def get_model_by_type(model_type, cfg):
from donkeycar.parts.keras import KerasRNN_LSTM, KerasBehavioral, KerasCategorical, KerasIMU, KerasLinear, Keras3D_CNN, KerasLocalizer, KerasLatent
if model_type is None:
model_type = "categorical"
input_shape = (cfg.IMAGE_H, cfg.IMAGE_W, cfg.IMAGE_DEPTH)
roi_crop = (cfg.ROI_CROP_TOP, cfg.ROI_CROP_BOTTOM)
if model_type == "localizer" or cfg.TRAIN_LOCALIZER:
kl = KerasLocalizer(num_outputs=2, num_behavior_inputs=len(cfg.BEHAVIOR_LIST), num_locations=cfg.NUM_LOCATIONS, input_shape=input_shape)
elif model_type == "behavior" or cfg.TRAIN_BEHAVIORS:
kl = KerasBehavioral(num_outputs=2, num_behavior_inputs=len(cfg.BEHAVIOR_LIST), input_shape=input_shape)
elif model_type == "imu":
kl = KerasIMU(num_outputs=2, num_imu_inputs=6, input_shape=input_shape)
elif model_type == "linear":
kl = KerasLinear(input_shape=input_shape, roi_crop=roi_crop)
elif model_type == "3d":
kl = Keras3D_CNN(image_w=cfg.IMAGE_W, image_h=cfg.IMAGE_H, image_d=cfg.IMAGE_DEPTH, seq_length=cfg.SEQUENCE_LENGTH)
elif model_type == "rnn":
kl = KerasRNN_LSTM(image_w=cfg.IMAGE_W, image_h=cfg.IMAGE_H, image_d=cfg.IMAGE_DEPTH, seq_length=cfg.SEQUENCE_LENGTH)
elif model_type == "categorical":
kl = KerasCategorical(input_shape=input_shape, throttle_range=cfg.MODEL_CATEGORICAL_MAX_THROTTLE_RANGE, roi_crop=roi_crop)
elif model_type == "latent":
kl = KerasLatent(input_shape=input_shape)
else:
raise Exception("unknown model type: %s" % model_type)
return kl
def get_model_by_type(model_type, cfg):
'''
given the string model_type and the configuration settings in cfg
create a Keras model and return it.
'''
from donkeycar.parts.keras import KerasRNN_LSTM, KerasBehavioral, KerasCategorical, KerasIMU, KerasLinear, Keras3D_CNN, KerasLocalizer, KerasLatent
from donkeycar.parts.tflite import TFLitePilot
if model_type is None:
model_type = cfg.DEFAULT_MODEL_TYPE
print("\"get_model_by_type\" model Type is: {}".format(model_type))
input_shape = (cfg.IMAGE_H, cfg.IMAGE_W, cfg.IMAGE_DEPTH)
roi_crop = (cfg.ROI_CROP_TOP, cfg.ROI_CROP_BOTTOM)
if model_type == "tflite_linear":
kl = TFLitePilot()
elif model_type == "localizer" or cfg.TRAIN_LOCALIZER:
kl = KerasLocalizer(num_outputs=2,
num_behavior_inputs=len(cfg.BEHAVIOR_LIST),
num_locations=cfg.NUM_LOCATIONS,
input_shape=input_shape)
elif model_type == "behavior" or cfg.TRAIN_BEHAVIORS:
kl = KerasBehavioral(num_outputs=2,
num_behavior_inputs=len(cfg.BEHAVIOR_LIST),
input_shape=input_shape)
elif model_type == "imu":
kl = KerasIMU(num_outputs=2, num_imu_inputs=6, input_shape=input_shape)
elif model_type == "linear":
kl = KerasLinear(input_shape=input_shape, roi_crop=roi_crop)
elif model_type == "tensorrt_linear":
# Aggressively lazy load this. This module imports pycuda.autoinit which causes a lot of unexpected things
# to happen when using TF-GPU for training.
from donkeycar.parts.tensorrt import TensorRTLinear
kl = TensorRTLinear(cfg=cfg)
elif model_type == "3d":
kl = Keras3D_CNN(image_w=cfg.IMAGE_W,
image_h=cfg.IMAGE_H,
image_d=cfg.IMAGE_DEPTH,
seq_length=cfg.SEQUENCE_LENGTH)
elif model_type == "rnn":
kl = KerasRNN_LSTM(image_w=cfg.IMAGE_W,
image_h=cfg.IMAGE_H,
image_d=cfg.IMAGE_DEPTH,
seq_length=cfg.SEQUENCE_LENGTH)
elif model_type == "categorical":
kl = KerasCategorical(
input_shape=input_shape,
throttle_range=cfg.MODEL_CATEGORICAL_MAX_THROTTLE_RANGE,
roi_crop=roi_crop)
elif model_type == "latent":
kl = KerasLatent(input_shape=input_shape)
else:
raise Exception("unknown model type: %s" % model_type)
return kl
