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1"use strict";
2/**
3 * @license
4 * Copyright 2020 Google LLC. All Rights Reserved.
5 * Licensed under the Apache License, Version 2.0 (the "License");
6 * you may not use this file except in compliance with the License.
7 * You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 * =============================================================================
17 */
18Object.defineProperty(exports, "__esModule", { value: true });
19var tfjs_1 = require("@tensorflow/tfjs");
20var nodejs_kernel_backend_1 = require("../nodejs_kernel_backend");
21exports.conv2DConfig = {
22 kernelName: tfjs_1.Conv2D,
23 backendName: 'tensorflow',
24 kernelFunc: function (args) {
25 var _a = args.inputs, x = _a.x, filter = _a.filter;
26 var backend = args.backend;
27 var _b = args.attrs, strides = _b.strides, pad = _b.pad, dataFormat = _b.dataFormat, dilations = _b.dilations, dimRoundingMode = _b.dimRoundingMode;
28 var $dataFormat = tfjs_1.backend_util.convertConv2DDataFormat(dataFormat);
29 var convInfo = tfjs_1.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
30 return conv2dImpl(x, filter, convInfo, backend);
31 }
32};
33function conv2dImpl(x, filter, convInfo, backend) {
34 if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME' &&
35 convInfo.padInfo.type !== 'EXPLICIT') {
36 throw new Error("TF Backend supports only 'valid' and 'same' padding " +
37 ("while padding was " + convInfo.padInfo.type));
38 }
39 var strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
40 var padding = convInfo.padInfo.type;
41 var dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
42 var dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
43 var opAttrs = [
44 nodejs_kernel_backend_1.createTensorsTypeOpAttr('T', x.dtype),
45 { name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides },
46 { name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding },
47 {
48 name: 'data_format',
49 type: backend.binding.TF_ATTR_STRING,
50 value: dataFormat
51 },
52 { name: 'use_cudnn_on_gpu', type: backend.binding.TF_ATTR_BOOL, value: true },
53 { name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations },
54 ];
55 if (padding === 'EXPLICIT') {
56 var padValue = [
57 convInfo.padInfo.top, convInfo.padInfo.bottom, convInfo.padInfo.left,
58 convInfo.padInfo.right
59 ];
60 opAttrs.push({
61 name: 'explicit_paddings',
62 type: backend.binding.TF_ATTR_INT,
63 value: dataFormat === 'NHWC' ? [0, 0].concat(padValue, [0, 0]) : [0, 0, 0, 0].concat(padValue)
64 });
65 }
66 return backend.executeSingleOutput(tfjs_1.Conv2D, opAttrs, [x, filter]);
67}
68exports.conv2dImpl = conv2dImpl;