fix for cpu, fix the version of scikit-image

.gitignore

@@ -8,4 +8,5 @@ tensorboard/*
 *.json
 core/data/*
 core/temp.py
-*.tar
+*.tar
+samples/*

README.md

@@ -4,7 +4,7 @@
 <a href="https://arxiv.org/abs/2107.07235"><img  src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg" ></a>
 <a href=""><img  src="https://img.shields.io/badge/license-MIT-blue"></a>
 <a href="https://www.ijcai.org/proceedings/2021/111"><img  src="https://img.shields.io/static/v1?label=inproceedings&message=Paper&color=orange"></a>
-
+<a href="https://paperswithcode.com/sota/image-matting-on-aim-500?p=deep-automatic-natural-image-matting"><img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/deep-automatic-natural-image-matting/image-matting-on-aim-500"></a>
 </p>
 
 
@@ -300,7 +300,7 @@ This project is under the MIT license. For further questions, please contact <st
 
 ## Relevant Projects
 
-[1] <strong>End-to-end Animal Image Matting, arxiv, 2020</strong> | [Paper](https://arxiv.org/pdf/2010.16188v1.pdf) | [Github](https://github.com/JizhiziLi/animal-matting)
+[1] <strong>Bridging Composite and Real: Towards End-to-end Deep Image Mattin, IJCV, 2021g</strong> | [Paper](https://arxiv.org/pdf/2010.16188v1.pdf) | [Github](https://github.com/JizhiziLi/GFM)
 <br><em>&ensp; &ensp; &ensp;Jizhizi Li, Jing Zhang, Stephen J. Maybank, Dacheng Tao</em>
 
 [2] <strong>Privacy-Preserving Portrait Matting, ACM MM, 2021</strong> | [Paper](https://arxiv.org/pdf/2104.14222.pdf) | [Github](https://github.com/JizhiziLi/P3M)

core/test.py

@@ -39,7 +39,12 @@ def get_args():
 
 def inference_once(args, model, scale_img, scale_trimap=None):
 	pred_list = []
-	tensor_img = torch.from_numpy(scale_img.astype(np.float32)[:, :, :]).permute(2, 0, 1).cuda()
+
+	if args.cuda:
+		tensor_img = torch.from_numpy(scale_img.astype(np.float32)[:, :, :]).permute(2, 0, 1).cuda()
+	else:
+		tensor_img = torch.from_numpy(scale_img.astype(np.float32)[:, :, :]).permute(2, 0, 1)
+
 	input_t = tensor_img
 	input_t = input_t/255.0
 	normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
@@ -185,7 +190,8 @@ def test_aim500(args, model):
 		alpha = alpha[:,:,0] if alpha.ndim>2 else alpha
 
 		with torch.no_grad():
-			torch.cuda.empty_cache()
+			if args.cuda:
+				torch.cuda.empty_cache()
 			predict = inference_img(args, model, img)
 			sad_trimap_diff, mse_trimap_diff, mad_trimap_diff = calculate_sad_mse_mad(predict, alpha, trimap)
 			sad_diff, mse_diff, mad_diff = calculate_sad_mse_mad_whole_img(predict, alpha)
@@ -288,7 +294,7 @@ def load_model_and_deploy(args):
 	print(f'Test stategy: {args.test_choice}')
 	print(f'Test dataset: {args.dataset_choice}')	
 	model = AimNet()
-
+	
 	if torch.cuda.device_count()==0:
 		print(f'Running on CPU...')
 		args.cuda = False

requirements.txt

@@ -1,8 +1,6 @@
 numpy==1.19.2
 opencv-python==4.4.0.46
 Pillow==8.0.0
-scikit-image==0.17.2
+scikit-image==0.14.5
 scipy==1.5.3
-torch==1.4.0
-torchvision==0.5.0
 tqdm==4.51.0