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Inference.py modified
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@ -17,9 +17,9 @@ from models import *
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if __name__ == "__main__":
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dataset_path="" #Your dataset path
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model_path="" #Your model path
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result_path="" #The folder path that you want to save the results
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dataset_path="/home/jiayi.zhu/DIS_projects/demo" #Your dataset path
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model_path="/home/jiayi.zhu/DIS_projects/saved_models/IS-Net-New/gpu_itr_654000_traLoss_0.1955_traTarLoss_0.0186_valLoss_0.9414_valTarLoss_0.1418_maxF1_0.9117_mae_0.0388_time_0.030502.pth" #Your model path
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result_path="/home/jiayi.zhu/DIS_projects/demo_result" #The folder path that you want to save the results
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input_size=[1024,1024]
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net=ISNetDIS()
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@ -29,7 +29,7 @@ if __name__ == "__main__":
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else:
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net.load_state_dict(torch.load(model_path,map_location="cpu"))
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net.eval()
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im_list = glob(dataset_path+"/*.jpg")
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im_list = glob(dataset_path+"/*.jpg")+glob(dataset_path+"/*.JPG")+glob(dataset_path+"/*.jpeg")+glob(dataset_path+"/*.JPEG")+glob(dataset_path+"/*.png")+glob(dataset_path+"/*.PNG")+glob(dataset_path+"/*.bmp")+glob(dataset_path+"/*.BMP")+glob(dataset_path+"/*.tiff")+glob(dataset_path+"/*.TIFF")
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for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
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print("im_path: ", im_path)
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im = io.imread(im_path)
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