def _build_for_predict(self, ds):
ds.name = 'predict'
program = F.Program()
startup_prog = F.Program()
with F.program_guard(program, startup_prog):
#share var with Train net
log.info('Building Predict Graph')
fea = ds.features()
fea = unflatten(fea, ds.data_schema)
model_spec = _build_net(self.model_fn, fea, RunMode.PREDICT,
self.params, self.run_config)
log.info('Done')
optimizer_ops = {'sgd', 'adam', 'adagrad'}
for op in program.global_block().ops:
if op.type == 'dropout':
op._set_attr('is_test', True)
if op.type == 'batch_norm':
op._set_attr('is_test', True)
if op.type in optimizer_ops:
raise RuntimeError('Found optimizer op in eval graph, op: %s' %
repr(op))
#program = program.clone(for_test=True)
log.info(
'Predict with: \n> Run_config: %s\n> Params: %s\n> Train_model_spec: %s\n'
% (repr(self.run_config), repr(self.params), repr(model_spec)))
return ProgramPair(train_program=program,
startup_program=startup_prog), model_spec
def _build_for_train(self, train_dataset):
train_dataset.name = 'train'
train_program = F.Program()
startup_prog = F.Program()
with F.program_guard(train_program, startup_prog):
with collection.Collections() as collections:
log.info('Building Train Graph...')
fea = train_dataset.features()
fea = unflatten(fea, train_dataset.data_schema)
model_spec = _build_net(self.model_fn, fea, RunMode.TRAIN,
self.params, self.run_config)
log.info('Building Train Graph: Done')
scalars = collections.get(collection.Key.SUMMARY_SCALAR)
histograms = collections.get(collection.Key.SUMMARY_HISTOGRAM)
skip_optimize_ops = collections.get(collection.Key.SKIP_OPTIMIZE)
skip_opt = set()
if skip_optimize_ops is not None:
skip_opt |= set(skip_optimize_ops)
if scalars is not None:
skip_opt |= {t for _, t in scalars}
if histograms is not None:
skip_opt |= {t for _, t in histograms}
skip_opt = list(skip_opt)
log.info(
'Train with: \n> Run_config: %s\n> Params: %s\n> Train_model_spec: %s\n'
% (repr(self.run_config), repr(self.params), repr(model_spec)))
summary_record = SummaryRecord(
scalar=collections.get(collection.Key.SUMMARY_SCALAR),
histogram=collections.get(collection.Key.SUMMARY_HISTOGRAM),
)
return ProgramPair(
train_program=train_program,
startup_program=startup_prog), model_spec, summary_record