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luni
2023-03-10 17:25:44 +00:00
parent cb76d36c4f
commit 4b1cb0b06d
27 changed files with 1890 additions and 207 deletions
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get_error.py
+33 -35
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@@ -1,5 +1,6 @@
import os
import csv
import glob
import numpy as np
@@ -7,15 +8,12 @@ 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/iccv'
checkpoint_name = 'iccv_pyr_32_64_25p'
checkpoint_name_skirt = 'iccv_pyr_32_64_128_256_512_skirt'
iteration = '155000'
iteration_skirt : str = '115000'
checkpoint_name = 'iccv_pyr_32_64_128_skin'
checkpoint_name_skirt = 'iccv_pyr_32_64_128_skin_skirt'
iteration = '75' + '000'
iteration_skirt : str = '155' + '000'
error_type = 'collision' # 'euclidean' 'collision' edge normal
# checkpoint_name = 'abl_skin'
# checkpoint_name_skirt = checkpoint_name + '_skirt'
# iteration = '70000'
# iteration_skirt : str = iteration
errors = []
errors_skirt = []
@@ -23,20 +21,20 @@ if __name__ == '__main__':
per_garment_errors_skirt = { 'Tshirt': [], 'Dress': [], 'Skirt': [], '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(float(row[1]))
# with open(os.path.join(folder, f'{checkpoint_name}-{iteration}-{error_type}.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(float(row[1]))
print('\nNon-skirt:')
for key in per_garment_errors.keys():
avg = np.mean(per_garment_errors[key])
errors.append(avg)
print(key + ': ' + str(avg))
# print('\nNon-skirt:')
# for key in per_garment_errors.keys():
# avg = np.mean(per_garment_errors[key])
# errors.append(avg)
# print(key + ': ' + str(avg))
print('\nAvg non-skirt: ' + str(np.mean(errors)))
# print('\nAvg non-skirt: ' + str(np.mean(errors)))
with open(os.path.join(folder, checkpoint_name_skirt + '-' + iteration_skirt + '.csv'), 'r') as f:
with open(os.path.join(folder, f'{checkpoint_name_skirt}-{iteration_skirt}-{error_type}.csv'), 'r') as f:
csv_reader = csv.reader(f, delimiter=',')
for row in csv_reader:
per_garment_errors_skirt[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
@@ -49,24 +47,24 @@ if __name__ == '__main__':
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'])
# 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 top avg: ' + str(cum_avg_top))
# print('Global tshirt avg: ' + str(cum_avg_shirt))
# print('Global top avg: ' + str(cum_avg_top))
print('\nGlobal per garment: ')
# print('\nGlobal per garment: ')
global_errors = []
per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
per_garment_errors['Skirt'] = per_garment_errors_skirt['Skirt']
per_garment_errors['Dress'] = per_garment_errors_skirt['Dress']
for key in per_garment_errors.keys():
avg = np.mean(per_garment_errors[key])
global_errors.append(avg)
print(key + ': ' + str(avg), 'samples: ' + str(len(per_garment_errors[key])))
# global_errors = []
# per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
# per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
# per_garment_errors['Skirt'] = per_garment_errors_skirt['Skirt']
# per_garment_errors['Dress'] = per_garment_errors_skirt['Dress']
# for key in per_garment_errors.keys():
# avg = np.mean(per_garment_errors[key])
# global_errors.append(avg)
# print(key + ': ' + str(avg), 'samples: ' + str(len(per_garment_errors[key])))
print('Global avg: ' + str(np.mean(global_errors)))
print(len(global_errors))
# print('Global avg: ' + str(np.mean(global_errors)))
# print(len(global_errors))