Upload example_inference.py

example_inference.py

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+import os
+import numpy as np
+from skimage import io
+import cv2
+import torch
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+from briarmbg import BriaRMBG
+
+
+def example_inference():
+
+    input_size=[1024,1024]
+    net=BriaRMBG()
+
+    model_path = "./model.pth"
+    im_path = "./example_image.jpg"
+    result_path = "."
+
+    if torch.cuda.is_available():
+        net.load_state_dict(torch.load(model_path))
+        net=net.cuda()
+    else:
+        net.load_state_dict(torch.load(model_path,map_location="cpu"))
+    net.eval()    
+
+    # prepare input
+    im = io.imread(im_path)
+    if len(im.shape) < 3:
+        im = im[:, :, np.newaxis]
+    im_size=im.shape[0:2]
+    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=input_size, mode='bilinear').type(torch.uint8)
+    image = torch.divide(im_tensor,255.0)
+    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
+
+    if torch.cuda.is_available():
+        image=image.cuda()
+    
+    #inference
+    result=net(image)
+
+    # post process
+    result = torch.squeeze(F.interpolate(result[0][0], size=im_size, mode='bilinear') ,0)
+    ma = torch.max(result)
+    mi = torch.min(result)
+    result = (result-mi)/(ma-mi)
+
+    # save result
+    im_name=im_path.split('/')[-1].split('.')[0]
+    im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
+    cv2.imwrite(os.path.join(result_path, im_name+".png"), im_array)
+
+
+if __name__ == "__main__":
+    example_inference()