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3238 lines
120 KiB
3238 lines
120 KiB
// Copyright 2020 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#include <xnnpack.h>
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#include <array>
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#include <algorithm>
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#include <functional>
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#include <iostream>
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#include <limits>
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#include <random>
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#include <fp16/fp16.h>
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#include "models/models.h"
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namespace models {
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ExecutionPlan FP16MobileNetV3Small(pthreadpool_t threadpool) {
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alignas(16) static std::array<uint16_t, 150528> v0;
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alignas(16) static std::array<uint16_t, 200704> v1;
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alignas(16) static std::array<uint16_t, 200704> v2;
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alignas(16) static std::array<uint16_t, 50176> v3;
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alignas(16) static std::array<uint16_t, 16> v4;
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alignas(16) static std::array<uint16_t, 8> v5;
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alignas(16) static std::array<uint16_t, 16> v6;
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alignas(16) static std::array<uint16_t, 50176> v7;
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alignas(16) static std::array<uint16_t, 50176> v8;
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alignas(16) static std::array<uint16_t, 225792> v9;
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alignas(16) static std::array<uint16_t, 56448> v10;
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alignas(16) static std::array<uint16_t, 18816> v11;
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alignas(16) static std::array<uint16_t, 68992> v12;
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alignas(16) static std::array<uint16_t, 68992> v13;
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alignas(16) static std::array<uint16_t, 18816> v14;
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alignas(16) static std::array<uint16_t, 18816> v15;
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alignas(16) static std::array<uint16_t, 75264> v16;
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alignas(16) static std::array<uint16_t, 75264> v17;
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alignas(16) static std::array<uint16_t, 18816> v18;
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alignas(16) static std::array<uint16_t, 18816> v19;
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alignas(16) static std::array<uint16_t, 96> v20;
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alignas(16) static std::array<uint16_t, 24> v21;
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alignas(16) static std::array<uint16_t, 96> v22;
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alignas(16) static std::array<uint16_t, 18816> v23;
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alignas(16) static std::array<uint16_t, 7840> v24;
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alignas(16) static std::array<uint16_t, 47040> v25;
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alignas(16) static std::array<uint16_t, 47040> v26;
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alignas(16) static std::array<uint16_t, 47040> v27;
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alignas(16) static std::array<uint16_t, 47040> v28;
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alignas(16) static std::array<uint16_t, 240> v29;
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alignas(16) static std::array<uint16_t, 64> v30;
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alignas(16) static std::array<uint16_t, 240> v31;
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alignas(16) static std::array<uint16_t, 47040> v32;
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alignas(16) static std::array<uint16_t, 7840> v33;
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alignas(16) static std::array<uint16_t, 7840> v34;
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alignas(16) static std::array<uint16_t, 47040> v35;
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alignas(16) static std::array<uint16_t, 47040> v36;
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alignas(16) static std::array<uint16_t, 47040> v37;
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alignas(16) static std::array<uint16_t, 47040> v38;
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alignas(16) static std::array<uint16_t, 240> v39;
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alignas(16) static std::array<uint16_t, 64> v40;
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alignas(16) static std::array<uint16_t, 240> v41;
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alignas(16) static std::array<uint16_t, 47040> v42;
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alignas(16) static std::array<uint16_t, 7840> v43;
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alignas(16) static std::array<uint16_t, 7840> v44;
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alignas(16) static std::array<uint16_t, 23520> v45;
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alignas(16) static std::array<uint16_t, 23520> v46;
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alignas(16) static std::array<uint16_t, 23520> v47;
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alignas(16) static std::array<uint16_t, 23520> v48;
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alignas(16) static std::array<uint16_t, 120> v49;
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alignas(16) static std::array<uint16_t, 32> v50;
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alignas(16) static std::array<uint16_t, 120> v51;
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alignas(16) static std::array<uint16_t, 23520> v52;
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alignas(16) static std::array<uint16_t, 9408> v53;
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alignas(16) static std::array<uint16_t, 28224> v54;
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alignas(16) static std::array<uint16_t, 28224> v55;
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alignas(16) static std::array<uint16_t, 28224> v56;
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alignas(16) static std::array<uint16_t, 28224> v57;
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alignas(16) static std::array<uint16_t, 144> v58;
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alignas(16) static std::array<uint16_t, 40> v59;
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alignas(16) static std::array<uint16_t, 144> v60;
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alignas(16) static std::array<uint16_t, 28224> v61;
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alignas(16) static std::array<uint16_t, 9408> v62;
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alignas(16) static std::array<uint16_t, 9408> v63;
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alignas(16) static std::array<uint16_t, 56448> v64;
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alignas(16) static std::array<uint16_t, 56448> v65;
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alignas(16) static std::array<uint16_t, 14112> v66;
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alignas(16) static std::array<uint16_t, 14112> v67;
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alignas(16) static std::array<uint16_t, 288> v68;
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alignas(16) static std::array<uint16_t, 72> v69;
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alignas(16) static std::array<uint16_t, 288> v70;
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alignas(16) static std::array<uint16_t, 14112> v71;
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alignas(16) static std::array<uint16_t, 4704> v72;
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alignas(16) static std::array<uint16_t, 28224> v73;
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alignas(16) static std::array<uint16_t, 28224> v74;
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alignas(16) static std::array<uint16_t, 28224> v75;
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alignas(16) static std::array<uint16_t, 28224> v76;
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alignas(16) static std::array<uint16_t, 576> v77;
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alignas(16) static std::array<uint16_t, 144> v78;
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alignas(16) static std::array<uint16_t, 576> v79;
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alignas(16) static std::array<uint16_t, 28224> v80;
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alignas(16) static std::array<uint16_t, 4704> v81;
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alignas(16) static std::array<uint16_t, 4704> v82;
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alignas(16) static std::array<uint16_t, 28224> v83;
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alignas(16) static std::array<uint16_t, 28224> v84;
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alignas(16) static std::array<uint16_t, 28224> v85;
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alignas(16) static std::array<uint16_t, 28224> v86;
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alignas(16) static std::array<uint16_t, 576> v87;
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alignas(16) static std::array<uint16_t, 144> v88;
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alignas(16) static std::array<uint16_t, 576> v89;
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alignas(16) static std::array<uint16_t, 28224> v90;
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alignas(16) static std::array<uint16_t, 4704> v91;
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alignas(16) static std::array<uint16_t, 4704> v92;
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alignas(16) static std::array<uint16_t, 28224> v93;
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alignas(16) static std::array<uint16_t, 28224> v94;
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alignas(16) static std::array<uint16_t, 576> v95;
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alignas(16) static std::array<uint16_t, 1024> v96;
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alignas(16) static std::array<uint16_t, 1024> v97;
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alignas(16) static std::array<uint16_t, 1024> v98;
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alignas(16) static std::array<uint16_t, 1001> v99;
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alignas(16) static std::array<uint16_t, 432> w100;
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alignas(16) static std::array<uint16_t, 16> w101;
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alignas(16) static std::array<uint16_t, 144> w102;
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alignas(16) static std::array<uint16_t, 16> w103;
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alignas(16) static std::array<uint16_t, 128> w104;
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alignas(16) static std::array<uint16_t, 8> w105;
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alignas(16) static std::array<uint16_t, 128> w106;
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alignas(16) static std::array<uint16_t, 16> w107;
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alignas(16) static std::array<uint16_t, 256> w108;
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alignas(16) static std::array<uint16_t, 16> w109;
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alignas(16) static std::array<uint16_t, 1152> w110;
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alignas(16) static std::array<uint16_t, 72> w111;
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alignas(16) static std::array<uint16_t, 648> w112;
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alignas(16) static std::array<uint16_t, 72> w113;
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alignas(16) static std::array<uint16_t, 1728> w114;
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alignas(16) static std::array<uint16_t, 24> w115;
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alignas(16) static std::array<uint16_t, 2112> w116;
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alignas(16) static std::array<uint16_t, 88> w117;
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alignas(16) static std::array<uint16_t, 792> w118;
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alignas(16) static std::array<uint16_t, 88> w119;
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alignas(16) static std::array<uint16_t, 2112> w120;
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alignas(16) static std::array<uint16_t, 24> w121;
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alignas(16) static std::array<uint16_t, 2304> w122;
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alignas(16) static std::array<uint16_t, 96> w123;
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alignas(16) static std::array<uint16_t, 2400> w124;
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alignas(16) static std::array<uint16_t, 96> w125;
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alignas(16) static std::array<uint16_t, 2304> w126;
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alignas(16) static std::array<uint16_t, 24> w127;
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alignas(16) static std::array<uint16_t, 2304> w128;
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alignas(16) static std::array<uint16_t, 96> w129;
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alignas(16) static std::array<uint16_t, 3840> w130;
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alignas(16) static std::array<uint16_t, 40> w131;
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alignas(16) static std::array<uint16_t, 9600> w132;
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alignas(16) static std::array<uint16_t, 240> w133;
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alignas(16) static std::array<uint16_t, 6000> w134;
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alignas(16) static std::array<uint16_t, 240> w135;
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alignas(16) static std::array<uint16_t, 15360> w136;
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alignas(16) static std::array<uint16_t, 64> w137;
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alignas(16) static std::array<uint16_t, 15360> w138;
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alignas(16) static std::array<uint16_t, 240> w139;
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alignas(16) static std::array<uint16_t, 9600> w140;
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alignas(16) static std::array<uint16_t, 40> w141;
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alignas(16) static std::array<uint16_t, 9600> w142;
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alignas(16) static std::array<uint16_t, 240> w143;
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alignas(16) static std::array<uint16_t, 6000> w144;
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alignas(16) static std::array<uint16_t, 240> w145;
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alignas(16) static std::array<uint16_t, 15360> w146;
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alignas(16) static std::array<uint16_t, 64> w147;
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alignas(16) static std::array<uint16_t, 15360> w148;
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alignas(16) static std::array<uint16_t, 240> w149;
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alignas(16) static std::array<uint16_t, 9600> w150;
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alignas(16) static std::array<uint16_t, 40> w151;
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alignas(16) static std::array<uint16_t, 4800> w152;
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alignas(16) static std::array<uint16_t, 120> w153;
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alignas(16) static std::array<uint16_t, 3000> w154;
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alignas(16) static std::array<uint16_t, 120> w155;
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alignas(16) static std::array<uint16_t, 3840> w156;
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alignas(16) static std::array<uint16_t, 32> w157;
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alignas(16) static std::array<uint16_t, 3840> w158;
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alignas(16) static std::array<uint16_t, 120> w159;
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alignas(16) static std::array<uint16_t, 5760> w160;
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alignas(16) static std::array<uint16_t, 48> w161;
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alignas(16) static std::array<uint16_t, 6912> w162;
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alignas(16) static std::array<uint16_t, 144> w163;
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alignas(16) static std::array<uint16_t, 3600> w164;
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alignas(16) static std::array<uint16_t, 144> w165;
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alignas(16) static std::array<uint16_t, 5760> w166;
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alignas(16) static std::array<uint16_t, 40> w167;
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alignas(16) static std::array<uint16_t, 5760> w168;
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alignas(16) static std::array<uint16_t, 144> w169;
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alignas(16) static std::array<uint16_t, 6912> w170;
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alignas(16) static std::array<uint16_t, 48> w171;
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alignas(16) static std::array<uint16_t, 13824> w172;
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alignas(16) static std::array<uint16_t, 288> w173;
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alignas(16) static std::array<uint16_t, 7200> w174;
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alignas(16) static std::array<uint16_t, 288> w175;
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alignas(16) static std::array<uint16_t, 20736> w176;
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alignas(16) static std::array<uint16_t, 72> w177;
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alignas(16) static std::array<uint16_t, 20736> w178;
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alignas(16) static std::array<uint16_t, 288> w179;
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alignas(16) static std::array<uint16_t, 27648> w180;
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alignas(16) static std::array<uint16_t, 96> w181;
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alignas(16) static std::array<uint16_t, 55296> w182;
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alignas(16) static std::array<uint16_t, 576> w183;
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alignas(16) static std::array<uint16_t, 14400> w184;
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alignas(16) static std::array<uint16_t, 576> w185;
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alignas(16) static std::array<uint16_t, 82944> w186;
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alignas(16) static std::array<uint16_t, 144> w187;
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alignas(16) static std::array<uint16_t, 82944> w188;
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alignas(16) static std::array<uint16_t, 576> w189;
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alignas(16) static std::array<uint16_t, 55296> w190;
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alignas(16) static std::array<uint16_t, 96> w191;
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alignas(16) static std::array<uint16_t, 55296> w192;
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alignas(16) static std::array<uint16_t, 576> w193;
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alignas(16) static std::array<uint16_t, 14400> w194;
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alignas(16) static std::array<uint16_t, 576> w195;
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alignas(16) static std::array<uint16_t, 82944> w196;
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alignas(16) static std::array<uint16_t, 144> w197;
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alignas(16) static std::array<uint16_t, 82944> w198;
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alignas(16) static std::array<uint16_t, 576> w199;
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alignas(16) static std::array<uint16_t, 55296> w200;
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alignas(16) static std::array<uint16_t, 96> w201;
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alignas(16) static std::array<uint16_t, 55296> w202;
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alignas(16) static std::array<uint16_t, 576> w203;
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alignas(16) static std::array<uint16_t, 589824> w204;
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alignas(16) static std::array<uint16_t, 1024> w205;
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alignas(16) static std::array<uint16_t, 1025024> w206;
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alignas(16) static std::array<uint16_t, 1001> w207;
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, +1.0f), std::ref(rng));
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auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
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std::generate(v0.begin(), v0.end(), std::ref(f16rng));
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std::generate(v1.begin(), v1.end(), std::ref(f16rng));
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std::generate(v2.begin(), v2.end(), std::ref(f16rng));
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std::generate(v3.begin(), v3.end(), std::ref(f16rng));
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std::generate(v4.begin(), v4.end(), std::ref(f16rng));
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std::generate(v5.begin(), v5.end(), std::ref(f16rng));
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std::generate(v6.begin(), v6.end(), std::ref(f16rng));
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std::generate(v7.begin(), v7.end(), std::ref(f16rng));
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std::generate(v8.begin(), v8.end(), std::ref(f16rng));
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std::generate(v9.begin(), v9.end(), std::ref(f16rng));
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std::generate(v10.begin(), v10.end(), std::ref(f16rng));
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std::generate(v11.begin(), v11.end(), std::ref(f16rng));
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std::generate(v12.begin(), v12.end(), std::ref(f16rng));
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std::generate(v13.begin(), v13.end(), std::ref(f16rng));
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std::generate(v14.begin(), v14.end(), std::ref(f16rng));
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std::generate(v15.begin(), v15.end(), std::ref(f16rng));
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std::generate(v16.begin(), v16.end(), std::ref(f16rng));
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std::generate(v17.begin(), v17.end(), std::ref(f16rng));
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std::generate(v18.begin(), v18.end(), std::ref(f16rng));
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std::generate(v19.begin(), v19.end(), std::ref(f16rng));
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std::generate(v20.begin(), v20.end(), std::ref(f16rng));
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std::generate(v21.begin(), v21.end(), std::ref(f16rng));
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std::generate(v22.begin(), v22.end(), std::ref(f16rng));
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std::generate(v23.begin(), v23.end(), std::ref(f16rng));
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std::generate(v24.begin(), v24.end(), std::ref(f16rng));
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std::generate(v25.begin(), v25.end(), std::ref(f16rng));
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std::generate(v26.begin(), v26.end(), std::ref(f16rng));
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std::generate(v27.begin(), v27.end(), std::ref(f16rng));
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std::generate(v28.begin(), v28.end(), std::ref(f16rng));
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std::generate(v29.begin(), v29.end(), std::ref(f16rng));
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std::generate(v30.begin(), v30.end(), std::ref(f16rng));
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std::generate(v31.begin(), v31.end(), std::ref(f16rng));
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std::generate(v32.begin(), v32.end(), std::ref(f16rng));
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std::generate(v33.begin(), v33.end(), std::ref(f16rng));
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std::generate(v34.begin(), v34.end(), std::ref(f16rng));
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std::generate(v35.begin(), v35.end(), std::ref(f16rng));
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std::generate(v36.begin(), v36.end(), std::ref(f16rng));
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std::generate(v37.begin(), v37.end(), std::ref(f16rng));
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std::generate(v38.begin(), v38.end(), std::ref(f16rng));
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std::generate(v39.begin(), v39.end(), std::ref(f16rng));
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std::generate(v40.begin(), v40.end(), std::ref(f16rng));
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std::generate(v41.begin(), v41.end(), std::ref(f16rng));
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std::generate(v42.begin(), v42.end(), std::ref(f16rng));
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std::generate(v43.begin(), v43.end(), std::ref(f16rng));
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std::generate(v44.begin(), v44.end(), std::ref(f16rng));
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std::generate(v45.begin(), v45.end(), std::ref(f16rng));
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std::generate(v46.begin(), v46.end(), std::ref(f16rng));
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std::generate(v47.begin(), v47.end(), std::ref(f16rng));
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std::generate(v48.begin(), v48.end(), std::ref(f16rng));
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std::generate(v49.begin(), v49.end(), std::ref(f16rng));
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std::generate(v50.begin(), v50.end(), std::ref(f16rng));
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std::generate(v51.begin(), v51.end(), std::ref(f16rng));
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std::generate(v52.begin(), v52.end(), std::ref(f16rng));
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std::generate(v53.begin(), v53.end(), std::ref(f16rng));
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std::generate(v54.begin(), v54.end(), std::ref(f16rng));
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std::generate(v55.begin(), v55.end(), std::ref(f16rng));
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std::generate(v56.begin(), v56.end(), std::ref(f16rng));
|
|
std::generate(v57.begin(), v57.end(), std::ref(f16rng));
|
|
std::generate(v58.begin(), v58.end(), std::ref(f16rng));
|
|
std::generate(v59.begin(), v59.end(), std::ref(f16rng));
|
|
std::generate(v60.begin(), v60.end(), std::ref(f16rng));
|
|
std::generate(v61.begin(), v61.end(), std::ref(f16rng));
|
|
std::generate(v62.begin(), v62.end(), std::ref(f16rng));
|
|
std::generate(v63.begin(), v63.end(), std::ref(f16rng));
|
|
std::generate(v64.begin(), v64.end(), std::ref(f16rng));
|
|
std::generate(v65.begin(), v65.end(), std::ref(f16rng));
|
|
std::generate(v66.begin(), v66.end(), std::ref(f16rng));
|
|
std::generate(v67.begin(), v67.end(), std::ref(f16rng));
|
|
std::generate(v68.begin(), v68.end(), std::ref(f16rng));
|
|
std::generate(v69.begin(), v69.end(), std::ref(f16rng));
|
|
std::generate(v70.begin(), v70.end(), std::ref(f16rng));
|
|
std::generate(v71.begin(), v71.end(), std::ref(f16rng));
|
|
std::generate(v72.begin(), v72.end(), std::ref(f16rng));
|
|
std::generate(v73.begin(), v73.end(), std::ref(f16rng));
|
|
std::generate(v74.begin(), v74.end(), std::ref(f16rng));
|
|
std::generate(v75.begin(), v75.end(), std::ref(f16rng));
|
|
std::generate(v76.begin(), v76.end(), std::ref(f16rng));
|
|
std::generate(v77.begin(), v77.end(), std::ref(f16rng));
|
|
std::generate(v78.begin(), v78.end(), std::ref(f16rng));
|
|
std::generate(v79.begin(), v79.end(), std::ref(f16rng));
|
|
std::generate(v80.begin(), v80.end(), std::ref(f16rng));
|
|
std::generate(v81.begin(), v81.end(), std::ref(f16rng));
|
|
std::generate(v82.begin(), v82.end(), std::ref(f16rng));
|
|
std::generate(v83.begin(), v83.end(), std::ref(f16rng));
|
|
std::generate(v84.begin(), v84.end(), std::ref(f16rng));
|
|
std::generate(v85.begin(), v85.end(), std::ref(f16rng));
|
|
std::generate(v86.begin(), v86.end(), std::ref(f16rng));
|
|
std::generate(v87.begin(), v87.end(), std::ref(f16rng));
|
|
std::generate(v88.begin(), v88.end(), std::ref(f16rng));
|
|
std::generate(v89.begin(), v89.end(), std::ref(f16rng));
|
|
std::generate(v90.begin(), v90.end(), std::ref(f16rng));
|
|
std::generate(v91.begin(), v91.end(), std::ref(f16rng));
|
|
std::generate(v92.begin(), v92.end(), std::ref(f16rng));
|
|
std::generate(v93.begin(), v93.end(), std::ref(f16rng));
|
|
std::generate(v94.begin(), v94.end(), std::ref(f16rng));
|
|
std::generate(v95.begin(), v95.end(), std::ref(f16rng));
|
|
std::generate(v96.begin(), v96.end(), std::ref(f16rng));
|
|
std::generate(v97.begin(), v97.end(), std::ref(f16rng));
|
|
std::generate(v98.begin(), v98.end(), std::ref(f16rng));
|
|
std::generate(v99.begin(), v99.end(), std::ref(f16rng));
|
|
std::generate(w100.begin(), w100.end(), std::ref(f16rng));
|
|
std::generate(w101.begin(), w101.end(), std::ref(f16rng));
|
|
std::generate(w102.begin(), w102.end(), std::ref(f16rng));
|
|
std::generate(w103.begin(), w103.end(), std::ref(f16rng));
|
|
std::generate(w104.begin(), w104.end(), std::ref(f16rng));
|
|
std::generate(w105.begin(), w105.end(), std::ref(f16rng));
|
|
std::generate(w106.begin(), w106.end(), std::ref(f16rng));
|
|
std::generate(w107.begin(), w107.end(), std::ref(f16rng));
|
|
std::generate(w108.begin(), w108.end(), std::ref(f16rng));
|
|
std::generate(w109.begin(), w109.end(), std::ref(f16rng));
|
|
std::generate(w110.begin(), w110.end(), std::ref(f16rng));
|
|
std::generate(w111.begin(), w111.end(), std::ref(f16rng));
|
|
std::generate(w112.begin(), w112.end(), std::ref(f16rng));
|
|
std::generate(w113.begin(), w113.end(), std::ref(f16rng));
|
|
std::generate(w114.begin(), w114.end(), std::ref(f16rng));
|
|
std::generate(w115.begin(), w115.end(), std::ref(f16rng));
|
|
std::generate(w116.begin(), w116.end(), std::ref(f16rng));
|
|
std::generate(w117.begin(), w117.end(), std::ref(f16rng));
|
|
std::generate(w118.begin(), w118.end(), std::ref(f16rng));
|
|
std::generate(w119.begin(), w119.end(), std::ref(f16rng));
|
|
std::generate(w120.begin(), w120.end(), std::ref(f16rng));
|
|
std::generate(w121.begin(), w121.end(), std::ref(f16rng));
|
|
std::generate(w122.begin(), w122.end(), std::ref(f16rng));
|
|
std::generate(w123.begin(), w123.end(), std::ref(f16rng));
|
|
std::generate(w124.begin(), w124.end(), std::ref(f16rng));
|
|
std::generate(w125.begin(), w125.end(), std::ref(f16rng));
|
|
std::generate(w126.begin(), w126.end(), std::ref(f16rng));
|
|
std::generate(w127.begin(), w127.end(), std::ref(f16rng));
|
|
std::generate(w128.begin(), w128.end(), std::ref(f16rng));
|
|
std::generate(w129.begin(), w129.end(), std::ref(f16rng));
|
|
std::generate(w130.begin(), w130.end(), std::ref(f16rng));
|
|
std::generate(w131.begin(), w131.end(), std::ref(f16rng));
|
|
std::generate(w132.begin(), w132.end(), std::ref(f16rng));
|
|
std::generate(w133.begin(), w133.end(), std::ref(f16rng));
|
|
std::generate(w134.begin(), w134.end(), std::ref(f16rng));
|
|
std::generate(w135.begin(), w135.end(), std::ref(f16rng));
|
|
std::generate(w136.begin(), w136.end(), std::ref(f16rng));
|
|
std::generate(w137.begin(), w137.end(), std::ref(f16rng));
|
|
std::generate(w138.begin(), w138.end(), std::ref(f16rng));
|
|
std::generate(w139.begin(), w139.end(), std::ref(f16rng));
|
|
std::generate(w140.begin(), w140.end(), std::ref(f16rng));
|
|
std::generate(w141.begin(), w141.end(), std::ref(f16rng));
|
|
std::generate(w142.begin(), w142.end(), std::ref(f16rng));
|
|
std::generate(w143.begin(), w143.end(), std::ref(f16rng));
|
|
std::generate(w144.begin(), w144.end(), std::ref(f16rng));
|
|
std::generate(w145.begin(), w145.end(), std::ref(f16rng));
|
|
std::generate(w146.begin(), w146.end(), std::ref(f16rng));
|
|
std::generate(w147.begin(), w147.end(), std::ref(f16rng));
|
|
std::generate(w148.begin(), w148.end(), std::ref(f16rng));
|
|
std::generate(w149.begin(), w149.end(), std::ref(f16rng));
|
|
std::generate(w150.begin(), w150.end(), std::ref(f16rng));
|
|
std::generate(w151.begin(), w151.end(), std::ref(f16rng));
|
|
std::generate(w152.begin(), w152.end(), std::ref(f16rng));
|
|
std::generate(w153.begin(), w153.end(), std::ref(f16rng));
|
|
std::generate(w154.begin(), w154.end(), std::ref(f16rng));
|
|
std::generate(w155.begin(), w155.end(), std::ref(f16rng));
|
|
std::generate(w156.begin(), w156.end(), std::ref(f16rng));
|
|
std::generate(w157.begin(), w157.end(), std::ref(f16rng));
|
|
std::generate(w158.begin(), w158.end(), std::ref(f16rng));
|
|
std::generate(w159.begin(), w159.end(), std::ref(f16rng));
|
|
std::generate(w160.begin(), w160.end(), std::ref(f16rng));
|
|
std::generate(w161.begin(), w161.end(), std::ref(f16rng));
|
|
std::generate(w162.begin(), w162.end(), std::ref(f16rng));
|
|
std::generate(w163.begin(), w163.end(), std::ref(f16rng));
|
|
std::generate(w164.begin(), w164.end(), std::ref(f16rng));
|
|
std::generate(w165.begin(), w165.end(), std::ref(f16rng));
|
|
std::generate(w166.begin(), w166.end(), std::ref(f16rng));
|
|
std::generate(w167.begin(), w167.end(), std::ref(f16rng));
|
|
std::generate(w168.begin(), w168.end(), std::ref(f16rng));
|
|
std::generate(w169.begin(), w169.end(), std::ref(f16rng));
|
|
std::generate(w170.begin(), w170.end(), std::ref(f16rng));
|
|
std::generate(w171.begin(), w171.end(), std::ref(f16rng));
|
|
std::generate(w172.begin(), w172.end(), std::ref(f16rng));
|
|
std::generate(w173.begin(), w173.end(), std::ref(f16rng));
|
|
std::generate(w174.begin(), w174.end(), std::ref(f16rng));
|
|
std::generate(w175.begin(), w175.end(), std::ref(f16rng));
|
|
std::generate(w176.begin(), w176.end(), std::ref(f16rng));
|
|
std::generate(w177.begin(), w177.end(), std::ref(f16rng));
|
|
std::generate(w178.begin(), w178.end(), std::ref(f16rng));
|
|
std::generate(w179.begin(), w179.end(), std::ref(f16rng));
|
|
std::generate(w180.begin(), w180.end(), std::ref(f16rng));
|
|
std::generate(w181.begin(), w181.end(), std::ref(f16rng));
|
|
std::generate(w182.begin(), w182.end(), std::ref(f16rng));
|
|
std::generate(w183.begin(), w183.end(), std::ref(f16rng));
|
|
std::generate(w184.begin(), w184.end(), std::ref(f16rng));
|
|
std::generate(w185.begin(), w185.end(), std::ref(f16rng));
|
|
std::generate(w186.begin(), w186.end(), std::ref(f16rng));
|
|
std::generate(w187.begin(), w187.end(), std::ref(f16rng));
|
|
std::generate(w188.begin(), w188.end(), std::ref(f16rng));
|
|
std::generate(w189.begin(), w189.end(), std::ref(f16rng));
|
|
std::generate(w190.begin(), w190.end(), std::ref(f16rng));
|
|
std::generate(w191.begin(), w191.end(), std::ref(f16rng));
|
|
std::generate(w192.begin(), w192.end(), std::ref(f16rng));
|
|
std::generate(w193.begin(), w193.end(), std::ref(f16rng));
|
|
std::generate(w194.begin(), w194.end(), std::ref(f16rng));
|
|
std::generate(w195.begin(), w195.end(), std::ref(f16rng));
|
|
std::generate(w196.begin(), w196.end(), std::ref(f16rng));
|
|
std::generate(w197.begin(), w197.end(), std::ref(f16rng));
|
|
std::generate(w198.begin(), w198.end(), std::ref(f16rng));
|
|
std::generate(w199.begin(), w199.end(), std::ref(f16rng));
|
|
std::generate(w200.begin(), w200.end(), std::ref(f16rng));
|
|
std::generate(w201.begin(), w201.end(), std::ref(f16rng));
|
|
std::generate(w202.begin(), w202.end(), std::ref(f16rng));
|
|
std::generate(w203.begin(), w203.end(), std::ref(f16rng));
|
|
std::generate(w204.begin(), w204.end(), std::ref(f16rng));
|
|
std::generate(w205.begin(), w205.end(), std::ref(f16rng));
|
|
std::generate(w206.begin(), w206.end(), std::ref(f16rng));
|
|
std::generate(w207.begin(), w207.end(), std::ref(f16rng));
|
|
|
|
ExecutionPlan operators;
|
|
xnn_status status;
|
|
|
|
xnn_operator_t op0 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 1 /* right padding */,
|
|
1 /* bottom padding */, 0 /* left padding */,
|
|
3 /* kernel height */, 3 /* kernel width */,
|
|
2 /* subsampling height */, 2 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
3 /* input channels per group */,
|
|
16 /* output_channels_per_group */,
|
|
3 /* input pixel stride */,
|
|
16 /* output pixel stride */,
|
|
w100.data(), w101.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op0);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #0" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op0, xnn_delete_operator);
|
|
|
|
xnn_operator_t op1 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
16 /* channels */,
|
|
16 /* input stride */,
|
|
16 /* output stride */,
|
|
0 /* flags */,
|
|
&op1);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #1" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op1, xnn_delete_operator);
|
|
|
|
xnn_operator_t op2 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 1 /* right padding */,
|
|
1 /* bottom padding */, 0 /* left padding */,
|
|
3 /* kernel height */, 3 /* kernel width */,
|
|
2 /* subsampling height */, 2 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
16 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
16 /* input pixel stride */,
|
|
16 /* output pixel stride */,
|
|
w102.data(), w103.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op2);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #2" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op2, xnn_delete_operator);
|
|
|
|
xnn_operator_t op3 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
16 /* channels */, 16 /* input stride */, 16 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op3);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #3" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op3, xnn_delete_operator);
|
|
|
|
xnn_operator_t op4 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
16 /* input channels per group */,
|
|
8 /* output_channels_per_group */,
|
|
16 /* input pixel stride */,
|
|
8 /* output pixel stride */,
|
|
w104.data(), w105.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op4);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #4" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op4, xnn_delete_operator);
|
|
|
|
xnn_operator_t op5 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
8 /* input channels per group */,
|
|
16 /* output_channels_per_group */,
|
|
8 /* input pixel stride */,
|
|
16 /* output pixel stride */,
|
|
w106.data(), w107.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op5);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #5" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op5, xnn_delete_operator);
|
|
|
|
xnn_operator_t op6 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op6);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #6" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op6, xnn_delete_operator);
|
|
|
|
xnn_operator_t op7 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
16 /* input channels per group */,
|
|
16 /* output_channels_per_group */,
|
|
16 /* input pixel stride */,
|
|
16 /* output pixel stride */,
|
|
w108.data(), w109.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op7);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #7" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op7, xnn_delete_operator);
|
|
|
|
xnn_operator_t op8 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
16 /* input channels per group */,
|
|
72 /* output_channels_per_group */,
|
|
16 /* input pixel stride */,
|
|
72 /* output pixel stride */,
|
|
w110.data(), w111.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op8);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #8" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op8, xnn_delete_operator);
|
|
|
|
xnn_operator_t op9 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 1 /* right padding */,
|
|
1 /* bottom padding */, 0 /* left padding */,
|
|
3 /* kernel height */, 3 /* kernel width */,
|
|
2 /* subsampling height */, 2 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
72 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
72 /* input pixel stride */,
|
|
72 /* output pixel stride */,
|
|
w112.data(), w113.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op9);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #9" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op9, xnn_delete_operator);
|
|
|
|
xnn_operator_t op10 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
72 /* input channels per group */,
|
|
24 /* output_channels_per_group */,
|
|
72 /* input pixel stride */,
|
|
24 /* output pixel stride */,
|
|
w114.data(), w115.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op10);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #10" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op10, xnn_delete_operator);
|
|
|
|
xnn_operator_t op11 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
24 /* input channels per group */,
|
|
88 /* output_channels_per_group */,
|
|
24 /* input pixel stride */,
|
|
88 /* output pixel stride */,
|
|
w116.data(), w117.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op11);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #11" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op11, xnn_delete_operator);
|
|
|
|
xnn_operator_t op12 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
1 /* top padding */, 1 /* right padding */,
|
|
1 /* bottom padding */, 1 /* left padding */,
|
|
3 /* kernel height */, 3 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
88 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
88 /* input pixel stride */,
|
|
88 /* output pixel stride */,
|
|
w118.data(), w119.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op12);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #12" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op12, xnn_delete_operator);
|
|
|
|
xnn_operator_t op13 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
88 /* input channels per group */,
|
|
24 /* output_channels_per_group */,
|
|
88 /* input pixel stride */,
|
|
24 /* output pixel stride */,
|
|
w120.data(), w121.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op13);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #13" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op13, xnn_delete_operator);
|
|
|
|
xnn_operator_t op14 = nullptr;
|
|
status = xnn_create_add_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op14);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #14" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op14, xnn_delete_operator);
|
|
|
|
xnn_operator_t op15 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
24 /* input channels per group */,
|
|
96 /* output_channels_per_group */,
|
|
24 /* input pixel stride */,
|
|
96 /* output pixel stride */,
|
|
w122.data(), w123.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op15);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #15" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op15, xnn_delete_operator);
|
|
|
|
xnn_operator_t op16 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
96 /* channels */,
|
|
96 /* input stride */,
|
|
96 /* output stride */,
|
|
0 /* flags */,
|
|
&op16);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #16" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op16, xnn_delete_operator);
|
|
|
|
xnn_operator_t op17 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
1 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 1 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
2 /* subsampling height */, 2 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
96 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
96 /* input pixel stride */,
|
|
96 /* output pixel stride */,
|
|
w124.data(), w125.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op17);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #17" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op17, xnn_delete_operator);
|
|
|
|
xnn_operator_t op18 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
96 /* channels */,
|
|
96 /* input stride */,
|
|
96 /* output stride */,
|
|
0 /* flags */,
|
|
&op18);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #18" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op18, xnn_delete_operator);
|
|
|
|
xnn_operator_t op19 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
96 /* channels */, 96 /* input stride */, 96 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op19);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #19" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op19, xnn_delete_operator);
|
|
|
|
xnn_operator_t op20 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
96 /* input channels per group */,
|
|
24 /* output_channels_per_group */,
|
|
96 /* input pixel stride */,
|
|
24 /* output pixel stride */,
|
|
w126.data(), w127.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op20);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #20" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op20, xnn_delete_operator);
|
|
|
|
xnn_operator_t op21 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
24 /* input channels per group */,
|
|
96 /* output_channels_per_group */,
|
|
24 /* input pixel stride */,
|
|
96 /* output pixel stride */,
|
|
w128.data(), w129.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op21);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #21" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op21, xnn_delete_operator);
|
|
|
|
xnn_operator_t op22 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op22);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #22" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op22, xnn_delete_operator);
|
|
|
|
xnn_operator_t op23 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
96 /* input channels per group */,
|
|
40 /* output_channels_per_group */,
|
|
96 /* input pixel stride */,
|
|
40 /* output pixel stride */,
|
|
w130.data(), w131.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op23);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #23" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op23, xnn_delete_operator);
|
|
|
|
xnn_operator_t op24 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
40 /* input channels per group */,
|
|
240 /* output_channels_per_group */,
|
|
40 /* input pixel stride */,
|
|
240 /* output pixel stride */,
|
|
w132.data(), w133.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op24);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #24" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op24, xnn_delete_operator);
|
|
|
|
xnn_operator_t op25 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
240 /* channels */,
|
|
240 /* input stride */,
|
|
240 /* output stride */,
|
|
0 /* flags */,
|
|
&op25);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #25" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op25, xnn_delete_operator);
|
|
|
|
xnn_operator_t op26 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
2 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 2 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
240 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
240 /* input pixel stride */,
|
|
240 /* output pixel stride */,
|
|
w134.data(), w135.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op26);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #26" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op26, xnn_delete_operator);
|
|
|
|
xnn_operator_t op27 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
240 /* channels */,
|
|
240 /* input stride */,
|
|
240 /* output stride */,
|
|
0 /* flags */,
|
|
&op27);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #27" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op27, xnn_delete_operator);
|
|
|
|
xnn_operator_t op28 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
240 /* channels */, 240 /* input stride */, 240 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op28);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #28" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op28, xnn_delete_operator);
|
|
|
|
xnn_operator_t op29 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
240 /* input channels per group */,
|
|
64 /* output_channels_per_group */,
|
|
240 /* input pixel stride */,
|
|
64 /* output pixel stride */,
|
|
w136.data(), w137.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op29);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #29" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op29, xnn_delete_operator);
|
|
|
|
xnn_operator_t op30 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
64 /* input channels per group */,
|
|
240 /* output_channels_per_group */,
|
|
64 /* input pixel stride */,
|
|
240 /* output pixel stride */,
|
|
w138.data(), w139.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op30);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #30" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op30, xnn_delete_operator);
|
|
|
|
xnn_operator_t op31 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op31);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #31" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op31, xnn_delete_operator);
|
|
|
|
xnn_operator_t op32 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
240 /* input channels per group */,
|
|
40 /* output_channels_per_group */,
|
|
240 /* input pixel stride */,
|
|
40 /* output pixel stride */,
|
|
w140.data(), w141.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op32);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #32" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op32, xnn_delete_operator);
|
|
|
|
xnn_operator_t op33 = nullptr;
|
|
status = xnn_create_add_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op33);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #33" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op33, xnn_delete_operator);
|
|
|
|
xnn_operator_t op34 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
40 /* input channels per group */,
|
|
240 /* output_channels_per_group */,
|
|
40 /* input pixel stride */,
|
|
240 /* output pixel stride */,
|
|
w142.data(), w143.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op34);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #34" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op34, xnn_delete_operator);
|
|
|
|
xnn_operator_t op35 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
240 /* channels */,
|
|
240 /* input stride */,
|
|
240 /* output stride */,
|
|
0 /* flags */,
|
|
&op35);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #35" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op35, xnn_delete_operator);
|
|
|
|
xnn_operator_t op36 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
2 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 2 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
240 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
240 /* input pixel stride */,
|
|
240 /* output pixel stride */,
|
|
w144.data(), w145.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op36);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #36" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op36, xnn_delete_operator);
|
|
|
|
xnn_operator_t op37 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
240 /* channels */,
|
|
240 /* input stride */,
|
|
240 /* output stride */,
|
|
0 /* flags */,
|
|
&op37);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #37" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op37, xnn_delete_operator);
|
|
|
|
xnn_operator_t op38 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
240 /* channels */, 240 /* input stride */, 240 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op38);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #38" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op38, xnn_delete_operator);
|
|
|
|
xnn_operator_t op39 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
240 /* input channels per group */,
|
|
64 /* output_channels_per_group */,
|
|
240 /* input pixel stride */,
|
|
64 /* output pixel stride */,
|
|
w146.data(), w147.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op39);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #39" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op39, xnn_delete_operator);
|
|
|
|
xnn_operator_t op40 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
64 /* input channels per group */,
|
|
240 /* output_channels_per_group */,
|
|
64 /* input pixel stride */,
|
|
240 /* output pixel stride */,
|
|
w148.data(), w149.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op40);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #40" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op40, xnn_delete_operator);
|
|
|
|
xnn_operator_t op41 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op41);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #41" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op41, xnn_delete_operator);
|
|
|
|
xnn_operator_t op42 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
240 /* input channels per group */,
|
|
40 /* output_channels_per_group */,
|
|
240 /* input pixel stride */,
|
|
40 /* output pixel stride */,
|
|
w150.data(), w151.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op42);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #42" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op42, xnn_delete_operator);
|
|
|
|
xnn_operator_t op43 = nullptr;
|
|
status = xnn_create_add_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op43);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #43" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op43, xnn_delete_operator);
|
|
|
|
xnn_operator_t op44 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
40 /* input channels per group */,
|
|
120 /* output_channels_per_group */,
|
|
40 /* input pixel stride */,
|
|
120 /* output pixel stride */,
|
|
w152.data(), w153.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op44);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #44" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op44, xnn_delete_operator);
|
|
|
|
xnn_operator_t op45 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
120 /* channels */,
|
|
120 /* input stride */,
|
|
120 /* output stride */,
|
|
0 /* flags */,
|
|
&op45);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #45" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op45, xnn_delete_operator);
|
|
|
|
xnn_operator_t op46 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
2 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 2 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
120 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
120 /* input pixel stride */,
|
|
120 /* output pixel stride */,
|
|
w154.data(), w155.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op46);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #46" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op46, xnn_delete_operator);
|
|
|
|
xnn_operator_t op47 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
120 /* channels */,
|
|
120 /* input stride */,
|
|
120 /* output stride */,
|
|
0 /* flags */,
|
|
&op47);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #47" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op47, xnn_delete_operator);
|
|
|
|
xnn_operator_t op48 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
120 /* channels */, 120 /* input stride */, 120 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op48);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #48" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op48, xnn_delete_operator);
|
|
|
|
xnn_operator_t op49 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
120 /* input channels per group */,
|
|
32 /* output_channels_per_group */,
|
|
120 /* input pixel stride */,
|
|
32 /* output pixel stride */,
|
|
w156.data(), w157.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op49);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #49" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op49, xnn_delete_operator);
|
|
|
|
xnn_operator_t op50 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
32 /* input channels per group */,
|
|
120 /* output_channels_per_group */,
|
|
32 /* input pixel stride */,
|
|
120 /* output pixel stride */,
|
|
w158.data(), w159.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op50);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #50" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op50, xnn_delete_operator);
|
|
|
|
xnn_operator_t op51 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op51);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #51" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op51, xnn_delete_operator);
|
|
|
|
xnn_operator_t op52 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
120 /* input channels per group */,
|
|
48 /* output_channels_per_group */,
|
|
120 /* input pixel stride */,
|
|
48 /* output pixel stride */,
|
|
w160.data(), w161.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op52);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #52" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op52, xnn_delete_operator);
|
|
|
|
xnn_operator_t op53 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
48 /* input channels per group */,
|
|
144 /* output_channels_per_group */,
|
|
48 /* input pixel stride */,
|
|
144 /* output pixel stride */,
|
|
w162.data(), w163.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op53);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #53" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op53, xnn_delete_operator);
|
|
|
|
xnn_operator_t op54 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
144 /* channels */,
|
|
144 /* input stride */,
|
|
144 /* output stride */,
|
|
0 /* flags */,
|
|
&op54);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #54" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op54, xnn_delete_operator);
|
|
|
|
xnn_operator_t op55 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
2 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 2 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
144 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
144 /* input pixel stride */,
|
|
144 /* output pixel stride */,
|
|
w164.data(), w165.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op55);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #55" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op55, xnn_delete_operator);
|
|
|
|
xnn_operator_t op56 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
144 /* channels */,
|
|
144 /* input stride */,
|
|
144 /* output stride */,
|
|
0 /* flags */,
|
|
&op56);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #56" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op56, xnn_delete_operator);
|
|
|
|
xnn_operator_t op57 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
144 /* channels */, 144 /* input stride */, 144 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op57);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #57" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op57, xnn_delete_operator);
|
|
|
|
xnn_operator_t op58 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
144 /* input channels per group */,
|
|
40 /* output_channels_per_group */,
|
|
144 /* input pixel stride */,
|
|
40 /* output pixel stride */,
|
|
w166.data(), w167.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op58);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #58" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op58, xnn_delete_operator);
|
|
|
|
xnn_operator_t op59 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
40 /* input channels per group */,
|
|
144 /* output_channels_per_group */,
|
|
40 /* input pixel stride */,
|
|
144 /* output pixel stride */,
|
|
w168.data(), w169.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op59);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #59" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op59, xnn_delete_operator);
|
|
|
|
xnn_operator_t op60 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op60);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #60" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op60, xnn_delete_operator);
|
|
|
|
xnn_operator_t op61 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
144 /* input channels per group */,
|
|
48 /* output_channels_per_group */,
|
|
144 /* input pixel stride */,
|
|
48 /* output pixel stride */,
|
|
w170.data(), w171.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op61);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #61" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op61, xnn_delete_operator);
|
|
|
|
xnn_operator_t op62 = nullptr;
|
|
status = xnn_create_add_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op62);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #62" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op62, xnn_delete_operator);
|
|
|
|
xnn_operator_t op63 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
48 /* input channels per group */,
|
|
288 /* output_channels_per_group */,
|
|
48 /* input pixel stride */,
|
|
288 /* output pixel stride */,
|
|
w172.data(), w173.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op63);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #63" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op63, xnn_delete_operator);
|
|
|
|
xnn_operator_t op64 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
288 /* channels */,
|
|
288 /* input stride */,
|
|
288 /* output stride */,
|
|
0 /* flags */,
|
|
&op64);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #64" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op64, xnn_delete_operator);
|
|
|
|
xnn_operator_t op65 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
1 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 1 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
2 /* subsampling height */, 2 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
288 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
288 /* input pixel stride */,
|
|
288 /* output pixel stride */,
|
|
w174.data(), w175.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op65);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #65" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op65, xnn_delete_operator);
|
|
|
|
xnn_operator_t op66 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
288 /* channels */,
|
|
288 /* input stride */,
|
|
288 /* output stride */,
|
|
0 /* flags */,
|
|
&op66);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #66" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op66, xnn_delete_operator);
|
|
|
|
xnn_operator_t op67 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
288 /* channels */, 288 /* input stride */, 288 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op67);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #67" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op67, xnn_delete_operator);
|
|
|
|
xnn_operator_t op68 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
288 /* input channels per group */,
|
|
72 /* output_channels_per_group */,
|
|
288 /* input pixel stride */,
|
|
72 /* output pixel stride */,
|
|
w176.data(), w177.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op68);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #68" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op68, xnn_delete_operator);
|
|
|
|
xnn_operator_t op69 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
72 /* input channels per group */,
|
|
288 /* output_channels_per_group */,
|
|
72 /* input pixel stride */,
|
|
288 /* output pixel stride */,
|
|
w178.data(), w179.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op69);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #69" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op69, xnn_delete_operator);
|
|
|
|
xnn_operator_t op70 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op70);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #70" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op70, xnn_delete_operator);
|
|
|
|
xnn_operator_t op71 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
288 /* input channels per group */,
|
|
96 /* output_channels_per_group */,
|
|
288 /* input pixel stride */,
|
|
96 /* output pixel stride */,
|
|
w180.data(), w181.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op71);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #71" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op71, xnn_delete_operator);
|
|
|
|
xnn_operator_t op72 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
96 /* input channels per group */,
|
|
576 /* output_channels_per_group */,
|
|
96 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w182.data(), w183.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op72);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #72" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op72, xnn_delete_operator);
|
|
|
|
xnn_operator_t op73 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
576 /* channels */,
|
|
576 /* input stride */,
|
|
576 /* output stride */,
|
|
0 /* flags */,
|
|
&op73);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #73" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op73, xnn_delete_operator);
|
|
|
|
xnn_operator_t op74 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
2 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 2 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
576 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w184.data(), w185.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op74);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #74" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op74, xnn_delete_operator);
|
|
|
|
xnn_operator_t op75 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
576 /* channels */,
|
|
576 /* input stride */,
|
|
576 /* output stride */,
|
|
0 /* flags */,
|
|
&op75);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #75" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op75, xnn_delete_operator);
|
|
|
|
xnn_operator_t op76 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
576 /* channels */, 576 /* input stride */, 576 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op76);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #76" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op76, xnn_delete_operator);
|
|
|
|
xnn_operator_t op77 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
576 /* input channels per group */,
|
|
144 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
144 /* output pixel stride */,
|
|
w186.data(), w187.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op77);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #77" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op77, xnn_delete_operator);
|
|
|
|
xnn_operator_t op78 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
144 /* input channels per group */,
|
|
576 /* output_channels_per_group */,
|
|
144 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w188.data(), w189.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op78);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #78" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op78, xnn_delete_operator);
|
|
|
|
xnn_operator_t op79 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op79);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #79" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op79, xnn_delete_operator);
|
|
|
|
xnn_operator_t op80 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
576 /* input channels per group */,
|
|
96 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
96 /* output pixel stride */,
|
|
w190.data(), w191.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op80);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #80" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op80, xnn_delete_operator);
|
|
|
|
xnn_operator_t op81 = nullptr;
|
|
status = xnn_create_add_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op81);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #81" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op81, xnn_delete_operator);
|
|
|
|
xnn_operator_t op82 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
96 /* input channels per group */,
|
|
576 /* output_channels_per_group */,
|
|
96 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w192.data(), w193.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op82);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #82" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op82, xnn_delete_operator);
|
|
|
|
xnn_operator_t op83 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
576 /* channels */,
|
|
576 /* input stride */,
|
|
576 /* output stride */,
|
|
0 /* flags */,
|
|
&op83);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #83" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op83, xnn_delete_operator);
|
|
|
|
xnn_operator_t op84 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
2 /* top padding */, 2 /* right padding */,
|
|
2 /* bottom padding */, 2 /* left padding */,
|
|
5 /* kernel height */, 5 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
576 /* groups */,
|
|
1 /* input channels per group */,
|
|
1 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w194.data(), w195.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op84);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #84" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op84, xnn_delete_operator);
|
|
|
|
xnn_operator_t op85 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
576 /* channels */,
|
|
576 /* input stride */,
|
|
576 /* output stride */,
|
|
0 /* flags */,
|
|
&op85);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #85" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op85, xnn_delete_operator);
|
|
|
|
xnn_operator_t op86 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
576 /* channels */, 576 /* input stride */, 576 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op86);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #86" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op86, xnn_delete_operator);
|
|
|
|
xnn_operator_t op87 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
576 /* input channels per group */,
|
|
144 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
144 /* output pixel stride */,
|
|
w196.data(), w197.data(),
|
|
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op87);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #87" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op87, xnn_delete_operator);
|
|
|
|
xnn_operator_t op88 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
144 /* input channels per group */,
|
|
576 /* output_channels_per_group */,
|
|
144 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w198.data(), w199.data(),
|
|
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
|
|
0 /* flags */,
|
|
&op88);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #88" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op88, xnn_delete_operator);
|
|
|
|
xnn_operator_t op89 = nullptr;
|
|
status = xnn_create_multiply_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op89);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #89" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op89, xnn_delete_operator);
|
|
|
|
xnn_operator_t op90 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
576 /* input channels per group */,
|
|
96 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
96 /* output pixel stride */,
|
|
w200.data(), w201.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op90);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #90" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op90, xnn_delete_operator);
|
|
|
|
xnn_operator_t op91 = nullptr;
|
|
status = xnn_create_add_nd_f16(
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op91);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #91" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op91, xnn_delete_operator);
|
|
|
|
xnn_operator_t op92 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
96 /* input channels per group */,
|
|
576 /* output_channels_per_group */,
|
|
96 /* input pixel stride */,
|
|
576 /* output pixel stride */,
|
|
w202.data(), w203.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op92);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #92" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op92, xnn_delete_operator);
|
|
|
|
xnn_operator_t op93 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
576 /* channels */,
|
|
576 /* input stride */,
|
|
576 /* output stride */,
|
|
0 /* flags */,
|
|
&op93);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #93" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op93, xnn_delete_operator);
|
|
|
|
xnn_operator_t op94 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
576 /* channels */, 576 /* input stride */, 576 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op94);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #94" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op94, xnn_delete_operator);
|
|
|
|
xnn_operator_t op95 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
576 /* input channels per group */,
|
|
1024 /* output_channels_per_group */,
|
|
576 /* input pixel stride */,
|
|
1024 /* output pixel stride */,
|
|
w204.data(), w205.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op95);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #95" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op95, xnn_delete_operator);
|
|
|
|
xnn_operator_t op96 = nullptr;
|
|
status = xnn_create_hardswish_nc_f16(
|
|
1024 /* channels */,
|
|
1024 /* input stride */,
|
|
1024 /* output stride */,
|
|
0 /* flags */,
|
|
&op96);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #96" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op96, xnn_delete_operator);
|
|
|
|
xnn_operator_t op97 = nullptr;
|
|
status = xnn_create_global_average_pooling_nwc_f16(
|
|
1024 /* channels */, 1024 /* input stride */, 1024 /* output stride */,
|
|
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
|
|
0 /* flags */,
|
|
&op97);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #97" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op97, xnn_delete_operator);
|
|
|
|
xnn_operator_t op98 = nullptr;
|
|
status = xnn_create_convolution2d_nhwc_f16(
|
|
0 /* top padding */, 0 /* right padding */,
|
|
0 /* bottom padding */, 0 /* left padding */,
|
|
1 /* kernel height */, 1 /* kernel width */,
|
|
1 /* subsampling height */, 1 /* subsampling width */,
|
|
1 /* dilation_height */, 1 /* dilation_width */,
|
|
1 /* groups */,
|
|
1024 /* input channels per group */,
|
|
1001 /* output_channels_per_group */,
|
|
1024 /* input pixel stride */,
|
|
1001 /* output pixel stride */,
|
|
w206.data(), w207.data(),
|
|
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
|
|
0 /* flags */,
|
|
&op98);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to create operation #98" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
operators.emplace_back(op98, xnn_delete_operator);
|
|
|
|
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op0,
|
|
1 /* batch size */, 224 /* input height */, 224 /* input width */,
|
|
v0.data() /* input */, v1.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #0" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op1,
|
|
12544 /* batch size */,
|
|
v1.data() /* input */, v2.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #1" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op2,
|
|
1 /* batch size */, 112 /* input height */, 112 /* input width */,
|
|
v2.data() /* input */, v3.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #2" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op3,
|
|
1 /* batch size */, 3136 /* width */,
|
|
v3.data() /* input */, v4.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #3" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op4,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v4.data() /* input */, v5.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #4" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op5,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v5.data() /* input */, v6.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #5" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 56, 56, 16 };
|
|
const size_t b_shape[] = { 1, 1, 1, 16 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op6,
|
|
4, a_shape, 4, b_shape,
|
|
v3.data() /* a */, v6.data() /* b */, v7.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #6" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op7,
|
|
1 /* batch size */, 56 /* input height */, 56 /* input width */,
|
|
v7.data() /* input */, v8.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #7" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op8,
|
|
1 /* batch size */, 56 /* input height */, 56 /* input width */,
|
|
v8.data() /* input */, v9.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #8" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op9,
|
|
1 /* batch size */, 56 /* input height */, 56 /* input width */,
|
|
v9.data() /* input */, v10.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #9" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op10,
|
|
1 /* batch size */, 28 /* input height */, 28 /* input width */,
|
|
v10.data() /* input */, v11.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #10" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op11,
|
|
1 /* batch size */, 28 /* input height */, 28 /* input width */,
|
|
v11.data() /* input */, v12.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #11" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op12,
|
|
1 /* batch size */, 28 /* input height */, 28 /* input width */,
|
|
v12.data() /* input */, v13.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #12" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op13,
|
|
1 /* batch size */, 28 /* input height */, 28 /* input width */,
|
|
v13.data() /* input */, v14.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #13" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 28, 28, 24 };
|
|
const size_t b_shape[] = { 1, 28, 28, 24 };
|
|
status = xnn_setup_add_nd_f16(
|
|
op14,
|
|
4, a_shape, 4, b_shape,
|
|
v14.data() /* a */, v11.data() /* b */, v15.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #14" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op15,
|
|
1 /* batch size */, 28 /* input height */, 28 /* input width */,
|
|
v15.data() /* input */, v16.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #15" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op16,
|
|
784 /* batch size */,
|
|
v16.data() /* input */, v17.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #16" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op17,
|
|
1 /* batch size */, 28 /* input height */, 28 /* input width */,
|
|
v17.data() /* input */, v18.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #17" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op18,
|
|
196 /* batch size */,
|
|
v18.data() /* input */, v19.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #18" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op19,
|
|
1 /* batch size */, 196 /* width */,
|
|
v19.data() /* input */, v20.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #19" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op20,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v20.data() /* input */, v21.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #20" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op21,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v21.data() /* input */, v22.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #21" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 96 };
|
|
const size_t b_shape[] = { 1, 1, 1, 96 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op22,
|
|
4, a_shape, 4, b_shape,
|
|
v19.data() /* a */, v22.data() /* b */, v23.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #22" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op23,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v23.data() /* input */, v24.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #23" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op24,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v24.data() /* input */, v25.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #24" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op25,
|
|
196 /* batch size */,
|
|
v25.data() /* input */, v26.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #25" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op26,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v26.data() /* input */, v27.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #26" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op27,
|
|
196 /* batch size */,
|
|
v27.data() /* input */, v28.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #27" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op28,
|
|
1 /* batch size */, 196 /* width */,
|
|
v28.data() /* input */, v29.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #28" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op29,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v29.data() /* input */, v30.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #29" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op30,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v30.data() /* input */, v31.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #30" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 240 };
|
|
const size_t b_shape[] = { 1, 1, 1, 240 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op31,
|
|
4, a_shape, 4, b_shape,
|
|
v28.data() /* a */, v31.data() /* b */, v32.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #31" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op32,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v32.data() /* input */, v33.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #32" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 40 };
|
|
const size_t b_shape[] = { 1, 14, 14, 40 };
|
|
status = xnn_setup_add_nd_f16(
|
|
op33,
|
|
4, a_shape, 4, b_shape,
|
|
v33.data() /* a */, v24.data() /* b */, v34.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #33" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op34,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v34.data() /* input */, v35.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #34" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op35,
|
|
196 /* batch size */,
|
|
v35.data() /* input */, v36.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #35" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op36,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v36.data() /* input */, v37.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #36" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op37,
|
|
196 /* batch size */,
|
|
v37.data() /* input */, v38.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #37" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op38,
|
|
1 /* batch size */, 196 /* width */,
|
|
v38.data() /* input */, v39.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #38" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op39,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v39.data() /* input */, v40.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #39" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op40,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v40.data() /* input */, v41.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #40" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 240 };
|
|
const size_t b_shape[] = { 1, 1, 1, 240 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op41,
|
|
4, a_shape, 4, b_shape,
|
|
v38.data() /* a */, v41.data() /* b */, v42.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #41" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op42,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v42.data() /* input */, v43.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #42" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 40 };
|
|
const size_t b_shape[] = { 1, 14, 14, 40 };
|
|
status = xnn_setup_add_nd_f16(
|
|
op43,
|
|
4, a_shape, 4, b_shape,
|
|
v43.data() /* a */, v34.data() /* b */, v44.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #43" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op44,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v44.data() /* input */, v45.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #44" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op45,
|
|
196 /* batch size */,
|
|
v45.data() /* input */, v46.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #45" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op46,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v46.data() /* input */, v47.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #46" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op47,
|
|
196 /* batch size */,
|
|
v47.data() /* input */, v48.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #47" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op48,
|
|
1 /* batch size */, 196 /* width */,
|
|
v48.data() /* input */, v49.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #48" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op49,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v49.data() /* input */, v50.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #49" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op50,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v50.data() /* input */, v51.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #50" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 120 };
|
|
const size_t b_shape[] = { 1, 1, 1, 120 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op51,
|
|
4, a_shape, 4, b_shape,
|
|
v48.data() /* a */, v51.data() /* b */, v52.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #51" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op52,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v52.data() /* input */, v53.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #52" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op53,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v53.data() /* input */, v54.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #53" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op54,
|
|
196 /* batch size */,
|
|
v54.data() /* input */, v55.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #54" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op55,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v55.data() /* input */, v56.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #55" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op56,
|
|
196 /* batch size */,
|
|
v56.data() /* input */, v57.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #56" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op57,
|
|
1 /* batch size */, 196 /* width */,
|
|
v57.data() /* input */, v58.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #57" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op58,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v58.data() /* input */, v59.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #58" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op59,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v59.data() /* input */, v60.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #59" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 144 };
|
|
const size_t b_shape[] = { 1, 1, 1, 144 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op60,
|
|
4, a_shape, 4, b_shape,
|
|
v57.data() /* a */, v60.data() /* b */, v61.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #60" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op61,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v61.data() /* input */, v62.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #61" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 14, 14, 48 };
|
|
const size_t b_shape[] = { 1, 14, 14, 48 };
|
|
status = xnn_setup_add_nd_f16(
|
|
op62,
|
|
4, a_shape, 4, b_shape,
|
|
v62.data() /* a */, v53.data() /* b */, v63.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #62" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op63,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v63.data() /* input */, v64.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #63" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op64,
|
|
196 /* batch size */,
|
|
v64.data() /* input */, v65.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #64" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op65,
|
|
1 /* batch size */, 14 /* input height */, 14 /* input width */,
|
|
v65.data() /* input */, v66.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #65" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op66,
|
|
49 /* batch size */,
|
|
v66.data() /* input */, v67.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #66" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op67,
|
|
1 /* batch size */, 49 /* width */,
|
|
v67.data() /* input */, v68.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #67" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op68,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v68.data() /* input */, v69.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #68" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op69,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v69.data() /* input */, v70.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #69" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 7, 7, 288 };
|
|
const size_t b_shape[] = { 1, 1, 1, 288 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op70,
|
|
4, a_shape, 4, b_shape,
|
|
v67.data() /* a */, v70.data() /* b */, v71.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #70" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op71,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v71.data() /* input */, v72.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #71" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op72,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v72.data() /* input */, v73.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #72" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op73,
|
|
49 /* batch size */,
|
|
v73.data() /* input */, v74.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #73" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op74,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v74.data() /* input */, v75.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #74" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op75,
|
|
49 /* batch size */,
|
|
v75.data() /* input */, v76.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #75" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op76,
|
|
1 /* batch size */, 49 /* width */,
|
|
v76.data() /* input */, v77.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #76" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op77,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v77.data() /* input */, v78.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #77" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op78,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v78.data() /* input */, v79.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #78" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 7, 7, 576 };
|
|
const size_t b_shape[] = { 1, 1, 1, 576 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op79,
|
|
4, a_shape, 4, b_shape,
|
|
v76.data() /* a */, v79.data() /* b */, v80.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #79" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op80,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v80.data() /* input */, v81.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #80" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 7, 7, 96 };
|
|
const size_t b_shape[] = { 1, 7, 7, 96 };
|
|
status = xnn_setup_add_nd_f16(
|
|
op81,
|
|
4, a_shape, 4, b_shape,
|
|
v81.data() /* a */, v72.data() /* b */, v82.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #81" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op82,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v82.data() /* input */, v83.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #82" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op83,
|
|
49 /* batch size */,
|
|
v83.data() /* input */, v84.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #83" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op84,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v84.data() /* input */, v85.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #84" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op85,
|
|
49 /* batch size */,
|
|
v85.data() /* input */, v86.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #85" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op86,
|
|
1 /* batch size */, 49 /* width */,
|
|
v86.data() /* input */, v87.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #86" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op87,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v87.data() /* input */, v88.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #87" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op88,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v88.data() /* input */, v89.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #88" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 7, 7, 576 };
|
|
const size_t b_shape[] = { 1, 1, 1, 576 };
|
|
status = xnn_setup_multiply_nd_f16(
|
|
op89,
|
|
4, a_shape, 4, b_shape,
|
|
v86.data() /* a */, v89.data() /* b */, v90.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #89" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op90,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v90.data() /* input */, v91.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #90" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
{
|
|
const size_t a_shape[] = { 1, 7, 7, 96 };
|
|
const size_t b_shape[] = { 1, 7, 7, 96 };
|
|
status = xnn_setup_add_nd_f16(
|
|
op91,
|
|
4, a_shape, 4, b_shape,
|
|
v91.data() /* a */, v82.data() /* b */, v92.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
}
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #91" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op92,
|
|
1 /* batch size */, 7 /* input height */, 7 /* input width */,
|
|
v92.data() /* input */, v93.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #92" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op93,
|
|
49 /* batch size */,
|
|
v93.data() /* input */, v94.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #93" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op94,
|
|
1 /* batch size */, 49 /* width */,
|
|
v94.data() /* input */, v95.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #94" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op95,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v95.data() /* input */, v96.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #95" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_hardswish_nc_f16(
|
|
op96,
|
|
1 /* batch size */,
|
|
v96.data() /* input */, v97.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #96" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_global_average_pooling_nwc_f16(
|
|
op97,
|
|
1 /* batch size */, 1 /* width */,
|
|
v97.data() /* input */, v98.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #97" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
status = xnn_setup_convolution2d_nhwc_f16(
|
|
op98,
|
|
1 /* batch size */, 1 /* input height */, 1 /* input width */,
|
|
v98.data() /* input */, v99.data() /* output */,
|
|
threadpool /* threadpool */);
|
|
if (status != xnn_status_success) {
|
|
std::cerr << "failed to setup operation #98" << std::endl;
|
|
return ExecutionPlan();
|
|
}
|
|
|
|
#pragma clang diagnostic push
|
|
#pragma clang diagnostic ignored "-Wpessimizing-move"
|
|
return operators;
|
|
#pragma clang diagnostic pop
|
|
}
|
|
|
|
} // namespace models
|