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[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

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UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers

Paper Paper Webiste

🧐 A Quick Look

  • What is it: UPop is the first structured pruning framework for vision-language Transformers. It enables effective structured pruning on various multi-modal & uni-modal tasks (including Visual Reasoning, Image Captioning, Visual Question Answer, Image-Text Retrieval, Text-Image Retrieval, Image Classification and Image Segmentation), datasets (including NLVR2, COCO Caption, VQAv2, COCO, Flickr30K, ImageNet and ADE20K), and model architectures (including BLIP, CLIP, DeiT and Segmenter).

    overview.mp4

  • What challenge does it tackle: The above video demonstrates that Unified Search adopted by UPop rescues us from the burden of repeated experiments (e.g., doing grid search) for searching optimal compression ratios among different modalities and structures. Furthermore, Progressive Pruning adopted by UPop eliminates the weight gap between the searched model and the pruned subnet to be retrained, therefore gaining better convergence and performance, especially at high compression ratios.

  • How about the performance: On multimodal tasks, for example, UPop can achieve 2x compression with only 1.2% and 2.0% accuracy loss on the VQAv2 dataset for Visual Question Answer and the NLVR2 dataset for Visual Reasoning, respectively. On unimodal tasks, for example, UPop can achieve 1.5x and 1.2x compression without any loss of accuracy on the ImageNet dataset for Image Classification and the ADE20K dataset for Image Segmentation, respectively. Some examples of vector-level structured granularity are as follows.

    Example (Task • Dataset • Model • Metric) Performance Parameters (M) FLOPs (G)
    Visual Reasoning • NLVR2 • BLIP • Acc $83.1 \rightarrow 81.1_{\color{red}\downarrow 2.0}$ $259.5 \rightarrow 150.2_{\color{ForestGreen}\downarrow 42\%}$ $132.5 \rightarrow 89.4_{\color{ForestGreen}\downarrow 33\%}$
    Image Caption • Caption COCO • BLIP • SPICE $23.8 \rightarrow 23.3_{\color{red}\downarrow 0.5}$ $224.0 \rightarrow 127.1_{\color{ForestGreen}\downarrow 43\%}$ $65.7 \rightarrow 39.8_{\color{ForestGreen}\downarrow 39\%}$
    Visual Question Answer • VQAv2 • BLIP • Acc $77.5 \rightarrow 76.3_{\color{red}\downarrow 1.2}$ $361.6 \rightarrow 211.3_{\color{ForestGreen}\downarrow 42\%}$ $186.1 \rightarrow 109.4_{\color{ForestGreen}\downarrow 41\%}$
    Image-Text Retrieval • COCO • BLIP • R@1 $81.9 \rightarrow 77.4_{\color{red}\downarrow 4.5}$ $447.6 \rightarrow 248.9_{\color{ForestGreen}\downarrow 44\%}$ $153.2\rightarrow 88.3_{\color{ForestGreen}\downarrow 42\%}$
    Image-Text Retrieval • COCO • CLIP • R@1 $71.5 \rightarrow 70.8_{\color{red}\downarrow 0.7}$ $856.0 \rightarrow 473.7_{\color{ForestGreen}\downarrow 45\%}$ $395.7\rightarrow 196.3_{\color{ForestGreen}\downarrow 50\%}$
    Text-Image Retrieval • COCO • BLIP • R@1 $64.3\rightarrow 59.8_{\color{red}\downarrow 4.5}$ $447.6 \rightarrow 248.9_{\color{ForestGreen}\downarrow 44\%}$ $153.2\rightarrow 88.3_{\color{ForestGreen}\downarrow 42\%}$
    Text-Image Retrieval • COCO • CLIP • R@1 $56.8\rightarrow 53.1_{\color{red}\downarrow 3.7}$ $856.0 \rightarrow 473.7_{\color{ForestGreen}\downarrow 45\%}$ $395.7\rightarrow 196.3_{\color{ForestGreen}\downarrow 50\%}$
    Image-Text Retrieval • Flickr30K • BLIP • R@1 $96.8\rightarrow 92.2_{\color{red}\downarrow 4.4}$ $447.6\rightarrow 250.5_{\color{ForestGreen}\downarrow 44\%}$ $153.2\rightarrow 91.0_{\color{ForestGreen}\downarrow 41\%}$
    Image-Text Retrieval • Flickr30K • CLIP • R@1 $96.8\rightarrow 93.2_{\color{red}\downarrow 3.6}$ $856.0\rightarrow 474.3_{\color{ForestGreen}\downarrow 45\%}$ $395.7 \rightarrow 201.1_{\color{ForestGreen}\downarrow 49\%}$
    Text-Image Retrieval • Flickr30K • BLIP • R@1 $86.9 \rightarrow 82.0_{\color{red}\downarrow 4.9}$ $447.6\rightarrow 250.5_{\color{ForestGreen}\downarrow 44\%}$ $153.2\rightarrow 91.0_{\color{ForestGreen}\downarrow 41\%}$
    Text-Image Retrieval • Flickr30K • CLIP • R@1 $86.6\rightarrow 80.5_{\color{red}\downarrow 6.1}$ $856.0\rightarrow 474.3_{\color{ForestGreen}\downarrow 45\%}$ $395.7 \rightarrow 201.1_{\color{ForestGreen}\downarrow 49\%}$
    Classification • ImageNet • DeiT • Acc@1 $79.9\rightarrow 80.2_{\color{ForestGreen}\uparrow 0.3}$ $22.0 \rightarrow 15.7_{\color{ForestGreen}\downarrow 29\%}$ $4.6 \rightarrow 3.2_{\color{ForestGreen}\downarrow 30\%}$
    Classification • ImageNet • DeiT • Acc@5 $95.0 \rightarrow 95.1_{\color{ForestGreen}\uparrow 0.1}$ $22.0 \rightarrow 15.7_{\color{ForestGreen}\downarrow 29\%}$ $4.6 \rightarrow 3.2_{\color{ForestGreen}\downarrow 30\%}$
    Segmentation • ADE20K • Segmenter • $\text{mIoU}^s$ $45.3\rightarrow 45.3_{\color{ForestGreen}\uparrow 0.0}$ $26.4 \rightarrow 21.5_{\color{ForestGreen}\downarrow 19\%}$ $38.6 \rightarrow 30.4_{\color{ForestGreen}\downarrow 21\%}$
    Segmentation • ADE20K • Segmenter • $\text{mIoU}^m$ $46.9 \rightarrow 47.1_{\color{ForestGreen}\uparrow 0.2}$ $26.4 \rightarrow 21.5_{\color{ForestGreen}\downarrow 19\%}$ $38.6 \rightarrow 30.4_{\color{ForestGreen}\downarrow 21\%}$

🥳 What's New

  • (Jun 2023), we worked on a new project CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers, which reduces computational costs effectively for accelerating. [Paper] [Code] 💡

  • (Jun 2023), we released the implementation, scripts, checkpoints, and logs. [Code] [Website] 🚩

  • (Apr 2023), our work UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers was accepted by ICML 2023. [Paper] [ArXiv] 🎉

🏃 Installation

The code is tested on Pytorch==1.11.0, cuda==11.3.1, and python==3.8.13. The dependencies can be installed by:

conda env create -f environment.yml

The status of installing dependencies: build

🚀 Visual Reasoning on the NLVR2 Dataset

  • Dataset & Annotation

    Download the NLVR2 dataset, unzip it under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations (including annotations for Visual Reasoning, Image Caption, VQA, Image-Text Retrieval, and Text-Image Retrieval tasks) from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_nlvr.py --evaluate \
--pretrained output/nlvr_nlvr2_compression_2x/model_base_nlvr_nlvr2_2x_compressed.pth \
--config ./configs/nlvr.yaml \
--output_dir output/nlvr_nlvr2_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_nlvr.py --p 0.5 --epoch 15 \
--pretrained pretrained/model_base_nlvr.pth \
--config ./configs/nlvr.yaml \
--output_dir output/nlvr_nlvr2_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    3x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link
    5x Google/Baidu Link Google/Baidu Google/Baidu Link
    10x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image Caption on the COCO Caption Dataset

  • Dataset & Annotation

    Download the COCO Caption dataset, unzip it under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_caption.py --evaluate \
--pretrained output/caption_coco_compression_2x/model_base_caption_capfilt_large_coco_2x_compressed.pth \
--config ./configs/caption_coco.yaml \
--output_dir output/caption_coco_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_caption.py --p 0.5 --epoch 5 \
--pretrained pretrained/model_base_caption_capfilt_large.pth \
--config ./configs/caption_coco.yaml \
--output_dir output/caption_coco_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Visual Question Answer on the VQAv2 Dataset

  • Dataset & Annotation

    Download the VQAv2 dataset and Visual Genome dataset, unzip them under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    [!Note] Note that the scripts will generate answers vqa_result.json, which should be submitted to the official server to obtain evaluation results.

    python -m torch.distributed.run --nproc_per_node=8 compress_vqa.py --evaluate \
--pretrained output/vqa_vqa2_compression_2x/model_base_vqa_capfilt_large_vqa2_2x_compressed.pth \
--config ./configs/vqa.yaml \
--output_dir output/vqa_vqa2_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_vqa.py --p 0.5 --epoch 10 \
--pretrained pretrained/model_base_vqa_capfilt_large.pth \
--config ./configs/vqa.yaml \
--output_dir output/vqa_vqa2_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image-Text and Text-Image Retrieval on the COCO Dataset

  • Dataset & Annotation

    Download the COCO dataset, unzip it under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval.py --evaluate \
--pretrained output/retrieval_coco_compression_2x/model_base_retrieval_coco_2x_compressed.pth --config ./configs/retrieval_coco.yaml \
--output_dir output/retrieval_coco_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval.py --p 0.5 --epoch 6 \
--pretrained pretrained/model_base_retrieval_coco.pth \
--config ./configs/retrieval_coco.yaml \
--output_dir output/retrieval_coco_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image-Text and Text-Image Retrieval on the Flickr30K Dataset

  • Dataset & Annotation

    Download the Flickr30k dataset, unzip it under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval_flickr.py --evaluate \
--pretrained output/retrieval_flickr_compression_2x/model_base_retrieval_flickr_2x_compressed.pth \
--config ./configs/retrieval_flickr.yaml \
--output_dir output/retrieval_flickr_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval_flickr.py --p 0.5 --epoch 12 \
--pretrained pretrained/model_base_retrieval_flickr.pth \
--config ./configs/retrieval_flickr.yaml \
--output_dir output/retrieval_flickr_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image-Text and Text-Image Retrieval on the COCO Dataset with CLIP

  • Dataset & Annotation

    Download the COCO dataset, unzip it under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval_clip.py --evaluate \
--pretrained output/retrieval_coco_clip_compression_2x/clip_large_retrieval_coco_2x_compressed.pth \
--config ./configs/retrieval_coco_clip.yaml \
--output_dir output/retrieval_coco_clip_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval_clip.py --p 0.5 --epoch 6 \
--pretrained pretrained/clip_large_retrieval_coco.pth \
--config ./configs/retrieval_coco_clip.yaml \
--output_dir output/retrieval_coco_clip_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image-Text and Text-Image Retrieval on the Flickr30K Dataset with CLIP

  • Dataset & Annotation

    Download the Flickr30k dataset, unzip it under the datasets folder, and accordingly modify the image_root in config. Download all-in-one annotations from Google Drive or Baidu Drive, unzip it under the annotation folder, and accordingly modify the annotation in config. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval_clip.py --evaluate \
--pretrained output/retrieval_flickr_clip_compression_2x/clip_large_retrieval_flickr_2x_compressed.pth \
--config ./configs/retrieval_flickr_clip.yaml \
--output_dir output/retrieval_flickr_clip_compression_2x

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the pretrained in config. For example, to conduct a 2x compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_retrieval_clip.py --p 0.5 --epoch 12 \
--pretrained pretrained/clip_large_retrieval_flickr.pth \
--config ./configs/retrieval_flickr_clip.yaml \
--output_dir output/retrieval_flickr_clip_compression_2x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    2x Google/Baidu Link Google/Baidu Google/Baidu Link
    4x Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image Classification on the ImageNet Dataset

  • Dataset & Annotation

    Download the ImageNet dataset, unzip it under the datasets folder, and accordingly modify the option --data-path in compression and evaluation scripts. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the option --resume of the scripts. For example, to evaluate a 50% compressed model:

    python -m torch.distributed.run --nproc_per_node=8 compress_deit.py --eval --dist-eval \
--data-path datasets/vision/imagenet \
--model deit_small_patch16_224 \
--resume output/train_deit_small_patch16_224_60s_300r_050x/deit_small_patch16_224_050x_compressed.pth

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, and accordingly modify the option --finetune of the scripts. For example, to conduct a 50% compression on 8 A100 GPUs:

    python -m torch.distributed.run --nproc_per_node=8 compress_deit.py \
--data-path datasets/vision/imagenet \
--finetune pretrained/deit_small_patch16_224-cd65a155.pth \
--model deit_small_patch16_224 \
--epochs-search 60 \
--epochs 300 \
--batch-size 512 \
--lr-search 1e-4 \
--lr 1e-4 \
--warmup-epochs 0 \
--p 0.5 \
--interval 800 \
--output_dir output/train_deit_small_patch16_224_60s_300r_050x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    10% Google/Baidu Link Google/Baidu Google/Baidu Link
    20% Google/Baidu Link Google/Baidu Google/Baidu Link
    30% Google/Baidu Link Google/Baidu Google/Baidu Link
    40% Google/Baidu Link Google/Baidu Google/Baidu Link
    50% Google/Baidu Link Google/Baidu Google/Baidu Link

🚀 Image Segmentation on the Ade20k Dataset

  • Dataset & Annotation

    Download the Ade20k dataset, unzip it under the datasets folder, and accordingly modify the option --dataset in compression and evaluation scripts. See here for expected folder structres.

  • Evaluation

    Download compressed checkpoints from the table below, put them under the output folder, accordingly modify the path option of the scripts, and export the folder of datasets as the environment variable DATASET. For example, to evaluate a 30% compressed model:

    export DATASET=datasets/vision

# for single-scale testing
python -m torch.distributed.run --nproc_per_node=4 segm/eval/miou.py \
output/seg_small_mask_16s_64r_030x/seg_small_mask_030x_compressed.pth ade20k --singlescale

# for multi-scale testing
python -m torch.distributed.run --nproc_per_node=4 segm/eval/miou.py \
output/seg_small_mask_16s_64r_030x/seg_small_mask_030x_compressed.pth ade20k --multiscale

  • Compression

    Download the uncompressed model from the table below, put it under the pretrained folder, accordingly modify the option --pretrained of the scripts, and export the folder of datasets as the environment variable DATASET. For example, to conduct a 30% compression on 4 A100 GPUs:

    export DATASET=datasets/vision

python -m torch.distributed.run --nproc_per_node=4 segm/train.py --dataset ade20k \
--backbone vit_small_patch16_384 --decoder mask_transformer --no-resume \
--pretrained pretrained/seg_small_mask.pth \
--epochs-search 16 \
--epochs 64 \
--batch-size 64 \
--lr-search 4e-3 \
-lr 4e-3  \
--p 0.30 \
--interval 200 \
--log-dir output/seg_small_mask_16s_64r_030x

  • Download

    Reduction Uncompressed Model Compression Script Training Log Compressed Checkpoint Evaluation Script
    10% Google/Baidu Link Google/Baidu Google/Baidu Link
    15% Google/Baidu Link Google/Baidu Google/Baidu Link
    20% Google/Baidu Link Google/Baidu Google/Baidu Link
    30% Google/Baidu Link Google/Baidu Google/Baidu Link

📑 Common Issues

1. Evaluation with single GPU

  • For BLIP and CLIP models, evaluate the 2x compressed BLIP model on the NLVR2 dataset as an example:

    python compress_nlvr.py --evaluate \
--pretrained output/caption_coco_compression_2x/model_base_caption_capfilt_large_coco_2x_compressed.pth \
--config ./configs/caption_coco.yaml \
--output_dir output/caption_coco_compression_2x

  • For DeiT, evaluate the 50% compressed model on the ImageNet dataset as an example:

    [!Note] Note that without the option ---dist-eval

    python compress_deit.py --eval \
--data-path datasets/vision/imagenet \
--model deit_small_patch16_224 \
--resume output/train_deit_small_patch16_224_60s_300r_050x/deit_small_patch16_224_050x_compressed.pth

  • For Segmenter, evaluate the 30% compressed model on the ADE20k dataset as an example:

    export DATASET=datasets/vision

# for single-scale testing
python segm/eval/miou.py \
output/seg_small_mask_16s_64r_030x/seg_small_mask_030x_compressed.pth ade20k --singlescale

# for multi-scale testing
python segm/eval/miou.py \
output/seg_small_mask_16s_64r_030x/seg_small_mask_030x_compressed.pth ade20k --multiscale

2. Compress with single GPU

  • For BLIP and CLIP models, compress the BLIP model to half on the NLVR2 dataset as an example:

    python compress_nlvr.py --p 0.5 --epoch 15 \
--pretrained pretrained/model_base_nlvr.pth \
--config ./configs/nlvr.yaml \
--output_dir output/nlvr_nlvr2_compression_2x

  • For DeiT, conduct a 50% compression on the ImageNet dataset as an example:

    python compress_deit.py \
--data-path datasets/vision/imagenet \
--finetune pretrained/deit_small_patch16_224-cd65a155.pth \
--model deit_small_patch16_224 \
--epochs-search 60 \
--epochs 300 \
--batch-size 512 \
--lr-search 1e-4 \
--lr 1e-4 \
--warmup-epochs 0 \
--p 0.5 \
--interval 800 \
--output_dir output/train_deit_small_patch16_224_60s_300r_050x

  • For Segmenter, conduct a 30% compression on the Ade20k dataset as an example:

    export DATASET=datasets/vision

python segm/train.py --dataset ade20k \
--backbone vit_small_patch16_384 --decoder mask_transformer --no-resume \
--pretrained pretrained/seg_small_mask.pth \
--epochs-search 16 \
--epochs 64 \
--batch-size 64 \
--lr-search 4e-3 \
-lr 4e-3  \
--p 0.30 \
--interval 200 \
--log-dir output/seg_small_mask_16s_64r_030x

3. Out of memory during the evaluation

  • For BLIP and CLIP models, change the batch_size_test (or the batch_size for the Image Caption task) in the corresponding config file to a smaller number.
  • For DeiT, modify the option --batch-size of the scripts to a smaller number.
  • For Segmenter, the default batch size of the evaluation is 1. For the single-scale testing, the peak of used GPU memory on a single card is less than 5G, which should be able to run on most types of GPUs. For the multi-scale testing, the peak of used GPU memory on a single card is about 13G, which may require a GPU with relatively larger memory.

4. Out of memory during the compression

  • For BLIP and CLIP models, change the batch_size_train and batch_size_test (or the batch_size for the Image Caption task) in the corresponding config file to a smaller number. Besides, the option --amp for compression scripts can be used to enable mixed precision. Compress the BLIP model to half on the NLVR2 dataset as an example:

    python -m torch.distributed.run --nproc_per_node=8 compress_nlvr.py --p 0.5 --epoch 15 --amp \
--pretrained pretrained/model_base_nlvr.pth \
--config ./configs/nlvr.yaml \
--output_dir output/nlvr_nlvr2_compression_2x

    [!WARNING]
    Note that using mixed precision may produce nan gradients. Since UPop take gradients as metrics to determine pruned positions, nan gradients may disrupt the determination and degrade the performance.

  • For DeiT and Segmenter, modify the option --batch-size of the scripts to a smaller number. Mixed precision is not supported temporarily, as it frequently causes nan gradients.

🌲 Expected Folder Structures

├── annotation
│   ├── answer_list.json
│   ├── coco_gt
│   │   ├── coco_karpathy_test_gt.json
│   │   └── coco_karpathy_val_gt.json
│   ├── ...
├── clip                                               
├── compress_caption.py       
├── compress_deit.py        
├── compress_nlvr.py                  
├── compress ...    
├── configs                                             
├── data                                        
├── datasets
│   └── vision
│       ├── coco
│       ├── flickr
│       ├── NLVR2     
│       ├── ...                                                                              
├── deit   
├── log                                     
├── models            
├── output                                    
├── pretrained
│   ├── bert-base-uncased
│   ├── clip_large_retrieval_coco.pth
│   ├── clip_large_retrieval_flickr.pth
│   ├── ...       
├── segm                                                                                   
├── transform                                                                           
└── utils.py

💬 Acknowledgments

This code is built upon BLIP, CLIP, DeiT, Segmenter, and timm. Thanks for these awesome open-source projects!

✨ Citation

@InProceedings{pmlr-v202-shi23e,
  title = {{UP}op: Unified and Progressive Pruning for Compressing Vision-Language Transformers},
  author = {Shi, Dachuan and Tao, Chaofan and Jin, Ying and Yang, Zhendong and Yuan, Chun and Wang, Jiaqi},
  booktitle = {Proceedings of the 40th International Conference on Machine Learning},
  pages = {31292--31311},
  year = {2023},
  volume = {202},
  publisher = {PMLR}
}

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[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

dachuanshi.com/UPop-Project/

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