def drive(self,
ip='127.0.0.1',
port=50007,
model='cnn_18.pkl',
crop_start=126,
crop_end=226):
print("Loading model: %s" % model)
cnn = CNNDriver()
# Model file trained with gpu need to be remaped on CPU
if self.__device.type == 'cpu':
cnn.load_state_dict(torch.load(model, map_location='cpu'))
else:
cnn.load_state_dict(torch.load(model))
cnn.eval()
cnn = cnn.to(self.__device)
transformations = transforms.Compose([transforms.ToTensor()])
comm = game_communication.GameTelemetry(ip, port)
comm.connect()
# Run until Crtl-C
try:
list_records = []
degrees = 0
while True:
# Sleep for 50ms
time.sleep(0.05)
# Get telemetry and image
telemetry = comm.get_game_data()
cam_img = comm.get_image()
# Skip entire record if image is invalid
if (cam_img is None) or (telemetry is None):
continue
start = time.time()
# Resize image to the format expected by the model
cam_img_res = (scipy.misc.imresize(
np.array(cam_img)[crop_start:crop_end], [66, 200]))
torch_tensor = transformations(cam_img_res)
cam_img_res = torch_tensor.unsqueeze(0)
cam_img_res = cam_img_res.to(self.__device)
# Get steering angle from model
degrees = cnn(cam_img_res)
# Convert Variable to numpy
degrees = float(degrees.data.cpu().numpy())
end = time.time()
elapsed_seconds = float("%.2f" % (end - start))
print('Elapsed time:', elapsed_seconds, 'angle:', degrees)
# Send command to game here...
commands = [degrees, 0.5]
comm.send_command(commands)
except KeyboardInterrupt:
pass
def game_pilot(ip, port, model_path, gpu, crop_start=126, crop_end=226):
# Set enviroment variable to set the GPU to use
if gpu != -1:
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu)
else:
print('Set tensorflow on CPU')
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Build model and get references to placeholders
driving_model = model.DrivingModel(training_mode=False)
model_in = driving_model.input
model_out = driving_model.output
model_drop = driving_model.dropout_control
train_mode = driving_model.train_mode
# Load tensorflow model
print("Loading model: %s" % model_path)
sess = tf.InteractiveSession()
saver = tf.train.Saver()
saver.restore(sess, model_path)
print(ip)
print(port)
comm = game_communication.GameTelemetry(ip, port)
comm.connect()
# Run until Crtl-C
try:
list_records = []
while True:
# Get telemetry and image
telemetry = comm.get_game_data()
cam_img = comm.get_image()
# Skip entire record if image is invalid
if (cam_img is None) or (telemetry is None):
continue
# Sleep for 50ms
time.sleep(0.05)
# Resize image to the format expected by the model
cam_img_res = scipy.misc.imresize(np.array(cam_img)[crop_start:crop_end], [66, 200]) / 255.0
# Get steering angle from tensorflow model (Also convert from rad to degree), dropout 0 at test time.
degrees = model_out.eval(feed_dict={model_in: [cam_img_res], model_drop: 0.0, train_mode: False})[0][0]
print(degrees)
# Send command to game here...
commands = [degrees, 0.5]
comm.send_command(commands)
except KeyboardInterrupt:
pass
def connect_and_create_dataset(ip, port):
print(ip)
print(port)
comm = game_communication.GameTelemetry(ip, port)
comm.connect()
# Run until Crtl-C
img_index = 0
try:
list_records = []
while True:
# Get telemetry and image
telemetry = comm.get_game_data()
cam_img = comm.get_image()
# Skip entire record if image is invalid
if (cam_img is None) or (telemetry is None):
continue
# Sleep for 30ms
time.sleep(0.03)
img_index += 1
# Add elements on the list until it's bigger than 20 elements
if len(list_records) < 20:
list_records.append(GameRecord(img_index, cam_img, telemetry))
else:
# From the main thread add item on the queue
if not list_game_state.full():
#print("Add elements to assync queue")
list_game_state.put(list_records)
list_records = []
list_records.append(GameRecord(img_index, cam_img, telemetry))
except KeyboardInterrupt:
pass
def game_pilot(ip, port, model_path, gpu, crop_start=126, crop_end=226):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Set enviroment variable to set the GPU to use
# Load model
# https://stackoverflow.com/questions/42703500/best-way-to-save-a-trained-model-in-pytroch
print("Loading model: %s" % model_path)
cnn = CNNDriver()
# Model file trained with gpu need to be remaped on CPU
if device.type == 'cpu':
cnn.load_state_dict(torch.load(model_path, map_location='cpu'))
else:
cnn.load_state_dict(torch.load(model_path))
cnn.eval()
cnn = cnn.to(device)
#transformations = transforms.Compose([
#transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
transformations = transforms.Compose([transforms.ToTensor()])
print(ip)
print(port)
comm = game_communication.GameTelemetry(ip, port)
comm.connect()
# Run until Crtl-C
try:
list_records = []
degrees = 0
while True:
# Sleep for 50ms
time.sleep(0.05)
# Get telemetry and image
telemetry = comm.get_game_data()
cam_img = comm.get_image()
# Skip entire record if image is invalid
if (cam_img is None) or (telemetry is None):
continue
start = time.time()
# Resize image to the format expected by the model
cam_img_res = (scipy.misc.imresize(
np.array(cam_img)[crop_start:crop_end], [66, 200]))
#cam_img_res = cam_img_res.transpose([2, 0, 1]).astype(np.float32)
#cam_img_res = np.expand_dims(cam_img_res, axis=0)
#torch_tensor = torch.from_numpy(cam_img_res).unsqueeze(0)
#torch_tensor = trfNorm(torch_tensor)
torch_tensor = transformations(cam_img_res)
cam_img_res = torch_tensor.unsqueeze(0)
cam_img_res = cam_img_res.to(device)
# Get steering angle from model
degrees = cnn(cam_img_res)
# Convert Variable to numpy
degrees = float(degrees.data.cpu().numpy())
#time.sleep(0.55)
end = time.time()
elapsed_seconds = float("%.2f" % (end - start))
print('Elapsed time:', elapsed_seconds, 'angle:', degrees)
# Send command to game here...
commands = [degrees, 0.5]
comm.send_command(commands)
except KeyboardInterrupt:
pass