From 58b2395d2d09036852e12ee7f751b26997c63741 Mon Sep 17 00:00:00 2001
From: MORITA Kazutaka <morita.kazutaka@lab.ntt.co.jp>
Date: Thu, 23 Aug 2018 07:48:01 +0900
Subject: [PATCH] [NNVM][KERAS] Fixed padding in pooling (#1635)
---
nnvm/python/nnvm/frontend/keras.py | 4 +---
nnvm/tests/python/frontend/keras/test_forward.py | 15 ++++++++++++++-
2 files changed, 15 insertions(+), 4 deletions(-)
diff --git a/nnvm/python/nnvm/frontend/keras.py b/nnvm/python/nnvm/frontend/keras.py
index 3cfa48176..3a0a25aa4 100644
--- a/nnvm/python/nnvm/frontend/keras.py
+++ b/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':
diff --git a/nnvm/tests/python/frontend/keras/test_forward.py b/nnvm/tests/python/frontend/keras/test_forward.py
index 5c27a3e38..3e80c7439 100644
--- a/nnvm/tests/python/frontend/keras/test_forward.py
+++ b/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()
--
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