forked from tensorflow/tfjs
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcrop_and_resize_gpu.ts
127 lines (113 loc) · 4.44 KB
/
crop_and_resize_gpu.ts
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
/**
* @license
* Copyright 2017 Google LLC. 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 {GPGPUProgram} from './gpgpu_math';
export class CropAndResizeProgram implements GPGPUProgram {
variableNames = ['Image', 'Boxes', 'BoxInd'];
outputShape: number[] = [];
userCode: string;
constructor(
imageShape: [number, number, number, number], boxShape: [number, number],
cropSize: [number, number], method: 'bilinear'|'nearest',
extrapolationValue: number) {
const [batch, imageHeight, imageWidth, depth] = imageShape;
const [numBoxes, ] = boxShape;
const [cropHeight, cropWidth] = cropSize;
this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
const methodId = method === 'bilinear' ? 1 : 0;
const [inputHeightFloat, inputWidthFloat] =
[`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];
const [heightRatio, heightScale, inY] = cropHeight > 1 ?
[
`${(imageHeight - 1) / (cropHeight - 1)}`,
'(y2-y1) * height_ratio',
`y1*${inputHeightFloat} + float(y)*(height_scale)`,
] :
[
'0.0',
'0.0',
`0.5 * (y1+y2) * ${inputHeightFloat}`,
];
const [widthRatio, widthScale, inX] = cropWidth > 1 ?
[
`${(imageWidth - 1) / (cropWidth - 1)}`,
'(x2-x1) * width_ratio',
`x1*${inputWidthFloat} + float(x)*(width_scale)`,
] :
[
'0.0',
'0.0',
`0.5 * (x1+x2) * ${inputWidthFloat}`,
];
// Reference implementation
// tslint:disable-next-line:max-line-length
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
this.userCode = `
const float height_ratio = float(${heightRatio});
const float width_ratio = float(${widthRatio});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${batch}) {
return;
}
float height_scale = ${heightScale};
float width_scale = ${widthScale};
float in_y = ${inY};
if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
setOutput(float(${extrapolationValue}));
return;
}
float in_x = ${inX};
if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
setOutput(float(${extrapolationValue}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${methodId} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`;
}
}