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PaddleOCR
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e6878b21
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体验新版 GitCode,发现更多精彩内容 >>
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e6878b21
编写于
12月 14, 2020
作者:
W
WenmuZhou
浏览文件
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电子邮件补丁
差异文件
trans numpy to paddle
上级
7b53596c
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
56 addition
and
48 deletion
+56
-48
ppocr/modeling/transforms/tps.py
ppocr/modeling/transforms/tps.py
+56
-48
未找到文件。
ppocr/modeling/transforms/tps.py
浏览文件 @
e6878b21
...
...
@@ -128,7 +128,7 @@ class LocalizationNetwork(nn.Layer):
i
=
0
for
block
in
self
.
block_list
:
x
=
block
(
x
)
x
=
x
.
reshape
([
B
,
-
1
]
)
x
=
x
.
squeeze
(
axis
=
2
).
squeeze
(
axis
=
2
)
x
=
self
.
fc1
(
x
)
x
=
F
.
relu
(
x
)
...
...
@@ -176,14 +176,15 @@ class GridGenerator(nn.Layer):
Return:
batch_P_prime: the grid for the grid_sampler
"""
C
=
self
.
build_C
()
P
=
self
.
build_P
(
I_r_size
)
inv_delta_C
=
self
.
build_inv_delta_C
(
C
).
astype
(
'float32'
)
P_hat
=
self
.
build_P_hat
(
C
,
P
).
astype
(
'float32'
)
C
=
self
.
build_C_paddle
()
P
=
self
.
build_P_paddle
(
I_r_size
)
inv_delta_C_tensor
=
self
.
build_inv_delta_C_paddle
(
C
).
astype
(
'float32'
)
# inv_delta_C_tensor = paddle.zeros((23,23)).astype('float32')
P_hat_tensor
=
self
.
build_P_hat_paddle
(
C
,
paddle
.
to_tensor
(
P
)).
astype
(
'float32'
)
inv_delta_C_tensor
=
paddle
.
to_tensor
(
inv_delta_C
)
inv_delta_C_tensor
.
stop_gradient
=
True
P_hat_tensor
=
paddle
.
to_tensor
(
P_hat
)
P_hat_tensor
.
stop_gradient
=
True
batch_C_ex_part_tensor
=
self
.
get_expand_tensor
(
batch_C_prime
)
...
...
@@ -196,71 +197,80 @@ class GridGenerator(nn.Layer):
batch_P_prime
=
paddle
.
matmul
(
P_hat_tensor
,
batch_T
)
return
batch_P_prime
def
build_C
(
self
):
def
build_C
_paddle
(
self
):
""" Return coordinates of fiducial points in I_r; C """
F
=
self
.
F
ctrl_pts_x
=
np
.
linspace
(
-
1.0
,
1.0
,
int
(
F
/
2
))
ctrl_pts_y_top
=
-
1
*
np
.
ones
(
int
(
F
/
2
)
)
ctrl_pts_y_bottom
=
np
.
ones
(
int
(
F
/
2
)
)
ctrl_pts_top
=
np
.
stack
([
ctrl_pts_x
,
ctrl_pts_y_top
],
axis
=
1
)
ctrl_pts_bottom
=
np
.
stack
([
ctrl_pts_x
,
ctrl_pts_y_bottom
],
axis
=
1
)
C
=
np
.
concatenate
([
ctrl_pts_top
,
ctrl_pts_bottom
],
axis
=
0
)
ctrl_pts_x
=
paddle
.
linspace
(
-
1.0
,
1.0
,
int
(
F
/
2
))
ctrl_pts_y_top
=
-
1
*
paddle
.
ones
([
int
(
F
/
2
)]
)
ctrl_pts_y_bottom
=
paddle
.
ones
([
int
(
F
/
2
)]
)
ctrl_pts_top
=
paddle
.
stack
([
ctrl_pts_x
,
ctrl_pts_y_top
],
axis
=
1
)
ctrl_pts_bottom
=
paddle
.
stack
([
ctrl_pts_x
,
ctrl_pts_y_bottom
],
axis
=
1
)
C
=
paddle
.
concat
([
ctrl_pts_top
,
ctrl_pts_bottom
],
axis
=
0
)
return
C
# F x 2
def
build_P
(
self
,
I_r_size
):
I_r_width
,
I_r_height
=
I_r_size
I_r_grid_x
=
(
np
.
arange
(
-
I_r_width
,
I_r_width
,
2
)
+
1.0
)
\
/
I_r_width
# self.I_r_width
I_r_grid_y
=
(
np
.
arange
(
-
I_r_height
,
I_r_height
,
2
)
+
1.0
)
\
/
I_r_height
# self.I_r_height
def
build_P_paddle
(
self
,
I_r_size
):
I_r_height
,
I_r_width
=
I_r_size
I_r_grid_x
=
(
paddle
.
arange
(
-
I_r_width
,
I_r_width
,
2
).
astype
(
'float32'
)
+
1.0
)
/
I_r_width
# self.I_r_width
I_r_grid_y
=
(
paddle
.
arange
(
-
I_r_height
,
I_r_height
,
2
).
astype
(
'float32'
)
+
1.0
)
/
I_r_height
# self.I_r_height
# P: self.I_r_width x self.I_r_height x 2
P
=
np
.
stack
(
np
.
meshgrid
(
I_r_grid_x
,
I_r_grid_y
),
axis
=
2
)
P
=
paddle
.
stack
(
paddle
.
meshgrid
(
I_r_grid_x
,
I_r_grid_y
),
axis
=
2
)
P
=
paddle
.
transpose
(
P
,
perm
=
[
1
,
0
,
2
])
# n (= self.I_r_width x self.I_r_height) x 2
return
P
.
reshape
([
-
1
,
2
])
def
build_inv_delta_C
(
self
,
C
):
def
build_inv_delta_C
_paddle
(
self
,
C
):
""" Return inv_delta_C which is needed to calculate T """
F
=
self
.
F
hat_C
=
np
.
zeros
((
F
,
F
),
dtype
=
float
)
# F x F
hat_C
=
paddle
.
zeros
((
F
,
F
),
dtype
=
'float32'
)
# F x F
for
i
in
range
(
0
,
F
):
for
j
in
range
(
i
,
F
):
r
=
np
.
linalg
.
norm
(
C
[
i
]
-
C
[
j
])
hat_C
[
i
,
j
]
=
r
hat_C
[
j
,
i
]
=
r
np
.
fill_diagonal
(
hat_C
,
1
)
hat_C
=
(
hat_C
**
2
)
*
np
.
log
(
hat_C
)
# print(C.shape, hat_C.shape)
delta_C
=
np
.
concatenate
(
# F+3 x F+3
if
i
==
j
:
hat_C
[
i
,
j
]
=
1
else
:
r
=
paddle
.
norm
(
C
[
i
]
-
C
[
j
])
hat_C
[
i
,
j
]
=
r
hat_C
[
j
,
i
]
=
r
hat_C
=
(
hat_C
**
2
)
*
paddle
.
log
(
hat_C
)
delta_C
=
paddle
.
concat
(
# F+3 x F+3
[
np
.
concatenate
(
[
np
.
ones
((
F
,
1
)),
C
,
hat_C
],
axis
=
1
),
# F x F+3
np
.
concatenate
(
[
np
.
zeros
((
2
,
3
)),
np
.
transpose
(
C
)],
axis
=
1
),
# 2 x F+3
np
.
concatenate
(
[
np
.
zeros
((
1
,
3
)),
np
.
ones
((
1
,
F
))],
axis
=
1
)
# 1 x F+3
paddle
.
concat
(
[
paddle
.
ones
((
F
,
1
)),
C
,
hat_C
],
axis
=
1
),
# F x F+3
paddle
.
concat
(
[
paddle
.
zeros
((
2
,
3
)),
paddle
.
transpose
(
C
,
perm
=
[
1
,
0
])],
axis
=
1
),
# 2 x F+3
paddle
.
concat
(
[
paddle
.
zeros
((
1
,
3
)),
paddle
.
ones
((
1
,
F
))],
axis
=
1
)
# 1 x F+3
],
axis
=
0
)
inv_delta_C
=
np
.
linalg
.
inv
(
delta_C
)
inv_delta_C
=
paddle
.
inverse
(
delta_C
)
return
inv_delta_C
# F+3 x F+3
def
build_P_hat
(
self
,
C
,
P
):
def
build_P_hat
_paddle
(
self
,
C
,
P
):
F
=
self
.
F
eps
=
self
.
eps
n
=
P
.
shape
[
0
]
# n (= self.I_r_width x self.I_r_height)
# P_tile: n x 2 -> n x 1 x 2 -> n x F x 2
P_tile
=
np
.
tile
(
np
.
expand_dims
(
P
,
axis
=
1
),
(
1
,
F
,
1
))
C_tile
=
np
.
expand_dims
(
C
,
axis
=
0
)
# 1 x F x 2
P_tile
=
paddle
.
tile
(
paddle
.
unsqueeze
(
P
,
axis
=
1
),
(
1
,
F
,
1
))
C_tile
=
paddle
.
unsqueeze
(
C
,
axis
=
0
)
# 1 x F x 2
P_diff
=
P_tile
-
C_tile
# n x F x 2
# rbf_norm: n x F
rbf_norm
=
np
.
linalg
.
norm
(
P_diff
,
ord
=
2
,
axis
=
2
,
keepdims
=
False
)
rbf_norm
=
paddle
.
norm
(
P_diff
,
p
=
2
,
axis
=
2
,
keepdim
=
False
)
# rbf: n x F
rbf
=
np
.
multiply
(
np
.
square
(
rbf_norm
),
np
.
log
(
rbf_norm
+
eps
))
P_hat
=
np
.
concatenate
([
np
.
ones
((
n
,
1
)),
P
,
rbf
],
axis
=
1
)
rbf
=
paddle
.
multiply
(
paddle
.
square
(
rbf_norm
),
paddle
.
log
(
rbf_norm
+
eps
))
P_hat
=
paddle
.
concat
([
paddle
.
ones
((
n
,
1
)),
P
,
rbf
],
axis
=
1
)
return
P_hat
# n x F+3
def
get_expand_tensor
(
self
,
batch_C_prime
):
B
=
batch_C_prime
.
shape
[
0
]
batch_C_prime
=
batch_C_prime
.
reshape
([
B
,
-
1
])
B
,
H
,
C
=
batch_C_prime
.
shape
batch_C_prime
=
batch_C_prime
.
reshape
([
B
,
H
*
C
])
batch_C_ex_part_tensor
=
self
.
fc
(
batch_C_prime
)
batch_C_ex_part_tensor
=
batch_C_ex_part_tensor
.
reshape
([
-
1
,
3
,
2
])
return
batch_C_ex_part_tensor
...
...
@@ -277,10 +287,8 @@ class TPS(nn.Layer):
def
forward
(
self
,
image
):
image
.
stop_gradient
=
False
I_r_size
=
[
image
.
shape
[
3
],
image
.
shape
[
2
]]
batch_C_prime
=
self
.
loc_net
(
image
)
batch_P_prime
=
self
.
grid_generator
(
batch_C_prime
,
I_r_size
)
batch_P_prime
=
self
.
grid_generator
(
batch_C_prime
,
image
.
shape
[
2
:]
)
batch_P_prime
=
batch_P_prime
.
reshape
(
[
-
1
,
image
.
shape
[
2
],
image
.
shape
[
3
],
2
])
batch_I_r
=
F
.
grid_sample
(
x
=
image
,
grid
=
batch_P_prime
)
...
...
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