Commit @c28c2ce8a36e074df672a432fe0de31474650dae

윤영준 2023-07-12

... mask was inverted...

@c28c2ce8a36e074df672a432fe0de31474650dae

6e2a279

c28c2ce

model/Generator.py

--- model/Generator.py

+++ model/Generator.py


         diff = abs(clean - dirty)
         diff = sum(diff, dim=1)
 
-        bin_diff = (diff < thresold).to(clean.dtype)
+        bin_diff = (diff >= thresold).to(clean.dtype)
 
         return bin_diff
 

6e2a279

c28c2ce

train.py

--- train.py

+++ train.py


     pass
 
 # 이건 땜빵이고 차후에 데이터 관리 모듈 만들꺼임
-rainy_data_path = glob.glob("data/source/Oxford_raindrop_dataset/dirty/*.png")
+rainy_data_path = glob.glob("data/source/Peking_raindrop_dataset/dirty/*.png")
 rainy_data_path = sorted(rainy_data_path)
-clean_data_path = glob.glob("data/source/Oxford_raindrop_dataset/clean/*.png")
+clean_data_path = glob.glob("data/source/Peking_raindrop_dataset/clean/*.png")
 clean_data_path = sorted(clean_data_path)
 
-resize = torchvision.transforms.Resize((692, 776), antialias=True)
+resize = torchvision.transforms.Resize((480, 720), antialias=True)
 dataset = ImagePairDataset(clean_data_path, rainy_data_path, transform=resize)
 dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
 # declare generator loss

 
         attentiveRNNresults = generator.attentiveRNN(rainy_img)
         generator_attention_map = attentiveRNNresults['attention_map_list']
-        binary_difference_mask = generator.binary_diff_mask(clean_img, rainy_img, thresold=0.2)
+        binary_difference_mask = generator.binary_diff_mask(clean_img, rainy_img, thresold=0.24)
         generator_loss_ARNN = generator.attentiveRNN.loss(rainy_img, binary_difference_mask)
         generator_loss_ARNN.backward()
         optimizer_G_ARNN.step()

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...	...	@@ -43,7 +43,7 @@
43	43	diff = abs(clean - dirty)
44	44	diff = sum(diff, dim=1)
45	45
46		- bin_diff = (diff < thresold).to(clean.dtype)
	46	+ bin_diff = (diff >= thresold).to(clean.dtype)
47	47
48	48	return bin_diff
49	49

...	...	@@ -66,12 +66,12 @@
66	66	pass
67	67
68	68	# 이건 땜빵이고 차후에 데이터 관리 모듈 만들꺼임
69		-rainy_data_path = glob.glob("data/source/Oxford_raindrop_dataset/dirty/*.png")
	69	+rainy_data_path = glob.glob("data/source/Peking_raindrop_dataset/dirty/*.png")
70	70	rainy_data_path = sorted(rainy_data_path)
71		-clean_data_path = glob.glob("data/source/Oxford_raindrop_dataset/clean/*.png")
	71	+clean_data_path = glob.glob("data/source/Peking_raindrop_dataset/clean/*.png")
72	72	clean_data_path = sorted(clean_data_path)
73	73
74		-resize = torchvision.transforms.Resize((692, 776), antialias=True)
	74	+resize = torchvision.transforms.Resize((480, 720), antialias=True)
75	75	dataset = ImagePairDataset(clean_data_path, rainy_data_path, transform=resize)
76	76	dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
77	77	# declare generator loss
...	...	@@ -116,7 +116,7 @@
116	116
117	117	attentiveRNNresults = generator.attentiveRNN(rainy_img)
118	118	generator_attention_map = attentiveRNNresults['attention_map_list']
119		- binary_difference_mask = generator.binary_diff_mask(clean_img, rainy_img, thresold=0.2)
	119	+ binary_difference_mask = generator.binary_diff_mask(clean_img, rainy_img, thresold=0.24)
120	120	generator_loss_ARNN = generator.attentiveRNN.loss(rainy_img, binary_difference_mask)
121	121	generator_loss_ARNN.backward()
122	122	optimizer_G_ARNN.step()

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