Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,7 @@ 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
+images/1641173_2291260800.jpg filter=lfs diff=lfs merge=lfs -text
+images/demo/demo1.jpg filter=lfs diff=lfs merge=lfs -text
+images/demo/demo2.jpg filter=lfs diff=lfs merge=lfs -text
+images/demo/demo3.jpg filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,12 +1,6 @@
 ---
-title: Tag2Text Demo
-emoji: 👀
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
+title: Tag2Text_Demo
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.35.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,51 @@
+from IPython.display import clear_output, Image
+import argparse
+import numpy as np
+import random
+from PIL import Image
+from ram.models import tag2text
+from ram import inference_tag2text as inference
+from ram import get_transform
+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+evice = torch.device('cpu')
+model = tag2text(pretrained='/content/recognize-anything/tag2text.pth', image_size=384, vit='swin_b')
+model.threshold = 0.68
+model.eval()
+
+def tag_image(input_image):
+    transform = get_transform(image_size=384) 
+
+    if isinstance(input_image, Image.Image):
+        img = input_image
+    else:
+        # Convert Gradio Image datatype (NumPy array) to PIL Image
+        img = Image.fromarray(input_image)
+
+    # Process the image
+    print(f"Start processing, image size {img.size}")
+    image = transform(img).unsqueeze(0)
+    # Generate Tags and Captions
+    res = inference(image, model)
+    tags = res[0].strip(' ').replace('  ', ' ')
+    caption = res[2]
+    print(tags, caption)
+    return tags, caption
+
+# Interface for the demo
+inputs = gr.inputs.Image()
+outputs = [gr.outputs.Textbox(label='Tags'), gr.outputs.Textbox(label='Caption')]
+
+# Launch the Gradio app
+gr.Interface(
+    fn=tag_image,
+    inputs=inputs,
+    outputs=outputs,
+    title="Tags and Captioning using Tag2Text",
+    description="Upload an image and see the results in text boxes.",
+    live=True,  
+    
+).launch(share=True, enable_queue=True, debug=True)

batch_inference.py

@@ -0,0 +1,407 @@
+from argparse import ArgumentParser
+from pathlib import Path
+from typing import Dict, List, Optional, TextIO, Tuple
+
+import torch
+from PIL import Image, UnidentifiedImageError
+from torch import Tensor
+from torch.nn import Module, Parameter
+from torch.nn.functional import relu, sigmoid
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
+
+from ram import get_transform
+from ram.models import ram, tag2text
+from ram.utils import build_openset_label_embedding, get_mAP, get_PR
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def parse_args():
+    parser = ArgumentParser()
+    # model
+    parser.add_argument("--model-type",
+                        type=str,
+                        choices=("ram", "tag2text"),
+                        required=True)
+    parser.add_argument("--checkpoint",
+                        type=str,
+                        required=True)
+    parser.add_argument("--backbone",
+                        type=str,
+                        choices=("swin_l", "swin_b"),
+                        default=None,
+                        help="If `None`, will judge from `--model-type`")
+    parser.add_argument("--open-set",
+                        action="store_true",
+                        help=(
+                            "Treat all categories in the taglist file as "
+                            "unseen and perform open-set classification. Only "
+                            "works with RAM."
+                        ))
+    # data
+    parser.add_argument("--dataset",
+                        type=str,
+                        choices=(
+                            "openimages_common_214",
+                            "openimages_rare_200"
+                        ),
+                        required=True)
+    parser.add_argument("--input-size",
+                        type=int,
+                        default=384)
+    # threshold
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument("--threshold",
+                       type=float,
+                       default=None,
+                       help=(
+                           "Use custom threshold for all classes. Mutually "
+                           "exclusive with `--threshold-file`. If both "
+                           "`--threshold` and `--threshold-file` is `None`, "
+                           "will use a default threshold setting."
+                       ))
+    group.add_argument("--threshold-file",
+                       type=str,
+                       default=None,
+                       help=(
+                           "Use custom class-wise thresholds by providing a "
+                           "text file. Each line is a float-type threshold, "
+                           "following the order of the tags in taglist file. "
+                           "See `ram/data/ram_tag_list_threshold.txt` as an "
+                           "example. Mutually exclusive with `--threshold`. "
+                           "If both `--threshold` and `--threshold-file` is "
+                           "`None`, will use default threshold setting."
+                       ))
+    # miscellaneous
+    parser.add_argument("--output-dir", type=str, default="./outputs")
+    parser.add_argument("--batch-size", type=int, default=128)
+    parser.add_argument("--num-workers", type=int, default=4)
+
+    args = parser.parse_args()
+
+    # post process and validity check
+    args.model_type = args.model_type.lower()
+
+    assert not (args.model_type == "tag2text" and args.open_set)
+
+    if args.backbone is None:
+        args.backbone = "swin_l" if args.model_type == "ram" else "swin_b"
+
+    return args
+
+
+def load_dataset(
+    dataset: str,
+    model_type: str,
+    input_size: int,
+    batch_size: int,
+    num_workers: int
+) -> Tuple[DataLoader, Dict]:
+    dataset_root = str(Path(__file__).resolve().parent / "datasets" / dataset)
+    img_root = dataset_root + "/imgs"
+    # Label system of tag2text contains duplicate tag texts, like
+    # "train" (noun) and "train" (verb). Therefore, for tag2text, we use
+    # `tagid` instead of `tag`.
+    if model_type == "ram":
+        tag_file = dataset_root + f"/{dataset}_ram_taglist.txt"
+        annot_file = dataset_root + f"/{dataset}_{model_type}_annots.txt"
+    else:
+        tag_file = dataset_root + f"/{dataset}_tag2text_tagidlist.txt"
+        annot_file = dataset_root + f"/{dataset}_{model_type}_idannots.txt"
+
+    with open(tag_file, "r", encoding="utf-8") as f:
+        taglist = [line.strip() for line in f]
+
+    with open(annot_file, "r", encoding="utf-8") as f:
+        imglist = [img_root + "/" + line.strip().split(",")[0] for line in f]
+
+    class _Dataset(Dataset):
+        def __init__(self):
+            self.transform = get_transform(input_size)
+
+        def __len__(self):
+            return len(imglist)
+
+        def __getitem__(self, index):
+            try:
+                img = Image.open(imglist[index])
+            except (OSError, FileNotFoundError, UnidentifiedImageError):
+                img = Image.new('RGB', (10, 10), 0)
+                print("Error loading image:", imglist[index])
+            return self.transform(img)
+
+    loader = DataLoader(
+        dataset=_Dataset(),
+        shuffle=False,
+        drop_last=False,
+        pin_memory=True,
+        batch_size=batch_size,
+        num_workers=num_workers
+    )
+    info = {
+        "taglist": taglist,
+        "imglist": imglist,
+        "annot_file": annot_file,
+        "img_root": img_root
+    }
+    return loader, info
+
+
+def get_class_idxs(
+    model_type: str,
+    open_set: bool,
+    taglist: List[str]
+) -> Optional[List[int]]:
+    """Get indices of required categories in the label system."""
+    if model_type == "ram":
+        if not open_set:
+            model_taglist_file = "ram/data/ram_tag_list.txt"
+            with open(model_taglist_file, "r", encoding="utf-8") as f:
+                model_taglist = [line.strip() for line in f]
+            return [model_taglist.index(tag) for tag in taglist]
+        else:
+            return None
+    else:  # for tag2text, we directly use tagid instead of text-form of tag.
+        # here tagid equals to tag index.
+        return [int(tag) for tag in taglist]
+
+
+def load_thresholds(
+    threshold: Optional[float],
+    threshold_file: Optional[str],
+    model_type: str,
+    open_set: bool,
+    class_idxs: List[int],
+    num_classes: int,
+) -> List[float]:
+    """Decide what threshold(s) to use."""
+    if not threshold_file and not threshold:  # use default
+        if model_type == "ram":
+            if not open_set:  # use class-wise tuned thresholds
+                ram_threshold_file = "ram/data/ram_tag_list_threshold.txt"
+                with open(ram_threshold_file, "r", encoding="utf-8") as f:
+                    idx2thre = {
+                        idx: float(line.strip()) for idx, line in enumerate(f)
+                    }
+                    return [idx2thre[idx] for idx in class_idxs]
+            else:
+                return [0.5] * num_classes
+        else:
+            return [0.68] * num_classes
+    elif threshold_file:
+        with open(threshold_file, "r", encoding="utf-8") as f:
+            thresholds = [float(line.strip()) for line in f]
+        assert len(thresholds) == num_classes
+        return thresholds
+    else:
+        return [threshold] * num_classes
+
+
+def gen_pred_file(
+    imglist: List[str],
+    tags: List[List[str]],
+    img_root: str,
+    pred_file: str
+) -> None:
+    """Generate text file of tag prediction results."""
+    with open(pred_file, "w", encoding="utf-8") as f:
+        for image, tag in zip(imglist, tags):
+            # should be relative to img_root to match the gt file.
+            s = str(Path(image).relative_to(img_root))
+            if tag:
+                s = s + "," + ",".join(tag)
+            f.write(s + "\n")
+
+
+def load_ram(
+    backbone: str,
+    checkpoint: str,
+    input_size: int,
+    taglist: List[str],
+    open_set: bool,
+    class_idxs: List[int],
+) -> Module:
+    model = ram(pretrained=checkpoint, image_size=input_size, vit=backbone)
+    # trim taglist for faster inference
+    if open_set:
+        print("Building tag embeddings ...")
+        label_embed, _ = build_openset_label_embedding(taglist)
+        model.label_embed = Parameter(label_embed.float())
+    else:
+        model.label_embed = Parameter(model.label_embed[class_idxs, :])
+    return model.to(device).eval()
+
+
+def load_tag2text(
+    backbone: str,
+    checkpoint: str,
+    input_size: int
+) -> Module:
+    model = tag2text(
+        pretrained=checkpoint,
+        image_size=input_size,
+        vit=backbone
+    )
+    return model.to(device).eval()
+
+
+@torch.no_grad()
+def forward_ram(model: Module, imgs: Tensor) -> Tensor:
+    image_embeds = model.image_proj(model.visual_encoder(imgs.to(device)))
+    image_atts = torch.ones(
+        image_embeds.size()[:-1], dtype=torch.long).to(device)
+    label_embed = relu(model.wordvec_proj(model.label_embed)).unsqueeze(0)\
+        .repeat(imgs.shape[0], 1, 1)
+    tagging_embed, _ = model.tagging_head(
+        encoder_embeds=label_embed,
+        encoder_hidden_states=image_embeds,
+        encoder_attention_mask=image_atts,
+        return_dict=False,
+        mode='tagging',
+    )
+    return sigmoid(model.fc(tagging_embed).squeeze(-1))
+
+
+@torch.no_grad()
+def forward_tag2text(
+    model: Module,
+    class_idxs: List[int],
+    imgs: Tensor
+) -> Tensor:
+    image_embeds = model.visual_encoder(imgs.to(device))
+    image_atts = torch.ones(
+        image_embeds.size()[:-1], dtype=torch.long).to(device)
+    label_embed = model.label_embed.weight.unsqueeze(0)\
+        .repeat(imgs.shape[0], 1, 1)
+    tagging_embed, _ = model.tagging_head(
+        encoder_embeds=label_embed,
+        encoder_hidden_states=image_embeds,
+        encoder_attention_mask=image_atts,
+        return_dict=False,
+        mode='tagging',
+    )
+    return sigmoid(model.fc(tagging_embed))[:, class_idxs]
+
+
+def print_write(f: TextIO, s: str):
+    print(s)
+    f.write(s + "\n")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    # set up output paths
+    output_dir = args.output_dir
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+    pred_file, pr_file, ap_file, summary_file, logit_file = [
+        output_dir + "/" + name for name in
+        ("pred.txt", "pr.txt", "ap.txt", "summary.txt", "logits.pth")
+    ]
+    with open(summary_file, "w", encoding="utf-8") as f:
+        print_write(f, "****************")
+        for key in (
+            "model_type", "backbone", "checkpoint", "open_set",
+            "dataset", "input_size",
+            "threshold", "threshold_file",
+            "output_dir", "batch_size", "num_workers"
+        ):
+            print_write(f, f"{key}: {getattr(args, key)}")
+        print_write(f, "****************")
+
+    # prepare data
+    loader, info = load_dataset(
+        dataset=args.dataset,
+        model_type=args.model_type,
+        input_size=args.input_size,
+        batch_size=args.batch_size,
+        num_workers=args.num_workers
+    )
+    taglist, imglist, annot_file, img_root = \
+        info["taglist"], info["imglist"], info["annot_file"], info["img_root"]
+
+    # get class idxs
+    class_idxs = get_class_idxs(
+        model_type=args.model_type,
+        open_set=args.open_set,
+        taglist=taglist
+    )
+
+    # set up threshold(s)
+    thresholds = load_thresholds(
+        threshold=args.threshold,
+        threshold_file=args.threshold_file,
+        model_type=args.model_type,
+        open_set=args.open_set,
+        class_idxs=class_idxs,
+        num_classes=len(taglist)
+    )
+
+    # inference
+    if Path(logit_file).is_file():
+
+        logits = torch.load(logit_file)
+
+    else:
+
+        # load model
+        if args.model_type == "ram":
+            model = load_ram(
+                backbone=args.backbone,
+                checkpoint=args.checkpoint,
+                input_size=args.input_size,
+                taglist=taglist,
+                open_set=args.open_set,
+                class_idxs=class_idxs
+            )
+        else:
+            model = load_tag2text(
+                backbone=args.backbone,
+                checkpoint=args.checkpoint,
+                input_size=args.input_size
+            )
+
+        # inference
+        logits = torch.empty(len(imglist), len(taglist))
+        pos = 0
+        for imgs in tqdm(loader, desc="inference"):
+            if args.model_type == "ram":
+                out = forward_ram(model, imgs)
+            else:
+                out = forward_tag2text(model, class_idxs, imgs)
+            bs = imgs.shape[0]
+            logits[pos:pos+bs, :] = out.cpu()
+            pos += bs
+
+        # save logits, making threshold-tuning super fast
+        torch.save(logits, logit_file)
+
+    # filter with thresholds
+    pred_tags = []
+    for scores in logits.tolist():
+        pred_tags.append([
+            taglist[i] for i, s in enumerate(scores) if s >= thresholds[i]
+        ])
+
+    # generate result file
+    gen_pred_file(imglist, pred_tags, img_root, pred_file)
+
+    # evaluate and record
+    mAP, APs = get_mAP(logits.numpy(), annot_file, taglist)
+    CP, CR, Ps, Rs = get_PR(pred_file, annot_file, taglist)
+
+    with open(ap_file, "w", encoding="utf-8") as f:
+        f.write("Tag,AP\n")
+        for tag, AP in zip(taglist, APs):
+            f.write(f"{tag},{AP*100.0:.2f}\n")
+
+    with open(pr_file, "w", encoding="utf-8") as f:
+        f.write("Tag,Precision,Recall\n")
+        for tag, P, R in zip(taglist, Ps, Rs):
+            f.write(f"{tag},{P*100.0:.2f},{R*100.0:.2f}\n")
+
+    with open(summary_file, "w", encoding="utf-8") as f:
+        print_write(f, f"mAP: {mAP*100.0}")
+        print_write(f, f"CP: {CP*100.0}")
+        print_write(f, f"CR: {CR*100.0}")

inference_tag2text.py

@@ -0,0 +1,69 @@
+'''
+ * The Tag2Text Model
+ * Written by Xinyu Huang
+'''
+import argparse
+import numpy as np
+import random
+
+import torch
+
+from PIL import Image
+from ram.models import tag2text
+from ram import inference_tag2text as inference
+from ram import get_transform
+
+
+parser = argparse.ArgumentParser(
+    description='Tag2Text inferece for tagging and captioning')
+parser.add_argument('--image',
+                    metavar='DIR',
+                    help='path to dataset',
+                    default='images/1641173_2291260800.jpg')
+parser.add_argument('--pretrained',
+                    metavar='DIR',
+                    help='path to pretrained model',
+                    default='pretrained/tag2text_swin_14m.pth')
+parser.add_argument('--image-size',
+                    default=384,
+                    type=int,
+                    metavar='N',
+                    help='input image size (default: 448)')
+parser.add_argument('--thre',
+                    default=0.68,
+                    type=float,
+                    metavar='N',
+                    help='threshold value')
+parser.add_argument('--specified-tags',
+                    default='None',
+                    help='User input specified tags')
+
+
+if __name__ == "__main__":
+
+    args = parser.parse_args()
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    transform = get_transform(image_size=args.image_size)
+
+    # delete some tags that may disturb captioning
+    # 127: "quarter"; 2961: "back", 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one"
+    delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359]
+
+    #######load model
+    model = tag2text(pretrained=args.pretrained,
+                             image_size=args.image_size,
+                             vit='swin_b',
+                             delete_tag_index=delete_tag_index)
+    model.threshold = args.thre  # threshold for tagging
+    model.eval()
+
+    model = model.to(device)
+
+    image = transform(Image.open(args.image)).unsqueeze(0).to(device)
+
+    res = inference(image, model, args.specified_tags)
+    print("Model Identified Tags: ", res[0])
+    print("User Specified Tags: ", res[1])
+    print("Image Caption: ", res[2])

ram/__init__.py

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

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/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
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+vector cartoon illustration
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+violinist
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+waste container
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+water buffalo
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+water drop
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+water level
+water lily
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+water pipe
+water purifier
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+water sport
+water surface
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+watercolor illustration
+watercolor painting
+waterfall
+watering can
+watermark overlay stamp
+watermelon
+waterproof jacket
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+wave
+wax
+weapon
+wear
+weather
+vane
+web
+webcam
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+wedding ring
+wedding bouquet
+wedding cake
+wedding couple
+wedding invitation
+wedding party
+wedding photo
+wedding photographer
+wedding photography
+wedding reception
+wedge
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+welder
+well
+western food
+western restaurant
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+wetland
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+whale shark
+wheat
+wheat field
+wheel
+wheelchair
+wheelie
+whipped cream
+whisk
+whisker
+whiskey
+whistle
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+white house
+white wine
+whiteboard
+wicket
+wide
+wield
+wig
+Wii
+Wii controller
+wild
+wildebeest
+wildfire
+wildflower
+wildlife
+willow
+wind
+wind chime
+wind farm
+wind turbine
+windmill
+window
+window box
+window display
+window frame
+window screen
+window seat
+window sill
+wiper
+windshield
+windy
+wine bottle
+wine cooler
+wine cabinet
+wine cellar
+wine glass
+wine rack
+wine tasting
+winery
+wing
+winter
+winter melon
+winter morning
+winter scene
+winter sport
+winter storm
+wire
+wisteria
+witch
+witch hat
+wok
+wolf
+woman
+wood
+wood duck
+wood floor
+wood wall
+wood-burning stove
+wooden spoon
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+wool
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+writing brush
+writing desk
+yacht
+yak
+yard
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+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
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+安慰食物
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+指挥官
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+指南针
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+电脑机箱
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+概念车
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+调味品
+避孕套
+独立产权的公寓
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+锥形物
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+五彩纸屑
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+集装箱船
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+控制塔
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+交谈
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+可转换的
+输送机
+厨师/烹饪
+烹饪
+烹饪喷雾剂
+炊具
+凉的
+冷却器
+铜
+一本/一册
+珊瑚
+珊瑚礁
+粗绳
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+迪斯科灯秋
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+潜水员
+跳水板
+纸杯
+流行音乐播音员
+杜宾犬
+码头
+医生
+文件
+纪录片
+狗
+狗窝
+犬种
+狗项圈
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+狗窝
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+美元
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+洋娃娃
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+蜻蜓
+排水沟
+剧本
+戏剧电影
+画
+抽屉里
+图画/画画
+图钉
+辫子
+连衣裙/特定场合的服装
+礼帽
+正装衬衫
+皮鞋
+大礼服
+梳妆台
+更衣室
+运球
+漂移
+浮木
+钻
+饮品/喝
+饮用水
+开车
+司机
+车道
+无人机
+水滴/下降
+吊灯
+滴管
+干旱
+药物
+药店
+鼓
+鼓手
+鸡腿
+干的
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+鸭子
+鸭嘴兽
+小鸭子
+布基胶带
+伙计
+二重唱
+粗呢
+独木舟
+哑铃
+饺子
+沙丘
+扣篮
+榴莲
+黄昏
+灰尘
+垃圾车
+簸箕
+羽绒被
+DVD
+染料
+鹰
+耳朵
+御寒耳罩
+耳机
+耳塞
+耳环
+地震
+画架
+复活节
+复活节兔子
+复活节彩蛋
+吃
+餐厅
+泡芙
+日食
+生态系统
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+教育家
+鳗鱼
+蛋
+蛋卷
+蛋挞
+打蛋器
+白鹭
+埃菲尔铁塔
+橡皮筋
+上级
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+电子
+电子器件
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+电梯轿厢
+电梯门
+电梯大堂
+电梯井
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+大使馆
+装饰
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+会徽
+刺绣
+翡翠
+紧急
+紧急服务
+紧急车辆
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+帝国大厦
+搪瓷
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+茶几
+能源
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+订婚戒指
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+机舱
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+工程
+英国短毛猫
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+回车键
+演艺人员
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+娱乐中心
+入口
+入口大厅
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+设备
+橡皮擦
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+房地产
+河口
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+晚礼服
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+傍晚天空
+晚上的太阳
+事件
+常绿的
+母羊
+挖掘
+运动
+排气罩
+展览
+出口
+探险者
+爆炸
+延长线
+灭火器
+排气扇
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+眼影
+眉
+眼线笔
+布料
+纺织品商店
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+脸部特写
+蜜粉
+毛巾
+面巾纸架
+设施
+工厂
+工厂车间
+集市
+露天市场
+仙女
+猎鹰
+秋天
+家庭
+家庭轿车
+全家福
+家庭房
+风扇/扇子
+尖牙
+农场
+农民
+农民市场
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+时尚配饰
+时装设计师
+时尚的女孩
+时装插图
+时装大片
+时装模特
+时装表演
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+西式快餐
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+水龙头
+故障
+动物
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+传真
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+羽毛
+软呢帽
+饲料
+一餐
+饲养
+喂养的椅子
+猫科
+美洲狮
+栅栏
+芬达
+蕨类植物
+雪貂
+摩天轮
+渡船
+肥料
+节日
+纤维
+小说
+小说书
+田野/场地/野外
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+无花果
+打架
+花样滑冰运动员
+小雕像
+文件
+档案照片
+文件柜
+填满
+胶片相机
+电影导演
+电影格式
+电影首映礼
+电影制片人
+拍摄
+过滤器
+鳍
+手
+终点线
+冷杉
+冷杉树
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+火灾报警
+消防部门
+消防车
+消防通道
+消防水带
+火坑
+消防站
+爆竹
+消防队员
+壁炉
+烟花
+烟花表演
+急救箱
+鱼
+鱼船
+海鲜市场
+鱼塘
+鱼缸
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+钓鱼
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+渔网
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+渔村
+健身
+健身课程
+五个
+固定装置
+峡湾
+国旗
+旗杆
+小薄片
+火焰
+火烈鸟
+法兰绒
+拍打
+耀斑
+闪光
+烧瓶
+平
+比目鱼
+风味
+跳蚤
+跳蚤市场
+舰队
+飞行
+空中乘务员
+翻转
+触发器
+翻转图
+浮动
+群
+洪水
+地板/地面
+落地扇
+脚垫
+楼层平面图
+落地窗
+插花艺术
+花店
+牙线
+面粉
+流动
+花
+花篮
+花坛
+花箱
+花田
+花童
+花卉市场
+流体
+冲洗
+长笛
+飞
+飞行钓鱼
+传单
+马
+泡沫
+雾
+多雾的
+鹅肝酱
+箔纸
+折椅
+树叶
+民间艺术家
+民间舞蹈
+民间摇滚艺术家
+方旦糖
+火锅
+圣洗池
+食物
+食用色素
+美食广场
+食品加工机
+小吃摊
+快餐车
+桌上足球
+脚
+人行桥
+足球
+足球教练
+大学橄榄球赛
+足球比赛
+足球场
+足球比赛
+橄榄球头盔
+足球运动员
+足球场
+足球队
+小路
+脚印
+脚踏板
+台座
+鞋子
+故宫
+浅滩
+额头 
+森林 
+森林大火 
+森林地面 
+森林小路
+森林公路
+锻造
+餐叉
+叉车 
+表格
+园林
+队列/形成物
+F1方程式赛车
+堡垒
+碉堡
+追逐
+化石
+粉底
+喷泉
+钢笔
+狐狸 
+框架 
+雀斑 
+高速公路 
+卡车 
+法国
+法国斗牛犬 
+薯条 
+法式吐司 
+化妆水
+冰箱 
+炸鸡 
+煎蛋 
+炒饭 
+友谊
+飞盘 
+青蛙 
+霜 
+结霜 
+严寒
+结冰
+水果 
+水果蛋糕 
+水果盘 
+水果市场 
+水果沙拉 
+水果摊
+果树 
+水果商店 
+油炸食品
+煎锅 
+软糖
+燃料 
+吸烟罩
+有趣的 
+葬礼 
+真菌 
+漏斗 
+毛皮衣服
+毛皮大衣 
+家具 
+蒲团 
+小工具 
+枪口
+星云/星系
+美术馆
+游戏 
+游戏棋盘
+游戏手柄
+火腿 
+团伙
+车库 
+车库门 
+手工模型
+垃圾 
+花园 
+花园芦笋 
+橡胶软管 
+花园蜘蛛
+园丁 
+园艺 
+加菲猫
+滴水嘴 
+花环 
+大蒜 
+衣服
+气体 
+加油站 
+煤气炉 
+防毒面具
+收集 
+聚集
+测量仪器
+露台 
+齿轮 
+壁虎 
+艺妓
+凝胶 
+百货商店 
+发电机 
+天竺葵 
+幽灵
+礼物 
+礼品袋 
+礼品篮 
+礼物盒 
+礼品卡 
+礼品商店 
+礼物包装 
+演唱会
+杜松子酒
+姜 
+姜饼 
+姜饼屋 
+银杏树 
+长颈鹿 
+女孩 
+给 
+冰川 
+角斗士
+玻璃珠 
+玻璃瓶 
+玻璃碗 
+玻璃箱
+玻璃建筑 
+玻璃门 
+玻璃地板 
+玻璃屋
+玻璃罐 
+玻璃板 
+玻璃桌子 
+玻璃花瓶 
+玻璃墙
+玻璃窗 
+眼镜 
+光滑面
+滑翔机 
+地球 
+手套 
+发光 
+汤圆 
+去 
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+球门
+守门员 
+山羊 
+羊奶酪
+戈壁 
+护目镜/墨镜
+黄金 
+金牌 
+金门大桥 
+金毛猎犬 
+金鱼 
+高尔夫运动
+高尔夫球帽 
+高尔夫球车 
+高尔夫球杆
+高尔夫球场 
+高尔夫球手 
+鹅 
+大猩猩 
+哥特式
+葫芦 
+政府 
+政府机构 
+礼服
+毕业生
+毕业典礼
+谷物
+逆戟鲸 
+大奖赛 
+祖父 
+祖母 
+祖父母 
+花岗岩 
+格兰诺拉麦片 
+葡萄 
+西柚
+葡萄酒
+草 
+蚱蜢 
+草原 
+长满草的 
+擦菜器
+坟墓 
+碎石
+墓��� 
+肉汁 
+调味汁瓶
+灰色
+吃草 
+放牧 
+绿色 
+绿色植物 
+欢迎
+问候 
+贺卡 
+灰狗 
+网格 
+筛子
+烧烤架
+格栅 
+烤鳗鱼 
+磨 
+研磨机
+粗燕麦粉 
+杂货袋
+洞穴
+地松鼠 
+群体
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+小树林
+生长
+牛油果酱
+警卫 
+看门狗 
+宾馆 
+客房 
+指南 
+豚鼠 
+吉他 
+吉他手 
+海湾 
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+枪 
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+古筝 
+健身房 
+体操运动员 
+栖息地 
+黑客 
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+头发 
+头发颜色 
+发胶 
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+发型 
+发夹 
+发网 
+发夹 
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+一半 
+礼堂
+万圣节 
+万圣节服装 
+万圣节南瓜 
+露背装 
+汉堡 
+汉堡包
+哈密瓜 
+锤子
+吊床 
+阻碍 
+仓鼠 
+烘手机
+放大镜
+擦手巾 
+手提包 
+手球 
+手铐
+手枪 
+手帕 
+把手
+手锯 
+握手 
+倒立 
+手写
+汉服 
+悬挂
+飞机库
+衣架
+幸福 
+海港
+斑海豹
+硬摇滚艺术家 
+精装书 
+建筑工人
+硬件 
+五金店 
+硬木 
+硬木地板 
+口琴 
+管风琴 
+羽管键琴
+收获 
+收割机 
+坐垫/搁脚凳/草丛
+帽子 
+帽盒 
+双簧管
+山楂 
+干草 
+干草地
+榛子
+头 
+主教练 
+大灯
+床头板 
+头饰 
+海岬 
+总部 
+听力 
+心脏
+心形
+热能
+加热器 
+帚石楠
+树篱
+刺猬 
+脚后跟
+直升机 
+直升机机场
+头盔 
+帮助 
+母鸡 
+指甲花 
+药草
+兽群
+寄居蟹 
+英雄 
+苍鹭
+芙蓉花
+芙蓉花 
+隐藏/隐蔽处
+高杠
+高跟鞋 
+高地 
+突出 
+徒步旅行 
+徒步旅行者 
+徒步靴
+登山设备 
+山丘
+丘陵地
+别墅
+山坡
+印度教寺庙 
+铰链 
+臀部 
+嘻哈艺人 
+河马 
+历史学家 
+历史遗迹
+历史 
+曲棍球 
+冰球馆
+曲棍球比赛 
+曲棍球运动员 
+曲棍球棒
+锄头 
+洞 
+假日
+冬青树
+海参 
+家/住宅
+家用电器
+基地 
+家居装饰 
+室内设计 
+内政部
+家庭影院 
+家庭作业 
+鹰嘴豆泥
+蜂蜜 
+蜂窝
+蜜月
+风帽
+连帽衫 
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ram/data/tag_list.txt

@@ -0,0 +1,3429 @@
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+cereal
+cafe
+cartoon character
+sugar
+fiction book
+glass floor
+umpire
+guitar
+hamster
+protester
+airplane
+garment
+blazer
+railway line
+wedding
+shoe box
+parking lot
+construction
+graduation ceremony
+tram
+telescope
+copper
+pain
+autumn forest
+guest house
+partner
+crayon
+dip
+boot
+corridor
+computer keyboard
+hockey player
+chicken coop
+bus station
+gathering
+ankle
+bunk bed
+wood table
+football coach
+monarch
+pharmacy
+legging
+mannequin
+female
+train track
+stack
+canopy
+design element
+grandmother
+symbol
+beach hut
+zucchini
+bomb
+businessman
+skyscraper
+tongue
+case
+sparkle
+highland
+ballroom
+prom
+estate
+customer
+archipelago
+cheese
+debate
+carriage
+bulldozer
+pumpkin
+sitting room
+gas station
+wedding reception
+camp
+dog bed
+tower
+property
+river bed
+pop latin artist
+fridge
+wine glass
+coast
+beer
+tow truck
+fire truck
+mountain bike
+thigh
+heron
+boat ride
+gondola
+turquoise
+lake
+llama
+kitty
+tin
+waiting room
+coffee cup
+socialite
+guard
+tap
+waterway
+forehead
+list
+erosion
+box
+sea lion
+pollen
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+wasp
+salon
+tennis tournament
+flower box
+aquarium
+rain cloud
+clothing store
+lead singer
+cupcake
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+lettering
+sport facility
+dance
+dog house
+nature
+football
+rooster
+footballer
+railway track
+crowd
+fishing rod
+silhouette
+wind turbine
+sari
+bus window
+cloud
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+event
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+fashion menswear milan week
+news
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+print
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+bunny
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+dinner
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+thread
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+plastic
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+urinal
+seagull
+volunteer
+hockey
+fir tree
+pollution
+trial
+collar
+area
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+circus
+yogurt
+orangutan
+viaduct
+comedian
+drone
+scissor
+pop rock artist
+biscuit
+panda
+water feature
+air balloon
+remote control
+watercolor painting
+show
+walk
+post office
+bike path
+rap gangsta artist
+microphone
+crack
+sunset sky
+glass
+tv show
+cartoon style
+stripe
+foyer
+signal
+calligraphy
+bulb
+gardener
+coffee bean
+spider
+tapestry
+city skyline
+necklace
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+traveler
+veteran
+frosting
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+tennis court
+tank top
+butterfly house
+mist
+drummer
+water level
+scale
+baseball glove
+music video performer
+champagne
+camping
+clothing
+water drop
+telephone box
+pen
+morning mist
+fire engine
+porch
+opening ceremony
+style
+palm tree
+fashion show
+universe
+scratch
+axe
+ottoman
+explosion
+rib
+boutique
+game
+cucumber
+fruit
+stone bridge
+nature reserve
+track
+train window
+punch
+telephone pole
+velvet
+sauce
+moon
+contrast
+flamingo
+bat
+vending machine
+ship
+equestrian
+shade
+comforter
+pallet
+sparrow
+wii
+glaze
+grocery
+steeple
+soccer player
+contract
+advertising
+runner
+chimpanzee
+world
+seat
+project
+chihuahua
+bubble
+willow
+pedestal
+soul hip hop artist
+curb
+drawer
+leaf
+banner
+launch party
+coach
+government
+snowball
+toy
+portrait
+doctor
+whiteboard
+electronic
+tiger
+graffiti
+column
+nightstand
+whistle
+maxi dress
+bench
+wetsuit
+bird feeder
+football game
+basketball
+class
+bathroom door
+store window
+text message
+wreath
+street view
+binocular
+pet
+facade
+drought
+lemon
+new year
+night view
+airplane window
+specie
+rule
+jaw
+wheat field
+diet
+pop artist
+habitat
+screenshot
+scoreboard
+shore
+mane
+quilt
+ski lift
+orchid
+turban
+christmas
+airport
+marina
+glass door
+glass bottle
+restaurant
+conductor
+logo
+sleep
+tape
+tomato
+river bank
+lilac
+tooth
+training
+pottery
+shop
+steam engine
+mason jar
+base
+procession
+border
+shoot
+footprint
+hotdog
+bull
+stocking
+recreation
+automobile model
+design
+country pop artist
+river
+retriever
+department store
+auditorium
+sport car
+supermarket
+belt
+cricket
+window box
+dress shirt
+letter
+residence
+megaphone
+pant
+wildfire
+bird nest
+crab
+swimsuit
+candle
+funeral
+mill
+national park
+plant
+cop
+power line
+perch
+blue
+finger
+ferris wheel
+globe
+skateboard
+helmet
+movie theater
+uniform
+hammer
+material
+kid
+well
+butterfly
+sideline
+fashion fall show
+planet earth
+lift
+male
+sauna
+gray
+flour
+sand sculpture
+program
+cabinet
+infant
+wheel
+aircraft model
+dough
+garlic
+skate
+arrow
+wrapping paper
+ripple
+lamp
+iron
+banknote
+beaver
+ferry
+courtyard
+bassist
+countryside
+steak
+comfort
+boxer
+laundry room
+campsite
+brick building
+golf
+subway
+headphone
+fort
+handbag
+drum
+flood
+saddle
+bass
+labyrinth
+needle
+sun ray
+app
+menu
+president
+cardigan
+dandelion
+wetland
+ice hockey player
+number
+city hall
+fishing
+portrait session
+pug
+key
+art print
+minister
+hurdle
+emergency
+painting artist
+flag pole
+evening
+purse
+recipe
+golf ball
+coloring book
+mountain peak
+senior
+holiday
+bud
+cousin
+pantry
+lap
+skin
+flag
+tissue paper
+ridge
+wire fence
+surfer
+climber
+photograph
+sewing machine
+cooler
+actress
+apple tree
+cancer
+starfish
+automobile make
+dumbbell
+brace
+tunnel
+window
+paint artist
+composition
+school student
+condo
+convertible
+cushion
+selfie
+territory
+guide
+tree
+court
+shrimp
+stone house
+dress
+eyelash
+juice
+broccoli
+chain
+tourism
+mountain top
+concept car
+film premiere
+light bulb
+cafeteria
+badge
+flower bed
+theater
+root
+racecar driver
+basketball boy game
+glove
+skyline
+wall
+glacier
+airport terminal
+bug
+trim
+railway station
+briefcase
+flat
+fountain
+person
+lane
+asparagus
+art
+lantern
+dishwasher
+director
+snake
+lecture
+game controller
+tree branch
+pub
+bathing suit
+queue
+belly
+poppy
+bow
+pitcher
+ice cream cone
+cave
+candy
+road bridge
+host
+traffic jam
+earring
+file
+foot
+watermark overlay stamp
+mailbox
+supercar
+railing
+bedroom
+seafood
+waffle
+bronze statue
+plan
+flow
+marble
+basketball game
+automobile
+scene
+cypress tree
+soldier
+skateboarder
+glass building
+cherry tree
+pump
+grain
+wildebeest
+loop
+frame
+bathtub
+saxophone
+diver
+stalk
+lily
+bead
+alley
+flock
+family room
+manufacturing
+pointer
+worker
+navy
+potato
+teacher
+photography
+dolly
+boardwalk
+water fountain
+athlete
+side dish
+bay
+ice hockey
+phone
+hero
+face
+gold medal
+blind
+swamp
+researcher
+swim
+meatball
+iguana
+leather jacket
+jellyfish
+site
+smoke
+traffic signal
+melon
+beetle
+calculator
+skirt
+plantation
+sculptor
+barrier
+catcher
+security guard
+sketch
+awning
+steering wheel
+mountain view
+bus stop
+pool
+leg
+spotlight
+apron
+mineral
+inlet
+sleeve
+torch
+emotion
+march
+police officer
+performance
+lamp post
+fishing boat
+summer
+presentation
+saucer
+suitcase
+supermodel
+goalkeeper
+shrub
+rock artist
+document
+beach house
+man
+blue artist
+cigar
+railroad track
+gown
+mosaic
+bungalow
+alphabet
+baseball field
+shed
+pedestrian
+rail
+soap
+kitchen counter
+dessert
+dunk
+blossom
+conversation
+fruit market
+glass jar
+military
+beer bottle
+photographer
+tennis racket
+competition
+escalator
+bell tower
+stilt
+ballerina
+television
+feather
+fence post
+rear
+dahlia
+red carpet
+tub
+hole
+fortress
+pack
+telephone
+cardboard
+city park
+platform
+college student
+arch bridge
+wind
+blender
+bloom
+ice rink
+birthday
+raven
+fairy
+embankment
+hall
+flower shop
+suburb
+barrel
+biker
+steam
+dragonfly
+formation
+electricity
+business people
+symmetry
+walkway
+fisherman
+gas mask
+loch
+youth
+hanger
+dot
+fish
+street market
+animation film
+crime fiction film
+boar
+emblem
+halloween costume
+kangaroo
+couple
+spoon
+squirrel
+neon sign
+sky
+office desk
+beauty salon
+breakwater
+fashion look
+toaster
+author
+news conference
+outdoor
+canoe
+dragon
+tool
+shopping centre
+ladybug
+swimming pool
+landscaping
+ski pole
+red
+truck
+fly
+temple
+level
+sunday
+railroad bridge
+car mirror
+lawn mower
+flute
+aircraft carrier
+fashion menswear london week
+sunshine
+tile floor
+skull
+fossil
+flower arrangement
+diaper
+sea turtle
+cherry blossom
+fireman
+shack
+lens
+waiter
+animal
+basement
+snow
+autumn park
+glass box
+kick
+head
+anniversary
+vine
+back
+paper lantern
+fish tank
+cellphone
+silk
+coral
+notebook
+photo
+gazebo
+ketchup
+driver
+farmer
+bonfire
+chestnut
+photoshoot
+football field
+olive tree
+pheasant
+sandal
+toilet
+fireplace
+music
+deity
+fish market
+fig
+bell
+neck
+grave
+villa
+cyclist
+crate
+grey
+asphalt road
+soccer
+hostel
+municipality
+courthouse
+roof
+end table
+pot
+sedan
+structure
+folk artist
+sport
+sport team
+protest
+syringe
+fashion designer
+jersey
+heart shape
+kayak
+stare
+sit with
+direct
+read
+photograph
+spin
+teach
+laugh
+carve
+grow on
+warm
+watch
+stretch
+smell
+decorate
+shine
+light
+dance
+send
+park
+chase
+collect
+lead
+kiss
+lead to
+lick
+smile
+cheer
+sit
+point
+block
+rock
+drop
+cut
+ski
+wrap
+lose
+serve
+provide
+sleep
+dress
+embrace
+burn
+pack
+stir
+create
+touch
+wash
+stick
+reveal
+shop
+train
+paint
+groom
+hunt
+bloom
+play
+pay
+brush
+shoot
+hold
+picture
+carry
+sip
+contain
+turn
+pour
+pitch
+give
+add
+blow
+look in
+show
+walk
+illuminate
+kneel
+cover
+drag
+post
+present
+fit
+operate
+fish
+race
+write
+deliver
+peel
+push
+run
+sit around
+buy
+jump
+walk on
+attend
+clean
+sell
+ride on
+mount
+host
+dry
+plant
+sing
+row
+shake
+perch
+ride
+fight
+skateboard
+live
+call
+surround
+practice
+play on
+work on
+step
+relax
+hit
+fall in
+flow
+greet
+launch
+wear
+hang on
+drive
+sit in
+break
+learn
+fly
+connect
+display
+locate
+compete
+go for
+sail
+lift
+toast
+help
+run on
+reflect
+pose
+scratch
+frame
+dribble
+herd
+enter
+exit
+place
+inspect
+build
+pick
+fill
+grind
+skate
+offer
+float
+sit by
+stand
+release
+rest
+singe
+climb
+tie
+mark
+lay
+stand around
+capture
+set
+land
+swinge
+run in
+kick
+lean
+head
+sign
+approach
+swim
+close
+crash
+control
+fall
+remove
+repair
+open
+appear
+travel
+load
+miss
+check
+surf
+moor
+smoke
+drink
+board
+seat
+feed
+rise
+sit on
+swing
+grow
+strike
+date
+slide
+share
+graze
+jump in
+lie
+extrude
+roll
+move
+gather
+eat
+pull
+run through
+squeeze
+lay on
+draw
+play with
+wave
+assemble
+perform
+march
+score
+attach
+adjust
+hang
+hug
+sleep on
+throw
+live in
+talk
+pet
+work
+run with
+see
+flip
+catch
+cook
+receive
+celebrate
+look
+classic
+bridal
+indoor
+industrial
+teenage
+mini
+grassy
+aged
+long
+warm
+light
+handsome
+happy
+three
+pregnant
+circular
+urban
+silver
+ceramic
+3d
+green
+blonde
+golden
+dark
+tropical
+ripe
+deep
+fat
+musical
+giant
+medical
+medieval
+bare
+stunning
+bold
+geographical
+huge
+plastic
+foggy
+stormy
+gothic
+biological
+empty
+clear
+antique
+pink
+steep
+brown
+striped
+aerial
+rainy
+cool
+flying
+commercial
+purple
+trendy
+blank
+haired
+dead
+wooden
+flat
+high
+beige
+panoramic
+angry
+dozen
+rural
+solar
+big
+small
+stained
+thick
+many
+fresh
+clean
+strong
+abstract
+crowded
+retro
+dry
+gorgeous
+martial
+modern
+blue
+cloudy
+low
+four
+outdoor
+single
+much
+beautiful
+snowy
+pretty
+new
+short
+sunny
+closed
+rocky
+red
+two
+double
+male
+gray
+five
+colorful
+automotive
+various
+one
+old
+rusty
+tall
+wild
+narrow
+natural
+several
+frozen
+textured
+lush
+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[0],tags_chinese[0]
+
+
+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/ram.py

@@ -0,0 +1,273 @@
+'''
+ * 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'
+            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 generate_tag(self,
+                 image,
+                 threshold=0.68,
+                 tag_input=None,
+                 ):
+            
+        label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))
+
+        image_embeds = self.image_proj(self.visual_encoder(image))
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        # recognized image tags using image-tag recogntiion decoder
+        image_cls_embeds = image_embeds[:, 0, :]
+        image_spatial_embeds = image_embeds[:, 1:, :]
+
+        bs = image_spatial_embeds.shape[0]
+        label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1)
+        tagging_embed = self.tagging_head(
+            encoder_embeds=label_embed,
+            encoder_hidden_states=image_embeds,
+            encoder_attention_mask=image_atts,
+            return_dict=False,
+            mode='tagging',
+        )
+
+        logits = self.fc(tagging_embed[0]).squeeze(-1)
+
+        targets = torch.where(
+            torch.sigmoid(logits) > self.class_threshold.to(image.device),
+            torch.tensor(1.0).to(image.device),
+            torch.zeros(self.num_class).to(image.device))
+
+        tag = targets.cpu().numpy()
+        tag[:,self.delete_tag_index] = 0
+        tag_output = []
+        tag_output_chinese = []
+        for b in range(bs):
+            index = np.argwhere(tag[b] == 1)
+            token = self.tag_list[index].squeeze(axis=1)
+            tag_output.append(' | '.join(token))
+            token_chinese = self.tag_list_chinese[index].squeeze(axis=1)
+            tag_output_chinese.append(' | '.join(token_chinese))
+
+
+        return tag_output, tag_output_chinese
+
+    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/swin_transformer.py

@@ -0,0 +1,654 @@
+# --------------------------------------------------------
+# Swin Transformer
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu
+# --------------------------------------------------------
+
+import numpy as np
+from scipy import interpolate
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def forward(self, x):
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
+
+
+class SwinTransformer(nn.Module):
+    r""" Swin Transformer
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 224
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, **kwargs):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
+                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                                 patches_resolution[1] // (2 ** i_layer)),
+                               depth=depths[i_layer],
+                               num_heads=num_heads[i_layer],
+                               window_size=window_size,
+                               mlp_ratio=self.mlp_ratio,
+                               qkv_bias=qkv_bias, qk_scale=qk_scale,
+                               drop=drop_rate, attn_drop=attn_drop_rate,
+                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                               norm_layer=norm_layer,
+                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                               use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        self.norm = norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward(self, x, idx_to_group_img=None, image_atts=None, **kwargs):
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x)
+
+        x = self.norm(x)  # B L C
+
+        x_cls = self.avgpool(x.transpose(1, 2))  # B C 1
+
+        if idx_to_group_img is None:
+            return torch.cat([x_cls.transpose(1, 2), x], dim=1)
+        else:
+            x_bs = torch.gather(x, dim=0, index=idx_to_group_img.view(-1, 1, 1).expand(-1, x.shape[1], x.shape[2]))
+            weights = image_atts[:, 1:].unsqueeze(2)  # B L 1
+            x_bs_cls = torch.sum((weights * x_bs).transpose(1, 2), dim=-1, keepdim=True)   # B C 1
+            x_bs_cls = x_bs_cls / torch.sum(weights.transpose(1, 2), dim=-1, keepdim=True)  # avgpool
+
+            return torch.cat([x_bs_cls.transpose(1, 2), x_bs], dim=1), \
+                   torch.cat([x_cls.transpose(1, 2), x], dim=1)
+
+    def flops(self):
+        flops = 0
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
+        flops += self.num_features * self.num_classes
+        return flops
+
+
+def interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''):
+    # from: https://github.com/microsoft/unilm/blob/8a0a1c1f4e7326938ea7580a00d56d7f17d65612/beit/run_class_finetuning.py#L348
+
+    # rel_pos_bias: relative_position_bias_table
+    src_num_pos, num_attn_heads = rel_pos_bias.size()
+
+    num_extra_tokens = 0
+    src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
+    dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
+    if src_size != dst_size:
+        print("Position interpolate %s from %dx%d to %dx%d" % (param_name, src_size, src_size, dst_size, dst_size))
+
+        # extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
+        # rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
+
+        def geometric_progression(a, r, n):
+            return a * (1.0 - r ** n) / (1.0 - r)
+
+        left, right = 1.01, 1.5
+        while right - left > 1e-6:
+            q = (left + right) / 2.0
+            gp = geometric_progression(1, q, src_size // 2)
+            if gp > dst_size // 2:
+                right = q
+            else:
+                left = q
+
+        # if q > 1.090307:
+        #     q = 1.090307
+
+        dis = []
+        cur = 1
+        for i in range(src_size // 2):
+            dis.append(cur)
+            cur += q ** (i + 1)
+
+        r_ids = [-_ for _ in reversed(dis)]
+
+        x = r_ids + [0] + dis
+        y = r_ids + [0] + dis
+
+        t = dst_size // 2.0
+        dx = np.arange(-t, t + 0.1, 1.0)
+        dy = np.arange(-t, t + 0.1, 1.0)
+
+        # print("Original positions = %s" % str(x))
+        # print("Target positions = %s" % str(dx))
+
+        all_rel_pos_bias = []
+
+        for i in range(num_attn_heads):
+            z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
+            f = interpolate.interp2d(x, y, z, kind='cubic')
+            all_rel_pos_bias.append(
+                torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
+
+        rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
+
+    return rel_pos_bias

ram/models/tag2text.py

@@ -0,0 +1,370 @@
+'''
+ * 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(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 +1,326 @@
+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.add_special_tokens({'bos_token': '[DEC]'})
+    tokenizer.add_special_tokens({'additional_special_tokens': ['[ENC]']})
+    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
+    return tokenizer
+
+
+def create_vit(vit,
+               image_size,
+               use_grad_checkpointing=False,
+               ckpt_layer=0,
+               drop_path_rate=0):
+
+    assert vit in ['base', 'large'], "vit parameter must be base or large"
+    if vit == 'base':
+        vision_width = 768
+        visual_encoder = VisionTransformer(
+            img_size=image_size,
+            patch_size=16,
+            embed_dim=vision_width,
+            depth=12,
+            num_heads=12,
+            use_grad_checkpointing=use_grad_checkpointing,
+            ckpt_layer=ckpt_layer,
+            drop_path_rate=0 or drop_path_rate)
+    elif vit == 'large':
+        vision_width = 1024
+        visual_encoder = VisionTransformer(
+            img_size=image_size,
+            patch_size=16,
+            embed_dim=vision_width,
+            depth=24,
+            num_heads=16,
+            use_grad_checkpointing=use_grad_checkpointing,
+            ckpt_layer=ckpt_layer,
+            drop_path_rate=0.1 or drop_path_rate)
+    return visual_encoder, vision_width
+
+
+def is_url(url_or_filename):
+    parsed = urlparse(url_or_filename)
+    return parsed.scheme in ("http", "https")
+
+
+def load_checkpoint(model, url_or_filename):
+    if is_url(url_or_filename):
+        cached_file = download_cached_file(url_or_filename,
+                                           check_hash=False,
+                                           progress=True)
+        checkpoint = torch.load(cached_file, map_location='cpu')
+    elif os.path.isfile(url_or_filename):
+        checkpoint = torch.load(url_or_filename, map_location='cpu')
+    else:
+        raise RuntimeError('checkpoint url or path is invalid')
+
+    state_dict = checkpoint['model']
+
+    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(
+        state_dict['visual_encoder.pos_embed'], model.visual_encoder)
+    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
+        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(
+            state_dict['visual_encoder_m.pos_embed'], model.visual_encoder_m)
+    for key in model.state_dict().keys():
+        if key in state_dict.keys():
+            if state_dict[key].shape != model.state_dict()[key].shape:
+                del state_dict[key]
+
+    msg = model.load_state_dict(state_dict, strict=False)
+    print('load checkpoint from %s' % url_or_filename)
+    return model, msg
+
+
+def load_checkpoint_swinbase(model, url_or_filename, kwargs):
+    if kwargs['image_size'] == 224:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'
+    elif kwargs['image_size'] == 384:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'
+    window_size = read_json(vision_config_path)['window_size']
+    print('--------------')
+    print(url_or_filename)
+    print('--------------')
+    if is_url(url_or_filename):
+        cached_file = download_cached_file(url_or_filename,
+                                           check_hash=False,
+                                           progress=True)
+        checkpoint = torch.load(cached_file, map_location='cpu')
+    elif os.path.isfile(url_or_filename):
+        checkpoint = torch.load(url_or_filename, map_location='cpu')
+    else:
+        raise RuntimeError('checkpoint url or path is invalid')
+
+    state_dict = checkpoint['model']
+
+    for k in list(state_dict.keys()):
+        if 'relative_position_bias_table' in k:
+            dst_num_pos = (2 * window_size - 1)**2
+            state_dict[k] = interpolate_relative_pos_embed(state_dict[k],
+                                                           dst_num_pos,
+                                                           param_name=k)
+        elif ('relative_position_index' in k) or ('attn_mask' in k):
+            del state_dict[k]
+        elif "vision_multi" in k:
+            state_dict[k.replace("vision_multi",
+                                 "tagging_head")] = state_dict.pop(k)
+
+    msg = model.load_state_dict(state_dict, strict=False)
+    print('load checkpoint from %s' % url_or_filename)
+    return model, msg
+
+
+def load_checkpoint_swinlarge(model, url_or_filename, kwargs):
+    if kwargs['image_size'] == 224:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json'
+    elif kwargs['image_size'] == 384:
+        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json'
+    window_size = read_json(vision_config_path)['window_size']
+    print('--------------')
+    print(url_or_filename)
+    print('--------------')
+    if is_url(url_or_filename):
+        cached_file = download_cached_file(url_or_filename,
+                                           check_hash=False,
+                                           progress=True)
+        checkpoint = torch.load(cached_file, map_location='cpu')
+    elif os.path.isfile(url_or_filename):
+        checkpoint = torch.load(url_or_filename, map_location='cpu')
+    else:
+        raise RuntimeError('checkpoint url or path is invalid')
+
+    state_dict = checkpoint['model']
+
+    for k in list(state_dict.keys()):
+        if 'relative_position_bias_table' in k:
+            dst_num_pos = (2 * window_size - 1)**2
+            state_dict[k] = interpolate_relative_pos_embed(state_dict[k],
+                                                           dst_num_pos,
+                                                           param_name=k)
+        elif ('relative_position_index' in k) or ('attn_mask' in k):
+            del state_dict[k]
+        elif "vision_multi" in k:
+            state_dict[k.replace("vision_multi",
+                                 "tagging_head")] = state_dict.pop(k)
+
+    msg = model.load_state_dict(state_dict, strict=False)
+    print('load checkpoint from %s' % url_or_filename)
+    return model, msg
+
+
+# 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 +1,10 @@
+from torchvision.transforms import Normalize, Compose, Resize, ToTensor
+
+
+def get_transform(image_size=384):
+    return Compose([
+        lambda image: image.convert("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 +1,332 @@
+
+
+
+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")
+    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 +1,10 @@
+timm==0.4.12
+transformers==4.15.0
+fairscale==0.4.4
+pycocoevalcap
+torch
+torchvision
+Pillow
+scipy
+git+https://github.com/openai/CLIP.git
+git+https://github.com/xinyu1205/recognize-anything.git

tag2text.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5276c154e450ed5276c104d049bb1e1d52a137eeadfd2a281ea460d8c9d8d3a
+size 4478785381