def _build(self):
# inference
self.infer_obs_t = nn.Variable((1,) + self.obs_shape)
with nn.parameter_scope('trainable'):
self.infer_policy_t = policy_network(self.infer_obs_t,
self.action_size, 'actor')
# training
self.obss_t = nn.Variable((self.batch_size,) + self.obs_shape)
self.acts_t = nn.Variable((self.batch_size, self.action_size))
self.rews_tp1 = nn.Variable((self.batch_size, 1))
self.obss_tp1 = nn.Variable((self.batch_size,) + self.obs_shape)
self.ters_tp1 = nn.Variable((self.batch_size, 1))
# critic training
with nn.parameter_scope('trainable'):
q_t = q_network(self.obss_t, self.acts_t, 'critic')
with nn.parameter_scope('target'):
policy_tp1 = policy_network(self.obss_tp1, self.action_size,
'actor')
q_tp1 = q_network(self.obss_tp1, policy_tp1, 'critic')
y = self.rews_tp1 + self.gamma * q_tp1 * (1.0 - self.ters_tp1)
self.critic_loss = F.mean(F.squared_error(q_t, y))
# actor training
with nn.parameter_scope('trainable'):
policy_t = policy_network(self.obss_t, self.action_size, 'actor')
q_t_with_actor = q_network(self.obss_t, policy_t, 'critic')
self.actor_loss = -F.mean(q_t_with_actor)
# get neural network parameters
with nn.parameter_scope('trainable'):
with nn.parameter_scope('critic'):
critic_params = nn.get_parameters()
with nn.parameter_scope('actor'):
actor_params = nn.get_parameters()
# setup optimizers
self.critic_solver = S.Adam(self.critic_lr)
self.critic_solver.set_parameters(critic_params)
self.actor_solver = S.Adam(self.actor_lr)
self.actor_solver.set_parameters(actor_params)
with nn.parameter_scope('trainable'):
trainable_params = nn.get_parameters()
with nn.parameter_scope('target'):
target_params = nn.get_parameters()
# build target update
update_targets = []
sync_targets = []
for key, src in trainable_params.items():
dst = target_params[key]
updated_dst = (1.0 - self.tau) * dst + self.tau * src
update_targets.append(F.assign(dst, updated_dst))
sync_targets.append(F.assign(dst, src))
self.update_target_expr = F.sink(*update_targets)
self.sync_target_expr = F.sink(*sync_targets)
def test_assign_forward_backward(seed, ctx, func_name):
rng = np.random.RandomState(seed)
dst = nn.Variable((2, 3, 4), need_grad=True)
src = nn.Variable((2, 3, 4), need_grad=True)
assign = F.assign(dst, src)
src.d = rng.rand(2, 3, 4)
assign.forward()
# destination variable should be equal to source variable
assert_allclose(dst.d, src.d)
# output variable of assign function should be equal to soure variable
assert_allclose(assign.d, src.d)
dummy = assign + rng.rand()
dst.grad.zero()
src.grad.zero()
dummy.forward()
dummy.backward()
# gradients at destination are identical to gradients at assign operation
assert not np.all(dst.g == np.zeros((2, 3, 4)))
assert np.all(dst.g == assign.g)
assert np.all(src.g == np.zeros((2, 3, 4)))
# check accum=False
assign.grad.zero()
dst.g = rng.rand(2, 3, 4)
f = assign.parent
f.forward([dst, src], [assign])
f.backward([dst, src], [assign], accum=[False])
assert np.all(dst.g == assign.g)
assert np.all(src.g == np.zeros((2, 3, 4)))
def build_static_graph(self):
real_img = nn.Variable(shape=(self.batch_size, 3, self.img_size,
self.img_size))
noises = [
F.randn(shape=(self.batch_size, self.config['latent_dim']))
for _ in range(2)
]
if self.config['regularize_gen']:
fake_img, dlatents = self.generator(self.batch_size,
noises,
return_latent=True)
else:
fake_img = self.generator(self.batch_size, noises)
fake_img_test = self.generator_ema(self.batch_size, noises)
gen_loss = gen_nonsaturating_loss(self.discriminator(fake_img))
fake_disc_out = self.discriminator(fake_img)
real_disc_out = self.discriminator(real_img)
disc_loss = disc_logistic_loss(real_disc_out, fake_disc_out)
var_name_list = [
'real_img', 'noises', 'fake_img', 'gen_loss', 'disc_loss',
'fake_disc_out', 'real_disc_out', 'fake_img_test'
]
var_list = [
real_img, noises, fake_img, gen_loss, disc_loss, fake_disc_out,
real_disc_out, fake_img_test
]
if self.config['regularize_gen']:
dlatents.need_grad = True
mean_path_length = nn.Variable()
pl_reg, path_mean, _ = gen_path_regularize(
fake_img=fake_img,
latents=dlatents,
mean_path_length=mean_path_length)
path_mean_update = F.assign(mean_path_length, path_mean)
path_mean_update.name = 'path_mean_update'
pl_reg += 0 * path_mean_update
gen_loss_reg = gen_loss + pl_reg
var_name_list.append('gen_loss_reg')
var_list.append(gen_loss_reg)
if self.config['regularize_disc']:
real_img.need_grad = True
real_disc_out = self.discriminator(real_img)
disc_loss_reg = disc_loss + self.config[
'r1_coeff'] * 0.5 * disc_r1_loss(
real_disc_out, real_img) * self.config['disc_reg_step']
real_img.need_grad = False
var_name_list.append('disc_loss_reg')
var_list.append(disc_loss_reg)
Parameters = namedtuple('Parameters', var_name_list)
self.parameters = Parameters(*var_list)
def make_ema_updater(scope_ema, scope_cur, ema_decay):
with nn.parameter_scope(scope_cur):
params_cur = nn.get_parameters()
with nn.parameter_scope(scope_ema):
params_ema = nn.get_parameters()
update_ema_list = []
for name in params_ema.keys():
params_ema_updated = ema_decay * \
params_ema[name] + (1.0 - ema_decay) * params_cur[name]
update_ema_list.append(F.assign(params_ema[name], params_ema_updated))
return F.sink(*update_ema_list)
def ema_update(self):
with nn.parameter_scope('Generator'):
g_params = nn.get_parameters(grad_only=False)
with nn.parameter_scope('GeneratorEMA'):
g_ema_params = nn.get_parameters(grad_only=False)
update_ema_list = []
for name in g_ema_params.keys():
params_ema_updated = self.gen_exp_weight * \
g_ema_params[name] + \
(1.0 - self.gen_exp_weight) * g_params[name]
update_ema_list.append(
F.assign(g_ema_params[name], params_ema_updated))
return F.sink(*update_ema_list)
def _build(self):
# inference graph
self.infer_obs_t = nn.Variable((1, ) + self.obs_shape)
with nn.parameter_scope('trainable'):
infer_dist = policy_network(self.infer_obs_t, self.action_size,
'actor')
self.infer_act_t, _ = _squash_action(infer_dist)
self.deterministic_act_t = infer_dist.mean()
# training graph
self.obss_t = nn.Variable((self.batch_size, ) + self.obs_shape)
self.acts_t = nn.Variable((self.batch_size, self.action_size))
self.rews_tp1 = nn.Variable((self.batch_size, 1))
self.obss_tp1 = nn.Variable((self.batch_size, ) + self.obs_shape)
self.ters_tp1 = nn.Variable((self.batch_size, 1))
with nn.parameter_scope('trainable'):
self.log_temp = get_parameter_or_create('temp', [1, 1],
ConstantInitializer(0.0))
dist_t = policy_network(self.obss_t, self.action_size, 'actor')
dist_tp1 = policy_network(self.obss_tp1, self.action_size, 'actor')
squashed_act_t, log_prob_t = _squash_action(dist_t)
squashed_act_tp1, log_prob_tp1 = _squash_action(dist_tp1)
q1_t = q_network(self.obss_t, self.acts_t, 'critic/1')
q2_t = q_network(self.obss_t, self.acts_t, 'critic/2')
q1_t_with_actor = q_network(self.obss_t, squashed_act_t,
'critic/1')
q2_t_with_actor = q_network(self.obss_t, squashed_act_t,
'critic/2')
with nn.parameter_scope('target'):
q1_tp1 = q_network(self.obss_tp1, squashed_act_tp1, 'critic/1')
q2_tp1 = q_network(self.obss_tp1, squashed_act_tp1, 'critic/2')
# q function loss
q_tp1 = F.minimum2(q1_tp1, q2_tp1)
entropy_tp1 = F.exp(self.log_temp) * log_prob_tp1
mask = (1.0 - self.ters_tp1)
q_target = self.rews_tp1 + self.gamma * (q_tp1 - entropy_tp1) * mask
q_target.need_grad = False
q1_loss = 0.5 * F.mean(F.squared_error(q1_t, q_target))
q2_loss = 0.5 * F.mean(F.squared_error(q2_t, q_target))
self.critic_loss = q1_loss + q2_loss
# policy function loss
q_t = F.minimum2(q1_t_with_actor, q2_t_with_actor)
entropy_t = F.exp(self.log_temp) * log_prob_t
self.actor_loss = F.mean(entropy_t - q_t)
# temperature loss
temp_target = log_prob_t - self.action_size
temp_target.need_grad = False
self.temp_loss = -F.mean(F.exp(self.log_temp) * temp_target)
# trainable parameters
with nn.parameter_scope('trainable'):
with nn.parameter_scope('critic'):
critic_params = nn.get_parameters()
with nn.parameter_scope('actor'):
actor_params = nn.get_parameters()
# target parameters
with nn.parameter_scope('target/critic'):
target_params = nn.get_parameters()
# target update
update_targets = []
sync_targets = []
for key, src in critic_params.items():
dst = target_params[key]
updated_dst = (1.0 - self.tau) * dst + self.tau * src
update_targets.append(F.assign(dst, updated_dst))
sync_targets.append(F.assign(dst, src))
self.update_target_expr = F.sink(*update_targets)
self.sync_target_expr = F.sink(*sync_targets)
# setup solvers
self.critic_solver = S.Adam(self.critic_lr)
self.critic_solver.set_parameters(critic_params)
self.actor_solver = S.Adam(self.actor_lr)
self.actor_solver.set_parameters(actor_params)
self.temp_solver = S.Adam(self.temp_lr)
self.temp_solver.set_parameters({'temp': self.log_temp})
def _build(self):
# inference graph
self.infer_obs_t = nn.Variable((1, ) + self.obs_shape)
with nn.parameter_scope('trainable'):
infer_dist = policy_network(self.infer_obs_t, self.action_size,
'actor')
self.infer_act_t, _ = _squash_action(infer_dist)
self.deterministic_act_t = infer_dist.mean()
# training graph
self.obss_t = nn.Variable((self.batch_size, ) + self.obs_shape)
self.acts_t = nn.Variable((self.batch_size, self.action_size))
self.rews_tp1 = nn.Variable((self.batch_size, 1))
self.obss_tp1 = nn.Variable((self.batch_size, ) + self.obs_shape)
self.ters_tp1 = nn.Variable((self.batch_size, 1))
with nn.parameter_scope('trainable'):
dist = policy_network(self.obss_t, self.action_size, 'actor')
squashed_act_t, log_prob_t = _squash_action(dist)
v_t = v_network(self.obss_t, 'value')
q_t1 = q_network(self.obss_t, self.acts_t, 'critic/1')
q_t2 = q_network(self.obss_t, self.acts_t, 'critic/2')
q_t1_with_actor = q_network(self.obss_t, squashed_act_t,
'critic/1')
q_t2_with_actor = q_network(self.obss_t, squashed_act_t,
'critic/2')
with nn.parameter_scope('target'):
v_tp1 = v_network(self.obss_tp1, 'value')
# value loss
q_t = F.minimum2(q_t1_with_actor, q_t2_with_actor)
v_target = q_t - log_prob_t
v_target.need_grad = False
self.value_loss = 0.5 * F.mean(F.squared_error(v_t, v_target))
# q function loss
scaled_rews_tp1 = self.rews_tp1 * self.reward_scale
q_target = scaled_rews_tp1 + self.gamma * v_tp1 * (1.0 - self.ters_tp1)
q_target.need_grad = False
q1_loss = 0.5 * F.mean(F.squared_error(q_t1, q_target))
q2_loss = 0.5 * F.mean(F.squared_error(q_t2, q_target))
self.critic_loss = q1_loss + q2_loss
# policy function loss
mean_loss = 0.5 * F.mean(dist.mean()**2)
logstd_loss = 0.5 * F.mean(F.log(dist.stddev())**2)
policy_reg_loss = self.policy_reg * (mean_loss + logstd_loss)
self.objective_loss = F.mean(log_prob_t - q_t)
self.actor_loss = self.objective_loss + policy_reg_loss
# trainable parameters
with nn.parameter_scope('trainable'):
with nn.parameter_scope('value'):
value_params = nn.get_parameters()
with nn.parameter_scope('critic'):
critic_params = nn.get_parameters()
with nn.parameter_scope('actor'):
actor_params = nn.get_parameters()
# target parameters
with nn.parameter_scope('target/value'):
target_params = nn.get_parameters()
# target update
update_targets = []
sync_targets = []
for key, src in value_params.items():
dst = target_params[key]
updated_dst = (1.0 - self.tau) * dst + self.tau * src
update_targets.append(F.assign(dst, updated_dst))
sync_targets.append(F.assign(dst, src))
self.update_target_expr = F.sink(*update_targets)
self.sync_target_expr = F.sink(*sync_targets)
# setup solvers
self.value_solver = S.Adam(self.value_lr)
self.value_solver.set_parameters(value_params)
self.critic_solver = S.Adam(self.critic_lr)
self.critic_solver.set_parameters(critic_params)
self.actor_solver = S.Adam(self.actor_lr)
self.actor_solver.set_parameters(actor_params)
def __call__(self,
batch_size,
style_noises,
truncation_psi=1.0,
return_latent=False,
mixing_layer_index=None,
dlatent_avg_beta=0.995):
with nn.parameter_scope(self.global_scope):
# normalize noise inputs
for i in range(len(style_noises)):
style_noises[i] = F.div2(
style_noises[i],
F.pow_scalar(F.add_scalar(F.mean(style_noises[i]**2.,
axis=1,
keepdims=True),
1e-8,
inplace=False),
0.5,
inplace=False))
# get latent code
w = [
mapping_network(style_noises[0],
outmaps=self.mapping_network_dim,
num_layers=self.mapping_network_num_layers)
]
w += [
mapping_network(style_noises[1],
outmaps=self.mapping_network_dim,
num_layers=self.mapping_network_num_layers)
]
dlatent_avg = nn.parameter.get_parameter_or_create(
name="dlatent_avg", shape=(1, 512))
# Moving average update of dlatent_avg
batch_avg = F.mean((w[0] + w[1]) * 0.5, axis=0, keepdims=True)
update_op = F.assign(
dlatent_avg, lerp(batch_avg, dlatent_avg, dlatent_avg_beta))
update_op.name = 'dlatent_avg_update'
dlatent_avg = F.identity(dlatent_avg) + 0 * update_op
# truncation trick
w = [lerp(dlatent_avg, _, truncation_psi) for _ in w]
# generate output from generator
constant_bc = nn.parameter.get_parameter_or_create(
name="G_synthesis/4x4/Const/const",
shape=(1, 512, 4, 4),
initializer=np.random.randn(1, 512, 4, 4).astype(np.float32))
constant_bc = F.broadcast(constant_bc,
(batch_size, ) + constant_bc.shape[1:])
if mixing_layer_index is None:
mixing_layer_index_var = F.randint(1,
len(self.resolutions) * 2,
(1, ))
else:
mixing_layer_index_var = F.constant(val=mixing_layer_index,
shape=(1, ))
mixing_switch_var = F.clip_by_value(
F.arange(0,
len(self.resolutions) * 2) - mixing_layer_index_var,
0, 1)
mixing_switch_var_re = F.reshape(
mixing_switch_var, (1, mixing_switch_var.shape[0], 1),
inplace=False)
w0 = F.reshape(w[0], (batch_size, 1, w[0].shape[1]), inplace=False)
w1 = F.reshape(w[1], (batch_size, 1, w[0].shape[1]), inplace=False)
w_mixed = w0 * mixing_switch_var_re + \
w1 * (1 - mixing_switch_var_re)
rgb_output = self.synthesis(w_mixed, constant_bc)
if return_latent:
return rgb_output, w_mixed
else:
return rgb_output
def sample_loop(self, model, shape, sampler,
noise=None,
dump_interval=-1,
progress=False,
without_auto_forward=False):
"""
Iteratively Sample data from model from t=T to t=0.
T is specified as the length of betas given to __init__().
Args:
model (collable):
A callable that takes x_t and t and predict noise (and sigma related parameters).
shape (list like object): A data shape.
sampler (callable): A function to sample x_{t-1} given x_{t} and t. Typically, self.p_sample or self.ddim_sample.
noise (collable): A noise generator. If None, F.randn(shape) will be used.
interval (int):
If > 0, all intermediate results at every `interval` step will be returned as a list.
e.g. if interval = 10, the predicted results at {10, 20, 30, ...} will be returned.
progress (bool): If True, tqdm will be used to show the sampling progress.
Returns:
- x_0 (nn.Variable): the final sampled result of x_0
- samples (a list of nn.Variable): the sampled results at every `interval`
- pred_x_starts (a list of nn.Variable): the predicted x_0 from each x_t at every `interval`:
"""
T = self.num_timesteps
indices = list(range(T))[::-1]
samples = []
pred_x_starts = []
if progress:
from tqdm.auto import tqdm
indices = tqdm(indices)
if without_auto_forward:
if noise is None:
noise = np.random.randn(*shape)
else:
assert isinstance(noise, np.ndarray)
assert noise.shape == shape
x_t = nn.Variable.from_numpy_array(noise)
t = nn.Variable.from_numpy_array([T - 1 for _ in range(shape[0])])
# build graph
y, pred_x_start = sampler(model, x_t, t)
up_x_t = F.assign(x_t, y)
up_t = F.assign(t, t - 1)
update = F.sink(up_x_t, up_t)
cnt = 0
for step in indices:
y.forward(clear_buffer=True)
update.forward(clear_buffer=True)
cnt += 1
if dump_interval > 0 and cnt % dump_interval == 0:
samples.append((step, y.d.copy()))
pred_x_starts.append((step, pred_x_start.d.copy()))
else:
with nn.auto_forward():
if noise is None:
x_t = F.randn(shape=shape)
else:
assert isinstance(noise, np.ndarray)
assert noise.shape == shape
x_t = nn.Variable.from_numpy_array(noise)
cnt = 0
for step in indices:
t = F.constant(step, shape=(shape[0], ))
x_t, pred_x_start = sampler(
model, x_t, t, no_noise=step == 0)
cnt += 1
if dump_interval > 0 and cnt % dump_interval == 0:
samples.append((step, x_t.d.copy()))
pred_x_starts.append((step, pred_x_start.d.copy()))
assert x_t.shape == shape
return x_t.d.copy(), samples, pred_x_starts
def build_static_graph(self):
real_img = nn.Variable(shape=(self.batch_size, 3, self.img_size,
self.img_size))
noises = [
F.randn(shape=(self.batch_size, self.config['latent_dim']))
for _ in range(2)
]
if self.few_shot_config['common']['type'] == 'cdc':
NT_class = NoiseTop(n_train=self.train_loader.size,
latent_dim=self.config['latent_dim'],
batch_size=self.batch_size)
noises = NT_class()
self.PD_switch_var = NT_class.PD_switch_var
if self.config['regularize_gen']:
fake_img, dlatents = self.generator(self.batch_size,
noises,
return_latent=True)
else:
fake_img = self.generator(self.batch_size, noises)
fake_img_test = self.generator_ema(self.batch_size, noises)
if self.few_shot_config['common']['type'] != 'cdc':
fake_disc_out = self.discriminator(fake_img)
real_disc_out = self.discriminator(real_img)
disc_loss = disc_logistic_loss(real_disc_out, fake_disc_out)
gen_loss = 0
if self.few_shot_config['common']['type'] == 'cdc':
fake_img_s = self.generator_s(self.batch_size, noises)
cdc_loss = CrossDomainCorrespondence(
fake_img,
fake_img_s,
_choice_num=self.few_shot_config['cdc']['feature_num'],
_layer_fix_switch=self.few_shot_config['cdc']['layer_fix'])
gen_loss += self.few_shot_config['cdc']['lambda'] * cdc_loss
# --- PatchDiscriminator ---
fake_disc_out, fake_feature_var = self.discriminator(
fake_img, patch_switch=True, index=0)
real_disc_out, real_feature_var = self.discriminator(
real_img, patch_switch=True, index=0)
disc_loss = disc_logistic_loss(real_disc_out, fake_disc_out)
disc_loss_patch = disc_logistic_loss(fake_feature_var,
real_feature_var)
disc_loss += self.PD_switch_var * disc_loss_patch
gen_loss += gen_nonsaturating_loss(fake_disc_out)
var_name_list = [
'real_img', 'noises', 'fake_img', 'gen_loss', 'disc_loss',
'fake_disc_out', 'real_disc_out', 'fake_img_test'
]
var_list = [
real_img, noises, fake_img, gen_loss, disc_loss, fake_disc_out,
real_disc_out, fake_img_test
]
if self.config['regularize_gen']:
dlatents.need_grad = True
mean_path_length = nn.Variable()
pl_reg, path_mean, _ = gen_path_regularize(
fake_img=fake_img,
latents=dlatents,
mean_path_length=mean_path_length)
path_mean_update = F.assign(mean_path_length, path_mean)
path_mean_update.name = 'path_mean_update'
pl_reg += 0 * path_mean_update
gen_loss_reg = gen_loss + pl_reg
var_name_list.append('gen_loss_reg')
var_list.append(gen_loss_reg)
if self.config['regularize_disc']:
real_img.need_grad = True
real_disc_out = self.discriminator(real_img)
disc_loss_reg = disc_loss + self.config[
'r1_coeff'] * 0.5 * disc_r1_loss(
real_disc_out, real_img) * self.config['disc_reg_step']
real_img.need_grad = False
var_name_list.append('disc_loss_reg')
var_list.append(disc_loss_reg)
Parameters = namedtuple('Parameters', var_name_list)
self.parameters = Parameters(*var_list)
def ema_update(p_ema, p_train):
return F.assign(p_ema, ema_decay * p_ema + (1. - ema_decay) * p_train)
