def get_lr_scheduler(optimizer, config, trainer_config=None):
"""Return a new LR Scheduler."""
trainer_config = trainer_config or {}
lr_type, lr_args = parse_spec(config)
if lr_type == 'CosineAnnealingLR':
if 'T_max' not in lr_args and 'epochs' in trainer_config:
lr_args['T_max'] = trainer_config['epochs']
return build(lr_type, optimizer, **lr_args)
def default_constructor(model, construct_config=None, arch_desc=None):
"""Apply all constructors on model."""
if arch_desc:
reg_id, args = parse_spec(construct_config['arch_desc'])
args['arch_desc'] = arch_desc
construct_config['arch_desc'] = to_spec(reg_id, args)
construct_fn = build_constructor_all(construct_config or {})
for name, con_fn in construct_fn.items():
logger.info('Running constructor: {} type: {}'.format(name, con_fn.__class__.__name__))
model = con_fn(model)
return model
def get_optimizer(params, config, trainer_config=None):
"""Return a new Optimizer."""
trainer_config = trainer_config or {}
optim_type, optim_args = parse_spec(config)
device_ids = trainer_config.get('device', [None])
n_parallel = len(device_ids)
if trainer_config.get('scale_lr', True) and 'lr' in optim_args:
optim_args['lr'] *= n_parallel
optimizer = build(optim_type, params, **optim_args)
if n_parallel > 1:
optimizer = torch.nn.DataParallel(optimizer,
device_ids=device_ids).module
return optimizer
def convert(self, slot):
"""Convert Slot to mixed operator."""
arch_params = self.param_map.get(slot.name, None)
old_conf = self.mixed_op_conf
reg_id, args = parse_spec(old_conf)
if arch_params is not None:
args['arch_param'] = arch_params
self.mixed_op_conf = to_spec(reg_id, args)
ent = super().convert(slot)
self.mixed_op_conf = old_conf
args.pop('arch_param', None)
if slot.name not in self.param_map:
self.param_map[slot.name] = ent.arch_param
return ent
def run(self, optim):
"""Run Estimator routine."""
del optim
logger = self.logger
config = self.config
pipeconf = config.pipeline
pending = queue.Queue()
for pn in pipeconf.keys():
pending.put(pn)
finished = set()
ret_values, ret = dict(), None
while not pending.empty():
pname = pending.get()
pconf = pipeconf.get(pname)
dep_sat = True
for dep in pconf.get('depends', []):
if dep not in finished:
dep_sat = False
break
if not dep_sat:
pending.put(pname)
continue
ptype, pargs = parse_spec(pconf)
pargs['name'] = pargs.get('name', pname)
for inp_kw, inp_idx in pconf.get('inputs', {}).items():
keys = inp_idx.split('.')
inp_val = ret_values
for k in keys:
if not inp_val or k not in inp_val:
raise RuntimeError(
'input key {} not found in return {}'.format(
inp_idx, ret_values))
inp_val = inp_val[k]
pargs[inp_kw] = inp_val
logger.info('pipeline: running {}, type={}'.format(pname, ptype))
ret = self.step(pconf)
ret_values[pname] = ret
logger.info('pipeline: finished {}, results={}'.format(pname, ret))
finished.add(pname)
ret_values['final'] = ret
logger.info('pipeline: all finished')
return ret_values