Delete Inference.py

Inference.py

@@ -1,53 +0,0 @@
-import os
-import numpy as np
-from skimage import io
-from glob import glob
-from tqdm import tqdm
-import cv2
-import torch
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-from models import ISNetDIS
-
-
-if __name__ == "__main__":
-    dataset_path="input_images"  #Your dataset path 
-    model_path="model.pth"    
-    result_path="output_results"  #The folder path that you want to save the results
-    
-    if not os.path.exists(result_path):
-        os.makedirs(result_path)
-
-    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"))
-    net.eval()    
-
-    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")
-    with torch.no_grad():
-        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").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()
-
-            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]
-            cv2.imwrite(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))