onnx · jiafatom · May 22, 2020 · May 13, 2020 · May 20, 2020 · May 20, 2020
diff --git a/.azure-pipelines/linux-CI-keras-applications-nightly.yml b/.azure-pipelines/linux-CI-keras-applications-nightly.yml
@@ -54,14 +54,14 @@ jobs:
         INSTALL_KERAS: pip install keras
         UNINSTALL_KERAS:
         INSTALL_TENSORFLOW: pip install tensorflow==1.15.0
-        INSTALL_ORT: pip install onnxruntime==1.1.1
+        INSTALL_ORT: pip install onnxruntime==1.3.0
         INSTALL_KERAS_RESNET: pip install keras-resnet
         INSTALL_TRANSFORMERS:
         NIGHTLY_BUILD_TEST: python run_all.py --exclude "test_keras_applications_v2.py"
 
       Python37-tf2:
-        python.version: '3.7.3'
-        ONNX_PATH: onnx==1.6.0
+        python.version: '3.8'
+        ONNX_PATH: onnx==1.7.0
         INSTALL_KERAS:
         UNINSTALL_KERAS: pip uninstall keras -y
         INSTALL_TENSORFLOW: pip install tensorflow==2.2.0

diff --git a/.azure-pipelines/linux-conda-CI-tf-keras.yml b/.azure-pipelines/linux-conda-CI-tf-keras.yml
@@ -32,6 +32,12 @@ jobs:
         TENSORFLOW_PATH: tensorflow-cpu
         INSTALL_ORT:
 
+      Python38:
+        python.version: '3.8'
+        ONNX_PATH: onnx==1.7.0
+        TENSORFLOW_PATH: tensorflow-cpu
+        INSTALL_ORT:
+
     maxParallel: 3
 
   steps:

diff --git a/.azure-pipelines/linux-conda-CI.yml b/.azure-pipelines/linux-conda-CI.yml
@@ -28,13 +28,21 @@ jobs:
         TENSORFLOW_PATH: tensorflow==1.15.0
         INSTALL_ORT: pip install onnxruntime==1.1.1
 
-      Python37-tf200:
+      # UT for standalone keras need tensorflow <= 2.0.0
+      Python37-tf200-onnx160:
         python.version: '3.7'
         ONNX_PATH: onnx==1.6.0
         KERAS: keras
         TENSORFLOW_PATH: tensorflow==2.0.0
         INSTALL_ORT:
 
+      Python37-tf200-onnx170:
+        python.version: '3.7'
+        ONNX_PATH: onnx==1.7.0
+        KERAS: keras
+        TENSORFLOW_PATH: tensorflow==2.0.0
+        INSTALL_ORT:
+
     maxParallel: 3
 
   steps:

diff --git a/.azure-pipelines/win32-CI-keras-applications-nightly.yml b/.azure-pipelines/win32-CI-keras-applications-nightly.yml
@@ -59,13 +59,13 @@ jobs:
         INSTALL_TRANSFORMERS:
         NIGHTLY_BUILD_TEST: python run_all.py --exclude "test_keras_applications_v2.py test_mask_rcnn.py"
 
-      Python37-tf2:
-        python.version: '3.7'
-        ONNX_PATH: onnx==1.6.0
+      Python38-tf2:
+        python.version: '3.8'
+        ONNX_PATH: onnx==1.7.0
         INSTALL_KERAS:
         UNINSTALL_KERAS: pip uninstall keras -y
         INSTALL_TENSORFLOW: pip install tensorflow==2.2.0
-        INSTALL_ORT: pip install onnxruntime==1.1.1
+        INSTALL_ORT: pip install onnxruntime==1.3.0
         INSTALL_KERAS_RESNET: pip install keras-resnet
         INSTALL_TRANSFORMERS: pip install transformers
         NIGHTLY_BUILD_TEST: pytest test_keras_applications_v2.py test_transformers.py test_efn.py --doctest-modules --junitxml=junit/test-results-v2.xml

diff --git a/.azure-pipelines/win32-conda-CI-tf-keras.yml b/.azure-pipelines/win32-conda-CI-tf-keras.yml
@@ -32,6 +32,12 @@ jobs:
         TENSORFLOW_PATH: tensorflow-cpu
         INSTALL_ORT:
 
+      Python38:
+        python.version: '3.8'
+        ONNX_PATH: onnx==1.7.0
+        TENSORFLOW_PATH: tensorflow-cpu
+        INSTALL_ORT:
+
     maxParallel: 3
 
   steps:

diff --git a/.azure-pipelines/win32-conda-CI.yml b/.azure-pipelines/win32-conda-CI.yml
@@ -28,13 +28,21 @@ jobs:
         TENSORFLOW_PATH: tensorflow==1.15.0
         INSTALL_ORT: pip install onnxruntime==1.1.1
 
-      Python37-tf200:
+      # UT for standalone keras need tensorflow <= 2.0.0
+      Python37-tf200-onnx160:
         python.version: '3.7'
         ONNX_PATH: onnx==1.6.0
         KERAS: keras
         TENSORFLOW_PATH: tensorflow==2.0.0
         INSTALL_ORT:
 
+      Python37-tf200-onnx170:
+        python.version: '3.7'
+        ONNX_PATH: onnx==1.7.0
+        KERAS: keras
+        TENSORFLOW_PATH: tensorflow==2.0.0
+        INSTALL_ORT:
+
     maxParallel: 3
 
   steps:

diff --git a/applications/nightly_build/test_transformers.py b/applications/nightly_build/test_transformers.py
@@ -201,19 +201,18 @@ def test_TFGPT2(self):
     def test_TFXLNet(self):
         if enable_full_transformer_test:
             from transformers import XLNetConfig, TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, \
-                TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple
+                TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple, XLNetTokenizer
             model_list = [TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, \
                 TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple]
         else:
-            from transformers import XLNetConfig, TFXLNetModel
+            from transformers import XLNetConfig, TFXLNetModel, XLNetTokenizer
             model_list = [TFXLNetModel]
 
-        # pretrained_weights = 'xlnet-large-cased'
-        tokenizer_file = 'xlnet_xlnet-large-cased.pickle'
-        tokenizer = self._get_tokenzier(tokenizer_file)
+        # XLNetTokenizer need SentencePiece, so the pickle file does not work here.
+        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
         config = XLNetConfig(n_layer=2)
         # The model with input mask has MatrixDiagV3 which is not a registered function/op
-        token = tokenizer.encode(self.text_str, add_special_tokens=True)
+        token = np.asarray(tokenizer.encode(self.text_str, add_special_tokens=True), dtype=np.int32)
         inputs_onnx = {'input_1': np.expand_dims(token, axis=0)}
         inputs = tf.constant(token)[None, :]  # Batch size 1
 

diff --git a/keras2onnx/_builtin.py b/keras2onnx/_builtin.py
@@ -1689,6 +1689,26 @@ def convert_tf_one_hot(scope, operator, container):
                               name=operator.full_name + '_one_hot', axis=axis)
 
 
+@converter_func(TYPES.Pow)
+def convert_tf_pow(scope, operator, container):
+    oopb = OnnxOperatorBuilder(container, scope)
+    node = operator.raw_operator
+    if container.target_opset < 12:
+        supported_types = [oopb.float16, oopb.float, oopb.double]
+        for input_idx_ in range(2):
+            dtype = _to_onnx_type(node.inputs[input_idx_].dtype)
+            if dtype not in supported_types:
+                raise ValueError("The input type of Pow is not supported for opset < 12.")
+        dtype = _to_onnx_type(node.outputs[0].dtype)
+        if dtype not in supported_types:
+            raise ValueError("The output type of Pow is not supported for opset < 12.")
+
+    oopb.apply_op_with_output("apply_pow",
+                              operator.input_full_names,
+                              operator.output_full_names,
+                              name=operator.full_name)
+
+
 @converter_func(TYPES.ReadVariableOp)
 def convert_tf_read_variable_op(scope, operator, container):
     oopb = OnnxOperatorBuilder(container, scope)
@@ -1835,7 +1855,6 @@ def _prepare_StridedSlice(node, target_opset):
     # onnx slice op can't remove a axis, track axis and add a squeeze op if needed
     needs_squeeze = []
     ellipsis_gap = 0
-    data_input = node.inputs[0]
 
     new_axis_len = 0
     cur_new_axis_mask = new_axis_mask
@@ -2121,7 +2140,6 @@ def convert_tf_zeros_like(scope, operator, container):
     "Log": ("apply_log",),
     "Mul": ("apply_mul",),
     "Neg": ("apply_neg",),
-    "Pow": ("apply_pow",),
     "RealDiv": ("apply_div",),
     "Reciprocal": ("apply_reciprocal",),
     "Relu": ("apply_relu",),

diff --git a/keras2onnx/_consts.py b/keras2onnx/_consts.py
@@ -57,6 +57,7 @@ class TYPES:
     Pack = 'Pack'
     Pad = 'Pad'
     PadV2 = 'PadV2'
+    Pow = 'Pow'
     Prod = 'Prod'
     Range = 'Range'
     ReadVariableOp = 'ReadVariableOp'

diff --git a/tests/test_layers.py b/tests/test_layers.py
@@ -205,6 +205,27 @@ def test_tf_bias_add(runner):
     assert runner('onnx_bias_add', onnx_model, data, expected)
 
 
+def test_tf_clip(runner):
+    model = Sequential()
+    model.add(Lambda(lambda x: K.clip(x, 0, 10), input_shape=[5, 5]))
+    data = np.random.randint(-5, 15, size=(1, 5, 5)).astype(np.float32)
+    expected = model.predict(data)
+    onnx_model = keras2onnx.convert_keras(model, 'test_tf_clip')
+    assert runner('onnx_tf_clip', onnx_model, data, expected)
+
+
+@pytest.mark.skipif(get_maximum_opset_supported() < 12,
+                    reason="Result mismatch on ORT, skip conversion for unsupported types.")
+def test_tf_pow(runner):
+    model = Sequential()
+    y = tf.constant([[2.0, 2.0], [2.0, 2.0]])
+    model.add(Lambda(lambda x: tf.math.pow(tf.cast(x, tf.int32), tf.cast(y, tf.int32)), input_shape=[2, 2]))
+    data = (100 * np.random.rand(3, 2, 2)).astype(np.float32)
+    expected = model.predict(data)
+    onnx_model = keras2onnx.convert_keras(model, 'test_tf_pow')
+    assert runner('onnx_tf_pow', onnx_model, data, expected)
+
+
 def test_tf_concat(runner):
     def my_func_1(x):
         return tf.concat([x[0], x[1]], 1)