def before_run(self, state):
ls = [m.tensor for m in self.metrics]
for i in ls:
if not (isinstance(i, list) or isinstance(i, tuple)):
raise ValueError(
'metrics should return tuple or list of tensors, got %s' %
repr(i))
for ii in i:
if not isinstance(ii, F.framework.Variable):
raise ValueError(
'metrics tensor be propeller.train.Metrics, got %s of type %s'
% (repr(ii), type(ii)))
ls_flt, self.schema = util.flatten(ls)
#log.debug(ls_flt)
return ls_flt
def run(self, fetch_list=[], *args, **kwargs):
"""
wrapper for Executor.run
"""
#log.debug('Executor running step %d' % self._state.gstep)
if self._hooks:
fetch_list = [fetch_list]
for h in self._hooks:
#log.debug('calling hook.before_run %s' % h)
fetch = h.before_run(self._state)
fetch_list.append(fetch)
fetch_list_len = map(len, fetch_list)
fetch_list, schema = util.flatten(fetch_list)
fetch_list = [
f.name if not isinstance(f, six.string_types) else f
for f in fetch_list
]
#if len(set(fetch_list)) != len(fetch_list):
# log.error('strange shit happend when fetch list has idetity tensors %s' % fetch_list)
#log.debug(fetch_list)
res = self._exe.run(self._program.train_program,
fetch_list=fetch_list,
*args,
**kwargs)
res = [self._merge_result(r) for r in res]
#log.debug(res)
res = util.unflatten(res, schema)
ret, res = res[0], res[1:]
for r, h in zip(res, self._hooks):
#log.debug('calling hook.after_run')
h.after_run(r, self._state)
if any(map(lambda i: i.should_stop(self._state), self._hooks)):
raise StopException('hook call stop')
else:
ret = self._exe.run(self._program.train_program,
fetch_list=fetch_list,
*args,
**kwargs)
self._state = self._state.next()
return ret