breaking: Move _NumpyDeserializer to sagemaker.deserializers.NumpyDeserializer

src/sagemaker/chainer/model.py

@@ -24,7 +24,8 @@
 )
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.chainer import defaults
-from sagemaker.predictor import Predictor, npy_serializer, numpy_deserializer
+from sagemaker.deserializers import NumpyDeserializer
+from sagemaker.predictor import Predictor, npy_serializer
 
 logger = logging.getLogger("sagemaker")
 
@@ -48,7 +49,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 using the default AWS configuration chain.
         """
         super(ChainerPredictor, self).__init__(
-            endpoint_name, sagemaker_session, npy_serializer, numpy_deserializer
+            endpoint_name, sagemaker_session, npy_serializer, NumpyDeserializer()
         )
 
 

src/sagemaker/deserializers.py

@@ -14,6 +14,11 @@
 from __future__ import absolute_import
 
 import abc
+import codecs
+import io
+import json
+
+import numpy as np
 
 
 class BaseDeserializer(abc.ABC):
@@ -111,3 +116,41 @@ def deserialize(self, data, content_type):
             tuple: A two-tuple containing the stream and content-type.
         """
         return data, content_type
+
+
+class NumpyDeserializer(BaseDeserializer):
+    """Deserialize a stream of data in the .npy format."""
+
+    ACCEPT = "application/x-npy"
+
+    def __init__(self, dtype=None):
+        """Initialize the dtype.
+
+        Args:
+            dtype (str): The dtype of the data.
+        """
+        self.dtype = dtype
+
+    def deserialize(self, data, content_type):
+        """Deserialize data from an inference endpoint into a NumPy array.
+
+        Args:
+            data (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            numpy.ndarray: The data deserialized into a NumPy array.
+        """
+        try:
+            if content_type == "text/csv":
+                return np.genfromtxt(
+                    codecs.getreader("utf-8")(data), delimiter=",", dtype=self.dtype
+                )
+            if content_type == "application/json":
+                return np.array(json.load(codecs.getreader("utf-8")(data)), dtype=self.dtype)
+            if content_type == "application/x-npy":
+                return np.load(io.BytesIO(data.read()))
+        finally:
+            data.close()
+
+        raise ValueError("%s cannot read content type %s." % (__class__.__name__, content_type))

src/sagemaker/predictor.py

@@ -698,50 +698,6 @@ def __call__(self, stream, content_type):
 json_deserializer = _JsonDeserializer()
 
 
-class _NumpyDeserializer(object):
-    """Placeholder docstring"""
-
-    def __init__(self, accept=CONTENT_TYPE_NPY, dtype=None):
-        """
-        Args:
-            accept:
-            dtype:
-        """
-        self.accept = accept
-        self.dtype = dtype
-
-    def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
-        """Decode from serialized data into a Numpy array.
-
-        Args:
-            stream (stream): The response stream to be deserialized.
-            content_type (str): The content type of the response. Can accept
-                CSV, JSON, or NPY data.
-
-        Returns:
-            object: Body of the response deserialized into a Numpy array.
-        """
-        try:
-            if content_type == CONTENT_TYPE_CSV:
-                return np.genfromtxt(
-                    codecs.getreader("utf-8")(stream), delimiter=",", dtype=self.dtype
-                )
-            if content_type == CONTENT_TYPE_JSON:
-                return np.array(json.load(codecs.getreader("utf-8")(stream)), dtype=self.dtype)
-            if content_type == CONTENT_TYPE_NPY:
-                return np.load(BytesIO(stream.read()))
-        finally:
-            stream.close()
-        raise ValueError(
-            "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
-                content_type
-            )
-        )
-
-
-numpy_deserializer = _NumpyDeserializer()
-
-
 class _NPYSerializer(object):
     """Placeholder docstring"""
 

src/sagemaker/pytorch/model.py

@@ -17,6 +17,7 @@
 import packaging.version
 
 import sagemaker
+from sagemaker.deserializers import NumpyDeserializer
 from sagemaker.fw_utils import (
     create_image_uri,
     model_code_key_prefix,
@@ -25,7 +26,7 @@
 )
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.pytorch import defaults
-from sagemaker.predictor import Predictor, npy_serializer, numpy_deserializer
+from sagemaker.predictor import Predictor, npy_serializer
 
 logger = logging.getLogger("sagemaker")
 
@@ -49,7 +50,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 using the default AWS configuration chain.
         """
         super(PyTorchPredictor, self).__init__(
-            endpoint_name, sagemaker_session, npy_serializer, numpy_deserializer
+            endpoint_name, sagemaker_session, npy_serializer, NumpyDeserializer()
         )
 
 

src/sagemaker/sklearn/model.py

@@ -16,10 +16,11 @@
 import logging
 
 import sagemaker
+from sagemaker.deserializers import NumpyDeserializer
 from sagemaker.fw_registry import default_framework_uri
 from sagemaker.fw_utils import model_code_key_prefix, validate_version_or_image_args
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
-from sagemaker.predictor import Predictor, npy_serializer, numpy_deserializer
+from sagemaker.predictor import Predictor, npy_serializer
 from sagemaker.sklearn import defaults
 
 logger = logging.getLogger("sagemaker")
@@ -44,7 +45,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 using the default AWS configuration chain.
         """
         super(SKLearnPredictor, self).__init__(
-            endpoint_name, sagemaker_session, npy_serializer, numpy_deserializer
+            endpoint_name, sagemaker_session, npy_serializer, NumpyDeserializer()
         )
 
 

tests/unit/sagemaker/test_deserializers.py

@@ -14,7 +14,15 @@
 
 import io
 
-from sagemaker.deserializers import StringDeserializer, BytesDeserializer, StreamDeserializer
+import numpy as np
+import pytest
+
+from sagemaker.deserializers import (
+    StringDeserializer,
+    BytesDeserializer,
+    StreamDeserializer,
+    NumpyDeserializer,
+)
 
 
 def test_string_deserializer():
@@ -44,3 +52,70 @@ def test_stream_deserializer():
 
     assert result == b"[1, 2, 3]"
     assert content_type == "application/json"
+
+
+@pytest.fixture
+def numpy_deserializer():
+    return NumpyDeserializer()
+
+
+def test_numpy_deserializer_from_csv(numpy_deserializer):
+    stream = io.BytesIO(b"1,2,3\n4,5,6")
+    array = numpy_deserializer.deserialize(stream, "text/csv")
+    assert np.array_equal(array, np.array([[1, 2, 3], [4, 5, 6]]))
+
+
+def test_numpy_deserializer_from_csv_ragged(numpy_deserializer):
+    stream = io.BytesIO(b"1,2,3\n4,5,6,7")
+    with pytest.raises(ValueError) as error:
+        numpy_deserializer.deserialize(stream, "text/csv")
+    assert "errors were detected" in str(error)
+
+
+def test_numpy_deserializer_from_csv_alpha():
+    numpy_deserializer = NumpyDeserializer(dtype="U5")
+    stream = io.BytesIO(b"hello,2,3\n4,5,6")
+    array = numpy_deserializer.deserialize(stream, "text/csv")
+    assert np.array_equal(array, np.array([["hello", 2, 3], [4, 5, 6]]))
+
+
+def test_numpy_deserializer_from_json(numpy_deserializer):
+    stream = io.BytesIO(b"[[1,2,3],\n[4,5,6]]")
+    array = numpy_deserializer.deserialize(stream, "application/json")
+    assert np.array_equal(array, np.array([[1, 2, 3], [4, 5, 6]]))
+
+
+# Sadly, ragged arrays work fine in JSON (giving us a 1D array of Python lists)
+def test_numpy_deserializer_from_json_ragged(numpy_deserializer):
+    stream = io.BytesIO(b"[[1,2,3],\n[4,5,6,7]]")
+    array = numpy_deserializer.deserialize(stream, "application/json")
+    assert np.array_equal(array, np.array([[1, 2, 3], [4, 5, 6, 7]]))
+
+
+def test_numpy_deserializer_from_json_alpha():
+    numpy_deserializer = NumpyDeserializer(dtype="U5")
+    stream = io.BytesIO(b'[["hello",2,3],\n[4,5,6]]')
+    array = numpy_deserializer.deserialize(stream, "application/json")
+    assert np.array_equal(array, np.array([["hello", 2, 3], [4, 5, 6]]))
+
+
+def test_numpy_deserializer_from_npy(numpy_deserializer):
+    array = np.ones((2, 3))
+    stream = io.BytesIO()
+    np.save(stream, array)
+    stream.seek(0)
+
+    result = numpy_deserializer.deserialize(stream, "application/x-npy")
+
+    assert np.array_equal(array, result)
+
+
+def test_numpy_deserializer_from_npy_object_array(numpy_deserializer):
+    array = np.array(["one", "two"])
+    stream = io.BytesIO()
+    np.save(stream, array)
+    stream.seek(0)
+
+    result = numpy_deserializer.deserialize(stream, "application/x-npy")
+
+    assert np.array_equal(array, result)

tests/unit/test_predictor.py

@@ -26,9 +26,7 @@
     json_deserializer,
     csv_serializer,
     csv_deserializer,
-    numpy_deserializer,
     npy_serializer,
-    _NumpyDeserializer,
 )
 from tests.unit import DATA_DIR
 
@@ -259,62 +257,6 @@ def test_npy_serializer_python_invalid_empty():
     assert "empty array" in str(error)
 
 
-def test_numpy_deser_from_csv():
-    arr = numpy_deserializer(io.BytesIO(b"1,2,3\n4,5,6"), "text/csv")
-    assert np.array_equal(arr, np.array([[1, 2, 3], [4, 5, 6]]))
-
-
-def test_numpy_deser_from_csv_ragged():
-    with pytest.raises(ValueError) as error:
-        numpy_deserializer(io.BytesIO(b"1,2,3\n4,5,6,7"), "text/csv")
-    assert "errors were detected" in str(error)
-
-
-def test_numpy_deser_from_csv_alpha():
-    arr = _NumpyDeserializer(dtype="U5")(io.BytesIO(b"hello,2,3\n4,5,6"), "text/csv")
-    assert np.array_equal(arr, np.array([["hello", 2, 3], [4, 5, 6]]))
-
-
-def test_numpy_deser_from_json():
-    arr = numpy_deserializer(io.BytesIO(b"[[1,2,3],\n[4,5,6]]"), "application/json")
-    assert np.array_equal(arr, np.array([[1, 2, 3], [4, 5, 6]]))
-
-
-# Sadly, ragged arrays work fine in JSON (giving us a 1D array of Python lists
-def test_numpy_deser_from_json_ragged():
-    arr = numpy_deserializer(io.BytesIO(b"[[1,2,3],\n[4,5,6,7]]"), "application/json")
-    assert np.array_equal(arr, np.array([[1, 2, 3], [4, 5, 6, 7]]))
-
-
-def test_numpy_deser_from_json_alpha():
-    arr = _NumpyDeserializer(dtype="U5")(
-        io.BytesIO(b'[["hello",2,3],\n[4,5,6]]'), "application/json"
-    )
-    assert np.array_equal(arr, np.array([["hello", 2, 3], [4, 5, 6]]))
-
-
-def test_numpy_deser_from_npy():
-    array = np.ones((2, 3))
-    stream = io.BytesIO()
-    np.save(stream, array)
-    stream.seek(0)
-
-    result = numpy_deserializer(stream)
-
-    assert np.array_equal(array, result)
-
-
-def test_numpy_deser_from_npy_object_array():
-    array = np.array(["one", "two"])
-    stream = io.BytesIO()
-    np.save(stream, array)
-    stream.seek(0)
-
-    result = numpy_deserializer(stream)
-
-    assert np.array_equal(array, result)
-
-
 # testing 'predict' invocations