Update app.py

app.py

@@ -87,35 +87,35 @@ def sentence_prob_mean(text):
 
 
 
-# def cloze_prob(text):
-
-# 	whole_text_encoding = tokenizer.encode(text)
-# 	text_list = text.split()
-# 	stem = ' '.join(text_list[:-1])
-# 	stem_encoding = tokenizer.encode(stem)
-# 	cw_encoding = whole_text_encoding[len(stem_encoding):]
-# 	tokens_tensor = torch.tensor([whole_text_encoding])
+def cloze_prob(text):
+
+	whole_text_encoding = tokenizer.encode(text)
+	text_list = text.split()
+	stem = ' '.join(text_list[:-1])
+	stem_encoding = tokenizer.encode(stem)
+	cw_encoding = whole_text_encoding[len(stem_encoding):]
+	tokens_tensor = torch.tensor([whole_text_encoding])
 	
-# 	with torch.no_grad():
-# 		outputs = model(tokens_tensor)
-# 		predictions = outputs[0]   
-
-# 	logprobs = []
-# 	start = -1-len(cw_encoding)
-# 	for j in range(start,-1,1):
-# 			raw_output = []
-# 			for i in predictions[-1][j]:
-# 					raw_output.append(i.item())
+	with torch.no_grad():
+		outputs = model(tokens_tensor)
+		predictions = outputs[0]   
+
+	logprobs = []
+	start = -1-len(cw_encoding)
+	for j in range(start,-1,1):
+			raw_output = []
+			for i in predictions[-1][j]:
+					raw_output.append(i.item())
 	
-# 			logprobs.append(np.log(softmax(raw_output)))
+			logprobs.append(np.log(softmax(raw_output)))
 			
 	
-# 	conditional_probs = []
-# 	for cw,prob in zip(cw_encoding,logprobs):
-# 			conditional_probs.append(prob[cw])
+	conditional_probs = []
+	for cw,prob in zip(cw_encoding,logprobs):
+			conditional_probs.append(prob[cw])
 
 
-# 	return np.exp(np.sum(conditional_probs))
+	return np.exp(np.sum(conditional_probs))
 
 
 
@@ -145,11 +145,11 @@ def Visual_re_ranker(caption_man, caption_woman, context_label, context_prob):
     sim_w = get_sim(sim_w)
 
 
-    LM_man =  sentence_prob_mean(caption_man)
-    LM_woman = sentence_prob_mean(caption_woman)
+    #LM_man =  sentence_prob_mean(caption_man)
+    #LM_woman = sentence_prob_mean(caption_woman)
 
-    # LM_man = cloze_prob(caption_man)
-    # LM_woman = cloze_prob(caption_woman)
+    LM_man = cloze_prob(caption_man)
+    LM_woman = cloze_prob(caption_woman)