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!5346 Fix bug of DepthwiseConv2d deleting arg 'group' 

Merge pull request !5346 from chenfei_mindspore/r0.7
This commit is contained in:
mindspore-ci-bot 2020-08-27 19:21:47 +08:00 committed by Gitee
parent 2f189543b9 c545422384
commit d670bcb003
1 changed files with 24 additions and 7 deletions
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31
mindspore/nn/layer/conv.py
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@ -21,7 +21,7 @@ from mindspore.ops.primitive import constexpr
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor
from mindspore._checkparam import Rel
from mindspore._checkparam import ParamValidator as validator, Rel
from mindspore._checkparam import Validator
from mindspore._checkparam import check_bool, twice, check_int_positive
from mindspore._extends import cell_attr_register
@ -29,10 +29,12 @@ from ..cell import Cell
__all__ = ['Conv2d', 'Conv2dTranspose', 'DepthwiseConv2d', 'Conv1d', 'Conv1dTranspose']
class _Conv(Cell):
"""
Applies a N-D convolution over an input signal composed of several input planes.
"""
def __init__(self,
in_channels,
out_channels,
@ -68,15 +70,16 @@ class _Conv(Cell):
self.group = check_int_positive(group)
self.has_bias = has_bias
if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
kernel_size[0] < 1 or kernel_size[1] < 1:
isinstance(kernel_size[0], bool) or isinstance(kernel_size[1], bool) or \
kernel_size[0] < 1 or kernel_size[1] < 1:
raise ValueError("Attr 'kernel_size' of 'Conv2D' Op passed "
+ str(self.kernel_size) + ", should be a int or tuple and equal to or greater than 1.")
if (not isinstance(stride[0], int)) or (not isinstance(stride[1], int)) or \
isinstance(stride[0], bool) or isinstance(stride[1], bool) or stride[0] < 1 or stride[1] < 1:
isinstance(stride[0], bool) or isinstance(stride[1], bool) or stride[0] < 1 or stride[1] < 1:
raise ValueError("Attr 'stride' of 'Conv2D' Op passed "
+ str(self.stride) + ", should be a int or tuple and equal to or greater than 1.")
if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \
dilation[0] < 1 or dilation[1] < 1:
isinstance(dilation[0], bool) or isinstance(dilation[1], bool) or dilation[0] < 1 or dilation[1] < 1:
raise ValueError("Attr 'dilation' of 'Conv2D' Op passed "
+ str(self.dilation) + ", should equal to or greater than 1.")
if in_channels % group != 0:
@ -192,6 +195,7 @@ class Conv2d(_Conv):
>>> net(input).shape
(1, 240, 1024, 640)
"""
@cell_attr_register
def __init__(self,
in_channels,
@ -263,6 +267,7 @@ def _check_input_3d(input_shape):
if len(input_shape) != 3:
raise ValueError(f"Input should be 3d, but got shape {input_shape}")
class Conv1d(_Conv):
r"""
1D convolution layer.
@ -343,6 +348,7 @@ class Conv1d(_Conv):
>>> net(input).shape
(1, 240, 640)
"""
@cell_attr_register
def __init__(self,
in_channels,
@ -497,6 +503,7 @@ class Conv2dTranspose(_Conv):
>>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
>>> net(input)
"""
def __init__(self,
in_channels,
out_channels,
@ -661,6 +668,7 @@ class Conv1dTranspose(_Conv):
>>> input = Tensor(np.ones([1, 3, 50]), mindspore.float32)
>>> net(input)
"""
def __init__(self,
in_channels,
out_channels,
@ -808,7 +816,8 @@ class DepthwiseConv2d(Cell):
filter and :math:`out_{j}` corresponds to the :math:`j`-th channel of the output. :math:`W_{ij}` is a slice
of kernel and it has shape :math:`(\text{ks_h}, \text{ks_w})`, where :math:`\text{ks_h}` and
:math:`\text{ks_w}` are the height and width of the convolution kernel. The full kernel has shape
:math:`(C_{out}, C_{in}, \text{ks_h}, \text{ks_w})` to split the input in the channel dimension.
:math:`(C_{out}, C_{in} // \text{group}, \text{ks_h}, \text{ks_w})`, where group is the group number
to split the input in the channel dimension.
If the 'pad_mode' is set to be "valid", the output height and width will be
:math:`\left \lfloor{1 + \frac{H_{in} + 2 \times \text{padding} - \text{ks_h} -
@ -851,6 +860,8 @@ class DepthwiseConv2d(Cell):
be :math:`k - 1` pixels skipped for each sampling location. Its value should
be greater than or equal to 1 and bounded by the height and width of the
input. Default: 1.
group (int): Split filter into groups, `in_ channels` and `out_channels` should be
divisible by the number of groups. Default: 1.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
It can be a Tensor, a string, an Initializer or a number. When a string is specified,
@ -874,6 +885,7 @@ class DepthwiseConv2d(Cell):
>>> net(input).shape
(1, 240, 1024, 640)
"""
def __init__(self,
in_channels,
out_channels,
@ -882,6 +894,7 @@ class DepthwiseConv2d(Cell):
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
@ -891,9 +904,13 @@ class DepthwiseConv2d(Cell):
self.dilation = twice(dilation)
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
validator.check_integer('group', group, in_channels, Rel.EQ)
validator.check_integer('group', group, out_channels, Rel.EQ)
validator.check_integer('group', group, 1, Rel.GE)
self.pad_mode = pad_mode
self.padding = padding
self.dilation = dilation
self.group = group
self.has_bias = has_bias
self.weight_init = weight_init
self.bias_init = bias_init
@ -921,10 +938,10 @@ class DepthwiseConv2d(Cell):
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, stride={}, ' \
'pad_mode={}, padding={}, dilation={}' \
'pad_mode={}, padding={}, dilation={}, group={}, ' \
'has_bias={}, weight_init={}, bias_init={}'.format(
self.in_channels, self.out_channels, self.kernel_size, self.stride,
self.pad_mode, self.padding, self.dilation,
self.pad_mode, self.padding, self.dilation, self.group,
self.has_bias, self.weight_init, self.bias_init)
if self.has_bias: