def sample(self):
"""Sample a work id and model from search algorithm."""
res = self.search_alg.search()
if not res:
return None
if not isinstance(res, list):
res = [res]
if len(res) == 0:
return None
out = []
for sample in res:
desc = sample.get("desc") if isinstance(sample, dict) else sample[1]
desc = self._decode_hps(desc)
model_desc = deepcopy(desc)
if "modules" in desc:
PipeStepConfig.model.model_desc = deepcopy(desc)
elif "network" in desc:
origin_desc = PipeStepConfig.model.model_desc
desc = update_dict(desc["network"], origin_desc)
PipeStepConfig.model.model_desc = deepcopy(desc)
if self.quota.is_filtered(desc):
continue
record = self.record.from_sample(sample, desc)
Report().broadcast(record)
out.append((record.worker_id, model_desc))
return out
def _set_startup(args):
if args.startup in ['benchmark', 'b']:
cfg = Config(args.config_file)
config = deepcopy(cfg)
if 'benchmark' in cfg.keys():
benchmark_config = cfg.pop('benchmark')
config = update_dict(benchmark_config, cfg)
else:
config = Config(args.config_file)
return config
def _desc_from_choices(self, choices):
"""Create description object from choices."""
desc = {}
pos = 0
for key in self.search_space.modules:
config_space = copy.deepcopy(self.search_space[key])
module_cfg, pos = self._sub_config_choice(config_space, choices,
pos)
desc[key] = module_cfg
desc = update_dict(desc, copy.deepcopy(self.search_space))
return desc
def decode(self, code):
"""Decode the code to Network Desc.
:param code: input code
:type code: list of int
:return: network desc
:rtype: NetworkDesc
"""
chn_info = self._code_to_chninfo(code)
desc = {
'backbone': chn_info,
}
desc = update_dict(desc, copy.deepcopy(self.search_space))
return desc
def decode(self, code):
"""Decode the code.
:param code: code of network
:type code: list
:return: network desc
:rtype: NetworkDesc
"""
length = len(code)
desc = {
"nbit_w_list": code[: length // 2],
"nbit_a_list": code[length // 2:]
}
desc = update_dict(desc, copy.deepcopy(self.search_space))
return desc
def sample(self):
"""Sample a work id and model from search algorithm."""
res = self.search_alg.search()
if not res:
return None
if not isinstance(res, list):
res = [res]
if len(res) == 0:
return None
out = []
for sample in res:
if isinstance(sample, dict):
id = sample["worker_id"]
desc = self._decode_hps(sample["encoded_desc"])
sample.pop("worker_id")
sample.pop("encoded_desc")
kwargs = sample
sample = _split_sample((id, desc))
else:
kwargs = {}
sample = _split_sample(sample)
(id, desc, hps) = sample
if "modules" in desc:
PipeStepConfig.model.model_desc = deepcopy(desc)
elif "network" in desc:
origin_desc = PipeStepConfig.model.model_desc
model_desc = update_dict(desc["network"], origin_desc)
PipeStepConfig.model.model_desc = model_desc
desc.pop('network')
desc.update(model_desc)
if self.quota.is_filtered(desc):
continue
if self.affinity and not self.affinity.is_affinity(desc):
continue
record = self.record.init(step_name=General.step_name,
worker_id=id,
desc=desc,
hps=hps,
**kwargs)
ReportClient.broadcast(record)
out.append((id, desc, hps))
return out
def decode(self, code):
"""Decode the code to Network Desc.
:param code: input code
:type code: list of int
:return: network desc
:rtype: NetworkDesc
"""
chn_info = self._code_to_chninfo(code)
desc = {
"backbone": chn_info,
"head": {
"base_channel": chn_info['chn_node'][-1]
}
}
desc = update_dict(desc, copy.deepcopy(self.search_space))
return desc
def decode(self, desc):
"""Decode hps: `trainer.optim.lr : 0.1` to dict format.
And convert to `zeus.common.config import Config` object
This Config will be override in Trainer or Datasets class
The override priority is: input hps > user configuration > default configuration
:param hps: hyper params
:return: dict
"""
hps_dict = {}
if desc is None:
return None
if isinstance(desc, tuple):
return desc
for hp_name, value in desc.items():
hp_dict = {}
for key in list(reversed(hp_name.split('.'))):
if hp_dict:
hp_dict = {key: hp_dict}
else:
hp_dict = {key: value}
# update cfg with hps
hps_dict = update_dict(hps_dict, hp_dict, [])
return Config(hps_dict)
def _init_hps(self, hps):
"""Convert trainer values in hps to cfg."""
if hps is not None:
self.args = Config(update_dict(hps, self.args))
