def _plot(self, updater, rollouts):
plt.ion()
if updater.dataset.gym_dataset.image_obs:
obs = rollouts.obs
else:
obs = rollouts.image
fig, axes = square_subplots(rollouts.batch_size, figsize=(5, 5))
plt.subplots_adjust(top=0.95, bottom=0, left=0, right=1, wspace=0.1, hspace=0.1)
images = []
for i, ax in enumerate(axes.flatten()):
ax.set_aspect("equal")
ax.set_axis_off()
image = ax.imshow(np.zeros(obs.shape[2:]))
images.append(image)
def animate(t):
for i in range(rollouts.batch_size):
images[i].set_array(obs[t, i, :, :, :])
anim = animation.FuncAnimation(fig, animate, frames=len(rollouts), interval=500)
path = updater.exp_dir.path_for('plots', '{}_animation.gif'.format(self.name))
anim.save(path, writer='imagemagick')
plt.close(fig)
def __call__(self, updater):
plt.ion()
for learner in updater.learners:
with learner:
rollouts = updater.env.do_rollouts(policy=learner.pi,
n_rollouts=self.N,
T=cfg.T,
mode='val')
if updater.env.gym_env.image_obs:
obs = rollouts.obs
else:
obs = rollouts.image
fig, axes = square_subplots(self.N, figsize=(5, 5))
plt.subplots_adjust(top=0.95,
bottom=0,
left=0,
right=1,
wspace=0.1,
hspace=0.1)
images = []
for i, ax in enumerate(axes.flatten()):
ax.set_aspect("equal")
ax.set_axis_off()
image = ax.imshow(np.zeros(obs.shape[2:]))
images.append(image)
def animate(t):
for i in range(self.N):
images[i].set_array(obs[t, i, :, :, :])
anim = animation.FuncAnimation(fig,
animate,
frames=len(rollouts),
interval=500)
path = updater.exp_dir.path_for(
'plots', 'animation.gif') # Don't open gifs with VLC
anim.save(path, writer='imagemagick')
if cfg.show_plots:
plt.show()
plt.close(fig)
d = {}
d.update({ph: a for ph, a in zip(sprites_ph, sprites)})
d.update({ph: a for ph, a in zip(scales_ph, scales)})
d.update({ph: a for ph, a in zip(offsets_ph, offsets)})
d[backgrounds_ph] = backgrounds
result = sess.run(images, feed_dict=d)
from dps.utils import image_to_string
print(image_to_string(result[0, ..., 0]))
print()
print(image_to_string(result[0, ..., 1]))
print()
print(image_to_string(result[0, ..., 2]))
print()
print(result)
print("Done running.")
# Sometimes we get values like 1.0001, nothing really bad.
result = np.clip(result, 1e-6, 1 - 1e-6)
import matplotlib.pyplot as plt
from dps.utils import square_subplots
fig, axes = square_subplots(len(sprites[0]))
fig.suptitle(device)
for img, ax in zip(result, axes.flatten()):
ax.imshow(img)
plt.show()