def _new_episode(self, args, episode):
""" New navigation episode. """
scene = episode["scene"]
if self._env is None:
self._env = Environment(
offline_data_dir=args.offline_data_dir,
use_offline_controller=True,
grid_size=args.grid_size,
images_file_name=args.images_file_name,
local_executable_path=args.local_executable_path,
rotate_by=args.rotate_by,
state_decimal=args.state_decimal,
actions = self.actions
)
self._env.start(scene)
else:
self._env.reset(scene)
self.environment.controller.state = episode["state"]
self.task_data = episode["task_data"]
self.target_object = episode["goal_object_type"]
if args.verbose:
print("Scene", scene, "Navigating towards:", self.target_object)
self.glove_embedding = gpuify(episode["glove_embedding"], self.gpu_id)
return True
def _new_episode(self, args, episode):
""" New navigation episode. """
scene = episode["scene"]
if self._env is None:
self._env = Environment(
offline_data_dir=args.offline_data_dir,
use_offline_controller=True,
grid_size=0.25,
images_file_name=args.images_file_name,
local_executable_path=args.local_executable_path,
)
# print('###################')
self._env.start(scene)
# print('$$$$$$$$$$$$$$$$$$$')
else:
# print("^^^^^^^^^^^^^^^^^^^^")
self._env.reset(scene)
# print('&&&&&&&&&&&&')
# print('@@@@@@@@@@@@@@@@@@@@@@@')
self.environment.controller.state = episode["state"]
self.task_data = episode["task_data"]
self.target_object = episode["goal_object_type"]
if args.verbose:
# print("!!!!!!!!!!!!!!!!!!!!!!")
print("Scene", scene, "Navigating towards:", self.target_object)
self.glove_embedding = gpuify(episode["glove_embedding"], self.gpu_id)
return True
def eval_at_state(self):
model_input = ModelInput()
model_input.state = self.preprocess_frame(
self.episode.state_for_agent())
model_input.hidden = self.hidden
model_input.additional_state_info = gpuify(
torch.Tensor(self.info[1]).unsqueeze(0), self.gpu_id)
model_output = self.model.forward(model_input)
return model_output
def reset_hidden(self):
if self.gpu_id >= 0:
with torch.cuda.device(self.gpu_id):
self.hidden = (
torch.zeros(1, self.hidden_state_sz).cuda(),
torch.zeros(1, self.hidden_state_sz).cuda(),
)
else:
self.hidden = (
torch.zeros(1, self.hidden_state_sz),
torch.zeros(1, self.hidden_state_sz),
)
self.last_action_probs = gpuify(torch.zeros((1, self.action_space)),
self.gpu_id)
def eval_at_state(self, model_options, frame):
model_input = ModelInput()
# if self.episode.current_frame is None:
# model_input.state = self.state()
# else:
# model_input.state = self.episode.current_frame
#process_frame to shape [1,3,224,224], for input to resnet18
processed_frame = self.preprocess_frame(
resnet_input_transform(frame, 224).unsqueeze(0))
resnet18_features = self.resnet18(processed_frame)
model_input.state = resnet18_features
model_input.hidden = self.hidden
model_input.target_class_embedding = gpuify(torch.Tensor(
self.target_glove_embedding),
gpu_id=self.gpu_id)
model_input.action_probs = self.last_action_probs
return model_input, self.model.forward(model_input, model_options)
def preprocess_frame(self, frame):
""" Preprocess the current frame for input into the model. """
state = torch.Tensor(frame)
return gpuify(state, self.gpu_id)
def target_object_index(self, target_object_index):
""" Set the target object by specifying the index. """
self._target_object_index = gpuify(
torch.LongTensor([target_object_index]), self.gpu_id)
def preprocess_frame(self, frame):
""" Preprocess the current frame for input into the model. """
frame = resnet_input_transform(frame, 84)
state = torch.Tensor(frame)
return gpuify(state.unsqueeze(0), self.gpu_id)
def preprocess_memory(self, memory):
""" Preprocess the current memory for input into the model. """
memory = torch.Tensor(memory)
return gpuify(memory.unsqueeze(0), self.gpu_id)
def augState(self):
## convert self.episode.additional_state_info to tensor compatible with agent
augTensor = torch.tensor(
self.episode.additional_state_info.astype(float))
return gpuify(augTensor.unsqueeze(0), self.gpu_id)
def preprocess_frame(self, frame):
""" Preprocess the current frame for input into the model. """
frame_numpy = np.ascontiguousarray(frame)
state = torch.Tensor(frame_numpy)
return gpuify(state, self.gpu_id)
def preprocess_triedFind(self, triedFind):
state = torch.Tensor([triedFind['Tomato'], triedFind['Bowl']])
return gpuify(state.unsqueeze(0), self.gpu_id)
def eval_at_state(self, params=None):
critic = torch.ones(1, 1)
actor = torch.ones(1, self.action_space)
critic = gpuify(critic, self.gpu_id)
actor = gpuify(actor, self.gpu_id)
return ModelInput(), ModelOutput(value=critic, logit=actor)
