def optimize(self, num_samples, grad_steps, pbar=None):
"""
Args
num_samples (int): number of samples to optimize over
grad_steps (int): number of gradient descent updates.
pbar: progress bar such as tqdm or st.progress [Default: None]
"""
self.losses, self.outs = [], []
variables = self.var_manager.initialize(num_samples=num_samples)
t_st = time.time()
for i in range(grad_steps):
self.step(variables, optimize=True, transform=(i == 0))
if pbar is not None:
pbar.progress(i / grad_steps)
if self.log:
if ((i + 1) % self.log_iter == 0) or (i + 1 == grad_steps):
self.log_result(variables, i + 1)
if (i + 1) % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print('optimize', i + 1, grad_steps, 'c', t_avg)
t_st = time.time()
if self.log:
return variables, self.outs, self.losses
transform_out = to_grid(torch.stack(list(self.out.cpu().detach())))
return variables, [transform_out], [[grad_steps, {'loss':self.loss}]]
def optimize(self,
num_samples,
meta_steps,
grad_steps,
last_grad_steps=300,
pbar=None):
"""
Args
num_samples (int): number of samples to optimize
meta_steps (int): number of Nevergrad updates
grad_steps (int): number of gradient updates per Nevergrad update.
last_grad_steps (int): after the final iteration of hybrid
optimization further optimize the last drawn samples using
gradient descent.
pbar: progress bar such as tqdm or st.progress
"""
self.losses, self.outs, i = [], [], 0
total_steps = meta_steps * grad_steps + last_grad_steps
self.setup_ng(self.var_manager, budget=meta_steps * grad_steps)
#####
# -- Hybrid optimization (outerloop Nevergrad) -- #
t_st = time.time()
for meta_iter in range(meta_steps + 1):
is_last_iter = (meta_iter == meta_steps)
_grad_steps = last_grad_steps if is_last_iter else grad_steps
variables = self.ng_init(self.var_manager, num_samples)
#####
# -- Gradient optimization (innerloop SGD) -- #
for j in range(_grad_steps):
self.step(variables, optimize=True, transform=(j == 0))
i += 1
if self.log:
if ((i + 1) % self.log_iter == 0) or (i + 1 == grad_steps):
self.log_result(variables, i + 1)
if pbar is not None:
pbar.progress(i / total_steps)
else:
if (i + 1) % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print('optimize', i + 1, total_steps, 'c',
t_avg)
t_st = time.time()
if not is_last_iter:
self.ng_update(variables, inverted_loss=True)
if self.log:
return variables, self.outs, self.losses
transform_out = to_grid(torch.stack(list(self.out.cpu().detach())))
return variables, [transform_out], [[total_steps, {'loss': self.loss}]]
def optimize(self, meta_steps, grad_steps=0, pbar=None, num_samples=None):
"""
Args
grad_steps (int): number of gradient descent updates.
meta_steps (int): number of CMA updates
grad_steps (int): number of gradient updates to apply after CMA
optimization. [Default: 0]
pbar: progress bar such as tqdm or st.progress
num_samples: must be None
"""
assert num_samples == None, 'PyCMA optimizer has fixed sample size'
self.setup_cma(self.var_manager)
self.losses, self.outs, i = [], [], 0
total_steps = meta_steps + grad_steps
#####
# -- CMA optimization (no gradient descent) -- #
t_st = time.time()
for _ in range(meta_steps):
variables = self.cma_init(self.var_manager)
self.step(variables, optimize=False, transform=False)
i += 1
if self.log:
if (i % self.log_iter == 0) or (i == grad_steps):
self.log_result(variables, i)
self.cma_update(variables, inverted_loss=True)
if pbar is not None:
pbar.progress(i / total_steps)
else:
if i % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print('optimize', i, total_steps, 'c', t_avg)
t_st = time.time()
#####
# -- Finetune CMA with ADAM optimization -- #
variables = self.cma_init(self.var_manager)
for j in range(grad_steps):
self.step(variables, optimize=True, transform=(j == 0))
i += 1
if self.log:
if ((i + 1) % self.log_iter == 0) or (i + 1 == grad_steps):
self.log_result(variables, i + 1)
if pbar is not None:
pbar.progress(i / total_steps)
else:
if (i + 1) % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print('optimize', i + 1, total_steps, 'c', t_avg)
t_st = time.time()
if self.log:
return variables, self.outs, self.losses
transform_out = to_grid(torch.stack(list(self.out.cpu().detach())))
return variables, [transform_out], [[total_steps, {'loss': self.loss}]]
def optimize(self, num_samples, meta_steps, grad_steps=0, pbar=None):
"""
Args
num_samples (int): number of samples to optimize
grad_steps (int): number of gradient descent updates.
meta_steps (int): number of Nevergrad updates
grad_steps (int): number of gradient updates to apply after
Nevergrad optimization. [Default: 0]
pbar:
progress bar such as tqdm or st.progress
"""
self.setup_ng(self.var_manager, meta_steps) # double check if budget is number of times you call or call sequentially
self.losses, self.outs, i = [], [], 0
total_steps = meta_steps + grad_steps
#####
# -- Nevergrad optimization (no gradient descent) -- #
t_st = time.time()
for _ in range(meta_steps):
variables = self.ng_init(self.var_manager, num_samples)
self.step(variables, optimize=False, transform=False)
i += 1
if self.log:
if (i % self.log_iter == 0) or (i == grad_steps):
self.log_result(variables, i)
self.ng_update(variables, inverted_loss=True)
if pbar is not None:
pbar.progress(i / total_steps)
else:
if i % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print('optimize', i, total_steps, 'c', t_avg)
t_st = time.time()
#####
# -- Finetune Nevergrad result with ADAM optimization -- #
variables = self.ng_init(self.var_manager, num_samples)
for j in range(grad_steps):
self.step(variables, optimize=True, transform=(j == 0))
i += 1
if self.log:
if ((i + 1) % self.log_iter == 0) or (i + 1 == grad_steps):
self.log_result(variables, i + 1)
if pbar is not None:
pbar.progress(i / total_steps)
else:
if (i + 1) % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print('optimize', i + 1, total_steps, 'c', t_avg)
t_st = time.time()
if self.log:
return variables, self.outs, self.losses
transform_out = to_grid(torch.stack(list(self.out.cpu().detach())))
return variables, [transform_out], [[total_steps, {'loss':self.loss}]]
def optimize(self, meta_steps, grad_steps, last_grad_steps=None, pbar=None):
"""
Args
meta_steps (int): number of CMA updates
grad_steps (int): number of gradient updates per CMA update.
pbar: progress bar such as tqdm or st.progress
"""
self.setup_cma(self.var_manager)
self.losses, self.outs, self.transform_outs, i = [], [], [], 0
self._best_loss, self._candidate = 999, None
self.vp_means = {}
self.transform_tracked = []
if last_grad_steps is None:
last_grad_steps = grad_steps
total_steps = (meta_steps - 1) * grad_steps + last_grad_steps
#####
# -- BasinCMA optimization (outerloop CMA) -- #
t_st = time.time()
for meta_iter in range(meta_steps):
is_last_iter = (meta_iter + 1 == meta_steps)
_grad_steps = last_grad_steps if is_last_iter else grad_steps
variables = self.cma_init(self.var_manager)
if meta_iter > 0:
self.propagate_variable(variables, meta_iter, meta_steps)
self.transform_tracked.append(
torch.stack(variables.transform.t.data).cpu().detach().clone()
)
#####
# -- Gradient optimization (innerloop SGD) -- #
for j in range(_grad_steps):
self.step(variables, optimize=True, transform=(j == 0))
i += 1
if self.log and (j == 0):
self.vis_transform(variables)
if self.log:
if (i % self.log_iter == 0) or (i == grad_steps):
self.log_result(variables, i)
if pbar is not None:
pbar.progress(i / total_steps)
else:
if i % self.show_iter == 0:
t_avg = (time.time() - t_st) / self.show_iter
progress_print(
'optimize', i, total_steps, 'c', t_avg)
t_st = time.time()
if not is_last_iter:
loss = self.cma_update(variables, inverted_loss=True)
self.update_propagation_variable_statistic(variables)
if np.min(loss) < self._best_loss:
self._candidate = \
variables.transform.t.data[np.argmin(loss)].cpu().detach()
self._best_loss = np.min(loss)
candidate_out = variables.output.target.data[np.argmin(loss)]
if self.log:
return variables, (self.outs, self.transform_outs, candidate_out),\
self.losses
transform_target = \
to_grid(torch.stack(variables.output.target.data).cpu())
transform_out = to_grid(torch.stack(list(self.out.cpu().detach())))
results = ([transform_out], [transform_target], candidate_out)
return variables, results, self.loss