backup

2022-11-04 16:17:58 +00:00
parent ebe8522905
commit 1c6b805519
18 changed files with 1413 additions and 486 deletions
@@ -0,0 +1,60 @@
+import os
+import csv
+import numpy as np
+
+
+if __name__ == '__main__':
+    garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
+    folder = '/home/workspace/data/results/errors'
+
+    checkpoint_name = 'abl_all'
+    iteration = '70000'
+
+    errors = dict()
+    errors_skirt = dict()
+    per_garment_errors = { 'Tshirt': [], 'Dress': [], 'Skirt': [], 'Top': [] }
+    per_garment_errors_skirt = { 'Tshirt': [], 'Jumpsuit': [], 'Trousers': [], 'Top': [] }
+
+
+    with open(os.path.join(folder, checkpoint_name + '-' + iteration + '.csv'), 'r') as f:
+        csv_reader = csv.reader(f, delimiter=',')
+        for row in csv_reader:
+            per_garment_errors[garment_type_table[row[0].split('_')[0]]].append(row[1])
+
+    with open(os.path.join(folder, checkpoint_name + '_skirt' + '-' + iteration + '.csv'), 'r') as f:
+        csv_reader = csv.reader(os.path.join(folder, checkpoint_name + '_skirt' + '-' + iteration + '.csv'), delimiter=',')
+        for row in csv_reader:
+            per_garment_errors_skirt[garment_type_table[row[0]]].append(row[1])
+
+    print('Non-skirt:')
+    for key in per_garment_errors.keys():
+        avg = np.mean(per_garment_errors[key])
+        errors.append(avg)
+        print(key + ': ' + str(avg))
+    print('Skirt:')
+    for key in per_garment_errors_skirt.keys():
+        avg = np.mean(per_garment_errors_skirt[key])
+        errors_skirt.append(avg)
+        print(key + ': ' + str(avg))
+
+    print('Avg non-skirt: ' + str(np.mean(errors)))
+    print('Avg skirt: ' + str(np.mean(errors_skirt)))
+
+    cum_avg_shirt = np.mean(per_garment_errors['Tshirt'] + per_garment_errors_skirt['Tshirt'])
+    cum_avg_top = np.mean(per_garment_errors['Top'] + per_garment_errors_skirt['Top'])
+
+    print('Global tshirt avg: ' + str(cum_avg_shirt))
+    print('Global tshirt avg: ' + str(cum_avg_top))
+
+    print('Globl per garment: ')
+
+    global_errors = []
+    per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
+    per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
+    for key in per_garment_errors.keys():
+        avg = np.mean(per_garment_errors[key])
+        global_errors.append(avg)
+        print(key + ': ' + str(avg))
+
+    print('Global avg: ' + str(np.mean(global_errors)))
+