def test_save_model(self):
this = os.path.dirname(__file__)
onnx_file = os.path.join(this, "models", "coreml_OneHotEncoder_BikeSharing.onnx")
new_onnx_file = os.path.join(this, "models", "coreml_OneHotEncoder_BikeSharing2.onnx")
onnx_model = load_model(onnx_file)
save_model(onnx_model, new_onnx_file)
self.assertTrue(os.path.exists(new_onnx_file))
def save(self):
# Onnx Save (can't save a list of model for now)
onx = convert_sklearn(self.pipe, 'Pipe',
[('input', StringTensorType([1, 1]))])
save_model(onx, "Model.onnx")
print ("Model saved")
def convert_model_from_float_to_float16(model_path):
# from onnxmltools.utils.float16_converter import convert_float_to_float16
from onnxmltools.utils import load_model, save_model
from float16 import convert_float_to_float16
onnx_model = load_model(model_path)
new_onnx_model = convert_float_to_float16(onnx_model)
save_model(new_onnx_model, 'new_fp16_model.onnx')
return os.path.join(os.getcwd(), "new_fp16_model.onnx")
def export(self, output_dir):
onnx_model = convert_sklearn(self.pipe,
initial_types=[("input",
FloatTensorType([1, 1]))])
save_model(onnx_model, path.join(output_dir, f'{self.name()}.onnx'))
X_test.head()
#%%
# Make a CSV backup of our test data without column names or indexes
test_data = X_test.copy()
test_data['target'] = y_test
test_data.to_csv("test_data.csv", header=False, index=False)
#%%
test_data.head()
#%%
model = xgbr()
model.fit(X_train, y_train, eval_set=[(X_test, y_test)])
#%%
conv_model = convert_xgboost(model, initial_types=[('float_input', FloatTensorType(shape=[1, 4]))])
assert(conv_model is not None)
save_model(conv_model, 'model.onnx')
#%%
`keras <https://keras.io/>`_,
`tensorflow <https://www.tensorflow.org/>`_,
`onnxmltools <https://pypi.org/project/onnxmltools/>`_
but then only *onnxruntime* is required
to compute the predictions.
"""
import os
if not os.path.exists('dense121.onnx'):
from keras.applications.densenet import DenseNet121
model = DenseNet121(include_top=True, weights='imagenet')
from onnxmltools import convert_keras
onx = convert_keras(model, 'dense121.onnx')
from onnxmltools.utils import save_model
save_model(onx, "dense121.onnx")
##################################
# Let's load an image (source: wikipedia).
from keras.preprocessing.image import array_to_img, img_to_array, load_img
img = load_img('Sannosawa1.jpg')
ximg = img_to_array(img)
import matplotlib.pyplot as plt
plt.imshow(ximg / 255)
plt.axis('off')
#############################################
# Let's load the model with onnxruntime.
import onnxruntime as rt
# Conversion to ONNX format
# +++++++++++++++++++++++++
#
# We use module
# `onnxmltools <https://github.com/onnx/onnxmltools>`_
# to convert the model into ONNX format.
from onnxmltools import convert_sklearn
from onnxmltools.utils import save_model
from onnxmltools.convert.common.data_types import FloatTensorType, Int64TensorType, DictionaryType, SequenceType
# initial_type = [('float_input', DictionaryType(Int64TensorType([1]), FloatTensorType([])))]
initial_type = [('float_input',
DictionaryType(Int64TensorType([1]), FloatTensorType([])))]
onx = convert_sklearn(pipe, initial_types=initial_type)
save_model(onx, "pipeline_vectorize.onnx")
##################################
# We load the model with ONNX Runtime and look at
# its input and output.
import onnxruntime as rt
sess = rt.InferenceSession("pipeline_vectorize.onnx")
import numpy
inp, out = sess.get_inputs()[0], sess.get_outputs()[0]
print("input name='{}' and shape={} and type={}".format(
inp.name, inp.shape, inp.type))
print("output name='{}' and shape={} and type={}".format(
out.name, out.shape, out.type))
##################################
