py

compare.py

@@ -0,0 +1,53 @@
+import numpy as np
+import json
+datum = np.load('dump.npz.npy')
+
+
+with open('dump.json','r') as rr:
+    js = json.load(rr)
+
+
+emb_dict = dict()
+for embed in js[1]:
+    fr = embed['frame_number']
+    vec = embed['vector']
+    emb_dict[fr] = np.asarray(vec)
+
+
+
+
+
+def cosine_sim(emb_in_1, emb_in_2):
+    emb_in_1 = emb_in_1.astype(np.float32)
+    emb_in_2 = emb_in_2.astype(np.float32)    
+    emb1_norm = np.linalg.norm(emb_in_1)
+    emb2_norm = np.linalg.norm(emb_in_2)
+    dot_prod = np.dot(emb_in_1, emb_in_2)
+    similarity = dot_prod/(emb1_norm * emb2_norm)
+
+    return np.round(emb1_norm,5),np.round( emb2_norm,5), np.round( dot_prod,5),np.round( similarity,5)
+
+
+arr_in_deepstream = np.asarray([y for _,y in emb_dict.items()])
+
+normed = np.divide(datum.T, np.linalg.norm(datum, axis=1)).T
+print('_________________________')
+
+for fr, emb in emb_dict.items():
+    emb1 = np.linalg.norm(emb)
+    emb2 = np.linalg.norm(datum[fr])
+#    print( cosine_sim(emb, datum[fr]))
+
+
+print('Deepstream and Actual norm')    
+print(np.max(np.dot(arr_in_deepstream, normed.T),axis=1))
+
+print('_________________________')
+for dat in datum:
+#    print(cosine_sim(dat, datum[0]))
+     pass
+
+
+#print(cosine_sim(datum[fr], datum[fr+1]))
+
+#print(cosine_sim(emb_dict[fr], emb_dict[fr+1]))