def update(self, aug_obs):
obs = aug_obs
enc_features = self.enc(obs)
mu = self.enc_mu(enc_features)
logvar = self.enc_logvar(enc_features)
stds = (0.5 * logvar).exp()
epsilon = ptu.randn(*mu.size())
code = epsilon * stds + mu
kle = -0.5 * torch.sum(
1 + logvar - mu.pow(2) - logvar.exp(), dim=1
).mean()
obs_distribution_params = self.dec(code)
log_prob = -1. * F.mse_loss(obs, obs_distribution_params,
reduction='elementwise_mean')
loss = self.beta * kle - log_prob
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.cpu().item()
def debug_statistics(self):
"""
Given an image $$x$$, samples a bunch of latents from the prior
$$z_i$$ and decode them $$\hat x_i$$.
Compare this to $$\hat x$$, the reconstruction of $$x$$.
Ideally
- All the $$\hat x_i$$s do worse than $$\hat x$$ (makes sure VAE
isn’t ignoring the latent)
- Some $$\hat x_i$$ do better than other $$\hat x_i$$ (tests for
coverage)
"""
debug_batch_size = 64
data = self.get_batch(train=False)
reconstructions, _, _ = self.model(data)
img = data[0]
recon_mse = ((reconstructions[0] - img)**2).mean().view(-1)
img_repeated = img.expand((debug_batch_size, img.shape[0]))
samples = ptu.randn(debug_batch_size, self.representation_size)
random_imgs, _ = self.model.decode(samples)
random_mses = (random_imgs - img_repeated)**2
mse_improvement = ptu.get_numpy(random_mses.mean(dim=1) - recon_mse)
stats = create_stats_ordered_dict(
'debug/MSE improvement over random',
mse_improvement,
)
stats.update(
create_stats_ordered_dict(
'debug/MSE of random decoding',
ptu.get_numpy(random_mses),
))
stats['debug/MSE of reconstruction'] = ptu.get_numpy(recon_mse)[0]
return stats
def load_dataset(self, dataset_path):
dataset = load_local_or_remote_file(dataset_path)
dataset = dataset.item()
observations = dataset['observations']
actions = dataset['actions']
# dataset['observations'].shape # (2000, 50, 6912)
# dataset['actions'].shape # (2000, 50, 2)
# dataset['env'].shape # (2000, 6912)
N, H, imlength = observations.shape
self.vae.eval()
for n in range(N):
x0 = ptu.from_numpy(dataset['env'][n:n + 1, :] / 255.0)
x = ptu.from_numpy(observations[n, :, :] / 255.0)
latents = self.vae.encode(x, x0, distrib=False)
r1, r2 = self.vae.latent_sizes
conditioning = latents[0, r1:]
goal = torch.cat(
[ptu.randn(self.vae.latent_sizes[0]), conditioning])
goal = ptu.get_numpy(goal) # latents[-1, :]
latents = ptu.get_numpy(latents)
latent_delta = latents - goal
distances = np.zeros((H - 1, 1))
for i in range(H - 1):
distances[i, 0] = np.linalg.norm(latent_delta[i + 1, :])
terminals = np.zeros((H - 1, 1))
# terminals[-1, 0] = 1
path = dict(
observations=[],
actions=actions[n, :H - 1, :],
next_observations=[],
rewards=-distances,
terminals=terminals,
)
for t in range(H - 1):
# reward = -np.linalg.norm(latent_delta[i, :])
obs = dict(
latent_observation=latents[t, :],
latent_achieved_goal=latents[t, :],
latent_desired_goal=goal,
)
next_obs = dict(
latent_observation=latents[t + 1, :],
latent_achieved_goal=latents[t + 1, :],
latent_desired_goal=goal,
)
path['observations'].append(obs)
path['next_observations'].append(next_obs)
# import ipdb; ipdb.set_trace()
self.replay_buffer.add_path(path)
def dump_samples(self, epoch):
self.model.eval()
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample)[0].cpu()
save_dir = osp.join(self.log_dir, 's%d.png' % epoch)
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize).transpose(2, 3), save_dir)
def dump_samples(self, epoch):
self.model.eval()
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample)[0].cpu()
save_dir = osp.join(logger.get_snapshot_dir(), 's%d.png' % epoch)
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize), save_dir)
def get_encoding_and_suff_stats(self, x):
output = self(x)
z_dim = output.shape[1] // 2
means, log_var = (output[:, :z_dim], output[:, z_dim:])
stds = (0.5 * log_var).exp()
epsilon = ptu.randn(means.shape)
latents = epsilon * stds + means
return latents, means, log_var, stds
def dump_samples(self, epoch, save_prefix='s'):
self.model.eval()
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample)[0].cpu()
save_dir = osp.join(logger.get_snapshot_dir(),
'{}{}.png'.format(save_prefix, epoch))
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize).transpose(2, 3), save_dir)
def sample_prior(self, batch_size, x_0):
if x_0.shape[0] == 1:
x_0 = x_0.repeat(batch_size, 1)
x_0 = x_0.reshape(-1, self.input_channels, self.imsize, self.imsize)
z_cond, _, _, _ = self.netE.cond_encoder(x_0)
z_delta = ptu.randn(batch_size, self.latent_size, 1, 1)
cond_sample = torch.cat([z_delta, z_cond], dim=1)
return cond_sample
def dump_samples(self, epoch):
self.model.eval()
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample)[0].cpu()
# save_dir = osp.join(logger.get_snapshot_dir(), 's%d.png' % epoch)
# save_dir = osp.join('/mnt/manh/project/visual_RL_imaged_goal', 's%d.png' % epoch)
project_path = osp.abspath(os.curdir)
save_dir = osp.join(project_path + str('/result_image/'),
's%d.png' % epoch)
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize).transpose(2, 3), save_dir)
def sample_prior(self, batch_size, x_0, true_prior=True):
if x_0.shape[0] == 1:
x_0 = x_0.repeat(batch_size, 1)
z_sample = ptu.randn(batch_size, self.latent_sizes[0])
if not true_prior:
stds = np.exp(0.5 * self.prior_logvar)
z_sample = z_sample * stds + self.prior_mu
conditioning = self.bn_c(self.c(self.dropout(self.cond_encoder(x_0))))
cond_sample = torch.cat([z_sample, conditioning], dim=1)
return cond_sample
def sample_prior(self, batch_size, cond=None, image_cond=True):
if cond.shape[0] == 1:
cond = cond.repeat(batch_size, axis=0)
cond = ptu.from_numpy(cond)
if image_cond:
z_cond = self.encode_cond(batch_size, cond)
else:
z_cat = cond.reshape(batch_size, 2 * self.embedding_dim, self.root_len, self.root_len)
z_cond = z_cat[:, self.embedding_dim:]
z_delta = ptu.randn(batch_size, self.embedding_dim, self.root_len, self.root_len)
z_cat = torch.cat([z_delta, z_cond], dim=1).view(-1, self.representation_size)
return ptu.get_numpy(z_cat)
def compute_density(self, data):
orig_data_length = len(data)
data = np.vstack([data for _ in range(self.n_average)])
data = ptu.from_numpy(data)
if self.mode == 'biased':
latents, means, log_vars, stds = (
self.encoder.get_encoding_and_suff_stats(data))
importance_weights = ptu.ones(data.shape[0])
elif self.mode == 'prior':
latents = ptu.randn(len(data), self.z_dim)
importance_weights = ptu.ones(data.shape[0])
elif self.mode == 'importance_sampling':
latents, means, log_vars, stds = (
self.encoder.get_encoding_and_suff_stats(data))
prior = Normal(ptu.zeros(1), ptu.ones(1))
prior_log_prob = prior.log_prob(latents).sum(dim=1)
encoder_distrib = Normal(means, stds)
encoder_log_prob = encoder_distrib.log_prob(latents).sum(dim=1)
importance_weights = (prior_log_prob - encoder_log_prob).exp()
else:
raise NotImplementedError()
unweighted_data_log_prob = self.compute_log_prob(
data, self.decoder, latents).squeeze(1)
unweighted_data_prob = unweighted_data_log_prob.exp()
unnormalized_data_prob = unweighted_data_prob * importance_weights
"""
Average over `n_average`
"""
dp_split = torch.split(unnormalized_data_prob, orig_data_length, dim=0)
# pre_avg.shape = ORIG_LEN x N_AVERAGE
dp_stacked = torch.stack(dp_split, dim=1)
# final.shape = ORIG_LEN
unnormalized_dp = torch.sum(dp_stacked, dim=1, keepdim=False)
"""
Compute the importance weight denomintors.
This requires summing across the `n_average` dimension.
"""
iw_split = torch.split(importance_weights, orig_data_length, dim=0)
iw_stacked = torch.stack(iw_split, dim=1)
iw_denominators = iw_stacked.sum(dim=1, keepdim=False)
final = unnormalized_dp / iw_denominators
return ptu.get_numpy(final)
def sample_prior(self, batch_size, cond=None, image_cond=True):
if cond.shape[0] == 1:
cond = cond.repeat(batch_size, axis=0)
cond = ptu.from_numpy(cond)
if image_cond:
cond = cond.reshape(-1, self.input_channels, self.imsize,
self.imsize)
z_cond, _, _, _ = self.netE.cond_encoder(cond)
z_cond = z_cond.reshape(-1, self.latent_size)
else:
z_cond = cond[:, self.latent_size:]
z_delta = ptu.randn(batch_size, self.latent_size)
cond_sample = torch.cat([z_delta, z_cond], dim=1)
return ptu.get_numpy(cond_sample)
def dump_samples(self, epoch):
self.model.eval()
batch, _ = self.eval_data["test/last_batch"]
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample, batch["observations"])[0].cpu()
save_dir = osp.join(self.log_dir, 's%d.png' % epoch)
save_image(
sample.data.view(64, 3, self.imsize, self.imsize).transpose(2, 3),
save_dir)
x0 = batch["x_0"]
x0_img = x0[:64].narrow(start=0, length=self.imlength // 2,
dim=1).contiguous().view(
-1, 3, self.imsize,
self.imsize).transpose(2, 3)
save_dir = osp.join(self.log_dir, 'x0_%d.png' % epoch)
save_image(x0_img.data.cpu(), save_dir)
def get_output_for(self, aug_obs, sample=True):
"""
Returns the log probability of the given observation.
"""
obs = aug_obs
with torch.no_grad():
enc_features = self.enc(obs)
mu = self.enc_mu(enc_features)
logvar = self.enc_logvar(enc_features)
stds = (0.5 * logvar).exp()
if sample:
epsilon = ptu.randn(*mu.size())
else:
epsilon = torch.ones_like(mu)
code = epsilon * stds + mu
obs_distribution_params = self.dec(code)
log_prob = -1. * F.mse_loss(obs, obs_distribution_params,
reduction='none')
log_prob = torch.sum(log_prob, -1, keepdim=True)
return log_prob.detach()
def sample(self, num_samples):
return ptu.get_numpy(
self.sample_given_z(ptu.randn(num_samples, self.z_dim)))
def fixed_noise(self, b_size):
return ptu.randn(b_size, self.representation_size, 1, 1)
def noise(self, size, num_epochs, epoch):
noise = ptu.randn(size)
std = 0.1 * (num_epochs - epoch) / num_epochs
return std * noise
def fixed_noise(self, b_size, latent):
z_cond = latent[:, self.model.latent_size:]
z_delta = ptu.randn(b_size, self.model.latent_size, 1, 1)
return torch.cat([z_delta, z_cond], dim=1)
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Critic operations.
"""
next_actions = self.target_policy(next_obs)
noise = ptu.randn(next_actions.shape) * self.target_policy_noise
noise = torch.clamp(
noise,
-self.target_policy_noise_clip,
self.target_policy_noise_clip
)
noisy_next_actions = next_actions + noise
target_q1_values = self.target_qf1(next_obs, noisy_next_actions)
target_q2_values = self.target_qf2(next_obs, noisy_next_actions)
target_q_values = torch.min(target_q1_values, target_q2_values)
q_target = self.reward_scale * rewards + (1. - terminals) * self.discount * target_q_values
q_target = q_target.detach()
q1_pred = self.qf1(obs, actions)
bellman_errors_1 = (q1_pred - q_target) ** 2
qf1_loss = bellman_errors_1.mean()
q2_pred = self.qf2(obs, actions)
bellman_errors_2 = (q2_pred - q_target) ** 2
qf2_loss = bellman_errors_2.mean()
"""
Update Networks
"""
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
policy_actions = policy_loss = None
if self._n_train_steps_total % self.policy_and_target_update_period == 0:
policy_actions = self.policy(obs)
q_output = self.qf1(obs, policy_actions)
policy_loss = - q_output.mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
ptu.soft_update_from_to(self.policy, self.target_policy, self.tau)
ptu.soft_update_from_to(self.qf1, self.target_qf1, self.tau)
ptu.soft_update_from_to(self.qf2, self.target_qf2, self.tau)
if self._need_to_update_eval_statistics:
self._need_to_update_eval_statistics = False
if policy_loss is None:
policy_actions = self.policy(obs)
q_output = self.qf1(obs, policy_actions)
policy_loss = - q_output.mean()
self.eval_statistics['QF1 Loss'] = np.mean(ptu.get_numpy(qf1_loss))
self.eval_statistics['QF2 Loss'] = np.mean(ptu.get_numpy(qf2_loss))
self.eval_statistics['Policy Loss'] = np.mean(ptu.get_numpy(
policy_loss
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q1 Predictions',
ptu.get_numpy(q1_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q2 Predictions',
ptu.get_numpy(q2_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q Targets',
ptu.get_numpy(q_target),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Bellman Errors 1',
ptu.get_numpy(bellman_errors_1),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Bellman Errors 2',
ptu.get_numpy(bellman_errors_2),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy Action',
ptu.get_numpy(policy_actions),
))
self._n_train_steps_total += 1
all_imgs = [
x0.narrow(start=0, length=imlength,
dim=1).contiguous().view(-1, 3, imsize, imsize).transpose(2, 3),
]
comparison = torch.cat(all_imgs)
save_dir = "/home/ashvin/data/s3doodad/share/multiobj/%sx0.png" % prefix
save_image(comparison.data.cpu(), save_dir, nrow=8)
vae = load_local_or_remote_file(vae_path).to("cpu")
vae.eval()
model = vae
all_imgs = []
for i in range(N_ROWS):
latent = ptu.randn(
n,
model.representation_size) # model.sample_prior(self.batch_size, env)
samples = model.decode(latent)[0]
all_imgs.extend([samples.view(
n,
3,
imsize,
imsize,
)[:n].transpose(2, 3)])
comparison = torch.cat(all_imgs)
save_dir = "/home/ashvin/data/s3doodad/share/multiobj/%svae_samples.png" % prefix
save_image(comparison.data.cpu(), save_dir, nrow=8)
cvae = load_local_or_remote_file(cvae_path).to("cpu")
cvae.eval()
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
gt.stamp('preback_start', unique=False)
"""
Update QF
"""
with torch.no_grad():
next_actions = self.target_policy(next_obs)
noise = ptu.randn(next_actions.shape) * self.target_policy_noise
noise = torch.clamp(noise, -self.target_policy_noise_clip,
self.target_policy_noise_clip)
noisy_next_actions = torch.clamp(next_actions + noise,
-self.max_action, self.max_action)
next_tau, next_tau_hat, next_presum_tau = self.get_tau(
next_obs, noisy_next_actions, fp=self.target_fp)
target_z1_values = self.target_zf1(next_obs, noisy_next_actions,
next_tau_hat)
target_z2_values = self.target_zf2(next_obs, noisy_next_actions,
next_tau_hat)
target_z_values = torch.min(target_z1_values, target_z2_values)
z_target = self.reward_scale * rewards + (
1. - terminals) * self.discount * target_z_values
tau, tau_hat, presum_tau = self.get_tau(obs, actions, fp=self.fp)
z1_pred = self.zf1(obs, actions, tau_hat)
z2_pred = self.zf2(obs, actions, tau_hat)
zf1_loss = self.zf_criterion(z1_pred, z_target, tau_hat,
next_presum_tau)
zf2_loss = self.zf_criterion(z2_pred, z_target, tau_hat,
next_presum_tau)
gt.stamp('preback_zf', unique=False)
self.zf1_optimizer.zero_grad()
zf1_loss.backward()
self.zf1_optimizer.step()
gt.stamp('backward_zf1', unique=False)
self.zf2_optimizer.zero_grad()
zf2_loss.backward()
self.zf2_optimizer.step()
gt.stamp('backward_zf2', unique=False)
"""
Update FP
"""
if self.tau_type == 'fqf':
with torch.no_grad():
dWdtau = 0.5 * (2 * self.zf1(obs, actions, tau[:, :-1]) -
z1_pred[:, :-1] - z1_pred[:, 1:] +
2 * self.zf2(obs, actions, tau[:, :-1]) -
z2_pred[:, :-1] - z2_pred[:, 1:])
dWdtau /= dWdtau.shape[0] # (N, T-1)
gt.stamp('preback_fp', unique=False)
self.fp_optimizer.zero_grad()
tau[:, :-1].backward(gradient=dWdtau)
self.fp_optimizer.step()
gt.stamp('backward_fp', unique=False)
"""
Policy Loss
"""
policy_actions = self.policy(obs)
risk_param = self.risk_schedule(self._n_train_steps_total)
if self.risk_type == 'VaR':
tau_ = ptu.ones_like(rewards) * risk_param
q_new_actions = self.zf1(obs, policy_actions, tau_)
else:
with torch.no_grad():
new_tau, new_tau_hat, new_presum_tau = self.get_tau(
obs, policy_actions, fp=self.fp)
z_new_actions = self.zf1(obs, policy_actions, new_tau_hat)
if self.risk_type in ['neutral', 'std']:
q_new_actions = torch.sum(new_presum_tau * z_new_actions,
dim=1,
keepdims=True)
if self.risk_type == 'std':
q_std = new_presum_tau * (z_new_actions -
q_new_actions).pow(2)
q_new_actions -= risk_param * q_std.sum(
dim=1, keepdims=True).sqrt()
else:
with torch.no_grad():
risk_weights = distortion_de(new_tau_hat, self.risk_type,
risk_param)
q_new_actions = torch.sum(risk_weights * new_presum_tau *
z_new_actions,
dim=1,
keepdims=True)
policy_loss = -q_new_actions.mean()
gt.stamp('preback_policy', unique=False)
if self._n_train_steps_total % self.policy_and_target_update_period == 0:
self.policy_optimizer.zero_grad()
policy_loss.backward()
policy_grad = ptu.fast_clip_grad_norm(self.policy.parameters(),
self.clip_norm)
self.policy_optimizer.step()
gt.stamp('backward_policy', unique=False)
ptu.soft_update_from_to(self.policy, self.target_policy,
self.soft_target_tau)
ptu.soft_update_from_to(self.zf1, self.target_zf1,
self.soft_target_tau)
ptu.soft_update_from_to(self.zf2, self.target_zf2,
self.soft_target_tau)
if self.tau_type == 'fqf':
ptu.soft_update_from_to(self.fp, self.target_fp,
self.soft_target_tau)
gt.stamp('soft_update', unique=False)
"""
Save some statistics for eval
"""
if self._need_to_update_eval_statistics:
self._need_to_update_eval_statistics = False
"""
Eval should set this to None.
This way, these statistics are only computed for one batch.
"""
self.eval_statistics['ZF1 Loss'] = zf1_loss.item()
self.eval_statistics['ZF2 Loss'] = zf2_loss.item()
self.eval_statistics['Policy Loss'] = policy_loss.item()
self.eval_statistics['Policy Grad'] = policy_grad
self.eval_statistics.update(
create_stats_ordered_dict(
'Z1 Predictions',
ptu.get_numpy(z1_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Z2 Predictions',
ptu.get_numpy(z2_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Z Targets',
ptu.get_numpy(z_target),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy Action',
ptu.get_numpy(policy_actions),
))
self._n_train_steps_total += 1
def sample_prior(self, batch_size):
z_s = ptu.randn(batch_size, self.representation_size)
return ptu.get_numpy(z_s)
def dump_samples(self, epoch):
self.model.eval()
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample)
save_dir = osp.join(self.log_dir, 's%d.png' % epoch)
save_image(sample.data.transpose(2, 3), save_dir)
def rsample(self, latent_distribution_params):
mu, logvar = latent_distribution_params
stds = (0.5 * logvar).exp()
epsilon = ptu.randn(*mu.size())
latents = epsilon * stds + mu
return latents
