fix bugs for CPU mode

README.md

@@ -22,7 +22,7 @@ Official pytorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.0
 - **Flexible.** Collaboration with Stable Diffusion for Image Editting.
 
 ## News
-[2023/03/28] Add a [demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo) on Hugging Face Spce! \
+[2023/03/28] Add a [demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo) on Hugging Face Sapce! \
 [2023/03/27] Support CPU-only mode. Now the model can run on machines without GPUs.\
 [2023/03/25] A [demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) for Grounding DINO is available at Colab. Thanks to @Piotr! \
 [2023/03/22] Code is available Now!

demo/gradio_app.py

@@ -7,16 +7,21 @@ from io import BytesIO
 from PIL import Image
 import numpy as np
 from pathlib import Path
-import gradio as gr
+
 
 import warnings
 
 import torch
 
+# prepare the environment
 os.system("python setup.py build develop --user")
 os.system("pip install packaging==21.3")
+os.system("pip install gradio")
+
+
 warnings.filterwarnings("ignore")
 
+import gradio as gr
 
 from groundingdino.models import build_model
 from groundingdino.util.slconfig import SLConfig
@@ -34,10 +39,10 @@ ckpt_repo_id = "ShilongLiu/GroundingDINO"
 ckpt_filenmae = "groundingdino_swint_ogc.pth"
 
 
-def load_model_hf(model_config_path, repo_id, filename):
+def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
     args = SLConfig.fromfile(model_config_path) 
-    args.device = 'cuda' 
     model = build_model(args)
+    args.device = device
 
     cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
     checkpoint = torch.load(cache_file, map_location='cpu')
@@ -72,7 +77,7 @@ def run_grounding(input_image, grounding_caption, box_threshold, text_threshold)
     image_pil: Image = image_transform_grounding_for_vis(init_image)
 
     # run grounidng
-    boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold)
+    boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu')
     annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases)
     image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
 
@@ -83,14 +88,12 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True)
     parser.add_argument("--debug", action="store_true", help="using debug mode")
-    parser.add_argument("--non-share", action="store_true", help="not share the app")
+    parser.add_argument("--share", action="store_true", help="share the app")
     args = parser.parse_args()
 
-    args.share = (not args.non_share)
-
     block = gr.Blocks().queue()
     with block:
-        gr.Markdown("# Grounding DINO")
+        gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)")
         gr.Markdown("### Open-World Detection with Grounding DINO")
 
         with gr.Row():
@@ -117,5 +120,6 @@ if __name__ == "__main__":
         run_button.click(fn=run_grounding, inputs=[
                         input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
 
+
     block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share)
 

groundingdino/util/inference.py

@@ -21,9 +21,9 @@ def preprocess_caption(caption: str) -> str:
     return result + "."
 
 
-def load_model(model_config_path: str, model_checkpoint_path: str):
+def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
     args = SLConfig.fromfile(model_config_path)
-    args.device = "cuda"
+    args.device = device
     model = build_model(args)
     checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
     model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
@@ -50,12 +50,13 @@ def predict(
         image: torch.Tensor,
         caption: str,
         box_threshold: float,
-        text_threshold: float
+        text_threshold: float,
+        device: str = "cuda"
 ) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
     caption = preprocess_caption(caption=caption)
 
-    model = model.cuda()
+    model = model.to(device)
-    image = image.cuda()
+    image = image.to(device)
 
     with torch.no_grad():
         outputs = model(image[None], captions=[caption])