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体验新版 GitCode,发现更多精彩内容 >>
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baa1f663
编写于
11月 23, 2022
作者:
V
Vvsmile
提交者:
GitHub
11月 23, 2022
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差异文件
Remove API: mean_iou (#47971)
remove mean_iou which is not used in paddle 2.0
上级
b7d3143f
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
0 addition
and
93 deletion
+0
-93
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+0
-68
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+0
-7
python/paddle/fluid/tests/unittests/test_mean_iou.py
python/paddle/fluid/tests/unittests/test_mean_iou.py
+0
-18
未找到文件。
python/paddle/fluid/layers/nn.py
浏览文件 @
baa1f663
...
...
@@ -114,7 +114,6 @@ __all__ = [
'gather_nd'
,
'scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'log'
,
'crop_tensor'
,
...
...
@@ -7626,73 +7625,6 @@ def relu(x, name=None):
return
out
def
mean_iou
(
input
,
label
,
num_classes
):
r
"""
Mean Intersection-Over-Union is a common evaluation metric for
semantic image segmentation, which first computes the IOU for each
semantic class and then computes the average over classes.
IOU is defined as follows:
.. math::
IOU = \\frac{true\_positive}{(true\_positive + false\_positive + false\_negative)}.
The predictions are accumulated in a confusion matrix and mean-IOU
is then calculated from it.
Parameters:
input (Tensor): A n-D Tensor of prediction results for semantic labels with type int32 or int64.
label (Tensor): A Tensor of ground truth labels with type int32 or int64.
Its shape should be the same as input.
num_classes (int32): The possible number of labels.
Returns:
Three Tensors.
- mean_iou(Tensor) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \
Data type is float32.
- out_wrong(Tensor) : A 1-D Tensor with shape [num_classes]. Data type is int32. \
The wrong numbers of each class.
- out_correct(Tensor): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class.
Examples:
.. code-block:: python
import paddle
iou_shape = [64, 32, 32]
num_classes = 5
predict = paddle.randint(low=0, high=255, shape=iou_shape, dtype='int64')
label = paddle.randint(low=0, high=255, shape=iou_shape, dtype='int64')
mean_iou, out_wrong, out_correct = paddle.metric.mean_iou(predict, label, num_classes)
"""
if
_non_static_mode
():
return
_legacy_C_ops
.
mean_iou
(
input
,
label
,
'num_classes'
,
num_classes
)
helper
=
LayerHelper
(
'mean_iou'
,
**
locals
())
check_variable_and_dtype
(
input
,
'Predictions'
,
[
'int32'
,
'int64'
],
'mean_iou'
)
check_variable_and_dtype
(
label
,
'Labels'
,
[
'int32'
,
'int64'
],
'mean_iou'
)
out_mean_iou
=
helper
.
create_variable_for_type_inference
(
dtype
=
'float32'
)
out_wrong
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
out_correct
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
helper
.
append_op
(
type
=
"mean_iou"
,
inputs
=
{
"Predictions"
:
input
,
"Labels"
:
label
},
outputs
=
{
"OutMeanIou"
:
out_mean_iou
,
"OutWrong"
:
out_wrong
,
"OutCorrect"
:
out_correct
,
},
attrs
=
{
"num_classes"
:
num_classes
},
)
return
out_mean_iou
,
out_wrong
,
out_correct
def
crop_tensor
(
x
,
shape
=
None
,
offsets
=
None
,
name
=
None
):
"""
Crop input into output, as specified by offsets and shape.
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
baa1f663
...
...
@@ -3476,13 +3476,6 @@ class TestBook(LayerTest):
output
=
layers
.
l2_normalize
(
x
,
axis
=
1
)
return
output
def
make_mean_iou
(
self
):
with
fluid
.
framework
.
_dygraph_place_guard
(
place
=
fluid
.
CPUPlace
()):
x
=
self
.
_get_data
(
name
=
'x'
,
shape
=
[
16
],
dtype
=
'int32'
)
y
=
self
.
_get_data
(
name
=
'label'
,
shape
=
[
16
],
dtype
=
'int32'
)
iou
=
layers
.
mean_iou
(
x
,
y
,
self
.
_high_data_bound
)
return
iou
def
make_argsort
(
self
):
with
program_guard
(
fluid
.
default_main_program
(),
fluid
.
default_startup_program
()
...
...
python/paddle/fluid/tests/unittests/test_mean_iou.py
浏览文件 @
baa1f663
...
...
@@ -15,7 +15,6 @@
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle.fluid
as
fluid
def
compute_mean_iou
(
...
...
@@ -140,22 +139,5 @@ class TestCase1(TestMeanIOUOp):
self
.
check_output
(
check_dygraph
=
False
,
check_eager
=
False
)
class
TestMeanIOUOpError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
# The input type of accuracy_op must be Variable.
x1
=
fluid
.
create_lod_tensor
(
np
.
array
([[
-
1
]]),
[[
1
]],
fluid
.
CPUPlace
()
)
y1
=
fluid
.
create_lod_tensor
(
np
.
array
([[
-
1
]]),
[[
1
]],
fluid
.
CPUPlace
()
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
mean_iou
,
x1
,
y1
)
# The input dtype of accuracy_op must be float32 or float64.
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
4
],
dtype
=
"float32"
)
y2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
4
],
dtype
=
"float32"
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
mean_iou
,
x2
,
y2
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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