add dump results to hf model demo

grounded_sam2_gd1.5_demo.py

@@ -22,11 +22,12 @@ from sam2.sam2_image_predictor import SAM2ImagePredictor
 Hyper parameters
 """
 API_TOKEN = "Your API token"
-TEXT_PROMPT = "car"
+TEXT_PROMPT = "car . building ."
 IMG_PATH = "notebooks/images/cars.jpg"
 SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
 SAM2_MODEL_CONFIG = "sam2_hiera_l.yaml"
 GROUNDING_MODEL = DetectionModel.GDino1_5_Pro # DetectionModel.GDino1_6_Pro
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 OUTPUT_DIR = Path("outputs/grounded_sam2_gd1.5_demo")
 DUMP_JSON_RESULTS = True
 
@@ -79,7 +80,7 @@ Init SAM 2 Model and Predict Mask with Box Prompt
 
 # environment settings
 # use bfloat16
-torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+torch.autocast(device_type=DEVICE, dtype=torch.bfloat16).__enter__()
 
 if torch.cuda.get_device_properties(0).major >= 8:
     # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
@@ -89,7 +90,7 @@ if torch.cuda.get_device_properties(0).major >= 8:
 # build SAM2 image predictor
 sam2_checkpoint = SAM2_CHECKPOINT
 model_cfg = SAM2_MODEL_CONFIG
-sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
+sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=DEVICE)
 sam2_predictor = SAM2ImagePredictor(sam2_model)
 
 image = Image.open(img_path)
@@ -160,6 +161,8 @@ if DUMP_JSON_RESULTS:
 
     input_boxes = input_boxes.tolist()
     scores = scores.tolist()
+    # FIXME: class_names should be a list of strings without spaces
+    class_names = [class_name.strip() for class_name in class_names]
     # save the results in standard format
     results = {
         "image_path": img_path,