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!4969 delete group parameter from nn.DepthwiseConv2d 

Merge pull request !4969 from chenzhongming/lite
This commit is contained in:
mindspore-ci-bot 2020-08-22 16:13:09 +08:00 committed by Gitee
parent 94a109f476 8337ae710e
commit d2641bbf79
1 changed files with 5 additions and 12 deletions
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17
mindspore/nn/layer/conv.py
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@ -13,6 +13,7 @@
# limitations under the License.
# ============================================================================
"""conv"""
import numpy as np
from mindspore import log as logger
from mindspore.ops import operations as P
@ -20,7 +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 ParamValidator as validator, Rel
from mindspore._checkparam import Rel
from mindspore._checkparam import Validator
from mindspore._checkparam import check_bool, twice, check_int_positive
from mindspore._extends import cell_attr_register
@ -807,8 +808,7 @@ 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{group}, \text{ks_h}, \text{ks_w})`, where group is the group number
to split the input in the channel dimension.
:math:`(C_{out}, C_{in}, \text{ks_h}, \text{ks_w})` 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} -
@ -851,8 +851,6 @@ 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,
@ -884,7 +882,6 @@ class DepthwiseConv2d(Cell):
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
@ -894,13 +891,9 @@ 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
@ -928,10 +921,10 @@ class DepthwiseConv2d(Cell):
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, stride={}, ' \
'pad_mode={}, padding={}, dilation={}, group={},' \
'pad_mode={}, padding={}, dilation={}' \
'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.group,
self.pad_mode, self.padding, self.dilation,
self.has_bias, self.weight_init, self.bias_init)
if self.has_bias: