1. fix warnings. \n 2. support CPU mode. \n 3. update README.

README.md

@@ -1,9 +1,10 @@
 # Grounding DINO 
+[📃Paper](https://arxiv.org/abs/2303.05499) | 
+[📽️Video](https://www.youtube.com/watch?v=wxWDt5UiwY8) |
+[📯Demo on Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) | 
+[🤗Demo on HF (Coming soon)]() 
 
----
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) \
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)
-
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-mscoco)](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-odinw)](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) \
@@ -20,8 +21,10 @@ Official pytorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.0
 - **High Performancce.** COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**.
 - **Flexible.** Collaboration with Stable Diffusion for Image Editting.
 
-<!-- [![Watch the video](https://i.imgur.com/vKb2F1B.png)](https://youtu.be/wxWDt5UiwY8)
+## News
-<iframe width="560" height="315" src="https://youtu.be/wxWDt5UiwY8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> -->
+[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!
 
 <details open>
 <summary><font size="4">
@@ -30,15 +33,18 @@ Description
 <img src=".asset/hero_figure.png" alt="ODinW" width="100%">
 </details>
 
+
+
 ## TODO 
 
 - [x] Release inference code and demo.
 - [x] Release checkpoints.
 - [ ] Grounding DINO with Stable Diffusion and GLIGEN demos.
+- [ ] Release training codes.
 
 ## Install 
 
-If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set.
+If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set. It will be compiled under CPU-only mode if no CUDA available.
 
 ```bash
 pip install -e .
@@ -46,15 +52,16 @@ pip install -e .
 
 ## Demo
 
-See the `demo/inference_on_a_image.py` for more details.
 ```bash
 CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
   -c /path/to/config \
   -p /path/to/checkpoint \
   -i .asset/cats.png \
   -o "outputs/0" \
-  -t "cat ear."
+  -t "cat ear." \
+  [--cpu-only] # open it for cpu mode
 ```
+See the `demo/inference_on_a_image.py` for more details.
 
 ## Checkpoints
 
@@ -68,6 +75,7 @@ CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
       <th>Data</th>
       <th>box AP on COCO</th>
       <th>Checkpoint</th>
+      <th>Config</th>
     </tr>
   </thead>
   <tbody>
@@ -78,6 +86,7 @@ CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
       <td>O365,GoldG,Cap4M</td>
       <td>48.4 (zero-shot) / 57.2 (fine-tune)</td>
       <td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth">link</a></td>
+      <td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py">link</a></td>
     </tr>
   </tbody>
 </table>

demo/inference_on_a_image.py

@@ -39,7 +39,13 @@ def plot_boxes_to_image(image_pil, tgt):
         draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
         # draw.text((x0, y0), str(label), fill=color)
 
-        bbox = draw.textbbox((x0, y0), str(label))
+        font = ImageFont.load_default()
+        if hasattr(font, "getbbox"):
+            bbox = draw.textbbox((x0, y0), str(label), font)
+        else:
+            w, h = draw.textsize(str(label), font)
+            bbox = (x0, y0, w + x0, y0 + h)
+        # bbox = draw.textbbox((x0, y0), str(label))
         draw.rectangle(bbox, fill=color)
         draw.text((x0, y0), str(label), fill="white")
 
@@ -63,9 +69,9 @@ def load_image(image_path):
     return image_pil, image
 
 
-def load_model(model_config_path, model_checkpoint_path):
+def load_model(model_config_path, model_checkpoint_path, cpu_only=False):
     args = SLConfig.fromfile(model_config_path)
-    args.device = "cuda"
+    args.device = "cuda" if not cpu_only else "cpu"
     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)
@@ -74,13 +80,14 @@ def load_model(model_config_path, model_checkpoint_path):
     return model
 
 
-def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True):
+def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, cpu_only=False):
     caption = caption.lower()
     caption = caption.strip()
     if not caption.endswith("."):
         caption = caption + "."
-    model = model.cuda()
+    device = "cuda" if not cpu_only else "cpu"
-    image = image.cuda()
+    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)
@@ -125,6 +132,8 @@ if __name__ == "__main__":
 
     parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
     parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
+
+    parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False")
     args = parser.parse_args()
 
     # cfg
@@ -141,14 +150,14 @@ if __name__ == "__main__":
     # load image
     image_pil, image = load_image(image_path)
     # load model
-    model = load_model(config_file, checkpoint_path)
+    model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only)
 
     # visualize raw image
     image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
 
     # run model
     boxes_filt, pred_phrases = get_grounding_output(
-        model, image, text_prompt, box_threshold, text_threshold
+        model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only
     )
 
     # visualize pred

groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -111,11 +111,11 @@ class PositionEmbeddingSineHW(nn.Module):
             x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
 
         dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_tx = self.temperatureW ** (2 * (dim_tx // 2) / self.num_pos_feats)
+        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
         pos_x = x_embed[:, :, :, None] / dim_tx
 
         dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_ty = self.temperatureH ** (2 * (dim_ty // 2) / self.num_pos_feats)
+        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
         pos_y = y_embed[:, :, :, None] / dim_ty
 
         pos_x = torch.stack(

groundingdino/models/GroundingDINO/ms_deform_attn.py

@@ -25,7 +25,10 @@ from torch.autograd import Function
 from torch.autograd.function import once_differentiable
 from torch.nn.init import constant_, xavier_uniform_
 
-from groundingdino import _C
+try:
+    from groundingdino import _C
+except:
+    warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
 
 
 # helpers
@@ -323,6 +326,7 @@ class MultiScaleDeformableAttention(nn.Module):
                     reference_points.shape[-1]
                 )
             )
+    
         if torch.cuda.is_available() and value.is_cuda:
             halffloat = False
             if value.dtype == torch.float16:

groundingdino/models/GroundingDINO/utils.py

@@ -206,7 +206,7 @@ def gen_sineembed_for_position(pos_tensor):
     # sineembed_tensor = torch.zeros(n_query, bs, 256)
     scale = 2 * math.pi
     dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (dim_t // 2) / 128)
+    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
     x_embed = pos_tensor[:, :, 0] * scale
     y_embed = pos_tensor[:, :, 1] * scale
     pos_x = x_embed[:, :, None] / dim_t

requirements.txt

@@ -6,4 +6,5 @@ yapf
 timm
 numpy
 opencv-python
-supervision==0.3.2
+supervision==0.3.2
+pycocotools