Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
magicwindyyd
mindspore
提交
f9088f46
M
mindspore
项目概览
magicwindyyd
/
mindspore
与 Fork 源项目一致
Fork自
MindSpore / mindspore
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
M
mindspore
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
f9088f46
编写于
7月 03, 2020
作者:
X
xutianchun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
change crop_size from tensor to tuple
上级
2711a628
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
17 addition
and
14 deletion
+17
-14
mindspore/ops/operations/image_ops.py
mindspore/ops/operations/image_ops.py
+15
-12
tests/st/ops/ascend/test_aicpu_ops/test_crop_and_reszie.py
tests/st/ops/ascend/test_aicpu_ops/test_crop_and_reszie.py
+2
-2
未找到文件。
mindspore/ops/operations/image_ops.py
浏览文件 @
f9088f46
...
@@ -45,10 +45,9 @@ class CropAndResize(PrimitiveWithInfer):
...
@@ -45,10 +45,9 @@ class CropAndResize(PrimitiveWithInfer):
extrapolate the input image values. Types allowd: float32.
extrapolate the input image values. Types allowd: float32.
- **box_index** (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
- **box_index** (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
The value of box_ind[i] specifies the image that the i-th box refers to. Types allowd: int32.
The value of box_ind[i] specifies the image that the i-th box refers to. Types allowd: int32.
- **crop_size** (Tensor) - Only constant value is allowd. Types allowed: int32.
- **crop_size** (Tuple[int]) - A tuple of two int32 elements: (crop_height, crop_width).
A 1-D tensor of 2 elements, size = [crop_height, crop_width].
Only constant value is allowed. All cropped image patches are resized to this size.
All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved.
The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.
Both crop_height and crop_width need to be positive.
Outputs:
Outputs:
A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.
A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.
...
@@ -70,8 +69,8 @@ class CropAndResize(PrimitiveWithInfer):
...
@@ -70,8 +69,8 @@ class CropAndResize(PrimitiveWithInfer):
>>> image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
>>> image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
>>> boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
>>> boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
>>> box_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
>>> box_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
>>> crop_size =
np.array([24, 24]).astype(np.int32
)
>>> crop_size =
(24, 24
)
>>> crop_and_resize = CropAndResizeNet(crop_size=
Tensor(crop_size)
)
>>> crop_and_resize = CropAndResizeNet(crop_size=
crop_size
)
>>> output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index))
>>> output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index))
>>> print(output.asnumpy())
>>> print(output.asnumpy())
"""
"""
...
@@ -91,11 +90,10 @@ class CropAndResize(PrimitiveWithInfer):
...
@@ -91,11 +90,10 @@ class CropAndResize(PrimitiveWithInfer):
x_shape
=
list
(
x
[
'shape'
])
x_shape
=
list
(
x
[
'shape'
])
boxes_shape
=
list
(
boxes
[
'shape'
])
boxes_shape
=
list
(
boxes
[
'shape'
])
box_index_shape
=
list
(
box_index
[
'shape'
])
box_index_shape
=
list
(
box_index
[
'shape'
])
crop_size_shape
=
list
(
crop_size
[
'shape'
])
# get value
# get value
if
crop_size
[
'value'
]
is
None
:
if
crop_size
[
'value'
]
is
None
:
raise
ValueError
(
f
"For
{
self
.
name
}
, crop_size must be const."
)
raise
ValueError
(
f
"For
{
self
.
name
}
, crop_size must be const
ant
."
)
crop_size_value
=
crop_size
[
'value'
]
.
asnumpy
()
crop_size_value
=
crop_size
[
'value'
]
# get dtype
# get dtype
x_dtype
=
x
[
'dtype'
]
x_dtype
=
x
[
'dtype'
]
boxes_dtype
=
boxes
[
'dtype'
]
boxes_dtype
=
boxes
[
'dtype'
]
...
@@ -107,15 +105,20 @@ class CropAndResize(PrimitiveWithInfer):
...
@@ -107,15 +105,20 @@ class CropAndResize(PrimitiveWithInfer):
mstype
.
float32
,
mstype
.
float64
,
mstype
.
uint8
,
mstype
.
uint16
],
self
.
name
)
mstype
.
float32
,
mstype
.
float64
,
mstype
.
uint8
,
mstype
.
uint16
],
self
.
name
)
validator
.
check_tensor_type_same
({
"boxes"
:
boxes_dtype
},
[
mstype
.
float32
],
self
.
name
)
validator
.
check_tensor_type_same
({
"boxes"
:
boxes_dtype
},
[
mstype
.
float32
],
self
.
name
)
validator
.
check_tensor_type_same
({
"box_index"
:
box_index_dtype
},
[
mstype
.
int32
],
self
.
name
)
validator
.
check_tensor_type_same
({
"box_index"
:
box_index_dtype
},
[
mstype
.
int32
],
self
.
name
)
validator
.
check_
tensor_type_same
({
"crop_size"
:
crop_size_dtype
},
[
mstype
.
int32
],
self
.
name
)
validator
.
check_
value_type
(
"crop_size"
,
crop_size_value
,
[
tuple
],
self
.
name
)
# check input shape rank
# check input shape rank
validator
.
check
(
"x rank"
,
len
(
x_shape
),
"expected"
,
4
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"x rank"
,
len
(
x_shape
),
"expected"
,
4
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"boxes rank"
,
len
(
boxes_shape
),
"expected"
,
2
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"boxes rank"
,
len
(
boxes_shape
),
"expected"
,
2
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"box_index rank"
,
len
(
box_index_shape
),
"expected"
,
1
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"box_index rank"
,
len
(
box_index_shape
),
"expected"
,
1
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"crop_size rank"
,
len
(
crop_size_shape
),
"expected"
,
1
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"crop_size size"
,
len
(
crop_size_value
),
"expected"
,
2
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"boxes dim_0"
,
boxes_shape
[
0
],
"box_index dim_0"
,
box_index_shape
[
0
],
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"boxes dim_0"
,
boxes_shape
[
0
],
"box_index dim_0"
,
box_index_shape
[
0
],
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"boxes dim_1"
,
boxes_shape
[
1
],
"expected"
,
4
,
Rel
.
EQ
,
self
.
name
)
validator
.
check
(
"boxes dim_1"
,
boxes_shape
[
1
],
"expected"
,
4
,
Rel
.
EQ
,
self
.
name
)
# check crop_size_value
validator
.
check
(
"crop_height"
,
crop_size_value
[
0
],
"minimum"
,
0
,
Rel
.
GT
,
self
.
name
)
validator
.
check
(
"crop_width"
,
crop_size_value
[
1
],
"minimum"
,
0
,
Rel
.
GT
,
self
.
name
)
# check crop_size element type
validator
.
check
(
"crop_height dtype"
,
crop_size_dtype
[
0
],
mstype
.
int32
,
self
.
name
)
validator
.
check
(
"crop_width dtype"
,
crop_size_dtype
[
1
],
mstype
.
int32
,
self
.
name
)
num_boxes
=
boxes_shape
[
0
]
num_boxes
=
boxes_shape
[
0
]
crop_height
=
crop_size_value
[
0
]
crop_height
=
crop_size_value
[
0
]
...
...
tests/st/ops/ascend/test_aicpu_ops/test_crop_and_reszie.py
浏览文件 @
f9088f46
...
@@ -43,7 +43,7 @@ def test_net_float32():
...
@@ -43,7 +43,7 @@ def test_net_float32():
image
=
np
.
random
.
normal
(
size
=
[
batch_size
,
image_height
,
image_width
,
channels
]).
astype
(
np
.
float32
)
image
=
np
.
random
.
normal
(
size
=
[
batch_size
,
image_height
,
image_width
,
channels
]).
astype
(
np
.
float32
)
boxes
=
np
.
random
.
uniform
(
size
=
[
num_boxes
,
4
]).
astype
(
np
.
float32
)
boxes
=
np
.
random
.
uniform
(
size
=
[
num_boxes
,
4
]).
astype
(
np
.
float32
)
box_index
=
np
.
random
.
uniform
(
size
=
[
num_boxes
],
low
=
0
,
high
=
batch_size
).
astype
(
np
.
int32
)
box_index
=
np
.
random
.
uniform
(
size
=
[
num_boxes
],
low
=
0
,
high
=
batch_size
).
astype
(
np
.
int32
)
crop_size
=
np
.
array
([
24
,
24
]).
astype
(
np
.
int32
)
crop_size
=
(
24
,
24
)
net
=
Net
(
crop_size
=
Tensor
(
crop_size
)
)
net
=
Net
(
crop_size
=
crop_size
)
output
=
net
(
Tensor
(
image
),
Tensor
(
boxes
),
Tensor
(
box_index
))
output
=
net
(
Tensor
(
image
),
Tensor
(
boxes
),
Tensor
(
box_index
))
print
(
output
.
asnumpy
())
print
(
output
.
asnumpy
())
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录