Commit @0106b7baecee1d51788a0c71bc312453e2ce828d

윤영준 2023-11-08

fixed a critical mistake, misaligned neural network

@0106b7baecee1d51788a0c71bc312453e2ce828d

dd75006

0106b7b

action.py

--- action.py

+++ action.py


 arnn.load_state_dict(torch.load("weights/ARNN_trained_weight_6_3_2.pt"))
 arnn.to(device=device)
 
-classifier = Classifier()
+classifier = Classifier(in_ch=1)
 classifier.eval()
-classifier.load_state_dict(torch.load("weights/Classifier_512.pt"))
+classifier.load_state_dict(torch.load("weights/classifier_e19_weight_1080p_512512_fixed_wrong_resolution_and_ch.pt"))
 classifier.to(device=device)
 
 tf_toTensor = ToTensor()

             image_arnn = arnn(image_tensor)
             image_tensor.cpu()
             del image_tensor
-            result = classifier(image_arnn['x'])
+            result = classifier(image_arnn['attention_map_list'][-1])
             image_arnn['x'].cpu()
             del image_arnn
 

dd75006

0106b7b

app.py

--- app.py

+++ app.py


 api.add_namespace(Action, '/action')
 
 if __name__ == "__main__":
-    app.run(debug=False, host='0.0.0.0', port=8000)
+    app.run(debug=False, host='0.0.0.0', port=7700)
     print("Flask Start")

dd75006

0106b7b

demonstration.py

--- demonstration.py

+++ demonstration.py


     random.shuffle(img_path)
 
     for i in iter(range(len(img_path))):
-        image = crop_image(img_path[i], crop_size, start_point)
+        ori_img = cv2.imread(img_path[i])
+        image = crop_image(ori_img, crop_size, start_point)
         if not image.any():
             continue
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

dd75006

0106b7b

model/AttentiveRNN.py

--- model/AttentiveRNN.py

+++ model/AttentiveRNN.py


             lstm_feats.append(lstm_feats_i)
         ret = {
             'x' : x,
-            # 'attention_map_list' : attention_map,
-            # 'lstm_feats' : lstm_feats
+            'attention_map_list' : attention_map,
+            'lstm_feats' : lstm_feats
         }
         return ret
 

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...	...	@@ -18,5 +18,5 @@
18	18	api.add_namespace(Action, '/action')
19	19
20	20	if __name__ == "__main__":
21		- app.run(debug=False, host='0.0.0.0', port=8000)
	21	+ app.run(debug=False, host='0.0.0.0', port=7700)
22	22	print("Flask Start") (파일 끝에 줄바꿈 문자 없음)

...	...	@@ -38,7 +38,8 @@
38	38	random.shuffle(img_path)
39	39
40	40	for i in iter(range(len(img_path))):
41		- image = crop_image(img_path[i], crop_size, start_point)
	41	+ ori_img = cv2.imread(img_path[i])
	42	+ image = crop_image(ori_img, crop_size, start_point)
42	43	if not image.any():
43	44	continue
44	45	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

...	...	@@ -217,8 +217,8 @@
217	217	lstm_feats.append(lstm_feats_i)
218	218	ret = {
219	219	'x' : x,
220		- # 'attention_map_list' : attention_map,
221		- # 'lstm_feats' : lstm_feats
	220	+ 'attention_map_list' : attention_map,
	221	+ 'lstm_feats' : lstm_feats
222	222	}
223	223	return ret
224	224

...	...	@@ -20,9 +20,9 @@
20	20	arnn.load_state_dict(torch.load("weights/ARNN_trained_weight_6_3_2.pt"))
21	21	arnn.to(device=device)
22	22
23		-classifier = Classifier()
	23	+classifier = Classifier(in_ch=1)
24	24	classifier.eval()
25		-classifier.load_state_dict(torch.load("weights/Classifier_512.pt"))
	25	+classifier.load_state_dict(torch.load("weights/classifier_e19_weight_1080p_512512_fixed_wrong_resolution_and_ch.pt"))
26	26	classifier.to(device=device)
27	27
28	28	tf_toTensor = ToTensor()
...	...	@@ -69,7 +69,7 @@
69	69	image_arnn = arnn(image_tensor)
70	70	image_tensor.cpu()
71	71	del image_tensor
72		- result = classifier(image_arnn['x'])
	72	+ result = classifier(image_arnn['attention_map_list'][-1])
73	73	image_arnn['x'].cpu()
74	74	del image_arnn
75	75

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