Fix typos in exporter

Summary:
Fix typos in exporter

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/45

Reviewed By: wat3rBro

Differential Revision: D27779963

Pulled By: zhanghang1989

fbshipit-source-id: bcf7922afe6d4cccc074615069538eb5a6098b98

d2go/export/api.py

@@ -4,7 +4,7 @@
 
 """
 API for exporting a pytorch model to a predictor, the predictor contains model(s) in
-deployable format and predifined functions as glue code. The exported predictor should
+deployable format and predefined functions as glue code. The exported predictor should
 generate same output as the original pytorch model. (See predictor/api.py for details of
 predictor)
 
@@ -17,7 +17,7 @@ This API defines two customizable methods for the pytorch model:
 
 NOTE:
     1: There's a difference between predictor type and model type. model type
-        refers to predifined deployable format such as caffe2, torchscript(_int8),
+        refers to predefined deployable format such as caffe2, torchscript(_int8),
         while the predictor type can be anything that "export_predictor" can
         recognize.
     2: The standard model exporting methods are provided by the library code, they're
@@ -54,7 +54,7 @@ class PredictorExportConfig(NamedTuple):
         model (any nested iterable structure of nn.Module): the model(s) to be exported
             (via tracing/onnx or scripting). This can be sub-model(s) when the predictor
             consists of multiple models in deployable format, and/or pre/post processing
-            is excluded due to requirement of tracing or hardward incompatibility.
+            is excluded due to requirement of tracing or hardware incompatibility.
         data_generator (Callable): a function to generate all data needed for tracing,
             such that data = data_generator(x), the returned data has the same nested
             structure as model. The data for each model will be treated as positional
@@ -83,7 +83,7 @@ def convert_and_export_predictor(
     cfg, pytorch_model, predictor_type, output_dir, data_loader
 ):
     """
-    Entry point for convert and export model. This involes two steps:
+    Entry point for convert and export model. This involves two steps:
         - convert: converting the given `pytorch_model` to another format, currently
             mainly for quantizing the model.
         - export: exporting the converted `pytorch_model` to predictor. This step
@@ -122,7 +122,7 @@ def convert_and_export_predictor(
 def export_predictor(cfg, pytorch_model, predictor_type, output_dir, data_loader):
     """
     Interface for exporting a pytorch model to predictor of given type. This function
-    can be override to arhieve customized exporting procedure, eg. using non-default
+    can be override to achieve customized exporting procedure, eg. using non-default
     optimization passes, composing traced models, etc.
 
     Args:
@@ -176,7 +176,7 @@ def default_export_predictor(
     model_export_kwargs = export_config.model_export_kwargs or {}
     # the default implementation assumes model type is the same as the predictor type
     model_type = predictor_type
-    model_path = predictor_path  # maye be sub dir for multipe models
+    model_path = predictor_path  # might be sub dir for multiple models
 
     standard_model_export(
         model,
@@ -209,14 +209,14 @@ def default_export_predictor(
 # TODO: define the supported model types, current caffe2/torchscript/torchscript_int8
 # is not enough.
 # TODO: determine if registry is needed (probably not since we only need to support
-# a few known formats) as libarary code.
+# a few known formats) as library code.
 def standard_model_export(model, model_type, save_path, input_args, **kwargs):
     if model_type.startswith("torchscript"):
         from d2go.export.torchscript import trace_and_save_torchscript
         trace_and_save_torchscript(model, input_args, save_path, **kwargs)
     elif model_type == "caffe2":
         from d2go.export.caffe2 import export_caffe2
-        # TODO: export_caffe2 depends on D2, need to make a copy of the implemetation
+        # TODO: export_caffe2 depends on D2, need to make a copy of the implementation
         # TODO: support specifying optimization pass via kwargs
         export_caffe2(model, input_args[0], save_path, **kwargs)
     else:

d2go/utils/export_utils.py

@@ -66,7 +66,7 @@ class D2Caffe2MetaArchPostprocessFunc(object):
         encoded_info = fake_predict_net.SerializeToString().decode("ascii")
 
         # HACK: Caffe2MetaArch's post processing requires the blob name of model output,
-        # this information is missed for torchscript. There'no easy way to know this
+        # this information is missed for torchscript. There's no easy way to know this
         # unless using NamedTuple for tracing.
         external_input = ["data", "im_info"]
         if cfg.MODEL.META_ARCHITECTURE == "GeneralizedRCNN":
@@ -100,7 +100,6 @@ def dataclass_object_dump(ob):
     return {**{f: getattr(ob, f) for f in fields}, "__dataclass__": ref}
 
 
-
 def dataclass_object_load(d):
     ref = d.pop("__dataclass__", None)
     if ref is None:

tools/exporter.py

@@ -101,7 +101,7 @@ def get_parser():
         "--compare-accuracy",
         action="store_true",
         help="If true, all exported models and the original pytorch model will be"
-        " evaluted on cfg.DATASETS.TEST",
+        " evaluated on cfg.DATASETS.TEST",
     )
     parser.add_argument(
         "--skip-if-fail",