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magicwindyyd
mindspore
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5e9cfaf6
M
mindspore
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5e9cfaf6
编写于
4月 10, 2020
作者:
高东海
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
syn-code1
上级
40647516
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
5 addition
and
56 deletion
+5
-56
mindspore/ccsrc/pipeline/pipeline_ge.cc
mindspore/ccsrc/pipeline/pipeline_ge.cc
+1
-1
mindspore/ccsrc/utils/callbacks.h
mindspore/ccsrc/utils/callbacks.h
+1
-1
mindspore/nn/optim/rmsprop.py
mindspore/nn/optim/rmsprop.py
+1
-1
tests/st/networks/test_network_main.py
tests/st/networks/test_network_main.py
+2
-53
未找到文件。
mindspore/ccsrc/pipeline/pipeline_ge.cc
浏览文件 @
5e9cfaf6
...
@@ -533,4 +533,4 @@ void ExportDFGraph(const std::string& file_name, const std::string& phase) {
...
@@ -533,4 +533,4 @@ void ExportDFGraph(const std::string& file_name, const std::string& phase) {
MS_LOG
(
DEBUG
)
<<
"ExportGraph End"
;
MS_LOG
(
DEBUG
)
<<
"ExportGraph End"
;
}
}
}
// namespace pipeline
}
// namespace pipeline
}
// namespace mindspore
}
// namespace mindspore
\ No newline at end of file
mindspore/ccsrc/utils/callbacks.h
浏览文件 @
5e9cfaf6
...
@@ -40,7 +40,7 @@ const int kCallbackOk = 0;
...
@@ -40,7 +40,7 @@ const int kCallbackOk = 0;
const
int
kCallbackFalied
=
1
;
const
int
kCallbackFalied
=
1
;
bool
GetParameterShape
(
const
FuncGraphPtr
&
anf_graph
,
const
std
::
string
&
param_name
,
bool
GetParameterShape
(
const
FuncGraphPtr
&
anf_graph
,
const
std
::
string
&
param_name
,
const
std
::
shared_ptr
<
std
::
vector
<
int
>>&
shape
)
const
std
::
shared_ptr
<
std
::
vector
<
int
>>&
shape
)
;
uint32_t
SummarySaveCallback
(
uint32_t
,
const
std
::
map
<
std
::
string
,
TensorPtr
>&
);
uint32_t
SummarySaveCallback
(
uint32_t
,
const
std
::
map
<
std
::
string
,
TensorPtr
>&
);
}
// namespace callbacks
}
// namespace callbacks
...
...
mindspore/nn/optim/rmsprop.py
浏览文件 @
5e9cfaf6
...
@@ -194,4 +194,4 @@ class RMSProp(Optimizer):
...
@@ -194,4 +194,4 @@ class RMSProp(Optimizer):
else
:
else
:
success
=
self
.
hyper_map
(
F
.
partial
(
rmsprop_opt
,
self
.
opt
,
lr
,
self
.
decay
,
self
.
epsilon
,
success
=
self
.
hyper_map
(
F
.
partial
(
rmsprop_opt
,
self
.
opt
,
lr
,
self
.
decay
,
self
.
epsilon
,
self
.
momentum
),
params
,
self
.
ms
,
self
.
moment
,
gradients
)
self
.
momentum
),
params
,
self
.
ms
,
self
.
moment
,
gradients
)
return
success
return
success
\ No newline at end of file
tests/st/networks/test_network_main.py
浏览文件 @
5e9cfaf6
...
@@ -12,11 +12,10 @@
...
@@ -12,11 +12,10 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
# ============================================================================
# ============================================================================
<<<<<<<
HEAD
:
tests
/
st
/
networks
/
test_network_main
.
py
"""
"""
Function:
Function:
test network
test network
Usage:
Usage:
python test_network_main.py --net lenet --target Ascend
python test_network_main.py --net lenet --target Ascend
"""
"""
import
os
import
os
...
@@ -32,47 +31,6 @@ from models.lenet import LeNet
...
@@ -32,47 +31,6 @@ from models.lenet import LeNet
from
models.resnetv1_5
import
resnet50
from
models.resnetv1_5
import
resnet50
from
models.alexnet
import
AlexNet
from
models.alexnet
import
AlexNet
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
"Ascend"
)
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
"Ascend"
)
=======
import
pytest
from
mindspore.nn
import
TrainOneStepCell
,
WithLossCell
import
mindspore.context
as
context
from
mindspore.nn.optim
import
Momentum
import
numpy
as
np
import
mindspore.nn
as
nn
from
mindspore.ops
import
operations
as
P
from
mindspore
import
Tensor
class
LeNet
(
nn
.
Cell
):
def
__init__
(
self
):
super
(
LeNet
,
self
).
__init__
()
self
.
relu
=
P
.
ReLU
()
self
.
batch_size
=
32
self
.
conv1
=
nn
.
Conv2d
(
1
,
6
,
kernel_size
=
5
,
stride
=
1
,
padding
=
0
,
has_bias
=
False
,
pad_mode
=
'valid'
)
self
.
conv2
=
nn
.
Conv2d
(
6
,
16
,
kernel_size
=
5
,
stride
=
1
,
padding
=
0
,
has_bias
=
False
,
pad_mode
=
'valid'
)
self
.
pool
=
nn
.
MaxPool2d
(
kernel_size
=
2
,
stride
=
2
)
self
.
reshape
=
P
.
Reshape
()
self
.
fc1
=
nn
.
Dense
(
400
,
120
)
self
.
fc2
=
nn
.
Dense
(
120
,
84
)
self
.
fc3
=
nn
.
Dense
(
84
,
10
)
def
construct
(
self
,
input_x
):
output
=
self
.
conv1
(
input_x
)
output
=
self
.
relu
(
output
)
output
=
self
.
pool
(
output
)
output
=
self
.
conv2
(
output
)
output
=
self
.
relu
(
output
)
output
=
self
.
pool
(
output
)
output
=
self
.
reshape
(
output
,
(
self
.
batch_size
,
-
1
))
output
=
self
.
fc1
(
output
)
output
=
self
.
relu
(
output
)
output
=
self
.
fc2
(
output
)
output
=
self
.
relu
(
output
)
output
=
self
.
fc3
(
output
)
return
output
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
"CPU"
)
>>>>>>>
add
cpu
st
lenet
:
tests
/
st
/
networks
/
test_cpu_lenet
.
py
def
train
(
net
,
data
,
label
):
def
train
(
net
,
data
,
label
):
learning_rate
=
0.01
learning_rate
=
0.01
...
@@ -89,24 +47,17 @@ def train(net, data, label):
...
@@ -89,24 +47,17 @@ def train(net, data, label):
print
(
"+++++++++++++++++++++++++++"
)
print
(
"+++++++++++++++++++++++++++"
)
assert
res
assert
res
<<<<<<<
HEAD
:
tests
/
st
/
networks
/
test_network_main
.
py
def
test_resnet50
():
def
test_resnet50
():
data
=
Tensor
(
np
.
ones
([
32
,
3
,
224
,
224
]).
astype
(
np
.
float32
)
*
0.01
)
data
=
Tensor
(
np
.
ones
([
32
,
3
,
224
,
224
]).
astype
(
np
.
float32
)
*
0.01
)
label
=
Tensor
(
np
.
ones
([
32
]).
astype
(
np
.
int32
))
label
=
Tensor
(
np
.
ones
([
32
]).
astype
(
np
.
int32
))
net
=
resnet50
(
32
,
10
)
net
=
resnet50
(
32
,
10
)
train
(
net
,
data
,
label
)
train
(
net
,
data
,
label
)
=======
@
pytest
.
mark
.
level0
@
pytest
.
mark
.
platform_x86_cpu
@
pytest
.
mark
.
env_onecard
>>>>>>>
add
cpu
st
lenet
:
tests
/
st
/
networks
/
test_cpu_lenet
.
py
def
test_lenet
():
def
test_lenet
():
data
=
Tensor
(
np
.
ones
([
32
,
1
,
32
,
32
]).
astype
(
np
.
float32
)
*
0.01
)
data
=
Tensor
(
np
.
ones
([
32
,
1
,
32
,
32
]).
astype
(
np
.
float32
)
*
0.01
)
label
=
Tensor
(
np
.
ones
([
32
]).
astype
(
np
.
int32
))
label
=
Tensor
(
np
.
ones
([
32
]).
astype
(
np
.
int32
))
net
=
LeNet
()
net
=
LeNet
()
train
(
net
,
data
,
label
)
train
(
net
,
data
,
label
)
<<<<<<<
HEAD
:
tests
/
st
/
networks
/
test_network_main
.
py
def
test_alexnet
():
def
test_alexnet
():
data
=
Tensor
(
np
.
ones
([
32
,
3
,
227
,
227
]).
astype
(
np
.
float32
)
*
0.01
)
data
=
Tensor
(
np
.
ones
([
32
,
3
,
227
,
227
]).
astype
(
np
.
float32
)
*
0.01
)
...
@@ -128,5 +79,3 @@ if __name__ == "__main__":
...
@@ -128,5 +79,3 @@ if __name__ == "__main__":
test_alexnet
()
test_alexnet
()
else
:
else
:
print
(
"Please add net name like --net lenet"
)
print
(
"Please add net name like --net lenet"
)
=======
>>>>>>>
add
cpu
st
lenet
:
tests
/
st
/
networks
/
test_cpu_lenet
.
py
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