from os.path import dirname, abspath
from keras2onnx.proto import keras
import numpy as np
import tensorflow as tf
from onnxconverter_common.onnx_ex import get_maximum_opset_supported
sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../tests/'))
from test_utils import run_onnx_runtime
from keras2onnx.proto import is_tensorflow_older_than
enable_full_transformer_test = False
if os.environ.get('ENABLE_FULL_TRANSFORMER_TEST', '0') != '0':
enable_transformer_test = True
@unittest.skipIf(is_tensorflow_older_than('2.1.0'),
"Transformers conversion need tensorflow 2.1.0+")
class TestTransformers(unittest.TestCase):
text_str = 'The quick brown fox jumps over lazy dog.'
def setUp(self):
self.model_files = []
def tearDown(self):
for fl in self.model_files:
os.remove(fl)
def _get_token_path(self, file_name):
return 'https://lotus.blob.core.windows.net/converter-models/transformer_tokenizer/' + file_name
def test_auto_encoder(runner):
tf.keras.backend.clear_session()
original_dim = 20
vae = VariationalAutoEncoder(original_dim, 64, 32)
x = tf.random.normal((7, original_dim))
expected = vae.predict(x)
oxml = keras2onnx.convert_keras(vae)
# assert runner('variational_auto_encoder', oxml, [x.numpy()], expected)
# The random generator is not same between different engines.
import onnx
onnx.checker.check_model(oxml)
@pytest.mark.skipif(is_tensorflow_older_than('2.2.0'),
reason="only supported on tf 2.2 and above.")
def test_tf_where(runner):
def _tf_where(input_0):
a = tf.where(True, input_0, [0, 1, 2, 5, 7])
b = tf.where([True], tf.expand_dims(input_0, axis=0),
tf.expand_dims([0, 1, 2, 5, 7], axis=0))
c = tf.logical_or(tf.cast(a, tf.bool), tf.cast(b, tf.bool))
return c
swm = SimpleWrapperModel(_tf_where)
const_in = [np.array([2, 4, 6, 8, 10]).astype(np.int32)]
expected = swm(const_in)
swm._set_inputs(const_in)
oxml = keras2onnx.convert_keras(swm)
assert runner('where_test', oxml, const_in, expected)
def test_auto_encoder(runner):
tf.keras.backend.clear_session()
original_dim = 20
vae = VariationalAutoEncoder(original_dim, 64, 32)
x = tf.random.normal((7, original_dim))
expected = vae.predict(x)
oxml = keras2onnx.convert_keras(vae)
# assert runner('variational_auto_encoder', oxml, [x.numpy()], expected)
# The random generator is not same between different engines.
import onnx
onnx.checker.check_model(oxml)
@pytest.mark.skipif(is_tensorflow_older_than('2.2.0'), reason="only supported on tf 2.2 and above.")
def test_tf_where(runner):
def _tf_where(input_0):
a = tf.where(True, input_0, [0, 1, 2, 5, 7])
b = tf.where([True], tf.expand_dims(input_0, axis=0), tf.expand_dims([0, 1, 2, 5, 7], axis=0))
c = tf.logical_or(tf.cast(a, tf.bool), tf.cast(b, tf.bool))
return c
swm = SimpleWrapperModel(_tf_where)
const_in = [np.array([2, 4, 6, 8, 10]).astype(np.int32)]
expected = swm(const_in)
swm._set_inputs(const_in)
oxml = keras2onnx.convert_keras(swm)
assert runner('where_test', oxml, const_in, expected)
# Licensed under the MIT License. See License.txt in the project root for
# license information.
###############################################################################
import os
import sys
import unittest
from os.path import dirname, abspath
from keras2onnx.proto import keras, is_tensorflow_older_than
sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../tests/'))
from test_utils import run_image
img_path = os.path.join(os.path.dirname(__file__), '../data', 'street.jpg')
@unittest.skipIf(is_tensorflow_older_than('2.1.0'), "efficientnet needs tensorflow >= 2.1.0")
class TestEfn(unittest.TestCase):
def setUp(self):
self.model_files = []
def tearDown(self):
for fl in self.model_files:
os.remove(fl)
@unittest.skip("TODO: model discrepancy")
def test_custom(self):
from efficientnet import tfkeras as efn
keras.backend.set_learning_phase(0)
base_model = efn.EfficientNetB0(input_shape=(600, 600, 3), weights=None)
backbone = keras.Model(base_model.input, base_model.get_layer("top_activation").output)
import sys
import unittest
import keras2onnx
import json
from os.path import dirname, abspath
sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../tests/'))
from test_utils import run_onnx_runtime
from keras2onnx.proto import is_tensorflow_older_than
enable_transformer_test = False
if os.environ.get('ENABLE_TRANSFORMER_TEST', '0') != '0':
enable_transformer_test = True
@unittest.skipIf(
is_tensorflow_older_than('2.1.0') or not enable_transformer_test,
"Need enable transformer test before Transformers conversion.")
class TestTransformers(unittest.TestCase):
def setUp(self):
self.model_files = []
def tearDown(self):
for fl in self.model_files:
os.remove(fl)
def _prepare_inputs(self, tokenizer):
raw_data = json.dumps(
{'text': 'The quick brown fox jumps over the lazy dog.'})
text = json.loads(raw_data)['text']
inputs = tokenizer.encode_plus(text,
add_special_tokens=True,
