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new c97e41b [FRONTEND][KERAS]Max_pool3d and Averagepool3d operator
support (#5085)
c97e41b is described below
commit c97e41b0c079c1181541501b14151be7499eccb9
Author: Samuel <siju.sam...@huawei.com>
AuthorDate: Tue Mar 31 14:11:41 2020 +0530
[FRONTEND][KERAS]Max_pool3d and Averagepool3d operator support (#5085)
* [KERAS]Pool3d support added
* Keras pool3d testcase added
---
python/tvm/relay/frontend/keras.py | 41 +++++++++++++++++++++++++++--
tests/python/frontend/keras/test_forward.py | 23 ++++++++++++++++
2 files changed, 62 insertions(+), 2 deletions(-)
diff --git a/python/tvm/relay/frontend/keras.py
b/python/tvm/relay/frontend/keras.py
index f5f9ad5..7c189f2 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -510,6 +510,43 @@ def _convert_pooling(inexpr, keras_layer, etab):
raise tvm.error.OpNotImplemented(
'Operator {} is not supported for frontend Keras.'.format(keras_layer))
+def _convert_pooling3d(inexpr, keras_layer, etab):
+ _check_data_format(keras_layer)
+ pool_type = type(keras_layer).__name__
+
+ if pool_type not in ['MaxPooling3D', 'AveragePooling3D']:
+ raise tvm.error.OpNotImplemented(
+ 'Operator {} is not supported for frontend
Keras.'.format(keras_layer))
+
+ pool_d1, pool_d2, pool_d3 = keras_layer.pool_size
+ stride_d1, stride_d2, stride_d3 = keras_layer.strides
+ params = {'pool_size': [pool_d1, pool_d2, pool_d3],
+ 'strides': [stride_d1, stride_d2, stride_d3],
+ 'padding': [0, 0, 0],
+ 'layout': etab.data_layout}
+
+ if keras_layer.padding == 'valid':
+ pass
+ elif keras_layer.padding == 'same':
+ in_d1 = keras_layer.input_shape[1]
+ in_d2 = keras_layer.input_shape[2]
+ in_d3 = keras_layer.input_shape[3]
+ pad_d1 = _get_pad_pair(in_d1, pool_d1, stride_d1)
+ pad_d2 = _get_pad_pair(in_d2, pool_d2, stride_d2)
+ pad_d3 = _get_pad_pair(in_d3, pool_d3, stride_d3)
+ params['padding'] = [pad_d1[0], pad_d2[0], pad_d3[0], pad_d1[1],
pad_d2[1], pad_d3[1]]
+ else:
+ raise tvm.error.OpAttributeUnImplemented(
+ 'Padding with {} is not supported in operator
Pooling3D.'.format(keras_layer.padding))
+
+ out = _op.transpose(inexpr, axes=(0, 4, 1, 2, 3))
+ params['layout'] = "NCDHW"
+ if pool_type == 'MaxPooling3D':
+ out = _op.nn.max_pool3d(out, **params)
+ elif pool_type == 'AveragePooling3D':
+ out = _op.nn.avg_pool3d(out, **params)
+
+ return _op.transpose(out, axes=(0, 2, 3, 4, 1))
def _convert_upsample(inexpr, keras_layer, etab):
_check_data_format(keras_layer)
@@ -817,8 +854,8 @@ _convert_map = {
'Conv3D' : _convert_convolution3d,
# 'Conv3DTranspose' : _convert_convolution3d,
# 'SeparableConv3D' : _convert_convolution3d,
- # 'MaxPooling3D' : _convert_pooling3d,
- # 'AveragePooling3D' : _convert_pooling3d,
+ 'MaxPooling3D' : _convert_pooling3d,
+ 'AveragePooling3D' : _convert_pooling3d,
# 'GlobalMaxPooling3D' : _convert_pooling3d,
# 'GlobalAveragePooling3D' : _convert_pooling3d,
# 'UpSampling3D' : _convert_upsample3d,
diff --git a/tests/python/frontend/keras/test_forward.py
b/tests/python/frontend/keras/test_forward.py
index 57f6401..5f4bcb1 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -421,6 +421,28 @@ class TestKeras:
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, layout='NDHWC')
+ def test_forward_pool3d(self, keras):
+ data = keras.layers.Input(shape=(32, 32, 32, 1))
+ pool_funcs = [# maxpool
+ keras.layers.MaxPooling3D(pool_size=(2, 2, 2),
+ strides=(1, 1, 1),
+ padding='same'),
+ keras.layers.MaxPooling3D(pool_size=(3, 3, 3),
+ strides=(2, 2, 2),
+ padding='valid'),
+ # avgpool
+ keras.layers.AveragePooling3D(pool_size=(3, 3, 3),
+ strides=(2, 2, 2),
+ padding='same'),
+ keras.layers.AveragePooling3D(pool_size=(2, 2, 2),
+ strides=(1, 1, 1),
+ padding='valid'),
+ ]
+ for pool_func in pool_funcs:
+ x = pool_func(data)
+ keras_model = keras.models.Model(data, x)
+ verify_keras_frontend(keras_model, layout='NDHWC')
+
if __name__ == '__main__':
for k in [keras, tf_keras]:
sut = TestKeras()
@@ -449,3 +471,4 @@ if __name__ == '__main__':
sut.test_forward_mobilenet(keras=k)
sut.test_forward_mobilenet(keras=k, layout='NHWC')
sut.test_forward_conv3d(keras=k)
+ sut.test_forward_pool3d(keras=k)
