def load_pretrained_model(self, model):
model_name = get_model_name(model)
pretrained_filename = self.get_last_checkpoints_filename(model_name)
if pretrained_filename is not None and os.path.isfile(
pretrained_filename):
info(
f"Found pretrained model at {pretrained_filename}, loading...")
model = type(model).load_from_checkpoint(pretrained_filename)
return model
def __init__(self, images_dir):
super(ImageClassificationDataset, self).__init__()
self.images_dir = images_dir
self.images = self.get_images_list()
info(f"images count = {len(self.images)}")
self.means = [0.13499917, 0.13499917, 0.13499917]
self.stdevs = [0.29748289, 0.29748289, 0.29748289]
if self.image_channels == 1:
self.means = [0.13500238, 0.13500238, 0.13500238]
self.stdevs = [0.29748997, 0.29748997, 0.29748997]
def move_yolo_txt_files(self, yolo_txt_files, train_or_valid):
target_dir = f"{self.work_dir}/{train_or_valid}/labels"
confirm_dirs(target_dir)
for txt_file in yolo_txt_files:
txt_name = os.path.basename(txt_file)
target_file = f"{target_dir}/{txt_name}"
if os.path.isfile(target_file):
continue
info(f"{txt_file} -> {target_file}")
shutil.move(txt_file, target_file)
def move_image_files(self, yolo_txt_files, train_or_valid):
target_dir = f"{self.work_dir}/{train_or_valid}/images"
confirm_dirs(target_dir)
for txt_file in yolo_txt_files:
txt_name = os.path.basename(txt_file).split('.')[0]
source_file = f"{self.work_dir}/images/{txt_name}.jpg"
target_file = f"{target_dir}/{txt_name}.jpg"
if os.path.isfile(target_file):
continue
info(f"{source_file} -> {target_file}")
shutil.move(source_file, target_file)
def load_pretrained_model(self, save_name):
# Check whether pretrained model exists. If yes, load it and skip training
pretrained_filename = self.get_pretrained_filename(save_name)
if os.path.isfile(pretrained_filename):
info("Found pretrained model at %s, loading..." %
pretrained_filename)
model = LitModelTrainer.load_from_checkpoint(pretrained_filename)
else:
# pl.seed_everything(42) # To be reproducable
info(f"model name = {save_name}")
model = self
return model
def update_anchors_txt_file(darknet_anchors_txt_path, cfg_file_path):
#anchors_txt_path = f"{work_dir}/../darknet/anchors.txt"
with open(darknet_anchors_txt_path, 'r') as f:
anchors_result = f.readline()
info(f"{anchors_result}")
regex = re.compile(r'anchors = (.+)')
match = regex.search(anchors_result)
if not match:
raise Exception("fail")
anchors_txt = match.group(1)
sed(cfg_file_path, f"219s/anchors = .+/anchors = {anchors_txt}")
sed(cfg_file_path, f"268s/anchors = .+/anchors = {anchors_txt}")
def split_train_valid_data(self):
self.restore_train_data()
yolo_txt_files = glob.glob(f"{self.yolo_txt_dir}/*.txt")
info(f"yolo_txt_files = {len(yolo_txt_files)}")
train_yolo_txt_files, valid_yolo_txt_files = shuffle_split(
yolo_txt_files)
info(
f"train_size={len(train_yolo_txt_files)} valid_size={len(valid_yolo_txt_files)}"
)
self.clean_yolo_txt_files()
self.move_image_files(train_yolo_txt_files, "train")
self.move_yolo_txt_files(train_yolo_txt_files, "train")
self.move_image_files(valid_yolo_txt_files, "valid")
self.move_yolo_txt_files(valid_yolo_txt_files, "valid")
def get_train_validation_loaders(self):
train_dataset = self
dataset_size = len(train_dataset)
indices = list(range(dataset_size))
split = int(np.floor(self.validation_split * dataset_size))
if self.shuffle_dataset:
np.random.seed(self.random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
info(f"train size={len(train_indices)} val size={len(val_indices)}")
train_loader = self.__get_data_loader(train_indices)
validation_loader = self.__get_data_loader(val_indices)
return train_loader, validation_loader
def main():
args = parse_args()
images_dir = args.images
dataset_dir = args.dataset_dir
classes_dict = ClassesDict()
train_images_dir = f"{dataset_dir}/train2021"
val_images_dir = f"{dataset_dir}/val2021"
info(f"images_dir = {images_dir}")
images = glob.glob(f"{images_dir}/*.jpg")
train_images, val_images = shuffle_split(images)
info(f"train_images = {len(train_images)}")
info(f"val_images = {len(val_images)}")
restore_images_folder(train_images_dir, images_dir)
restore_images_folder(val_images_dir, images_dir)
move_image_files_to_dataset_folder(train_images, train_images_dir)
move_image_files_to_dataset_folder(val_images, val_images_dir)
generate_annotations_files(classes_dict, dataset_dir, train_images_dir,
"train2021")
generate_annotations_files(classes_dict, dataset_dir, val_images_dir,
"val2021")
def compute_mean_std(self):
"""
計算數據集的均值和標準差
: return :
"""
info(f"compute mean std...")
dataset = self.__get_all_images()
num_imgs = len(self.images)
for data in dataset:
data = np.asarray(data) / 255.0
mean = np.mean(data, axis=(0, 1))
std = np.std(data, axis=(0, 1))
self.means += mean
self.stdevs += std
# img = data[0]
# for i in range(3):
# # 一個通道的均值和標準差
# self.means[i] += img[i, :, :].mean()
# self.stdevs[i] += img[i, :, :].std()
self.means = np.asarray(self.means) / num_imgs
self.stdevs = np.asarray(self.stdevs) / num_imgs
return self.means, self.stdevs
def main():
ap = argparse.ArgumentParser()
# ap.add_argument('--toggle', '--no-toggle', dest='toggle', action=NegateAction, nargs=0)
ap.add_argument('--convert', dest='toggle', nargs=0)
ap.add_argument('--update_anchors', dest='toggle', nargs=0)
args = ap.parse_args()
work_dir = "d:/demo/training_yolo",
cfg_dir = f"{work_dir}/cfg",
cfg_file_path = f"{cfg_dir}/yolov4-tiny-obj.cfg",
config = {work_dir, cfg_dir, cfg_file_path}
if args.update_anchors:
run_update_anchors(args, config)
return
if args.convert:
run_convert_annotation_xml_to_yolo_data(args, config)
return
info(f"--convert")
info(f"--update_anchors")
def run_convert_annotation_xml_to_yolo_data(args, **config):
info(f"convert annotation xml to yolo data")
p = ConvertAnnotationXmlToYoloHelper()
p.convert()
def run_update_anchors(args, **config):
info(f"update anchars")
update_anchors_txt_file(f"{config.work_dir}/../darknet/anchors.txt",
config.cfg_file_path)
