main.py

main.py

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+import gradio as gd
+import torch
+from torchvision import
+
+# -- get torch and cuda version
+TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
+CUDA_VERSION = torch.__version__.split("+")[-1]
+
+# -- install pre-build detectron2
+!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/{CUDA_VERSION}/{TORCH_VERSION}/index.html
+
+import detectron2
+from detectron2.utils.logger import setup_logger # ????
+
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+
+# ????
+setup_logger()
+
+# -- load rcnn model
+cfg = get_cfg()
+# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
+# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
+predictor = DefaultPredictor(cfg)
+
+!wget http://images.cocodataset.org/val2017/000000439715.jpg -q -O input.jpg
+im = cv2.imread("./input.jpg")
+cv2_imshow(im)
+
+outputs = predictor(im)
+
+print(outputs["instances"].pred_classes)
+print(outputs["instances"].pred_boxes)
+
+# -- load Mask R-CNN model for segmentation
+MaskRCNN = torch.load("MaskRCNN.pt")
+
+
+
+