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@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/example_result.png filter=lfs diff=lfs merge=lfs -text
+assets/teaser.jpg filter=lfs diff=lfs merge=lfs -text
+samples/0064.png filter=lfs diff=lfs merge=lfs -text
+samples/0245.png filter=lfs diff=lfs merge=lfs -text
+samples/0393.png filter=lfs diff=lfs merge=lfs -text
+samples/0457.png filter=lfs diff=lfs merge=lfs -text
+samples/0479.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+inference_results/
+__pycache__/
+ram_swin_large_14m.pth

LICENSE

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+MIT License
+
+Copyright (c) 2025 Bryan Sangwoo Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,89 @@
+# Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
+
+This repository is the official implementation of [Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment](https://arxiv.org/abs/2505.18600), led by
+
+[Bryan Sangwoo Kim](https://scholar.google.com/citations?user=ndWU-84AAAAJ&hl=en), [Jeongsol Kim](https://jeongsol.dev/), [Jong Chul Ye](https://bispl.weebly.com/professor.html)
+
+![main figure](assets/teaser.jpg)
+
+[![Project Website](https://img.shields.io/badge/Project-Website-blue)](https://bryanswkim.github.io/chain-of-zoom/)
+[![arXiv](https://img.shields.io/badge/arXiv-2505.18600-b31b1b.svg)](https://arxiv.org/abs/2505.18600)
+
 ---
-title: Chain Of Zoom
-emoji: 🚀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 5.32.0
-app_file: app.py
-pinned: false
----
+## 🔥 Summary
+
+Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but show notable drawbacks:
+
+1. **Blur and artifacts** when pushed to magnify beyond its training regime
+2. **High computational costs and inefficiency** of retraining models when we want to magnify further
+
+This brings us to the fundamental question: \
+_How can we effectively utilize super-resolution models to explore much higher resolutions than they were originally trained for?_
+
+We address this via **Chain-of-Zoom** 🔎, a model-agnostic framework that factorizes SISR into an autoregressive chain of intermediate scale-states with multi-scale-aware prompts.
+CoZ repeatedly re-uses a backbone SR model, decomposing the conditional probability into tractable sub-problems to achieve extreme resolutions without additional training.
+Because visual cues diminish at high magnifications, we augment each zoom step with multi-scale-aware text prompts generated by a prompt extractor VLM.
+This prompt extractor can be fine-tuned through GRPO with a critic VLM to further align text guidance towards human preference.
+
+## 🗓 ️News
+- [May 2025] Code and paper are uploaded.
+
+## 🛠️ Setup
+First, create your environment. We recommend using the following commands. 
+
+```
+git clone https://github.com/bryanswkim/Chain-of-Zoom.git
+cd Chain-of-Zoom
+
+conda create -n coz python=3.10
+conda activate coz
+pip install -r requirements.txt
+```
+
+## ⏳ Models
+
+|Models|Checkpoints|
+|:---------|:--------|
+|Stable Diffusion v3|[Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
+|Qwen2.5-VL-3B-Instruct|[Hugging Face](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
+|RAM|[Hugging Face](https://huggingface.co/spaces/xinyu1205/recognize-anything/blob/main/ram_swin_large_14m.pth)
+
+## 🌄 Example
+You can quickly check the results of using **CoZ** with the following example:
+```
+python inference_coz.py \
+  -i samples \
+  -o inference_results/coz_vlmprompt \
+  --rec_type recursive_multiscale \
+  --prompt_type vlm \
+  --lora_path ckpt/SR_LoRA/model_20001.pkl \
+  --vae_path ckpt/SR_VAE/vae_encoder_20001.pt \
+  --pretrained_model_name_or_path 'stabilityai/stable-diffusion-3-medium-diffusers' \
+  --ram_ft_path ckpt/DAPE/DAPE.pth \
+  --ram_path ckpt/RAM/ram_swin_large_14m.pth \
+```
+Which will give a result like below:
+
+![main figure](assets/example_result.png)
+
+## 🔬 Efficient Memory
+Using ```--efficient_memory``` allows CoZ to run on a single GPU with 24GB VRAM, but highly increases inference time due to offloading. \
+We recommend using two GPUs.
+
+## 📝 Citation
+If you find our method useful, please cite as below or leave a star to this repository.
+
+```
+@article{kim2025chain,
+  title={Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment},
+  author={Kim, Bryan Sangwoo and Kim, Jeongsol and Ye, Jong Chul},
+  journal={arXiv preprint arXiv:2505.18600},
+  year={2025}
+}
+```
+
+## 🤗 Acknowledgements
+We thank the authors of [OSEDiff](https://github.com/cswry/OSEDiff) for sharing their awesome work!
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+> [!note]
+> This work is currently in the preprint stage, and there may be some changes to the code.

app.py

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+import gradio as gr
+import subprocess
+import os
+import shutil
+from pathlib import Path
+from PIL import Image
+import spaces
+
+# -----------------------------------------------------------------------------
+# CONFIGURE THESE PATHS TO MATCH YOUR PROJECT STRUCTURE
+# -----------------------------------------------------------------------------
+
+INPUT_DIR  = "samples"
+OUTPUT_DIR = "inference_results/coz_vlmprompt"
+
+# -----------------------------------------------------------------------------
+# HELPER FUNCTION TO RUN INFERENCE AND RETURN THE OUTPUT IMAGE
+# -----------------------------------------------------------------------------
+
+@spaces.GPU()
+def run_with_upload(uploaded_image_path):
+    """
+    1) Clear out INPUT_DIR (so old samples don’t linger).
+    2) Copy the uploaded image into INPUT_DIR.
+    3) Run your inference_coz.py command (which reads from -i INPUT_DIR).
+    4) After it finishes, find the most recently‐modified PNG in OUTPUT_DIR.
+    5) Return a PIL.Image, which Gradio will display.
+    """
+
+    # 1) Make sure INPUT_DIR exists; if it does, delete everything inside.
+    os.makedirs(INPUT_DIR, exist_ok=True)
+    for fn in os.listdir(INPUT_DIR):
+        full_path = os.path.join(INPUT_DIR, fn)
+        try:
+            if os.path.isfile(full_path) or os.path.islink(full_path):
+                os.remove(full_path)
+            elif os.path.isdir(full_path):
+                shutil.rmtree(full_path)
+        except Exception as e:
+            print(f"Warning: could not delete {full_path}: {e}")
+
+    # 2) Copy the uploaded image into INPUT_DIR.
+    #    Gradio will give us a path like "/tmp/gradio_xyz.png"
+    if uploaded_image_path is None:
+        return None
+
+    try:
+        # Open with PIL (this handles JPEG, BMP, TIFF, etc.)
+        pil_img = Image.open(uploaded_image_path).convert("RGB")
+    except Exception as e:
+        print(f"Error: could not open uploaded image: {e}")
+        return None
+
+    # Save it as "input.png" in our INPUT_DIR
+    save_path = Path(INPUT_DIR) / "input.png"
+    try:
+        pil_img.save(save_path, format="PNG")
+    except Exception as e:
+        print(f"Error: could not save as PNG: {e}")
+        return None
+
+    # 3) Build and run your inference_coz.py command.
+    #    This will block until it completes.
+    cmd = [
+        "python", "inference_coz.py",
+        "-i", INPUT_DIR,
+        "-o", OUTPUT_DIR,
+        "--rec_type", "recursive_multiscale",
+        "--prompt_type", "vlm",
+        "--upscale", "2",
+        "--lora_path", "ckpt/SR_LoRA/model_20001.pkl",
+        "--vae_path", "ckpt/SR_VAE/vae_encoder_20001.pt",
+        "--pretrained_model_name_or_path", "stabilityai/stable-diffusion-3-medium-diffusers",
+        "--ram_ft_path", "ckpt/DAPE/DAPE.pth",
+        "--ram_path", "ckpt/RAM/ram_swin_large_14m.pth"
+    ]
+    try:
+        subprocess.run(cmd, check=True)
+    except subprocess.CalledProcessError as err:
+        # If inference_coz.py crashes, we can print/log the error.
+        print("Inference failed:", err)
+        return None
+
+    # 4) After it finishes, scan OUTPUT_DIR for .png files.
+
+    RECUSIVE_DIR = f'{OUTPUT_DIR}/recursive'
+
+    if not os.path.isdir(RECUSIVE_DIR):
+        return None
+
+    png_files = [
+        os.path.join(RECUSIVE_DIR, fn)
+        for fn in os.listdir(RECUSIVE_DIR)
+        if fn.lower().endswith(".png")
+    ]
+    if not png_files:
+        return None
+
+    # 5) Pick the most recently‐modified PNG
+    latest_png = max(png_files, key=os.path.getmtime)
+
+    # 6) Open and return a PIL.Image. Gradio will display it automatically.
+    try:
+        img = Image.open(latest_png).convert("RGB")
+    except Exception as e:
+        print(f"Error opening {latest_png}: {e}")
+        return None
+
+    return img
+
+# -----------------------------------------------------------------------------
+# BUILD THE GRADIO INTERFACE
+# -----------------------------------------------------------------------------
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Upload an image, then click **Run Inference** to process it.")
+
+    # 1) Image upload component. We set type="filepath" so the callback
+    #    (run_with_upload) will receive a local path to the uploaded file.
+    upload_image = gr.Image(
+        label="Upload your input image",
+        type="filepath"
+    )
+
+    # 2) A button that the user will click to launch inference.
+    run_button = gr.Button("Run Inference")
+
+    # 3) An output <Image> where we will show the final PNG.
+    output_image = gr.Image(
+        label="Inference Result",
+        type="pil"          # because run_with_upload() returns a PIL.Image
+    )
+
+    # Wire the button: when clicked, call run_with_upload(upload_image), put
+    # its return value into output_image.
+    run_button.click(
+        fn=run_with_upload,
+        inputs=upload_image,
+        outputs=output_image
+    )
+
+# -----------------------------------------------------------------------------
+# START THE GRADIO SERVER
+# -----------------------------------------------------------------------------
+
+demo.launch(share=True)

ckpt/DAPE/DAPE.pth

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ckpt/SR_LoRA/model_20001.pkl

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ckpt/SR_VAE/vae_encoder_20001.pt

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inference_coz.py

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+import os
+import sys
+sys.path.append(os.getcwd())
+import glob
+import argparse
+import torch
+from torchvision import transforms
+import torchvision.transforms.functional as F
+import numpy as np
+from PIL import Image
+
+from ram.models.ram_lora import ram
+from ram import inference_ram as inference
+from utils.wavelet_color_fix import adain_color_fix, wavelet_color_fix
+
+tensor_transforms = transforms.Compose([
+    transforms.ToTensor(),
+])
+ram_transforms = transforms.Compose([
+    transforms.Resize((384, 384)),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+def resize_and_center_crop(img: Image.Image, size: int) -> Image.Image:
+    w, h = img.size
+    scale = size / min(w, h)
+    new_w, new_h = int(w * scale), int(h * scale)
+    img = img.resize((new_w, new_h), Image.LANCZOS)
+    left = (new_w - size) // 2
+    top  = (new_h - size) // 2
+    return img.crop((left, top, left + size, top + size))
+
+def get_validation_prompt(args, image, prompt_image_path, dape_model=None, vlm_model=None, device='cuda'):
+    # prepare low-res tensor for SR input
+    lq = tensor_transforms(image).unsqueeze(0).to(device)
+    # select prompt source
+    if args.prompt_type == "null":
+        prompt_text = args.prompt or ""
+    elif args.prompt_type == "dape":
+        lq_ram = ram_transforms(lq).to(dtype=weight_dtype)
+        captions = inference(lq_ram, dape_model)
+        prompt_text = f"{captions[0]}, {args.prompt}," if args.prompt else captions[0]
+    elif args.prompt_type in ("vlm"):
+        message_text = None
+        
+        if args.rec_type == "recursive":
+            message_text = "What is in this image? Give me a set of words."
+            print(f'MESSAGE TEXT: {message_text}')
+            messages = [
+                {"role": "system", "content": f"{message_text}"},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": prompt_image_path}
+                    ]
+                }
+            ]
+            text = vlm_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            image_inputs, video_inputs = process_vision_info(messages)
+            inputs = vlm_processor(
+                text=[text],
+                images=image_inputs,
+                videos=video_inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+            
+        elif args.rec_type == "recursive_multiscale":
+            start_image_path = prompt_image_path[0]
+            input_image_path = prompt_image_path[1]
+            message_text = "The second image is a zoom-in of the first image. Based on this knowledge, what is in the second image? Give me a set of words."
+            print(f'START IMAGE PATH: {start_image_path}\nINPUT IMAGE PATH: {input_image_path}\nMESSAGE TEXT: {message_text}')
+            messages = [
+                {"role": "system", "content": f"{message_text}"},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": start_image_path},
+                        {"type": "image", "image": input_image_path}
+                    ]
+                }
+            ]
+            print(f'MESSAGES\n{messages}')
+
+            text = vlm_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            image_inputs, video_inputs = process_vision_info(messages)
+            inputs = vlm_processor(
+                text=[text],
+                images=image_inputs,
+                videos=video_inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+
+        else:
+            raise ValueError(f"VLM prompt generation not implemented for rec_type: {args.rec_type}")
+
+        inputs = inputs.to("cuda")
+
+        original_sr_devices = {}
+        if args.efficient_memory and 'model' in globals() and hasattr(model, 'text_enc_1'): # Check if SR model is defined
+            print("Moving SR model components to CPU for VLM inference.")
+            original_sr_devices['text_enc_1'] = model.text_enc_1.device
+            original_sr_devices['text_enc_2'] = model.text_enc_2.device
+            original_sr_devices['text_enc_3'] = model.text_enc_3.device
+            original_sr_devices['transformer'] = model.transformer.device
+            original_sr_devices['vae'] = model.vae.device
+            
+            model.text_enc_1.to('cpu')
+            model.text_enc_2.to('cpu')
+            model.text_enc_3.to('cpu')
+            model.transformer.to('cpu')
+            model.vae.to('cpu')
+            vlm_model.to('cuda') # vlm_model should already be on its device_map="auto" device
+
+        generated_ids = vlm_model.generate(**inputs, max_new_tokens=128)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = vlm_processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        prompt_text = f"{output_text[0]}, {args.prompt}," if args.prompt else output_text[0]
+
+        if args.efficient_memory and 'model' in globals() and hasattr(model, 'text_enc_1'):
+            print("Restoring SR model components to original devices.")
+            vlm_model.to('cpu') # If vlm_model was moved to a specific cuda device and needs to be offloaded
+            model.text_enc_1.to(original_sr_devices['text_enc_1'])
+            model.text_enc_2.to(original_sr_devices['text_enc_2'])
+            model.text_enc_3.to(original_sr_devices['text_enc_3'])
+            model.transformer.to(original_sr_devices['transformer'])
+            model.vae.to(original_sr_devices['vae'])
+    else:
+        raise ValueError(f"Unknown prompt_type: {args.prompt_type}")
+    return prompt_text, lq
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_image', '-i', type=str, default='preset/datasets/test_dataset/input', help='path to the input image')
+    parser.add_argument('--output_dir', '-o', type=str, default='preset/datasets/test_dataset/output', help='the directory to save the output')
+    parser.add_argument('--pretrained_model_name_or_path', type=str, default=None, help='sd model path')
+    parser.add_argument('--seed', type=int, default=42, help='Random seed to be used')
+    parser.add_argument('--process_size', type=int, default=512)
+    parser.add_argument('--upscale', type=int, default=4)
+    parser.add_argument('--align_method', type=str, choices=['wavelet', 'adain', 'nofix'], default='nofix')
+    parser.add_argument('--lora_path', type=str, default=None, help='for LoRA of SR model')
+    parser.add_argument('--vae_path', type=str, default=None)
+    parser.add_argument('--prompt', type=str, default='', help='user prompts')
+    parser.add_argument('--prompt_type', type=str, choices=['null','dape','vlm'], default='dape', help='type of prompt to use')
+    parser.add_argument('--ram_path', type=str, default=None)
+    parser.add_argument('--ram_ft_path', type=str, default=None)
+    parser.add_argument('--save_prompts', type=bool, default=True)
+    parser.add_argument('--mixed_precision', type=str, choices=['fp16', 'fp32'], default='fp16')
+    parser.add_argument('--merge_and_unload_lora', action='store_true', help='merge lora weights before inference')
+    parser.add_argument('--lora_rank', type=int, default=4)
+    parser.add_argument('--vae_decoder_tiled_size', type=int, default=224)
+    parser.add_argument('--vae_encoder_tiled_size', type=int, default=1024)
+    parser.add_argument('--latent_tiled_size', type=int, default=96)
+    parser.add_argument('--latent_tiled_overlap', type=int, default=32)
+    parser.add_argument('--rec_type', type=str, choices=['nearest', 'bicubic','onestep','recursive','recursive_multiscale'], default='recursive', help='type of inference to use')
+    parser.add_argument('--rec_num', type=int, default=4)
+    parser.add_argument('--efficient_memory', default=False, action='store_true')
+    args = parser.parse_args()
+
+    global weight_dtype
+    weight_dtype = torch.float32
+    if args.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+
+    # initialize SR model
+    model = None
+    if args.rec_type not in ('nearest', 'bicubic'):
+        if not args.efficient_memory:
+            from osediff_sd3 import OSEDiff_SD3_TEST, SD3Euler
+            model = SD3Euler()
+            model.text_enc_1.to('cuda:0')
+            model.text_enc_2.to('cuda:0')
+            model.text_enc_3.to('cuda:0')
+            model.transformer.to('cuda:1', dtype=torch.float32)
+            model.vae.to('cuda:1', dtype=torch.float32)
+            for p in [model.text_enc_1, model.text_enc_2, model.text_enc_3, model.transformer, model.vae]:
+                p.requires_grad_(False)
+            model_test = OSEDiff_SD3_TEST(args, model)
+        else:
+            # For efficient memory, text encoders are moved to CPU/GPU on demand in get_validation_prompt
+            # Only load transformer and VAE initially if they are always on GPU
+            from osediff_sd3 import OSEDiff_SD3_TEST_efficient, SD3Euler
+            model = SD3Euler()
+            model.transformer.to('cuda', dtype=torch.float32)
+            model.vae.to('cuda', dtype=torch.float32)
+            for p in [model.text_enc_1, model.text_enc_2, model.text_enc_3, model.transformer, model.vae]:
+                p.requires_grad_(False)
+            model_test = OSEDiff_SD3_TEST_efficient(args, model)
+
+    # gather input images
+    if os.path.isdir(args.input_image):
+        image_names = sorted(glob.glob(f'{args.input_image}/*.png'))
+    else:
+        image_names = [args.input_image]
+
+    # load DAPE if needed
+    DAPE = None
+    if args.prompt_type == "dape":
+        DAPE = ram(pretrained=args.ram_path,
+                   pretrained_condition=args.ram_ft_path,
+                   image_size=384,
+                   vit='swin_l')
+        DAPE.eval().to("cuda")
+        DAPE = DAPE.to(dtype=weight_dtype)
+
+    # load VLM pipeline if needed
+    vlm_model = None
+    global vlm_processor
+    global process_vision_info
+    vlm_processor = None
+    if args.prompt_type == "vlm":
+        from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+        from qwen_vl_utils import process_vision_info
+
+        vlm_model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
+        print(f"Loading base VLM model: {vlm_model_name}")
+        vlm_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            vlm_model_name,
+            torch_dtype="auto",
+            device_map="auto"
+        )
+        vlm_processor = AutoProcessor.from_pretrained(vlm_model_name)
+        print('Base VLM LOADING COMPLETE')
+
+    os.makedirs(args.output_dir, exist_ok=True)
+    os.makedirs(os.path.join(args.output_dir, 'per-sample'), exist_ok=True)
+    os.makedirs(os.path.join(args.output_dir, 'per-scale'), exist_ok=True)
+    os.makedirs(os.path.join(args.output_dir, 'recursive'), exist_ok=True)
+    print(f'There are {len(image_names)} images.')
+    print(f'Align Method Used: {args.align_method}')
+    print(f'Prompt Type: {args.prompt_type}')
+
+    # inference loop
+    for image_name in image_names:
+        bname = os.path.basename(image_name)
+        rec_dir = os.path.join(args.output_dir, 'per-sample', bname[:-4])
+        os.makedirs(rec_dir, exist_ok=True)
+        if args.save_prompts:
+            txt_path = os.path.join(rec_dir, 'txt')
+            os.makedirs(txt_path, exist_ok=True)
+        print(f'#### IMAGE: {bname}')
+
+        # first image
+        os.makedirs(os.path.join(args.output_dir, 'per-scale', 'scale0'), exist_ok=True)
+        first_image = Image.open(image_name).convert('RGB')
+        first_image = resize_and_center_crop(first_image, args.process_size)
+        first_image.save(f'{rec_dir}/0.png')
+        first_image.save(os.path.join(args.output_dir, 'per-scale', 'scale0', bname))
+
+        # recursion
+        for rec in range(args.rec_num):
+            print(f'RECURSION: {rec}')
+            os.makedirs(os.path.join(args.output_dir, 'per-scale', f'scale{rec+1}'), exist_ok=True)
+            start_image_path = None
+            input_image_path = None
+            prompt_image_path = None    # this will hold the path(s) for prompt extraction
+            
+            current_sr_input_image_pil = None
+
+            if args.rec_type in ('nearest', 'bicubic', 'onestep'):
+                start_image_pil_path = f'{rec_dir}/0.png'
+                start_image_pil = Image.open(start_image_pil_path).convert('RGB')
+                rscale = pow(args.upscale, rec+1)
+                w, h = start_image_pil.size
+                new_w, new_h = w // rscale, h // rscale
+                
+                # crop from the original highest-res image available for this step
+                cropped_region = start_image_pil.crop(((w-new_w)//2, (h-new_h)//2, (w+new_w)//2, (h+new_h)//2))
+                
+                if args.rec_type == 'onestep':
+                    current_sr_input_image_pil = cropped_region.resize((w, h), Image.BICUBIC)
+                    prompt_image_path = f'{rec_dir}/0_input_for_{rec+1}.png'
+                    current_sr_input_image_pil.save(prompt_image_path)
+                elif args.rec_type == 'bicubic':
+                    current_sr_input_image_pil = cropped_region.resize((w, h), Image.BICUBIC)
+                    current_sr_input_image_pil.save(f'{rec_dir}/{rec+1}.png')
+                    current_sr_input_image_pil.save(os.path.join(args.output_dir, 'per-scale', f'scale{rec+1}', bname))
+                    continue
+                elif args.rec_type == 'nearest':
+                    current_sr_input_image_pil = cropped_region.resize((w, h), Image.NEAREST)
+                    current_sr_input_image_pil.save(f'{rec_dir}/{rec+1}.png')
+                    current_sr_input_image_pil.save(os.path.join(args.output_dir, 'per-scale', f'scale{rec+1}', bname))
+                    continue
+
+            elif args.rec_type == 'recursive':
+                # input for SR is based on the previous SR output, cropped and resized
+                prev_sr_output_path = f'{rec_dir}/{rec}.png'
+                prev_sr_output_pil = Image.open(prev_sr_output_path).convert('RGB')
+                rscale = args.upscale
+                w, h = prev_sr_output_pil.size
+                new_w, new_h = w // rscale, h // rscale
+                cropped_region = prev_sr_output_pil.crop(((w-new_w)//2, (h-new_h)//2, (w+new_w)//2, (h+new_h)//2))
+                current_sr_input_image_pil = cropped_region.resize((w, h), Image.BICUBIC)
+
+                # this resized image is also the input for VLM
+                input_image_path = f'{rec_dir}/{rec+1}_input.png'
+                current_sr_input_image_pil.save(input_image_path)
+                prompt_image_path = input_image_path
+
+            elif args.rec_type == 'recursive_multiscale':
+                prev_sr_output_path = f'{rec_dir}/{rec}.png'
+                prev_sr_output_pil = Image.open(prev_sr_output_path).convert('RGB')
+                rscale = args.upscale
+                w, h = prev_sr_output_pil.size
+                new_w, new_h = w // rscale, h // rscale
+                cropped_region = prev_sr_output_pil.crop(((w-new_w)//2, (h-new_h)//2, (w+new_w)//2, (h+new_h)//2))
+                current_sr_input_image_pil = cropped_region.resize((w, h), Image.BICUBIC)
+
+                # save the SR input image (which is the "zoomed-in" image for VLM)
+                zoomed_image_path = f'{rec_dir}/{rec+1}_input.png'
+                current_sr_input_image_pil.save(zoomed_image_path)
+                prompt_image_path = [prev_sr_output_path, zoomed_image_path]
+
+            else:
+                raise ValueError(f"Unknown recursion_type: {args.rec_type}")
+
+            # generate prompts
+            validation_prompt, lq = get_validation_prompt(args, current_sr_input_image_pil, prompt_image_path, DAPE, vlm_model)
+            if args.save_prompts:
+                with open(os.path.join(txt_path, f'{rec}.txt'), 'w', encoding='utf-8') as f:
+                    f.write(validation_prompt)
+            print(f'TAG: {validation_prompt}')
+
+            # super-resolution
+            with torch.no_grad():
+                lq = lq * 2 - 1
+
+                if args.efficient_memory and model is not None:
+                    print("Ensuring SR model components are on CUDA for SR inference.")
+                    if not isinstance(model_test, OSEDiff_SD3_TEST_efficient):
+                        model.text_enc_1.to('cuda:0')
+                        model.text_enc_2.to('cuda:0')
+                        model.text_enc_3.to('cuda:0')
+                    # transformer and VAE should already be on CUDA per initialization
+                    model.transformer.to('cuda', dtype=torch.float32)
+                    model.vae.to('cuda', dtype=torch.float32)
+
+                output_image = model_test(lq, prompt=validation_prompt)
+                output_image = torch.clamp(output_image[0].cpu(), -1.0, 1.0)
+                output_pil = transforms.ToPILImage()(output_image * 0.5 + 0.5)
+                if args.align_method == 'adain':
+                    output_pil = adain_color_fix(target=output_pil, source=current_sr_input_image_pil)
+                elif args.align_method == 'wavelet':
+                    output_pil = wavelet_color_fix(target=output_pil, source=current_sr_input_image_pil)
+
+            output_pil.save(f'{rec_dir}/{rec+1}.png')   # this is the SR output
+            output_pil.save(os.path.join(args.output_dir, 'per-scale', f'scale{rec+1}', bname))
+
+        # concatenate and save
+        imgs = [Image.open(os.path.join(rec_dir, f'{i}.png')).convert('RGB') for i in range(args.rec_num+1)]
+        concat = Image.new('RGB', (sum(im.width for im in imgs), max(im.height for im in imgs)))
+        x_off = 0
+        for im in imgs:
+            concat.paste(im, (x_off, 0))
+            x_off += im.width
+        concat.save(os.path.join(rec_dir, bname))
+        concat.save(os.path.join(args.output_dir, 'recursive', bname))

lora/lora_layers.py

@@ -0,0 +1,137 @@
+from typing import List, Optional, Set, Type, Union
+
+import torch
+from torch import nn
+
+
+class LoraInjectedLinear(nn.Module):
+    """
+    Linear layer with LoRA injection.
+    Taken from https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py
+    """
+    def __init__(
+        self, in_features, out_features, bias=False, r=4, dropout_p=0.1, scale=1.0
+    ):
+        super().__init__()
+
+        if r > min(in_features, out_features):
+            raise ValueError(
+                f"LoRA rank {r} must be less or equal than {min(in_features, out_features)}"
+            )
+        self.r = r
+        self.linear = nn.Linear(in_features, out_features, bias)
+        self.lora_down = nn.Linear(in_features, r, bias=False)
+        self.dropout = nn.Dropout(dropout_p)
+        self.lora_up = nn.Linear(r, out_features, bias=False)
+        self.scale = scale
+        self.selector = nn.Identity()
+
+        nn.init.normal_(self.lora_down.weight, std=1 / r)
+        nn.init.zeros_(self.lora_up.weight)
+
+    def forward(self, input):
+        return (
+            self.linear(input.float())
+            + self.dropout(self.lora_up(self.selector(self.lora_down(input.float()))))
+            * self.scale
+        ).half()
+
+    def realize_as_lora(self):
+        return self.lora_up.weight.data * self.scale, self.lora_down.weight.data
+
+    def set_selector_from_diag(self, diag: torch.Tensor):
+        # diag is a 1D tensor of size (r,)
+        assert diag.shape == (self.r,)
+        self.selector = nn.Linear(self.r, self.r, bias=False)
+        self.selector.weight.data = torch.diag(diag)
+        self.selector.weight.data = self.selector.weight.data.to(
+            self.lora_up.weight.device
+        ).to(self.lora_up.weight.dtype)
+
+class LoraInjectedConv2d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups: int = 1,
+        bias: bool = True,
+        r: int = 4,
+        dropout_p: float = 0.1,
+        scale: float = 1.0,
+    ):
+        super().__init__()
+        if r > min(in_channels, out_channels):
+            raise ValueError(
+                f"LoRA rank {r} must be less or equal than {min(in_channels, out_channels)}"
+            )
+        self.r = r
+        self.conv = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+
+        self.lora_down = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=r,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=False,
+        )
+        self.dropout = nn.Dropout(dropout_p)
+        self.lora_up = nn.Conv2d(
+            in_channels=r,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=False,
+        )
+        self.selector = nn.Identity()
+        self.scale = scale
+
+        nn.init.normal_(self.lora_down.weight, std=1 / r)
+        nn.init.zeros_(self.lora_up.weight)
+
+    def forward(self, input):
+        return (
+            self.conv(input)
+            + self.dropout(self.lora_up(self.selector(self.lora_down(input))))
+            * self.scale
+        )
+
+    def realize_as_lora(self):
+        return self.lora_up.weight.data * self.scale, self.lora_down.weight.data
+
+    def set_selector_from_diag(self, diag: torch.Tensor):
+        # diag is a 1D tensor of size (r,)
+        assert diag.shape == (self.r,)
+        self.selector = nn.Conv2d(
+            in_channels=self.r,
+            out_channels=self.r,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=False,
+        )
+        self.selector.weight.data = torch.diag(diag)
+
+        # same device + dtype as lora_up
+        self.selector.weight.data = self.selector.weight.data.to(
+            self.lora_up.weight.device
+        ).to(self.lora_up.weight.dtype)
+
+
+

lora/lora_utils.py

@@ -0,0 +1,61 @@
+import torch
+from torch import nn
+from lora.lora_layers import LoraInjectedLinear, LoraInjectedConv2d
+
+def _find_modules(model, ancestor_class=None, search_class=[nn.Linear], exclude_children_of=[LoraInjectedLinear]):
+    # Get the targets we should replace all linears under
+    if ancestor_class is not None:
+        ancestors = (
+            module
+            for module in model.modules()
+            if module.__class__.__name__ in ancestor_class
+        )
+    else:
+        # this, incase you want to naively iterate over all modules.
+        ancestors = [module for module in model.modules()]
+
+    for ancestor in ancestors:
+        for fullname, module in ancestor.named_modules():
+            # if 'norm1_context' in fullname:
+                if any([isinstance(module, _class) for _class in search_class]):
+                    *path, name = fullname.split(".")
+                    parent = ancestor
+                    while path:
+                        parent = parent.get_submodule(path.pop(0))
+                    if exclude_children_of and any(
+                        [isinstance(parent, _class) for _class in exclude_children_of]
+                    ):
+                        continue
+                    yield parent, name, module
+
+def extract_lora_ups_down(model, target_replace_module={'AdaLayerNormZero'}):   # Attention for kv_lora
+
+    loras = []
+
+    for _m, _n, _child_module in _find_modules(
+        model,
+        target_replace_module,
+        search_class=[LoraInjectedLinear, LoraInjectedConv2d],
+    ):
+        loras.append((_child_module.lora_up, _child_module.lora_down))
+
+    if len(loras) == 0:
+        raise ValueError("No lora injected.")
+
+    return loras
+
+def save_lora_weight(
+    model,
+    path="./lora.pt",
+    target_replace_module={'AdaLayerNormZero'},     # Attention for kv_lora
+    save_half:bool=False
+):
+    weights = []
+    for _up, _down in extract_lora_ups_down(
+        model, target_replace_module=target_replace_module
+    ):
+        dtype = torch.float16 if save_half else torch.float32
+        weights.append(_up.weight.to("cpu").to(dtype))
+        weights.append(_down.weight.to("cpu").to(dtype))
+
+    torch.save(weights, path)

osediff_sd3.py

@@ -0,0 +1,725 @@
+import os
+import sys
+sys.path.append(os.getcwd())
+import yaml
+import copy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import List, Tuple, Optional
+import numpy as np
+import lpips
+from torchvision import transforms
+from PIL import Image
+from peft import LoraConfig, get_peft_model
+
+from copy import deepcopy
+from tqdm import tqdm
+
+from diffusers import StableDiffusion3Pipeline, FluxPipeline
+from lora.lora_layers import LoraInjectedLinear, LoraInjectedConv2d
+
+def inject_lora_vae(vae, lora_rank=4, init_lora_weights="gaussian", verbose=False):
+    """
+    Inject LoRA into the VAE's encoder
+    """
+    vae.requires_grad_(False)
+    vae.train() 
+
+    # Identify modules to LoRA-ify in the encoder
+    l_grep = ["conv1", "conv2", "conv_in", "conv_shortcut", 
+              "conv", "conv_out", "to_k", "to_q", "to_v", "to_out.0"]
+    l_target_modules_encoder = []
+    for n, p in vae.named_parameters():
+        if "bias" in n or "norm" in n:
+            continue
+        for pattern in l_grep:
+            if (pattern in n) and ("encoder" in n):
+                l_target_modules_encoder.append(n.replace(".weight", ""))
+            elif ("quant_conv" in n) and ("post_quant_conv" not in n):
+                l_target_modules_encoder.append(n.replace(".weight", ""))
+
+    if verbose:
+        print("The following VAE parameters will get LoRA:")
+        print(l_target_modules_encoder)
+
+    # Create and add a LoRA adapter
+    lora_conf_encoder = LoraConfig(
+        r=lora_rank,
+        init_lora_weights=init_lora_weights,
+        target_modules=l_target_modules_encoder
+    )
+    
+    adapter_name = "default_encoder"
+    try:
+        vae.add_adapter(lora_conf_encoder, adapter_name=adapter_name)
+        vae.set_adapter(adapter_name)
+    except ValueError as e:
+        if "already exists" in str(e):
+            print(f"Adapter with name {adapter_name} already exists. Skipping injection.")
+        else:
+            raise e
+
+    return vae, l_target_modules_encoder
+
+def _find_modules(model, ancestor_class=None, search_class=[nn.Linear], exclude_children_of=[LoraInjectedLinear]):
+    # Get the targets we should replace all linears under
+    if ancestor_class is not None:
+        ancestors = (
+            module
+            for module in model.modules()
+            if module.__class__.__name__ in ancestor_class
+        )
+    else:
+        # this, in case you want to naively iterate over all modules.
+        ancestors = [module for module in model.modules()]
+
+    for ancestor in ancestors:
+        for fullname, module in ancestor.named_modules():
+            if any([isinstance(module, _class) for _class in search_class]):
+                *path, name = fullname.split(".")
+                parent = ancestor
+                while path:
+                    parent = parent.get_submodule(path.pop(0))
+                if exclude_children_of and any(
+                    [isinstance(parent, _class) for _class in exclude_children_of]
+                ):
+                    continue
+                yield parent, name, module
+
+def inject_lora(model, ancestor_class, loras=None, r:int=4, dropout_p:float=0.0, scale:float=1.0, verbose:bool=False):
+    
+    model.requires_grad_(False)
+    model.train()
+    
+    names = []
+    require_grad_params = []  # to be updated
+
+    total_lora_params = 0  
+
+    if loras is not None:
+        loras = torch.load(loras, map_location=model.device, weights_only=True)
+        loras = [lora.float() for lora in loras]
+
+    for _module, name, _child_module in _find_modules(model, ancestor_class):  # SiLU + Linear Block
+        weight = _child_module.weight
+        bias = _child_module.bias
+
+        if verbose:
+            print(f'LoRA Injection : injecting lora into {name}')
+
+        _tmp = LoraInjectedLinear(
+            _child_module.in_features,
+            _child_module.out_features,
+            _child_module.bias is not None,
+            r=r,
+            dropout_p=dropout_p,
+            scale=scale,
+        )
+        _tmp.linear.weight = nn.Parameter(weight.float())
+        if bias is not None:
+            _tmp.linear.bias = nn.Parameter(bias.float())
+        
+        # switch the module
+        _tmp.to(device=_child_module.weight.device, dtype=torch.float) # keep as float / mixed precision
+        _module._modules[name] = _tmp
+
+        require_grad_params.append(_module._modules[name].lora_up.parameters())
+        require_grad_params.append(_module._modules[name].lora_down.parameters())
+
+        if loras != None:
+            _module._modules[name].lora_up.weight = nn.Parameter(loras.pop(0))
+            _module._modules[name].lora_down.weight = nn.Parameter(loras.pop(0))
+
+        _module._modules[name].lora_up.weight.requires_grad = True
+        _module._modules[name].lora_down.weight.requires_grad = True
+        names.append(name)
+
+        if verbose:
+            # -------- Count LoRA parameters just added --------
+            lora_up_count = sum(p.numel() for p in _tmp.lora_up.parameters())
+            lora_down_count = sum(p.numel() for p in _tmp.lora_down.parameters())
+            lora_total_for_this_layer = lora_up_count + lora_down_count
+            total_lora_params += lora_total_for_this_layer
+            print(f"  Added {lora_total_for_this_layer} params "
+                  f"(lora_up={lora_up_count}, lora_down={lora_down_count})")
+    
+    if verbose:
+        print(f"Total new LoRA parameters added: {total_lora_params}")
+    
+    return require_grad_params, names
+
+def add_mp_hook(transformer):
+    '''
+    For mixed precision of LoRA. (i.e. keep LoRA as float and others as half)
+    '''
+    def pre_hook(module, input):
+        return input.float()
+    
+    def post_hook(module, input, output):
+        return output.half()
+
+    hooks = []
+    for _module, name, _child_module in _find_modules(transformer):
+        if isinstance(_child_module, LoraInjectedLinear):
+            hook = _child_module.lora_up.register_forward_pre_hook(pre_hook)
+            hooks.append(hook)
+            hook = _child_module.lora_down.register_forward_hook(post_hook)
+            hooks.append(hook)
+    
+    return transformer, hooks
+
+def compute_density_for_timestep_sampling(
+    weighting_scheme: str, batch_size: int, logit_mean: float = 0.0, logit_std: float = 1.0, mode_scale: Optional[float] = None
+):
+    """
+    Compute the density for sampling the timesteps when doing SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "logit_normal":
+        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
+        u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
+        u = torch.nn.functional.sigmoid(u)
+    elif weighting_scheme == "mode":
+        u = torch.rand(size=(batch_size,), device="cpu")
+        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
+    else:
+        u = torch.rand(size=(batch_size,), device="cpu")
+    return u
+
+def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas):
+    """
+    Computes loss weighting scheme for SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "sigma_sqrt":
+        weighting = (sigmas**-2.0).float()
+    elif weighting_scheme == "cosmap":
+        bot = 1 - 2 * sigmas + 2 * sigmas**2
+        weighting = 2 / (math.pi * bot)
+    else:
+        weighting = torch.ones_like(sigmas)
+    return weighting
+
+
+class StableDiffusion3Base():
+    def __init__(self, model_key:str='stabilityai/stable-diffusion-3-medium-diffusers', device='cuda', dtype=torch.float16):
+        self.device = device
+        self.dtype = dtype
+
+        pipe = StableDiffusion3Pipeline.from_pretrained(model_key, torch_dtype=self.dtype)
+
+        self.scheduler = pipe.scheduler
+
+        self.tokenizer_1 = pipe.tokenizer
+        self.tokenizer_2 = pipe.tokenizer_2
+        self.tokenizer_3 = pipe.tokenizer_3
+        self.text_enc_1 = pipe.text_encoder.to(device)
+        self.text_enc_2 = pipe.text_encoder_2.to(device)
+        self.text_enc_3 = pipe.text_encoder_3.to(device)
+
+        self.vae=pipe.vae.to(device)
+
+        self.transformer = pipe.transformer.to(device)
+        self.transformer.eval()
+        self.transformer.requires_grad_(False)
+
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels)-1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+
+        del pipe
+
+    def encode_prompt(self, prompt: List[str], batch_size:int=1) -> List[torch.Tensor]:
+        '''
+        We assume that
+        1. number of tokens < max_length
+        2. one prompt for one image
+        '''
+        # CLIP encode (used for modulation of adaLN-zero)
+        # now, we have two CLIPs
+        text_clip1_ids = self.tokenizer_1(prompt,
+                                          padding="max_length",
+                                          max_length=77,
+                                          truncation=True,
+                                          return_tensors='pt').input_ids
+        text_clip1_emb = self.text_enc_1(text_clip1_ids.to(self.device), output_hidden_states=True)
+        pool_clip1_emb = text_clip1_emb[0].to(dtype=self.dtype, device=self.device)
+        text_clip1_emb = text_clip1_emb.hidden_states[-2].to(dtype=self.dtype, device=self.device)
+
+        text_clip2_ids = self.tokenizer_2(prompt,
+                                          padding="max_length",
+                                          max_length=77,
+                                          truncation=True,
+                                          return_tensors='pt').input_ids
+        text_clip2_emb = self.text_enc_2(text_clip2_ids.to(self.device), output_hidden_states=True)
+        pool_clip2_emb = text_clip2_emb[0].to(dtype=self.dtype, device=self.device)
+        text_clip2_emb = text_clip2_emb.hidden_states[-2].to(dtype=self.dtype, device=self.device)
+        
+        # T5 encode (used for text condition)
+        text_t5_ids = self.tokenizer_3(prompt,
+                                       padding="max_length",
+                                       max_length=512,
+                                       truncation=True,
+                                       add_special_tokens=True,
+                                       return_tensors='pt').input_ids
+        text_t5_emb = self.text_enc_3(text_t5_ids.to(self.device))[0]
+        text_t5_emb = text_t5_emb.to(dtype=self.dtype, device=self.device)
+
+        # Merge
+        clip_prompt_emb = torch.cat([text_clip1_emb, text_clip2_emb], dim=-1)
+        clip_prompt_emb = torch.nn.functional.pad(
+            clip_prompt_emb, (0, text_t5_emb.shape[-1] - clip_prompt_emb.shape[-1])
+        )
+        prompt_emb = torch.cat([clip_prompt_emb, text_t5_emb], dim=-2)
+        pooled_prompt_emb = torch.cat([pool_clip1_emb, pool_clip2_emb], dim=-1)
+
+        return prompt_emb, pooled_prompt_emb
+
+    def initialize_latent(self, img_size:Tuple[int], batch_size:int=1, **kwargs):
+        H, W = img_size
+        lH, lW = H//self.vae_scale_factor, W//self.vae_scale_factor
+        lC = self.transformer.config.in_channels
+        latent_shape = (batch_size, lC, lH, lW)
+
+        z = torch.randn(latent_shape, device=self.device, dtype=self.dtype)
+
+        return z
+
+    def encode(self, image: torch.Tensor) -> torch.Tensor:
+        z = self.vae.encode(image).latent_dist.sample()
+        z = (z-self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        return z
+    
+    def decode(self, z: torch.Tensor) -> torch.Tensor:
+        z = (z/self.vae.config.scaling_factor) + self.vae.config.shift_factor
+        return self.vae.decode(z, return_dict=False)[0]
+    
+    
+class SD3Euler(StableDiffusion3Base):
+    def __init__(self, model_key:str='stabilityai/stable-diffusion-3-medium-diffusers', device='cuda'):
+        super().__init__(model_key=model_key, device=device)
+
+    def inversion(self, src_img, prompts: List[str], NFE:int, cfg_scale: float=1.0, batch_size: int=1):
+
+        # encode text prompts
+        prompt_emb, pooled_emb = self.encode_prompt(prompts, batch_size)
+        null_prompt_emb, null_pooled_emb = self.encode_prompt([""], batch_size)
+
+        # initialize latent
+        src_img = src_img.to(device=self.device, dtype=self.dtype)
+        with torch.no_grad():
+            z = self.encode(src_img)
+            z0 = z.clone()
+
+        # timesteps (default option. You can make your custom here.)
+        self.scheduler.set_timesteps(NFE, device=self.device)
+        timesteps = self.scheduler.timesteps
+        timesteps = torch.cat([timesteps, torch.zeros(1, device=self.device)])
+        timesteps = reversed(timesteps)
+        sigmas = timesteps / self.scheduler.config.num_train_timesteps
+
+        # Solve ODE
+        pbar = tqdm(timesteps[:-1], total=NFE, desc='SD3 Euler Inversion')
+        for i, t in enumerate(pbar):
+            timestep = t.expand(z.shape[0]).to(self.device)
+            pred_v = self.predict_vector(z, timestep, prompt_emb, pooled_emb)
+            if cfg_scale != 1.0:
+                pred_null_v = self.predict_vector(z, timestep, null_prompt_emb, null_pooled_emb)
+            else:
+                pred_null_v = 0.0
+
+            sigma = sigmas[i]
+            sigma_next = sigmas[i+1]
+
+            z = z + (sigma_next - sigma) * (pred_null_v + cfg_scale * (pred_v - pred_null_v))
+
+        return z
+
+    def sample(self, prompts: List[str], NFE:int, img_shape: Optional[Tuple[int]]=None, cfg_scale: float=1.0, batch_size: int = 1, latent:Optional[torch.Tensor]=None):
+        imgH, imgW = img_shape if img_shape is not None else (512, 512)
+
+        # encode text prompts
+        with torch.no_grad():
+            prompt_emb, pooled_emb = self.encode_prompt(prompts, batch_size)
+            null_prompt_emb, null_pooled_emb = self.encode_prompt([""], batch_size)
+
+        # initialize latent
+        if latent is None:
+            z = self.initialize_latent((imgH, imgW), batch_size)
+        else:
+            z = latent
+
+        # timesteps (default option. You can make your custom here.)
+        self.scheduler.set_timesteps(NFE, device=self.device)
+        timesteps = self.scheduler.timesteps
+        sigmas = timesteps / self.scheduler.config.num_train_timesteps
+
+        # Solve ODE
+        pbar = tqdm(timesteps, total=NFE, desc='SD3 Euler')
+        for i, t in enumerate(pbar):
+            timestep = t.expand(z.shape[0]).to(self.device)
+            pred_v = self.predict_vector(z, timestep, prompt_emb, pooled_emb)
+            if cfg_scale != 1.0:
+                pred_null_v = self.predict_vector(z, timestep, null_prompt_emb, null_pooled_emb)
+            else:
+                pred_null_v = 0.0
+
+            sigma = sigmas[i]
+            sigma_next = sigmas[i+1] if i+1 < NFE else 0.0
+
+            z = z + (sigma_next - sigma) * (pred_null_v + cfg_scale * (pred_v - pred_null_v))
+
+        # decode
+        with torch.no_grad():
+            img = self.decode(z)
+        return img
+
+
+class OSEDiff_SD3_GEN(torch.nn.Module):
+    def __init__(self, args, base_model):
+        super().__init__()
+        
+        self.args = args
+        self.model = base_model
+        
+        # Add lora to transformer
+        print('Adding Lora to OSEDiff_SD3_GEN')
+        self.transformer_gen = copy.deepcopy(self.model.transformer)
+        self.transformer_gen.to('cuda:1')
+        # self.transformer_gen = self.transformer_gen.float()
+        
+        self.transformer_gen.requires_grad_(False)
+        self.transformer_gen.train()
+        self.transformer_gen, hooks = add_mp_hook(self.transformer_gen)
+        self.hooks = hooks
+
+        lora_params, _ = inject_lora(self.transformer_gen, {"AdaLayerNormZero"}, r=args.lora_rank, verbose=True)
+        # self.lora_params = lora_params
+        for name, param in self.transformer_gen.named_parameters():
+            if "lora_" in name:
+                param.requires_grad = True   # LoRA up/down
+            else:
+                param.requires_grad = False  # everything else
+        
+        # Insert LoRA into VAE
+        print("Adding Lora to VAE")
+        self.model.vae, self.lora_vae_modules_encoder = inject_lora_vae(self.model.vae, lora_rank=args.lora_rank, verbose=True)
+
+    def predict_vector(self, z, t, prompt_emb, pooled_emb):
+        v = self.transformer_gen(hidden_states=z,
+                             timestep=t,
+                             pooled_projections=pooled_emb,
+                             encoder_hidden_states=prompt_emb,
+                             return_dict=False)[0]
+        return v
+
+    def forward(self, x_src, batch=None, args=None):
+        
+        z_src = self.model.encode(x_src.to(dtype=torch.float32, device=self.model.vae.device))
+        z_src = z_src.to(self.transformer_gen.device)
+        
+        # calculate prompt_embeddings and neg_prompt_embeddings
+        batch_size, _, _, _ = x_src.shape
+        with torch.no_grad():
+            prompt_embeds, pooled_embeds = self.model.encode_prompt(batch["prompt"], batch_size)
+            neg_prompt_embeds, neg_pooled_embeds = self.model.encode_prompt(batch["neg_prompt"], batch_size)
+        
+        NFE = 1
+        self.model.scheduler.set_timesteps(NFE, device=self.model.device)
+        timesteps = self.model.scheduler.timesteps
+        sigmas = timesteps / self.model.scheduler.config.num_train_timesteps
+        sigmas = sigmas.to(self.transformer_gen.device)
+
+        # Solve ODE
+        i = 0
+        t = timesteps[0]
+
+        timestep = t.expand(z_src.shape[0]).to(self.transformer_gen.device)
+        prompt_embeds = prompt_embeds.to(self.transformer_gen.device, dtype=torch.float32)
+        pooled_embeds = pooled_embeds.to(self.transformer_gen.device, dtype=torch.float32)
+        pred_v = self.predict_vector(z_src, timestep, prompt_embeds, pooled_embeds)
+        pred_null_v = 0.0
+
+        sigma = sigmas[i]
+        sigma_next = sigmas[i+1] if i+1 < NFE else 0.0
+        
+        z_src = z_src + (sigma_next - sigma) * (pred_null_v + 1 * (pred_v - pred_null_v))
+        
+        output_image = self.model.decode(z_src.to(dtype=torch.float32, device=self.model.vae.device))
+
+        return output_image, z_src, prompt_embeds, pooled_embeds
+
+
+class OSEDiff_SD3_REG(torch.nn.Module):
+    def __init__(self, args, base_model):
+        super().__init__()
+        
+        self.args = args
+        self.model = base_model
+        self.transformer_org = self.model.transformer
+        
+        # Add lora to transformer
+        print('Adding Lora to OSEDiff_SD3_REG')
+        self.transformer_reg = copy.deepcopy(self.transformer_org)
+        self.transformer_reg.to('cuda:1')
+        
+        self.transformer_reg.requires_grad_(False)
+        self.transformer_reg.train()
+        self.transformer_reg, hooks = add_mp_hook(self.transformer_reg)
+        self.hooks = hooks
+        
+        lora_params, _ = inject_lora(self.transformer_reg, {"AdaLayerNormZero"}, r=args.lora_rank, verbose=True)
+        for name, param in self.transformer_reg.named_parameters():
+            if "lora_" in name:
+                param.requires_grad = True   # LoRA up/down
+            else:
+                param.requires_grad = False  # everything else
+        
+    def predict_vector_reg(self, z, t, prompt_emb, pooled_emb):
+        v = self.transformer_reg(hidden_states=z,
+                             timestep=t,
+                             pooled_projections=pooled_emb,
+                             encoder_hidden_states=prompt_emb,
+                             return_dict=False)[0]
+        return v
+    
+    def predict_vector_org(self, z, t, prompt_emb, pooled_emb):
+        v = self.transformer_org(hidden_states=z,
+                             timestep=t,
+                             pooled_projections=pooled_emb,
+                             encoder_hidden_states=prompt_emb,
+                             return_dict=False)[0]
+        return v
+
+    def distribution_matching_loss(self, z0, prompt_embeds, pooled_embeds, global_step, args):
+        
+        with torch.no_grad():
+            device = self.transformer_reg.device
+            # get timesteps and sigma
+            u = compute_density_for_timestep_sampling(
+                        weighting_scheme="uniform",
+                        batch_size=1,
+                        logit_mean=0.0,
+                        logit_std=1.0,
+                        mode_scale=1.29,
+                    )
+            
+            t_idx = (u*1000).long().to(device)
+            self.model.scheduler.set_timesteps(1000, device=device)
+            times = self.model.scheduler.timesteps
+            t = times[t_idx]
+            sigma = t / 1000
+            
+            # get noise and xt
+            z0 = z0.to(device)
+            noise = torch.randn_like(z0)
+            sigma = sigma.half()
+            zt = (1-sigma) * z0 + sigma * noise
+            
+            # Get x0_prediction of transformer_reg
+            v_pred_reg = self.predict_vector_reg(zt, t, prompt_embeds.to(device), pooled_embeds.to(device))
+            reg_model_pred = v_pred_reg * (-sigma) + zt         # this is x0_prediction for reg
+            
+            # Get x0_prediction of transformer_org
+            org_device = self.transformer_org.device
+            v_pred_org = self.predict_vector_org(zt.to(org_device), t.to(org_device), prompt_embeds.to(org_device), pooled_embeds.to(org_device))
+            org_model_pred = v_pred_org * (-sigma.to(org_device)) + zt.to(org_device)         # this is x0_prediction for org
+            
+            # Visualization
+            if global_step % 100 == 1:
+                self.vsd_visualization(z0, noise, zt, reg_model_pred, org_model_pred, global_step, args)
+            
+        weighting_factor = torch.abs(z0 - org_model_pred.to(device)).mean(dim=[1, 2, 3], keepdim=True)
+
+        grad = (reg_model_pred - org_model_pred.to(device)) / weighting_factor
+        loss = F.mse_loss(z0, (z0 - grad).detach())
+
+        return loss
+    
+    def vsd_visualization(self, z0, noise, zt, reg_model_pred, org_model_pred, global_step, args):
+        #-------- Visualization --------#
+        # 1. Visualize latents, noise, zt
+        z0_img = self.model.decode(z0.to(dtype=torch.float32, device=self.model.vae.device))
+        ns_img = self.model.decode(noise.to(dtype=torch.float32, device=self.model.vae.device))
+        zt_img = self.model.decode(zt.to(dtype=torch.float32, device=self.model.vae.device))
+        
+        z0_img_pil = transforms.ToPILImage()(torch.clamp(z0_img[0].cpu(), -1.0, 1.0) * 0.5 + 0.5)
+        ns_img_pil = transforms.ToPILImage()(torch.clamp(ns_img[0].cpu(), -1.0, 1.0) * 0.5 + 0.5)
+        zt_img_pil = transforms.ToPILImage()(torch.clamp(zt_img[0].cpu(), -1.0, 1.0) * 0.5 + 0.5)
+        
+        # 2. Visualize reg_img, org_img
+        reg_img = self.model.decode(reg_model_pred.to(dtype=torch.float32, device=self.model.vae.device))
+        org_img = self.model.decode(org_model_pred.to(dtype=torch.float32, device=self.model.vae.device))
+        
+        reg_img_pil = transforms.ToPILImage()(torch.clamp(reg_img[0].cpu(), -1.0, 1.0) * 0.5 + 0.5)
+        org_img_pil = transforms.ToPILImage()(torch.clamp(org_img[0].cpu(), -1.0, 1.0) * 0.5 + 0.5)
+        
+        # Concatenate images side by side
+        w, h = z0_img_pil.width, z0_img_pil.height
+        combined_image = Image.new('RGB', (w*5, h))
+        combined_image.paste(z0_img_pil, (0, 0))
+        combined_image.paste(ns_img_pil, (w, 0))
+        combined_image.paste(zt_img_pil, (w*2, 0))
+        combined_image.paste(reg_img_pil, (w*3, 0))
+        combined_image.paste(org_img_pil, (w*4, 0))
+        combined_image.save(os.path.join(args.output_dir, f'visualization/vsd/{global_step}.png'))
+        #-------- Visualization --------#
+    
+    def diff_loss(self, z0, prompt_embeds, pooled_embeds, net_lpips, args):
+        
+        device = self.transformer_reg.device
+        u = compute_density_for_timestep_sampling(
+                        weighting_scheme="uniform",
+                        batch_size=1,
+                        logit_mean=0.0,
+                        logit_std=1.0,
+                        mode_scale=1.29,
+                    )
+        
+        t_idx = (u*1000).long().to(device)
+        self.model.scheduler.set_timesteps(1000, device=device)
+        times = self.model.scheduler.timesteps
+        t = times[t_idx]
+        sigma = t / 1000
+        
+        z0 = z0.to(device)
+        z0, prompt_embeds = z0.detach(), prompt_embeds.detach()
+        noise = torch.randn_like(z0)
+        sigma = sigma.half()
+        zt = (1-sigma) * z0 + sigma * noise         # noisy latents
+
+        # v-prediction
+        v_pred = self.predict_vector_reg(zt, t, prompt_embeds.to(device), pooled_embeds.to(device))
+        model_pred = v_pred * (-sigma) + zt
+        target = z0
+        
+        loss_weight = compute_loss_weighting_for_sd3("logit_normal", sigma)
+        diffusion_loss = loss_weight.float() * F.mse_loss(model_pred.float(), target.float()) 
+        
+        loss_d = diffusion_loss
+
+        return loss_d.mean()
+
+class OSEDiff_SD3_TEST(torch.nn.Module):
+    def __init__(self, args, base_model):
+        super().__init__()
+        
+        self.args = args
+        self.model = base_model
+        self.lora_path = args.lora_path
+        self.vae_path = args.vae_path
+        
+        # Add lora to transformer
+        print(f'Loading LoRA to Transformer from {self.lora_path}')
+        self.model.transformer.requires_grad_(False)
+        lora_params, _ = inject_lora(self.model.transformer, {"AdaLayerNormZero"}, loras=self.lora_path, r=args.lora_rank, verbose=False)
+        for name, param in self.model.transformer.named_parameters():
+            param.requires_grad = False
+        
+        # Insert LoRA into VAE
+        print(f"Loading LoRA to VAE from {self.vae_path}")
+        self.model.vae, self.lora_vae_modules_encoder = inject_lora_vae(self.model.vae, lora_rank=args.lora_rank, verbose=False)
+        encoder_state_dict_fp16 = torch.load(self.vae_path, map_location="cpu")
+        self.model.vae.encoder.load_state_dict(encoder_state_dict_fp16)
+
+    def predict_vector(self, z, t, prompt_emb, pooled_emb):
+        v = self.model.transformer(hidden_states=z,
+                             timestep=t,
+                             pooled_projections=pooled_emb,
+                             encoder_hidden_states=prompt_emb,
+                             return_dict=False)[0]
+        return v
+
+    @torch.no_grad()
+    def forward(self, x_src, prompt):
+        
+        z_src = self.model.vae.encode(x_src.to(dtype=torch.float32, device=self.model.vae.device)).latent_dist.sample() * self.model.vae.config.scaling_factor
+        
+        z_src = z_src.to(self.model.transformer.device)
+        
+        # calculate prompt_embeddings and neg_prompt_embeddings
+        batch_size, _, _, _ = x_src.shape
+        with torch.no_grad():
+            prompt_embeds, pooled_embeds = self.model.encode_prompt([prompt], batch_size)
+        
+        self.model.scheduler.set_timesteps(1, device=self.model.device)
+        timesteps = self.model.scheduler.timesteps
+
+        # Solve ODE
+        t = timesteps[0]
+        timestep = t.expand(z_src.shape[0]).to(self.model.transformer.device)
+        prompt_embeds = prompt_embeds.to(self.model.transformer.device, dtype=torch.float32)
+        pooled_embeds = pooled_embeds.to(self.model.transformer.device, dtype=torch.float32)
+        pred_v = self.predict_vector(z_src, timestep, prompt_embeds, pooled_embeds)
+        
+        z_src = z_src - pred_v
+        
+        with torch.no_grad():
+            output_image = self.model.decode(z_src.to(dtype=torch.float32, device=self.model.vae.device))
+
+        return output_image
+
+
+class OSEDiff_SD3_TEST_efficient(torch.nn.Module):
+    def __init__(self, args, base_model):
+        super().__init__()
+        
+        self.args = args
+        self.model = base_model
+        self.lora_path = args.lora_path
+        self.vae_path = args.vae_path
+        
+        # Add lora to transformer
+        print(f'Loading LoRA to Transformer from {self.lora_path}')
+        self.model.transformer.requires_grad_(False)
+        lora_params, _ = inject_lora(self.model.transformer, {"AdaLayerNormZero"}, loras=self.lora_path, r=args.lora_rank, verbose=False)
+        for name, param in self.model.transformer.named_parameters():
+            param.requires_grad = False
+        
+        # Insert LoRA into VAE
+        print(f"Loading LoRA to VAE from {self.vae_path}")
+        self.model.vae, self.lora_vae_modules_encoder = inject_lora_vae(self.model.vae, lora_rank=args.lora_rank, verbose=False)
+        encoder_state_dict_fp16 = torch.load(self.vae_path, map_location="cpu")
+        self.model.vae.encoder.load_state_dict(encoder_state_dict_fp16)
+
+    def predict_vector(self, z, t, prompt_emb, pooled_emb):
+        v = self.model.transformer(hidden_states=z,
+                             timestep=t,
+                             pooled_projections=pooled_emb,
+                             encoder_hidden_states=prompt_emb,
+                             return_dict=False)[0]
+        return v
+
+    @torch.no_grad()
+    def forward(self, x_src, prompt):
+        
+        z_src = self.model.vae.encode(x_src.to(dtype=torch.float32, device=self.model.vae.device)).latent_dist.sample() * self.model.vae.config.scaling_factor
+        
+        z_src = z_src.to(self.model.transformer.device)
+        
+        # calculate prompt_embeddings
+        batch_size, _, _, _ = x_src.shape
+        prompt_embeds, pooled_embeds = self.model.encode_prompt([prompt], batch_size)
+        
+        self.model.scheduler.set_timesteps(1, device=self.model.device)
+        timesteps = self.model.scheduler.timesteps
+
+        # Solve ODE
+        t = timesteps[0]
+        timestep = t.expand(z_src.shape[0]).to(self.model.transformer.device)
+        prompt_embeds = prompt_embeds.to(self.model.transformer.device, dtype=torch.float32)
+        pooled_embeds = pooled_embeds.to(self.model.transformer.device, dtype=torch.float32)
+        pred_v = self.predict_vector(z_src, timestep, prompt_embeds, pooled_embeds)
+        z_src = z_src - pred_v
+        
+        output_image = self.model.decode(z_src.to(dtype=torch.float32, device=self.model.vae.device))
+
+        return output_image
+

ram/__init__.py

@@ -0,0 +1,2 @@
+from .inference import inference_tag2text, inference_ram, inference_ram_openset
+from .transform import get_transform

ram/configs/condition_config.json

@@ -0,0 +1,3 @@
+{
+    "nf": 64
+  }

ram/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   
+  }

ram/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
+  }

ram/configs/swin/config_swinB_384.json

@@ -0,0 +1,9 @@
+{
+    "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 ]
+  }

ram/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 ]
+  }

ram/configs/swin/config_swinL_444.json

@@ -0,0 +1,9 @@
+{
+    "ckpt": "pretrain_model/swin_large_patch4_window12_384_22k.pth",
+    "vision_width": 1536,
+    "image_res": 444,
+    "window_size": 12,
+    "embed_dim": 192,
+    "depths": [ 2, 2, 18, 2 ],
+    "num_heads": [ 6, 12, 24, 48 ]
+  }

ram/data/ram_tag_list.txt

@@ -0,0 +1,4585 @@
+3D CG rendering
+3D glasses
+abacus
+abalone
+monastery
+belly
+academy
+accessory
+accident
+accordion
+acorn
+acrylic paint
+act
+action
+action film
+activity
+actor
+adaptation
+add
+adhesive tape
+adjust
+adult
+adventure
+advertisement
+antenna
+aerobics
+spray can
+afro
+agriculture
+aid
+air conditioner
+air conditioning
+air sock
+aircraft cabin
+aircraft model
+air field
+air line
+airliner
+airman
+plane
+airplane window
+airport
+airport runway
+airport terminal
+airship
+airshow
+aisle
+alarm
+alarm clock
+mollymawk
+album
+album cover
+alcohol
+alcove
+algae
+alley
+almond
+aloe vera
+alp
+alpaca
+alphabet
+german shepherd
+altar
+amber
+ambulance
+bald eagle
+American shorthair
+amethyst
+amphitheater
+amplifier
+amusement park
+amusement ride
+anchor
+ancient
+anemone
+angel
+angle
+animal
+animal sculpture
+animal shelter
+animation
+animation film
+animator
+anime
+ankle
+anklet
+anniversary
+trench coat
+ant
+antelope
+antique
+antler
+anvil
+apartment
+ape
+app
+app icon
+appear
+appearance
+appetizer
+applause
+apple
+apple juice
+apple pie
+apple tree
+applesauce
+appliance
+appointment
+approach
+apricot
+apron
+aqua
+aquarium
+aquarium fish
+aqueduct
+arcade
+arcade machine
+arch
+arch bridge
+archaelogical excavation
+archery
+archipelago
+architect
+architecture
+archive
+archway
+area
+arena
+argument
+arm
+armadillo
+armband
+armchair
+armoire
+armor
+army
+army base
+army tank
+array
+arrest
+arrow
+art
+art exhibition
+art gallery
+art print
+art school
+art studio
+art vector illustration
+artichoke
+article
+artifact
+artist
+artists loft
+ash
+ashtray
+asia temple
+asparagus
+asphalt road
+assemble
+assembly
+assembly line
+association
+astronaut
+astronomer
+athlete
+athletic
+atlas
+atm
+atmosphere
+atrium
+attach
+fighter jet
+attend
+attraction
+atv
+eggplant
+auction
+audi
+audio
+auditorium
+aurora
+author
+auto factory
+auto mechanic
+auto part
+auto show
+auto showroom
+car battery
+automobile make
+automobile model
+motor vehicle
+autumn
+autumn forest
+autumn leave
+autumn park
+autumn tree
+avatar
+avenue
+aviator sunglasses
+avocado
+award
+award ceremony
+award winner
+shed
+ax
+azalea
+baboon
+baby
+baby bottle
+baby carriage
+baby clothe
+baby elephant
+baby food
+baby seat
+baby shower
+back
+backdrop
+backlight
+backpack
+backyard
+bacon
+badge
+badger
+badlands
+badminton
+badminton racket
+bag
+bagel
+bagpipe
+baguette
+bait
+baked goods
+baker
+bakery
+baking
+baking sheet
+balance
+balance car
+balcony
+ball
+ball pit
+ballerina
+ballet
+ballet dancer
+ballet skirt
+balloon
+balloon arch
+baseball player
+ballroom
+bamboo
+bamboo forest
+banana
+banana bread
+banana leaf
+banana tree
+band
+band aid
+bandage
+headscarf
+bandeau
+bangs
+bracelet
+balustrade
+banjo
+bank
+bank card
+bank vault
+banknote
+banner
+banquet
+banquet hall
+banyan tree
+baozi
+baptism
+bar
+bar code
+bar stool
+barbecue
+barbecue grill
+barbell
+barber
+barber shop
+barbie
+barge
+barista
+bark
+barley
+barn
+barn owl
+barn door
+barrel
+barricade
+barrier
+handcart
+bartender
+baseball
+baseball base
+baseball bat
+baseball hat
+baseball stadium
+baseball game
+baseball glove
+baseball pitcher
+baseball team
+baseball uniform
+basement
+basil
+basin
+basket
+basket container
+basketball
+basketball backboard
+basketball coach
+basketball court
+basketball game
+basketball hoop
+basketball player
+basketball stadium
+basketball team
+bass
+bass guitar
+bass horn
+bassist
+bat
+bath
+bath heater
+bath mat
+bath towel
+swimwear
+bathrobe
+bathroom
+bathroom accessory
+bathroom cabinet
+bathroom door
+bathroom mirror
+bathroom sink
+toilet paper
+bathroom window
+batman
+wand
+batter
+battery
+battle
+battle rope
+battleship
+bay
+bay bridge
+bay window
+bayberry
+bazaar
+beach
+beach ball
+beach chair
+beach house
+beach hut
+beach towel
+beach volleyball
+lighthouse
+bead
+beagle
+beak
+beaker
+beam
+bean
+bean bag chair
+beanbag
+bear
+bear cub
+beard
+beast
+beat
+beautiful
+beauty
+beauty salon
+beaver
+bed
+bedcover
+bed frame
+bedroom
+bedding
+bedpan
+bedroom window
+bedside lamp
+bee
+beech tree
+beef
+beekeeper
+beeper
+beer
+beer bottle
+beer can
+beer garden
+beer glass
+beer hall
+beet
+beetle
+beige
+clock
+bell pepper
+bell tower
+belt
+belt buckle
+bench
+bend
+bengal tiger
+bento
+beret
+berry
+berth
+beverage
+bib
+bibimbap
+bible
+bichon
+bicycle
+bicycle helmet
+bicycle wheel
+biker
+bidet
+big ben
+bike lane
+bike path
+bike racing
+bike ride
+bikini
+bikini top
+bill
+billard
+billboard
+billiard table
+bin
+binder
+binocular
+biology laboratory
+biplane
+birch
+birch tree
+bird
+bird bath
+bird feeder
+bird house
+bird nest
+birdbath
+bird cage
+birth
+birthday
+birthday cake
+birthday candle
+birthday card
+birthday party
+biscuit
+bishop
+bison
+bit
+bite
+black
+black sheep
+blackberry
+blackbird
+blackboard
+blacksmith
+blade
+blanket
+sports coat
+bleacher
+blender
+blessing
+blind
+eye mask
+flasher
+snowstorm
+block
+blog
+blood
+bloom
+blossom
+blouse
+blow
+hair drier
+blowfish
+blue
+blue artist
+blue jay
+blue sky
+blueberry
+bluebird
+pig
+board
+board eraser
+board game
+boardwalk
+boat
+boat deck
+boat house
+paddle
+boat ride
+bobfloat
+bobcat
+body
+bodyboard
+bodybuilder
+boiled egg
+boiler
+bolo tie
+bolt
+bomb
+bomber
+bonasa umbellu
+bone
+bonfire
+bonnet
+bonsai
+book
+book cover
+bookcase
+folder
+bookmark
+bookshelf
+bookstore
+boom microphone
+boost
+boot
+border
+Border collie
+botanical garden
+bottle
+bottle cap
+bottle opener
+bottle screw
+bougainvillea
+boulder
+bouquet
+boutique
+boutique hotel
+bow
+bow tie
+bow window
+bowl
+bowling
+bowling alley
+bowling ball
+bowling equipment
+box
+box girder bridge
+box turtle
+boxer
+underdrawers
+boxing
+boxing glove
+boxing ring
+boy
+brace
+bracket
+braid
+brain
+brake
+brake light
+branch
+brand
+brandy
+brass
+brass plaque
+bread
+breadbox
+break
+breakfast
+seawall
+chest
+brewery
+brick
+brick building
+wall
+brickwork
+wedding dress
+bride
+groom
+bridesmaid
+bridge
+bridle
+briefcase
+bright
+brim
+broach
+broadcasting
+broccoli
+bronze
+bronze medal
+bronze sculpture
+bronze statue
+brooch
+creek
+broom
+broth
+brown
+brown bear
+brownie
+brunch
+brunette
+brush
+coyote
+brussels sprout
+bubble
+bubble gum
+bubble tea
+bucket cabinet
+shield
+bud
+buddha
+buffalo
+buffet
+bug
+build
+builder
+building
+building block
+building facade
+building material
+lamp
+bull
+bulldog
+bullet
+bullet train
+bulletin board
+bulletproof vest
+bullfighting
+megaphone
+bullring
+bumblebee
+bumper
+roll
+bundle
+bungee
+bunk bed
+bunker
+bunny
+buoy
+bureau
+burial chamber
+burn
+burrito
+bus
+bus driver
+bus interior
+bus station
+bus stop
+bus window
+bush
+business
+business card
+business executive
+business suit
+business team
+business woman
+businessman
+bust
+butcher
+butchers shop
+butte
+butter
+cream
+butterfly
+butterfly house
+button
+buttonwood
+buy
+taxi
+cabana
+cabbage
+cabin
+cabin car
+cabinet
+cabinetry
+cable
+cable car
+cactus
+cafe
+canteen
+cage
+cake
+cake stand
+calculator
+caldron
+calendar
+calf
+call
+phone box
+calligraphy
+calm
+camcorder
+camel
+camera
+camera lens
+camouflage
+camp
+camper
+campfire
+camping
+campsite
+campus
+can
+can opener
+canal
+canary
+cancer
+candle
+candle holder
+candy
+candy bar
+candy cane
+candy store
+cane
+jar
+cannon
+canopy
+canopy bed
+cantaloupe
+cantilever bridge
+canvas
+canyon
+cap
+cape
+cape cod
+cappuccino
+capsule
+captain
+capture
+car
+car dealership
+car door
+car interior
+car logo
+car mirror
+parking lot
+car seat
+car show
+car wash
+car window
+caramel
+card
+card game
+cardboard
+cardboard box
+cardigan
+cardinal
+cargo
+cargo aircraft
+cargo ship
+caribbean
+carnation
+carnival
+carnivore
+carousel
+carp
+carpenter
+carpet
+slipper
+house finch
+coach
+dalmatian
+aircraft carrier
+carrot
+carrot cake
+carry
+cart
+carton
+cartoon
+cartoon character
+cartoon illustration
+cartoon style
+carve
+case
+cash
+cashew
+casino
+casserole
+cassette
+cassette deck
+plaster bandage
+casting
+castle
+cat
+cat bed
+cat food
+cat furniture
+cat tree
+catacomb
+catamaran
+catamount
+catch
+catcher
+caterpillar
+catfish
+cathedral
+cattle
+catwalk
+catwalk show
+cauliflower
+cave
+caviar
+CD
+CD player
+cedar
+ceiling
+ceiling fan
+celebrate
+celebration
+celebrity
+celery
+cello
+smartphone
+cement
+graveyard
+centerpiece
+centipede
+ceramic
+ceramic tile
+cereal
+ceremony
+certificate
+chain
+chain saw
+chair
+chairlift
+daybed
+chalet
+chalice
+chalk
+chamber
+chameleon
+champagne
+champagne flute
+champion
+championship
+chandelier
+changing table
+channel
+chap
+chapel
+character sculpture
+charcoal
+charge
+charger
+chariot
+charity
+charity event
+charm
+graph
+chase
+chassis
+check
+checkbook
+chessboard
+checklist
+cheer
+cheerlead
+cheese
+cheeseburger
+cheesecake
+cheetah
+chef
+chemical compound
+chemist
+chemistry
+chemistry lab
+cheongsam
+cherry
+cherry blossom
+cherry tomato
+cherry tree
+chess
+chestnut
+chicken
+chicken breast
+chicken coop
+chicken salad
+chicken wing
+garbanzo
+chiffonier
+chihuahua
+child
+child actor
+childs room
+chile
+chili dog
+chimney
+chimpanzee
+chinaware
+chinese cabbage
+chinese garden
+chinese knot
+chinese rose
+chinese tower
+chip
+chipmunk
+chisel
+chocolate
+chocolate bar
+chocolate cake
+chocolate chip
+chocolate chip cookie
+chocolate milk
+chocolate mousse
+truffle
+choir
+kitchen knife
+cutting board
+chopstick
+christmas
+christmas ball
+christmas card
+christmas decoration
+christmas dinner
+christmas eve
+christmas hat
+christmas light
+christmas market
+christmas ornament
+christmas tree
+chrysanthemum
+church
+church tower
+cider
+cigar
+cigar box
+cigarette
+cigarette case
+waistband
+cinema
+photographer
+cinnamon
+circle
+circuit
+circuit board
+circus
+water tank
+citrus fruit
+city
+city bus
+city hall
+city nightview
+city park
+city skyline
+city square
+city street
+city wall
+city view
+clam
+clarinet
+clasp
+class
+classic
+classroom
+clavicle
+claw
+clay
+pottery
+clean
+clean room
+cleaner
+cleaning product
+clear
+cleat
+clementine
+client
+cliff
+climb
+climb mountain
+climber
+clinic
+clip
+clip art
+clipboard
+clipper
+clivia
+cloak
+clogs
+close-up
+closet
+cloth
+clothe
+clothing
+clothespin
+clothesline
+clothing store
+cloud
+cloud forest
+cloudy
+clover
+joker
+clown fish
+club
+clutch
+clutch bag
+coal
+coast
+coat
+coatrack
+cob
+cock
+cockatoo
+cocker
+cockpit
+roach
+cocktail
+cocktail dress
+cocktail shaker
+cocktail table
+cocoa
+coconut
+coconut tree
+coffee
+coffee bean
+coffee cup
+coffee machine
+coffee shop
+coffeepot
+coffin
+cognac
+spiral
+coin
+coke
+colander
+cold
+slaw
+collaboration
+collage
+collection
+college student
+sheepdog
+crash
+color
+coloring book
+coloring material
+pony
+pillar
+comb
+combination lock
+comic
+comedy
+comedy film
+comet
+comfort
+comfort food
+comic book
+comic book character
+comic strip
+commander
+commentator
+community
+commuter
+company
+compass
+compete
+contest
+competitor
+composer
+composition
+compost
+computer
+computer box
+computer chair
+computer desk
+keyboard
+computer monitor
+computer room
+computer screen
+computer tower
+concept car
+concert
+concert hall
+conch
+concrete
+condiment
+condom
+condominium
+conductor
+cone
+meeting
+conference center
+conference hall
+meeting room
+confetti
+conflict
+confluence
+connect
+connector
+conservatory
+constellation
+construction site
+construction worker
+contain
+container
+container ship
+continent
+profile
+contract
+control
+control tower
+convenience store
+convention
+conversation
+converter
+convertible
+transporter
+cook
+cooking
+cooking spray
+cooker
+cool
+cooler
+copper
+copy
+coral
+coral reef
+rope
+corded phone
+liquor
+corgi
+cork
+corkboard
+cormorant
+corn
+corn field
+cornbread
+corner
+trumpet
+cornice
+cornmeal
+corral
+corridor
+corset
+cosmetic
+cosmetics brush
+cosmetics mirror
+cosplay
+costume
+costumer film designer
+infant bed
+cottage
+cotton
+cotton candy
+couch
+countdown
+counter
+counter top
+country artist
+country house
+country lane
+country pop artist
+countryside
+coupe
+couple
+couple photo
+courgette
+course
+court
+courthouse
+courtyard
+cousin
+coverall
+cow
+cowbell
+cowboy
+cowboy boot
+cowboy hat
+crab
+crabmeat
+crack
+cradle
+craft
+craftsman
+cranberry
+crane
+crape
+crapper
+crate
+crater lake
+lobster
+crayon
+cream cheese
+cream pitcher
+create
+creature
+credit card
+crescent
+croissant
+crest
+crew
+cricket
+cricket ball
+cricket team
+cricketer
+crochet
+crock pot
+crocodile
+crop
+crop top
+cross
+crossbar
+crossroad
+crosstalk
+crosswalk
+crouton
+crow
+crowbar
+crowd
+crowded
+crown
+crt screen
+crucifix
+cruise
+cruise ship
+cruiser
+crumb
+crush
+crutch
+crystal
+cub
+cube
+cucumber
+cue
+cuff
+cufflink
+cuisine
+farmland
+cup
+cupcake
+cupid
+curb
+curl
+hair roller
+currant
+currency
+curry
+curtain
+curve
+pad
+customer
+cut
+cutlery
+cycle
+cycling
+cyclone
+cylinder
+cymbal
+cypress
+cypress tree
+dachshund
+daffodil
+dagger
+dahlia
+daikon
+dairy
+daisy
+dam
+damage
+damp
+dance
+dance floor
+dance room
+dancer
+dandelion
+dark
+darkness
+dart
+dartboard
+dashboard
+date
+daughter
+dawn
+day bed
+daylight
+deadbolt
+death
+debate
+debris
+decanter
+deck
+decker bus
+decor
+decorate
+decorative picture
+deer
+defender
+deity
+delicatessen
+deliver
+demolition
+monster
+demonstration
+den
+denim jacket
+dentist
+department store
+depression
+derby
+dermopathy
+desert
+desert road
+design
+designer
+table
+table lamp
+desktop
+desktop computer
+dessert
+destruction
+detective
+detergent
+dew
+dial
+diamond
+diaper
+diaper bag
+journal
+die
+diet
+excavator
+number
+digital clock
+dill
+dinner
+rowboat
+dining room
+dinner party
+dinning table
+dinosaur
+dip
+diploma
+direct
+director
+dirt
+dirt bike
+dirt field
+dirt road
+dirt track
+disaster
+disciple
+disco
+disco ball
+discotheque
+disease
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+window display
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+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
+job
+work card
+workbench
+worker
+workplace
+workshop
+world
+worm
+worship
+wound
+wrap
+wrap dress
+wrapping paper
+wrestle
+wrestler
+wrinkle
+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

ram/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 
+羚羊 
+古董 
+鹿角 
+铁砧 
+公寓 
+猿 
+应用程序 
+应用图标 
+出现 
+外观 
+开胃菜 
+掌声 
+苹果 
+苹果汁 
+苹果派 
+苹果树 
+苹果酱 
+设备 
+约定
+通道
+杏子
+围裙 
+浅绿色
+水族馆 
+观赏鱼
+渡槽 
+游乐中心
+商场游戏机
+拱门
+拱桥 
+考古现场
+射箭 
+群岛 
+建筑师
+建筑设计
+档案
+拱门 
+地区
+竞技场 
+争论
+手臂 
+穿山甲
+臂章 
+扶手椅 
+衣柜
+盔甲
+军队 
+军事基地 
+坦克 
+阵列
+逮捕 
+箭头 
+艺术 
+艺术展
+美术馆
+艺术印刷品
+艺术学校 
+艺术工作室 
+艺术矢量插图 
+洋蓟 
+文章 
+手工艺品
+艺术家 
+艺术阁楼 
+灰 
+烟灰缸 
+亚洲寺庙 
+芦笋 
+沥青道路
+组装
+集会
+生产流水线
+协会 
+宇航员 
+天文学家 
+运动员 
+运动 
+地图集
+自助取款机 
+大气层
+中庭 
+连接
+战斗机 
+参加 
+吸引力 
+全地形车
+茄子 
+拍卖 
+奥迪汽车
+音频 
+礼堂 
+极光 
+作者 
+汽车厂 
+汽车修理工 
+汽车零件
+车展 
+汽车展厅 
+汽车电池 
+汽车制造
+汽车模型 
+汽车
+秋天 
+秋天的森林 
+秋天的叶子
+秋天的公园 
+秋天的树 
+阿凡达
+林荫大道
+飞行员太阳镜 
+牛油果
+奖品
+颁奖典礼 
+获奖者
+棚
+斧头 
+杜鹃花 
+狒狒 
+婴儿 
+奶瓶 
+婴儿车 
+婴儿衣服 
+小象 
+婴儿食品 
+婴儿座椅
+迎婴派对
+背后/后面
+背景 
+背光 
+背包 
+后院 
+培根 
+徽章 
+獾 
+荒地 
+羽毛球运动
+羽毛球拍 
+袋子
+面包圈
+风笛 
+法棍
+诱饵 
+焙烤食品 
+面包师
+面包店 
+烘焙 
+烤盘 
+平衡 
+平衡车 
+阳台 
+球 
+球池
+芭蕾舞女演员 
+芭蕾舞 
+芭蕾舞演员 
+芭蕾舞裙 
+气球 
+气球拱门 
+棒球手 
+舞厅 
+竹子 
+竹林 
+香蕉 
+香蕉面包 
+香蕉叶子 
+香蕉树 
+乐队 
+创可贴 
+绷带 
+头巾 
+束发带 
+刘海
+手镯 
+栏杆
+五弦琴
+银行 
+银行卡 
+银行金库
+纸币
+横幅/旗帜
+宴会 
+宴会厅 
+榕树
+包子 
+洗礼 
+酒吧 
+条形码 
+高脚凳
+烧烤 
+烧烤架 
+杠铃 
+理发师 
+理发店 
+芭比娃娃 
+驳船 
+咖啡师 
+树皮 
+大麦 
+谷仓 
+仓鸮
+挡光板
+桶 
+路障
+屏障
+手推车 
+酒保 
+棒球 
+棒球基地 
+棒球棒 
+棒球帽 
+棒球场
+棒球比赛 
+棒球手套 
+棒球投手 
+棒球队 
+棒球制服 
+地下室 
+罗勒 
+水盆
+篮子 
+篮子
+篮球 
+篮球篮板 
+篮球教练 
+篮球场 
+篮球比赛 
+篮球框
+篮球运动员 
+篮球馆 
+篮球队 
+贝斯
+低音吉他 
+低音喇叭 
+贝斯手 
+球棒/球拍
+浴室
+水浴加热器
+浴垫 
+浴巾 
+泳装 
+浴袍 
+浴室 
+浴室配件 
+浴室柜 
+浴室门 
+浴室镜子
+浴室水槽 
+卫生纸
+浴室窗户
+蝙蝠侠 
+棒子
+接连猛打/击球员
+电池 
+战斗 
+战绳
+战舰 
+海湾
+海湾大桥 
+凸窗 
+杨梅
+集市 
+海滩 
+沙滩球 
+沙滩椅 
+海滨别墅 
+海滩小屋 
+沙滩毛巾 
+沙滩排球 
+灯塔 
+珠子
+比格犬
+鸟嘴
+烧杯 
+横梁
+豆子
+豆袋椅 
+豆袋 
+熊 
+幼熊 
+胡子 
+野兽 
+击打/击败
+美丽的 
+美丽
+美容院 
+海狸 
+床
+床单
+床架
+卧室 
+床上用品 
+便盆 
+卧室窗户 
+床头灯 
+蜜蜂 
+山毛榉
+牛肉 
+养蜂人 
+蜂鸣器 
+啤酒 
+啤酒瓶 
+啤酒罐 
+啤酒花园 
+啤酒杯 
+啤酒馆
+甜菜 
+甲虫 
+米色 
+时钟 
+甜椒 
+钟楼 
+皮带
+皮带扣 
+长凳
+弯曲 
+孟加拉虎 
+盒饭 
+贝雷帽 
+浆果 
+停泊位 
+饮料 
+围嘴 
+拌饭 
+圣经 
+比熊
+自行车 
+自行车头盔 
+自行车车轮 
+自行车骑士
+坐浴盆 
+大本钟 
+自行车道 
+自行车道 
+自行车赛
+骑车
+比基尼 
+比基尼上衣
+账单
+台球
+广告牌 
+台球台
+垃圾箱
+活页夹
+双筒望远镜 
+生物学实验室 
+双翼飞机 
+桦木 
+桦树 
+鸟 
+鸟池
+喂鸟器 
+鸟舍
+鸟巢 
+鸟池
+鸟笼 
+出生 
+生日 
+生日蛋糕 
+生日蜡烛 
+生日贺卡 
+生日聚会 
+饼干 
+主教 
+野牛 
+钻头
+咬 
+黑色
+黑山羊
+黑莓 
+乌鸦
+黑板
+铁匠 
+叶片/刀片
+毯子/覆盖层
+��动外套 
+看台
+搅拌机 
+祝福 
+窗帘
+眼罩 
+闪光 
+暴风雪 
+块 
+博客 
+血 
+开花
+花
+女装衬衫
+吹
+吹风机
+河豚 
+蓝色
+蓝色艺术家
+蓝松鸦
+蓝天
+蓝莓 
+蓝知更鸟 
+猪 
+板子
+板擦
+棋盘游戏 
+木板路 
+船 
+船甲板
+船屋
+桨 
+乘船 
+浮标
+山猫 
+躯干
+身体冲浪板
+健美运动员 
+水煮鸡蛋 
+锅炉 
+饰扣式领带
+门闩
+炸弹 
+轰炸机 
+披肩榛鸡
+骨骼
+篝火 
+阀盖 
+盆景 
+书 
+书籍封面
+书柜 
+文件夹 
+书签 
+书架
+书店 
+远程拾音器
+推动
+靴子
+边界
+边境牧羊犬 
+植物园 
+瓶 
+瓶盖 
+开瓶器 
+螺旋开瓶器
+三角梅
+巨石
+花束 
+时装店
+精品酒店 
+鞠躬/蝴蝶结
+领结 
+弓形窗
+碗 
+保龄球运动
+保龄球馆 
+保龄球 
+保龄球设备 
+盒子 
+箱形梁桥
+箱龟 
+拳击手 
+内裤 
+拳击 
+拳击手套 
+拳击台
+男孩 
+支撑物
+支架 
+辫子
+大脑 
+刹车 
+刹车灯 
+树枝
+商标
+白兰地 
+黄铜 
+黄铜牌匾 
+面包 
+面包箱 
+休息
+早餐 
+防浪堤 
+胸部
+啤酒厂 
+砖块
+砖建筑物
+墙 
+砖块
+婚纱 
+新娘 
+新郎 
+伴娘 
+桥 
+缰绳 
+公文包 
+明亮的 
+边沿
+钻头
+广播 
+西兰花 
+青铜 
+铜牌 
+青铜雕塑 
+青铜雕像 
+胸针 
+小溪
+扫帚 
+肉汤 
+棕色
+棕熊 
+巧克力蛋糕 
+早午餐 
+浅黑肤色的女人 
+刷子
+郊狼
+包菜
+气泡
+泡泡糖 
+珍珠奶茶
+斗柜 
+盾牌
+芽 
+佛 
+水牛 
+自助餐 
+昆虫
+建造
+建造者
+建筑 
+积木
+建筑立面 
+建筑材料 
+灯 
+牛 
+斗牛犬 
+子弹 
+动车 
+公告栏 
+防弹背心 
+斗牛 
+扩音器 
+斗牛场 
+大黄蜂 
+保险杠 
+卷/地形起伏
+捆
+蹦极 
+双层床 
+地堡/击球
+兔子 
+浮标 
+书桌
+墓室 
+燃烧 
+玉米煎饼 
+公交车
+公交车司机
+公交车内部
+公交车站 
+公交车站 
+公交车窗户
+灌木
+商业
+名片 
+业务主管 
+商务西装
+业务团队 
+女商人
+商人 
+半身像
+屠夫 
+肉铺
+孤峰 
+黄油 
+奶油 
+蝴蝶 
+蝴蝶馆
+按钮 
+梧桐树
+购买
+出租车 
+小屋 
+卷心菜 
+小屋/机舱
+守车
+储藏柜
+橱柜
+电缆 
+缆车 
+仙人掌 
+咖啡馆 
+食堂 
+笼子
+蛋糕 
+蛋糕台
+计算器 
+大锅 
+日历 
+小腿 
+通话
+电话亭
+书法 
+平静的
+摄像机 
+骆驼 
+相机 
+相机镜头 
+迷彩
+露营
+露营者 
+篝火 
+露营
+营地 
+校园 
+罐
+开罐器 
+运河 
+金丝雀 
+癌症 
+蜡烛 
+烛台 
+糖果 
+块状糖
+柺杖糖 
+糖果店 
+拐杖
+罐子
+大炮 
+树冠/顶棚
+四柱床
+香瓜 
+悬臂桥 
+帆布 
+峡谷 
+帽子
+斗篷
+科德角 
+卡布奇诺
+胶囊 
+队长 
+捕获 
+车 
+汽车经销商 
+车门 
+汽车内饰
+车标
+后视镜
+停车场 
+汽车座椅
+车展
+洗车 
+车窗 
+焦糖 
+卡片
+纸牌游戏
+纸板 
+纸板盒 
+羊毛衫
+红衣凤头鸟
+货物 
+货运飞机 
+货船 
+加勒比 
+康乃馨 
+狂欢节 
+食肉动物 
+旋转木马 
+鲤鱼 
+木匠
+地毯 
+拖鞋 
+红雀
+长途客车
+斑点狗
+航空母舰
+胡萝卜 
+胡萝卜蛋糕 
+携带 
+手推车
+纸箱/纸盒
+卡通 
+卡通人物 
+卡通插图 
+卡通风格 
+雕刻 
+容器
+现金 
+腰果 
+赌场 
+砂锅 
+磁带 
+盒式录音机
+石膏绷带
+铸造 
+城堡 
+猫 
+猫窝
+猫粮 
+猫器具
+猫架
+地下墓穴 
+双体船 
+美洲狮
+握着/抓着
+捕手 
+毛毛虫 
+鲶鱼 
+教堂 
+牛 
+猫步 
+走秀 
+菜花 
+洞穴 
+鱼子酱 
+光盘
+CD播放器
+雪松 
+天花板 
+吊扇 
+庆祝 
+庆典
+名人 
+芹菜 
+大提琴 
+手机 
+水泥 
+墓地 
+中心装饰品
+蜈蚣 
+陶瓷 
+瓷砖 
+麦片 
+仪式 
+证书 
+链条
+链锯 
+椅子 
+升降椅 
+躺椅
+木屋
+圣杯
+粉笔 
+房间
+变色龙 
+香槟酒
+香槟杯
+冠军
+锦标赛
+吊灯
+婴儿换尿布台
+通道
+皴裂处
+小教堂
+人物雕塑
+木炭
+充电
+充电器
+战车
+慈善机构
+慈善活动
+魅力
+图表
+追逐
+底盘
+检查/支票
+支票簿
+棋盘
+检查表
+欢呼声
+鼓励/啦啦队
+奶酪
+奶酪汉堡
+奶酪蛋糕
+猎豹
+厨师
+化合物
+化学家
+化学
+化学实验室
+旗袍
+樱桃
+樱花
+樱桃番茄
+樱桃树
+国际象棋
+栗子
+鸡
+鸡胸肉
+鸡笼
+鸡肉沙拉
+鸡翅
+鹰嘴豆
+小衣橱
+吉娃娃
+孩子
+童星
+孩子的房间
+红番椒
+辣热狗
+烟囱
+黑猩猩
+瓷器
+白菜
+中国园林
+中国结
+月季
+中国塔
+炸薯条/炸薯条
+花栗鼠
+凿子
+巧克力
+巧克力棒
+巧克力蛋糕
+巧克力碎片
+巧克力饼干
+巧克力牛奶
+巧克力慕斯
+松露
+唱诗班
+厨房刀
+砧板
+筷子
+圣诞节
+圣诞球
+圣诞贺卡
+圣诞装饰
+圣诞晚宴
+平安夜
+圣诞帽
+圣诞灯
+圣诞市场
+圣诞装饰
+圣诞树
+菊花
+教堂
+教堂塔
+苹果酒
+雪茄
+雪茄盒
+香烟
+烟盒
+腰带
+电影院
+摄影师
+肉桂
+圆
+电路
+电路板
+马戏团
+水箱
+柑橘类水果
+城市
+城市公交
+市政厅
+城市夜景
+城市公园
+城市天际线
+城市广场
+城市街道
+城墙
+城市景观
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ram/data/ram_tag_list_threshold.txt

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ram/data/tag_list.txt

@@ -0,0 +1,3429 @@
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+bear cub
+observatory
+bicycle
+hillside
+judge
+watercolor illustration
+granite
+lobster
+livery
+stone
+ceramic
+ranch
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+building
+tattoo
+cricketer
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+source
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+spray
+ceremony
+magic
+curve
+container
+fair
+medicine
+baby
+tennis racquet
+ornament
+bamboo
+duckling
+song
+safari
+team presentation
+daffodil
+cross
+toothpaste
+shield
+fashion model
+capsule
+map
+creek
+glass house
+glass plate
+siding
+corner
+water buffalo
+bison
+figure skater
+diploma
+tire
+race
+cable car
+brain
+gas stove
+soap bubble
+palette
+snowboard
+school child
+trench coat
+monk
+fiber
+kitchen window
+sunglass
+coffee
+security
+strawberry
+penguin
+tree root
+loaf
+engagement ring
+lamb
+vector cartoon illustration
+sandwich
+mountain village
+shape
+charm
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+automotive
+various
+one
+old
+rusty
+tall
+wild
+narrow
+natural
+several
+frozen
+textured
+lush
+young
+hot
+mixed
+white
+float
+quiet
+round
+bright
+religious
+female
+historical
+shiny
+traditional
+tourist
+yellow
+bald
+coastal
+lovely
+little
+broken
+romantic
+wide
+royal
+rich
+open
+cute
+ancient
+cold
+political
+elderly
+gold
+full
+rustic
+metallic
+floral
+sad
+wet
+fancy
+senior
+tiny
+stylish
+large
+frosty
+orange
+transparent
+electronic
+shallow
+scared
+armed
+dirty
+historic
+black
+few
+windy
+some
+square
+ornamental
+sandy
+thin

ram/inference.py

@@ -0,0 +1,46 @@
+'''
+ * The Inference of RAM and Tag2Text Models
+ * Written by Xinyu Huang
+'''
+import torch
+
+
+def inference_tag2text(image, model, input_tag="None"):
+
+    with torch.no_grad():
+        caption, tag_predict = model.generate(image,
+                                              tag_input=None,
+                                              max_length=50,
+                                              return_tag_predict=True)
+
+    if input_tag == '' or input_tag == 'none' or input_tag == 'None':
+        return tag_predict[0], None, caption[0]
+
+    # If user input specified tags:
+    else:
+        input_tag_list = []
+        input_tag_list.append(input_tag.replace(',', ' | '))
+
+        with torch.no_grad():
+            caption, input_tag = model.generate(image,
+                                                tag_input=input_tag_list,
+                                                max_length=50,
+                                                return_tag_predict=True)
+
+        return tag_predict[0], input_tag[0], caption[0]
+
+
+def inference_ram(image, model):
+
+    with torch.no_grad():
+        tags, tags_chinese = model.generate_tag(image)
+
+    return tags
+
+
+def inference_ram_openset(image, model):
+
+    with torch.no_grad():
+        tags = model.generate_tag_openset(image)
+
+    return tags[0]

ram/models/__init__.py

@@ -0,0 +1,2 @@
+from .ram import ram
+from .tag2text import tag2text

ram/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
+
+

ram/models/bert_lora.py

@@ -0,0 +1,1040 @@
+'''
+ * 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
+
+import loralib as lora
+
+
+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)
+        self.query = lora.Linear(config.hidden_size, self.all_head_size, r=8)
+        if is_cross_attention:
+            # self.key = nn.Linear(config.encoder_width, self.all_head_size)
+            self.key = lora.Linear(config.encoder_width, self.all_head_size, r=8)
+            self.value = nn.Linear(config.encoder_width, self.all_head_size)
+        else:
+            # self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.key = lora.Linear(config.hidden_size, self.all_head_size, r=8)
+            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
+
+

ram/models/ram.py

@@ -0,0 +1,317 @@
+'''
+ * The Recognize Anything Model (RAM)
+ * Written by Xinyu Huang
+'''
+import json
+import warnings
+
+import numpy as np
+import torch
+from torch import nn
+
+from .bert import BertConfig, BertLMHeadModel, BertModel
+from .swin_transformer import SwinTransformer
+from .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'
+            elif image_size == 444:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_444.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)
+        self.label_embed = nn.Parameter(torch.zeros(self.num_class, q2l_config.encoder_width))
+
+        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(f'{CONFIG_PATH}/data/textual_label_embedding.pth',map_location='cpu').float())
+
+        # adjust thresholds for some tags
+        self.class_threshold = torch.ones(self.num_class) * self.threshold
+        ram_class_threshold_path = f'{CONFIG_PATH}/data/ram_tag_list_threshold.txt'
+        with open(ram_class_threshold_path, 'r', encoding='utf-8') as f:
+            ram_class_threshold = [float(s.strip()) for s in f]
+        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="utf-8") 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 condition_forward(self,
+                 image,
+                 threshold=0.68,
+                 condition_flag=None,
+                 tag_input=None,
+                 only_feature=True,
+                 ):
+            
+        label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))
+
+        image_embeds = self.image_proj(self.visual_encoder(image))
+        if only_feature:
+            return image_embeds
+        else:
+            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))
+
+            return image_embeds, logits, targets
+
+    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))
+            tag_output.append(', '.join(token))
+            token_chinese = self.tag_list_chinese[index].squeeze(axis=1)
+            # tag_output_chinese.append(' | '.join(token_chinese))
+            tag_output_chinese.append(', '.join(token_chinese))
+
+
+        return tag_output, tag_output_chinese
+
+    def generate_tag_openset(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 = []
+        for b in range(bs):
+            index = np.argwhere(tag[b] == 1)
+            token = self.tag_list[index].squeeze(axis=1)
+            tag_output.append(' | '.join(token))
+
+        return tag_output
+
+
+# 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

ram/models/ram_lora.py

@@ -0,0 +1,344 @@
+'''
+ * The Recognize Anything Model (RAM)
+ * Written by Xinyu Huang
+'''
+import json
+import warnings
+
+import numpy as np
+import torch
+from torch import nn
+
+
+from .bert_lora import BertConfig, BertLMHeadModel, BertModel
+from .swin_transformer_lora import SwinTransformer
+from .utils import *
+
+warnings.filterwarnings("ignore")
+
+
+
+class RAMLora(nn.Module):
+    def __init__(self,
+                 condition_config=f'{CONFIG_PATH}/configs/condition_config.json',
+                 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,
+                 max_threthold=0.9,
+                 add_threthold=0,
+                 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'
+            elif image_size == 444:
+                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_444.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)
+        self.label_embed = nn.Parameter(torch.zeros(self.num_class, q2l_config.encoder_width))
+
+        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(f'{CONFIG_PATH}/data/textual_label_embedding.pth',map_location='cpu').float())
+
+        # adjust thresholds for some tags
+        self.class_threshold = torch.ones(self.num_class) * self.threshold
+
+        print(f'Loading default thretholds from .txt....')
+        ram_class_threshold_path = f'{CONFIG_PATH}/data/ram_tag_list_threshold.txt'
+        with open(ram_class_threshold_path, 'r', encoding='utf-8') as f:
+            ram_class_threshold = [float(s.strip()) for s in f]
+        for key,value in enumerate(ram_class_threshold):
+            if value > max_threthold:
+                self.class_threshold[key] = value
+            else:
+                self.class_threshold[key] = min(value + add_threthold, max_threthold)
+
+        
+
+    def load_tag_list(self, tag_list_file):
+        with open(tag_list_file, 'r', encoding="utf-8") 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_image_embeds(self,
+                 image,
+                 condition=False
+                 ):
+            
+        image_embeds = self.image_proj(self.visual_encoder(image))
+        
+        return image_embeds
+
+    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))
+            tag_output.append(', '.join(token))
+            token_chinese = self.tag_list_chinese[index].squeeze(axis=1)
+            # tag_output_chinese.append(' | '.join(token_chinese))
+            tag_output_chinese.append(', '.join(token_chinese))
+
+
+        return tag_output, tag_output_chinese
+
+    
+
+    def condition_forward(self,
+                 image,
+                 threshold=0.68,
+                 condition_flag=None,
+                 tag_input=None,
+                 only_feature=True
+                 ):
+            
+        label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))
+        image_embeds = self.image_proj(self.visual_encoder(image))
+
+        if only_feature:
+            return image_embeds
+        else:
+            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))
+
+            return image_embeds, logits, targets
+
+    def generate_tag_openset(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 = []
+        for b in range(bs):
+            index = np.argwhere(tag[b] == 1)
+            token = self.tag_list[index].squeeze(axis=1)
+            tag_output.append(' | '.join(token))
+
+        return tag_output
+
+
+# load RAM pretrained model parameters
+def ram(pretrained='', pretrained_condition='', **kwargs):
+    model = RAMLora(**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'])
+    
+    if pretrained_condition:
+        model.load_state_dict(torch.load(pretrained_condition), strict=False)
+        print(f'load lora from {pretrained_condition}')
+
+    return model

ram/models/swin_transformer.py

@@ -0,0 +1,696 @@
+# --------------------------------------------------------
+# 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)
+
+        ## condition from LR
+        self.condition_attention = nn.Sequential(
+            nn.Linear(256, dim*2, bias=False),
+        )
+        self.condition_ffn = nn.Sequential(
+            nn.Linear(256, dim*2, bias=False),
+        )
+
+        zero_module(self.condition_attention)
+        zero_module(self.condition_ffn)
+
+    def forward(self, x, condition=None):
+        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)
+
+        # add condition before attention
+        # input B,H,W,C
+        if condition is not None:
+            x = x.permute(0, 3, 1, 2) # BCHW
+            condition_attention = self.condition_attention(condition).view(-1, 2*C, 1, 1)
+            condition_attn_multiplication, condition_attn_addition = condition_attention.chunk(2, dim=1)
+            x = x*condition_attn_multiplication + condition_attn_multiplication
+            x = x.permute(0, 2, 3, 1)
+
+
+
+        # 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)))
+        
+        # add condition before ffn
+        # input B,H*W,C
+        if condition is not None:
+            res = x
+            x = self.norm2(x)
+            x = x.view(B, H, W, C)
+            x = x.permute(0, 3, 1, 2) # BCHW
+            condition_ffn = self.condition_ffn(condition).view(-1, 2*C, 1, 1)
+            condition_ffn_multiplication, condition_ffn_addition = condition_ffn.chunk(2, dim=1)
+            x = x*condition_ffn_multiplication + condition_ffn_addition
+            x = x.permute(0, 2, 3, 1)
+            x = x.view(B, H*W, C)
+            x = res + self.drop_path(self.mlp(x))
+        else:
+            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, condition=None):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x, condition=condition)
+        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, condition=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, condition=condition)
+
+        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
+
+def zero_module(module):
+    for p in module.parameters():
+        nn.init.zeros_(p)
+    return module

ram/models/swin_transformer_lora.py

@@ -0,0 +1,660 @@
+# --------------------------------------------------------
+# 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_
+
+import loralib as lora
+
+
+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.fc1 = lora.Linear(in_features, hidden_features, r=16)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        # self.fc2 = lora.Linear(hidden_features, out_features, r=16)
+        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)
+        # lora version
+        self.qkv = lora.MergedLinear(dim, 3*dim, r=8, enable_lora=[True, False, True])
+        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

ram/models/tag2text.py

@@ -0,0 +1,419 @@
+'''
+ * The Tag2Text Model
+ * Written by Xinyu Huang
+'''
+import numpy as np
+import json
+import torch
+import warnings
+
+from torch import nn
+from .bert import BertConfig, BertModel, BertLMHeadModel
+from .swin_transformer import SwinTransformer
+
+from .utils import *
+
+warnings.filterwarnings("ignore")
+
+
+class Tag2Text(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()
+
+        self.tagging_loss_function = AsymmetricLoss(gamma_neg=7,
+                                                    gamma_pos=0,
+                                                    clip=0.05)
+
+        # 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 forward(self, image, caption, tag):
+        """
+        call function as forward
+
+        Args:
+            image: type: torch.Tensor  shape: batch_size * 3 * 384 * 384
+            caption: type: list[string]  len: batch_size
+            tag: type: torch.Tensor   shape: batch * class_num (e.g. 3429)   value: positive sample is 1.0, negative sample is 0.0
+
+        Returns:
+            loss: type: torch.Tensor
+        """
+
+        image_embeds = self.visual_encoder(image)
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        ##================= Image Tagging ================##
+        bs = image_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])
+
+        loss_tag = self.tagging_loss_function(logits, tag)
+
+        ##================= Image-Tag-Text Generation ================##
+        tag = tag.cpu().numpy()
+        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))
+        
+        # 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,
+        )
+
+        text = self.tokenizer(caption,
+                              padding='longest',
+                              truncation=True,
+                              max_length=40,
+                                return_tensors="pt").to(
+                                    image.device)
+        
+        decoder_input_ids = text.input_ids
+        decoder_input_ids[:,0] = self.tokenizer.bos_token_id
+
+        decoder_targets = decoder_input_ids.masked_fill(
+            decoder_input_ids == self.tokenizer.pad_token_id, -100) 
+        decoder_targets[:,:self.prompt_length] = -100
+        
+        decoder_output = self.text_decoder(decoder_input_ids, 
+                                           attention_mask = text.attention_mask, 
+                                           encoder_hidden_states = output_tagembedding.last_hidden_state,
+                                           encoder_attention_mask = None,                  
+                                           labels = decoder_targets,
+                                           return_dict = True,   
+                                          )   
+        
+        loss_t2t = decoder_output.loss
+
+        # balance loss scale
+        loss = loss_t2t + loss_tag/(loss_tag/loss_t2t).detach()
+
+        return loss
+
+    def generate_image_embeds(self,
+                 image,
+                 condition=False
+                 ):
+            
+        image_embeds = self.visual_encoder(image)
+        
+        return image_embeds
+
+    def condition_forward(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
+
+
+        bs = image_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))
+
+        # delete some tags that may disturb captioning
+        targets[:, self.delete_tag_index] = 0
+
+        return image_embeds, logits, targets
+
+
+    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:
+
+            bs = image_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(pretrained='', **kwargs):
+    model = Tag2Text(**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
+

ram/models/tag2text_lora.py

@@ -0,0 +1,419 @@
+'''
+ * The Tag2Text Model
+ * Written by Xinyu Huang
+'''
+import numpy as np
+import json
+import torch
+import warnings
+
+from torch import nn
+from .bert_lora import BertConfig, BertModel, BertLMHeadModel
+from .swin_transformer_lora import SwinTransformer
+
+from .utils import *
+
+warnings.filterwarnings("ignore")
+
+
+class Tag2Text(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()
+
+        self.tagging_loss_function = AsymmetricLoss(gamma_neg=7,
+                                                    gamma_pos=0,
+                                                    clip=0.05)
+
+        # 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 forward(self, image, caption, tag):
+        """
+        call function as forward
+
+        Args:
+            image: type: torch.Tensor  shape: batch_size * 3 * 384 * 384
+            caption: type: list[string]  len: batch_size
+            tag: type: torch.Tensor   shape: batch * class_num (e.g. 3429)   value: positive sample is 1.0, negative sample is 0.0
+
+        Returns:
+            loss: type: torch.Tensor
+        """
+
+        image_embeds = self.visual_encoder(image)
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        ##================= Image Tagging ================##
+        bs = image_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])
+
+        loss_tag = self.tagging_loss_function(logits, tag)
+
+        ##================= Image-Tag-Text Generation ================##
+        tag = tag.cpu().numpy()
+        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))
+        
+        # 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,
+        )
+
+        text = self.tokenizer(caption,
+                              padding='longest',
+                              truncation=True,
+                              max_length=40,
+                                return_tensors="pt").to(
+                                    image.device)
+        
+        decoder_input_ids = text.input_ids
+        decoder_input_ids[:,0] = self.tokenizer.bos_token_id
+
+        decoder_targets = decoder_input_ids.masked_fill(
+            decoder_input_ids == self.tokenizer.pad_token_id, -100) 
+        decoder_targets[:,:self.prompt_length] = -100
+        
+        decoder_output = self.text_decoder(decoder_input_ids, 
+                                           attention_mask = text.attention_mask, 
+                                           encoder_hidden_states = output_tagembedding.last_hidden_state,
+                                           encoder_attention_mask = None,                  
+                                           labels = decoder_targets,
+                                           return_dict = True,   
+                                          )   
+        
+        loss_t2t = decoder_output.loss
+
+        # balance loss scale
+        loss = loss_t2t + loss_tag/(loss_tag/loss_t2t).detach()
+
+        return loss
+
+    def generate_image_embeds(self,
+                 image,
+                 condition=False
+                 ):
+            
+        image_embeds = self.visual_encoder(image)
+        
+        return image_embeds
+
+    def condition_forward(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
+
+
+        bs = image_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))
+
+        # delete some tags that may disturb captioning
+        targets[:, self.delete_tag_index] = 0
+
+        return image_embeds, logits, targets
+
+
+    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:
+
+            bs = image_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(pretrained='', **kwargs):
+    model = Tag2Text(**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
+

ram/models/utils.py

@@ -0,0 +1,365 @@
+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 .vit import interpolate_pos_embed
+from .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 = BertTokenizer.from_pretrained('/home/notebook/data/group/LowLevelLLM/LLM/bert-base-uncased', local_files_only=True)
+    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_condition(model, url_or_filename):
+def load_checkpoint_swinlarge_condition(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['params']
+
+    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_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
+
+
+# Tagging loss function
+# copy from https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py
+class AsymmetricLoss(nn.Module):
+    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=True):
+        super(AsymmetricLoss, self).__init__()
+
+        self.gamma_neg = gamma_neg
+        self.gamma_pos = gamma_pos
+        self.clip = clip
+        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
+        self.eps = eps
+
+    def forward(self, x, y):
+        """"
+        Parameters
+        ----------
+        x: input logits
+        y: targets (multi-label binarized vector)
+        """
+
+        # Calculating Probabilities
+        x_sigmoid = torch.sigmoid(x)
+        xs_pos = x_sigmoid
+        xs_neg = 1 - x_sigmoid
+
+        # Asymmetric Clipping
+        if self.clip is not None and self.clip > 0:
+            xs_neg = (xs_neg + self.clip).clamp(max=1)
+
+        # Basic CE calculation
+        los_pos = y * torch.log(xs_pos.clamp(min=self.eps))
+        los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))
+        loss = los_pos + los_neg
+
+        # Asymmetric Focusing
+        if self.gamma_neg > 0 or self.gamma_pos > 0:
+            if self.disable_torch_grad_focal_loss:
+                torch.set_grad_enabled(False)
+            pt0 = xs_pos * y
+            pt1 = xs_neg * (1 - y)  # pt = p if t > 0 else 1-p
+            pt = pt0 + pt1
+            one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)
+            one_sided_w = torch.pow(1 - pt, one_sided_gamma)
+            if self.disable_torch_grad_focal_loss:
+                torch.set_grad_enabled(True)
+            loss *= one_sided_w
+
+        return -loss.sum()

ram/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/transform.py

@@ -0,0 +1,13 @@
+from torchvision.transforms import Normalize, Compose, Resize, ToTensor
+
+
+def convert_to_rgb(image):
+    return image.convert("RGB")
+
+def get_transform(image_size=384):
+    return Compose([
+        convert_to_rgb,
+        Resize((image_size, image_size)),
+        ToTensor(),
+        Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])

ram/utils/__init__.py

@@ -0,0 +1,2 @@
+from .metrics import get_mAP, get_PR
+from .openset_utils import build_openset_label_embedding

ram/utils/metrics.py

@@ -0,0 +1,102 @@
+from typing import List, Tuple
+
+import numpy as np
+from numpy import ndarray
+
+
+def get_mAP(
+    preds: ndarray,
+    gt_file: str,
+    taglist: List[str]
+) -> Tuple[float, ndarray]:
+    assert preds.shape[1] == len(taglist)
+
+    # When mapping categories from test datasets to our system, there might be
+    # multiple vs one situation due to different semantic definitions of tags.
+    # So there can be duplicate tags in `taglist`. This special case is taken
+    # into account.
+    tag2idxs = {}
+    for idx, tag in enumerate(taglist):
+        if tag not in tag2idxs:
+            tag2idxs[tag] = []
+        tag2idxs[tag].append(idx)
+
+    # build targets
+    targets = np.zeros_like(preds)
+    with open(gt_file, "r") as f:
+        lines = [line.strip("\n").split(",") for line in f.readlines()]
+    assert len(lines) == targets.shape[0]
+    for i, line in enumerate(lines):
+        for tag in line[1:]:
+            targets[i, tag2idxs[tag]] = 1.0
+
+    # compute average precision for each class
+    APs = np.zeros(preds.shape[1])
+    for k in range(preds.shape[1]):
+        APs[k] = _average_precision(preds[:, k], targets[:, k])
+
+    return APs.mean(), APs
+
+
+def _average_precision(output: ndarray, target: ndarray) -> float:
+    epsilon = 1e-8
+
+    # sort examples
+    indices = output.argsort()[::-1]
+    # Computes prec@i
+    total_count_ = np.cumsum(np.ones((len(output), 1)))
+
+    target_ = target[indices]
+    ind = target_ == 1
+    pos_count_ = np.cumsum(ind)
+    total = pos_count_[-1]
+    pos_count_[np.logical_not(ind)] = 0
+    pp = pos_count_ / total_count_
+    precision_at_i_ = np.sum(pp)
+    precision_at_i = precision_at_i_ / (total + epsilon)
+
+    return precision_at_i
+
+
+def get_PR(
+    pred_file: str,
+    gt_file: str,
+    taglist: List[str]
+) -> Tuple[float, float, ndarray, ndarray]:
+    # When mapping categories from test datasets to our system, there might be
+    # multiple vs one situation due to different semantic definitions of tags.
+    # So there can be duplicate tags in `taglist`. This special case is taken
+    # into account.
+    tag2idxs = {}
+    for idx, tag in enumerate(taglist):
+        if tag not in tag2idxs:
+            tag2idxs[tag] = []
+        tag2idxs[tag].append(idx)
+
+    # build preds
+    with open(pred_file, "r", encoding="utf-8") as f:
+        lines = [line.strip().split(",") for line in f.readlines()]
+    preds = np.zeros((len(lines), len(tag2idxs)), dtype=bool)
+    for i, line in enumerate(lines):
+        for tag in line[1:]:
+            preds[i, tag2idxs[tag]] = True
+
+    # build targets
+    with open(gt_file, "r", encoding="utf-8") as f:
+        lines = [line.strip().split(",") for line in f.readlines()]
+    targets = np.zeros((len(lines), len(tag2idxs)), dtype=bool)
+    for i, line in enumerate(lines):
+        for tag in line[1:]:
+            targets[i, tag2idxs[tag]] = True
+
+    assert preds.shape == targets.shape
+
+    # calculate P and R
+    TPs = ( preds &  targets).sum(axis=0)  # noqa: E201, E222
+    FPs = ( preds & ~targets).sum(axis=0)  # noqa: E201, E222
+    FNs = (~preds &  targets).sum(axis=0)  # noqa: E201, E222
+    eps = 1.e-9
+    Ps = TPs / (TPs + FPs + eps)
+    Rs = TPs / (TPs + FNs + eps)
+
+    return Ps.mean(), Rs.mean(), Ps, Rs

ram/utils/openset_utils.py

@@ -0,0 +1,333 @@
+
+
+
+import torch
+import torch.nn as nn
+from clip import clip
+
+
+def article(name):
+    return "an" if name[0] in "aeiou" else "a"
+
+
+def processed_name(name, rm_dot=False):
+    # _ for lvis
+    # / for obj365
+    res = name.replace("_", " ").replace("/", " or ").lower()
+    if rm_dot:
+        res = res.rstrip(".")
+    return res
+
+
+single_template = ["a photo of a {}."]
+
+multiple_templates = [
+    "There is {article} {} in the scene.",
+    "There is the {} in the scene.",
+    "a photo of {article} {} in the scene.",
+    "a photo of the {} in the scene.",
+    "a photo of one {} in the scene.",
+    "itap of {article} {}.",
+    "itap of my {}.",  # itap: I took a picture of
+    "itap of the {}.",
+    "a photo of {article} {}.",
+    "a photo of my {}.",
+    "a photo of the {}.",
+    "a photo of one {}.",
+    "a photo of many {}.",
+    "a good photo of {article} {}.",
+    "a good photo of the {}.",
+    "a bad photo of {article} {}.",
+    "a bad photo of the {}.",
+    "a photo of a nice {}.",
+    "a photo of the nice {}.",
+    "a photo of a cool {}.",
+    "a photo of the cool {}.",
+    "a photo of a weird {}.",
+    "a photo of the weird {}.",
+    "a photo of a small {}.",
+    "a photo of the small {}.",
+    "a photo of a large {}.",
+    "a photo of the large {}.",
+    "a photo of a clean {}.",
+    "a photo of the clean {}.",
+    "a photo of a dirty {}.",
+    "a photo of the dirty {}.",
+    "a bright photo of {article} {}.",
+    "a bright photo of the {}.",
+    "a dark photo of {article} {}.",
+    "a dark photo of the {}.",
+    "a photo of a hard to see {}.",
+    "a photo of the hard to see {}.",
+    "a low resolution photo of {article} {}.",
+    "a low resolution photo of the {}.",
+    "a cropped photo of {article} {}.",
+    "a cropped photo of the {}.",
+    "a close-up photo of {article} {}.",
+    "a close-up photo of the {}.",
+    "a jpeg corrupted photo of {article} {}.",
+    "a jpeg corrupted photo of the {}.",
+    "a blurry photo of {article} {}.",
+    "a blurry photo of the {}.",
+    "a pixelated photo of {article} {}.",
+    "a pixelated photo of the {}.",
+    "a black and white photo of the {}.",
+    "a black and white photo of {article} {}.",
+    "a plastic {}.",
+    "the plastic {}.",
+    "a toy {}.",
+    "the toy {}.",
+    "a plushie {}.",
+    "the plushie {}.",
+    "a cartoon {}.",
+    "the cartoon {}.",
+    "an embroidered {}.",
+    "the embroidered {}.",
+    "a painting of the {}.",
+    "a painting of a {}.",
+]
+
+
+openimages_rare_unseen = ['Aerial photography',
+'Aircraft engine',
+'Ale',
+'Aloe',
+'Amphibian',
+'Angling',
+'Anole',
+'Antique car',
+'Arcade game',
+'Arthropod',
+'Assault rifle',
+'Athletic shoe',
+'Auto racing',
+'Backlighting',
+'Bagpipes',
+'Ball game',
+'Barbecue chicken',
+'Barechested',
+'Barquentine',
+'Beef tenderloin',
+'Billiard room',
+'Billiards',
+'Bird of prey',
+'Black swan',
+'Black-and-white',
+'Blond',
+'Boating',
+'Bonbon',
+'Bottled water',
+'Bouldering',
+'Bovine',
+'Bratwurst',
+'Breadboard',
+'Briefs',
+'Brisket',
+'Brochette',
+'Calabaza',
+'Camera operator',
+'Canola',
+'Childbirth',
+'Chordophone',
+'Church bell',
+'Classical sculpture',
+'Close-up',
+'Cobblestone',
+'Coca-cola',
+'Combat sport',
+'Comics',
+'Compact car',
+'Computer speaker',
+'Cookies and crackers',
+'Coral reef fish',
+'Corn on the cob',
+'Cosmetics',
+'Crocodilia',
+'Digital camera',
+'Dishware',
+'Divemaster',
+'Dobermann',
+'Dog walking',
+'Domestic rabbit',
+'Domestic short-haired cat',
+'Double-decker bus',
+'Drums',
+'Electric guitar',
+'Electric piano',
+'Electronic instrument',
+'Equestrianism',
+'Equitation',
+'Erinaceidae',
+'Extreme sport',
+'Falafel',
+'Figure skating',
+'Filling station',
+'Fire apparatus',
+'Firearm',
+'Flatbread',
+'Floristry',
+'Forklift truck',
+'Freight transport',
+'Fried food',
+'Fried noodles',
+'Frigate',
+'Frozen yogurt',
+'Frying',
+'Full moon',
+'Galleon',
+'Glacial landform',
+'Gliding',
+'Go-kart',
+'Goats',
+'Grappling',
+'Great white shark',
+'Gumbo',
+'Gun turret',
+'Hair coloring',
+'Halter',
+'Headphones',
+'Heavy cruiser',
+'Herding',
+'High-speed rail',
+'Holding hands',
+'Horse and buggy',
+'Horse racing',
+'Hound',
+'Hunting knife',
+'Hurdling',
+'Inflatable',
+'Jackfruit',
+'Jeans',
+'Jiaozi',
+'Junk food',
+'Khinkali',
+'Kitesurfing',
+'Lawn game',
+'Leaf vegetable',
+'Lechon',
+'Lifebuoy',
+'Locust',
+'Lumpia',
+'Luxury vehicle',
+'Machine tool',
+'Medical imaging',
+'Melee weapon',
+'Microcontroller',
+'Middle ages',
+'Military person',
+'Military vehicle',
+'Milky way',
+'Miniature Poodle',
+'Modern dance',
+'Molluscs',
+'Monoplane',
+'Motorcycling',
+'Musical theatre',
+'Narcissus',
+'Nest box',
+'Newsagent\'s shop',
+'Nile crocodile',
+'Nordic skiing',
+'Nuclear power plant',
+'Orator',
+'Outdoor shoe',
+'Parachuting',
+'Pasta salad',
+'Peafowl',
+'Pelmeni',
+'Perching bird',
+'Performance car',
+'Personal water craft',
+'Pit bull',
+'Plant stem',
+'Pork chop',
+'Portrait photography',
+'Primate',
+'Procyonidae',
+'Prosciutto',
+'Public speaking',
+'Racewalking',
+'Ramen',
+'Rear-view mirror',
+'Residential area',
+'Ribs',
+'Rice ball',
+'Road cycling',
+'Roller skating',
+'Roman temple',
+'Rowing',
+'Rural area',
+'Sailboat racing',
+'Scaled reptile',
+'Scuba diving',
+'Senior citizen',
+'Shallot',
+'Shinto shrine',
+'Shooting range',
+'Siberian husky',
+'Sledding',
+'Soba',
+'Solar energy',
+'Sport climbing',
+'Sport utility vehicle',
+'Steamed rice',
+'Stemware',
+'Sumo',
+'Surfing Equipment',
+'Team sport',
+'Touring car',
+'Toy block',
+'Trampolining',
+'Underwater diving',
+'Vegetarian food',
+'Wallaby',
+'Water polo',
+'Watercolor paint',
+'Whiskers',
+'Wind wave',
+'Woodwind instrument',
+'Yakitori',
+'Zeppelin']
+
+
+def build_openset_label_embedding(categories=None):
+    if categories is None:
+        categories = openimages_rare_unseen
+    # model, _ = clip.load("ViT-B/16")
+    model, _ = clip.load("ViT-B-16.pt")
+    templates = multiple_templates
+
+    run_on_gpu = torch.cuda.is_available()
+
+    with torch.no_grad():
+        openset_label_embedding = []
+        for category in categories:
+            texts = [
+                template.format(
+                    processed_name(category, rm_dot=True), article=article(category)
+                )
+                for template in templates
+            ]
+            texts = [
+                "This is " + text if text.startswith("a") or text.startswith("the") else text
+                for text in texts
+            ]
+            texts = clip.tokenize(texts)  # tokenize
+            if run_on_gpu:
+                texts = texts.cuda()
+                model = model.cuda()
+            text_embeddings = model.encode_text(texts)
+            text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)
+            text_embedding = text_embeddings.mean(dim=0)
+            text_embedding /= text_embedding.norm()
+            openset_label_embedding.append(text_embedding)
+        openset_label_embedding = torch.stack(openset_label_embedding, dim=1)
+        if run_on_gpu:
+            openset_label_embedding = openset_label_embedding.cuda()
+
+    openset_label_embedding = openset_label_embedding.t()
+    return openset_label_embedding, categories
+
+
+
+

requirements.txt

@@ -0,0 +1,52 @@
+accelerate==1.4.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+diffusers==0.32.1
+fairscale==0.4.13
+filelock==3.13.1
+fsspec==2024.6.1
+huggingface-hub==0.29.3
+idna==3.10
+importlib_metadata==8.6.1
+Jinja2==3.1.4
+loralib==0.1.2
+lpips==0.1.4
+MarkupSafe==2.1.5
+mpmath==1.3.0
+munch==4.0.0
+networkx==3.3
+numpy==2.1.2
+nvidia-cublas-cu11==11.11.3.6
+nvidia-cuda-cupti-cu11==11.8.87
+nvidia-cuda-nvrtc-cu11==11.8.89
+nvidia-cuda-runtime-cu11==11.8.89
+nvidia-cudnn-cu11==9.1.0.70
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.3.0.86
+nvidia-cusolver-cu11==11.4.1.48
+nvidia-cusparse-cu11==11.7.5.86
+nvidia-nccl-cu11==2.20.5
+nvidia-nvtx-cu11==11.8.86
+packaging==24.2
+peft==0.15.2
+pillow==11.1.0
+protobuf==6.30.0
+psutil==7.0.0
+PyYAML==6.0.2
+qwen-vl-utils[decord]==0.0.8
+regex==2024.11.6
+requests==2.32.3
+safetensors==0.5.3
+scipy==1.15.2
+sentencepiece==0.2.0
+sympy==1.13.1
+timm==1.0.15
+tokenizers==0.21.0
+torch==2.4.1
+torchvision==0.19.1
+tqdm==4.67.1
+transformers==4.49.0
+triton==3.0.0
+typing_extensions==4.12.2
+urllib3==2.3.0
+zipp==3.21.0

scripts/inference/inference_coz_dapeprompt.sh

@@ -0,0 +1,17 @@
+#----------------- CoZ with DAPE Prompts -----------------#
+# REQUIRED ENVIRONMENT: coz
+
+INPUT_FOLDER="samples"
+OUTPUT_FOLDER="inference_results/coz_dapeprompt"
+
+CUDA_VISIBLE_DEVICES=0,1, python inference_coz.py \
+-i $INPUT_FOLDER \
+-o $OUTPUT_FOLDER \
+--rec_type recursive \
+--prompt_type dape \
+--lora_path ckpt/SR_LoRA/model_20001.pkl \
+--vae_path ckpt/SR_VAE/vae_encoder_20001.pt \
+--pretrained_model_name_or_path 'stabilityai/stable-diffusion-3-medium-diffusers' \
+--ram_ft_path ckpt/DAPE/DAPE.pth \
+--ram_path ckpt/RAM/ram_swin_large_14m.pth \
+

scripts/inference/inference_coz_nullprompt.sh

@@ -0,0 +1,15 @@
+#----------------- CoZ with Null Prompts -----------------#
+# REQUIRED ENVIRONMENT: coz
+
+INPUT_FOLDER="samples"
+OUTPUT_FOLDER="inference_results/coz_nullprompt"
+
+CUDA_VISIBLE_DEVICES=0,1, python inference_coz.py \
+-i $INPUT_FOLDER \
+-o $OUTPUT_FOLDER \
+--rec_type recursive \
+--prompt_type null \
+--lora_path ckpt/SR_LoRA/model_20001.pkl \
+--vae_path ckpt/SR_VAE/vae_encoder_20001.pt \
+--pretrained_model_name_or_path 'stabilityai/stable-diffusion-3-medium-diffusers' \
+