def run(self):
common_s2_prepare_run(self.logger, self.trainer, self.s1_path,
self.tmp_load_path, self.reset_bn, self.methods)
checkpoint_dir = self.checkpoint_dir(self.save_dir)
candidate_dir = '%s/candidates/' % checkpoint_dir
# get problem, run
xu = np.array([
n - 1 for n in self.get_method().strategy_manager.get_num_choices(
unique=True)
])
xl = np.zeros_like(xu)
problem = PymooProblem(estimators=self.estimators,
xl=xl,
xu=xu,
n_var=len(xu))
wrapper = PymooHPOUtils.run(problem, self.algorithm, self.termination,
self.seed, self.logger, checkpoint_dir)
# save results
for sr in wrapper.sorted_best():
self.get_method().get_network().forward_strategy(
fixed_arc=tuple(sr.x))
Builder.save_config(
self.get_method().get_network().config(finalize=True),
candidate_dir,
'candidate-%s' % '-'.join([str(xs) for xs in sr.x]))
def _run(self, save=True):
common_s2_prepare_run(self.logger, self.trainer, self.s1_path,
self.tmp_load_path, self.reset_bn, self.methods)
checkpoint_dir = self.checkpoint_dir(self.save_dir)
candidate_dir = '%s/candidates/' % checkpoint_dir
file_viz = '%sx.pdf' % checkpoint_dir
self.get_method().eval()
# run
algorithm = self.hpo.run_opt(hparams=self.args,
logger=self.logger,
checkpoint_dir=checkpoint_dir,
value_space=self._architecture_space,
constraints=self.constraints,
objectives=self.objectives)
population = algorithm.get_total_population(sort=True)
# save results
if save:
population.plot(self.objectives[0].key,
self.objectives[1].key,
show=False,
save_path=file_viz)
for candidate in population.fronts[0]:
self.get_method().get_network().forward_strategy(
fixed_arc=candidate.values)
Builder.save_config(
self.get_method().get_network().config(finalize=True),
candidate_dir, 'candidate-%s' %
'-'.join([str(g) for g in candidate.values]))
return algorithm, population
def assert_stats_match(name,
task_cfg,
cfg: dict,
num_params=None,
num_macs=None):
cfg_dir = replace_standard_paths('{path_tmp}/tests/cfgs/')
cfg_path = Builder.save_config(cfg, cfg_dir, name)
exp = Main.new_task(
task_cfg,
args_changes={
'{cls_data}.fake': True,
'{cls_data}.batch_size_train': 2,
'{cls_data}.batch_size_test': -1,
'{cls_task}.is_test_run': True,
'{cls_task}.save_dir': '{path_tmp}/tests/workdir/',
"{cls_network}.config_path": cfg_path,
"{cls_trainer}.ema_decay": -1,
'cls_network_heads':
'ClassificationHead', # necessary for the DARTS search space to disable the aux heads
},
raise_unparsed=False)
net = exp.get_method().get_network()
macs = exp.get_method().profile_macs()
net.eval()
# print(net)
cp = count_parameters(net)
if num_params is not None:
assert cp == num_params, 'Got unexpected num params for %s: %d, expected %d, diff: %d'\
% (name, cp, num_params, abs(cp - num_params))
if num_macs is not None:
assert macs == num_macs, 'Got unexpected num macs for %s: %d, expected %d, diff: %d'\
% (name, macs, num_macs, abs(macs - num_macs))
def save(net: nn.Module, save_dir: str, name: str, verbose=True) -> dict:
# saving config now will only use the currently active connections, since we have a search network
cfg = net.config(finalize=True)
if (save_dir is not None) and (name is not None):
path = Builder.save_config(cfg, save_dir, name)
if verbose:
print('Saved config: %s' % path)
return cfg
def generate_from_name(name: str, save=True, verbose=True):
genotype, compact = compact_from_name(name, verbose=verbose)
run_configs = '{path_conf_tasks}/d1_dartsv1.run_config, {path_conf_net_search}darts.run_config'
# create weight sharing cell model
changes = {
'cls_data':
'Cifar10Data',
'{cls_data}.fake':
True,
'{cls_task}.save_del_old':
False,
'{cls_network_body}.cell_order':
'n, r',
'{cls_network_body}.features_first_cell':
36 * 4,
'{cls_network_stem}.features':
36 * 3,
'cls_network_cells_primitives':
"%s, %s" % (compact.get('primitives'), compact.get('primitives')),
}
task = Main.new_task(run_configs, args_changes=changes)
net = task.get_method().get_network()
args = task.args
wss = StrategyManager().get_strategies()
assert len(wss) == 1
ws = wss[list(wss.keys())[0]]
# fix arc, all block inputs use different weights
# go through all weights in the search cell
for n, w in ws.named_parameters_single():
# figure out cell type ("normal", "reduce"), block index, and if it's the first, second, ... op of that block
c_type, block_idx, num_inputs, num_idx = n.split('/')[-4:]
block_idx = int(block_idx.split('-')[-1])
num_idx = int(num_idx.split('-')[-1])
# set all paths weights to zero
w.data.zero_()
# go through the cell description of the genotype, if input and op number match, set the weight to be higher
for op_idx, from_idx in compact.get(c_type)[block_idx]:
if num_idx == from_idx:
w[op_idx] = 1
ws.forward()
# saving config now will only use the highest weighted connections, since we have a search network
cfg = net.config(finalize=True, num_block_ops=2)
if save:
path = Builder.save_config(cfg, get_net_config_dir(genotype.source),
name)
print('Saved config: %s' % path)
return net, cfg, args
def visualize_config(config: dict, save_path: str):
save_path = replace_standard_paths(save_path)
cfg_path = Builder.save_config(config, replace_standard_paths('{path_tmp}/viz/'), 'viz')
exp = Main.new_task(run_config, args_changes={
'{cls_data}.fake': True,
'{cls_data}.batch_size_train': 4,
'{cls_task}.is_test_run': True,
'{cls_task}.save_dir': '{path_tmp}/viz/task/',
'{cls_task}.save_del_old': True,
"{cls_network}.config_path": cfg_path,
})
net = exp.get_method().get_network()
vt = VizTree(net)
vt.print()
vt.plot(save_path + 'net', add_subgraphs=True)
print('Saved cell viz to %s' % save_path)
def visualize_config(config: dict, save_path: str):
save_path = replace_standard_paths(save_path)
cfg_path = Builder.save_config(config, replace_standard_paths('{path_tmp}/viz/'), 'viz')
exp = Main.new_task(run_config, args_changes={
'{cls_data}.fake': True,
'{cls_data}.batch_size_train': 2,
'{cls_task}.is_test_run': True,
'{cls_task}.save_dir': '{path_tmp}/viz/task/',
'{cls_task}.save_del_old': True,
"{cls_task}.note": "viz",
"{cls_network}.config_path": cfg_path,
})
net = exp.get_method().get_network()
for s in ['n', 'r']:
for cell in net.get_cells():
if cell.name.startswith(s):
visualize_cell(cell, save_path, s)
break
print('Saved cell viz to %s' % save_path)