[NNVM][KERAS] Fixed padding in pooling (#1635)

nnvm/python/nnvm/frontend/keras.py

@@ -269,14 +269,12 @@ def _convert_pooling(insym, keras_layer, symtab):
                   'padding': [0, 0]}
         if keras_layer.padding == 'valid':
             pass
-        # we insert a separate pad operator
         elif keras_layer.padding == 'same':
             in_h = keras_layer.input_shape[1]
             in_w = keras_layer.input_shape[2]
             pad_t, pad_b = _get_pad_pair(in_h, pool_h, stride_h)
             pad_l, pad_r = _get_pad_pair(in_w, pool_w, stride_w)
-            insym = _sym.pad(data=insym, pad_width=(
-                (0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)))
+            params['padding'] = [pad_t, pad_l, pad_b, pad_r]
         else:
             raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
         if pool_type == 'MaxPooling2D':

nnvm/tests/python/frontend/keras/test_forward.py

@@ -38,7 +38,7 @@ def verify_keras_frontend(keras_model):
         out = m.get_output(0, tvm.nd.empty(out_shape, dtype))
         return out.asnumpy()
 
-    xs = [np.random.uniform(size=shape) for shape in in_shapes]
+    xs = [np.random.uniform(size=shape, low=-1.0, high=1.0) for shape in in_shapes]
     keras_out = get_keras_output(xs)
     for target, ctx in ctx_list():
         tvm_out = get_tvm_output([x.transpose([0,3,1,2]) for x in xs], target, ctx)
@@ -74,6 +74,18 @@ def test_forward_dense():
     verify_keras_frontend(keras_model)
 
 
+def test_forward_pool():
+    data = keras.layers.Input(shape=(2,2,1))
+    # maxpool
+    x = keras.layers.MaxPooling2D((3, 3), strides=(1, 1), padding='same')(data)
+    keras_model = keras.models.Model(data, x)
+    verify_keras_frontend(keras_model)
+    # avgpool
+    y = keras.layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(data)
+    keras_model = keras.models.Model(data, y)
+    verify_keras_frontend(keras_model)
+
+
 def test_forward_transpose_conv():
     data = keras.layers.Input(shape=(32,32,3))
     x = keras.layers.Conv2D(filters=10, kernel_size=(3,3), strides=(2,2), padding='same')(data)
@@ -206,6 +218,7 @@ if __name__ == '__main__':
     test_forward_elemwise_add()
     test_forward_activations()
     test_forward_dense()
+    test_forward_pool()
     test_forward_transpose_conv()
     test_forward_separable_conv()
     test_forward_upsample()