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
def get_model_by_type(model_type, cfg):
from donkeycar.parts.keras import KerasRNN_LSTM, KerasBehavioral, KerasCategorical, KerasIMU, KerasLinear, Keras3D_CNN, KerasLocalizer
if model_type is None:
model_type = "categorical"
input_shape = (cfg.IMAGE_H, cfg.IMAGE_W, cfg.IMAGE_DEPTH)
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)
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)
else:
raise Exception("unknown model type: %s" % model_type)
return kl
def get_model_by_type(model_type: str, cfg: 'Config') -> 'KerasPilot':
'''
given the string model_type and the configuration settings in cfg
create a Keras model and return it.
'''
from donkeycar.parts.keras import KerasCategorical, KerasLinear, \
KerasInferred, KerasIMU, KerasMemory, KerasBehavioral, KerasLocalizer, \
KerasLSTM, Keras3D_CNN
from donkeycar.parts.interpreter import KerasInterpreter, TfLite, TensorRT
if model_type is None:
model_type = cfg.DEFAULT_MODEL_TYPE
logger.info(f'get_model_by_type: model type is: {model_type}')
input_shape = (cfg.IMAGE_H, cfg.IMAGE_W, cfg.IMAGE_DEPTH)
if 'tflite_' in model_type:
interpreter = TfLite()
used_model_type = model_type.replace('tflite_', '')
elif 'tensorrt_' in model_type:
interpreter = TensorRT()
used_model_type = model_type.replace('tensorrt_', '')
else:
interpreter = KerasInterpreter()
used_model_type = model_type
used_model_type = EqMemorizedString(used_model_type)
if used_model_type == "linear":
kl = KerasLinear(interpreter=interpreter, input_shape=input_shape)
elif used_model_type == "categorical":
kl = KerasCategorical(
interpreter=interpreter,
input_shape=input_shape,
throttle_range=cfg.MODEL_CATEGORICAL_MAX_THROTTLE_RANGE)
elif used_model_type == 'inferred':
kl = KerasInferred(interpreter=interpreter, input_shape=input_shape)
elif used_model_type == "imu":
kl = KerasIMU(interpreter=interpreter, input_shape=input_shape)
elif used_model_type == "memory":
mem_length = getattr(cfg, 'SEQUENCE_LENGTH', 3)
mem_depth = getattr(cfg, 'MEM_DEPTH', 0)
kl = KerasMemory(interpreter=interpreter,
input_shape=input_shape,
mem_length=mem_length,
mem_depth=mem_depth)
elif used_model_type == "behavior":
kl = KerasBehavioral(
interpreter=interpreter,
input_shape=input_shape,
throttle_range=cfg.MODEL_CATEGORICAL_MAX_THROTTLE_RANGE,
num_behavior_inputs=len(cfg.BEHAVIOR_LIST))
elif used_model_type == 'localizer':
kl = KerasLocalizer(interpreter=interpreter,
input_shape=input_shape,
num_locations=cfg.NUM_LOCATIONS)
elif used_model_type == 'rnn':
kl = KerasLSTM(interpreter=interpreter,
input_shape=input_shape,
seq_length=cfg.SEQUENCE_LENGTH)
elif used_model_type == '3d':
kl = Keras3D_CNN(interpreter=interpreter,
input_shape=input_shape,
seq_length=cfg.SEQUENCE_LENGTH)
else:
known = [
k + u for k in ('', 'tflite_', 'tensorrt_')
for u in used_model_type.mem
]
raise ValueError(
f"Unknown model type {model_type}, supported types are"
f" { ', '.join(known)}")
return kl