml --resume flash_intern_image_b_1k_224.pth --data-path <imagenet-path>
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```
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### Training from Scratch on ImageNet-1K
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> The paper results were obtained from models trained with configs in `configs/without_lr_decay`.
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To train an `InternImage` on ImageNet from scratch, run:
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```bash
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python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py \
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--cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
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```
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### Manage Jobs with Slurm.
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For example, to train `FlashInternImage` with 8 GPU on a single node for 300 epochs, run:
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`FlashInternImage-T`:
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```bash
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GPUS=8 sh train_in1k.sh <partition> <job-name> configs/flash_intern_image_t_1k_224.yaml --resume flash_intern_image_t_1k_224.pth --eval
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```
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`FlashInternImage-S`:
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```bash
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GPUS=8 sh train_in1k.sh <partition> <job-name> configs/flash_intern_image_s_1k_224.yaml --resume flash_intern_image_s_1k_224.pth --eval
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```
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<!--
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### Test pretrained model on ImageNet-22K
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For example, to evaluate the `InternImage-L-22k`:
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```bash
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python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py \
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--cfg configs/internimage_xl_22k_192to384.yaml --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory>] \
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--resume internimage_xl_22k_192to384.pth --eval
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``` -->
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<!-- ### Fine-tuning from a ImageNet-22K pretrained model
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For example, to fine-tune a `InternImage-XL-22k` model pretrained on ImageNet-22K:
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```bashs
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GPUS=8 sh train_in1k.sh <partition> <job-name> configs/intern_image_.yaml --pretrained intern_image_b.pth --eval
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python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
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--cfg configs/.yaml --pretrained swin_base_patch4_window7_224_22k.pth \
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--data-path <imagenet-path> --batch-size 64 --accumulation-steps 2 [--use-checkpoint]
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``` -->
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