Revert to difference code

@@ -10,17 +10,15 @@ import src.video_util as videoutil
 import json
 import json
 import urllib3
 import urllib3
 
 
-# Erase logs
 logging.disable(logging.WARNING)
 logging.disable(logging.WARNING)
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
 
-# Define model paths.
-MODEL_PATH = "./new_model/inference_model/segment_inference_model"
-# TAG_VECTOR_MODEL_PATH = "./new_model/twitter100_tag_vectors.gz"
-TAG_VECTOR_MODEL_PATH = "glove-wiki-gigaword-100"
-VIDEO_VECTOR_MODEL_PATH = "./new_model/gigaword100_video_vectors.model"
-VIDEO_TAGS_PATH = "./statics/new_kaggle_solution_40k.csv"
+# Old model
+MODEL_PATH = "./model/inference_model/segment_inference_model"
+TAG_VECTOR_MODEL_PATH = "./model/tag_vectors.model"
+VIDEO_VECTOR_MODEL_PATH = "./model/video_vectors.model"
+VIDEO_TAGS_PATH = "./statics/kaggle_solution_40k.csv"
 
 
 # Define static file paths.
 # Define static file paths.
 SEGMENT_LABEL_PATH = "./statics/segment_label_ids.csv"
 SEGMENT_LABEL_PATH = "./statics/segment_label_ids.csv"
@@ -28,11 +26,10 @@ VOCAB_PATH = "./statics/vocabulary.csv"
 
 
 # Define parameters.
 # Define parameters.
 TAG_TOP_K = 5
 TAG_TOP_K = 5
-VIDEO_TOP_K = 5
+VIDEO_TOP_K = 10
 
 
 # Target featuremap.
 # Target featuremap.
-FEATUREMAP_PATH = "./featuremaps/toy-3-features.pb"
-
+FEATUREMAP_PATH = "./featuremaps/concert-1-features.pb"
 
 
 def get_segments(batch_video_mtx, batch_num_frames, segment_size):
 def get_segments(batch_video_mtx, batch_num_frames, segment_size):
     """Get segment-level inputs from frame-level features."""
     """Get segment-level inputs from frame-level features."""
@@ -235,5 +232,17 @@ def inference_pb(file_path, threshold):
 
 
 
 
 if __name__ == '__main__':
 if __name__ == '__main__':
-    result = inference_pb(FEATUREMAP_PATH, VIDEO_TOP_K)
-    print(json.dumps(result, sort_keys=True, indent=2))
+    result = inference_pb(FEATUREMAP_PATH, 5)
+    print("=============== Old Model ===============")
+    print(result["tag_result"])
+    print(json.dumps(result["video_result"], sort_keys=True, indent=2))
+
+    # New model
+    MODEL_PATH = "./new_model/inference_model/segment_inference_model"
+    TAG_VECTOR_MODEL_PATH = "./new_model/tag_vectors.model"
+    VIDEO_VECTOR_MODEL_PATH = "./new_model/video_vectors.model"
+    VIDEO_TAGS_PATH = "./statics/new_kaggle_solution_40k.csv"
+    result = inference_pb(FEATUREMAP_PATH, 5)
+    print("=============== New Model ===============")
+    print(result["tag_result"])
+    print(json.dumps(result["video_result"], sort_keys=True, indent=2))

@@ -4,9 +4,9 @@ import numpy as np
 
 
 
 
 def recommend_videos(tags, tag_model_path, video_model_path, top_k):
 def recommend_videos(tags, tag_model_path, video_model_path, top_k):
-    # tag_vectors = Word2Vec.load(tag_model_path).wv
+    tag_vectors = Word2Vec.load(tag_model_path).wv
     # tag_vectors = KeyedVectors.load_word2vec_format(tag_model_path, binary=True)
     # tag_vectors = KeyedVectors.load_word2vec_format(tag_model_path, binary=True)
-    tag_vectors = api.load(tag_model_path)
+    # tag_vectors = api.load(tag_model_path)
     video_vectors = Word2Vec().wv.load(video_model_path)
     video_vectors = Word2Vec().wv.load(video_model_path)
     error_tags = []
     error_tags = []