def testSelectRecordByContext(self):
float_features = self.model.input_feature_schema.float_features
float_array = np.array([1.0, 2.0], dtype=np.float32)
schema.FeedRecord(float_features, [float_array])
with Tags(Tags.EXCLUDE_FROM_PREDICTION):
log_float_features = self.model.Log(float_features, 1)
joined = self.model.SelectRecordByContext(
schema.Struct(
(InstantiationContext.PREDICTION, float_features),
(InstantiationContext.TRAINING, log_float_features),
# TODO: TRAIN_ONLY layers are also generated in eval
(InstantiationContext.EVAL, log_float_features),
))
# model.output_schema has to a struct
self.model.output_schema = schema.Struct(('joined', joined))
predict_net = layer_model_instantiator.generate_predict_net(self.model)
workspace.RunNetOnce(predict_net)
predict_output = schema.FetchRecord(predict_net.output_record())
npt.assert_array_equal(float_array, predict_output['joined']())
eval_net = layer_model_instantiator.generate_eval_net(self.model)
workspace.RunNetOnce(eval_net)
eval_output = schema.FetchRecord(eval_net.output_record())
npt.assert_array_equal(np.log(float_array), eval_output['joined']())
_, train_net = (
layer_model_instantiator.generate_training_nets_forward_only(
self.model))
workspace.RunNetOnce(train_net)
train_output = schema.FetchRecord(train_net.output_record())
npt.assert_array_equal(np.log(float_array), train_output['joined']())
def run_train_net_forward_only(self):
self.model.output_schema = schema.Struct()
train_init_net, train_net = \
layer_model_instantiator.generate_training_nets_forward_only(
self.model)
workspace.RunNetOnce(train_init_net)
workspace.RunNetOnce(train_net)
def run_train_net_forward_only(self, num_iter=1):
self.model.output_schema = schema.Struct()
train_init_net, train_net = \
layer_model_instantiator.generate_training_nets_forward_only(
self.model)
workspace.RunNetOnce(train_init_net)
assert num_iter > 0, 'num_iter must be larger than 0'
workspace.CreateNet(train_net)
workspace.RunNet(train_net.Proto().name, num_iter=num_iter)
def run_train_net_forward_only(self, num_iter=1):
self.model.output_schema = schema.Struct()
train_init_net, train_net = \
layer_model_instantiator.generate_training_nets_forward_only(
self.model)
workspace.RunNetOnce(train_init_net)
assert num_iter > 0, 'num_iter must be larger than 0'
workspace.CreateNet(train_net)
workspace.RunNet(train_net.Proto().name, num_iter=num_iter)
def testSelectRecordByContext(self):
float_features = self.model.input_feature_schema.float_features
float_array = np.array([1.0, 2.0], dtype=np.float32)
schema.FeedRecord(float_features, [float_array])
with Tags(Tags.EXCLUDE_FROM_PREDICTION):
log_float_features = self.model.Log(float_features, 1)
joined = self.model.SelectRecordByContext(
schema.Struct(
(InstantiationContext.PREDICTION, float_features),
(InstantiationContext.TRAINING, log_float_features),
# TODO: TRAIN_ONLY layers are also generated in eval
(InstantiationContext.EVAL, log_float_features),
)
)
# model.output_schema has to a struct
self.model.output_schema = schema.Struct((
'joined', joined
))
predict_net = layer_model_instantiator.generate_predict_net(self.model)
workspace.RunNetOnce(predict_net)
predict_output = schema.FetchRecord(predict_net.output_record())
npt.assert_array_equal(float_array,
predict_output['joined']())
eval_net = layer_model_instantiator.generate_eval_net(self.model)
workspace.RunNetOnce(eval_net)
eval_output = schema.FetchRecord(eval_net.output_record())
npt.assert_array_equal(np.log(float_array),
eval_output['joined']())
_, train_net = (
layer_model_instantiator.generate_training_nets_forward_only(
self.model
)
)
workspace.RunNetOnce(train_net)
train_output = schema.FetchRecord(train_net.output_record())
npt.assert_array_equal(np.log(float_array),
train_output['joined']())
