def config_datasets(data):
train_path = data["train_path"]
valid_path = data["valid_path"]
minibatch_size = int(data["minibatch_size"])
rgb_noise = float(data["data_augmentation"]["rgb_noise"])
depth_noise = float(data["data_augmentation"]["depth_noise"])
occluder_path = data["data_augmentation"]["occluder_path"]
background_path = data["data_augmentation"]["background_path"]
blur_noise = int(data["data_augmentation"]["blur_noise"])
h_noise = float(data["data_augmentation"]["h_noise"])
s_noise = float(data["data_augmentation"]["s_noise"])
v_noise = float(data["data_augmentation"]["v_noise"])
channel_hide = data["data_augmentation"]["channel_hide"] == "True"
data_augmentation = DataAugmentation()
data_augmentation.set_rgb_noise(rgb_noise)
data_augmentation.set_depth_noise(depth_noise)
if occluder_path != "":
data_augmentation.set_occluder(occluder_path)
if background_path != "":
data_augmentation.set_background(background_path)
if channel_hide:
data_augmentation.set_channel_hide(0.25)
data_augmentation.set_blur(blur_noise)
data_augmentation.set_hsv_noise(h_noise, s_noise, v_noise)
message_logger.info("Setup Train : {}".format(train_path))
train_dataset = Dataset(train_path, minibatch_size=minibatch_size)
if not train_dataset.load():
message_logger.error("Train dataset empty")
sys.exit(-1)
train_dataset.set_data_augmentation(data_augmentation)
train_dataset.compute_mean_std()
message_logger.info("Computed mean : {}\nComputed Std : {}".format(
train_dataset.mean, train_dataset.std))
message_logger.info("Setup Valid : {}".format(valid_path))
valid_dataset = Dataset(valid_path,
minibatch_size=minibatch_size,
max_samples=20000)
if not valid_dataset.load():
message_logger.error("Valid dataset empty")
sys.exit(-1)
valid_dataset.set_data_augmentation(data_augmentation)
valid_dataset.mean = train_dataset.mean
valid_dataset.std = train_dataset.std
return train_dataset, valid_dataset
def config_datasets(data):
train_path = data["train_path"]
valid_path = data["valid_path"]
minibatch_size = int(data["minibatch_size"])
rgb_noise = float(data["data_augmentation"]["rgb_noise"])
depth_noise = float(data["data_augmentation"]["depth_noise"])
occluder_path = data["data_augmentation"]["occluder_path"]
background_path = data["data_augmentation"]["background_path"]
blur_noise = int(data["data_augmentation"]["blur_noise"])
hue_noise = float(data["data_augmentation"]["hue_noise"])
data_augmentation = DataAugmentation()
data_augmentation.set_rgb_noise(rgb_noise)
data_augmentation.set_depth_noise(depth_noise)
if occluder_path != "":
data_augmentation.set_occluder(occluder_path)
if background_path != "":
data_augmentation.set_background(background_path)
data_augmentation.set_blur(blur_noise)
data_augmentation.set_hue_noise(hue_noise)
train_dataset = Dataset(train_path, minibatch_size=minibatch_size)
if not train_dataset.load():
message_logger.error("Train dataset empty")
sys.exit(-1)
train_dataset.set_data_augmentation(data_augmentation)
train_dataset.compute_mean_std()
message_logger.info("Computed mean : {}\nComputed Std : {}".format(train_dataset.mean, train_dataset.std))
valid_dataset = Dataset(valid_path, minibatch_size=minibatch_size)
if not valid_dataset.load():
message_logger.error("Valid dataset empty")
sys.exit(-1)
valid_dataset.set_data_augmentation(data_augmentation)
valid_dataset.mean = train_dataset.mean
valid_dataset.std = train_dataset.std
return train_dataset, valid_dataset
