remove Grounded-SAM part due to limited resource

README.md

@@ -10,12 +10,10 @@ pinned: false
 license: mit
 ---
 
-# Recognize Anything Model + Grounded-SAM DEMO
+# Recognize Anything Model / Tag2Text Model
 
 Please refer to these homepages for details of these projects:
 
 - Recognize Anthing Model: [https://recognize-anything.github.io/](https://recognize-anything.github.io/)
 
 - Tag2Text Model: [https://https://tag2text.github.io/](https://https://tag2text.github.io/)
-
-- Grounded-SAM: [https://github.com/IDEA-Research/Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)

app.py

@@ -1,307 +1,78 @@
-import os
-import sys
-from pathlib import Path
+import torch
+from ram import get_transform, inference_ram, inference_tag2text
+from ram.models import ram, tag2text_caption
 
-# setup Grouded-Segment-Anything
-# building GroundingDINO requires torch but imports it before installing,
-# so directly installing in requirements.txt causes dependency error.
-# 1. build with "-e" option to keep the bin file in ./GroundingDINO/groundingdino/, rather than in site-package dir.
-os.system("pip install -e ./GroundingDINO/")
-# 2. for unknown reason, "import groundingdino" will fill due to unable to find the module, even after installing.
-# add ./GroundingDINO/ to PATH, so package "groundingdino" can be imported.
-sys.path.append(str(Path(__file__).parent / "GroundingDINO"))
-
-import random  # noqa: E402
-
-import cv2  # noqa: E402
-import groundingdino.datasets.transforms as T  # noqa: E402
-import numpy as np  # noqa: E402
-import torch  # noqa: E402
-import torchvision  # noqa: E402
-import torchvision.transforms as TS  # noqa: E402
-from groundingdino.models import build_model  # noqa: E402
-from groundingdino.util.slconfig import SLConfig  # noqa: E402
-from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap  # noqa: E402
-from PIL import Image, ImageDraw, ImageFont  # noqa: E402
-from ram import inference_ram  # noqa: E402
-from ram import inference_tag2text  # noqa: E402
-from ram.models import ram  # noqa: E402
-from ram.models import tag2text_caption  # noqa: E402
-from segment_anything import SamPredictor, build_sam  # noqa: E402
-
-
-# args
-config_file = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py"
 ram_checkpoint = "./ram_swin_large_14m.pth"
 tag2text_checkpoint = "./tag2text_swin_14m.pth"
-grounded_checkpoint = "./groundingdino_swint_ogc.pth"
-sam_checkpoint = "./sam_vit_h_4b8939.pth"
-box_threshold = 0.25
-text_threshold = 0.2
-iou_threshold = 0.5
-device = "cpu"
-
-
-def load_model(model_config_path, model_checkpoint_path, device):
-    args = SLConfig.fromfile(model_config_path)
-    args.device = device
-    model = build_model(args)
-    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
-    load_res = model.load_state_dict(
-        clean_state_dict(checkpoint["model"]), strict=False)
-    print(load_res)
-    _ = model.eval()
-    return model
-
-
-def get_grounding_output(model, image, caption, box_threshold, text_threshold, device="cpu"):
-    caption = caption.lower()
-    caption = caption.strip()
-    if not caption.endswith("."):
-        caption = caption + "."
-    model = model.to(device)
-    image = image.to(device)
-    with torch.no_grad():
-        outputs = model(image[None], captions=[caption])
-    logits = outputs["pred_logits"].cpu().sigmoid()[0]  # (nq, 256)
-    boxes = outputs["pred_boxes"].cpu()[0]  # (nq, 4)
-    logits.shape[0]
-
-    # filter output
-    logits_filt = logits.clone()
-    boxes_filt = boxes.clone()
-    filt_mask = logits_filt.max(dim=1)[0] > box_threshold
-    logits_filt = logits_filt[filt_mask]  # num_filt, 256
-    boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
-    logits_filt.shape[0]
-
-    # get phrase
-    tokenlizer = model.tokenizer
-    tokenized = tokenlizer(caption)
-    # build pred
-    pred_phrases = []
-    scores = []
-    for logit, box in zip(logits_filt, boxes_filt):
-        pred_phrase = get_phrases_from_posmap(
-            logit > text_threshold, tokenized, tokenlizer)
-        pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
-        scores.append(logit.max().item())
-
-    return boxes_filt, torch.Tensor(scores), pred_phrases
-
-
-def draw_mask(mask, draw, random_color=False):
-    if random_color:
-        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153)
-    else:
-        color = (30, 144, 255, 153)
-
-    nonzero_coords = np.transpose(np.nonzero(mask))
-
-    for coord in nonzero_coords:
-        draw.point(coord[::-1], fill=color)
-
-
-def draw_box(box, draw, label):
-    # random color
-    color = tuple(np.random.randint(0, 255, size=3).tolist())
-    line_width = int(max(4, min(20, 0.006*max(draw.im.size))))
-    draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color,  width=line_width)
-
-    if label:
-        font_path = os.path.join(
-            cv2.__path__[0], 'qt', 'fonts', 'DejaVuSans.ttf')
-        font_size = int(max(12, min(60, 0.02*max(draw.im.size))))
-        font = ImageFont.truetype(font_path, size=font_size)
-        if hasattr(font, "getbbox"):
-            bbox = draw.textbbox((box[0], box[1]), str(label), font)
-        else:
-            w, h = draw.textsize(str(label), font)
-            bbox = (box[0], box[1], w + box[0], box[1] + h)
-        draw.rectangle(bbox, fill=color)
-        draw.text((box[0], box[1]), str(label), fill="white", font=font)
-
-        draw.text((box[0], box[1]), label, font=font)
+image_size = 384
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 
 @torch.no_grad()
-def inference(
-    raw_image, specified_tags, do_det_seg,
-    tagging_model_type, tagging_model, grounding_dino_model, sam_model
-):
+def inference(raw_image, specified_tags, tagging_model_type, tagging_model, transform):
     print(f"Start processing, image size {raw_image.size}")
-    raw_image = raw_image.convert("RGB")
-
-    # run tagging model
-    normalize = TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    transform = TS.Compose([
-        TS.Resize((384, 384)),
-        TS.ToTensor(),
-        normalize
-    ])
 
-    image = raw_image.resize((384, 384))
-    image = transform(image).unsqueeze(0).to(device)
+    image = transform(raw_image).unsqueeze(0).to(device)
 
-    # Currently ", " is better for detecting single tags
-    # while ". " is a little worse in some case
     if tagging_model_type == "RAM":
         res = inference_ram(image, tagging_model)
-        tags = res[0].strip(' ').replace('  ', ' ').replace(' |', ',')
-        tags_chinese = res[1].strip(' ').replace('  ', ' ').replace(' |', ',')
+        tags = res[0].strip(' ').replace('  ', ' ')
+        tags_chinese = res[1].strip(' ').replace('  ', ' ')
         print("Tags: ", tags)
-        print("图像标签: ", tags_chinese)
+        print("标签: ", tags_chinese)
+        return tags, tags_chinese
     else:
         res = inference_tag2text(image, tagging_model, specified_tags)
-        tags = res[0].strip(' ').replace('  ', ' ').replace(' |', ',')
+        tags = res[0].strip(' ').replace('  ', ' ')
         caption = res[2]
         print(f"Tags: {tags}")
         print(f"Caption: {caption}")
+        return tags, caption
 
-    # return
-    if not do_det_seg:
-        if tagging_model_type == "RAM":
-            return tags.replace(", ", " | "), tags_chinese.replace(", ", " | "), None
-        else:
-            return tags.replace(", ", " | "), caption, None
-
-    # run groundingDINO
-    transform = T.Compose([
-        T.RandomResize([800], max_size=1333),
-        T.ToTensor(),
-        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ])
-
-    image, _ = transform(raw_image, None)  # 3, h, w
-
-    boxes_filt, scores, pred_phrases = get_grounding_output(
-        grounding_dino_model, image, tags, box_threshold, text_threshold, device=device
-    )
-    print("GroundingDINO finished")
 
-    # run SAM
-    image = np.asarray(raw_image)
-    sam_model.set_image(image)
+def inference_with_ram(img):
+    return inference(img, None, "RAM", ram_model, transform)
 
-    size = raw_image.size
-    H, W = size[1], size[0]
-    for i in range(boxes_filt.size(0)):
-        boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
-        boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
-        boxes_filt[i][2:] += boxes_filt[i][:2]
 
-    boxes_filt = boxes_filt.cpu()
-    # use NMS to handle overlapped boxes
-    print(f"Before NMS: {boxes_filt.shape[0]} boxes")
-    nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()
-    boxes_filt = boxes_filt[nms_idx]
-    pred_phrases = [pred_phrases[idx] for idx in nms_idx]
-    print(f"After NMS: {boxes_filt.shape[0]} boxes")
-
-    transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)
-
-    masks, _, _ = sam_model.predict_torch(
-        point_coords=None,
-        point_labels=None,
-        boxes=transformed_boxes.to(device),
-        multimask_output=False,
-    )
-    print("SAM finished")
-
-    # draw output image
-    mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0))
-
-    mask_draw = ImageDraw.Draw(mask_image)
-    for mask in masks:
-        draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True)
-
-    image_draw = ImageDraw.Draw(raw_image)
-
-    for box, label in zip(boxes_filt, pred_phrases):
-        draw_box(box, image_draw, label)
-
-    out_image = raw_image.convert('RGBA')
-    out_image.alpha_composite(mask_image)
-
-    # return
-    if tagging_model_type == "RAM":
-        return tags.replace(", ", " | "), tags_chinese.replace(", ", " | "), out_image
-    else:
-        return tags.replace(", ", " | "), caption, out_image
+def inference_with_t2t(img, input_tags):
+    return inference(img, input_tags, "Tag2Text", tag2text_model, transform)
 
 
 if __name__ == "__main__":
     import gradio as gr
 
-    # load RAM
-    ram_model = ram(pretrained=ram_checkpoint, image_size=384, vit='swin_l')
-    ram_model.eval()
-    ram_model = ram_model.to(device)
-
-    # load Tag2Text
-    delete_tag_index = []  # filter out attributes and action categories which are difficult to grounding
-    for i in range(3012, 3429):
-        delete_tag_index.append(i)
-
-    tag2text_model = tag2text_caption(pretrained=tag2text_checkpoint,
-                                      image_size=384,
-                                      vit='swin_b',
-                                      delete_tag_index=delete_tag_index)
-    tag2text_model.threshold = 0.64  # we reduce the threshold to obtain more tags
-    tag2text_model.eval()
-    tag2text_model = tag2text_model.to(device)
-
-    # load groundingDINO
-    grounding_dino_model = load_model(config_file, grounded_checkpoint, device=device)
-
-    # load SAM
-    sam_model = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))
+    # get transform and load models
+    transform = get_transform(image_size=image_size)
+    ram_model = ram(pretrained=ram_checkpoint, image_size=image_size, vit='swin_l').eval().to(device)
+    tag2text_model = tag2text_caption(
+        pretrained=tag2text_checkpoint, image_size=image_size, vit='swin_b').eval().to(device)
 
     # build GUI
     def build_gui():
 
         description = """
-            <center><strong><font size='10'>Recognize Anything Model + Grounded-SAM</font></strong></center>
+            <center><strong><font size='10'>Recognize Anything Model</font></strong></center>
             <br>
-            Welcome to the RAM/Tag2Text + Grounded-SAM demo! <br><br>
+            <p>Welcome to the <a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything Model</a> / <a href='https://tag2text.github.io/Tag2Text' target='_blank'>Tag2Text Model</a> demo!</p>
             <li>
                 <b>Recognize Anything Model:</b> Upload your image to get the <b>English and Chinese tags</b>!
             </li>
             <li>
-                <b>Tag2Text Model:</b> Upload your image to get the <b>tags and caption</b>!
-                (Optional: Specify tags to get the corresponding caption.)
-            </li>
-            <li>
-                <b>Grounded-SAM:</b> Tick the checkbox to get <b>boxes</b> and <b>masks</b> of tags!
+                <b>Tag2Text Model:</b> Upload your image to get the <b>tags and caption</b>! (Optional: Specify tags to get the corresponding caption.)
             </li>
-            <br>
-            Great thanks to <a href='https://huggingface.co/majinyu' target='_blank'>Ma Jinyu</a>, the major contributor of this demo!
+            <p><b>More over:</b> Combine with <a href='https://github.com/IDEA-Research/Grounded-Segment-Anything' target='_blank'>Grounded-SAM</a>, you can get <b>boxes and masks</b>! Please run <a href='https://github.com/xinyu1205/recognize-anything/blob/main/gui_demo.ipynb' target='_blank'>this notebook</a> to try out!</p>
+            <p>Great thanks to <a href='https://huggingface.co/majinyu' target='_blank'>Ma Jinyu</a>, the major contributor of this demo!</p>
         """  # noqa
 
         article = """
             <p style='text-align: center'>
                 RAM and Tag2Text are trained on open-source datasets, and we are persisting in refining and iterating upon it.<br/>
-                Grounded-SAM is a combination of Grounding DINO and SAM aming to detect and segment anything with text inputs.<br/>
                 <a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything: A Strong Image Tagging Model</a>
                 |
                 <a href='https://https://tag2text.github.io/' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a>
-                |
-                <a href='https://github.com/IDEA-Research/Grounded-Segment-Anything' target='_blank'>Grounded-Segment-Anything</a>
             </p>
         """  # noqa
 
-        def inference_with_ram(img, do_det_seg):
-            return inference(
-                img, None, do_det_seg,
-                "RAM", ram_model, grounding_dino_model, sam_model
-            )
-
-        def inference_with_t2t(img, input_tags, do_det_seg):
-            return inference(
-                img, input_tags, do_det_seg,
-                "Tag2Text", tag2text_model, grounding_dino_model, sam_model
-            )
-
         with gr.Blocks(title="Recognize Anything Model") as demo:
             ###############
             # components
@@ -312,23 +83,24 @@ if __name__ == "__main__":
                 with gr.Row():
                     with gr.Column():
                         ram_in_img = gr.Image(type="pil")
-                        ram_opt_det_seg = gr.Checkbox(label="Get Boxes and Masks with Grounded-SAM", value=True)
                         with gr.Row():
                             ram_btn_run = gr.Button(value="Run")
-                            ram_btn_clear = gr.ClearButton()
+                            try:
+                                ram_btn_clear = gr.ClearButton()
+                            except AttributeError:  # old gradio does not have ClearButton, not big problem
+                                ram_btn_clear = None
                     with gr.Column():
-                        ram_out_img = gr.Image(type="pil")
                         ram_out_tag = gr.Textbox(label="Tags")
                         ram_out_biaoqian = gr.Textbox(label="标签")
                 gr.Examples(
                     examples=[
-                        ["images/demo1.jpg", True],
-                        ["images/demo2.jpg", True],
-                        ["images/demo4.jpg", True],
+                        ["images/demo1.jpg"],
+                        ["images/demo2.jpg"],
+                        ["images/demo4.jpg"],
                     ],
                     fn=inference_with_ram,
-                    inputs=[ram_in_img, ram_opt_det_seg],
-                    outputs=[ram_out_tag, ram_out_biaoqian, ram_out_img],
+                    inputs=[ram_in_img],
+                    outputs=[ram_out_tag, ram_out_biaoqian],
                     cache_examples=True
                 )
 
@@ -337,23 +109,24 @@ if __name__ == "__main__":
                     with gr.Column():
                         t2t_in_img = gr.Image(type="pil")
                         t2t_in_tag = gr.Textbox(label="User Specified Tags (Optional, separated by comma)")
-                        t2t_opt_det_seg = gr.Checkbox(label="Get Boxes and Masks with Grounded-SAM", value=True)
                         with gr.Row():
                             t2t_btn_run = gr.Button(value="Run")
-                            t2t_btn_clear = gr.ClearButton()
+                            try:
+                                t2t_btn_clear = gr.ClearButton()
+                            except AttributeError:  # old gradio does not have ClearButton, not big problem
+                                t2t_btn_clear = None
                     with gr.Column():
-                        t2t_out_img = gr.Image(type="pil")
                         t2t_out_tag = gr.Textbox(label="Tags")
                         t2t_out_cap = gr.Textbox(label="Caption")
                 gr.Examples(
                     examples=[
-                        ["images/demo4.jpg", "", True],
-                        ["images/demo4.jpg", "power line", False],
-                        ["images/demo4.jpg", "track, train", False],
+                        ["images/demo4.jpg", ""],
+                        ["images/demo4.jpg", "power line"],
+                        ["images/demo4.jpg", "track, train"],
                     ],
                     fn=inference_with_t2t,
-                    inputs=[t2t_in_img, t2t_in_tag, t2t_opt_det_seg],
-                    outputs=[t2t_out_tag, t2t_out_cap, t2t_out_img],
+                    inputs=[t2t_in_img, t2t_in_tag],
+                    outputs=[t2t_out_tag, t2t_out_cap],
                     cache_examples=True
                 )
 
@@ -365,22 +138,20 @@ if __name__ == "__main__":
             # run inference
             ram_btn_run.click(
                 fn=inference_with_ram,
-                inputs=[ram_in_img, ram_opt_det_seg],
-                outputs=[ram_out_tag, ram_out_biaoqian, ram_out_img]
+                inputs=[ram_in_img],
+                outputs=[ram_out_tag, ram_out_biaoqian]
             )
             t2t_btn_run.click(
                 fn=inference_with_t2t,
-                inputs=[t2t_in_img, t2t_in_tag, t2t_opt_det_seg],
-                outputs=[t2t_out_tag, t2t_out_cap, t2t_out_img]
+                inputs=[t2t_in_img, t2t_in_tag],
+                outputs=[t2t_out_tag, t2t_out_cap]
             )
 
-            # hide or show image output
-            ram_opt_det_seg.change(fn=lambda b: gr.update(visible=b), inputs=[ram_opt_det_seg], outputs=[ram_out_img])
-            t2t_opt_det_seg.change(fn=lambda b: gr.update(visible=b), inputs=[t2t_opt_det_seg], outputs=[t2t_out_img])
-
             # clear
-            ram_btn_clear.add([ram_in_img, ram_out_img, ram_out_tag, ram_out_biaoqian])
-            t2t_btn_clear.add([t2t_in_img, t2t_in_tag, t2t_out_img, t2t_out_tag, t2t_out_cap])
+            if ram_btn_clear is not None:
+                ram_btn_clear.add([ram_in_img, ram_out_tag, ram_out_biaoqian])
+            if t2t_btn_clear is not None:
+                t2t_btn_clear.add([t2t_in_img, t2t_in_tag, t2t_out_tag, t2t_out_cap])
 
         return demo
 

requirements.txt

@@ -1,5 +1,5 @@
 timm==0.4.12
-transformers
+transformers==4.15.0
 fairscale==0.4.4
 pycocoevalcap
 torch
@@ -7,21 +7,4 @@ torchvision
 Pillow
 scipy
 git+https://github.com/openai/CLIP.git
-git+https://github.com/IDEA-Research/Grounded-Segment-Anything.git/#subdirectory=segment_anything
 git+https://github.com/xinyu1205/recognize-anything.git
-addict
-diffusers[torch]
-gradio
-huggingface_hub
-matplotlib
-numpy
-onnxruntime
-opencv_python
-pycocotools
-PyYAML
-requests
-setuptools
-supervision
-termcolor
-yapf
-nltk