def test_rename_output(self):
rename_feature(self.spec, 'output', 'renamed_output', rename_inputs=False, rename_outputs=True)
model = MLModel(self.spec)
preds = model.predict({'feature_1': 1.0, 'feature_2': 1.0})
self.assertIsNotNone(preds)
self.assertEquals(preds['renamed_output'], 3.1)
rename_feature(self.spec, 'renamed_output', 'output', rename_inputs=False, rename_outputs=True)
def test_rename_feature_mlprogram(self):
@mb.program(input_specs=[mb.TensorSpec(shape=(3, ))])
def linear_prog(input):
W = np.ones((10, 3), dtype=np.float)
out = mb.linear(x=input, weight=W, name="output")
return out
model = coremltools.convert(linear_prog, convert_to='mlprogram')
spec = model.get_spec()
input_name = spec.description.input[0].name
output_name = spec.description.output[0].name
# rename input
rename_feature(spec, input_name, "new_input_name")
self.assertEqual(spec.description.input[0].name, "new_input_name")
model = coremltools.models.MLModel(spec, weights_dir=model.weights_dir)
out = model.predict({"new_input_name": np.array([1.0, 2.0,
3.0])})[output_name]
self.assertEqual(out.shape, (10, ))
self.assertEqual(out[0], 6.0)
# rename output
rename_feature(spec, output_name, "new_output_name")
self.assertEqual(spec.description.output[0].name, "new_output_name")
model = coremltools.models.MLModel(spec, weights_dir=model.weights_dir)
out = model.predict({"new_input_name":
np.array([1.0, 2.0, 3.0])})["new_output_name"]
self.assertEqual(out.shape, (10, ))
self.assertEqual(out[1], 6.0)
def test_rename_input(self):
rename_feature(self.spec,
"feature_1",
"renamed_feature",
rename_inputs=True)
model = MLModel(self.spec)
package = tempfile.TemporaryDirectory(suffix=".mlpackage")
package.cleanup()
model.save(package.name)
loaded_model = MLModel(package.name)
if utils._macos_version() >= (12, 0):
preds = loaded_model.predict({
"renamed_feature": 1.0,
"feature_2": 1.0
})
self.assertIsNotNone(preds)
self.assertEqual(preds["output"], 3.1)
# reset the spec for next run
rename_feature(self.spec,
"renamed_feature",
"feature_1",
rename_inputs=True)
# cleanup
MLModelTest._remove_path(package.name)
def test_rename_output_bad(self):
rename_feature(
self.spec, "blah", "bad_name", rename_inputs=False, rename_outputs=True
)
model = MLModel(self.spec)
preds = model.predict({"feature_1": 1.0, "feature_2": 1.0})
self.assertIsNotNone(preds)
self.assertEqual(preds["output"], 3.1)
def test_rename_input(self):
rename_feature(self.spec, "feature_1", "renamed_feature", rename_inputs=True)
model = MLModel(self.spec)
preds = model.predict({"renamed_feature": 1.0, "feature_2": 1.0})
self.assertIsNotNone(preds)
self.assertEqual(preds["output"], 3.1)
# reset the spec for next run
rename_feature(self.spec, "renamed_feature", "feature_1", rename_inputs=True)
def test_rename_input(self):
rename_feature(self.spec, 'feature_1', 'renamed_feature', rename_inputs=True)
model = MLModel(self.spec)
preds = model.predict({'renamed_feature': 1.0, 'feature_2': 1.0})
self.assertIsNotNone(preds)
self.assertEquals(preds['output'], 3.1)
# reset the spec for next run
rename_feature(self.spec, 'renamed_feature', 'feature_1', rename_inputs=True)
def test_rename_feature_classifier_mlprogram(self):
torch_model = _torch.nn.ReLU().eval()
model = coremltools.convert(
_torch.jit.trace(torch_model, _torch.rand(3, )),
inputs=[coremltools.TensorType(shape=(3,))],
classifier_config=coremltools.ClassifierConfig(['a', 'b', 'c']),
convert_to='mlprogram'
)
spec = model.get_spec()
input_name = spec.description.input[0].name
rename_feature(spec, 'classLabel', 'highestProbClass')
model = coremltools.models.MLModel(spec)
output_class = model.predict({input_name: np.array([1.0, 2.0, 3.0])})['highestProbClass']
self.assertEqual(output_class, 'c')
def test_pipeline_rename(self):
# Convert
scikit_spec = converter.convert(self.scikit_model).get_spec()
model = MLModel(scikit_spec)
sample_data = self.scikit_data.data[0]
# Rename
rename_feature(scikit_spec, 'input', 'renamed_input')
renamed_model = MLModel(scikit_spec)
# Check the predictions
self.assertEquals(
model.predict({'input': sample_data}),
renamed_model.predict({'renamed_input': sample_data}))
def test_pipeline_rename(self):
# Convert
scikit_spec = converter.convert(self.scikit_model).get_spec()
model = MLModel(scikit_spec)
sample_data = self.scikit_data.data[0]
# Rename
rename_feature(scikit_spec, "input", "renamed_input")
renamed_model = MLModel(scikit_spec)
# Check the predictions
if _is_macos() and _macos_version() >= (10, 13):
out_dict = model.predict({"input": sample_data})
out_dict_renamed = renamed_model.predict({"renamed_input": sample_data})
self.assertAlmostEqual(list(out_dict.keys()), list(out_dict_renamed.keys()))
self.assertAlmostEqual(
list(out_dict.values()), list(out_dict_renamed.values())
)
def test_rename_image_input(self):
input_features = [("data", datatypes.Array(3, 1, 1))]
output_features = [("out", datatypes.Array(3, 1, 1))]
builder = NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_activation("linear", "LINEAR", "data", "out")
spec = builder.spec
# make an image input
mlmodel = make_image_input(MLModel(spec), "data", image_format="NCHW", scale=2.0)
# rename the input
spec = mlmodel.get_spec()
rename_feature(spec, "data", "new_input_name")
mlmodel = MLModel(spec)
# test
x = np.array([4, 5, 6], dtype=np.uint8).reshape(1, 1, 3)
pil_img = PIL.Image.fromarray(x)
out = mlmodel.predict({"new_input_name": pil_img}, useCPUOnly=True)['out']
np.testing.assert_equal(out, np.array([8.0, 10.0, 12.0]).reshape(3, 1, 1))
def test_rename_output_nn_classifier(self):
input_features = [("data", datatypes.Array(3))]
output_features = [("out", datatypes.Array(3))]
builder = NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_activation("linear", "LINEAR", "data", "out")
spec = builder.spec
mlmodel = MLModel(spec)
class_labels = ["a", "b", "c"]
mlmodel = make_nn_classifier(mlmodel, class_labels=["a", "b", "c"])
# rename output
spec = mlmodel.get_spec()
rename_feature(spec, "out", "new_out_name")
mlmodel = MLModel(spec)
out_dict = mlmodel.predict({"data": np.array([4.0, 5.5, 6.0])}, useCPUOnly=True)
self.assertEqual(out_dict["classLabel"], "c")
self.assertTrue("new_out_name" in out_dict)
self.assertTrue(isinstance(out_dict["new_out_name"], dict))
def test_rename_feature_mlprogram(self):
torch_model = _torch.nn.ReLU().eval()
model = coremltools.convert(
_torch.jit.trace(torch_model, _torch.rand(3, )),
inputs=[coremltools.TensorType(shape=(3,))],
convert_to='mlprogram'
)
spec = model.get_spec()
input_name = spec.description.input[0].name
output_name = spec.description.output[0].name
# rename input
rename_feature(spec, input_name, "new_input_name")
self.assertEqual(spec.description.input[0].name, "new_input_name")
model = coremltools.models.MLModel(spec)
out = model.predict({"new_input_name": np.array([1.0, 2.0, 3.0])})[output_name]
self.assertEqual(out[0], 1.0)
# rename output
rename_feature(spec, output_name, "new_output_name")
self.assertEqual(spec.description.output[0].name, "new_output_name")
model = coremltools.models.MLModel(spec)
out = model.predict({"new_input_name": np.array([1.0, 2.0, 3.0])})["new_output_name"]
self.assertEqual(out[1], 2.0)
def test_rename_input_bad(self):
rename_feature(self.spec, 'blah', 'bad_name', rename_inputs=True)
model = MLModel(self.spec)
preds = model.predict({'feature_1': 1.0, 'feature_2': 1.0})
self.assertIsNotNone(preds)
self.assertEqual(preds['output'], 3.1)
