def save_replay_gif(self):
dir_name = "replay_render"
if not os.path.exists(dir_name):
os.makedirs(dir_name)
state = self.env_wrapper.reset()
self.env_wrapper.env.resume_simulator()
for step in range(self.max_steps):
action = self.actor.get_action(state)
action = action.cpu().detach().numpy()
next_state, reward, done = self.env_wrapper.step(action)
img = self.env_wrapper.render()
plt.imsave(fname=f"{dir_name}/{step}.png", arr=img)
state = next_state
if done:
break
fn = f"{self.config['env']}-{self.config['model']}-{step}.gif"
make_gif(dir_name, f"{self.log_dir}/{fn}")
shutil.rmtree(dir_name, ignore_errors=False, onerror=None)
print("fig saved to ", f"{self.log_dir}/{fn}")
_, cond2, _, caps2 = dataset.test.next_batch_test(
1, dataset_pos2, 1)
cond1 = np.squeeze(cond1, axis=0)
cond2 = np.squeeze(cond2, axis=0)
cap1, cap2 = caps1[0][0], caps2[0][0]
samples = gen_cond_interp_img(sess, gen_no_noise, cond1, cond2,
z_dim, batch_size)
samples = np.clip(samples, -1, 1)
save_interp_cap_batch(
samples, cap1, cap2,
'{}/{}_visual/cond_interp/cond_interp{}.png'.format(
samples_dir, dataset.name, idx))
make_gif(samples,
'{}/{}_visual/cond_interp/gifs/cond_interp{}.gif'.format(
samples_dir, dataset.name, idx),
duration=10)
# Generate captioned image
# ---------------------------------------------------------------------------------------------------------
_, conditions, _, captions = dataset.test.next_batch_test(
1, dataset_pos, 1)
conditions = np.squeeze(conditions, axis=0)
caption = captions[0][0]
samples = gen_captioned_img(sess, gen_op, conditions, z_dim,
batch_size)
samples = np.clip(samples, -1., 1.)
save_cap_batch(
samples, caption,
'{}/{}_visual/cap/cap{}.png'.format(samples_dir, dataset.name,