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体验新版 GitCode,发现更多精彩内容 >>
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76ba55e8
编写于
10月 07, 2019
作者:
L
lvmengsi
提交者:
GitHub
10月 07, 2019
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电子邮件补丁
差异文件
add error log for python api and c++ (#20061)
* add error log
上级
01ad8d2e
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
255 addition
and
35 deletion
+255
-35
paddle/fluid/operators/batch_norm_op.cc
paddle/fluid/operators/batch_norm_op.cc
+30
-6
paddle/fluid/operators/conv_op.cc
paddle/fluid/operators/conv_op.cc
+34
-10
paddle/fluid/operators/cross_entropy_op.cc
paddle/fluid/operators/cross_entropy_op.cc
+40
-14
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+58
-4
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
+16
-0
python/paddle/fluid/tests/unittests/test_conv2d_op.py
python/paddle/fluid/tests/unittests/test_conv2d_op.py
+24
-1
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
+29
-0
python/paddle/fluid/tests/unittests/test_dropout_op.py
python/paddle/fluid/tests/unittests/test_dropout_op.py
+24
-0
未找到文件。
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @
76ba55e8
...
...
@@ -58,8 +58,18 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
const
DataLayout
data_layout
=
framework
::
StringToDataLayout
(
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"data_layout"
));
PADDLE_ENFORCE
(
x_dims
.
size
()
>=
2
&&
x_dims
.
size
()
<=
5
,
"Input X must have 2 to 5 dimensions."
);
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
"ShapeError: the dimension of input X must greater than or equal to 2."
"But received: the shape of input X = [%s], the dimension of input X ="
"[%d]"
,
x_dims
,
x_dims
.
size
());
PADDLE_ENFORCE_LE
(
x_dims
.
size
(),
5
,
"ShapeError: the dimension of input X must smaller than or equal to 5."
"But received: the shape of input X = [%s], the dimension of input X ="
"[%d]"
,
x_dims
,
x_dims
.
size
());
const
int64_t
C
=
(
data_layout
==
DataLayout
::
kNCHW
?
x_dims
[
1
]
...
...
@@ -68,8 +78,16 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
auto
scale_dim
=
ctx
->
GetInputDim
(
"Scale"
);
auto
bias_dim
=
ctx
->
GetInputDim
(
"Bias"
);
PADDLE_ENFORCE_EQ
(
scale_dim
.
size
(),
1UL
);
PADDLE_ENFORCE_EQ
(
scale_dim
.
size
(),
1UL
);
PADDLE_ENFORCE_EQ
(
scale_dim
.
size
(),
1UL
,
"ShapeError: the dimension of scale must equal to 1."
"But received: the shape of scale is [%s], the dimension "
"of scale is [%d]"
,
scale_dim
,
scale_dim
.
size
());
PADDLE_ENFORCE_EQ
(
bias_dim
.
size
(),
1UL
,
"ShapeError: the dimension of bias must equal to 1."
"But received: the shape of bias is [%s],the dimension of bias is [%d]"
,
bias_dim
,
bias_dim
.
size
());
bool
check
=
true
;
if
((
!
ctx
->
IsRuntime
())
&&
(
framework
::
product
(
scale_dim
)
<=
0
||
...
...
@@ -78,8 +96,14 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
}
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
scale_dim
[
0
],
C
);
PADDLE_ENFORCE_EQ
(
scale_dim
[
0
],
C
);
PADDLE_ENFORCE_EQ
(
scale_dim
[
0
],
C
,
"ShapeError: the shape of scale must equal to [%d]"
"But received: the shape of scale is [%d]"
,
C
,
scale_dim
[
0
]);
PADDLE_ENFORCE_EQ
(
bias_dim
[
0
],
C
,
"ShapeError: the shape of bias must equal to [%d]"
"But received: the shape of bias is [%d]"
,
C
,
bias_dim
[
0
]);
}
ctx
->
SetOutputDim
(
"Y"
,
x_dims
);
ctx
->
SetOutputDim
(
"MeanOut"
,
{
C
});
...
...
paddle/fluid/operators/conv_op.cc
浏览文件 @
76ba55e8
...
...
@@ -51,25 +51,49 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
const
bool
channel_last
=
(
data_format
==
"NHWC"
||
data_format
==
"NDHWC"
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
true
,
"Conv intput should be 4-D or 5-D tensor, get %u"
,
in_dims
.
size
());
"ShapeError: Conv input should be 4-D or 5-D tensor. But "
"received: %u-D Tensor,"
"the shape of Conv input is [%s]"
,
in_dims
.
size
(),
in_dims
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
filter_dims
.
size
(),
"Conv input dimension and filter dimension should be the same."
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
()
-
strides
.
size
()
==
2U
,
true
,
"Conv input dimension and strides dimension should be consistent."
);
"ShapeError: Conv input dimension and filter dimension should be the "
"equal."
"But received: the shape of Conv input is [%s], input dimension of Conv "
"input is [%d],"
"the shape of filter is [%s], the filter dimension of Conv is [%d]"
,
in_dims
,
in_dims
.
size
(),
filter_dims
,
filter_dims
.
size
());
int
in_sub_stride_size
=
in_dims
.
size
()
-
strides
.
size
();
PADDLE_ENFORCE_EQ
(
in_dims
.
size
()
-
strides
.
size
()
==
2U
,
true
,
"ShapeError: the dimension of input minus the dimension of "
"stride must be euqal to 2."
"But received: the dimension of input minus the dimension "
"of stride is [%d], the"
"input dimension of Conv is [%d], the shape of Conv input "
"is [%s], the stride"
"dimension of Conv is [%d]"
,
in_sub_stride_size
,
in_dims
.
size
(),
in_dims
,
strides
.
size
());
const
auto
input_channels
=
channel_last
?
in_dims
[
in_dims
.
size
()
-
1
]
:
in_dims
[
1
];
PADDLE_ENFORCE_EQ
(
input_channels
,
filter_dims
[
1
]
*
groups
,
"The number of input channels should be equal to filter "
"channels * groups."
);
PADDLE_ENFORCE_EQ
(
input_channels
,
filter_dims
[
1
]
*
groups
,
"ShapeError: The number of input channels should be equal to filter "
"channels * groups. But received: the input channels is [%d], the shape"
"of input is [%s], the filter channel is [%d], the shape of filter is "
"[%s],"
"the groups is [%d]"
,
in_dims
[
1
],
in_dims
,
filter_dims
[
1
],
filter_dims
,
groups
);
PADDLE_ENFORCE_EQ
(
filter_dims
[
0
]
%
groups
,
0
,
"The number of output channels should be divided by groups."
);
"ShapeError: The number of output channels should be divided by groups."
"But received: the output channels is [%d], the shape of filter is [%s]"
"(the first dimension of filter is output channel), the groups is [%d]"
,
filter_dims
[
0
],
filter_dims
,
groups
);
framework
::
DDim
in_data_dims
;
if
(
channel_last
)
{
...
...
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @
76ba55e8
...
...
@@ -41,30 +41,56 @@ class CrossEntropyOpBase : public framework::OperatorWithKernel {
bool
check
=
ctx
->
IsRuntime
()
||
!
contain_unknown_dim
;
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
"ShapeError: Input(X) and Input(Label) shall have the same shape "
"except the last dimension. But received: the shape of Input(X) is "
"[%s],"
"the shape of Input(Label) is [%s]."
,
x_dims
,
label_dims
);
}
if
(
IsSoftLabel
(
ctx
))
{
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
(),
"If Attr(soft_label) == true, Input(X) and Input(Label) "
"shall have the same rank."
);
"ShapeError: If Attr(soft_label) == true, Input(X) and Input(Label) "
"shall have the same dimensions. But received: the dimensions of "
"Input(X) is [%d],"
"the shape of Input(X) is [%s], the dimensions of Input(Label) is "
"[%d], the shape of"
"Input(Label) is [%s]"
,
rank
,
x_dims
,
label_dims
.
size
(),
label_dims
);
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
x_dims
[
rank
-
1
],
label_dims
[
rank
-
1
],
"If Attr(soft_label) == true, the last dimension of "
"Input(X) and Input(Label) should be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
rank
-
1
],
label_dims
[
rank
-
1
],
"ShapeError: If Attr(soft_label) == true, the last dimension of "
"Input(X) and Input(Label) should be equal. But received: the"
"last dimension of Input(X) is [%d], the shape of Input(X) is [%s],"
"the last dimension of Input(Label) is [%d], the shape of "
"Input(Label)"
"is [%s], the last dimension is [%d]."
,
x_dims
[
rank
-
1
],
x_dims
,
label_dims
[
rank
-
1
],
label_dims
,
rank
-
1
);
}
}
else
{
if
(
rank
==
label_dims
.
size
())
{
PADDLE_ENFORCE_EQ
(
label_dims
[
rank
-
1
],
1UL
,
"the last dimension of Input(Label) should be 1."
);
}
else
{
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
()
+
1
,
"The rank of Input(X) should be equal to Input(Label) plus 1."
);
label_dims
[
rank
-
1
],
1UL
,
"ShapeError: the last dimension of Input(Label) should be 1."
"But received: the last dimension of Input(Label) is [%d],"
"the last dimension is [%d]"
,
label_dims
[
rank
-
1
],
rank
-
1
);
}
else
{
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
()
+
1
,
"ShapeError: The rank of Input(X) should be equal to "
"Input(Label) plus 1."
"But received: The dimension of Input(X) is [%d], "
"the shape of Input(X) is [%s],"
"the dimension of Input(Label) is [%d], the shape of "
"Input(Label) is [%s]"
,
rank
,
x_dims
,
label_dims
.
size
(),
label_dims
);
}
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
76ba55e8
...
...
@@ -1677,6 +1677,20 @@ def dropout(x,
"""
helper = LayerHelper('dropout', **locals())
if not isinstance(x, Variable):
raise TypeError(
"The type of 'input' in dropout must be Variable, but received %s" %
(type(x)))
if convert_dtype(x.dtype) in ['float16']:
warnings.warn(
"The data type of 'input' in dropout only support float16 on GPU now."
)
if convert_dtype(x.dtype) not in ['float16', 'float32', 'float64']:
raise TypeError(
"The data type of 'input' in dropout must be float16 or float32 or float64, but received %s."
% (convert_dtype(x.dtype)))
out = helper.create_variable_for_type_inference(dtype=x.dtype)
mask = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.UINT8, stop_gradient=True)
...
...
@@ -1749,6 +1763,19 @@ def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
predict = fluid.layers.fc(input=x, size=class_num, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
"""
if not isinstance(input, Variable):
raise TypeError(
"The type of 'input' in cross_entropy must be Variable, but received %s"
% (type(input)))
if convert_dtype(input.dtype) in ['float16']:
warnings.warn(
"The data type of 'input' in cross_entropy only support float16 on GPU now."
)
if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
raise TypeError(
"The data type of 'input' in cross_entropy must be float16 or float32 or float64, but received %s."
% (convert_dtype(input.dtype)))
if not soft_label:
return cross_entropy2(input, label, ignore_index)
helper = LayerHelper('cross_entropy', **locals())
...
...
@@ -2397,6 +2424,20 @@ def conv2d(input,
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
if not isinstance(input, Variable):
raise TypeError(
"The type of 'input' in conv2d must be Variable, but received %s" %
(type(input)))
if convert_dtype(input.dtype) in ['float16']:
warnings.warn(
"The data type of 'input' in conv2d only support float16 on GPU now."
)
if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
raise TypeError(
"The data type of 'input' in conv2d must be float16 or float32 or float64, but received %s."
% (convert_dtype(input.dtype)))
num_channels = input.shape[1]
if not isinstance(use_cudnn, bool):
raise ValueError("Attr(use_cudnn) should be True or False. Received "
"Attr(use_cudnn): %s. " % str(use_cudnn))
...
...
@@ -2427,9 +2468,9 @@ def conv2d(input,
else:
if num_channels % groups != 0:
raise ValueError(
"
The number of input channels must be divisible by Attr(groups).
"
"
Received: number of channels(%s), groups(%s)." %
(str(num_channels), str(groups)
))
"
the channel of input must be divisible by groups,
"
"
received: the channel of input is {}, the shape of input is {}"
", the groups is {}".format(num_channels, input.shape, groups
))
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
...
...
@@ -3740,8 +3781,21 @@ def batch_norm(input,
"""
assert bias_attr is not False, "bias_attr should not be False in batch_norm."
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
if not isinstance(input, Variable):
raise TypeError(
"The type of 'input' in batch_norm must be Variable, but received %s"
% (type(input)))
if convert_dtype(input.dtype) in ['float16']:
warnings.warn(
"The data type of 'input' in batch_norm only support float16 on GPU now."
)
if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
raise TypeError(
"The data type of 'input' in batch_norm must be float16 or float32 or float64, but received %s."
% (convert_dtype(input.dtype)))
dtype = helper.input_dtype()
# use fp32 for bn parameter
if dtype == core.VarDesc.VarType.FP16:
dtype = core.VarDesc.VarType.FP32
...
...
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
浏览文件 @
76ba55e8
...
...
@@ -21,6 +21,8 @@ from paddle.fluid.op import Operator
import
paddle.fluid
as
fluid
from
op_test
import
OpTest
from
paddle.fluid.framework
import
grad_var_name
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
def
_reference_testing
(
x
,
scale
,
offset
,
mean
,
var
,
epsilon
,
data_format
):
...
...
@@ -530,5 +532,19 @@ class TestBatchNormOpFreezeStatsAndScaleBiasTraining(
self
.
fetch_list
=
[
'y'
,
'mean'
,
'variance'
,
'x@GRAD'
]
class
TestBatchNormOpError
(
OpTest
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
# the input of batch_norm must be Variable.
x1
=
fluid
.
create_lod_tensor
(
np
.
array
([
-
1
,
3
,
5
,
5
]),
[[
1
,
1
,
1
,
1
]],
fluid
.
CPUPlace
())
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
batch_norm
,
x1
)
# the input dtype of batch_norm must be float16 or float32 or float64
# float16 only can be set on GPU place
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
3
,
4
,
5
,
6
],
dtype
=
"int32"
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
batch_norm
,
x2
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @
76ba55e8
...
...
@@ -18,8 +18,9 @@ import unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
import
paddle.fluid
as
fluid
from
op_test
import
OpTest
from
paddle.fluid
import
Program
,
program_guard
def
conv2d_forward_naive
(
input
,
...
...
@@ -647,6 +648,28 @@ class TestCUDNNExhaustiveSearch(TestConv2dOp):
self
.
exhaustive_search
=
True
class
TestConv2dOpError
(
OpTest
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
def
test_Variable
():
# the input of conv2d must be Variable.
x1
=
fluid
.
create_lod_tensor
(
np
.
array
([
-
1
,
3
,
5
,
5
]),
[[
1
,
1
,
1
,
1
]],
fluid
.
CPUPlace
())
fluid
.
layers
.
conv2d
(
x1
,
1
,
1
)
self
.
assertRaises
(
TypeError
,
test_Variable
)
def
test_dtype
():
# the input dtype of conv2d must be float16 or float32 or float64
# float16 only can be set on GPU place
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
3
,
4
,
5
,
6
],
dtype
=
"int32"
)
fluid
.
layers
.
conv2d
(
x2
,
1
,
1
)
self
.
assertRaises
(
TypeError
,
test_dtype
)
# Please Don't remove the following code.
# Currently, CI use cudnn V5.0 which not support dilation conv.
# class TestCUDNNWithDilation(TestWithDilation):
...
...
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
浏览文件 @
76ba55e8
...
...
@@ -18,6 +18,8 @@ import unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
,
randomize_probability
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
class
TestCrossEntropyOp
(
OpTest
):
...
...
@@ -356,5 +358,32 @@ create_test_class(TestCrossEntropyOp7, "TestCrossEntropyF16Op7")
create_test_class
(
TestCrossEntropyOp7RemoveLastDim
,
"TestCrossEntropyF16Op7RemoveLastDim"
)
class
TestCrossEntropyOpError
(
OpTest
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
def
test_Variable
():
# the input of cross_entropy must be Variable.
x1
=
fluid
.
create_lod_tensor
(
np
.
array
([
-
1
,
3
,
5
,
5
]),
[[
1
,
1
,
1
,
1
]],
fluid
.
CPUPlace
())
lab1
=
fluid
.
create_lod_tensor
(
np
.
array
([
-
1
,
3
,
5
,
5
]),
[[
1
,
1
,
1
,
1
]],
fluid
.
CPUPlace
())
fluid
.
layers
.
cross_entropy
(
x1
,
lab1
)
self
.
assertRaises
(
TypeError
,
test_Variable
)
def
test_dtype
():
# the input dtype of cross_entropy must be float16 or float32 or float64
# float16 only can be set on GPU place
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
3
,
4
,
5
,
6
],
dtype
=
"int32"
)
lab2
=
fluid
.
layers
.
data
(
name
=
'lab2'
,
shape
=
[
3
,
4
,
5
,
6
],
dtype
=
"int32"
)
fluid
.
layers
.
cross_entropy
(
x2
,
lab2
)
self
.
assertRaises
(
TypeError
,
test_dtype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_dropout_op.py
浏览文件 @
76ba55e8
...
...
@@ -18,6 +18,8 @@ import unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
class
TestDropoutOp
(
OpTest
):
...
...
@@ -180,5 +182,27 @@ class TestFP16DropoutOp2(TestFP16DropoutOp):
self
.
fix_seed
=
False
class
TestDropoutOpError
(
OpTest
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
def
test_Variable
():
# the input of dropout must be Variable.
x1
=
fluid
.
create_lod_tensor
(
np
.
array
([
-
1
,
3
,
5
,
5
]),
[[
1
,
1
,
1
,
1
]],
fluid
.
CPUPlace
())
fluid
.
layers
.
dropout
(
x1
,
dropout_prob
=
0.5
)
self
.
assertRaises
(
TypeError
,
test_Variable
)
def
test_dtype
():
# the input dtype of dropout must be float16 or float32 or float64
# float16 only can be set on GPU place
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
3
,
4
,
5
,
6
],
dtype
=
"int32"
)
fluid
.
layers
.
dropout
(
x2
,
dropout_prob
=
0.5
)
self
.
assertRaises
(
TypeError
,
test_dtype
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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