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0bb67049
编写于
8月 24, 2018
作者:
R
Ruilong Liu
提交者:
GitHub
8月 24, 2018
浏览文件
操作
浏览文件
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差异文件
Merge pull request #833 from codeWorm2015/metal
fix crash
上级
ca22a7a5
6e5e698d
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
273 addition
and
129 deletion
+273
-129
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BoxcoderKernel.swift
...bile/paddle-mobile/Operators/Kernels/BoxcoderKernel.swift
+1
-1
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
...le/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
+112
-110
metal/paddle-mobile/paddle-mobile/Operators/Kernels/MulticlassNMSKernel.swift
...paddle-mobile/Operators/Kernels/MulticlassNMSKernel.swift
+1
-1
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
...bile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
+1
-1
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Texture2DTo2DArrayKernel.swift
...e-mobile/Operators/Kernels/Texture2DTo2DArrayKernel.swift
+16
-15
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvKernel.metal
...le/paddle-mobile/Operators/Kernels/metal/ConvKernel.metal
+141
-0
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
+1
-1
未找到文件。
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BoxcoderKernel.swift
浏览文件 @
0bb67049
...
...
@@ -34,6 +34,6 @@ class BoxcoderKernel<P: PrecisionType>: Kernel, Computable{
required
init
(
device
:
MTLDevice
,
param
:
BoxcoderParam
<
P
>
)
{
param
.
output
.
initTexture
(
device
:
device
)
super
.
init
(
device
:
device
,
inFunctionName
:
"
priorbox
"
)
super
.
init
(
device
:
device
,
inFunctionName
:
"
boxcoder
"
)
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
浏览文件 @
0bb67049
...
...
@@ -16,124 +16,126 @@ import Foundation
import
MetalPerformanceShaders
struct
ConvBNReluTestParam
:
TestParam
{
let
inputTexture
:
MTLTexture
let
outputTexture
:
MTLTexture
var
metalParam
:
MetalConvParam
let
filterBuffer
:
MTLBuffer
let
biaseBuffer
:
MTLBuffer
let
newScaleBuffer
:
MTLBuffer
let
newBiaseBuffer
:
MTLBuffer
let
filterSize
:
(
width
:
Int
,
height
:
Int
,
channel
:
Int
)
init
(
inInputTexture
:
MTLTexture
,
inOutputTexture
:
MTLTexture
,
inMetalParam
:
MetalConvParam
,
inFilterBuffer
:
MTLBuffer
,
inBiaseBuffer
:
MTLBuffer
,
inNewScaleBuffer
:
MTLBuffer
,
inNewBiaseBuffer
:
MTLBuffer
,
inFilterSize
:
(
width
:
Int
,
height
:
Int
,
channel
:
Int
))
{
inputTexture
=
inInputTexture
outputTexture
=
inOutputTexture
metalParam
=
inMetalParam
filterBuffer
=
inFilterBuffer
biaseBuffer
=
inBiaseBuffer
newScaleBuffer
=
inNewScaleBuffer
newBiaseBuffer
=
inNewBiaseBuffer
filterSize
=
inFilterSize
}
let
inputTexture
:
MTLTexture
let
outputTexture
:
MTLTexture
var
metalParam
:
MetalConvParam
let
filterBuffer
:
MTLBuffer
let
biaseBuffer
:
MTLBuffer
let
newScaleBuffer
:
MTLBuffer
let
newBiaseBuffer
:
MTLBuffer
let
filterSize
:
(
width
:
Int
,
height
:
Int
,
channel
:
Int
)
init
(
inInputTexture
:
MTLTexture
,
inOutputTexture
:
MTLTexture
,
inMetalParam
:
MetalConvParam
,
inFilterBuffer
:
MTLBuffer
,
inBiaseBuffer
:
MTLBuffer
,
inNewScaleBuffer
:
MTLBuffer
,
inNewBiaseBuffer
:
MTLBuffer
,
inFilterSize
:
(
width
:
Int
,
height
:
Int
,
channel
:
Int
))
{
inputTexture
=
inInputTexture
outputTexture
=
inOutputTexture
metalParam
=
inMetalParam
filterBuffer
=
inFilterBuffer
biaseBuffer
=
inBiaseBuffer
newScaleBuffer
=
inNewScaleBuffer
newBiaseBuffer
=
inNewBiaseBuffer
filterSize
=
inFilterSize
}
}
class
ConvBNReluKernel
<
P
:
PrecisionType
>
:
Kernel
,
Computable
,
Testable
{
required
init
(
device
:
MTLDevice
,
testParam
:
ConvBNReluTestParam
)
{
if
testParam
.
filterSize
.
width
==
1
&&
testParam
.
filterSize
.
height
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_add_batch_norm_relu_1x1"
)
}
else
if
testParam
.
filterSize
.
channel
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"depthwise_conv_add_batch_norm_relu_3x3"
)
}
else
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_add_batch_norm_relu_3x3"
)
}
required
init
(
device
:
MTLDevice
,
testParam
:
ConvBNReluTestParam
)
{
if
testParam
.
filterSize
.
width
==
1
&&
testParam
.
filterSize
.
height
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_batch_norm_relu_1x1"
)
}
else
if
testParam
.
filterSize
.
channel
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"depthwise_conv_batch_norm_relu_3x3"
)
}
else
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_batch_norm_relu_3x3"
)
}
}
var
metalParam
:
MetalConvParam
!
required
init
(
device
:
MTLDevice
,
param
:
ConvBNReluParam
<
P
>
)
{
if
param
.
filter
.
width
==
1
&&
param
.
filter
.
height
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_batch_norm_relu_1x1"
)
}
else
if
param
.
filter
.
channel
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"depthwise_conv_batch_norm_relu_3x3"
)
}
else
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_batch_norm_relu_3x3"
)
}
param
.
output
.
initTexture
(
device
:
device
,
transpose
:
[
0
,
2
,
3
,
1
])
param
.
filter
.
initBuffer
(
device
:
device
,
precision
:
Tensor
.
BufferPrecision
.
Float32
)
param
.
variance
.
initBuffer
(
device
:
device
)
param
.
mean
.
initBuffer
(
device
:
device
)
param
.
scale
.
initBuffer
(
device
:
device
)
param
.
bias
.
initBuffer
(
device
:
device
)
let
offsetX
=
param
.
filter
.
width
/
2
-
Int
(
param
.
paddings
[
0
])
let
offsetY
=
param
.
filter
.
height
/
2
-
Int
(
param
.
paddings
[
1
])
print
(
"offset x:
\(
offsetX
)
"
)
print
(
"offset y:
\(
offsetY
)
"
)
let
offsetZ
=
0.0
print
(
" fuck "
)
metalParam
=
MetalConvParam
.
init
(
offsetX
:
Int16
(
offsetX
),
offsetY
:
Int16
(
offsetY
),
offsetZ
:
Int16
(
offsetZ
),
strideX
:
UInt16
(
param
.
stride
[
0
]),
strideY
:
UInt16
(
param
.
stride
[
1
]),
paddedZ
:
UInt16
(
param
.
input
.
metalTexture
.
arrayLength
*
4
-
param
.
input
.
dim
[
3
]))
var
invs
:
[
P
]
=
[]
let
varianceContents
=
param
.
variance
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
for
i
in
0
..<
param
.
variance
.
buffer
.
length
/
MemoryLayout
<
P
>.
stride
{
let
inv
=
1.0
/
pow
(
Float32
.
init
(
varianceContents
[
i
])
+
param
.
epsilon
,
0.5
)
invs
.
append
(
P
(
inv
))
}
var
metalParam
:
MetalConvParam
!
required
init
(
device
:
MTLDevice
,
param
:
ConvBNReluParam
<
P
>
)
{
if
param
.
filter
.
width
==
1
&&
param
.
filter
.
height
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_add_batch_norm_relu_1x1"
)
}
else
if
param
.
filter
.
channel
==
1
{
super
.
init
(
device
:
device
,
inFunctionName
:
"depthwise_conv_add_batch_norm_relu_3x3"
)
}
else
{
super
.
init
(
device
:
device
,
inFunctionName
:
"conv_add_batch_norm_relu_3x3"
)
}
param
.
output
.
initTexture
(
device
:
device
,
transpose
:
[
0
,
2
,
3
,
1
])
param
.
filter
.
initBuffer
(
device
:
device
,
precision
:
Tensor
.
BufferPrecision
.
Float32
)
param
.
variance
.
initBuffer
(
device
:
device
)
param
.
mean
.
initBuffer
(
device
:
device
)
param
.
scale
.
initBuffer
(
device
:
device
)
param
.
bias
.
initBuffer
(
device
:
device
)
let
offsetX
=
param
.
filter
.
width
/
2
-
Int
(
param
.
paddings
[
0
])
let
offsetY
=
param
.
filter
.
height
/
2
-
Int
(
param
.
paddings
[
1
])
print
(
"offset x:
\(
offsetX
)
"
)
print
(
"offset y:
\(
offsetY
)
"
)
let
offsetZ
=
0.0
metalParam
=
MetalConvParam
.
init
(
offsetX
:
Int16
(
offsetX
),
offsetY
:
Int16
(
offsetY
),
offsetZ
:
Int16
(
offsetZ
),
strideX
:
UInt16
(
param
.
stride
[
0
]),
strideY
:
UInt16
(
param
.
stride
[
1
]),
paddedZ
:
UInt16
(
param
.
input
.
metalTexture
.
arrayLength
*
4
-
param
.
input
.
dim
[
3
]))
var
invs
:
[
P
]
=
[]
let
varianceContents
=
param
.
variance
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
for
i
in
0
..<
param
.
variance
.
buffer
.
length
/
MemoryLayout
<
P
>.
stride
{
let
inv
=
1.0
/
pow
(
Float32
.
init
(
varianceContents
[
i
])
+
param
.
epsilon
,
0.5
)
invs
.
append
(
P
(
inv
))
}
let
newScale
:
UnsafeMutablePointer
<
P
>
=
UnsafeMutablePointer
<
P
>.
allocate
(
capacity
:
param
.
scale
.
buffer
.
length
)
let
newBiase
:
UnsafeMutablePointer
<
P
>
=
UnsafeMutablePointer
<
P
>.
allocate
(
capacity
:
param
.
bias
.
buffer
.
length
)
let
scaleContents
=
param
.
scale
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
let
biaseContents
=
param
.
bias
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
let
meanContents
=
param
.
mean
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
for
i
in
0
..<
param
.
scale
.
buffer
.
length
/
MemoryLayout
<
P
>.
stride
{
newScale
[
i
]
=
invs
[
i
]
*
scaleContents
[
i
]
newBiase
[
i
]
=
biaseContents
[
i
]
-
meanContents
[
i
]
*
invs
[
i
]
*
scaleContents
[
i
]
}
param
.
newBiase
=
device
.
makeBuffer
(
bytes
:
newBiase
,
length
:
param
.
bias
.
buffer
.
length
)
param
.
newScale
=
device
.
makeBuffer
(
bytes
:
newScale
,
length
:
param
.
scale
.
buffer
.
length
)
newScale
.
deinitialize
(
count
:
param
.
scale
.
buffer
.
length
)
newScale
.
deallocate
()
newBiase
.
deinitialize
(
count
:
param
.
bias
.
buffer
.
length
)
newBiase
.
deallocate
()
let
newScale
:
UnsafeMutablePointer
<
P
>
=
UnsafeMutablePointer
<
P
>.
allocate
(
capacity
:
param
.
scale
.
buffer
.
length
)
let
newBiase
:
UnsafeMutablePointer
<
P
>
=
UnsafeMutablePointer
<
P
>.
allocate
(
capacity
:
param
.
bias
.
buffer
.
length
)
let
scaleContents
=
param
.
scale
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
let
biaseContents
=
param
.
bias
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
let
meanContents
=
param
.
mean
.
buffer
.
contents
()
.
assumingMemoryBound
(
to
:
P
.
self
)
for
i
in
0
..<
param
.
scale
.
buffer
.
length
/
MemoryLayout
<
P
>.
stride
{
newScale
[
i
]
=
invs
[
i
]
*
scaleContents
[
i
]
newBiase
[
i
]
=
biaseContents
[
i
]
-
meanContents
[
i
]
*
invs
[
i
]
*
scaleContents
[
i
]
}
func
compute
(
commandBuffer
:
MTLCommandBuffer
,
param
:
ConvBNReluParam
<
P
>
)
throws
{
guard
let
encoder
=
commandBuffer
.
makeComputeCommandEncoder
()
else
{
throw
PaddleMobileError
.
predictError
(
message
:
" encode is nil"
)
}
encoder
.
setTexture
(
param
.
input
.
metalTexture
,
index
:
0
)
encoder
.
setTexture
(
param
.
output
.
metalTexture
,
index
:
1
)
encoder
.
setBytes
(
&
metalParam
,
length
:
MemoryLayout
<
MetalConvParam
>.
size
,
index
:
0
)
encoder
.
setBuffer
(
param
.
filter
.
buffer
,
offset
:
0
,
index
:
1
)
encoder
.
setBuffer
(
param
.
newScale
!
,
offset
:
0
,
index
:
3
)
encoder
.
setBuffer
(
param
.
newBiase
!
,
offset
:
0
,
index
:
4
)
encoder
.
dispatch
(
computePipline
:
pipline
,
outTexture
:
param
.
output
.
metalTexture
)
encoder
.
endEncoding
(
)
param
.
newBiase
=
device
.
makeBuffer
(
bytes
:
newBiase
,
length
:
param
.
bias
.
buffer
.
length
)
param
.
newScale
=
device
.
makeBuffer
(
bytes
:
newScale
,
length
:
param
.
scale
.
buffer
.
length
)
newScale
.
deinitialize
(
count
:
param
.
scale
.
buffer
.
length
)
newScale
.
deallocate
()
newBiase
.
deinitialize
(
count
:
param
.
bias
.
buffer
.
length
)
newBiase
.
deallocate
(
)
}
func
compute
(
commandBuffer
:
MTLCommandBuffer
,
param
:
ConvBNReluParam
<
P
>
)
throws
{
guard
let
encoder
=
commandBuffer
.
makeComputeCommandEncoder
()
else
{
throw
PaddleMobileError
.
predictError
(
message
:
" encode is nil"
)
}
public
func
test
(
commandBuffer
:
MTLCommandBuffer
,
param
:
ConvBNReluTestParam
)
{
guard
let
encoder
=
commandBuffer
.
makeComputeCommandEncoder
()
else
{
fatalError
()
}
encoder
.
setTexture
(
param
.
inputTexture
,
index
:
0
)
encoder
.
setTexture
(
param
.
outputTexture
,
index
:
1
)
var
inMetalParam
=
param
.
metalParam
encoder
.
setBytes
(
&
inMetalParam
,
length
:
MemoryLayout
<
MetalConvParam
>.
size
,
index
:
0
)
encoder
.
setBuffer
(
param
.
filterBuffer
,
offset
:
0
,
index
:
1
)
encoder
.
setBuffer
(
param
.
biaseBuffer
,
offset
:
0
,
index
:
2
)
encoder
.
setBuffer
(
param
.
newScaleBuffer
,
offset
:
0
,
index
:
3
)
encoder
.
setBuffer
(
param
.
newBiaseBuffer
,
offset
:
0
,
index
:
4
)
encoder
.
dispatch
(
computePipline
:
pipline
,
outTexture
:
param
.
outputTexture
)
encoder
.
endEncoding
()
encoder
.
setTexture
(
param
.
input
.
metalTexture
,
index
:
0
)
encoder
.
setTexture
(
param
.
output
.
metalTexture
,
index
:
1
)
encoder
.
setBytes
(
&
metalParam
,
length
:
MemoryLayout
<
MetalConvParam
>.
size
,
index
:
0
)
encoder
.
setBuffer
(
param
.
filter
.
buffer
,
offset
:
0
,
index
:
1
)
encoder
.
setBuffer
(
param
.
newScale
!
,
offset
:
0
,
index
:
3
)
encoder
.
setBuffer
(
param
.
newBiase
!
,
offset
:
0
,
index
:
4
)
encoder
.
dispatch
(
computePipline
:
pipline
,
outTexture
:
param
.
output
.
metalTexture
)
encoder
.
endEncoding
()
}
public
func
test
(
commandBuffer
:
MTLCommandBuffer
,
param
:
ConvBNReluTestParam
)
{
guard
let
encoder
=
commandBuffer
.
makeComputeCommandEncoder
()
else
{
fatalError
()
}
encoder
.
setTexture
(
param
.
inputTexture
,
index
:
0
)
encoder
.
setTexture
(
param
.
outputTexture
,
index
:
1
)
var
inMetalParam
=
param
.
metalParam
encoder
.
setBytes
(
&
inMetalParam
,
length
:
MemoryLayout
<
MetalConvParam
>.
size
,
index
:
0
)
encoder
.
setBuffer
(
param
.
filterBuffer
,
offset
:
0
,
index
:
1
)
encoder
.
setBuffer
(
param
.
biaseBuffer
,
offset
:
0
,
index
:
2
)
encoder
.
setBuffer
(
param
.
newScaleBuffer
,
offset
:
0
,
index
:
3
)
encoder
.
setBuffer
(
param
.
newBiaseBuffer
,
offset
:
0
,
index
:
4
)
encoder
.
dispatch
(
computePipline
:
pipline
,
outTexture
:
param
.
outputTexture
)
encoder
.
endEncoding
()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/MulticlassNMSKernel.swift
浏览文件 @
0bb67049
...
...
@@ -26,6 +26,6 @@ class MulticlassNMSKernel<P: PrecisionType>: Kernel, Computable{
}
required
init
(
device
:
MTLDevice
,
param
:
MulticlassNMSParam
<
P
>
)
{
super
.
init
(
device
:
device
,
inFunctionName
:
"priorbox"
)
super
.
init
(
device
:
device
,
inFunctionName
:
"prior
_
box"
)
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @
0bb67049
...
...
@@ -33,7 +33,7 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
var
metalParam
:
PriorBoxMetalParam
!
required
init
(
device
:
MTLDevice
,
param
:
PriorBoxParam
<
P
>
)
{
super
.
init
(
device
:
device
,
inFunctionName
:
"priorbox"
)
super
.
init
(
device
:
device
,
inFunctionName
:
"prior
_
box"
)
param
.
output
.
initTexture
(
device
:
device
,
transpose
:
[
2
,
0
,
1
,
3
])
param
.
outputVariances
.
initTexture
(
device
:
device
,
transpose
:
[
2
,
0
,
1
,
3
])
...
...
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Texture2DTo2DArrayKernel.swift
浏览文件 @
0bb67049
...
...
@@ -15,23 +15,24 @@
import
Foundation
struct
Texture2DTo2DArrayParam
{
let
input
:
MTLTexture
let
output
:
MTLTexture
let
expectDim
:
Dim
let
input
:
MTLTexture
let
output
:
MTLTexture
let
expectDim
:
Dim
}
class
Texture2DTo2DArrayKernel
<
P
:
PrecisionType
>
:
Kernel
,
Computable
{
func
compute
(
commandBuffer
:
MTLCommandBuffer
,
param
:
FeedParam
<
P
>
)
throws
{
guard
let
encoder
=
commandBuffer
.
makeComputeCommandEncoder
()
else
{
throw
PaddleMobileError
.
predictError
(
message
:
" encode is nil"
)
}
encoder
.
setTexture
(
param
.
input
.
mtlTexture
,
index
:
0
)
encoder
.
setTexture
(
param
.
output
.
metalTexture
,
index
:
1
)
encoder
.
dispatch
(
computePipline
:
pipline
,
outTexture
:
param
.
input
.
mtlTexture
)
encoder
.
endEncoding
()
}
required
init
(
device
:
MTLDevice
,
param
:
FeedParam
<
P
>
)
{
super
.
init
(
device
:
device
,
inFunctionName
:
"texture2d_to_2d_array"
)
func
compute
(
commandBuffer
:
MTLCommandBuffer
,
param
:
FeedParam
<
P
>
)
throws
{
guard
let
encoder
=
commandBuffer
.
makeComputeCommandEncoder
()
else
{
throw
PaddleMobileError
.
predictError
(
message
:
" encode is nil"
)
}
encoder
.
setTexture
(
param
.
input
.
mtlTexture
,
index
:
0
)
encoder
.
setTexture
(
param
.
output
.
metalTexture
,
index
:
1
)
encoder
.
dispatch
(
computePipline
:
pipline
,
outTexture
:
param
.
input
.
mtlTexture
)
encoder
.
endEncoding
()
}
required
init
(
device
:
MTLDevice
,
param
:
FeedParam
<
P
>
)
{
param
.
output
.
initTexture
(
device
:
device
,
transpose
:
[
0
,
2
,
3
,
1
])
super
.
init
(
device
:
device
,
inFunctionName
:
"texture2d_to_2d_array"
)
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvKernel.metal
浏览文件 @
0bb67049
...
...
@@ -699,3 +699,144 @@ kernel void depthwise_conv_add_3x3(texture2d_array<float, access::sample> inText
outTexture.write(output, gid.xy, gid.z);
}
#pragma mark - conv bn relu
kernel void conv_batch_norm_relu_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam ¶m [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 1;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = fmax(output * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam ¶m [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
float4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), i);
for (int j = 0; j < 9; ++j) {
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = fmax(output * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam ¶m [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
uint output_slice = gid.z;
ushort2 stride = ushort2(param.strideX, param.strideY);
ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint weithTo = gid.z * kernelHXW * 4;
float4 output = float4(0.0);
float4 inputs[9];
inputs[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), output_slice);
inputs[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), output_slice);
inputs[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), output_slice);
inputs[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), output_slice);
inputs[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), output_slice);
inputs[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), output_slice);
inputs[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), output_slice);
inputs[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), output_slice);
inputs[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), output_slice);
for (int j = 0; j < 9; ++j) {
float4 input = inputs[j];
output.x += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = fmax(output * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
浏览文件 @
0bb67049
...
...
@@ -27,7 +27,7 @@ class PriorBoxParam<P: PrecisionType>: OpParam {
aspectRatios
=
try
PriorBoxParam
.
getAttr
(
key
:
"aspect_ratios"
,
attrs
:
opDesc
.
attrs
)
variances
=
try
PriorBoxParam
.
getAttr
(
key
:
"variances"
,
attrs
:
opDesc
.
attrs
)
flip
=
try
PriorBoxParam
.
getAttr
(
key
:
"flip"
,
attrs
:
opDesc
.
attrs
)
clip
=
try
PriorBoxParam
.
getAttr
(
key
:
"cl
o
p"
,
attrs
:
opDesc
.
attrs
)
clip
=
try
PriorBoxParam
.
getAttr
(
key
:
"cl
i
p"
,
attrs
:
opDesc
.
attrs
)
stepW
=
try
PriorBoxParam
.
getAttr
(
key
:
"step_w"
,
attrs
:
opDesc
.
attrs
)
stepH
=
try
PriorBoxParam
.
getAttr
(
key
:
"step_h"
,
attrs
:
opDesc
.
attrs
)
offset
=
try
PriorBoxParam
.
getAttr
(
key
:
"offset"
,
attrs
:
opDesc
.
attrs
)
...
...
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