From 28dd0b6c289f6de9b361d4bbe90987539ee5af23 Mon Sep 17 00:00:00 2001
From: David Fan <30608893+jiafatom@users.noreply.github.com>
Date: Fri, 27 Sep 2019 15:21:07 -0700
Subject: [PATCH] Add a unit test for MergePadConvSolution: [0, 0, 1, 1] +
 Conv2D (#254)

* Add a unit test: ZeroPadding2D (0, 0, 1, 1) + Conv2D to test MergePadConvSolution

* Add a unit test: ZeroPadding2D (0, 0, 1, 1) + Conv2D to test MergePadConvSolution
---
 tests/test_layers.py | 57 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/tests/test_layers.py b/tests/test_layers.py
index ff538eb6..e88a4e80 100644
--- a/tests/test_layers.py
+++ b/tests/test_layers.py
@@ -46,6 +46,7 @@
 InputLayer = keras.layers.InputLayer
 Lambda = keras.layers.Lambda
 Layer = keras.layers.Layer
+LeakyReLU = keras.layers.LeakyReLU
 LSTM = keras.layers.LSTM
 Maximum = keras.layers.Maximum
 MaxPool1D = keras.layers.MaxPool1D
@@ -1120,6 +1121,62 @@ def _model():
         expected = model.predict(x)
         self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, x, expected, self.model_files))
 
+    @unittest.skipIf(is_keras_older_than("2.2.4") or is_tf_keras,
+                     "ReLU support requires keras 2.2.4 or later.")
+    def test_shared_model_3(self):
+        def _bottleneck(x, filters, activation, strides, block_id):
+            padding = 'same' if strides == 1 else 'valid'
+            ch_axis = 1 if K.image_data_format() == 'channels_first' else -1
+            if strides > 1:
+                x = ZeroPadding2D(((0, 1), (0, 1)), data_format=K.image_data_format())(x)
+
+            x = Conv2D(filters // 2, (1, 1), padding='same', name='bottleneck_'+str(block_id)+'_conv_0',
+                          use_bias=False, data_format=K.image_data_format())(x)
+
+            x = BatchNormalization(axis=ch_axis, name='bottleneck_'+str(block_id)+'_bnorm_0')(x)
+
+            if activation == 'relu':
+                x = ReLU(name='bottleneck_'+str(block_id)+'_relu_0')(x)
+            elif activation == 'leaky':
+                x = LeakyReLU(name='bottleneck_'+str(block_id)+'_leaky_0')(x)
+            else:
+                assert False
+
+            x = Conv2D(filters // 2, (3, 3), padding=padding, name='bottleneck_'+str(block_id)+'_conv_1',
+                          strides=strides, use_bias=False, data_format=K.image_data_format())(x)
+            x = BatchNormalization(axis=ch_axis, name='bottleneck_'+str(block_id)+'_bnorm_1')(x)
+            if activation == 'relu':
+                x = ReLU(name='bottleneck_'+str(block_id)+'_relu_1')(x)
+            elif activation == 'leaky':
+                x = LeakyReLU(name='bottleneck_'+str(block_id)+'_leaky_1')(x)
+            else:
+                assert False
+
+            x = Conv2D(filters, (1, 1), padding='same', name='bottleneck_'+str(block_id)+'_conv_2',
+                          use_bias=False, data_format=K.image_data_format())(x)
+            x = BatchNormalization(axis=ch_axis, name='bottleneck_'+str(block_id)+'_bnorm_2')(x)
+            if activation == 'relu':
+                x = ReLU(name='bottleneck_'+str(block_id)+'_relu_2')(x)
+            elif activation == 'leaky':
+                x = LeakyReLU(name='bottleneck_'+str(block_id)+'_leaky_2')(x)
+            else:
+                assert False
+
+            return x
+
+        def convnet_7(input_shape, activation):
+            input = Input(shape=input_shape, name='input_1')
+            x = _bottleneck(input, filters=16, strides=1, activation=activation, block_id=1)
+            x = _bottleneck(x, filters=32, strides=2, activation=activation, block_id=2)
+            return Model(inputs=input, outputs=x, name='convnet_7')
+
+        for activation in ['relu', 'leaky']:
+            model = convnet_7(input_shape=(3, 96, 128), activation=activation)
+            onnx_model = keras2onnx.convert_keras(model, model.name)
+            x = np.random.rand(1, 3, 96, 128).astype(np.float32)
+            expected = model.predict(x)
+            self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, x, expected, self.model_files))
+
     def test_masking(self):
         timesteps, features = (3, 5)
         model = Sequential([