add unittest

Author: lucienwang1009

tests/backend_test_base.py

@@ -130,7 +130,7 @@ def run_test_case(self, feed_dict, input_names_with_port, output_names_with_port
         graph_def = tf_optimize(input_names_with_port, output_names_with_port,
                                 sess.graph_def, constant_fold)
 
-        if self.config.is_debug_mode and constant_fold:
+        if self.config.is_debug_mode:
             model_path = os.path.join(self.test_data_directory, self._testMethodName + "_after_tf_optimize.pb")
             utils.save_protobuf(model_path, graph_def)
             self.log.debug("created file  %s", model_path)

tests/run_pretrained_models.py

@@ -242,6 +242,8 @@ def run_test(self, name, backend="caffe2", debug=False, onnx_file=None, opset=No
                 inputs[k] = v
 
         graph_def = tf2onnx.tfonnx.tf_optimize(inputs.keys(), self.output_names, graph_def, fold_const)
+        if debug:
+            utils.save_protobuf(os.path.join(TEMP_DIR, name + "_after_tf_optimize.pb"), graph_def)
         shape_override = {}
         g = tf.import_graph_def(graph_def, name='')
         with tf.Session(config=tf.ConfigProto(allow_soft_placement=True), graph=g) as sess:

tests/test_backend.py

@@ -353,6 +353,21 @@ def test_dropout(self):
         output_names_with_port = ["output:0"]
         self.run_test_case(feed_dict, input_names_with_port, output_names_with_port)
 
+    def test_nn_dropout(self):
+        keep_prob = tf.placeholder_with_default(1., (), "keep_prob")
+        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_, keep_prob)
+
+        _ = 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]