Merge pull request #452 from mindest/dev_ci_build

fix ci build errors in tf 1.13

Author: nbcsm

tests/common.py

@@ -124,6 +124,13 @@ def _append_message(reason, message):
     return reason
 
 
+def check_tf_max_version(max_accepted_version, message=""):
+    """ Skip if tf_version > max_required_version """
+    config = get_test_config()
+    reason = _append_message("conversion requires tf <= {}".format(max_accepted_version), message)
+    return unittest.skipIf(config.tf_version > LooseVersion(max_accepted_version), reason)
+
+
 def check_tf_min_version(min_required_version, message=""):
     """ Skip if tf_version < min_required_version """
     config = get_test_config()

tests/test_backend.py

@@ -370,6 +370,22 @@ def test_nn_dropout(self):
         # here we set it False to test PlaceholderWithDefault bug: https://github.com/onnx/tensorflow-onnx/pull/446
         self.run_test_case(feed_dict, input_names_with_port, output_names_with_port, constant_fold=False)
 
+    @check_tf_min_version("1.13")
+    def test_nn_dropout_with_rate(self):
+        rate = tf.placeholder_with_default(0., (), "rate")
+        x_val = np.ones([1, 24, 24, 3], dtype=np.float32)
+        # Define a scope for reusing the variables
+        x = tf.placeholder(tf.float32, shape=x_val.shape, name="input_1")
+        x_ = tf.identity(x)
+
+        fc1 = tf.nn.dropout(x_, rate=rate)
+
+        _ = tf.identity(fc1, name="output")
+        feed_dict = {"input_1:0": x_val}
+        input_names_with_port = ["input_1:0"]
+        output_names_with_port = ["output:0"]
+        self.run_test_case(feed_dict, input_names_with_port, output_names_with_port, constant_fold=False)
+
     def test_conv2d_with_input_transpose(self):
         x_shape = [2, 32, 32, 3]
         kernel_shape = [3, 3, 3, 3]
@@ -784,13 +800,15 @@ def test_relu(self):
     @skip_caffe2_backend("fails on caffe2 with dim issue")
     @check_onnxruntime_incompatibility("Mul")
     def test_leaky_relu(self):
-        for alpha in [0.1, -0.1, 1.0, -1.0, 10.0, -10.0]:
-            x_val = 1000 * np.random.random_sample([1000, 100]).astype(np.float32)
-            x = tf.placeholder(tf.float32, [None] * x_val.ndim, name=_TFINPUT)
-            x_ = tf.nn.leaky_relu(x, alpha)
-            _ = tf.identity(x_, name=_TFOUTPUT)
-            self._run_test_case([_OUTPUT], {_INPUT: x_val})
-            tf.reset_default_graph()
+        x_types = [np.float32, np.int32, np.int64]
+        for x_type in x_types:
+            x_val = 1000 * np.random.random_sample([1000, 100]).astype(x_type)
+            for alpha in [0.1, -0.1, 1.0, -1.0]:
+                x = tf.placeholder(x_val.dtype, [None] * x_val.ndim, name=_TFINPUT)
+                x_ = tf.nn.leaky_relu(x, alpha)
+                _ = tf.identity(x_, name=_TFOUTPUT)
+                self._run_test_case([_OUTPUT], {_INPUT: x_val})
+                tf.reset_default_graph()
 
     @check_onnxruntime_incompatibility("Elu")
     def test_elu(self):

tests/test_graph.py

@@ -24,7 +24,7 @@
 from tf2onnx.tfonnx import process_tf_graph
 from tf2onnx.handler import tf_op
 
-from common import get_test_config, unittest_main
+from common import get_test_config, unittest_main, check_tf_min_version, check_tf_max_version
 
 
 # pylint: disable=missing-docstring,unused-argument,unused-variable
@@ -152,6 +152,7 @@ def test_randomnormal(self):
                        'RandomNormal__2:0 -> output }'
             self.assertEqual(expected, actual)
 
+    @check_tf_max_version("1.12")
     def test_dropout(self):
         with tf.Session() as sess:
             x1 = tf.placeholder(tf.float32, [2, 3], name="input1")
@@ -170,6 +171,28 @@ def test_dropout(self):
                        'Add:0 -> output1 output1:0 -> output2 output2:0 -> output }'
             self.assertEqual(expected, actual)
 
+    @check_tf_min_version("1.13")
+    def test_dropout_2(self):
+        with tf.Session() as sess:
+            x1 = tf.placeholder(tf.float32, [2, 3], name="input1")
+            x2 = tf.placeholder(tf.float32, [1, 3], name="input2")
+            prop = tf.placeholder(tf.float32, name="prob")
+            x_ = tf.add(x1, x2)
+            x_ = tf.nn.dropout(x_, prop)
+            x_ = tf.identity(x_, name="output1")
+            x_ = tf.identity(x_, name="output2")
+            _ = tf.identity(x_, name="output")
+            g = process_tf_graph(sess.graph, opset=self.config.opset)
+            actual = onnx_to_graphviz(g)
+            expected = 'digraph { "dropout/sub/x" [op_type=Const] "sub/x" [op_type=Const] ' \
+                       'prob [op_type=Placeholder shape="[]"] sub [op_type=Sub] "dropout/sub" [op_type=Sub] ' \
+                       'input2 [op_type=Placeholder shape="[1, 3]"] input1 [op_type=Placeholder shape="[2, 3]"] ' \
+                       'Add [op_type=Add] output1 [op_type=Identity] output2 [op_type=Identity] ' \
+                       'output [op_type=Identity] "sub/x":0 -> sub prob:0 -> sub "dropout/sub/x":0 -> ' \
+                       '"dropout/sub" sub:0 -> "dropout/sub" input1:0 -> Add input2:0 -> Add Add:0 -> ' \
+                       'output1 output1:0 -> output2 output2:0 -> output }'
+            self.assertEqual(expected, actual)
+
     def test_add(self):
         with tf.Session() as sess:
             x1 = tf.placeholder(tf.float32, [2, 3], name="input1")

tf2onnx/onnx_opset/math.py

@@ -32,8 +32,8 @@ class RealDiv(common.BroadcastOp):
     pass
 
 
-@tf_op(["Abs", "Ceil", "Elu", "Exp", "Floor", "Log", "LogSoftmax", "Neg", "Relu", "Sigmoid", "Sqrt", "Tanh",
-        "Softplus", "Softsign", "Reciprocal"])
+@tf_op(["Abs", "Ceil", "Elu", "Exp", "Floor", "LeakyRelu", "Log", "LogSoftmax", "Neg", "Relu", "Sigmoid", "Sqrt",
+        "Tanh", "Softplus", "Softsign", "Reciprocal"])
 class DirectOp:
     @classmethod
     def version_4(cls, ctx, node, **kwargs):