def create_generator(self):
train_ann_fnames = self._get_train_anns()
valid_ann_fnames = self._get_valid_anns()
train_generator = BatchGenerator(train_ann_fnames,
self._train_config["train_image_folder"],
batch_size=self._train_config["batch_size"],
labels=self._model_config["labels"],
anchors=self._model_config["anchors"],
min_net_size=self._train_config["min_size"],
max_net_size=self._train_config["max_size"],
jitter=self._train_config["jitter"],
shuffle=True)
if len(valid_ann_fnames) > 0:
valid_generator = BatchGenerator(valid_ann_fnames,
self._train_config["valid_image_folder"],
batch_size=self._train_config["batch_size"],
labels=self._model_config["labels"],
anchors=self._model_config["anchors"],
min_net_size=self._model_config["net_size"],
max_net_size=self._model_config["net_size"],
jitter=False,
shuffle=False)
else:
valid_generator = None
print("Training samples : {}, Validation samples : {}".format(len(train_ann_fnames), len(valid_ann_fnames)))
return train_generator, valid_generator
def setup_generator(setup_train_dirs):
ann_fnames, image_root = setup_train_dirs
generator = BatchGenerator(ann_fnames,
image_root,
batch_size=2,
labels=["raccoon"],
min_net_size=288,
max_net_size=288,
jitter=False)
return generator
def create_generator(self, split_train_valid=False):
train_ann_fnames = self._get_train_anns()
valid_ann_fnames = self._get_valid_anns()
img_folder = 'valid_image_folder'
if split_train_valid:
train_valid_split = int(0.8 * len(train_ann_fnames))
np.random.seed(55)
np.random.shuffle(train_ann_fnames)
np.random.seed()
img_folder = 'train_image_folder'
train_ann_fnames, valid_ann_fnames = train_ann_fnames[:train_valid_split], train_ann_fnames[
train_valid_split:]
# valid_generator = None
valid_generator = BatchGenerator(
valid_ann_fnames,
self._train_config[img_folder],
batch_size=self._train_config['batch_size'],
labels=self._model_config['labels'],
anchors=self._model_config['anchors'],
min_net_size=self._model_config['net_size'],
max_net_size=self._model_config['net_size'],
jitter=False,
shuffle=False)
train_generator = BatchGenerator(
train_ann_fnames,
self._train_config['train_image_folder'],
batch_size=self._train_config['batch_size'],
labels=self._model_config['labels'],
anchors=self._model_config['anchors'],
min_net_size=self._train_config['min_size'],
max_net_size=self._train_config['max_size'],
jitter=self._train_config['jitter'],
shuffle=True)
print('Training samples : {}, Validation samples : {}'.format(
len(train_ann_fnames), len(valid_ann_fnames)))
return train_generator, valid_generator
def test_train(setup_tf_eager, setup_darknet_weights, setup_train_dirs):
ann_fnames, image_root = setup_train_dirs
darknet_weights = setup_darknet_weights
# 1. create generator
generator = BatchGenerator(ann_fnames,
image_root,
batch_size=2,
labels_naming=["raccoon"],
jitter=False)
valid_generator = BatchGenerator(ann_fnames,
image_root,
batch_size=2,
labels_naming=["raccoon"],
jitter=False)
# 2. create model
model = Yolonet(n_classes=1)
model.load_darknet_params(darknet_weights, True)
# 3. training
loss_history = train_fn(model, generator, valid_generator, num_epoches=3)
assert loss_history[0] > loss_history[-1]
'--config',
default="configs/raccoon.json",
help='config file')
if __name__ == '__main__':
args = argparser.parse_args()
with open(args.config) as data_file:
config = json.load(data_file)
# 1. create generator
ann_fnames = glob.glob(os.path.join(config["train"]["train_annot_folder"], "*.xml"))
print(len(ann_fnames))
train_generator = BatchGenerator(ann_fnames,
config["train"]["train_image_folder"],
batch_size=config["train"]["batch_size"],
labels_naming=config["model"]["labels"],
anchors=config["model"]["anchors"],
jitter=config["train"]["jitter"])
valid_generator = BatchGenerator(ann_fnames,
config["train"]["train_image_folder"],
batch_size=config["train"]["batch_size"],
labels_naming=config["model"]["labels"],
anchors=config["model"]["anchors"],
jitter=False,
shuffle=False)
print(train_generator.steps_per_epoch)
# 2. create model
model = Yolonet(n_classes=len(config["model"]["labels"]))