diff --git a/BLIP/.DS_Store b/BLIP/.DS_Store deleted file mode 100644 index e7d690565b681946d9be69a21f45bf295a034b5d..0000000000000000000000000000000000000000 Binary files a/BLIP/.DS_Store and /dev/null differ diff --git a/BLIP/CODEOWNERS b/BLIP/CODEOWNERS deleted file mode 100644 index 522fa4a0f715cd0328b9b9dbacae00e060193f43..0000000000000000000000000000000000000000 --- a/BLIP/CODEOWNERS +++ /dev/null @@ -1,2 +0,0 @@ -# Comment line immediately above ownership line is reserved for related gus information. Please be careful while editing. -#ECCN:Open Source diff --git a/BLIP/CODE_OF_CONDUCT.md b/BLIP/CODE_OF_CONDUCT.md deleted file mode 100644 index b6724718c9512d730bb7f1bcc5848cd420241407..0000000000000000000000000000000000000000 --- a/BLIP/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,105 +0,0 @@ -# Salesforce Open Source Community Code of Conduct - -## About the Code of Conduct - -Equality is a core value at Salesforce. We believe a diverse and inclusive -community fosters innovation and creativity, and are committed to building a -culture where everyone feels included. - -Salesforce open-source projects are committed to providing a friendly, safe, and -welcoming environment for all, regardless of gender identity and expression, -sexual orientation, disability, physical appearance, body size, ethnicity, nationality, -race, age, religion, level of experience, education, socioeconomic status, or -other similar personal characteristics. - -The goal of this code of conduct is to specify a baseline standard of behavior so -that people with different social values and communication styles can work -together effectively, productively, and respectfully in our open source community. -It also establishes a mechanism for reporting issues and resolving conflicts. - -All questions and reports of abusive, harassing, or otherwise unacceptable behavior -in a Salesforce open-source project may be reported by contacting the Salesforce -Open Source Conduct Committee at ossconduct@salesforce.com. - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of gender -identity and expression, sexual orientation, disability, physical appearance, -body size, ethnicity, nationality, race, age, religion, level of experience, education, -socioeconomic status, or other similar personal characteristics. - -## Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy toward other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or -advances -* Personal attacks, insulting/derogatory comments, or trolling -* Public or private harassment -* Publishing, or threatening to publish, others' private information—such as -a physical or electronic address—without explicit permission -* Other conduct which could reasonably be considered inappropriate in a -professional setting -* Advocating for or encouraging any of the above behaviors - -## Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned with this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project email -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the Salesforce Open Source Conduct Committee -at ossconduct@salesforce.com. All complaints will be reviewed and investigated -and will result in a response that is deemed necessary and appropriate to the -circumstances. The committee is obligated to maintain confidentiality with -regard to the reporter of an incident. Further details of specific enforcement -policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership and the Salesforce Open Source Conduct -Committee. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][contributor-covenant-home], -version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html. -It includes adaptions and additions from [Go Community Code of Conduct][golang-coc], -[CNCF Code of Conduct][cncf-coc], and [Microsoft Open Source Code of Conduct][microsoft-coc]. - -This Code of Conduct is licensed under the [Creative Commons Attribution 3.0 License][cc-by-3-us]. - -[contributor-covenant-home]: https://www.contributor-covenant.org (https://www.contributor-covenant.org/) -[golang-coc]: https://golang.org/conduct -[cncf-coc]: https://github.com/cncf/foundation/blob/master/code-of-conduct.md -[microsoft-coc]: https://opensource.microsoft.com/codeofconduct/ -[cc-by-3-us]: https://creativecommons.org/licenses/by/3.0/us/ diff --git a/BLIP/LICENSE.txt b/BLIP/LICENSE.txt deleted file mode 100644 index a63e87f4e1e90c96861648a16a7304d97d3c3f7b..0000000000000000000000000000000000000000 --- a/BLIP/LICENSE.txt +++ /dev/null @@ -1,12 +0,0 @@ -Copyright (c) 2022, Salesforce.com, Inc. -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. - -* Neither the name of Salesforce.com nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/BLIP/README.md b/BLIP/README.md deleted file mode 100644 index 7923e2119361be467ef4da36ad167cdcc1344e08..0000000000000000000000000000000000000000 --- a/BLIP/README.md +++ /dev/null @@ -1,116 +0,0 @@ -## BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation - -## Announcement: BLIP is now officially integrated into [LAVIS](https://github.com/salesforce/LAVIS) - a one-stop library for language-and-vision research and applications! - - - -This is the PyTorch code of the BLIP paper [[blog](https://blog.salesforceairesearch.com/blip-bootstrapping-language-image-pretraining/)]. The code has been tested on PyTorch 1.10. -To install the dependencies, run 
pip install -r requirements.txt
- -Catalog: -- [x] Inference demo -- [x] Pre-trained and finetuned checkpoints -- [x] Finetuning code for Image-Text Retrieval, Image Captioning, VQA, and NLVR2 -- [x] Pre-training code -- [x] Zero-shot video-text retrieval -- [x] Download of bootstrapped pre-training datasets - - -### Inference demo: -Run our interactive demo using [Colab notebook](https://colab.research.google.com/github/salesforce/BLIP/blob/main/demo.ipynb) (no GPU needed). -The demo includes code for: -1. Image captioning -2. Open-ended visual question answering -3. Multimodal / unimodal feature extraction -4. Image-text matching - -Try out the [Web demo](https://huggingface.co/spaces/Salesforce/BLIP), integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). - -Replicate web demo and Docker image is also available at [![Replicate](https://replicate.com/salesforce/blip/badge)](https://replicate.com/salesforce/blip) - -### Pre-trained checkpoints: -Num. pre-train images | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L ---- | :---: | :---: | :---: -14M | Download| - | - -129M | Download| Download | Download - -### Finetuned checkpoints: -Task | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L ---- | :---: | :---: | :---: -Image-Text Retrieval (COCO) | Download| - | Download -Image-Text Retrieval (Flickr30k) | Download| - | Download -Image Captioning (COCO) | - | Download| Download | -VQA | Download| Download | - -NLVR2 | Download| - | - - - -### Image-Text Retrieval: -1. Download COCO and Flickr30k datasets from the original websites, and set 'image_root' in configs/retrieval_{dataset}.yaml accordingly. -2. To evaluate the finetuned BLIP model on COCO, run: -
python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
---config ./configs/retrieval_coco.yaml \
---output_dir output/retrieval_coco \
---evaluate
-3. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/retrieval_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run: -
python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
---config ./configs/retrieval_coco.yaml \
---output_dir output/retrieval_coco
- -### Image-Text Captioning: -1. Download COCO and NoCaps datasets from the original websites, and set 'image_root' in configs/caption_coco.yaml and configs/nocaps.yaml accordingly. -2. To evaluate the finetuned BLIP model on COCO, run: -
python -m torch.distributed.run --nproc_per_node=8 train_caption.py --evaluate
-3. To evaluate the finetuned BLIP model on NoCaps, generate results with: (evaluation needs to be performed on official server) -
python -m torch.distributed.run --nproc_per_node=8 eval_nocaps.py
-4. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/caption_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run: -
python -m torch.distributed.run --nproc_per_node=8 train_caption.py
- -### VQA: -1. Download VQA v2 dataset and Visual Genome dataset from the original websites, and set 'vqa_root' and 'vg_root' in configs/vqa.yaml. -2. To evaluate the finetuned BLIP model, generate results with: (evaluation needs to be performed on official server) -
python -m torch.distributed.run --nproc_per_node=8 train_vqa.py --evaluate
-3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/vqa.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run: -
python -m torch.distributed.run --nproc_per_node=16 train_vqa.py
- -### NLVR2: -1. Download NLVR2 dataset from the original websites, and set 'image_root' in configs/nlvr.yaml. -2. To evaluate the finetuned BLIP model, run -
python -m torch.distributed.run --nproc_per_node=8 train_nlvr.py --evaluate
-3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/nlvr.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run: -
python -m torch.distributed.run --nproc_per_node=16 train_nlvr.py
- -### Finetune with ViT-L: -In order to finetune a model with ViT-L, simply change the config file to set 'vit' as large. Batch size and learning rate may also need to be adjusted accordingly (please see the paper's appendix for hyper-parameter details). Gradient checkpoint can also be activated in the config file to reduce GPU memory usage. - -### Pre-train: -1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. -2. In configs/pretrain.yaml, set 'train_file' as the paths for the json files . -3. Pre-train the model using 8 A100 GPUs: -
python -m torch.distributed.run --nproc_per_node=8 pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain
- -### Zero-shot video-text retrieval: -1. Download MSRVTT dataset following the instructions from https://github.com/salesforce/ALPRO, and set 'video_root' accordingly in configs/retrieval_msrvtt.yaml. -2. Install [decord](https://github.com/dmlc/decord) with 
pip install decord
-3. To perform zero-shot evaluation, run -
python -m torch.distributed.run --nproc_per_node=8 eval_retrieval_video.py
- -### Pre-training datasets download: -We provide bootstrapped pre-training datasets as json files. Each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'url': url_of_image, 'caption': text_of_image}. - -Image source | Filtered web caption | Filtered synthetic caption by ViT-B | Filtered synthetic caption by ViT-L ---- | :---: | :---: | :---: -CC3M+CC12M+SBU | Download| Download| Download -LAION115M | Download| Download| Download - -### Citation -If you find this code to be useful for your research, please consider citing. -
-@inproceedings{li2022blip,
-      title={BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, 
-      author={Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi},
-      year={2022},
-      booktitle={ICML},
-}
- -### Acknowledgement -The implementation of BLIP relies on resources from ALBEF, Huggingface Transformers, and timm. We thank the original authors for their open-sourcing. diff --git a/BLIP/SECURITY.md b/BLIP/SECURITY.md deleted file mode 100644 index 8249025739809035264e7776583b2f3ec100553c..0000000000000000000000000000000000000000 --- a/BLIP/SECURITY.md +++ /dev/null @@ -1,7 +0,0 @@ -## Security - -Please report any security issue to [security@salesforce.com](mailto:security@salesforce.com) -as soon as it is discovered. This library limits its runtime dependencies in -order to reduce the total cost of ownership as much as can be, but all consumers -should remain vigilant and have their security stakeholders review all third-party -products (3PP) like this one and their dependencies. diff --git a/BLIP/cog.yaml b/BLIP/cog.yaml deleted file mode 100644 index c1dfcc430a4cab0fdd2a60a682336219a61c4a4f..0000000000000000000000000000000000000000 --- a/BLIP/cog.yaml +++ /dev/null @@ -1,17 +0,0 @@ -build: - gpu: true - cuda: "11.1" - python_version: "3.8" - system_packages: - - "libgl1-mesa-glx" - - "libglib2.0-0" - python_packages: - - "ipython==7.30.1" - - "torchvision==0.11.1" - - "torch==1.10.0" - - "timm==0.4.12" - - "transformers==4.15.0" - - "fairscale==0.4.4" - - "pycocoevalcap==1.2" - -predict: "predict.py:Predictor" diff --git a/BLIP/configs/bert_config.json b/BLIP/configs/bert_config.json deleted file mode 100644 index 3ef38aabc7f966b53079e9d559dc59e459cc0051..0000000000000000000000000000000000000000 --- a/BLIP/configs/bert_config.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 0, - "type_vocab_size": 2, - "vocab_size": 30522, - "encoder_width": 768, - "add_cross_attention": true -} diff --git a/BLIP/configs/caption_coco.yaml b/BLIP/configs/caption_coco.yaml deleted file mode 100644 index 42eab7030c0310ba2f265baf36fa1400aa6e5846..0000000000000000000000000000000000000000 --- a/BLIP/configs/caption_coco.yaml +++ /dev/null @@ -1,33 +0,0 @@ -image_root: '/export/share/datasets/vision/coco/images/' -ann_root: 'annotation' -coco_gt_root: 'annotation/coco_gt' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' - -# size of vit model; base or large -vit: 'base' -vit_grad_ckpt: False -vit_ckpt_layer: 0 -batch_size: 32 -init_lr: 1e-5 - -# vit: 'large' -# vit_grad_ckpt: True -# vit_ckpt_layer: 5 -# batch_size: 16 -# init_lr: 2e-6 - -image_size: 384 - -# generation configs -max_length: 20 -min_length: 5 -num_beams: 3 -prompt: 'a picture of ' - -# optimizer -weight_decay: 0.05 -min_lr: 0 -max_epoch: 5 - diff --git a/BLIP/configs/med_config.json b/BLIP/configs/med_config.json deleted file mode 100644 index 0ffad0a6f3c2f9f11b8faa84529d9860bb70327a..0000000000000000000000000000000000000000 --- a/BLIP/configs/med_config.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 0, - "type_vocab_size": 2, - "vocab_size": 30524, - "encoder_width": 768, - "add_cross_attention": true -} diff --git a/BLIP/configs/nlvr.yaml b/BLIP/configs/nlvr.yaml deleted file mode 100644 index 2d1122aadb1a776bd347068233096b0c984f648b..0000000000000000000000000000000000000000 --- a/BLIP/configs/nlvr.yaml +++ /dev/null @@ -1,21 +0,0 @@ -image_root: '/export/share/datasets/vision/NLVR2/' -ann_root: 'annotation' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth' - -#size of vit model; base or large -vit: 'base' -batch_size_train: 16 -batch_size_test: 64 -vit_grad_ckpt: False -vit_ckpt_layer: 0 -max_epoch: 15 - -image_size: 384 - -# optimizer -weight_decay: 0.05 -init_lr: 3e-5 -min_lr: 0 - diff --git a/BLIP/configs/nocaps.yaml b/BLIP/configs/nocaps.yaml deleted file mode 100644 index 9028135859b94aef5324c85c80e376c609d8a089..0000000000000000000000000000000000000000 --- a/BLIP/configs/nocaps.yaml +++ /dev/null @@ -1,15 +0,0 @@ -image_root: '/export/share/datasets/vision/nocaps/' -ann_root: 'annotation' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' - -vit: 'base' -batch_size: 32 - -image_size: 384 - -max_length: 20 -min_length: 5 -num_beams: 3 -prompt: 'a picture of ' \ No newline at end of file diff --git a/BLIP/configs/pretrain.yaml b/BLIP/configs/pretrain.yaml deleted file mode 100644 index 02355ee0228932803c661616485bf315e862b826..0000000000000000000000000000000000000000 --- a/BLIP/configs/pretrain.yaml +++ /dev/null @@ -1,27 +0,0 @@ -train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json', - '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json', - ] -laion_path: '' - -# size of vit model; base or large -vit: 'base' -vit_grad_ckpt: False -vit_ckpt_layer: 0 - -image_size: 224 -batch_size: 75 - -queue_size: 57600 -alpha: 0.4 - -# optimizer -weight_decay: 0.05 -init_lr: 3e-4 -min_lr: 1e-6 -warmup_lr: 1e-6 -lr_decay_rate: 0.9 -max_epoch: 20 -warmup_steps: 3000 - - - diff --git a/BLIP/configs/retrieval_coco.yaml b/BLIP/configs/retrieval_coco.yaml deleted file mode 100644 index a8569e9b67112fe3605ac25e4fdc0231f7975378..0000000000000000000000000000000000000000 --- a/BLIP/configs/retrieval_coco.yaml +++ /dev/null @@ -1,34 +0,0 @@ -image_root: '/export/share/datasets/vision/coco/images/' -ann_root: 'annotation' -dataset: 'coco' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' - -# size of vit model; base or large - -vit: 'base' -batch_size_train: 32 -batch_size_test: 64 -vit_grad_ckpt: True -vit_ckpt_layer: 4 -init_lr: 1e-5 - -# vit: 'large' -# batch_size_train: 16 -# batch_size_test: 32 -# vit_grad_ckpt: True -# vit_ckpt_layer: 12 -# init_lr: 5e-6 - -image_size: 384 -queue_size: 57600 -alpha: 0.4 -k_test: 256 -negative_all_rank: True - -# optimizer -weight_decay: 0.05 -min_lr: 0 -max_epoch: 6 - diff --git a/BLIP/configs/retrieval_flickr.yaml b/BLIP/configs/retrieval_flickr.yaml deleted file mode 100644 index d75ea4eed87c9a001523c5e5914998c5e737594d..0000000000000000000000000000000000000000 --- a/BLIP/configs/retrieval_flickr.yaml +++ /dev/null @@ -1,34 +0,0 @@ -image_root: '/export/share/datasets/vision/flickr30k/' -ann_root: 'annotation' -dataset: 'flickr' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth' - -# size of vit model; base or large - -vit: 'base' -batch_size_train: 32 -batch_size_test: 64 -vit_grad_ckpt: True -vit_ckpt_layer: 4 -init_lr: 1e-5 - -# vit: 'large' -# batch_size_train: 16 -# batch_size_test: 32 -# vit_grad_ckpt: True -# vit_ckpt_layer: 10 -# init_lr: 5e-6 - -image_size: 384 -queue_size: 57600 -alpha: 0.4 -k_test: 128 -negative_all_rank: False - -# optimizer -weight_decay: 0.05 -min_lr: 0 -max_epoch: 6 - diff --git a/BLIP/configs/retrieval_msrvtt.yaml b/BLIP/configs/retrieval_msrvtt.yaml deleted file mode 100644 index 395f62542bb22d706b8e19e2455d2c7298984d0b..0000000000000000000000000000000000000000 --- a/BLIP/configs/retrieval_msrvtt.yaml +++ /dev/null @@ -1,12 +0,0 @@ -video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos' -ann_root: 'annotation' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' - -# size of vit model; base or large -vit: 'base' -batch_size: 64 -k_test: 128 -image_size: 384 -num_frm_test: 8 \ No newline at end of file diff --git a/BLIP/configs/vqa.yaml b/BLIP/configs/vqa.yaml deleted file mode 100644 index 74327e6d0a34672023b44569558fe8beeb052548..0000000000000000000000000000000000000000 --- a/BLIP/configs/vqa.yaml +++ /dev/null @@ -1,25 +0,0 @@ -vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/ -vg_root: '/export/share/datasets/vision/visual-genome/' #followed by image/ -train_files: ['vqa_train','vqa_val','vg_qa'] -ann_root: 'annotation' - -# set pretrained as a file path or an url -pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth' - -# size of vit model; base or large -vit: 'base' -batch_size_train: 16 -batch_size_test: 32 -vit_grad_ckpt: False -vit_ckpt_layer: 0 -init_lr: 2e-5 - -image_size: 480 - -k_test: 128 -inference: 'rank' - -# optimizer -weight_decay: 0.05 -min_lr: 0 -max_epoch: 10 \ No newline at end of file diff --git a/BLIP/data/__init__.py b/BLIP/data/__init__.py deleted file mode 100644 index 0be209acf415855ea6ef753efedf903b5decb6b9..0000000000000000000000000000000000000000 --- a/BLIP/data/__init__.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -from torch.utils.data import DataLoader -from torchvision import transforms -from torchvision.transforms.functional import InterpolationMode - -from data.coco_karpathy_dataset import coco_karpathy_train, coco_karpathy_caption_eval, coco_karpathy_retrieval_eval -from data.nocaps_dataset import nocaps_eval -from data.flickr30k_dataset import flickr30k_train, flickr30k_retrieval_eval -from data.vqa_dataset import vqa_dataset -from data.nlvr_dataset import nlvr_dataset -from data.pretrain_dataset import pretrain_dataset -from transform.randaugment import RandomAugment - -def create_dataset(dataset, config, min_scale=0.5): - - normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) - - transform_train = transforms.Compose([ - transforms.RandomResizedCrop(config['image_size'],scale=(min_scale, 1.0),interpolation=InterpolationMode.BICUBIC), - transforms.RandomHorizontalFlip(), - RandomAugment(2,5,isPIL=True,augs=['Identity','AutoContrast','Brightness','Sharpness','Equalize', - 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']), - transforms.ToTensor(), - normalize, - ]) - transform_test = transforms.Compose([ - transforms.Resize((config['image_size'],config['image_size']),interpolation=InterpolationMode.BICUBIC), - transforms.ToTensor(), - normalize, - ]) - - if dataset=='pretrain': - dataset = pretrain_dataset(config['train_file'], config['laion_path'], transform_train) - return dataset - - elif dataset=='caption_coco': - train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root'], prompt=config['prompt']) - val_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return train_dataset, val_dataset, test_dataset - - elif dataset=='nocaps': - val_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return val_dataset, test_dataset - - elif dataset=='retrieval_coco': - train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root']) - val_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return train_dataset, val_dataset, test_dataset - - elif dataset=='retrieval_flickr': - train_dataset = flickr30k_train(transform_train, config['image_root'], config['ann_root']) - val_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return train_dataset, val_dataset, test_dataset - - elif dataset=='vqa': - train_dataset = vqa_dataset(transform_train, config['ann_root'], config['vqa_root'], config['vg_root'], - train_files = config['train_files'], split='train') - test_dataset = vqa_dataset(transform_test, config['ann_root'], config['vqa_root'], config['vg_root'], split='test') - return train_dataset, test_dataset - - elif dataset=='nlvr': - train_dataset = nlvr_dataset(transform_train, config['image_root'], config['ann_root'],'train') - val_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'val') - test_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'test') - return train_dataset, val_dataset, test_dataset - - -def create_sampler(datasets, shuffles, num_tasks, global_rank): - samplers = [] - for dataset,shuffle in zip(datasets,shuffles): - sampler = torch.utils.data.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank, shuffle=shuffle) - samplers.append(sampler) - return samplers - - -def create_loader(datasets, samplers, batch_size, num_workers, is_trains, collate_fns): - loaders = [] - for dataset,sampler,bs,n_worker,is_train,collate_fn in zip(datasets,samplers,batch_size,num_workers,is_trains,collate_fns): - if is_train: - shuffle = (sampler is None) - drop_last = True - else: - shuffle = False - drop_last = False - loader = DataLoader( - dataset, - batch_size=bs, - num_workers=n_worker, - pin_memory=True, - sampler=sampler, - shuffle=shuffle, - collate_fn=collate_fn, - drop_last=drop_last, - ) - loaders.append(loader) - return loaders - diff --git a/BLIP/data/coco_karpathy_dataset.py b/BLIP/data/coco_karpathy_dataset.py deleted file mode 100644 index a34d29205f42aa09695b160ac9c91958ba041bb3..0000000000000000000000000000000000000000 --- a/BLIP/data/coco_karpathy_dataset.py +++ /dev/null @@ -1,126 +0,0 @@ -import os -import json - -from torch.utils.data import Dataset -from torchvision.datasets.utils import download_url - -from PIL import Image - -from data.utils import pre_caption - -class coco_karpathy_train(Dataset): - def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''): - ''' - image_root (string): Root directory of images (e.g. coco/images/) - ann_root (string): directory to store the annotation file - ''' - url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json' - filename = 'coco_karpathy_train.json' - - download_url(url,ann_root) - - self.annotation = json.load(open(os.path.join(ann_root,filename),'r')) - self.transform = transform - self.image_root = image_root - self.max_words = max_words - self.prompt = prompt - - self.img_ids = {} - n = 0 - for ann in self.annotation: - img_id = ann['image_id'] - if img_id not in self.img_ids.keys(): - self.img_ids[img_id] = n - n += 1 - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - image_path = os.path.join(self.image_root,ann['image']) - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - caption = self.prompt+pre_caption(ann['caption'], self.max_words) - - return image, caption, self.img_ids[ann['image_id']] - - -class coco_karpathy_caption_eval(Dataset): - def __init__(self, transform, image_root, ann_root, split): - ''' - image_root (string): Root directory of images (e.g. coco/images/) - ann_root (string): directory to store the annotation file - split (string): val or test - ''' - urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json', - 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'} - filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'} - - download_url(urls[split],ann_root) - - self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r')) - self.transform = transform - self.image_root = image_root - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - image_path = os.path.join(self.image_root,ann['image']) - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - img_id = ann['image'].split('/')[-1].strip('.jpg').split('_')[-1] - - return image, int(img_id) - - -class coco_karpathy_retrieval_eval(Dataset): - def __init__(self, transform, image_root, ann_root, split, max_words=30): - ''' - image_root (string): Root directory of images (e.g. coco/images/) - ann_root (string): directory to store the annotation file - split (string): val or test - ''' - urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json', - 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'} - filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'} - - download_url(urls[split],ann_root) - - self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r')) - self.transform = transform - self.image_root = image_root - - self.text = [] - self.image = [] - self.txt2img = {} - self.img2txt = {} - - txt_id = 0 - for img_id, ann in enumerate(self.annotation): - self.image.append(ann['image']) - self.img2txt[img_id] = [] - for i, caption in enumerate(ann['caption']): - self.text.append(pre_caption(caption,max_words)) - self.img2txt[img_id].append(txt_id) - self.txt2img[txt_id] = img_id - txt_id += 1 - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - image_path = os.path.join(self.image_root, self.annotation[index]['image']) - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - return image, index \ No newline at end of file diff --git a/BLIP/data/flickr30k_dataset.py b/BLIP/data/flickr30k_dataset.py deleted file mode 100644 index 018ab387014ddaf554c4d3184cfc0e2ba8b2d487..0000000000000000000000000000000000000000 --- a/BLIP/data/flickr30k_dataset.py +++ /dev/null @@ -1,93 +0,0 @@ -import os -import json - -from torch.utils.data import Dataset -from torchvision.datasets.utils import download_url - -from PIL import Image - -from data.utils import pre_caption - -class flickr30k_train(Dataset): - def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''): - ''' - image_root (string): Root directory of images (e.g. flickr30k/) - ann_root (string): directory to store the annotation file - ''' - url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_train.json' - filename = 'flickr30k_train.json' - - download_url(url,ann_root) - - self.annotation = json.load(open(os.path.join(ann_root,filename),'r')) - self.transform = transform - self.image_root = image_root - self.max_words = max_words - self.prompt = prompt - - self.img_ids = {} - n = 0 - for ann in self.annotation: - img_id = ann['image_id'] - if img_id not in self.img_ids.keys(): - self.img_ids[img_id] = n - n += 1 - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - image_path = os.path.join(self.image_root,ann['image']) - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - caption = self.prompt+pre_caption(ann['caption'], self.max_words) - - return image, caption, self.img_ids[ann['image_id']] - - -class flickr30k_retrieval_eval(Dataset): - def __init__(self, transform, image_root, ann_root, split, max_words=30): - ''' - image_root (string): Root directory of images (e.g. flickr30k/) - ann_root (string): directory to store the annotation file - split (string): val or test - ''' - urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json', - 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json'} - filenames = {'val':'flickr30k_val.json','test':'flickr30k_test.json'} - - download_url(urls[split],ann_root) - - self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r')) - self.transform = transform - self.image_root = image_root - - self.text = [] - self.image = [] - self.txt2img = {} - self.img2txt = {} - - txt_id = 0 - for img_id, ann in enumerate(self.annotation): - self.image.append(ann['image']) - self.img2txt[img_id] = [] - for i, caption in enumerate(ann['caption']): - self.text.append(pre_caption(caption,max_words)) - self.img2txt[img_id].append(txt_id) - self.txt2img[txt_id] = img_id - txt_id += 1 - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - image_path = os.path.join(self.image_root, self.annotation[index]['image']) - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - return image, index \ No newline at end of file diff --git a/BLIP/data/nlvr_dataset.py b/BLIP/data/nlvr_dataset.py deleted file mode 100644 index a8d6b2d7cd8d3260bd279c7dca80de53bacc691a..0000000000000000000000000000000000000000 --- a/BLIP/data/nlvr_dataset.py +++ /dev/null @@ -1,78 +0,0 @@ -import os -import json -import random - -from torch.utils.data import Dataset -from torchvision.datasets.utils import download_url - -from PIL import Image - -from data.utils import pre_caption - -class nlvr_dataset(Dataset): - def __init__(self, transform, image_root, ann_root, split): - ''' - image_root (string): Root directory of images - ann_root (string): directory to store the annotation file - split (string): train, val or test - ''' - urls = {'train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_train.json', - 'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_dev.json', - 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_test.json'} - filenames = {'train':'nlvr_train.json','val':'nlvr_dev.json','test':'nlvr_test.json'} - - download_url(urls[split],ann_root) - self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r')) - - self.transform = transform - self.image_root = image_root - - - def __len__(self): - return len(self.annotation) - - - def __getitem__(self, index): - - ann = self.annotation[index] - - image0_path = os.path.join(self.image_root,ann['images'][0]) - image0 = Image.open(image0_path).convert('RGB') - image0 = self.transform(image0) - - image1_path = os.path.join(self.image_root,ann['images'][1]) - image1 = Image.open(image1_path).convert('RGB') - image1 = self.transform(image1) - - sentence = pre_caption(ann['sentence'], 40) - - if ann['label']=='True': - label = 1 - else: - label = 0 - - words = sentence.split(' ') - - if 'left' not in words and 'right' not in words: - if random.random()<0.5: - return image0, image1, sentence, label - else: - return image1, image0, sentence, label - else: - if random.random()<0.5: - return image0, image1, sentence, label - else: - new_words = [] - for word in words: - if word=='left': - new_words.append('right') - elif word=='right': - new_words.append('left') - else: - new_words.append(word) - - sentence = ' '.join(new_words) - return image1, image0, sentence, label - - - \ No newline at end of file diff --git a/BLIP/data/nocaps_dataset.py b/BLIP/data/nocaps_dataset.py deleted file mode 100644 index ba0bed06d8af3dbaccf18a56e725f101e585503e..0000000000000000000000000000000000000000 --- a/BLIP/data/nocaps_dataset.py +++ /dev/null @@ -1,32 +0,0 @@ -import os -import json - -from torch.utils.data import Dataset -from torchvision.datasets.utils import download_url - -from PIL import Image - -class nocaps_eval(Dataset): - def __init__(self, transform, image_root, ann_root, split): - urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json', - 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json'} - filenames = {'val':'nocaps_val.json','test':'nocaps_test.json'} - - download_url(urls[split],ann_root) - - self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r')) - self.transform = transform - self.image_root = image_root - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - image_path = os.path.join(self.image_root,ann['image']) - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - return image, int(ann['img_id']) \ No newline at end of file diff --git a/BLIP/data/pretrain_dataset.py b/BLIP/data/pretrain_dataset.py deleted file mode 100644 index 703d543ab5267fdc6fe2b7c84ef6a631d8af90ad..0000000000000000000000000000000000000000 --- a/BLIP/data/pretrain_dataset.py +++ /dev/null @@ -1,59 +0,0 @@ -import json -import os -import random - -from torch.utils.data import Dataset - -from PIL import Image -from PIL import ImageFile -ImageFile.LOAD_TRUNCATED_IMAGES = True -Image.MAX_IMAGE_PIXELS = None - -from data.utils import pre_caption -import os,glob - -class pretrain_dataset(Dataset): - def __init__(self, ann_file, laion_path, transform): - - self.ann_pretrain = [] - for f in ann_file: - print('loading '+f) - ann = json.load(open(f,'r')) - self.ann_pretrain += ann - - self.laion_path = laion_path - if self.laion_path: - self.laion_files = glob.glob(os.path.join(laion_path,'*.json')) - - print('loading '+self.laion_files[0]) - with open(self.laion_files[0],'r') as f: - self.ann_laion = json.load(f) - - self.annotation = self.ann_pretrain + self.ann_laion - else: - self.annotation = self.ann_pretrain - - self.transform = transform - - - def reload_laion(self, epoch): - n = epoch%len(self.laion_files) - print('loading '+self.laion_files[n]) - with open(self.laion_files[n],'r') as f: - self.ann_laion = json.load(f) - - self.annotation = self.ann_pretrain + self.ann_laion - - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - image = Image.open(ann['image']).convert('RGB') - image = self.transform(image) - caption = pre_caption(ann['caption'],30) - - return image, caption \ No newline at end of file diff --git a/BLIP/data/utils.py b/BLIP/data/utils.py deleted file mode 100644 index 628894844becd462d444584b8b2b01a84ee4b8f7..0000000000000000000000000000000000000000 --- a/BLIP/data/utils.py +++ /dev/null @@ -1,112 +0,0 @@ -import re -import json -import os - -import torch -import torch.distributed as dist - -import utils - -def pre_caption(caption,max_words=50): - caption = re.sub( - r"([.!\"()*#:;~])", - ' ', - caption.lower(), - ) - caption = re.sub( - r"\s{2,}", - ' ', - caption, - ) - caption = caption.rstrip('\n') - caption = caption.strip(' ') - - #truncate caption - caption_words = caption.split(' ') - if len(caption_words)>max_words: - caption = ' '.join(caption_words[:max_words]) - - return caption - -def pre_question(question,max_ques_words=50): - question = re.sub( - r"([.!\"()*#:;~])", - '', - question.lower(), - ) - question = question.rstrip(' ') - - #truncate question - question_words = question.split(' ') - if len(question_words)>max_ques_words: - question = ' '.join(question_words[:max_ques_words]) - - return question - - -def save_result(result, result_dir, filename, remove_duplicate=''): - result_file = os.path.join(result_dir, '%s_rank%d.json'%(filename,utils.get_rank())) - final_result_file = os.path.join(result_dir, '%s.json'%filename) - - json.dump(result,open(result_file,'w')) - - dist.barrier() - - if utils.is_main_process(): - # combine results from all processes - result = [] - - for rank in range(utils.get_world_size()): - result_file = os.path.join(result_dir, '%s_rank%d.json'%(filename,rank)) - res = json.load(open(result_file,'r')) - result += res - - if remove_duplicate: - result_new = [] - id_list = [] - for res in result: - if res[remove_duplicate] not in id_list: - id_list.append(res[remove_duplicate]) - result_new.append(res) - result = result_new - - json.dump(result,open(final_result_file,'w')) - print('result file saved to %s'%final_result_file) - - return final_result_file - - - -from pycocotools.coco import COCO -from pycocoevalcap.eval import COCOEvalCap -from torchvision.datasets.utils import download_url - -def coco_caption_eval(coco_gt_root, results_file, split): - urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json', - 'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json'} - filenames = {'val':'coco_karpathy_val_gt.json','test':'coco_karpathy_test_gt.json'} - - download_url(urls[split],coco_gt_root) - annotation_file = os.path.join(coco_gt_root,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 on a subset of images by setting - # coco_eval.params['image_id'] = coco_result.getImgIds() - # please remove this line when evaluating the full validation set - # coco_eval.params['image_id'] = coco_result.getImgIds() - - # evaluate results - # SPICE will take a few minutes the first time, but speeds up due to caching - coco_eval.evaluate() - - # print output evaluation scores - for metric, score in coco_eval.eval.items(): - print(f'{metric}: {score:.3f}') - - return coco_eval \ No newline at end of file diff --git a/BLIP/data/video_dataset.py b/BLIP/data/video_dataset.py deleted file mode 100644 index 0a6f8a61105bbd4285f98b3abe9445b73fd4c7ef..0000000000000000000000000000000000000000 --- a/BLIP/data/video_dataset.py +++ /dev/null @@ -1,110 +0,0 @@ -from torch.utils.data import Dataset -from torchvision.datasets.utils import download_url - -from PIL import Image -import torch -import numpy as np -import random -import decord -from decord import VideoReader -import json -import os -from data.utils import pre_caption - -decord.bridge.set_bridge("torch") - -class ImageNorm(object): - """Apply Normalization to Image Pixels on GPU - """ - def __init__(self, mean, std): - self.mean = torch.tensor(mean).view(1, 3, 1, 1) - self.std = torch.tensor(std).view(1, 3, 1, 1) - - def __call__(self, img): - - if torch.max(img) > 1 and self.mean.max() <= 1: - img.div_(255.) - return img.sub_(self.mean).div_(self.std) - -def load_jsonl(filename): - with open(filename, "r") as f: - return [json.loads(l.strip("\n")) for l in f.readlines()] - - -class VideoDataset(Dataset): - - def __init__(self, video_root, ann_root, num_frm=4, frm_sampling_strategy="rand", max_img_size=384, video_fmt='.mp4'): - ''' - image_root (string): Root directory of video - ann_root (string): directory to store the annotation file - ''' - url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/msrvtt_test.jsonl' - filename = 'msrvtt_test.jsonl' - - download_url(url,ann_root) - self.annotation = load_jsonl(os.path.join(ann_root,filename)) - - self.num_frm = num_frm - self.frm_sampling_strategy = frm_sampling_strategy - self.max_img_size = max_img_size - self.video_root = video_root - self.video_fmt = video_fmt - self.img_norm = ImageNorm(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)) - - self.text = [pre_caption(ann['caption'],40) for ann in self.annotation] - self.txt2video = [i for i in range(len(self.annotation))] - self.video2txt = self.txt2video - - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - video_path = os.path.join(self.video_root, ann['clip_name'] + self.video_fmt) - - vid_frm_array = self._load_video_from_path_decord(video_path, height=self.max_img_size, width=self.max_img_size) - - video = self.img_norm(vid_frm_array.float()) - - return video, ann['clip_name'] - - - - def _load_video_from_path_decord(self, video_path, height=None, width=None, start_time=None, end_time=None, fps=-1): - try: - if not height or not width: - vr = VideoReader(video_path) - else: - vr = VideoReader(video_path, width=width, height=height) - - vlen = len(vr) - - if start_time or end_time: - assert fps > 0, 'must provide video fps if specifying start and end time.' - - start_idx = min(int(start_time * fps), vlen) - end_idx = min(int(end_time * fps), vlen) - else: - start_idx, end_idx = 0, vlen - - if self.frm_sampling_strategy == 'uniform': - frame_indices = np.arange(start_idx, end_idx, vlen / self.num_frm, dtype=int) - elif self.frm_sampling_strategy == 'rand': - frame_indices = sorted(random.sample(range(vlen), self.num_frm)) - elif self.frm_sampling_strategy == 'headtail': - frame_indices_head = sorted(random.sample(range(vlen // 2), self.num_frm // 2)) - frame_indices_tail = sorted(random.sample(range(vlen // 2, vlen), self.num_frm // 2)) - frame_indices = frame_indices_head + frame_indices_tail - else: - raise NotImplementedError('Invalid sampling strategy {} '.format(self.frm_sampling_strategy)) - - raw_sample_frms = vr.get_batch(frame_indices) - except Exception as e: - return None - - raw_sample_frms = raw_sample_frms.permute(0, 3, 1, 2) - - return raw_sample_frms diff --git a/BLIP/data/vqa_dataset.py b/BLIP/data/vqa_dataset.py deleted file mode 100644 index 92ec1df429b3910316ddd554bfea01c6e7922cae..0000000000000000000000000000000000000000 --- a/BLIP/data/vqa_dataset.py +++ /dev/null @@ -1,88 +0,0 @@ -import os -import json -import random -from PIL import Image - -import torch -from torch.utils.data import Dataset -from data.utils import pre_question - -from torchvision.datasets.utils import download_url - -class vqa_dataset(Dataset): - def __init__(self, transform, ann_root, vqa_root, vg_root, train_files=[], split="train"): - self.split = split - - self.transform = transform - self.vqa_root = vqa_root - self.vg_root = vg_root - - if split=='train': - urls = {'vqa_train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json', - 'vqa_val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json', - 'vg_qa':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json'} - - self.annotation = [] - for f in train_files: - download_url(urls[f],ann_root) - self.annotation += json.load(open(os.path.join(ann_root,'%s.json'%f),'r')) - else: - download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json',ann_root) - self.annotation = json.load(open(os.path.join(ann_root,'vqa_test.json'),'r')) - - download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json',ann_root) - self.answer_list = json.load(open(os.path.join(ann_root,'answer_list.json'),'r')) - - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - if ann['dataset']=='vqa': - image_path = os.path.join(self.vqa_root,ann['image']) - elif ann['dataset']=='vg': - image_path = os.path.join(self.vg_root,ann['image']) - - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - if self.split == 'test': - question = pre_question(ann['question']) - question_id = ann['question_id'] - return image, question, question_id - - - elif self.split=='train': - - question = pre_question(ann['question']) - - if ann['dataset']=='vqa': - answer_weight = {} - for answer in ann['answer']: - if answer in answer_weight.keys(): - answer_weight[answer] += 1/len(ann['answer']) - else: - answer_weight[answer] = 1/len(ann['answer']) - - answers = list(answer_weight.keys()) - weights = list(answer_weight.values()) - - elif ann['dataset']=='vg': - answers = [ann['answer']] - weights = [0.2] - - return image, question, answers, weights - - -def vqa_collate_fn(batch): - image_list, question_list, answer_list, weight_list, n = [], [], [], [], [] - for image, question, answer, weights in batch: - image_list.append(image) - question_list.append(question) - weight_list += weights - answer_list += answer - n.append(len(answer)) - return torch.stack(image_list,dim=0), question_list, answer_list, torch.Tensor(weight_list), n \ No newline at end of file diff --git a/BLIP/demo.ipynb b/BLIP/demo.ipynb deleted file mode 100644 index 3077a1a42c584f3ef535903f64cf6b3bb722490e..0000000000000000000000000000000000000000 --- a/BLIP/demo.ipynb +++ /dev/null @@ -1,301 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2b949f9f", - "metadata": {}, - "source": [ - "# BLIP: Inference Demo\n", - " - [Image Captioning](#Image-Captioning)\n", - " - [VQA](#VQA)\n", - " - [Feature Extraction](#Feature-Extraction)\n", - " - [Image Text Matching](#Image-Text-Matching)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cbcb066b", - "metadata": {}, - "outputs": [], - "source": [ - "# install requirements\n", - "import sys\n", - "if 'google.colab' in sys.modules:\n", - " print('Running in Colab.')\n", - " !pip3 install transformers==4.15.0 timm==0.4.12 fairscale==0.4.4\n", - " !git clone https://github.com/salesforce/BLIP\n", - " %cd BLIP" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a811a65f", - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image\n", - "import requests\n", - "import torch\n", - "from torchvision import transforms\n", - "from torchvision.transforms.functional import InterpolationMode\n", - "\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "def load_demo_image(image_size,device):\n", - " img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' \n", - " raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') \n", - "\n", - " w,h = raw_image.size\n", - " display(raw_image.resize((w//5,h//5)))\n", - " \n", - " transform = transforms.Compose([\n", - " transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))\n", - " ]) \n", - " image = transform(raw_image).unsqueeze(0).to(device) \n", - " return image" - ] - }, - { - "cell_type": "markdown", - "id": "f72f4406", - "metadata": {}, - "source": [ - "# Image Captioning\n", - "Perform image captioning using finetuned BLIP model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6835daef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth\n", - "caption: a woman sitting on the beach with a dog\n" - ] - } - ], - "source": [ - "from models.blip import blip_decoder\n", - "\n", - "image_size = 384\n", - "image = load_demo_image(image_size=image_size, device=device)\n", - "\n", - "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'\n", - " \n", - "model = blip_decoder(pretrained=model_url, image_size=image_size, vit='base')\n", - "model.eval()\n", - "model = model.to(device)\n", - "\n", - "with torch.no_grad():\n", - " # beam search\n", - " caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) \n", - " # nucleus sampling\n", - " # caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) \n", - " print('caption: '+caption[0])" - ] - }, - { - "cell_type": "markdown", - "id": "fac320a2", - "metadata": {}, - "source": [ - "# VQA\n", - "Perform visual question answering using finetuned BLIP model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5e6f3fb1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth\n", - "answer: on beach\n" - ] - } - ], - "source": [ - "from models.blip_vqa import blip_vqa\n", - "\n", - "image_size = 480\n", - "image = load_demo_image(image_size=image_size, device=device) \n", - "\n", - "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'\n", - " \n", - "model = blip_vqa(pretrained=model_url, image_size=image_size, vit='base')\n", - "model.eval()\n", - "model = model.to(device)\n", - "\n", - "question = 'where is the woman sitting?'\n", - "\n", - "with torch.no_grad():\n", - " answer = model(image, question, train=False, inference='generate') \n", - " print('answer: '+answer[0])" - ] - }, - { - "cell_type": "markdown", - "id": "6100e519", - "metadata": {}, - "source": [ - "# Feature Extraction" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4f8f21ed", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth\n" - ] - } - ], - "source": [ - "from models.blip import blip_feature_extractor\n", - "\n", - "image_size = 224\n", - "image = load_demo_image(image_size=image_size, device=device) \n", - "\n", - "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth'\n", - " \n", - "model = blip_feature_extractor(pretrained=model_url, image_size=image_size, vit='base')\n", - "model.eval()\n", - "model = model.to(device)\n", - "\n", - "caption = 'a woman sitting on the beach with a dog'\n", - "\n", - "multimodal_feature = model(image, caption, mode='multimodal')[0,0]\n", - "image_feature = model(image, caption, mode='image')[0,0]\n", - "text_feature = model(image, caption, mode='text')[0,0]" - ] - }, - { - "cell_type": "markdown", - "id": "201e1146", - "metadata": {}, - "source": [ - "# Image-Text Matching" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "49ba5906", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth\n", - "text: a woman sitting on the beach with a dog\n", - "The image and text is matched with a probability of 0.9960\n", - "The image feature and text feature has a cosine similarity of 0.5262\n" - ] - } - ], - "source": [ - "from models.blip_itm import blip_itm\n", - "\n", - "image_size = 384\n", - "image = load_demo_image(image_size=image_size,device=device)\n", - "\n", - "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'\n", - " \n", - "model = blip_itm(pretrained=model_url, image_size=image_size, vit='base')\n", - "model.eval()\n", - "model = model.to(device='cpu')\n", - "\n", - "caption = 'a woman sitting on the beach with a dog'\n", - "\n", - "print('text: %s' %caption)\n", - "\n", - "itm_output = model(image,caption,match_head='itm')\n", - "itm_score = torch.nn.functional.softmax(itm_output,dim=1)[:,1]\n", - "print('The image and text is matched with a probability of %.4f'%itm_score)\n", - "\n", - "itc_score = model(image,caption,match_head='itc')\n", - "print('The image feature and text feature has a cosine similarity of %.4f'%itc_score)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/BLIP/eval_nocaps.py b/BLIP/eval_nocaps.py deleted file mode 100644 index 3cbb09a8cc7771605c013583d721aa95d9413b42..0000000000000000000000000000000000000000 --- a/BLIP/eval_nocaps.py +++ /dev/null @@ -1,118 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.backends.cudnn as cudnn -import torch.distributed as dist -from torch.utils.data import DataLoader - -from models.blip import blip_decoder -import utils -from data import create_dataset, create_sampler, create_loader -from data.utils import save_result - -@torch.no_grad() -def evaluate(model, data_loader, device, config): - # evaluate - model.eval() - - metric_logger = utils.MetricLogger(delimiter=" ") - header = 'Evaluation:' - print_freq = 10 - - result = [] - for image, image_id in metric_logger.log_every(data_loader, print_freq, header): - - image = image.to(device) - - captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], - min_length=config['min_length'], repetition_penalty=1.1) - - for caption, img_id in zip(captions, image_id): - result.append({"image_id": img_id.item(), "caption": caption}) - - return result - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating captioning dataset") - val_dataset, test_dataset = create_dataset('nocaps', config) - - if args.distributed: - num_tasks = utils.get_world_size() - global_rank = utils.get_rank() - samplers = create_sampler([val_dataset,test_dataset], [False,False], num_tasks, global_rank) - else: - samplers = [None,None] - - val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, - batch_size=[config['batch_size']]*2,num_workers=[4,4], - is_trains=[False, False], collate_fns=[None,None]) - - #### Model #### - print("Creating model") - model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], - prompt=config['prompt']) - - model = model.to(device) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - val_result = evaluate(model_without_ddp, val_loader, device, config) - val_result_file = save_result(val_result, args.result_dir, 'val', remove_duplicate='image_id') - test_result = evaluate(model_without_ddp, test_loader, device, config) - test_result_file = save_result(test_result, args.result_dir, 'test', remove_duplicate='image_id') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/nocaps.yaml') - parser.add_argument('--output_dir', default='output/NoCaps') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - args.result_dir = os.path.join(args.output_dir, 'result') - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - Path(args.result_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/eval_retrieval_video.py b/BLIP/eval_retrieval_video.py deleted file mode 100644 index 07ebab7f41f6466f6f46130002e2e0df1266486a..0000000000000000000000000000000000000000 --- a/BLIP/eval_retrieval_video.py +++ /dev/null @@ -1,250 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.backends.cudnn as cudnn -import torch.distributed as dist -from torch.utils.data import DataLoader - -from models.blip_retrieval import blip_retrieval -import utils -from data.video_dataset import VideoDataset - - -@torch.no_grad() -def evaluation(model, data_loader, tokenizer, device, config): - # test - model.eval() - - metric_logger = utils.MetricLogger(delimiter=" ") - header = 'Evaluation:' - - print('Computing features for evaluation...') - start_time = time.time() - - texts = data_loader.dataset.text - num_text = len(texts) - text_bs = 256 - text_ids = [] - text_embeds = [] - text_atts = [] - for i in range(0, num_text, text_bs): - text = texts[i: min(num_text, i+text_bs)] - text_input = tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device) - text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text') - text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:])) - text_embeds.append(text_embed) - text_ids.append(text_input.input_ids) - text_atts.append(text_input.attention_mask) - - text_embeds = torch.cat(text_embeds,dim=0) - text_ids = torch.cat(text_ids,dim=0) - text_atts = torch.cat(text_atts,dim=0) - text_ids[:,0] = tokenizer.additional_special_tokens_ids[0] - - video_feats = [] - video_embeds = [] - for video, video_id in data_loader: - - B,N,C,W,H = video.size() - video = video.view(-1,C,W,H) - video = video.to(device,non_blocking=True) - video_feat = model.visual_encoder(video) - video_embed = model.vision_proj(video_feat[:,0,:]) - video_embed = video_embed.view(B,N,-1).mean(dim=1) - video_embed = F.normalize(video_embed,dim=-1) - - video_feat = video_feat.view(B,-1,video_feat.shape[-1]) - video_feats.append(video_feat.cpu()) - video_embeds.append(video_embed) - - video_feats = torch.cat(video_feats,dim=0) - video_embeds = torch.cat(video_embeds,dim=0) - - sims_matrix = video_embeds @ text_embeds.t() - score_matrix_v2t = torch.full((len(texts),len(texts)),-100.0).to(device) - - num_tasks = utils.get_world_size() - rank = utils.get_rank() - step = sims_matrix.size(0)//num_tasks + 1 - start = rank*step - end = min(sims_matrix.size(0),start+step) - - for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): - topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0) - - encoder_output = video_feats[start+i].repeat(config['k_test'],1,1).to(device,non_blocking=True) - encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) - output = model.text_encoder(text_ids[topk_idx], - attention_mask = text_atts[topk_idx], - encoder_hidden_states = encoder_output, - encoder_attention_mask = encoder_att, - return_dict = True, - ) - score = model.itm_head(output.last_hidden_state[:,0,:])[:,1] - score_matrix_v2t[start+i,topk_idx] = score + topk_sim - - sims_matrix = sims_matrix.t() - score_matrix_t2v = torch.full((len(texts),len(texts)),-100.0).to(device) - - step = sims_matrix.size(0)//num_tasks + 1 - start = rank*step - end = min(sims_matrix.size(0),start+step) - - for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): - - topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0) - encoder_output = video_feats[topk_idx].to(device,non_blocking=True) - encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) - output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1), - attention_mask = text_atts[start+i].repeat(config['k_test'],1), - encoder_hidden_states = encoder_output, - encoder_attention_mask = encoder_att, - return_dict = True, - ) - score = model.itm_head(output.last_hidden_state[:,0,:])[:,1] - score_matrix_t2v[start+i,topk_idx] = score + topk_sim - - if args.distributed: - dist.barrier() - torch.distributed.all_reduce(score_matrix_v2t, op=torch.distributed.ReduceOp.SUM) - torch.distributed.all_reduce(score_matrix_t2v, op=torch.distributed.ReduceOp.SUM) - - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print('Evaluation time {}'.format(total_time_str)) - - return score_matrix_v2t.cpu().numpy(), score_matrix_t2v.cpu().numpy() - - - -@torch.no_grad() -def itm_eval(scores_v2t, scores_t2v, txt2vmg, vid2txt): - - #Video->Text - ranks = np.zeros(scores_v2t.shape[0]) - for index,score in enumerate(scores_v2t): - inds = np.argsort(score)[::-1] - ranks[index] = np.where(inds == vid2txt[index])[0][0] - - # Compute metrics - tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) - tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) - tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) - - #Text->Video - ranks = np.zeros(scores_t2v.shape[0]) - - for index,score in enumerate(scores_t2v): - inds = np.argsort(score)[::-1] - ranks[index] = np.where(inds == txt2vmg[index])[0][0] - - mdR = np.median(ranks+1) - - # Compute metrics - vr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) - vr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) - vr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) - - tr_mean = (tr1 + tr5 + tr10) / 3 - vr_mean = (vr1 + vr5 + vr10) / 3 - r_mean = (tr_mean + vr_mean) / 2 - - eval_result = {'txt_r1': tr1, - 'txt_r5': tr5, - 'txt_r10': tr10, - 'txt_r_mean': tr_mean, - 'vid_r1': vr1, - 'vid_r5': vr5, - 'vid_r10': vr10, - 'vid_r_mean': vr_mean, - 'vid_mdR': mdR, - 'r_mean': r_mean} - return eval_result - - - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating retrieval dataset") - test_dataset = VideoDataset(config['video_root'],config['ann_root'],num_frm=config['num_frm_test'], - max_img_size=config['image_size'], frm_sampling_strategy='uniform') - - test_loader = DataLoader( - test_dataset, - batch_size=config['batch_size'], - num_workers=4, - pin_memory=True, - drop_last=False, - shuffle=False, - ) - - #### Model #### - print("Creating model") - model = blip_retrieval(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit']) - - model = model.to(device) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - score_v2t, score_t2v, = evaluation(model_without_ddp, test_loader, model_without_ddp.tokenizer, device, config) - - if utils.is_main_process(): - - test_result = itm_eval(score_v2t, score_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt) - print(test_result) - - log_stats = {**{f'{k}': v for k, v in test_result.items()},} - with open(os.path.join(args.output_dir, "test_result.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/retrieval_msrvtt.yaml') - parser.add_argument('--output_dir', default='output/Retrieval_msrvtt') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/models/__init__.py b/BLIP/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/BLIP/models/__pycache__/__init__.cpython-37.pyc b/BLIP/models/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index 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a/BLIP/models/__pycache__/vit.cpython-37.pyc and /dev/null differ diff --git a/BLIP/models/blip.py b/BLIP/models/blip.py deleted file mode 100644 index 0a189c6a1c8ba264b4e10d72edc4ba48437c5982..0000000000000000000000000000000000000000 --- a/BLIP/models/blip.py +++ /dev/null @@ -1,238 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import warnings -warnings.filterwarnings("ignore") - -from BLIP.models.vit import VisionTransformer, interpolate_pos_embed -from BLIP.models.med import BertConfig, BertModel, BertLMHeadModel -from transformers import BertTokenizer - -import torch -from torch import nn -import torch.nn.functional as F - -import os -from urllib.parse import urlparse -from timm.models.hub import download_cached_file - -class BLIP_Base(nn.Module): - def __init__(self, - med_config = 'BLIP/configs/med_config.json', - image_size = 224, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) - - - def forward(self, image, caption, mode): - - assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" - text = self.tokenizer(caption, return_tensors="pt").to(image.device) - - if mode=='image': - # return image features - image_embeds = self.visual_encoder(image) - return image_embeds - - elif mode=='text': - # return text features - text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - return text_output.last_hidden_state - - elif mode=='multimodal': - # return multimodel features - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - text.input_ids[:,0] = self.tokenizer.enc_token_id - output = self.text_encoder(text.input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True, - ) - return output.last_hidden_state - - - -class BLIP_Decoder(nn.Module): - def __init__(self, - med_config = 'BLIP/configs/med_config.json', - image_size = 384, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - prompt = 'a picture of ', - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_decoder = BertLMHeadModel(config=med_config) - - self.prompt = prompt - self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 - - - def forward(self, image, caption): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) - - text.input_ids[:,0] = self.tokenizer.bos_token_id - - decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) - decoder_targets[:,:self.prompt_length] = -100 - - decoder_output = self.text_decoder(text.input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - labels = decoder_targets, - return_dict = True, - ) - loss_lm = decoder_output.loss - - return loss_lm - - def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): - image_embeds = self.visual_encoder(image) - - if not sample: - image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) - - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} - - prompt = [self.prompt] * image.size(0) - input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) - input_ids[:,0] = self.tokenizer.bos_token_id - input_ids = input_ids[:, :-1] - - if sample: - #nucleus sampling - outputs = self.text_decoder.generate(input_ids=input_ids, - max_length=max_length, - min_length=min_length, - do_sample=True, - top_p=top_p, - num_return_sequences=1, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - repetition_penalty=1.1, - **model_kwargs) - else: - #beam search - outputs = self.text_decoder.generate(input_ids=input_ids, - max_length=max_length, - min_length=min_length, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - repetition_penalty=repetition_penalty, - **model_kwargs) - - captions = [] - for output in outputs: - caption = self.tokenizer.decode(output, skip_special_tokens=True) - captions.append(caption[len(self.prompt):]) - return captions - - -def blip_decoder(pretrained='',**kwargs): - model = BLIP_Decoder(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - assert(len(msg.missing_keys)==0) - return model - -def blip_feature_extractor(pretrained='',**kwargs): - model = BLIP_Base(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - assert(len(msg.missing_keys)==0) - return model - -def init_tokenizer(): - tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') - tokenizer.add_special_tokens({'bos_token':'[DEC]'}) - tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) - tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] - return tokenizer - - -def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): - - assert vit in ['base', 'large'], "vit parameter must be base or large" - if vit=='base': - vision_width = 768 - visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, - num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, - drop_path_rate=0 or drop_path_rate - ) - elif vit=='large': - vision_width = 1024 - visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, - num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, - drop_path_rate=0.1 or drop_path_rate - ) - return visual_encoder, vision_width - -def is_url(url_or_filename): - parsed = urlparse(url_or_filename) - return parsed.scheme in ("http", "https") - -def load_checkpoint(model,url_or_filename): - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) - checkpoint = torch.load(cached_file, map_location='cpu') - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location='cpu') - else: - raise RuntimeError('checkpoint url or path is invalid') - - state_dict = checkpoint['model'] - - state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) - if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): - state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], - model.visual_encoder_m) - for key in model.state_dict().keys(): - if key in state_dict.keys(): - if state_dict[key].shape!=model.state_dict()[key].shape: - del state_dict[key] - - msg = model.load_state_dict(state_dict,strict=False) - print('load checkpoint from %s'%url_or_filename) - return model,msg - diff --git a/BLIP/models/blip_itm.py b/BLIP/models/blip_itm.py deleted file mode 100644 index cf354c829564bf5a1f56089a2d745093d51e0fa2..0000000000000000000000000000000000000000 --- a/BLIP/models/blip_itm.py +++ /dev/null @@ -1,76 +0,0 @@ -from models.med import BertConfig, BertModel -from transformers import BertTokenizer - -import torch -from torch import nn -import torch.nn.functional as F - -from models.blip import create_vit, init_tokenizer, load_checkpoint - -class BLIP_ITM(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 384, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - embed_dim = 256, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) - - text_width = self.text_encoder.config.hidden_size - - self.vision_proj = nn.Linear(vision_width, embed_dim) - self.text_proj = nn.Linear(text_width, embed_dim) - - self.itm_head = nn.Linear(text_width, 2) - - - def forward(self, image, caption, match_head='itm'): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, - return_tensors="pt").to(image.device) - - - if match_head=='itm': - output = self.text_encoder(text.input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True, - ) - itm_output = self.itm_head(output.last_hidden_state[:,0,:]) - return itm_output - - elif match_head=='itc': - text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) - text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) - - sim = image_feat @ text_feat.t() - return sim - - -def blip_itm(pretrained='',**kwargs): - model = BLIP_ITM(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - assert(len(msg.missing_keys)==0) - return model - \ No newline at end of file diff --git a/BLIP/models/blip_nlvr.py b/BLIP/models/blip_nlvr.py deleted file mode 100644 index 84837167bfa6874d3c3e41fb9b37271113910b7f..0000000000000000000000000000000000000000 --- a/BLIP/models/blip_nlvr.py +++ /dev/null @@ -1,103 +0,0 @@ -from models.med import BertConfig -from models.nlvr_encoder import BertModel -from models.vit import interpolate_pos_embed -from models.blip import create_vit, init_tokenizer, is_url - -from timm.models.hub import download_cached_file - -import torch -from torch import nn -import torch.nn.functional as F -from transformers import BertTokenizer -import numpy as np - -class BLIP_NLVR(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 480, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) - - self.cls_head = nn.Sequential( - nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size), - nn.ReLU(), - nn.Linear(self.text_encoder.config.hidden_size, 2) - ) - - def forward(self, image, text, targets, train=True): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0)) - - text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) - text.input_ids[:,0] = self.tokenizer.enc_token_id - - output = self.text_encoder(text.input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = [image0_embeds,image1_embeds], - encoder_attention_mask = [image_atts[:image0_embeds.size(0)], - image_atts[image0_embeds.size(0):]], - return_dict = True, - ) - hidden_state = output.last_hidden_state[:,0,:] - prediction = self.cls_head(hidden_state) - - if train: - loss = F.cross_entropy(prediction, targets) - return loss - else: - return prediction - -def blip_nlvr(pretrained='',**kwargs): - model = BLIP_NLVR(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - print("missing keys:") - print(msg.missing_keys) - return model - - -def load_checkpoint(model,url_or_filename): - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) - checkpoint = torch.load(cached_file, map_location='cpu') - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location='cpu') - else: - raise RuntimeError('checkpoint url or path is invalid') - state_dict = checkpoint['model'] - - state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) - - for key in list(state_dict.keys()): - if 'crossattention.self.' in key: - new_key0 = key.replace('self','self0') - new_key1 = key.replace('self','self1') - state_dict[new_key0] = state_dict[key] - state_dict[new_key1] = state_dict[key] - elif 'crossattention.output.dense.' in key: - new_key0 = key.replace('dense','dense0') - new_key1 = key.replace('dense','dense1') - state_dict[new_key0] = state_dict[key] - state_dict[new_key1] = state_dict[key] - - msg = model.load_state_dict(state_dict,strict=False) - print('load checkpoint from %s'%url_or_filename) - return model,msg - \ No newline at end of file diff --git a/BLIP/models/blip_pretrain.py b/BLIP/models/blip_pretrain.py deleted file mode 100644 index e42ce5f998b0a51e6f731ee6b5c8bae6d02a8664..0000000000000000000000000000000000000000 --- a/BLIP/models/blip_pretrain.py +++ /dev/null @@ -1,339 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -from models.med import BertConfig, BertModel, BertLMHeadModel -from transformers import BertTokenizer -import transformers -transformers.logging.set_verbosity_error() - -import torch -from torch import nn -import torch.nn.functional as F - -from models.blip import create_vit, init_tokenizer, load_checkpoint - -class BLIP_Pretrain(nn.Module): - def __init__(self, - med_config = 'configs/bert_config.json', - image_size = 224, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - embed_dim = 256, - queue_size = 57600, - momentum = 0.995, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) - - if vit=='base': - checkpoint = torch.hub.load_state_dict_from_url( - url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth", - map_location="cpu", check_hash=True) - state_dict = checkpoint["model"] - msg = self.visual_encoder.load_state_dict(state_dict,strict=False) - elif vit=='large': - from timm.models.helpers import load_custom_pretrained - from timm.models.vision_transformer import default_cfgs - load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k']) - - self.tokenizer = init_tokenizer() - encoder_config = BertConfig.from_json_file(med_config) - encoder_config.encoder_width = vision_width - self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False) - self.text_encoder.resize_token_embeddings(len(self.tokenizer)) - - text_width = self.text_encoder.config.hidden_size - - self.vision_proj = nn.Linear(vision_width, embed_dim) - self.text_proj = nn.Linear(text_width, embed_dim) - - self.itm_head = nn.Linear(text_width, 2) - - # create momentum encoders - self.visual_encoder_m, vision_width = create_vit(vit,image_size) - self.vision_proj_m = nn.Linear(vision_width, embed_dim) - self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False) - self.text_proj_m = nn.Linear(text_width, embed_dim) - - self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], - [self.vision_proj,self.vision_proj_m], - [self.text_encoder,self.text_encoder_m], - [self.text_proj,self.text_proj_m], - ] - self.copy_params() - - # create the queue - self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) - self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) - self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) - - self.image_queue = nn.functional.normalize(self.image_queue, dim=0) - self.text_queue = nn.functional.normalize(self.text_queue, dim=0) - - self.queue_size = queue_size - self.momentum = momentum - self.temp = nn.Parameter(0.07*torch.ones([])) - - # create the decoder - decoder_config = BertConfig.from_json_file(med_config) - decoder_config.encoder_width = vision_width - self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config) - self.text_decoder.resize_token_embeddings(len(self.tokenizer)) - tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention') - - - def forward(self, image, caption, alpha): - with torch.no_grad(): - self.temp.clamp_(0.001,0.5) - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) - - text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, - return_tensors="pt").to(image.device) - text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) - - # get momentum features - with torch.no_grad(): - self._momentum_update() - image_embeds_m = self.visual_encoder_m(image) - image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) - image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) - - text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) - text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) - - sim_i2t_m = image_feat_m @ text_feat_all / self.temp - sim_t2i_m = text_feat_m @ image_feat_all / self.temp - - sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device) - sim_targets.fill_diagonal_(1) - - sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets - sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets - - sim_i2t = image_feat @ text_feat_all / self.temp - sim_t2i = text_feat @ image_feat_all / self.temp - - loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() - loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() - - loss_ita = (loss_i2t+loss_t2i)/2 - - self._dequeue_and_enqueue(image_feat_m, text_feat_m) - - ###============== Image-text Matching ===================### - encoder_input_ids = text.input_ids.clone() - encoder_input_ids[:,0] = self.tokenizer.enc_token_id - - # forward the positve image-text pair - bs = image.size(0) - output_pos = self.text_encoder(encoder_input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True, - ) - with torch.no_grad(): - weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 - weights_t2i.fill_diagonal_(0) - weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4 - weights_i2t.fill_diagonal_(0) - - # select a negative image for each text - image_embeds_neg = [] - for b in range(bs): - neg_idx = torch.multinomial(weights_t2i[b], 1).item() - image_embeds_neg.append(image_embeds[neg_idx]) - image_embeds_neg = torch.stack(image_embeds_neg,dim=0) - - # select a negative text for each image - text_ids_neg = [] - text_atts_neg = [] - for b in range(bs): - neg_idx = torch.multinomial(weights_i2t[b], 1).item() - text_ids_neg.append(encoder_input_ids[neg_idx]) - text_atts_neg.append(text.attention_mask[neg_idx]) - - text_ids_neg = torch.stack(text_ids_neg,dim=0) - text_atts_neg = torch.stack(text_atts_neg,dim=0) - - text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) - text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) - - image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) - image_atts_all = torch.cat([image_atts,image_atts],dim=0) - - output_neg = self.text_encoder(text_ids_all, - attention_mask = text_atts_all, - encoder_hidden_states = image_embeds_all, - encoder_attention_mask = image_atts_all, - return_dict = True, - ) - - vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) - vl_output = self.itm_head(vl_embeddings) - - itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], - dim=0).to(image.device) - loss_itm = F.cross_entropy(vl_output, itm_labels) - - ##================= LM ========================## - decoder_input_ids = text.input_ids.clone() - decoder_input_ids[:,0] = self.tokenizer.bos_token_id - decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) - - decoder_output = self.text_decoder(decoder_input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - labels = decoder_targets, - return_dict = True, - ) - - loss_lm = decoder_output.loss - return loss_ita, loss_itm, loss_lm - - - - @torch.no_grad() - def copy_params(self): - for model_pair in self.model_pairs: - for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): - param_m.data.copy_(param.data) # initialize - param_m.requires_grad = False # not update by gradient - - - @torch.no_grad() - def _momentum_update(self): - for model_pair in self.model_pairs: - for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): - param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) - - - @torch.no_grad() - def _dequeue_and_enqueue(self, image_feat, text_feat): - # gather keys before updating queue - image_feats = concat_all_gather(image_feat) - text_feats = concat_all_gather(text_feat) - - batch_size = image_feats.shape[0] - - ptr = int(self.queue_ptr) - assert self.queue_size % batch_size == 0 # for simplicity - - # replace the keys at ptr (dequeue and enqueue) - self.image_queue[:, ptr:ptr + batch_size] = image_feats.T - self.text_queue[:, ptr:ptr + batch_size] = text_feats.T - ptr = (ptr + batch_size) % self.queue_size # move pointer - - self.queue_ptr[0] = ptr - - -def blip_pretrain(**kwargs): - model = BLIP_Pretrain(**kwargs) - return model - - -@torch.no_grad() -def concat_all_gather(tensor): - """ - Performs all_gather operation on the provided tensors. - *** Warning ***: torch.distributed.all_gather has no gradient. - """ - tensors_gather = [torch.ones_like(tensor) - for _ in range(torch.distributed.get_world_size())] - torch.distributed.all_gather(tensors_gather, tensor, async_op=False) - - output = torch.cat(tensors_gather, dim=0) - return output - - -from typing import List -def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str): - uninitialized_encoder_weights: List[str] = [] - if decoder.__class__ != encoder.__class__: - logger.info( - f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized." - ) - - def tie_encoder_to_decoder_recursively( - decoder_pointer: nn.Module, - encoder_pointer: nn.Module, - module_name: str, - uninitialized_encoder_weights: List[str], - skip_key: str, - depth=0, - ): - assert isinstance(decoder_pointer, nn.Module) and isinstance( - encoder_pointer, nn.Module - ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module" - if hasattr(decoder_pointer, "weight") and skip_key not in module_name: - assert hasattr(encoder_pointer, "weight") - encoder_pointer.weight = decoder_pointer.weight - if hasattr(decoder_pointer, "bias"): - assert hasattr(encoder_pointer, "bias") - encoder_pointer.bias = decoder_pointer.bias - print(module_name+' is tied') - return - - encoder_modules = encoder_pointer._modules - decoder_modules = decoder_pointer._modules - if len(decoder_modules) > 0: - assert ( - len(encoder_modules) > 0 - ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}" - - all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()]) - encoder_layer_pos = 0 - for name, module in decoder_modules.items(): - if name.isdigit(): - encoder_name = str(int(name) + encoder_layer_pos) - decoder_name = name - if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len( - encoder_modules - ) != len(decoder_modules): - # this can happen if the name corresponds to the position in a list module list of layers - # in this case the decoder has added a cross-attention that the encoder does not have - # thus skip this step and subtract one layer pos from encoder - encoder_layer_pos -= 1 - continue - elif name not in encoder_modules: - continue - elif depth > 500: - raise ValueError( - "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model." - ) - else: - decoder_name = encoder_name = name - tie_encoder_to_decoder_recursively( - decoder_modules[decoder_name], - encoder_modules[encoder_name], - module_name + "/" + name, - uninitialized_encoder_weights, - skip_key, - depth=depth + 1, - ) - all_encoder_weights.remove(module_name + "/" + encoder_name) - - uninitialized_encoder_weights += list(all_encoder_weights) - - # tie weights recursively - tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key) diff --git a/BLIP/models/blip_retrieval.py b/BLIP/models/blip_retrieval.py deleted file mode 100644 index 1debe7e2e664f8dd603f8d4c537e3599c68638d7..0000000000000000000000000000000000000000 --- a/BLIP/models/blip_retrieval.py +++ /dev/null @@ -1,319 +0,0 @@ -from models.med import BertConfig, BertModel -from transformers import BertTokenizer - -import torch -from torch import nn -import torch.nn.functional as F - -from models.blip import create_vit, init_tokenizer, load_checkpoint - -class BLIP_Retrieval(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 384, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - embed_dim = 256, - queue_size = 57600, - momentum = 0.995, - negative_all_rank = False, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) - - text_width = self.text_encoder.config.hidden_size - - self.vision_proj = nn.Linear(vision_width, embed_dim) - self.text_proj = nn.Linear(text_width, embed_dim) - - self.itm_head = nn.Linear(text_width, 2) - - # create momentum encoders - self.visual_encoder_m, vision_width = create_vit(vit,image_size) - self.vision_proj_m = nn.Linear(vision_width, embed_dim) - self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False) - self.text_proj_m = nn.Linear(text_width, embed_dim) - - self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], - [self.vision_proj,self.vision_proj_m], - [self.text_encoder,self.text_encoder_m], - [self.text_proj,self.text_proj_m], - ] - self.copy_params() - - # create the queue - self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) - self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) - self.register_buffer("idx_queue", torch.full((1,queue_size),-100)) - self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long)) - - self.image_queue = nn.functional.normalize(self.image_queue, dim=0) - self.text_queue = nn.functional.normalize(self.text_queue, dim=0) - - self.queue_size = queue_size - self.momentum = momentum - self.temp = nn.Parameter(0.07*torch.ones([])) - - self.negative_all_rank = negative_all_rank - - - def forward(self, image, caption, alpha, idx): - with torch.no_grad(): - self.temp.clamp_(0.001,0.5) - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) - - text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, - return_tensors="pt").to(image.device) - - text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) - - ###============== Image-text Contrastive Learning ===================### - idx = idx.view(-1,1) - idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1) - pos_idx = torch.eq(idx, idx_all).float() - sim_targets = pos_idx / pos_idx.sum(1,keepdim=True) - - # get momentum features - with torch.no_grad(): - self._momentum_update() - image_embeds_m = self.visual_encoder_m(image) - image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) - image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) - - text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) - text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) - - sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp - sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp - - sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets - sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets - - sim_i2t = image_feat @ text_feat_m_all / self.temp - sim_t2i = text_feat @ image_feat_m_all / self.temp - - loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() - loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() - - loss_ita = (loss_i2t+loss_t2i)/2 - - idxs = concat_all_gather(idx) - self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs) - - ###============== Image-text Matching ===================### - encoder_input_ids = text.input_ids.clone() - encoder_input_ids[:,0] = self.tokenizer.enc_token_id - - # forward the positve image-text pair - bs = image.size(0) - output_pos = self.text_encoder(encoder_input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True, - ) - - - if self.negative_all_rank: - # compute sample similarity - with torch.no_grad(): - mask = torch.eq(idx, idxs.t()) - - image_feat_world = concat_all_gather(image_feat) - text_feat_world = concat_all_gather(text_feat) - - sim_i2t = image_feat @ text_feat_world.t() / self.temp - sim_t2i = text_feat @ image_feat_world.t() / self.temp - - weights_i2t = F.softmax(sim_i2t,dim=1) - weights_i2t.masked_fill_(mask, 0) - - weights_t2i = F.softmax(sim_t2i,dim=1) - weights_t2i.masked_fill_(mask, 0) - - image_embeds_world = all_gather_with_grad(image_embeds) - - # select a negative image (from all ranks) for each text - image_embeds_neg = [] - for b in range(bs): - neg_idx = torch.multinomial(weights_t2i[b], 1).item() - image_embeds_neg.append(image_embeds_world[neg_idx]) - image_embeds_neg = torch.stack(image_embeds_neg,dim=0) - - # select a negative text (from all ranks) for each image - input_ids_world = concat_all_gather(encoder_input_ids) - att_mask_world = concat_all_gather(text.attention_mask) - - text_ids_neg = [] - text_atts_neg = [] - for b in range(bs): - neg_idx = torch.multinomial(weights_i2t[b], 1).item() - text_ids_neg.append(input_ids_world[neg_idx]) - text_atts_neg.append(att_mask_world[neg_idx]) - - else: - with torch.no_grad(): - mask = torch.eq(idx, idx.t()) - - sim_i2t = image_feat @ text_feat.t() / self.temp - sim_t2i = text_feat @ image_feat.t() / self.temp - - weights_i2t = F.softmax(sim_i2t,dim=1) - weights_i2t.masked_fill_(mask, 0) - - weights_t2i = F.softmax(sim_t2i,dim=1) - weights_t2i.masked_fill_(mask, 0) - - # select a negative image (from same rank) for each text - image_embeds_neg = [] - for b in range(bs): - neg_idx = torch.multinomial(weights_t2i[b], 1).item() - image_embeds_neg.append(image_embeds[neg_idx]) - image_embeds_neg = torch.stack(image_embeds_neg,dim=0) - - # select a negative text (from same rank) for each image - text_ids_neg = [] - text_atts_neg = [] - for b in range(bs): - neg_idx = torch.multinomial(weights_i2t[b], 1).item() - text_ids_neg.append(encoder_input_ids[neg_idx]) - text_atts_neg.append(text.attention_mask[neg_idx]) - - text_ids_neg = torch.stack(text_ids_neg,dim=0) - text_atts_neg = torch.stack(text_atts_neg,dim=0) - - text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) - text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) - - image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) - image_atts_all = torch.cat([image_atts,image_atts],dim=0) - - output_neg = self.text_encoder(text_ids_all, - attention_mask = text_atts_all, - encoder_hidden_states = image_embeds_all, - encoder_attention_mask = image_atts_all, - return_dict = True, - ) - - - vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) - vl_output = self.itm_head(vl_embeddings) - - itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], - dim=0).to(image.device) - loss_itm = F.cross_entropy(vl_output, itm_labels) - - return loss_ita, loss_itm - - - @torch.no_grad() - def copy_params(self): - for model_pair in self.model_pairs: - for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): - param_m.data.copy_(param.data) # initialize - param_m.requires_grad = False # not update by gradient - - - @torch.no_grad() - def _momentum_update(self): - for model_pair in self.model_pairs: - for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): - param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) - - - @torch.no_grad() - def _dequeue_and_enqueue(self, image_feat, text_feat, idxs): - # gather keys before updating queue - image_feats = concat_all_gather(image_feat) - text_feats = concat_all_gather(text_feat) - - - batch_size = image_feats.shape[0] - - ptr = int(self.ptr_queue) - assert self.queue_size % batch_size == 0 # for simplicity - - # replace the keys at ptr (dequeue and enqueue) - self.image_queue[:, ptr:ptr + batch_size] = image_feats.T - self.text_queue[:, ptr:ptr + batch_size] = text_feats.T - self.idx_queue[:, ptr:ptr + batch_size] = idxs.T - ptr = (ptr + batch_size) % self.queue_size # move pointer - - self.ptr_queue[0] = ptr - - -def blip_retrieval(pretrained='',**kwargs): - model = BLIP_Retrieval(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - print("missing keys:") - print(msg.missing_keys) - return model - - -@torch.no_grad() -def concat_all_gather(tensor): - """ - Performs all_gather operation on the provided tensors. - *** Warning ***: torch.distributed.all_gather has no gradient. - """ - tensors_gather = [torch.ones_like(tensor) - for _ in range(torch.distributed.get_world_size())] - torch.distributed.all_gather(tensors_gather, tensor, async_op=False) - - output = torch.cat(tensors_gather, dim=0) - return output - - -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(torch.distributed.get_world_size())] - torch.distributed.all_gather(output, x) - return tuple(output) - - @staticmethod - def backward(ctx, *grads): - all_gradients = torch.stack(grads) - torch.distributed.all_reduce(all_gradients) - return all_gradients[torch.distributed.get_rank()] - - -def all_gather_with_grad(tensors): - """ - Performs all_gather operation on the provided tensors. - Graph remains connected for backward grad computation. - """ - # Queue the gathered tensors - world_size = torch.distributed.get_world_size() - # There is no need for reduction in the single-proc case - if world_size == 1: - return tensors - - tensor_all = GatherLayer.apply(tensors) - - return torch.cat(tensor_all, dim=0) diff --git a/BLIP/models/blip_vqa.py b/BLIP/models/blip_vqa.py deleted file mode 100644 index d4cb3688fad03888f8568ec65437ee20452c6cb8..0000000000000000000000000000000000000000 --- a/BLIP/models/blip_vqa.py +++ /dev/null @@ -1,186 +0,0 @@ -from models.med import BertConfig, BertModel, BertLMHeadModel -from models.blip import create_vit, init_tokenizer, load_checkpoint - -import torch -from torch import nn -import torch.nn.functional as F -from transformers import BertTokenizer -import numpy as np - -class BLIP_VQA(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 480, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) - self.tokenizer = init_tokenizer() - - encoder_config = BertConfig.from_json_file(med_config) - encoder_config.encoder_width = vision_width - self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) - - decoder_config = BertConfig.from_json_file(med_config) - self.text_decoder = BertLMHeadModel(config=decoder_config) - - - def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, - return_tensors="pt").to(image.device) - question.input_ids[:,0] = self.tokenizer.enc_token_id - - if train: - ''' - n: number of answers for each question - weights: weight for each answer - ''' - answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) - answer.input_ids[:,0] = self.tokenizer.bos_token_id - answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) - - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - question_states = [] - question_atts = [] - for b, n in enumerate(n): - question_states += [question_output.last_hidden_state[b]]*n - question_atts += [question.attention_mask[b]]*n - question_states = torch.stack(question_states,0) - question_atts = torch.stack(question_atts,0) - - answer_output = self.text_decoder(answer.input_ids, - attention_mask = answer.attention_mask, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - labels = answer_targets, - return_dict = True, - reduction = 'none', - ) - - loss = weights * answer_output.loss - loss = loss.sum()/image.size(0) - - return loss - - - else: - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - if inference=='generate': - num_beams = 3 - question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) - question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) - model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} - - bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) - - outputs = self.text_decoder.generate(input_ids=bos_ids, - max_length=10, - min_length=1, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - **model_kwargs) - - answers = [] - for output in outputs: - answer = self.tokenizer.decode(output, skip_special_tokens=True) - answers.append(answer) - return answers - - elif inference=='rank': - max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, - answer.input_ids, answer.attention_mask, k_test) - return max_ids - - - - def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): - - num_ques = question_states.size(0) - start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token - - start_output = self.text_decoder(start_ids, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - return_dict = True, - reduction = 'none') - logits = start_output.logits[:,0,:] # first token's logit - - # topk_probs: top-k probability - # topk_ids: [num_question, k] - answer_first_token = answer_ids[:,1] - prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) - topk_probs, topk_ids = prob_first_token.topk(k,dim=1) - - # answer input: [num_question*k, answer_len] - input_ids = [] - input_atts = [] - for b, topk_id in enumerate(topk_ids): - input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) - input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) - input_ids = torch.cat(input_ids,dim=0) - input_atts = torch.cat(input_atts,dim=0) - - targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) - - # repeat encoder's output for top-k answers - question_states = tile(question_states, 0, k) - question_atts = tile(question_atts, 0, k) - - output = self.text_decoder(input_ids, - attention_mask = input_atts, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - labels = targets_ids, - return_dict = True, - reduction = 'none') - - log_probs_sum = -output.loss - log_probs_sum = log_probs_sum.view(num_ques,k) - - max_topk_ids = log_probs_sum.argmax(dim=1) - max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] - - return max_ids - - -def blip_vqa(pretrained='',**kwargs): - model = BLIP_VQA(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) -# assert(len(msg.missing_keys)==0) - return model - - -def tile(x, dim, n_tile): - init_dim = x.size(dim) - repeat_idx = [1] * x.dim() - repeat_idx[dim] = n_tile - x = x.repeat(*(repeat_idx)) - order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) - return torch.index_select(x, dim, order_index.to(x.device)) - - \ No newline at end of file diff --git a/BLIP/models/med.py b/BLIP/models/med.py deleted file mode 100644 index 7b00a35450b736180a805d4f4664b4fb95aeba01..0000000000000000000000000000000000000000 --- a/BLIP/models/med.py +++ /dev/null @@ -1,955 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li - * Based on huggingface code base - * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert -''' - -import math -import os -import warnings -from dataclasses import dataclass -from typing import Optional, Tuple - -import torch -from torch import Tensor, device, dtype, nn -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss -import torch.nn.functional as F - -from transformers.activations import ACT2FN -from transformers.file_utils import ( - ModelOutput, -) -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MaskedLMOutput, - MultipleChoiceModelOutput, - NextSentencePredictorOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.modeling_utils import ( - PreTrainedModel, - apply_chunking_to_forward, - find_pruneable_heads_and_indices, - prune_linear_layer, -) -from transformers.utils import logging -from transformers.models.bert.configuration_bert import BertConfig - - -logger = logging.get_logger(__name__) - - -class BertEmbeddings(nn.Module): - """Construct the embeddings from word and position embeddings.""" - - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - - self.config = config - - def forward( - self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - if position_ids is None: - position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - embeddings = inputs_embeds - - if self.position_embedding_type == "absolute": - position_embeddings = self.position_embeddings(position_ids) - embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class BertSelfAttention(nn.Module): - def __init__(self, config, is_cross_attention): - super().__init__() - self.config = config - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads) - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - if is_cross_attention: - self.key = nn.Linear(config.encoder_width, self.all_head_size) - self.value = nn.Linear(config.encoder_width, self.all_head_size) - else: - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - self.max_position_embeddings = config.max_position_embeddings - self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) - self.save_attention = False - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - mixed_query_layer = self.query(hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention: - key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) - value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([past_key_value[0], key_layer], dim=2) - value_layer = torch.cat([past_key_value[1], value_layer], dim=2) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - - query_layer = self.transpose_for_scores(mixed_query_layer) - - past_key_value = (key_layer, value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - seq_length = hidden_states.size()[1] - position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) - position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) - distance = position_ids_l - position_ids_r - positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility - - if self.position_embedding_type == "relative_key": - relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores - elif self.position_embedding_type == "relative_key_query": - relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key - - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - if is_cross_attention and self.save_attention: - self.save_attention_map(attention_probs) - attention_probs.register_hook(self.save_attn_gradients) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs_dropped = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs_dropped = attention_probs_dropped * head_mask - - context_layer = torch.matmul(attention_probs_dropped, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - - outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) - - outputs = outputs + (past_key_value,) - return outputs - - -class BertSelfOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertAttention(nn.Module): - def __init__(self, config, is_cross_attention=False): - super().__init__() - self.self = BertSelfAttention(config, is_cross_attention) - self.output = BertSelfOutput(config) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) - - # Prune linear layers - self.self.query = prune_linear_layer(self.self.query, index) - self.self.key = prune_linear_layer(self.self.key, index) - self.self.value = prune_linear_layer(self.self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.self.num_attention_heads = self.self.num_attention_heads - len(heads) - self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - self_outputs = self.self( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them - return outputs - - -class BertIntermediate(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = ACT2FN[config.hidden_act] - else: - self.intermediate_act_fn = config.hidden_act - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -class BertOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertLayer(nn.Module): - def __init__(self, config, layer_num): - super().__init__() - self.config = config - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = BertAttention(config) - self.layer_num = layer_num - if self.config.add_cross_attention: - self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) - self.intermediate = BertIntermediate(config) - self.output = BertOutput(config) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - mode=None, - ): - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - hidden_states, - attention_mask, - head_mask, - output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, - ) - attention_output = self_attention_outputs[0] - - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - - if mode=='multimodal': - assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" - - cross_attention_outputs = self.crossattention( - attention_output, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions=output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) - outputs = (layer_output,) + outputs - - outputs = outputs + (present_key_value,) - - return outputs - - def feed_forward_chunk(self, attention_output): - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class BertEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=False, - output_hidden_states=False, - return_dict=True, - mode='multimodal', - ): - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - next_decoder_cache = () if use_cache else None - - for i in range(self.config.num_hidden_layers): - layer_module = self.layer[i] - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - if use_cache: - logger.warn( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - mode=mode, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - mode=mode, - ) - - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - -class BertPooler(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states): - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class BertPredictionHeadTransform(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if isinstance(config.hidden_act, str): - self.transform_act_fn = ACT2FN[config.hidden_act] - else: - self.transform_act_fn = config.hidden_act - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - return hidden_states - - -class BertLMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - - # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` - self.decoder.bias = self.bias - - def forward(self, hidden_states): - hidden_states = self.transform(hidden_states) - hidden_states = self.decoder(hidden_states) - return hidden_states - - -class BertOnlyMLMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertLMPredictionHead(config) - - def forward(self, sequence_output): - prediction_scores = self.predictions(sequence_output) - return prediction_scores - - -class BertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = BertConfig - base_model_prefix = "bert" - _keys_to_ignore_on_load_missing = [r"position_ids"] - - def _init_weights(self, module): - """ Initialize the weights """ - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - - -class BertModel(BertPreTrainedModel): - """ - The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of - cross-attention is added between the self-attention layers, following the architecture described in `Attention is - all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, - Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. - argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an - input to the forward pass. - """ - - def __init__(self, config, add_pooling_layer=True): - super().__init__(config) - self.config = config - - self.embeddings = BertEmbeddings(config) - - self.encoder = BertEncoder(config) - - self.pooler = BertPooler(config) if add_pooling_layer else None - - self.init_weights() - - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - for layer, heads in heads_to_prune.items(): - self.encoder.layer[layer].attention.prune_heads(heads) - - - def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: - """ - Makes broadcastable attention and causal masks so that future and masked tokens are ignored. - - Arguments: - attention_mask (:obj:`torch.Tensor`): - Mask with ones indicating tokens to attend to, zeros for tokens to ignore. - input_shape (:obj:`Tuple[int]`): - The shape of the input to the model. - device: (:obj:`torch.device`): - The device of the input to the model. - - Returns: - :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. - """ - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - if attention_mask.dim() == 3: - extended_attention_mask = attention_mask[:, None, :, :] - elif attention_mask.dim() == 2: - # Provided a padding mask of dimensions [batch_size, seq_length] - # - if the model is a decoder, apply a causal mask in addition to the padding mask - # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] - if is_decoder: - batch_size, seq_length = input_shape - - seq_ids = torch.arange(seq_length, device=device) - causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] - # in case past_key_values are used we need to add a prefix ones mask to the causal mask - # causal and attention masks must have same type with pytorch version < 1.3 - causal_mask = causal_mask.to(attention_mask.dtype) - - if causal_mask.shape[1] < attention_mask.shape[1]: - prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] - causal_mask = torch.cat( - [ - torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), - causal_mask, - ], - axis=-1, - ) - - extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] - else: - extended_attention_mask = attention_mask[:, None, None, :] - else: - raise ValueError( - "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( - input_shape, attention_mask.shape - ) - ) - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and -10000.0 for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility - extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 - return extended_attention_mask - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - is_decoder=False, - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - batch_size, seq_length = input_shape - device = input_ids.device - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = inputs_embeds.device - elif encoder_embeds is not None: - input_shape = encoder_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = encoder_embeds.device - else: - raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, - device, is_decoder) - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() - else: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - - if type(encoder_attention_mask) == list: - encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] - elif encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - - if encoder_embeds is None: - embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - else: - embedding_output = encoder_embeds - - encoder_outputs = self.encoder( - embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - mode=mode, - ) - sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - if not return_dict: - return (sequence_output, pooled_output) + encoder_outputs[1:] - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - past_key_values=encoder_outputs.past_key_values, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - cross_attentions=encoder_outputs.cross_attentions, - ) - - - -class BertLMHeadModel(BertPreTrainedModel): - - _keys_to_ignore_on_load_unexpected = [r"pooler"] - _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] - - def __init__(self, config): - super().__init__(config) - - self.bert = BertModel(config, add_pooling_layer=False) - self.cls = BertOnlyMLMHead(config) - - self.init_weights() - - def get_output_embeddings(self): - return self.cls.predictions.decoder - - def set_output_embeddings(self, new_embeddings): - self.cls.predictions.decoder = new_embeddings - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - labels=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - return_logits=False, - is_decoder=True, - reduction='mean', - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are - ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - Returns: - Example:: - >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig - >>> import torch - >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') - >>> config = BertConfig.from_pretrained("bert-base-cased") - >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) - >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") - >>> outputs = model(**inputs) - >>> prediction_logits = outputs.logits - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - if labels is not None: - use_cache = False - - outputs = self.bert( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - is_decoder=is_decoder, - mode=mode, - ) - - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - if return_logits: - return prediction_scores[:, :-1, :].contiguous() - - lm_loss = None - if labels is not None: - # we are doing next-token prediction; shift prediction scores and input ids by one - shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) - lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - if reduction=='none': - lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((lm_loss,) + output) if lm_loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=lm_loss, - logits=prediction_scores, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - - def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): - input_shape = input_ids.shape - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_shape) - - # cut decoder_input_ids if past is used - if past is not None: - input_ids = input_ids[:, -1:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "past_key_values": past, - "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), - "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), - "is_decoder": True, - } - - def _reorder_cache(self, past, beam_idx): - reordered_past = () - for layer_past in past: - reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) - return reordered_past diff --git a/BLIP/models/nlvr_encoder.py b/BLIP/models/nlvr_encoder.py deleted file mode 100644 index 1946bb4a300f75afa4848f6622839445903c34a9..0000000000000000000000000000000000000000 --- a/BLIP/models/nlvr_encoder.py +++ /dev/null @@ -1,843 +0,0 @@ -import math -import os -import warnings -from dataclasses import dataclass -from typing import Optional, Tuple - -import torch -from torch import Tensor, device, dtype, nn -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss -import torch.nn.functional as F - -from transformers.activations import ACT2FN -from transformers.file_utils import ( - ModelOutput, -) -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MaskedLMOutput, - MultipleChoiceModelOutput, - NextSentencePredictorOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.modeling_utils import ( - PreTrainedModel, - apply_chunking_to_forward, - find_pruneable_heads_and_indices, - prune_linear_layer, -) -from transformers.utils import logging -from transformers.models.bert.configuration_bert import BertConfig - - -logger = logging.get_logger(__name__) - - -class BertEmbeddings(nn.Module): - """Construct the embeddings from word and position embeddings.""" - - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - - self.config = config - - def forward( - self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - if position_ids is None: - position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - embeddings = inputs_embeds - - if self.position_embedding_type == "absolute": - position_embeddings = self.position_embeddings(position_ids) - embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class BertSelfAttention(nn.Module): - def __init__(self, config, is_cross_attention): - super().__init__() - self.config = config - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads) - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - if is_cross_attention: - self.key = nn.Linear(config.encoder_width, self.all_head_size) - self.value = nn.Linear(config.encoder_width, self.all_head_size) - else: - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - self.max_position_embeddings = config.max_position_embeddings - self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) - self.save_attention = False - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - mixed_query_layer = self.query(hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention: - key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) - value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([past_key_value[0], key_layer], dim=2) - value_layer = torch.cat([past_key_value[1], value_layer], dim=2) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - - query_layer = self.transpose_for_scores(mixed_query_layer) - - past_key_value = (key_layer, value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - seq_length = hidden_states.size()[1] - position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) - position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) - distance = position_ids_l - position_ids_r - positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility - - if self.position_embedding_type == "relative_key": - relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores - elif self.position_embedding_type == "relative_key_query": - relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key - - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - if is_cross_attention and self.save_attention: - self.save_attention_map(attention_probs) - attention_probs.register_hook(self.save_attn_gradients) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs_dropped = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs_dropped = attention_probs_dropped * head_mask - - context_layer = torch.matmul(attention_probs_dropped, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - - outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) - - outputs = outputs + (past_key_value,) - return outputs - - -class BertSelfOutput(nn.Module): - def __init__(self, config, twin=False, merge=False): - super().__init__() - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - if twin: - self.dense0 = nn.Linear(config.hidden_size, config.hidden_size) - self.dense1 = nn.Linear(config.hidden_size, config.hidden_size) - else: - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if merge: - self.act = ACT2FN[config.hidden_act] - self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size) - self.merge = True - else: - self.merge = False - - def forward(self, hidden_states, input_tensor): - if type(hidden_states) == list: - hidden_states0 = self.dense0(hidden_states[0]) - hidden_states1 = self.dense1(hidden_states[1]) - if self.merge: - #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1))) - hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1)) - else: - hidden_states = (hidden_states0+hidden_states1)/2 - else: - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertAttention(nn.Module): - def __init__(self, config, is_cross_attention=False, layer_num=-1): - super().__init__() - if is_cross_attention: - self.self0 = BertSelfAttention(config, is_cross_attention) - self.self1 = BertSelfAttention(config, is_cross_attention) - else: - self.self = BertSelfAttention(config, is_cross_attention) - self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6)) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) - - # Prune linear layers - self.self.query = prune_linear_layer(self.self.query, index) - self.self.key = prune_linear_layer(self.self.key, index) - self.self.value = prune_linear_layer(self.self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.self.num_attention_heads = self.self.num_attention_heads - len(heads) - self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - if type(encoder_hidden_states)==list: - self_outputs0 = self.self0( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states[0], - encoder_attention_mask[0], - past_key_value, - output_attentions, - ) - self_outputs1 = self.self1( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states[1], - encoder_attention_mask[1], - past_key_value, - output_attentions, - ) - attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states) - - outputs = (attention_output,) + self_outputs0[1:] # add attentions if we output them - else: - self_outputs = self.self( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them - return outputs - - -class BertIntermediate(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = ACT2FN[config.hidden_act] - else: - self.intermediate_act_fn = config.hidden_act - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -class BertOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertLayer(nn.Module): - def __init__(self, config, layer_num): - super().__init__() - self.config = config - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = BertAttention(config) - self.layer_num = layer_num - if self.config.add_cross_attention: - self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num) - self.intermediate = BertIntermediate(config) - self.output = BertOutput(config) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - mode=None, - ): - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - hidden_states, - attention_mask, - head_mask, - output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, - ) - attention_output = self_attention_outputs[0] - - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - - if mode=='multimodal': - assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" - cross_attention_outputs = self.crossattention( - attention_output, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions=output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) - outputs = (layer_output,) + outputs - - outputs = outputs + (present_key_value,) - - return outputs - - def feed_forward_chunk(self, attention_output): - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class BertEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=False, - output_hidden_states=False, - return_dict=True, - mode='multimodal', - ): - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - next_decoder_cache = () if use_cache else None - - for i in range(self.config.num_hidden_layers): - layer_module = self.layer[i] - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - if use_cache: - logger.warn( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - mode=mode, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - mode=mode, - ) - - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - -class BertPooler(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states): - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class BertPredictionHeadTransform(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if isinstance(config.hidden_act, str): - self.transform_act_fn = ACT2FN[config.hidden_act] - else: - self.transform_act_fn = config.hidden_act - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - return hidden_states - - -class BertLMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - - # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` - self.decoder.bias = self.bias - - def forward(self, hidden_states): - hidden_states = self.transform(hidden_states) - hidden_states = self.decoder(hidden_states) - return hidden_states - - -class BertOnlyMLMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertLMPredictionHead(config) - - def forward(self, sequence_output): - prediction_scores = self.predictions(sequence_output) - return prediction_scores - - -class BertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = BertConfig - base_model_prefix = "bert" - _keys_to_ignore_on_load_missing = [r"position_ids"] - - def _init_weights(self, module): - """ Initialize the weights """ - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - - -class BertModel(BertPreTrainedModel): - """ - The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of - cross-attention is added between the self-attention layers, following the architecture described in `Attention is - all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, - Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. - argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an - input to the forward pass. - """ - - def __init__(self, config, add_pooling_layer=True): - super().__init__(config) - self.config = config - - self.embeddings = BertEmbeddings(config) - - self.encoder = BertEncoder(config) - - self.pooler = BertPooler(config) if add_pooling_layer else None - - self.init_weights() - - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - for layer, heads in heads_to_prune.items(): - self.encoder.layer[layer].attention.prune_heads(heads) - - - def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: - """ - Makes broadcastable attention and causal masks so that future and masked tokens are ignored. - - Arguments: - attention_mask (:obj:`torch.Tensor`): - Mask with ones indicating tokens to attend to, zeros for tokens to ignore. - input_shape (:obj:`Tuple[int]`): - The shape of the input to the model. - device: (:obj:`torch.device`): - The device of the input to the model. - - Returns: - :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. - """ - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - if attention_mask.dim() == 3: - extended_attention_mask = attention_mask[:, None, :, :] - elif attention_mask.dim() == 2: - # Provided a padding mask of dimensions [batch_size, seq_length] - # - if the model is a decoder, apply a causal mask in addition to the padding mask - # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] - if is_decoder: - batch_size, seq_length = input_shape - - seq_ids = torch.arange(seq_length, device=device) - causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] - # in case past_key_values are used we need to add a prefix ones mask to the causal mask - # causal and attention masks must have same type with pytorch version < 1.3 - causal_mask = causal_mask.to(attention_mask.dtype) - - if causal_mask.shape[1] < attention_mask.shape[1]: - prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] - causal_mask = torch.cat( - [ - torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), - causal_mask, - ], - axis=-1, - ) - - extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] - else: - extended_attention_mask = attention_mask[:, None, None, :] - else: - raise ValueError( - "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( - input_shape, attention_mask.shape - ) - ) - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and -10000.0 for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility - extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 - return extended_attention_mask - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - is_decoder=False, - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - batch_size, seq_length = input_shape - device = input_ids.device - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = inputs_embeds.device - elif encoder_embeds is not None: - input_shape = encoder_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = encoder_embeds.device - else: - raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, - device, is_decoder) - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() - else: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - - if type(encoder_attention_mask) == list: - encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] - elif encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - - if encoder_embeds is None: - embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - else: - embedding_output = encoder_embeds - - encoder_outputs = self.encoder( - embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - mode=mode, - ) - sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - if not return_dict: - return (sequence_output, pooled_output) + encoder_outputs[1:] - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - past_key_values=encoder_outputs.past_key_values, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - cross_attentions=encoder_outputs.cross_attentions, - ) - diff --git a/BLIP/models/vit.py b/BLIP/models/vit.py deleted file mode 100644 index cec3d8e08ed4451d65392feb2e9f4848d1ef3899..0000000000000000000000000000000000000000 --- a/BLIP/models/vit.py +++ /dev/null @@ -1,305 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li - * Based on timm code base - * https://github.com/rwightman/pytorch-image-models/tree/master/timm -''' - -import torch -import torch.nn as nn -import torch.nn.functional as F -from functools import partial - -from timm.models.vision_transformer import _cfg, PatchEmbed -from timm.models.registry import register_model -from timm.models.layers import trunc_normal_, DropPath -from timm.models.helpers import named_apply, adapt_input_conv - -from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper - -class Mlp(nn.Module): - """ MLP as used in Vision Transformer, MLP-Mixer and related networks - """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class Attention(nn.Module): - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights - self.scale = qk_scale or head_dim ** -0.5 - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - self.attn_gradients = None - self.attention_map = None - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def forward(self, x, register_hook=False): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - if register_hook: - self.save_attention_map(attn) - attn.register_hook(self.save_attn_gradients) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Block(nn.Module): - - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): - super().__init__() - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if use_grad_checkpointing: - self.attn = checkpoint_wrapper(self.attn) - self.mlp = checkpoint_wrapper(self.mlp) - - def forward(self, x, register_hook=False): - x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) - x = x + self.drop_path(self.mlp(self.norm2(x))) - return x - - -class VisionTransformer(nn.Module): - """ Vision Transformer - A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - - https://arxiv.org/abs/2010.11929 - """ - def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, - num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, - use_grad_checkpointing=False, ckpt_layer=0): - """ - Args: - img_size (int, tuple): input image size - patch_size (int, tuple): patch size - in_chans (int): number of input channels - num_classes (int): number of classes for classification head - embed_dim (int): embedding dimension - depth (int): depth of transformer - num_heads (int): number of attention heads - mlp_ratio (int): ratio of mlp hidden dim to embedding dim - qkv_bias (bool): enable bias for qkv if True - qk_scale (float): override default qk scale of head_dim ** -0.5 if set - representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set - drop_rate (float): dropout rate - attn_drop_rate (float): attention dropout rate - drop_path_rate (float): stochastic depth rate - norm_layer: (nn.Module): normalization layer - """ - super().__init__() - self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models - norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) - - num_patches = self.patch_embed.num_patches - - self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) - self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) - self.pos_drop = nn.Dropout(p=drop_rate) - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule - self.blocks = nn.ModuleList([ - Block( - dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, - use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) - ) - for i in range(depth)]) - self.norm = norm_layer(embed_dim) - - trunc_normal_(self.pos_embed, std=.02) - trunc_normal_(self.cls_token, std=.02) - self.apply(self._init_weights) - - 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) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token'} - - def forward(self, x, register_blk=-1): - B = x.shape[0] - x = self.patch_embed(x) - - cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks - x = torch.cat((cls_tokens, x), dim=1) - - x = x + self.pos_embed[:,:x.size(1),:] - x = self.pos_drop(x) - - for i,blk in enumerate(self.blocks): - x = blk(x, register_blk==i) - x = self.norm(x) - - return x - - @torch.jit.ignore() - def load_pretrained(self, checkpoint_path, prefix=''): - _load_weights(self, checkpoint_path, prefix) - - -@torch.no_grad() -def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): - """ Load weights from .npz checkpoints for official Google Brain Flax implementation - """ - import numpy as np - - def _n2p(w, t=True): - if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: - w = w.flatten() - if t: - if w.ndim == 4: - w = w.transpose([3, 2, 0, 1]) - elif w.ndim == 3: - w = w.transpose([2, 0, 1]) - elif w.ndim == 2: - w = w.transpose([1, 0]) - return torch.from_numpy(w) - - w = np.load(checkpoint_path) - if not prefix and 'opt/target/embedding/kernel' in w: - prefix = 'opt/target/' - - if hasattr(model.patch_embed, 'backbone'): - # hybrid - backbone = model.patch_embed.backbone - stem_only = not hasattr(backbone, 'stem') - stem = backbone if stem_only else backbone.stem - stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) - stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) - stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) - if not stem_only: - for i, stage in enumerate(backbone.stages): - for j, block in enumerate(stage.blocks): - bp = f'{prefix}block{i + 1}/unit{j + 1}/' - for r in range(3): - getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) - getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) - getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) - if block.downsample is not None: - block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) - block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) - block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) - embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) - else: - embed_conv_w = adapt_input_conv( - model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) - model.patch_embed.proj.weight.copy_(embed_conv_w) - model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) - model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) - pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) - if pos_embed_w.shape != model.pos_embed.shape: - pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights - pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) - model.pos_embed.copy_(pos_embed_w) - model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) - model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) -# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: -# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) -# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) -# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: -# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) -# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) - for i, block in enumerate(model.blocks.children()): - block_prefix = f'{prefix}Transformer/encoderblock_{i}/' - mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' - block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) - block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) - block.attn.qkv.weight.copy_(torch.cat([ - _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) - block.attn.qkv.bias.copy_(torch.cat([ - _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) - block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) - block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) - for r in range(2): - getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) - getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) - block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) - block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) - - -def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): - # interpolate position embedding - embedding_size = pos_embed_checkpoint.shape[-1] - num_patches = visual_encoder.patch_embed.num_patches - num_extra_tokens = visual_encoder.pos_embed.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) - - if orig_size!=new_size: - # class_token and dist_token are kept unchanged - 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) - print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) - - return new_pos_embed - else: - return pos_embed_checkpoint \ No newline at end of file diff --git a/BLIP/predict.py b/BLIP/predict.py deleted file mode 100644 index 35426cadcbb3bf8c3d8cb9c910511c154e451f4e..0000000000000000000000000000000000000000 --- a/BLIP/predict.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -Download the weights in ./checkpoints beforehand for fast inference -wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth -wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth -wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth -""" - -from pathlib import Path - -from PIL import Image -import torch -from torchvision import transforms -from torchvision.transforms.functional import InterpolationMode -import cog - -from models.blip import blip_decoder -from models.blip_vqa import blip_vqa -from models.blip_itm import blip_itm - - -class Predictor(cog.Predictor): - def setup(self): - self.device = "cuda:0" - - self.models = { - 'image_captioning': blip_decoder(pretrained='checkpoints/model*_base_caption.pth', - image_size=384, vit='base'), - 'visual_question_answering': blip_vqa(pretrained='checkpoints/model*_vqa.pth', - image_size=480, vit='base'), - 'image_text_matching': blip_itm(pretrained='checkpoints/model_base_retrieval_coco.pth', - image_size=384, vit='base') - } - - @cog.input( - "image", - type=Path, - help="input image", - ) - @cog.input( - "task", - type=str, - default='image_captioning', - options=['image_captioning', 'visual_question_answering', 'image_text_matching'], - help="Choose a task.", - ) - @cog.input( - "question", - type=str, - default=None, - help="Type question for the input image for visual question answering task.", - ) - @cog.input( - "caption", - type=str, - default=None, - help="Type caption for the input image for image text matching task.", - ) - def predict(self, image, task, question, caption): - if task == 'visual_question_answering': - assert question is not None, 'Please type a question for visual question answering task.' - if task == 'image_text_matching': - assert caption is not None, 'Please type a caption for mage text matching task.' - - im = load_image(image, image_size=480 if task == 'visual_question_answering' else 384, device=self.device) - model = self.models[task] - model.eval() - model = model.to(self.device) - - if task == 'image_captioning': - with torch.no_grad(): - caption = model.generate(im, sample=False, num_beams=3, max_length=20, min_length=5) - return 'Caption: ' + caption[0] - - if task == 'visual_question_answering': - with torch.no_grad(): - answer = model(im, question, train=False, inference='generate') - return 'Answer: ' + answer[0] - - # image_text_matching - itm_output = model(im, caption, match_head='itm') - itm_score = torch.nn.functional.softmax(itm_output, dim=1)[:, 1] - itc_score = model(im, caption, match_head='itc') - return f'The image and text is matched with a probability of {itm_score.item():.4f}.\n' \ - f'The image feature and text feature has a cosine similarity of {itc_score.item():.4f}.' - - -def load_image(image, image_size, device): - raw_image = Image.open(str(image)).convert('RGB') - - w, h = raw_image.size - - transform = transforms.Compose([ - transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC), - transforms.ToTensor(), - transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) - ]) - image = transform(raw_image).unsqueeze(0).to(device) - return image diff --git a/BLIP/pretrain.py b/BLIP/pretrain.py deleted file mode 100644 index c9490ec8eb8ff5f074b5772ada55cd27ec673a12..0000000000000000000000000000000000000000 --- a/BLIP/pretrain.py +++ /dev/null @@ -1,173 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.backends.cudnn as cudnn -import torch.distributed as dist -from torch.utils.data import DataLoader - -from models.blip_pretrain import blip_pretrain -import utils -from utils import warmup_lr_schedule, step_lr_schedule -from data import create_dataset, create_sampler, create_loader - -def train(model, data_loader, optimizer, epoch, device, config): - # train - model.train() - - metric_logger = utils.MetricLogger(delimiter=" ") - metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}')) - metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) - metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) - metric_logger.add_meter('loss_lm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) - - header = 'Train Epoch: [{}]'.format(epoch) - print_freq = 50 - - if config['laion_path']: - data_loader.dataset.reload_laion(epoch) - - data_loader.sampler.set_epoch(epoch) - - for i, (image, caption) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): - - if epoch==0: - warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr']) - - optimizer.zero_grad() - - image = image.to(device,non_blocking=True) - - # ramp up alpha in the first 2 epochs - alpha = config['alpha']*min(1,(epoch*len(data_loader)+i)/(2*len(data_loader))) - - loss_ita, loss_itm, loss_lm = model(image, caption, alpha = alpha) - loss = loss_ita + loss_itm + loss_lm - - loss.backward() - optimizer.step() - - metric_logger.update(loss_ita=loss_ita.item()) - metric_logger.update(loss_itm=loss_itm.item()) - metric_logger.update(loss_lm=loss_lm.item()) - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - - - # gather the stats from all processes - metric_logger.synchronize_between_processes() - print("Averaged stats:", metric_logger.global_avg()) - return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating dataset") - datasets = [create_dataset('pretrain', config, min_scale=0.2)] - print('number of training samples: %d'%len(datasets[0])) - - num_tasks = utils.get_world_size() - global_rank = utils.get_rank() - samplers = create_sampler(datasets, [True], num_tasks, global_rank) - - data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0] - - #### Model #### - print("Creating model") - model = blip_pretrain(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], - vit_ckpt_layer=config['vit_ckpt_layer'], queue_size=config['queue_size']) - - model = model.to(device) - - optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) - - start_epoch = 0 - if args.checkpoint: - checkpoint = torch.load(args.checkpoint, map_location='cpu') - state_dict = checkpoint['model'] - model.load_state_dict(state_dict) - - optimizer.load_state_dict(checkpoint['optimizer']) - start_epoch = checkpoint['epoch']+1 - print('resume checkpoint from %s'%args.checkpoint) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - print("Start training") - start_time = time.time() - for epoch in range(start_epoch, config['max_epoch']): - - step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate']) - - train_stats = train(model, data_loader, optimizer, epoch, device, config) - if utils.is_main_process(): - log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, - 'epoch': epoch, - } - save_obj = { - 'model': model_without_ddp.state_dict(), - 'optimizer': optimizer.state_dict(), - 'config': config, - 'epoch': epoch, - } - torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch)) - - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - - dist.barrier() - - 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__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/pretrain.yaml') - parser.add_argument('--output_dir', default='output/Pretrain') - parser.add_argument('--checkpoint', default='') - parser.add_argument('--evaluate', action='store_true') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/requirements.txt b/BLIP/requirements.txt deleted file mode 100644 index d897bc6a08712f4beb2f78ca2592dcbe06a3e2db..0000000000000000000000000000000000000000 --- a/BLIP/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -timm==0.4.12 -transformers==4.15.0 -fairscale==0.4.4 -pycocoevalcap diff --git a/BLIP/train_caption.py b/BLIP/train_caption.py deleted file mode 100644 index 7c639ac646b9a1b8074b6e9c2343b961de76db05..0000000000000000000000000000000000000000 --- a/BLIP/train_caption.py +++ /dev/null @@ -1,206 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.backends.cudnn as cudnn -import torch.distributed as dist -from torch.utils.data import DataLoader - -from models.blip import blip_decoder -import utils -from utils import cosine_lr_schedule -from data import create_dataset, create_sampler, create_loader -from data.utils import save_result, coco_caption_eval - -def train(model, data_loader, optimizer, epoch, device): - # train - model.train() - - metric_logger = utils.MetricLogger(delimiter=" ") - metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) - metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}')) - header = 'Train Caption Epoch: [{}]'.format(epoch) - print_freq = 50 - - for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): - image = image.to(device) - - loss = model(image, caption) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - metric_logger.update(loss=loss.item()) - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - - # gather the stats from all processes - metric_logger.synchronize_between_processes() - print("Averaged stats:", metric_logger.global_avg()) - return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} - - -@torch.no_grad() -def evaluate(model, data_loader, device, config): - # evaluate - model.eval() - - metric_logger = utils.MetricLogger(delimiter=" ") - header = 'Caption generation:' - print_freq = 10 - - result = [] - for image, image_id in metric_logger.log_every(data_loader, print_freq, header): - - image = image.to(device) - - captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], - min_length=config['min_length']) - - for caption, img_id in zip(captions, image_id): - result.append({"image_id": img_id.item(), "caption": caption}) - - return result - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating captioning dataset") - train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', config) - - if args.distributed: - num_tasks = utils.get_world_size() - global_rank = utils.get_rank() - samplers = create_sampler([train_dataset,val_dataset,test_dataset], [True,False,False], num_tasks, global_rank) - else: - samplers = [None, None, None] - - train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers, - batch_size=[config['batch_size']]*3,num_workers=[4,4,4], - is_trains=[True, False, False], collate_fns=[None,None,None]) - - #### Model #### - print("Creating model") - model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], - vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], - prompt=config['prompt']) - - model = model.to(device) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) - - best = 0 - best_epoch = 0 - - print("Start training") - start_time = time.time() - for epoch in range(0, config['max_epoch']): - if not args.evaluate: - if args.distributed: - train_loader.sampler.set_epoch(epoch) - - cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr']) - - train_stats = train(model, train_loader, optimizer, epoch, device) - - val_result = evaluate(model_without_ddp, val_loader, device, config) - val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d'%epoch, remove_duplicate='image_id') - - test_result = evaluate(model_without_ddp, test_loader, device, config) - test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d'%epoch, remove_duplicate='image_id') - - if utils.is_main_process(): - coco_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val') - coco_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test') - - if args.evaluate: - log_stats = {**{f'val_{k}': v for k, v in coco_val.eval.items()}, - **{f'test_{k}': v for k, v in coco_test.eval.items()}, - } - with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - else: - save_obj = { - 'model': model_without_ddp.state_dict(), - 'optimizer': optimizer.state_dict(), - 'config': config, - 'epoch': epoch, - } - - if coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best: - best = coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] - best_epoch = epoch - torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) - - log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, - **{f'val_{k}': v for k, v in coco_val.eval.items()}, - **{f'test_{k}': v for k, v in coco_test.eval.items()}, - 'epoch': epoch, - 'best_epoch': best_epoch, - } - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - - if args.evaluate: - break - dist.barrier() - - 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__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/caption_coco.yaml') - parser.add_argument('--output_dir', default='output/Caption_coco') - parser.add_argument('--evaluate', action='store_true') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - args.result_dir = os.path.join(args.output_dir, 'result') - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - Path(args.result_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/train_nlvr.py b/BLIP/train_nlvr.py deleted file mode 100644 index 84b247bda2334c1fd894b6c11d33ef48c8e7df28..0000000000000000000000000000000000000000 --- a/BLIP/train_nlvr.py +++ /dev/null @@ -1,213 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path -import json -import pickle - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader -import torch.backends.cudnn as cudnn -import torch.distributed as dist - -from models.blip_nlvr import blip_nlvr - -import utils -from utils import cosine_lr_schedule, warmup_lr_schedule -from data import create_dataset, create_sampler, create_loader - -def train(model, data_loader, optimizer, epoch, device, config): - # train - model.train() - - metric_logger = utils.MetricLogger(delimiter=" ") - metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}')) - metric_logger.add_meter('loss', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) - - header = 'Train Epoch: [{}]'.format(epoch) - print_freq = 50 - step_size = 10 - - for i,(image0, image1, text, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): - - images = torch.cat([image0, image1], dim=0) - images, targets = images.to(device), targets.to(device) - - loss = model(images, text, targets=targets, train=True) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - metric_logger.update(loss=loss.item()) - - # gather the stats from all processes - metric_logger.synchronize_between_processes() - print("Averaged stats:", metric_logger.global_avg()) - return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} - - -@torch.no_grad() -def evaluate(model, data_loader, device, config): - # test - model.eval() - - metric_logger = utils.MetricLogger(delimiter=" ") - - header = 'Evaluation:' - print_freq = 50 - - for image0, image1, text, targets in metric_logger.log_every(data_loader, print_freq, header): - images = torch.cat([image0, image1], dim=0) - images, targets = images.to(device), targets.to(device) - - prediction = model(images, text, targets=targets, train=False) - - _, pred_class = prediction.max(1) - accuracy = (targets==pred_class).sum() / targets.size(0) - - metric_logger.meters['acc'].update(accuracy.item(), n=image0.size(0)) - - # gather the stats from all processes - metric_logger.synchronize_between_processes() - - print("Averaged stats:", metric_logger.global_avg()) - return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} - - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating dataset") - datasets = create_dataset('nlvr', config) - - if args.distributed: - num_tasks = utils.get_world_size() - global_rank = utils.get_rank() - samplers = create_sampler(datasets, [True,False,False], num_tasks, global_rank) - else: - samplers = [None, None, None] - - batch_size=[config['batch_size_train'],config['batch_size_test'],config['batch_size_test']] - train_loader, val_loader, test_loader = create_loader(datasets,samplers,batch_size=batch_size, - num_workers=[4,4,4],is_trains=[True,False,False], - collate_fns=[None,None,None]) - - #### Model #### - print("Creating model") - model = blip_nlvr(pretrained=config['pretrained'], image_size=config['image_size'], - vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer']) - - model = model.to(device) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) - - print("Start training") - start_time = time.time() - best = 0 - best_epoch = 0 - - for epoch in range(0, config['max_epoch']): - if not args.evaluate: - if args.distributed: - train_loader.sampler.set_epoch(epoch) - - cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr']) - - train_stats = train(model, train_loader, optimizer, epoch, device, config) - - val_stats = evaluate(model, val_loader, device, config) - test_stats = evaluate(model, test_loader, device, config) - - if utils.is_main_process(): - if args.evaluate: - log_stats = {**{f'val_{k}': v for k, v in val_stats.items()}, - **{f'test_{k}': v for k, v in test_stats.items()}, - } - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - - else: - log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, - **{f'val_{k}': v for k, v in val_stats.items()}, - **{f'test_{k}': v for k, v in test_stats.items()}, - 'epoch': epoch, - } - - if float(val_stats['acc'])>best: - save_obj = { - 'model': model_without_ddp.state_dict(), - 'optimizer': optimizer.state_dict(), - 'config': config, - 'epoch': epoch, - } - torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) - best = float(val_stats['acc']) - best_epoch = epoch - - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - if args.evaluate: - break - - dist.barrier() - - if utils.is_main_process(): - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write("best epoch: %d"%best_epoch) - - 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__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/nlvr.yaml') - parser.add_argument('--output_dir', default='output/NLVR') - parser.add_argument('--evaluate', action='store_true') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/train_retrieval.py b/BLIP/train_retrieval.py deleted file mode 100644 index 574f03382cc8197b97971a11ae54b632bcfe6655..0000000000000000000000000000000000000000 --- a/BLIP/train_retrieval.py +++ /dev/null @@ -1,345 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.backends.cudnn as cudnn -import torch.distributed as dist -from torch.utils.data import DataLoader - -from models.blip_retrieval import blip_retrieval -import utils -from utils import cosine_lr_schedule -from data import create_dataset, create_sampler, create_loader - - -def train(model, data_loader, optimizer, epoch, device, config): - # train - model.train() - - metric_logger = utils.MetricLogger(delimiter=" ") - metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) - metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=1, fmt='{value:.4f}')) - metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=1, fmt='{value:.4f}')) - header = 'Train Epoch: [{}]'.format(epoch) - print_freq = 50 - - for i,(image, caption, idx) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): - image = image.to(device,non_blocking=True) - idx = idx.to(device,non_blocking=True) - - if epoch>0: - alpha = config['alpha'] - else: - alpha = config['alpha']*min(1,i/len(data_loader)) - - loss_ita, loss_itm = model(image, caption, alpha=alpha, idx=idx) - loss = loss_ita + loss_itm - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - metric_logger.update(loss_itm=loss_itm.item()) - metric_logger.update(loss_ita=loss_ita.item()) - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - - # gather the stats from all processes - metric_logger.synchronize_between_processes() - print("Averaged stats:", metric_logger.global_avg()) - return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} - - -@torch.no_grad() -def evaluation(model, data_loader, device, config): - # test - model.eval() - - metric_logger = utils.MetricLogger(delimiter=" ") - header = 'Evaluation:' - - print('Computing features for evaluation...') - start_time = time.time() - - texts = data_loader.dataset.text - num_text = len(texts) - text_bs = 256 - text_ids = [] - text_embeds = [] - text_atts = [] - for i in range(0, num_text, text_bs): - text = texts[i: min(num_text, i+text_bs)] - text_input = model.tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device) - text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text') - text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:])) - text_embeds.append(text_embed) - text_ids.append(text_input.input_ids) - text_atts.append(text_input.attention_mask) - - text_embeds = torch.cat(text_embeds,dim=0) - text_ids = torch.cat(text_ids,dim=0) - text_atts = torch.cat(text_atts,dim=0) - text_ids[:,0] = model.tokenizer.enc_token_id - - image_feats = [] - image_embeds = [] - for image, img_id in data_loader: - image = image.to(device) - image_feat = model.visual_encoder(image) - image_embed = model.vision_proj(image_feat[:,0,:]) - image_embed = F.normalize(image_embed,dim=-1) - - image_feats.append(image_feat.cpu()) - image_embeds.append(image_embed) - - image_feats = torch.cat(image_feats,dim=0) - image_embeds = torch.cat(image_embeds,dim=0) - - sims_matrix = image_embeds @ text_embeds.t() - score_matrix_i2t = torch.full((len(data_loader.dataset.image),len(texts)),-100.0).to(device) - - num_tasks = utils.get_world_size() - rank = utils.get_rank() - step = sims_matrix.size(0)//num_tasks + 1 - start = rank*step - end = min(sims_matrix.size(0),start+step) - - for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): - topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0) - - encoder_output = image_feats[start+i].repeat(config['k_test'],1,1).to(device) - encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device) - output = model.text_encoder(text_ids[topk_idx], - attention_mask = text_atts[topk_idx], - encoder_hidden_states = encoder_output, - encoder_attention_mask = encoder_att, - return_dict = True, - ) - score = model.itm_head(output.last_hidden_state[:,0,:])[:,1] - score_matrix_i2t[start+i,topk_idx] = score + topk_sim - - sims_matrix = sims_matrix.t() - score_matrix_t2i = torch.full((len(texts),len(data_loader.dataset.image)),-100.0).to(device) - - step = sims_matrix.size(0)//num_tasks + 1 - start = rank*step - end = min(sims_matrix.size(0),start+step) - - for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): - - topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0) - encoder_output = image_feats[topk_idx].to(device) - encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device) - output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1), - attention_mask = text_atts[start+i].repeat(config['k_test'],1), - encoder_hidden_states = encoder_output, - encoder_attention_mask = encoder_att, - return_dict = True, - ) - score = model.itm_head(output.last_hidden_state[:,0,:])[:,1] - score_matrix_t2i[start+i,topk_idx] = score + topk_sim - - if args.distributed: - dist.barrier() - torch.distributed.all_reduce(score_matrix_i2t, op=torch.distributed.ReduceOp.SUM) - torch.distributed.all_reduce(score_matrix_t2i, op=torch.distributed.ReduceOp.SUM) - - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print('Evaluation time {}'.format(total_time_str)) - - return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy() - - - -@torch.no_grad() -def itm_eval(scores_i2t, scores_t2i, txt2img, img2txt): - - #Images->Text - ranks = np.zeros(scores_i2t.shape[0]) - for index,score in enumerate(scores_i2t): - inds = np.argsort(score)[::-1] - # Score - rank = 1e20 - for i in img2txt[index]: - tmp = np.where(inds == i)[0][0] - if tmp < rank: - rank = tmp - ranks[index] = rank - - # Compute metrics - tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) - tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) - tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) - - #Text->Images - ranks = np.zeros(scores_t2i.shape[0]) - - for index,score in enumerate(scores_t2i): - inds = np.argsort(score)[::-1] - ranks[index] = np.where(inds == txt2img[index])[0][0] - - # Compute metrics - ir1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) - ir5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) - ir10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) - - tr_mean = (tr1 + tr5 + tr10) / 3 - ir_mean = (ir1 + ir5 + ir10) / 3 - r_mean = (tr_mean + ir_mean) / 2 - - eval_result = {'txt_r1': tr1, - 'txt_r5': tr5, - 'txt_r10': tr10, - 'txt_r_mean': tr_mean, - 'img_r1': ir1, - 'img_r5': ir5, - 'img_r10': ir10, - 'img_r_mean': ir_mean, - 'r_mean': r_mean} - return eval_result - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating retrieval dataset") - train_dataset, val_dataset, test_dataset = create_dataset('retrieval_%s'%config['dataset'], config) - - if args.distributed: - num_tasks = utils.get_world_size() - global_rank = utils.get_rank() - samplers = create_sampler([train_dataset], [True], num_tasks, global_rank) + [None, None] - else: - samplers = [None, None, None] - - train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers, - batch_size=[config['batch_size_train']]+[config['batch_size_test']]*2, - num_workers=[4,4,4], - is_trains=[True, False, False], - collate_fns=[None,None,None]) - - - #### Model #### - print("Creating model") - model = blip_retrieval(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], - vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], - queue_size=config['queue_size'], negative_all_rank=config['negative_all_rank']) - - model = model.to(device) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) - - best = 0 - best_epoch = 0 - - print("Start training") - start_time = time.time() - - for epoch in range(0, config['max_epoch']): - if not args.evaluate: - if args.distributed: - train_loader.sampler.set_epoch(epoch) - - cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr']) - - train_stats = train(model, train_loader, optimizer, epoch, device, config) - - score_val_i2t, score_val_t2i, = evaluation(model_without_ddp, val_loader, device, config) - score_test_i2t, score_test_t2i = evaluation(model_without_ddp, test_loader, device, config) - - if utils.is_main_process(): - - val_result = itm_eval(score_val_i2t, score_val_t2i, val_loader.dataset.txt2img, val_loader.dataset.img2txt) - print(val_result) - - if val_result['r_mean']>best: - save_obj = { - 'model': model_without_ddp.state_dict(), - 'optimizer': optimizer.state_dict(), - 'config': config, - 'epoch': epoch, - } - torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) - best = val_result['r_mean'] - best_epoch = epoch - - test_result = itm_eval(score_test_i2t, score_test_t2i, test_loader.dataset.txt2img, test_loader.dataset.img2txt) - print(test_result) - - if args.evaluate: - log_stats = {**{f'val_{k}': v for k, v in val_result.items()}, - **{f'test_{k}': v for k, v in test_result.items()}, - } - with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - else: - log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, - **{f'val_{k}': v for k, v in val_result.items()}, - **{f'test_{k}': v for k, v in test_result.items()}, - 'epoch': epoch, - 'best_epoch': best_epoch, - } - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - - if args.evaluate: - break - - dist.barrier() - torch.cuda.empty_cache() - - 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__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/retrieval_flickr.yaml') - parser.add_argument('--output_dir', default='output/Retrieval_flickr') - parser.add_argument('--evaluate', action='store_true') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/train_vqa.py b/BLIP/train_vqa.py deleted file mode 100644 index 89eb7490862e517cc660f842396033c21d441a20..0000000000000000000000000000000000000000 --- a/BLIP/train_vqa.py +++ /dev/null @@ -1,202 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import argparse -import os -import ruamel_yaml as yaml -import numpy as np -import random -import time -import datetime -import json -from pathlib import Path - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader -import torch.backends.cudnn as cudnn -import torch.distributed as dist - -from models.blip_vqa import blip_vqa -import utils -from utils import cosine_lr_schedule -from data import create_dataset, create_sampler, create_loader -from data.vqa_dataset import vqa_collate_fn -from data.utils import save_result - - -def train(model, data_loader, optimizer, epoch, device): - # train - model.train() - - metric_logger = utils.MetricLogger(delimiter=" ") - metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) - metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}')) - - header = 'Train Epoch: [{}]'.format(epoch) - print_freq = 50 - - for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): - image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True) - - loss = model(image, question, answer, train=True, n=n, weights=weights) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - metric_logger.update(loss=loss.item()) - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - - # gather the stats from all processes - metric_logger.synchronize_between_processes() - print("Averaged stats:", metric_logger.global_avg()) - return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} - - -@torch.no_grad() -def evaluation(model, data_loader, device, config) : - # test - model.eval() - - metric_logger = utils.MetricLogger(delimiter=" ") - header = 'Generate VQA test result:' - print_freq = 50 - - result = [] - - if config['inference']=='rank': - answer_list = data_loader.dataset.answer_list - answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device) - answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id - - for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): - image = image.to(device,non_blocking=True) - - if config['inference']=='generate': - answers = model(image, question, train=False, inference='generate') - - for answer, ques_id in zip(answers, question_id): - ques_id = int(ques_id.item()) - result.append({"question_id":ques_id, "answer":answer}) - - elif config['inference']=='rank': - answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test']) - - for ques_id, answer_id in zip(question_id, answer_ids): - result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]}) - - return result - - -def main(args, config): - utils.init_distributed_mode(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 - - #### Dataset #### - print("Creating vqa datasets") - datasets = create_dataset('vqa', config) - - if args.distributed: - num_tasks = utils.get_world_size() - global_rank = utils.get_rank() - samplers = create_sampler(datasets, [True, False], num_tasks, global_rank) - else: - samplers = [None, None] - - train_loader, test_loader = create_loader(datasets,samplers, - batch_size=[config['batch_size_train'],config['batch_size_test']], - num_workers=[4,4],is_trains=[True, False], - collate_fns=[vqa_collate_fn,None]) - #### Model #### - print("Creating model") - model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'], - vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer']) - - model = model.to(device) - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - model_without_ddp = model.module - - optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) - - best = 0 - best_epoch = 0 - - print("Start training") - start_time = time.time() - for epoch in range(0, config['max_epoch']): - if not args.evaluate: - if args.distributed: - train_loader.sampler.set_epoch(epoch) - - cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr']) - - train_stats = train(model, train_loader, optimizer, epoch, device) - - else: - break - - if utils.is_main_process(): - log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, - 'epoch': epoch, - } - with open(os.path.join(args.output_dir, "log.txt"),"a") as f: - f.write(json.dumps(log_stats) + "\n") - - save_obj = { - 'model': model_without_ddp.state_dict(), - 'optimizer': optimizer.state_dict(), - 'config': config, - 'epoch': epoch, - } - torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch)) - - dist.barrier() - - vqa_result = evaluation(model_without_ddp, test_loader, device, config) - result_file = save_result(vqa_result, args.result_dir, 'vqa_result') - - 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__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='./configs/vqa.yaml') - parser.add_argument('--output_dir', default='output/VQA') - parser.add_argument('--evaluate', action='store_true') - parser.add_argument('--device', default='cuda') - parser.add_argument('--seed', default=42, type=int) - parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') - parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') - parser.add_argument('--distributed', default=True, type=bool) - args = parser.parse_args() - - config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) - - args.result_dir = os.path.join(args.output_dir, 'result') - - Path(args.output_dir).mkdir(parents=True, exist_ok=True) - Path(args.result_dir).mkdir(parents=True, exist_ok=True) - - yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) - - main(args, config) \ No newline at end of file diff --git a/BLIP/transform/randaugment.py b/BLIP/transform/randaugment.py deleted file mode 100644 index 094d9f4cacc93146d2bab7311d9dc04feb07032c..0000000000000000000000000000000000000000 --- a/BLIP/transform/randaugment.py +++ /dev/null @@ -1,340 +0,0 @@ -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) \ No newline at end of file diff --git a/BLIP/utils.py b/BLIP/utils.py deleted file mode 100644 index ebe0e1dc2f5d200156d5dd1acc305a8b7b7b98da..0000000000000000000000000000000000000000 --- a/BLIP/utils.py +++ /dev/null @@ -1,278 +0,0 @@ -import math -def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr): - """Decay the learning rate""" - lr = (init_lr - min_lr) * 0.5 * (1. + math.cos(math.pi * epoch / max_epoch)) + min_lr - for param_group in optimizer.param_groups: - param_group['lr'] = lr - -def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr): - """Warmup the learning rate""" - lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max_step) - for param_group in optimizer.param_groups: - param_group['lr'] = lr - -def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate): - """Decay the learning rate""" - lr = max(min_lr, init_lr * (decay_rate**epoch)) - for param_group in optimizer.param_groups: - param_group['lr'] = lr - -import numpy as np -import io -import os -import time -from collections import defaultdict, deque -import datetime - -import torch -import torch.distributed as dist - -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 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 global_avg(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append( - "{}: {:.4f}".format(name, meter.global_avg) - ) - 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 AttrDict(dict): - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - -def compute_acc(logits, label, reduction='mean'): - ret = (torch.argmax(logits, dim=1) == label).float() - if reduction == 'none': - return ret.detach() - elif reduction == 'mean': - return ret.mean().item() - -def compute_n_params(model, return_str=True): - tot = 0 - for p in model.parameters(): - w = 1 - for x in p.shape: - w *= x - tot += w - if return_str: - if tot >= 1e6: - return '{:.1f}M'.format(tot / 1e6) - else: - return '{:.1f}K'.format(tot / 1e3) - else: - return tot - -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 init_distributed_mode(args): - if '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 {}, word {}): {}'.format( - args.rank, args.world_size, args.dist_url), flush=True) - torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - torch.distributed.barrier() - setup_for_distributed(args.rank == 0) - - \ No newline at end of file diff --git a/app.py b/app.py index cb592abf39ec7bc23f8dc022b5c1ed24f5eee407..d5000547872aec72ae1582e7688b158b60343568 100644 --- a/app.py +++ b/app.py @@ -1,33 +1,19 @@ import gradio as gr -import sys -from BLIP.models.blip import blip_decoder from PIL import Image -import requests import torch -from torchvision import transforms -from torchvision.transforms.functional import InterpolationMode -from urllib.parse import urlparse +from transformers import BlipProcessor, BlipForConditionalGeneration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -image_size = 384 -transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC), - transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) - ]) - -model_url = "https://technionmail-my.sharepoint.com/personal/snoamr_campus_technion_ac_il/_layouts/15/download.aspx?share=EZxgXQaBXGREgDsQiaTcwAAB0z8jQA_hgAnwwPQDt8Dgew" -model = blip_decoder(pretrained=model_url, image_size=384, vit='base') -model.eval() -model = model.to(device) +processor = BlipProcessor.from_pretrained("noamrot/FuseCap") +model = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap").to(device) def inference(raw_image): - # raw_image = torch.tensor(raw_image) - image = transform(raw_image).unsqueeze(0).to(device) - with torch.no_grad(): - caption = model.generate(image, sample=False, num_beams=3, max_length=60, min_length=5) - return caption[0] + text = "a picture of " + inputs = processor(raw_image, text, return_tensors="pt").to(device) + out = model.generate(**inputs) + caption = processor.decode(out[0], skip_special_tokens=True) + return caption inputs = [gr.Image(type='pil', interactive=False),] @@ -47,3 +33,4 @@ iface = gr.Interface(fn=inference, examples=examples, enable_queue=True) iface.launch() + diff --git a/requirements.txt b/requirements.txt index 8c6c9668dee2e0f24ab87c75d3c63e9b82141d76..1a7e9f850caa465e3b2358361fcc2a96db37f146 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,4 @@ -timm==0.4.12 transformers==4.15.0 -fairscale==0.4.4 torch torchvision Pillow \ No newline at end of file