Awesome
CAE: Context AutoEncoder for Self-Supervised Representation Learning
<p align="center"> <img src='furnace/CAE.png'> </p>This is a PyTorch implementation of CAE: Context AutoEncoder for Self-Supervised Representation Learning.
Highlights
- State-of-the-art MIM performance. Results in the paper are successfully reproduced.
Installation
Clone the repo and install required packages.
pip install -r requirements.txt
# install apex
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
Data Preparation
First, download ImageNet-1k from http://image-net.org/.
The directory structure is the standard layout of torchvision's datasets.ImageFolder. The training and validation data are expected to be in the train/ folder and val folder, respectively:
/path/to/imagenet/
train/
class1/
img1.jpeg
class2/
img2.jpeg
val/
class1/
img3.jpeg
class/2
img4.jpeg
Second, download the pretrained tokenizer.
TOKENIZER_PATH=/path/to/save/dall_e_tokenizer_weight
mkdir -p $TOKENIZER_PATH
wget -o $TOKENIZER_PATH/encoder.pkl https://cdn.openai.com/dall-e/encoder.pkl
wget -o $TOKENIZER_PATH/decoder.pkl https://cdn.openai.com/dall-e/decoder.pkl
Pretraining
Here is an example that pretrains CAE-base on ImageNet-1K with 32 GPUs. Please see scripts/cae_base_800e.sh for complete script.
OMP_NUM_THREADS=1 $PYTHON -m torch.distributed.launch \
--nproc_per_node=8 \
tools/run_pretraining.py \
--data_path ${DATA_PATH} \
--output_dir ${OUTPUT_DIR} \
--model cae_base_patch16_224_8k_vocab --discrete_vae_weight_path ${TOKENIZER_PATH} \
--batch_size 64 --lr 1.5e-3 --warmup_epochs 20 --epochs 800 \
--clip_grad 3.0 --layer_scale_init_value 0.1 \
--imagenet_default_mean_and_std \
--color_jitter 0 \
--drop_path 0.1 \
--sincos_pos_emb \
--mask_generator block \
--num_mask_patches 98 \
--decoder_layer_scale_init_value 0.1 \
--no_auto_resume \
--save_ckpt_freq 100 \
--exp_name $my_name \
--regressor_depth 4 \
--decoder_depth 4 \
--align_loss_weight 2
--num_mask_patches
: number of the input patches need be masked.--batch_size
: batch size per GPU.- Effective batch size =
number of GPUs
*--batch_size
. So in the above example, the effective batch size is64*32 = 2048
. --lr
: learning rate.--warmup_epochs
: learning rate warmup epochs. Warm up [10, 20, 40] epochs for [300, 800, 1600] pretrain epochs respectively.--epochs
: total pretraining epochs.--clip_grad
: clip gradient norm.--drop_path
: stochastic depth rate.--imagenet_default_mean_and_std
: enable this for ImageNet-1k pretraining, i.e.,(0.485, 0.456, 0.406)
for mean and(0.229, 0.224, 0.225)
for std. For other pretraining data, use(0.5, 0.5, 0.5)
for mean and(0.5, 0.5, 0.5)
for std by default.--layer_scale_init_value
: 0.1 for base, 1e-5 for large, set 0 to disable layerscale. We set--decoder_layer_scale_init_value
the same as this.--sincos_pos_emb
: adopt sin-cos positional embedding during pretraining.--regressor_depth
: length of the regressor.--decoder_depth
: length of the decoder.--align_loss_weight
: weight for alignment loss. 2 by default.
Warmup epochs for 300/800/1600 epochs pretraining are 10/20/40.
For CAE-large, please refer to scripts/cae_large_1600e.sh.
Results
Here provides the results of CAE-base/CAE-large for these evaluation tasks:
- Linear probing
- Attentive probing
- Fine-tuning
- Semantic segmentation
- Object detection and instance segmentation
Pretrained weights and logs are available (Google Drive, Baidu Cloud [Code: 4kil]). *: from CAE paper.
Model | Pretraining data | #Epoch | Linear | Attentive | Fine-tuning | ADE Seg | COCO Det | COCO InstSeg |
---|---|---|---|---|---|---|---|---|
MAE-base* | ImageNet-1K | 1600 | 67.8 | 74.2 | 83.6 | 48.1 | 48.4 | 42.6 |
MAE-large* | ImageNet-1K | 1600 | 76.0 | 78.8 | 86.0 | 53.6 | 54.0 | 47.1 |
CAE-base | ImageNet-1K | 300 | 64.5 | 74.0 | 83.6 | 48.1 | 48.3 | 42.7 |
CAE-base | ImageNet-1K | 800 | 68.9 | 75.9 | 83.8 | 49.7 | 49.9 | 43.9 |
CAE-base | ImageNet-1K | 1600 | 70.3 | 77.2 | 83.9 | 50.3 | 50.3 | 44.2 |
CAE-large | ImageNet-1K | 1600 | 77.8 | 81.2 | 86.2 | 54.9 | 54.5 | 47.5 |
Linear Probing
- Please refer to scripts/cae_base_800e.sh (32 GPUs).
- For CAE-large, just replace
--model cae_base_patch16_224
with--model cae_large_patch16_224
.
Attentive Probing
- Please refer to scripts/cae_base_800e.sh (32 GPUs).
- For CAE-large, just replace
--model cae_base_patch16_224
with--model cae_large_patch16_224
.
Fine-tuning
- Please refer to scripts/cae_base_finetune.sh (32 GPUs).
- For CAE-large, please refer to scripts/cae_large_finetune.sh (32 GPUs).
Segmentation & Detection
- Please refer to downstream_tasks dir to get started.
Acknowledgement
This repository is built using the BEiT and MMSelfSup, thanks for their open-source code! Thanks also to the CAE authors for their excellent work!
Citation
@article{ContextAutoencoder2022,
title={Context Autoencoder for Self-Supervised Representation Learning},
author={Chen, Xiaokang and Ding, Mingyu and Wang, Xiaodi and Xin, Ying and Mo, Shentong and Wang, Yunhao and Han, Shumin and Luo, Ping and Zeng, Gang and Wang, Jingdong},
journal={arXiv preprint arXiv:2202.03026},
year={2022}
}