def eval(self,
eval_ckpt=None,
log_diagnostics=[],
output_dir=".",
label="eval") -> None:
r"""Main method of trainer evaluation. Calls _eval_checkpoint() that
is specified in Trainer class that inherits from BaseRLTrainer
Returns:
None
"""
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
if "tensorboard" in self.config.VIDEO_OPTION:
assert (len(self.config.TENSORBOARD_DIR) > 0
), "Must specify a tensorboard directory for video display"
if "disk" in self.config.VIDEO_OPTION:
assert (len(self.config.VIDEO_DIR) >
0), "Must specify a directory for storing videos on disk"
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
if eval_ckpt is not None: # evaluate a single checkpoint from path
ckpt_index = os.path.split(eval_ckpt)[1].split(".")[-2]
self._eval_checkpoint(eval_ckpt,
writer,
checkpoint_index=ckpt_index,
log_diagnostics=log_diagnostics,
output_dir=output_dir,
label=label)
else:
if os.path.isfile(self.config.EVAL_CKPT_PATH_DIR):
# evaluate singe checkpoint
# parse checkpoint from filename
ckpt_index = self.config.EVAL_CKPT_PATH_DIR.split('.')[-2]
self._eval_checkpoint(self.config.EVAL_CKPT_PATH_DIR,
writer,
checkpoint_index=ckpt_index)
else:
# evaluate multiple checkpoints in order
prev_ckpt_ind = -1
while True:
current_ckpt = None
while current_ckpt is None:
current_ckpt = poll_checkpoint_folder(
self.config.EVAL_CKPT_PATH_DIR, prev_ckpt_ind)
time.sleep(
2) # sleep for 2 secs before polling again
logger.info(
f"=======current_ckpt: {current_ckpt}=======")
prev_ckpt_ind += 1
self._eval_checkpoint(
checkpoint_path=current_ckpt,
writer=writer,
checkpoint_index=prev_ckpt_ind,
)
def eval(self) -> None:
r"""
Main method of evaluating PPO
Returns:
None
"""
ppo_cfg = self.config.TRAINER.RL.PPO
self.device = torch.device("cuda", ppo_cfg.pth_gpu_id)
self.video_option = ppo_cfg.video_option.strip().split(",")
if "tensorboard" in self.video_option:
assert (ppo_cfg.tensorboard_dir is not None
), "Must specify a tensorboard directory for video display"
if "disk" in self.video_option:
assert (ppo_cfg.video_dir is not None
), "Must specify a directory for storing videos on disk"
with get_tensorboard_writer(ppo_cfg.tensorboard_dir,
purge_step=0,
flush_secs=30) as writer:
if os.path.isfile(ppo_cfg.eval_ckpt_path_or_dir):
# evaluate singe checkpoint
self._eval_checkpoint(ppo_cfg.eval_ckpt_path_or_dir, writer)
else:
# evaluate multiple checkpoints in order
prev_ckpt_ind = -1
while True:
current_ckpt = None
while current_ckpt is None:
current_ckpt = poll_checkpoint_folder(
ppo_cfg.eval_ckpt_path_or_dir, prev_ckpt_ind)
time.sleep(2) # sleep for 2 secs before polling again
logger.warning(
"=============current_ckpt: {}=============".format(
current_ckpt))
prev_ckpt_ind += 1
self._eval_checkpoint(
checkpoint_path=current_ckpt,
writer=writer,
cur_ckpt_idx=prev_ckpt_ind,
)
def eval(self) -> None:
r"""Main method of trainer evaluation. Calls _eval_checkpoint() that
is specified in Trainer class that inherits from BaseRLTrainer
Returns:
None
"""
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
if "tensorboard" in self.config.VIDEO_OPTION:
assert (len(self.config.TENSORBOARD_DIR) > 0
), "Must specify a tensorboard directory for video display"
os.makedirs(self.config.TENSORBOARD_DIR, exist_ok=True)
if "disk" in self.config.VIDEO_OPTION:
assert (len(self.config.VIDEO_DIR) >
0), "Must specify a directory for storing videos on disk"
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
if os.path.isfile(self.config.EVAL_CKPT_PATH_DIR):
# evaluate singe checkpoint
self._eval_checkpoint(self.config.EVAL_CKPT_PATH_DIR, writer)
else:
# evaluate multiple checkpoints in order
prev_ckpt_ind = -1
while True:
current_ckpt = None
while current_ckpt is None:
current_ckpt = poll_checkpoint_folder(
self.config.EVAL_CKPT_PATH_DIR, prev_ckpt_ind)
time.sleep(2) # sleep for 2 secs before polling again
logger.info(f"=======current_ckpt: {current_ckpt}=======")
prev_ckpt_ind += 1
self._eval_checkpoint(
checkpoint_path=current_ckpt,
writer=writer,
checkpoint_index=prev_ckpt_ind,
)
def eval_bruce(self):
r"""Trainer evaluation method by Bruce. Stripped Tensorflow stuff
Returns:
device
actor_critic
batch
not_done_masks
test_recurrent_hidden_states
"""
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
actor_critic = None
batch = None
not_done_masks = None
test_recurrent_hidden_states = None
if os.path.isfile(self.config.EVAL_CKPT_PATH_DIR):
# evaluate singe checkpoint
actor_critic, batch, not_done_masks, test_recurrent_hidden_states = self._eval_checkpoint_bruce(
self.config.EVAL_CKPT_PATH_DIR)
else:
# evaluate multiple checkpoints in order
prev_ckpt_ind = -1
while True:
current_ckpt = None
while current_ckpt is None:
current_ckpt = poll_checkpoint_folder(
self.config.EVAL_CKPT_PATH_DIR, prev_ckpt_ind)
time.sleep(2) # sleep for 2 secs before polling again
logger.info(f"=======current_ckpt: {current_ckpt}=======")
prev_ckpt_ind += 1
actor_critic, batch, not_done_masks, test_recurrent_hidden_states = self._eval_checkpoint_bruce(
checkpoint_path=current_ckpt,
checkpoint_index=prev_ckpt_ind,
)
return actor_critic, batch, self.device, not_done_masks, test_recurrent_hidden_states