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new 2bdf73f [KERAS]Global MaxPool3d and AvgPool3d support (#5098)
2bdf73f is described below
commit 2bdf73fabbea128501df33e912a5405a5e4051d6
Author: Samuel <[email protected]>
AuthorDate: Tue May 19 17:54:25 2020 +0530
[KERAS]Global MaxPool3d and AvgPool3d support (#5098)
---
python/tvm/relay/frontend/keras.py | 23 ++++++++--
python/tvm/relay/op/nn/nn.py | 69 +++++++++++++++++++++++++++++
tests/python/frontend/keras/test_forward.py | 12 +++++
3 files changed, 101 insertions(+), 3 deletions(-)
diff --git a/python/tvm/relay/frontend/keras.py
b/python/tvm/relay/frontend/keras.py
index 43065be..9869958 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -373,7 +373,7 @@ def _convert_convolution3d(inexpr, keras_layer, etab):
pad_d3 = _get_pad_pair(in_d3, dilated_kernel_d3, stride_d3)
params['padding'] = [pad_d1[0], pad_d2[0], pad_d3[0], pad_d1[1],
pad_d2[1], pad_d3[1]]
else:
- msg = 'Padding with {} is not supported for operator Convolution ' \
+ msg = 'Padding with {} is not supported for operator Convolution3D ' \
'in frontend Keras.'
raise
tvm.error.OpAttributeUnImplemented(msg.format(keras_layer.padding))
out = _op.nn.conv3d(data=inexpr, **params)
@@ -543,6 +543,23 @@ def _convert_pooling3d(inexpr, keras_layer, etab):
return _op.transpose(out, axes=(0, 2, 3, 4, 1))
+
+def _convert_global_pooling3d(inexpr, keras_layer, etab):
+ _check_data_format(keras_layer)
+ pool_type = type(keras_layer).__name__
+
+ global_pool_params = {'layout': etab.data_layout}
+ if pool_type == 'GlobalMaxPooling3D':
+ out = _op.nn.global_max_pool3d(inexpr, **global_pool_params)
+ elif pool_type == 'GlobalAveragePooling3D':
+ out = _op.nn.global_avg_pool3d(inexpr, **global_pool_params)
+ else:
+ raise tvm.error.OpNotImplemented(
+ 'Operator {} is not supported for frontend
Keras.'.format(keras_layer))
+
+ return _convert_flatten(out, keras_layer, etab)
+
+
def _convert_upsample(inexpr, keras_layer, etab):
_check_data_format(keras_layer)
upsample_type = type(keras_layer).__name__
@@ -885,8 +902,8 @@ _convert_map = {
# 'SeparableConv3D' : _convert_convolution3d,
'MaxPooling3D' : _convert_pooling3d,
'AveragePooling3D' : _convert_pooling3d,
- # 'GlobalMaxPooling3D' : _convert_pooling3d,
- # 'GlobalAveragePooling3D' : _convert_pooling3d,
+ 'GlobalMaxPooling3D' : _convert_global_pooling3d,
+ 'GlobalAveragePooling3D' : _convert_global_pooling3d,
'UpSampling3D' : _convert_upsample3d,
'ZeroPadding3D' : _convert_padding3d,
diff --git a/python/tvm/relay/op/nn/nn.py b/python/tvm/relay/op/nn/nn.py
index 888e0b6..96708c9 100644
--- a/python/tvm/relay/op/nn/nn.py
+++ b/python/tvm/relay/op/nn/nn.py
@@ -2692,3 +2692,72 @@ def adaptive_avg_pool3d(data,
"""
output_size = [] or output_size
return _make.adaptive_avg_pool3d(data, output_size, layout)
+
+
+def global_max_pool3d(data,
+ layout="NCDHW"):
+ r"""3D global maximum pooling operator.
+
+ This operator takes data as input and does 3D max value calculation
+ across each window represented by DxWxH.
+
+ In the default case, where the data_layout is `NCDHW`
+ a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
+ to produce an output Tensor with the following rule:
+
+ with data of shape (b, c, d, h, w)
+ .. math::
+
+ \mbox{out}(b, c, 1, 1, 1) = \max_{l=0, \ldots, d}, \max_{m=0,
\ldots, h},
+ \max_{n=0, \ldots, w} \mbox{data}(b, c, l, m, n)
+
+ Parameters
+ ----------
+ data : tvm.relay.Expr
+ The input data to the operator.
+
+ layout : str, optional
+ Layout of the input.
+
+ Returns
+ -------
+ result : tvm.relay.Expr
+ The computed result.
+ """
+ output_size = [1, 1, 1]
+ return _make.adaptive_max_pool3d(data, output_size, layout)
+
+
+def global_avg_pool3d(data,
+ layout="NCDHW"):
+ r"""3D global average pooling operator.
+
+ This operator takes data as input and does 3D average value calculation
+ across each window represented by DxWxH.
+
+ In the default case, where the data_layout is `NCDHW`
+ a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
+ to produce an output Tensor with the following rule:
+
+ with data of shape (b, c, d, h, w)
+
+ .. math::
+
+ \mbox{out}(b, c, 1, 1, 1) = \frac{1}{d * h * w} \sum_{l=0}^{d-1}
\sum_{m=0}^{h-1}
+ \sum_{n=0}^{w-1} \mbox{data}(b, c, l, m, n)
+
+ Parameters
+ ----------
+ data : tvm.relay.Expr
+ The input data to the operator.
+
+ layout : str, optional
+ Layout of the input.
+
+ Returns
+ -------
+ result : tvm.relay.Expr
+ The computed result.
+ """
+ output_size = [1, 1, 1]
+ return _make.adaptive_avg_pool3d(data, output_size, layout)
diff --git a/tests/python/frontend/keras/test_forward.py
b/tests/python/frontend/keras/test_forward.py
index b4a1816..ed0181f 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -483,6 +483,17 @@ class TestKeras:
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)
+ def test_forward_global_pool3d(self, keras):
+ data = keras.layers.Input(shape=(32, 32, 32, 1))
+ pool_funcs = [# global maxpool
+ keras.layers.GlobalMaxPooling3D(),
+ # global avgpool
+ keras.layers.GlobalAveragePooling3D()
+ ]
+ 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]:
@@ -513,6 +524,7 @@ if __name__ == '__main__':
sut.test_forward_mobilenet(keras=k, layout='NHWC')
sut.test_forward_conv3d(keras=k)
sut.test_forward_pool3d(keras=k)
+ sut.test_forward_global_pool3d(keras=k)
sut.test_forward_upsample3d(keras=k)
sut.test_forward_zero_padding3d(keras=k)
sut.test_forward_embedding(keras=k)
