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lot more training

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luni
2023-03-03 12:45:49 +00:00
parent d93678ee1a
commit 3b414e76d3
25 changed files with 1632 additions and 54 deletions
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calculate_error.py
+15 -10
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@@ -1,6 +1,6 @@
import os
import sys
from configs.config_abl_all_post import config
from configs.iccv.config_abl_pyr_32_curv import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
# import tensorflow as tf
@@ -59,21 +59,26 @@ if __name__ == '__main__':
folder = config['test_dir']
checkpoint_name = config['name']
iteration = ''
# iteration = '200000'
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration
output_folder = '/home/workspace/local-data/results/iccv/' + checkpoint_name + '-'
# check if there is a folder that starts with this name using glob
folders = glob.glob(output_folder + '*')
assert len(folders) == 1, 'There should be only one checkpoint that starts with this name'
output_folder = folders[0]
iteration = output_folder.split('-')[-1]
faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
ids = os.listdir(faces_folder)
ids = [id.split('.')[0] for id in ids]
faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
# faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
# ids = os.listdir(faces_folder)
# ids = [id.split('.')[0] for id in ids]
# faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
masks_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
masks_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
ids = os.listdir(masks_folder)
ids = [id.split('.')[0] for id in ids]
masks = {id : np.load(masks_folder + '/' + id + '.npy') for id in ids }
@@ -86,7 +91,7 @@ if __name__ == '__main__':
else:
gt_name = 'mesh_posed'
orig_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
orig_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
errors = dict()
if config['is_skirt']:
@@ -107,7 +112,7 @@ if __name__ == '__main__':
for key in per_garment_errors.keys():
print(key + ': ' + str(np.mean(per_garment_errors[key])))
with open('/home/workspace/data/results/errors/post/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
with open('/home/workspace/data/results/errors/iccv/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
file_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
for key in sorted(errors.keys()):