Duplicate from xinyu1205/Recognize_Anything-Tag2Text

Co-authored-by: Xinyu Huang <[email protected]>

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README.md

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+---
+license: mit
+title: RAM_Tag2Text
+sdk: gradio
+emoji: 🐠
+colorFrom: blue
+colorTo: yellow
+app_file: app.py
+pinned: false
+duplicated_from: xinyu1205/Recognize_Anything-Tag2Text
+---
+
+
+# :label: Recognize Anything: A Strong Image Tagging Model & Tag2Text: Guiding Vision-Language Model via Image Tagging
+
+Official PyTorch Implementation of the <a href="https://recognize-anything.github.io/">Recognize Anything Model (RAM)</a> and the <a href="https://tag2text.github.io/">Tag2Text Model</a>.
+
+- RAM is a strong image tagging model, which can recognize any common category with high accuracy.
+- Tag2Text is an efficient and controllable vision-language model with tagging guidance.
+
+
+When combined with localization models ([Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything)), Tag2Text and RAM form a strong and general pipeline for visual semantic analysis.
+
+![](./images/ram_grounded_sam.jpg)
+
+## :sun_with_face: Helpful Tutorial
+
+
+- :apple:  [[Access RAM Homepage](https://recognize-anything.github.io/)]
+- :grapes: [[Access Tag2Text Homepage](https://tag2text.github.io/)]
+- :sunflower:  [[Read RAM arXiv Paper](https://arxiv.org/abs/2306.03514)]
+- :rose: [[Read Tag2Text arXiv Paper](https://arxiv.org/abs/2303.05657)]
+- :mushroom: [[Try our Tag2Text web Demo! 🤗](https://huggingface.co/spaces/xinyu1205/Tag2Text)]
+
+
+
+## :bulb: Highlight
+**Recognition and localization are two foundation computer vision tasks.**
+- **The Segment Anything Model (SAM)** excels in **localization capabilities**, while it falls short when it comes to **recognition tasks**.
+- **The Recognize Anything Model (RAM) and Tag2Text** exhibits **exceptional recognition abilities**, in terms of **both accuracy and scope**.
+
+<p align="center">
+ <table class="tg">
+  <tr>
+    <td class="tg-c3ow"><img src="images/localization_and_recognition.jpg" align="center" width="800" ></td>
+  </tr>
+</table>
+</p>
+
+
+<details close>
+<summary><font size="4">
+Tag2Text for Vision-Language Tasks.
+</font></summary>
+
+- **Tagging.** Without manual annotations, Tag2Text achieves **superior** image tag recognition ability of [**3,429**](./data/tag_list.txt) commonly human-used categories.
+- **Efficient.** Tagging guidance effectively enhances the performance of vision-language models on both **generation-based** and **alignment-based** tasks.
+- **Controllable.** Tag2Text permits users to input **desired tags**, providing the flexibility in composing corresponding texts based on the input tags.
+
+
+<p align="center">
+ <table class="tg">
+  <tr>
+    <td class="tg-c3ow"><img src="images/tag2text_framework.png" align="center" width="800" ></td>
+  </tr>
+</table>
+</p>
+</details>
+
+
+<details close>
+<summary><font size="4">
+Advancements of RAM on Tag2Text.
+</font></summary>
+
+- **Accuracy.** RAM utilizes a data engine to generate additional annotations and clean incorrect ones, resulting higher accuracy compared to Tag2Text.
+- **Scope.** Tag2Text recognizes 3,400+ fixed tags. RAM upgrades the number to 6,400+, covering more valuable categories. With open-set capability, RAM is feasible to recognize any common category.
+
+
+</details>
+
+
+## :sparkles: Highlight Projects with other Models
+- [Tag2Text/RAM with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) is trong and general pipeline for visual semantic analysis, which can automatically **recognize**, detect, and segment for an image!
+- [Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) is a multifunctional video question answering tool. Tag2Text provides powerful tagging and captioning capabilities as a fundamental component.
+- [Prompt-can-anything](https://github.com/positive666/Prompt-Can-Anything) is a gradio web library that integrates SOTA multimodal large models, including Tag2text as the core model for graphic understanding
+
+
+
+
+## :fire: News
+
+- **`2023/06/07`**: We release the [Recognize Anything Model (RAM)](https://recognize-anything.github.io/), a strong image tagging model!
+- **`2023/06/05`**: Tag2Text is combined with [Prompt-can-anything](https://github.com/OpenGVLab/Ask-Anything).
+- **`2023/05/20`**: Tag2Text is combined with [VideoChat](https://github.com/OpenGVLab/Ask-Anything).
+- **`2023/04/20`**: We marry Tag2Text with with [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything).
+- **`2023/04/10`**: Code and checkpoint is available Now!
+- **`2023/03/14`**: [Tag2Text web demo 🤗](https://huggingface.co/spaces/xinyu1205/Tag2Text) is available on Hugging Face Space! 
+
+
+
+
+
+
+## :writing_hand: TODO 
+
+- [x] Release Tag2Text demo.
+- [x] Release checkpoints.
+- [x] Release inference code.
+- [ ] Release RAM demo and checkpoints (until June 14th at the latest).
+- [ ] Release training codes (until August 1st at the latest).
+- [ ] Release training datasets (until August 1st at the latest).
+
+
+
+## :toolbox: Checkpoints
+
+<!-- insert a table -->
+<table>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>name</th>
+      <th>backbone</th>
+      <th>Data</th>
+      <th>Illustration</th>
+      <th>Checkpoint</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1</th>
+      <td>Tag2Text-Swin</td>
+      <td>Swin-Base</td>
+      <td>COCO, VG, SBU, CC-3M, CC-12M</td>
+      <td>Demo version with comprehensive captions.</td>
+      <td><a href="https://huggingface.co/spaces/xinyu1205/Tag2Text/blob/main/tag2text_swin_14m.pth">Download  link</a></td>
+    </tr>
+  </tbody>
+</table>
+
+
+## :running: Tag2Text Inference
+
+1. Install the dependencies, run:
+
+<pre/>pip install -r requirements.txt</pre> 
+
+2. Download Tag2Text pretrained checkpoints.
+
+3. Get the tagging and captioning results:
+<pre/>
+python inference.py  --image images/1641173_2291260800.jpg \
+--pretrained pretrained/tag2text_swin_14m.pth
+</pre>
+Or get the tagging and sepcifed captioning results (optional):
+<pre/>python inference.py  --image images/1641173_2291260800.jpg \
+--pretrained pretrained/tag2text_swin_14m.pth \
+--specified-tags "cloud,sky"</pre>
+
+
+## :black_nib: Citation
+If you find our work to be useful for your research, please consider citing.
+
+```
+@misc{zhang2023recognize,
+  title={Recognize Anything: A Strong Image Tagging Model}, 
+  author={Youcai Zhang and Xinyu Huang and Jinyu Ma and Zhaoyang Li and Zhaochuan Luo and Yanchun Xie and Yuzhuo Qin and Tong Luo and Yaqian Li and Shilong Liu and Yandong Guo and Lei Zhang},
+  year={2023},
+  eprint={2306.03514},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+
+@article{huang2023tag2text,
+  title={Tag2Text: Guiding Vision-Language Model via Image Tagging},
+  author={Huang, Xinyu and Zhang, Youcai and Ma, Jinyu and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Li, Yaqian and Guo, Yandong and Zhang, Lei},
+  journal={arXiv preprint arXiv:2303.05657},
+  year={2023}
+}
+```
+
+## :hearts: Acknowledgements
+This work is done with the help of the amazing code base of [BLIP](https://github.com/salesforce/BLIP), thanks very much!
+
+We also want to thank @Cheng Rui @Shilong Liu @Ren Tianhe for their help in [marrying Tag2Text with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything).
+
+
+

app.py

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+import numpy as np
+import random
+
+import torch
+import torchvision.transforms as transforms
+
+from PIL import Image
+from models.tag2text import tag2text_caption, ram
+
+import gradio as gr
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+image_size = 384
+
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                std=[0.229, 0.224, 0.225
+])
+transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize])
+
+#######Tag2Text Model
+pretrained = 'tag2text_swin_14m.pth'
+
+model_tag2text = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' )
+
+model_tag2text.eval()
+model_tag2text = model_tag2text.to(device)
+
+
+#######RAM Model
+pretrained = 'ram_swin_large_14m.pth'
+
+model_ram = ram(pretrained=pretrained, image_size=image_size, vit='swin_l' )
+
+model_ram.eval()
+model_ram = model_ram.to(device)
+
+
+def inference(raw_image, model_n , input_tag):
+    raw_image = raw_image.resize((image_size, image_size))
+
+    image = transform(raw_image).unsqueeze(0).to(device) 
+    if model_n == 'Recognize Anything Model':  
+        model = model_ram
+        with torch.no_grad():
+            tags, tags_chinese = model.generate_tag(image)
+        return tags[0],tags_chinese[0], 'none'
+    else:
+        model = model_tag2text
+        model.threshold = 0.68
+        if input_tag == '' or input_tag == 'none' or input_tag == 'None':
+            input_tag_list = None
+        else:
+            input_tag_list = []
+            input_tag_list.append(input_tag.replace(',',' | '))
+        with torch.no_grad():
+
+
+            caption, tag_predict = model.generate(image,tag_input = input_tag_list,max_length = 50, return_tag_predict = True)
+            if input_tag_list == None:
+                tag_1 = tag_predict
+                tag_2 = ['none']
+            else:
+                _, tag_1 = model.generate(image,tag_input = None, max_length = 50, return_tag_predict = True)
+                tag_2 = tag_predict
+
+        return tag_1[0],'none',caption[0]
+
+
+def build_gui():
+
+    description = """
+        <center><strong><font size='10'>Recognize Anything Model</font></strong></center>
+        <br>
+        Welcome to the Recognize Anything Model (RAM) and Tag2Text Model demo! <br><br>
+        <li>
+            <b>Recognize Anything Model:</b> Upload your image to get the <b>English and Chinese outputs of the image tags</b>!
+        </li>
+        <li>
+            <b>Tag2Text Model:</b> Upload your image to get the <b>tags</b> and <b>caption</b> of the image.
+            Optional: You can also input specified tags to get the corresponding caption.
+        </li>
+    """  # noqa
+
+    article = """
+        <p style='text-align: center'>
+            RAM and Tag2Text is training on open-source datasets, and we are persisting in refining and iterating upon it.<br/>
+            <a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything: A Strong Image Tagging Model</a>
+            |
+            <a href='https://https://tag2text.github.io/' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a>
+            |
+            <a href='https://github.com/xinyu1205/Tag2Text' target='_blank'>Github Repo</a>
+        </p>
+    """  # noqa
+
+    def inference_with_ram(img):
+        res = inference(img, "Recognize Anything Model", None)
+        return res[0], res[1]
+
+    def inference_with_t2t(img, input_tags):
+        res = inference(img, "Tag2Text Model", input_tags)
+        return res[0], res[2]
+
+    with gr.Blocks(title="Recognize Anything Model") as demo:
+        ###############
+        # components
+        ###############
+        gr.HTML(description)
+
+        with gr.Tab(label="Recognize Anything Model"):
+            with gr.Row():
+                with gr.Column():
+                    ram_in_img = gr.Image(type="pil")
+                    with gr.Row():
+                        ram_btn_run = gr.Button(value="Run")
+                        ram_btn_clear = gr.Button(value="Clear")
+                with gr.Column():
+                    ram_out_tag = gr.Textbox(label="Tags")
+                    ram_out_biaoqian = gr.Textbox(label="标签")
+            gr.Examples(
+                examples=[
+                    ["images/demo1.jpg"],
+                    ["images/demo2.jpg"],
+                    ["images/demo4.jpg"],
+                ],
+                fn=inference_with_ram,
+                inputs=[ram_in_img],
+                outputs=[ram_out_tag, ram_out_biaoqian],
+                cache_examples=True
+            )
+
+        with gr.Tab(label="Tag2Text Model"):
+            with gr.Row():
+                with gr.Column():
+                    t2t_in_img = gr.Image(type="pil")
+                    t2t_in_tag = gr.Textbox(label="User Specified Tags (Optional, separated by comma)")
+                    with gr.Row():
+                        t2t_btn_run = gr.Button(value="Run")
+                        t2t_btn_clear = gr.Button(value="Clear")
+                with gr.Column():
+                    t2t_out_tag = gr.Textbox(label="Tags")
+                    t2t_out_cap = gr.Textbox(label="Caption")
+            gr.Examples(
+                examples=[
+                    ["images/demo4.jpg", ""],
+                    ["images/demo4.jpg", "power line"],
+                    ["images/demo4.jpg", "track, train"],
+                ],
+                fn=inference_with_t2t,
+                inputs=[t2t_in_img, t2t_in_tag],
+                outputs=[t2t_out_tag, t2t_out_cap],
+                cache_examples=True
+            )
+
+        gr.HTML(article)
+
+        ###############
+        # events
+        ###############
+        # run inference
+        ram_btn_run.click(
+            fn=inference_with_ram,
+            inputs=[ram_in_img],
+            outputs=[ram_out_tag, ram_out_biaoqian]
+        )
+        t2t_btn_run.click(
+            fn=inference_with_t2t,
+            inputs=[t2t_in_img, t2t_in_tag],
+            outputs=[t2t_out_tag, t2t_out_cap]
+        )
+
+        # # images of two image panels should keep the same
+        # # and clear old outputs when image changes
+        # # slow due to internet latency when deployed on huggingface, comment out
+        # def sync_img(v):
+        #     return [gr.update(value=v)] + [gr.update(value="")] * 4
+
+        # ram_in_img.upload(fn=sync_img, inputs=[ram_in_img], outputs=[
+        #     t2t_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
+        # ])
+        # ram_in_img.clear(fn=sync_img, inputs=[ram_in_img], outputs=[
+        #     t2t_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
+        # ])
+        # t2t_in_img.clear(fn=sync_img, inputs=[t2t_in_img], outputs=[
+        #     ram_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
+        # ])
+        # t2t_in_img.upload(fn=sync_img, inputs=[t2t_in_img], outputs=[
+        #     ram_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
+        # ])
+
+        # clear all
+        def clear_all():
+            return [gr.update(value=None)] * 2 + [gr.update(value="")] * 5
+
+        ram_btn_clear.click(fn=clear_all, inputs=[], outputs=[
+            ram_in_img, t2t_in_img,
+            ram_out_tag, ram_out_biaoqian, t2t_in_tag, t2t_out_tag, t2t_out_cap
+        ])
+        t2t_btn_clear.click(fn=clear_all, inputs=[], outputs=[
+            ram_in_img, t2t_in_img,
+            ram_out_tag, ram_out_biaoqian, t2t_in_tag, t2t_out_tag, t2t_out_cap
+        ])
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_gui()
+    demo.launch(enable_queue=True)

configs/med_config.json

@@ -0,0 +1,21 @@
+{
+    "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   
+  }

configs/q2l_config.json

@@ -0,0 +1,22 @@
+{
+    "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": 4,
+    "num_hidden_layers": 2,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 30522,
+    "encoder_width": 768,
+    "add_cross_attention": true,
+    "add_tag_cross_attention": false
+  }

configs/swin/config_swinB_224.json

@@ -0,0 +1,10 @@
+{
+    "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth",
+    "vision_width": 1024,
+    "image_res": 224,
+    "window_size": 7,
+    "embed_dim": 128,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 4, 8, 16, 32 ]
+  }
+  

configs/swin/config_swinB_384.json

@@ -0,0 +1,10 @@
+{
+    "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth",
+    "vision_width": 1024,
+    "image_res": 384,
+    "window_size": 12,
+    "embed_dim": 128,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 4, 8, 16, 32 ]
+  }
+  

configs/swin/config_swinB_480.json

@@ -0,0 +1,9 @@
+{
+  "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth",
+  "vision_width": 1024,
+  "image_res": 480,
+  "window_size": 15,
+  "embed_dim": 128,
+  "depths": [ 2, 2, 18, 2 ],
+  "num_heads": [ 4, 8, 16, 32 ]
+}

configs/swin/config_swinB_576.json

@@ -0,0 +1,9 @@
+{
+  "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth",
+  "vision_width": 1024,
+  "image_res": 576,
+  "window_size": 18,
+  "embed_dim": 128,
+  "depths": [ 2, 2, 18, 2 ],
+  "num_heads": [ 4, 8, 16, 32 ]
+}

configs/swin/config_swinB_608.json

@@ -0,0 +1,9 @@
+{
+  "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth",
+  "vision_width": 1024,
+  "image_res": 608,
+  "window_size": 19,
+  "embed_dim": 128,
+  "depths": [ 2, 2, 18, 2 ],
+  "num_heads": [ 4, 8, 16, 32 ]
+}

configs/swin/config_swinL_384.json

@@ -0,0 +1,9 @@
+{
+    "ckpt": "pretrain_model/swin_large_patch4_window12_384_22k.pth",
+    "vision_width": 1536,
+    "image_res": 384,
+    "window_size": 12,
+    "embed_dim": 192,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 6, 12, 24, 48 ]
+  }

configs/tag2text_caption.yaml

@@ -0,0 +1,33 @@
+image_root: '/home/notebook/data/group/projects/tagging/caption/datasets/public/coco/'
+
+ann_root: 'dataset/caption_dataset'
+coco_gt_root: 'dataset/caption_dataset'
+
+pretrained: '/home/notebook/code/personal/S9049611/BLIP/output/pretrain_caption_tagtotext_v2_bert_asl'
+
+# size of vit model; base or large
+vit: 'swin_b'
+vit_grad_ckpt: False
+vit_ckpt_layer: 0
+
+batch_size: 35
+init_lr: 5e-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: 10
+
+text_pretrain: 'bert'
+
+class_num: 3429
+threshold: 0.7
+

data/ram_tag_list.txt

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+3D CG rendering
+3D glasses
+abacus
+abalone
+monastery
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+vault
+vector cartoon illustration
+vector icon
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+vegetable garden
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+vent
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+video game
+videotape
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+violinist
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+walking cane
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+walrus
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+waste container
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+water bird
+water buffalo
+water cooler
+water drop
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+water heater
+water level
+water lily
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+water purifier
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+water sport
+water surface
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+watercolor
+watercolor illustration
+watercolor painting
+waterfall
+watering can
+watermark overlay stamp
+watermelon
+waterproof jacket
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+wave
+wax
+weapon
+wear
+weather
+vane
+web
+webcam
+wedding
+wedding ring
+wedding bouquet
+wedding cake
+wedding couple
+wedding invitation
+wedding party
+wedding photo
+wedding photographer
+wedding photography
+wedding reception
+wedge
+weed
+weight
+weight scale
+welder
+well
+western food
+western restaurant
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+wet bar
+wet suit
+wetland
+wetsuit
+whale
+whale shark
+wheat
+wheat field
+wheel
+wheelchair
+wheelie
+whipped cream
+whisk
+whisker
+whiskey
+whistle
+white
+white house
+white wine
+whiteboard
+wicket
+wide
+wield
+wig
+Wii
+Wii controller
+wild
+wildebeest
+wildfire
+wildflower
+wildlife
+willow
+wind
+wind chime
+wind farm
+wind turbine
+windmill
+window
+window box
+window display
+window frame
+window screen
+window seat
+window sill
+wiper
+windshield
+windy
+wine bottle
+wine cooler
+wine cabinet
+wine cellar
+wine glass
+wine rack
+wine tasting
+winery
+wing
+winter
+winter melon
+winter morning
+winter scene
+winter sport
+winter storm
+wire
+wisteria
+witch
+witch hat
+wok
+wolf
+woman
+wood
+wood duck
+wood floor
+wood wall
+wood-burning stove
+wooden spoon
+woodland
+woodpecker
+woodworking plane
+wool
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+work card
+workbench
+worker
+workplace
+workshop
+world
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+wrap dress
+wrapping paper
+wrestle
+wrestler
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+wristband
+write
+writer
+writing
+writing brush
+writing desk
+yacht
+yak
+yard
+yellow
+yoga
+yoga mat
+yoghurt
+yoke
+yolk
+youth
+youth hostel
+yurt
+zebra
+zebra crossing
+zen garden
+zip
+zipper
+zombie
+zongzi
+zoo

data/ram_tag_list_chinese.txt

@@ -0,0 +1,4585 @@
+三维CG渲染 
+3d眼镜
+算盘 
+鲍鱼 
+修道院 
+肚子 
+学院 
+附件 
+事故 
+手风琴 
+橡子 
+丙烯颜料
+表演
+行动 
+动作电影 
+活动 
+演员 
+改编本
+添加 
+胶带 
+调整 
+成人 
+冒险 
+广告 
+天线 
+有氧运动 
+喷雾罐
+爆炸头
+农业 
+帮助
+空调 
+空调系统
+风向标
+飞机客舱 
+飞机模型 
+机场 
+航线
+客机 
+飞行员 
+飞机 
+飞机窗口 
+机场 
+机场跑道 
+航站楼 
+飞艇 
+航展
+过道 
+警报
+闹钟 
+信天翁 
+唱片
+唱片封面 
+酒精 
+壁龛
+水藻
+胡同/球道
+杏仁 
+芦荟
+高山 
+羊驼 
+字母表
+德国牧羊犬
+圣坛
+琥珀
+救护车 
+秃鹰
+美国短毛猫 
+紫水晶 
+圆形剧场 
+扩音器
+游乐园 
+游乐设施 
+锚 
+古老的 
+海葵 
+天使 
+角 
+动物 
+动物雕塑 
+动物收容所 
+动画片
+动画电影 
+动画师 
+动漫 
+脚踝 
+短袜 
+周年庆
+风衣 
+蚂蚁 
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+古董 
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+独立产权的公寓
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+冲突
+合流
+连接
+连接器
+温室
+星座
+建筑工地
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+鼠标/鼠
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+烤干酪辣味玉米片 
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+修女 
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+猩猩 
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+教区 
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+停车 
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+护照 
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+牧场 
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+意大利辣香肠 
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+花瓣 
+佩妮 
+教堂的长椅
+野鸡 
+现象 
+哲学家 
+电话 
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+照片 
+照相亭 
+相框 
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+钢琴家 
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+图片 
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+乒乓球桌 
+乒乓球 
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+一品脱的量 
+琵琶 
+管子
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+海盗旗 
+海盗船 
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+滑雪场 
+口袋里的面包 
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+橡皮泥 
+高原 
+平台 
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+大浅盘
+玩/演奏/运动
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+技术员 
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+T字形物
+青少年 
+电线杆 
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+电视 
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+阳台 
+地形 
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+领土 
+测试 
+测试赛 
+试管
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+短信 
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data/ram_tag_list_threshold.py

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+mountain peak
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+ridge
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+dumbbell
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+inlet
+sleeve
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+march
+police officer
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+suitcase
+supermodel
+goalkeeper
+shrub
+rock artist
+document
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+man
+blue artist
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+gown
+mosaic
+bungalow
+alphabet
+baseball field
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+rail
+soap
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+dessert
+dunk
+blossom
+conversation
+fruit market
+glass jar
+military
+beer bottle
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+tennis racket
+competition
+escalator
+bell tower
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+ballerina
+television
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+dahlia
+red carpet
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+fortress
+pack
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+city park
+platform
+college student
+arch bridge
+wind
+blender
+bloom
+ice rink
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+raven
+fairy
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+hall
+flower shop
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+electricity
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+symmetry
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+fisherman
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+loch
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+hanger
+dot
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+animation film
+crime fiction film
+boar
+emblem
+halloween costume
+kangaroo
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+spoon
+squirrel
+neon sign
+sky
+office desk
+beauty salon
+breakwater
+fashion look
+toaster
+author
+news conference
+outdoor
+canoe
+dragon
+tool
+shopping centre
+ladybug
+swimming pool
+landscaping
+ski pole
+red
+truck
+fly
+temple
+level
+sunday
+railroad bridge
+car mirror
+lawn mower
+flute
+aircraft carrier
+fashion menswear london week
+sunshine
+tile floor
+skull
+fossil
+flower arrangement
+diaper
+sea turtle
+cherry blossom
+fireman
+shack
+lens
+waiter
+animal
+basement
+snow
+autumn park
+glass box
+kick
+head
+anniversary
+vine
+back
+paper lantern
+fish tank
+cellphone
+silk
+coral
+notebook
+photo
+gazebo
+ketchup
+driver
+farmer
+bonfire
+chestnut
+photoshoot
+football field
+olive tree
+pheasant
+sandal
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+fireplace
+music
+deity
+fish market
+fig
+bell
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+asphalt road
+soccer
+hostel
+municipality
+courthouse
+roof
+end table
+pot
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+sport
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+protest
+syringe
+fashion designer
+jersey
+heart shape
+kayak
+stare
+sit with
+direct
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+spin
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+carve
+grow on
+warm
+watch
+stretch
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+send
+park
+chase
+collect
+lead
+kiss
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+lick
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+shoot
+hold
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+carry
+sip
+contain
+turn
+pour
+pitch
+give
+add
+blow
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+show
+walk
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+kneel
+cover
+drag
+post
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+operate
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+peel
+push
+run
+sit around
+buy
+jump
+walk on
+attend
+clean
+sell
+ride on
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+host
+dry
+plant
+sing
+row
+shake
+perch
+ride
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+skateboard
+live
+call
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+practice
+play on
+work on
+step
+relax
+hit
+fall in
+flow
+greet
+launch
+wear
+hang on
+drive
+sit in
+break
+learn
+fly
+connect
+display
+locate
+compete
+go for
+sail
+lift
+toast
+help
+run on
+reflect
+pose
+scratch
+frame
+dribble
+herd
+enter
+exit
+place
+inspect
+build
+pick
+fill
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+skate
+offer
+float
+sit by
+stand
+release
+rest
+singe
+climb
+tie
+mark
+lay
+stand around
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+run in
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+remove
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+open
+appear
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+miss
+check
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+seat
+feed
+rise
+sit on
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+date
+slide
+share
+graze
+jump in
+lie
+extrude
+roll
+move
+gather
+eat
+pull
+run through
+squeeze
+lay on
+draw
+play with
+wave
+assemble
+perform
+march
+score
+attach
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+sleep on
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+live in
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+flip
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+receive
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+look
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+happy
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+pregnant
+circular
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+silver
+ceramic
+3d
+green
+blonde
+golden
+dark
+tropical
+ripe
+deep
+fat
+musical
+giant
+medical
+medieval
+bare
+stunning
+bold
+geographical
+huge
+plastic
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+gothic
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+empty
+clear
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+striped
+aerial
+rainy
+cool
+flying
+commercial
+purple
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+blank
+haired
+dead
+wooden
+flat
+high
+beige
+panoramic
+angry
+dozen
+rural
+solar
+big
+small
+stained
+thick
+many
+fresh
+clean
+strong
+abstract
+crowded
+retro
+dry
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+martial
+modern
+blue
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+four
+outdoor
+single
+much
+beautiful
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+pretty
+new
+short
+sunny
+closed
+rocky
+red
+two
+double
+male
+gray
+five
+colorful
+automotive
+various
+one
+old
+rusty
+tall
+wild
+narrow
+natural
+several
+frozen
+textured
+lush
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+hot
+mixed
+white
+float
+quiet
+round
+bright
+religious
+female
+historical
+shiny
+traditional
+tourist
+yellow
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+coastal
+lovely
+little
+broken
+romantic
+wide
+royal
+rich
+open
+cute
+ancient
+cold
+political
+elderly
+gold
+full
+rustic
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+floral
+sad
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+fancy
+senior
+tiny
+stylish
+large
+frosty
+orange
+transparent
+electronic
+shallow
+scared
+armed
+dirty
+historic
+black
+few
+windy
+some
+square
+ornamental
+sandy
+thin

data/textual_label_embedding.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe873d76eb8190eb7f60362a08904ebd2bab00b6e2af026c91f866d0e6a037bb
+size 4695787

models/bert.py

@@ -0,0 +1,1035 @@
+'''
+ * 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_nopos(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)
+        #     # print('add position_embeddings!!!!')
+        #     embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+
+
+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)
+            # print('add position_embeddings!!!!')
+            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:
+            # print(self.key.weight.shape)
+            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)
+
+        # compatible with higher versions of transformers 
+        if key_layer.shape[0] > query_layer.shape[0]:
+            key_layer = key_layer[:query_layer.shape[0], :, :, :]
+            attention_mask = attention_mask[:query_layer.shape[0], :, :]
+            value_layer = value_layer[:query_layer.shape[0], :, :, :]
+
+        # 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,
+    ):
+        
+        if mode == 'tagging':
+            
+            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
+
+            cross_attention_outputs = self.crossattention(
+                hidden_states,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions=output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights  
+
+            present_key_value = cross_attention_outputs[-1]
+
+        else:
+            # 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 <https://arxiv.org/abs/1706.03762>`__ 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)
+        # sequence_output.shape torch.Size([85, 30, 768])
+        # prediction_scores.shape torch.Size([85, 30, 30524])
+        # labels.shape torch.Size([85, 30])
+
+
+        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
+
+

models/swin_transformer.py

@@ -0,0 +1,654 @@
+# --------------------------------------------------------
+# Swin Transformer
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu
+# --------------------------------------------------------
+
+import numpy as np
+from scipy import interpolate
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    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
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        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)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        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)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        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 self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def forward(self, x):
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
+
+
+class SwinTransformer(nn.Module):
+    r""" Swin Transformer
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 224
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, **kwargs):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
+                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                                 patches_resolution[1] // (2 ** i_layer)),
+                               depth=depths[i_layer],
+                               num_heads=num_heads[i_layer],
+                               window_size=window_size,
+                               mlp_ratio=self.mlp_ratio,
+                               qkv_bias=qkv_bias, qk_scale=qk_scale,
+                               drop=drop_rate, attn_drop=attn_drop_rate,
+                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                               norm_layer=norm_layer,
+                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                               use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        self.norm = norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        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 {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward(self, x, idx_to_group_img=None, image_atts=None, **kwargs):
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x)
+
+        x = self.norm(x)  # B L C
+
+        x_cls = self.avgpool(x.transpose(1, 2))  # B C 1
+
+        if idx_to_group_img is None:
+            return torch.cat([x_cls.transpose(1, 2), x], dim=1)
+        else:
+            x_bs = torch.gather(x, dim=0, index=idx_to_group_img.view(-1, 1, 1).expand(-1, x.shape[1], x.shape[2]))
+            weights = image_atts[:, 1:].unsqueeze(2)  # B L 1
+            x_bs_cls = torch.sum((weights * x_bs).transpose(1, 2), dim=-1, keepdim=True)   # B C 1
+            x_bs_cls = x_bs_cls / torch.sum(weights.transpose(1, 2), dim=-1, keepdim=True)  # avgpool
+
+            return torch.cat([x_bs_cls.transpose(1, 2), x_bs], dim=1), \
+                   torch.cat([x_cls.transpose(1, 2), x], dim=1)
+
+    def flops(self):
+        flops = 0
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
+        flops += self.num_features * self.num_classes
+        return flops
+
+
+def interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''):
+    # from: https://github.com/microsoft/unilm/blob/8a0a1c1f4e7326938ea7580a00d56d7f17d65612/beit/run_class_finetuning.py#L348
+
+    # rel_pos_bias: relative_position_bias_table
+    src_num_pos, num_attn_heads = rel_pos_bias.size()
+
+    num_extra_tokens = 0
+    src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
+    dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
+    if src_size != dst_size:
+        print("Position interpolate %s from %dx%d to %dx%d" % (param_name, src_size, src_size, dst_size, dst_size))
+
+        # extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
+        # rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
+
+        def geometric_progression(a, r, n):
+            return a * (1.0 - r ** n) / (1.0 - r)
+
+        left, right = 1.01, 1.5
+        while right - left > 1e-6:
+            q = (left + right) / 2.0
+            gp = geometric_progression(1, q, src_size // 2)
+            if gp > dst_size // 2:
+                right = q
+            else:
+                left = q
+
+        # if q > 1.090307:
+        #     q = 1.090307
+
+        dis = []
+        cur = 1
+        for i in range(src_size // 2):
+            dis.append(cur)
+            cur += q ** (i + 1)
+
+        r_ids = [-_ for _ in reversed(dis)]
+
+        x = r_ids + [0] + dis
+        y = r_ids + [0] + dis
+
+        t = dst_size // 2.0
+        dx = np.arange(-t, t + 0.1, 1.0)
+        dy = np.arange(-t, t + 0.1, 1.0)
+
+        # print("Original positions = %s" % str(x))
+        # print("Target positions = %s" % str(dx))
+
+        all_rel_pos_bias = []
+
+        for i in range(num_attn_heads):
+            z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
+            f = interpolate.interp2d(x, y, z, kind='cubic')
+            all_rel_pos_bias.append(
+                torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
+
+        rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
+
+    return rel_pos_bias

models/tag2text.py

@@ -0,0 +1,487 @@
+'''
+ * The Recognize Anything Model (RAM) & Tag2Text Model
+ * Written by Xinyu Huang
+'''
+import numpy as np
+import json
+import torch
+import warnings
+
+from torch import nn
+from models.bert import BertConfig, BertModel, BertLMHeadModel
+from models.vit import VisionTransformer
+from models.swin_transformer import SwinTransformer
+from data.ram_tag_list_threshold import ram_class_threshold
+
+from models.utils import *
+
+warnings.filterwarnings("ignore")
+
+class RAM(nn.Module):
+    def __init__(self,
+                 med_config=f'{CONFIG_PATH}/configs/med_config.json',
+                 image_size=384,
+                 vit='base',
+                 vit_grad_ckpt=False,
+                 vit_ckpt_layer=0,
+                 prompt='a picture of ',
+                 threshold=0.68,
+                 delete_tag_index=[],
+                 tag_list=f'{CONFIG_PATH}/data/ram_tag_list.txt',
+                 tag_list_chinese=f'{CONFIG_PATH}/data/ram_tag_list_chinese.txt'):
+        r""" The Recognize Anything Model (RAM) inference module.
+        RAM is a strong image tagging model, which can recognize any common category with high accuracy.
+        Described in the paper " Recognize Anything: A Strong Image Tagging Model" https://recognize-anything.github.io/
+        
+        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
+            threshold (int): tagging threshold
+            delete_tag_index (list): delete some tags that may disturb captioning
+        """
+        super().__init__()
+
+        # create image encoder
+        if vit == 'swin_b':
+            if image_size == 224:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'
+            elif image_size == 384:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'
+            vision_config = read_json(vision_config_path)
+            assert image_size == vision_config['image_res']
+            # assert config['patch_size'] == 32
+            vision_width = vision_config['vision_width']
+
+            self.visual_encoder = SwinTransformer(
+                img_size=vision_config['image_res'],
+                patch_size=4,
+                in_chans=3,
+                embed_dim=vision_config['embed_dim'],
+                depths=vision_config['depths'],
+                num_heads=vision_config['num_heads'],
+                window_size=vision_config['window_size'],
+                mlp_ratio=4.,
+                qkv_bias=True,
+                drop_rate=0.0,
+                drop_path_rate=0.1,
+                ape=False,
+                patch_norm=True,
+                use_checkpoint=False)
+
+        elif vit == 'swin_l':
+            if image_size == 224:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json'
+            elif image_size == 384:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json'
+            vision_config = read_json(vision_config_path)
+            assert image_size == vision_config['image_res']
+            # assert config['patch_size'] == 32
+            vision_width = vision_config['vision_width']
+
+            self.visual_encoder = SwinTransformer(
+                img_size=vision_config['image_res'],
+                patch_size=4,
+                in_chans=3,
+                embed_dim=vision_config['embed_dim'],
+                depths=vision_config['depths'],
+                num_heads=vision_config['num_heads'],
+                window_size=vision_config['window_size'],
+                mlp_ratio=4.,
+                qkv_bias=True,
+                drop_rate=0.0,
+                drop_path_rate=0.1,
+                ape=False,
+                patch_norm=True,
+                use_checkpoint=False)
+
+        else:
+            self.visual_encoder, vision_width = create_vit(
+                vit, image_size, vit_grad_ckpt, vit_ckpt_layer)
+
+        # create tokenzier
+        self.tokenizer = init_tokenizer()
+
+        # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder
+        # create image-tag interaction encoder
+        encoder_config = BertConfig.from_json_file(med_config)
+        encoder_config.encoder_width = 512
+        self.tag_encoder = BertModel(config=encoder_config,
+                                     add_pooling_layer=False)
+
+        # create image-tag-text decoder
+        decoder_config = BertConfig.from_json_file(med_config)
+        self.text_decoder = BertLMHeadModel(config=decoder_config)
+
+        self.delete_tag_index = delete_tag_index
+        self.prompt = prompt
+        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1
+
+        # load tag list
+        self.tag_list = self.load_tag_list(tag_list)
+        self.tag_list_chinese = self.load_tag_list(tag_list_chinese)
+
+        # create image-tag recognition decoder
+        self.threshold = threshold
+        self.num_class = len(self.tag_list)
+        q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json')
+        q2l_config.encoder_width = 512
+        self.tagging_head = BertModel(config=q2l_config,
+                                      add_pooling_layer=False)
+        self.tagging_head.resize_token_embeddings(len(self.tokenizer))
+        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
+
+        if q2l_config.hidden_size != 512:
+            self.wordvec_proj = nn.Linear(512, q2l_config.hidden_size)
+        else:
+            self.wordvec_proj = nn.Identity()
+
+        self.fc = nn.Linear(q2l_config.hidden_size, 1)
+
+        self.del_selfattention()
+
+        # share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder"
+        tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '',
+                                    ' ')
+        self.image_proj = nn.Linear(vision_width, 512)
+        self.label_embed = nn.Parameter(torch.load('data/textual_label_embedding.pth',map_location='cpu').float())
+
+        # adjust thresholds for some tags
+        self.class_threshold = torch.ones(self.num_class) * self.threshold
+        for key,value in enumerate(ram_class_threshold):
+            self.class_threshold[key] = value
+
+    def load_tag_list(self, tag_list_file):
+        with open(tag_list_file, 'r', encoding="utf8") as f:
+            tag_list = f.read().splitlines()
+        tag_list = np.array(tag_list)
+        return tag_list
+
+    # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label
+    def del_selfattention(self):
+        del self.tagging_head.embeddings
+        for layer in self.tagging_head.encoder.layer:
+            del layer.attention
+
+    def generate_tag(self,
+                 image,
+                 threshold=0.68,
+                 tag_input=None,
+                 ):
+            
+        label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))
+
+        image_embeds = self.image_proj(self.visual_encoder(image))
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        # recognized image tags using image-tag recogntiion decoder
+        image_cls_embeds = image_embeds[:, 0, :]
+        image_spatial_embeds = image_embeds[:, 1:, :]
+
+        bs = image_spatial_embeds.shape[0]
+        label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1)
+        tagging_embed = self.tagging_head(
+            encoder_embeds=label_embed,
+            encoder_hidden_states=image_embeds,
+            encoder_attention_mask=image_atts,
+            return_dict=False,
+            mode='tagging',
+        )
+
+        logits = self.fc(tagging_embed[0]).squeeze(-1)
+
+        targets = torch.where(
+            torch.sigmoid(logits) > self.class_threshold.to(image.device),
+            torch.tensor(1.0).to(image.device),
+            torch.zeros(self.num_class).to(image.device))
+
+        tag = targets.cpu().numpy()
+        tag[:,self.delete_tag_index] = 0
+        tag_output = []
+        tag_output_chinese = []
+        for b in range(bs):
+            index = np.argwhere(tag[b] == 1)
+            token = self.tag_list[index].squeeze(axis=1)
+            tag_output.append(' | '.join(token))
+            token_chinese = self.tag_list_chinese[index].squeeze(axis=1)
+            tag_output_chinese.append(' | '.join(token_chinese))
+
+
+        return tag_output, tag_output_chinese
+
+
+class Tag2Text_Caption(nn.Module):
+
+    def __init__(self,
+                 med_config=f'{CONFIG_PATH}/configs/med_config.json',
+                 image_size=384,
+                 vit='base',
+                 vit_grad_ckpt=False,
+                 vit_ckpt_layer=0,
+                 prompt='a picture of ',
+                 threshold=0.68,
+                 delete_tag_index=[127,2961, 3351, 3265, 3338, 3355, 3359],
+                 tag_list=f'{CONFIG_PATH}/data/tag_list.txt'):
+        r""" Tag2Text inference module, both captioning and tagging are included.
+        Tag2Text is an efficient and controllable vision-language pre-training framework.
+        Described in the paper "Tag2Text: Guiding Vision-Language Model via Image Tagging" https://arxiv.org/abs/2303.05657
+
+        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
+            threshold (int): tagging threshold
+            delete_tag_index (list): delete some tags that may disturb captioning
+        """
+        super().__init__()
+
+        # create image encoder
+        if vit == 'swin_b':
+            if image_size == 224:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'
+            elif image_size == 384:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'
+            vision_config = read_json(vision_config_path)
+            assert image_size == vision_config['image_res']
+            # assert config['patch_size'] == 32
+            vision_width = vision_config['vision_width']
+
+            self.visual_encoder = SwinTransformer(
+                img_size=vision_config['image_res'],
+                patch_size=4,
+                in_chans=3,
+                embed_dim=vision_config['embed_dim'],
+                depths=vision_config['depths'],
+                num_heads=vision_config['num_heads'],
+                window_size=vision_config['window_size'],
+                mlp_ratio=4.,
+                qkv_bias=True,
+                drop_rate=0.0,
+                drop_path_rate=0.1,
+                ape=False,
+                patch_norm=True,
+                use_checkpoint=False)
+
+        else:
+            self.visual_encoder, vision_width = create_vit(
+                vit, image_size, vit_grad_ckpt, vit_ckpt_layer)
+
+        # create tokenzier
+        self.tokenizer = init_tokenizer()
+
+        # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder
+        # create image-tag interaction encoder
+        encoder_config = BertConfig.from_json_file(med_config)
+        encoder_config.encoder_width = vision_width
+        self.tag_encoder = BertModel(config=encoder_config,
+                                     add_pooling_layer=False)
+
+        # create image-tag-text decoder
+        decoder_config = BertConfig.from_json_file(med_config)
+        self.text_decoder = BertLMHeadModel(config=decoder_config)
+
+        # delete some tags that may disturb captioning
+        # 127: "quarter"; 2961: "back"; 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one"
+        self.delete_tag_index = delete_tag_index
+        self.prompt = prompt
+        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1
+
+        # load tag list
+        self.tag_list = self.load_tag_list(tag_list)
+
+        # create image-tag recognition decoder
+        self.threshold = threshold
+        self.num_class = len(self.tag_list)
+        q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json')
+        q2l_config.encoder_width = vision_width
+        self.tagging_head = BertModel(config=q2l_config,
+                                      add_pooling_layer=False)
+        self.tagging_head.resize_token_embeddings(len(self.tokenizer))
+        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
+        self.fc = GroupWiseLinear(self.num_class,
+                                  q2l_config.hidden_size,
+                                  bias=True)
+        self.del_selfattention()
+
+        # share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder"
+        tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '',
+                                    ' ')
+
+        # adjust thresholds for some tags
+        # default threshold: 0.68
+        # 2701: "person"; 2828: "man"; 1167: "woman"; 
+        tag_thrshold = {2701:0.7, 2828: 0.7, 1167: 0.7}
+        self.class_threshold = torch.ones(self.num_class) * self.threshold
+        for key,value in tag_thrshold.items():
+            self.class_threshold[key] = value
+
+    def load_tag_list(self, tag_list_file):
+        with open(tag_list_file, 'r') as f:
+            tag_list = f.read().splitlines()
+        tag_list = np.array(tag_list)
+        return tag_list
+
+    # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label
+    def del_selfattention(self):
+        del self.tagging_head.embeddings
+        for layer in self.tagging_head.encoder.layer:
+            del layer.attention
+
+    def generate(self,
+                 image,
+                 sample=False,
+                 num_beams=3,
+                 max_length=30,
+                 min_length=10,
+                 top_p=0.9,
+                 repetition_penalty=1.0,
+                 tag_input=None,
+                 return_tag_predict=False):
+
+        image_embeds = self.visual_encoder(image)
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        # if not user specified tags, recognized image tags using image-tag recogntiion decoder
+        if tag_input == None:
+            image_cls_embeds = image_embeds[:, 0, :]
+            image_spatial_embeds = image_embeds[:, 1:, :]
+
+            bs = image_spatial_embeds.shape[0]
+            label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
+            tagging_embed = self.tagging_head(
+                encoder_embeds=label_embed,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=False,
+                mode='tagging',
+            )
+
+            logits = self.fc(tagging_embed[0])
+
+            targets = torch.where(
+                torch.sigmoid(logits) > self.class_threshold.to(image.device),
+                torch.tensor(1.0).to(image.device),
+                torch.zeros(self.num_class).to(image.device))
+
+            tag = targets.cpu().numpy()
+
+            # delete some tags that may disturb captioning
+            tag[:, self.delete_tag_index] = 0
+
+            tag_input = []
+            for b in range(bs):
+                index = np.argwhere(tag[b] == 1)
+                token = self.tag_list[index].squeeze(axis=1)
+                tag_input.append(' | '.join(token))
+                
+        tag_output = tag_input
+
+        # beam search for text generation(default)
+        if not sample:
+            image_embeds = image_embeds.repeat_interleave(num_beams, dim=0)
+            tag_input_temp = []
+            for tag in tag_input:
+                for i in range(num_beams):
+                    tag_input_temp.append(tag)
+            tag_input = tag_input_temp
+
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        # tokenizer input tags
+        tag_input_tokenzier = self.tokenizer(tag_input,
+                                             padding='max_length',
+                                             truncation=True,
+                                             max_length=40,
+                                             return_tensors="pt").to(
+                                                 image.device)
+        encoder_input_ids = tag_input_tokenzier.input_ids
+        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id
+
+        # put input tag into image-tag interaction encoder to interact with image embeddings
+        output_tagembedding = self.tag_encoder(
+            encoder_input_ids,
+            attention_mask=tag_input_tokenzier.attention_mask,
+            encoder_hidden_states=image_embeds,
+            encoder_attention_mask=image_atts,
+            return_dict=True,
+        )
+
+        # prompt trick for better captioning, followed BLIP
+        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
+            model_kwargs = {
+                "encoder_hidden_states": output_tagembedding.last_hidden_state,
+                "encoder_attention_mask": None
+            }
+            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 (default)
+            model_kwargs = {
+                "encoder_hidden_states": output_tagembedding.last_hidden_state,
+                "encoder_attention_mask": None
+            }
+            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):])
+        if return_tag_predict == True:
+            return  captions, tag_output
+        return captions
+
+
+# load Tag2Text pretrained model parameters
+def tag2text_caption(pretrained='', **kwargs):
+    model = Tag2Text_Caption(**kwargs)
+    if pretrained:
+        if kwargs['vit'] == 'swin_b':
+            model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)
+        else:
+            model, msg = load_checkpoint(model, pretrained)
+        print('vit:', kwargs['vit'])
+        print('msg', msg)
+    return model
+
+
+# load RAM pretrained model parameters
+def ram(pretrained='', **kwargs):
+    model = RAM(**kwargs)
+    if pretrained:
+        if kwargs['vit'] == 'swin_b':
+            model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)
+        elif kwargs['vit'] == 'swin_l':
+            model, msg = load_checkpoint_swinlarge(model, pretrained, kwargs)
+        else:
+            model, msg = load_checkpoint(model, pretrained)
+        print('vit:', kwargs['vit'])
+        print('msg', msg)
+    return model
+

models/utils.py

@@ -0,0 +1,278 @@
+import os
+import json
+import torch
+import math
+
+from torch import nn
+from typing import List
+from transformers import BertTokenizer
+from urllib.parse import urlparse
+from timm.models.hub import download_cached_file
+from models.vit import interpolate_pos_embed
+from models.swin_transformer import interpolate_relative_pos_embed
+from pathlib import Path
+CONFIG_PATH=(Path(__file__).resolve().parents[1])
+
+def read_json(rpath):
+    with open(rpath, 'r') as f:
+        return json.load(f)
+
+
+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)
+
+
+class GroupWiseLinear(nn.Module):
+    # could be changed to:
+    # output = torch.einsum('ijk,zjk->ij', x, self.W)
+    # or output = torch.einsum('ijk,jk->ij', x, self.W[0])
+    def __init__(self, num_class, hidden_dim, bias=True):
+        super().__init__()
+        self.num_class = num_class
+        self.hidden_dim = hidden_dim
+        self.bias = bias
+
+        self.W = nn.Parameter(torch.Tensor(1, num_class, hidden_dim))
+        if bias:
+            self.b = nn.Parameter(torch.Tensor(1, num_class))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        stdv = 1. / math.sqrt(self.W.size(2))
+        for i in range(self.num_class):
+            self.W[0][i].data.uniform_(-stdv, stdv)
+        if self.bias:
+            for i in range(self.num_class):
+                self.b[0][i].data.uniform_(-stdv, stdv)
+
+    def forward(self, x):
+        # x: B,K,d
+        x = (self.W * x).sum(-1)
+        if self.bias:
+            x = x + self.b
+        return x
+
+
+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
+
+
+def load_checkpoint_swinbase(model, url_or_filename, kwargs):
+    if kwargs['image_size'] == 224:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'
+    elif kwargs['image_size'] == 384:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'
+    window_size = read_json(vision_config_path)['window_size']
+    print('--------------')
+    print(url_or_filename)
+    print('--------------')
+    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']
+
+    for k in list(state_dict.keys()):
+        if 'relative_position_bias_table' in k:
+            dst_num_pos = (2 * window_size - 1)**2
+            state_dict[k] = interpolate_relative_pos_embed(state_dict[k],
+                                                           dst_num_pos,
+                                                           param_name=k)
+        elif ('relative_position_index' in k) or ('attn_mask' in k):
+            del state_dict[k]
+        elif "vision_multi" in k:
+            state_dict[k.replace("vision_multi",
+                                 "tagging_head")] = state_dict.pop(k)
+
+    msg = model.load_state_dict(state_dict, strict=False)
+    print('load checkpoint from %s' % url_or_filename)
+    return model, msg
+
+
+def load_checkpoint_swinlarge(model, url_or_filename, kwargs):
+    if kwargs['image_size'] == 224:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json'
+    elif kwargs['image_size'] == 384:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json'
+    window_size = read_json(vision_config_path)['window_size']
+    print('--------------')
+    print(url_or_filename)
+    print('--------------')
+    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']
+
+    for k in list(state_dict.keys()):
+        if 'relative_position_bias_table' in k:
+            dst_num_pos = (2 * window_size - 1)**2
+            state_dict[k] = interpolate_relative_pos_embed(state_dict[k],
+                                                           dst_num_pos,
+                                                           param_name=k)
+        elif ('relative_position_index' in k) or ('attn_mask' in k):
+            del state_dict[k]
+        elif "vision_multi" in k:
+            state_dict[k.replace("vision_multi",
+                                 "tagging_head")] = state_dict.pop(k)
+
+    msg = model.load_state_dict(state_dict, strict=False)
+    print('load checkpoint from %s' % url_or_filename)
+    return model, msg
+
+

models/vit.py

@@ -0,0 +1,305 @@
+'''
+ * 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

ram_swin_large_14m.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15c729c793af28b9d107c69f85836a1356d76ea830d4714699fb62e55fcc08ed
+size 5625634877

requirements.txt

@@ -0,0 +1,8 @@
+timm==0.4.12
+transformers
+fairscale==0.4.4
+pycocoevalcap
+torch
+torchvision
+Pillow
+scipy

tag2text_swin_14m.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ce96f0ce98f940a6680d567f66a38ccc9ca8c4e638e5f5c5c2e881a0e3502ac
+size 4478705095