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The following commit(s) were added to refs/heads/master by this push:
new d048615 temporary solution for instancenorm, will refactor using
backend (#9807)
d048615 is described below
commit d04861575f18cd476132e56f4e738e9e6771338b
Author: Hang Zhang <8041160+zhanghang1...@users.noreply.github.com>
AuthorDate: Mon Feb 19 11:47:14 2018 -0800
temporary solution for instancenorm, will refactor using backend (#9807)
* temporary solution for instancenorm, will refactor using backend
* fix typo
* rm space
* fix doc
* fix doc
* Update conv_layers.py
* Update basic_layers.py
* Update conv_layers.py
* Update basic_layers.py
* fix typo
* fix typo
---
python/mxnet/gluon/nn/basic_layers.py | 19 +++++++++++++++----
python/mxnet/gluon/nn/conv_layers.py | 20 ++++++++++++++------
2 files changed, 29 insertions(+), 10 deletions(-)
diff --git a/python/mxnet/gluon/nn/basic_layers.py
b/python/mxnet/gluon/nn/basic_layers.py
index 0f38119..b61540d 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -420,6 +420,10 @@ class InstanceNorm(HybridBlock):
Parameters
----------
+ axis : int, default 1
+ The axis that should be normalized. This is typically the channels
+ (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,
+ set `axis=1` in `InstanceNorm`. If `layout='NHWC'`, then set `axis=3`.
epsilon: float, default 1e-5
Small float added to variance to avoid dividing by zero.
center: bool, default True
@@ -439,6 +443,7 @@ class InstanceNorm(HybridBlock):
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
+
Inputs:
- **data**: input tensor with arbitrary shape.
@@ -463,11 +468,13 @@ class InstanceNorm(HybridBlock):
[[-0.99998319 0.99998361]]]
<NDArray 2x1x2 @cpu(0)>
"""
- def __init__(self, epsilon=1e-5, center=True, scale=False,
+ def __init__(self, axis=1, epsilon=1e-5, center=True, scale=False,
beta_initializer='zeros', gamma_initializer='ones',
in_channels=0, **kwargs):
super(InstanceNorm, self).__init__(**kwargs)
- self._kwargs = {'eps': epsilon}
+ self._kwargs = {'eps': epsilon, 'axis': axis}
+ self._axis = axis
+ self._epsilon = epsilon
self.gamma = self.params.get('gamma', grad_req='write' if scale else
'null',
shape=(in_channels,),
init=gamma_initializer,
allow_deferred_init=True)
@@ -476,8 +483,12 @@ class InstanceNorm(HybridBlock):
allow_deferred_init=True)
def hybrid_forward(self, F, x, gamma, beta):
- return F.InstanceNorm(x, gamma, beta,
- name='fwd', **self._kwargs)
+ if self._axis == 1:
+ return F.InstanceNorm(x, gamma, beta,
+ name='fwd', eps=self._epsilon)
+ x = x.swapaxes(1, self._axis)
+ return F.InstanceNorm(x, gamma, beta, name='fwd',
+ eps=self._epsilon).swapaxes(1, self._axis)
def __repr__(self):
s = '{name}({content}'
diff --git a/python/mxnet/gluon/nn/conv_layers.py
b/python/mxnet/gluon/nn/conv_layers.py
index a69cb8a..87a62bc 100644
--- a/python/mxnet/gluon/nn/conv_layers.py
+++ b/python/mxnet/gluon/nn/conv_layers.py
@@ -1011,18 +1011,26 @@ class GlobalAvgPool3D(_Pooling):
class ReflectionPad2D(HybridBlock):
- """Pads the input tensor using the reflection of the input boundary.
+ r"""Pads the input tensor using the reflection of the input boundary.
Parameters
----------
padding: int
An integer padding size
- Shape:
- - Input: :math:`(N, C, H_{in}, W_{in})`
- - Output: :math:`(N, C, H_{out}, W_{out})` where
- :math:`H_{out} = H_{in} + 2 * padding
- :math:`W_{out} = W_{in} + 2 * padding
+
+ Inputs:
+ - **data**: input tensor with the shape :math:`(N, C, H_{in}, W_{in})`.
+
+ Outputs:
+ - **out**: output tensor with the shape :math:`(N, C, H_{out},
W_{out})`, where
+
+ .. math::
+
+ H_{out} = H_{in} + 2 \cdot padding
+
+ W_{out} = W_{in} + 2 \cdot padding
+
Examples
--------
--
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