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forwardOnBigImages.py
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Created
Fri, Apr 19, 16:00
Size
1 KB
Mime Type
text/x-python
Expires
Sun, Apr 21, 16:00 (2 d)
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blob
Format
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Handle
17075760
Attached To
R8206 networkTraining
forwardOnBigImages.py
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import
numpy
as
np
import
networkTraining.cropRoutines
as
cropRoutines
import
torch
def
targetCoords
(
sourceCoords
,
validCoords
):
cc
=
sourceCoords
vc
=
validCoords
tc
=
[]
for
i
in
range
(
len
(
cc
)):
tc
.
append
(
slice
(
cc
[
i
]
.
start
+
vc
[
i
]
.
start
,
cc
[
i
]
.
start
+
vc
[
i
]
.
stop
))
return
tc
def
processChunk
(
inChunk
,
cropSize
,
marginSize
,
startDim
,
net
,
outChannels
=
None
):
nc
=
cropRoutines
.
noCrops
(
inChunk
.
shape
,
cropSize
,
marginSize
,
startDim
)
size
=
np
.
array
(
inChunk
.
shape
)
if
outChannels
:
size
[
1
]
=
outChannels
outChunk
=
np
.
zeros
(
tuple
(
size
))
net
.
eval
()
for
i
in
range
(
nc
):
cc
,
vc
=
cropRoutines
.
cropCoords
(
i
,
cropSize
,
marginSize
,
inChunk
.
shape
,
startDim
)
tc
=
targetCoords
(
cc
,
vc
)
crop
=
inChunk
[
tuple
(
cc
)]
o
=
net
.
forward
(
torch
.
from_numpy
(
crop
)
.
cuda
())
tc
[
1
]
=
slice
(
0
,
size
[
1
])
vc
[
1
]
=
slice
(
0
,
size
[
1
])
outChunk
[
tuple
(
tc
)]
=
o
.
cpu
()
.
data
.
numpy
()[
tuple
(
vc
)]
return
outChunk
def
processChunk_v2
(
inChunk
,
cropSize
,
marginSize
,
startDim
,
net
,
outDims
=
None
):
# outDims can be a tuple of sizes...
nc
=
cropRoutines
.
noCrops
(
inChunk
.
shape
,
cropSize
,
marginSize
,
startDim
)
size
=
inChunk
.
shape
if
outDims
:
size
=
[]
for
k
in
range
(
len
(
outDims
)):
size
.
append
(
outDims
[
k
])
for
k
in
range
(
startDim
,
inChunk
.
ndim
):
size
.
append
(
inChunk
.
shape
[
k
])
outChunk
=
np
.
zeros
(
tuple
(
size
))
net
.
eval
()
for
i
in
range
(
nc
):
cc
,
vc
=
cropRoutines
.
cropCoords
(
i
,
cropSize
,
marginSize
,
inChunk
.
shape
,
startDim
)
tc
=
targetCoords
(
cc
,
vc
)
crop
=
inChunk
[
tuple
(
cc
)]
o
=
net
.
forward
(
torch
.
from_numpy
(
crop
)
.
cuda
())
if
outDims
:
ttc
=
[]
tvc
=
[]
for
k
in
range
(
len
(
outDims
)):
ttc
.
append
(
slice
(
0
,
outDims
[
k
]))
tvc
.
append
(
slice
(
0
,
outDims
[
k
]))
for
k
in
range
(
startDim
,
inChunk
.
ndim
):
ttc
.
append
(
tc
[
k
])
tvc
.
append
(
vc
[
k
])
tc
=
ttc
vc
=
tvc
print
(
"osize"
,
o
.
size
())
print
(
"cropshape"
,
crop
.
shape
)
print
(
"tc,vc"
,
tc
,
vc
)
print
(
"outchanksjape"
,
outChunk
.
shape
)
print
(
"oshape"
,
o
.
cpu
()
.
data
.
numpy
()
.
shape
)
outChunk
[
tuple
(
tc
)]
=
o
.
cpu
()
.
data
.
numpy
()[
tuple
(
vc
)]
return
outChunk
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