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plotting.py
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plotting.py
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import os, pdb, torch, torchvision
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def save_plots(config, experiment, epoch, stats, split, pretrain=True, eps=None, goal='explore'):
# folder to save visualizations
viz_dir = f'{experiment}/viz/ep{epoch:05d}/{split}/{goal}'
if not os.path.isdir(viz_dir):
os.makedirs(viz_dir)
if pretrain:
'''
# plot uncertainty across time
plt.plot(range(u_list.size(0)), u_list.mean(1).numpy(), '.')
plt.xlabel('rollout')
plt.ylabel('mean uncertainty')
plt.savefig(f'{experiment}/u_vs_time_ep{epoch}.pdf')
plt.close()
u_list_ = u_list.view(-1).numpy()
mse_list_ = mse_list.view(-1).numpy()
u_expert_ = u_expert.cpu().numpy()
mse_expert_ = mse_expert.cpu().numpy()
'''
# plot uncertainty vs. MSE
plt.plot(stats['uncertainty'].view(-1).numpy(), stats['mse'].view(-1).numpy(), '.', markersize=1, color='black')
plt.xlabel('uncertainty')
plt.ylabel('MSE')
plt.savefig(f'{experiment}/u_vs_mse_ep{epoch}.pdf')
# plot some trajectories in the replay buffer
print('[saving trajectory viz]')
ep_dir = viz_dir + '/experience/'
os.system('mkdir -p ' + ep_dir)
n_episodes = stats['uncertainty'].size(0)
if config.input_type == 'image':
# latest ones
ep_list = [i for i in range(0, 5)]
else:
# a few older trajectories
ep_list = [i for i in range(0, n_episodes, int(config.n_exploration_episodes/2))]
ep_list += [i for i in range(0, 5)]
ep_list = list(set(ep_list))
# get top MSE quantile, so all plots are comparable scale
mse_top_quant = torch.sort(stats['mse'].view(-1), descending=False)[0][round(config.u_quantile*stats['mse'].numel())]
for j in ep_list:
s_real = stats['s_real'][j]
s_pred = stats['s_pred'][j]
actions = stats['actions'][j]
u = stats['uncertainty'][j]
mse = stats['mse'][j]
search_graph = stats['search_graph'][j]
timestep = torch.arange(s_pred.size(0))
if s_pred.dim() == 3:
timestep_pred = timestep.unsqueeze(1).repeat(1, s_pred.size(1)).view(-1)
s_pred_mean = s_pred.mean(1)
s_pred = s_pred.view(s_pred.size(0)*s_pred.size(1), -1)
else:
timestep_pred = timestep
s_pred_mean = s_pred
if eps is None:
eps = stats['eps']
u = u / eps # plotting doesn't like small numbers so normalize
if config.env == 'mountaincar' or config.env == 'acrobot':
plt.close()
fig = plt.figure()
dotsize=2
ax = plt.subplot(3, 2, 1)
ax.set_title('Truth (z=time)')
ax.scatter(s_real[:, 0].numpy(), s_real[:, 1].numpy(), s=dotsize, c=timestep.numpy())
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
ax = plt.subplot(3, 2, 2)
ax.set_title('Pred (z=time)')
ax.scatter(s_pred[:, 0].numpy(), s_pred[:, 1].numpy(), s=dotsize, c=timestep_pred.numpy())
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
ax = plt.subplot(3, 2, 3)
ax.set_title('Truth (z=uncertainty)')
ax.scatter(s_real[:, 0].numpy(), s_real[:, 1].numpy(), s=dotsize, c=u.numpy(), vmin=0, vmax=1)
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
ax = plt.subplot(3, 2, 4)
ax.set_title('Pred (z=uncertainty)')
ax.scatter(s_pred_mean[:, 0].numpy(), s_pred_mean[:, 1].numpy(), s=dotsize, c=u.numpy(), vmin=0, vmax=1)
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
ax = plt.subplot(3, 2, 5)
ax.set_title('Truth (z=MSE)')
ax.scatter(s_real[:, 0].numpy(), s_real[:, 1].numpy(), s=dotsize, c=mse.numpy(), vmin=0, vmax=mse_top_quant)
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
ax = plt.subplot(3, 2, 4)
ax.set_title('Pred (z=MSE)')
ax.scatter(s_pred_mean[:, 0].numpy(), s_pred_mean[:, 1].numpy(), s=dotsize, c=mse.numpy(), vmin=0, vmax=mse_top_quant)
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
save_file = f'{ep_dir}/traj{j}'
if pretrain:
save_file += '_pretrain.pdf'
else:
save_file += '_posttrain.pdf'
print(f'[saving to: {save_file}]')
plt.savefig(save_file)
plt.close()
if search_graph is not None:
if search_graph.dim() == 3:
search_graph = torch.mean(search_graph, 1)
plt.scatter(search_graph[:, 0].cpu().numpy(), search_graph[:, 1].cpu().numpy(), s=1)
plt.xlim([0, 1])
plt.ylim([0, 1])
save_file = f'{ep_dir}/search_graph{j}.pdf'
print(f'[saving to: {save_file}]')
plt.savefig(save_file)
plt.close()
elif config.input_type == 'image':
if config.phi == 'learned' or config.phi == 'ae':
pred = stats['s_pred'][:, :200].float()
if config.loss == 'softmax':
pred = pred / 255.0
images = torch.cat((stats['s_real'][:, :200].unsqueeze(2), pred), 2)
elif config.phi == 'random':
images = stats['s_real']
ep_dir = viz_dir + f'/ep{j}/'
print(f'[saving movie {ep_dir}]')
if not os.path.isdir(ep_dir):
os.makedirs(ep_dir)
for t in range(images.size(1)):
torchvision.utils.save_image(images[j][t], f'{ep_dir}/step{t:03d}_a{actions[t]}.png', nrow=config.n_ensemble+1)
os.system(f'tar -cf {experiment}/viz_ep{epoch}.tar.gz {experiment}/viz')