def vision_predict(source, target, train_pid_path, train_score_path, test_pid_path, test_score_path):
source_model_path, target_dataset_path = get_source_target_info(source, target)
target_probe_path = target_dataset_path + '/probe'
target_train_path = target_dataset_path + '/train'
target_gallery_path = target_dataset_path + '/test'
train_pair_predict(source_model_path, target_train_path, train_pid_path, train_score_path)
test_pair_predict(source_model_path, target_probe_path, target_gallery_path, test_pid_path, test_score_path)
predict_eval(target, test_pid_path)
batch_size=batch_size
)
def pair_transfer_2grid():
DATASET = '/home/cwh/coding/grid_train_probe_gallery/cross0'
LIST = os.path.join(DATASET, 'pretrain/test_track.txt')
TRAIN = os.path.join(DATASET, 'pretrain')
train_images = reid_img_prepare(LIST, TRAIN)
batch_size = 64
# similar_persons = np.genfromtxt('../pretrain/grid_cross0/train_renew_pid.log', delimiter=' ')
# similar_matrix = np.genfromtxt('../pretrain/grid_cross0/train_renew_ac.log', delimiter=' ')
similar_persons = np.genfromtxt('../pretrain/grid_cross0/cross_filter_pid.log', delimiter=' ') - 1
similar_matrix = np.genfromtxt('../pretrain/grid_cross0/cross_filter_score.log', delimiter=' ')
pair_transfer(
pair_generator_by_rank_list(train_images, batch_size, similar_persons, similar_matrix, train=True),
pair_generator_by_rank_list(train_images, batch_size, similar_persons, similar_matrix, train=False),
'../pretrain/pair_pretrain.h5',
batch_size=batch_size
)
if __name__ == '__main__':
pair_transfer_2grid()
test_pair_predict('../transfer/pair_transfer.h5',
'/home/cwh/coding/grid_train_probe_gallery/cross0/probe',
'/home/cwh/coding/grid_train_probe_gallery/cross0/gallery',
'pid_path', 'score_path'
)
pair_generator_by_rank_list(train_images, batch_size, similar_persons, similar_matrix, train=False),
target,
batch_size=batch_size, num_classes=class_count
)
if __name__ == '__main__':
# sources = ['cuhk_grid_viper_mix']
sources = ['cuhk']
target = 'market'
pair_model('../pretrain/cuhk_pair_pretrain.h5', 751)
for source in sources:
pair_pretrain_on_dataset(source, target)
transform_dir = '/home/cwh/coding/Market-1501'
safe_remove('pair_transfer_pid.log')
test_pair_predict('market_pair_pretrain.h5',
transform_dir + '/probe', transform_dir + '/test',
'pair_transfer_pid.log', 'pair_transfer_score.log')
market_result_eval('pair_transfer_pid.log', TEST='/home/cwh/coding/Market-1501/test',
QUERY='/home/cwh/coding/Market-1501/probe')
# sources = ['grid-cv-%d' % i for i in range(10)]
# for source in sources:
# softmax_pretrain_on_dataset(source,
# project_path='/home/cwh/coding/rank-reid',
# dataset_parent='/home/cwh/coding')
# pair_pretrain_on_dataset(source,
# project_path='/home/cwh/coding/rank-reid',
# dataset_parent='/home/cwh/coding')