.

beit3/README.md

+# [(BEiT-3) Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks](https://arxiv.org/abs/2208.10442)
+
+Official PyTorch implementation and pretrained models of BEiT-3. 
+
+The code and pretrained models of **BEiT** can be found at [here](https://github.com/microsoft/unilm/tree/master/beit).
+
+The code and pretrained models of **BEiT v2** can be found at [here](https://github.com/microsoft/unilm/tree/master/beit2).
+
+- March, 2023: release [the code and pretrained models of **BEiT-3**](https://github.com/microsoft/unilm/tree/master/beit3)
+- March, 2023: [**BEiT-3**](https://arxiv.org/abs/2208.10442) was accepted by **CVPR 2023**.
+- Sept 2022: release [the code and pretrained models of **BEiT v2**](https://github.com/microsoft/unilm/tree/master/beit2)
+- Aug 2022: release preprint [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://arxiv.org/abs/2208.10442)
+- Aug 2022: release preprint [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://arxiv.org/abs/2208.06366)
+- June 2022: release preprint [VL-BEiT: Generative Vision-Language Pretraining](https://arxiv.org/abs/2206.01127)
+- March, 2022: add [linear probe examples](https://github.com/microsoft/unilm/blob/master/beit/get_started_for_image_classification.md#example-linear-probe-on-imagenet)
+- January, 2022: [**BEiT**](https://openreview.net/forum?id=p-BhZSz59o4) was accepted by **ICLR 2022 as Oral presentation** (54 out of 3391).
+- August 2021: [**BEiT**](https://huggingface.co/transformers/master/model_doc/beit.html) is on [HuggingFace](https://github.com/huggingface/transformers)
+- July 2021: BEiT-large achieves **[state-of-the-art results on ADE20K](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k) (a big jump to 57.0 mIoU) for semantic segmentation**.
+- July 2021: BEiT-large achieves **state-of-the-art ImageNet top-1 accuracy (88.6%) under the setting without extra data other than ImageNet-22k**.
+- July 2021: release [the code and pretrained models of **BEiT**](https://github.com/microsoft/unilm/tree/master/beit)
+- June 2021: release preprint [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
+
+## Pretrained models
+
+We provide BEiT-3 weights pretrained on monomodal and multimodal data. Our large-size model outperforms previous large-size models across various vision-language and vision downstream tasks. The models were pretrained with 224x224 resolution.
+
+### Tips
+- For vision-language tasks that require deep fusion, we recommend using `BEiT3-base` and `BEiT3-large`.
+- For image-text retrieval or vision tasks, using `BEiT3-base-itc` and `BEiT3-large-itc` usually achieve better performance.
+
+### Download Checkpoints
+
+1. Models pretrained on ImageNet-21k images, 160 GB text documents, and web-scale image-text pairs (collected from [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/), [English LAION-2B](https://laion.ai/blog/laion-5b/), [COYO-700M](https://github.com/kakaobrain/coyo-dataset), and CC15M). 
+   - [`BEiT3-base`](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=12; hidden=768; FFN factor=4x; #head=12; patch=16x16; #parameters: 276M
+   - [`BEiT3-large`](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=24; hidden=1024; FFN factor=4x; #head=16; patch=16x16; #parameters: 746M
+
+2. Perform image-text contrastive intermediate tuning on `BEiT3-base` and `BEiT3-large`. 
+   - [`BEiT3-base-itc`](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_itc_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=12; hidden=768; FFN factor=4x; #head=12; patch=16x16; #parameters: 222M
+   - [`BEiT3-large-itc`](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_itc_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=24; hidden=1024; FFN factor=4x; #head=16; patch=16x16; #parameters: 674M
+
+3. Add indomain image-text pairs (COCO and VG) to continue training `BEiT3-base` and `BEiT3-large` using masked data modeling. The indomain models achieve better performance on VQAv2 and NLVR2 tasks.
+   - [`BEiT3-base-indomain`](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=12; hidden=768; FFN factor=4x; #head=12; patch=16x16; #parameters: 276M
+   - [`BEiT3-large-indomain`](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=24; hidden=1024; FFN factor=4x; #head=16; patch=16x16; #parameters: 746M
+
+### Text Tokenizer
+
+[beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from transformers import XLMRobertaTokenizer
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+```
+
+### Architecture
+
+We use [Magneto](https://arxiv.org/abs/2210.06423) with decoupled Multiway Transformer as the backbone architecture. Magneto can have better training stability and obtain better performance across modalities (such as vision, and language). The implementation is based on the [torchscale](https://github.com/microsoft/torchscale/blob/main/torchscale/model/BEiT3.py) package.
+
+
+## Setup
+
+```
+alias=`whoami | cut -d'.' -f2`; docker run -it --rm --runtime=nvidia --ipc=host --privileged -v /home/${alias}:/home/${alias} pytorch/pytorch:1.8.1-cuda11.1-cudnn8-devel bash
+```
+
+Clone the repo and install required packages:
+```
+git clone https://github.com/microsoft/unilm.git
+cd unilm/beit3
+pip install -r requirements.txt
+```
+
+
+## Fine-tuning on ImageNet-1k (Image Classification)
+
+The detailed instructions can be found at [`get_started_for_image_classification.md`](get_started/get_started_for_image_classification.md). We only use vision-related parameters for image classification fine-tuning.
+
+| initialized checkpoint | resolution | acc@1 | acc@5 | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-----:|:-------:|-------------------|
+| [beit3_base_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 85.4 | 97.6 | 87M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/in1k/beit3_base_patch16_224_in1k.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_base_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 85.4 | 97.6 | 87M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/in1k/beit3_base_indomain_patch16_224_in1k.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 87.6 | 98.3 | 305M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/in1k/beit3_large_patch16_224_in1k.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 87.5 | 98.3 | 305M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/in1k/beit3_large_indomain_patch16_224_in1k.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+
+## Fine-tuning on VQAv2 (Visual Question Answering)
+
+The detailed instructions can be found at [`get_started_for_vqav2.md`](get_started/get_started_for_vqav2.md).
+
+| initialized checkpoint | resolution | augmented data | test-dev | test-std | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-----:|:-----:|:-------:|-------------------|
+| [beit3_base_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | - | 77.65 | - | 228M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/vqa/beit3_base_patch16_480_vqa.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_base_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | - | 78.46 | - | 228M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/vqa/beit3_base_indomain_patch16_480_vqa.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | - | 81.85 | - | 683M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/vqa/beit3_large_patch16_480_vqa.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | - | 82.53 | - | 683M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/vqa/beit3_large_indomain_patch16_480_vqa.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 768x768 | VGQA | 82.97 | 83.03 | 684M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/vqa/beit3_large_indomain_patch16_768_vgqaaug_vqa.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+
+## Fine-tuning on NLVR2 (Visual Reasoning)
+
+The detailed instructions can be found at [`get_started_for_nlvr2.md`](get_started/get_started_for_nlvr2.md).
+
+| initialized checkpoint | resolution | dev | test-P | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-----:|:-------:|-------------------|
+| [beit3_base_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 83.6 | 84.4 | 226M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nlvr2/beit3_base_patch16_224_nlvr2.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_base_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 84.6 | 85.3 | 226M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nlvr2/beit3_base_indomain_patch16_224_nlvr2.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 88.5 | 89.4 | 681M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nlvr2/beit3_large_patch16_224_nlvr2.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 224x224 | 89.2 | 90.0 | 681M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nlvr2/beit3_large_indomain_patch16_224_nlvr2.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+
+## Fine-tuning on COCO Captioning and NoCaps (Image Captioning)
+
+The detailed instructions can be found at [`get_started_for_image_captioning.md`](get_started/get_started_for_captioning.md).
+
+### COCO Captioning
+
+| initialized checkpoint | resolution | test CIDEr | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-------:|-------------------|
+| [beit3_base_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | 133.6 | 271M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/coco_captioning/beit3_base_patch16_480_coco_captioning.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_base_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | 135.0 | 271M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/coco_captioning/beit3_base_indomain_patch16_480_coco_captioning.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | 143.2 | 739M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/coco_captioning/beit3_large_patch16_480_coco_captioning.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+### NoCaps
+
+| initialized checkpoint | resolution | val CIDEr | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-------:|-------------------|
+| [beit3_base_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | 104.4 | 271M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nocaps/beit3_base_patch16_480_nocaps.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_base_indomain_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_indomain_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | 105.6 | 271M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nocaps/beit3_base_indomain_patch16_480_nocaps.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 480x480 | 120.2 | 739M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nocaps/beit3_large_patch16_480_nocaps.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+
+## Fine-tuning on COCO and Flickr30k Retrieval (Image-Text Retrieval)
+
+The detailed instructions can be found at [`get_started_for_retrieval.md`](get_started/get_started_for_retrieval.md).
+
+### COCO Retrieval
+
+| initialized checkpoint | resolution | IR@1 | TR@1 | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-----:|:-------:|-------------------|
+| [beit3_base_itc_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_itc_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 384x384 | 61.4 | 79.1 | 222M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/coco_retrieval/beit3_base_patch16_384_coco_retrieval.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_itc_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_itc_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 384x384 | 63.4 | 82.1 | 675M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/coco_retrieval/beit3_large_patch16_384_coco_retrieval.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+### Flickr30k Retrieval
+
+| initialized checkpoint | resolution | IR@1 | TR@1 | #params | weight |
+|:----------------------------------------|:----------:|:-----:|:-----:|:-------:|-------------------|
+| [beit3_base_itc_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_base_itc_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 384x384 | 86.2 | 96.3 | 222M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/f30k_retrieval/beit3_base_patch16_384_f30k_retrieval.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+| [beit3_large_itc_patch16_224](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/pretraining/beit3_large_itc_patch16_224.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) | 384x384 | 88.1 | 97.2 | 675M | [link](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/f30k_retrieval/beit3_large_patch16_384_f30k_retrieval.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) |
+
+
+## Citation
+
+If you find this repository useful, please consider citing our work:
+```
+@inproceedings{beit3,
+title={Image as a foreign language: {BEiT} pretraining for vision and vision-language tasks},
+author={Wenhui Wang and Hangbo Bao and Li Dong and Johan Bjorck and Zhiliang Peng and Qiang Liu and Kriti Aggarwal and Owais Khan Mohammed and Saksham Singhal and Subhojit Som and Furu Wei},
+booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+year={2023}
+}
+
+@article{beitv2,
+title={{BEiT v2}: Masked Image Modeling with Vector-Quantized Visual Tokenizers},
+author={Zhiliang Peng and Li Dong and Hangbo Bao and Qixiang Ye and Furu Wei},
+year={2022},
+eprint={2208.06366},
+archivePrefix={arXiv},
+primaryClass={cs.CV}
+}
+
+@inproceedings{beit,
+title={{BEiT}: {BERT} Pre-Training of Image Transformers},
+author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
+booktitle={International Conference on Learning Representations},
+year={2022},
+url={https://openreview.net/forum?id=p-BhZSz59o4}
+}
+```
+
+
+## Acknowledgement
+
+This repository is built using the [BEiT](https://github.com/microsoft/unilm/tree/master/beit), the [BEiTv2](https://github.com/microsoft/unilm/tree/master/beit2), the [CLIP](https://github.com/openai/CLIP), the [open_clip](https://github.com/mlfoundations/open_clip), the [Oscar](https://github.com/microsoft/Oscar), the [DeiT](https://github.com/facebookresearch/deit), the [Dino](https://github.com/facebookresearch/dino) repository and the [timm](https://github.com/rwightman/pytorch-image-models) library.
+
+
+## License
+This project is licensed under the license found in the LICENSE file in the root directory of this source tree.
+
+[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
+
+### Contact Information
+
+For help or issues using BEiT-3 models, please submit a GitHub issue.

beit3/datasets.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+import os
+import json
+import random
+import torch
+import glob
+from collections import defaultdict, Counter
+from torchvision import transforms
+from torchvision.datasets.folder import default_loader
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
+from timm.data.transforms import RandomResizedCropAndInterpolation
+from timm.data import create_transform
+
+import utils
+from glossary import normalize_word
+from randaug import RandomAugment
+
+
+class BaseDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, data_path, split, transform, 
+        tokenizer, num_max_bpe_tokens, task=None,
+    ):
+        index_files = self.get_index_files(split, task=task)
+        self.tokenizer = tokenizer
+        self.num_max_bpe_tokens = num_max_bpe_tokens
+        self.data_path = data_path
+        items = []
+        self.index_files = index_files
+
+        offset = 0
+        for _index_file in index_files:
+            index_file = os.path.join(data_path, _index_file)
+            with open(index_file, mode="r", encoding="utf-8") as reader:
+                for line in reader:
+                    data = json.loads(line)
+                    items.append(data)
+                print("Load %d image-text pairs from %s. " % (len(items) - offset, index_file))
+                offset = len(items)
+        self.items = items
+        self.bos_token_id = tokenizer.bos_token_id
+        self.eos_token_id = tokenizer.eos_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+        self.loader = default_loader
+        self.transform = transform
+        self.split = split
+
+    @staticmethod
+    def get_index_files(split):
+        raise NotImplementedError()
+
+    def _get_image(self, image_path: str):
+        image_path = os.path.join(self.data_path, image_path)
+        image = self.loader(image_path)
+        return self.transform(image)
+
+    def _get_text_segment(self, text_segment, max_len=None):
+        if isinstance(text_segment, str):
+            tokens = self.tokenizer.tokenize(text_segment)
+        else:
+            tokens = text_segment[:]
+        if len(tokens) == 0:
+            raise RuntimeError("The text segment should contains at least one tokens!")
+        if max_len is None:
+            max_len = self.num_max_bpe_tokens
+
+        if len(tokens) > max_len - 2:
+            tokens = tokens[:max_len - 2]
+
+        tokens = [self.bos_token_id] + tokens[:] + [self.eos_token_id]
+        num_tokens = len(tokens)
+        padding_mask = [0] * num_tokens + [1] * (max_len - num_tokens)
+        return tokens + [self.pad_token_id] * (max_len - num_tokens), padding_mask, num_tokens
+
+    def _get_image_text_example(self, index: int, data: dict):
+        item = self.items[index]
+        img_path = item["image_path"]
+        img = self._get_image(img_path)
+        data["image"] = img
+
+        text_segment = item["text_segment"]
+        language_tokens, padding_mask, _ = self._get_text_segment(text_segment)
+        data["language_tokens"] = language_tokens
+        data["padding_mask"] = padding_mask
+
+    def __getitem__(self, index: int):
+        data = dict()
+        self._get_image_text_example(index, data)
+        return data
+
+    def __len__(self) -> int:
+        return len(self.items)
+
+    def __repr__(self) -> str:
+        head = "Dataset " + self.__class__.__name__
+        body = '{' + "\n  Number of items: %s," % self.__len__()
+        body += "\n  data root = %s," % self.data_path
+        body += "\n  split = %s," % self.split
+        body += "\n  dataset index files = %s" % str(self.index_files)
+        body += "\n  num max bpe tokens = %s" % self.num_max_bpe_tokens
+        body += "\n  transforms = ["
+        for t in self.transform.transforms:
+            body += "\n    %s" % str(t)
+        body += "\n  ]"
+        body += "\n}"
+
+        return head + body
+
+
+def _write_data_into_jsonl(items, jsonl_file):
+    with open(jsonl_file, mode="w", encoding="utf-8") as writer:
+        for data in items:
+            writer.write(json.dumps(data, indent=None))
+            writer.write('\n')
+    print("Write %s with %d items !" % (jsonl_file, len(items)))
+
+
+def _make_retrieval_coco_karpathy_dataset_index(
+        data_path, 
+        tokenizer, 
+        split=("train", "restval"), 
+        split_name="train", 
+):
+    coco_karpathy_split_json_file = os.path.join(data_path, "dataset_coco.json")
+    items = []
+    image_counter = set()
+    print("read %s" % coco_karpathy_split_json_file)
+    with open(coco_karpathy_split_json_file, mode="r", encoding="utf-8") as reader:
+        data = json.loads(reader.read())
+        for item in data["images"]:
+            if item["split"] in split:
+                image_path = os.path.join(item["filepath"], item["filename"])
+                for sent in item["sentences"]:
+                    tokens = tokenizer.tokenize(sent["raw"])
+                    token_ids = tokenizer.convert_tokens_to_ids(tokens)
+                    items.append({
+                            "image_path": image_path, 
+                            "text_segment": token_ids, 
+                            "image_id": len(image_counter), 
+                    })
+                if image_path not in image_counter:
+                    image_counter.add(image_path)
+    print("Find %d images and %d image-text pairs for karpathy dataset %s split !" % \
+        (len(image_counter), len(items), split_name))
+    index_file = os.path.join(data_path, "coco_retrieval.%s.jsonl" % split_name)
+    _write_data_into_jsonl(items, index_file)
+    pass
+
+
+def _make_captioning_coco_karpathy_dataset_index(
+        data_path, 
+        tokenizer, 
+        split=("train", "restval"), 
+        split_name="train", 
+):
+    coco_karpathy_split_json_file = os.path.join(data_path, "dataset_coco.json")
+    items = []
+    image_counter = set()
+    print("read %s" % coco_karpathy_split_json_file)
+    with open(coco_karpathy_split_json_file, mode="r", encoding="utf-8") as reader:
+        data = json.loads(reader.read())
+        for item in data["images"]:
+            if item["split"] in split:
+                image_path = os.path.join(item["filepath"], item["filename"])
+                if item["split"] in ["train", "restval"]:
+                    for sent in item["sentences"]:
+                        tokens = tokenizer.tokenize(sent["raw"])
+                        token_ids = tokenizer.convert_tokens_to_ids(tokens)
+                        items.append({
+                                "image_path": image_path, 
+                                "text_segment": token_ids, 
+                                "image_id": item["cocoid"], 
+                        })
+                else:
+                    items.append({
+                                "image_path": image_path, 
+                                "text_segment": None, 
+                                "image_id": item["cocoid"], 
+                    })
+                if image_path not in image_counter:
+                    image_counter.add(image_path)
+    print("Find %d images and %d image-text pairs for karpathy dataset %s split !" % \
+        (len(image_counter), len(items), split_name))
+    index_file = os.path.join(data_path, "coco_captioning.%s.jsonl" % split_name)
+    _write_data_into_jsonl(items, index_file)
+    pass
+
+
+def _make_nocaps_dataset_index(
+        data_path,  
+        split="val", 
+):
+    if split == "val":
+        json_file = "nocaps_val_4500_captions.json"
+    elif split == "test":
+        json_file = "nocaps_test_image_info.json"
+    nocaps_split_json_file = os.path.join(data_path, json_file)
+    items = []
+    image_counter = set()
+    print("read %s" % nocaps_split_json_file)
+    with open(nocaps_split_json_file, mode="r", encoding="utf-8") as reader:
+        data = json.loads(reader.read())
+        for item in data["images"]:
+            image_path = os.path.join(split, item["file_name"])
+            items.append({
+                "image_path": image_path, 
+                "text_segment": None, 
+                "image_id": item["id"], 
+            })
+
+            if image_path not in image_counter:
+                image_counter.add(image_path)
+
+    print("Find %d images and %d image-text pairs for nocaps dataset %s split !" % \
+        (len(image_counter), len(items), split))
+    index_file = os.path.join(data_path, "nocaps.%s.jsonl" % split)
+    _write_data_into_jsonl(items, index_file)
+
+
+class NLVR2Dataset(BaseDataset):
+    @staticmethod
+    def get_index_files(split, task=None):
+        if split == "train":
+            return ("nlvr2.train.index.jsonl", )
+        elif split == "val":
+            return ("nlvr2.dev.index.jsonl", )
+        elif split == "test":
+            return ("nlvr2.test-P.index.jsonl", )
+        else:
+            raise RuntimeError("split %s is not found!" % split)
+
+    def __getitem__(self, index: int):
+        data = super().__getitem__(index)
+        item = self.items[index]
+        img_path = item["image2_path"]
+        img = self._get_image(img_path)
+        data["image2"] = img
+        data["label"] = self.items[index]["label"]
+        return data
+
+    @staticmethod
+    def __preprocess_json(preifx, json_file, tokenizer, index_file):
+        items = []
+        with open(json_file, mode="r", encoding="utf-8") as reader:
+            for line in reader:
+                data = json.loads(line)
+                path = os.path.join(preifx, str(data["directory"])) if "directory" in data else preifx
+                path = os.path.join(path, "-".join(data["identifier"].split("-")[:-1]))
+                tokens = tokenizer.tokenize(data["sentence"])
+                token_ids = tokenizer.convert_tokens_to_ids(tokens)
+                items.append({
+                    "image_path": path + "-img0.png",
+                    "image2_path": path + "-img1.png",
+                    "text_segment": token_ids,
+                    "label": 1 if data["label"] == "True" else 0,
+                    "identifier": data["identifier"], 
+                })
+        _write_data_into_jsonl(items, index_file)
+
+    @classmethod
+    def make_dataset_index(cls, data_path, tokenizer, nlvr_repo_path):
+        cls.__preprocess_json(
+            preifx="images/train", json_file=os.path.join(nlvr_repo_path, "nlvr2/data/train.json"), 
+            tokenizer=tokenizer, index_file=os.path.join(data_path, cls.get_index_files("train")[0]), 
+        )
+        cls.__preprocess_json(
+            preifx="dev", json_file=os.path.join(nlvr_repo_path, "nlvr2/data/dev.json"), 
+            tokenizer=tokenizer, index_file=os.path.join(data_path, cls.get_index_files("val")[0]), 
+        )
+        cls.__preprocess_json(
+            preifx="test1", json_file=os.path.join(nlvr_repo_path, "nlvr2/data/test1.json"), 
+            tokenizer=tokenizer, index_file=os.path.join(data_path, cls.get_index_files("test")[0]), 
+        )
+
+
+class ImageNetDataset(BaseDataset):
+    @staticmethod
+    def get_index_files(split, task=None):
+        if split == "train":
+            return ("imagenet.train.index.jsonl", )
+        elif split == "val":
+            return ("imagenet.val.index.jsonl", )
+        elif split == "test":
+            return ("imagenet.val.index.jsonl", )
+        else:
+            raise RuntimeError("split %s is not found!" % split)
+
+    def __getitem__(self, index: int):
+        data = dict()
+        item = self.items[index]
+        img_path = item["image_path"]
+        img = self._get_image(img_path)
+        data["image"] = img
+        data["label"] = item["label"]
+        return data
+    
+    @staticmethod
+    def _find_classes(dir):
+        """
+        Finds the class folders in a dataset.
+        Args:
+            dir (string): Root directory path.
+        Returns:
+            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.
+        Ensures:
+            No class is a subdirectory of another.
+        """
+        classes = [d.name for d in os.scandir(dir) if d.is_dir()]
+        classes.sort()
+        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
+        return classes, class_to_idx
+
+    @staticmethod
+    def _make_imagenet_index(data_path, index_path, data_path_prefix, class_to_idx, split):
+        items = []
+        index_file = os.path.join(index_path, f"imagenet.{split}.index.jsonl")
+        for target_class in sorted(class_to_idx.keys()):
+            class_index = class_to_idx[target_class]
+            target_dir = os.path.join(data_path, target_class)
+            if not os.path.isdir(target_dir):
+                continue
+            for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
+                for fname in sorted(fnames):
+                    path = os.path.join(root, fname)
+                    path = path.replace(data_path_prefix, "")
+                    items.append({
+                        "image_path": path,
+                        "label": class_index,
+                    })
+
+        _write_data_into_jsonl(items, index_file)
+
+    @classmethod
+    def make_dataset_index(cls, train_data_path, val_data_path, index_path):
+        data_path_prefix = train_data_path[:[x[0]==x[1] for x in zip(train_data_path, val_data_path)].index(0)]
+        classes, class_to_idx = cls._find_classes(train_data_path)
+        cls._make_imagenet_index(
+             data_path=train_data_path, index_path=index_path, data_path_prefix=data_path_prefix,
+             class_to_idx=class_to_idx, split="train",
+        )
+        cls._make_imagenet_index(
+             data_path=val_data_path, index_path=index_path, data_path_prefix=data_path_prefix,
+             class_to_idx=class_to_idx, split="val",
+        )
+
+
+class VQAv2Dataset(BaseDataset):
+    def __init__(self, data_path, **kwargs):
+        super().__init__(data_path=data_path, **kwargs)
+        ans2label_file = os.path.join(data_path, "answer2label.txt")
+        ans2label = {}
+        label2ans = []
+        with open(ans2label_file, mode="r", encoding="utf-8") as reader:
+            for i, line in enumerate(reader):
+                data = json.loads(line)
+                ans = data["answer"]
+                label = data["label"]
+                label = int(label)
+                assert label == i
+                ans2label[ans] = i
+                label2ans.append(ans)
+        
+        self.ans2label = ans2label
+        self.label2ans = label2ans
+
+    @staticmethod
+    def get_index_files(split, task=None):
+        if split == "train":
+            return ("vqa.train.jsonl", "vqa.trainable_val.jsonl")
+        elif split == "val":
+            return ("vqa.rest_val.jsonl", )
+        elif split == "test":
+            return ("vqa.test.jsonl", )
+        elif split == "test-dev":
+            return ("vqa.test-dev.jsonl", )            
+        else:
+            raise RuntimeError("split %s is not found!" % split)
+
+    def __getitem__(self, index: int):
+        data = super().__getitem__(index)
+        if "labels" in self.items[index] and len(self.items[index]["labels"]) > 0:
+            labels = [0.] * len(self.label2ans)
+            for l, s in zip(self.items[index]["labels"], self.items[index]["scores"]):
+                labels[l] = s
+            data["labels"] = torch.FloatTensor(labels)
+        else:
+            data["qid"] = self.items[index]["qid"]
+        return data
+
+    @staticmethod
+    def get_score(occurences):
+        if occurences == 0:
+            return 0.0
+        elif occurences == 1:
+            return 0.3
+        elif occurences == 2:
+            return 0.6
+        elif occurences == 3:
+            return 0.9
+        else:
+            return 1.0
+
+    @classmethod
+    def make_dataset_index(cls, data_path, tokenizer, annotation_data_path):
+        with open(os.path.join(annotation_data_path, "v2_OpenEnded_mscoco_train2014_questions.json"), "r") as fp:
+            questions_train2014 = json.load(fp)["questions"]
+        with open(os.path.join(annotation_data_path, "v2_OpenEnded_mscoco_val2014_questions.json"), "r") as fp:
+            questions_val2014 = json.load(fp)["questions"]
+        with open(os.path.join(annotation_data_path, "v2_OpenEnded_mscoco_test2015_questions.json"), "r") as fp:
+            questions_test2015 = json.load(fp)["questions"]
+        with open(os.path.join(annotation_data_path, "v2_OpenEnded_mscoco_test-dev2015_questions.json"), "r") as fp:
+            questions_test_dev2015 = json.load(fp)["questions"]
+
+        with open(os.path.join(annotation_data_path, "v2_mscoco_train2014_annotations.json"), "r") as fp:
+            annotations_train2014 = json.load(fp)["annotations"]
+        with open(os.path.join(annotation_data_path, "v2_mscoco_val2014_annotations.json"), "r") as fp:
+            annotations_val2014 = json.load(fp)["annotations"]
+
+        annotations = dict()
+
+        for split, questions in zip(
+            ["train", "val", "test", "test-dev"],
+            [questions_train2014, questions_val2014, questions_test2015, questions_test_dev2015],
+        ):
+            _annot = defaultdict(dict)
+            for q in questions:
+                question_text = q["question"]
+                tokens = tokenizer.tokenize(question_text)
+                token_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+                assert q["question_id"] not in _annot[q["image_id"]]
+                _annot[q["image_id"]][q["question_id"]] = {
+                    "question": question_text, 
+                    "token_ids": token_ids, 
+                }
+
+            annotations[split] = _annot
+
+        all_major_answers = list()
+
+        for split, annots in zip(
+            ["train", "val"], [annotations_train2014, annotations_val2014],
+        ):
+            # _annot = annotations[split]
+            for q in annots:
+                all_major_answers.append(q["multiple_choice_answer"])
+
+        all_major_answers = [normalize_word(word) for word in all_major_answers]
+        counter = {k: v for k, v in Counter(all_major_answers).items() if v >= 9}
+        ans2label = {k: i for i, k in enumerate(counter.keys())}
+        label2ans = list(counter.keys())
+
+        for split, annots in zip(
+            ["train", "val"], [annotations_train2014, annotations_val2014],
+        ):
+            _annot = annotations[split]
+            for q in annots:
+                answers = q["answers"]
+                answer_count = {}
+                for answer in answers:
+                    answer_ = answer["answer"]
+                    answer_count[answer_] = answer_count.get(answer_, 0) + 1
+
+                labels = []
+                scores = []
+                for answer in answer_count:
+                    if answer not in ans2label:
+                        continue
+                    labels.append(ans2label[answer])
+                    score = cls.get_score(answer_count[answer])
+                    scores.append(score)
+
+                assert "labels" not in _annot[q["image_id"]][q["question_id"]]
+                assert "question" in _annot[q["image_id"]][q["question_id"]]
+                _annot[q["image_id"]][q["question_id"]]["labels"] = labels
+                _annot[q["image_id"]][q["question_id"]]["scores"] = scores
+
+        for split in ["train", "val"]:
+            filtered_annot = dict()
+            for ik, iv in annotations[split].items():
+                new_q = dict()
+                for qk, qv in iv.items():
+                    if len(qv["labels"]) != 0:
+                        new_q[qk] = qv
+                if len(new_q) != 0:
+                    filtered_annot[ik] = new_q
+            annotations[split] = filtered_annot
+
+        split2items = {}
+        for split in ["train", "val", "test", "test-dev"]:
+            annot = annotations[split]
+            split_name = {
+                "train": "train2014",
+                "val": "val2014",
+                "test": "test2015",
+                "test-dev": "test2015",
+            }[split]
+            paths = list(glob.glob(f"{data_path}/{split_name}/*.jpg"))
+            random.shuffle(paths)
+            annot_paths = [path for path in paths \
+                if int(path.split("/")[-1].split("_")[-1][:-4]) in annot]
+
+            if len(paths) == len(annot_paths):
+                print("all images have caption annotations")
+            else:
+                print("not all images have caption annotations")
+            print(len(paths), len(annot_paths), len(annot))
+
+            items = []
+            for path in annot_paths:
+                iid = int(path.split("/")[-1].split("_")[-1][:-4])
+                _annot = annotations[split][iid]
+                for qid in _annot:
+                    q = _annot[qid]
+                    if split in ["train", "val"]:
+                        labels = q["labels"]
+                        scores = q["scores"]
+                    else:
+                        labels, scores = [], []
+
+                    items.append({
+                        "image_path": os.path.join(split_name, path.split('/')[-1]), 
+                        "text_segment": q["token_ids"], 
+                        "labels": labels, 
+                        "scores": scores, 
+                        "qid": qid, 
+                    })
+            split2items[split] = items
+
+            _write_data_into_jsonl(items=items, jsonl_file=os.path.join(data_path, "vqa.%s.jsonl" % split))
+
+        # Following ViLT, we use 1000 images of the original val set as the final val set        
+        val_image2items = defaultdict(list)
+        for item in split2items["val"]:
+            val_image2items[item["image_path"]].append(item)
+        
+        print("Contains %d image and %d pairs for val set!" % (len(val_image2items), len(split2items["val"])))
+
+        val_images = list(val_image2items.keys())
+        random.shuffle(val_images)
+        trainable_val = []
+        rest_val = []
+        for i, image_id in enumerate(val_images):
+            if i < 1000:
+                rest_val += val_image2items[image_id]
+            else:
+                trainable_val += val_image2items[image_id]
+        
+        _write_data_into_jsonl(items=trainable_val, jsonl_file=os.path.join(data_path, "vqa.trainable_val.jsonl"))
+        _write_data_into_jsonl(items=rest_val, jsonl_file=os.path.join(data_path, "vqa.rest_val.jsonl"))
+
+        with open(os.path.join(data_path, "answer2label.txt"), mode="w", encoding="utf-8") as writer:
+            for ans in ans2label:
+                to_json = {
+                    "answer": ans, 
+                    "label": ans2label[ans]
+                }
+                writer.write("%s\n" % json.dumps(to_json))
+
+
+class RetrievalDataset(BaseDataset):
+    @staticmethod
+    def get_index_files(split, task=None):
+        if split == "train":
+            return (f"{task}.train.jsonl", )
+        elif split == "val":
+            return (f"{task}.val.jsonl", )
+        elif split == "test":
+            return (f"{task}.test.jsonl", )
+        else:
+            raise RuntimeError("split %s is not found!" % split)
+
+    def __getitem__(self, index: int):
+        data = super().__getitem__(index)
+        data["image_id"] = self.items[index]["image_id"]
+        return data
+
+    @staticmethod
+    def make_flickr30k_dataset_index(data_path, tokenizer, karpathy_path):
+
+        with open(os.path.join(karpathy_path, "dataset_flickr30k.json"), "r") as reader:
+            captions = json.loads(reader.read())
+
+        captions = captions["images"]
+        split2items = defaultdict(list)
+        split2images = defaultdict(set)
+
+        for each_item in captions:
+            image_path = os.path.join("flickr30k-images", each_item["filename"])
+            split = each_item["split"]
+
+            for text_segment in each_item["sentences"]:
+                tokens = tokenizer.tokenize(text_segment["raw"])
+                token_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+                split2items[split].append({
+                    "image_path": image_path, 
+                    "text_segment": token_ids, 
+                    "image_id": len(split2images[split]), 
+                })
+
+            assert each_item["filename"] not in split2images[split]
+            split2images[split].add(each_item["filename"])
+
+        for split in split2items:
+            print("%d images and %d image-text pairs!" % (len(split2images[split]), len(split2items[split])))
+            _write_data_into_jsonl(split2items[split], os.path.join(data_path, "flickr30k.%s.jsonl" % split))
+
+    @staticmethod
+    def make_coco_dataset_index(data_path, tokenizer):
+        _make_retrieval_coco_karpathy_dataset_index(data_path, tokenizer, split=("train", "restval"), split_name="train")
+        _make_retrieval_coco_karpathy_dataset_index(data_path, tokenizer, split=("val", ), split_name="val")
+        _make_retrieval_coco_karpathy_dataset_index(data_path, tokenizer, split=("test", ), split_name="test")
+
+
+class CaptioningDataset(BaseDataset):
+
+    def __init__(self, data_path, split, transform, 
+                tokenizer, num_max_bpe_tokens, task, mask_prob):
+        super().__init__(
+            data_path=data_path, split=split, 
+            transform=transform, tokenizer=tokenizer, 
+            num_max_bpe_tokens=num_max_bpe_tokens, task=task, 
+        )
+        self.mask_token_id = tokenizer.mask_token_id
+        self.language_vocab_size = tokenizer.vocab_size
+        self.mask_prob = mask_prob
+
+    @staticmethod
+    def get_index_files(split, task=None):
+        if split == "train":
+            return ("coco_captioning.train.jsonl", )
+        elif split == "val":
+            return (f"{task}.val.jsonl", )
+        elif split == "test":
+            return (f"{task}.test.jsonl", )
+        else:
+            raise RuntimeError("split %s is not found!" % split)
+
+    def _get_mask_token(self, token):
+        p = random.random()
+        if p < 0.8:
+            return self.mask_token_id
+        elif p < 0.9:
+            return token
+        else:
+            return random.randint(3, self.language_vocab_size - 1)
+
+    def _masking_on_text_tokens(self, tokens, num_tokens, mask_prob):
+        bool_masked_pos = [0] * len(tokens)
+        to_mask = min(int(num_tokens * mask_prob + 0.5), num_tokens - 1)
+        to_mask = max(to_mask, 1)
+        num_masked_tokens = 0
+        while num_masked_tokens < to_mask:
+            i = random.randint(1, num_tokens - 1)
+            if bool_masked_pos[i] == 0:
+                bool_masked_pos[i] = 1
+                tokens[i] = self._get_mask_token(tokens[i])
+                num_masked_tokens += 1
+
+        return tokens, bool_masked_pos
+
+    def __getitem__(self, index: int):
+        data = dict()
+        item = self.items[index]
+        img_path = item["image_path"]
+        img = self._get_image(img_path)
+        data["image"] = img
+        data["image_id"] = item["image_id"]
+
+        text_segment = item["text_segment"]
+        if text_segment is not None:
+            language_tokens, padding_mask, num_tokens = self._get_text_segment(text_segment)
+            masked_tokens = language_tokens[:]
+            masked_tokens, language_masked_pos = \
+                self._masking_on_text_tokens(masked_tokens, num_tokens, self.mask_prob)
+            data["language_tokens"] = language_tokens
+            data["masked_tokens"] = masked_tokens
+            data["language_masked_pos"] = language_masked_pos
+            data["padding_mask"] = padding_mask
+        return data
+
+    @staticmethod
+    def make_coco_captioning_dataset_index(data_path, tokenizer):
+        _make_captioning_coco_karpathy_dataset_index(data_path, tokenizer, split=("train", "restval"), split_name="train")
+        _make_captioning_coco_karpathy_dataset_index(data_path, tokenizer, split=("val", ), split_name="val")
+        _make_captioning_coco_karpathy_dataset_index(data_path, tokenizer, split=("test", ), split_name="test")
+
+    @staticmethod
+    def make_nocaps_captioning_dataset_index(data_path):
+        _make_nocaps_dataset_index(data_path, split="val")
+        _make_nocaps_dataset_index(data_path, split="test")
+
+
+task2dataset = {
+    "nlvr2": NLVR2Dataset, 
+    "vqav2": VQAv2Dataset, 
+    "flickr30k": RetrievalDataset, 
+    "coco_retrieval": RetrievalDataset,  
+    "coco_captioning": CaptioningDataset,
+    "nocaps": CaptioningDataset,
+    "imagenet": ImageNetDataset,
+}
+
+
+def create_dataloader(dataset, is_train, batch_size, num_workers, pin_mem, dist_eval=False):
+    if is_train or dist_eval:
+        num_tasks = utils.get_world_size()
+        global_rank = utils.get_rank()
+
+        if not is_train and dist_eval and len(dataset) % num_tasks != 0:
+            print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
+                    'This will slightly alter validation results as extra duplicate entries are added to achieve '
+                    'equal num of samples per-process.')
+
+        sampler = torch.utils.data.DistributedSampler(
+            dataset, num_replicas=num_tasks, rank=global_rank, shuffle=is_train
+        )
+    else:
+        sampler = torch.utils.data.SequentialSampler(dataset)
+    
+    return torch.utils.data.DataLoader(
+        dataset, sampler=sampler,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        pin_memory=pin_mem,
+        drop_last=is_train,
+        collate_fn=utils.merge_batch_tensors_by_dict_key,
+    )
+
+
+def build_transform(is_train, args):
+    if args.task in ["imagenet"]:
+        return build_imagenet_transform(is_train, args)
+
+    if is_train:
+        t = [
+            RandomResizedCropAndInterpolation(args.input_size, scale=(0.5, 1.0), interpolation=args.train_interpolation), 
+            transforms.RandomHorizontalFlip(),
+        ]
+        if args.randaug:
+            t.append(
+                RandomAugment(
+                    2, 7, isPIL=True, 
+                    augs=[
+                        'Identity','AutoContrast','Equalize','Brightness','Sharpness', 
+                        'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate', 
+                    ]))
+        t += [
+            transforms.ToTensor(),
+            transforms.Normalize(mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 
+        ]
+        t = transforms.Compose(t)
+    else:
+        t = transforms.Compose([
+            transforms.Resize((args.input_size, args.input_size), interpolation=3), 
+            transforms.ToTensor(),
+            transforms.Normalize(mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD)
+        ])
+
+    return t
+
+
+def build_imagenet_transform(is_train, args):
+    resize_im = args.input_size > 32
+    if is_train:
+        # this should always dispatch to transforms_imagenet_train
+        transform = create_transform(
+            input_size=args.input_size,
+            is_training=True,
+            color_jitter=args.color_jitter,
+            auto_augment=args.aa,
+            interpolation=args.train_interpolation,
+            re_prob=args.reprob,
+            re_mode=args.remode,
+            re_count=args.recount,
+            mean=IMAGENET_DEFAULT_MEAN,
+            std=IMAGENET_DEFAULT_STD,
+        )
+        if not resize_im:
+            # replace RandomResizedCropAndInterpolation with
+            # RandomCrop
+            transform.transforms[0] = transforms.RandomCrop(
+                args.input_size, padding=4)
+        return transform
+
+    t = []
+    if resize_im:
+        if args.crop_pct is None:
+            args.crop_pct = 1.0
+        size = int(args.input_size / args.crop_pct)
+        t.append(
+            transforms.Resize(size, interpolation=3),  # to maintain same ratio w.r.t. 224 images
+        )
+        t.append(transforms.CenterCrop(args.input_size))
+
+    t.append(transforms.ToTensor())
+    t.append(transforms.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD))
+    return transforms.Compose(t)
+
+
+def get_sentencepiece_model_for_beit3(args):
+    from transformers import XLMRobertaTokenizer
+    return XLMRobertaTokenizer(args.sentencepiece_model)
+
+
+def create_dataset_by_split(args, split, is_train=True):
+    transform = build_transform(is_train=is_train, args=args)
+    dataset_class = task2dataset[args.task]
+    tokenizer = get_sentencepiece_model_for_beit3(args)
+
+    opt_kwargs = {}
+    if args.task in ["coco_captioning", "nocaps"]:
+        opt_kwargs["mask_prob"] = args.captioning_mask_prob
+
+    dataset = dataset_class(
+        data_path=args.data_path, split=split, 
+        transform=transform, tokenizer=tokenizer, 
+        num_max_bpe_tokens=args.num_max_bpe_tokens, 
+        task=args.task, **opt_kwargs, 
+    )
+    if is_train:
+        batch_size = args.batch_size
+    elif hasattr(args, "eval_batch_size") and args.eval_batch_size is not None:
+        batch_size = args.eval_batch_size
+    else:
+        batch_size = int(args.batch_size * 1.5)
+
+    return create_dataloader(
+        dataset, is_train=is_train, batch_size=batch_size, 
+        num_workers=args.num_workers, pin_mem=args.pin_mem, dist_eval=args.dist_eval, 
+    )
+
+
+def create_downstream_dataset(args, is_eval=False):
+    if is_eval:
+        return create_dataset_by_split(args, split="test", is_train=False)
+    else:
+        return \
+            create_dataset_by_split(args, split="train", is_train=True), \
+            create_dataset_by_split(args, split="val", is_train=True)

beit3/engine_for_finetuning.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+import math
+import sys
+import json
+from typing import Iterable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from timm.utils import ModelEma
+from timm.utils import accuracy, ModelEma
+from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
+from datasets import get_sentencepiece_model_for_beit3
+
+import utils
+
+
+class TaskHandler(object):
+    def __init__(self) -> None:
+        self.metric_logger = None
+        self.split = None
+
+    def train_batch(self, model, **kwargs):
+        raise NotImplementedError()
+
+    def eval_batch(self, model, **kwargs):
+        raise NotImplementedError()
+
+    def before_eval(self, metric_logger, data_loader, **kwargs):
+        self.metric_logger = metric_logger
+        self.split = data_loader.dataset.split
+
+    def after_eval(self, **kwargs):
+        raise NotImplementedError()
+
+
+class NLVR2Handler(TaskHandler):
+    def __init__(self) -> None:
+        super().__init__()
+        self.criterion = torch.nn.CrossEntropyLoss()
+
+    def train_batch(self, model, image, image2, language_tokens, padding_mask, label):
+        logits = model(
+            image_a=image, image_b=image2, 
+            text_description=language_tokens, 
+            padding_mask=padding_mask)
+        acc = (logits.max(-1)[-1] == label).float().mean()
+        return {
+            "loss": self.criterion(input=logits, target=label), 
+            "acc": acc, 
+        }
+
+    def eval_batch(self, model, image, image2, language_tokens, padding_mask, label):
+        logits = model(
+            image_a=image, image_b=image2, 
+            text_description=language_tokens, 
+            padding_mask=padding_mask)
+        batch_size = language_tokens.shape[0]
+        acc = (logits.max(-1)[-1] == label).float().sum(0) * 100.0 / batch_size
+        self.metric_logger.meters['acc'].update(acc.item(), n=batch_size)
+    
+    def after_eval(self, **kwargs):
+        print('* Acc {acc.global_avg:.3f}'.format(acc=self.metric_logger.acc))
+        return {k: meter.global_avg for k, meter in self.metric_logger.meters.items()}, "acc"
+    
+
+class ImageNetHandler(TaskHandler):
+    def __init__(self, args) -> None:
+        super().__init__()
+        mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
+        if mixup_active:
+            # smoothing is handled with mixup label transform
+            self.criterion = SoftTargetCrossEntropy()
+        elif args.label_smoothing > 0.:
+            self.criterion = LabelSmoothingCrossEntropy(smoothing=args.label_smoothing)
+        else:
+            self.criterion = torch.nn.CrossEntropyLoss()
+
+    def train_batch(self, model, image, label):
+        logits = model(image=image)
+        return {
+            "loss": self.criterion(logits, label), 
+        }
+
+    def eval_batch(self, model, image, label):
+        logits = model(image=image)
+        batch_size = image.shape[0]
+        acc1, acc5 = accuracy(logits, label, topk=(1, 5))
+        self.metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+        self.metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
+    
+    def after_eval(self, **kwargs):
+        print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}'
+            .format(top1=self.metric_logger.acc1, top5=self.metric_logger.acc5))
+        return {k: meter.global_avg for k, meter in self.metric_logger.meters.items()}, "acc1"
+
+
+class RetrievalHandler(TaskHandler):
+    def __init__(self) -> None:
+        super().__init__()
+        self.image_feats = []
+        self.text_feats = []
+        self.image_ids = []
+        self.metric_logger = None
+
+    def train_batch(self, model, image, language_tokens, padding_mask, image_id):
+        loss, vision_cls, language_cls = model(
+            image=image, text_description=language_tokens, padding_mask=padding_mask)
+        return {
+            "loss": loss, 
+        }
+
+    def before_eval(self, metric_logger, **kwargs):
+        self.image_feats.clear()
+        self.text_feats.clear()
+        self.image_ids.clear()
+        self.metric_logger = metric_logger
+
+    def eval_batch(self, model, image, language_tokens, padding_mask, image_id):
+        vision_cls, _ = model(image=image, only_infer=True)
+        _, language_cls = model(
+            text_description=language_tokens, padding_mask=padding_mask, only_infer=True)
+
+        self.image_feats.append(vision_cls.clone())
+        self.text_feats.append(language_cls.clone())
+        self.image_ids.append(image_id.clone())
+    
+    def after_eval(self, **kwargs):
+        image_feats = {}
+        for feats, ids in zip(self.image_feats, self.image_ids):
+            for i, _idx in enumerate(ids):
+                idx = _idx.item()
+                if idx not in image_feats:
+                    image_feats[idx] = feats[i]
+        
+        tiids = torch.cat(self.image_ids, dim=0)
+        iids = []
+        sorted_tensors = []
+        for key in sorted(image_feats.keys()):
+            sorted_tensors.append(image_feats[key].view(1, -1))
+            iids.append(key)
+
+        image_cls_feats = torch.cat(sorted_tensors, dim=0)
+        text_cls_feats = torch.cat(self.text_feats, dim=0)
+
+        scores = image_cls_feats @ text_cls_feats.t()
+        iids = torch.LongTensor(iids).to(scores.device)
+
+        print("scores: {}".format(scores.size()))
+        print("iids: {}".format(iids.size()))
+        print("tiids: {}".format(tiids.size()))
+
+        topk10 = scores.topk(10, dim=1)
+        topk5 = scores.topk(5, dim=1)
+        topk1 = scores.topk(1, dim=1)
+        
+        topk10_iids = tiids[topk10.indices]
+        topk5_iids = tiids[topk5.indices]
+        topk1_iids = tiids[topk1.indices]
+
+        tr_r10 = (iids.unsqueeze(1) == topk10_iids).float().max(dim=1)[0].mean()
+        tr_r5 = (iids.unsqueeze(1) == topk5_iids).float().max(dim=1)[0].mean()
+        tr_r1 = (iids.unsqueeze(1) == topk1_iids).float().max(dim=1)[0].mean()
+
+        topk10 = scores.topk(10, dim=0)
+        topk5 = scores.topk(5, dim=0)
+        topk1 = scores.topk(1, dim=0)
+        topk10_iids = iids[topk10.indices]
+        topk5_iids = iids[topk5.indices]
+        topk1_iids = iids[topk1.indices]
+
+        ir_r10 = (tiids.unsqueeze(0) == topk10_iids).float().max(dim=0)[0].mean()
+        ir_r5 = (tiids.unsqueeze(0) == topk5_iids).float().max(dim=0)[0].mean()
+        ir_r1 = (tiids.unsqueeze(0) == topk1_iids).float().max(dim=0)[0].mean()
+
+        eval_result = {
+            "tr_r10": tr_r10.item() * 100.0, 
+            "tr_r5": tr_r5.item() * 100.0, 
+            "tr_r1": tr_r1.item() * 100.0, 
+            "ir_r10": ir_r10.item() * 100.0, 
+            "ir_r5": ir_r5.item() * 100.0, 
+            "ir_r1": ir_r1.item() * 100.0, 
+            "average_score": 100.0 * (tr_r1 + tr_r5 + tr_r10 + ir_r1 + ir_r5 + ir_r10).item() / 6.0, 
+        }
+
+        print('* Eval result = %s' % json.dumps(eval_result))
+        return eval_result, "average_score"
+
+
+class VQAHandler(TaskHandler):
+    def __init__(self) -> None:
+        super().__init__()
+        self.predictions = []
+        self.criterion = nn.BCEWithLogitsLoss(reduction='mean')
+        self.label2ans = None
+
+    def train_batch(self, model, image, language_tokens, padding_mask, labels):
+        logits = model(
+            image=image, question=language_tokens, 
+            padding_mask=padding_mask)
+        return {
+            "loss": self.criterion(input=logits.float(), target=labels.float()) * labels.shape[1], 
+        }
+
+    def before_eval(self, metric_logger, data_loader, **kwargs):
+        self.predictions.clear()
+        self.metric_logger = metric_logger
+        self.label2ans = data_loader.dataset.label2ans
+
+    def eval_batch(self, model, image, language_tokens, padding_mask, labels=None, qid=None):
+        logits = model(
+            image=image, question=language_tokens, 
+            padding_mask=padding_mask)
+        batch_size = language_tokens.shape[0]
+        if labels is not None:
+            scores = utils.VQAScore()(logits, labels) * 100.0
+            self.metric_logger.meters['score'].update(scores.item(), n=batch_size)
+        else:
+            _, preds = logits.max(-1)
+            for image_id, pred in zip(qid, preds):
+                self.predictions.append({
+                    "question_id": image_id.item(), 
+                    "answer": self.label2ans[pred.item()], 
+                })
+
+    def after_eval(self, **kwargs):
+        if len(self.predictions) == 0:
+            print('* Score {score.global_avg:.3f}'.format(score=self.metric_logger.score))
+            return {k: meter.global_avg for k, meter in self.metric_logger.meters.items()}, "score"
+        else:
+            return self.predictions, "prediction"
+
+
+class CaptioningHandler(TaskHandler):
+    def __init__(self, args) -> None:
+        super().__init__()
+        self.predictions = []
+        self.criterion = utils.BertCaptioningLoss(args.label_smoothing, args.drop_worst_ratio, args.drop_worst_after)
+        self.tokenizer = get_sentencepiece_model_for_beit3(args)
+        self.num_beams = args.num_beams
+        self.max_len = args.num_max_bpe_tokens
+        self.length_penalty = args.length_penalty
+        self.vocab_size = args.vocab_size
+
+    def train_batch(self, model, image, language_tokens, masked_tokens, language_masked_pos, padding_mask, image_id, global_step):
+        logits, _ = model(
+            image=image, text_ids=masked_tokens, padding_mask=padding_mask, language_masked_pos=language_masked_pos, image_id=image_id)
+        masked_labels = language_tokens[language_masked_pos.bool()]
+        score = torch.max(logits, -1)[1].data == masked_labels
+        acc = torch.sum(score.float()) / torch.sum(language_masked_pos)
+        return {
+            "loss": self.criterion(logits, masked_labels, global_step),
+            "acc": acc
+        }
+
+    def before_eval(self, metric_logger, data_loader, **kwargs):
+        self.predictions.clear()
+        self.metric_logger = metric_logger
+
+    def eval_batch(self, model, image, image_id=None):
+        cur_len = 2
+        num_keep_best = 1
+        TOPN_PER_BEAM = 3
+
+        batch_size = image.size(0)
+        mask_id = self.tokenizer.mask_token_id
+        cls_id = self.tokenizer.cls_token_id
+        pad_id = self.tokenizer.pad_token_id
+        sep_id = self.tokenizer.sep_token_id
+        eos_token_ids = [sep_id]
+
+        cls_ids = torch.full(
+            (batch_size, 1), cls_id, dtype=torch.long, device=image.device
+        )
+        mask_ids = torch.full(
+            (batch_size, 1), mask_id, dtype=torch.long, device=image.device
+        )
+        cur_input_ids = torch.cat([cls_ids, mask_ids], dim=1)
+        tmp_ids = torch.full(
+            (batch_size, self.max_len-1), mask_id, dtype=torch.long, device=image.device
+        )
+        decoding_results = torch.cat([cls_ids, tmp_ids], dim=1)
+        
+        # Expand input to num beams
+        cur_input_ids = cur_input_ids.unsqueeze(1).expand(batch_size, self.num_beams, cur_len)
+        cur_input_ids = cur_input_ids.contiguous().view(batch_size * self.num_beams, cur_len)  # (batch_size * num_beams, cur_len)
+        decoding_results = decoding_results.unsqueeze(1).expand(batch_size, self.num_beams, self.max_len)
+        decoding_results = decoding_results.contiguous().view(batch_size * self.num_beams, self.max_len)  # (batch_size * num_beams, cur_len)
+        image = image.unsqueeze(1).expand(batch_size, self.num_beams, image.size(-3), image.size(-2), image.size(-1))
+        image = image.contiguous().view(batch_size * self.num_beams, image.size(-3), image.size(-2), image.size(-1))
+
+        generated_hyps = [
+            utils.BeamHypotheses(
+                num_keep_best, self.max_len, length_penalty=self.length_penalty, early_stopping=False
+            ) for _ in range(batch_size)
+        ]
+        # scores for each sentence in the beam
+        beam_scores = torch.zeros((batch_size, self.num_beams), dtype=torch.float, device=cur_input_ids.device)
+        beam_scores[:, 1:] = -1e9
+        beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)
+
+        # done sentences
+        done = [False for _ in range(batch_size)]
+        incremental_state = {}
+
+        while cur_len <= self.max_len:
+            next_token_idx = 1
+            padding_masks = torch.full(
+                cur_input_ids.shape, 0, dtype=torch.long, device=image.device
+            )
+            input_image = image
+            if cur_len != 2:
+                input_image = None
+
+            outputs, incremental_state_next = model(
+                image=input_image, text_ids=cur_input_ids, language_masked_pos=None,
+                padding_mask=padding_masks, text_len=cur_len, incremental_state=incremental_state)
+            incremental_state = incremental_state_next
+
+            # assert outputs.shape[1] == token_len
+            scores = outputs[:, next_token_idx, :] # (batch_size * num_beams, vocab_size)
+            scores = F.log_softmax(scores, dim=-1)  # (batch_size * num_beams, vocab_size)
+            assert scores.size() == (batch_size * self.num_beams, self.vocab_size)
+            # Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
+            _scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
+            # re-organize to group the beam together (we are keeping top hypothesis accross beams)
+            _scores = _scores.view(batch_size, self.num_beams * self.vocab_size)  # (batch_size, num_beams * vocab_size)
+            next_scores, next_words = torch.topk(_scores, TOPN_PER_BEAM * self.num_beams, dim=1, largest=True, sorted=True)
+            assert next_scores.size() == next_words.size() == (batch_size, TOPN_PER_BEAM * self.num_beams)
+
+            # next batch beam content
+            # list of (batch_size * num_beams) tuple(next hypothesis score, next word, current position in the batch)
+            next_batch_beam = []
+            # for each sentence
+            for batch_ex in range(batch_size):
+                # if we are done with this sentence
+                done[batch_ex] = done[batch_ex] or generated_hyps[batch_ex].is_done(next_scores[batch_ex].max().item())
+                if done[batch_ex]:
+                    next_batch_beam.extend([(0, pad_id, 0)] * self.num_beams)  # pad the batch
+                    continue
+
+                # next sentence beam content
+                next_sent_beam = []
+                for idx, score in zip(next_words[batch_ex], next_scores[batch_ex]):
+                    # get beam and word IDs
+                    beam_id = idx // self.vocab_size
+                    word_id = idx % self.vocab_size
+                    # end of sentence, or next word
+                    # if word_id.item() in eos_token_ids or cur_len + 1 == max_len:
+                    if (word_id.item() in eos_token_ids and cur_len + 1 <= self.max_len) or (cur_len + 1 == self.max_len):
+                        generated_hyps[batch_ex].add(
+                            decoding_results[batch_ex * self.num_beams + beam_id, :cur_len].clone(), score.item()
+                        )
+                    else:
+                        next_sent_beam.append((score, word_id, batch_ex * self.num_beams + beam_id))
+                    # the beam for next step is full
+                    if len(next_sent_beam) == self.num_beams:
+                        break
+
+                # update next beam content
+                if cur_len + 1 == self.max_len:
+                    assert len(next_sent_beam) == 0
+                else:
+                    assert len(next_sent_beam) == self.num_beams
+
+                if len(next_sent_beam) == 0:
+                    next_sent_beam = [(0, pad_id, 0)] * self.num_beams  # pad the batch
+                next_batch_beam.extend(next_sent_beam)
+                assert len(next_batch_beam) == self.num_beams * (batch_ex + 1)
+            
+            # sanity check / prepare next batch
+            assert len(next_batch_beam) == batch_size * self.num_beams
+            beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
+            beam_words = cur_input_ids.new([x[1] for x in next_batch_beam])
+            beam_idx = cur_input_ids.new([x[2] for x in next_batch_beam])
+
+            # re-order batch
+            cur_input_ids = cur_input_ids[beam_idx, :]
+            decoding_results = decoding_results[beam_idx, :]
+            for module in incremental_state:
+                for key in incremental_state[module]:
+                    result = incremental_state[module][key].index_select(0, beam_idx)
+                    incremental_state[module][key] = result[:,:,:-1,:]
+            
+            next_ids = torch.full(
+                (batch_size * self.num_beams, 1), mask_id, dtype=torch.long, device=image.device
+            )
+            cur_input_ids = torch.cat([beam_words.unsqueeze(1), next_ids], dim=1)
+            decoding_results[:, cur_len-1] = beam_words
+            # update current length
+            cur_len = cur_len + 1
+            # stop when we are done with each sentence
+            if all(done):
+                break
+        
+        # select the best hypotheses
+        tgt_len = torch.ones(batch_size, num_keep_best, dtype=torch.long)
+        logprobs = torch.zeros(batch_size, num_keep_best,
+                    dtype=torch.float).fill_(-1e5).to(cur_input_ids.device)
+        all_best = []
+
+        for i, hypotheses in enumerate(generated_hyps):
+                best = []
+                hyp_scores = torch.tensor([x[0] for x in hypotheses.hyp])
+                _, best_indices = torch.topk(hyp_scores,
+                        min(num_keep_best, len(hyp_scores)), largest=True)
+                for best_idx, hyp_idx in enumerate(best_indices):
+                    conf, best_hyp = hypotheses.hyp[hyp_idx]
+                    best.append(best_hyp)
+                    logprobs[i, best_idx] = conf
+                    tgt_len[i, best_idx] = len(best_hyp) + 1  # +1 for the <EOS> symbol
+                all_best.append(best)
+        
+        # generate target batch, pad to the same length
+        decoded = cur_input_ids.new(batch_size, num_keep_best, self.max_len).fill_(pad_id)
+        for batch_idx, best in enumerate(all_best):
+            for best_idx, hypo in enumerate(best):
+                decoded[batch_idx, best_idx, : tgt_len[batch_idx, best_idx] - 1] = hypo
+                decoded[batch_idx, best_idx, tgt_len[batch_idx, best_idx] - 1] = eos_token_ids[0]
+        
+        captions = self.tokenizer.batch_decode(decoded.squeeze(1), skip_special_tokens=True)
+        for qid, pred in zip(image_id, captions):
+            self.predictions.append({
+                "image_id": qid.item(), 
+                "caption": pred, 
+            })
+
+    def after_eval(self, **kwargs):
+        return self.predictions, "prediction"
+
+
+def get_handler(args):
+    if args.task == "nlvr2":
+        return NLVR2Handler()
+    elif args.task == "vqav2":
+        return VQAHandler()
+    elif args.task in ("flickr30k", "coco_retrieval"):
+        return RetrievalHandler()
+    elif args.task in ("coco_captioning", "nocaps"):
+        return CaptioningHandler(args)
+    elif args.task in ("imagenet"):
+        return ImageNetHandler(args)
+    else:
+        raise NotImplementedError("Sorry, %s is not support." % args.task)
+
+
+def train_one_epoch(
+        model: torch.nn.Module, data_loader: Iterable, 
+        optimizer: torch.optim.Optimizer, device: torch.device, 
+        handler: TaskHandler, epoch: int, start_steps: int, 
+        lr_schedule_values: list, loss_scaler, max_norm: float = 0, 
+        update_freq: int = 1, model_ema: Optional[ModelEma] = None, 
+        log_writer: Optional[utils.TensorboardLogger] = None, 
+        task = None, mixup_fn=None,
+):
+    model.train(True)
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
+    metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    print_freq = 10
+
+    if loss_scaler is None:
+        model.zero_grad()
+        model.micro_steps = 0
+    else:
+        optimizer.zero_grad()
+
+    for data_iter_step, data in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
+        step = data_iter_step // update_freq
+        global_step = start_steps + step  # global training iteration
+        # Update LR & WD for the first acc
+        if lr_schedule_values is not None and data_iter_step % update_freq == 0:
+            for i, param_group in enumerate(optimizer.param_groups):
+                if lr_schedule_values is not None:
+                    param_group["lr"] = lr_schedule_values[global_step] * param_group["lr_scale"]
+        # put input data into cuda
+        for tensor_key in data.keys():
+            data[tensor_key] = data[tensor_key].to(device, non_blocking=True)
+            # print("input %s = %s" % (tensor_key, data[tensor_key]))
+            if loss_scaler is None and tensor_key.startswith("image"):
+                data[tensor_key] = data[tensor_key].half()
+
+        # mixup for imagenet finetuning
+        if mixup_fn is not None:
+            data["image"], data["label"] = mixup_fn(data["image"], data["label"])
+        
+        if task in ["coco_captioning", "nocaps"]:
+            data["global_step"] = global_step
+
+        if loss_scaler is None:
+            results = handler.train_batch(model, **data)
+        else:
+            with torch.cuda.amp.autocast():
+                results = handler.train_batch(model, **data)
+
+        loss = results.pop("loss")
+        loss_value = loss.item()
+
+        if not math.isfinite(loss_value):
+            print("Loss is {}, stopping training".format(loss_value))
+            sys.exit(1)
+
+        if loss_scaler is None:
+            loss /= update_freq
+            model.backward(loss)
+            model.step()
+
+            if (data_iter_step + 1) % update_freq == 0:
+                # model.zero_grad()
+                # Deepspeed will call step() & model.zero_grad() automatic
+                if model_ema is not None:
+                    model_ema.update(model)
+            grad_norm = None
+            loss_scale_value = utils.get_loss_scale_for_deepspeed(model)
+        else:
+            # this attribute is added by timm on one optimizer (adahessian)
+            is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
+            loss /= update_freq
+            grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
+                                    parameters=model.parameters(), create_graph=is_second_order,
+                                    update_grad=(data_iter_step + 1) % update_freq == 0)
+            if (data_iter_step + 1) % update_freq == 0:
+                optimizer.zero_grad()
+                if model_ema is not None:
+                    model_ema.update(model)
+            loss_scale_value = loss_scaler.state_dict()["scale"]
+
+        torch.cuda.synchronize()
+
+        metric_logger.update(loss=loss_value)
+        metric_logger.update(loss_scale=loss_scale_value)
+        min_lr = 10.
+        max_lr = 0.
+        for group in optimizer.param_groups:
+            min_lr = min(min_lr, group["lr"])
+            max_lr = max(max_lr, group["lr"])
+
+        metric_logger.update(lr=max_lr)
+        metric_logger.update(min_lr=min_lr)
+        weight_decay_value = None
+        for group in optimizer.param_groups:
+            if group["weight_decay"] > 0:
+                weight_decay_value = group["weight_decay"]
+        metric_logger.update(weight_decay=weight_decay_value)
+        metric_logger.update(grad_norm=grad_norm)
+
+        if log_writer is not None:
+            kwargs = {
+                "loss": loss_value, 
+            }
+            for key in results:
+                kwargs[key] = results[key]
+            log_writer.update(head="train", **kwargs)
+
+            kwargs = {
+                "loss_scale": loss_scale_value, 
+                "lr": max_lr, 
+                "min_lr": min_lr, 
+                "weight_decay": weight_decay_value, 
+                "grad_norm": grad_norm, 
+            }
+            log_writer.update(head="opt", **kwargs)
+            log_writer.set_step()
+
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+    print("Averaged stats:", metric_logger)
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+
+
+@torch.no_grad()
+def evaluate(data_loader, model, device, handler):
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+
+    # switch to evaluation mode
+    model.eval()
+    handler.before_eval(metric_logger=metric_logger, data_loader=data_loader)
+
+    for data in metric_logger.log_every(data_loader, 10, header):
+        for tensor_key in data.keys():
+            data[tensor_key] = data[tensor_key].to(device, non_blocking=True)
+
+        with torch.cuda.amp.autocast():
+            handler.eval_batch(model=model, **data)
+
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+
+    return handler.after_eval()

beit3/get_started/get_started_for_captioning.md

+# Fine-tuning BEiT-3 on Image Captioning
+
+## COCO Captioning Setup
+
+1. [Setup environment](../README.md#setup).
+2. Download [2014 train images](http://images.cocodataset.org/zips/train2014.zip), [2014 val images](http://images.cocodataset.org/zips/val2014.zip) and [karpathy split](https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip), then organize the dataset as following structure:
+
+```
+/path/to/your_data/
+  train2014/            
+    COCO_train2014_000000000009.jpg                
+    ...
+  val2014/              
+    COCO_val2014_000000000042.jpg
+    ...       
+  dataset_coco.json
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import CaptioningDataset
+from transformers import XLMRobertaTokenizer
+
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+
+CaptioningDataset.make_coco_captioning_dataset_index(
+    data_path="/path/to/your_data",
+    tokenizer=tokenizer,
+)
+```
+
+
+## NoCaps Setup
+
+1. [Setup environment](README.md#setup).
+2. Download [NoCaps val set](https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json), [NoCaps test set](https://s3.amazonaws.com/nocaps/nocaps_test_image_info.json) and download imags using the urls in val and test json files, then organize the dataset as following structure:
+
+```
+/path/to/your_data/
+  val/            
+    09c863d76bcf6b00.jpg                
+    ...
+  test/              
+    19dc6913830a0a21.jpg
+    ...       
+  nocaps_val_4500_captions.json
+  nocaps_test_image_info.json
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import CaptioningDataset
+from transformers import XLMRobertaTokenizer
+
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+
+CaptioningDataset.make_nocaps_captioning_dataset_index(
+    data_path="/path/to/your_data",
+)
+```
+We use COCO captioning training set as the training data of NoCaps.
+
+
+## Example: Fine-tuning BEiT-3 on Captioning
+
+The BEiT-3 **base** model can be fine-tuned on captioning tasks using 8 V100-32GB:
+
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_480 \
+        --input_size 480 \
+        --task coco_captioning \
+        --batch_size 32 \
+        --layer_decay 1.0 \
+        --lr 4e-5 \
+        --randaug \
+        --epochs 10 \
+        --warmup_epochs 1 \
+        --drop_path 0.1 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --num_max_bpe_tokens 32 \
+        --captioning_mask_prob 0.7 \
+        --drop_worst_after 12000 \
+        --dist_eval \
+        --checkpoint_activations \
+        --enable_deepspeed
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*32 = 256`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models).
+- `--task`: **coco_captioning** for COCO captioning and **nocaps** for NoCaps dataset.
+- `lr`: 4e-5 for COCO captioning and 1e-5 for NoCaps.
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory.
+
+
+The BEiT-3 **large** model can be fine-tuned on captioning tasks using 8 V100-32GB:
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_480 \
+        --input_size 480 \
+        --task coco_captioning \
+        --batch_size 32 \
+        --layer_decay 1.0 \
+        --lr 8e-6 \
+        --randaug \
+        --epochs 10 \
+        --warmup_epochs 1 \
+        --drop_path 0.1 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --num_max_bpe_tokens 32 \
+        --captioning_mask_prob 0.7 \
+        --drop_worst_after 12000 \
+        --dist_eval \
+        --checkpoint_activations \
+        --enable_deepspeed
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*32 = 256`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models).
+- `--task`: **coco_captioning** for COCO captioning and **nocaps** for NoCaps dataset.
+- `lr`: 8e-6 for COCO captioning and NoCaps.
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory.
+
+
+## Example: Evaluate BEiT-3 Fine-tuned model on Captioning
+
+- Get the prediction file of the fine-tuned BEiT3-base model on captioning with 8 V100-32GB:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_480 \
+        --input_size 480 \
+        --task coco_captioning \
+        --batch_size 16 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_480_coco_captioning.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_prediction \
+        --eval \
+        --dist_eval
+```
+- `--task`: **coco_captioning** for COCO captioning and **nocaps** for NoCaps dataset.
+- `--finetune`: **beit3_base_patch16_480_coco_captioning.pth** for COCO captioning and **beit3_base_patch16_480_nocaps.pth** for NoCaps dataset.
+
+- Get the prediction file of the fine-tuned BEiT3-large model on captioning with 8 V100-32GB:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_480 \
+        --input_size 480 \
+        --task coco_captioning \
+        --batch_size 16 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_480_coco_captioning.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_prediction \
+        --eval \
+        --dist_eval
+```
+- `--task`: **coco_captioning** for COCO captioning and **nocaps** for NoCaps dataset.
+- `--finetune`: **beit3_large_patch16_480_coco_captioning.pth** for COCO captioning and **beit3_large_patch16_480_nocaps.pth** for NoCaps dataset.
+
+Please then submit the prediction file in the `output_dir` to the [evaluation server](https://eval.ai/web/challenges/challenge-page/355/overview) to obtain the NoCaps val and test results.

beit3/get_started/get_started_for_image_classification.md

+# Fine-tuning BEiT-3 on ImageNet-1k (Image Classification)
+
+
+## Setup
+
+1. [Setup environment](../README.md#setup).
+2. Download and extract ImageNet-1k from http://image-net.org/.
+
+The directory structure is the standard layout of torchvision's [`datasets.ImageFolder`](https://pytorch.org/docs/stable/torchvision/datasets.html#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
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import ImageNetDataset
+
+ImageNetDataset.make_dataset_index(
+    train_data_path = "/path/to/your_data/train",
+    val_data_path = "/path/to/your_data/val",
+    index_path = "/path/to/your_data"
+)
+```
+
+
+## Example: Fine-tuning BEiT-3 on ImageNet-1k (Image Classification)
+
+The BEiT-3 **base** model can be finetuned on ImageNet-1k using 8 V100-32GB:
+
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_224 \
+        --task imagenet \
+        --batch_size 128 \
+        --layer_decay 0.65 \
+        --lr 7e-4 \
+        --update_freq 1 \
+        --epochs 50 \
+        --warmup_epochs 5 \
+        --drop_path 0.15 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --dist_eval \
+        --mixup 0.8 \
+        --cutmix 1.0 \
+        --enable_deepspeed
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*128*1 = 1024`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models)
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+
+
+The BEiT-3 **large** model can be finetuned on ImageNet-1k using a DGX box (8 V100-32GB):
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_224 \
+        --task imagenet \
+        --batch_size 128 \
+        --layer_decay 0.8 \
+        --lr 2e-4 \
+        --update_freq 1 \
+        --epochs 50 \
+        --warmup_epochs 5 \
+        --drop_path 0.25 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --dist_eval \
+        --mixup 0.8 \
+        --cutmix 1.0 \
+        --enable_deepspeed \
+        --checkpoint_activations
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*128 = 1024`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models)
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory
+
+## Example: Evaluate BEiT-3 Finetuned model on ImageNet-1k (Image Classification)
+
+- Evaluate our fine-tuned BEiT3-base model on ImageNet val with a single GPU:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=1 run_beit3_finetuning.py \
+        --model beit3_base_patch16_224 \
+        --task imagenet \
+        --batch_size 128 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_224_in1k.pth \
+        --data_path /path/to/your_data \
+        --eval \
+        --dist_eval
+```
+
+Expected results:
+```
+* Acc@1 85.400 Acc@5 97.630
+```
+
+- Evaluate our fine-tuned BEiT3-large model on ImageNet val with a single GPU:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=1 run_beit3_finetuning.py \
+        --model beit3_large_patch16_224 \
+        --task imagenet \
+        --batch_size 128 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_224_in1k.pth \
+        --data_path /path/to/your_data \
+        --eval \
+        --dist_eval
+```
+
+Expected results:
+```
+* Acc@1 87.580 Acc@5 98.326
+```

beit3/get_started/get_started_for_nlvr2.md

+# Fine-tuning BEiT-3 on NLVR2 (Visual Reasoning)
+
+
+## Setup
+
+1. [Setup environment](../README.md#setup).
+2. Clone the [repository](https://github.com/lil-lab/nlvr) and sign the [request form](https://goo.gl/forms/yS29stWnFWzrDBFH3) to download the images, then organize the dataset as following structure:
+
+```
+/path/to/your_data/
+  images/train/                            
+    0/train-11670-0-img0.png
+    ...
+  dev/              
+    dev-269-0-img0.png
+    ...  
+  test1/              
+    test1-261-0-img0.png
+    ...         
+  nlvr/ (nlvr repo)
+    nlvr/
+    nlvr2/
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import NLVR2Dataset
+from transformers import XLMRobertaTokenizer
+
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+
+NLVR2Dataset.make_dataset_index(
+    data_path="/path/to/your_data", 
+    tokenizer=tokenizer, 
+    nlvr_repo_path="/path/to/your_data/nlvr"
+)
+```
+
+
+## Example: Fine-tuning BEiT-3 on NLVR2 (Visual Reasoning)
+
+The BEiT-3 **base** model can be finetuned on NLVR2 using 8 V100-32GB:
+
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_224 \
+        --task nlvr2 \
+        --batch_size 32 \
+        --layer_decay 0.65 \
+        --lr 7e-4 \
+        --epochs 20 \
+        --warmup_epochs 5 \
+        --drop_path 0.2 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.2 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --enable_deepspeed
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*32 = 256`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models).
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+- `--lr`: 7e-4 for `BEiT3-base`, 5e-4 for `BEiT3-base-indomain`.
+
+
+The BEiT-3 **large** model can be finetuned on NLVR2 using 8 V100-32GB:
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_224 \
+        --task nlvr2 \
+        --batch_size 32 \
+        --layer_decay 0.85 \
+        --lr 3e-4 \
+        --epochs 20 \
+        --warmup_epochs 5 \
+        --drop_path 0.2 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.2 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --enable_deepspeed \
+        --checkpoint_activations
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*32 = 256`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models).
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+- `--lr`: 3e-4 for `BEiT3-large`, 1e-4 for `BEiT3-large-indomain`.
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory.
+
+
+## Example: Evaluate BEiT-3 Finetuned model on NLVR2 (Visual Reasoning)
+
+- Get the result of our fine-tuned BEiT3-base model on NLVR2 test with 8 V100-32GB:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_224 \
+        --task nlvr2 \
+        --batch_size 32 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_224_nlvr2.pth \
+        --data_path /path/to/your_data \
+        --eval \
+        --dist_eval
+```
+
+Expected results:
+```
+* Acc 84.386
+```
+
+- Get the result of our fine-tuned BEiT3-large model on NLVR2 test with 8 V100-32GB:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_224 \
+        --task nlvr2 \
+        --batch_size 32 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_224_nlvr2.pth \
+        --data_path /path/to/your_data \
+        --eval \
+        --dist_eval
+```
+
+Expected results:
+```
+* Acc 89.437
+```

beit3/get_started/get_started_for_retrieval.md

+# Fine-tuning BEiT-3 on Image-text Retrieval
+
+## COCO Retrieval Setup
+
+1. [Setup environment](../README.md#setup).
+2. Download [2014 train images](http://images.cocodataset.org/zips/train2014.zip), [2014 val images](http://images.cocodataset.org/zips/val2014.zip) and [karpathy split](https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip), then organize the dataset as following structure:
+
+```
+/path/to/your_data/
+  train2014/            
+    COCO_train2014_000000000009.jpg                
+    ...
+  val2014/              
+    COCO_val2014_000000000042.jpg
+    ...       
+  dataset_coco.json
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import RetrievalDataset
+from transformers import XLMRobertaTokenizer
+
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+
+RetrievalDataset.make_coco_dataset_index(
+    data_path="/path/to/your_data",
+    tokenizer=tokenizer,
+)
+```
+
+
+## Flickr30k Retrieval Setup
+
+1. [Setup environment](README.md#setup).
+2. Sign [flickr images request form](https://forms.illinois.edu/sec/229675) and download [karpathy split](https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip), then organize the dataset as following structure:
+
+```
+/path/to/your_data/
+  flickr30k-images/            
+    2923475135.jpg                
+    ...      
+  dataset_flickr30k.json
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import RetrievalDataset
+from transformers import XLMRobertaTokenizer
+
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+
+RetrievalDataset.make_flickr30k_dataset_index(
+    data_path="/path/to/your_data",
+    tokenizer=tokenizer,
+    karpathy_path="/path/to/your_data",
+)
+```
+
+
+## Example: Fine-tuning BEiT-3 on Retrieval
+
+The BEiT-3 **base** model can be finetuned on retrieval tasks using 16 V100-32GB:
+
+```bash       
+python -m torch.distributed.launch --nproc_per_node=16 run_beit3_finetuning.py \
+        --model beit3_base_patch16_384 \
+        --input_size 384 \
+        --task coco_retrieval \
+        --batch_size 192 \
+        --layer_decay 0.65 \
+        --lr 2e-4 \
+        --epochs 15 \
+        --warmup_epochs 3 \
+        --drop_path 0.2 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_itc_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --enable_deepspeed \
+        --checkpoint_activations
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `192*16 = 3072`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models)
+- `--task`: **coco_retrieval** for COCO retrieval, **flickr30k** for Flickr30k retrieval
+- `--lr`: 2e-4 for COCO retrieval, 1e-4 for Flickr30k retrieval
+- `--epochs`: 15 for COCO retrieval, 20 for Flickr30k retrieval
+- `--warmup_epochs`: 3 for COCO retrieval, 5 for Flickr30k retrieval
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory
+
+
+The BEiT-3 **large** model can be finetuned on retrieval tasks using 2x16 V100-32GB:
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=16 --nnodes=2 --node_rank=$NODE_RANK \
+       --master_addr=$MASTER_ADDR --master_port=$MASTER_PORT run_beit3_finetuning.py \
+        --model beit3_large_patch16_384 \
+        --input_size 384 \
+        --task coco_retrieval \
+        --batch_size 96 \
+        --layer_decay 0.85 \
+        --lr 5e-5 \
+        --epochs 15 \
+        --warmup_epochs 3 \
+        --drop_path 0.2 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_itc_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --enable_deepspeed \
+        --checkpoint_activations
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `96*32 = 3072`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models)
+- `--task`: **coco_retrieval** for COCO retrieval, **flickr30k** for Flickr30k retrieval
+- `--epochs`: 15 for COCO retrieval, 20 for Flickr30k retrieval
+- `--warmup_epochs`: 3 for COCO retrieval, 5 for Flickr30k retrieval
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory
+
+
+## Example: Evaluate BEiT-3 Fine-tuned model on COCO Retrieval and Flickr30k Retrieval
+
+- Get the results of our fine-tuned BEiT3-base model on retrieval tasks using a single GPU:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=1 run_beit3_finetuning.py \
+        --model beit3_base_patch16_384 \
+        --input_size 384 \
+        --task coco_retrieval \
+        --batch_size 16 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_384_coco_retrieval.pth \
+        --data_path /path/to/your_data \
+        --eval \
+        --dist_eval
+```
+- `--task`: **coco_retrieval** for COCO retrieval, **flickr30k** for Flickr30k retrieval
+- `--finetune`: **beit3_base_patch16_384_coco_retrieval.pth** for COCO retrieval, **beit3_base_patch16_384_f30k_retrieval.pth** for Flickr30k retrieval
+
+- Get the results of our fine-tuned BEiT3-large model on retrieval tasks using a single GPU:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=1 run_beit3_finetuning.py \
+        --model beit3_large_patch16_384 \
+        --input_size 384 \
+        --task coco_retrieval \
+        --batch_size 16 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_384_coco_retrieval.pth \
+        --data_path /path/to/your_data \
+        --eval \
+        --dist_eval
+```
+- `--task`: **coco_retrieval** for COCO retrieval, **flickr30k** for Flickr30k retrieval
+- `--finetune`: **beit3_large_patch16_384_coco_retrieval.pth** for COCO retrieval, **beit3_large_patch16_384_f30k_retrieval.pth** for Flickr30k retrieval

beit3/get_started/get_started_for_vqav2.md

+# Fine-tuning BEiT-3 on VQAv2 (Visual Question Answering)
+
+
+## Setup
+
+1. [Setup environment](../README.md#setup).
+2. Download COCO [2014 train images](http://images.cocodataset.org/zips/train2014.zip), [2014 val images](http://images.cocodataset.org/zips/val2014.zip), [2015 test images](http://images.cocodataset.org/zips/test2015.zip), annotations ([train](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Train_mscoco.zip), [val](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip)), and questions ([train](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Train_mscoco.zip), [val](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip), [test](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Test_mscoco.zip)), then organize the dataset as following structure:
+
+```
+/path/to/your_data/
+  train2014/            
+    COCO_train2014_000000000009.jpg                
+    ...
+  val2014/              
+    COCO_val2014_000000000042.jpg
+    ...  
+  test2015/              
+    COCO_test2015_000000000001.jpg
+    ...         
+  vqa/
+    v2_OpenEnded_mscoco_train2014_questions.json
+    v2_OpenEnded_mscoco_val2014_questions.json
+    v2_OpenEnded_mscoco_test2015_questions.json
+    v2_OpenEnded_mscoco_test-dev2015_questions.json
+    v2_mscoco_train2014_annotations.json
+    v2_mscoco_val2014_annotations.json
+```
+
+We then generate the index json files using the following command. [beit3.spm](https://conversationhub.blob.core.windows.net/beit-share-public/beit3/sentencepiece/beit3.spm?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D) is the sentencepiece model used for tokenizing texts.
+```
+from datasets import VQAv2Dataset
+from transformers import XLMRobertaTokenizer
+
+tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")
+
+VQAv2Dataset.make_dataset_index(
+    data_path="/path/to/your_data",
+    tokenizer=tokenizer,
+    annotation_data_path="/path/to/your_data/vqa",
+)
+```
+
+
+## Example: Fine-tuning BEiT-3 on VQAv2 (Visual Question Answering)
+
+The BEiT-3 **base** model can be finetuned on VQAv2 using 8 V100-32GB:
+
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_480 \
+        --input_size 480 \
+        --task vqav2 \
+        --batch_size 16 \
+        --layer_decay 1.0 \
+        --lr 3e-5 \
+        --update_freq 1 \
+        --randaug \
+        --epochs 10 \
+        --warmup_epochs 1 \
+        --drop_path 0.1 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.01 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --task_head_lr_weight 20 \
+        --opt_betas 0.9 0.98 \
+        --enable_deepspeed
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*16 = 128`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models)
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+
+
+The BEiT-3 **large** model can be finetuned on VQAv2 using 8 V100-32GB:
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_480 \
+        --input_size 480 \
+        --task vqav2 \
+        --batch_size 16 \
+        --layer_decay 1.0 \
+        --lr 2e-5 \
+        --update_freq 1 \
+        --randaug \
+        --epochs 10 \
+        --warmup_epochs 1 \
+        --drop_path 0.15 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_224.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_model \
+        --log_dir /path/to/save/your_model/log \
+        --weight_decay 0.01 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --task_head_lr_weight 20 \
+        --opt_betas 0.9 0.98 \
+        --enable_deepspeed \
+        --checkpoint_activations
+```
+- `--batch_size`: batch size per GPU. Effective batch size = `number of GPUs` * `--batch_size` * `--update_freq`. So in the above example, the effective batch size is `8*16 = 128`.
+- `--finetune`: weight path of your pretrained models; please download the pretrained model weights in [README.md](../README.md#pretrained-models)
+- `--enable_deepspeed`: optional. If you use apex, please enable deepspeed.
+- `--checkpoint_activations`: using gradient checkpointing for saving GPU memory
+
+
+## Example: Evaluate BEiT-3 Finetuned model on VQAv2 (Visual Question Answering)
+
+- Get the prediction file of the fine-tuned BEiT3-base model on VQAv2 test with 8 V100-32GB:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_base_patch16_480 \
+        --input_size 480 \
+        --task vqav2 \
+        --batch_size 16 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_base_patch16_480_vqa.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_prediction \
+        --eval \
+        --dist_eval
+```
+
+- Get the prediction file of the fine-tuned BEiT3-large model on VQAv2 test with 8 V100-32GB:
+```bash       
+python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
+        --model beit3_large_patch16_480 \
+        --input_size 480 \
+        --task vqav2 \
+        --batch_size 16 \
+        --sentencepiece_model /your_beit3_model_path/beit3.spm \
+        --finetune /your_beit3_model_path/beit3_large_patch16_480_vqa.pth \
+        --data_path /path/to/your_data \
+        --output_dir /path/to/save/your_prediction \
+        --eval \
+        --dist_eval
+```
+
+Please then submit the prediction file in the `output_dir` to the [evaluation server](https://eval.ai/web/challenges/challenge-page/830/overview) to obtain the VQAv2 test-dev and test-std results.

beit3/glossary.py

+import re
+
+contractions = {
+    "aint": "ain't",
+    "arent": "aren't",
+    "cant": "can't",
+    "couldve": "could've",
+    "couldnt": "couldn't",
+    "couldn'tve": "couldn't've",
+    "couldnt've": "couldn't've",
+    "didnt": "didn't",
+    "doesnt": "doesn't",
+    "dont": "don't",
+    "hadnt": "hadn't",
+    "hadnt've": "hadn't've",
+    "hadn'tve": "hadn't've",
+    "hasnt": "hasn't",
+    "havent": "haven't",
+    "hed": "he'd",
+    "hed've": "he'd've",
+    "he'dve": "he'd've",
+    "hes": "he's",
+    "howd": "how'd",
+    "howll": "how'll",
+    "hows": "how's",
+    "Id've": "I'd've",
+    "I'dve": "I'd've",
+    "Im": "I'm",
+    "Ive": "I've",
+    "isnt": "isn't",
+    "itd": "it'd",
+    "itd've": "it'd've",
+    "it'dve": "it'd've",
+    "itll": "it'll",
+    "let's": "let's",
+    "maam": "ma'am",
+    "mightnt": "mightn't",
+    "mightnt've": "mightn't've",
+    "mightn'tve": "mightn't've",
+    "mightve": "might've",
+    "mustnt": "mustn't",
+    "mustve": "must've",
+    "neednt": "needn't",
+    "notve": "not've",
+    "oclock": "o'clock",
+    "oughtnt": "oughtn't",
+    "ow's'at": "'ow's'at",
+    "'ows'at": "'ow's'at",
+    "'ow'sat": "'ow's'at",
+    "shant": "shan't",
+    "shed've": "she'd've",
+    "she'dve": "she'd've",
+    "she's": "she's",
+    "shouldve": "should've",
+    "shouldnt": "shouldn't",
+    "shouldnt've": "shouldn't've",
+    "shouldn'tve": "shouldn't've",
+    "somebody'd": "somebodyd",
+    "somebodyd've": "somebody'd've",
+    "somebody'dve": "somebody'd've",
+    "somebodyll": "somebody'll",
+    "somebodys": "somebody's",
+    "someoned": "someone'd",
+    "someoned've": "someone'd've",
+    "someone'dve": "someone'd've",
+    "someonell": "someone'll",
+    "someones": "someone's",
+    "somethingd": "something'd",
+    "somethingd've": "something'd've",
+    "something'dve": "something'd've",
+    "somethingll": "something'll",
+    "thats": "that's",
+    "thered": "there'd",
+    "thered've": "there'd've",
+    "there'dve": "there'd've",
+    "therere": "there're",
+    "theres": "there's",
+    "theyd": "they'd",
+    "theyd've": "they'd've",
+    "they'dve": "they'd've",
+    "theyll": "they'll",
+    "theyre": "they're",
+    "theyve": "they've",
+    "twas": "'twas",
+    "wasnt": "wasn't",
+    "wed've": "we'd've",
+    "we'dve": "we'd've",
+    "weve": "we've",
+    "werent": "weren't",
+    "whatll": "what'll",
+    "whatre": "what're",
+    "whats": "what's",
+    "whatve": "what've",
+    "whens": "when's",
+    "whered": "where'd",
+    "wheres": "where's",
+    "whereve": "where've",
+    "whod": "who'd",
+    "whod've": "who'd've",
+    "who'dve": "who'd've",
+    "wholl": "who'll",
+    "whos": "who's",
+    "whove": "who've",
+    "whyll": "why'll",
+    "whyre": "why're",
+    "whys": "why's",
+    "wont": "won't",
+    "wouldve": "would've",
+    "wouldnt": "wouldn't",
+    "wouldnt've": "wouldn't've",
+    "wouldn'tve": "wouldn't've",
+    "yall": "y'all",
+    "yall'll": "y'all'll",
+    "y'allll": "y'all'll",
+    "yall'd've": "y'all'd've",
+    "y'alld've": "y'all'd've",
+    "y'all'dve": "y'all'd've",
+    "youd": "you'd",
+    "youd've": "you'd've",
+    "you'dve": "you'd've",
+    "youll": "you'll",
+    "youre": "you're",
+    "youve": "you've",
+}
+
+manual_map = {
+    "none": "0",
+    "zero": "0",
+    "one": "1",
+    "two": "2",
+    "three": "3",
+    "four": "4",
+    "five": "5",
+    "six": "6",
+    "seven": "7",
+    "eight": "8",
+    "nine": "9",
+    "ten": "10",
+}
+articles = ["a", "an", "the"]
+period_strip = re.compile("(?!<=\d)(\.)(?!\d)")
+comma_strip = re.compile("(\d)(\,)(\d)")
+punct = [
+    ";",
+    r"/",
+    "[",
+    "]",
+    '"',
+    "{",
+    "}",
+    "(",
+    ")",
+    "=",
+    "+",
+    "\\",
+    "_",
+    "-",
+    ">",
+    "<",
+    "@",
+    "`",
+    ",",
+    "?",
+    "!",
+]
+
+
+def normalize_word(token):
+    _token = token
+    for p in punct:
+        if (p + " " in token or " " + p in token) or (
+            re.search(comma_strip, token) != None
+        ):
+            _token = _token.replace(p, "")
+        else:
+            _token = _token.replace(p, " ")
+    token = period_strip.sub("", _token, re.UNICODE)
+
+    _token = []
+    temp = token.lower().split()
+    for word in temp:
+        word = manual_map.setdefault(word, word)
+        if word not in articles:
+            _token.append(word)
+    for i, word in enumerate(_token):
+        if word in contractions:
+            _token[i] = contractions[word]
+    token = " ".join(_token)
+    token = token.replace(",", "")
+    return token

beit3/modeling_finetune.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.registry import register_model
+import numpy as np
+
+import utils
+from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
+
+
+class TwoLayerMLP(nn.Module):
+    def __init__(
+            self, 
+            in_features, 
+            hidden_features, 
+            out_features, 
+            norm_layer, 
+            norm_input=True, 
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(in_features) if norm_input else nn.Identity()
+        self.dense1 = nn.Linear(in_features, hidden_features)
+        self.norm2 = norm_layer(hidden_features)
+        self.act = nn.GELU()
+        self.dense2 = nn.Linear(hidden_features, out_features)
+
+    def forward(self, x):
+        x = self.norm1(x)
+        x = self.dense1(x)
+        x = self.norm2(x)
+        x = self.act(x)
+        return self.dense2(x)
+
+
+class Pooler(nn.Module):
+    def __init__(self, input_features, output_features, norm_layer):
+        super().__init__()
+        self.norm = norm_layer(input_features)
+        self.dense = nn.Linear(input_features, output_features)
+        self.activation = nn.Tanh()
+
+    def forward(self, x):
+        cls_rep = x[:, 0, :]
+        cls_rep = self.norm(cls_rep)
+        pooled_output = self.dense(cls_rep)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BEiT3ForVisualReasoning(BEiT3Wrapper):
+    def __init__(
+            self, 
+            args, 
+            num_classes, 
+            norm_layer=nn.LayerNorm, 
+            **kwargs
+    ):
+        super(BEiT3ForVisualReasoning, self).__init__(args=args)
+        embed_dim = args.encoder_embed_dim
+        self.head = TwoLayerMLP(
+            in_features=embed_dim * 4, 
+            hidden_features=embed_dim * 2,
+            out_features=num_classes, 
+            norm_layer=norm_layer, 
+        )
+        init_scale = 0.001
+        self.head.apply(self._init_weights)
+        if isinstance(self.head.dense1, nn.Linear):
+            self.head.dense1.weight.data.mul_(init_scale)
+            self.head.dense1.bias.data.mul_(init_scale)
+
+        if isinstance(self.head.dense2, nn.Linear):
+            self.head.dense2.weight.data.mul_(init_scale)
+            self.head.dense2.bias.data.mul_(init_scale)
+
+    def forward(self, image_a, image_b, text_description, padding_mask, **kwargs):
+        bsz, _ = text_description.size()
+        
+        vision_input = torch.cat((image_a, image_b), dim=0)
+        language_input = torch.cat((text_description, text_description), dim=0)
+        padding_mask = torch.cat((padding_mask, padding_mask), dim=0)
+
+        outputs = self.beit3(
+            textual_tokens=language_input, 
+            visual_tokens=vision_input, 
+            text_padding_position=padding_mask, 
+        )
+        x = outputs["encoder_out"]
+        multiway_split_position = outputs["multiway_split_position"]
+
+        vision_cls = x[:, 0, :]
+        language_cls = x[:, multiway_split_position, :]
+        cls_rep = torch.cat((vision_cls, language_cls), dim=-1)
+        a, b = torch.split(cls_rep, split_size_or_sections=[bsz, bsz], dim=0)
+        cls_rep = torch.cat((a, b), dim=-1)
+        return self.head(cls_rep)
+    
+
+class BEiT3ForImageClassification(BEiT3Wrapper):
+    def __init__(
+            self, 
+            args, 
+            num_classes, 
+            norm_layer=nn.LayerNorm, 
+            **kwargs
+    ):
+        super(BEiT3ForImageClassification, self).__init__(args=args)
+        embed_dim = args.encoder_embed_dim
+        self.fc_norm = norm_layer(embed_dim)
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.fc_norm.apply(self._init_weights)
+        self.head.apply(self._init_weights)
+        init_scale = 0.001
+        if isinstance(self.head, nn.Linear):
+            self.head.weight.data.mul_(init_scale)
+            self.head.bias.data.mul_(init_scale)
+
+    def forward(self, image, **kwargs):
+        x = self.beit3(textual_tokens=None, visual_tokens=image)["encoder_out"]
+        t = x[:, 1:, :]
+        cls_x = self.fc_norm(t.mean(1))
+        return self.head(cls_x)
+
+
+class BEiT3ForCaptioning(BEiT3Wrapper):
+    def __init__(
+            self, 
+            args, 
+            **kwargs
+    ):
+        super(BEiT3ForCaptioning, self).__init__(args=args)
+        embed_dim = args.encoder_embed_dim
+        self.mlm_head = nn.Linear(embed_dim, args.vocab_size)
+        self.mlm_head.apply(self._init_weights)
+
+    def forward(self, image, text_ids, padding_mask, language_masked_pos, text_len=None, incremental_state=None, **kwargs):
+        text_len = text_len if text_len is not None else text_ids.size(1)
+        image_len = self.beit3.vision_embed.num_position_embeddings()
+        max_len = text_len + image_len
+        uni_mask = torch.zeros((max_len, max_len), dtype=torch.long, device=text_ids.device)
+        i_start, i_end = 0, image_len
+        t_start, t_end = image_len, max_len
+        # triangle mask for caption to caption
+        uni_mask[t_start:t_end, t_start:t_end] = torch.tril(torch.ones(text_len, text_len, dtype=torch.long, device=text_ids.device))
+        # full attention for caption to image
+        uni_mask[t_start:t_end, i_start:i_end] = 1
+        # full attention for image to image
+        uni_mask[i_start:i_end, i_start:i_end] = 1
+        uni_mask = 1-uni_mask
+
+        if incremental_state is not None:
+            for idx in range(self.get_num_layers()):
+                if idx not in incremental_state:
+                    incremental_state[idx] = {}
+        
+        # for incremental decoding
+        positions = None
+        if image is None:
+            uni_mask = uni_mask[-2:]
+            padding_mask = None
+            # start position (2 (fairseq starts at 2) + cur_position) is equal to text_len
+            positions = torch.arange(text_len, text_ids.size(1) + text_len, device=text_ids.device).long().unsqueeze(0)
+
+        outputs = self.beit3(
+            textual_tokens=text_ids, 
+            visual_tokens=image, 
+            text_padding_position=padding_mask,
+            attn_mask=uni_mask,
+            incremental_state=incremental_state,
+            positions=positions,
+        )
+        if image is not None:
+            text_feats = outputs["encoder_out"][:, image_len:]
+        else:
+            text_feats = outputs["encoder_out"]
+
+        if language_masked_pos is not None:
+            text_feats = text_feats[language_masked_pos.bool()]
+
+        return self.mlm_head(text_feats), incremental_state
+
+
+class BEiT3ForVisualQuestionAnswering(BEiT3Wrapper):
+    def __init__(
+            self, 
+            args, 
+            num_classes, 
+            norm_layer=nn.LayerNorm, 
+            **kwargs
+    ):
+        super(BEiT3ForVisualQuestionAnswering, self).__init__(args=args)
+        embed_dim = args.encoder_embed_dim
+        self.pooler = Pooler(
+            input_features=embed_dim, 
+            output_features=embed_dim, 
+            norm_layer=norm_layer, 
+        )
+        self.pooler.apply(self._init_weights)
+        self.head = nn.Sequential(
+            nn.Linear(embed_dim, embed_dim * 2), 
+            norm_layer(embed_dim * 2), 
+            nn.GELU(), 
+            nn.Linear(embed_dim * 2, num_classes), 
+        )
+        self.head.apply(self._init_weights)
+
+    def forward(self, image, question, padding_mask, **kwargs):
+        outputs = self.beit3(
+            textual_tokens=question, 
+            visual_tokens=image, 
+            text_padding_position=padding_mask, 
+        )
+        x = outputs["encoder_out"]
+        cls_rep = self.pooler(x)
+        return self.head(cls_rep)
+
+
+class BEiT3ForRetrieval(BEiT3Wrapper):
+    def __init__(
+            self, 
+            args,
+            **kwargs
+    ):
+        super(BEiT3ForRetrieval, self).__init__(args=args)
+        embed_dim = args.encoder_embed_dim
+        self.language_head = nn.Linear(embed_dim, embed_dim, bias=False)
+        self.vision_head = nn.Linear(embed_dim, embed_dim, bias=False)
+        self.language_head.apply(self._init_weights)
+        self.vision_head.apply(self._init_weights)
+        self.criterion = utils.ClipLoss(
+            rank=utils.get_rank(), 
+            world_size=utils.get_world_size(), 
+        )
+        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+    def forward(self, image=None, text_description=None, padding_mask=None, only_infer=False, **kwargs):
+        if image is not None:
+            outputs = self.beit3(
+                textual_tokens=None, 
+                visual_tokens=image, 
+                text_padding_position=None, 
+            )
+            x = outputs["encoder_out"]
+            vision_cls = self.vision_head(x[:, 0, :])
+            vision_cls = F.normalize(vision_cls, dim=-1)
+        else:
+            vision_cls = None
+
+        if text_description is not None:
+            outputs = self.beit3(
+                textual_tokens=text_description, 
+                visual_tokens=None, 
+                text_padding_position=padding_mask, 
+            )
+            x = outputs["encoder_out"]
+            language_cls = self.language_head(x[:, 0, :])
+            language_cls = F.normalize(language_cls, dim=-1)
+        else:
+            language_cls = None
+        
+        if only_infer:
+            return vision_cls, language_cls
+        else:
+            loss, logits_per_image, logits_per_text = self.criterion(
+                vision_cls, language_cls, self.logit_scale.exp())
+            return loss, vision_cls, language_cls
+
+
+@register_model
+def beit3_base_patch16_224_imageclassification(pretrained=False, **kwargs):
+    args = _get_base_config(**kwargs)
+    args.normalize_output = False
+    model = BEiT3ForImageClassification(args, num_classes=1000, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_224_imageclassification(pretrained=False, **kwargs):
+    args = _get_large_config(**kwargs)
+    args.normalize_output = False
+    model = BEiT3ForImageClassification(args, num_classes=1000, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_224_nlvr2(pretrained=False, **kwargs):
+    args = _get_base_config(**kwargs)
+    model = BEiT3ForVisualReasoning(args, num_classes=2, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_224_nlvr2(pretrained=False, **kwargs):
+    args = _get_large_config(**kwargs)
+    model = BEiT3ForVisualReasoning(args, num_classes=2, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_384_vqav2(pretrained=False, **kwargs):
+    args = _get_base_config(img_size=384, **kwargs)
+    args.normalize_output = False
+    model = BEiT3ForVisualQuestionAnswering(args, num_classes=3129, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_480_vqav2(pretrained=False, **kwargs):
+    args = _get_base_config(img_size=480, **kwargs)
+    args.normalize_output = False
+    model = BEiT3ForVisualQuestionAnswering(args, num_classes=3129, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_384_vqav2(pretrained=False, **kwargs):
+    args = _get_large_config(img_size=384, **kwargs)
+    args.normalize_output = False
+    model = BEiT3ForVisualQuestionAnswering(args, num_classes=3129, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_480_vqav2(pretrained=False, **kwargs):
+    args = _get_large_config(img_size=480, **kwargs)
+    args.normalize_output = False
+    model = BEiT3ForVisualQuestionAnswering(args, num_classes=3129, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_768_vqav2(pretrained=False, **kwargs):
+    args = _get_large_config(img_size=768, **kwargs)
+    args.normalize_output = False
+    model = BEiT3ForVisualQuestionAnswering(args, num_classes=3129, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_224_captioning(pretrained=False, **kwargs):
+    args = _get_base_config(**kwargs)
+    model = BEiT3ForCaptioning(args, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_480_captioning(pretrained=False, **kwargs):
+    args = _get_base_config(img_size=480, **kwargs)
+    model = BEiT3ForCaptioning(args, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_480_captioning(pretrained=False, **kwargs):
+    args = _get_large_config(img_size=480, **kwargs)
+    model = BEiT3ForCaptioning(args, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_224_retrieval(pretrained=False, **kwargs):
+    args = _get_base_config(**kwargs)
+    model = BEiT3ForRetrieval(args, **kwargs)
+    return model
+
+
+@register_model
+def beit3_base_patch16_384_retrieval(pretrained=False, **kwargs):
+    args = _get_base_config(img_size=384, **kwargs)
+    model = BEiT3ForRetrieval(args, **kwargs)
+    return model
+
+
+@register_model
+def beit3_large_patch16_384_retrieval(pretrained=False, **kwargs):
+    args = _get_large_config(img_size=384, **kwargs)
+    model = BEiT3ForRetrieval(args, **kwargs)
+    return model

beit3/modeling_utils.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+import math
+import torch
+import torch.nn as nn
+from timm.models.layers import trunc_normal_ as __call_trunc_normal_
+
+from torchscale.model.BEiT3 import BEiT3
+from torchscale.architecture.config import EncoderConfig
+
+
+def trunc_normal_(tensor, mean=0., std=1.):
+    __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std)
+
+
+def _get_base_config(
+        img_size=224, patch_size=16, drop_path_rate=0, 
+        checkpoint_activations=None, mlp_ratio=4, vocab_size=64010, **kwargs
+):
+    return EncoderConfig(
+        img_size=img_size, patch_size=patch_size, vocab_size=vocab_size, multiway=True, 
+        layernorm_embedding=False, normalize_output=True, no_output_layer=True, 
+        drop_path_rate=drop_path_rate, encoder_embed_dim=768, encoder_attention_heads=12, 
+        encoder_ffn_embed_dim=int(768 * mlp_ratio), encoder_layers=12, 
+        checkpoint_activations=checkpoint_activations, 
+    )
+
+
+def _get_large_config(
+        img_size=224, patch_size=16, drop_path_rate=0, 
+        checkpoint_activations=None, mlp_ratio=4, vocab_size=64010, **kwargs
+):
+    return EncoderConfig(
+        img_size=img_size, patch_size=patch_size, vocab_size=vocab_size, multiway=True, 
+        layernorm_embedding=False, normalize_output=True, no_output_layer=True, 
+        drop_path_rate=drop_path_rate, encoder_embed_dim=1024, encoder_attention_heads=16, 
+        encoder_ffn_embed_dim=int(1024 * mlp_ratio), encoder_layers=24, 
+        checkpoint_activations=checkpoint_activations, 
+    )
+
+
+class BEiT3Wrapper(nn.Module):
+    def __init__(self, args, **kwargs):
+        super().__init__()
+        self.args = args
+        self.beit3 = BEiT3(args)
+        self.apply(self._init_weights)
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def get_num_layers(self):
+        return self.beit3.encoder.num_layers
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token', 'beit3.encoder.embed_positions.A.weight', 'beit3.vision_embed.cls_token', 'logit_scale'}
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)

beit3/optim_factory.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+from torch import optim as optim
+from timm.optim.lookahead import Lookahead
+
+import json
+
+
+def get_num_layer_for_vit(var_name, num_max_layer):
+    if "embed" in var_name:
+        return 0
+    elif var_name in (
+        "cls_token", "mask_token", "pos_embed", "language_pos_embed", 
+        "word_embeddings.weight", "vision_cls_token", "vision_pos_embed"
+    ):
+        return 0
+    elif var_name.startswith("patch_embed"):
+        return 0
+    elif var_name.startswith("rel_pos_bias"):
+        return num_max_layer - 1
+    elif "layers." in var_name:
+        layer_id = int(var_name.split('layers.')[1].split('.')[0])
+        return layer_id + 1
+    else:
+        return num_max_layer - 1
+
+
+def get_is_head_flag_for_vit(var_name, num_max_layer):
+    if var_name.startswith("head"):
+        return 1
+    # elif var_name.startswith("pooler"):
+    #     return 1
+    else:
+        return 0
+
+
+class LayerDecayValueAssigner(object):
+    def __init__(self, values, scale_handler=None):
+        self.scale_handler = scale_handler or get_num_layer_for_vit
+        self.values = values
+
+    def get_scale(self, layer_id):
+        return self.values[layer_id]
+
+    def get_layer_id(self, var_name):
+        return self.scale_handler(var_name, len(self.values))
+
+
+# The implementation code is modified from Timm (https://github.com/huggingface/pytorch-image-models/tree/main/timm
+def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num_layer=None, get_layer_scale=None):
+    parameter_group_names = {}
+    parameter_group_vars = {}
+
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue  # frozen weights
+        if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
+            group_name = "no_decay"
+            this_weight_decay = 0.
+        else:
+            group_name = "decay"
+            this_weight_decay = weight_decay
+        if get_num_layer is not None:
+            layer_id = get_num_layer(name)
+            group_name = "layer_%d_%s" % (layer_id, group_name)
+        else:
+            layer_id = None
+
+        if group_name not in parameter_group_names:
+            if get_layer_scale is not None:
+                scale = get_layer_scale(layer_id)
+            else:
+                scale = 1.
+
+            parameter_group_names[group_name] = {
+                "weight_decay": this_weight_decay,
+                "params": [],
+                "lr_scale": scale
+            }
+            parameter_group_vars[group_name] = {
+                "weight_decay": this_weight_decay,
+                "params": [],
+                "lr_scale": scale
+            }
+
+        parameter_group_vars[group_name]["params"].append(param)
+        parameter_group_names[group_name]["params"].append(name)
+    print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
+    return list(parameter_group_vars.values())
+
+
+def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None):
+    opt_lower = args.opt.lower()
+    weight_decay = args.weight_decay
+    if weight_decay and filter_bias_and_bn:
+        skip = {}
+        if skip_list is not None:
+            skip = skip_list
+        elif hasattr(model, 'no_weight_decay'):
+            skip = model.no_weight_decay()
+        parameters = get_parameter_groups(model, weight_decay, skip, get_num_layer, get_layer_scale)
+        weight_decay = 0.
+    else:
+        parameters = model.parameters()
+
+    opt_args = dict(lr=args.lr, weight_decay=weight_decay)
+    if hasattr(args, 'opt_eps') and args.opt_eps is not None:
+        opt_args['eps'] = args.opt_eps
+    if hasattr(args, 'opt_betas') and args.opt_betas is not None:
+        opt_args['betas'] = args.opt_betas
+
+    opt_split = opt_lower.split('_')
+    opt_lower = opt_split[-1]
+    if opt_lower == 'adamw':
+        optimizer = optim.AdamW(parameters, **opt_args)
+    else:
+        raise ValueError("Invalid optimizer")
+
+    if len(opt_split) > 1:
+        if opt_split[0] == 'lookahead':
+            optimizer = Lookahead(optimizer)
+
+    return optimizer

beit3/randaug.py

+import cv2
+import numpy as np
+
+
+## aug functions
+def identity_func(img):
+    return img
+
+
+def autocontrast_func(img, cutoff=0):
+    '''
+        same output as PIL.ImageOps.autocontrast
+    '''
+    n_bins = 256
+
+    def tune_channel(ch):
+        n = ch.size
+        cut = cutoff * n // 100
+        if cut == 0:
+            high, low = ch.max(), ch.min()
+        else:
+            hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
+            low = np.argwhere(np.cumsum(hist) > cut)
+            low = 0 if low.shape[0] == 0 else low[0]
+            high = np.argwhere(np.cumsum(hist[::-1]) > cut)
+            high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
+        if high <= low:
+            table = np.arange(n_bins)
+        else:
+            scale = (n_bins - 1) / (high - low)
+            offset = -low * scale
+            table = np.arange(n_bins) * scale + offset
+            table[table < 0] = 0
+            table[table > n_bins - 1] = n_bins - 1
+        table = table.clip(0, 255).astype(np.uint8)
+        return table[ch]
+
+    channels = [tune_channel(ch) for ch in cv2.split(img)]
+    out = cv2.merge(channels)
+    return out
+
+
+def equalize_func(img):
+    '''
+        same output as PIL.ImageOps.equalize
+        PIL's implementation is different from cv2.equalize
+    '''
+    n_bins = 256
+
+    def tune_channel(ch):
+        hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
+        non_zero_hist = hist[hist != 0].reshape(-1)
+        step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
+        if step == 0: return ch
+        n = np.empty_like(hist)
+        n[0] = step // 2
+        n[1:] = hist[:-1]
+        table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
+        return table[ch]
+
+    channels = [tune_channel(ch) for ch in cv2.split(img)]
+    out = cv2.merge(channels)
+    return out
+
+
+def rotate_func(img, degree, fill=(0, 0, 0)):
+    '''
+    like PIL, rotate by degree, not radians
+    '''
+    H, W = img.shape[0], img.shape[1]
+    center = W / 2, H / 2
+    M = cv2.getRotationMatrix2D(center, degree, 1)
+    out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
+    return out
+
+
+def solarize_func(img, thresh=128):
+    '''
+        same output as PIL.ImageOps.posterize
+    '''
+    table = np.array([el if el < thresh else 255 - el for el in range(256)])
+    table = table.clip(0, 255).astype(np.uint8)
+    out = table[img]
+    return out
+
+
+def color_func(img, factor):
+    '''
+        same output as PIL.ImageEnhance.Color
+    '''
+    ## implementation according to PIL definition, quite slow
+    #  degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
+    #  out = blend(degenerate, img, factor)
+    #  M = (
+    #      np.eye(3) * factor
+    #      + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
+    #  )[np.newaxis, np.newaxis, :]
+    M = (
+            np.float32([
+                [0.886, -0.114, -0.114],
+                [-0.587, 0.413, -0.587],
+                [-0.299, -0.299, 0.701]]) * factor
+            + np.float32([[0.114], [0.587], [0.299]])
+    )
+    out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
+    return out
+
+
+def contrast_func(img, factor):
+    """
+        same output as PIL.ImageEnhance.Contrast
+    """
+    mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
+    table = np.array([(
+        el - mean) * factor + mean
+        for el in range(256)
+    ]).clip(0, 255).astype(np.uint8)
+    out = table[img]
+    return out
+
+
+def brightness_func(img, factor):
+    '''
+        same output as PIL.ImageEnhance.Contrast
+    '''
+    table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
+    out = table[img]
+    return out
+
+
+def sharpness_func(img, factor):
+    '''
+    The differences the this result and PIL are all on the 4 boundaries, the center
+    areas are same
+    '''
+    kernel = np.ones((3, 3), dtype=np.float32)
+    kernel[1][1] = 5
+    kernel /= 13
+    degenerate = cv2.filter2D(img, -1, kernel)
+    if factor == 0.0:
+        out = degenerate
+    elif factor == 1.0:
+        out = img
+    else:
+        out = img.astype(np.float32)
+        degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
+        out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
+        out = out.astype(np.uint8)
+    return out
+
+
+def shear_x_func(img, factor, fill=(0, 0, 0)):
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, factor, 0], [0, 1, 0]])
+    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
+    return out
+
+
+def translate_x_func(img, offset, fill=(0, 0, 0)):
+    '''
+        same output as PIL.Image.transform
+    '''
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, 0, -offset], [0, 1, 0]])
+    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
+    return out
+
+
+def translate_y_func(img, offset, fill=(0, 0, 0)):
+    '''
+        same output as PIL.Image.transform
+    '''
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, 0, 0], [0, 1, -offset]])
+    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
+    return out
+
+
+def posterize_func(img, bits):
+    '''
+        same output as PIL.ImageOps.posterize
+    '''
+    out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
+    return out
+
+
+def shear_y_func(img, factor, fill=(0, 0, 0)):
+    H, W = img.shape[0], img.shape[1]
+    M = np.float32([[1, 0, 0], [factor, 1, 0]])
+    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
+    return out
+
+
+def cutout_func(img, pad_size, replace=(0, 0, 0)):
+    replace = np.array(replace, dtype=np.uint8)
+    H, W = img.shape[0], img.shape[1]
+    rh, rw = np.random.random(2)
+    pad_size = pad_size // 2
+    ch, cw = int(rh * H), int(rw * W)
+    x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
+    y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
+    out = img.copy()
+    out[x1:x2, y1:y2, :] = replace
+    return out
+
+
+### level to args
+def enhance_level_to_args(MAX_LEVEL):
+    def level_to_args(level):
+        return ((level / MAX_LEVEL) * 1.8 + 0.1,)
+    return level_to_args
+
+
+def shear_level_to_args(MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = (level / MAX_LEVEL) * 0.3
+        if np.random.random() > 0.5: level = -level
+        return (level, replace_value)
+
+    return level_to_args
+
+
+def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = (level / MAX_LEVEL) * float(translate_const)
+        if np.random.random() > 0.5: level = -level
+        return (level, replace_value)
+
+    return level_to_args
+
+
+def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = int((level / MAX_LEVEL) * cutout_const)
+        return (level, replace_value)
+
+    return level_to_args
+
+
+def solarize_level_to_args(MAX_LEVEL):
+    def level_to_args(level):
+        level = int((level / MAX_LEVEL) * 256)
+        return (level, )
+    return level_to_args
+
+
+def none_level_to_args(level):
+    return ()
+
+
+def posterize_level_to_args(MAX_LEVEL):
+    def level_to_args(level):
+        level = int((level / MAX_LEVEL) * 4)
+        return (level, )
+    return level_to_args
+
+
+def rotate_level_to_args(MAX_LEVEL, replace_value):
+    def level_to_args(level):
+        level = (level / MAX_LEVEL) * 30
+        if np.random.random() < 0.5:
+            level = -level
+        return (level, replace_value)
+
+    return level_to_args
+
+
+func_dict = {
+    'Identity': identity_func,
+    'AutoContrast': autocontrast_func,
+    'Equalize': equalize_func,
+    'Rotate': rotate_func,
+    'Solarize': solarize_func,
+    'Color': color_func,
+    'Contrast': contrast_func,
+    'Brightness': brightness_func,
+    'Sharpness': sharpness_func,
+    'ShearX': shear_x_func,
+    'TranslateX': translate_x_func,
+    'TranslateY': translate_y_func,
+    'Posterize': posterize_func,
+    'ShearY': shear_y_func,
+}
+
+translate_const = 10
+MAX_LEVEL = 10
+replace_value = (128, 128, 128)
+arg_dict = {
+    'Identity': none_level_to_args,
+    'AutoContrast': none_level_to_args,
+    'Equalize': none_level_to_args,
+    'Rotate': rotate_level_to_args(MAX_LEVEL, replace_value),
+    'Solarize': solarize_level_to_args(MAX_LEVEL),
+    'Color': enhance_level_to_args(MAX_LEVEL),
+    'Contrast': enhance_level_to_args(MAX_LEVEL),
+    'Brightness': enhance_level_to_args(MAX_LEVEL),
+    'Sharpness': enhance_level_to_args(MAX_LEVEL),
+    'ShearX': shear_level_to_args(MAX_LEVEL, replace_value),
+    'TranslateX': translate_level_to_args(
+        translate_const, MAX_LEVEL, replace_value
+    ),
+    'TranslateY': translate_level_to_args(
+        translate_const, MAX_LEVEL, replace_value
+    ),
+    'Posterize': posterize_level_to_args(MAX_LEVEL),
+    'ShearY': shear_level_to_args(MAX_LEVEL, replace_value),
+}
+
+
+class RandomAugment(object):
+
+    def __init__(self, N=2, M=10, isPIL=False, augs=[]):
+        self.N = N
+        self.M = M
+        self.isPIL = isPIL
+        if augs:
+            self.augs = augs       
+        else:
+            self.augs = list(arg_dict.keys())
+
+    def get_random_ops(self):
+        sampled_ops = np.random.choice(self.augs, self.N)
+        return [(op, 0.5, self.M) for op in sampled_ops]
+
+    def __call__(self, img):
+        if self.isPIL:
+            img = np.array(img)            
+        ops = self.get_random_ops()
+        for name, prob, level in ops:
+            if np.random.random() > prob:
+                continue
+            args = arg_dict[name](level)
+            img = func_dict[name](img, *args) 
+        return img
+
+
+if __name__ == '__main__':
+    a = RandomAugment()
+    img = np.random.randn(32, 32, 3)
+    a(img)

beit3/requirements.txt

+timm==0.4.12
+Pillow
+blobfile
+mypy
+numpy
+pytest
+requests
+einops
+tensorboardX
+scipy
+ftfy
+opencv-python
+sentencepiece
+pyarrow
+torchmetrics==0.7.3
+transformers
+pycocotools
+pycocoevalcap
+torchscale==0.2.0

beit3/run_beit3_finetuning.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+import argparse
+import datetime
+import numpy as np
+import time
+import torch
+import torch.backends.cudnn as cudnn
+import json
+import os
+
+from pathlib import Path
+
+from timm.data.mixup import Mixup
+from timm.models import create_model
+from timm.utils import ModelEma
+from optim_factory import create_optimizer, get_parameter_groups, \
+    LayerDecayValueAssigner, get_is_head_flag_for_vit
+
+from engine_for_finetuning import train_one_epoch, get_handler, evaluate
+from datasets import create_downstream_dataset
+from utils import NativeScalerWithGradNormCount as NativeScaler
+import utils
+import modeling_finetune
+
+
+def get_args():
+    parser = argparse.ArgumentParser('BEiT fine-tuning and evaluation script for image classification', add_help=False)
+
+    # Model parameters
+    parser.add_argument('--model', default='beit_base_patch16_224', type=str, metavar='MODEL',
+                        help='Name of model to train')
+    parser.add_argument('--task', type=str, required=True, 
+                        choices=['nlvr2', 'vqav2', 'flickr30k', 'coco_retrieval', 'coco_captioning', 'nocaps', 'imagenet'], 
+                        help='Name of task to fine-tuning')
+
+    parser.add_argument('--input_size', default=224, type=int,
+                        help='images input size')
+    parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
+                        help='Drop path rate (default: 0.1)')
+
+    parser.add_argument('--checkpoint_activations', action='store_true', default=None, 
+                        help='Enable checkpointing to save your memory.')
+    parser.add_argument('--sentencepiece_model', type=str, required=True, 
+                        help='Sentencepiece model path for the pretrained model.')
+    parser.add_argument('--vocab_size', type=int, default=64010)
+    parser.add_argument('--num_max_bpe_tokens', type=int, default=64)
+
+    parser.add_argument('--model_ema', action='store_true', default=False)
+    parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
+    parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='')
+
+    # Optimizer parameters
+    parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
+                        help='Optimizer (default: "adamw"')
+    parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
+                        help='Optimizer Epsilon (default: 1e-8)')
+    parser.add_argument('--opt_betas', default=[0.9, 0.999], type=float, nargs='+', metavar='BETA',
+                        help='Optimizer Betas (default: 0.9, 0.999, use opt default)')
+    parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
+                        help='Clip gradient norm (default: None, no clipping)')
+    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
+                        help='SGD momentum (default: 0.9)')
+    parser.add_argument('--weight_decay', type=float, default=0.05,
+                        help='weight decay (default: 0.05)')
+
+    parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
+                        help='learning rate (default: 5e-4)')
+    parser.add_argument('--layer_decay', type=float, default=0.9)
+    parser.add_argument('--task_head_lr_weight', type=float, default=0)
+
+    parser.add_argument('--warmup_lr', type=float, default=1e-6, metavar='LR',
+                        help='warmup learning rate (default: 1e-6)')
+    parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
+                        help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
+    parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
+                        help='epochs to warmup LR, if scheduler supports')
+    parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
+                        help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
+
+    parser.add_argument('--batch_size', default=64, type=int)
+    parser.add_argument('--eval_batch_size', default=None, type=int)
+    parser.add_argument('--epochs', default=20, type=int)
+    parser.add_argument('--update_freq', default=1, type=int)
+    parser.add_argument('--save_ckpt_freq', default=5, type=int)
+
+    # Augmentation parameters
+    parser.add_argument('--randaug', action='store_true', default=False)
+    parser.add_argument('--train_interpolation', type=str, default='bicubic',
+                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
+
+    # Finetuning params
+    parser.add_argument('--finetune', default='',
+                        help='finetune from checkpoint')
+    parser.add_argument('--model_key', default='model|module', type=str)
+    parser.add_argument('--model_prefix', default='', type=str)
+
+    # Dataset parameters
+    parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
+                        help='dataset path')
+
+    parser.add_argument('--output_dir', default='',
+                        help='path where to save, empty for no saving')
+    parser.add_argument('--log_dir', default=None,
+                        help='path where to tensorboard log')
+    parser.add_argument('--device', default='cuda',
+                        help='device to use for training / testing')
+    parser.add_argument('--seed', default=0, type=int)
+    parser.add_argument('--resume', default='',
+                        help='resume from checkpoint')
+    parser.add_argument('--auto_resume', action='store_true')
+    parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
+    parser.set_defaults(auto_resume=True)
+
+    parser.add_argument('--save_ckpt', action='store_true')
+    parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt')
+    parser.set_defaults(save_ckpt=True)
+
+    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
+                        help='start epoch')
+    parser.add_argument('--eval', action='store_true',
+                        help='Perform evaluation only')
+    parser.add_argument('--dist_eval', action='store_true', default=False,
+                        help='Enabling distributed evaluation')
+    parser.add_argument('--num_workers', default=10, type=int)
+    parser.add_argument('--pin_mem', action='store_true',
+                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
+    parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
+    parser.set_defaults(pin_mem=True)
+
+    # distributed training parameters
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='number of distributed processes')
+    parser.add_argument('--local_rank', default=-1, type=int)
+    parser.add_argument('--dist_on_itp', action='store_true')
+    parser.add_argument('--dist_url', default='env://',
+                        help='url used to set up distributed training')
+
+    # parameter for dump predictions (VQA, COCO captioning, NoCaps)
+    parser.add_argument('--task_cache_path', default=None, type=str)
+
+    # parameter for imagenet finetuning
+    parser.add_argument('--nb_classes', default=1000, type=int,
+                        help='number of the classification types')
+    parser.add_argument('--mixup', type=float, default=0,
+                        help='mixup alpha, mixup enabled if > 0.')
+    parser.add_argument('--cutmix', type=float, default=0,
+                        help='cutmix alpha, cutmix enabled if > 0.')
+    parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
+                        help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
+    parser.add_argument('--mixup_prob', type=float, default=1.0,
+                        help='Probability of performing mixup or cutmix when either/both is enabled')
+    parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
+                        help='Probability of switching to cutmix when both mixup and cutmix enabled')
+    parser.add_argument('--mixup_mode', type=str, default='batch',
+                        help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
+    
+    # augmentation parameters for imagenet finetuning
+    parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
+                        help='Color jitter factor (default: 0.4)')
+    parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
+                        help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)')
+    parser.add_argument('--smoothing', type=float, default=0.1,
+                        help='Label smoothing (default: 0.1)')
+
+    # evaluation parameters for imagenet
+    parser.add_argument('--crop_pct', type=float, default=None)
+
+    # random Erase params for imagenet finetuning
+    parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
+                        help='Random erase prob (default: 0.25)')
+    parser.add_argument('--remode', type=str, default='pixel',
+                        help='Random erase mode (default: "pixel")')
+    parser.add_argument('--recount', type=int, default=1,
+                        help='Random erase count (default: 1)')
+    parser.add_argument('--resplit', action='store_true', default=False,
+                        help='Do not random erase first (clean) augmentation split')
+
+    # parameter for captioning finetuning
+    parser.add_argument('--captioning_mask_prob', type=float, default=0.6)
+    parser.add_argument('--drop_worst_ratio', type=float, default=0.2)
+    parser.add_argument('--drop_worst_after', type=int, default=12000)
+    parser.add_argument('--num_beams', type=int, default=3)
+    parser.add_argument('--length_penalty', type=float, default=0.6)
+
+    # label smoothing for imagenet and captioning
+    parser.add_argument('--label_smoothing', type=float, default=0.1)
+
+    # deepspeed parameters
+    parser.add_argument('--enable_deepspeed', action='store_true', default=False)
+    parser.add_argument('--initial_scale_power', type=int, default=16)
+    parser.add_argument('--zero_stage', default=0, type=int,
+                        help='ZeRO optimizer stage (default: 0)')
+
+    known_args, _ = parser.parse_known_args()
+
+    if known_args.enable_deepspeed:
+        try:
+            import deepspeed
+            from deepspeed import DeepSpeedConfig
+            parser = deepspeed.add_config_arguments(parser)
+            ds_init = deepspeed.initialize
+        except:
+            print("Please 'pip install deepspeed==0.4.0'")
+            exit(0)
+    else:
+        ds_init = None
+
+    return parser.parse_args(), ds_init
+
+
+def main(args, ds_init):
+    utils.init_distributed_mode(args)
+
+    if ds_init is not None:
+        utils.create_ds_config(args)
+
+    if args.task_cache_path is None:
+        args.task_cache_path = args.output_dir
+
+    print(args)
+
+    device = torch.device(args.device)
+
+    # fix the seed for reproducibility
+    seed = args.seed + utils.get_rank()
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    # random.seed(seed)
+
+    cudnn.benchmark = True
+
+    if utils.get_rank() == 0 and args.log_dir is not None:
+        os.makedirs(args.log_dir, exist_ok=True)
+        log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
+    else:
+        log_writer = None
+
+    data_loader_train, data_loader_val = create_downstream_dataset(args)
+
+    if not args.model.endswith(args.task):
+        if args.task in ("flickr30k", "coco_retrieval"):
+            model_config = "%s_retrieval" % args.model
+        elif args.task in ("coco_captioning", "nocaps"):
+            model_config = "%s_captioning" % args.model
+        elif args.task in ("imagenet"):
+            model_config = "%s_imageclassification" % args.model
+        else:
+            model_config = "%s_%s" % (args.model, args.task)
+    else:
+        model_config = args.model
+    print("model_config = %s" % model_config)
+    model = create_model(
+        model_config,
+        pretrained=False,
+        drop_path_rate=args.drop_path,
+        vocab_size=args.vocab_size,
+        checkpoint_activations=args.checkpoint_activations,
+    )
+
+    if args.finetune:
+        utils.load_model_and_may_interpolate(args.finetune, model, args.model_key, args.model_prefix)
+
+    model.to(device)
+
+    model_ema = None
+    if args.model_ema:
+        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
+        model_ema = ModelEma(
+            model,
+            decay=args.model_ema_decay,
+            device='cpu' if args.model_ema_force_cpu else '',
+            resume='')
+        print("Using EMA with decay = %.8f" % args.model_ema_decay)
+
+    model_without_ddp = model
+    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+    print("Model = %s" % str(model_without_ddp))
+    print('number of params:', n_parameters)
+
+    total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
+    num_training_steps_per_epoch = len(data_loader_train.dataset) // total_batch_size
+    print("LR = %.8f" % args.lr)
+    print("Batch size = %d" % total_batch_size)
+    print("Update frequent = %d" % args.update_freq)
+    print("Number of training examples = %d" % len(data_loader_train.dataset))
+    print("Number of training training per epoch = %d" % num_training_steps_per_epoch)
+
+    num_layers = model_without_ddp.get_num_layers()
+    if args.layer_decay < 1.0:
+        lrs = list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2))
+        assigner = LayerDecayValueAssigner(lrs)
+    elif args.task_head_lr_weight > 1:
+        assigner = LayerDecayValueAssigner([1.0, args.task_head_lr_weight], scale_handler=get_is_head_flag_for_vit)
+    else:
+        assigner = None
+
+    if assigner is not None:
+        print("Assigned values = %s" % str(assigner.values))
+
+    skip_weight_decay_list = model.no_weight_decay()
+
+    if args.distributed:
+        torch.distributed.barrier()
+    if args.enable_deepspeed:
+        loss_scaler = None
+        optimizer_params = get_parameter_groups(
+            model, args.weight_decay, skip_weight_decay_list,
+            assigner.get_layer_id if assigner is not None else None,
+            assigner.get_scale if assigner is not None else None)
+        model, optimizer, _, _ = ds_init(
+            args=args, model=model, model_parameters=optimizer_params,
+            dist_init_required=not args.distributed,
+        )
+
+        print("model.gradient_accumulation_steps() = %d" % model.gradient_accumulation_steps())
+        assert model.gradient_accumulation_steps() == args.update_freq
+    else:
+        if args.distributed:
+            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
+            model_without_ddp = model.module
+
+        optimizer = create_optimizer(
+            args, model_without_ddp, skip_list=skip_weight_decay_list,
+            get_num_layer=assigner.get_layer_id if assigner is not None else None, 
+            get_layer_scale=assigner.get_scale if assigner is not None else None)
+        loss_scaler = NativeScaler()
+
+    lr_schedule_values = utils.cosine_scheduler(
+        args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
+        warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
+    )
+
+    utils.auto_load_model(
+        args=args, model=model, model_without_ddp=model_without_ddp,
+        optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)
+
+    task_handler = get_handler(args)
+
+    # mixup for imagenet
+    mixup_fn = None
+    if args.task in ["imagenet", "in1k"]:
+        mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
+        if mixup_active:
+            print("Mixup is activated!")
+            mixup_fn = Mixup(
+                mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
+                prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
+                label_smoothing=args.label_smoothing, num_classes=args.nb_classes)
+
+    if args.eval:
+        data_loader_test = create_downstream_dataset(args, is_eval=True)
+        if args.task in ["nlvr2", "flickr30k", "coco_retrieval", "imagenet"]:
+            ext_test_stats, task_key = evaluate(data_loader_test, model, device, task_handler)
+            print(f"Accuracy of the network on the {len(data_loader_test.dataset)} test images: {ext_test_stats[task_key]:.3f}%")
+            exit(0)
+        elif args.task == "vqav2":
+            result, _ = evaluate(data_loader_test, model, device, task_handler)
+            utils.dump_predictions(args, result, "vqav2_test")
+            exit(0)
+        elif args.task in ["coco_captioning", "nocaps"]:
+            predictions, _ = evaluate(data_loader_test, model, device, task_handler)
+            prediction_file = utils.dump_predictions(args, predictions, "{}_test".format(args.task))
+            if utils.is_main_process() and args.task == "coco_captioning":
+                captioning_result = utils.coco_caption_eval(args.output_dir, prediction_file, "{}_test".format(args.task))
+                result_file = os.path.join(args.output_dir, f"{args.task}_result.json")
+                print(json.dumps(captioning_result))
+                utils.write_result_to_jsonl(captioning_result, result_file)
+            exit(0)
+
+    print(f"Start training for {args.epochs} epochs")
+    start_time = time.time()
+
+    max_accuracy = 0.0
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            data_loader_train.sampler.set_epoch(epoch)
+        if log_writer is not None:
+            log_writer.set_step(epoch * num_training_steps_per_epoch * args.update_freq)
+        train_stats = train_one_epoch(
+            model, data_loader_train, optimizer, device, task_handler, epoch, 
+            epoch * num_training_steps_per_epoch, lr_schedule_values, loss_scaler, 
+            args.clip_grad, args.update_freq, model_ema, log_writer, args.task, mixup_fn,
+        )
+        if args.output_dir and args.save_ckpt:
+            if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
+                utils.save_model(
+                    args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
+                    loss_scaler=loss_scaler, epoch=epoch, model_ema=model_ema)
+        if data_loader_val is not None:
+            if args.task not in ["coco_captioning", "nocaps"]:
+                test_stats, task_key = evaluate(data_loader_val, model, device, task_handler)
+            else:
+                predictions, _ = evaluate(data_loader_val, model, device, task_handler)
+                prediction_file = utils.dump_predictions(args, predictions, f"{args.task}_val_e{epoch}")
+                result_file = os.path.join(args.output_dir, f"{args.task}_result_val_e{epoch}.json")
+                task_key = "CIDEr"
+                if utils.is_main_process():
+                    test_stats = utils.coco_caption_eval(args.output_dir, prediction_file, "{}_val".format(args.task))
+                    utils.write_result_to_jsonl(test_stats, result_file)
+                torch.distributed.barrier()
+                if not utils.is_main_process():
+                    test_stats = utils.read_result_from_jsonl(result_file)
+
+            print(f"Performance of the network on the {len(data_loader_val.dataset)} val images: {test_stats[task_key]:.1f}%")
+            if max_accuracy < test_stats[task_key]:
+                max_accuracy = test_stats[task_key]
+                if args.output_dir and args.save_ckpt:
+                    utils.save_model(
+                        args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
+                        loss_scaler=loss_scaler, epoch="best", model_ema=model_ema)
+
+            print(f'Max performance: {max_accuracy:.2f}%')
+            if log_writer is not None:
+                log_writer.update(acc=test_stats[task_key], head="perf", step=epoch)
+            
+            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
+                        **{f'val_{k}': v for k, v in test_stats.items()},
+                        'epoch': epoch,
+                        'n_parameters': n_parameters}
+        else:
+            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
+                         # **{f'test_{k}': v for k, v in test_stats.items()},
+                         'epoch': epoch,
+                         'n_parameters': n_parameters}
+
+        if args.output_dir and utils.is_main_process():
+            if log_writer is not None:
+                log_writer.flush()
+            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
+                f.write(json.dumps(log_stats) + "\n")
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+
+
+if __name__ == '__main__':
+    opts, ds_init = get_args()
+    if opts.output_dir:
+        Path(opts.output_dir).mkdir(parents=True, exist_ok=True)
+    main(opts, ds_init)

beit3/train.sh

+#!/bin/bash/
+
+export HIP_VISIBLE_DEVICES=0,1,2,3 # 自行修改为训练的卡号和数量
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
+
+python -m torch.distributed.launch --nproc_per_node=4 run_beit3_finetuning.py \
+        --model beit3_base_patch16_480 \
+        --input_size 480 \
+        --task coco_captioning \
+        --batch_size 32 \
+        --layer_decay 1.0 \
+        --lr 4e-5 \
+        --randaug \
+        --epochs 10 \
+        --warmup_epochs 1 \
+        --drop_path 0.1 \
+        --sentencepiece_model ./pretrained_models/beit3.spm \
+        --finetune ./pretrained_models/beit3_base_patch16_224.pth \
+        --data_path /home/data/coco2014 \
+        --output_dir ./save_models/ \
+        --log_dir ./logs \
+        --weight_decay 0.05 \
+        --seed 42 \
+        --save_ckpt_freq 5 \
+        --num_max_bpe_tokens 32 \
+        --captioning_mask_prob 0.7 \
+        --drop_worst_after 12000 \
+        --dist_eval \
+        --checkpoint_activations \
+        --enable_deepspeed
\ No newline at end of file

beit3/utils.py

+# --------------------------------------------------------
+# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
+# Github source: https://github.com/microsoft/unilm/tree/master/beit3
+# Copyright (c) 2023 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------'
+
+import datetime
+import io
+import os
+import math
+import time
+import json
+import argparse
+import numpy as np
+from pathlib import Path
+from collections import defaultdict, deque
+from timm.utils import get_state_dict
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch._six import inf
+from torchmetrics import Metric
+from tensorboardX import SummaryWriter
+
+
+def bool_flag(s):
+    """
+    Parse boolean arguments from the command line.
+    """
+    FALSY_STRINGS = {"off", "false", "0"}
+    TRUTHY_STRINGS = {"on", "true", "1"}
+    if s.lower() in FALSY_STRINGS:
+        return False
+    elif s.lower() in TRUTHY_STRINGS:
+        return True
+    else:
+        raise argparse.ArgumentTypeError("invalid value for a boolean flag")
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value)
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if v is None:
+                continue
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(
+            type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(
+                "{}: {}".format(name, str(meter))
+            )
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None):
+        i = 0
+        if not header:
+            header = ''
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt='{avg:.4f}')
+        data_time = SmoothedValue(fmt='{avg:.4f}')
+        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        log_msg = [
+            header,
+            '[{0' + space_fmt + '}/{1}]',
+            'eta: {eta}',
+            '{meters}',
+            'time: {time}',
+            'data: {data}'
+        ]
+        if torch.cuda.is_available():
+            log_msg.append('max mem: {memory:.0f}')
+        log_msg = self.delimiter.join(log_msg)
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time),
+                        memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time)))
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print('{} Total time: {} ({:.4f} s / it)'.format(
+            header, total_time_str, total_time / len(iterable)))
+
+
+class TensorboardLogger(object):
+    def __init__(self, log_dir):
+        self.writer = SummaryWriter(logdir=log_dir)
+        self.step = 0
+
+    def set_step(self, step=None):
+        if step is not None:
+            self.step = step
+        else:
+            self.step += 1
+
+    def update(self, head='scalar', step=None, **kwargs):
+        for k, v in kwargs.items():
+            if v is None:
+                continue
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.writer.add_scalar(head + "/" + k, v, self.step if step is None else step)
+
+    def flush(self):
+        self.writer.flush()
+
+
+def _load_checkpoint_for_ema(model_ema, checkpoint):
+    """
+    Workaround for ModelEma._load_checkpoint to accept an already-loaded object
+    """
+    mem_file = io.BytesIO()
+    torch.save(checkpoint, mem_file)
+    mem_file.seek(0)
+    model_ema._load_checkpoint(mem_file)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop('force', False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def _get_rank_env():
+    if "RANK" in os.environ:
+        return int(os.environ["RANK"])
+    else:
+        return int(os.environ['OMPI_COMM_WORLD_RANK'])
+
+
+def _get_local_rank_env():
+    if "LOCAL_RANK" in os.environ:
+        return int(os.environ["LOCAL_RANK"])
+    else:
+        return int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
+
+
+def _get_world_size_env():
+    if "WORLD_SIZE" in os.environ:
+        return int(os.environ["WORLD_SIZE"])
+    else:
+        return int(os.environ['OMPI_COMM_WORLD_SIZE'])
+
+
+# The implementation code is modified from DeiT (https://github.com/facebookresearch/deit.git)
+def init_distributed_mode(args):
+    if args.dist_on_itp:
+        args.rank = _get_rank_env()
+        args.world_size = _get_world_size_env()  # int(os.environ['OMPI_COMM_WORLD_SIZE'])
+        args.gpu = _get_local_rank_env()
+        args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
+        os.environ['LOCAL_RANK'] = str(args.gpu)
+        os.environ['RANK'] = str(args.rank)
+        os.environ['WORLD_SIZE'] = str(args.world_size)
+        # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
+    elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.gpu = int(os.environ['LOCAL_RANK'])
+    elif 'SLURM_PROCID' in os.environ:
+        args.rank = int(os.environ['SLURM_PROCID'])
+        args.gpu = args.rank % torch.cuda.device_count()
+    else:
+        print('Not using distributed mode')
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = 'nccl'
+    print('| distributed init (rank {}): {}, gpu {}'.format(
+        args.rank, args.dist_url, args.gpu), flush=True)
+    torch.distributed.init_process_group(
+        backend=args.dist_backend, init_method=args.dist_url,
+        world_size=args.world_size, rank=args.rank,
+        timeout=datetime.timedelta(0, 7200)
+    )
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
+    missing_keys = []
+    unexpected_keys = []
+    error_msgs = []
+    # copy state_dict so _load_from_state_dict can modify it
+    metadata = getattr(state_dict, '_metadata', None)
+    state_dict = state_dict.copy()
+    if metadata is not None:
+        state_dict._metadata = metadata
+
+    def load(module, prefix=''):
+        local_metadata = {} if metadata is None else metadata.get(
+            prefix[:-1], {})
+        module._load_from_state_dict(
+            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
+        for name, child in module._modules.items():
+            if child is not None:
+                load(child, prefix + name + '.')
+
+    load(model, prefix=prefix)
+
+    warn_missing_keys = []
+    ignore_missing_keys = []
+    for key in missing_keys:
+        keep_flag = True
+        for ignore_key in ignore_missing.split('|'):
+            if ignore_key in key:
+                keep_flag = False
+                break
+        if keep_flag:
+            warn_missing_keys.append(key)
+        else:
+            ignore_missing_keys.append(key)
+
+    missing_keys = warn_missing_keys
+
+    if len(missing_keys) > 0:
+        print("Weights of {} not initialized from pretrained model: {}".format(
+            model.__class__.__name__, missing_keys))
+    if len(unexpected_keys) > 0:
+        print("Weights from pretrained model not used in {}: {}".format(
+            model.__class__.__name__, unexpected_keys))
+    if len(ignore_missing_keys) > 0:
+        print("Ignored weights of {} not initialized from pretrained model: {}".format(
+            model.__class__.__name__, ignore_missing_keys))
+    if len(error_msgs) > 0:
+        print('\n'.join(error_msgs))
+
+
+class NativeScalerWithGradNormCount:
+    state_dict_key = "amp_scaler"
+
+    def __init__(self):
+        self._scaler = torch.cuda.amp.GradScaler()
+
+    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
+        self._scaler.scale(loss).backward(create_graph=create_graph)
+        if update_grad:
+            if clip_grad is not None:
+                assert parameters is not None
+                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
+                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
+            else:
+                self._scaler.unscale_(optimizer)
+                norm = get_grad_norm_(parameters)
+            self._scaler.step(optimizer)
+            self._scaler.update()
+        else:
+            norm = None
+        return norm
+
+    def state_dict(self):
+        return self._scaler.state_dict()
+
+    def load_state_dict(self, state_dict):
+        self._scaler.load_state_dict(state_dict)
+
+
+def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = [p for p in parameters if p.grad is not None]
+    norm_type = float(norm_type)
+    if len(parameters) == 0:
+        return torch.tensor(0.)
+    device = parameters[0].grad.device
+    if norm_type == inf:
+        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
+    else:
+        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
+    return total_norm
+
+
+def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0,
+                     start_warmup_value=0, warmup_steps=-1, sched_type="cos"):
+    warmup_schedule = np.array([])
+    warmup_iters = warmup_epochs * niter_per_ep
+    if warmup_steps > 0:
+        warmup_iters = warmup_steps
+    print("Set warmup steps = %d" % warmup_iters)
+    if warmup_epochs > 0:
+        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
+
+    if sched_type == "cos":
+        iters = np.arange(epochs * niter_per_ep - warmup_iters)
+        schedule = np.array([
+            final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])
+    elif sched_type == "linear":
+        schedule = np.linspace(base_value, final_value, epochs * niter_per_ep - warmup_iters)
+    else:
+        raise NotImplementedError()
+
+    schedule = np.concatenate((warmup_schedule, schedule))
+
+    assert len(schedule) == epochs * niter_per_ep
+    return schedule
+
+
+def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
+    output_dir = Path(args.output_dir)
+    if loss_scaler is not None:
+        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch)]
+        for checkpoint_path in checkpoint_paths:
+            to_save = {
+                'model': model_without_ddp.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'epoch': epoch,
+                'scaler': loss_scaler.state_dict(),
+                'args': args,
+            }
+
+            if model_ema is not None:
+                to_save['model_ema'] = get_state_dict(model_ema)
+
+            save_on_master(to_save, checkpoint_path)
+    else:
+        client_state = {'epoch': epoch, "args": args}
+        if model_ema is not None:
+            client_state['model_ema'] = get_state_dict(model_ema)
+        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch, client_state=client_state)
+
+
+def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
+    output_dir = Path(args.output_dir)
+    if loss_scaler is not None:
+        # torch.amp
+        if args.auto_resume and len(args.resume) == 0:
+            import glob
+            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
+            latest_ckpt = -1
+            for ckpt in all_checkpoints:
+                t = ckpt.split('-')[-1].split('.')[0]
+                if t.isdigit():
+                    latest_ckpt = max(int(t), latest_ckpt)
+            if latest_ckpt >= 0:
+                args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
+            print("Auto resume checkpoint: %s" % args.resume)
+
+        if args.resume:
+            if args.resume.startswith('https'):
+                checkpoint = torch.hub.load_state_dict_from_url(
+                    args.resume, map_location='cpu', check_hash=True)
+            else:
+                checkpoint = torch.load(args.resume, map_location='cpu')
+            model_without_ddp.load_state_dict(checkpoint['model'])
+            print("Resume checkpoint %s" % args.resume)
+            if 'optimizer' in checkpoint and 'epoch' in checkpoint:
+                optimizer.load_state_dict(checkpoint['optimizer'])
+                args.start_epoch = checkpoint['epoch'] + 1
+                if hasattr(args, 'model_ema') and args.model_ema:
+                    _load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
+                if 'scaler' in checkpoint:
+                    loss_scaler.load_state_dict(checkpoint['scaler'])
+                print("With optim & sched!")
+    else:
+        # deepspeed, only support '--auto_resume'.
+        if args.auto_resume:
+            import glob
+            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*'))
+            latest_ckpt = -1
+            for ckpt in all_checkpoints:
+                t = ckpt.split('-')[-1].split('.')[0]
+                if t.isdigit():
+                    latest_ckpt = max(int(t), latest_ckpt)
+            if latest_ckpt >= 0:
+                args.resume = os.path.join(output_dir, 'checkpoint-%d' % latest_ckpt)
+                print("Auto resume checkpoint: %d" % latest_ckpt)
+                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint-%d' % latest_ckpt)
+                args.start_epoch = client_states['epoch'] + 1
+                if model_ema is not None:
+                    if args.model_ema:
+                        _load_checkpoint_for_ema(model_ema, client_states['model_ema'])
+
+
+# The implementation code is modified from DeiT (https://github.com/facebookresearch/deit.git)
+def load_model_and_may_interpolate(ckpt_path, model, model_key, model_prefix):
+    if ckpt_path.startswith('https'):
+        checkpoint = torch.hub.load_state_dict_from_url(
+            ckpt_path, map_location='cpu', check_hash=True)
+    else:
+        checkpoint = torch.load(ckpt_path, map_location='cpu')
+
+    print("Load ckpt from %s" % ckpt_path)
+    checkpoint_model = None
+    for model_key in model_key.split('|'):
+        if model_key in checkpoint:
+            checkpoint_model = checkpoint[model_key]
+            print("Load state_dict by model_key = %s" % model_key)
+            break
+    
+    if checkpoint_model is None:
+        checkpoint_model = checkpoint
+    
+    state_dict = model.state_dict()
+    for k in ['head.weight', 'head.bias']:
+        if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
+            print(f"Removing key {k} from pretrained checkpoint")
+            del checkpoint_model[k]
+
+    # interpolate position embedding
+    for pos_embed_key in ("vision_pos_embed", "pos_embed", "beit3.encoder.embed_positions.A.weight"):
+        if pos_embed_key in checkpoint_model:
+            pos_embed_checkpoint = checkpoint_model[pos_embed_key]
+            embedding_size = pos_embed_checkpoint.shape[-1]
+            if pos_embed_key == "beit3.encoder.embed_positions.A.weight":
+                # being consistent with Fairseq, which starts from 2 for position embedding
+                torchscale_model = True
+                num_patches = model.beit3.vision_embed.num_patches
+                num_extra_tokens = model.beit3.vision_embed.num_position_embeddings() + 2 - num_patches
+            else:
+                torchscale_model = False
+                num_patches = model.patch_embed.num_patches
+                num_extra_tokens = getattr(model, pos_embed_key).shape[-2] - num_patches
+            # height (== width) for the checkpoint position embedding
+            orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+            # height (== width) for the new position embedding
+            new_size = int(num_patches ** 0.5)
+            # class_token and dist_token are kept unchanged
+            if orig_size != new_size:
+                print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+                if torchscale_model:
+                    extra_tokens = pos_embed_checkpoint[:num_extra_tokens].unsqueeze(0)
+                    # only the position tokens are interpolated
+                    pos_tokens = pos_embed_checkpoint[num_extra_tokens:]
+                else:
+                    extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+                    # only the position tokens are interpolated
+                    pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+                pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+                pos_tokens = torch.nn.functional.interpolate(
+                    pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+                pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+                new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+                if torchscale_model:
+                    new_pos_embed = new_pos_embed.squeeze(0)
+                checkpoint_model[pos_embed_key] = new_pos_embed
+
+    load_state_dict(model, checkpoint_model, prefix=model_prefix)
+
+
+def create_ds_config(args):
+    args.deepspeed_config = os.path.join(args.output_dir, "deepspeed_config.json")
+    with open(args.deepspeed_config, mode="w") as writer:
+        ds_config = {
+            "train_batch_size": args.batch_size * args.update_freq * get_world_size(),
+            "train_micro_batch_size_per_gpu": args.batch_size,
+            "steps_per_print": 1000,
+            "optimizer": {
+                "type": "Adam",
+                "adam_w_mode": True,
+                "params": {
+                    "lr": args.lr,
+                    "weight_decay": args.weight_decay,
+                    "bias_correction": True,
+                    "betas": [
+                        args.opt_betas[0],
+                        args.opt_betas[1]
+                    ],
+                    "eps": args.opt_eps
+                }
+            },
+            "fp16": {
+                "enabled": True,
+                "loss_scale": 0,
+                "initial_scale_power": getattr(args, "initial_scale_power", 12),
+                "loss_scale_window": 1000,
+                "hysteresis": 2,
+                "min_loss_scale": 1
+            },
+            "amp": {
+                "enabled": False,
+                "opt_level": "O2"
+            }
+        }
+
+        if args.clip_grad is not None:
+            ds_config.update({'gradient_clipping': args.clip_grad})
+
+        if args.zero_stage == 1:
+            ds_config.update({"zero_optimization": {"stage": args.zero_stage, "reduce_bucket_size": 5e8}})
+        elif args.zero_stage > 1:
+            raise NotImplementedError()
+
+        writer.write(json.dumps(ds_config, indent=2))
+
+
+def merge_batch_tensors_by_dict_key(batch):
+    batch_tensors = {}
+    for tensor_key in batch[0]:
+        if isinstance(batch[0][tensor_key], torch.Tensor):
+            batch_tensors[tensor_key] = torch.stack([d[tensor_key] for d in batch])
+        else:
+            batch_tensors[tensor_key] = torch.tensor([d[tensor_key] for d in batch], dtype=torch.long)
+    return batch_tensors
+
+
+def get_loss_scale_for_deepspeed(model):
+    optimizer = model.optimizer
+    loss_scale = None
+    if hasattr(optimizer, 'loss_scale'):
+        loss_scale = optimizer.loss_scale
+    elif hasattr(optimizer, 'cur_scale'):
+        loss_scale = optimizer.cur_scale
+    return loss_scale
+
+
+class GatherLayer(torch.autograd.Function):
+    """
+    Gather tensors from all workers with support for backward propagation:
+    This implementation does not cut the gradients as torch.distributed.all_gather does.
+    """
+    @staticmethod
+    def forward(ctx, x):
+        output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
+        dist.all_gather(output, x)
+        return tuple(output)
+    @staticmethod
+    def backward(ctx, *grads):
+        all_gradients = torch.stack(grads)
+        dist.all_reduce(all_gradients)
+        return all_gradients[dist.get_rank()]
+
+
+def gather_features(
+        image_features,
+        text_features,
+):
+    gathered_image_features = GatherLayer.apply(image_features)
+    gathered_text_features = GatherLayer.apply(text_features)
+    all_image_features = torch.cat(gathered_image_features)
+    all_text_features = torch.cat(gathered_text_features)
+
+    return all_image_features, all_text_features
+
+
+# The implementation code is modified from open_clip (https://github.com/mlfoundations/open_clip.git)
+class ClipLoss(nn.Module):
+
+    def __init__(
+            self,
+            cache_labels=False,
+            rank=0,
+            world_size=1,
+    ):
+        super().__init__()
+        self.cache_labels = cache_labels
+        self.rank = rank
+        self.world_size = world_size
+
+        # cache state
+        self.prev_num_logits = 0
+        self.labels = {}
+
+    def forward(self, image_features, text_features, logit_scale):
+        device = image_features.device
+        if self.world_size > 1:
+            all_image_features, all_text_features = gather_features(
+                image_features, text_features
+            )
+
+            logits_per_image = logit_scale * image_features @ all_text_features.T
+            logits_per_text = logit_scale * text_features @ all_image_features.T
+        else:
+            logits_per_image = logit_scale * image_features @ text_features.T
+            logits_per_text = logit_scale * text_features @ image_features.T
+
+        # calculated ground-truth and cache if enabled
+        num_logits = logits_per_image.shape[0]
+        if self.prev_num_logits != num_logits or device not in self.labels:
+            labels = torch.arange(num_logits, device=device, dtype=torch.long)
+            if self.world_size > 1:
+                labels = labels + num_logits * self.rank
+            if self.cache_labels:
+                self.labels[device] = labels
+                self.prev_num_logits = num_logits
+        else:
+            labels = self.labels[device]
+
+        total_loss = (
+            F.cross_entropy(logits_per_image, labels) +
+            F.cross_entropy(logits_per_text, labels)
+            ) / 2
+        return total_loss, logits_per_image, logits_per_text
+
+
+def write_result_to_jsonl(test_stats, result_file):
+    with open(result_file, mode="w", encoding="utf-8") as writer:
+        writer.write(json.dumps(test_stats, indent=None))
+
+
+def read_result_from_jsonl(result_file):
+    with open(result_file, mode="r", encoding="utf-8") as reader:
+        return json.load(reader)
+
+
+# The implementation code is from ViLT (https://github.com/dandelin/ViLT.git)
+class VQAScore(Metric):
+    def __init__(self, dist_sync_on_step=False):
+        super().__init__(dist_sync_on_step=dist_sync_on_step)
+        self.add_state("score", default=torch.tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("total", default=torch.tensor(0.0), dist_reduce_fx="sum")
+
+    def update(self, logits, target):
+        logits, target = (
+            logits.detach().float().to(self.score.device),
+            target.detach().float().to(self.score.device),
+        )
+        logits = torch.max(logits, 1)[1]
+        one_hots = torch.zeros(*target.size()).to(target)
+        one_hots.scatter_(1, logits.view(-1, 1), 1)
+        scores = one_hots * target
+
+        self.score += scores.sum()
+        self.total += len(logits)
+
+    def compute(self):
+        return self.score / self.total
+
+
+class BertCaptioningLoss(nn.Module):
+    def __init__(self, label_smoothing, drop_worst_ratio, drop_worst_after):
+        super().__init__()
+        self.label_smoothing = label_smoothing
+        self.drop_worst_ratio = drop_worst_ratio
+        self.drop_worst_after = drop_worst_after
+        self.log_soft = nn.LogSoftmax(dim=1)
+        self.kl = nn.KLDivLoss(reduction='none')
+        self.iter = 0
+
+    def forward(self, logits, target, iter):
+        eps = self.label_smoothing
+        n_class = logits.size(1)
+        one_hot = torch.zeros_like(logits).scatter(1, target.view(-1, 1), 1)
+        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
+        log_prb = self.log_soft(logits)
+        loss = self.kl(log_prb, one_hot).sum(1)
+
+        if self.drop_worst_ratio > 0 and iter > self.drop_worst_after:
+            loss, _ = torch.topk(loss,
+                    k=int(loss.shape[0] * (1-self.drop_worst_ratio)),
+                    largest=False)
+        loss = loss.mean()
+
+        return loss
+
+
+class BeamHypotheses(object):
+    def __init__(self, n_hyp, max_length, length_penalty, early_stopping):
+        """
+        Initialize n-best list of hypotheses.
+        """
+        self.max_length = max_length - 1  # ignoring bos_token
+        self.length_penalty = length_penalty
+        self.early_stopping = early_stopping
+        self.n_hyp = n_hyp
+        self.hyp = []
+        self.worst_score = 1e9
+
+    def __len__(self):
+        """
+        Number of hypotheses in the list.
+        """
+        return len(self.hyp)
+
+    def add(self, hyp, sum_logprobs):
+        """
+        Add a new hypothesis to the list.
+        """
+        score = sum_logprobs / len(hyp) ** self.length_penalty
+        if len(self) < self.n_hyp or score > self.worst_score:
+            self.hyp.append((score, hyp))
+            if len(self) > self.n_hyp:
+                sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.hyp)])
+                del self.hyp[sorted_scores[0][1]]
+                self.worst_score = sorted_scores[1][0]
+            else:
+                self.worst_score = min(score, self.worst_score)
+
+    def is_done(self, best_sum_logprobs):
+        """
+        If there are enough hypotheses and that none of the hypotheses being generated
+        can become better than the worst one in the heap, then we are done with this sentence.
+        """
+        if len(self) < self.n_hyp:
+            return False
+        elif self.early_stopping:
+            return True
+        else:
+            return self.worst_score >= best_sum_logprobs / self.max_length ** self.length_penalty
+
+
+def dump_predictions(args, result, file_suffix):
+    global_rank = get_rank()
+    jsons = None
+    if global_rank >= 0:
+        output_file = os.path.join(args.task_cache_path, f"submit_{global_rank}_{file_suffix}.json")
+        with open(output_file, "w") as fp:
+            json.dump(result, fp, indent=2)
+        torch.distributed.barrier()
+
+        if global_rank == 0:
+            world_size = get_world_size()
+            jsons = []
+            for i in range(world_size):
+                each_file = os.path.join(args.task_cache_path, f"submit_{i}_{file_suffix}.json")
+                with open(each_file, "r") as fp:
+                    jsons += json.load(fp)
+            
+            new_jsons = []
+            res_dict = dict()
+            if args.task in ["coco_captioning", "nocaps"]:
+                qid_key = "image_id"
+            else:
+                # for VQAv2
+                qid_key = "question_id"
+            for item in jsons:
+                if item[qid_key] in res_dict:
+                    continue
+                new_jsons.append(item)
+                res_dict[item[qid_key]] = item
+            jsons = new_jsons
+
+        torch.distributed.barrier()
+        os.remove(output_file)
+    else:
+        jsons = result
+    
+    result_file = os.path.join(args.output_dir, f"submit_{file_suffix}.json")
+    if jsons is not None:
+        with open(result_file, "w") as fp:
+            json.dump(jsons, fp, indent=2)
+        print("Infer %d examples into %s" % (len(jsons), result_file))
+    return result_file
+
+
+# The evaluation code is from BLIP (https://github.com/salesforce/BLIP)
+# For nocaps, please submit the prediction file to the evaluate server (https://eval.ai/web/challenges/challenge-page/355/overview) to obtain the final results
+def coco_caption_eval(gt_dir, results_file, split):
+    from pycocotools.coco import COCO
+    from pycocoevalcap.eval import COCOEvalCap
+    from torchvision.datasets.utils import download_url
+
+    urls = {'coco_captioning_val': 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json',
+            'coco_captioning_test': 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json',
+            'nocaps_val': 'https://conversationhub.blob.core.windows.net/beit-share-public/beit3/nocaps/nocaps_val_gt.json?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D'}
+    filenames = {'coco_captioning_val':'coco_karpathy_val_gt.json',
+                 'coco_captioning_test':'coco_karpathy_test_gt.json',
+                 'nocaps_val':'nocaps_val_gt.json'}
+    
+    download_url(urls[split], gt_dir)
+    annotation_file = os.path.join(gt_dir, filenames[split])
+    
+    # create coco object and coco_result object
+    coco = COCO(annotation_file)
+    coco_result = coco.loadRes(results_file)
+
+    # create coco_eval object by taking coco and coco_result
+    coco_eval = COCOEvalCap(coco, coco_result)
+
+    # evaluate results
+    # SPICE will take a few minutes the first time, but speeds up due to caching
+    coco_eval.evaluate()
+    
+    res_dict = dict()
+    for metric, score in coco_eval.eval.items():
+        res_dict[metric] = score
+    
+    return res_dict

beit3/val.sh

+#!/bin/bash/
+
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
+
+python -m torch.distributed.launch --nproc_per_node=4 run_beit3_finetuning.py \
+        --model beit3_base_patch16_480 \
+        --input_size 480 \
+        --task coco_captioning \
+        --batch_size 16 \
+        --sentencepiece_model ./pretrained_models/beit3.spm \
+        --finetune ./pretrained_models/beit3_base_patch16_480_coco_captioning.pth \
+        --data_path ../../data/coco2014/ \
+        --output_dir ./save_models \
+        --eval \
+        --dist_eval