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体验新版 GitCode,发现更多精彩内容 >>
提交
fcaa378c
编写于
7月 25, 2020
作者:
L
liuxiao93
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix bug for con1d with 3d input.
上级
7fbed0ce
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
36 addition
and
9 deletion
+36
-9
mindspore/nn/layer/conv.py
mindspore/nn/layer/conv.py
+33
-8
mindspore/ops/operations/nn_ops.py
mindspore/ops/operations/nn_ops.py
+3
-1
未找到文件。
mindspore/nn/layer/conv.py
浏览文件 @
fcaa378c
...
...
@@ -13,10 +13,13 @@
# limitations under the License.
# ============================================================================
"""conv"""
import
numpy
as
np
from
mindspore
import
log
as
logger
from
mindspore.ops
import
operations
as
P
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
Validator
from
mindspore._checkparam
import
check_bool
,
twice
,
check_int_positive
...
...
@@ -254,6 +257,11 @@ class Conv2d(_Conv):
return
s
@
constexpr
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.
...
...
@@ -359,6 +367,15 @@ class Conv1d(_Conv):
kernel_size
=
(
1
,
kernel_size
)
stride
=
(
1
,
stride
)
dilation
=
(
1
,
dilation
)
get_shape
=
P
.
Shape
()
get_dtype
=
P
.
DType
()
if
isinstance
(
weight_init
,
Tensor
):
weight_init_shape
=
get_shape
(
weight_init
)
Validator
.
check_integer
(
'weight_init_shape'
,
len
(
weight_init_shape
),
3
,
Rel
.
EQ
,
self
.
cls_name
)
weight_init_dtype
=
get_dtype
(
weight_init
)
weight_init_value
=
weight_init
.
asnumpy
()
weight_init_value
=
np
.
expand_dims
(
weight_init_value
,
2
)
weight_init
=
Tensor
(
weight_init_value
,
weight_init_dtype
)
super
(
Conv1d
,
self
).
__init__
(
in_channels
,
...
...
@@ -391,13 +408,13 @@ class Conv1d(_Conv):
def
construct
(
self
,
x
):
x_shape
=
self
.
shape
(
x
)
if
len
(
x_shape
)
==
3
:
x
=
self
.
expand_dims
(
x
,
2
)
_check_input_3d
(
x_shape
)
x
=
self
.
expand_dims
(
x
,
2
)
output
=
self
.
conv2d
(
x
,
self
.
weight
)
if
self
.
has_bias
:
output
=
self
.
bias_add
(
output
,
self
.
bias
)
if
len
(
x_shape
)
==
3
:
output
=
self
.
squeeze
(
output
)
output
=
self
.
squeeze
(
output
)
return
output
def
extend_repr
(
self
):
...
...
@@ -669,6 +686,15 @@ class Conv1dTranspose(_Conv):
kernel_size
=
(
1
,
kernel_size
)
stride
=
(
1
,
stride
)
dilation
=
(
1
,
dilation
)
get_shape
=
P
.
Shape
()
get_dtype
=
P
.
DType
()
if
isinstance
(
weight_init
,
Tensor
):
weight_init_shape
=
get_shape
(
weight_init
)
Validator
.
check_integer
(
'weight_init_shape'
,
len
(
weight_init_shape
),
3
,
Rel
.
EQ
,
self
.
cls_name
)
weight_init_dtype
=
get_dtype
(
weight_init
)
weight_init_value
=
weight_init
.
asnumpy
()
weight_init_value
=
np
.
expand_dims
(
weight_init_value
,
2
)
weight_init
=
Tensor
(
weight_init_value
,
weight_init_dtype
)
# out_channels and in_channels swap.
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv1dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
...
...
@@ -733,8 +759,8 @@ class Conv1dTranspose(_Conv):
def
construct
(
self
,
x
):
x_shape
=
self
.
shape
(
x
)
if
len
(
x_shape
)
==
3
:
x
=
self
.
expand_dims
(
x
,
2
)
_check_input_3d
(
x_shape
)
x
=
self
.
expand_dims
(
x
,
2
)
n
,
_
,
h
,
w
=
self
.
shape
(
x
)
...
...
@@ -746,8 +772,7 @@ class Conv1dTranspose(_Conv):
if
self
.
has_bias
:
output
=
self
.
bias_add
(
output
,
self
.
bias
)
if
len
(
x_shape
)
==
3
:
output
=
self
.
squeeze
(
output
)
output
=
self
.
squeeze
(
output
)
return
output
def
extend_repr
(
self
):
...
...
mindspore/ops/operations/nn_ops.py
浏览文件 @
fcaa378c
...
...
@@ -1690,7 +1690,9 @@ class L2Loss(PrimitiveWithInfer):
Set `input_x` as x and output as loss.
.. math::
loss = sum(x ** 2) / 2
loss = sum(x ** 2) / nelement(x)
:math:`nelement(x)` represents the number of `input_x`.
Inputs:
- **input_x** (Tensor) - A input Tensor.
...
...
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