save all

scripts/get_error_hood.py

@@ -2,30 +2,9 @@ import os
 import sys
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
 
-import importlib
-import argparse
-parser = argparse.ArgumentParser(description='')
-parser.add_argument('--name', type=str)
-parser.add_argument('--config', type=str)
-parser.add_argument('--debug', action='store_true')
-parser.add_argument('--iteration', type=str, default='')
-parser.add_argument('-g', '--gpu', type=str)
-parser.add_argument('-n', '--normal', action='store_true')
-parser.add_argument('-e', '--edge', action='store_true')
-parser.add_argument('-cr', '--collision_ratio', action='store_true')
-parser.add_argument('-a', '--all', action='store_true')
-args = vars(parser.parse_args())
-
-if args['debug']:
-    from configs.iccvw.pyr_64_128_256_512_cape_f_e2e import config as config_1
-else:
-    config_module = importlib.import_module(args['config'])
-    config_1 = config_module.config
-
-
 import time
 import argparse
-import tqdm
+from tqdm import tqdm
 import random
 import numpy as np
 import plotly.graph_objects as go
@@ -35,50 +14,53 @@ from data.utils import Frame
 
 
 
-def calculate(type=''):
+def calculate(type='chamfer'):
     errors = dict()
     errors_mean = dict()
 
-    with open(f'{folder}/{hood_csv}', 'r') as f:
-        lines_hood = f.readlines()
-    with open(f'{folder}/{ours_csv}', 'r') as f:
-        lines_ours = f.readlines()
+    files_hood = sorted(os.listdir(f'{folder}/{run}_hood/{type}'))
+    errors_hood = [np.load(f'{folder}/{run}_hood/{type}/{file}') for file in tqdm(files_hood)]
+    files_ours = sorted(os.listdir(f'{folder}/hood_3_var_ours/{type}'))
+    errors_ours = [np.load(f'{folder}/hood_3_var_ours/{type}/{file}') for file in tqdm(files_ours)]
 
-    for line in lines_hood:
-        line = line.split(',')
-        category = line[2]
+    for error, frame in zip(errors_hood, files_hood):
+        frame_id, frame_nr, frame_garment = frame.split('_')
+        category = frame_garment
 
         if category not in errors:
             errors[category] = []
-        errors[category].append(float(line[3]))
+        errors[category].append(error)
 
     for category in errors:
         errors_mean[category] = round(np.mean(errors[category]), 5)
     print(errors_mean) 
 
     mean = round(np.mean(list(errors_mean.values())), 5)
-    print(f'Final HOOD error{type}: {mean}')
+    print(f'Final HOOD error_{type}: {mean}')
 
-    for line in lines_ours:
-        line = line.split(',')
-        category = line[2]
+    # errors = dict()
+    # errors_mean = dict()
 
-        if category not in errors:
-            errors[category] = []
-        errors[category].append(float(line[3]))
+    # for error, frame in zip(errors_ours, files_ours):
+    #     frame_id, frame_nr, frame_garment = frame.split('_')
+    #     category = frame_garment
 
-    for category in errors:
-        errors_mean[category] = round(np.mean(errors[category]), 5)
-    print(errors_mean) 
+    #     if category not in errors:
+    #         errors[category] = []
+    #     errors[category].append(error)
 
-    mean = round(np.mean(list(errors_mean.values())), 5)
-    print(f'Final OURS error{type}: {mean}')
+    # for category in errors:
+    #     errors_mean[category] = round(np.mean(errors[category]), 5)
+    # print(errors_mean) 
+
+    # mean = round(np.mean(list(errors_mean.values())), 5)
+    # print(f'Final OURS error_{type}: {mean}')
 
 
 if __name__ == '__main__':
 
     folder = '/data-local/127_2/results/hood'
-    hood_csv = 'hood_2_hood.csv'
-    ours_csv = 'hood_2_ours.csv'
+    run = 'hood_3'
 
-    calculate()
+    for type in ['chamfer', 'area', 'collision_ratio']: # ['chamfer', 'collision', 'area', 'collision_ratio']:
+        calculate(type)