def test_model(dataset: dataset_json.Dataset, input_components, model_path):
model = architectures.safe_load_model(model_path, compile=False)
# architectures.apply_predict_decorator(model)
host = "127.0.0.1"
port = 9091
window = windowInterface() # create a window
config = {
"host": host,
"port": port,
"window": window,
"use_speed": (True, True),
"sleep_time": 0.01,
"PID_settings": [17, 0.5, 0.3, 1.0, 1.0],
"loop_settings": [True, False, False, True, False, False],
"buffer_time": 11,
"track": "warren",
"model": model,
"dataset": dataset,
"input_components": input_components,
}
universal_client(config, True, "model_test")
window.mainloop()
from custom_modules.datasets import dataset_json
from custom_modules.vis import vis_fe, vis_lab
physical_devices = tensorflow.config.list_physical_devices("GPU")
for gpu_instance in physical_devices:
tensorflow.config.experimental.set_memory_growth(gpu_instance, True)
base_path = os.path.expanduser("~") + "\\random_data"
dos = f"{base_path}\\donkeycar\\20-11-21\\"
Dataset = dataset_json.Dataset(["direction", "speed", "throttle"])
input_components = []
gdos = Dataset.load_dataset_sorted(dos, flat=True)
np.random.shuffle(gdos)
model = architectures.safe_load_model("test_model\\models\\maxpool_renault.h5", compile=True)
# architectures.apply_predict_decorator(model)
model.summary()
vis_model = tensorflow.keras.models.Model(model.inputs, model.layers[-1].output)
for labpath in gdos:
img, annotation = Dataset.load_img_and_annotation(labpath, to_list=False)
to_pred = Dataset.make_to_pred_annotations([img], [annotation], input_components)
prediction_dict, dt = model.predict(to_pred)
img = img / 255
for class_idx in range(vis_model.output.shape[1]):
vis_img = vis_fe.get_gradcam(vis_model, to_pred, class_idx, penultimate_layer=11)
vis_img = np.transpose(vis_img[0], (1, 2, 0))
base_path = os.path.expanduser("~") + "\\random_data"
dos = f"{base_path}\\donkey\\"
physical_devices = tensorflow.config.list_physical_devices("GPU")
for gpu_instance in physical_devices:
tensorflow.config.experimental.set_memory_growth(gpu_instance, True)
Dataset = dataset_json.Dataset(["direction", "speed", "throttle"])
input_components = []
gdos = Dataset.load_dataset(dos, flat=True)
np.random.shuffle(gdos)
model = architectures.safe_load_model("test_model\\models\\test_renault.h5", compile=False)
# architectures.apply_predict_decorator(model)
fe = architectures.get_fe(model)
fe.summary()
filter_indexes = []
for it, layer in enumerate(fe.layers):
if "activation" in layer.name:
filter_indexes.append(it)
for labpath in gdos:
img, annotation = Dataset.load_img_and_annotation(labpath, to_list=False)
for index in filter_indexes:
flip=True,
augm=True,
use_earlystop=False,
use_tensorboard=False,
use_plateau_lr=False,
verbose=True,
epochs=10,
batch_size=32,
show_distr=False,
)
# print(architectures.get_flops(save_path))
if simTest:
import sim_client
sim_client.test_model(Dataset, input_components, save_path)
else:
if testdosdir:
paths = Dataset.load_dataset_sorted(test_path, flat=True)
else:
paths = Dataset.load_dos_sorted(test_path)
model = architectures.safe_load_model(save_path, compile=False)
pred_function.test_compare_paths(Dataset,
input_components,
model,
paths,
waitkey=1,
apply_decorator=False)
dos = f"{base_path}\\donkeycar\\11-12-21\\1\\"
physical_devices = tensorflow.config.list_physical_devices("GPU")
for gpu_instance in physical_devices:
tensorflow.config.experimental.set_memory_growth(gpu_instance, True)
Dataset = dataset_json.Dataset(["direction", "speed", "throttle"])
input_components = []
# model = architectures.safe_load_model(
# "test_model\\models\\pretrained_1.h5", compile=False)
# architectures.apply_predict_decorator(model)
# model_outputs = architectures.get_model_output_names(model)
model1 = architectures.safe_load_model(
"test_model\\models\\test2_renault.tflite",
output_names=["direction", "throttle"])
model2 = architectures.safe_load_model(
"test_model\\models\\test3_renault.tflite",
output_names=["direction", "throttle"])
gdos = Dataset.load_dos_sorted(dos)
fig = plt.figure()
# plt.style.use("classic")
ax1 = fig.add_subplot(1, 1, 1)
ax2 = ax1.twinx()
ax3 = ax1.twinx()
###
ax1.clear()
joy = controller.XboxOneJoystick()
joy.init()
assert joy.connected is True
print("joy working")
# cap = camera.usbWebcam(topcrop=0.2, botcrop=0.0)
cap = cv2.VideoCapture(0)
print("cam working")
basedir = os.path.dirname(os.path.abspath(__file__))
# model = architectures.safe_load_model(f"{basedir}/models/auto_label7.h5", compile=False)
# Load TFLite model
model = architectures.safe_load_model(
f"{basedir}/../test_model/models/test2_renault.tflite", ["direction"])
# checking if the controller is working properly
joy_leftX = 0
while joy_leftX <= 0.9:
joy_leftX = joy.axis_states["x"]
print(joy_leftX, end="\r")
time.sleep(0.01)
while joy_leftX >= -0.9:
joy_leftX = joy.axis_states["x"]
print(joy_leftX, end="\r")
time.sleep(0.01)
print("Starting mainloop")
"loop_settings": [True, False, False, True, False, False],
"buffer_time": 11,
"track": "warren",
"model": model,
"dataset": dataset,
"input_components": input_components,
}
universal_client(config, True, "model_test")
window.mainloop()
if __name__ == "__main__":
model_path = os.getcwd() + os.path.normpath(
"/test_model/models/test_renault.tflite")
model = architectures.safe_load_model(model_path,
output_names=["direction"])
# model.summary()
dataset = dataset_json.Dataset(["direction", "speed", "throttle", "time"])
input_components = []
hosts = ["127.0.0.1", "donkey-sim.roboticist.dev", "sim.diyrobocars.fr"]
host = hosts[0]
port = 9091
window = windowInterface() # create a window
config = {
"host": host,
"port": port,
"window": window,
def __init__(self, Dataset, model_path):
self.Dataset = Dataset
model = architectures.safe_load_model(model_path, compile=False)
self.fe = architectures.get_flat_fe(model)