Inference.py

IS-Net/Inference.py

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+import os
+import time
+import numpy as np
+from skimage import io
+import time
+from glob import glob
+from tqdm import tqdm
+
+import torch, gc
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.optim as optim
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+
+from models import *
+
+
+if __name__ == "__main__":
+    dataset_path=""  #Your dataset path
+    model_path=""  #Your model path
+    result_path=""  #The folder path that you want to save the results
+    input_size=[1024,1024]
+    net=ISNetDIS()
+
+    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"))
+
+    im_list = glob(dataset_path+"/*.jpg")
+    for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
+        print("im_path: ", im_path)
+        im = io.imread(im_path)
+        if len(im.shape) < 3:
+            im = im[:, :, np.newaxis]
+        im_shp=im.shape[0:2]
+        im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+        im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear")
+        image = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0]).type(torch.uint8)
+        if torch.cuda.is_available():
+            image=image.cuda()
+        image = torch.divide(image,255.0)
+        result=net(image)
+        result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
+        ma = torch.max(result)
+        mi = torch.min(result)
+        result = (result-mi)/(ma-mi)
+        im_name=im_path.split('/')[-1].split('.')[0]
+        io.imsave(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))