batch renaming export_scheme to predictor_type

Reviewed By: zhanghang1989

Differential Revision: D27898376

fbshipit-source-id: 87549b0cc24bd38f114977503f4eba97e9166ab8

d2go/export/api.py

@@ -179,9 +179,7 @@ def default_export_predictor(
     # according to the "predictor_type".
     assert hasattr(pytorch_model, "prepare_for_export"), pytorch_model
     inputs = next(iter(data_loader))
-    export_config = pytorch_model.prepare_for_export(
-        cfg, inputs, export_scheme=predictor_type
-    )
+    export_config = pytorch_model.prepare_for_export(cfg, inputs, predictor_type)
     model_inputs = (
         export_config.data_generator(inputs)
         if export_config.data_generator is not None

d2go/utils/prepare_for_export.py

@@ -18,9 +18,9 @@ from d2go.utils.export_utils import (D2Caffe2MetaArchPreprocessFunc,
 logger = logging.getLogger(__name__)
 
 
-def d2_meta_arch_prepare_for_export(self, cfg, inputs, export_scheme):
+def d2_meta_arch_prepare_for_export(self, cfg, inputs, predictor_type):
 
-    if "torchscript" in export_scheme and "@tracing" in export_scheme:
+    if "torchscript" in predictor_type and "@tracing" in predictor_type:
 
         def inference_func(model, image):
             inputs = [{"image": image}]