def render(self, mode='human'):
imgs = self.get_images()
bigimg = tile_images(imgs)
if mode == 'human':
self.get_viewer().imshow(bigimg)
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def render(self, mode='human'):
imgs = self.get_images()
bigimg = tile_images(imgs)
if mode == 'human':
self.get_viewer().imshow(bigimg)
return self.get_viewer().isopen
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def render(self, mode='human'):
for pipe in self.remotes:
pipe.send(('render', None))
imgs = [pipe.recv() for pipe in self.remotes]
bigimg = tile_images(imgs)
if mode == 'human':
import cv2
cv2.imshow('vecenv', bigimg[:, :, ::-1])
cv2.waitKey(1)
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def render(self, mode='human'):
for pipe in self.parent_pipes:
pipe.send(('render', None))
imgs = [pipe.recv() for pipe in self.parent_pipes]
bigimg = tile_images(imgs)
if mode == 'human':
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(bigimg[:, :, ::-1])
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def render(self, mode='human'):
for pipe in self.remotes:
pipe.send(('render', None))
imgs = [pipe.recv() for pipe in self.remotes]
bigimg = tile_images(imgs)
if mode == 'human':
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(bigimg[:, :, ::-1])
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def render(self, mode='human'):
if self.num_workers <= 1:
return self.envs.render(mode=mode)
for pipe in self.remotes:
pipe.send(('render', None))
imgs = np.asarray([pipe.recv() for pipe in self.remotes])
imgs = imgs.reshape((-1, *imgs.shape[2:]))
bigimg = tile_images(imgs)
if mode == 'human':
import cv2
cv2.imshow('vecenv', bigimg[:, :, ::-1])
cv2.waitKey(1)
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def render(self, mode='human'):
# code doesn't work all that well
# TODO: need to clean up
for pipe in self.remotes:
pipe.send(('render', None))
imgs = np.asarray([pipe.recv() for pipe in self.remotes],
dtype=np.uint8)
# tile_images expects a 4 dimensional shape
imgs = np.squeeze(imgs, axis=1)
bigimg = tile_images(imgs)
if mode == 'human':
import cv2
cv2.imshow('vecenv', bigimg[:, :, ::-1])
cv2.waitKey(1)
elif mode == 'rgb_array':
return bigimg.astype(np.uint8)
else:
raise NotImplementedError
def evaluate_model(
model, network_name: str,
env_name: str, num_env: int, seed: int,
evals_per_env=1,
eval_dir: Path = None,
use_logger=True,
max_eplen=None,
frame_stack_size=4,
noop_reset=True,
fire_reset=True,
return_raw=False,
return_prc=False,
video_recorder=False,
progress=False):
tqdm = maybe_tqdm(progress)
if eval_dir is not None:
eval_dir.mkdir(exist_ok=True, parents=True)
logging.debug(f'Creating {num_env} instances of {env_name}...')
with closing(make_envs(
env_name=env_name,
num_env=num_env,
seed=seed,
max_eplen=max_eplen,
frame_stack_size=frame_stack_size,
noop_reset=noop_reset,
fire_reset=fire_reset,
eval_dir=eval_dir,
use_logger=use_logger,
video_recorder=video_recorder,
)) as eval_envs:
logging.debug(f'Done creating envs. Running each for {evals_per_env} episodes, at most {max_eplen} each...')
eval_results, raw_observations, prc_observations, attention = run_model(
model,
eval_envs,
evals_per_env=evals_per_env,
return_raw=return_raw,
return_prc=return_prc,
return_attn=return_raw or return_prc,
progress=progress,
)
if eval_dir is not None:
with (eval_dir / 'results.json').open('w') as fp:
json.dump(eval_results, fp, indent=4)
if return_raw or return_prc:
logging.debug(f'Rendering {len(attention)} frames of attention...')
rh, rw, rc = 210, 160, 3
ph, pw, pc = 84, 84, frame_stack_size
if return_raw:
assert raw_observations[0].shape == (num_env, rh, rw, rc)
if return_prc:
assert prc_observations[0].shape == (num_env, ph, pw, pc)
saliency_maps = render_attention(
attention,
(num_env, ph, pw, pc),
**models_baselines.attention_visualization_params[network_name]
)
logging.debug('Done rendering.')
if eval_dir is not None:
with VideoWriter(eval_dir / 'perception.mkv') as writer:
it = tqdm(
render_perception(raw_observations, prc_observations, saliency_maps),
postfix='writing video',
total=len(saliency_maps)
)
for frame in it:
frame = tile_images(frame)
frame = (frame * 255).astype(np.uint8)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
writer.write(frame)
else:
saliency_maps = []
return eval_results, raw_observations, prc_observations, saliency_maps
