update ranker_test.pth

app.py

@@ -27,7 +27,7 @@ def txt_embed(t_txt, g_txt, fb_txt, net, batch_size, device1):
 
 
 def start(modelName):
-    device1 = "cuda" if torch.cuda.is_available() else "cpu"
+    device1 = "cpu"
     if 'model' not in st.session_state:
         # load model
         sentence_clip_model, sentence_clip_preprocess = load_from_name("ViT-B-16", device=device1,
@@ -49,7 +49,7 @@ def start(modelName):
     if 'model' in st.session_state:
         if st.session_state['model'] != modelName:
             # load model
-            device1 = "cuda" if torch.cuda.is_available() else "cpu"
+            device1 = "cpu"
             sentence_clip_model, sentence_clip_preprocess = load_from_name("ViT-B-16", device=device1,
                                                                            download_root='../../data/pretrained_weights/',
                                                                            resume='./models/ClipEncoder.pt')
@@ -195,7 +195,7 @@ def start(modelName):
 
 
 def main():
-    modelName = st.selectbox('Select model', ('p1-t10-g.pth', 'p2-t10-g.pth'))
+    modelName = st.selectbox('Select model', ('p1-t10-g.pth', 'p2-t10-g.pth', 'only CLIP'))
     start(modelName)
 
 

recommendation/utils/ranker_1.py

@@ -144,9 +144,9 @@ class Ranker:
     def actions_metric(self, f_embed_his, f_embed_his_t, f_embed_his_g, batch_size=32, lens=4):
         # f_embed_his: [turn, batch, emb]
         # distance: [batch, turn, data]
-        distance_ = torch.zeros(batch_size, lens, self.data_emb.size(0)).to(self.device)
-        distance_t = torch.zeros(batch_size, lens, self.data_emb.size(0)).to(self.device)
-        distance_g = torch.zeros(batch_size, lens, self.data_emb.size(0)).to(self.device)
+        distance_ = torch.zeros(batch_size, lens, len(self.data_emb))
+        distance_t = torch.zeros(batch_size, lens, len(self.data_emb))
+        distance_g = torch.zeros(batch_size, lens, len(self.data_emb))
         for turn_i in range(lens):
             for batch_i in range(batch_size):     # batch
                 # dot distance