Add read tsv to compute distances

CNN_embeddings_projector/read_tsv.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# File       : read_tsv.py
+# Modified   : 01.02.2022
+# By         : Sandra Carrasco <[email protected]>
+
+"""
+Compute cosine distance between embeddings from tsv file.
+"""
+
+import pandas as pd
+import csv
+from scipy.spatial import distance
+import numpy as np
+import matplotlib.pyplot as plt
+
+metadata = csv.reader(open("/workspace/stylegan2-ada-pytorch/CNN_embeddings_projector/projections_vs_reals/00000/default/metadata.tsv"), delimiter="\t")
+embeddings = list(csv.reader(open("/workspace/stylegan2-ada-pytorch/CNN_embeddings_projector/projections_vs_reals_nosprite/00000/default/tensors.tsv"), delimiter="\t"))
+
+#embeddings already ordered from x1, to x1, from x2, to x2 ....
+distances = []
+for i in range(0,len(embeddings),2):
+    emb_from = list(map(float, embeddings[i]))
+    emb_to = list(map(float, embeddings[i+1]))
+    distances.append( distance.cosine(emb_from,emb_to) )
+
+textfile = open("/workspace/stylegan2-ada-pytorch/CNN_embeddings_projector/projections_vs_reals_nosprite/distances.txt", "w")
+for element in distances:
+    textfile.write(str(element) + "\n")
+textfile.close()
+
+distances = np.array(distances)
+Q1 = np.quantile(distances, 0.25)
+Q2 = np.quantile(distances, 0.5)
+Q3 = np.quantile(distances, 0.75)
+his = plt.hist(distances)
+distances_indeces_ordered = np.argsort(distances)