| // 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 "models/models.h" |
| |
| namespace models { |
| |
| ExecutionPlan FP32SparseMobileNetV3Small(float sparsity, pthreadpool_t threadpool) { |
| alignas(16) static std::array<float, 150528> v0; |
| alignas(16) static std::array<float, 200704> v1; |
| alignas(16) static std::array<float, 200704> v2; |
| alignas(16) static std::array<float, 50176> v3; |
| alignas(16) static std::array<float, 16> v4; |
| alignas(16) static std::array<float, 8> v5; |
| alignas(16) static std::array<float, 16> v6; |
| alignas(16) static std::array<float, 50176> v7; |
| alignas(16) static std::array<float, 50176> v8; |
| alignas(16) static std::array<float, 225792> v9; |
| alignas(16) static std::array<float, 56448> v10; |
| alignas(16) static std::array<float, 18816> v11; |
| alignas(16) static std::array<float, 68992> v12; |
| alignas(16) static std::array<float, 68992> v13; |
| alignas(16) static std::array<float, 18816> v14; |
| alignas(16) static std::array<float, 18816> v15; |
| alignas(16) static std::array<float, 75264> v16; |
| alignas(16) static std::array<float, 75264> v17; |
| alignas(16) static std::array<float, 18816> v18; |
| alignas(16) static std::array<float, 18816> v19; |
| alignas(16) static std::array<float, 96> v20; |
| alignas(16) static std::array<float, 24> v21; |
| alignas(16) static std::array<float, 96> v22; |
| alignas(16) static std::array<float, 18816> v23; |
| alignas(16) static std::array<float, 7840> v24; |
| alignas(16) static std::array<float, 47040> v25; |
| alignas(16) static std::array<float, 47040> v26; |
| alignas(16) static std::array<float, 47040> v27; |
| alignas(16) static std::array<float, 47040> v28; |
| alignas(16) static std::array<float, 240> v29; |
| alignas(16) static std::array<float, 64> v30; |
| alignas(16) static std::array<float, 240> v31; |
| alignas(16) static std::array<float, 47040> v32; |
| alignas(16) static std::array<float, 7840> v33; |
| alignas(16) static std::array<float, 7840> v34; |
| alignas(16) static std::array<float, 47040> v35; |
| alignas(16) static std::array<float, 47040> v36; |
| alignas(16) static std::array<float, 47040> v37; |
| alignas(16) static std::array<float, 47040> v38; |
| alignas(16) static std::array<float, 240> v39; |
| alignas(16) static std::array<float, 64> v40; |
| alignas(16) static std::array<float, 240> v41; |
| alignas(16) static std::array<float, 47040> v42; |
| alignas(16) static std::array<float, 7840> v43; |
| alignas(16) static std::array<float, 7840> v44; |
| alignas(16) static std::array<float, 23520> v45; |
| alignas(16) static std::array<float, 23520> v46; |
| alignas(16) static std::array<float, 23520> v47; |
| alignas(16) static std::array<float, 23520> v48; |
| alignas(16) static std::array<float, 120> v49; |
| alignas(16) static std::array<float, 32> v50; |
| alignas(16) static std::array<float, 120> v51; |
| alignas(16) static std::array<float, 23520> v52; |
| alignas(16) static std::array<float, 9408> v53; |
| alignas(16) static std::array<float, 28224> v54; |
| alignas(16) static std::array<float, 28224> v55; |
| alignas(16) static std::array<float, 28224> v56; |
| alignas(16) static std::array<float, 28224> v57; |
| alignas(16) static std::array<float, 144> v58; |
| alignas(16) static std::array<float, 40> v59; |
| alignas(16) static std::array<float, 144> v60; |
| alignas(16) static std::array<float, 28224> v61; |
| alignas(16) static std::array<float, 9408> v62; |
| alignas(16) static std::array<float, 9408> v63; |
| alignas(16) static std::array<float, 56448> v64; |
| alignas(16) static std::array<float, 56448> v65; |
| alignas(16) static std::array<float, 14112> v66; |
| alignas(16) static std::array<float, 14112> v67; |
| alignas(16) static std::array<float, 288> v68; |
| alignas(16) static std::array<float, 72> v69; |
| alignas(16) static std::array<float, 288> v70; |
| alignas(16) static std::array<float, 14112> v71; |
| alignas(16) static std::array<float, 4704> v72; |
| alignas(16) static std::array<float, 28224> v73; |
| alignas(16) static std::array<float, 28224> v74; |
| alignas(16) static std::array<float, 28224> v75; |
| alignas(16) static std::array<float, 28224> v76; |
| alignas(16) static std::array<float, 576> v77; |
| alignas(16) static std::array<float, 144> v78; |
| alignas(16) static std::array<float, 576> v79; |
| alignas(16) static std::array<float, 28224> v80; |
| alignas(16) static std::array<float, 4704> v81; |
| alignas(16) static std::array<float, 4704> v82; |
| alignas(16) static std::array<float, 28224> v83; |
| alignas(16) static std::array<float, 28224> v84; |
| alignas(16) static std::array<float, 28224> v85; |
| alignas(16) static std::array<float, 28224> v86; |
| alignas(16) static std::array<float, 576> v87; |
| alignas(16) static std::array<float, 144> v88; |
| alignas(16) static std::array<float, 576> v89; |
| alignas(16) static std::array<float, 28224> v90; |
| alignas(16) static std::array<float, 4704> v91; |
| alignas(16) static std::array<float, 4704> v92; |
| alignas(16) static std::array<float, 28224> v93; |
| alignas(16) static std::array<float, 28224> v94; |
| alignas(16) static std::array<float, 576> v95; |
| alignas(16) static std::array<float, 1024> v96; |
| alignas(16) static std::array<float, 1024> v97; |
| alignas(16) static std::array<float, 1024> v98; |
| alignas(16) static std::array<float, 1001> v99; |
| alignas(16) static std::array<float, 432> w100; |
| alignas(16) static std::array<float, 16> w101; |
| alignas(16) static std::array<float, 144> w102; |
| alignas(16) static std::array<float, 16> w103; |
| alignas(16) static std::array<float, 128> w104; |
| alignas(16) static std::array<float, 8> w105; |
| alignas(16) static std::array<float, 128> w106; |
| alignas(16) static std::array<float, 16> w107; |
| alignas(16) static std::array<float, 256> w108; |
| alignas(16) static std::array<float, 16> w109; |
| alignas(16) static std::array<float, 1152> w110; |
| alignas(16) static std::array<float, 72> w111; |
| alignas(16) static std::array<float, 648> w112; |
| alignas(16) static std::array<float, 72> w113; |
| alignas(16) static std::array<float, 1728> w114; |
| alignas(16) static std::array<float, 24> w115; |
| alignas(16) static std::array<float, 2112> w116; |
| alignas(16) static std::array<float, 88> w117; |
| alignas(16) static std::array<float, 792> w118; |
| alignas(16) static std::array<float, 88> w119; |
| alignas(16) static std::array<float, 2112> w120; |
| alignas(16) static std::array<float, 24> w121; |
| alignas(16) static std::array<float, 2304> w122; |
| alignas(16) static std::array<float, 96> w123; |
| alignas(16) static std::array<float, 2400> w124; |
| alignas(16) static std::array<float, 96> w125; |
| alignas(16) static std::array<float, 2304> w126; |
| alignas(16) static std::array<float, 24> w127; |
| alignas(16) static std::array<float, 2304> w128; |
| alignas(16) static std::array<float, 96> w129; |
| alignas(16) static std::array<float, 3840> w130; |
| alignas(16) static std::array<float, 40> w131; |
| alignas(16) static std::array<float, 9600> w132; |
| alignas(16) static std::array<float, 240> w133; |
| alignas(16) static std::array<float, 6000> w134; |
| alignas(16) static std::array<float, 240> w135; |
| alignas(16) static std::array<float, 15360> w136; |
| alignas(16) static std::array<float, 64> w137; |
| alignas(16) static std::array<float, 15360> w138; |
| alignas(16) static std::array<float, 240> w139; |
| alignas(16) static std::array<float, 9600> w140; |
| alignas(16) static std::array<float, 40> w141; |
| alignas(16) static std::array<float, 9600> w142; |
| alignas(16) static std::array<float, 240> w143; |
| alignas(16) static std::array<float, 6000> w144; |
| alignas(16) static std::array<float, 240> w145; |
| alignas(16) static std::array<float, 15360> w146; |
| alignas(16) static std::array<float, 64> w147; |
| alignas(16) static std::array<float, 15360> w148; |
| alignas(16) static std::array<float, 240> w149; |
| alignas(16) static std::array<float, 9600> w150; |
| alignas(16) static std::array<float, 40> w151; |
| alignas(16) static std::array<float, 4800> w152; |
| alignas(16) static std::array<float, 120> w153; |
| alignas(16) static std::array<float, 3000> w154; |
| alignas(16) static std::array<float, 120> w155; |
| alignas(16) static std::array<float, 3840> w156; |
| alignas(16) static std::array<float, 32> w157; |
| alignas(16) static std::array<float, 3840> w158; |
| alignas(16) static std::array<float, 120> w159; |
| alignas(16) static std::array<float, 5760> w160; |
| alignas(16) static std::array<float, 48> w161; |
| alignas(16) static std::array<float, 6912> w162; |
| alignas(16) static std::array<float, 144> w163; |
| alignas(16) static std::array<float, 3600> w164; |
| alignas(16) static std::array<float, 144> w165; |
| alignas(16) static std::array<float, 5760> w166; |
| alignas(16) static std::array<float, 40> w167; |
| alignas(16) static std::array<float, 5760> w168; |
| alignas(16) static std::array<float, 144> w169; |
| alignas(16) static std::array<float, 6912> w170; |
| alignas(16) static std::array<float, 48> w171; |
| alignas(16) static std::array<float, 13824> w172; |
| alignas(16) static std::array<float, 288> w173; |
| alignas(16) static std::array<float, 7200> w174; |
| alignas(16) static std::array<float, 288> w175; |
| alignas(16) static std::array<float, 20736> w176; |
| alignas(16) static std::array<float, 72> w177; |
| alignas(16) static std::array<float, 20736> w178; |
| alignas(16) static std::array<float, 288> w179; |
| alignas(16) static std::array<float, 27648> w180; |
| alignas(16) static std::array<float, 96> w181; |
| alignas(16) static std::array<float, 55296> w182; |
| alignas(16) static std::array<float, 576> w183; |
| alignas(16) static std::array<float, 14400> w184; |
| alignas(16) static std::array<float, 576> w185; |
| alignas(16) static std::array<float, 82944> w186; |
| alignas(16) static std::array<float, 144> w187; |
| alignas(16) static std::array<float, 82944> w188; |
| alignas(16) static std::array<float, 576> w189; |
| alignas(16) static std::array<float, 55296> w190; |
| alignas(16) static std::array<float, 96> w191; |
| alignas(16) static std::array<float, 55296> w192; |
| alignas(16) static std::array<float, 576> w193; |
| alignas(16) static std::array<float, 14400> w194; |
| alignas(16) static std::array<float, 576> w195; |
| alignas(16) static std::array<float, 82944> w196; |
| alignas(16) static std::array<float, 144> w197; |
| alignas(16) static std::array<float, 82944> w198; |
| alignas(16) static std::array<float, 576> w199; |
| alignas(16) static std::array<float, 55296> w200; |
| alignas(16) static std::array<float, 96> w201; |
| alignas(16) static std::array<float, 55296> w202; |
| alignas(16) static std::array<float, 576> w203; |
| alignas(16) static std::array<float, 589824> w204; |
| alignas(16) static std::array<float, 1024> w205; |
| alignas(16) static std::array<float, 1025024> w206; |
| alignas(16) static std::array<float, 1001> w207; |
| |
| 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(w100.begin(), w100.end(), std::ref(f32rng)); |
| std::generate(w101.begin(), w101.end(), std::ref(f32rng)); |
| std::generate(w102.begin(), w102.end(), std::ref(f32rng)); |
| std::generate(w103.begin(), w103.end(), std::ref(f32rng)); |
| std::fill(w104.begin(), w104.end(), 0.0f); |
| std::generate(w104.begin(), w104.end() - size_t(sparsity * w104.size()), std::ref(f32rng)); |
| std::shuffle(w104.begin(), w104.end(), rng); |
| std::generate(w105.begin(), w105.end(), std::ref(f32rng)); |
| std::fill(w106.begin(), w106.end(), 0.0f); |
| std::generate(w106.begin(), w106.end() - size_t(sparsity * w106.size()), std::ref(f32rng)); |
| std::shuffle(w106.begin(), w106.end(), rng); |
| std::generate(w107.begin(), w107.end(), std::ref(f32rng)); |
| std::fill(w108.begin(), w108.end(), 0.0f); |
| std::generate(w108.begin(), w108.end() - size_t(sparsity * w108.size()), std::ref(f32rng)); |
| std::shuffle(w108.begin(), w108.end(), rng); |
| std::generate(w109.begin(), w109.end(), std::ref(f32rng)); |
| std::fill(w110.begin(), w110.end(), 0.0f); |
| std::generate(w110.begin(), w110.end() - size_t(sparsity * w110.size()), std::ref(f32rng)); |
| std::shuffle(w110.begin(), w110.end(), rng); |
| std::generate(w111.begin(), w111.end(), std::ref(f32rng)); |
| std::generate(w112.begin(), w112.end(), std::ref(f32rng)); |
| std::generate(w113.begin(), w113.end(), std::ref(f32rng)); |
| std::fill(w114.begin(), w114.end(), 0.0f); |
| std::generate(w114.begin(), w114.end() - size_t(sparsity * w114.size()), std::ref(f32rng)); |
| std::shuffle(w114.begin(), w114.end(), rng); |
| std::generate(w115.begin(), w115.end(), std::ref(f32rng)); |
| std::fill(w116.begin(), w116.end(), 0.0f); |
| std::generate(w116.begin(), w116.end() - size_t(sparsity * w116.size()), std::ref(f32rng)); |
| std::shuffle(w116.begin(), w116.end(), rng); |
| std::generate(w117.begin(), w117.end(), std::ref(f32rng)); |
| std::generate(w118.begin(), w118.end(), std::ref(f32rng)); |
| 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::fill(w122.begin(), w122.end(), 0.0f); |
| std::generate(w122.begin(), w122.end() - size_t(sparsity * w122.size()), std::ref(f32rng)); |
| std::shuffle(w122.begin(), w122.end(), rng); |
| std::generate(w123.begin(), w123.end(), std::ref(f32rng)); |
| std::generate(w124.begin(), w124.end(), std::ref(f32rng)); |
| 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::fill(w128.begin(), w128.end(), 0.0f); |
| std::generate(w128.begin(), w128.end() - size_t(sparsity * w128.size()), std::ref(f32rng)); |
| std::shuffle(w128.begin(), w128.end(), rng); |
| 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::fill(w170.begin(), w170.end(), 0.0f); |
| std::generate(w170.begin(), w170.end() - size_t(sparsity * w170.size()), std::ref(f32rng)); |
| std::shuffle(w170.begin(), w170.end(), rng); |
| 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::generate(w174.begin(), w174.end(), std::ref(f32rng)); |
| std::generate(w175.begin(), w175.end(), std::ref(f32rng)); |
| std::fill(w176.begin(), w176.end(), 0.0f); |
| std::generate(w176.begin(), w176.end() - size_t(sparsity * w176.size()), std::ref(f32rng)); |
| std::shuffle(w176.begin(), w176.end(), rng); |
| 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::fill(w182.begin(), w182.end(), 0.0f); |
| std::generate(w182.begin(), w182.end() - size_t(sparsity * w182.size()), std::ref(f32rng)); |
| std::shuffle(w182.begin(), w182.end(), rng); |
| std::generate(w183.begin(), w183.end(), std::ref(f32rng)); |
| std::generate(w184.begin(), w184.end(), std::ref(f32rng)); |
| 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::fill(w188.begin(), w188.end(), 0.0f); |
| std::generate(w188.begin(), w188.end() - size_t(sparsity * w188.size()), std::ref(f32rng)); |
| std::shuffle(w188.begin(), w188.end(), rng); |
| 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::generate(w194.begin(), w194.end(), std::ref(f32rng)); |
| 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::fill(w198.begin(), w198.end(), 0.0f); |
| std::generate(w198.begin(), w198.end() - size_t(sparsity * w198.size()), std::ref(f32rng)); |
| std::shuffle(w198.begin(), w198.end(), rng); |
| 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::generate(w206.begin(), w206.end(), std::ref(f32rng)); |
| std::generate(w207.begin(), w207.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 */, |
| w100.data(), w101.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| XNN_FLAG_INPUT_NHWC /* flags */, |
| &op0); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #0" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op0, xnn_delete_operator); |
| |
| xnn_operator_t op1 = nullptr; |
| status = xnn_create_hardswish_nc_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 */, |
| 2 /* subsampling height */, 2 /* subsampling width */, |
| 1 /* dilation_height */, 1 /* dilation_width */, |
| 16 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 16 /* input pixel stride */, |
| 16 /* output pixel stride */, |
| w102.data(), w103.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op2); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #2" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op2, xnn_delete_operator); |
| |
| xnn_operator_t op3 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 16 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op3); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #3" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op3, xnn_delete_operator); |
| |
| xnn_operator_t op4 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 8 /* output_channels_per_group */, |
| 16 /* input pixel stride */, |
| 8 /* output pixel stride */, |
| w104.data(), w105.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op4); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #4" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op4, xnn_delete_operator); |
| |
| xnn_operator_t op5 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 8 /* input channels per group */, |
| 16 /* output_channels_per_group */, |
| 8 /* input pixel stride */, |
| 16 /* output pixel stride */, |
| w106.data(), w107.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op5); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #5" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op5, xnn_delete_operator); |
| |
| xnn_operator_t op6 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op6); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #6" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op6, xnn_delete_operator); |
| |
| xnn_operator_t op7 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| w108.data(), w109.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op7); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #7" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op7, xnn_delete_operator); |
| |
| xnn_operator_t op8 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 72 /* output_channels_per_group */, |
| 16 /* input pixel stride */, |
| 72 /* output pixel stride */, |
| w110.data(), w111.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op8); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #8" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op8, xnn_delete_operator); |
| |
| xnn_operator_t op9 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 72 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 72 /* input pixel stride */, |
| 72 /* output pixel stride */, |
| w112.data(), w113.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op9); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #9" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op9, xnn_delete_operator); |
| |
| xnn_operator_t op10 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| w114.data(), w115.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op10); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #10" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op10, xnn_delete_operator); |
| |
| xnn_operator_t op11 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 88 /* output_channels_per_group */, |
| 24 /* input pixel stride */, |
| 88 /* output pixel stride */, |
| w116.data(), w117.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op11); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #11" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op11, xnn_delete_operator); |
| |
| xnn_operator_t op12 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 88 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 88 /* input pixel stride */, |
| 88 /* output pixel stride */, |
| w118.data(), w119.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op12); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #12" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op12, xnn_delete_operator); |
| |
| xnn_operator_t op13 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 88 /* input channels per group */, |
| 24 /* output_channels_per_group */, |
| 88 /* input pixel stride */, |
| 24 /* output pixel stride */, |
| w120.data(), w121.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op13); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #13" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op13, xnn_delete_operator); |
| |
| xnn_operator_t op14 = nullptr; |
| status = xnn_create_add_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op14); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #14" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op14, xnn_delete_operator); |
| |
| xnn_operator_t op15 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* output_channels_per_group */, |
| 24 /* input pixel stride */, |
| 96 /* output pixel stride */, |
| w122.data(), w123.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op15); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #15" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op15, xnn_delete_operator); |
| |
| xnn_operator_t op16 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 96 /* channels */, |
| 96 /* input stride */, |
| 96 /* output stride */, |
| 0 /* flags */, |
| &op16); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #16" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op16, xnn_delete_operator); |
| |
| xnn_operator_t op17 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 96 /* input pixel stride */, |
| 96 /* output pixel stride */, |
| w124.data(), w125.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op17); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #17" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op17, xnn_delete_operator); |
| |
| xnn_operator_t op18 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 96 /* channels */, |
| 96 /* input stride */, |
| 96 /* output stride */, |
| 0 /* flags */, |
| &op18); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #18" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op18, xnn_delete_operator); |
| |
| xnn_operator_t op19 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 96 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op19); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #19" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op19, xnn_delete_operator); |
| |
| xnn_operator_t op20 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* input channels per group */, |
| 24 /* output_channels_per_group */, |
| 96 /* input pixel stride */, |
| 24 /* output pixel stride */, |
| w126.data(), w127.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op20); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #20" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op20, xnn_delete_operator); |
| |
| xnn_operator_t op21 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* output_channels_per_group */, |
| 24 /* input pixel stride */, |
| 96 /* output pixel stride */, |
| w128.data(), w129.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op21); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #21" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op21, xnn_delete_operator); |
| |
| xnn_operator_t op22 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op22); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #22" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op22, xnn_delete_operator); |
| |
| xnn_operator_t op23 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* input channels per group */, |
| 40 /* output_channels_per_group */, |
| 96 /* input pixel stride */, |
| 40 /* output pixel stride */, |
| w130.data(), w131.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op23); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #23" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op23, xnn_delete_operator); |
| |
| xnn_operator_t op24 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| w132.data(), w133.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op24); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #24" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op24, xnn_delete_operator); |
| |
| xnn_operator_t op25 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 240 /* channels */, |
| 240 /* input stride */, |
| 240 /* output stride */, |
| 0 /* flags */, |
| &op25); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #25" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op25, xnn_delete_operator); |
| |
| xnn_operator_t op26 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 240 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 240 /* input pixel stride */, |
| 240 /* output pixel stride */, |
| w134.data(), w135.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op26); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #26" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op26, xnn_delete_operator); |
| |
| xnn_operator_t op27 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 240 /* channels */, |
| 240 /* input stride */, |
| 240 /* output stride */, |
| 0 /* flags */, |
| &op27); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #27" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op27, xnn_delete_operator); |
| |
| xnn_operator_t op28 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 240 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op28); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #28" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op28, xnn_delete_operator); |
| |
| xnn_operator_t op29 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 64 /* output_channels_per_group */, |
| 240 /* input pixel stride */, |
| 64 /* output pixel stride */, |
| w136.data(), w137.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op29); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #29" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op29, xnn_delete_operator); |
| |
| xnn_operator_t op30 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 240 /* output_channels_per_group */, |
| 64 /* input pixel stride */, |
| 240 /* output pixel stride */, |
| w138.data(), w139.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op30); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #30" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op30, xnn_delete_operator); |
| |
| xnn_operator_t op31 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op31); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #31" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op31, xnn_delete_operator); |
| |
| xnn_operator_t op32 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 40 /* output_channels_per_group */, |
| 240 /* input pixel stride */, |
| 40 /* output pixel stride */, |
| w140.data(), w141.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op32); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #32" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op32, xnn_delete_operator); |
| |
| xnn_operator_t op33 = nullptr; |
| status = xnn_create_add_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op33); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #33" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op33, xnn_delete_operator); |
| |
| xnn_operator_t op34 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| w142.data(), w143.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op34); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #34" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op34, xnn_delete_operator); |
| |
| xnn_operator_t op35 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 240 /* channels */, |
| 240 /* input stride */, |
| 240 /* output stride */, |
| 0 /* flags */, |
| &op35); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #35" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op35, xnn_delete_operator); |
| |
| xnn_operator_t op36 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 240 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 240 /* input pixel stride */, |
| 240 /* output pixel stride */, |
| w144.data(), w145.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op36); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #36" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op36, xnn_delete_operator); |
| |
| xnn_operator_t op37 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 240 /* channels */, |
| 240 /* input stride */, |
| 240 /* output stride */, |
| 0 /* flags */, |
| &op37); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #37" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op37, xnn_delete_operator); |
| |
| xnn_operator_t op38 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 240 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op38); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #38" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op38, xnn_delete_operator); |
| |
| xnn_operator_t op39 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 64 /* output_channels_per_group */, |
| 240 /* input pixel stride */, |
| 64 /* output pixel stride */, |
| w146.data(), w147.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op39); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #39" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op39, xnn_delete_operator); |
| |
| xnn_operator_t op40 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 240 /* output_channels_per_group */, |
| 64 /* input pixel stride */, |
| 240 /* output pixel stride */, |
| w148.data(), w149.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op40); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #40" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op40, xnn_delete_operator); |
| |
| xnn_operator_t op41 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op41); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #41" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op41, xnn_delete_operator); |
| |
| xnn_operator_t op42 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 40 /* output_channels_per_group */, |
| 240 /* input pixel stride */, |
| 40 /* output pixel stride */, |
| w150.data(), w151.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op42); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #42" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op42, xnn_delete_operator); |
| |
| xnn_operator_t op43 = nullptr; |
| status = xnn_create_add_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op43); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #43" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op43, xnn_delete_operator); |
| |
| xnn_operator_t op44 = nullptr; |
| status = xnn_create_convolution2d_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(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op44); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #44" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op44, xnn_delete_operator); |
| |
| xnn_operator_t op45 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 120 /* channels */, |
| 120 /* input stride */, |
| 120 /* output stride */, |
| 0 /* flags */, |
| &op45); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #45" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op45, xnn_delete_operator); |
| |
| xnn_operator_t op46 = nullptr; |
| status = xnn_create_convolution2d_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(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op46); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #46" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op46, xnn_delete_operator); |
| |
| xnn_operator_t op47 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 120 /* channels */, |
| 120 /* input stride */, |
| 120 /* output stride */, |
| 0 /* flags */, |
| &op47); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #47" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op47, xnn_delete_operator); |
| |
| xnn_operator_t op48 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 120 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op48); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #48" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op48, xnn_delete_operator); |
| |
| xnn_operator_t op49 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| &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 */, |
| 32 /* input channels per group */, |
| 120 /* output_channels_per_group */, |
| 32 /* input pixel stride */, |
| 120 /* output pixel stride */, |
| w158.data(), w159.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op50); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #50" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op50, xnn_delete_operator); |
| |
| xnn_operator_t op51 = nullptr; |
| status = xnn_create_multiply_nd_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 */, |
| 120 /* input channels per group */, |
| 48 /* output_channels_per_group */, |
| 120 /* input pixel stride */, |
| 48 /* output pixel stride */, |
| w160.data(), w161.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op52); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #52" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op52, xnn_delete_operator); |
| |
| xnn_operator_t op53 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 48 /* input channels per group */, |
| 144 /* output_channels_per_group */, |
| 48 /* input pixel stride */, |
| 144 /* output pixel stride */, |
| w162.data(), w163.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op53); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #53" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op53, xnn_delete_operator); |
| |
| xnn_operator_t op54 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 144 /* channels */, |
| 144 /* input stride */, |
| 144 /* output stride */, |
| 0 /* flags */, |
| &op54); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #54" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op54, xnn_delete_operator); |
| |
| xnn_operator_t op55 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 144 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 144 /* input pixel stride */, |
| 144 /* output pixel stride */, |
| w164.data(), w165.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op55); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #55" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op55, xnn_delete_operator); |
| |
| xnn_operator_t op56 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 144 /* channels */, |
| 144 /* input stride */, |
| 144 /* output stride */, |
| 0 /* flags */, |
| &op56); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #56" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op56, xnn_delete_operator); |
| |
| xnn_operator_t op57 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 144 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op57); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #57" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op57, xnn_delete_operator); |
| |
| xnn_operator_t op58 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 144 /* input channels per group */, |
| 40 /* output_channels_per_group */, |
| 144 /* input pixel stride */, |
| 40 /* output pixel stride */, |
| w166.data(), w167.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op58); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #58" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op58, xnn_delete_operator); |
| |
| xnn_operator_t op59 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 144 /* output_channels_per_group */, |
| 40 /* input pixel stride */, |
| 144 /* output pixel stride */, |
| w168.data(), w169.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op59); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #59" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op59, xnn_delete_operator); |
| |
| xnn_operator_t op60 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op60); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #60" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op60, xnn_delete_operator); |
| |
| xnn_operator_t op61 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 144 /* input channels per group */, |
| 48 /* output_channels_per_group */, |
| 144 /* input pixel stride */, |
| 48 /* output pixel stride */, |
| w170.data(), w171.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op61); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #61" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op61, xnn_delete_operator); |
| |
| xnn_operator_t op62 = nullptr; |
| status = xnn_create_add_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op62); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #62" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op62, xnn_delete_operator); |
| |
| xnn_operator_t op63 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 48 /* input channels per group */, |
| 288 /* output_channels_per_group */, |
| 48 /* input pixel stride */, |
| 288 /* output pixel stride */, |
| w172.data(), w173.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op63); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #63" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op63, xnn_delete_operator); |
| |
| xnn_operator_t op64 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 288 /* channels */, |
| 288 /* input stride */, |
| 288 /* output stride */, |
| 0 /* flags */, |
| &op64); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #64" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op64, xnn_delete_operator); |
| |
| xnn_operator_t op65 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 288 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 288 /* input pixel stride */, |
| 288 /* output pixel stride */, |
| w174.data(), w175.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op65); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #65" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op65, xnn_delete_operator); |
| |
| xnn_operator_t op66 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 288 /* channels */, |
| 288 /* input stride */, |
| 288 /* output stride */, |
| 0 /* flags */, |
| &op66); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #66" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op66, xnn_delete_operator); |
| |
| xnn_operator_t op67 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 288 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op67); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #67" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op67, xnn_delete_operator); |
| |
| xnn_operator_t op68 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 288 /* input channels per group */, |
| 72 /* output_channels_per_group */, |
| 288 /* input pixel stride */, |
| 72 /* output pixel stride */, |
| w176.data(), w177.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op68); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #68" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op68, xnn_delete_operator); |
| |
| xnn_operator_t op69 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 288 /* output_channels_per_group */, |
| 72 /* input pixel stride */, |
| 288 /* output pixel stride */, |
| w178.data(), w179.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op69); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #69" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op69, xnn_delete_operator); |
| |
| xnn_operator_t op70 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op70); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #70" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op70, xnn_delete_operator); |
| |
| xnn_operator_t op71 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 288 /* input channels per group */, |
| 96 /* output_channels_per_group */, |
| 288 /* input pixel stride */, |
| 96 /* output pixel stride */, |
| w180.data(), w181.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op71); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #71" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op71, xnn_delete_operator); |
| |
| xnn_operator_t op72 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* input channels per group */, |
| 576 /* output_channels_per_group */, |
| 96 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w182.data(), w183.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op72); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #72" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op72, xnn_delete_operator); |
| |
| xnn_operator_t op73 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 576 /* channels */, |
| 576 /* input stride */, |
| 576 /* output stride */, |
| 0 /* flags */, |
| &op73); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #73" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op73, xnn_delete_operator); |
| |
| xnn_operator_t op74 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 576 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w184.data(), w185.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op74); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #74" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op74, xnn_delete_operator); |
| |
| xnn_operator_t op75 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 576 /* channels */, |
| 576 /* input stride */, |
| 576 /* output stride */, |
| 0 /* flags */, |
| &op75); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #75" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op75, xnn_delete_operator); |
| |
| xnn_operator_t op76 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 576 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op76); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #76" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op76, xnn_delete_operator); |
| |
| xnn_operator_t op77 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 576 /* input channels per group */, |
| 144 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 144 /* output pixel stride */, |
| w186.data(), w187.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op77); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #77" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op77, xnn_delete_operator); |
| |
| xnn_operator_t op78 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 144 /* input channels per group */, |
| 576 /* output_channels_per_group */, |
| 144 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w188.data(), w189.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op78); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #78" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op78, xnn_delete_operator); |
| |
| xnn_operator_t op79 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op79); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #79" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op79, xnn_delete_operator); |
| |
| xnn_operator_t op80 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 576 /* input channels per group */, |
| 96 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 96 /* output pixel stride */, |
| w190.data(), w191.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op80); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #80" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op80, xnn_delete_operator); |
| |
| xnn_operator_t op81 = nullptr; |
| status = xnn_create_add_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op81); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #81" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op81, xnn_delete_operator); |
| |
| xnn_operator_t op82 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* input channels per group */, |
| 576 /* output_channels_per_group */, |
| 96 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w192.data(), w193.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op82); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #82" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op82, xnn_delete_operator); |
| |
| xnn_operator_t op83 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 576 /* channels */, |
| 576 /* input stride */, |
| 576 /* output stride */, |
| 0 /* flags */, |
| &op83); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #83" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op83, xnn_delete_operator); |
| |
| xnn_operator_t op84 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 576 /* groups */, |
| 1 /* input channels per group */, |
| 1 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w194.data(), w195.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op84); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #84" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op84, xnn_delete_operator); |
| |
| xnn_operator_t op85 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 576 /* channels */, |
| 576 /* input stride */, |
| 576 /* output stride */, |
| 0 /* flags */, |
| &op85); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #85" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op85, xnn_delete_operator); |
| |
| xnn_operator_t op86 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 576 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op86); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #86" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op86, xnn_delete_operator); |
| |
| xnn_operator_t op87 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 576 /* input channels per group */, |
| 144 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 144 /* output pixel stride */, |
| w196.data(), w197.data(), |
| 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op87); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #87" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op87, xnn_delete_operator); |
| |
| xnn_operator_t op88 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 144 /* input channels per group */, |
| 576 /* output_channels_per_group */, |
| 144 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w198.data(), w199.data(), |
| 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, |
| 0 /* flags */, |
| &op88); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #88" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op88, xnn_delete_operator); |
| |
| xnn_operator_t op89 = nullptr; |
| status = xnn_create_multiply_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op89); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #89" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op89, xnn_delete_operator); |
| |
| xnn_operator_t op90 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 576 /* input channels per group */, |
| 96 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 96 /* output pixel stride */, |
| w200.data(), w201.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op90); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #90" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op90, xnn_delete_operator); |
| |
| xnn_operator_t op91 = nullptr; |
| status = xnn_create_add_nd_f32( |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op91); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #91" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op91, xnn_delete_operator); |
| |
| xnn_operator_t op92 = nullptr; |
| status = xnn_create_convolution2d_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 */, |
| 96 /* input channels per group */, |
| 576 /* output_channels_per_group */, |
| 96 /* input pixel stride */, |
| 576 /* output pixel stride */, |
| w202.data(), w203.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op92); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #92" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op92, xnn_delete_operator); |
| |
| xnn_operator_t op93 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 576 /* channels */, |
| 576 /* input stride */, |
| 576 /* output stride */, |
| 0 /* flags */, |
| &op93); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #93" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op93, xnn_delete_operator); |
| |
| xnn_operator_t op94 = nullptr; |
| status = xnn_create_global_average_pooling_ncw_f32( |
| 576 /* channels */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op94); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #94" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op94, xnn_delete_operator); |
| |
| xnn_operator_t op95 = nullptr; |
| status = xnn_create_convolution2d_nhwc_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 */, |
| 576 /* input channels per group */, |
| 1024 /* output_channels_per_group */, |
| 576 /* input pixel stride */, |
| 1024 /* output pixel stride */, |
| w204.data(), w205.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op95); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #95" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op95, xnn_delete_operator); |
| |
| xnn_operator_t op96 = nullptr; |
| status = xnn_create_hardswish_nc_f32( |
| 1024 /* channels */, |
| 1024 /* input stride */, |
| 1024 /* output stride */, |
| 0 /* flags */, |
| &op96); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #96" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op96, xnn_delete_operator); |
| |
| xnn_operator_t op97 = nullptr; |
| status = xnn_create_global_average_pooling_nwc_f32( |
| 1024 /* channels */, 1024 /* input stride */, 1024 /* output stride */, |
| -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), |
| 0 /* flags */, |
| &op97); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #97" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op97, xnn_delete_operator); |
| |
| xnn_operator_t op98 = nullptr; |
| status = xnn_create_convolution2d_nhwc_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 */, |
| 1024 /* input channels per group */, |
| 1001 /* output_channels_per_group */, |
| 1024 /* input pixel stride */, |
| 1001 /* output pixel stride */, |
| w206.data(), w207.data(), |
| -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, |
| 0 /* flags */, |
| &op98); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to create operation #98" << std::endl; |
| return ExecutionPlan(); |
| } |
| operators.emplace_back(op98, xnn_delete_operator); |
| |
| |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op0, |
| 1 /* batch size */, 224 /* input height */, 224 /* input width */, |
| v0.data() /* input */, v1.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #0" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op1, |
| 12544 /* batch size */, |
| v1.data() /* input */, v2.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #1" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op2, |
| 1 /* batch size */, 112 /* input height */, 112 /* input width */, |
| v2.data() /* input */, v3.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #2" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op3, |
| 1 /* batch size */, 3136 /* width */, |
| v3.data() /* input */, v4.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #3" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op4, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v4.data() /* input */, v5.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #4" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op5, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v5.data() /* input */, v6.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #5" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 16, 56, 56 }; |
| const size_t b_shape[] = { 1, 16, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op6, |
| 4, a_shape, 4, b_shape, |
| v3.data() /* a */, v6.data() /* b */, v7.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #6" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op7, |
| 1 /* batch size */, 56 /* input height */, 56 /* input width */, |
| v7.data() /* input */, v8.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #7" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op8, |
| 1 /* batch size */, 56 /* input height */, 56 /* input width */, |
| v8.data() /* input */, v9.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #8" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op9, |
| 1 /* batch size */, 56 /* input height */, 56 /* input width */, |
| v9.data() /* input */, v10.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #9" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op10, |
| 1 /* batch size */, 28 /* input height */, 28 /* input width */, |
| v10.data() /* input */, v11.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #10" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op11, |
| 1 /* batch size */, 28 /* input height */, 28 /* input width */, |
| v11.data() /* input */, v12.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #11" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op12, |
| 1 /* batch size */, 28 /* input height */, 28 /* input width */, |
| v12.data() /* input */, v13.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #12" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op13, |
| 1 /* batch size */, 28 /* input height */, 28 /* input width */, |
| v13.data() /* input */, v14.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #13" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 24, 28, 28 }; |
| const size_t b_shape[] = { 1, 24, 28, 28 }; |
| status = xnn_setup_add_nd_f32( |
| op14, |
| 4, a_shape, 4, b_shape, |
| v14.data() /* a */, v11.data() /* b */, v15.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #14" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op15, |
| 1 /* batch size */, 28 /* input height */, 28 /* input width */, |
| v15.data() /* input */, v16.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #15" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op16, |
| 784 /* batch size */, |
| v16.data() /* input */, v17.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #16" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op17, |
| 1 /* batch size */, 28 /* input height */, 28 /* input width */, |
| v17.data() /* input */, v18.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #17" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op18, |
| 196 /* batch size */, |
| v18.data() /* input */, v19.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #18" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op19, |
| 1 /* batch size */, 196 /* width */, |
| v19.data() /* input */, v20.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #19" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op20, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v20.data() /* input */, v21.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #20" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op21, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v21.data() /* input */, v22.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #21" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 96, 14, 14 }; |
| const size_t b_shape[] = { 1, 96, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op22, |
| 4, a_shape, 4, b_shape, |
| v19.data() /* a */, v22.data() /* b */, v23.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #22" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op23, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v23.data() /* input */, v24.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #23" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op24, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v24.data() /* input */, v25.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #24" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op25, |
| 196 /* batch size */, |
| v25.data() /* input */, v26.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #25" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op26, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v26.data() /* input */, v27.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #26" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op27, |
| 196 /* batch size */, |
| v27.data() /* input */, v28.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #27" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op28, |
| 1 /* batch size */, 196 /* width */, |
| v28.data() /* input */, v29.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #28" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op29, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v29.data() /* input */, v30.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #29" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op30, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v30.data() /* input */, v31.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #30" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 240, 14, 14 }; |
| const size_t b_shape[] = { 1, 240, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op31, |
| 4, a_shape, 4, b_shape, |
| v28.data() /* a */, v31.data() /* b */, v32.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #31" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op32, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v32.data() /* input */, v33.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #32" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 40, 14, 14 }; |
| const size_t b_shape[] = { 1, 40, 14, 14 }; |
| status = xnn_setup_add_nd_f32( |
| op33, |
| 4, a_shape, 4, b_shape, |
| v33.data() /* a */, v24.data() /* b */, v34.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #33" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op34, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v34.data() /* input */, v35.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #34" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op35, |
| 196 /* batch size */, |
| v35.data() /* input */, v36.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #35" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op36, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v36.data() /* input */, v37.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #36" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op37, |
| 196 /* batch size */, |
| v37.data() /* input */, v38.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #37" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op38, |
| 1 /* batch size */, 196 /* width */, |
| v38.data() /* input */, v39.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #38" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op39, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v39.data() /* input */, v40.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #39" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op40, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v40.data() /* input */, v41.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #40" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 240, 14, 14 }; |
| const size_t b_shape[] = { 1, 240, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op41, |
| 4, a_shape, 4, b_shape, |
| v38.data() /* a */, v41.data() /* b */, v42.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #41" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op42, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v42.data() /* input */, v43.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #42" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 40, 14, 14 }; |
| const size_t b_shape[] = { 1, 40, 14, 14 }; |
| status = xnn_setup_add_nd_f32( |
| op43, |
| 4, a_shape, 4, b_shape, |
| v43.data() /* a */, v34.data() /* b */, v44.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #43" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op44, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v44.data() /* input */, v45.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #44" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op45, |
| 196 /* batch size */, |
| v45.data() /* input */, v46.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #45" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op46, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v46.data() /* input */, v47.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #46" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op47, |
| 196 /* batch size */, |
| v47.data() /* input */, v48.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #47" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op48, |
| 1 /* batch size */, 196 /* width */, |
| v48.data() /* input */, v49.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #48" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op49, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v49.data() /* input */, v50.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #49" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op50, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v50.data() /* input */, v51.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #50" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 120, 14, 14 }; |
| const size_t b_shape[] = { 1, 120, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op51, |
| 4, a_shape, 4, b_shape, |
| v48.data() /* a */, v51.data() /* b */, v52.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #51" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op52, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v52.data() /* input */, v53.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #52" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op53, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v53.data() /* input */, v54.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #53" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op54, |
| 196 /* batch size */, |
| v54.data() /* input */, v55.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #54" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op55, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v55.data() /* input */, v56.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #55" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op56, |
| 196 /* batch size */, |
| v56.data() /* input */, v57.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #56" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op57, |
| 1 /* batch size */, 196 /* width */, |
| v57.data() /* input */, v58.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #57" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op58, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v58.data() /* input */, v59.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #58" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op59, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v59.data() /* input */, v60.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #59" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 144, 14, 14 }; |
| const size_t b_shape[] = { 1, 144, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op60, |
| 4, a_shape, 4, b_shape, |
| v57.data() /* a */, v60.data() /* b */, v61.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #60" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op61, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v61.data() /* input */, v62.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #61" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 48, 14, 14 }; |
| const size_t b_shape[] = { 1, 48, 14, 14 }; |
| status = xnn_setup_add_nd_f32( |
| op62, |
| 4, a_shape, 4, b_shape, |
| v62.data() /* a */, v53.data() /* b */, v63.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #62" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op63, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v63.data() /* input */, v64.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #63" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op64, |
| 196 /* batch size */, |
| v64.data() /* input */, v65.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #64" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op65, |
| 1 /* batch size */, 14 /* input height */, 14 /* input width */, |
| v65.data() /* input */, v66.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #65" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op66, |
| 49 /* batch size */, |
| v66.data() /* input */, v67.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #66" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op67, |
| 1 /* batch size */, 49 /* width */, |
| v67.data() /* input */, v68.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #67" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op68, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v68.data() /* input */, v69.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #68" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op69, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v69.data() /* input */, v70.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #69" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 288, 7, 7 }; |
| const size_t b_shape[] = { 1, 288, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op70, |
| 4, a_shape, 4, b_shape, |
| v67.data() /* a */, v70.data() /* b */, v71.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #70" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op71, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v71.data() /* input */, v72.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #71" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op72, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v72.data() /* input */, v73.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #72" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op73, |
| 49 /* batch size */, |
| v73.data() /* input */, v74.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #73" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op74, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v74.data() /* input */, v75.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #74" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op75, |
| 49 /* batch size */, |
| v75.data() /* input */, v76.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #75" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op76, |
| 1 /* batch size */, 49 /* width */, |
| v76.data() /* input */, v77.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #76" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op77, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v77.data() /* input */, v78.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #77" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op78, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v78.data() /* input */, v79.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #78" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 576, 7, 7 }; |
| const size_t b_shape[] = { 1, 576, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op79, |
| 4, a_shape, 4, b_shape, |
| v76.data() /* a */, v79.data() /* b */, v80.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #79" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op80, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v80.data() /* input */, v81.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #80" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 96, 7, 7 }; |
| const size_t b_shape[] = { 1, 96, 7, 7 }; |
| status = xnn_setup_add_nd_f32( |
| op81, |
| 4, a_shape, 4, b_shape, |
| v81.data() /* a */, v72.data() /* b */, v82.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #81" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op82, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v82.data() /* input */, v83.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #82" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op83, |
| 49 /* batch size */, |
| v83.data() /* input */, v84.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #83" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op84, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v84.data() /* input */, v85.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #84" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op85, |
| 49 /* batch size */, |
| v85.data() /* input */, v86.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #85" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op86, |
| 1 /* batch size */, 49 /* width */, |
| v86.data() /* input */, v87.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #86" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op87, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v87.data() /* input */, v88.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #87" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op88, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v88.data() /* input */, v89.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #88" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 576, 7, 7 }; |
| const size_t b_shape[] = { 1, 576, 1, 1 }; |
| status = xnn_setup_multiply_nd_f32( |
| op89, |
| 4, a_shape, 4, b_shape, |
| v86.data() /* a */, v89.data() /* b */, v90.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #89" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op90, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v90.data() /* input */, v91.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #90" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| { |
| const size_t a_shape[] = { 1, 96, 7, 7 }; |
| const size_t b_shape[] = { 1, 96, 7, 7 }; |
| status = xnn_setup_add_nd_f32( |
| op91, |
| 4, a_shape, 4, b_shape, |
| v91.data() /* a */, v82.data() /* b */, v92.data() /* output */, |
| threadpool /* threadpool */); |
| } |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #91" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nchw_f32( |
| op92, |
| 1 /* batch size */, 7 /* input height */, 7 /* input width */, |
| v92.data() /* input */, v93.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #92" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op93, |
| 49 /* batch size */, |
| v93.data() /* input */, v94.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #93" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_ncw_f32( |
| op94, |
| 1 /* batch size */, 49 /* width */, |
| v94.data() /* input */, v95.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #94" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nhwc_f32( |
| op95, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v95.data() /* input */, v96.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #95" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_hardswish_nc_f32( |
| op96, |
| 1 /* batch size */, |
| v96.data() /* input */, v97.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #96" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_global_average_pooling_nwc_f32( |
| op97, |
| 1 /* batch size */, 1 /* width */, |
| v97.data() /* input */, v98.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #97" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| status = xnn_setup_convolution2d_nhwc_f32( |
| op98, |
| 1 /* batch size */, 1 /* input height */, 1 /* input width */, |
| v98.data() /* input */, v99.data() /* output */, |
| threadpool /* threadpool */); |
| if (status != xnn_status_success) { |
| std::cerr << "failed to setup operation #98" << std::endl; |
| return ExecutionPlan(); |
| } |
| |
| #pragma clang diagnostic push |
| #pragma clang diagnostic ignored "-Wpessimizing-move" |
| return operators; |
| #pragma clang diagnostic pop |
| } |
| |
| } // namespace models |
