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Paddle-Lite
提交 38e9f34c 编写于 作者: L liuruilong
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format files and adjust architecture

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浏览文件 @ 38e9f34c
......@@ -96,3 +96,6 @@ metal/paddle-mobile/paddle-mobile/CPU/libpaddle-mobile.a
metal/paddle-mobile-demo/paddle-mobile-demo/images
metal/paddle-mobile-demo/paddle-mobile-demo/models
metal/paddle-mobile-demo/paddle-mobile-demo/Resources
metal/paddle-mobile-demo/paddle-mobile-demo/Resources/images
metal/paddle-mobile-demo/paddle-mobile-demo/Resources/models
metal/paddle-mobile-demo/paddle-mobile-demo.xcodeproj/project.pbxproj
浏览文件 @ 38e9f34c
......@@ -10,13 +10,26 @@
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......@@ -31,7 +44,6 @@
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FC9797CF21D6506F00F2FD90 /* mingren.jpg in Resources */ = {isa = PBXBuildFile; fileRef = FC9797CE21D6506F00F2FD90 /* mingren.jpg */; };
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......@@ -59,7 +71,6 @@
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......@@ -69,6 +80,23 @@
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......@@ -84,7 +112,6 @@
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FC9797D021D6616600F2FD90 /* BufferToTexture.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = BufferToTexture.metal; sourceTree = "<group>"; };
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......@@ -145,8 +172,11 @@
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FC2BFD3F21DF5DDF00C262B2 /* OCInterface */,
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FC039B8120E11C550081E9F8 /* AppDelegate.swift */,
......@@ -155,10 +185,7 @@
FC039B8820E11C560081E9F8 /* Assets.xcassets */,
FC039B8A20E11C560081E9F8 /* LaunchScreen.storyboard */,
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......@@ -166,14 +193,16 @@
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FC9797BD21D6045B00F2FD90 /* banana.jpeg */,
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name = images;
path = ../../images;
path = images;
sourceTree = "<group>";
};
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......@@ -183,8 +212,37 @@
FC704C1B21D237FC00F98BAB /* vision_model */,
FC704C1621D2375300F98BAB /* superresoltion */,
);
name = models;
path = ../../models;
path = models;
sourceTree = "<group>";
};
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children = (
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FC2BFD4221DF5E1E00C262B2 /* SuperResolutionNet.swift */,
);
path = OCInterface;
sourceTree = "<group>";
};
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isa = PBXGroup;
children = (
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sourceTree = "<group>";
};
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......@@ -235,9 +293,15 @@
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FC2BFD2B21DF3FE900C262B2 /* Genet.swift */,
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sourceTree = "<group>";
......@@ -319,16 +383,19 @@
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......@@ -380,15 +447,24 @@
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FC039B8220E11C550081E9F8 /* AppDelegate.swift in Sources */,
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FC2BFD4421DF5E1E00C262B2 /* SuperResolutionNet.swift in Sources */,
FC2BFD3E21DF5CE800C262B2 /* PreProcessKernel.metal in Sources */,
FC2BFD3821DF46DE00C262B2 /* OCDemoViewController.m in Sources */,
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runOnlyForDeploymentPostprocessing = 0;
};
......
metal/paddle-mobile-demo/paddle-mobile-demo/Base.lproj/Main.storyboard
浏览文件 @ 38e9f34c
<?xml version="1.0" encoding="UTF-8"?>
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<capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
</dependencies>
......@@ -20,7 +19,7 @@
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<button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="roundedRect" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="TQt-X9-PdF">
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<state key="normal" title="Button"/>
<connections>
<action selector="predictAct:" destination="Vwd-lt-764" eventType="touchUpInside" id="d4z-Cv-6jY"/>
......@@ -60,7 +59,7 @@
<nil key="highlightedColor"/>
</label>
<pickerView contentMode="scaleToFill" translatesAutoresizingMaskIntoConstraints="NO" id="DlO-dk-RMr">
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</constraints>
......@@ -83,6 +82,9 @@
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<color key="backgroundColor" white="0.0" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
<constraints>
<constraint firstAttribute="width" secondItem="wUL-9N-u1V" secondAttribute="height" multiplier="21:10" id="cp7-bd-CvU"/>
</constraints>
<state key="normal" title="Image">
<color key="titleColor" white="1" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
</state>
......
metal/paddle-mobile-demo/paddle-mobile-demo/LoadPointerViewController.h 已删除 100644 → 0
浏览文件 @ 4dc6de9f
//
// LoadPointerViewController.h
// paddle-mobile-demo
//
// Created by Xiao,Haichun on 2018/9/19.
// Copyright © 2018年 orange. All rights reserved.
//
#import <UIKit/UIKit.h>
@interface LoadPointerViewController : UIViewController
@end
metal/paddle-mobile-demo/paddle-mobile-demo/Net/MetalHelper.swift → metal/paddle-mobile-demo/paddle-mobile-demo/MetalHelper.swift
浏览文件 @ 38e9f34c
......@@ -27,7 +27,4 @@ public class MetalHelper {
queue = device.makeCommandQueue()!
textureLoader = MTKTextureLoader.init(device: device)
}
}
metal/paddle-mobile/paddle-mobile/CustomNet/Genet.swift → metal/paddle-mobile-demo/paddle-mobile-demo/Net/Genet.swift
浏览文件 @ 38e9f34c
......@@ -13,41 +13,35 @@
limitations under the License. */
import Foundation
import paddle_mobile
public class Genet: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
means = [128.0, 128.0, 128.0]
scale = 0.017
except = 0
modelPath = Bundle.main.path(forResource: "genet_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "genet_params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = GenetPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 128, 128, 3])
inputDim = Dim.init(inDim: [1, 128, 128, 3])
}
@objc override public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
super.init(device:device,paramPointer:paramPointer,paramSize:paramSize,modePointer:modePointer,modelSize:modelSize)
means = [128.0, 128.0, 128.0]
scale = 0.017
except = 0
modelPath = ""
paramPath = ""
modelDir = ""
@objc override public init(device: MTLDevice, paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
super.init(device: device,
paramPointer: paramPointer,
paramSize: paramSize,
modePointer: modePointer,
modelSize: modelSize)
preprocessKernel = GenetPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 128, 128, 3])
inputDim = Dim.init(inDim: [1, 128, 128, 3])
}
class GenetPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = Shape.init(inWidth: 128, inHeight: 128, inChannel: 3)
super.init(device: device, inFunctionName: "genet_preprocess", outputDim: s, usePaddleMobileLib: false)
super.init(device: device, inFunctionName: "genet_preprocess", outputDim: s, metalLoadModel: .LoadMetalInDefaultLib, metalLibPath: nil)
}
}
override public func resultStr(res: ResultHolder) -> String {
// fatalError()
return " \(res.result[0]) ... "
}
......
metal/paddle-mobile/paddle-mobile/CustomNet/MobileNet.swift → metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobileNet.swift
浏览文件 @ 38e9f34c
......@@ -13,13 +13,14 @@
limitations under the License. */
import Foundation
import paddle_mobile
public class MobileNet: Net{
class MobilenetPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = Shape.init(inWidth: 224, inHeight: 224, inChannel: 3)
super.init(device: device, inFunctionName: "mobilenet_preprocess", outputDim: s, usePaddleMobileLib: false)
super.init(device: device, inFunctionName: "mobilenet_preprocess", outputDim: s, metalLoadModel: .LoadMetalInDefaultLib, metalLibPath: nil)
}
}
......@@ -53,14 +54,13 @@ public class MobileNet: Net{
override public init(device: MTLDevice) {
super.init(device: device)
means = [123.68, 116.78, 103.94]
scale = 0.017
except = 0
modelPath = Bundle.main.path(forResource: "mobilenet_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "mobilenet_params", ofType: nil) ?! "para null"
modelDir = ""
// metalLoadMode = .LoadMetalInCustomMetalLib
// metalLibPath = Bundle.main.path(forResource: "PaddleMobileMetal", ofType: "metallib") ?! " can't be nil "
preprocessKernel = MobilenetPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 224, 224, 3])
inputDim = Dim.init(inDim: [1, 224, 224, 3])
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobileNetCombined.swift 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
import paddle_mobile
public class MobileNetCombined: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
except = 0
modelPath = Bundle.main.path(forResource: "combined_mobilenet_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "combined_mobilenet_params", ofType: nil) ?! "para null"
inputDim = Dim.init(inDim: [1, 224, 224, 3])
// metalLoadMode = .LoadMetalInCustomMetalLib
// metalLibPath = Bundle.main.path(forResource: "PaddleMobileMetal", ofType: "metallib") ?! " can't be nil "
}
override public func resultStr(res: ResultHolder) -> String {
return " \(res.result[0]) ... "
}
}
metal/paddle-mobile/paddle-mobile/CustomNet/MobileNetSSD.swift → metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobileNetSSD.swift
浏览文件 @ 38e9f34c
......@@ -13,36 +13,35 @@
limitations under the License. */
import Foundation
import paddle_mobile
public class MobileNet_ssd_hand: Net{
public class MobileNet_ssd_hand: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
means = [123.68, 116.78, 103.94]
scale = 0.017
except = 2
modelPath = Bundle.main.path(forResource: "ssd_hand_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "ssd_hand_params", ofType: nil) ?! "para null"
modelDir = ""
// metalLoadMode = .LoadMetalInCustomMetalLib
// metalLibPath = Bundle.main.path(forResource: "PaddleMobileMetal", ofType: "metallib") ?! " can't be nil "
preprocessKernel = MobilenetssdPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 300, 300, 3])
inputDim = Dim.init(inDim: [1, 300, 300, 3])
}
@objc override public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
super.init(device:device,paramPointer:paramPointer,paramSize:paramSize,modePointer:modePointer,modelSize:modelSize)
means = [123.68, 116.78, 103.94]
scale = 0.017
except = 2
modelPath = ""
paramPath = ""
modelDir = ""
// metalLoadMode = .LoadMetalInCustomMetalLib
// metalLibPath = Bundle.main.path(forResource: "PaddleMobileMetal", ofType: "metallib") ?! " can't be nil "
preprocessKernel = MobilenetssdPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 300, 300, 3])
inputDim = Dim.init(inDim: [1, 300, 300, 3])
}
class MobilenetssdPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = Shape.init(inWidth: 300, inHeight: 300, inChannel: 3)
super.init(device: device, inFunctionName: "mobilenet_ssd_preprocess", outputDim: s, usePaddleMobileLib: false)
super.init(device: device, inFunctionName: "mobilenet_ssd_preprocess", outputDim: s, metalLoadModel: .LoadMetalInDefaultLib, metalLibPath: nil)
}
}
......@@ -50,7 +49,7 @@ public class MobileNet_ssd_hand: Net{
return " \(res)"
}
override func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
override public func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
// guard let interRes = paddleMobileRes.intermediateResults else {
// fatalError(" need have inter result ")
......
metal/paddle-mobile/paddle-mobile/CustomNet/MobilenetSSD_AR.swift → metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobilenetSSD_AR.swift
浏览文件 @ 38e9f34c
......@@ -13,36 +13,29 @@
limitations under the License. */
import Foundation
import paddle_mobile
public class MobileNet_ssd_AR: Net{
public class MobileNet_ssd_AR: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
means = [103.94, 116.78, 123.68]
scale = 1
except = 2
modelPath = Bundle.main.path(forResource: "ar_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "ar_params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = MobilenetssdPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 160, 160, 3])
inputDim = Dim.init(inDim: [1, 160, 160, 3])
}
@objc override public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
super.init(device:device,paramPointer:paramPointer,paramSize:paramSize,modePointer:modePointer,modelSize:modelSize)
means = [103.94, 116.78, 123.68]
scale = 1
except = 2
modelPath = ""
paramPath = ""
modelDir = ""
preprocessKernel = MobilenetssdPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 160, 160, 3])
inputDim = Dim.init(inDim: [1, 160, 160, 3])
}
class MobilenetssdPreProccess: CusomKernel {
init(device: MTLDevice) {
let s = Shape.init(inWidth: 160, inHeight: 160, inChannel: 3)
super.init(device: device, inFunctionName: "mobilent_ar_preprocess", outputDim: s, usePaddleMobileLib: false)
super.init(device: device, inFunctionName: "mobilent_ar_preprocess", outputDim: s, metalLoadModel: .LoadMetalInDefaultLib, metalLibPath: nil)
}
}
......@@ -50,18 +43,19 @@ public class MobileNet_ssd_AR: Net{
return " \(res.result[0])"
}
override func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
guard let interRes = paddleMobileRes.intermediateResults else {
fatalError(" need have inter result ")
}
guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? FetchHolder else {
fatalError(" need score ")
}
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? FetchHolder else {
override public func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
fatalError()
}
// guard let interRes = paddleMobileRes.intermediateResults else {
// fatalError(" need have inter result ")
// }
//
// guard let scores = interRes["Scores"], scores.count > 0, let score = scores[0] as? FetchHolder else {
// fatalError(" need score ")
// }
//
// guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? FetchHolder else {
// fatalError()
// }
// let startDate = Date.init()
......@@ -72,19 +66,19 @@ public class MobileNet_ssd_AR: Net{
//
// print((0..<bbox.capacity).map{ bbox.result[$0] }.strideArray())
let nmsCompute = NMSCompute.init()
nmsCompute.scoreThredshold = 0.25
nmsCompute.nmsTopK = 100
nmsCompute.keepTopK = 100
nmsCompute.nmsEta = 1.0
nmsCompute.nmsThreshold = 0.449999988
nmsCompute.background_label = 0;
nmsCompute.scoreDim = [NSNumber.init(value: score.dim[0]), NSNumber.init(value: score.dim[1]), NSNumber.init(value: score.dim[2])]
nmsCompute.bboxDim = [NSNumber.init(value: bbox.dim[0]), NSNumber.init(value: bbox.dim[1]), NSNumber.init(value: bbox.dim[2])]
guard let result = nmsCompute.compute(withScore: score.result, andBBoxs: bbox.result) else {
fatalError( " result error " )
}
let resultHolder = ResultHolder.init(inResult: result.output, inCapacity: Int(result.outputSize))
// let nmsCompute = NMSCompute.init()
// nmsCompute.scoreThredshold = 0.25
// nmsCompute.nmsTopK = 100
// nmsCompute.keepTopK = 100
// nmsCompute.nmsEta = 1.0
// nmsCompute.nmsThreshold = 0.449999988
// nmsCompute.background_label = 0;
// nmsCompute.scoreDim = [NSNumber.init(value: score.dim[0]), NSNumber.init(value: score.dim[1]), NSNumber.init(value: score.dim[2])]
// nmsCompute.bboxDim = [NSNumber.init(value: bbox.dim[0]), NSNumber.init(value: bbox.dim[1]), NSNumber.init(value: bbox.dim[2])]
// guard let result = nmsCompute.compute(withScore: score.result, andBBoxs: bbox.result) else {
// fatalError( " result error " )
// }
// let resultHolder = ResultHolder.init(inResult: result.output, inCapacity: Int(result.outputSize))
// for i in 0..<Int(result.outputSize) {
//
// print("i \(i) : \(result.output[i])")
......@@ -92,10 +86,11 @@ public class MobileNet_ssd_AR: Net{
// print(Date.init().timeIntervalSince(startDate))
// print(resultHolder.result![0])
return resultHolder
// return resultHolder
}
override func updateProgram(program: Program) {
// override func updateProgram(program: Program) {
// for i in [56, 66, 76, 86, 93, 99] {
// let opDesc = program.programDesc.blocks[0].ops[i]
// let output = opDesc.outputs["Out"]!.first!
......@@ -148,6 +143,6 @@ public class MobileNet_ssd_AR: Net{
// print(" split axis \(opDesc.attrs["axis"])")
// }
// 99
}
// }
}
metal/paddle-mobile-demo/paddle-mobile-demo/Net/PaddleMobile.swift 已删除 100644 → 0
浏览文件 @ 4dc6de9f
//
// PaddleMobile.swift
// paddle-mobile-demo
//
// Created by liuRuiLong on 2018/9/5.
// Copyright © 2018年 orange. All rights reserved.
//
import Foundation
metal/paddle-mobile-demo/paddle-mobile-demo/Net/PreProcessKernel.metal
浏览文件 @ 38e9f34c
......@@ -115,23 +115,3 @@ kernel void mobilent_ar_preprocess_half(texture2d<half, access::read> inTexture
const half4 inColor = (inTexture.read(gid) * 255.0 - means) * 0.017;
outTexture.write(half4(inColor.z, inColor.y, inColor.x, 0.0f), gid);
}
kernel void scale(texture2d<float, access::sample> inTexture [[texture(0)]], texture2d<float, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return;
float w_stride = inTexture.get_width() / outTexture.get_width();
float h_stride = inTexture.get_height() / outTexture.get_height();
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x * w_stride, gid.y * h_stride), 0);
outTexture.write(input, gid);
}
kernel void scale_half(texture2d<float, access::sample> inTexture [[texture(0)]], texture2d<half, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return;
float w_stride = inTexture.get_width() / outTexture.get_width();
float h_stride = inTexture.get_height() / outTexture.get_height();
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x * w_stride, gid.y * h_stride), 0);
outTexture.write(half4(input), gid);
}
metal/paddle-mobile-demo/paddle-mobile-demo/Net/YoloNet.swift 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Metal
import Foundation
import paddle_mobile
public class YoloNet: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
except = 0
modelPath = Bundle.main.path(forResource: "yolo_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "yolo_params", ofType: nil) ?! "para null"
inputDim = Dim.init(inDim: [1, 416, 416, 3])
// metalLoadMode = .LoadMetalInCustomMetalLib
// metalLibPath = Bundle.main.path(forResource: "PaddleMobileMetal", ofType: "metallib") ?! " can't be nil "
}
override public func resultStr(res: ResultHolder) -> String {
return " \(res.result[0]) ... "
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/OCDemo/LoadPointerViewController.h 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import <UIKit/UIKit.h>
@interface LoadPointerViewController : UIViewController
@end
metal/paddle-mobile-demo/paddle-mobile-demo/LoadPointerViewController.m → metal/paddle-mobile-demo/paddle-mobile-demo/OCDemo/LoadPointerViewController.m
浏览文件 @ 38e9f34c
//
// LoadPointerViewController.m
// paddle-mobile-demo
//
// Created by Xiao,Haichun on 2018/9/19.
// Copyright © 2018年 orange. All rights reserved.
//
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import "PaddleMobileGPU.h"
#import "LoadPointerViewController.h"
#import <Metal/Metal.h>
#import "paddle-mobile-demo-Bridging-Header.h"
#import <Metal/Metal.h>
@interface LoadPointerViewController ()
@property (strong, nonatomic) id<MTLDevice> device;
......
metal/paddle-mobile-demo/paddle-mobile-demo/OCDemo/OCDemoViewController.h 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import <Foundation/Foundation.h>
NS_ASSUME_NONNULL_BEGIN
@interface OCDemoViewController : NSObject
@end
NS_ASSUME_NONNULL_END
metal/paddle-mobile-demo/paddle-mobile-demo/OCDemo/OCDemoViewController.m 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import "OCDemoViewController.h"
@implementation OCDemoViewController
@end
metal/paddle-mobile/paddle-mobile/PaddleMobileGPU.h → metal/paddle-mobile-demo/paddle-mobile-demo/OCInterface/PaddleMobileGPU.h
浏览文件 @ 38e9f34c
......@@ -16,9 +16,8 @@
#import <Foundation/Foundation.h>
typedef enum : NSUInteger {
MobileNetType,
MobileNetSSDType,
GenetType,
SuperResolutionNetType,
MobileNetSSDType
} NetType;
@interface PaddleMobileGPUResult: NSObject
......
metal/paddle-mobile/paddle-mobile/PaddleMobileGPU.m → metal/paddle-mobile-demo/paddle-mobile-demo/OCInterface/PaddleMobileGPU.m
浏览文件 @ 38e9f34c
......@@ -12,11 +12,10 @@
See the License for the specific language governing permissions and
limitations under the License. */
#import "paddle_mobile.h"
#import "PaddleMobileGPU.h"
#import <Foundation/Foundation.h>
#import <paddle_mobile/paddle_mobile-Swift.h>
#import <paddle_mobile_demo-Swift.h>
@implementation ModelConfig
@end
......@@ -53,12 +52,10 @@
self = [super init];
if (self) {
Net *net = nil;
if (netType == GenetType) {
net = [[Genet alloc] initWithDevice:queue.device paramPointer:config.paramPointer paramSize:config.paramSize modePointer:config.modelPointer modelSize:config.modelSize];
if (netType == SuperResolutionNetType) {
net = [[SuperResolutionNet alloc] initWithDevice:queue.device];
} else if (netType == MobileNetSSDType) {
net = [[MobileNet_ssd_AR alloc] initWithDevice:queue.device paramPointer:config.paramPointer paramSize:config.paramSize modePointer:config.modelPointer modelSize:config.modelSize];
} else if (netType == MobileNetType) {
}
runner = [[Runner alloc] initInNet:net commandQueue:queue];
}
......
metal/paddle-mobile/paddle-mobile/CustomNet/SuperResolutionNet.swift → metal/paddle-mobile-demo/paddle-mobile-demo/OCInterface/SuperResolutionNet.swift
浏览文件 @ 38e9f34c
......@@ -13,31 +13,25 @@
limitations under the License. */
import Foundation
import paddle_mobile
public class SuperResolutionNet: Net{
@objc public class SuperResolutionNet: Net{
override public func resultStr(res: ResultHolder) -> String {
return "未实现"
}
override public init(device: MTLDevice) {
@objc override public init(device: MTLDevice) {
super.init(device: device)
means = [0.0, 0.0, 0.0]
scale = 1.0
except = 0
modelPath = Bundle.main.path(forResource: "super_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "super_params", ofType: nil) ?! "para null"
modelDir = ""
preprocessKernel = nil
// inputDim_ = Dim.init(inDim: [1, Int(552 * 1.414), Int(310 * 1.414), 1])
inputDim_ = Dim.init(inDim: [1, 224, 224, 1])
}
override func updateProgram(program: Program) {
guard needUpdateProgram else {
return
inputDim = Dim.init(inDim: [1, 224, 224, 1])
// metalLoadMode = .LoadMetalInCustomMetalLib
// metalLibPath = Bundle.main.path(forResource: "PaddleMobileMetal", ofType: "metallib") ?! " can't be nil "
}
override public func updateProgram(program: Program) {
// n h w c
for block in program.programDesc.blocks {
for varDesc in block.vars {
......@@ -47,8 +41,9 @@ public class SuperResolutionNet: Net{
if let texture = varEle as? Texture {
let newDim = Dim.init(inDim: [texture.dim[0], inputDim[1], inputDim[2], texture.tensorDim[1]])
print(" var desc name " + varDesc.name + " new dim" + "\(newDim)")
texture.updateDims(inTensorDim: Dim.init(inDim: [texture.tensorDim[0], texture.tensorDim[1], inputDim[1], inputDim[2]]), inDim: newDim)
texture.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: computePrecision)
texture.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: GlobalConfig.shared.computePrecision)
let output: FetchHolder = program.scope.output() as! FetchHolder
output.dim = newDim
......@@ -60,7 +55,6 @@ public class SuperResolutionNet: Net{
}
}
}
needUpdateProgram = false
}
}
metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift
浏览文件 @ 38e9f34c
......@@ -18,7 +18,6 @@ import CoreMedia
import paddle_mobile
import MetalPerformanceShaders
class FileReader {
let file: UnsafeMutablePointer<FILE>
let fileSize: Int
......@@ -53,16 +52,12 @@ enum Platform {
let platformSupport: [(Platform, String)] = [(.GPU, "GPU")]
enum SupportModel: String{
// case mobilenet = "mobilenet"
// case mobilenet_ssd = "mobilenetssd"
case yolo = "yolo"
case mobilenet_combined = "mobilenet_combined"
case super_resolution = "superresoltion"
case mobilenet = "mobilenet"
static func supportedModels() -> [SupportModel] {
// .mobilenet,
// .mobilenet_ssd,
return [.super_resolution, .yolo, .mobilenet_combined, .mobilenet]
}
}
......@@ -94,24 +89,25 @@ class ViewController: UIViewController {
@IBAction func loadAct(_ sender: Any) {
runner = Runner.init(inNet: netSupport[modelType]!, commandQueue: MetalHelper.shared.queue)
if platform == .GPU {
let filePath = Bundle.main.path(forResource: "mingren_input_data", ofType: nil)
let fileReader = try! FileReader.init(paramPath: filePath!)
let pointer: UnsafeMutablePointer<Float32> = fileReader.read()
let buffer = MetalHelper.shared.device.makeBuffer(length: fileReader.fileSize, options: .storageModeShared)
buffer?.contents().copyMemory(from: pointer, byteCount: fileReader.fileSize)
// let filePath = Bundle.main.path(forResource: "mingren_input_data", ofType: nil)
// let fileReader = try! FileReader.init(paramPath: filePath!)
// let pointer: UnsafeMutablePointer<Float32> = fileReader.read()
//
//
// let buffer = MetalHelper.shared.device.makeBuffer(length: fileReader.fileSize, options: .storageModeShared)
//
// buffer?.contents().copyMemory(from: pointer, byteCount: fileReader.fileSize)
if self.toPredictTexture == nil {
runner.getTexture(inBuffer: buffer!) { [weak self] (texture) in
// runner.getTexture(inBuffer: buffer!) { [weak self] (texture) in
// self?.toPredictTexture = texture
// }
runner.getTexture(image: selectImage!.cgImage!) { [weak self] (texture) in
self?.toPredictTexture = texture
}
// runner.getTexture(image: selectImage!.cgImage!) { [weak self] (texture) in
// }
}
} else {
fatalError( " unsupport " )
......@@ -178,13 +174,14 @@ class ViewController: UIViewController {
modelPickerView.dataSource = self
threadPickerView.delegate = self
threadPickerView.dataSource = self
if let image = UIImage.init(named: "test.jpg") {
if let image = UIImage.init(named: "classify-img-output.png") {
selectImage = image
selectImageView.image = image
} else {
print("请添加测试图片")
}
GlobalConfig.shared.computePrecision = .Float32
// if platform == .CPU {
// inputPointer = runner.preproccess(image: selectImage!.cgImage!)
......
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ 38e9f34c
......@@ -28,8 +28,6 @@
4AF9287921341661005B6C3A /* Softmax.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9287821341661005B6C3A /* Softmax.metal */; };
4AF928822135673D005B6C3A /* ConcatKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF928812135673D005B6C3A /* ConcatKernel.metal */; };
4AF9288421357BE3005B6C3A /* Elementwise.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9288321357BE3005B6C3A /* Elementwise.metal */; };
C28FDF8421B7858F0054EFAC /* MobileNetCombined.swift in Sources */ = {isa = PBXBuildFile; fileRef = C28FDF8221B7858F0054EFAC /* MobileNetCombined.swift */; };
C28FDF8521B7858F0054EFAC /* YoloNet.swift in Sources */ = {isa = PBXBuildFile; fileRef = C28FDF8321B7858F0054EFAC /* YoloNet.swift */; };
C28FE02F21BA68C00054EFAC /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = C28FE02C21BA68C00054EFAC /* Metal.framework */; };
C28FE03021BA68C00054EFAC /* MetalPerformanceShaders.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = C28FE02D21BA68C00054EFAC /* MetalPerformanceShaders.framework */; };
C28FE03121BA68C00054EFAC /* MetalKit.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = C28FE02E21BA68C00054EFAC /* MetalKit.framework */; };
......@@ -64,18 +62,16 @@
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */; };
FC1B16B320EC9A4F00678B91 /* Kernels.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC1B16B220EC9A4F00678B91 /* Kernels.metal */; };
FC1CF3F721D4B4C400F7392E /* Runner.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC1CF3F621D4B4C400F7392E /* Runner.swift */; };
FC292C5421421B2F00CF622F /* PaddleMobileGPU.h in Headers */ = {isa = PBXBuildFile; fileRef = FC292C5321421B2E00CF622F /* PaddleMobileGPU.h */; settings = {ATTRIBUTES = (Public, ); }; };
FC292C5621421B4600CF622F /* PaddleMobileGPU.m in Sources */ = {isa = PBXBuildFile; fileRef = FC292C5521421B4600CF622F /* PaddleMobileGPU.m */; };
FC292C81214255BD00CF622F /* CPUCompute.mm in Sources */ = {isa = PBXBuildFile; fileRef = FC292C7C214255BC00CF622F /* CPUCompute.mm */; };
FC292C82214255BD00CF622F /* MobileNetSSD.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC292C7E214255BC00CF622F /* MobileNetSSD.swift */; };
FC292C872142624800CF622F /* Genet.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC292C862142624800CF622F /* Genet.swift */; };
FC33B0F02147659000714A93 /* MobileNet.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC33B0EF2147659000714A93 /* MobileNet.swift */; };
FC2BFCC221DF2F9100C262B2 /* GlobalConfig.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC2BFCC121DF2F9100C262B2 /* GlobalConfig.swift */; };
FC2BFD4621DF685F00C262B2 /* Scale.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC2BFD4521DF685F00C262B2 /* Scale.swift */; };
FC2BFD4A21DF81DE00C262B2 /* Kernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC2BFD4921DF81DE00C262B2 /* Kernel.swift */; };
FC2BFD4E21DF820B00C262B2 /* ConvAddBatchNormReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC2BFD4D21DF820A00C262B2 /* ConvAddBatchNormReluOp.swift */; };
FC2BFD5121DF8E0400C262B2 /* Scale.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC2BFD5021DF8E0400C262B2 /* Scale.metal */; };
FC3602CC2108819F00FACB58 /* PaddleMobileUnitTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC3602CB2108819F00FACB58 /* PaddleMobileUnitTest.swift */; };
FC4CB74920F0B954007C0C6D /* ConvKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC4CB74820F0B954007C0C6D /* ConvKernel.metal */; };
FC4CB74B20F12C30007C0C6D /* ProgramOptimize.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC4CB74A20F12C30007C0C6D /* ProgramOptimize.swift */; };
FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC5163F520EF556E00636C28 /* Texture2DTo2DArrayKernel.swift */; };
FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC60DB8820E9AAA500FF203F /* MetalExtension.swift */; };
FC704C2721D2385100F98BAB /* SuperResolutionNet.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC704C2621D2385100F98BAB /* SuperResolutionNet.swift */; };
FC803BBF214CB65A0094B8E5 /* ConvAddPreluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC803BBE214CB65A0094B8E5 /* ConvAddPreluOp.swift */; };
FC803BC1214CB77A0094B8E5 /* ConvAddPreluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC0214CB77A0094B8E5 /* ConvAddPreluKernel.swift */; };
FC803BC3214CB79C0094B8E5 /* ConvAddPreluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC803BC2214CB79C0094B8E5 /* ConvAddPreluKernel.metal */; };
......@@ -85,8 +81,6 @@
FC82735920E3C04200BE430A /* OpCreator.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC82735820E3C04200BE430A /* OpCreator.swift */; };
FC9797C921D6101D00F2FD90 /* ResizeBilinearOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9797C821D6101D00F2FD90 /* ResizeBilinearOp.swift */; };
FC9797CB21D6102D00F2FD90 /* ResizeBilinearKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9797CA21D6102D00F2FD90 /* ResizeBilinearKernel.swift */; };
FC9797CD21D61B2E00F2FD90 /* CPUCompute.h in Headers */ = {isa = PBXBuildFile; fileRef = FC292C7D214255BC00CF622F /* CPUCompute.h */; settings = {ATTRIBUTES = (Public, ); }; };
FC9A19E32148C31300CD9CBF /* MobilenetSSD_AR.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9A19E22148C31300CD9CBF /* MobilenetSSD_AR.swift */; };
FC9C2A0D21D3D185005856C6 /* FetchKernel.inc.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC9C2A0C21D3D185005856C6 /* FetchKernel.inc.metal */; };
FC9D037920E229E4000F735A /* OpParam.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9D037820E229E4000F735A /* OpParam.swift */; };
FC9D038020E22FBB000F735A /* FeedOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC9D037F20E22FBB000F735A /* FeedOp.swift */; };
......@@ -138,9 +132,7 @@
FCE9D7B9214FAA4800B520C3 /* NMSFetchResultKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCE9D7B8214FAA4800B520C3 /* NMSFetchResultKernel.metal */; };
FCEB684A212F00DB00D2448E /* PreluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCEB6849212F00DB00D2448E /* PreluKernel.metal */; };
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCEB684B212F093800D2448E /* PreluOp.swift */; };
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCEBC0F320F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift */; };
FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCEBC0F520F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift */; };
FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCF2D73720E64E70007AC5F5 /* Kernel.swift */; };
/* End PBXBuildFile section */
/* Begin PBXFileReference section */
......@@ -166,8 +158,6 @@
4AF9287821341661005B6C3A /* Softmax.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = Softmax.metal; sourceTree = "<group>"; };
4AF928812135673D005B6C3A /* ConcatKernel.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = ConcatKernel.metal; sourceTree = "<group>"; };
4AF9288321357BE3005B6C3A /* Elementwise.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = Elementwise.metal; sourceTree = "<group>"; };
C28FDF8221B7858F0054EFAC /* MobileNetCombined.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = MobileNetCombined.swift; sourceTree = "<group>"; };
C28FDF8321B7858F0054EFAC /* YoloNet.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = YoloNet.swift; sourceTree = "<group>"; };
C28FE02C21BA68C00054EFAC /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
C28FE02D21BA68C00054EFAC /* MetalPerformanceShaders.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = MetalPerformanceShaders.framework; path = System/Library/Frameworks/MetalPerformanceShaders.framework; sourceTree = SDKROOT; };
C28FE02E21BA68C00054EFAC /* MetalKit.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = MetalKit.framework; path = System/Library/Frameworks/MetalKit.framework; sourceTree = SDKROOT; };
......@@ -206,20 +196,17 @@
FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ElementwiseAddKernel.swift; sourceTree = "<group>"; };
FC1B16B220EC9A4F00678B91 /* Kernels.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Kernels.metal; sourceTree = "<group>"; };
FC1CF3F621D4B4C400F7392E /* Runner.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Runner.swift; sourceTree = "<group>"; };
FC292C5321421B2E00CF622F /* PaddleMobileGPU.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = PaddleMobileGPU.h; sourceTree = "<group>"; };
FC292C5521421B4600CF622F /* PaddleMobileGPU.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = PaddleMobileGPU.m; sourceTree = "<group>"; };
FC292C7C214255BC00CF622F /* CPUCompute.mm */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.cpp.objcpp; path = CPUCompute.mm; sourceTree = "<group>"; };
FC292C7D214255BC00CF622F /* CPUCompute.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = CPUCompute.h; sourceTree = "<group>"; };
FC292C7E214255BC00CF622F /* MobileNetSSD.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = MobileNetSSD.swift; sourceTree = "<group>"; };
FC292C862142624800CF622F /* Genet.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = Genet.swift; sourceTree = "<group>"; };
FC33B0EF2147659000714A93 /* MobileNet.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = MobileNet.swift; sourceTree = "<group>"; };
FC2BFCC121DF2F9100C262B2 /* GlobalConfig.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = GlobalConfig.swift; sourceTree = "<group>"; };
FC2BFD4521DF685F00C262B2 /* Scale.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Scale.swift; sourceTree = "<group>"; };
FC2BFD4921DF81DE00C262B2 /* Kernel.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = Kernel.swift; sourceTree = "<group>"; };
FC2BFD4D21DF820A00C262B2 /* ConvAddBatchNormReluOp.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = ConvAddBatchNormReluOp.swift; sourceTree = "<group>"; };
FC2BFD5021DF8E0400C262B2 /* Scale.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Scale.metal; sourceTree = "<group>"; };
FC3602CB2108819F00FACB58 /* PaddleMobileUnitTest.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PaddleMobileUnitTest.swift; sourceTree = "<group>"; };
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvKernel.metal; sourceTree = "<group>"; };
FC4CB74A20F12C30007C0C6D /* ProgramOptimize.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ProgramOptimize.swift; sourceTree = "<group>"; };
FC4FD97D2140F2C30073E130 /* libstdc++.tbd */ = {isa = PBXFileReference; lastKnownFileType = "sourcecode.text-based-dylib-definition"; name = "libstdc++.tbd"; path = "usr/lib/libstdc++.tbd"; sourceTree = SDKROOT; };
FC5163F520EF556E00636C28 /* Texture2DTo2DArrayKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Texture2DTo2DArrayKernel.swift; sourceTree = "<group>"; };
FC60DB8820E9AAA500FF203F /* MetalExtension.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = MetalExtension.swift; sourceTree = "<group>"; };
FC704C2621D2385100F98BAB /* SuperResolutionNet.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = SuperResolutionNet.swift; sourceTree = "<group>"; };
FC803BBE214CB65A0094B8E5 /* ConvAddPreluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvAddPreluOp.swift; sourceTree = "<group>"; };
FC803BC0214CB77A0094B8E5 /* ConvAddPreluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvAddPreluKernel.swift; sourceTree = "<group>"; };
FC803BC2214CB79C0094B8E5 /* ConvAddPreluKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvAddPreluKernel.metal; sourceTree = "<group>"; };
......@@ -229,7 +216,6 @@
FC82735820E3C04200BE430A /* OpCreator.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = OpCreator.swift; sourceTree = "<group>"; };
FC9797C821D6101D00F2FD90 /* ResizeBilinearOp.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = ResizeBilinearOp.swift; sourceTree = "<group>"; };
FC9797CA21D6102D00F2FD90 /* ResizeBilinearKernel.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = ResizeBilinearKernel.swift; sourceTree = "<group>"; };
FC9A19E22148C31300CD9CBF /* MobilenetSSD_AR.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = MobilenetSSD_AR.swift; sourceTree = "<group>"; };
FC9C2A0C21D3D185005856C6 /* FetchKernel.inc.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = FetchKernel.inc.metal; sourceTree = "<group>"; };
FC9D037820E229E4000F735A /* OpParam.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = OpParam.swift; sourceTree = "<group>"; };
FC9D037F20E22FBB000F735A /* FeedOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = FeedOp.swift; sourceTree = "<group>"; };
......@@ -281,9 +267,7 @@
FCE9D7B8214FAA4800B520C3 /* NMSFetchResultKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = NMSFetchResultKernel.metal; sourceTree = "<group>"; };
FCEB6849212F00DB00D2448E /* PreluKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = PreluKernel.metal; sourceTree = "<group>"; };
FCEB684B212F093800D2448E /* PreluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PreluOp.swift; sourceTree = "<group>"; };
FCEBC0F320F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; name = ConvAddBatchNormReluOp.swift; path = "paddle-mobile/Operators/ConvAddBatchNormReluOp.swift"; sourceTree = SOURCE_ROOT; };
FCEBC0F520F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvAddBatchNormReluKernel.swift; sourceTree = "<group>"; };
FCF2D73720E64E70007AC5F5 /* Kernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; name = Kernel.swift; path = "paddle-mobile/Operators/Kernels/Base/Kernel.swift"; sourceTree = SOURCE_ROOT; };
/* End PBXFileReference section */
/* Begin PBXFrameworksBuildPhase section */
......@@ -343,17 +327,10 @@
FC039B6C20E11C3C0081E9F8 /* paddle-mobile */ = {
isa = PBXGroup;
children = (
FC9797CC21D61A5500F2FD90 /* CustomNet */,
FCE9D7B6214F869000B520C3 /* Net.swift */,
FC292C5521421B4600CF622F /* PaddleMobileGPU.m */,
FC292C5321421B2E00CF622F /* PaddleMobileGPU.h */,
FC039BAE20E11CC20081E9F8 /* Program */,
FC039BA320E11CBC0081E9F8 /* Operators */,
FC039B9C20E11CB20081E9F8 /* framework */,
FC039B9320E11C9A0081E9F8 /* Common */,
FC2BFD4721DF818000C262B2 /* API */,
FC2BFD4821DF818000C262B2 /* Src */,
FC039B6D20E11C3C0081E9F8 /* paddle_mobile.h */,
FC039B6E20E11C3C0081E9F8 /* Info.plist */,
FC1CF3F621D4B4C400F7392E /* Runner.swift */,
);
path = "paddle-mobile";
sourceTree = "<group>";
......@@ -386,10 +363,10 @@
FC039BA320E11CBC0081E9F8 /* Operators */ = {
isa = PBXGroup;
children = (
FC9797C821D6101D00F2FD90 /* ResizeBilinearOp.swift */,
FC086BA520E67E8500D85EF7 /* Kernels */,
FCD592FA20E248EC00252966 /* Base */,
FCEBC0F320F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift */,
FC9797C821D6101D00F2FD90 /* ResizeBilinearOp.swift */,
FC2BFD4D21DF820A00C262B2 /* ConvAddBatchNormReluOp.swift */,
FC039BA420E11CBC0081E9F8 /* ConvOp.swift */,
FC039BA520E11CBC0081E9F8 /* ElementwiseAddOp.swift */,
FC039BA720E11CBC0081E9F8 /* BatchNormOp.swift */,
......@@ -441,9 +418,9 @@
FC086BA520E67E8500D85EF7 /* Kernels */ = {
isa = PBXGroup;
children = (
FC9797CA21D6102D00F2FD90 /* ResizeBilinearKernel.swift */,
FCDDC6CD212FE02100E5EF74 /* Base */,
FCEB6837212F00B100D2448E /* metal */,
FC9797CA21D6102D00F2FD90 /* ResizeBilinearKernel.swift */,
FCDDC6C7212FA3CA00E5EF74 /* ConvTransposeKernel.swift */,
FC0E2DBB20EE45FE009C1FAC /* ConvKernel.swift */,
FC0E2DB920EE3B8D009C1FAC /* ReluKernel.swift */,
......@@ -469,24 +446,30 @@
FC803BC0214CB77A0094B8E5 /* ConvAddPreluKernel.swift */,
FCE3A1AA2153DE8C00C37CDE /* ConvAddAddPreluKernel.swift */,
FCE3A1AE2153E8EE00C37CDE /* ElementwiseAddPreluKernel.swift */,
FC2BFD4521DF685F00C262B2 /* Scale.swift */,
);
path = Kernels;
sourceTree = "<group>";
};
FC9797CC21D61A5500F2FD90 /* CustomNet */ = {
FC2BFD4721DF818000C262B2 /* API */ = {
isa = PBXGroup;
children = (
FCE9D7B6214F869000B520C3 /* Net.swift */,
FC1CF3F621D4B4C400F7392E /* Runner.swift */,
FC2BFCC121DF2F9100C262B2 /* GlobalConfig.swift */,
);
path = API;
sourceTree = "<group>";
};
FC2BFD4821DF818000C262B2 /* Src */ = {
isa = PBXGroup;
children = (
FC292C7C214255BC00CF622F /* CPUCompute.mm */,
FC292C7D214255BC00CF622F /* CPUCompute.h */,
FC704C2621D2385100F98BAB /* SuperResolutionNet.swift */,
C28FDF8221B7858F0054EFAC /* MobileNetCombined.swift */,
C28FDF8321B7858F0054EFAC /* YoloNet.swift */,
FC9A19E22148C31300CD9CBF /* MobilenetSSD_AR.swift */,
FC33B0EF2147659000714A93 /* MobileNet.swift */,
FC292C862142624800CF622F /* Genet.swift */,
FC292C7E214255BC00CF622F /* MobileNetSSD.swift */,
);
path = CustomNet;
FC039BAE20E11CC20081E9F8 /* Program */,
FC039BA320E11CBC0081E9F8 /* Operators */,
FC039B9C20E11CB20081E9F8 /* framework */,
FC039B9320E11C9A0081E9F8 /* Common */,
);
path = Src;
sourceTree = "<group>";
};
FCD592FA20E248EC00252966 /* Base */ = {
......@@ -502,7 +485,7 @@
FCDDC6CD212FE02100E5EF74 /* Base */ = {
isa = PBXGroup;
children = (
FCF2D73720E64E70007AC5F5 /* Kernel.swift */,
FC2BFD4921DF81DE00C262B2 /* Kernel.swift */,
);
path = Base;
sourceTree = "<group>";
......@@ -547,6 +530,7 @@
FCE3A1B02153E90F00C37CDE /* ElementwiseAddPreluKernel.inc.metal */,
FCE3A1B22153E91900C37CDE /* ElementwiseAddPreluKernel.metal */,
FC9C2A0C21D3D185005856C6 /* FetchKernel.inc.metal */,
FC2BFD5021DF8E0400C262B2 /* Scale.metal */,
);
path = metal;
sourceTree = "<group>";
......@@ -558,9 +542,7 @@
isa = PBXHeadersBuildPhase;
buildActionMask = 2147483647;
files = (
FC292C5421421B2F00CF622F /* PaddleMobileGPU.h in Headers */,
FC039B6F20E11C3C0081E9F8 /* paddle_mobile.h in Headers */,
FC9797CD21D61B2E00F2FD90 /* CPUCompute.h in Headers */,
);
runOnlyForDeploymentPostprocessing = 0;
};
......@@ -678,7 +660,6 @@
FCE3A1AB2153DE8C00C37CDE /* ConvAddAddPreluKernel.swift in Sources */,
FC9D037920E229E4000F735A /* OpParam.swift in Sources */,
FC3602CC2108819F00FACB58 /* PaddleMobileUnitTest.swift in Sources */,
FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */,
FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */,
FC0226562138F33800F395E2 /* TransposeKernel.metal in Sources */,
FCDDC6C6212F9FB800E5EF74 /* PreluKernel.swift in Sources */,
......@@ -686,13 +667,10 @@
FCA67CD52138272900BD58AA /* ConvAddMetal.metal in Sources */,
FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */,
4AA1EA8C2146640900D0F791 /* SplitOp.swift in Sources */,
FC292C81214255BD00CF622F /* CPUCompute.mm in Sources */,
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */,
4AA1EAAC214F55C800D0F791 /* Softmax.inc.metal in Sources */,
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */,
4AF928772133F1DB005B6C3A /* BoxCoder.metal in Sources */,
FC803BBF214CB65A0094B8E5 /* ConvAddPreluOp.swift in Sources */,
FC33B0F02147659000714A93 /* MobileNet.swift in Sources */,
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */,
4AA1EAA8214B7AFB00D0F791 /* BilinearInterp.inc.metal in Sources */,
FCA67CD92138287B00BD58AA /* ConvBNReluKernel.metal in Sources */,
......@@ -704,17 +682,19 @@
FC039BBA20E11CC20081E9F8 /* TensorDesc.swift in Sources */,
FC039BA020E11CB20081E9F8 /* Dim.swift in Sources */,
FC039BB820E11CC20081E9F8 /* framework.pb.swift in Sources */,
C28FDF8521B7858F0054EFAC /* YoloNet.swift in Sources */,
FC039B9920E11C9A0081E9F8 /* Types.swift in Sources */,
FC4CB74920F0B954007C0C6D /* ConvKernel.metal in Sources */,
FCA3A1632132A4AC00084FE5 /* ReshapeKernel.metal in Sources */,
FCBCCC592122F42700D94F7E /* ConvBNReluOp.swift in Sources */,
FC039BA920E11CBC0081E9F8 /* ConvOp.swift in Sources */,
FCCED5E121D71FC000BE8D5F /* PoolKernel.inc.metal in Sources */,
FC2BFD4A21DF81DE00C262B2 /* Kernel.swift in Sources */,
FC9D038420E23B01000F735A /* Texture.swift in Sources */,
FCE3A1B32153E91900C37CDE /* ElementwiseAddPreluKernel.metal in Sources */,
FC2BFD4E21DF820B00C262B2 /* ConvAddBatchNormReluOp.swift in Sources */,
4AA1EAA2214912CD00D0F791 /* FlattenKernel.swift in Sources */,
4AA1EA982146666500D0F791 /* FlattenOp.swift in Sources */,
FC2BFCC221DF2F9100C262B2 /* GlobalConfig.swift in Sources */,
FCBCCC652122FCD700D94F7E /* TransposeOp.swift in Sources */,
4AA1EAA6214B5F6800D0F791 /* Shape.metal in Sources */,
FCD04E6E20F31B4B0007374F /* ReshapeOp.swift in Sources */,
......@@ -724,13 +704,11 @@
FCD04E7420F3437E0007374F /* ConvAddKernel.swift in Sources */,
FC1CF3F721D4B4C400F7392E /* Runner.swift in Sources */,
FC039BB920E11CC20081E9F8 /* Scope.swift in Sources */,
FC292C5621421B4600CF622F /* PaddleMobileGPU.m in Sources */,
FCD04E6620F314C50007374F /* PoolOp.swift in Sources */,
FCE9D7B9214FAA4800B520C3 /* NMSFetchResultKernel.metal in Sources */,
FC039BAC20E11CBC0081E9F8 /* BatchNormOp.swift in Sources */,
FCBCCC6F2123097100D94F7E /* MulticlassNMSOp.swift in Sources */,
FC039BBC20E11CC20081E9F8 /* VarDesc.swift in Sources */,
FC292C872142624800CF622F /* Genet.swift in Sources */,
FC803BC5214CB8F00094B8E5 /* ConvAddPrelu.inc.metal in Sources */,
4AF928822135673D005B6C3A /* ConcatKernel.metal in Sources */,
FCBCCC632122FCC000D94F7E /* TransposeKernel.swift in Sources */,
......@@ -748,23 +726,20 @@
FCBCCC5D2122F8A100D94F7E /* DepthwiseConvOp.swift in Sources */,
FCE3A1AF2153E8EE00C37CDE /* ElementwiseAddPreluKernel.swift in Sources */,
FCE9D7B7214F869000B520C3 /* Net.swift in Sources */,
FC704C2721D2385100F98BAB /* SuperResolutionNet.swift in Sources */,
FC0E2DBE20EE460D009C1FAC /* BatchNormKernel.swift in Sources */,
FC039BAB20E11CBC0081E9F8 /* Operator.swift in Sources */,
C28FDF8421B7858F0054EFAC /* MobileNetCombined.swift in Sources */,
FCD04E6A20F319EC0007374F /* SoftmaxOp.swift in Sources */,
FC292C82214255BD00CF622F /* MobileNetSSD.swift in Sources */,
FCBCCC612122FBDF00D94F7E /* PriorBoxKernel.swift in Sources */,
FCBCCC5F2122FB3B00D94F7E /* PriorBoxOp.swift in Sources */,
FC9D038220E2312E000F735A /* FetchOp.swift in Sources */,
FCA67B1721364EF000BD58AA /* ConvTransposeKernel.metal in Sources */,
FC039BBD20E11CC20081E9F8 /* Program.swift in Sources */,
FC2BFD5121DF8E0400C262B2 /* Scale.metal in Sources */,
FC039BA220E11CB70081E9F8 /* Loader.swift in Sources */,
FCBCCC67212306B000D94F7E /* ConcatOp.swift in Sources */,
FCD04E6C20F31A280007374F /* SoftmaxKernel.swift in Sources */,
FCEB684A212F00DB00D2448E /* PreluKernel.metal in Sources */,
4AA1EAA02148DEEE00D0F791 /* ReshapeKernel.inc.metal in Sources */,
FC9A19E32148C31300CD9CBF /* MobilenetSSD_AR.swift in Sources */,
FCDDC6CF212FE14700E5EF74 /* PriorBoxKernel.metal in Sources */,
FC4CB74B20F12C30007C0C6D /* ProgramOptimize.swift in Sources */,
FCE3A1A92153DE5100C37CDE /* ConvAddAddPreluOp.swift in Sources */,
......@@ -780,6 +755,7 @@
FC039BBE20E11CC20081E9F8 /* OpDesc.swift in Sources */,
FC9797C921D6101D00F2FD90 /* ResizeBilinearOp.swift in Sources */,
4AA1EA88214662BD00D0F791 /* BilinearInterpKernel.swift in Sources */,
FC2BFD4621DF685F00C262B2 /* Scale.swift in Sources */,
FC039B9720E11C9A0081E9F8 /* Extensions.swift in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
......@@ -919,7 +895,7 @@
DYLIB_COMPATIBILITY_VERSION = 1;
DYLIB_CURRENT_VERSION = 1;
DYLIB_INSTALL_NAME_BASE = "@rpath";
ENABLE_BITCODE = NO;
ENABLE_BITCODE = YES;
INFOPLIST_FILE = "paddle-mobile/Info.plist";
INSTALL_PATH = "$(LOCAL_LIBRARY_DIR)/Frameworks";
IPHONEOS_DEPLOYMENT_TARGET = 9.0;
......@@ -956,7 +932,7 @@
DYLIB_COMPATIBILITY_VERSION = 1;
DYLIB_CURRENT_VERSION = 1;
DYLIB_INSTALL_NAME_BASE = "@rpath";
ENABLE_BITCODE = NO;
ENABLE_BITCODE = YES;
INFOPLIST_FILE = "paddle-mobile/Info.plist";
INSTALL_PATH = "$(LOCAL_LIBRARY_DIR)/Frameworks";
IPHONEOS_DEPLOYMENT_TARGET = 9.0;
......
metal/paddle-mobile/paddle-mobile/API/GlobalConfig.swift 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
@objc public enum MetalLoadMode: Int {
case
LoadMetalInPaddleMobile = 1, // 使用 paddle-mobile 中的 metal 代码
LoadMetalInDefaultLib = 2, // 使用 main bundle 中的 metal 代码
LoadMetalInCustomMetalLib = 3 // 使用 metal 库文件
}
@objc public enum ComputePrecision: Int {
case
Float32 = 1,
Float16 = 2
}
@objc public class GlobalConfig: NSObject {
/// 单例
@objc public static let shared: GlobalConfig = GlobalConfig.init()
/// 运算精度， runner 生命周期中不可变
@objc public var computePrecision: ComputePrecision = .Float16
}
metal/paddle-mobile/paddle-mobile/Net.swift → metal/paddle-mobile/paddle-mobile/API/Net.swift
浏览文件 @ 38e9f34c
......@@ -12,58 +12,52 @@
See the License for the specific language governing permissions and
limitations under the License. */
import Foundation
import Metal
import Foundation
public class ResultHolder: NSObject {
@objc public let result: UnsafeMutablePointer<Float32>
@objc public let capacity: Int
/// 网络的基类， 参数已经给了默认值，请在子类实现中修改需要改的参数
@objc open class Net: NSObject {
init(inResult: UnsafeMutablePointer<Float32>, inCapacity: Int) {
result = inResult
capacity = inCapacity
}
/// 默认为0， 如果指定个数， 后边 except 个op不使用 GPU 运算， 中间结果会通过 fetchResult 传参过来
@objc public var except: Int = 0
@objc public func releasePointer() {
result.deinitialize(count: capacity)
result.deallocate()
}
}
/// 预处理 kernel， 如果输入图像需要预处理， 则指定预处理 kernel
@objc public var preprocessKernel: CusomKernel? = nil
public class Net: NSObject {
var except: Int = 0
// 以下四个参数为从内存中读取模型时用到的参数
/// 模型在内存中的指针
@objc public var modelPointer: UnsafeMutableRawPointer? = nil
// for CPU
var means: [Float] = []
var scale: Float = 0.0
/// 模型大小 单位： 字节
@objc public var modelSize: Int = 0
var needUpdateProgram = true
/// 权重参数在内存中的指针
@objc public var paramPointer: UnsafeMutableRawPointer? = nil
public var inputDim: Dim {
get{
return inputDim_
}
set{
if inputDim_ != newValue {
needUpdateProgram = true
}
inputDim_ = newValue
}
}
/// 权重大小 单位： 字节
@objc public var paramSize: Int = 0
var inputDim_: Dim = Dim.init(inDim: [])
var preprocessKernel: CusomKernel? = nil
var paramPointer: UnsafeMutableRawPointer? = nil
var paramSize: Int = 0
var modelPointer: UnsafeMutableRawPointer? = nil
var modelSize: Int = 0
var modelPath: String = ""
var paramPath: String = ""
var modelDir: String = ""
let device: MTLDevice
@objc public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
// 以下两个为从文件中读取模型时用到的参数
/// 模型文件路径
@objc public var modelPath: String? = nil
/// 权重文件路径
@objc public var paramPath: String? = nil
/// 代表着 GPU 处理器
@objc public let device: MTLDevice
/// metal 代码加载方式 注意： 如果静态库只能使用 LoadMetalInDefaultLib LoadMetalInCustomMetalLib 进行 load metal 代码
@objc public var metalLoadMode: MetalLoadMode = .LoadMetalInPaddleMobile
/// 当 metalLoadMode 为 LoadMetalInCustomMetalLib 时， metal library 路径不能为空
@objc public var metalLibPath: String? = nil
/// 输入维度，按照 n h w c 方式传入
@objc public var inputDim: Dim = Dim.init(inDim: [])
@objc public init(device: MTLDevice, paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
self.paramPointer = paramPointer
self.paramSize = paramSize
self.modelPointer = modePointer
......@@ -77,22 +71,17 @@ public class Net: NSObject {
super.init()
}
@objc public func updateInputDim(inDim: [Int]) {
inputDim = Dim.init(inDim: inDim)
}
public func resultStr(res: ResultHolder) -> String {
@objc open func resultStr(res: ResultHolder) -> String {
fatalError()
}
func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
@objc open func fetchResult(paddleMobileRes: GPUResultHolder) -> ResultHolder {
guard let inResPointer = paddleMobileRes.resultPointer else {
fatalError()
}
return ResultHolder.init(inResult: inResPointer, inCapacity: paddleMobileRes.capacity)
}
func updateProgram(program: Program) {
open func updateProgram(program: Program) {
}
}
metal/paddle-mobile/paddle-mobile/Runner.swift → metal/paddle-mobile/paddle-mobile/API/Runner.swift
浏览文件 @ 38e9f34c
//
// Runner.swift
// paddle-mobile
//
// Created by liuRuiLong on 2018/12/27.
// Copyright © 2018 orange. All rights reserved.
//
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
import MetalKit
import Foundation
class ScaleKernel: CusomKernel {
init(device: MTLDevice, shape: Shape) {
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "scale", outputDim: shape, usePaddleMobileLib: false)
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "scale_half", outputDim: shape, usePaddleMobileLib: false)
} else {
fatalError(" unsupport ")
@objc public class ResultHolder: NSObject {
@objc public let result: UnsafeMutablePointer<Float32>
@objc public let capacity: Int
init(inResult: UnsafeMutablePointer<Float32>, inCapacity: Int) {
result = inResult
capacity = inCapacity
}
@objc public func releasePointer() {
result.deinitialize(count: capacity)
result.deallocate()
}
}
......@@ -29,15 +38,12 @@ class ScaleKernel: CusomKernel {
public let net: Net
let device: MTLDevice?
let numel: Int
let meansNumber: [NSNumber]
// dims num nchw
let dimsNum: [NSNumber]
/**
* inNet: 需要运行的网络
* commandQueue: GPU 是需要传入
* inPlatform: 需要使用的平台, GPU or CPU
*/
/// 初始化函数
///
/// - Parameters:
/// - inNet: 传入自定义的网络
/// - commandQueue: commandQueue
@objc public init(inNet: Net, commandQueue: MTLCommandQueue?) {
guard inNet.inputDim.cout() == 4 else {
fatalError(" input dim count must 4 ")
......@@ -49,21 +55,12 @@ class ScaleKernel: CusomKernel {
if let inDevice = device {
textureLoader = MTKTextureLoader.init(device: inDevice)
}
numel = net.inputDim.numel()
meansNumber = net.means.map {
NSNumber.init(value: $0)
}
dimsNum = [NSNumber.init(value: net.inputDim[0]),
NSNumber.init(value: net.inputDim[3]),
NSNumber.init(value: net.inputDim[1]),
NSNumber.init(value: net.inputDim[2])]
}
/**
* load 模型, 返回 true 可进行预测
*/
/// load 模型, 返回 true 可进行预测
///
/// - Returns: load 成功或失败
@objc public func load() -> Bool {
guard let inDevice = device, let inQueue = queue else {
print(" paddle mobile gpu load error, need MTLCommandQueue")
......@@ -71,10 +68,24 @@ class ScaleKernel: CusomKernel {
}
let loader = Loader<Float32>.init()
do {
// program = try loader.load(device: inDevice, paramPointer: net.paramPointer!, paramSize: net.paramSize,modePointer:net.modelPointer!,modelSize:net.modelSize)
program = try loader.load(device: inDevice, modelPath: net.modelPath, paraPath: net.paramPath)
executor = try Executor<Float32>.init(inDevice: inDevice, inQueue: inQueue, inProgram: program!)
if let inParamPointer = net.paramPointer, let inModelPointer = net.modelPointer {
guard net.paramSize > 0 && net.modelSize > 0 else {
print(" load from memory param size or model size can't 0 ")
return false
}
program = try loader.load(device: inDevice, paramPointer: inParamPointer, paramSize: net.paramSize,modePointer:inModelPointer,modelSize:net.modelSize)
} else if let inModelPath = net.modelPath, let inParamPath = net.paramPath {
program = try loader.load(device: inDevice, modelPath: inModelPath, paraPath: inParamPath)
} else {
print(" model pointer or model file path need be specified")
return false
}
let initContext: InitContext = InitContext.init()
initContext.metalLoadMode = net.metalLoadMode
initContext.metalLibPath = net.metalLibPath
executor = try Executor<Float32>.init(inDevice: inDevice, inQueue: inQueue, inProgram: program!, initContext: initContext)
net.updateProgram(program: program!)
} catch let error {
print(error)
......@@ -83,13 +94,12 @@ class ScaleKernel: CusomKernel {
return true
}
/**
* GPU 版本 predict
* texture: 需要预测的 texture 需要做过预处理
* ( _ success: Bool, _ time:TimeInterval, _ resultArray: [Float32]) -> Void : 回调闭包, 三个参数分别为: 是否成功, 预测耗时, 结果数组
*/
/// 预测
///
/// - Parameters:
/// - texture: 输入 texture 需要使用 getTexture 获得
/// - completion: 结果回调， 当 success 为 true 时 result 不为 nil
@objc public func predict(texture: MTLTexture, completion: @escaping ( _ success: Bool, _ result: ResultHolder?) -> Void) {
net.updateProgram(program: program!)
do {
try self.executor?.predict(input: texture, dim: self.net.inputDim, completionHandle: { [weak self] (res) in
guard let SSelf = self else {
......@@ -105,23 +115,28 @@ class ScaleKernel: CusomKernel {
}
}
/*
* 清理内存, 调用此函数后, 不能再使用, 需重新 load
*/
/// 清理内存, 调用此函数后, 不能再使用, 需重新 load
@objc public func clear() {
executor?.clear()
executor = nil
program = nil
}
/*
* 获取 texture, 对 texture 进行预处理, GPU 预测时使用
*/
/// 获取 texture, 对 texture 进行预处理, 预测时使用
///
/// - Parameters:
/// - image: 输入图像
/// - getTexture: 获取 texture 回调
@objc public func getTexture(image: CGImage, getTexture: @escaping (MTLTexture) -> Void) {
let texture = try? textureLoader?.newTexture(cgImage: image, options: [:]) ?! " texture loader error"
scaleTexture(input: texture!, complete: getTexture)
}
/// 通过 buffer 获取 texture， 内部会使用GPU进行转换操作
///
/// - Parameters:
/// - inBuffer: 输入buffer
/// - getTexture: 结果回调
@objc public func getTexture(inBuffer: MTLBuffer, getTexture: @escaping (MTLTexture) -> Void) {
guard let inQueue = queue, let inDevice = device else {
fatalError( " queue or devcie nil " )
......@@ -131,7 +146,7 @@ class ScaleKernel: CusomKernel {
fatalError( " make buffer error" )
}
let bufferToTextureKernel = BufferToTextureKernel.init(device: inDevice, outputDim: Shape.init(inWidth: net.inputDim[2], inHeight: net.inputDim[1], inChannel: net.inputDim[3]))
let bufferToTextureKernel = BufferToTextureKernel.init(device: inDevice, outputDim: Shape.init(inWidth: net.inputDim[2], inHeight: net.inputDim[1], inChannel: net.inputDim[3]), metalLoadMode: net.metalLoadMode, metalLibPath: net.metalLibPath)
do {
try bufferToTextureKernel.compute(inputBuffer: inBuffer, commandBuffer: buffer)
} catch {
......@@ -145,6 +160,19 @@ class ScaleKernel: CusomKernel {
buffer.commit()
}
/// 更新输入维度， 针对可变长输入模型
///
/// - Parameter inDim: 输入维度
@objc public func updateInputDim(inDim: Dim) {
if net.inputDim != inDim {
guard let inProgram = program else {
fatalError(" need load first ")
}
net.inputDim = inDim
net.updateProgram(program: inProgram)
}
}
public func scaleTexture(input: MTLTexture , complete: @escaping (MTLTexture) -> Void) {
guard let inQueue = queue, let inDevice = device else {
......@@ -155,7 +183,7 @@ class ScaleKernel: CusomKernel {
fatalError( " make buffer error" )
}
let scaleKernel = ScaleKernel.init(device: inDevice, shape: Shape.init(inWidth: net.inputDim[2], inHeight: net.inputDim[1], inChannel: 3))
let scaleKernel = ScaleKernel.init(device: inDevice, shape: Shape.init(inWidth: net.inputDim[2], inHeight: net.inputDim[1], inChannel: 3), metalLoadMode: net.metalLoadMode, metalLibPath: net.metalLibPath)
do {
try scaleKernel.compute(inputTexuture: input, commandBuffer: buffer)
......
metal/paddle-mobile/paddle-mobile/CustomNet/MobileNetCombined.swift 已删除 100644 → 0
浏览文件 @ 4dc6de9f
//
// MobileNetCombined.swift
// paddle-mobile
//
// Created by Xiao,Haichun on 2018/12/5.
// Copyright © 2018 orange. All rights reserved.
//
import Foundation
public class MobileNetCombined: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
means = [0, 0, 0]
scale = 1
except = 0
modelPath = Bundle.main.path(forResource: "combined_mobilenet_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "combined_mobilenet_params", ofType: nil) ?! "para null"
modelDir = ""
inputDim_ = Dim.init(inDim: [1, 224, 224, 3])
}
@objc override public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
super.init(device:device,paramPointer:paramPointer,paramSize:paramSize,modePointer:modePointer,modelSize:modelSize)
means = [0, 0, 0]
scale = 1
except = 0
modelPath = ""
paramPath = ""
modelDir = ""
inputDim_ = Dim.init(inDim: [1, 224, 224, 3])
}
// class GenetPreProccess: CusomKernel {
// init(device: MTLDevice) {
// let s = CusomKernel.Shape.init(inWidth: 128, inHeight: 128, inChannel: 3)
// super.init(device: device, inFunctionName: "genet_preprocess", outputDim: s, usePaddleMobileLib: false)
// }
// }
override public func resultStr(res: ResultHolder) -> String {
// fatalError()
return " \(res.result[0]) ... "
}
}
metal/paddle-mobile/paddle-mobile/CustomNet/YoloNet.swift 已删除 100644 → 0
浏览文件 @ 4dc6de9f
//
// YoloNet.swift
// paddle-mobile
//
// Created by Xiao,Haichun on 2018/12/5.
// Copyright © 2018 orange. All rights reserved.
//
import Foundation
import Metal
public class YoloNet: Net {
@objc public override init(device: MTLDevice) {
super.init(device: device)
means = [0, 0, 0]
scale = 1
except = 0
modelPath = Bundle.main.path(forResource: "yolo_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "yolo_params", ofType: nil) ?! "para null"
modelDir = ""
// preprocessKernel = GenetPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 416, 416, 3])
}
@objc override public init(device: MTLDevice,paramPointer: UnsafeMutableRawPointer, paramSize:Int, modePointer: UnsafeMutableRawPointer, modelSize: Int) {
super.init(device:device,paramPointer:paramPointer,paramSize:paramSize,modePointer:modePointer,modelSize:modelSize)
means = [0, 0, 0]
scale = 1
except = 0
modelPath = ""
paramPath = ""
modelDir = ""
//preprocessKernel = GenetPreProccess.init(device: device)
inputDim_ = Dim.init(inDim: [1, 416, 416, 3])
}
// class GenetPreProccess: CusomKernel {
// init(device: MTLDevice) {
// let s = CusomKernel.Shape.init(inWidth: 128, inHeight: 128, inChannel: 3)
// super.init(device: device, inFunctionName: "genet_preprocess", outputDim: s, usePaddleMobileLib: false)
// }
// }
override public func resultStr(res: ResultHolder) -> String {
return " \(res.result[0]) ... "
}
}
metal/paddle-mobile/paddle-mobile/Common/MetalExtension.swift → metal/paddle-mobile/paddle-mobile/Src/Common/MetalExtension.swift
浏览文件 @ 38e9f34c
......@@ -18,6 +18,7 @@ import CoreMedia
fileprivate var defaultMetalLibrary: MTLLibrary?
fileprivate var paddleMobileMetalLibrary: MTLLibrary?
fileprivate var customMetalLibrary: MTLLibrary?
extension MTLDevice {
func defaultLibrary() -> MTLLibrary {
......@@ -31,6 +32,22 @@ extension MTLDevice {
}
}
func customLibrary(metalLibPath: String) -> MTLLibrary {
if customMetalLibrary == nil {
do {
customMetalLibrary = try makeLibrary(filepath: metalLibPath)
} catch let error {
fatalError("\(error)")
}
}
if let inMetalLib = customMetalLibrary {
return inMetalLib
} else {
fatalError(" customlib is nil ")
}
}
func paddleMobileLibrary() -> MTLLibrary {
if paddleMobileMetalLibrary == nil {
guard let path = Bundle.init(for: Kernel.self).path(forResource: "default", ofType: "metallib") else {
......@@ -50,8 +67,19 @@ extension MTLDevice {
}
}
func pipeLine(funcName: String, inPaddleMobileLib: Bool = true) -> MTLComputePipelineState {
let useLib = inPaddleMobileLib ? paddleMobileLibrary() : defaultLibrary()
func pipeLine(funcName: String, metalLoadMode: MetalLoadMode, metalLibPath: String?) -> MTLComputePipelineState {
let useLib: MTLLibrary
switch metalLoadMode {
case .LoadMetalInDefaultLib:
useLib = defaultLibrary()
case .LoadMetalInPaddleMobile:
useLib = paddleMobileLibrary()
case .LoadMetalInCustomMetalLib:
useLib = customLibrary(metalLibPath: metalLibPath ?! " can't be nil ")
default:
fatalError()
}
guard let function = useLib.makeFunction(name: funcName) else {
fatalError(" function " + funcName + " not found")
}
......
metal/paddle-mobile/paddle-mobile/Common/PaddleMobileUnitTest.swift → metal/paddle-mobile/paddle-mobile/Src/Common/PaddleMobileUnitTest.swift
浏览文件 @ 38e9f34c
......@@ -324,9 +324,10 @@ public class PaddleMobileUnitTest {
let param = ConvAddBatchNormReluTestParam.init(inInputTexture: inputeTexture, inOutputTexture: outputTexture, inMetalParam: metalParam, inFilterBuffer: filterBuffer, inBiaseBuffer: biaseBuffer, inNewScaleBuffer: newScalueBuffer, inNewBiaseBuffer: newBiaseBuffer, inFilterSize: filterSize)
let initContext = InitContext.init()
initContext.metalLoadMode = .LoadMetalInDefaultLib
let convAddBnReluKernel = ConvAddBatchNormReluKernel<Float32>.init(device: device, testParam: param)
let convAddBnReluKernel = ConvAddBatchNormReluKernel<Float32>.init(device: device, testParam: param, initContext: initContext)
convAddBnReluKernel.test(commandBuffer: buffer, param: param)
......
metal/paddle-mobile/paddle-mobile/Common/Types.swift → metal/paddle-mobile/paddle-mobile/Src/Common/Types.swift
浏览文件 @ 38e9f34c
......@@ -252,11 +252,11 @@ extension InputTexture: Variant {
extension MTLTexture where Self: Variant {
}
class FetchHolder: Variant {
public class FetchHolder: Variant {
var resultBuffer: MTLBuffer?
var dim: Dim
var capacity: Int
var paddedCapacity: Int
public var dim: Dim
public var capacity: Int
public var paddedCapacity: Int
init(inPaddedCapacity: Int, inDim: Dim) {
paddedCapacity = inPaddedCapacity
......@@ -264,7 +264,7 @@ class FetchHolder: Variant {
dim = inDim
}
func initBuffer(device: MTLDevice) {
public func initBuffer(device: MTLDevice) {
resultBuffer = device.makeBuffer(length: paddedCapacity * 4, options: [])
}
......@@ -278,12 +278,12 @@ class FetchHolder: Variant {
}
extension FetchHolder: CustomStringConvertible, CustomDebugStringConvertible {
var description: String {
public var description: String {
fatalError()
// return "\(result)"
}
var debugDescription: String {
public var debugDescription: String {
fatalError()
// return "\(result)"
}
......
metal/paddle-mobile/paddle-mobile/Operators/Base/OpCreator.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Base/OpCreator.swift
浏览文件 @ 38e9f34c
......@@ -27,19 +27,19 @@ class OpCreator<P: PrecisionType> {
}
}
func creat(device: MTLDevice, opDesc: OpDesc, scope: Scope) throws -> Runable & InferShaperable {
func creat(device: MTLDevice, opDesc: OpDesc, scope: Scope, initContext: InitContext) throws -> Runable & InferShaperable {
guard let opCreator = opCreators[opDesc.type] else {
throw PaddleMobileError.opError(message: "there is no " + opDesc.type + " yet")
}
do {
return try opCreator(device, opDesc, scope)
return try opCreator(device, opDesc, scope, initContext)
} catch let error {
throw error
}
}
let opCreators: [String : (MTLDevice, OpDesc, Scope) throws -> Runable & InferShaperable] =
let opCreators: [String : (MTLDevice, OpDesc, Scope, InitContext) throws -> Runable & InferShaperable] =
[gConvType : ConvOp<P>.creat,
gBatchNormType : BatchNormOp<P>.creat,
gReluType : ReluOp<P>.creat,
......
metal/paddle-mobile/paddle-mobile/Operators/Base/Operator.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Base/Operator.swift
浏览文件 @ 38e9f34c
......@@ -31,7 +31,7 @@ protocol Runable {
func run(device: MTLDevice, buffer: MTLCommandBuffer) throws
func runImpl(device: MTLDevice,buffer: MTLCommandBuffer) throws
func delogOutput()
func inputVariant() -> [String : [Variant]]
func inputVariant() -> [String : [MTLBuffer]]
func computeMiddleResult(device: MTLDevice, buffer: MTLCommandBuffer)
}
......@@ -44,7 +44,7 @@ extension Runable where Self: OperatorProtocol{
}
}
func inputVariant() -> [String : [Variant]] {
func inputVariant() -> [String : [MTLBuffer]] {
// return [:]
fatalError(" op \(type) need implement inputVariant")
}
......@@ -59,15 +59,26 @@ extension Runable where Self: OperatorProtocol{
}
}
public class InitContext {
/// metal 代码加载方式
var metalLoadMode: MetalLoadMode = .LoadMetalInDefaultLib
/// 当 metalLoadMode 为 LoadMetalInCustomMetalLib 时， metal library 路径不能为空
var metalLibPath: String? = nil
init() {
metalLoadMode = .LoadMetalInDefaultLib
metalLibPath = nil
}
}
protocol Creator where Self: OperatorProtocol{
associatedtype OpType: OperatorProtocol & Runable & InferShaperable
static func creat(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws -> OpType
static func creat(device: MTLDevice, opDesc: OpDesc, inScope: Scope, initContext: InitContext) throws -> OpType
}
extension Creator where Self: OperatorProtocol {
static func creat(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws -> OpType {
static func creat(device: MTLDevice, opDesc: OpDesc, inScope: Scope, initContext: InitContext) throws -> OpType {
do {
return try OpType.provide(device:device, opDesc: opDesc, inScope: inScope)
return try OpType.provide(device:device, opDesc: opDesc, inScope: inScope, initContext: initContext)
} catch let error {
throw error
}
......@@ -89,13 +100,13 @@ protocol OperatorProtocol {
var attrs: [String : Attr] { get }
var para: ParamType { get }
var kernel: KerType { get }
init(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws
init(device: MTLDevice, opDesc: OpDesc, inScope: Scope, initContext: InitContext) throws
}
extension OperatorProtocol {
static func provide(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws -> Self {
static func provide(device: MTLDevice, opDesc: OpDesc, inScope: Scope, initContext: InitContext) throws -> Self {
do {
return try Self.init(device: device, opDesc: opDesc, inScope: inScope)
return try Self.init(device: device, opDesc: opDesc, inScope: inScope, initContext: initContext)
} catch let error {
throw error
}
......@@ -103,18 +114,7 @@ extension OperatorProtocol {
}
class Operator <KernelType: Computable , ParameterType>: OperatorProtocol where KernelType.ParamType == ParameterType {
typealias ParamType = ParameterType
typealias KerType = KernelType
let type: String
let inputs: [String : [String]]
var paraInputs: [String : [String]]
let outpus: [String : [String]]
let attrs: [String : Attr]
let para: ParamType
let scope: Scope
var kernel: KerType
required init(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws {
// print("create op: \(opDesc.type)")
required init(device: MTLDevice, opDesc: OpDesc, inScope: Scope, initContext: InitContext) throws {
type = opDesc.type
scope = inScope
inputs = opDesc.inputs
......@@ -126,8 +126,19 @@ class Operator <KernelType: Computable , ParameterType>: OperatorProtocol where
} catch let error {
throw error
}
kernel = KernelType.init(device: device, param: para)
kernel = KernelType.init(device: device, param: para, initContext: initContext)
}
typealias ParamType = ParameterType
typealias KerType = KernelType
let type: String
let inputs: [String : [String]]
var paraInputs: [String : [String]]
let outpus: [String : [String]]
let attrs: [String : Attr]
let para: ParamType
let scope: Scope
var kernel: KerType
}
// op infos
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/ConvAddOp.swift
浏览文件 @ 38e9f34c
......@@ -111,6 +111,7 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
// print(biase)
print(" \(type) output: ")
print(para.output.metalTexture)
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/DepthwiseConvOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/DepthwiseConvOp.swift
浏览文件 @ 38e9f34c
......@@ -18,14 +18,6 @@ class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runa
typealias OpType = DepthConvOp<P>
required init(device: MTLDevice, opDesc: OpDesc, inScope: Scope) throws {
do {
try super.init(device: device, opDesc: opDesc, inScope: inScope)
} catch let error {
throw error
}
}
func inferShape() {
let inDims = para.input.dim
let filterDim = para.filter.dim
......
metal/paddle-mobile/paddle-mobile/Operators/ElementwiseAddOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/ElementwiseAddOp.swift
浏览文件 @ 38e9f34c
......@@ -32,7 +32,7 @@ class ElementwiseAddParam<P: PrecisionType>: OpParam {
let device = inputX.metalTexture!.device
inputY = Texture.init(device: device, inDim: tensorY.dim)
let value: [P] = Array(UnsafeBufferPointer(start: tensorY.data.pointer, count: tensorY.dim.numel()))
inputY.metalTexture = device.tensor2texture(value: value, dim: tensorY.dim.dims, transpose: [0, 1, 2, 3], inComputePrecision: computePrecision)
inputY.metalTexture = device.tensor2texture(value: value, dim: tensorY.dim.dims, transpose: [0, 1, 2, 3], inComputePrecision: GlobalConfig.shared.computePrecision)
}
// required init(device: MTLDevice, param: ElementwiseAddParam<P>) {
......
metal/paddle-mobile/paddle-mobile/Operators/ElementwiseAddPreluOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/ElementwiseAddPreluOp.swift
浏览文件 @ 38e9f34c
......@@ -34,7 +34,7 @@ class ElementwiseAddPreluParam<P: PrecisionType>: OpParam {
let device = inputX.metalTexture!.device
inputY = Texture.init(device: device, inDim: tensorY.dim)
let value: [P] = Array(UnsafeBufferPointer(start: tensorY.data.pointer, count: tensorY.dim.numel()))
inputY.metalTexture = device.tensor2texture(value: value, dim: tensorY.dim.dims, transpose: [0, 1, 2, 3], inComputePrecision: computePrecision)
inputY.metalTexture = device.tensor2texture(value: value, dim: tensorY.dim.dims, transpose: [0, 1, 2, 3], inComputePrecision: GlobalConfig.shared.computePrecision)
}
// required init(device: MTLDevice, param: ElementwiseAddParam<P>) {
......
metal/paddle-mobile/paddle-mobile/Operators/FeedOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/FeedOp.swift
浏览文件 @ 38e9f34c
......@@ -63,7 +63,7 @@ class FeedOp<P: PrecisionType>: Operator<Texture2DTo2DArrayKernel<P>, FeedParam<
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture)
print(para.output.metalTexture.toTensor(dim: (n: para.output.padToFourDim[0], c: para.output.padToFourDim[3], h: para.output.padToFourDim[2], w: para.output.padToFourDim[1])).strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/FetchOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/FetchOp.swift
浏览文件 @ 38e9f34c
......@@ -45,17 +45,17 @@ class FetchKernel<P: PrecisionType>: Kernel, Computable {
encoder.endEncoding()
}
required init(device: MTLDevice, param: FetchParam<P>) {
required init(device: MTLDevice, param: FetchParam<P>, initContext: InitContext) {
param.output.initBuffer(device: device)
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
if param.input.transpose == [0, 2, 3, 1] {
super.init(device: device, inFunctionName: "fetch_half")
super.init(device: device, inFunctionName: "fetch_half", initContext: initContext)
} else {
fatalError(" not support ")
}
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
if param.input.transpose == [0, 2, 3, 1] {
super.init(device: device, inFunctionName: "fetch_float")
super.init(device: device, inFunctionName: "fetch_float", initContext: initContext)
} else {
fatalError(" not support ")
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Base/Kernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/Base/Kernel.swift
浏览文件 @ 38e9f34c
......@@ -21,14 +21,14 @@ public protocol TestParam {
public protocol Testable {
associatedtype TestParamType: TestParam
func test(commandBuffer: MTLCommandBuffer, param: TestParamType)
init(device: MTLDevice, testParam: TestParamType)
init(device: MTLDevice, testParam: TestParamType, initContext: InitContext)
}
protocol Computable {
associatedtype ParamType: OpParam
func compute(commandBuffer: MTLCommandBuffer, param: ParamType) throws
init(device: MTLDevice, param: ParamType)
init(device: MTLDevice, param: ParamType, initContext: InitContext)
}
protocol KernelProtocol {
......@@ -37,20 +37,20 @@ protocol KernelProtocol {
}
open class Kernel {
@objc open class Kernel: NSObject{
let pipline: MTLComputePipelineState
let functionName: String
public init(device: MTLDevice, inFunctionName: String, usePaddleMobileLib: Bool = true) {
pipline = device.pipeLine(funcName: inFunctionName, inPaddleMobileLib: usePaddleMobileLib)
public init(device: MTLDevice, inFunctionName: String, usePaddleMobileLib: Bool = false, initContext: InitContext) {
pipline = device.pipeLine(funcName: inFunctionName, metalLoadMode: initContext.metalLoadMode, metalLibPath: initContext.metalLibPath)
functionName = inFunctionName
}
}
public struct Shape {
@objc public class Shape: NSObject {
public let width: Int
public let height: Int
public let channel: Int
public init(inWidth: Int, inHeight: Int, inChannel: Int){
@objc public init(inWidth: Int, inHeight: Int, inChannel: Int){
width = inWidth
height = inHeight
channel = inChannel
......@@ -60,16 +60,16 @@ public struct Shape {
open class BufferToTextureKernel: Kernel {
public let outputTexture: MTLTexture
public init(device: MTLDevice, outputDim: Shape, usePaddleMobileLib: Bool = false) {
public init(device: MTLDevice, outputDim: Shape, metalLoadMode: MetalLoadMode, metalLibPath: String?) {
let textureDesc = MTLTextureDescriptor.init()
textureDesc.textureType = .type2D
textureDesc.width = outputDim.width
textureDesc.height = outputDim.height
textureDesc.depth = (outputDim.channel + 3) / 4
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
textureDesc.pixelFormat = .rgba16Float
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
textureDesc.pixelFormat = .rgba32Float
} else {
fatalError()
......@@ -78,10 +78,13 @@ open class BufferToTextureKernel: Kernel {
textureDesc.usage = [.shaderRead, .shaderWrite]
textureDesc.storageMode = .shared
outputTexture = device.makeTexture(descriptor: textureDesc) ?! " make texture error "
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "buffer_to_texture_kernel", usePaddleMobileLib: usePaddleMobileLib)
let initContext = InitContext.init()
initContext.metalLibPath = metalLibPath
initContext.metalLoadMode = metalLoadMode
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "buffer_to_texture_kernel", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "buffer_to_texture_kernel_half", usePaddleMobileLib: usePaddleMobileLib)
super.init(device: device, inFunctionName: "buffer_to_texture_kernel_half", initContext: initContext)
}
}
......@@ -98,19 +101,19 @@ open class BufferToTextureKernel: Kernel {
}
open class CusomKernel: Kernel {
@objc open class CusomKernel: Kernel {
public let outputTexture: MTLTexture
public init(device: MTLDevice, inFunctionName: String, outputDim: Shape, usePaddleMobileLib: Bool = false) {
public init(device: MTLDevice, inFunctionName: String, outputDim: Shape, metalLoadModel: MetalLoadMode, metalLibPath: String?) {
let textureDesc = MTLTextureDescriptor.init()
textureDesc.textureType = .type2D
textureDesc.width = outputDim.width
textureDesc.height = outputDim.height
textureDesc.depth = (outputDim.channel + 3) / 4
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
textureDesc.pixelFormat = .rgba16Float
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
textureDesc.pixelFormat = .rgba32Float
} else {
fatalError()
......@@ -120,7 +123,10 @@ open class CusomKernel: Kernel {
textureDesc.storageMode = .shared
outputTexture = device.makeTexture(descriptor: textureDesc) ?! " make texture error "
super.init(device: device, inFunctionName: inFunctionName, usePaddleMobileLib: usePaddleMobileLib)
let context = InitContext.init()
context.metalLoadMode = metalLoadModel
context.metalLibPath = metalLibPath
super.init(device: device, inFunctionName: inFunctionName, initContext: context)
}
public func compute(inputTexuture: MTLTexture, commandBuffer: MTLCommandBuffer) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BatchNormKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/BatchNormKernel.swift
浏览文件 @ 38e9f34c
......@@ -15,7 +15,7 @@
import Foundation
class BatchNormKernel<P: PrecisionType>: Kernel, Computable {
required init(device: MTLDevice, param: BatchNormParam<P>) {
required init(device: MTLDevice, param: BatchNormParam<P>, initContext: InitContext) {
let count = param.variance.dim.numel()
let varianceP = param.variance.data.pointer
let meanP = param.mean.data.pointer
......@@ -27,13 +27,13 @@ class BatchNormKernel<P: PrecisionType>: Kernel, Computable {
scaleP[i] = invStd * scaleP[i]
}
param.bias.initBuffer(device: device, precision: computePrecision)
param.scale.initBuffer(device: device, precision: computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "batchnorm")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "batchnorm_half")
param.bias.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.scale.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "batchnorm", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "batchnorm_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BilinearInterpKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/BilinearInterpKernel.swift
浏览文件 @ 38e9f34c
......@@ -41,12 +41,12 @@ class BilinearInterpKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: BilinearInterpParam<P>) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "bilinear_interp_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "bilinear_interp_half")
required init(device: MTLDevice, param: BilinearInterpParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "bilinear_interp_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "bilinear_interp_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BoxcoderKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/BoxcoderKernel.swift
浏览文件 @ 38e9f34c
......@@ -32,12 +32,12 @@ class BoxcoderKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: BoxcoderParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 3, 1, 2], computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "boxcoder_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "boxcoder_half")
required init(device: MTLDevice, param: BoxcoderParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 3, 1, 2], computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "boxcoder_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "boxcoder_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConcatKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConcatKernel.swift
浏览文件 @ 38e9f34c
......@@ -52,8 +52,8 @@ class ConcatKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: ConcatParam<P>) {
param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: computePrecision)
required init(device: MTLDevice, param: ConcatParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: GlobalConfig.shared.computePrecision)
let orank = param.output.tensorDim.cout()
let num = param.input.count
assert(num <= 6)
......@@ -133,16 +133,16 @@ class ConcatKernel<P: PrecisionType>: Kernel, Computable{
}
}
pm.vdim = (Int32(vdim[0]), Int32(vdim[1]), Int32(vdim[2]), Int32(vdim[3]), Int32(vdim[4]), Int32(vdim[5]))
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_half", initContext: initContext)
} else {
fatalError()
}
}
required init(device: MTLDevice, testParam: ConcatTestParam) {
super.init(device: device, inFunctionName: "concat")
required init(device: MTLDevice, testParam: ConcatTestParam, initContext: InitContext) {
super.init(device: device, inFunctionName: "concat", initContext: initContext)
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddAddPreluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvAddAddPreluKernel.swift
浏览文件 @ 38e9f34c
......@@ -16,99 +16,99 @@ import Foundation
class ConvAddAddPreluKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddAddPreluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
param.alpha.initBuffer(device: device, precision: computePrecision)
required init(device: MTLDevice, param: ConvAddAddPreluParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: GlobalConfig.shared.computePrecision)
param.filter.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.y.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.alpha.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_half", initContext: initContext)
}
} else if param.filter.channel == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_half", initContext: initContext)
}
} else if param.filter.width == 3 && param.filter.height == 3 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_half", initContext: initContext)
}
} else if param.filter.width == 1 && param.filter.height == 5 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_half", initContext: initContext)
}
} else if param.filter.width == 5 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_half", initContext: initContext)
}
} else {
fatalError(" unsupport yet ")
}
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_float", initContext: initContext)
}
} else if param.filter.channel == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_float")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_float")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_float")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_float", initContext: initContext)
}
} else if param.filter.width == 3 && param.filter.height == 3 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_float", initContext: initContext)
}
} else if param.filter.width == 1 && param.filter.height == 5 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_float", initContext: initContext)
}
} else if param.filter.width == 5 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_float", initContext: initContext)
}
} else {
fatalError(" unsupport yet ")
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddBatchNormReluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvAddBatchNormReluKernel.swift
浏览文件 @ 38e9f34c
......@@ -37,44 +37,44 @@ struct ConvAddBatchNormReluTestParam: TestParam {
}
class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
required init(device: MTLDevice, testParam: ConvAddBatchNormReluTestParam) {
required init(device: MTLDevice, testParam: ConvAddBatchNormReluTestParam, initContext: InitContext) {
if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1", initContext: initContext)
} else if testParam.filterSize.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3", initContext: initContext)
}
}
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: GlobalConfig.shared.computePrecision)
param.filter.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.y.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.variance.initBuffer(device: device, precision: .Float32)
param.mean.initBuffer(device: device, precision: .Float32)
param.scale.initBuffer(device: device, precision: .Float32)
param.bias.initBuffer(device: device, precision: .Float32)
if computePrecision == .Float32 {
if GlobalConfig.shared.computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1", initContext: initContext)
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3", initContext: initContext)
} else {
fatalError(" unsupport ")
}
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1_half")
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1_half", initContext: initContext)
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3_half", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3_half")
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3_half", initContext: initContext)
} else {
fatalError(" unsupport ")
}
......@@ -120,10 +120,10 @@ class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable
var newBiaseBuffer: MTLBuffer
var newScaleBuffer: MTLBuffer
if computePrecision == .Float32 {
if GlobalConfig.shared.computePrecision == .Float32 {
newBiaseBuffer = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)!
newScaleBuffer = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)!
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
newBiaseBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
newScaleBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvAddKernel.swift
浏览文件 @ 38e9f34c
......@@ -16,37 +16,37 @@ import Foundation
class ConvAddKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
required init(device: MTLDevice, param: ConvAddParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: GlobalConfig.shared.computePrecision)
let padWhenOneC = !(param.filter.channel == 1 && param.filter.n == param.input.tensorDim[1])
param.filter.initBuffer(device: device, precision: computePrecision, padWhenOneC: padWhenOneC)
param.y.initBuffer(device: device, precision: computePrecision)
param.filter.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision, padWhenOneC: padWhenOneC)
param.y.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1_half")
super.init(device: device, inFunctionName: "conv_add_1x1_half", initContext: initContext)
} else if param.filter.channel == 1 && param.filter.n == param.input.tensorDim[1] {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_half", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_3x3_half")
super.init(device: device, inFunctionName: "conv_add_3x3_half", initContext: initContext)
} else if param.filter.width == 1 && param.filter.height == 5 {
super.init(device: device, inFunctionName: "conv_add_5x1_half")
super.init(device: device, inFunctionName: "conv_add_5x1_half", initContext: initContext)
} else if param.filter.width == 5 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x5_half")
super.init(device: device, inFunctionName: "conv_add_1x5_half", initContext: initContext)
} else {
fatalError(" unsupport yet ")
}
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1")
super.init(device: device, inFunctionName: "conv_add_1x1", initContext: initContext)
} else if param.filter.channel == 1 && param.filter.n == param.input.tensorDim[1] {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3", initContext: initContext)
} else if param.filter.width == 1 && param.filter.height == 5 {
super.init(device: device, inFunctionName: "conv_add_5x1")
super.init(device: device, inFunctionName: "conv_add_5x1", initContext: initContext)
} else if param.filter.width == 5 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x5")
super.init(device: device, inFunctionName: "conv_add_1x5", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_3x3")
super.init(device: device, inFunctionName: "conv_add_3x3", initContext: initContext)
} else {
fatalError(" unsupport yet ")
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddPreluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvAddPreluKernel.swift
浏览文件 @ 38e9f34c
......@@ -16,99 +16,99 @@ import Foundation
class ConvAddPreluKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddPreluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
param.alpha.initBuffer(device: device, precision: computePrecision)
required init(device: MTLDevice, param: ConvAddPreluParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: GlobalConfig.shared.computePrecision)
param.filter.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.y.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.alpha.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_half", initContext: initContext)
}
} else if param.filter.channel == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_half")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_half", initContext: initContext)
}
} else if param.filter.width == 3 && param.filter.height == 3 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_half", initContext: initContext)
}
} else if param.filter.width == 1 && param.filter.height == 5 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_half", initContext: initContext)
}
} else if param.filter.width == 5 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_half")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_half")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_half")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_half", initContext: initContext)
}
} else {
fatalError(" unsupport yet ")
}
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_1x1_prelu_other_float", initContext: initContext)
}
} else if param.filter.channel == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_float")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_float")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_float")
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_prelu_other_float", initContext: initContext)
}
} else if param.filter.width == 3 && param.filter.height == 3 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_3x3_prelu_other_float", initContext: initContext)
}
} else if param.filter.width == 1 && param.filter.height == 5 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_5x1_prelu_other_float", initContext: initContext)
}
} else if param.filter.width == 5 && param.filter.height == 1 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_float")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_float")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_float")
super.init(device: device, inFunctionName: "conv_add_1x5_prelu_other_float", initContext: initContext)
}
} else {
fatalError(" unsupport yet ")
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvBNReluKernel.swift
浏览文件 @ 38e9f34c
......@@ -38,44 +38,44 @@ struct ConvBNReluTestParam: TestParam {
}
class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
required init(device: MTLDevice, testParam: ConvBNReluTestParam) {
required init(device: MTLDevice, testParam: ConvBNReluTestParam, initContext: InitContext) {
if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1")
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1", initContext: initContext)
} else if testParam.filterSize.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3", initContext: initContext)
}
}
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvBNReluParam<P>) {
required init(device: MTLDevice, param: ConvBNReluParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: GlobalConfig.shared.computePrecision)
param.filter.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.variance.initBuffer(device: device, precision: .Float32)
param.mean.initBuffer(device: device, precision: .Float32)
param.scale.initBuffer(device: device, precision: .Float32)
param.bias.initBuffer(device: device, precision: .Float32)
if computePrecision == .Float32 {
if GlobalConfig.shared.computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1")
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1", initContext: initContext)
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3", initContext: initContext)
} else {
fatalError(" unsupport ")
}
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1_half")
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1_half", initContext: initContext)
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3_half")
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3_half", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3_half")
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3_half", initContext: initContext)
} else {
fatalError(" unsupport ")
}
......@@ -122,10 +122,10 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
var newBiaseBuffer: MTLBuffer
var newScaleBuffer: MTLBuffer
if computePrecision == .Float32 {
if GlobalConfig.shared.computePrecision == .Float32 {
newBiaseBuffer = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)!
newScaleBuffer = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)!
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
newBiaseBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
newScaleBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvKernel.swift
浏览文件 @ 38e9f34c
......@@ -26,14 +26,14 @@ public struct MetalConvParam {
class ConvKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvParam<P>) {
required init(device: MTLDevice, param: ConvParam<P>, initContext: InitContext) {
param.filter.initBuffer(device: device, precision: ComputePrecision.Float32)
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_1x1")
super.init(device: device, inFunctionName: "conv_1x1", initContext: initContext)
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_3x3")
super.init(device: device, inFunctionName: "depthwise_conv_3x3", initContext: initContext)
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_3x3")
super.init(device: device, inFunctionName: "conv_3x3", initContext: initContext)
} else {
fatalError(" unsupport ")
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvTransposeKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ConvTransposeKernel.swift
浏览文件 @ 38e9f34c
......@@ -30,18 +30,18 @@ struct MetalConvTransposeParam {
class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: MetalConvTransposeParam!
required init(device: MTLDevice, param: ConvTransposeParam<P>) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision, convertToNHWC: false, withTranspose: true)
if computePrecision == .Float32 {
required init(device: MTLDevice, param: ConvTransposeParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
param.filter.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision, convertToNHWC: false, withTranspose: true)
if GlobalConfig.shared.computePrecision == .Float32 {
if param.stride == [2, 2] && param.stride == [2, 2] {
super.init(device: device, inFunctionName: "conv_transpose2x2_stride2")
super.init(device: device, inFunctionName: "conv_transpose2x2_stride2", initContext: initContext)
} else {
fatalError(" -- conv transpose unsupported yet -- ")
}
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
if param.stride == [2, 2] && param.stride == [2, 2] {
super.init(device: device, inFunctionName: "conv_transpose2x2_stride2_half")
super.init(device: device, inFunctionName: "conv_transpose2x2_stride2_half", initContext: initContext)
} else {
fatalError(" -- conv transpose unsupported yet -- ")
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ElementwiseAddKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ElementwiseAddKernel.swift
浏览文件 @ 38e9f34c
......@@ -26,8 +26,8 @@ struct ElementwiseAddMetalParam {
class ElementwiseAddKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: ElementwiseAddMetalParam
required init(device: MTLDevice, param: ElementwiseAddParam<P>) {
param.output.initTexture(device: device, inTranspose: param.inputX.transpose, computePrecision: computePrecision)
required init(device: MTLDevice, param: ElementwiseAddParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.inputX.transpose, computePrecision: GlobalConfig.shared.computePrecision)
metalParam = ElementwiseAddMetalParam.init()
......@@ -50,10 +50,10 @@ class ElementwiseAddKernel<P: PrecisionType>: Kernel, Computable {
// print("===> elementwise_add fast!!!")
metalParam.fast = 1
}
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "elementwise_add")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "elementwise_add_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "elementwise_add", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "elementwise_add_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ElementwiseAddPreluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ElementwiseAddPreluKernel.swift
浏览文件 @ 38e9f34c
......@@ -17,9 +17,9 @@ import Foundation
class ElementwiseAddPreluKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: ElementwiseAddMetalParam
required init(device: MTLDevice, param: ElementwiseAddPreluParam<P>) {
param.output.initTexture(device: device, inTranspose: param.inputX.transpose, computePrecision: computePrecision)
param.alpha.initBuffer(device: device, precision: computePrecision)
required init(device: MTLDevice, param: ElementwiseAddPreluParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.inputX.transpose, computePrecision: GlobalConfig.shared.computePrecision)
param.alpha.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
metalParam = ElementwiseAddMetalParam.init()
......@@ -43,21 +43,21 @@ class ElementwiseAddPreluKernel<P: PrecisionType>: Kernel, Computable {
metalParam.fast = 1
}
if computePrecision == .Float32 {
if GlobalConfig.shared.computePrecision == .Float32 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "elementwise_add_channel_float")
super.init(device: device, inFunctionName: "elementwise_add_channel_float", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "elementwise_add_element_float")
super.init(device: device, inFunctionName: "elementwise_add_element_float", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "elementwise_add_prelu_float")
super.init(device: device, inFunctionName: "elementwise_add_prelu_float", initContext: initContext)
}
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "elementwise_add_channel_half")
super.init(device: device, inFunctionName: "elementwise_add_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "elementwise_add_channel_half")
super.init(device: device, inFunctionName: "elementwise_add_channel_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "elementwise_add_channel_half")
super.init(device: device, inFunctionName: "elementwise_add_channel_half", initContext: initContext)
}
} else {
fatalError()
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/FlattenKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/FlattenKernel.swift
浏览文件 @ 38e9f34c
......@@ -26,8 +26,8 @@ class FlattenKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: FlattenMetalParam
required init(device: MTLDevice, param: FlattenParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
required init(device: MTLDevice, param: FlattenParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, computePrecision: GlobalConfig.shared.computePrecision)
var id: [Int32] = [1, 1, 1, 1]
for i in 0..<param.input.tensorDim.cout() {
id[4-param.input.tensorDim.cout()+i] = Int32(param.input.tensorDim[i])
......@@ -47,10 +47,10 @@ class FlattenKernel<P: PrecisionType>: Kernel, Computable{
let irank = param.input.tensorDim.cout()
let orank = param.output.tensorDim.cout()
assert(orank == 2)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape_\(irank)_2_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_\(irank)_2_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape_\(irank)_2_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_\(irank)_2_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/MulticlassNMSKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/MulticlassNMSKernel.swift
浏览文件 @ 38e9f34c
......@@ -17,16 +17,16 @@ import Foundation
class MulticlassNMSKernel<P: PrecisionType>: Kernel, Computable{
let pipline1: MTLComputePipelineState
required init(device: MTLDevice, param: MulticlassNMSParam<P>) {
required init(device: MTLDevice, param: MulticlassNMSParam<P>, initContext: InitContext) {
param.middleOutput.initBuffer(device: device)
param.bboxOutput.initBuffer(device: device)
if computePrecision == .Float32 {
pipline1 = device.pipeLine(funcName: "nms_fetch_bbox", inPaddleMobileLib: true)
super.init(device: device, inFunctionName: "nms_fetch_result")
} else if computePrecision == .Float16 {
pipline1 = device.pipeLine(funcName: "nms_fetch_bbox_half", inPaddleMobileLib: true)
super.init(device: device, inFunctionName: "nms_fetch_result_half")
if GlobalConfig.shared.computePrecision == .Float32 {
pipline1 = device.pipeLine(funcName: "nms_fetch_bbox", metalLoadMode: initContext.metalLoadMode, metalLibPath: initContext.metalLibPath)
super.init(device: device, inFunctionName: "nms_fetch_result", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
pipline1 = device.pipeLine(funcName: "nms_fetch_bbox_half", metalLoadMode: initContext.metalLoadMode, metalLibPath: initContext.metalLibPath)
super.init(device: device, inFunctionName: "nms_fetch_result_half", initContext: initContext)
} else {
fatalError( " unsupport precision " )
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PoolKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/PoolKernel.swift
浏览文件 @ 38e9f34c
......@@ -26,8 +26,8 @@ struct PoolMetalParam {
class PoolKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: PoolMetalParam
required init(device: MTLDevice, param: PoolParam<P>) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
required init(device: MTLDevice, param: PoolParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
var poolType: Int32
switch param.poolType {
......@@ -48,10 +48,10 @@ class PoolKernel<P: PrecisionType>: Kernel, Computable{
poolType: poolType
)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "pool_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "pool_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "pool_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "pool_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PreluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/PreluKernel.swift
浏览文件 @ 38e9f34c
......@@ -15,24 +15,24 @@
import Foundation
class PreluKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: PreluParam<P>) {
param.alpha.initBuffer(device: device, precision: computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
required init(device: MTLDevice, param: PreluParam<P>, initContext: InitContext) {
param.alpha.initBuffer(device: device, precision: GlobalConfig.shared.computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float32 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "prelu_channel")
super.init(device: device, inFunctionName: "prelu_channel", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "prelu_element")
super.init(device: device, inFunctionName: "prelu_element", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "prelu_other")
super.init(device: device, inFunctionName: "prelu_other", initContext: initContext)
}
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "prelu_channel_half")
super.init(device: device, inFunctionName: "prelu_channel_half", initContext: initContext)
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "prelu_element_half")
super.init(device: device, inFunctionName: "prelu_element_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "prelu_other_half")
super.init(device: device, inFunctionName: "prelu_other_half", initContext: initContext)
}
} else {
fatalError()
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @ 38e9f34c
......@@ -32,29 +32,28 @@ struct PriorBoxMetalParam {
class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: PriorBoxMetalParam!
required init(device: MTLDevice, param: PriorBoxParam<P>) {
required init(device: MTLDevice, param: PriorBoxParam<P>, initContext: InitContext) {
let originDim = param.output.tensorDim;
param.output.tensorDim = Dim.init(inDim: [1, originDim[0], originDim[1], originDim[2] * originDim[3]])
param.output.padToFourDim = Dim.init(inDim: [1, originDim[0], originDim[1], originDim[2] * originDim[3]])
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: computePrecision)
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: GlobalConfig.shared.computePrecision)
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: GlobalConfig.shared.computePrecision)
if computePrecision == .Float32 {
if GlobalConfig.shared.computePrecision == .Float32 {
if param.min_max_aspect_ratios_order {
super.init(device: device, inFunctionName: "prior_box_MinMaxAspectRatiosOrder")
super.init(device: device, inFunctionName: "prior_box_MinMaxAspectRatiosOrder", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "prior_box")
super.init(device: device, inFunctionName: "prior_box", initContext: initContext)
}
} else if computePrecision == .Float16 {
} else if GlobalConfig.shared.computePrecision == .Float16 {
if param.min_max_aspect_ratios_order {
super.init(device: device, inFunctionName: "prior_box_MinMaxAspectRatiosOrder_half")
super.init(device: device, inFunctionName: "prior_box_MinMaxAspectRatiosOrder_half", initContext: initContext)
} else {
super.init(device: device, inFunctionName: "prior_box_half")
super.init(device: device, inFunctionName: "prior_box_half", initContext: initContext)
}
} else {
fatalError()
......@@ -105,12 +104,12 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
}
}
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
let buffer = device.makeBuffer(length: outputAspectRatior.count * MemoryLayout<Float16>.size)
float32ToFloat16(input: &outputAspectRatior, output:(buffer?.contents())!, count: outputAspectRatior.count)
param.newAspectRatios = buffer
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
let buffer = device.makeBuffer(bytes: outputAspectRatior, length: outputAspectRatior.count * MemoryLayout<Float32>.size, options: [])
param.newAspectRatios = buffer
} else {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ReluKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ReluKernel.swift
浏览文件 @ 38e9f34c
......@@ -25,11 +25,11 @@ class ReluKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: ReluParam<P>) {
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "relu")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "relu_half")
required init(device: MTLDevice, param: ReluParam<P>, initContext: InitContext) {
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "relu", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "relu_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ReshapeKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ReshapeKernel.swift
浏览文件 @ 38e9f34c
......@@ -31,8 +31,8 @@ class ReshapeKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: ReshapeMetalParam
required init(device: MTLDevice, param: ReshapeParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
required init(device: MTLDevice, param: ReshapeParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, computePrecision: GlobalConfig.shared.computePrecision)
var id: [Int32] = [1, 1, 1, 1]
for i in 0..<param.input.tensorDim.cout() {
id[4-param.input.tensorDim.cout()+i] = Int32(param.input.tensorDim[i])
......@@ -51,23 +51,23 @@ class ReshapeKernel<P: PrecisionType>: Kernel, Computable{
)
let irank = param.input.tensorDim.cout()
let orank = param.output.tensorDim.cout()
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape_\(irank)_\(orank)_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_\(irank)_\(orank)_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape_\(irank)_\(orank)_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_\(irank)_\(orank)_half", initContext: initContext)
} else {
fatalError()
}
}
required init(device: MTLDevice, testParam: ReshapeTestParam) {
required init(device: MTLDevice, testParam: ReshapeTestParam, initContext: InitContext) {
metalParam = ReshapeMetalParam.init(
idim: (0, 0, 0, 0),
itrans: (0, 0, 0, 0),
odim: (0, 0, 0, 0),
otrans: (0, 0, 0, 0)
)
super.init(device: device, inFunctionName: "reshape")
super.init(device: device, inFunctionName: "reshape", initContext: initContext)
}
func compute(commandBuffer: MTLCommandBuffer, param: ReshapeParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ResizeBilinearKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ResizeBilinearKernel.swift
浏览文件 @ 38e9f34c
......@@ -20,6 +20,17 @@ struct ResizeBilinearMetalParam {
}
class ResizeBilinearKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: ResizeBilinearParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "resize_bilinear", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "resize_bilinear_half", initContext: initContext)
} else {
fatalError()
}
}
func compute(commandBuffer: MTLCommandBuffer, param: ResizeBilinearParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
......@@ -35,15 +46,6 @@ class ResizeBilinearKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: ResizeBilinearParam<P>) {
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "resize_bilinear")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "resize_bilinear_half")
} else {
fatalError()
}
}
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/Scale.swift 0 → 100644
浏览文件 @ 38e9f34c
//
// Scale.swift
// paddle-mobile
//
// Created by liuRuiLong on 2019/1/4.
// Copyright © 2019 orange. All rights reserved.
//
import Foundation
class ScaleKernel: CusomKernel {
init(device: MTLDevice, shape: Shape, metalLoadMode: MetalLoadMode, metalLibPath: String?) {
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "scale", outputDim: shape, metalLoadModel: metalLoadMode, metalLibPath: metalLibPath)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "scale_half", outputDim: shape, metalLoadModel: metalLoadMode, metalLibPath: metalLibPath)
} else {
fatalError(" unsupport ")
}
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ShapeKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/ShapeKernel.swift
浏览文件 @ 38e9f34c
......@@ -28,12 +28,12 @@ class ShapeKernel<P: PrecisionType>: Kernel, Computable{
// encoder.endEncoding()
}
required init(device: MTLDevice, param: ShapeParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "shape")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "shape_half")
required init(device: MTLDevice, param: ShapeParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "shape", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "shape_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/SoftmaxKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/SoftmaxKernel.swift
浏览文件 @ 38e9f34c
......@@ -22,16 +22,16 @@ struct SoftmaxMetalParam {
class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: SoftmaxMetalParam
required init(device: MTLDevice, param: SoftmaxParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
required init(device: MTLDevice, param: SoftmaxParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, computePrecision: GlobalConfig.shared.computePrecision)
metalParam = SoftmaxMetalParam.init(
N: Int32(param.input.tensorDim[0]),
K: Int32(param.input.tensorDim[1])
)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "softmax_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "softmax_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "softmax_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "softmax_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/SplitKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/SplitKernel.swift
浏览文件 @ 38e9f34c
......@@ -37,13 +37,13 @@ class SplitKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: SplitParam<P>) {
required init(device: MTLDevice, param: SplitParam<P>, initContext: InitContext) {
// param.output.initTexture(device: device, computePrecision: computePrecision)
let num = param.outputList.count
let rank = param.input.tensorDim.cout()
assert(num >= 2 && num <= 4)
for output in param.outputList {
output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: GlobalConfig.shared.computePrecision)
}
smp = SplitMetalParam.init()
smp.idim = (Int32(param.input.dim[0]), Int32(param.input.dim[1]), Int32(param.input.dim[2]), Int32(param.input.dim[3]))
......@@ -81,10 +81,10 @@ class SplitKernel<P: PrecisionType>: Kernel, Computable{
if v == "normal" {
fatalError("split unsupported")
}
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "split_\(rank)_\(num)_\(v)_float")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "split_\(rank)_\(num)_\(v)_half")
if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "split_\(rank)_\(num)_\(v)_float", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "split_\(rank)_\(num)_\(v)_half", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Texture2DTo2DArrayKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/Texture2DTo2DArrayKernel.swift
浏览文件 @ 38e9f34c
......@@ -33,12 +33,12 @@ class Texture2DTo2DArrayKernel<P: PrecisionType>: Kernel, Computable{
encoder.endEncoding()
}
required init(device: MTLDevice, param: FeedParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "texture2d_to_2d_array_half")
} else if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "texture2d_to_2d_array")
required init(device: MTLDevice, param: FeedParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: GlobalConfig.shared.computePrecision)
if GlobalConfig.shared.computePrecision == .Float16 {
super.init(device: device, inFunctionName: "texture2d_to_2d_array_half", initContext: initContext)
} else if GlobalConfig.shared.computePrecision == .Float32 {
super.init(device: device, inFunctionName: "texture2d_to_2d_array", initContext: initContext)
} else {
fatalError()
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/TransposeKernel.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/TransposeKernel.swift
浏览文件 @ 38e9f34c
......@@ -22,8 +22,8 @@ struct TransposeMetalParam {
class TransposeKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: TransposeMetalParam = TransposeMetalParam.init()
required init(device: MTLDevice, param: TransposeParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
required init(device: MTLDevice, param: TransposeParam<P>, initContext: InitContext) {
param.output.initTexture(device: device, computePrecision: GlobalConfig.shared.computePrecision)
let rank = param.input.tensorDim.cout()
var axis: [Int] = [0, 1, 2, 3]
for i in 0..<param.axis.count {
......@@ -43,13 +43,13 @@ class TransposeKernel<P: PrecisionType>: Kernel, Computable {
metalParam.oC = Int32(param.output.dim[3])
metalParam.axis = (Int32(naxis[0]), Int32(naxis[1]), Int32(naxis[2]), Int32(naxis[3]))
var kernelFunc = "transpose_undefined"
if computePrecision == .Float16 {
if GlobalConfig.shared.computePrecision == .Float16 {
if param.input.transpose == axis {
kernelFunc = "transpose_copy_half"
} else {
kernelFunc = "transpose_\(rank)_half"
}
} else if computePrecision == .Float32 {
} else if GlobalConfig.shared.computePrecision == .Float32 {
if param.input.transpose == axis {
kernelFunc = "transpose_copy_float"
} else {
......@@ -60,7 +60,7 @@ class TransposeKernel<P: PrecisionType>: Kernel, Computable {
}
print("===========>", kernelFunc)
print(metalParam)
super.init(device: device, inFunctionName: kernelFunc)
super.init(device: device, inFunctionName: kernelFunc, initContext: initContext)
}
func compute(commandBuffer: MTLCommandBuffer, param: TransposeParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/BatchNormKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void batchnorm(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 * nscale [[buffer(0)]],
const device float4 * nbias [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
const float4 input = inTexture.read(gid.xy, gid.z);
float4 output = input * nscale[gid.z] + nbias[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void batchnorm_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 * newScale [[buffer(0)]],
const device half4 * newBias [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
const half4 input = inTexture.read(gid.xy, gid.z);
half4 output = input * newScale[gid.z] + newBias[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/BatchNormRelu.metal 0 → 100644
浏览文件 @ 38e9f34c
//
// BatchNormRelu.metal
// paddle-mobile
//
#include <metal_stdlib>
using namespace metal;
struct MetalConvParam {
short offsetX;
short offsetY;
short offsetZ;
ushort strideX;
ushort strideY;
};
kernel void batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *new_scale [[buffer(0)]],
const device float4 *new_biase [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
float4 input;
float4 output;
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
input = inTexture.sample(sample, gid.x, gid.y, gid.z);
output = fmax(input * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/BilinearInterp.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define FUNC(f, p) CONCAT2_(f, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(bilinear_interp, P)(texture2d_array<P, access::read> input [[texture(0)]],
texture2d_array<P, access::write> output [[texture(1)]],
constant bilinear_interp_param & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r;
if ((input.get_width() == output.get_width()) && (input.get_height() == output.get_height())) {
r = input.read(gid.xy, gid.z);
} else {
P w = gid.x * pm.ratio_w;
P h = gid.y * pm.ratio_h;
uint w0 = w, h0 = h;
uint w1 = w0 + 1, h1 = h0 + 1;
P w1lambda = w - w0, h1lambda = h - h0;
P w2lambda = 1.0 - w1lambda, h2lambda = 1.0 - h1lambda;
if (w1 >= input.get_width()) w1 = w0;
if (h1 >= input.get_height()) h1 = h0;
VECTOR(P, 4) r0 = input.read(uint2(w0, h0), gid.z);
VECTOR(P, 4) r1 = input.read(uint2(w1, h0), gid.z);
VECTOR(P, 4) r2 = input.read(uint2(w0, h1), gid.z);
VECTOR(P, 4) r3 = input.read(uint2(w1, h1), gid.z);
r = h2lambda * (w2lambda * r0 + w1lambda * r1)
+ h1lambda * (w2lambda * r2 + w1lambda * r3);
}
output.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/BilinearInterp.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
struct bilinear_interp_param {
float ratio_h;
float ratio_w;
};
#define P float
#include "BilinearInterp.inc.metal"
#undef P
#define P half
#include "BilinearInterp.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/BoxCoder.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define FUNC(f, p) CONCAT2_(f, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(boxcoder, P)(texture2d_array<P, access::read> priorBox [[texture(0)]],
texture2d_array<P, access::read> priorBoxVar [[texture(1)]],
texture2d_array<P, access::read> targetBox [[texture(2)]],
texture2d_array<P, access::write> output[[texture(3)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) p = priorBox.read(uint2(0, gid.x), gid.z);
VECTOR(P, 4) pv = priorBoxVar.read(uint2(0, gid.x), gid.z);
VECTOR(P, 4) t;
t[0] = targetBox.read(uint2(0, gid.x), gid.z)[0];
t[1] = targetBox.read(uint2(1, gid.x), gid.z)[0];
t[2] = targetBox.read(uint2(2, gid.x), gid.z)[0];
t[3] = targetBox.read(uint2(3, gid.x), gid.z)[0];
P px = (p.x + p.z) / 2;
P py = (p.y + p.w) / 2;
P pw = p.z - p.x;
P ph = p.w - p.y;
P tx = pv.x * t.x * pw + px;
P ty = pv.y * t.y * ph + py;
P tw = exp(pv.z * t.z) * pw;
P th = exp(pv.w * t.w) * ph;
VECTOR(P, 4) r;
r.x = tx - tw / 2;
r.y = ty - th / 2;
r.z = tx + tw / 2;
r.w = ty + th / 2;
output.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/BoxCoder.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
#define P float
#include "BoxCoder.inc.metal"
#undef P
#define P half
#include "BoxCoder.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Common.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
inline void xyzn2abcd_1(int xyzn[4], int abcd[4]) {
abcd[0] = abcd[1] = abcd[2] = 0;
abcd[3] = xyzn[0] * 4 + xyzn[3];
}
inline void xyzn2abcd_2(int xyzn[4], int abcd[4]) {
abcd[0] = abcd[1] = 0;
abcd[2] = xyzn[1];
abcd[3] = xyzn[0] * 4 + xyzn[3];
}
inline void xyzn2abcd_3(int xyzn[4], int abcd[4]) {
abcd[0] = 0;
abcd[3] = xyzn[0];
abcd[2] = xyzn[1];
abcd[1] = xyzn[2] * 4 + xyzn[3];
}
inline void xyzn2abcd_4(int C, int xyzn[4], int abcd[4]) {
abcd[2] = xyzn[0];
abcd[1] = xyzn[1];
uint t = xyzn[2] * 4 + xyzn[3];
abcd[0] = t / C;
abcd[3] = t % C;
}
inline void abcd2xyzn_1(int abcd[4], int xyzn[4]) {
xyzn[1] = xyzn[2] = 0;
xyzn[0] = abcd[3] / 4;
xyzn[1] = abcd[3] % 4;
}
inline void abcd2xyzn_2(int abcd[4], int xyzn[4]) {
xyzn[2] = 0;
xyzn[1] = abcd[2];
xyzn[0] = abcd[3] / 4;
xyzn[3] = abcd[3] % 4;
}
inline void abcd2xyzn_3(int abcd[4], int xyzn[4]) {
xyzn[0] = abcd[3];
xyzn[1] = abcd[2];
xyzn[2] = abcd[1] / 4;
xyzn[3] = abcd[1] % 4;
}
inline void abcd2xyzn_4(int C, int abcd[4], int xyzn[4]) {
xyzn[0] = abcd[2];
xyzn[1] = abcd[1];
uint t = abcd[0] * C + abcd[3];
xyzn[2] = t / 4;
xyzn[3] = t % 4;
}
inline void xyzn2abcd(int C, int xyzn[4], int abcd[4]) {
abcd[2] = xyzn[0];
abcd[1] = xyzn[1];
uint t = xyzn[2] * 4 + xyzn[3];
abcd[0] = t / C;
abcd[3] = t % C;
}
inline void abcd2xyzn(int C, int abcd[4], int xyzn[4]) {
xyzn[0] = abcd[2];
xyzn[1] = abcd[1];
uint t = abcd[0] * C + abcd[3];
xyzn[2] = t / 4;
xyzn[3] = t % 4;
}
inline int32_t abcd2index(int32_t dim[4], int32_t abcd[4]) {
int32_t r = abcd[0];
r = r * dim[1] + abcd[1];
r = r * dim[2] + abcd[2];
r = r * dim[3] + abcd[3];
return r;
}
inline void index2abcd(int32_t dim[4], int32_t ind, int32_t abcd[4]) {
abcd[3] = ind % dim[3]; ind /= dim[3];
abcd[2] = ind % dim[2]; ind /= dim[2];
abcd[1] = ind % dim[1]; ind /= dim[1];
abcd[0] = ind;
}
inline void trans(int32_t trans[4], int32_t ipos[4], int32_t opos[4]) {
for (int i = 0; i < 4; i++) {
opos[i] = ipos[trans[i]];
}
}
inline void invtrans(int32_t trans[4], int32_t ipos[4], int32_t opos[4]) {
for (int i = 0; i < 4; i++) {
opos[trans[i]] = ipos[i];
}
}
struct MetalConvParam {
short offsetX;
short offsetY;
short offsetZ;
ushort strideX;
ushort strideY;
ushort dilationX;
ushort dilationY;
};
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConcatKernel.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define CONCAT5_(a, b, c, d, e) a ## _ ## b ## _ ## c ## _ ## d ## _ ## e
#define FUNC(f, r, n, v, p) CONCAT5_(f, r, n, v, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC_R(f, r) CONCAT2_(f, r)
#if V == VX
#define VV x
#elif V == VY
#define VV y
#elif V == VZ
#define VV z
#else
#define VV normal
#endif
#if V == VNORMAL
//kernel void FUNC(concat, R, N, normal, P)(array<texture2d_array<P, access::read>, N> in [[texture(0)]],
// texture2d_array<P, access::read> out_x [[texture(N)]],
// texture2d_array<P, access::write> out [[texture(N+1)]],
// constant ConcatParam & pm [[buffer(0)]],
// uint3 gid [[thread_position_in_grid]]) {
//}
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif
texture2d_array<P, access::read> inx [[texture(N)]],
texture2d_array<P, access::write> out [[texture(N+1)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
ConcatParam cp = pm;
int xyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, abcd[4], oxyzn[4];
VECTOR(P, 4) r = inx.read(gid.xy, gid.z);
for (int i = 0; i < 4; i++) {
xyzn[3] = i;
#if R == 4
xyzn2abcd_4(cp.odim[3], xyzn, abcd);
#else
FUNC_R(xyzn2abcd, R)(xyzn, abcd);
#endif
int k = abcd[cp.axis] - cp.offset;
if (k < 0) continue;
int j = 0;
for (; j < N; j++) {
if (k < cp.vdim[j]) {
break;
}
k -= cp.vdim[j];
}
if (j == N) {
continue;
}
int ta = cp.odim[cp.axis];
abcd[cp.axis] = k;
cp.odim[cp.axis] = cp.vdim[j];
#if R == 4
abcd2xyzn_4(cp.odim[3], abcd, oxyzn);
#else
FUNC_R(abcd2xyzn, R)(abcd, oxyzn);
#endif
cp.odim[cp.axis] = ta;
switch (j) {
case 0: r[i] = in0.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 1: r[i] = in1.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#if N >= 3
case 2: r[i] = in2.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
#if N >= 4
case 3: r[i] = in3.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
#if N >= 5
case 4: r[i] = in4.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
#if N >= 6
case 5: r[i] = in5.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
#endif
}
}
out.write(r, gid.xy, gid.z);
}
#endif // V == NORMAL
#if V == VX
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif // N >= 4
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif // N >= 5
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif // N >= 6
texture2d_array<P, access::write> out [[texture(N)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
int x = gid.x - pm.offset;
if (x < 0) return;
if (x < pm.vdim[0]) {
VECTOR(P, 4) r = in0.read(gid.xy, gid.z);
out.write(r, gid.xy, gid.z);
return;
}
x -= pm.vdim[0];
if (x < pm.vdim[1]) {
VECTOR(P, 4) r = in1.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#if N >= 3
x -= pm.vdim[1];
if (x < pm.vdim[2]) {
VECTOR(P, 4) r = in2.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 3
#if N >= 4
x -= pm.vdim[2];
if (x < pm.vdim[3]) {
VECTOR(P, 4) r = in3.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 4
#if N >= 5
x -= pm.vdim[3];
if (x < pm.vdim[4]) {
VECTOR(P, 4) r = in4.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 5
#if N >= 6
x -= pm.vdim[4];
if (x < pm.vdim[5]) {
VECTOR(P, 4) r = in5.read(uint2(x, gid.y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 6
}
#endif // V == VX
#if V == VY
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif // N >= 4
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif // N >= 5
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif // N >= 6
texture2d_array<P, access::write> out [[texture(N)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
int y = gid.y - pm.offset;
if (y < 0) return;
if (y < pm.vdim[0]) {
VECTOR(P, 4) r = in0.read(gid.xy, gid.z);
out.write(r, gid.xy, gid.z);
return;
}
y -= pm.vdim[0];
if (y < pm.vdim[1]) {
VECTOR(P, 4) r = in1.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#if N >= 3
y -= pm.vdim[1];
if (y < pm.vdim[2]) {
VECTOR(P, 4) r = in2.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 3
#if N >= 4
y -= pm.vdim[2];
if (y < pm.vdim[3]) {
VECTOR(P, 4) r = in3.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 4
#if N >= 5
y -= pm.vdim[3];
if (y < pm.vdim[4]) {
VECTOR(P, 4) r = in4.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 5
#if N >= 6
y -= pm.vdim[4];
if (y < pm.vdim[5]) {
VECTOR(P, 4) r = in5.read(uint2(gid.x, y), gid.z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 6
}
#endif // V == VY
#if V == VZ
kernel void FUNC(concat, R, N, VV, P)(texture2d_array<P, access::read> in0 [[texture(0)]],
texture2d_array<P, access::read> in1 [[texture(1)]],
#if N >= 3
texture2d_array<P, access::read> in2 [[texture(2)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::read> in3 [[texture(3)]],
#endif // N >= 4
#if N >= 5
texture2d_array<P, access::read> in4 [[texture(4)]],
#endif // N >= 5
#if N >= 6
texture2d_array<P, access::read> in5 [[texture(5)]],
#endif // N >= 6
texture2d_array<P, access::write> out [[texture(N)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
int z = gid.z - pm.offset;
if (z < 0) return;
if (z < pm.vdim[0]) {
VECTOR(P, 4) r = in0.read(gid.xy, gid.z);
out.write(r, gid.xy, gid.z);
return;
}
z -= pm.vdim[0];
if (z < pm.vdim[1]) {
VECTOR(P, 4) r = in1.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#if N >= 3
z -= pm.vdim[1];
if (z < pm.vdim[2]) {
VECTOR(P, 4) r = in2.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 3
#if N >= 4
z -= pm.vdim[2];
if (z < pm.vdim[3]) {
VECTOR(P, 4) r = in3.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 4
#if N >= 5
z -= pm.vdim[3];
if (z < pm.vdim[4]) {
VECTOR(P, 4) r = in4.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 5
#if N >= 6
z -= pm.vdim[4];
if (z < pm.vdim[5]) {
VECTOR(P, 4) r = in5.read(gid.xy, z);
out.write(r, gid.xy, gid.z);
return;
}
#endif // N >= 6
}
#endif // V == VZ
#undef VV
#endif // #ifdef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConcatKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct ConcatParam {
int32_t odim[4];
int32_t axis;
int32_t offset;
int32_t trans[4];
int32_t vdim[6];
};
#define VNORMAL 1
#define VX 2
#define VY 3
#define VZ 4
// >> fast mode
// only support concat_{2,3,4}_{2,3,4,5,6}_y_{float,half}
// only support concat_{3,4}_{2,3,4,5,6}_x_{float,half}
// only support concat_{1,2,3,4}_{2,3,4,5,6}_z_{float,half}
// >> normal mode (loop mode)
// ssd-ar: (R=4, N=3, V=z), (R=3, N=2, V=y), (R=2, N=5, V=x), (R=3, N=5, V=x)
// ssd: (R=2, N=6, V=y), (R=3, N=6, V=y)
// genet: (R=4, N=2, V=normal)
// ssd-ar: (R=3, N=5, V=x)
#define V VX
#define R 3
#define N 5
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd-ar: (R=2, N=5, V=x)
#define V VX
#define R 2
#define N 5
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd-ar: (R=3, N=2, V=y)
#define V VY
#define R 3
#define N 2
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd-ar: (R=4, N=3, V=z)
#define V VZ
#define R 4
#define N 3
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd: (R=2, N=6, V=y)
#define V VY
#define R 2
#define N 6
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
// ssd: (R=3, N=6, V=y)
#define V VY
#define R 3
#define N 6
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
#define V VNORMAL
#define R 4
#define N 2
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
#define V VY
#define R 2
#define N 2
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
#define V VY
#define R 2
#define N 5
#define P float
#include "ConcatKernel.inc.metal"
#undef P
#define P half
#include "ConcatKernel.inc.metal"
#undef P
#undef N
#undef R
#undef V
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvAddBNReluKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
kernel void conv_add_batch_norm_relu_1x1_half(
texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *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);
half4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = fmax((output + float4(biase[gid.z])) * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_add_batch_norm_relu_3x3_half(
texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *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);
half4 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) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = fmax((output + float4(biase[gid.z])) * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_add_batch_norm_relu_3x3_half(
texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *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);
half4 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) {
half4 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 + float4(biase[gid.z])) * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
/*---------------------------------------------*/
kernel void conv_add_batch_norm_relu_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[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 + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[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 + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *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;
}
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 + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvAddMetal.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
#pragma mark - convAdd
kernel void conv_add_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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 = biase[gid.z];
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 = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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 = biase[gid.z];
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
float4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), 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 = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_5x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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 = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = biase[gid.z];
ushort dilation_y = param.dilationY;
float4 input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 2 * dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 2 * dilation_y), i);
for (int j = 0; j < 5; ++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 = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_1x5(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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 = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = biase[gid.z];
ushort dilation_x = param.dilationX;
float4 input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 2 * dilation_x, posInInput.y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x + 2 * dilation_x, posInInput.y), i);
for (int j = 0; j < 5; ++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 = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
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 = biase[gid.z];
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 = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
#pragma mark - half
kernel void conv_add_1x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
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;
half4 output = biase[gid.z];
half4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
// output = output + float4(biase[gid.z]);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
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;
half4 output = biase[gid.z];
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
half4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), i);
for (int j = 0; j < 9; ++j) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(float4(input[j]), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(float4(input[j]), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(float4(input[j]), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(float4(input[j]), float4(weight_w));
}
}
// output = output + float4(biase[gid.z]);
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
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;
half4 output = biase[gid.z];
half4 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) {
half4 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 = output + float4(biase[gid.z]);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_5x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
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 = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
half4 output = biase[gid.z];
ushort dilation_y = param.dilationY;
half4 input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 2 * dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 2 * dilation_y), i);
for (int j = 0; j < 5; ++j) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
// output = output + float4(biase[gid.z]);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_1x5_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
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 = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
half4 output = biase[gid.z];
ushort dilation_x = param.dilationX;
half4 input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 2 * dilation_x, posInInput.y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x + 2 * dilation_x, posInInput.y), i);
for (int j = 0; j < 5; ++j) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
// output = output + float4(biase[gid.z]);
outTexture.write(output, gid.xy, gid.z);
}
kernel void test_conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
if (gid.x > 0 || gid.y > 0 || gid.z > 0) { 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);
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
float4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), 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 = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvAddPrelu.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#include "Macro.metal"
#pragma mark - convAdd
kernel void FUNC3_(conv_add_1x1, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
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;
VECTOR(P, 4) output = biase[gid.z];
VECTOR(P, 4) input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample,float2(posInInput.x, posInInput.y), i);
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
// output = output + float4(biase[gid.z]);
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(conv_add_3x3, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
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;
VECTOR(P, 4) output = biase[gid.z];
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
VECTOR(P, 4) input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), i);
for (int j = 0; j < 9; ++j) {
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
// output = output + float4(biase[gid.z]);
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(conv_add_5x1, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
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 = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
VECTOR(P, 4) output = biase[gid.z];;
ushort dilation_y = param.dilationY;
VECTOR(P, 4) input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 2 * dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 2 * dilation_y), i);
for (int j = 0; j < 5; ++j) {
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(conv_add_1x5, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device VECTOR(P, 4) *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
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 = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
VECTOR(P, 4) output = biase[gid.z];
ushort dilation_x = param.dilationX;
VECTOR(P, 4) input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 2 * dilation_x, posInInput.y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x + 2 * dilation_x, posInInput.y), i);
for (int j = 0; j < 5; ++j) {
VECTOR(P, 4) weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
VECTOR(P, 4) weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
VECTOR(P, 4) weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
VECTOR(P, 4) weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
kernel void FUNC3_(depthwise_conv_add_3x3, PRELU_TYPE, P)(texture2d_array<P, access::sample> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device P *weights [[buffer(1)]],
const device VECTOR(P, 4) *biase [[buffer(2)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(3)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(3)]],
#endif
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;
VECTOR(P, 4) output = biase[gid.z];
VECTOR(P, 4) 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) {
VECTOR(P, 4) 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];
}
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(VECTOR(P, 4)(output), gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvAddPreluKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
#define P float
#define PRELU_CHANNEL prelu_channel
#define PRELU_TYPE prelu_channel
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_CHANNEL
#define PRELU_ELEMENT prelu_element
#define PRELU_TYPE prelu_element
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_ELEMENT
#define PRELU_OTHER prelu_other
#define PRELU_TYPE prelu_other
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_OTHER
#undef P
#define P half
#define PRELU_CHANNEL prelu_channel
#define PRELU_TYPE prelu_channel
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_CHANNEL
#define PRELU_ELEMENT prelu_element
#define PRELU_TYPE prelu_element
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_ELEMENT
#define PRELU_OTHER prelu_other
#define PRELU_TYPE prelu_other
#include "ConvAddPrelu.inc.metal"
#undef PRELU_TYPE
#undef PRELU_OTHER
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvBNReluKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
#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 &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(2)]],
const device float4 *new_biase [[buffer(3)]],
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 &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(2)]],
const device float4 *new_biase [[buffer(3)]],
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 &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(2)]],
const device float4 *new_biase [[buffer(3)]],
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);
}
#pragma mark - half
kernel void conv_batch_norm_relu_1x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *new_scale [[buffer(2)]],
const device half4 *new_biase [[buffer(3)]],
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);
half4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(float4(input), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(float4(input), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(float4(input), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(float4(input), float4(weight_w));
}
output = fmax(output * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_batch_norm_relu_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *new_scale [[buffer(2)]],
const device half4 *new_biase [[buffer(3)]],
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);
half4 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) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(float4(input[j]), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(float4(input[j]), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(float4(input[j]), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(float4(input[j]), float4(weight_w));
}
}
output = fmax(output * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_batch_norm_relu_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half *weights [[buffer(1)]],
const device half4 *new_scale [[buffer(2)]],
const device half4 *new_biase [[buffer(3)]],
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);
half4 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) {
half4 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 * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
// conv
#pragma mark -- conv
kernel void conv_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
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);
}
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
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];
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
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);
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
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);
half4 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) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(float4(input[j]), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(float4(input[j]), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(float4(input[j]), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(float4(input[j]), float4(weight_w));
}
}
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half *weights [[buffer(1)]],
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);
half4 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) {
half4 input = inputs[j];
output.x += float(input.x) * float(weights[weithTo + 0 * kernelHXW + j]);
output.y += float(input.y) * float(weights[weithTo + 1 * kernelHXW + j]);
output.z += float(input.z) * float(weights[weithTo + 2 * kernelHXW + j]);
output.w += float(input.w) * float(weights[weithTo + 3 * kernelHXW + j]);
}
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_1x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
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);
half4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(float4(input), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(float4(input), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(float4(input), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(float4(input), float4(weight_w));
}
outTexture.write(half4(output), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ConvTransposeKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
struct MetalConvTransposeParam{
ushort kernelW;
ushort kernelH;
ushort strideX;
ushort strideY;
ushort paddingX;
ushort paddingY;
ushort dilationX;
ushort dilationY;
};
kernel void conv_transpose2x2_stride2(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvTransposeParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
int input_array_size = inTexture.get_array_size();
int kernel_index_x = gid.x % 2;
int kernel_index_y = gid.y % 2;
int kernel_index = kernel_index_y * 2 + kernel_index_x;
int kernel_to = gid.z * input_array_size * 4 * 4 + (kernel_index * input_array_size);
int input_x = gid.x / 2;
int input_y = gid.y / 2;
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 output = float4(0.0);
for (int i = 0; i < input_array_size; ++i) {
float4 input = inTexture.sample(sample, float2(input_x, input_y), i);
float4 kernel_slice0 = weights[kernel_to + input_array_size * 4 * 0 + i];
float4 kernel_slice1 = weights[kernel_to + input_array_size * 4 * 1 + i];
float4 kernel_slice2 = weights[kernel_to + input_array_size * 4 * 2 + i];
float4 kernel_slice3 = weights[kernel_to + input_array_size * 4 * 3 + i];
output.x += dot(input, kernel_slice0);
output.y += dot(input, kernel_slice1);
output.z += dot(input, kernel_slice2);
output.w += dot(input, kernel_slice3);
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_transpose2x2_stride2_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvTransposeParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
int input_array_size = inTexture.get_array_size();
int kernel_index_x = gid.x % 2;
int kernel_index_y = gid.y % 2;
int kernel_index = kernel_index_y * 2 + kernel_index_x;
int kernel_to = gid.z * input_array_size * 4 * 4 + (kernel_index * input_array_size);
int input_x = gid.x / 2;
int input_y = gid.y / 2;
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 output = float4(0.0);
for (int i = 0; i < input_array_size; ++i) {
half4 input = inTexture.sample(sample, float2(input_x, input_y), i);
half4 kernel_slice0 = weights[kernel_to + input_array_size * 4 * 0 + i];
half4 kernel_slice1 = weights[kernel_to + input_array_size * 4 * 1 + i];
half4 kernel_slice2 = weights[kernel_to + input_array_size * 4 * 2 + i];
half4 kernel_slice3 = weights[kernel_to + input_array_size * 4 * 3 + i];
output.x += dot(float4(input), float4(kernel_slice0));
output.y += dot(float4(input), float4(kernel_slice1));
output.z += dot(float4(input), float4(kernel_slice2));
output.w += dot(float4(input), float4(kernel_slice3));
}
outTexture.write(half4(output), gid.xy, gid.z);
}
//kernel void conv_transpose(texture2d_array<float, access::sample> inTexture [[texture(0)]],
// texture2d_array<float, access::write> outTexture [[texture(1)]],
// constant MetalConvTransposeParam &param [[buffer(0)]],
// const device float4 *weights [[buffer(1)]],
// uint3 gid [[thread_position_in_grid]]){
// if (gid.x >= outTexture.get_width() ||
// gid.y >= outTexture.get_height() ||
// gid.z >= outTexture.get_array_size()) {
// return;
// }
//
// int input_array_size = inTexture.get_array_size();
//
// uint kernel_one_output_slice = input_array_size * param.kernelW * param.kernelH;
//
// uint kernel_stride_z = gid.z * 4 * (kernel_one_output_slice);
//
// constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
//
// float4 output;
//
// for (int w = 0; w < param.kernelW; ++w) {
// int top = gid.x - w * param.dilationX + param.paddingX;
// int input_x = top / param.strideX;
// if (top < 0 || input_x >= int(inTexture.get_width())) {
// continue;
// }
//
// for (int h = 0; h < param.kernelH; ++h) {
// int top_y = gid.y - h * param.dilationY + param.paddingY;
// int input_y = top_y / param.strideY;
// if (top_y < 0 || input_y >= int(inTexture.get_height())) {
// continue;
// }
//
// uint kernel_index = (w * param.kernelH + h) * inTexture.get_array_size();
//
// for (int slice = 0; slice < input_array_size; ++slice) {
//
// float4 input;
// float4 kernel_slice = weights[kernel_stride_z + 0 * kernel_one_output_slice + kernel_index + slice];
// float4 kernel_slice1 = weights[kernel_stride_z + 1 * kernel_one_output_slice + kernel_index + slice];
//
// float4 kernel_slice2 = weights[kernel_stride_z + 2 * kernel_one_output_slice + kernel_index + slice];
//
// float4 kernel_slice3 = weights[kernel_stride_z + 3 * kernel_one_output_slice + kernel_index + slice];
//
// input = inTexture.sample(sample, float2(input_x, input_y), slice);
// output.x += dot(input, kernel_slice);
// output.y += dot(input, kernel_slice1);
// output.z += dot(input, kernel_slice2);
// output.w += dot(input, kernel_slice3);
// }
// }
// }
//
// outTexture.write(output, gid.xy, gid.z);
//}
//
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Elementwise.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct ElementwiseAddParam {
int32_t fast;
int32_t axis;
int32_t ylen;
int32_t xdim[4];
int32_t xtrans[4];
int32_t ydim[4];
int32_t ytrans[4];
};
kernel void elementwise_add(texture2d_array<float, access::read> inputX [[texture(0)]],
texture2d_array<float, access::read> inputY [[texture(1)]],
texture2d_array<float, access::write> outTexture [[texture(2)]],
constant ElementwiseAddParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
float4 rx, ry;
if (pm.fast == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(gid.xy, gid.z);
} else {
rx = inputX.read(gid.xy, gid.z);
int32_t x_xyzn[4] = {int32_t(gid.x), int32_t(gid.y), int32_t(gid.z), 0}, x_abcd[4], t_abcd[4];
int32_t y_abcd[4] = {0, 0, 0, 0}, y_xyzn[4];
int32_t xtrans[4] = {pm.xtrans[0], pm.xtrans[1], pm.xtrans[2], pm.xtrans[3]};
int32_t ytrans[4] = {pm.ytrans[0], pm.ytrans[1], pm.ytrans[2], pm.ytrans[3]};
int32_t yshift = 4 - pm.ylen - pm.axis;
for (int n = 0; n < 4; n++) {
x_xyzn[3] = n;
xyzn2abcd(pm.xdim[3], x_xyzn, x_abcd);
invtrans(xtrans, x_abcd, t_abcd);
for (int k = pm.axis; k < (pm.axis + pm.ylen); k++) {
y_abcd[yshift+k] = t_abcd[k];
}
trans(ytrans, y_abcd, t_abcd);
abcd2xyzn(pm.ydim[3], t_abcd, y_xyzn);
ry[n] = inputY.read(uint2(y_xyzn[0], y_xyzn[1]), y_xyzn[2])[y_xyzn[3]];
}
}
float4 r = rx + ry;
outTexture.write(r, gid.xy, gid.z);
}
kernel void elementwise_add_half(texture2d_array<half, access::read> inputX [[texture(0)]],
texture2d_array<half, access::read> inputY [[texture(1)]],
texture2d_array<half, access::write> outTexture [[texture(2)]],
constant ElementwiseAddParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
half4 rx, ry;
if (pm.fast == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(gid.xy, gid.z);
} else {
rx = inputX.read(gid.xy, gid.z);
int32_t x_xyzn[4] = {int32_t(gid.x), int32_t(gid.y), int32_t(gid.z), 0}, x_abcd[4], t_abcd[4];
int32_t y_abcd[4] = {0, 0, 0, 0}, y_xyzn[4];
int32_t xtrans[4] = {pm.xtrans[0], pm.xtrans[1], pm.xtrans[2], pm.xtrans[3]};
int32_t ytrans[4] = {pm.ytrans[0], pm.ytrans[1], pm.ytrans[2], pm.ytrans[3]};
int32_t yshift = 4 - pm.ylen - pm.axis;
for (int n = 0; n < 4; n++) {
x_xyzn[3] = n;
xyzn2abcd(pm.xdim[3], x_xyzn, x_abcd);
invtrans(xtrans, x_abcd, t_abcd);
for (int k = pm.axis; k < (pm.axis + pm.ylen); k++) {
y_abcd[yshift+k] = t_abcd[k];
}
trans(ytrans, y_abcd, t_abcd);
abcd2xyzn(pm.ydim[3], t_abcd, y_xyzn);
ry[n] = inputY.read(uint2(y_xyzn[0], y_xyzn[1]), y_xyzn[2])[y_xyzn[3]];
}
}
half4 r = rx + ry;
outTexture.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ElementwiseAddPreluKernel.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#include <metal_stdlib>
#include "Macro.metal"
using namespace metal;
kernel void FUNC3_(elementwise_add, PRELU_TYPE, P)(texture2d_array<P, access::read> inputX [[texture(0)]],
texture2d_array<P, access::read> inputY [[texture(1)]],
texture2d_array<P, access::write> outTexture [[texture(2)]],
constant ElementwiseAddParam &pm [[buffer(0)]],
#ifdef PRELU_CHANNEL
const device VECTOR(P, 4) *alpha [[buffer(1)]],
#endif
#ifdef PRELU_ELEMENT
const device VECTOR(P, 4) *alpha [[buffer(1)]],
#endif
#ifdef PRELU_OTHER
const device P *alpha [[buffer(1)]],
#endif
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
VECTOR(P, 4) rx, ry;
if (pm.fast == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(gid.xy, gid.z);
} else {
rx = inputX.read(gid.xy, gid.z);
int32_t x_xyzn[4] = {int32_t(gid.x), int32_t(gid.y), int32_t(gid.z), 0}, x_abcd[4], t_abcd[4];
int32_t y_abcd[4] = {0, 0, 0, 0}, y_xyzn[4];
int32_t xtrans[4] = {pm.xtrans[0], pm.xtrans[1], pm.xtrans[2], pm.xtrans[3]};
int32_t ytrans[4] = {pm.ytrans[0], pm.ytrans[1], pm.ytrans[2], pm.ytrans[3]};
int32_t yshift = 4 - pm.ylen - pm.axis;
for (int n = 0; n < 4; n++) {
x_xyzn[3] = n;
xyzn2abcd(pm.xdim[3], x_xyzn, x_abcd);
invtrans(xtrans, x_abcd, t_abcd);
for (int k = pm.axis; k < (pm.axis + pm.ylen); k++) {
y_abcd[yshift+k] = t_abcd[k];
}
trans(ytrans, y_abcd, t_abcd);
abcd2xyzn(pm.ydim[3], t_abcd, y_xyzn);
ry[n] = inputY.read(uint2(y_xyzn[0], y_xyzn[1]), y_xyzn[2])[y_xyzn[3]];
}
}
VECTOR(P, 4) output = rx + ry;
#ifdef PRELU_CHANNEL
VECTOR(P, 4) alpha_value = alpha[gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_ELEMENT
int alpha_to = (gid.y * outTexture.get_width() + gid.x) * outTexture.get_array_size();
VECTOR(P, 4) alpha_value = alpha[alpha_to + gid.z];
output.x = output.x > 0 ? output.x : (alpha_value.x * output.x);
output.y = output.y > 0 ? output.y : (alpha_value.y * output.y);
output.z = output.z > 0 ? output.z : (alpha_value.z * output.z);
output.w = output.w > 0 ? output.w : (alpha_value.w * output.w);
#endif
#ifdef PRELU_OTHER
P alpha_value = alpha[0];
output.x = output.x > 0 ? output.x : (alpha_value * output.x);
output.y = output.y > 0 ? output.y : (alpha_value * output.y);
output.z = output.z > 0 ? output.z : (alpha_value * output.z);
output.w = output.w > 0 ? output.w : (alpha_value * output.w);
#endif
outTexture.write(output, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ElementwiseAddPreluKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct ElementwiseAddParam {
int32_t fast;
int32_t axis;
int32_t ylen;
int32_t xdim[4];
int32_t xtrans[4];
int32_t ydim[4];
int32_t ytrans[4];
};
#define P float
#define PRELU_CHANNEL prelu_channel
#define PRELU_TYPE channel
#include "ElementwiseAddPreluKernel.inc.metal"
#undef PRELU_TYPE
#undef PRELU_CHANNEL
#define PRELU_ELEMENT element
#define PRELU_TYPE prelu_element
#include "ElementwiseAddPreluKernel.inc.metal"
#undef PRELU_TYPE
#undef PRELU_ELEMENT
#define PRELU_OTHER other
#define PRELU_TYPE prelu_other
#include "ElementwiseAddPreluKernel.inc.metal"
#undef PRELU_TYPE
#undef PRELU_OTHER
#undef P
#define P half
#define PRELU_CHANNEL channel
#define PRELU_TYPE channel
#include "ElementwiseAddPreluKernel.inc.metal"
#undef PRELU_TYPE
#undef PRELU_CHANNEL
#define PRELU_ELEMENT element
#define PRELU_TYPE prelu_element
#include "ElementwiseAddPreluKernel.inc.metal"
#undef PRELU_TYPE
#undef PRELU_ELEMENT
#define PRELU_OTHER other
#define PRELU_TYPE prelu_other
#include "ElementwiseAddPreluKernel.inc.metal"
#undef PRELU_TYPE
#undef PRELU_OTHER
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/FetchKernel.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT2(a, b) a ## b
#define FUNC(m, n, q) CONCAT3_(m, n, q)
#define FUNC_T(m, n) CONCAT2_(m, n)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC_T(fetch, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
int input_height = inTexture.get_height();
const VECTOR(P, 4) input = inTexture.read(gid.xy, gid.z);
int output_to = 4 * input_width * input_height;
output[gid.z * output_to + 0 * input_width * input_height + gid.y * input_width + gid.x] = input.x;
output[gid.z * output_to + 1 * input_width * input_height + gid.y * input_width + gid.x] = input.y;
output[gid.z * output_to + 2 * input_width * input_height + gid.y * input_width + gid.x] = input.z;
output[gid.z * output_to + 3 * input_width * input_height + gid.y * input_width + gid.x] = input.w;
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/FetchKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
#define P float
#include "FetchKernel.inc.metal"
#undef P
#define P half
#include "FetchKernel.inc.metal"
#undef P
kernel void fetch_placeholder(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
}
kernel void fetch_placeholder_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Kernels.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
// 占位函数, 啥也没干
kernel void place_holder(texture2d<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
}
struct OutputDim {
ushort width;
ushort height;
ushort strideX;
ushort strideY;
};
kernel void resize(texture2d<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant OutputDim &params [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
constexpr sampler s(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint2 pos = gid.xy * uint2(params.strideX, params.strideY);
const half4 input = inTexture.read(pos);
outTexture.write(half4(input.x, input.y, input.z, input.w), gid.xy, gid.z);
}
kernel void texture2d_to_2d_array(texture2d<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height()){
return;
}
const float4 input = inTexture.read(gid.xy);
outTexture.write(input, gid.xy, 0);
}
kernel void texture2d_to_2d_array_half(texture2d<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height()){
return;
}
const half4 input = inTexture.read(gid.xy);
outTexture.write(input, gid.xy, 0);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Macro.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define CONCAT5_(a, b, c, d, e) a ## _ ## b ## _ ## c ## _ ## d ## _ ## e
#define FUNC(f, r, n, v, p) CONCAT5_(f, r, n, v, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC2_(a, b) CONCAT2_(a, b)
#define FUNC3_(a, b, c) CONCAT3_(a, b, c)
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/NMSFetchResultKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void nms_fetch_result(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
const float4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = input.x;
}
kernel void nms_fetch_result_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
const half4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = input.x;
}
kernel void nms_fetch_bbox(texture2d_array<float, access::read> inTexture [[texture(0)]],
device float4 *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
// int input_height = inTexture.get_height();
const float4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = input;
}
kernel void nms_fetch_bbox_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
device float4 *output [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height() ||
gid.z >= inTexture.get_array_size()) {
return;
}
int input_width = inTexture.get_width();
// int input_height = inTexture.get_height();
const half4 input = inTexture.read(gid.xy, gid.z);
output[gid.y * input_width + gid.x] = float4(input);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/PoolKernel.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
//
// PoolKernel.inc.metal
// paddle-mobile
//
// Created by liuRuiLong on 2018/12/29.
// Copyright © 2018 orange. All rights reserved.
//
#ifdef P
kernel void FUNC2_(pool, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant PoolParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
int xmin = gid.x * pm.strideX - pm.paddingX;
int xmax = min(xmin + pm.ksizeX, int(inTexture.get_width()));
xmin = max(xmin, 0);
int ymin = gid.y * pm.strideX - pm.paddingX;
int ymax = min(ymin + pm.ksizeX, int(inTexture.get_height()));
ymin = max(ymin, 0);
VECTOR(P, 4) r = 0;
if (pm.poolType == 0) {
r = inTexture.read(uint2(xmin, ymin), gid.z);
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r = fmax(r, inTexture.read(uint2(x, y), gid.z));
}
}
} else if (pm.poolType == 1) {
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r += inTexture.read(uint2(x, y), gid.z);
}
}
r /= (xmax - xmin) * (ymax - ymin);
}
outTexture.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/PoolKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Macro.metal"
using namespace metal;
struct PoolParam {
int ksizeX;
int ksizeY;
int strideX;
int strideY;
int paddingX;
int paddingY;
int poolType;
};
#define P float
#import "PoolKernel.inc.metal"
#undef P
#define P half
#import "PoolKernel.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/PreluKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void prelu_channel(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
float4 alpha_value = alpha[gid.z];
float4 output;
output.x = input.x > 0 ? input.x : (alpha_value.x * input.x);
output.y = input.y > 0 ? input.y : (alpha_value.y * input.y);
output.z = input.z > 0 ? input.z : (alpha_value.z * input.z);
output.w = input.w > 0 ? input.w : (alpha_value.w * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_element(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
int alpha_to = (gid.y * inTexture.get_width() + gid.x) * inTexture.get_array_size();
float4 alpha_value = alpha[alpha_to + gid.z];
float4 output;
output.x = input.x > 0 ? input.x : (alpha_value.x * input.x);
output.y = input.y > 0 ? input.y : (alpha_value.y * input.y);
output.z = input.z > 0 ? input.z : (alpha_value.z * input.z);
output.w = input.w > 0 ? input.w : (alpha_value.w * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_other(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
float alpha_value = alpha[0];
float4 output;
output.x = input.x > 0 ? input.x : (alpha_value * input.x);
output.y = input.y > 0 ? input.y : (alpha_value * input.y);
output.z = input.z > 0 ? input.z : (alpha_value * input.z);
output.w = input.w > 0 ? input.w : (alpha_value * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_channel_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
half4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
half4 alpha_value = alpha[gid.z];
half4 output;
output.x = input.x > 0 ? input.x : (alpha_value.x * input.x);
output.y = input.y > 0 ? input.y : (alpha_value.y * input.y);
output.z = input.z > 0 ? input.z : (alpha_value.z * input.z);
output.w = input.w > 0 ? input.w : (alpha_value.w * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_element_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
half4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
int alpha_to = (gid.y * inTexture.get_width() + gid.x) * inTexture.get_array_size();
half4 alpha_value = alpha[alpha_to + gid.z];
half4 output;
output.x = input.x > 0 ? input.x : (alpha_value.x * input.x);
output.y = input.y > 0 ? input.y : (alpha_value.y * input.y);
output.z = input.z > 0 ? input.z : (alpha_value.z * input.z);
output.w = input.w > 0 ? input.w : (alpha_value.w * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_other_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
half4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
half alpha_value = alpha[0];
half4 output;
output.x = input.x > 0 ? input.x : (alpha_value * input.x);
output.y = input.y > 0 ? input.y : (alpha_value * input.y);
output.z = input.z > 0 ? input.z : (alpha_value * input.z);
output.w = input.w > 0 ? input.w : (alpha_value * input.w);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/PriorBoxKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
struct PriorBoxMetalParam {
float offset;
float stepWidth;
float stepHeight;
float minSize;
float maxSize;
float imageWidth;
float imageHeight;
bool clip;
uint numPriors;
uint aspecRatiosSize;
uint minSizeSize;
uint maxSizeSize;
};
kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outBoxTexture [[texture(1)]],
texture2d_array<float, access::write> varianceTexture [[texture(2)]],
const device float *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z < param.aspecRatiosSize) {
float ar = aspect_ratios[gid.z];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(res, gid.xy, gid.z);
} else if (gid.z >= param.aspecRatiosSize) {
if (param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(max_box, gid.xy, gid.z);
}
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(variances_output, gid.xy, gid.z);
}
}
kernel void prior_box_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outBoxTexture [[texture(1)]],
texture2d_array<half, access::write> varianceTexture [[texture(2)]],
const device half *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z < param.aspecRatiosSize) {
half ar = aspect_ratios[gid.z];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
} else if (gid.z >= param.aspecRatiosSize) {
if (param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(half4(max_box), gid.xy, gid.z);
}
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(half4(variances_output), gid.xy, gid.z);
}
}
kernel void prior_box_MinMaxAspectRatiosOrder(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outBoxTexture [[texture(1)]],
texture2d_array<float, access::write> varianceTexture [[texture(2)]],
const device float *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z == 0) {
box_width = box_height = param.minSize / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(res, gid.xy, gid.z);
}
if (gid.z == 1 && param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(res, gid.xy, gid.z);
}
int aspect_to = 0;
if (param.maxSizeSize > 0) {
aspect_to = gid.z - 2;
} else {
aspect_to = gid.z - 1;
}
if (aspect_to >= 0 && aspect_to < int(param.aspecRatiosSize)) {
int skip = 0;
for (int i = 0; i < aspect_to + 1; ++i) {
if (fabs(aspect_ratios[i] - 1.) < 1e-6) {
skip += 1;
}
}
aspect_to += skip;
float ar = aspect_ratios[aspect_to];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(res, gid.xy, gid.z);
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(variances_output, gid.xy, gid.z);
}
}
kernel void prior_box_MinMaxAspectRatiosOrder_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outBoxTexture [[texture(1)]],
texture2d_array<half, access::write> varianceTexture [[texture(2)]],
const device half *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z == 0) {
box_width = box_height = param.minSize / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
}
if (gid.z == 1 && param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
}
int aspect_to = 0;
if (param.maxSizeSize > 0) {
aspect_to = gid.z - 2;
} else {
aspect_to = gid.z - 1;
}
if (aspect_to > 0 && aspect_to < int(param.aspecRatiosSize) && fabs(aspect_ratios[aspect_to] - 1.) > 1e-6) {
float ar = aspect_ratios[aspect_to];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(half4(variances_output), gid.xy, gid.z);
}
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ReluKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void relu_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
constexpr sampler s(coord::pixel, filter::nearest, address::clamp_to_zero);
const half4 input = inTexture.read(gid.xy, gid.z);
const float4 relu = fmax((float4)input, 0.0);
outTexture.write(half4(relu), gid.xy, gid.z);
}
kernel void relu(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
constexpr sampler s(coord::pixel, filter::nearest, address::clamp_to_zero);
const float4 input = inTexture.read(gid.xy, gid.z);
const float4 relu = fmax((float4)input, 0.0);
outTexture.write(float4(relu), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ReshapeKernel.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define FUNC(f, r1, r2, p) CONCAT4_(f, r1, r2, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC_R(f, r) CONCAT2_(f, r)
kernel void FUNC(reshape, RIN, ROUT, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant ReshapeParam &rp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, oabcd[4], ixyzn[4], iabcd[4];
ReshapeParam lrp = rp;
int oC = lrp.odim[lrp.otrans[3]];
int iC = lrp.idim[lrp.itrans[3]];
int count = lrp.odim[0] * lrp.odim[1] * lrp.odim[2] * lrp.odim[3];
VECTOR(P, 4) r;
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
#if ROUT == 4
xyzn2abcd_4(oC, oxyzn, oabcd);
#else
FUNC_R(xyzn2abcd, ROUT)(oxyzn, oabcd);
#endif
int tabcd[4];
invtrans(lrp.otrans, oabcd, tabcd);
int index = abcd2index(lrp.odim, tabcd);
if (index < count) {
index2abcd(lrp.idim, index, tabcd);
trans(lrp.itrans, tabcd, iabcd);
#if RIN == 4
abcd2xyzn_4(iC, iabcd, ixyzn);
#else
FUNC_R(abcd2xyzn, RIN)(iabcd, ixyzn);
#endif
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
} else {
r[n] = 0;
}
}
outTexture.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ReshapeKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONRITIONS OF ANY KINR, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct ReshapeParam {
int32_t idim[4];
int32_t itrans[4];
int32_t odim[4];
int32_t otrans[4];
};
#define P float
#define RIN 4
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 3
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 2
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 1
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#undef P
#define P half
#define RIN 4
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 3
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 2
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#define RIN 1
#define ROUT 4
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 3
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 2
#include "ReshapeKernel.inc.metal"
#undef ROUT
#define ROUT 1
#include "ReshapeKernel.inc.metal"
#undef ROUT
#undef RIN
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/ResizeBilinear.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
struct resize_bilinear_param {
// int32_t out_h;
// int32_t out_w;
float ratio_h;
float ratio_w;
};
kernel void resize_bilinear(texture2d_array<float, access::read> input [[texture(0)]],
texture2d_array<float, access::write> output [[texture(2)]],
constant resize_bilinear_param & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
float4 r;
if ((input.get_width() == output.get_width()) && (input.get_height() == output.get_height())) {
r = input.read(gid.xy, gid.z);
} else {
float w = gid.x * pm.ratio_w;
float h = gid.y * pm.ratio_h;
uint w0 = w, h0 = h;
uint w1 = w0 + 1, h1 = h0 + 1;
float w1lambda = w - w0, h1lambda = h - h0;
float w2lambda = 1.0 - w1lambda, h2lambda = 1.0 - h1lambda;
if (w1 >= input.get_width()) w1 = w0;
if (h1 >= input.get_height()) h1 = h0;
float4 r0 = input.read(uint2(w0, h0), gid.z);
float4 r1 = input.read(uint2(w1, h0), gid.z);
float4 r2 = input.read(uint2(w0, h1), gid.z);
float4 r3 = input.read(uint2(w1, h1), gid.z);
r = h2lambda * (w2lambda * r0 + w1lambda * r1) + h1lambda * (w2lambda * r2 + w1lambda * r3);
}
output.write(r, gid.xy, gid.z);
}
kernel void resize_bilinear_half(texture2d_array<half, access::read> input [[texture(0)]],
texture2d_array<half, access::write> output [[texture(2)]],
constant resize_bilinear_param & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
half4 r;
if ((input.get_width() == output.get_width()) && (input.get_height() == output.get_height())) {
r = input.read(gid.xy, gid.z);
} else {
half w = gid.x * pm.ratio_w;
half h = gid.y * pm.ratio_h;
uint w0 = w, h0 = h;
uint w1 = w0 + 1, h1 = h0 + 1;
half w1lambda = w - w0, h1lambda = h - h0;
half w2lambda = 1.0 - w1lambda, h2lambda = 1.0 - h1lambda;
if (w1 >= input.get_width()) w1 = w0;
if (h1 >= input.get_height()) h1 = h0;
half4 r0 = input.read(uint2(w0, h0), gid.z);
half4 r1 = input.read(uint2(w1, h0), gid.z);
half4 r2 = input.read(uint2(w0, h1), gid.z);
half4 r3 = input.read(uint2(w1, h1), gid.z);
r = h2lambda * (w2lambda * r0 + w1lambda * r1) + h1lambda * (w2lambda * r2 + w1lambda * r3);
}
output.write(r, gid.xy, gid.z);
output.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Scale.metal 0 → 100644
浏览文件 @ 38e9f34c
//
// Scale.metal
// paddle-mobile
//
// Created by liuRuiLong on 2019/1/4.
// Copyright © 2019 orange. All rights reserved.
//
#include <metal_stdlib>
using namespace metal;
kernel void scale(texture2d<float, access::sample> inTexture [[texture(0)]], texture2d<float, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return;
float w_stride = inTexture.get_width() / outTexture.get_width();
float h_stride = inTexture.get_height() / outTexture.get_height();
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x * w_stride, gid.y * h_stride), 0);
outTexture.write(input, gid);
}
kernel void scale_half(texture2d<float, access::sample> inTexture [[texture(0)]], texture2d<half, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return;
float w_stride = inTexture.get_width() / outTexture.get_width();
float h_stride = inTexture.get_height() / outTexture.get_height();
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x * w_stride, gid.y * h_stride), 0);
outTexture.write(half4(input), gid);
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Shape.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
kernel void shape() {
}
kernel void shape_half() {
}
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Softmax.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define FUNC(f, p) CONCAT2_(f, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(softmax, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant SoftmaxParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
// int zsize = inTexture.get_array_size();
P maxv = inTexture.read(uint2(0, gid.y), 0)[0];
int group = sp.K / 4;
int remain = sp.K % 4;
for (int x = 0; x < group; x++) {
VECTOR(P, 4) r = inTexture.read(uint2(x, gid.y), 0);
maxv = max(maxv, max(r[0], max(r[1], max(r[2], r[3]))));
}
if (remain > 0) {
VECTOR(P, 4) r = inTexture.read(uint2(group, gid.y), 0);
for (int i = 0; i < remain; i++) {
maxv = max(maxv, r[i]);
}
}
VECTOR(P, 4) rsum = {0, 0, 0, 0};
for (int x = 0; x < group; x++) {
VECTOR(P, 4) r = inTexture.read(uint2(x, gid.y), 0);
rsum += exp(r - maxv);
}
P sum = rsum[0] + rsum[1] + rsum[2] + rsum[3];
if (remain > 0) {
VECTOR(P, 4) r = inTexture.read(uint2(group, gid.y), 0);
for (int i = 0; i < remain; i++) {
sum += exp(r[i] - maxv);
}
}
VECTOR(P, 4) rr = inTexture.read(gid.xy, gid.z);
rr = exp(rr - maxv) / sum;
outTexture.write(rr, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Softmax.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
using namespace metal;
struct SoftmaxParam {
int N;
int K;
};
#define P float
#include "Softmax.inc.metal"
#undef P
#define P half
#include "Softmax.inc.metal"
#undef P
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Split.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define CONCAT4_(a, b, c, d) a ## _ ## b ## _ ## c ## _ ## d
#define CONCAT5_(a, b, c, d, e) a ## _ ## b ## _ ## c ## _ ## d ## _ ## e
#define FUNC(f, r, n, v, p) CONCAT5_(f, r, n, v, p)
#define VECTOR(p, n) CONCAT2(p, n)
#define FUNC_R(f, r) CONCAT2_(f, r)
#if V == VX
#define VV x
#elif V == VY
#define VV y
#elif V == VZ
#define VV z
#else
#define VV normal
#endif
#if V == VY
kernel void FUNC(split, R, N, VV, P)(texture2d_array<P, access::read> input [[texture(0)]],
texture2d_array<P, access::write> out1 [[texture(1)]],
texture2d_array<P, access::write> out2 [[texture(2)]],
#if N >= 3
texture2d_array<P, access::write> out3 [[texture(3)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::write> out4 [[texture(4)]],
#endif // N >= 4
constant SplitParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r = input.read(gid.xy, gid.z);
int y = gid.y - sp.offset;
if (y < sp.vdim[0]) {
out1.write(r, gid.xy, gid.z);
return;
}
y -= sp.vdim[0];
if (y < sp.vdim[1]) {
out2.write(r, uint2(gid.x, y), gid.z);
return;
}
#if N >= 3
y -= sp.vdim[1];
if (y < sp.vdim[2]) {
out3.write(r, uint2(gid.x, y), gid.z);
return;
}
#endif // N >= 3
#if N >= 4
y -= sp.vdim[2];
if (y < sp.vdim[3]) {
out4.write(r, uint2(gid.x, y), gid.z);
return;
}
#endif // N >= 4
}
#endif // V == VY
#if V == VX
kernel void FUNC(split, R, N, VV, P)(texture2d_array<P, access::read> input [[texture(0)]],
texture2d_array<P, access::write> out1 [[texture(1)]],
texture2d_array<P, access::write> out2 [[texture(2)]],
#if N >= 3
texture2d_array<P, access::write> out3 [[texture(3)]],
#endif // N >= 3
#if N >= 4
texture2d_array<P, access::write> out4 [[texture(4)]],
#endif // N >= 4
constant SplitParam &sp [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r = input.read(gid.xy, gid.z);
int x = gid.x;
if (x < sp.vdim[0]) {
out1.write(r, gid.xy, gid.z);
return;
}
x -= sp.vdim[0];
if (x < sp.vdim[1]) {
out2.write(r, uint2(x, gid.y), gid.z);
return;
}
#if N >= 3
x -= sp.vdim[1];
if (x < sp.vdim[2]) {
out3.write(r, uint2(x, gid.y), gid.z);
return;
}
#endif // N >= 3
#if N >= 4
x -= sp.vdim[2];
if (x < sp.vdim[3]) {
out4.write(r, uint2(x, gid.y), gid.z);
return;
}
#endif // N >= 4
}
#endif // V == VX
#undef VV
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/Split.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct SplitParam {
int32_t idim[4];
int32_t axis;
int32_t offset;
int32_t trans[4];
int32_t vdim[4];
};
#define VNORMAL 1
#define VX 2
#define VY 3
#define VZ 4
// only support split_{2, 3, 4}_{2, 3, 4}_y_{float, half}
// only support split_{3, 4}_{2, 3, 4}_x_{float, half}
//// ssd-ar: (R=3, N=2, V=y)
#define V VY
#define R 3
#define N 2
#define P float
#include "Split.inc.metal"
#undef P
#define P half
#include "Split.inc.metal"
#undef P
#undef N
#undef R
#undef V
//// ssd-ar: (R=2, N=2, V=y)
#define V VY
#define R 2
#define N 2
#define P float
#include "Split.inc.metal"
#undef P
#define P half
#include "Split.inc.metal"
#undef P
#undef N
#undef R
#undef V
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/TransposeKernel.inc.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef P
#define CONCAT2(a, b) a ## b
#define CONCAT2_(a, b) a ## _ ## b
#define CONCAT3_(a, b, c) a ## _ ## b ## _ ## c
#define FUNC(f, r, p) CONCAT3_(f, r, p)
#define VECTOR(p, n) CONCAT2(p, n)
kernel void FUNC(transpose, R, P)(texture2d_array<P, access::read> inTexture [[texture(0)]],
texture2d_array<P, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
VECTOR(P, 4) r;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0};
int iabcd[4], oabcd[4], ixyzn[4];
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
#if R == 4
xyzn2abcd_4(pm.oC, oxyzn, iabcd);
#endif // R == 4
#if R == 3
xyzn2abcd_3(oxyzn, oabcd);
#endif // R == 3
#if R == 2
xyzn2abcd_2(oxyzn, oabcd);
#endif // R == 2
iabcd[pm.axis[0]] = oabcd[0];
iabcd[pm.axis[1]] = oabcd[1];
iabcd[pm.axis[2]] = oabcd[2];
iabcd[pm.axis[3]] = oabcd[3];
#if R == 4
abcd2xyzn_4(pm.iC, iabcd, ixyzn);
#endif // R == 4
#if R == 3
abcd2xyzn_3(iabcd, ixyzn);
#endif // R == 3
#if R == 2
abcd2xyzn_2(iabcd, ixyzn);
#endif // R == 2
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
#endif
metal/paddle-mobile/paddle-mobile/Src/Operators/Kernels/metal/TransposeKernel.metal 0 → 100644
浏览文件 @ 38e9f34c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct TransposeParam {
int iC;
int oC;
int axis[4];
};
kernel void transpose_copy_float(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
outTexture.write(inTexture.read(gid.xy, gid.z), gid.xy, gid.z);
}
kernel void transpose_copy_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
outTexture.write(inTexture.read(gid.xy, gid.z), gid.xy, gid.z);
}
#define R 4
#define P float
#include "TransposeKernel.inc.metal"
#undef P
#define P half
#include "TransposeKernel.inc.metal"
#undef P
#undef R
#define R 3
#define P float
#include "TransposeKernel.inc.metal"
#undef P
#define P half
#include "TransposeKernel.inc.metal"
#undef P
#undef R
#define R 2
#define P float
#include "TransposeKernel.inc.metal"
#undef P
#define P half
#include "TransposeKernel.inc.metal"
#undef P
#undef R
metal/paddle-mobile/paddle-mobile/Operators/MulticlassNMSOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/MulticlassNMSOp.swift
浏览文件 @ 38e9f34c
......@@ -38,8 +38,11 @@ class MulticlassNMSParam<P: PrecisionType>: OpParam {
class MulticlassNMSOp<P: PrecisionType>: Operator<MulticlassNMSKernel<P>, MulticlassNMSParam<P>>, Runable, Creator, InferShaperable{
func inputVariant() -> [String : [Variant]] {
return ["Scores" : [para.middleOutput], "BBoxes" : [para.bboxOutput]]
func inputVariant() -> [String : [MTLBuffer]] {
guard let scoreBuffer = para.middleOutput.resultBuffer, let bboxBuffer = para.middleOutput.resultBuffer else {
fatalError()
}
return ["Scores" : [scoreBuffer], "BBoxes" : [bboxBuffer]]
}
func computeMiddleResult(device: MTLDevice, buffer: MTLCommandBuffer) {
......
metal/paddle-mobile/paddle-mobile/Operators/ReluOp.swift → metal/paddle-mobile/paddle-mobile/Src/Operators/ReluOp.swift
浏览文件 @ 38e9f34c
......@@ -47,6 +47,7 @@ class ReluOp<P: PrecisionType>: Operator<ReluKernel<P>, ReluParam<P>>, Runable,
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture)
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
// let device = para.output.metalTexture!.device
// let outputArray: [Float32] = device.texture2tensor(texture: para.output.metalTexture, dim: para.output.tensorDim.dims, transpose: para.output.transpose)
......
metal/paddle-mobile/paddle-mobile/Program/BlockDesc.swift → metal/paddle-mobile/paddle-mobile/Src/Program/BlockDesc.swift
浏览文件 @ 38e9f34c
......@@ -14,10 +14,10 @@
import Foundation
class BlockDesc {
public class BlockDesc {
let index: Int
let parentIndex: Int
let vars: [VarDesc]
public let vars: [VarDesc]
let ops: [OpDesc]
init(block: PaddleMobile_Framework_Proto_BlockDesc) {
index = Int(block.idx)
......@@ -45,7 +45,7 @@ class BlockDesc {
}
extension BlockDesc: CustomStringConvertible, CustomDebugStringConvertible {
var description: String {
public var description: String {
var str = ""
for i in 0..<ops.count {
......@@ -61,9 +61,7 @@ extension BlockDesc: CustomStringConvertible, CustomDebugStringConvertible {
return str
}
var debugDescription: String {
public var debugDescription: String {
return description
}
}
metal/paddle-mobile/paddle-mobile/Program/Program.swift → metal/paddle-mobile/paddle-mobile/Src/Program/Program.swift
浏览文件 @ 38e9f34c
......@@ -14,10 +14,10 @@
import Foundation
public class Program {
let paramPath: String
let programDesc: ProgramDesc
let scope: Scope
@objc public class Program: NSObject {
public let paramPath: String
public let programDesc: ProgramDesc
public let scope: Scope
init(inProgramDesc: ProgramDesc, inParamPath: String, inScope: Scope) {
programDesc = inProgramDesc
paramPath = inParamPath
......
metal/paddle-mobile/paddle-mobile/Program/ProgramDesc.swift → metal/paddle-mobile/paddle-mobile/Src/Program/ProgramDesc.swift
浏览文件 @ 38e9f34c
......@@ -15,7 +15,7 @@
import Foundation
public class ProgramDesc {
var blocks: [BlockDesc] = []
public var blocks: [BlockDesc] = []
init(protoProgram: PaddleMobile_Framework_Proto_ProgramDesc) {
for block in protoProgram.blocks {
self.blocks.append(BlockDesc.init(block: block))
......
metal/paddle-mobile/paddle-mobile/Program/Scope.swift → metal/paddle-mobile/paddle-mobile/Src/Program/Scope.swift
浏览文件 @ 38e9f34c
......@@ -14,7 +14,7 @@
import Foundation
class Scope {
public class Scope {
let feedKey: String
let fetchKey: String
func setInput(input: Variant) {
......@@ -29,7 +29,7 @@ class Scope {
return vars[feedKey];
}
func output() -> Variant? {
public func output() -> Variant? {
return vars[fetchKey];
}
......@@ -38,7 +38,7 @@ class Scope {
fetchKey = inFetchKey
}
var vars: [String : Variant] = [:]
public var vars: [String : Variant] = [:]
subscript(key: String) -> Variant?{
get {
return vars[key]
......
metal/paddle-mobile/paddle-mobile/Program/VarDesc.swift → metal/paddle-mobile/paddle-mobile/Src/Program/VarDesc.swift
浏览文件 @ 38e9f34c
......@@ -14,7 +14,7 @@
import Foundation
enum VarTypeType: Int {
public enum VarTypeType: Int {
case ErrorType = -1,
Bool = 0,
Int16 = 1,
......@@ -56,10 +56,10 @@ enum VarTypeType: Int {
}
}
class VarDesc {
let name: String
let persistable: Bool
let type: VarTypeType
public class VarDesc {
public let name: String
public let persistable: Bool
public let type: VarTypeType
let tensorDesc: TensorDesc?
init(protoVarDesc: PaddleMobile_Framework_Proto_VarDesc) {
type = VarTypeType.init(rawValue: protoVarDesc.type.type.rawValue) ?? .ErrorType
......@@ -79,7 +79,7 @@ class VarDesc {
}
extension VarDesc: CustomStringConvertible, CustomDebugStringConvertible {
var description: String {
public var description: String {
var str = ""
str += "var name \(name): \n"
if let inTensorDesc = tensorDesc {
......@@ -93,7 +93,7 @@ extension VarDesc: CustomStringConvertible, CustomDebugStringConvertible {
return str
}
var debugDescription: String {
public var debugDescription: String {
return description
}
}
metal/paddle-mobile/paddle-mobile/framework/Dim.swift → metal/paddle-mobile/paddle-mobile/Src/framework/Dim.swift
浏览文件 @ 38e9f34c
......@@ -14,24 +14,18 @@
import Foundation
public struct Dim {
public init(inDim: [Int]) {
dims = inDim
}
public init(inDim: (n: Int, h: Int, w: Int, c: Int)) {
dims = [inDim.n, inDim.h, inDim.w, inDim.c]
}
@objc public class Dim: NSObject {
private(set) var dims: [Int]
mutating func swapeDimAt(index1: Int, index2: Int) {
dims.swapAt(index1, index2)
@objc public init(inDim: [Int]) {
dims = inDim
}
func cout() -> Int {
public func cout() -> Int {
return dims.count
}
func numel() -> Int {
public func numel() -> Int {
return dims.reduce(1) { $0 * $1 }
}
......@@ -47,14 +41,15 @@ public struct Dim {
return dims[index];
}
private(set) var dims: [Int]
private init(){
fatalError()
public override var description: String {
return "\(dims)"
}
}
extension Dim: CustomStringConvertible {
public var description: String {
return "\(dims)"
func swapeDimAt(index1: Int, index2: Int) {
dims.swapAt(index1, index2)
}
private override init(){
fatalError()
}
}
metal/paddle-mobile/paddle-mobile/framework/Executor.swift → metal/paddle-mobile/paddle-mobile/Src/framework/Executor.swift
浏览文件 @ 38e9f34c
......@@ -19,13 +19,12 @@ let testTo = 5
var isTest = false
let computePrecision: ComputePrecision = .Float32
public class GPUResultHolder {
public let dim: [Int]
public let capacity: Int
public var resultPointer: UnsafeMutablePointer<Float32>?
public var intermediateResults: [String : [Variant]]?
public init(inDim: [Int], inPointer: UnsafeMutablePointer<Float32>?, inCapacity: Int, inIntermediateResults: [String : [Variant]]? = nil) {
@objc public class GPUResultHolder: NSObject{
@objc public let dim: [Int]
@objc public let capacity: Int
@objc public var resultPointer: UnsafeMutablePointer<Float32>?
@objc public var intermediateResults: [String : [MTLBuffer]]?
public init(inDim: [Int], inPointer: UnsafeMutablePointer<Float32>?, inCapacity: Int, inIntermediateResults: [String : [MTLBuffer]]? = nil) {
dim = inDim
capacity = inCapacity
......@@ -37,29 +36,10 @@ public class GPUResultHolder {
intermediateResults = inIntermediateResults
}
}
extension GPUResultHolder: CustomDebugStringConvertible, CustomStringConvertible {
public var debugDescription: String {
// var str = ""
// str += "Dim: \(dim) \n value:[ "
// if resultArr.count < 20 {
// for d in resultArr {
// str += " \(d) "
// }
// } else {
// for d in stride(from: 0, to: resultArr.count, by: resultArr.count/20) {
// str += " \(resultArr[d]) "
// }
// }
// str += " ]"
// return str
public override var description: String {
fatalError()
}
public var description: String {
return debugDescription
}
}
public class Executor<P: PrecisionType> {
......@@ -69,32 +49,18 @@ public class Executor<P: PrecisionType> {
let device: MTLDevice
let inflightSemaphore: DispatchSemaphore
let queue: MTLCommandQueue
public init(inDevice:MTLDevice, inQueue: MTLCommandQueue, inProgram: Program) throws {
init(inDevice:MTLDevice, inQueue: MTLCommandQueue, inProgram: Program, initContext: InitContext) throws {
self.inflightSemaphore = DispatchSemaphore(value: 1)
program = inProgram
device = inDevice
queue = inQueue
// print("before for ")
//print(program.scope.vars["fea_pyramid1_mbox_conf_flat.Flatten.output.1.tmp_0"])
for block in inProgram.programDesc.blocks {
//block.ops.count
for i in 0..<block.ops.count {
let opDesc = block.ops[i]
do {
// print("in for i \(i): ")
// print(program.scope.vars["fea_pyramid1_mbox_conf_flat.Flatten.output.1.tmp_0"])
//
// if i == 56 {
// print(program.scope.vars["fea_pyramid1_mbox_conf_flat.Flatten.output.1.tmp_0"])
//
// }
let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: opDesc, scope: inProgram.scope)
let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: opDesc, scope: inProgram.scope, initContext: initContext)
ops.append(op)
} catch let error {
throw error
......@@ -134,7 +100,7 @@ public class Executor<P: PrecisionType> {
}
}
var outputTextures: [String : [Variant]]?
var outputTextures: [String : [MTLBuffer]]?
if except > 0 {
ops[ops.count - except].computeMiddleResult(device: device, buffer: buffer)
outputTextures = ops[ops.count - except].inputVariant()
......
metal/paddle-mobile/paddle-mobile/framework/Tensor.swift → metal/paddle-mobile/paddle-mobile/Src/framework/Tensor.swift
浏览文件 @ 38e9f34c
......@@ -28,9 +28,7 @@ extension Tensorial {
}
}
public enum ComputePrecision {
case Float32, Float16
}
class Tensor<P: PrecisionType>: Tensorial {
......
metal/paddle-mobile/paddle-mobile/framework/Texture.swift → metal/paddle-mobile/paddle-mobile/Src/framework/Texture.swift
浏览文件 @ 38e9f34c
......@@ -71,7 +71,7 @@ extension InputTexture {
public class Texture: Tensorial {
var dim: Dim
public var dim: Dim
public var tensorDim: Dim
public var padToFourDim: Dim
private var textureDesc: MTLTextureDescriptor!
......@@ -96,7 +96,7 @@ public class Texture: Tensorial {
return metalTexture.realNHWC(dim: (n: padToFourDim[0], h: padToFourDim[1], w: padToFourDim[2], c: padToFourDim[3]))
}
func initTexture(device: MTLDevice, inTranspose: [Int] = [0, 1, 2, 3], computePrecision: ComputePrecision = .Float16) {
public func initTexture(device: MTLDevice, inTranspose: [Int] = [0, 1, 2, 3], computePrecision: ComputePrecision = .Float16) {
transpose = inTranspose
for i in 0..<(4 - tensorDim.cout()) {
if i != inTranspose[i] {
......@@ -143,7 +143,7 @@ public class Texture: Tensorial {
metalTexture = device.makeTexture(descriptor: tmpTextureDes) ?! " texture nil "
}
func updateDims(inTensorDim: Dim, inDim: Dim) {
public func updateDims(inTensorDim: Dim, inDim: Dim) {
var fourDim: Dim
if inDim.cout() == 4 {
fourDim = inDim
......
metal/paddle-mobile/paddle-mobile/paddle_mobile.h
浏览文件 @ 38e9f34c
......@@ -14,8 +14,6 @@
#pragma once
#import "CPUCompute.h"
#import "PaddleMobileGPU.h"
#import <UIKit/UIKit.h>
//! Project version number for paddle_mobile.
......
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