test / models /fp32-sparse-mobilenet-v3-large.cc
Androidonnxfork's picture
Upload folder using huggingface_hub
8b7c501
raw
history blame
181 kB
// Copyright 2020 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <xnnpack.h>
#include <array>
#include <algorithm>
#include <functional>
#include <iostream>
#include <limits>
#include <random>
#include <xnnpack/cache.h>
#include <xnnpack/common.h>
#include <xnnpack/models.h>
namespace models {
ExecutionPlan FP32SparseMobileNetV3Large(float sparsity, pthreadpool_t threadpool) {
alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v0;
alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v1;
alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v2;
alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v3;
alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v4;
alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v5;
alignas(16) static std::array<float, 802816 + XNN_EXTRA_BYTES / sizeof(float)> v6;
alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v7;
alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v8;
alignas(16) static std::array<float, 225792 + XNN_EXTRA_BYTES / sizeof(float)> v9;
alignas(16) static std::array<float, 225792 + XNN_EXTRA_BYTES / sizeof(float)> v10;
alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v11;
alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v12;
alignas(16) static std::array<float, 225792 + XNN_EXTRA_BYTES / sizeof(float)> v13;
alignas(16) static std::array<float, 56448 + XNN_EXTRA_BYTES / sizeof(float)> v14;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> v15;
alignas(16) static std::array<float, 24 + XNN_EXTRA_BYTES / sizeof(float)> v16;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> v17;
alignas(16) static std::array<float, 56448 + XNN_EXTRA_BYTES / sizeof(float)> v18;
alignas(16) static std::array<float, 31360 + XNN_EXTRA_BYTES / sizeof(float)> v19;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v20;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v21;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> v22;
alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> v23;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> v24;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v25;
alignas(16) static std::array<float, 31360 + XNN_EXTRA_BYTES / sizeof(float)> v26;
alignas(16) static std::array<float, 31360 + XNN_EXTRA_BYTES / sizeof(float)> v27;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v28;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v29;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> v30;
alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> v31;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> v32;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v33;
alignas(16) static std::array<float, 31360 + XNN_EXTRA_BYTES / sizeof(float)> v34;
alignas(16) static std::array<float, 31360 + XNN_EXTRA_BYTES / sizeof(float)> v35;
alignas(16) static std::array<float, 188160 + XNN_EXTRA_BYTES / sizeof(float)> v36;
alignas(16) static std::array<float, 188160 + XNN_EXTRA_BYTES / sizeof(float)> v37;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v38;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v39;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v40;
alignas(16) static std::array<float, 39200 + XNN_EXTRA_BYTES / sizeof(float)> v41;
alignas(16) static std::array<float, 39200 + XNN_EXTRA_BYTES / sizeof(float)> v42;
alignas(16) static std::array<float, 39200 + XNN_EXTRA_BYTES / sizeof(float)> v43;
alignas(16) static std::array<float, 39200 + XNN_EXTRA_BYTES / sizeof(float)> v44;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v45;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v46;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v47;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v48;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v49;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v50;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v51;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v52;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v53;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v54;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v55;
alignas(16) static std::array<float, 36064 + XNN_EXTRA_BYTES / sizeof(float)> v56;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v57;
alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v58;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v59;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v60;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v61;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v62;
alignas(16) static std::array<float, 480 + XNN_EXTRA_BYTES / sizeof(float)> v63;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> v64;
alignas(16) static std::array<float, 480 + XNN_EXTRA_BYTES / sizeof(float)> v65;
alignas(16) static std::array<float, 94080 + XNN_EXTRA_BYTES / sizeof(float)> v66;
alignas(16) static std::array<float, 21952 + XNN_EXTRA_BYTES / sizeof(float)> v67;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v68;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v69;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v70;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v71;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> v72;
alignas(16) static std::array<float, 168 + XNN_EXTRA_BYTES / sizeof(float)> v73;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> v74;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v75;
alignas(16) static std::array<float, 21952 + XNN_EXTRA_BYTES / sizeof(float)> v76;
alignas(16) static std::array<float, 21952 + XNN_EXTRA_BYTES / sizeof(float)> v77;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v78;
alignas(16) static std::array<float, 131712 + XNN_EXTRA_BYTES / sizeof(float)> v79;
alignas(16) static std::array<float, 32928 + XNN_EXTRA_BYTES / sizeof(float)> v80;
alignas(16) static std::array<float, 32928 + XNN_EXTRA_BYTES / sizeof(float)> v81;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> v82;
alignas(16) static std::array<float, 168 + XNN_EXTRA_BYTES / sizeof(float)> v83;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> v84;
alignas(16) static std::array<float, 32928 + XNN_EXTRA_BYTES / sizeof(float)> v85;
alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v86;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v87;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v88;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v89;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v90;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> v91;
alignas(16) static std::array<float, 240 + XNN_EXTRA_BYTES / sizeof(float)> v92;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> v93;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v94;
alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v95;
alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v96;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v97;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v98;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v99;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v100;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> v101;
alignas(16) static std::array<float, 240 + XNN_EXTRA_BYTES / sizeof(float)> v102;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> v103;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v104;
alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v105;
alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v106;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v107;
alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v108;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> v109;
alignas(16) static std::array<float, 1280 + XNN_EXTRA_BYTES / sizeof(float)> v110;
alignas(16) static std::array<float, 1280 + XNN_EXTRA_BYTES / sizeof(float)> v111;
alignas(16) static std::array<float, 1280 + XNN_EXTRA_BYTES / sizeof(float)> v112;
alignas(16) static std::array<float, 1001 + XNN_EXTRA_BYTES / sizeof(float)> v113;
alignas(16) static std::array<float, 432 + XNN_EXTRA_BYTES / sizeof(float)> w114;
alignas(16) static std::array<float, 16 + XNN_EXTRA_BYTES / sizeof(float)> w115;
alignas(16) static std::array<float, 144 + XNN_EXTRA_BYTES / sizeof(float)> w116;
alignas(16) static std::array<float, 16 + XNN_EXTRA_BYTES / sizeof(float)> w117;
alignas(16) static std::array<float, 256 + XNN_EXTRA_BYTES / sizeof(float)> w118;
alignas(16) static std::array<float, 16 + XNN_EXTRA_BYTES / sizeof(float)> w119;
alignas(16) static std::array<float, 1024 + XNN_EXTRA_BYTES / sizeof(float)> w120;
alignas(16) static std::array<float, 64 + XNN_EXTRA_BYTES / sizeof(float)> w121;
alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w122;
alignas(16) static std::array<float, 64 + XNN_EXTRA_BYTES / sizeof(float)> w123;
alignas(16) static std::array<float, 1536 + XNN_EXTRA_BYTES / sizeof(float)> w124;
alignas(16) static std::array<float, 24 + XNN_EXTRA_BYTES / sizeof(float)> w125;
alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w126;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> w127;
alignas(16) static std::array<float, 648 + XNN_EXTRA_BYTES / sizeof(float)> w128;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> w129;
alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w130;
alignas(16) static std::array<float, 24 + XNN_EXTRA_BYTES / sizeof(float)> w131;
alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w132;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> w133;
alignas(16) static std::array<float, 1800 + XNN_EXTRA_BYTES / sizeof(float)> w134;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> w135;
alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w136;
alignas(16) static std::array<float, 24 + XNN_EXTRA_BYTES / sizeof(float)> w137;
alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w138;
alignas(16) static std::array<float, 72 + XNN_EXTRA_BYTES / sizeof(float)> w139;
alignas(16) static std::array<float, 2880 + XNN_EXTRA_BYTES / sizeof(float)> w140;
alignas(16) static std::array<float, 40 + XNN_EXTRA_BYTES / sizeof(float)> w141;
alignas(16) static std::array<float, 4800 + XNN_EXTRA_BYTES / sizeof(float)> w142;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w143;
alignas(16) static std::array<float, 3000 + XNN_EXTRA_BYTES / sizeof(float)> w144;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w145;
alignas(16) static std::array<float, 3840 + XNN_EXTRA_BYTES / sizeof(float)> w146;
alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w147;
alignas(16) static std::array<float, 3840 + XNN_EXTRA_BYTES / sizeof(float)> w148;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w149;
alignas(16) static std::array<float, 4800 + XNN_EXTRA_BYTES / sizeof(float)> w150;
alignas(16) static std::array<float, 40 + XNN_EXTRA_BYTES / sizeof(float)> w151;
alignas(16) static std::array<float, 4800 + XNN_EXTRA_BYTES / sizeof(float)> w152;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w153;
alignas(16) static std::array<float, 3000 + XNN_EXTRA_BYTES / sizeof(float)> w154;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w155;
alignas(16) static std::array<float, 3840 + XNN_EXTRA_BYTES / sizeof(float)> w156;
alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w157;
alignas(16) static std::array<float, 3840 + XNN_EXTRA_BYTES / sizeof(float)> w158;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w159;
alignas(16) static std::array<float, 4800 + XNN_EXTRA_BYTES / sizeof(float)> w160;
alignas(16) static std::array<float, 40 + XNN_EXTRA_BYTES / sizeof(float)> w161;
alignas(16) static std::array<float, 9600 + XNN_EXTRA_BYTES / sizeof(float)> w162;
alignas(16) static std::array<float, 240 + XNN_EXTRA_BYTES / sizeof(float)> w163;
alignas(16) static std::array<float, 2160 + XNN_EXTRA_BYTES / sizeof(float)> w164;
alignas(16) static std::array<float, 240 + XNN_EXTRA_BYTES / sizeof(float)> w165;
alignas(16) static std::array<float, 19200 + XNN_EXTRA_BYTES / sizeof(float)> w166;
alignas(16) static std::array<float, 80 + XNN_EXTRA_BYTES / sizeof(float)> w167;
alignas(16) static std::array<float, 16000 + XNN_EXTRA_BYTES / sizeof(float)> w168;
alignas(16) static std::array<float, 200 + XNN_EXTRA_BYTES / sizeof(float)> w169;
alignas(16) static std::array<float, 1800 + XNN_EXTRA_BYTES / sizeof(float)> w170;
alignas(16) static std::array<float, 200 + XNN_EXTRA_BYTES / sizeof(float)> w171;
alignas(16) static std::array<float, 16000 + XNN_EXTRA_BYTES / sizeof(float)> w172;
alignas(16) static std::array<float, 80 + XNN_EXTRA_BYTES / sizeof(float)> w173;
alignas(16) static std::array<float, 14720 + XNN_EXTRA_BYTES / sizeof(float)> w174;
alignas(16) static std::array<float, 184 + XNN_EXTRA_BYTES / sizeof(float)> w175;
alignas(16) static std::array<float, 1656 + XNN_EXTRA_BYTES / sizeof(float)> w176;
alignas(16) static std::array<float, 184 + XNN_EXTRA_BYTES / sizeof(float)> w177;
alignas(16) static std::array<float, 14720 + XNN_EXTRA_BYTES / sizeof(float)> w178;
alignas(16) static std::array<float, 80 + XNN_EXTRA_BYTES / sizeof(float)> w179;
alignas(16) static std::array<float, 14720 + XNN_EXTRA_BYTES / sizeof(float)> w180;
alignas(16) static std::array<float, 184 + XNN_EXTRA_BYTES / sizeof(float)> w181;
alignas(16) static std::array<float, 1656 + XNN_EXTRA_BYTES / sizeof(float)> w182;
alignas(16) static std::array<float, 184 + XNN_EXTRA_BYTES / sizeof(float)> w183;
alignas(16) static std::array<float, 14720 + XNN_EXTRA_BYTES / sizeof(float)> w184;
alignas(16) static std::array<float, 80 + XNN_EXTRA_BYTES / sizeof(float)> w185;
alignas(16) static std::array<float, 38400 + XNN_EXTRA_BYTES / sizeof(float)> w186;
alignas(16) static std::array<float, 480 + XNN_EXTRA_BYTES / sizeof(float)> w187;
alignas(16) static std::array<float, 4320 + XNN_EXTRA_BYTES / sizeof(float)> w188;
alignas(16) static std::array<float, 480 + XNN_EXTRA_BYTES / sizeof(float)> w189;
alignas(16) static std::array<float, 57600 + XNN_EXTRA_BYTES / sizeof(float)> w190;
alignas(16) static std::array<float, 120 + XNN_EXTRA_BYTES / sizeof(float)> w191;
alignas(16) static std::array<float, 57600 + XNN_EXTRA_BYTES / sizeof(float)> w192;
alignas(16) static std::array<float, 480 + XNN_EXTRA_BYTES / sizeof(float)> w193;
alignas(16) static std::array<float, 53760 + XNN_EXTRA_BYTES / sizeof(float)> w194;
alignas(16) static std::array<float, 112 + XNN_EXTRA_BYTES / sizeof(float)> w195;
alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> w196;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> w197;
alignas(16) static std::array<float, 6048 + XNN_EXTRA_BYTES / sizeof(float)> w198;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> w199;
alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> w200;
alignas(16) static std::array<float, 168 + XNN_EXTRA_BYTES / sizeof(float)> w201;
alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> w202;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> w203;
alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> w204;
alignas(16) static std::array<float, 112 + XNN_EXTRA_BYTES / sizeof(float)> w205;
alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> w206;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> w207;
alignas(16) static std::array<float, 16800 + XNN_EXTRA_BYTES / sizeof(float)> w208;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> w209;
alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> w210;
alignas(16) static std::array<float, 168 + XNN_EXTRA_BYTES / sizeof(float)> w211;
alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> w212;
alignas(16) static std::array<float, 672 + XNN_EXTRA_BYTES / sizeof(float)> w213;
alignas(16) static std::array<float, 107520 + XNN_EXTRA_BYTES / sizeof(float)> w214;
alignas(16) static std::array<float, 160 + XNN_EXTRA_BYTES / sizeof(float)> w215;
alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w216;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w217;
alignas(16) static std::array<float, 24000 + XNN_EXTRA_BYTES / sizeof(float)> w218;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w219;
alignas(16) static std::array<float, 230400 + XNN_EXTRA_BYTES / sizeof(float)> w220;
alignas(16) static std::array<float, 240 + XNN_EXTRA_BYTES / sizeof(float)> w221;
alignas(16) static std::array<float, 230400 + XNN_EXTRA_BYTES / sizeof(float)> w222;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w223;
alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w224;
alignas(16) static std::array<float, 160 + XNN_EXTRA_BYTES / sizeof(float)> w225;
alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w226;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w227;
alignas(16) static std::array<float, 24000 + XNN_EXTRA_BYTES / sizeof(float)> w228;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w229;
alignas(16) static std::array<float, 230400 + XNN_EXTRA_BYTES / sizeof(float)> w230;
alignas(16) static std::array<float, 240 + XNN_EXTRA_BYTES / sizeof(float)> w231;
alignas(16) static std::array<float, 230400 + XNN_EXTRA_BYTES / sizeof(float)> w232;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w233;
alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w234;
alignas(16) static std::array<float, 160 + XNN_EXTRA_BYTES / sizeof(float)> w235;
alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w236;
alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w237;
alignas(16) static std::array<float, 1228800 + XNN_EXTRA_BYTES / sizeof(float)> w238;
alignas(16) static std::array<float, 1280 + XNN_EXTRA_BYTES / sizeof(float)> w239;
alignas(16) static std::array<float, 1281280 + XNN_EXTRA_BYTES / sizeof(float)> w240;
alignas(16) static std::array<float, 1001 + XNN_EXTRA_BYTES / sizeof(float)> w241;
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, +1.0f), std::ref(rng));
std::generate(v0.begin(), v0.end(), std::ref(f32rng));
std::generate(v1.begin(), v1.end(), std::ref(f32rng));
std::generate(v2.begin(), v2.end(), std::ref(f32rng));
std::generate(v3.begin(), v3.end(), std::ref(f32rng));
std::generate(v4.begin(), v4.end(), std::ref(f32rng));
std::generate(v5.begin(), v5.end(), std::ref(f32rng));
std::generate(v6.begin(), v6.end(), std::ref(f32rng));
std::generate(v7.begin(), v7.end(), std::ref(f32rng));
std::generate(v8.begin(), v8.end(), std::ref(f32rng));
std::generate(v9.begin(), v9.end(), std::ref(f32rng));
std::generate(v10.begin(), v10.end(), std::ref(f32rng));
std::generate(v11.begin(), v11.end(), std::ref(f32rng));
std::generate(v12.begin(), v12.end(), std::ref(f32rng));
std::generate(v13.begin(), v13.end(), std::ref(f32rng));
std::generate(v14.begin(), v14.end(), std::ref(f32rng));
std::generate(v15.begin(), v15.end(), std::ref(f32rng));
std::generate(v16.begin(), v16.end(), std::ref(f32rng));
std::generate(v17.begin(), v17.end(), std::ref(f32rng));
std::generate(v18.begin(), v18.end(), std::ref(f32rng));
std::generate(v19.begin(), v19.end(), std::ref(f32rng));
std::generate(v20.begin(), v20.end(), std::ref(f32rng));
std::generate(v21.begin(), v21.end(), std::ref(f32rng));
std::generate(v22.begin(), v22.end(), std::ref(f32rng));
std::generate(v23.begin(), v23.end(), std::ref(f32rng));
std::generate(v24.begin(), v24.end(), std::ref(f32rng));
std::generate(v25.begin(), v25.end(), std::ref(f32rng));
std::generate(v26.begin(), v26.end(), std::ref(f32rng));
std::generate(v27.begin(), v27.end(), std::ref(f32rng));
std::generate(v28.begin(), v28.end(), std::ref(f32rng));
std::generate(v29.begin(), v29.end(), std::ref(f32rng));
std::generate(v30.begin(), v30.end(), std::ref(f32rng));
std::generate(v31.begin(), v31.end(), std::ref(f32rng));
std::generate(v32.begin(), v32.end(), std::ref(f32rng));
std::generate(v33.begin(), v33.end(), std::ref(f32rng));
std::generate(v34.begin(), v34.end(), std::ref(f32rng));
std::generate(v35.begin(), v35.end(), std::ref(f32rng));
std::generate(v36.begin(), v36.end(), std::ref(f32rng));
std::generate(v37.begin(), v37.end(), std::ref(f32rng));
std::generate(v38.begin(), v38.end(), std::ref(f32rng));
std::generate(v39.begin(), v39.end(), std::ref(f32rng));
std::generate(v40.begin(), v40.end(), std::ref(f32rng));
std::generate(v41.begin(), v41.end(), std::ref(f32rng));
std::generate(v42.begin(), v42.end(), std::ref(f32rng));
std::generate(v43.begin(), v43.end(), std::ref(f32rng));
std::generate(v44.begin(), v44.end(), std::ref(f32rng));
std::generate(v45.begin(), v45.end(), std::ref(f32rng));
std::generate(v46.begin(), v46.end(), std::ref(f32rng));
std::generate(v47.begin(), v47.end(), std::ref(f32rng));
std::generate(v48.begin(), v48.end(), std::ref(f32rng));
std::generate(v49.begin(), v49.end(), std::ref(f32rng));
std::generate(v50.begin(), v50.end(), std::ref(f32rng));
std::generate(v51.begin(), v51.end(), std::ref(f32rng));
std::generate(v52.begin(), v52.end(), std::ref(f32rng));
std::generate(v53.begin(), v53.end(), std::ref(f32rng));
std::generate(v54.begin(), v54.end(), std::ref(f32rng));
std::generate(v55.begin(), v55.end(), std::ref(f32rng));
std::generate(v56.begin(), v56.end(), std::ref(f32rng));
std::generate(v57.begin(), v57.end(), std::ref(f32rng));
std::generate(v58.begin(), v58.end(), std::ref(f32rng));
std::generate(v59.begin(), v59.end(), std::ref(f32rng));
std::generate(v60.begin(), v60.end(), std::ref(f32rng));
std::generate(v61.begin(), v61.end(), std::ref(f32rng));
std::generate(v62.begin(), v62.end(), std::ref(f32rng));
std::generate(v63.begin(), v63.end(), std::ref(f32rng));
std::generate(v64.begin(), v64.end(), std::ref(f32rng));
std::generate(v65.begin(), v65.end(), std::ref(f32rng));
std::generate(v66.begin(), v66.end(), std::ref(f32rng));
std::generate(v67.begin(), v67.end(), std::ref(f32rng));
std::generate(v68.begin(), v68.end(), std::ref(f32rng));
std::generate(v69.begin(), v69.end(), std::ref(f32rng));
std::generate(v70.begin(), v70.end(), std::ref(f32rng));
std::generate(v71.begin(), v71.end(), std::ref(f32rng));
std::generate(v72.begin(), v72.end(), std::ref(f32rng));
std::generate(v73.begin(), v73.end(), std::ref(f32rng));
std::generate(v74.begin(), v74.end(), std::ref(f32rng));
std::generate(v75.begin(), v75.end(), std::ref(f32rng));
std::generate(v76.begin(), v76.end(), std::ref(f32rng));
std::generate(v77.begin(), v77.end(), std::ref(f32rng));
std::generate(v78.begin(), v78.end(), std::ref(f32rng));
std::generate(v79.begin(), v79.end(), std::ref(f32rng));
std::generate(v80.begin(), v80.end(), std::ref(f32rng));
std::generate(v81.begin(), v81.end(), std::ref(f32rng));
std::generate(v82.begin(), v82.end(), std::ref(f32rng));
std::generate(v83.begin(), v83.end(), std::ref(f32rng));
std::generate(v84.begin(), v84.end(), std::ref(f32rng));
std::generate(v85.begin(), v85.end(), std::ref(f32rng));
std::generate(v86.begin(), v86.end(), std::ref(f32rng));
std::generate(v87.begin(), v87.end(), std::ref(f32rng));
std::generate(v88.begin(), v88.end(), std::ref(f32rng));
std::generate(v89.begin(), v89.end(), std::ref(f32rng));
std::generate(v90.begin(), v90.end(), std::ref(f32rng));
std::generate(v91.begin(), v91.end(), std::ref(f32rng));
std::generate(v92.begin(), v92.end(), std::ref(f32rng));
std::generate(v93.begin(), v93.end(), std::ref(f32rng));
std::generate(v94.begin(), v94.end(), std::ref(f32rng));
std::generate(v95.begin(), v95.end(), std::ref(f32rng));
std::generate(v96.begin(), v96.end(), std::ref(f32rng));
std::generate(v97.begin(), v97.end(), std::ref(f32rng));
std::generate(v98.begin(), v98.end(), std::ref(f32rng));
std::generate(v99.begin(), v99.end(), std::ref(f32rng));
std::generate(v100.begin(), v100.end(), std::ref(f32rng));
std::generate(v101.begin(), v101.end(), std::ref(f32rng));
std::generate(v102.begin(), v102.end(), std::ref(f32rng));
std::generate(v103.begin(), v103.end(), std::ref(f32rng));
std::generate(v104.begin(), v104.end(), std::ref(f32rng));
std::generate(v105.begin(), v105.end(), std::ref(f32rng));
std::generate(v106.begin(), v106.end(), std::ref(f32rng));
std::generate(v107.begin(), v107.end(), std::ref(f32rng));
std::generate(v108.begin(), v108.end(), std::ref(f32rng));
std::generate(v109.begin(), v109.end(), std::ref(f32rng));
std::generate(v110.begin(), v110.end(), std::ref(f32rng));
std::generate(v111.begin(), v111.end(), std::ref(f32rng));
std::generate(v112.begin(), v112.end(), std::ref(f32rng));
std::generate(v113.begin(), v113.end(), std::ref(f32rng));
std::generate(w114.begin(), w114.end(), std::ref(f32rng));
std::generate(w115.begin(), w115.end(), std::ref(f32rng));
std::generate(w116.begin(), w116.end(), std::ref(f32rng));
std::generate(w117.begin(), w117.end(), std::ref(f32rng));
std::fill(w118.begin(), w118.end(), 0.0f);
std::generate(w118.begin(), w118.end() - size_t(sparsity * w118.size()), std::ref(f32rng));
std::shuffle(w118.begin(), w118.end(), rng);
std::generate(w119.begin(), w119.end(), std::ref(f32rng));
std::fill(w120.begin(), w120.end(), 0.0f);
std::generate(w120.begin(), w120.end() - size_t(sparsity * w120.size()), std::ref(f32rng));
std::shuffle(w120.begin(), w120.end(), rng);
std::generate(w121.begin(), w121.end(), std::ref(f32rng));
std::generate(w122.begin(), w122.end(), std::ref(f32rng));
std::generate(w123.begin(), w123.end(), std::ref(f32rng));
std::fill(w124.begin(), w124.end(), 0.0f);
std::generate(w124.begin(), w124.end() - size_t(sparsity * w124.size()), std::ref(f32rng));
std::shuffle(w124.begin(), w124.end(), rng);
std::generate(w125.begin(), w125.end(), std::ref(f32rng));
std::fill(w126.begin(), w126.end(), 0.0f);
std::generate(w126.begin(), w126.end() - size_t(sparsity * w126.size()), std::ref(f32rng));
std::shuffle(w126.begin(), w126.end(), rng);
std::generate(w127.begin(), w127.end(), std::ref(f32rng));
std::generate(w128.begin(), w128.end(), std::ref(f32rng));
std::generate(w129.begin(), w129.end(), std::ref(f32rng));
std::fill(w130.begin(), w130.end(), 0.0f);
std::generate(w130.begin(), w130.end() - size_t(sparsity * w130.size()), std::ref(f32rng));
std::shuffle(w130.begin(), w130.end(), rng);
std::generate(w131.begin(), w131.end(), std::ref(f32rng));
std::fill(w132.begin(), w132.end(), 0.0f);
std::generate(w132.begin(), w132.end() - size_t(sparsity * w132.size()), std::ref(f32rng));
std::shuffle(w132.begin(), w132.end(), rng);
std::generate(w133.begin(), w133.end(), std::ref(f32rng));
std::generate(w134.begin(), w134.end(), std::ref(f32rng));
std::generate(w135.begin(), w135.end(), std::ref(f32rng));
std::fill(w136.begin(), w136.end(), 0.0f);
std::generate(w136.begin(), w136.end() - size_t(sparsity * w136.size()), std::ref(f32rng));
std::shuffle(w136.begin(), w136.end(), rng);
std::generate(w137.begin(), w137.end(), std::ref(f32rng));
std::fill(w138.begin(), w138.end(), 0.0f);
std::generate(w138.begin(), w138.end() - size_t(sparsity * w138.size()), std::ref(f32rng));
std::shuffle(w138.begin(), w138.end(), rng);
std::generate(w139.begin(), w139.end(), std::ref(f32rng));
std::fill(w140.begin(), w140.end(), 0.0f);
std::generate(w140.begin(), w140.end() - size_t(sparsity * w140.size()), std::ref(f32rng));
std::shuffle(w140.begin(), w140.end(), rng);
std::generate(w141.begin(), w141.end(), std::ref(f32rng));
std::fill(w142.begin(), w142.end(), 0.0f);
std::generate(w142.begin(), w142.end() - size_t(sparsity * w142.size()), std::ref(f32rng));
std::shuffle(w142.begin(), w142.end(), rng);
std::generate(w143.begin(), w143.end(), std::ref(f32rng));
std::generate(w144.begin(), w144.end(), std::ref(f32rng));
std::generate(w145.begin(), w145.end(), std::ref(f32rng));
std::fill(w146.begin(), w146.end(), 0.0f);
std::generate(w146.begin(), w146.end() - size_t(sparsity * w146.size()), std::ref(f32rng));
std::shuffle(w146.begin(), w146.end(), rng);
std::generate(w147.begin(), w147.end(), std::ref(f32rng));
std::fill(w148.begin(), w148.end(), 0.0f);
std::generate(w148.begin(), w148.end() - size_t(sparsity * w148.size()), std::ref(f32rng));
std::shuffle(w148.begin(), w148.end(), rng);
std::generate(w149.begin(), w149.end(), std::ref(f32rng));
std::fill(w150.begin(), w150.end(), 0.0f);
std::generate(w150.begin(), w150.end() - size_t(sparsity * w150.size()), std::ref(f32rng));
std::shuffle(w150.begin(), w150.end(), rng);
std::generate(w151.begin(), w151.end(), std::ref(f32rng));
std::fill(w152.begin(), w152.end(), 0.0f);
std::generate(w152.begin(), w152.end() - size_t(sparsity * w152.size()), std::ref(f32rng));
std::shuffle(w152.begin(), w152.end(), rng);
std::generate(w153.begin(), w153.end(), std::ref(f32rng));
std::generate(w154.begin(), w154.end(), std::ref(f32rng));
std::generate(w155.begin(), w155.end(), std::ref(f32rng));
std::fill(w156.begin(), w156.end(), 0.0f);
std::generate(w156.begin(), w156.end() - size_t(sparsity * w156.size()), std::ref(f32rng));
std::shuffle(w156.begin(), w156.end(), rng);
std::generate(w157.begin(), w157.end(), std::ref(f32rng));
std::fill(w158.begin(), w158.end(), 0.0f);
std::generate(w158.begin(), w158.end() - size_t(sparsity * w158.size()), std::ref(f32rng));
std::shuffle(w158.begin(), w158.end(), rng);
std::generate(w159.begin(), w159.end(), std::ref(f32rng));
std::fill(w160.begin(), w160.end(), 0.0f);
std::generate(w160.begin(), w160.end() - size_t(sparsity * w160.size()), std::ref(f32rng));
std::shuffle(w160.begin(), w160.end(), rng);
std::generate(w161.begin(), w161.end(), std::ref(f32rng));
std::fill(w162.begin(), w162.end(), 0.0f);
std::generate(w162.begin(), w162.end() - size_t(sparsity * w162.size()), std::ref(f32rng));
std::shuffle(w162.begin(), w162.end(), rng);
std::generate(w163.begin(), w163.end(), std::ref(f32rng));
std::generate(w164.begin(), w164.end(), std::ref(f32rng));
std::generate(w165.begin(), w165.end(), std::ref(f32rng));
std::fill(w166.begin(), w166.end(), 0.0f);
std::generate(w166.begin(), w166.end() - size_t(sparsity * w166.size()), std::ref(f32rng));
std::shuffle(w166.begin(), w166.end(), rng);
std::generate(w167.begin(), w167.end(), std::ref(f32rng));
std::fill(w168.begin(), w168.end(), 0.0f);
std::generate(w168.begin(), w168.end() - size_t(sparsity * w168.size()), std::ref(f32rng));
std::shuffle(w168.begin(), w168.end(), rng);
std::generate(w169.begin(), w169.end(), std::ref(f32rng));
std::generate(w170.begin(), w170.end(), std::ref(f32rng));
std::generate(w171.begin(), w171.end(), std::ref(f32rng));
std::fill(w172.begin(), w172.end(), 0.0f);
std::generate(w172.begin(), w172.end() - size_t(sparsity * w172.size()), std::ref(f32rng));
std::shuffle(w172.begin(), w172.end(), rng);
std::generate(w173.begin(), w173.end(), std::ref(f32rng));
std::fill(w174.begin(), w174.end(), 0.0f);
std::generate(w174.begin(), w174.end() - size_t(sparsity * w174.size()), std::ref(f32rng));
std::shuffle(w174.begin(), w174.end(), rng);
std::generate(w175.begin(), w175.end(), std::ref(f32rng));
std::generate(w176.begin(), w176.end(), std::ref(f32rng));
std::generate(w177.begin(), w177.end(), std::ref(f32rng));
std::fill(w178.begin(), w178.end(), 0.0f);
std::generate(w178.begin(), w178.end() - size_t(sparsity * w178.size()), std::ref(f32rng));
std::shuffle(w178.begin(), w178.end(), rng);
std::generate(w179.begin(), w179.end(), std::ref(f32rng));
std::fill(w180.begin(), w180.end(), 0.0f);
std::generate(w180.begin(), w180.end() - size_t(sparsity * w180.size()), std::ref(f32rng));
std::shuffle(w180.begin(), w180.end(), rng);
std::generate(w181.begin(), w181.end(), std::ref(f32rng));
std::generate(w182.begin(), w182.end(), std::ref(f32rng));
std::generate(w183.begin(), w183.end(), std::ref(f32rng));
std::fill(w184.begin(), w184.end(), 0.0f);
std::generate(w184.begin(), w184.end() - size_t(sparsity * w184.size()), std::ref(f32rng));
std::shuffle(w184.begin(), w184.end(), rng);
std::generate(w185.begin(), w185.end(), std::ref(f32rng));
std::fill(w186.begin(), w186.end(), 0.0f);
std::generate(w186.begin(), w186.end() - size_t(sparsity * w186.size()), std::ref(f32rng));
std::shuffle(w186.begin(), w186.end(), rng);
std::generate(w187.begin(), w187.end(), std::ref(f32rng));
std::generate(w188.begin(), w188.end(), std::ref(f32rng));
std::generate(w189.begin(), w189.end(), std::ref(f32rng));
std::fill(w190.begin(), w190.end(), 0.0f);
std::generate(w190.begin(), w190.end() - size_t(sparsity * w190.size()), std::ref(f32rng));
std::shuffle(w190.begin(), w190.end(), rng);
std::generate(w191.begin(), w191.end(), std::ref(f32rng));
std::fill(w192.begin(), w192.end(), 0.0f);
std::generate(w192.begin(), w192.end() - size_t(sparsity * w192.size()), std::ref(f32rng));
std::shuffle(w192.begin(), w192.end(), rng);
std::generate(w193.begin(), w193.end(), std::ref(f32rng));
std::fill(w194.begin(), w194.end(), 0.0f);
std::generate(w194.begin(), w194.end() - size_t(sparsity * w194.size()), std::ref(f32rng));
std::shuffle(w194.begin(), w194.end(), rng);
std::generate(w195.begin(), w195.end(), std::ref(f32rng));
std::fill(w196.begin(), w196.end(), 0.0f);
std::generate(w196.begin(), w196.end() - size_t(sparsity * w196.size()), std::ref(f32rng));
std::shuffle(w196.begin(), w196.end(), rng);
std::generate(w197.begin(), w197.end(), std::ref(f32rng));
std::generate(w198.begin(), w198.end(), std::ref(f32rng));
std::generate(w199.begin(), w199.end(), std::ref(f32rng));
std::fill(w200.begin(), w200.end(), 0.0f);
std::generate(w200.begin(), w200.end() - size_t(sparsity * w200.size()), std::ref(f32rng));
std::shuffle(w200.begin(), w200.end(), rng);
std::generate(w201.begin(), w201.end(), std::ref(f32rng));
std::fill(w202.begin(), w202.end(), 0.0f);
std::generate(w202.begin(), w202.end() - size_t(sparsity * w202.size()), std::ref(f32rng));
std::shuffle(w202.begin(), w202.end(), rng);
std::generate(w203.begin(), w203.end(), std::ref(f32rng));
std::fill(w204.begin(), w204.end(), 0.0f);
std::generate(w204.begin(), w204.end() - size_t(sparsity * w204.size()), std::ref(f32rng));
std::shuffle(w204.begin(), w204.end(), rng);
std::generate(w205.begin(), w205.end(), std::ref(f32rng));
std::fill(w206.begin(), w206.end(), 0.0f);
std::generate(w206.begin(), w206.end() - size_t(sparsity * w206.size()), std::ref(f32rng));
std::shuffle(w206.begin(), w206.end(), rng);
std::generate(w207.begin(), w207.end(), std::ref(f32rng));
std::generate(w208.begin(), w208.end(), std::ref(f32rng));
std::generate(w209.begin(), w209.end(), std::ref(f32rng));
std::fill(w210.begin(), w210.end(), 0.0f);
std::generate(w210.begin(), w210.end() - size_t(sparsity * w210.size()), std::ref(f32rng));
std::shuffle(w210.begin(), w210.end(), rng);
std::generate(w211.begin(), w211.end(), std::ref(f32rng));
std::fill(w212.begin(), w212.end(), 0.0f);
std::generate(w212.begin(), w212.end() - size_t(sparsity * w212.size()), std::ref(f32rng));
std::shuffle(w212.begin(), w212.end(), rng);
std::generate(w213.begin(), w213.end(), std::ref(f32rng));
std::fill(w214.begin(), w214.end(), 0.0f);
std::generate(w214.begin(), w214.end() - size_t(sparsity * w214.size()), std::ref(f32rng));
std::shuffle(w214.begin(), w214.end(), rng);
std::generate(w215.begin(), w215.end(), std::ref(f32rng));
std::fill(w216.begin(), w216.end(), 0.0f);
std::generate(w216.begin(), w216.end() - size_t(sparsity * w216.size()), std::ref(f32rng));
std::shuffle(w216.begin(), w216.end(), rng);
std::generate(w217.begin(), w217.end(), std::ref(f32rng));
std::generate(w218.begin(), w218.end(), std::ref(f32rng));
std::generate(w219.begin(), w219.end(), std::ref(f32rng));
std::fill(w220.begin(), w220.end(), 0.0f);
std::generate(w220.begin(), w220.end() - size_t(sparsity * w220.size()), std::ref(f32rng));
std::shuffle(w220.begin(), w220.end(), rng);
std::generate(w221.begin(), w221.end(), std::ref(f32rng));
std::fill(w222.begin(), w222.end(), 0.0f);
std::generate(w222.begin(), w222.end() - size_t(sparsity * w222.size()), std::ref(f32rng));
std::shuffle(w222.begin(), w222.end(), rng);
std::generate(w223.begin(), w223.end(), std::ref(f32rng));
std::fill(w224.begin(), w224.end(), 0.0f);
std::generate(w224.begin(), w224.end() - size_t(sparsity * w224.size()), std::ref(f32rng));
std::shuffle(w224.begin(), w224.end(), rng);
std::generate(w225.begin(), w225.end(), std::ref(f32rng));
std::fill(w226.begin(), w226.end(), 0.0f);
std::generate(w226.begin(), w226.end() - size_t(sparsity * w226.size()), std::ref(f32rng));
std::shuffle(w226.begin(), w226.end(), rng);
std::generate(w227.begin(), w227.end(), std::ref(f32rng));
std::generate(w228.begin(), w228.end(), std::ref(f32rng));
std::generate(w229.begin(), w229.end(), std::ref(f32rng));
std::fill(w230.begin(), w230.end(), 0.0f);
std::generate(w230.begin(), w230.end() - size_t(sparsity * w230.size()), std::ref(f32rng));
std::shuffle(w230.begin(), w230.end(), rng);
std::generate(w231.begin(), w231.end(), std::ref(f32rng));
std::fill(w232.begin(), w232.end(), 0.0f);
std::generate(w232.begin(), w232.end() - size_t(sparsity * w232.size()), std::ref(f32rng));
std::shuffle(w232.begin(), w232.end(), rng);
std::generate(w233.begin(), w233.end(), std::ref(f32rng));
std::fill(w234.begin(), w234.end(), 0.0f);
std::generate(w234.begin(), w234.end() - size_t(sparsity * w234.size()), std::ref(f32rng));
std::shuffle(w234.begin(), w234.end(), rng);
std::generate(w235.begin(), w235.end(), std::ref(f32rng));
std::fill(w236.begin(), w236.end(), 0.0f);
std::generate(w236.begin(), w236.end() - size_t(sparsity * w236.size()), std::ref(f32rng));
std::shuffle(w236.begin(), w236.end(), rng);
std::generate(w237.begin(), w237.end(), std::ref(f32rng));
std::generate(w238.begin(), w238.end(), std::ref(f32rng));
std::generate(w239.begin(), w239.end(), std::ref(f32rng));
std::generate(w240.begin(), w240.end(), std::ref(f32rng));
std::generate(w241.begin(), w241.end(), std::ref(f32rng));
ExecutionPlan operators;
xnn_status status;
xnn_operator_t op0 = nullptr;
status = xnn_create_convolution2d_nchw_f32(
1 /* top padding */, 1 /* right padding */,
1 /* bottom padding */, 1 /* 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 */,
w114.data(), w115.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
XNN_FLAG_INPUT_NHWC /* flags */,
nullptr,
nullptr,
&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_f32(
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_nchw_f32(
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 */,
16 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
16 /* input pixel stride */,
16 /* output pixel stride */,
w116.data(), w117.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
w118.data(), w119.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-std::numeric_limits<float>::infinity() /* 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_nchw_f32(
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 */,
64 /* output_channels_per_group */,
16 /* input pixel stride */,
64 /* output pixel stride */,
w120.data(), w121.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
1 /* top padding */, 1 /* right padding */,
1 /* bottom padding */, 1 /* left padding */,
3 /* kernel height */, 3 /* kernel width */,
2 /* subsampling height */, 2 /* subsampling width */,
1 /* dilation_height */, 1 /* dilation_width */,
64 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
64 /* input pixel stride */,
64 /* output pixel stride */,
w122.data(), w123.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
24 /* output_channels_per_group */,
64 /* input pixel stride */,
24 /* output pixel stride */,
w124.data(), w125.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
72 /* output_channels_per_group */,
24 /* input pixel stride */,
72 /* output pixel stride */,
w126.data(), w127.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
72 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
72 /* input pixel stride */,
72 /* output pixel stride */,
w128.data(), w129.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
w130.data(), w131.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-std::numeric_limits<float>::infinity() /* 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_nchw_f32(
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 */,
72 /* output_channels_per_group */,
24 /* input pixel stride */,
72 /* output pixel stride */,
w132.data(), w133.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
2 /* top padding */, 2 /* right padding */,
2 /* bottom padding */, 2 /* left padding */,
5 /* kernel height */, 5 /* 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 */,
w134.data(), w135.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_global_average_pooling_ncw_f32(
72 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_nchw_f32(
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 */,
w136.data(), w137.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
72 /* output_channels_per_group */,
24 /* input pixel stride */,
72 /* output pixel stride */,
w138.data(), w139.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
40 /* output_channels_per_group */,
72 /* 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 */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
w142.data(), w143.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
w144.data(), w145.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_global_average_pooling_ncw_f32(
120 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_convolution2d_nchw_f32(
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 */,
w146.data(), w147.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
w148.data(), w149.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
40 /* output_channels_per_group */,
120 /* 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 */,
nullptr,
nullptr,
&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_add_nd_f32(
-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_convolution2d_nchw_f32(
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(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_global_average_pooling_ncw_f32(
120 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_nchw_f32(
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 */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
40 /* output_channels_per_group */,
120 /* input pixel stride */,
40 /* output pixel stride */,
w160.data(), w161.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
w162.data(), w163.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
240 /* channels */,
240 /* input stride */,
240 /* output stride */,
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_convolution2d_nchw_f32(
1 /* top padding */, 1 /* right padding */,
1 /* bottom padding */, 1 /* left padding */,
3 /* kernel height */, 3 /* kernel width */,
2 /* subsampling height */, 2 /* 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 */,
w164.data(), w165.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
240 /* channels */,
240 /* input stride */,
240 /* output stride */,
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_nchw_f32(
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 */,
80 /* output_channels_per_group */,
240 /* input pixel stride */,
80 /* output pixel stride */,
w166.data(), w167.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
80 /* input channels per group */,
200 /* output_channels_per_group */,
80 /* input pixel stride */,
200 /* output pixel stride */,
w168.data(), w169.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
200 /* channels */,
200 /* input stride */,
200 /* output stride */,
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_nchw_f32(
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 */,
200 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
200 /* input pixel stride */,
200 /* output pixel stride */,
w170.data(), w171.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
200 /* channels */,
200 /* input stride */,
200 /* output stride */,
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_nchw_f32(
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 */,
200 /* input channels per group */,
80 /* output_channels_per_group */,
200 /* input pixel stride */,
80 /* output pixel stride */,
w172.data(), w173.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
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_nchw_f32(
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 */,
80 /* input channels per group */,
184 /* output_channels_per_group */,
80 /* input pixel stride */,
184 /* output pixel stride */,
w174.data(), w175.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_f32(
184 /* channels */,
184 /* input stride */,
184 /* 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_convolution2d_nchw_f32(
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 */,
184 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
184 /* input pixel stride */,
184 /* output pixel stride */,
w176.data(), w177.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
184 /* channels */,
184 /* input stride */,
184 /* output stride */,
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_nchw_f32(
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 */,
184 /* input channels per group */,
80 /* output_channels_per_group */,
184 /* input pixel stride */,
80 /* output pixel stride */,
w178.data(), w179.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-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_nchw_f32(
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 */,
80 /* input channels per group */,
184 /* output_channels_per_group */,
80 /* input pixel stride */,
184 /* output pixel stride */,
w180.data(), w181.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
184 /* channels */,
184 /* input stride */,
184 /* output stride */,
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_convolution2d_nchw_f32(
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 */,
184 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
184 /* input pixel stride */,
184 /* output pixel stride */,
w182.data(), w183.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
184 /* channels */,
184 /* input stride */,
184 /* output stride */,
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_convolution2d_nchw_f32(
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 */,
184 /* input channels per group */,
80 /* output_channels_per_group */,
184 /* input pixel stride */,
80 /* output pixel stride */,
w184.data(), w185.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
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_nchw_f32(
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 */,
80 /* input channels per group */,
480 /* output_channels_per_group */,
80 /* input pixel stride */,
480 /* output pixel stride */,
w186.data(), w187.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
480 /* channels */,
480 /* input stride */,
480 /* output stride */,
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_convolution2d_nchw_f32(
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 */,
480 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
480 /* input pixel stride */,
480 /* output pixel stride */,
w188.data(), w189.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
480 /* channels */,
480 /* input stride */,
480 /* output stride */,
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_global_average_pooling_ncw_f32(
480 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_nchw_f32(
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 */,
480 /* input channels per group */,
120 /* output_channels_per_group */,
480 /* input pixel stride */,
120 /* output pixel stride */,
w190.data(), w191.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
480 /* output_channels_per_group */,
120 /* input pixel stride */,
480 /* output pixel stride */,
w192.data(), w193.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
480 /* input channels per group */,
112 /* output_channels_per_group */,
480 /* input pixel stride */,
112 /* output pixel stride */,
w194.data(), w195.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
112 /* input channels per group */,
672 /* output_channels_per_group */,
112 /* input pixel stride */,
672 /* output pixel stride */,
w196.data(), w197.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
672 /* channels */,
672 /* input stride */,
672 /* output stride */,
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_nchw_f32(
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 */,
672 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
672 /* input pixel stride */,
672 /* output pixel stride */,
w198.data(), w199.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
672 /* channels */,
672 /* input stride */,
672 /* output stride */,
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_global_average_pooling_ncw_f32(
672 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_nchw_f32(
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 */,
672 /* input channels per group */,
168 /* output_channels_per_group */,
672 /* input pixel stride */,
168 /* output pixel stride */,
w200.data(), w201.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
168 /* input channels per group */,
672 /* output_channels_per_group */,
168 /* input pixel stride */,
672 /* output pixel stride */,
w202.data(), w203.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
672 /* input channels per group */,
112 /* output_channels_per_group */,
672 /* input pixel stride */,
112 /* output pixel stride */,
w204.data(), w205.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
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_nchw_f32(
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 */,
112 /* input channels per group */,
672 /* output_channels_per_group */,
112 /* input pixel stride */,
672 /* output pixel stride */,
w206.data(), w207.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
672 /* channels */,
672 /* input stride */,
672 /* output stride */,
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_convolution2d_nchw_f32(
2 /* top padding */, 2 /* right padding */,
2 /* bottom padding */, 2 /* left padding */,
5 /* kernel height */, 5 /* kernel width */,
2 /* subsampling height */, 2 /* subsampling width */,
1 /* dilation_height */, 1 /* dilation_width */,
672 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
672 /* input pixel stride */,
672 /* output pixel stride */,
w208.data(), w209.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
672 /* channels */,
672 /* input stride */,
672 /* output stride */,
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_global_average_pooling_ncw_f32(
672 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_nchw_f32(
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 */,
672 /* input channels per group */,
168 /* output_channels_per_group */,
672 /* input pixel stride */,
168 /* output pixel stride */,
w210.data(), w211.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
168 /* input channels per group */,
672 /* output_channels_per_group */,
168 /* input pixel stride */,
672 /* output pixel stride */,
w212.data(), w213.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
672 /* input channels per group */,
160 /* output_channels_per_group */,
672 /* input pixel stride */,
160 /* output pixel stride */,
w214.data(), w215.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_convolution2d_nchw_f32(
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 */,
160 /* input channels per group */,
960 /* output_channels_per_group */,
160 /* input pixel stride */,
960 /* output pixel stride */,
w216.data(), w217.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
960 /* channels */,
960 /* input stride */,
960 /* output stride */,
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_nchw_f32(
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 */,
960 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
960 /* input pixel stride */,
960 /* output pixel stride */,
w218.data(), w219.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
960 /* channels */,
960 /* input stride */,
960 /* output stride */,
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_global_average_pooling_ncw_f32(
960 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
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_convolution2d_nchw_f32(
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 */,
960 /* input channels per group */,
240 /* output_channels_per_group */,
960 /* input pixel stride */,
240 /* output pixel stride */,
w220.data(), w221.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_nchw_f32(
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 */,
960 /* output_channels_per_group */,
240 /* input pixel stride */,
960 /* output pixel stride */,
w222.data(), w223.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_multiply_nd_f32(
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
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_convolution2d_nchw_f32(
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 */,
960 /* input channels per group */,
160 /* output_channels_per_group */,
960 /* input pixel stride */,
160 /* output pixel stride */,
w224.data(), w225.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_add_nd_f32(
-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_convolution2d_nchw_f32(
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 */,
160 /* input channels per group */,
960 /* output_channels_per_group */,
160 /* input pixel stride */,
960 /* output pixel stride */,
w226.data(), w227.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&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_hardswish_nc_f32(
960 /* channels */,
960 /* input stride */,
960 /* output stride */,
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_nchw_f32(
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 */,
960 /* groups */,
1 /* input channels per group */,
1 /* output_channels_per_group */,
960 /* input pixel stride */,
960 /* output pixel stride */,
w228.data(), w229.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op98);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #98" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op98, xnn_delete_operator);
xnn_operator_t op99 = nullptr;
status = xnn_create_hardswish_nc_f32(
960 /* channels */,
960 /* input stride */,
960 /* output stride */,
0 /* flags */,
&op99);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #99" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op99, xnn_delete_operator);
xnn_operator_t op100 = nullptr;
status = xnn_create_global_average_pooling_ncw_f32(
960 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
0 /* flags */,
&op100);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #100" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op100, xnn_delete_operator);
xnn_operator_t op101 = nullptr;
status = xnn_create_convolution2d_nchw_f32(
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 */,
960 /* input channels per group */,
240 /* output_channels_per_group */,
960 /* input pixel stride */,
240 /* output pixel stride */,
w230.data(), w231.data(),
0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op101);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #101" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op101, xnn_delete_operator);
xnn_operator_t op102 = nullptr;
status = xnn_create_convolution2d_nchw_f32(
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 */,
960 /* output_channels_per_group */,
240 /* input pixel stride */,
960 /* output pixel stride */,
w232.data(), w233.data(),
0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op102);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #102" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op102, xnn_delete_operator);
xnn_operator_t op103 = nullptr;
status = xnn_create_multiply_nd_f32(
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
&op103);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #103" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op103, xnn_delete_operator);
xnn_operator_t op104 = nullptr;
status = xnn_create_convolution2d_nchw_f32(
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 */,
960 /* input channels per group */,
160 /* output_channels_per_group */,
960 /* input pixel stride */,
160 /* output pixel stride */,
w234.data(), w235.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op104);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #104" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op104, xnn_delete_operator);
xnn_operator_t op105 = nullptr;
status = xnn_create_add_nd_f32(
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
&op105);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #105" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op105, xnn_delete_operator);
xnn_operator_t op106 = nullptr;
status = xnn_create_convolution2d_nchw_f32(
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 */,
160 /* input channels per group */,
960 /* output_channels_per_group */,
160 /* input pixel stride */,
960 /* output pixel stride */,
w236.data(), w237.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op106);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #106" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op106, xnn_delete_operator);
xnn_operator_t op107 = nullptr;
status = xnn_create_hardswish_nc_f32(
960 /* channels */,
960 /* input stride */,
960 /* output stride */,
0 /* flags */,
&op107);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #107" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op107, xnn_delete_operator);
xnn_operator_t op108 = nullptr;
status = xnn_create_global_average_pooling_ncw_f32(
960 /* channels */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
0 /* flags */,
&op108);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #108" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op108, xnn_delete_operator);
xnn_operator_t op109 = nullptr;
status = xnn_create_convolution2d_nhwc_f32(
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 */,
960 /* input channels per group */,
1280 /* output_channels_per_group */,
960 /* input pixel stride */,
1280 /* output pixel stride */,
w238.data(), w239.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op109);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #109" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op109, xnn_delete_operator);
xnn_operator_t op110 = nullptr;
status = xnn_create_hardswish_nc_f32(
1280 /* channels */,
1280 /* input stride */,
1280 /* output stride */,
0 /* flags */,
&op110);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #110" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op110, xnn_delete_operator);
xnn_operator_t op111 = nullptr;
status = xnn_create_global_average_pooling_nwc_f32(
1280 /* channels */, 1280 /* input stride */, 1280 /* output stride */,
-std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
0 /* flags */,
&op111);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #111" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op111, xnn_delete_operator);
xnn_operator_t op112 = nullptr;
status = xnn_create_convolution2d_nhwc_f32(
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 */,
1280 /* input channels per group */,
1001 /* output_channels_per_group */,
1280 /* input pixel stride */,
1001 /* output pixel stride */,
w240.data(), w241.data(),
-std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
0 /* flags */,
nullptr,
nullptr,
&op112);
if (status != xnn_status_success) {
std::cerr << "failed to create operation #112" << std::endl;
return ExecutionPlan();
}
operators.emplace_back(op112, xnn_delete_operator);
status = xnn_reshape_convolution2d_nchw_f32(
op0,
/*batch_size=*/1, /*input_height=*/224, /*input_width=*/224,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #0" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op1,
/*batch_size=*/12544,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #1" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op2,
/*batch_size=*/1, /*input_height=*/112, /*input_width=*/112,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #2" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op3,
/*batch_size=*/1, /*input_height=*/112, /*input_width=*/112,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #3" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 16, 112, 112 };
const size_t b_shape[] = { 1, 16, 112, 112 };
status = xnn_reshape_add_nd_f32(
op4,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #4" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op5,
/*batch_size=*/1, /*input_height=*/112, /*input_width=*/112,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #5" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op6,
/*batch_size=*/1, /*input_height=*/112, /*input_width=*/112,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #6" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op7,
/*batch_size=*/1, /*input_height=*/56, /*input_width=*/56,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #7" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op8,
/*batch_size=*/1, /*input_height=*/56, /*input_width=*/56,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #8" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op9,
/*batch_size=*/1, /*input_height=*/56, /*input_width=*/56,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #9" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op10,
/*batch_size=*/1, /*input_height=*/56, /*input_width=*/56,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #10" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 24, 56, 56 };
const size_t b_shape[] = { 1, 24, 56, 56 };
status = xnn_reshape_add_nd_f32(
op11,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #11" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op12,
/*batch_size=*/1, /*input_height=*/56, /*input_width=*/56,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #12" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op13,
/*batch_size=*/1, /*input_height=*/56, /*input_width=*/56,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #13" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op14,
/*batch_size=*/1, 784 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #14" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op15,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #15" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op16,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #16" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 72, 28, 28 };
const size_t b_shape[] = { 1, 72, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op17,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #17" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op18,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #18" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op19,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #19" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op20,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #20" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op21,
/*batch_size=*/1, 784 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #21" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op22,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #22" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op23,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #23" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 120, 28, 28 };
const size_t b_shape[] = { 1, 120, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op24,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #24" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op25,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #25" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 40, 28, 28 };
const size_t b_shape[] = { 1, 40, 28, 28 };
status = xnn_reshape_add_nd_f32(
op26,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #26" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op27,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #27" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op28,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #28" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op29,
/*batch_size=*/1, 784 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #29" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op30,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #30" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op31,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #31" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 120, 28, 28 };
const size_t b_shape[] = { 1, 120, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op32,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #32" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op33,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #33" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 40, 28, 28 };
const size_t b_shape[] = { 1, 40, 28, 28 };
status = xnn_reshape_add_nd_f32(
op34,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #34" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op35,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #35" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op36,
/*batch_size=*/784,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #36" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op37,
/*batch_size=*/1, /*input_height=*/28, /*input_width=*/28,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #37" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op38,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #38" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op39,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #39" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op40,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #40" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op41,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #41" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op42,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #42" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op43,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #43" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op44,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #44" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 80, 14, 14 };
const size_t b_shape[] = { 1, 80, 14, 14 };
status = xnn_reshape_add_nd_f32(
op45,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #45" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op46,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #46" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op47,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #47" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op48,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #48" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op49,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #49" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op50,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #50" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 80, 14, 14 };
const size_t b_shape[] = { 1, 80, 14, 14 };
status = xnn_reshape_add_nd_f32(
op51,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #51" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op52,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #52" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op53,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #53" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op54,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #54" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op55,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #55" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op56,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #56" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 80, 14, 14 };
const size_t b_shape[] = { 1, 80, 14, 14 };
status = xnn_reshape_add_nd_f32(
op57,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #57" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op58,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #58" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op59,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #59" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op60,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #60" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op61,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #61" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op62,
/*batch_size=*/1, 196 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #62" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op63,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #63" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op64,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #64" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 480, 14, 14 };
const size_t b_shape[] = { 1, 480, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op65,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #65" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op66,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #66" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op67,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #67" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op68,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #68" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op69,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #69" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op70,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #70" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op71,
/*batch_size=*/1, 196 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #71" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op72,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #72" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op73,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #73" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 672, 14, 14 };
const size_t b_shape[] = { 1, 672, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op74,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #74" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op75,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #75" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 112, 14, 14 };
const size_t b_shape[] = { 1, 112, 14, 14 };
status = xnn_reshape_add_nd_f32(
op76,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #76" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op77,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #77" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op78,
/*batch_size=*/196,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #78" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op79,
/*batch_size=*/1, /*input_height=*/14, /*input_width=*/14,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #79" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op80,
/*batch_size=*/49,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #80" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op81,
/*batch_size=*/1, 49 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #81" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op82,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #82" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op83,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #83" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 672, 7, 7 };
const size_t b_shape[] = { 1, 672, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op84,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #84" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op85,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #85" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op86,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #86" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op87,
/*batch_size=*/49,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #87" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op88,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #88" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op89,
/*batch_size=*/49,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #89" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op90,
/*batch_size=*/1, 49 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #90" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op91,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #91" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op92,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #92" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 960, 7, 7 };
const size_t b_shape[] = { 1, 960, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op93,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #93" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op94,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #94" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 160, 7, 7 };
const size_t b_shape[] = { 1, 160, 7, 7 };
status = xnn_reshape_add_nd_f32(
op95,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #95" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op96,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #96" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op97,
/*batch_size=*/49,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #97" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op98,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #98" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op99,
/*batch_size=*/49,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #99" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op100,
/*batch_size=*/1, 49 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #100" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op101,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #101" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op102,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #102" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 960, 7, 7 };
const size_t b_shape[] = { 1, 960, 1, 1 };
status = xnn_reshape_multiply_nd_f32(
op103,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #103" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op104,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #104" << std::endl;
return ExecutionPlan();
}
{
const size_t a_shape[] = { 1, 160, 7, 7 };
const size_t b_shape[] = { 1, 160, 7, 7 };
status = xnn_reshape_add_nd_f32(
op105,
4, a_shape, 4, b_shape,
/*threadpool=*/threadpool);
}
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #105" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nchw_f32(
op106,
/*batch_size=*/1, /*input_height=*/7, /*input_width=*/7,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #106" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op107,
/*batch_size=*/49,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #107" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_ncw_f32(
op108,
/*batch_size=*/1, 49 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #108" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nhwc_f32(
op109,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #109" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_hardswish_nc_f32(
op110,
/*batch_size=*/1,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #110" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_global_average_pooling_nwc_f32(
op111,
/*batch_size=*/1, 1 /* width */,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #111" << std::endl;
return ExecutionPlan();
}
status = xnn_reshape_convolution2d_nhwc_f32(
op112,
/*batch_size=*/1, /*input_height=*/1, /*input_width=*/1,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/threadpool);
if (status != xnn_status_success) {
std::cerr << "failed to reshape operation #112" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op0,
/*input=*/v0.data(), /*output=*/v1.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #0" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op1,
/*input=*/v1.data(), /*output=*/v2.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #1" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op2,
/*input=*/v2.data(), /*output=*/v3.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #2" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op3,
/*input=*/v3.data(), /*output=*/v4.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #3" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op4,
v4.data() /* a */, v2.data() /* b */, /*output=*/v5.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #4" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op5,
/*input=*/v5.data(), /*output=*/v6.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #5" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op6,
/*input=*/v6.data(), /*output=*/v7.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #6" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op7,
/*input=*/v7.data(), /*output=*/v8.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #7" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op8,
/*input=*/v8.data(), /*output=*/v9.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #8" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op9,
/*input=*/v9.data(), /*output=*/v10.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #9" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op10,
/*input=*/v10.data(), /*output=*/v11.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #10" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op11,
v11.data() /* a */, v8.data() /* b */, /*output=*/v12.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #11" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op12,
/*input=*/v12.data(), /*output=*/v13.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #12" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op13,
/*input=*/v13.data(), /*output=*/v14.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #13" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op14,
/*input=*/v14.data(), /*output=*/v15.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #14" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op15,
/*input=*/v15.data(), /*output=*/v16.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #15" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op16,
/*input=*/v16.data(), /*output=*/v17.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #16" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op17,
v14.data() /* a */, v17.data() /* b */, /*output=*/v18.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #17" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op18,
/*input=*/v18.data(), /*output=*/v19.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #18" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op19,
/*input=*/v19.data(), /*output=*/v20.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #19" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op20,
/*input=*/v20.data(), /*output=*/v21.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #20" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op21,
/*input=*/v21.data(), /*output=*/v22.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #21" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op22,
/*input=*/v22.data(), /*output=*/v23.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #22" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op23,
/*input=*/v23.data(), /*output=*/v24.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #23" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op24,
v21.data() /* a */, v24.data() /* b */, /*output=*/v25.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #24" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op25,
/*input=*/v25.data(), /*output=*/v26.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #25" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op26,
v26.data() /* a */, v19.data() /* b */, /*output=*/v27.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #26" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op27,
/*input=*/v27.data(), /*output=*/v28.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #27" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op28,
/*input=*/v28.data(), /*output=*/v29.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #28" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op29,
/*input=*/v29.data(), /*output=*/v30.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #29" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op30,
/*input=*/v30.data(), /*output=*/v31.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #30" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op31,
/*input=*/v31.data(), /*output=*/v32.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #31" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op32,
v29.data() /* a */, v32.data() /* b */, /*output=*/v33.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #32" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op33,
/*input=*/v33.data(), /*output=*/v34.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #33" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op34,
v34.data() /* a */, v27.data() /* b */, /*output=*/v35.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #34" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op35,
/*input=*/v35.data(), /*output=*/v36.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #35" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op36,
/*input=*/v36.data(), /*output=*/v37.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #36" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op37,
/*input=*/v37.data(), /*output=*/v38.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #37" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op38,
/*input=*/v38.data(), /*output=*/v39.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #38" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op39,
/*input=*/v39.data(), /*output=*/v40.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #39" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op40,
/*input=*/v40.data(), /*output=*/v41.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #40" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op41,
/*input=*/v41.data(), /*output=*/v42.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #41" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op42,
/*input=*/v42.data(), /*output=*/v43.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #42" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op43,
/*input=*/v43.data(), /*output=*/v44.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #43" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op44,
/*input=*/v44.data(), /*output=*/v45.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #44" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op45,
v45.data() /* a */, v40.data() /* b */, /*output=*/v46.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #45" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op46,
/*input=*/v46.data(), /*output=*/v47.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #46" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op47,
/*input=*/v47.data(), /*output=*/v48.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #47" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op48,
/*input=*/v48.data(), /*output=*/v49.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #48" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op49,
/*input=*/v49.data(), /*output=*/v50.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #49" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op50,
/*input=*/v50.data(), /*output=*/v51.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #50" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op51,
v51.data() /* a */, v46.data() /* b */, /*output=*/v52.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #51" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op52,
/*input=*/v52.data(), /*output=*/v53.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #52" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op53,
/*input=*/v53.data(), /*output=*/v54.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #53" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op54,
/*input=*/v54.data(), /*output=*/v55.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #54" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op55,
/*input=*/v55.data(), /*output=*/v56.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #55" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op56,
/*input=*/v56.data(), /*output=*/v57.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #56" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op57,
v57.data() /* a */, v52.data() /* b */, /*output=*/v58.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #57" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op58,
/*input=*/v58.data(), /*output=*/v59.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #58" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op59,
/*input=*/v59.data(), /*output=*/v60.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #59" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op60,
/*input=*/v60.data(), /*output=*/v61.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #60" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op61,
/*input=*/v61.data(), /*output=*/v62.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #61" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op62,
/*input=*/v62.data(), /*output=*/v63.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #62" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op63,
/*input=*/v63.data(), /*output=*/v64.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #63" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op64,
/*input=*/v64.data(), /*output=*/v65.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #64" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op65,
v62.data() /* a */, v65.data() /* b */, /*output=*/v66.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #65" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op66,
/*input=*/v66.data(), /*output=*/v67.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #66" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op67,
/*input=*/v67.data(), /*output=*/v68.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #67" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op68,
/*input=*/v68.data(), /*output=*/v69.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #68" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op69,
/*input=*/v69.data(), /*output=*/v70.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #69" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op70,
/*input=*/v70.data(), /*output=*/v71.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #70" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op71,
/*input=*/v71.data(), /*output=*/v72.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #71" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op72,
/*input=*/v72.data(), /*output=*/v73.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #72" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op73,
/*input=*/v73.data(), /*output=*/v74.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #73" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op74,
v71.data() /* a */, v74.data() /* b */, /*output=*/v75.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #74" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op75,
/*input=*/v75.data(), /*output=*/v76.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #75" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op76,
v76.data() /* a */, v67.data() /* b */, /*output=*/v77.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #76" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op77,
/*input=*/v77.data(), /*output=*/v78.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #77" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op78,
/*input=*/v78.data(), /*output=*/v79.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #78" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op79,
/*input=*/v79.data(), /*output=*/v80.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #79" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op80,
/*input=*/v80.data(), /*output=*/v81.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #80" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op81,
/*input=*/v81.data(), /*output=*/v82.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #81" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op82,
/*input=*/v82.data(), /*output=*/v83.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #82" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op83,
/*input=*/v83.data(), /*output=*/v84.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #83" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op84,
v81.data() /* a */, v84.data() /* b */, /*output=*/v85.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #84" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op85,
/*input=*/v85.data(), /*output=*/v86.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #85" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op86,
/*input=*/v86.data(), /*output=*/v87.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #86" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op87,
/*input=*/v87.data(), /*output=*/v88.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #87" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op88,
/*input=*/v88.data(), /*output=*/v89.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #88" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op89,
/*input=*/v89.data(), /*output=*/v90.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #89" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op90,
/*input=*/v90.data(), /*output=*/v91.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #90" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op91,
/*input=*/v91.data(), /*output=*/v92.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #91" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op92,
/*input=*/v92.data(), /*output=*/v93.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #92" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op93,
v90.data() /* a */, v93.data() /* b */, /*output=*/v94.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #93" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op94,
/*input=*/v94.data(), /*output=*/v95.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #94" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op95,
v95.data() /* a */, v86.data() /* b */, /*output=*/v96.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #95" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op96,
/*input=*/v96.data(), /*output=*/v97.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #96" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op97,
/*input=*/v97.data(), /*output=*/v98.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #97" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op98,
/*input=*/v98.data(), /*output=*/v99.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #98" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op99,
/*input=*/v99.data(), /*output=*/v100.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #99" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op100,
/*input=*/v100.data(), /*output=*/v101.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #100" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op101,
/*input=*/v101.data(), /*output=*/v102.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #101" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op102,
/*input=*/v102.data(), /*output=*/v103.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #102" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_multiply_nd_f32(
op103,
v100.data() /* a */, v103.data() /* b */, /*output=*/v104.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #103" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op104,
/*input=*/v104.data(), /*output=*/v105.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #104" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_add_nd_f32(
op105,
v105.data() /* a */, v96.data() /* b */, /*output=*/v106.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #105" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nchw_f32(
op106,
/*input=*/v106.data(), /*output=*/v107.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #106" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op107,
/*input=*/v107.data(), /*output=*/v108.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #107" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_ncw_f32(
op108,
/*input=*/v108.data(), /*output=*/v109.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #108" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nhwc_f32(
op109,
/*input=*/v109.data(), /*output=*/v110.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #109" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_hardswish_nc_f32(
op110,
/*input=*/v110.data(), /*output=*/v111.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #110" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_global_average_pooling_nwc_f32(
op111,
/*input=*/v111.data(), /*output=*/v112.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #111" << std::endl;
return ExecutionPlan();
}
status = xnn_setup_convolution2d_nhwc_f32(
op112,
/*input=*/v112.data(), /*output=*/v113.data());
if (status != xnn_status_success) {
std::cerr << "failed to setup operation #112" << std::endl;
return ExecutionPlan();
}
XNN_PRAGMA_CLANG("clang diagnostic push")
XNN_PRAGMA_CLANG("clang diagnostic ignored \"-Wpessimizing-move\"")
return operators;
XNN_PRAGMA_CLANG("clang diagnostic pop")
}
} // namespace models