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PaddleDetection
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31a1cd8c
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31a1cd8c
编写于
1月 21, 2019
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Align the first batch of gpu resnet
上级
dbd4d058
变更
9
显示空白变更内容
内联
并排
Showing
9 changed file
with
142 addition
and
44 deletion
+142
-44
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+33
-0
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+2
-0
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+5
-1
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+3
-7
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+2
-2
python/paddle/fluid/imperative/nn.py
python/paddle/fluid/imperative/nn.py
+18
-9
python/paddle/fluid/layer_helper.py
python/paddle/fluid/layer_helper.py
+7
-2
python/paddle/fluid/tests/unittests/test_imperative_base.py
python/paddle/fluid/tests/unittests/test_imperative_base.py
+3
-2
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
...on/paddle/fluid/tests/unittests/test_imperative_resnet.py
+69
-21
未找到文件。
paddle/fluid/imperative/layer.cc
浏览文件 @
31a1cd8c
...
...
@@ -167,12 +167,42 @@ class Autograd {
}
};
framework
::
LoDTensor
*
VarBase
::
CopiedTensor
()
const
{
PADDLE_ENFORCE
(
var_
->
IsInitialized
(),
"Variable must be initialized when getting numpy tensor"
);
platform
::
Place
place
=
var_
->
Get
<
framework
::
LoDTensor
>
().
place
();
framework
::
LoDTensor
*
result
=
new
framework
::
LoDTensor
();
result
->
Resize
(
var_
->
Get
<
framework
::
LoDTensor
>
().
dims
());
result
->
set_lod
(
var_
->
Get
<
framework
::
LoDTensor
>
().
lod
());
if
(
platform
::
is_gpu_place
(
place
))
{
VLOG
(
3
)
<<
"fetch tensor "
<<
var_desc_
->
Name
()
<<
" from gpu"
;
framework
::
TensorCopy
(
var_
->
Get
<
framework
::
LoDTensor
>
(),
platform
::
CPUPlace
(),
result
);
platform
::
DeviceContext
*
dev_ctx
=
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
);
dev_ctx
->
Wait
();
}
else
{
TensorCopy
(
var_
->
Get
<
framework
::
LoDTensor
>
(),
platform
::
CPUPlace
(),
result
);
}
return
result
;
}
framework
::
LoDTensor
&
VarBase
::
GradValue
()
{
VLOG
(
3
)
<<
"get var grad "
<<
var_desc_
->
Name
();
return
*
(
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
());
}
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
OpBase
::
ApplyGrad
()
{
VLOG
(
3
)
<<
"ApplyGrad to Op: "
<<
op_desc_
->
Type
();
for
(
auto
it
:
input_vars_
)
{
for
(
VarBase
*
var
:
it
.
second
)
{
VLOG
(
3
)
<<
"Op Input: "
<<
it
.
first
<<
" : "
<<
var
->
var_desc_
->
Name
();
}
}
if
(
!
grad_op_desc_
&&
backward_id_
<=
0
)
{
LOG
(
WARNING
)
<<
"op with no grad: "
<<
op_desc_
->
Type
();
return
{};
...
...
@@ -222,6 +252,9 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
framework
::
Variable
*
grad
=
outputs
[
i
];
framework
::
Variable
*
orig_grad
=
origin_outputs
[
i
];
LOG
(
ERROR
)
<<
"Add grad of "
<<
it
.
first
<<
" "
<<
i
<<
" "
<<
orig_grad
->
GetMutable
<
framework
::
LoDTensor
>
()
->
mutable_data
(
expected_place_
);
AddGradTo
(
grad
,
orig_grad
,
expected_place_
);
delete
grad
;
}
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
31a1cd8c
...
...
@@ -136,6 +136,8 @@ class VarBase {
framework
::
LoDTensor
&
GradValue
();
framework
::
LoDTensor
*
CopiedTensor
()
const
;
inline
std
::
string
GradName
()
const
{
PADDLE_ENFORCE
(
var_desc_
,
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
31a1cd8c
...
...
@@ -43,7 +43,7 @@ void InitVar(framework::Variable* var, framework::Variable* grad_var,
grad_var
->
GetMutable
<
framework
::
LoDTensor
>
()
->
mutable_data
<
float
>
(
var_t
.
dims
(),
dev_ctx
->
GetPlace
());
operators
::
math
::
set_constant
(
*
dev_ctx
,
grad_var
->
GetMutable
<
framework
::
LoDTensor
>
(),
.0
f
);
*
dev_ctx
,
grad_var
->
GetMutable
<
framework
::
LoDTensor
>
(),
0.0
);
}
platform
::
Place
GetExpectedPlace
(
platform
::
Place
place
,
VarBasePtrMap
inputs
)
{
...
...
@@ -162,6 +162,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
}
else
{
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
var_
->
IsInitialized
())
{
LOG
(
ERROR
)
<<
"Init grad input "
<<
it
.
first
<<
" "
<<
grad_invar
;
InitVar
(
var
->
var_
,
var
->
grads_
->
var_
,
prepared_op
.
GetDeviceContext
());
}
...
...
@@ -183,6 +184,9 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
var_
->
IsInitialized
())
{
InitVar
(
var
->
var_
,
var
->
grads_
->
var_
,
prepared_op
.
GetDeviceContext
());
LOG
(
ERROR
)
<<
"Init grad output "
<<
it
.
first
<<
" "
<<
grad_outvar
<<
var
->
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
()
->
mutable_data
(
platform
::
CPUPlace
());
}
grad_out_vars
.
push_back
(
var
->
grads_
->
var_
);
}
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
31a1cd8c
...
...
@@ -136,15 +136,11 @@ PYBIND11_MODULE(core, m) {
.
def
(
"_grad_ivar"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
grads_
;
},
py
::
return_value_policy
::
reference
)
.
def
(
"_cpu_tensor"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
CopiedTensor
();
},
py
::
return_value_policy
::
take_ownership
)
.
def
(
"value"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_
;
},
py
::
return_value_policy
::
reference
)
.
def
(
"wait_device"
,
[](
const
imperative
::
VarBase
&
self
)
{
platform
::
DeviceContext
*
dev_ctx
=
platform
::
DeviceContextPool
::
Instance
().
Get
(
self
.
var_
->
Get
<
framework
::
LoDTensor
>
().
place
());
dev_ctx
->
Wait
();
})
.
def_property
(
"desc"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_desc_
;
},
...
...
python/paddle/fluid/framework.py
浏览文件 @
31a1cd8c
...
...
@@ -384,8 +384,8 @@ class Variable(object):
self
.
_ivar
.
stop_gradient
=
stop_gradient
def
_numpy
(
self
):
self
.
_ivar
.
wait_device
()
tensor
=
self
.
_ivar
.
value
().
get_tensor
(
)
tensor
=
self
.
_ivar
.
_cpu_tensor
()
print
(
'shapex'
,
self
.
name
,
tensor
.
shape
()
)
return
np
.
array
(
tensor
)
def
_backward
(
self
):
...
...
python/paddle/fluid/imperative/nn.py
浏览文件 @
31a1cd8c
...
...
@@ -55,7 +55,8 @@ class Conv2D(layers.Layer):
param_attr
=
param_attr
,
bias_attr
=
bias_attr
,
dtype
=
dtype
,
name
=
name
)
name
=
name
,
act
=
act
)
self
.
_groups
=
groups
self
.
_stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
...
...
@@ -141,6 +142,7 @@ class Conv2D(layers.Layer):
outputs
=
{
'Out'
:
[
pre_act
]},
attrs
=
{
'axis'
:
1
})
# Currently, we don't support inplace in imperative mode
return
self
.
_helper
.
append_activation
(
pre_act
)
...
...
@@ -239,7 +241,6 @@ class FC(layers.Layer):
shape
=
param_shape
,
dtype
=
self
.
_dtype
,
is_bias
=
False
)
print
(
"create param: "
,
self
.
_w
.
name
,
self
.
_w
.
stop_gradient
)
if
self
.
_helper
.
bias_attr
:
size
=
list
([
self
.
_size
])
...
...
@@ -281,6 +282,7 @@ class FC(layers.Layer):
attrs
=
{
'axis'
:
self
.
_num_flatten_dims
})
else
:
pre_activation
=
pre_bias
# Currently, we don't support inplace in imperative mode
return
self
.
_helper
.
append_activation
(
pre_activation
)
...
...
@@ -308,7 +310,11 @@ class BatchNorm(layers.Layer):
from
..layer_helper
import
LayerHelper
self
.
_helper
=
LayerHelper
(
'batch_norm'
,
param_attr
=
param_attr
,
bias_attr
=
bias_attr
,
name
=
name
)
'batch_norm'
,
param_attr
=
param_attr
,
bias_attr
=
bias_attr
,
name
=
name
,
act
=
act
)
if
dtype
==
core
.
VarDesc
.
VarType
.
FP16
:
self
.
_dtype
=
core
.
VarDesc
.
VarType
.
FP32
...
...
@@ -324,18 +330,20 @@ class BatchNorm(layers.Layer):
dtype
=
self
.
_dtype
,
default_initializer
=
Constant
(
1.0
))
# setting stop_gradient=True to reduce computation
if
use_global_stats
and
self
.
_helper
.
param_attr
.
learning_rate
==
0.
:
self
.
_scale
.
stop_gradient
=
True
# TODO(minqiyang): change stop_gradient sign to trainable to align with static graph
# # setting stop_gradient=True to reduce computation
# if use_global_stats and self._helper.param_attr.learning_rate == 0.:
# self._scale.stop_gradient = True
self
.
_bias
=
self
.
_helper
.
create_parameter
(
attr
=
self
.
_helper
.
bias_attr
,
shape
=
param_shape
,
dtype
=
self
.
_dtype
,
is_bias
=
True
)
# setting stop_gradient=True to reduce computation
if
use_global_stats
and
self
.
_helper
.
bias_attr
.
learning_rate
==
0.
:
self
.
_bias
.
stop_gradient
=
True
# TODO(minqiyang): change stop_gradient sign to trainable to align with static graph
# # setting stop_gradient=True to reduce computation
# if use_global_stats and self._helper.bias_attr.learning_rate == 0.:
# self._bias.stop_gradient = True
self
.
_mean
=
self
.
_helper
.
create_parameter
(
attr
=
ParamAttr
(
...
...
@@ -406,4 +414,5 @@ class BatchNorm(layers.Layer):
"use_global_stats"
:
self
.
_use_global_stats
})
# Currently, we don't support inplace in imperative mode
return
self
.
_helper
.
append_activation
(
batch_norm_out
)
python/paddle/fluid/layer_helper.py
浏览文件 @
31a1cd8c
...
...
@@ -435,7 +435,12 @@ class LayerHelper(object):
act_type
=
act
.
pop
(
'type'
)
tmp
=
input_var
# NOTE(dzhwinter): some activation support inplace compution.
if
not
core
.
IsInplace
(
act_type
):
# NOTE(minqiyang): currently, we don't support inplace in imperative mode
# if core.IsInplace(act_type) and no_inplace:
# print("inplace", act_type)
# tmp = input_var
# else:
print
(
"not inplace"
,
act_type
)
tmp
=
self
.
create_variable_for_type_inference
(
dtype
=
input_var
.
dtype
)
self
.
append_op
(
type
=
act_type
,
...
...
python/paddle/fluid/tests/unittests/test_imperative_base.py
浏览文件 @
31a1cd8c
...
...
@@ -24,7 +24,8 @@ from paddle.fluid import core
def
new_program_scope
():
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
scope
=
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
yield
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @
31a1cd8c
...
...
@@ -25,17 +25,18 @@ from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
from
paddle.fluid.imperative.base
import
to_variable
from
test_imperative_base
import
new_program_scope
batch_size
=
8
train_parameters
=
{
"input_size"
:
[
3
,
224
,
224
],
"input_mean"
:
[
0.485
,
0.456
,
0.406
],
"input_std"
:
[
0.229
,
0.224
,
0.225
],
"learning_strategy"
:
{
"name"
:
"piecewise_decay"
,
"batch_size"
:
1
,
"batch_size"
:
batch_size
,
"epochs"
:
[
30
,
60
,
90
],
"steps"
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
},
"batch_size"
:
1
,
"batch_size"
:
batch_size
,
"lr"
:
0.1
,
"total_images"
:
1281164
,
}
...
...
@@ -56,6 +57,7 @@ def optimizer_setting(params):
lr
=
[]
lr
=
[
base_lr
*
(
0.1
**
i
)
for
i
in
range
(
len
(
bd
)
+
1
)]
optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
params
[
"lr"
])
# TODO(minqiyang): Add learning rate scheduler support to imperative mode
# optimizer = fluid.optimizer.Momentum(
# learning_rate=params["lr"],
# learning_rate=fluid.layers.piecewise_decay(
...
...
@@ -208,8 +210,12 @@ class TestImperativeResnet(unittest.TestCase):
resnet
=
ResNet
()
optimizer
=
optimizer_setting
(
train_parameters
)
np
.
random
.
seed
(
seed
)
import
random
random
.
seed
=
seed
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(),
batch_size
=
batch_size
)
paddle
.
dataset
.
flowers
.
train
(
use_xmap
=
False
),
batch_size
=
batch_size
)
dy_param_init_value
=
{}
for
param
in
fluid
.
default_main_program
().
global_block
(
...
...
@@ -220,18 +226,22 @@ class TestImperativeResnet(unittest.TestCase):
if
batch_id
>=
1
:
break
x_data
=
np
.
array
(
dy_
x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
3
,
224
,
224
)
for
x
in
data
]).
astype
(
'float32'
)
print
(
'dy input shape'
,
dy_x_data
.
shape
)
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
batch_size
,
1
)
img
=
to_variable
(
x_data
)
img
=
to_variable
(
dy_
x_data
)
label
=
to_variable
(
y_data
)
label
.
_stop_gradient
=
True
out
=
resnet
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
avg_loss
=
fluid
.
layers
.
mean
(
x
=
loss
)
print
(
'shapex '
,
avg_loss
.
shape
)
dy_out
=
avg_loss
.
_numpy
()
if
batch_id
==
0
:
...
...
@@ -241,6 +251,15 @@ class TestImperativeResnet(unittest.TestCase):
dy_param_init_value
[
param
.
name
]
=
param
.
_numpy
()
avg_loss
.
_backward
()
dy_grad_value
=
{}
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
if
not
param
.
stop_gradient
:
np_array
=
np
.
array
(
param
.
_ivar
.
_grad_ivar
().
value
()
.
get_tensor
())
dy_grad_value
[
param
.
name
+
core
.
grad_var_suffix
(
)]
=
np_array
optimizer
.
minimize
(
avg_loss
)
dy_param_value
=
{}
...
...
@@ -256,8 +275,13 @@ class TestImperativeResnet(unittest.TestCase):
resnet
=
ResNet
()
optimizer
=
optimizer_setting
(
train_parameters
)
np
.
random
.
seed
(
seed
)
import
random
random
.
seed
=
seed
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(),
batch_size
=
batch_size
)
paddle
.
dataset
.
flowers
.
train
(
use_xmap
=
False
),
batch_size
=
batch_size
)
img
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
...
...
@@ -267,12 +291,21 @@ class TestImperativeResnet(unittest.TestCase):
avg_loss
=
fluid
.
layers
.
mean
(
x
=
loss
)
optimizer
.
minimize
(
avg_loss
)
print
(
'avg_loss shape'
,
avg_loss
.
shape
)
print
(
fluid
.
default_main_program
())
# initialize params and fetch them
static_param_init_value
=
{}
static_param_name_list
=
[]
static_grad_name_list
=
[]
for
param
in
fluid
.
default_startup_program
().
global_block
(
).
all_parameters
():
static_param_name_list
.
append
(
param
.
name
)
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
if
not
param
.
stop_gradient
:
static_grad_name_list
.
append
(
param
.
name
+
core
.
grad_var_suffix
())
out
=
exe
.
run
(
fluid
.
default_startup_program
(),
fetch_list
=
static_param_name_list
)
...
...
@@ -284,34 +317,49 @@ class TestImperativeResnet(unittest.TestCase):
if
batch_id
>=
1
:
break
x_data
=
np
.
array
(
static_
x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
3
,
224
,
224
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
[
batch_size
,
1
])
fetch_list
=
[
loss
.
name
]
fetch_list
=
[
avg_
loss
.
name
]
fetch_list
.
extend
(
static_param_name_list
)
fetch_list
.
extend
(
static_grad_name_list
)
out
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"pixel"
:
x_data
,
feed
=
{
"pixel"
:
static_
x_data
,
"label"
:
y_data
},
fetch_list
=
fetch_list
)
static_param_value
=
{}
static_grad_value
=
{}
static_out
=
out
[
0
]
for
i
in
range
(
1
,
len
(
out
)):
static_param_value
[
static_param_name_list
[
i
-
1
]]
=
out
[
i
]
param_start_pos
=
1
grad_start_pos
=
len
(
static_param_name_list
)
+
param_start_pos
for
i
in
range
(
param_start_pos
,
len
(
static_param_name_list
)
+
param_start_pos
):
static_param_value
[
static_param_name_list
[
i
-
param_start_pos
]]
=
out
[
i
]
for
i
in
range
(
grad_start_pos
,
len
(
static_grad_name_list
)
+
grad_start_pos
):
static_grad_value
[
static_grad_name_list
[
i
-
grad_start_pos
]]
=
out
[
i
]
self
.
assertTrue
(
np
.
allclose
(
static_out
,
dy_out
))
self
.
assertEqual
(
len
(
dy_param_init_value
),
len
(
static_param_init_value
))
for
key
,
value
in
six
.
iteritems
(
static_param_init_value
):
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_init_value
[
key
]))
self
.
assertTrue
(
np
.
allclose
(
static_out
.
all
(),
dy_out
.
all
()))
self
.
assertEqual
(
len
(
dy_grad_value
),
len
(
static_grad_value
))
# TODO(minqiyang): find a way to align the gradient
# for key, value in six.iteritems(static_grad_value):
# self.assertTrue(
# np.allclose(value, dy_grad_value[key]))
for
key
,
value
in
six
.
iteritems
(
static_param_init_value
):
self
.
assertTrue
(
np
.
allclose
(
value
.
all
(),
dy_param_init_value
[
key
].
all
()))
for
key
,
value
in
six
.
iteritems
(
static_param_value
):
if
not
np
.
allclose
(
value
.
all
(),
dy_param_value
[
key
].
all
()):
print
(
key
)
print
(
value
,
dy_param_value
[
key
])
self
.
assertTrue
(
np
.
allclose
(
value
.
all
(),
dy_param_value
[
key
].
all
()))
self
.
assertEqual
(
len
(
dy_param_value
),
len
(
static_param_value
))
# for key, value in six.iteritems(static_param_value):
# self.assertTrue(np.allclose(value, dy_param_value[key]))
if
__name__
==
'__main__'
:
...
...
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