def setup(self, path_to_config_file):
yaml_conf, checkpoint_number = checkpoint_parse_configuration_file(path_to_config_file)
# Take the checkpoint name and load it
checkpoint = torch.load(os.path.join(os.sep, os.path.join(*os.path.realpath(__file__).split(os.sep)[:-2]),
'_logs',
yaml_conf.split(os.sep)[-2], yaml_conf.split('/')[-1].split('.')[-2]
, 'checkpoints', str(checkpoint_number) + '.pth'))
# do the merge here
merge_with_yaml(os.path.join(os.sep, os.path.join(*os.path.realpath(__file__).split(os.sep)[:-2]),
yaml_conf))
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
logging.info("Setup Model")
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self.latest_image = None
self.latest_image_tensor = None
# We add more time to the curve commands
self._expand_command_front = 5
self._expand_command_back = 3
self.track = 2 # Track.CAMERAS
def setup(self, path_to_config_file):
yaml_conf, checkpoint_number = checkpoint_parse_configuration_file(path_to_config_file)
# Take the checkpoint name and load it
checkpoint = torch.load(os.path.join('/', os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
'_logs',
yaml_conf.split('/')[-2], yaml_conf.split('/')[-1].split('.')[-2]
, 'checkpoints', str(checkpoint_number) + '.pth'))
# merge the specific agent config with global config _g_conf
merge_with_yaml(os.path.join('/', os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
yaml_conf))
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
# TODO: retrain the model with MPSC
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
logging.info("Setup Model")
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self.latest_image = None
self.latest_image_tensor = None
# We add more time to the curve commands
self._expand_command_front = 5
self._expand_command_back = 3
# check map waypoint format => carla_data_provider & http://carla.org/2018/11/16/release-0.9.1/
# e.g. from map.get_waypoint Waypoint(Transform(Location(x=338.763, y=226.453, z=0), Rotation(pitch=360, yaw=270.035, roll=0)))
self.track = Track.ALL_SENSORS_HDMAP_WAYPOINTS # specify available track info, see autonomous_agent.py
def setup(self, path_to_config_file):
yaml_conf, checkpoint_number = checkpoint_parse_configuration_file(path_to_config_file)
# Take the checkpoint name and load it
checkpoint = torch.load(os.path.join('/', os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
'_logs',
yaml_conf.split('/')[-2], yaml_conf.split('/')[-1].split('.')[-2]
, 'checkpoints', str(checkpoint_number) + '.pth'))
# do the merge here
merge_with_yaml(os.path.join('/', os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
yaml_conf))
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
self.first_iter = True
logging.info("Setup Model")
# Load the model and prepare set it for evaluation
self._model.load_state_dict(checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
# Set ERFnet for segmentation
self.model_erf = ERFNet(20)
self.model_erf = torch.nn.DataParallel(self.model_erf)
self.model_erf = self.model_erf.cuda()
print("LOAD ERFNet - drive")
def load_my_state_dict(model, state_dict): #custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
return model
self.model_erf = load_my_state_dict(self.model_erf, torch.load(os.path.join('trained_models/erfnet_pretrained.pth')))
self.model_erf.eval()
print ("ERFNet and weights LOADED successfully")
self.latest_image = None
self.latest_image_tensor = None
# We add more time to the curve commands
self._expand_command_front = 5
self._expand_command_back = 3
self.track = Track.CAMERAS
def setup(self, path_to_config_file):
yaml_conf, checkpoint_number, agent_name, encoder_params = checkpoint_parse_configuration_file(
path_to_config_file)
# Take the checkpoint name and load it
if encoder_params is not None:
self.checkpoint = torch.load(
os.path.join(
'/',
os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
'_logs',
yaml_conf.split('/')[-2],
yaml_conf.split('/')[-1].split('.')[-2] + '_' +
str(encoder_params['encoder_checkpoint']), 'checkpoints',
str(checkpoint_number) + '.pth'))
# Once the ENCODER_MODEL_CONFIGURATION was defined, we use the pre-trained encoder model to extract bottleneck Z and drive the E-t-E agent
self.encoder_checkpoint = torch.load(
os.path.join(
'/',
os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
'_logs', encoder_params['encoder_folder'],
encoder_params['encoder_exp'], 'checkpoints',
str(encoder_params['encoder_checkpoint']) + '.pth'))
self.encoder_model = CoILModel(g_conf.ENCODER_MODEL_TYPE,
g_conf.ENCODER_MODEL_CONFIGURATION)
self.encoder_model.load_state_dict(
self.encoder_checkpoint['state_dict'])
self.encoder_model.cuda()
self.encoder_model.eval()
else:
self.checkpoint = torch.load(
os.path.join(
'/',
os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
'_logs',
yaml_conf.split('/')[-2],
yaml_conf.split('/')[-1].split('.')[-2], 'checkpoints',
str(checkpoint_number) + '.pth'))
# do the merge here
# TODO THE MERGE IS REQUIRED DEPENDING ON THE SITUATION
g_conf.immutable(False)
merge_with_yaml(
os.path.join(
'/', os.path.join(*os.path.realpath(__file__).split('/')[:-2]),
yaml_conf), encoder_params)
self._model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION,
g_conf.ENCODER_MODEL_CONFIGURATION)
self.first_iter = True
logging.info("Setup Model")
# Load the model and prepare set it for evaluation
self._model.load_state_dict(self.checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
self.latest_image = None
self.latest_image_tensor = None
# We add more time to the curve commands
self._expand_command_front = 5
self._expand_command_back = 3
# TODO: Merge with Felipe's code
self._msn = None
self._lat_ref = 0
self._lon_ref = 0
# Check the agent name
self._name = agent_name
self.count = 0
def setup(self, path_to_config_file):
self._agent = None
self.route_assigned = False
self.count = 0
exp_dir = os.path.join(
'/', os.path.join(*path_to_config_file.split('/')[:-1]))
yaml_conf, checkpoint_number, agent_name, encoder_params = checkpoint_parse_configuration_file(
path_to_config_file)
if encoder_params == "None":
encoder_params = None
g_conf.immutable(False)
merge_with_yaml(
os.path.join('/',
os.path.join(*path_to_config_file.split('/')[:-4]),
yaml_conf), encoder_params)
if g_conf.MODEL_TYPE in ['one-step-affordances']:
# one step training, no need to retrain FC layers, we just get the output of encoder model as prediciton
self._model = EncoderModel(g_conf.ENCODER_MODEL_TYPE,
g_conf.ENCODER_MODEL_CONFIGURATION)
self.checkpoint = torch.load(
os.path.join(exp_dir, 'checkpoints',
str(checkpoint_number) + '.pth'))
print("Affordances Model ",
str(checkpoint_number) + '.pth', "loaded from ",
os.path.join(exp_dir, 'checkpoints'))
self._model.load_state_dict(self.checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
elif g_conf.MODEL_TYPE in ['separate-affordances']:
if encoder_params is not None:
self.encoder_model = EncoderModel(
g_conf.ENCODER_MODEL_TYPE,
g_conf.ENCODER_MODEL_CONFIGURATION)
self.encoder_model.cuda()
# Here we load the pre-trained encoder (not fine-tunned)
if g_conf.FREEZE_ENCODER:
encoder_checkpoint = torch.load(
os.path.join(
os.path.join(
'/',
os.path.join(
*path_to_config_file.split('/')[:-4])),
'_logs', encoder_params['encoder_folder'],
encoder_params['encoder_exp'], 'checkpoints',
str(encoder_params['encoder_checkpoint']) +
'.pth'))
print(
"Encoder model ",
str(encoder_params['encoder_checkpoint']),
"loaded from ",
os.path.join('_logs', encoder_params['encoder_folder'],
encoder_params['encoder_exp'],
'checkpoints'))
self.encoder_model.load_state_dict(
encoder_checkpoint['state_dict'])
self.encoder_model.eval()
for param_ in self.encoder_model.parameters():
param_.requires_grad = False
else:
encoder_checkpoint = torch.load(
os.path.join(exp_dir, 'checkpoints',
str(checkpoint_number) + '_encoder.pth'))
print("FINE TUNNED encoder model ",
str(checkpoint_number) + '_encoder.pth',
"loaded from ", os.path.join(exp_dir, 'checkpoints'))
self.encoder_model.load_state_dict(
encoder_checkpoint['state_dict'])
self.encoder_model.eval()
for param_ in self.encoder_model.parameters():
param_.requires_grad = False
else:
raise RuntimeError(
'encoder_params can not be None in MODEL_TYPE --> separate-affordances'
)
self._model = CoILModel(g_conf.MODEL_TYPE,
g_conf.MODEL_CONFIGURATION,
g_conf.ENCODER_MODEL_CONFIGURATION)
self.checkpoint = torch.load(
os.path.join(exp_dir, 'checkpoints',
str(checkpoint_number) + '.pth'))
print("Affordances Model ",
str(checkpoint_number) + '.pth', "loaded from ",
os.path.join(exp_dir, 'checkpoints'))
self._model.load_state_dict(self.checkpoint['state_dict'])
self._model.cuda()
self._model.eval()
