diff options
author | Marat Dukhan <maratek@google.com> | 2021-03-09 09:35:36 -0800 |
---|---|---|
committer | XNNPACK Team <xnnpack-github-robot@google.com> | 2021-03-09 09:36:31 -0800 |
commit | 4cea2322fe9c00172f4ea9462bc3d0f6fe7a4a78 (patch) | |
tree | 53ec696d0256bc532a33ac53bcb821da9a2b16da | |
parent | 52e061d5bc43f56d67f446190e1f83b52c2b3c25 (diff) | |
download | XNNPACK-4cea2322fe9c00172f4ea9462bc3d0f6fe7a4a78.tar.gz |
Built-in end-to-end benchmark on sparse models
PiperOrigin-RevId: 361829000
-rw-r--r-- | BUILD.bazel | 52 | ||||
-rwxr-xr-x | CMakeLists.txt | 6 | ||||
-rw-r--r-- | bench/end2end.cc | 29 | ||||
-rw-r--r-- | emscripten.bzl | 2 | ||||
-rw-r--r-- | models/fp32-sparse-mobilenet-v1.cc | 1152 | ||||
-rw-r--r-- | models/fp32-sparse-mobilenet-v2.cc | 2416 | ||||
-rw-r--r-- | models/fp32-sparse-mobilenet-v3-large.cc | 3813 | ||||
-rw-r--r-- | models/fp32-sparse-mobilenet-v3-small.cc | 3316 | ||||
-rw-r--r-- | models/models.h | 5 |
9 files changed, 10789 insertions, 2 deletions
diff --git a/BUILD.bazel b/BUILD.bazel index d35b38fc6..1675b7437 100644 --- a/BUILD.bazel +++ b/BUILD.bazel @@ -5333,6 +5333,18 @@ cc_library( ) cc_library( + name = "fp32_sparse_mobilenet_v1", + srcs = ["models/fp32-sparse-mobilenet-v1.cc"], + hdrs = ["models/models.h"], + copts = xnnpack_std_cxxopts(), + linkstatic = True, + deps = [ + ":XNNPACK", + "@pthreadpool", + ], +) + +cc_library( name = "fp16_mobilenet_v1", srcs = ["models/fp16-mobilenet-v1.cc"], hdrs = ["models/models.h"], @@ -5394,6 +5406,18 @@ cc_library( ) cc_library( + name = "fp32_sparse_mobilenet_v2", + srcs = ["models/fp32-sparse-mobilenet-v2.cc"], + hdrs = ["models/models.h"], + copts = xnnpack_std_cxxopts(), + linkstatic = True, + deps = [ + ":XNNPACK", + "@pthreadpool", + ], +) + +cc_library( name = "fp16_mobilenet_v2", srcs = ["models/fp16-mobilenet-v2.cc"], hdrs = ["models/models.h"], @@ -5419,6 +5443,18 @@ cc_library( ) cc_library( + name = "fp32_sparse_mobilenet_v3_large", + srcs = ["models/fp32-sparse-mobilenet-v3-large.cc"], + hdrs = ["models/models.h"], + copts = xnnpack_std_cxxopts(), + linkstatic = True, + deps = [ + ":XNNPACK", + "@pthreadpool", + ], +) + +cc_library( name = "fp16_mobilenet_v3_large", srcs = ["models/fp16-mobilenet-v3-large.cc"], hdrs = ["models/models.h"], @@ -5444,6 +5480,18 @@ cc_library( ) cc_library( + name = "fp32_sparse_mobilenet_v3_small", + srcs = ["models/fp32-sparse-mobilenet-v3-small.cc"], + hdrs = ["models/models.h"], + copts = xnnpack_std_cxxopts(), + linkstatic = True, + deps = [ + ":XNNPACK", + "@pthreadpool", + ], +) + +cc_library( name = "fp16_mobilenet_v3_small", srcs = ["models/fp16-mobilenet-v3-small.cc"], hdrs = ["models/models.h"], @@ -5513,6 +5561,10 @@ xnnpack_benchmark( ":fp32_mobilenet_v2", ":fp32_mobilenet_v3_large", ":fp32_mobilenet_v3_small", + ":fp32_sparse_mobilenet_v1", + ":fp32_sparse_mobilenet_v2", + ":fp32_sparse_mobilenet_v3_large", + ":fp32_sparse_mobilenet_v3_small", ":qs8_mobilenet_v1", ":qs8_mobilenet_v2", ":qu8_mobilenet_v1", diff --git a/CMakeLists.txt b/CMakeLists.txt index aa38990fc..9aed708c2 100755 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -4738,7 +4738,11 @@ IF(XNNPACK_BUILD_BENCHMARKS) models/fp16-mobilenet-v3-large.cc models/fp32-mobilenet-v3-large.cc models/fp16-mobilenet-v3-small.cc - models/fp32-mobilenet-v3-small.cc) + models/fp32-mobilenet-v3-small.cc + models/fp32-sparse-mobilenet-v1.cc + models/fp32-sparse-mobilenet-v2.cc + models/fp32-sparse-mobilenet-v3-large.cc + models/fp32-sparse-mobilenet-v3-small.cc) SET_TARGET_PROPERTIES(bench-models PROPERTIES CXX_STANDARD 11 CXX_STANDARD_REQUIRED YES diff --git a/bench/end2end.cc b/bench/end2end.cc index 246e32c01..629e4c6a1 100644 --- a/bench/end2end.cc +++ b/bench/end2end.cc @@ -68,6 +68,30 @@ static void FP32MobileNetV3Small(benchmark::State& state) { End2EndBenchmark(state, models::FP32MobileNetV3Small); } +static void FP32Sparse80MobileNetV1(benchmark::State& state) { + End2EndBenchmark(state, [](pthreadpool_t threadpool) { + return models::FP32SparseMobileNetV1(0.8f, threadpool); + }); +} + +static void FP32Sparse80MobileNetV2(benchmark::State& state) { + End2EndBenchmark(state, [](pthreadpool_t threadpool) { + return models::FP32SparseMobileNetV2(0.8f, threadpool); + }); +} + +static void FP32Sparse80MobileNetV3Large(benchmark::State& state) { + End2EndBenchmark(state, [](pthreadpool_t threadpool) { + return models::FP32SparseMobileNetV3Large(0.8f, threadpool); + }); +} + +static void FP32Sparse80MobileNetV3Small(benchmark::State& state) { + End2EndBenchmark(state, [](pthreadpool_t threadpool) { + return models::FP32SparseMobileNetV3Small(0.8f, threadpool); + }); +} + static void FP16MobileNetV1(benchmark::State& state) { End2EndBenchmark(state, models::FP16MobileNetV1); } @@ -101,6 +125,11 @@ BENCHMARK(FP32MobileNetV2)->Apply(benchmark::utils::MultiThreadingParameters)->U BENCHMARK(FP32MobileNetV3Large)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); BENCHMARK(FP32MobileNetV3Small)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); +BENCHMARK(FP32Sparse80MobileNetV1)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); +BENCHMARK(FP32Sparse80MobileNetV2)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); +BENCHMARK(FP32Sparse80MobileNetV3Large)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); +BENCHMARK(FP32Sparse80MobileNetV3Small)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); + BENCHMARK(FP16MobileNetV1)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); BENCHMARK(FP16MobileNetV2)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); BENCHMARK(FP16MobileNetV3Large)->Apply(benchmark::utils::MultiThreadingParameters)->Unit(benchmark::kMicrosecond)->UseRealTime(); diff --git a/emscripten.bzl b/emscripten.bzl index faad0879c..0a0caedf5 100644 --- a/emscripten.bzl +++ b/emscripten.bzl @@ -26,7 +26,7 @@ def xnnpack_emscripten_benchmark_linkopts(): "-s ERROR_ON_UNDEFINED_SYMBOLS=1", "-s EXIT_RUNTIME=1", "-s ALLOW_MEMORY_GROWTH=1", - "-s TOTAL_MEMORY=268435456", # 256M + "-s TOTAL_MEMORY=436207616", # 416M "--pre-js $(location :preamble.js.lds)", ] diff --git a/models/fp32-sparse-mobilenet-v1.cc b/models/fp32-sparse-mobilenet-v1.cc new file mode 100644 index 000000000..aeb285f67 --- /dev/null +++ b/models/fp32-sparse-mobilenet-v1.cc @@ -0,0 +1,1152 @@ +// 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 FP32SparseMobileNetV1(float sparsity, pthreadpool_t threadpool) { + alignas(16) static std::array<float, 150528> v0; + alignas(16) static std::array<float, 401408> v1; + alignas(16) static std::array<float, 401408> v2; + alignas(16) static std::array<float, 802816> v3; + alignas(16) static std::array<float, 200704> v4; + alignas(16) static std::array<float, 401408> v5; + alignas(16) static std::array<float, 401408> v6; + alignas(16) static std::array<float, 401408> v7; + alignas(16) static std::array<float, 100352> v8; + alignas(16) static std::array<float, 200704> v9; + alignas(16) static std::array<float, 200704> v10; + alignas(16) static std::array<float, 200704> v11; + alignas(16) static std::array<float, 50176> v12; + alignas(16) static std::array<float, 100352> v13; + alignas(16) static std::array<float, 100352> v14; + alignas(16) static std::array<float, 100352> v15; + alignas(16) static std::array<float, 100352> v16; + alignas(16) static std::array<float, 100352> v17; + alignas(16) static std::array<float, 100352> v18; + alignas(16) static std::array<float, 100352> v19; + alignas(16) static std::array<float, 100352> v20; + alignas(16) static std::array<float, 100352> v21; + alignas(16) static std::array<float, 100352> v22; + alignas(16) static std::array<float, 100352> v23; + alignas(16) static std::array<float, 25088> v24; + alignas(16) static std::array<float, 50176> v25; + alignas(16) static std::array<float, 50176> v26; + alignas(16) static std::array<float, 50176> v27; + alignas(16) static std::array<float, 1024> v28; + alignas(16) static std::array<float, 1001> v29; + alignas(16) static std::array<float, 864> w30; + alignas(16) static std::array<float, 32> w31; + alignas(16) static std::array<float, 288> w32; + alignas(16) static std::array<float, 32> w33; + alignas(16) static std::array<float, 2048> w34; + alignas(16) static std::array<float, 64> w35; + alignas(16) static std::array<float, 576> w36; + alignas(16) static std::array<float, 64> w37; + alignas(16) static std::array<float, 8192> w38; + alignas(16) static std::array<float, 128> w39; + alignas(16) static std::array<float, 1152> w40; + alignas(16) static std::array<float, 128> w41; + alignas(16) static std::array<float, 16384> w42; + alignas(16) static std::array<float, 128> w43; + alignas(16) static std::array<float, 1152> w44; + alignas(16) static std::array<float, 128> w45; + alignas(16) static std::array<float, 32768> w46; + alignas(16) static std::array<float, 256> w47; + alignas(16) static std::array<float, 2304> w48; + alignas(16) static std::array<float, 256> w49; + alignas(16) static std::array<float, 65536> w50; + alignas(16) static std::array<float, 256> w51; + alignas(16) static std::array<float, 2304> w52; + alignas(16) static std::array<float, 256> w53; + alignas(16) static std::array<float, 131072> w54; + alignas(16) static std::array<float, 512> w55; + alignas(16) static std::array<float, 4608> w56; + alignas(16) static std::array<float, 512> w57; + alignas(16) static std::array<float, 262144> w58; + alignas(16) static std::array<float, 512> w59; + alignas(16) static std::array<float, 4608> w60; + alignas(16) static std::array<float, 512> w61; + alignas(16) static std::array<float, 262144> w62; + alignas(16) static std::array<float, 512> w63; + alignas(16) static std::array<float, 4608> w64; + alignas(16) static std::array<float, 512> w65; + alignas(16) static std::array<float, 262144> w66; + alignas(16) static std::array<float, 512> w67; + alignas(16) static std::array<float, 4608> w68; + alignas(16) static std::array<float, 512> w69; + alignas(16) static std::array<float, 262144> w70; + alignas(16) static std::array<float, 512> w71; + alignas(16) static std::array<float, 4608> w72; + alignas(16) static std::array<float, 512> w73; + alignas(16) static std::array<float, 262144> w74; + alignas(16) static std::array<float, 512> w75; + alignas(16) static std::array<float, 4608> w76; + alignas(16) static std::array<float, 512> w77; + alignas(16) static std::array<float, 524288> w78; + alignas(16) static std::array<float, 1024> w79; + alignas(16) static std::array<float, 9216> w80; + alignas(16) static std::array<float, 1024> w81; + alignas(16) static std::array<float, 1048576> w82; + alignas(16) static std::array<float, 1024> w83; + alignas(16) static std::array<float, 1025024> w84; + alignas(16) static std::array<float, 1001> w85; + + 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(w30.begin(), w30.end(), std::ref(f32rng)); + std::generate(w31.begin(), w31.end(), std::ref(f32rng)); + std::generate(w32.begin(), w32.end(), std::ref(f32rng)); + std::generate(w33.begin(), w33.end(), std::ref(f32rng)); + std::fill(w34.begin(), w34.end(), 0.0f); + std::generate(w34.begin(), w34.end() - size_t(sparsity * w34.size()), std::ref(f32rng)); + std::shuffle(w34.begin(), w34.end(), rng); + std::generate(w35.begin(), w35.end(), std::ref(f32rng)); + std::generate(w36.begin(), w36.end(), std::ref(f32rng)); + std::generate(w37.begin(), w37.end(), std::ref(f32rng)); + std::fill(w38.begin(), w38.end(), 0.0f); + std::generate(w38.begin(), w38.end() - size_t(sparsity * w38.size()), std::ref(f32rng)); + std::shuffle(w38.begin(), w38.end(), rng); + std::generate(w39.begin(), w39.end(), std::ref(f32rng)); + std::generate(w40.begin(), w40.end(), std::ref(f32rng)); + std::generate(w41.begin(), w41.end(), std::ref(f32rng)); + std::fill(w42.begin(), w42.end(), 0.0f); + std::generate(w42.begin(), w42.end() - size_t(sparsity * w42.size()), std::ref(f32rng)); + std::shuffle(w42.begin(), w42.end(), rng); + std::generate(w43.begin(), w43.end(), std::ref(f32rng)); + std::generate(w44.begin(), w44.end(), std::ref(f32rng)); + std::generate(w45.begin(), w45.end(), std::ref(f32rng)); + std::fill(w46.begin(), w46.end(), 0.0f); + std::generate(w46.begin(), w46.end() - size_t(sparsity * w46.size()), std::ref(f32rng)); + std::shuffle(w46.begin(), w46.end(), rng); + std::generate(w47.begin(), w47.end(), std::ref(f32rng)); + std::generate(w48.begin(), w48.end(), std::ref(f32rng)); + std::generate(w49.begin(), w49.end(), std::ref(f32rng)); + std::fill(w50.begin(), w50.end(), 0.0f); + std::generate(w50.begin(), w50.end() - size_t(sparsity * w50.size()), std::ref(f32rng)); + std::shuffle(w50.begin(), w50.end(), rng); + std::generate(w51.begin(), w51.end(), std::ref(f32rng)); + std::generate(w52.begin(), w52.end(), std::ref(f32rng)); + std::generate(w53.begin(), w53.end(), std::ref(f32rng)); + std::fill(w54.begin(), w54.end(), 0.0f); + std::generate(w54.begin(), w54.end() - size_t(sparsity * w54.size()), std::ref(f32rng)); + std::shuffle(w54.begin(), w54.end(), rng); + std::generate(w55.begin(), w55.end(), std::ref(f32rng)); + std::generate(w56.begin(), w56.end(), std::ref(f32rng)); + std::generate(w57.begin(), w57.end(), std::ref(f32rng)); + std::fill(w58.begin(), w58.end(), 0.0f); + std::generate(w58.begin(), w58.end() - size_t(sparsity * w58.size()), std::ref(f32rng)); + std::shuffle(w58.begin(), w58.end(), rng); + std::generate(w59.begin(), w59.end(), std::ref(f32rng)); + std::generate(w60.begin(), w60.end(), std::ref(f32rng)); + std::generate(w61.begin(), w61.end(), std::ref(f32rng)); + std::fill(w62.begin(), w62.end(), 0.0f); + std::generate(w62.begin(), w62.end() - size_t(sparsity * w62.size()), std::ref(f32rng)); + std::shuffle(w62.begin(), w62.end(), rng); + std::generate(w63.begin(), w63.end(), std::ref(f32rng)); + std::generate(w64.begin(), w64.end(), std::ref(f32rng)); + std::generate(w65.begin(), w65.end(), std::ref(f32rng)); + std::fill(w66.begin(), w66.end(), 0.0f); + std::generate(w66.begin(), w66.end() - size_t(sparsity * w66.size()), std::ref(f32rng)); + std::shuffle(w66.begin(), w66.end(), rng); + std::generate(w67.begin(), w67.end(), std::ref(f32rng)); + std::generate(w68.begin(), w68.end(), std::ref(f32rng)); + std::generate(w69.begin(), w69.end(), std::ref(f32rng)); + std::fill(w70.begin(), w70.end(), 0.0f); + std::generate(w70.begin(), w70.end() - size_t(sparsity * w70.size()), std::ref(f32rng)); + std::shuffle(w70.begin(), w70.end(), rng); + std::generate(w71.begin(), w71.end(), std::ref(f32rng)); + std::generate(w72.begin(), w72.end(), std::ref(f32rng)); + std::generate(w73.begin(), w73.end(), std::ref(f32rng)); + std::fill(w74.begin(), w74.end(), 0.0f); + std::generate(w74.begin(), w74.end() - size_t(sparsity * w74.size()), std::ref(f32rng)); + std::shuffle(w74.begin(), w74.end(), rng); + std::generate(w75.begin(), w75.end(), std::ref(f32rng)); + std::generate(w76.begin(), w76.end(), std::ref(f32rng)); + std::generate(w77.begin(), w77.end(), std::ref(f32rng)); + std::fill(w78.begin(), w78.end(), 0.0f); + std::generate(w78.begin(), w78.end() - size_t(sparsity * w78.size()), std::ref(f32rng)); + std::shuffle(w78.begin(), w78.end(), rng); + std::generate(w79.begin(), w79.end(), std::ref(f32rng)); + std::generate(w80.begin(), w80.end(), std::ref(f32rng)); + std::generate(w81.begin(), w81.end(), std::ref(f32rng)); + std::fill(w82.begin(), w82.end(), 0.0f); + std::generate(w82.begin(), w82.end() - size_t(sparsity * w82.size()), std::ref(f32rng)); + std::shuffle(w82.begin(), w82.end(), rng); + std::generate(w83.begin(), w83.end(), std::ref(f32rng)); + std::generate(w84.begin(), w84.end(), std::ref(f32rng)); + std::generate(w85.begin(), w85.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 */, + 32 /* output_channels_per_group */, + 3 /* input pixel stride */, + 32 /* output pixel stride */, + w30.data(), w31.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 32 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 32 /* input pixel stride */, + 32 /* output pixel stride */, + w32.data(), w33.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 64 /* output_channels_per_group */, + 32 /* input pixel stride */, + 64 /* output pixel stride */, + w34.data(), w35.data(), + 0.0f /* output min */, 6.0f /* 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_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 2 /* subsampling height */, 2 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 64 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 64 /* input pixel stride */, + 64 /* output pixel stride */, + w36.data(), w37.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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 */, + 64 /* input channels per group */, + 128 /* output_channels_per_group */, + 64 /* input pixel stride */, + 128 /* output pixel stride */, + w38.data(), w39.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 128 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 128 /* input pixel stride */, + 128 /* output pixel stride */, + w40.data(), w41.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 128 /* input channels per group */, + 128 /* output_channels_per_group */, + 128 /* input pixel stride */, + 128 /* output pixel stride */, + w42.data(), w43.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 128 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 128 /* input pixel stride */, + 128 /* output pixel stride */, + w44.data(), w45.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 128 /* input channels per group */, + 256 /* output_channels_per_group */, + 128 /* input pixel stride */, + 256 /* output pixel stride */, + w46.data(), w47.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 256 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 256 /* input pixel stride */, + 256 /* output pixel stride */, + w48.data(), w49.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 256 /* input channels per group */, + 256 /* output_channels_per_group */, + 256 /* input pixel stride */, + 256 /* output pixel stride */, + w50.data(), w51.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 256 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 256 /* input pixel stride */, + 256 /* output pixel stride */, + w52.data(), w53.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op11); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #11" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op11, xnn_delete_operator); + + xnn_operator_t op12 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 256 /* input channels per group */, + 512 /* output_channels_per_group */, + 256 /* input pixel stride */, + 512 /* output pixel stride */, + w54.data(), w55.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 512 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w56.data(), w57.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 512 /* input channels per group */, + 512 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w58.data(), w59.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 512 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w60.data(), w61.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 512 /* input channels per group */, + 512 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w62.data(), w63.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 512 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w64.data(), w65.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op17); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #17" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op17, xnn_delete_operator); + + xnn_operator_t op18 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 512 /* input channels per group */, + 512 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w66.data(), w67.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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_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 */, + 512 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w68.data(), w69.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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 */, + 512 /* input channels per group */, + 512 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w70.data(), w71.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 512 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w72.data(), w73.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 512 /* input channels per group */, + 512 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w74.data(), w75.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 512 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 512 /* input pixel stride */, + 512 /* output pixel stride */, + w76.data(), w77.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 512 /* input channels per group */, + 1024 /* output_channels_per_group */, + 512 /* input pixel stride */, + 1024 /* output pixel stride */, + w78.data(), w79.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op24); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #24" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op24, xnn_delete_operator); + + xnn_operator_t op25 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 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 */, + 1024 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 1024 /* input pixel stride */, + 1024 /* output pixel stride */, + w80.data(), w81.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 1024 /* output_channels_per_group */, + 1024 /* input pixel stride */, + 1024 /* output pixel stride */, + w82.data(), w83.data(), + 0.0f /* output min */, 6.0f /* 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_global_average_pooling_ncw_f32( + 1024 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 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_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 */, + w84.data(), w85.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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); + + + + 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_convolution2d_nchw_f32( + op1, + 1 /* batch size */, 112 /* input height */, 112 /* input width */, + 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_convolution2d_nchw_f32( + op3, + 1 /* batch size */, 112 /* input height */, 112 /* input 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 */, 56 /* input height */, 56 /* 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 */, 56 /* input height */, 56 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op6, + 1 /* batch size */, 56 /* input height */, 56 /* input width */, + v6.data() /* input */, 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 */, 28 /* input height */, 28 /* 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 */, 28 /* input height */, 28 /* 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 */, 14 /* input height */, 14 /* 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 */, 14 /* input height */, 14 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op14, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v14.data() /* input */, 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 */, 14 /* input height */, 14 /* 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_convolution2d_nchw_f32( + op16, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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 */, 14 /* input height */, 14 /* 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_convolution2d_nchw_f32( + op18, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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_convolution2d_nchw_f32( + op19, + 1 /* batch size */, 14 /* input height */, 14 /* input 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 */, 14 /* input height */, 14 /* 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 */, 14 /* input height */, 14 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op22, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v22.data() /* input */, 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 */, 7 /* input height */, 7 /* 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_convolution2d_nchw_f32( + op25, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + 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 */, 7 /* input height */, 7 /* 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_global_average_pooling_ncw_f32( + op27, + 1 /* batch size */, 49 /* width */, + 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_convolution2d_nhwc_f32( + op28, + 1 /* batch size */, 1 /* input height */, 1 /* input 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(); + } + + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wpessimizing-move" + return operators; + #pragma clang diagnostic pop +} + +} // namespace models diff --git a/models/fp32-sparse-mobilenet-v2.cc b/models/fp32-sparse-mobilenet-v2.cc new file mode 100644 index 000000000..10b6aba4a --- /dev/null +++ b/models/fp32-sparse-mobilenet-v2.cc @@ -0,0 +1,2416 @@ +// 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 FP32SparseMobileNetV2(float sparsity, pthreadpool_t threadpool) { + alignas(16) static std::array<float, 150528> v0; + alignas(16) static std::array<float, 401408> v1; + alignas(16) static std::array<float, 401408> v2; + alignas(16) static std::array<float, 200704> v3; + alignas(16) static std::array<float, 1204224> v4; + alignas(16) static std::array<float, 301056> v5; + alignas(16) static std::array<float, 75264> v6; + alignas(16) static std::array<float, 451584> v7; + alignas(16) static std::array<float, 451584> v8; + alignas(16) static std::array<float, 75264> v9; + alignas(16) static std::array<float, 75264> v10; + alignas(16) static std::array<float, 451584> v11; + alignas(16) static std::array<float, 112896> v12; + alignas(16) static std::array<float, 25088> v13; + alignas(16) static std::array<float, 150528> v14; + alignas(16) static std::array<float, 150528> v15; + alignas(16) static std::array<float, 25088> v16; + alignas(16) static std::array<float, 25088> v17; + alignas(16) static std::array<float, 150528> v18; + alignas(16) static std::array<float, 150528> v19; + alignas(16) static std::array<float, 25088> v20; + alignas(16) static std::array<float, 25088> v21; + alignas(16) static std::array<float, 150528> v22; + alignas(16) static std::array<float, 37632> v23; + alignas(16) static std::array<float, 12544> v24; + alignas(16) static std::array<float, 75264> v25; + alignas(16) static std::array<float, 75264> v26; + alignas(16) static std::array<float, 12544> v27; + alignas(16) static std::array<float, 12544> v28; + alignas(16) static std::array<float, 75264> v29; + alignas(16) static std::array<float, 75264> v30; + alignas(16) static std::array<float, 12544> v31; + alignas(16) static std::array<float, 12544> v32; + alignas(16) static std::array<float, 75264> v33; + alignas(16) static std::array<float, 75264> v34; + alignas(16) static std::array<float, 12544> v35; + alignas(16) static std::array<float, 12544> v36; + alignas(16) static std::array<float, 75264> v37; + alignas(16) static std::array<float, 75264> v38; + alignas(16) static std::array<float, 18816> v39; + alignas(16) static std::array<float, 112896> v40; + alignas(16) static std::array<float, 112896> v41; + alignas(16) static std::array<float, 18816> v42; + alignas(16) static std::array<float, 18816> v43; + alignas(16) static std::array<float, 112896> v44; + alignas(16) static std::array<float, 112896> v45; + alignas(16) static std::array<float, 18816> v46; + alignas(16) static std::array<float, 18816> v47; + alignas(16) static std::array<float, 112896> v48; + alignas(16) static std::array<float, 28224> v49; + alignas(16) static std::array<float, 7840> v50; + alignas(16) static std::array<float, 47040> v51; + alignas(16) static std::array<float, 47040> v52; + alignas(16) static std::array<float, 7840> v53; + alignas(16) static std::array<float, 7840> v54; + alignas(16) static std::array<float, 47040> v55; + alignas(16) static std::array<float, 47040> v56; + alignas(16) static std::array<float, 7840> v57; + alignas(16) static std::array<float, 7840> v58; + alignas(16) static std::array<float, 47040> v59; + alignas(16) static std::array<float, 47040> v60; + alignas(16) static std::array<float, 15680> v61; + alignas(16) static std::array<float, 62720> v62; + alignas(16) static std::array<float, 1280> v63; + alignas(16) static std::array<float, 1001> v64; + alignas(16) static std::array<float, 864> w65; + alignas(16) static std::array<float, 32> w66; + alignas(16) static std::array<float, 288> w67; + alignas(16) static std::array<float, 32> w68; + alignas(16) static std::array<float, 512> w69; + alignas(16) static std::array<float, 16> w70; + alignas(16) static std::array<float, 1536> w71; + alignas(16) static std::array<float, 96> w72; + alignas(16) static std::array<float, 864> w73; + alignas(16) static std::array<float, 96> w74; + alignas(16) static std::array<float, 2304> w75; + alignas(16) static std::array<float, 24> w76; + alignas(16) static std::array<float, 3456> w77; + alignas(16) static std::array<float, 144> w78; + alignas(16) static std::array<float, 1296> w79; + alignas(16) static std::array<float, 144> w80; + alignas(16) static std::array<float, 3456> w81; + alignas(16) static std::array<float, 24> w82; + alignas(16) static std::array<float, 3456> w83; + alignas(16) static std::array<float, 144> w84; + alignas(16) static std::array<float, 1296> w85; + alignas(16) static std::array<float, 144> w86; + alignas(16) static std::array<float, 4608> w87; + alignas(16) static std::array<float, 32> w88; + alignas(16) static std::array<float, 6144> w89; + alignas(16) static std::array<float, 192> w90; + alignas(16) static std::array<float, 1728> w91; + alignas(16) static std::array<float, 192> w92; + alignas(16) static std::array<float, 6144> w93; + alignas(16) static std::array<float, 32> w94; + alignas(16) static std::array<float, 6144> w95; + alignas(16) static std::array<float, 192> w96; + alignas(16) static std::array<float, 1728> w97; + alignas(16) static std::array<float, 192> w98; + alignas(16) static std::array<float, 6144> w99; + alignas(16) static std::array<float, 32> w100; + alignas(16) static std::array<float, 6144> w101; + alignas(16) static std::array<float, 192> w102; + alignas(16) static std::array<float, 1728> w103; + alignas(16) static std::array<float, 192> w104; + alignas(16) static std::array<float, 12288> w105; + alignas(16) static std::array<float, 64> w106; + alignas(16) static std::array<float, 24576> w107; + alignas(16) static std::array<float, 384> w108; + alignas(16) static std::array<float, 3456> w109; + alignas(16) static std::array<float, 384> w110; + alignas(16) static std::array<float, 24576> w111; + alignas(16) static std::array<float, 64> w112; + alignas(16) static std::array<float, 24576> w113; + alignas(16) static std::array<float, 384> w114; + alignas(16) static std::array<float, 3456> w115; + alignas(16) static std::array<float, 384> w116; + alignas(16) static std::array<float, 24576> w117; + alignas(16) static std::array<float, 64> w118; + alignas(16) static std::array<float, 24576> w119; + alignas(16) static std::array<float, 384> w120; + alignas(16) static std::array<float, 3456> w121; + alignas(16) static std::array<float, 384> w122; + alignas(16) static std::array<float, 24576> w123; + alignas(16) static std::array<float, 64> w124; + alignas(16) static std::array<float, 24576> w125; + alignas(16) static std::array<float, 384> w126; + alignas(16) static std::array<float, 3456> w127; + alignas(16) static std::array<float, 384> w128; + alignas(16) static std::array<float, 36864> w129; + alignas(16) static std::array<float, 96> w130; + alignas(16) static std::array<float, 55296> w131; + alignas(16) static std::array<float, 576> w132; + alignas(16) static std::array<float, 5184> w133; + alignas(16) static std::array<float, 576> w134; + alignas(16) static std::array<float, 55296> w135; + alignas(16) static std::array<float, 96> w136; + alignas(16) static std::array<float, 55296> w137; + alignas(16) static std::array<float, 576> w138; + alignas(16) static std::array<float, 5184> w139; + alignas(16) static std::array<float, 576> w140; + alignas(16) static std::array<float, 55296> w141; + alignas(16) static std::array<float, 96> w142; + alignas(16) static std::array<float, 55296> w143; + alignas(16) static std::array<float, 576> w144; + alignas(16) static std::array<float, 5184> w145; + alignas(16) static std::array<float, 576> w146; + alignas(16) static std::array<float, 92160> w147; + alignas(16) static std::array<float, 160> w148; + alignas(16) static std::array<float, 153600> w149; + alignas(16) static std::array<float, 960> w150; + alignas(16) static std::array<float, 8640> w151; + alignas(16) static std::array<float, 960> w152; + alignas(16) static std::array<float, 153600> w153; + alignas(16) static std::array<float, 160> w154; + alignas(16) static std::array<float, 153600> w155; + alignas(16) static std::array<float, 960> w156; + alignas(16) static std::array<float, 8640> w157; + alignas(16) static std::array<float, 960> w158; + alignas(16) static std::array<float, 153600> w159; + alignas(16) static std::array<float, 160> w160; + alignas(16) static std::array<float, 153600> w161; + alignas(16) static std::array<float, 960> w162; + alignas(16) static std::array<float, 8640> w163; + alignas(16) static std::array<float, 960> w164; + alignas(16) static std::array<float, 307200> w165; + alignas(16) static std::array<float, 320> w166; + alignas(16) static std::array<float, 409600> w167; + alignas(16) static std::array<float, 1280> w168; + alignas(16) static std::array<float, 1281280> w169; + alignas(16) static std::array<float, 1001> w170; + + 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(w65.begin(), w65.end(), std::ref(f32rng)); + std::generate(w66.begin(), w66.end(), std::ref(f32rng)); + std::generate(w67.begin(), w67.end(), std::ref(f32rng)); + std::generate(w68.begin(), w68.end(), std::ref(f32rng)); + std::fill(w69.begin(), w69.end(), 0.0f); + std::generate(w69.begin(), w69.end() - size_t(sparsity * w69.size()), std::ref(f32rng)); + std::shuffle(w69.begin(), w69.end(), rng); + std::generate(w70.begin(), w70.end(), std::ref(f32rng)); + std::fill(w71.begin(), w71.end(), 0.0f); + std::generate(w71.begin(), w71.end() - size_t(sparsity * w71.size()), std::ref(f32rng)); + std::shuffle(w71.begin(), w71.end(), rng); + std::generate(w72.begin(), w72.end(), std::ref(f32rng)); + std::generate(w73.begin(), w73.end(), std::ref(f32rng)); + std::generate(w74.begin(), w74.end(), std::ref(f32rng)); + std::fill(w75.begin(), w75.end(), 0.0f); + std::generate(w75.begin(), w75.end() - size_t(sparsity * w75.size()), std::ref(f32rng)); + std::shuffle(w75.begin(), w75.end(), rng); + std::generate(w76.begin(), w76.end(), std::ref(f32rng)); + std::fill(w77.begin(), w77.end(), 0.0f); + std::generate(w77.begin(), w77.end() - size_t(sparsity * w77.size()), std::ref(f32rng)); + std::shuffle(w77.begin(), w77.end(), rng); + std::generate(w78.begin(), w78.end(), std::ref(f32rng)); + std::generate(w79.begin(), w79.end(), std::ref(f32rng)); + std::generate(w80.begin(), w80.end(), std::ref(f32rng)); + std::fill(w81.begin(), w81.end(), 0.0f); + std::generate(w81.begin(), w81.end() - size_t(sparsity * w81.size()), std::ref(f32rng)); + std::shuffle(w81.begin(), w81.end(), rng); + std::generate(w82.begin(), w82.end(), std::ref(f32rng)); + std::fill(w83.begin(), w83.end(), 0.0f); + std::generate(w83.begin(), w83.end() - size_t(sparsity * w83.size()), std::ref(f32rng)); + std::shuffle(w83.begin(), w83.end(), rng); + std::generate(w84.begin(), w84.end(), std::ref(f32rng)); + std::generate(w85.begin(), w85.end(), std::ref(f32rng)); + std::generate(w86.begin(), w86.end(), std::ref(f32rng)); + std::fill(w87.begin(), w87.end(), 0.0f); + std::generate(w87.begin(), w87.end() - size_t(sparsity * w87.size()), std::ref(f32rng)); + std::shuffle(w87.begin(), w87.end(), rng); + std::generate(w88.begin(), w88.end(), std::ref(f32rng)); + std::fill(w89.begin(), w89.end(), 0.0f); + std::generate(w89.begin(), w89.end() - size_t(sparsity * w89.size()), std::ref(f32rng)); + std::shuffle(w89.begin(), w89.end(), rng); + std::generate(w90.begin(), w90.end(), std::ref(f32rng)); + std::generate(w91.begin(), w91.end(), std::ref(f32rng)); + std::generate(w92.begin(), w92.end(), std::ref(f32rng)); + std::fill(w93.begin(), w93.end(), 0.0f); + std::generate(w93.begin(), w93.end() - size_t(sparsity * w93.size()), std::ref(f32rng)); + std::shuffle(w93.begin(), w93.end(), rng); + std::generate(w94.begin(), w94.end(), std::ref(f32rng)); + std::fill(w95.begin(), w95.end(), 0.0f); + std::generate(w95.begin(), w95.end() - size_t(sparsity * w95.size()), std::ref(f32rng)); + std::shuffle(w95.begin(), w95.end(), rng); + std::generate(w96.begin(), w96.end(), std::ref(f32rng)); + std::generate(w97.begin(), w97.end(), std::ref(f32rng)); + std::generate(w98.begin(), w98.end(), std::ref(f32rng)); + std::fill(w99.begin(), w99.end(), 0.0f); + std::generate(w99.begin(), w99.end() - size_t(sparsity * w99.size()), std::ref(f32rng)); + std::shuffle(w99.begin(), w99.end(), rng); + std::generate(w100.begin(), w100.end(), std::ref(f32rng)); + std::fill(w101.begin(), w101.end(), 0.0f); + std::generate(w101.begin(), w101.end() - size_t(sparsity * w101.size()), std::ref(f32rng)); + std::shuffle(w101.begin(), w101.end(), rng); + std::generate(w102.begin(), w102.end(), std::ref(f32rng)); + std::generate(w103.begin(), w103.end(), std::ref(f32rng)); + std::generate(w104.begin(), w104.end(), std::ref(f32rng)); + std::fill(w105.begin(), w105.end(), 0.0f); + std::generate(w105.begin(), w105.end() - size_t(sparsity * w105.size()), std::ref(f32rng)); + std::shuffle(w105.begin(), w105.end(), rng); + std::generate(w106.begin(), w106.end(), std::ref(f32rng)); + std::fill(w107.begin(), w107.end(), 0.0f); + std::generate(w107.begin(), w107.end() - size_t(sparsity * w107.size()), std::ref(f32rng)); + std::shuffle(w107.begin(), w107.end(), rng); + std::generate(w108.begin(), w108.end(), std::ref(f32rng)); + std::generate(w109.begin(), w109.end(), std::ref(f32rng)); + std::generate(w110.begin(), w110.end(), std::ref(f32rng)); + std::fill(w111.begin(), w111.end(), 0.0f); + std::generate(w111.begin(), w111.end() - size_t(sparsity * w111.size()), std::ref(f32rng)); + std::shuffle(w111.begin(), w111.end(), rng); + std::generate(w112.begin(), w112.end(), std::ref(f32rng)); + std::fill(w113.begin(), w113.end(), 0.0f); + std::generate(w113.begin(), w113.end() - size_t(sparsity * w113.size()), std::ref(f32rng)); + std::shuffle(w113.begin(), w113.end(), rng); + std::generate(w114.begin(), w114.end(), std::ref(f32rng)); + std::generate(w115.begin(), w115.end(), std::ref(f32rng)); + std::generate(w116.begin(), w116.end(), std::ref(f32rng)); + std::fill(w117.begin(), w117.end(), 0.0f); + std::generate(w117.begin(), w117.end() - size_t(sparsity * w117.size()), std::ref(f32rng)); + std::shuffle(w117.begin(), w117.end(), rng); + std::generate(w118.begin(), w118.end(), std::ref(f32rng)); + std::fill(w119.begin(), w119.end(), 0.0f); + std::generate(w119.begin(), w119.end() - size_t(sparsity * w119.size()), std::ref(f32rng)); + std::shuffle(w119.begin(), w119.end(), rng); + std::generate(w120.begin(), w120.end(), std::ref(f32rng)); + std::generate(w121.begin(), w121.end(), std::ref(f32rng)); + std::generate(w122.begin(), w122.end(), std::ref(f32rng)); + std::fill(w123.begin(), w123.end(), 0.0f); + std::generate(w123.begin(), w123.end() - size_t(sparsity * w123.size()), std::ref(f32rng)); + std::shuffle(w123.begin(), w123.end(), rng); + std::generate(w124.begin(), w124.end(), std::ref(f32rng)); + std::fill(w125.begin(), w125.end(), 0.0f); + std::generate(w125.begin(), w125.end() - size_t(sparsity * w125.size()), std::ref(f32rng)); + std::shuffle(w125.begin(), w125.end(), rng); + std::generate(w126.begin(), w126.end(), std::ref(f32rng)); + std::generate(w127.begin(), w127.end(), std::ref(f32rng)); + std::generate(w128.begin(), w128.end(), std::ref(f32rng)); + std::fill(w129.begin(), w129.end(), 0.0f); + std::generate(w129.begin(), w129.end() - size_t(sparsity * w129.size()), std::ref(f32rng)); + std::shuffle(w129.begin(), w129.end(), rng); + std::generate(w130.begin(), w130.end(), std::ref(f32rng)); + std::fill(w131.begin(), w131.end(), 0.0f); + std::generate(w131.begin(), w131.end() - size_t(sparsity * w131.size()), std::ref(f32rng)); + std::shuffle(w131.begin(), w131.end(), rng); + std::generate(w132.begin(), w132.end(), std::ref(f32rng)); + std::generate(w133.begin(), w133.end(), std::ref(f32rng)); + std::generate(w134.begin(), w134.end(), std::ref(f32rng)); + std::fill(w135.begin(), w135.end(), 0.0f); + std::generate(w135.begin(), w135.end() - size_t(sparsity * w135.size()), std::ref(f32rng)); + std::shuffle(w135.begin(), w135.end(), rng); + std::generate(w136.begin(), w136.end(), std::ref(f32rng)); + std::fill(w137.begin(), w137.end(), 0.0f); + std::generate(w137.begin(), w137.end() - size_t(sparsity * w137.size()), std::ref(f32rng)); + std::shuffle(w137.begin(), w137.end(), rng); + std::generate(w138.begin(), w138.end(), std::ref(f32rng)); + std::generate(w139.begin(), w139.end(), std::ref(f32rng)); + std::generate(w140.begin(), w140.end(), std::ref(f32rng)); + std::fill(w141.begin(), w141.end(), 0.0f); + std::generate(w141.begin(), w141.end() - size_t(sparsity * w141.size()), std::ref(f32rng)); + std::shuffle(w141.begin(), w141.end(), rng); + std::generate(w142.begin(), w142.end(), std::ref(f32rng)); + std::fill(w143.begin(), w143.end(), 0.0f); + std::generate(w143.begin(), w143.end() - size_t(sparsity * w143.size()), std::ref(f32rng)); + std::shuffle(w143.begin(), w143.end(), rng); + std::generate(w144.begin(), w144.end(), std::ref(f32rng)); + std::generate(w145.begin(), w145.end(), std::ref(f32rng)); + std::generate(w146.begin(), w146.end(), std::ref(f32rng)); + std::fill(w147.begin(), w147.end(), 0.0f); + std::generate(w147.begin(), w147.end() - size_t(sparsity * w147.size()), std::ref(f32rng)); + std::shuffle(w147.begin(), w147.end(), rng); + std::generate(w148.begin(), w148.end(), std::ref(f32rng)); + std::fill(w149.begin(), w149.end(), 0.0f); + std::generate(w149.begin(), w149.end() - size_t(sparsity * w149.size()), std::ref(f32rng)); + std::shuffle(w149.begin(), w149.end(), rng); + std::generate(w150.begin(), w150.end(), std::ref(f32rng)); + std::generate(w151.begin(), w151.end(), std::ref(f32rng)); + std::generate(w152.begin(), w152.end(), std::ref(f32rng)); + std::fill(w153.begin(), w153.end(), 0.0f); + std::generate(w153.begin(), w153.end() - size_t(sparsity * w153.size()), std::ref(f32rng)); + std::shuffle(w153.begin(), w153.end(), rng); + std::generate(w154.begin(), w154.end(), std::ref(f32rng)); + std::fill(w155.begin(), w155.end(), 0.0f); + std::generate(w155.begin(), w155.end() - size_t(sparsity * w155.size()), std::ref(f32rng)); + std::shuffle(w155.begin(), w155.end(), rng); + std::generate(w156.begin(), w156.end(), std::ref(f32rng)); + std::generate(w157.begin(), w157.end(), std::ref(f32rng)); + std::generate(w158.begin(), w158.end(), std::ref(f32rng)); + std::fill(w159.begin(), w159.end(), 0.0f); + std::generate(w159.begin(), w159.end() - size_t(sparsity * w159.size()), std::ref(f32rng)); + std::shuffle(w159.begin(), w159.end(), rng); + std::generate(w160.begin(), w160.end(), std::ref(f32rng)); + std::fill(w161.begin(), w161.end(), 0.0f); + std::generate(w161.begin(), w161.end() - size_t(sparsity * w161.size()), std::ref(f32rng)); + std::shuffle(w161.begin(), w161.end(), rng); + std::generate(w162.begin(), w162.end(), std::ref(f32rng)); + std::generate(w163.begin(), w163.end(), std::ref(f32rng)); + std::generate(w164.begin(), w164.end(), std::ref(f32rng)); + std::fill(w165.begin(), w165.end(), 0.0f); + std::generate(w165.begin(), w165.end() - size_t(sparsity * w165.size()), std::ref(f32rng)); + std::shuffle(w165.begin(), w165.end(), rng); + std::generate(w166.begin(), w166.end(), std::ref(f32rng)); + std::fill(w167.begin(), w167.end(), 0.0f); + std::generate(w167.begin(), w167.end() - size_t(sparsity * w167.size()), std::ref(f32rng)); + std::shuffle(w167.begin(), w167.end(), rng); + std::generate(w168.begin(), w168.end(), std::ref(f32rng)); + std::fill(w169.begin(), w169.end(), 0.0f); + std::generate(w169.begin(), w169.end() - size_t(sparsity * w169.size()), std::ref(f32rng)); + std::shuffle(w169.begin(), w169.end(), rng); + std::generate(w170.begin(), w170.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 */, + 32 /* output_channels_per_group */, + 3 /* input pixel stride */, + 32 /* output pixel stride */, + w65.data(), w66.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 32 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 32 /* input pixel stride */, + 32 /* output pixel stride */, + w67.data(), w68.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 16 /* output_channels_per_group */, + 32 /* input pixel stride */, + 16 /* output pixel stride */, + w69.data(), w70.data(), + -std::numeric_limits<float>::infinity() /* 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_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 */, + 96 /* output_channels_per_group */, + 16 /* input pixel stride */, + 96 /* output pixel stride */, + w71.data(), w72.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 96 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 96 /* input pixel stride */, + 96 /* output pixel stride */, + w73.data(), w74.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 96 /* input channels per group */, + 24 /* output_channels_per_group */, + 96 /* input pixel stride */, + 24 /* output pixel stride */, + w75.data(), w76.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* 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_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 */, + 144 /* output_channels_per_group */, + 24 /* input pixel stride */, + 144 /* output pixel stride */, + w77.data(), w78.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 144 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 144 /* input pixel stride */, + 144 /* output pixel stride */, + w79.data(), w80.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 144 /* input channels per group */, + 24 /* output_channels_per_group */, + 144 /* input pixel stride */, + 24 /* output pixel stride */, + w81.data(), w82.data(), + -std::numeric_limits<float>::infinity() /* 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_add_nd_f32( + -std::numeric_limits<float>::infinity() /* 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 */, + 24 /* input channels per group */, + 144 /* output_channels_per_group */, + 24 /* input pixel stride */, + 144 /* output pixel stride */, + w83.data(), w84.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 144 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 144 /* input pixel stride */, + 144 /* output pixel stride */, + w85.data(), w86.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op11); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #11" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op11, xnn_delete_operator); + + xnn_operator_t op12 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 144 /* input channels per group */, + 32 /* output_channels_per_group */, + 144 /* input pixel stride */, + 32 /* output pixel stride */, + w87.data(), w88.data(), + -std::numeric_limits<float>::infinity() /* 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 */, + 32 /* input channels per group */, + 192 /* output_channels_per_group */, + 32 /* input pixel stride */, + 192 /* output pixel stride */, + w89.data(), w90.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 192 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 192 /* input pixel stride */, + 192 /* output pixel stride */, + w91.data(), w92.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 192 /* input channels per group */, + 32 /* output_channels_per_group */, + 192 /* input pixel stride */, + 32 /* output pixel stride */, + w93.data(), w94.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_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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( + 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 */, + 192 /* output_channels_per_group */, + 32 /* input pixel stride */, + 192 /* output pixel stride */, + w95.data(), w96.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op17); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #17" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op17, xnn_delete_operator); + + xnn_operator_t op18 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 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 */, + 192 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 192 /* input pixel stride */, + 192 /* output pixel stride */, + w97.data(), w98.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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_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 */, + 192 /* input channels per group */, + 32 /* output_channels_per_group */, + 192 /* input pixel stride */, + 32 /* output pixel stride */, + w99.data(), w100.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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_add_nd_f32( + -std::numeric_limits<float>::infinity() /* 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 */, + 32 /* input channels per group */, + 192 /* output_channels_per_group */, + 32 /* input pixel stride */, + 192 /* output pixel stride */, + w101.data(), w102.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 192 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 192 /* input pixel stride */, + 192 /* output pixel stride */, + w103.data(), w104.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 192 /* input channels per group */, + 64 /* output_channels_per_group */, + 192 /* input pixel stride */, + 64 /* output pixel stride */, + w105.data(), w106.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 */, + 64 /* input channels per group */, + 384 /* output_channels_per_group */, + 64 /* input pixel stride */, + 384 /* output pixel stride */, + w107.data(), w108.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op24); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #24" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op24, xnn_delete_operator); + + xnn_operator_t op25 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 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 */, + 384 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 384 /* input pixel stride */, + 384 /* output pixel stride */, + w109.data(), w110.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 384 /* input channels per group */, + 64 /* output_channels_per_group */, + 384 /* input pixel stride */, + 64 /* output pixel stride */, + w111.data(), w112.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_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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_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 */, + 384 /* output_channels_per_group */, + 64 /* input pixel stride */, + 384 /* output pixel stride */, + w113.data(), w114.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 384 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 384 /* input pixel stride */, + 384 /* output pixel stride */, + w115.data(), w116.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 384 /* input channels per group */, + 64 /* output_channels_per_group */, + 384 /* input pixel stride */, + 64 /* output pixel stride */, + w117.data(), w118.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* 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_add_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 */, + 64 /* input channels per group */, + 384 /* output_channels_per_group */, + 64 /* input pixel stride */, + 384 /* output pixel stride */, + w119.data(), w120.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op32); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #32" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op32, xnn_delete_operator); + + xnn_operator_t op33 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 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 */, + 384 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 384 /* input pixel stride */, + 384 /* output pixel stride */, + w121.data(), w122.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 384 /* input channels per group */, + 64 /* output_channels_per_group */, + 384 /* input pixel stride */, + 64 /* output pixel stride */, + w123.data(), w124.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_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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( + 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 */, + 384 /* output_channels_per_group */, + 64 /* input pixel stride */, + 384 /* output pixel stride */, + w125.data(), w126.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 384 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 384 /* input pixel stride */, + 384 /* output pixel stride */, + w127.data(), w128.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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_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 */, + 384 /* input channels per group */, + 96 /* output_channels_per_group */, + 384 /* input pixel stride */, + 96 /* output pixel stride */, + w129.data(), w130.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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 */, + 96 /* input channels per group */, + 576 /* output_channels_per_group */, + 96 /* input pixel stride */, + 576 /* output pixel stride */, + w131.data(), w132.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 576 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 576 /* input pixel stride */, + 576 /* output pixel stride */, + w133.data(), w134.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + w135.data(), w136.data(), + -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_add_nd_f32( + -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_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 */, + w137.data(), w138.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 576 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 576 /* input pixel stride */, + 576 /* output pixel stride */, + w139.data(), w140.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + w141.data(), w142.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op45); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #45" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op45, xnn_delete_operator); + + xnn_operator_t op46 = nullptr; + status = xnn_create_add_nd_f32( + -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_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 */, + w143.data(), w144.data(), + 0.0f /* output min */, 6.0f /* output max */, + 0 /* flags */, + &op47); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #47" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op47, xnn_delete_operator); + + xnn_operator_t op48 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 2 /* subsampling height */, 2 /* 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 */, + w145.data(), w146.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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 */, + 576 /* input channels per group */, + 160 /* output_channels_per_group */, + 576 /* input pixel stride */, + 160 /* output pixel stride */, + w147.data(), w148.data(), + -std::numeric_limits<float>::infinity() /* 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 */, + 160 /* input channels per group */, + 960 /* output_channels_per_group */, + 160 /* input pixel stride */, + 960 /* output pixel stride */, + w149.data(), w150.data(), + 0.0f /* output min */, 6.0f /* 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_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 */, + 960 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 960 /* input pixel stride */, + 960 /* output pixel stride */, + w151.data(), w152.data(), + 0.0f /* output min */, 6.0f /* 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 */, + 960 /* input channels per group */, + 160 /* output_channels_per_group */, + 960 /* input pixel stride */, + 160 /* output pixel stride */, + w153.data(), w154.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_add_nd_f32( + -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_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 160 /* input channels per group */, + 960 /* output_channels_per_group */, + 160 /* input pixel stride */, + 960 /* output pixel stride */, + w155.data(), w156.data(), + 0.0f /* output min */, 6.0f /* output max */, + 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( + 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 */, + 960 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 960 /* input pixel stride */, + 960 /* output pixel stride */, + w157.data(), w158.data(), + 0.0f /* output min */, 6.0f /* 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_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 160 /* output_channels_per_group */, + 960 /* input pixel stride */, + 160 /* output pixel stride */, + w159.data(), w160.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 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_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op57); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #57" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op57, xnn_delete_operator); + + xnn_operator_t op58 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 160 /* input channels per group */, + 960 /* output_channels_per_group */, + 160 /* input pixel stride */, + 960 /* output pixel stride */, + w161.data(), w162.data(), + 0.0f /* output min */, 6.0f /* 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( + 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 */, + 960 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 960 /* input pixel stride */, + 960 /* output pixel stride */, + w163.data(), w164.data(), + 0.0f /* output min */, 6.0f /* 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_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 320 /* output_channels_per_group */, + 960 /* input pixel stride */, + 320 /* output pixel stride */, + w165.data(), w166.data(), + -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 */, + 320 /* input channels per group */, + 1280 /* output_channels_per_group */, + 320 /* input pixel stride */, + 1280 /* output pixel stride */, + w167.data(), w168.data(), + 0.0f /* output min */, 6.0f /* 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_global_average_pooling_ncw_f32( + 1280 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op62); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #62" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op62, xnn_delete_operator); + + xnn_operator_t op63 = nullptr; + status = xnn_create_convolution2d_nhwc_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 1280 /* input channels per group */, + 1001 /* output_channels_per_group */, + 1280 /* input pixel stride */, + 1001 /* output pixel stride */, + w169.data(), w170.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); + + + + 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_convolution2d_nchw_f32( + op1, + 1 /* batch size */, 112 /* input height */, 112 /* input width */, + 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_convolution2d_nchw_f32( + op3, + 1 /* batch size */, 112 /* input height */, 112 /* input 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 */, 112 /* input height */, 112 /* 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 */, 56 /* input height */, 56 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op6, + 1 /* batch size */, 56 /* input height */, 56 /* input width */, + v6.data() /* input */, 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(); + } + + { + const size_t a_shape[] = { 1, 24, 56, 56 }; + const size_t b_shape[] = { 1, 24, 56, 56 }; + status = xnn_setup_add_nd_f32( + op9, + 4, a_shape, 4, b_shape, + v9.data() /* a */, v6.data() /* b */, 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 */, 56 /* input height */, 56 /* 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 */, 56 /* input height */, 56 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op14, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + v14.data() /* input */, 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(); + } + + { + const size_t a_shape[] = { 1, 32, 28, 28 }; + const size_t b_shape[] = { 1, 32, 28, 28 }; + status = xnn_setup_add_nd_f32( + op16, + 4, a_shape, 4, b_shape, + v16.data() /* a */, v13.data() /* b */, 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_convolution2d_nchw_f32( + op18, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + 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_convolution2d_nchw_f32( + op19, + 1 /* batch size */, 28 /* input height */, 28 /* input 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(); + } + + { + const size_t a_shape[] = { 1, 32, 28, 28 }; + const size_t b_shape[] = { 1, 32, 28, 28 }; + status = xnn_setup_add_nd_f32( + op20, + 4, a_shape, 4, b_shape, + v20.data() /* a */, v17.data() /* b */, 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 */, 28 /* input height */, 28 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op22, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + v22.data() /* input */, 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_convolution2d_nchw_f32( + op25, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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(); + } + + { + const size_t a_shape[] = { 1, 64, 14, 14 }; + const size_t b_shape[] = { 1, 64, 14, 14 }; + status = xnn_setup_add_nd_f32( + op27, + 4, a_shape, 4, b_shape, + v27.data() /* a */, v24.data() /* b */, v28.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #27" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op28, + 1 /* batch size */, 14 /* input height */, 14 /* input 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 */, 14 /* input height */, 14 /* 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 */, 14 /* input height */, 14 /* 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, 64, 14, 14 }; + const size_t b_shape[] = { 1, 64, 14, 14 }; + status = xnn_setup_add_nd_f32( + op31, + 4, a_shape, 4, b_shape, + v31.data() /* a */, v28.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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op33, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v33.data() /* input */, 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(); + } + + { + const size_t a_shape[] = { 1, 64, 14, 14 }; + const size_t b_shape[] = { 1, 64, 14, 14 }; + status = xnn_setup_add_nd_f32( + op35, + 4, a_shape, 4, b_shape, + v35.data() /* a */, v32.data() /* b */, 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_convolution2d_nchw_f32( + op37, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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_convolution2d_nchw_f32( + op38, + 1 /* batch size */, 14 /* input height */, 14 /* input 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 */, 14 /* input height */, 14 /* 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 */, 14 /* input height */, 14 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op41, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v41.data() /* input */, v42.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #41" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 96, 14, 14 }; + const size_t b_shape[] = { 1, 96, 14, 14 }; + status = xnn_setup_add_nd_f32( + op42, + 4, a_shape, 4, b_shape, + v42.data() /* a */, v39.data() /* b */, v43.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #42" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op43, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v43.data() /* input */, 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_convolution2d_nchw_f32( + op45, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v45.data() /* input */, v46.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #45" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 96, 14, 14 }; + const size_t b_shape[] = { 1, 96, 14, 14 }; + status = xnn_setup_add_nd_f32( + op46, + 4, a_shape, 4, b_shape, + v46.data() /* a */, v43.data() /* b */, v47.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #46" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op47, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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_convolution2d_nchw_f32( + op48, + 1 /* batch size */, 14 /* input height */, 14 /* input 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 */, 7 /* input height */, 7 /* 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 */, 7 /* input height */, 7 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op51, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + v51.data() /* input */, 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 */, 7 /* input height */, 7 /* 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(); + } + + { + const size_t a_shape[] = { 1, 160, 7, 7 }; + const size_t b_shape[] = { 1, 160, 7, 7 }; + status = xnn_setup_add_nd_f32( + op53, + 4, a_shape, 4, b_shape, + v53.data() /* a */, v50.data() /* b */, v54.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #53" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op54, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + 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 */, 7 /* input height */, 7 /* 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_convolution2d_nchw_f32( + op56, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + v56.data() /* input */, v57.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #56" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 160, 7, 7 }; + const size_t b_shape[] = { 1, 160, 7, 7 }; + status = xnn_setup_add_nd_f32( + op57, + 4, a_shape, 4, b_shape, + v57.data() /* a */, v54.data() /* b */, 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 */, 7 /* input height */, 7 /* 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 */, 7 /* input height */, 7 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op60, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + v60.data() /* input */, 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 */, 7 /* input height */, 7 /* 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(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op62, + 1 /* batch size */, 49 /* width */, + v62.data() /* input */, v63.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #62" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nhwc_f32( + op63, + 1 /* batch size */, 1 /* input height */, 1 /* 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(); + } + + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wpessimizing-move" + return operators; + #pragma clang diagnostic pop +} + +} // namespace models diff --git a/models/fp32-sparse-mobilenet-v3-large.cc b/models/fp32-sparse-mobilenet-v3-large.cc new file mode 100644 index 000000000..06eecf35e --- /dev/null +++ b/models/fp32-sparse-mobilenet-v3-large.cc @@ -0,0 +1,3813 @@ +// 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 FP32SparseMobileNetV3Large(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, 200704> v3; + alignas(16) static std::array<float, 200704> v4; + alignas(16) static std::array<float, 200704> v5; + alignas(16) static std::array<float, 802816> v6; + alignas(16) static std::array<float, 200704> v7; + alignas(16) static std::array<float, 75264> v8; + alignas(16) static std::array<float, 225792> v9; + alignas(16) static std::array<float, 225792> v10; + alignas(16) static std::array<float, 75264> v11; + alignas(16) static std::array<float, 75264> v12; + alignas(16) static std::array<float, 225792> v13; + alignas(16) static std::array<float, 56448> v14; + alignas(16) static std::array<float, 72> v15; + alignas(16) static std::array<float, 24> v16; + alignas(16) static std::array<float, 72> v17; + alignas(16) static std::array<float, 56448> v18; + alignas(16) static std::array<float, 31360> v19; + alignas(16) static std::array<float, 94080> v20; + alignas(16) static std::array<float, 94080> v21; + alignas(16) static std::array<float, 120> v22; + alignas(16) static std::array<float, 32> v23; + alignas(16) static std::array<float, 120> v24; + alignas(16) static std::array<float, 94080> v25; + alignas(16) static std::array<float, 31360> v26; + alignas(16) static std::array<float, 31360> v27; + alignas(16) static std::array<float, 94080> v28; + alignas(16) static std::array<float, 94080> v29; + alignas(16) static std::array<float, 120> v30; + alignas(16) static std::array<float, 32> v31; + alignas(16) static std::array<float, 120> v32; + alignas(16) static std::array<float, 94080> v33; + alignas(16) static std::array<float, 31360> v34; + alignas(16) static std::array<float, 31360> v35; + alignas(16) static std::array<float, 188160> v36; + alignas(16) static std::array<float, 188160> v37; + alignas(16) static std::array<float, 47040> v38; + alignas(16) static std::array<float, 47040> v39; + alignas(16) static std::array<float, 15680> v40; + alignas(16) static std::array<float, 39200> v41; + alignas(16) static std::array<float, 39200> v42; + alignas(16) static std::array<float, 39200> v43; + alignas(16) static std::array<float, 39200> v44; + alignas(16) static std::array<float, 15680> v45; + alignas(16) static std::array<float, 15680> v46; + alignas(16) static std::array<float, 36064> v47; + alignas(16) static std::array<float, 36064> v48; + alignas(16) static std::array<float, 36064> v49; + alignas(16) static std::array<float, 36064> v50; + alignas(16) static std::array<float, 15680> v51; + alignas(16) static std::array<float, 15680> v52; + alignas(16) static std::array<float, 36064> v53; + alignas(16) static std::array<float, 36064> v54; + alignas(16) static std::array<float, 36064> v55; + alignas(16) static std::array<float, 36064> v56; + alignas(16) static std::array<float, 15680> v57; + alignas(16) static std::array<float, 15680> v58; + alignas(16) static std::array<float, 94080> v59; + alignas(16) static std::array<float, 94080> v60; + alignas(16) static std::array<float, 94080> v61; + alignas(16) static std::array<float, 94080> v62; + alignas(16) static std::array<float, 480> v63; + alignas(16) static std::array<float, 120> v64; + alignas(16) static std::array<float, 480> v65; + alignas(16) static std::array<float, 94080> v66; + alignas(16) static std::array<float, 21952> v67; + alignas(16) static std::array<float, 131712> v68; + alignas(16) static std::array<float, 131712> v69; + alignas(16) static std::array<float, 131712> v70; + alignas(16) static std::array<float, 131712> v71; + alignas(16) static std::array<float, 672> v72; + alignas(16) static std::array<float, 168> v73; + alignas(16) static std::array<float, 672> v74; + alignas(16) static std::array<float, 131712> v75; + alignas(16) static std::array<float, 21952> v76; + alignas(16) static std::array<float, 21952> v77; + alignas(16) static std::array<float, 131712> v78; + alignas(16) static std::array<float, 131712> v79; + alignas(16) static std::array<float, 32928> v80; + alignas(16) static std::array<float, 32928> v81; + alignas(16) static std::array<float, 672> v82; + alignas(16) static std::array<float, 168> v83; + alignas(16) static std::array<float, 672> v84; + alignas(16) static std::array<float, 32928> v85; + alignas(16) static std::array<float, 7840> v86; + alignas(16) static std::array<float, 47040> v87; + alignas(16) static std::array<float, 47040> v88; + alignas(16) static std::array<float, 47040> v89; + alignas(16) static std::array<float, 47040> v90; + alignas(16) static std::array<float, 960> v91; + alignas(16) static std::array<float, 240> v92; + alignas(16) static std::array<float, 960> v93; + alignas(16) static std::array<float, 47040> v94; + alignas(16) static std::array<float, 7840> v95; + alignas(16) static std::array<float, 7840> v96; + alignas(16) static std::array<float, 47040> v97; + alignas(16) static std::array<float, 47040> v98; + alignas(16) static std::array<float, 47040> v99; + alignas(16) static std::array<float, 47040> v100; + alignas(16) static std::array<float, 960> v101; + alignas(16) static std::array<float, 240> v102; + alignas(16) static std::array<float, 960> v103; + alignas(16) static std::array<float, 47040> v104; + alignas(16) static std::array<float, 7840> v105; + alignas(16) static std::array<float, 7840> v106; + alignas(16) static std::array<float, 47040> v107; + alignas(16) static std::array<float, 47040> v108; + alignas(16) static std::array<float, 960> v109; + alignas(16) static std::array<float, 1280> v110; + alignas(16) static std::array<float, 1280> v111; + alignas(16) static std::array<float, 1280> v112; + alignas(16) static std::array<float, 1001> v113; + alignas(16) static std::array<float, 432> w114; + alignas(16) static std::array<float, 16> w115; + alignas(16) static std::array<float, 144> w116; + alignas(16) static std::array<float, 16> w117; + alignas(16) static std::array<float, 256> w118; + alignas(16) static std::array<float, 16> w119; + alignas(16) static std::array<float, 1024> w120; + alignas(16) static std::array<float, 64> w121; + alignas(16) static std::array<float, 576> w122; + alignas(16) static std::array<float, 64> w123; + alignas(16) static std::array<float, 1536> w124; + alignas(16) static std::array<float, 24> w125; + alignas(16) static std::array<float, 1728> w126; + alignas(16) static std::array<float, 72> w127; + alignas(16) static std::array<float, 648> w128; + alignas(16) static std::array<float, 72> w129; + alignas(16) static std::array<float, 1728> w130; + alignas(16) static std::array<float, 24> w131; + alignas(16) static std::array<float, 1728> w132; + alignas(16) static std::array<float, 72> w133; + alignas(16) static std::array<float, 1800> w134; + alignas(16) static std::array<float, 72> w135; + alignas(16) static std::array<float, 1728> w136; + alignas(16) static std::array<float, 24> w137; + alignas(16) static std::array<float, 1728> w138; + alignas(16) static std::array<float, 72> w139; + alignas(16) static std::array<float, 2880> w140; + alignas(16) static std::array<float, 40> w141; + alignas(16) static std::array<float, 4800> w142; + alignas(16) static std::array<float, 120> w143; + alignas(16) static std::array<float, 3000> w144; + alignas(16) static std::array<float, 120> w145; + alignas(16) static std::array<float, 3840> w146; + alignas(16) static std::array<float, 32> w147; + alignas(16) static std::array<float, 3840> w148; + alignas(16) static std::array<float, 120> w149; + alignas(16) static std::array<float, 4800> 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, 4800> w160; + alignas(16) static std::array<float, 40> w161; + alignas(16) static std::array<float, 9600> w162; + alignas(16) static std::array<float, 240> w163; + alignas(16) static std::array<float, 2160> w164; + alignas(16) static std::array<float, 240> w165; + alignas(16) static std::array<float, 19200> w166; + alignas(16) static std::array<float, 80> w167; + alignas(16) static std::array<float, 16000> w168; + alignas(16) static std::array<float, 200> w169; + alignas(16) static std::array<float, 1800> w170; + alignas(16) static std::array<float, 200> w171; + alignas(16) static std::array<float, 16000> w172; + alignas(16) static std::array<float, 80> w173; + alignas(16) static std::array<float, 14720> w174; + alignas(16) static std::array<float, 184> w175; + alignas(16) static std::array<float, 1656> w176; + alignas(16) static std::array<float, 184> w177; + alignas(16) static std::array<float, 14720> w178; + alignas(16) static std::array<float, 80> w179; + alignas(16) static std::array<float, 14720> w180; + alignas(16) static std::array<float, 184> w181; + alignas(16) static std::array<float, 1656> w182; + alignas(16) static std::array<float, 184> w183; + alignas(16) static std::array<float, 14720> w184; + alignas(16) static std::array<float, 80> w185; + alignas(16) static std::array<float, 38400> w186; + alignas(16) static std::array<float, 480> w187; + alignas(16) static std::array<float, 4320> w188; + alignas(16) static std::array<float, 480> w189; + alignas(16) static std::array<float, 57600> w190; + alignas(16) static std::array<float, 120> w191; + alignas(16) static std::array<float, 57600> w192; + alignas(16) static std::array<float, 480> w193; + alignas(16) static std::array<float, 53760> w194; + alignas(16) static std::array<float, 112> w195; + alignas(16) static std::array<float, 75264> w196; + alignas(16) static std::array<float, 672> w197; + alignas(16) static std::array<float, 6048> w198; + alignas(16) static std::array<float, 672> w199; + alignas(16) static std::array<float, 112896> w200; + alignas(16) static std::array<float, 168> w201; + alignas(16) static std::array<float, 112896> w202; + alignas(16) static std::array<float, 672> w203; + alignas(16) static std::array<float, 75264> w204; + alignas(16) static std::array<float, 112> w205; + alignas(16) static std::array<float, 75264> w206; + alignas(16) static std::array<float, 672> w207; + alignas(16) static std::array<float, 16800> w208; + alignas(16) static std::array<float, 672> w209; + alignas(16) static std::array<float, 112896> w210; + alignas(16) static std::array<float, 168> w211; + alignas(16) static std::array<float, 112896> w212; + alignas(16) static std::array<float, 672> w213; + alignas(16) static std::array<float, 107520> w214; + alignas(16) static std::array<float, 160> w215; + alignas(16) static std::array<float, 153600> w216; + alignas(16) static std::array<float, 960> w217; + alignas(16) static std::array<float, 24000> w218; + alignas(16) static std::array<float, 960> w219; + alignas(16) static std::array<float, 230400> w220; + alignas(16) static std::array<float, 240> w221; + alignas(16) static std::array<float, 230400> w222; + alignas(16) static std::array<float, 960> w223; + alignas(16) static std::array<float, 153600> w224; + alignas(16) static std::array<float, 160> w225; + alignas(16) static std::array<float, 153600> w226; + alignas(16) static std::array<float, 960> w227; + alignas(16) static std::array<float, 24000> w228; + alignas(16) static std::array<float, 960> w229; + alignas(16) static std::array<float, 230400> w230; + alignas(16) static std::array<float, 240> w231; + alignas(16) static std::array<float, 230400> w232; + alignas(16) static std::array<float, 960> w233; + alignas(16) static std::array<float, 153600> w234; + alignas(16) static std::array<float, 160> w235; + alignas(16) static std::array<float, 153600> w236; + alignas(16) static std::array<float, 960> w237; + alignas(16) static std::array<float, 1228800> w238; + alignas(16) static std::array<float, 1280> w239; + alignas(16) static std::array<float, 1281280> w240; + alignas(16) static std::array<float, 1001> w241; + + std::random_device random_device; + auto rng = std::mt19937(random_device()); + auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, +1.0f), std::ref(rng)); + std::generate(v0.begin(), v0.end(), std::ref(f32rng)); + std::generate(v1.begin(), v1.end(), std::ref(f32rng)); + std::generate(v2.begin(), v2.end(), std::ref(f32rng)); + std::generate(v3.begin(), v3.end(), std::ref(f32rng)); + std::generate(v4.begin(), v4.end(), std::ref(f32rng)); + std::generate(v5.begin(), v5.end(), std::ref(f32rng)); + std::generate(v6.begin(), v6.end(), std::ref(f32rng)); + std::generate(v7.begin(), v7.end(), std::ref(f32rng)); + std::generate(v8.begin(), v8.end(), std::ref(f32rng)); + std::generate(v9.begin(), v9.end(), std::ref(f32rng)); + std::generate(v10.begin(), v10.end(), std::ref(f32rng)); + std::generate(v11.begin(), v11.end(), std::ref(f32rng)); + std::generate(v12.begin(), v12.end(), std::ref(f32rng)); + std::generate(v13.begin(), v13.end(), std::ref(f32rng)); + std::generate(v14.begin(), v14.end(), std::ref(f32rng)); + std::generate(v15.begin(), v15.end(), std::ref(f32rng)); + std::generate(v16.begin(), v16.end(), std::ref(f32rng)); + std::generate(v17.begin(), v17.end(), std::ref(f32rng)); + std::generate(v18.begin(), v18.end(), std::ref(f32rng)); + std::generate(v19.begin(), v19.end(), std::ref(f32rng)); + std::generate(v20.begin(), v20.end(), std::ref(f32rng)); + std::generate(v21.begin(), v21.end(), std::ref(f32rng)); + std::generate(v22.begin(), v22.end(), std::ref(f32rng)); + std::generate(v23.begin(), v23.end(), std::ref(f32rng)); + std::generate(v24.begin(), v24.end(), std::ref(f32rng)); + std::generate(v25.begin(), v25.end(), std::ref(f32rng)); + std::generate(v26.begin(), v26.end(), std::ref(f32rng)); + std::generate(v27.begin(), v27.end(), std::ref(f32rng)); + std::generate(v28.begin(), v28.end(), std::ref(f32rng)); + std::generate(v29.begin(), v29.end(), std::ref(f32rng)); + std::generate(v30.begin(), v30.end(), std::ref(f32rng)); + std::generate(v31.begin(), v31.end(), std::ref(f32rng)); + std::generate(v32.begin(), v32.end(), std::ref(f32rng)); + std::generate(v33.begin(), v33.end(), std::ref(f32rng)); + std::generate(v34.begin(), v34.end(), std::ref(f32rng)); + std::generate(v35.begin(), v35.end(), std::ref(f32rng)); + std::generate(v36.begin(), v36.end(), std::ref(f32rng)); + std::generate(v37.begin(), v37.end(), std::ref(f32rng)); + std::generate(v38.begin(), v38.end(), std::ref(f32rng)); + std::generate(v39.begin(), v39.end(), std::ref(f32rng)); + std::generate(v40.begin(), v40.end(), std::ref(f32rng)); + std::generate(v41.begin(), v41.end(), std::ref(f32rng)); + std::generate(v42.begin(), v42.end(), std::ref(f32rng)); + std::generate(v43.begin(), v43.end(), std::ref(f32rng)); + std::generate(v44.begin(), v44.end(), std::ref(f32rng)); + std::generate(v45.begin(), v45.end(), std::ref(f32rng)); + std::generate(v46.begin(), v46.end(), std::ref(f32rng)); + std::generate(v47.begin(), v47.end(), std::ref(f32rng)); + std::generate(v48.begin(), v48.end(), std::ref(f32rng)); + std::generate(v49.begin(), v49.end(), std::ref(f32rng)); + std::generate(v50.begin(), v50.end(), std::ref(f32rng)); + std::generate(v51.begin(), v51.end(), std::ref(f32rng)); + std::generate(v52.begin(), v52.end(), std::ref(f32rng)); + std::generate(v53.begin(), v53.end(), std::ref(f32rng)); + std::generate(v54.begin(), v54.end(), std::ref(f32rng)); + std::generate(v55.begin(), v55.end(), std::ref(f32rng)); + std::generate(v56.begin(), v56.end(), std::ref(f32rng)); + std::generate(v57.begin(), v57.end(), std::ref(f32rng)); + std::generate(v58.begin(), v58.end(), std::ref(f32rng)); + std::generate(v59.begin(), v59.end(), std::ref(f32rng)); + std::generate(v60.begin(), v60.end(), std::ref(f32rng)); + std::generate(v61.begin(), v61.end(), std::ref(f32rng)); + std::generate(v62.begin(), v62.end(), std::ref(f32rng)); + std::generate(v63.begin(), v63.end(), std::ref(f32rng)); + std::generate(v64.begin(), v64.end(), std::ref(f32rng)); + std::generate(v65.begin(), v65.end(), std::ref(f32rng)); + std::generate(v66.begin(), v66.end(), std::ref(f32rng)); + std::generate(v67.begin(), v67.end(), std::ref(f32rng)); + std::generate(v68.begin(), v68.end(), std::ref(f32rng)); + std::generate(v69.begin(), v69.end(), std::ref(f32rng)); + std::generate(v70.begin(), v70.end(), std::ref(f32rng)); + std::generate(v71.begin(), v71.end(), std::ref(f32rng)); + std::generate(v72.begin(), v72.end(), std::ref(f32rng)); + std::generate(v73.begin(), v73.end(), std::ref(f32rng)); + std::generate(v74.begin(), v74.end(), std::ref(f32rng)); + std::generate(v75.begin(), v75.end(), std::ref(f32rng)); + std::generate(v76.begin(), v76.end(), std::ref(f32rng)); + std::generate(v77.begin(), v77.end(), std::ref(f32rng)); + std::generate(v78.begin(), v78.end(), std::ref(f32rng)); + std::generate(v79.begin(), v79.end(), std::ref(f32rng)); + std::generate(v80.begin(), v80.end(), std::ref(f32rng)); + std::generate(v81.begin(), v81.end(), std::ref(f32rng)); + std::generate(v82.begin(), v82.end(), std::ref(f32rng)); + std::generate(v83.begin(), v83.end(), std::ref(f32rng)); + std::generate(v84.begin(), v84.end(), std::ref(f32rng)); + std::generate(v85.begin(), v85.end(), std::ref(f32rng)); + std::generate(v86.begin(), v86.end(), std::ref(f32rng)); + std::generate(v87.begin(), v87.end(), std::ref(f32rng)); + std::generate(v88.begin(), v88.end(), std::ref(f32rng)); + std::generate(v89.begin(), v89.end(), std::ref(f32rng)); + std::generate(v90.begin(), v90.end(), std::ref(f32rng)); + std::generate(v91.begin(), v91.end(), std::ref(f32rng)); + std::generate(v92.begin(), v92.end(), std::ref(f32rng)); + std::generate(v93.begin(), v93.end(), std::ref(f32rng)); + std::generate(v94.begin(), v94.end(), std::ref(f32rng)); + std::generate(v95.begin(), v95.end(), std::ref(f32rng)); + std::generate(v96.begin(), v96.end(), std::ref(f32rng)); + std::generate(v97.begin(), v97.end(), std::ref(f32rng)); + std::generate(v98.begin(), v98.end(), std::ref(f32rng)); + std::generate(v99.begin(), v99.end(), std::ref(f32rng)); + std::generate(v100.begin(), v100.end(), std::ref(f32rng)); + std::generate(v101.begin(), v101.end(), std::ref(f32rng)); + std::generate(v102.begin(), v102.end(), std::ref(f32rng)); + std::generate(v103.begin(), v103.end(), std::ref(f32rng)); + std::generate(v104.begin(), v104.end(), std::ref(f32rng)); + std::generate(v105.begin(), v105.end(), std::ref(f32rng)); + std::generate(v106.begin(), v106.end(), std::ref(f32rng)); + std::generate(v107.begin(), v107.end(), std::ref(f32rng)); + std::generate(v108.begin(), v108.end(), std::ref(f32rng)); + std::generate(v109.begin(), v109.end(), std::ref(f32rng)); + std::generate(v110.begin(), v110.end(), std::ref(f32rng)); + std::generate(v111.begin(), v111.end(), std::ref(f32rng)); + std::generate(v112.begin(), v112.end(), std::ref(f32rng)); + std::generate(v113.begin(), v113.end(), std::ref(f32rng)); + std::generate(w114.begin(), w114.end(), std::ref(f32rng)); + std::generate(w115.begin(), w115.end(), std::ref(f32rng)); + std::generate(w116.begin(), w116.end(), std::ref(f32rng)); + std::generate(w117.begin(), w117.end(), std::ref(f32rng)); + std::fill(w118.begin(), w118.end(), 0.0f); + std::generate(w118.begin(), w118.end() - size_t(sparsity * w118.size()), std::ref(f32rng)); + std::shuffle(w118.begin(), w118.end(), rng); + std::generate(w119.begin(), w119.end(), std::ref(f32rng)); + std::fill(w120.begin(), w120.end(), 0.0f); + std::generate(w120.begin(), w120.end() - size_t(sparsity * w120.size()), std::ref(f32rng)); + std::shuffle(w120.begin(), w120.end(), rng); + std::generate(w121.begin(), w121.end(), std::ref(f32rng)); + std::generate(w122.begin(), w122.end(), std::ref(f32rng)); + std::generate(w123.begin(), w123.end(), std::ref(f32rng)); + std::fill(w124.begin(), w124.end(), 0.0f); + std::generate(w124.begin(), w124.end() - size_t(sparsity * w124.size()), std::ref(f32rng)); + std::shuffle(w124.begin(), w124.end(), rng); + std::generate(w125.begin(), w125.end(), std::ref(f32rng)); + std::fill(w126.begin(), w126.end(), 0.0f); + std::generate(w126.begin(), w126.end() - size_t(sparsity * w126.size()), std::ref(f32rng)); + std::shuffle(w126.begin(), w126.end(), rng); + std::generate(w127.begin(), w127.end(), std::ref(f32rng)); + std::generate(w128.begin(), w128.end(), std::ref(f32rng)); + std::generate(w129.begin(), w129.end(), std::ref(f32rng)); + std::fill(w130.begin(), w130.end(), 0.0f); + std::generate(w130.begin(), w130.end() - size_t(sparsity * w130.size()), std::ref(f32rng)); + std::shuffle(w130.begin(), w130.end(), rng); + std::generate(w131.begin(), w131.end(), std::ref(f32rng)); + std::fill(w132.begin(), w132.end(), 0.0f); + std::generate(w132.begin(), w132.end() - size_t(sparsity * w132.size()), std::ref(f32rng)); + std::shuffle(w132.begin(), w132.end(), rng); + std::generate(w133.begin(), w133.end(), std::ref(f32rng)); + std::generate(w134.begin(), w134.end(), std::ref(f32rng)); + std::generate(w135.begin(), w135.end(), std::ref(f32rng)); + std::fill(w136.begin(), w136.end(), 0.0f); + std::generate(w136.begin(), w136.end() - size_t(sparsity * w136.size()), std::ref(f32rng)); + std::shuffle(w136.begin(), w136.end(), rng); + std::generate(w137.begin(), w137.end(), std::ref(f32rng)); + std::fill(w138.begin(), w138.end(), 0.0f); + std::generate(w138.begin(), w138.end() - size_t(sparsity * w138.size()), std::ref(f32rng)); + std::shuffle(w138.begin(), w138.end(), rng); + std::generate(w139.begin(), w139.end(), std::ref(f32rng)); + std::fill(w140.begin(), w140.end(), 0.0f); + std::generate(w140.begin(), w140.end() - size_t(sparsity * w140.size()), std::ref(f32rng)); + std::shuffle(w140.begin(), w140.end(), rng); + std::generate(w141.begin(), w141.end(), std::ref(f32rng)); + std::fill(w142.begin(), w142.end(), 0.0f); + std::generate(w142.begin(), w142.end() - size_t(sparsity * w142.size()), std::ref(f32rng)); + std::shuffle(w142.begin(), w142.end(), rng); + std::generate(w143.begin(), w143.end(), std::ref(f32rng)); + std::generate(w144.begin(), w144.end(), std::ref(f32rng)); + std::generate(w145.begin(), w145.end(), std::ref(f32rng)); + std::fill(w146.begin(), w146.end(), 0.0f); + std::generate(w146.begin(), w146.end() - size_t(sparsity * w146.size()), std::ref(f32rng)); + std::shuffle(w146.begin(), w146.end(), rng); + std::generate(w147.begin(), w147.end(), std::ref(f32rng)); + std::fill(w148.begin(), w148.end(), 0.0f); + std::generate(w148.begin(), w148.end() - size_t(sparsity * w148.size()), std::ref(f32rng)); + std::shuffle(w148.begin(), w148.end(), rng); + std::generate(w149.begin(), w149.end(), std::ref(f32rng)); + std::fill(w150.begin(), w150.end(), 0.0f); + std::generate(w150.begin(), w150.end() - size_t(sparsity * w150.size()), std::ref(f32rng)); + std::shuffle(w150.begin(), w150.end(), rng); + std::generate(w151.begin(), w151.end(), std::ref(f32rng)); + std::fill(w152.begin(), w152.end(), 0.0f); + std::generate(w152.begin(), w152.end() - size_t(sparsity * w152.size()), std::ref(f32rng)); + std::shuffle(w152.begin(), w152.end(), rng); + std::generate(w153.begin(), w153.end(), std::ref(f32rng)); + std::generate(w154.begin(), w154.end(), std::ref(f32rng)); + std::generate(w155.begin(), w155.end(), std::ref(f32rng)); + std::fill(w156.begin(), w156.end(), 0.0f); + std::generate(w156.begin(), w156.end() - size_t(sparsity * w156.size()), std::ref(f32rng)); + std::shuffle(w156.begin(), w156.end(), rng); + std::generate(w157.begin(), w157.end(), std::ref(f32rng)); + std::fill(w158.begin(), w158.end(), 0.0f); + std::generate(w158.begin(), w158.end() - size_t(sparsity * w158.size()), std::ref(f32rng)); + std::shuffle(w158.begin(), w158.end(), rng); + std::generate(w159.begin(), w159.end(), std::ref(f32rng)); + std::fill(w160.begin(), w160.end(), 0.0f); + std::generate(w160.begin(), w160.end() - size_t(sparsity * w160.size()), std::ref(f32rng)); + std::shuffle(w160.begin(), w160.end(), rng); + std::generate(w161.begin(), w161.end(), std::ref(f32rng)); + std::fill(w162.begin(), w162.end(), 0.0f); + std::generate(w162.begin(), w162.end() - size_t(sparsity * w162.size()), std::ref(f32rng)); + std::shuffle(w162.begin(), w162.end(), rng); + std::generate(w163.begin(), w163.end(), std::ref(f32rng)); + std::generate(w164.begin(), w164.end(), std::ref(f32rng)); + std::generate(w165.begin(), w165.end(), std::ref(f32rng)); + std::fill(w166.begin(), w166.end(), 0.0f); + std::generate(w166.begin(), w166.end() - size_t(sparsity * w166.size()), std::ref(f32rng)); + std::shuffle(w166.begin(), w166.end(), rng); + std::generate(w167.begin(), w167.end(), std::ref(f32rng)); + std::fill(w168.begin(), w168.end(), 0.0f); + std::generate(w168.begin(), w168.end() - size_t(sparsity * w168.size()), std::ref(f32rng)); + std::shuffle(w168.begin(), w168.end(), rng); + std::generate(w169.begin(), w169.end(), std::ref(f32rng)); + std::generate(w170.begin(), w170.end(), std::ref(f32rng)); + std::generate(w171.begin(), w171.end(), std::ref(f32rng)); + std::fill(w172.begin(), w172.end(), 0.0f); + std::generate(w172.begin(), w172.end() - size_t(sparsity * w172.size()), std::ref(f32rng)); + std::shuffle(w172.begin(), w172.end(), rng); + std::generate(w173.begin(), w173.end(), std::ref(f32rng)); + std::fill(w174.begin(), w174.end(), 0.0f); + std::generate(w174.begin(), w174.end() - size_t(sparsity * w174.size()), std::ref(f32rng)); + std::shuffle(w174.begin(), w174.end(), rng); + std::generate(w175.begin(), w175.end(), std::ref(f32rng)); + std::generate(w176.begin(), w176.end(), std::ref(f32rng)); + std::generate(w177.begin(), w177.end(), std::ref(f32rng)); + std::fill(w178.begin(), w178.end(), 0.0f); + std::generate(w178.begin(), w178.end() - size_t(sparsity * w178.size()), std::ref(f32rng)); + std::shuffle(w178.begin(), w178.end(), rng); + std::generate(w179.begin(), w179.end(), std::ref(f32rng)); + std::fill(w180.begin(), w180.end(), 0.0f); + std::generate(w180.begin(), w180.end() - size_t(sparsity * w180.size()), std::ref(f32rng)); + std::shuffle(w180.begin(), w180.end(), rng); + std::generate(w181.begin(), w181.end(), std::ref(f32rng)); + std::generate(w182.begin(), w182.end(), std::ref(f32rng)); + std::generate(w183.begin(), w183.end(), std::ref(f32rng)); + std::fill(w184.begin(), w184.end(), 0.0f); + std::generate(w184.begin(), w184.end() - size_t(sparsity * w184.size()), std::ref(f32rng)); + std::shuffle(w184.begin(), w184.end(), rng); + std::generate(w185.begin(), w185.end(), std::ref(f32rng)); + std::fill(w186.begin(), w186.end(), 0.0f); + std::generate(w186.begin(), w186.end() - size_t(sparsity * w186.size()), std::ref(f32rng)); + std::shuffle(w186.begin(), w186.end(), rng); + std::generate(w187.begin(), w187.end(), std::ref(f32rng)); + std::generate(w188.begin(), w188.end(), std::ref(f32rng)); + std::generate(w189.begin(), w189.end(), std::ref(f32rng)); + std::fill(w190.begin(), w190.end(), 0.0f); + std::generate(w190.begin(), w190.end() - size_t(sparsity * w190.size()), std::ref(f32rng)); + std::shuffle(w190.begin(), w190.end(), rng); + std::generate(w191.begin(), w191.end(), std::ref(f32rng)); + std::fill(w192.begin(), w192.end(), 0.0f); + std::generate(w192.begin(), w192.end() - size_t(sparsity * w192.size()), std::ref(f32rng)); + std::shuffle(w192.begin(), w192.end(), rng); + std::generate(w193.begin(), w193.end(), std::ref(f32rng)); + std::fill(w194.begin(), w194.end(), 0.0f); + std::generate(w194.begin(), w194.end() - size_t(sparsity * w194.size()), std::ref(f32rng)); + std::shuffle(w194.begin(), w194.end(), rng); + std::generate(w195.begin(), w195.end(), std::ref(f32rng)); + std::fill(w196.begin(), w196.end(), 0.0f); + std::generate(w196.begin(), w196.end() - size_t(sparsity * w196.size()), std::ref(f32rng)); + std::shuffle(w196.begin(), w196.end(), rng); + std::generate(w197.begin(), w197.end(), std::ref(f32rng)); + std::generate(w198.begin(), w198.end(), std::ref(f32rng)); + std::generate(w199.begin(), w199.end(), std::ref(f32rng)); + std::fill(w200.begin(), w200.end(), 0.0f); + std::generate(w200.begin(), w200.end() - size_t(sparsity * w200.size()), std::ref(f32rng)); + std::shuffle(w200.begin(), w200.end(), rng); + std::generate(w201.begin(), w201.end(), std::ref(f32rng)); + std::fill(w202.begin(), w202.end(), 0.0f); + std::generate(w202.begin(), w202.end() - size_t(sparsity * w202.size()), std::ref(f32rng)); + std::shuffle(w202.begin(), w202.end(), rng); + std::generate(w203.begin(), w203.end(), std::ref(f32rng)); + std::fill(w204.begin(), w204.end(), 0.0f); + std::generate(w204.begin(), w204.end() - size_t(sparsity * w204.size()), std::ref(f32rng)); + std::shuffle(w204.begin(), w204.end(), rng); + std::generate(w205.begin(), w205.end(), std::ref(f32rng)); + std::fill(w206.begin(), w206.end(), 0.0f); + std::generate(w206.begin(), w206.end() - size_t(sparsity * w206.size()), std::ref(f32rng)); + std::shuffle(w206.begin(), w206.end(), rng); + std::generate(w207.begin(), w207.end(), std::ref(f32rng)); + std::generate(w208.begin(), w208.end(), std::ref(f32rng)); + std::generate(w209.begin(), w209.end(), std::ref(f32rng)); + std::fill(w210.begin(), w210.end(), 0.0f); + std::generate(w210.begin(), w210.end() - size_t(sparsity * w210.size()), std::ref(f32rng)); + std::shuffle(w210.begin(), w210.end(), rng); + std::generate(w211.begin(), w211.end(), std::ref(f32rng)); + std::fill(w212.begin(), w212.end(), 0.0f); + std::generate(w212.begin(), w212.end() - size_t(sparsity * w212.size()), std::ref(f32rng)); + std::shuffle(w212.begin(), w212.end(), rng); + std::generate(w213.begin(), w213.end(), std::ref(f32rng)); + std::fill(w214.begin(), w214.end(), 0.0f); + std::generate(w214.begin(), w214.end() - size_t(sparsity * w214.size()), std::ref(f32rng)); + std::shuffle(w214.begin(), w214.end(), rng); + std::generate(w215.begin(), w215.end(), std::ref(f32rng)); + std::fill(w216.begin(), w216.end(), 0.0f); + std::generate(w216.begin(), w216.end() - size_t(sparsity * w216.size()), std::ref(f32rng)); + std::shuffle(w216.begin(), w216.end(), rng); + std::generate(w217.begin(), w217.end(), std::ref(f32rng)); + std::generate(w218.begin(), w218.end(), std::ref(f32rng)); + std::generate(w219.begin(), w219.end(), std::ref(f32rng)); + std::fill(w220.begin(), w220.end(), 0.0f); + std::generate(w220.begin(), w220.end() - size_t(sparsity * w220.size()), std::ref(f32rng)); + std::shuffle(w220.begin(), w220.end(), rng); + std::generate(w221.begin(), w221.end(), std::ref(f32rng)); + std::fill(w222.begin(), w222.end(), 0.0f); + std::generate(w222.begin(), w222.end() - size_t(sparsity * w222.size()), std::ref(f32rng)); + std::shuffle(w222.begin(), w222.end(), rng); + std::generate(w223.begin(), w223.end(), std::ref(f32rng)); + std::fill(w224.begin(), w224.end(), 0.0f); + std::generate(w224.begin(), w224.end() - size_t(sparsity * w224.size()), std::ref(f32rng)); + std::shuffle(w224.begin(), w224.end(), rng); + std::generate(w225.begin(), w225.end(), std::ref(f32rng)); + std::fill(w226.begin(), w226.end(), 0.0f); + std::generate(w226.begin(), w226.end() - size_t(sparsity * w226.size()), std::ref(f32rng)); + std::shuffle(w226.begin(), w226.end(), rng); + std::generate(w227.begin(), w227.end(), std::ref(f32rng)); + std::generate(w228.begin(), w228.end(), std::ref(f32rng)); + std::generate(w229.begin(), w229.end(), std::ref(f32rng)); + std::fill(w230.begin(), w230.end(), 0.0f); + std::generate(w230.begin(), w230.end() - size_t(sparsity * w230.size()), std::ref(f32rng)); + std::shuffle(w230.begin(), w230.end(), rng); + std::generate(w231.begin(), w231.end(), std::ref(f32rng)); + std::fill(w232.begin(), w232.end(), 0.0f); + std::generate(w232.begin(), w232.end() - size_t(sparsity * w232.size()), std::ref(f32rng)); + std::shuffle(w232.begin(), w232.end(), rng); + std::generate(w233.begin(), w233.end(), std::ref(f32rng)); + std::fill(w234.begin(), w234.end(), 0.0f); + std::generate(w234.begin(), w234.end() - size_t(sparsity * w234.size()), std::ref(f32rng)); + std::shuffle(w234.begin(), w234.end(), rng); + std::generate(w235.begin(), w235.end(), std::ref(f32rng)); + std::fill(w236.begin(), w236.end(), 0.0f); + std::generate(w236.begin(), w236.end() - size_t(sparsity * w236.size()), std::ref(f32rng)); + std::shuffle(w236.begin(), w236.end(), rng); + std::generate(w237.begin(), w237.end(), std::ref(f32rng)); + std::generate(w238.begin(), w238.end(), std::ref(f32rng)); + std::generate(w239.begin(), w239.end(), std::ref(f32rng)); + std::generate(w240.begin(), w240.end(), std::ref(f32rng)); + std::generate(w241.begin(), w241.end(), std::ref(f32rng)); + + ExecutionPlan operators; + xnn_status status; + + xnn_operator_t op0 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 2 /* subsampling height */, 2 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 3 /* input channels per group */, + 16 /* output_channels_per_group */, + 3 /* input pixel stride */, + 16 /* output pixel stride */, + w114.data(), w115.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + XNN_FLAG_INPUT_NHWC /* flags */, + &op0); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #0" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op0, xnn_delete_operator); + + xnn_operator_t op1 = nullptr; + status = xnn_create_hardswish_nc_f32( + 16 /* channels */, + 16 /* input stride */, + 16 /* output stride */, + 0 /* flags */, + &op1); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #1" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op1, xnn_delete_operator); + + xnn_operator_t op2 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 16 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 16 /* input pixel stride */, + 16 /* output pixel stride */, + w116.data(), w117.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op2); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #2" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op2, xnn_delete_operator); + + xnn_operator_t op3 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 16 /* input channels per group */, + 16 /* output_channels_per_group */, + 16 /* input pixel stride */, + 16 /* output pixel stride */, + w118.data(), w119.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op3); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #3" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op3, xnn_delete_operator); + + xnn_operator_t op4 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op4); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #4" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op4, xnn_delete_operator); + + xnn_operator_t op5 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 16 /* input channels per group */, + 64 /* output_channels_per_group */, + 16 /* input pixel stride */, + 64 /* output pixel stride */, + w120.data(), w121.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op5); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #5" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op5, xnn_delete_operator); + + xnn_operator_t op6 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 2 /* subsampling height */, 2 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 64 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 64 /* input pixel stride */, + 64 /* output pixel stride */, + w122.data(), w123.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op6); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #6" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op6, xnn_delete_operator); + + xnn_operator_t op7 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 64 /* input channels per group */, + 24 /* output_channels_per_group */, + 64 /* input pixel stride */, + 24 /* output pixel stride */, + w124.data(), w125.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op7); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #7" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op7, xnn_delete_operator); + + xnn_operator_t op8 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 24 /* input channels per group */, + 72 /* output_channels_per_group */, + 24 /* input pixel stride */, + 72 /* output pixel stride */, + w126.data(), w127.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op8); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #8" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op8, xnn_delete_operator); + + xnn_operator_t op9 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 72 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 72 /* input pixel stride */, + 72 /* output pixel stride */, + w128.data(), w129.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op9); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #9" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op9, xnn_delete_operator); + + xnn_operator_t op10 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 72 /* input channels per group */, + 24 /* output_channels_per_group */, + 72 /* input pixel stride */, + 24 /* output pixel stride */, + w130.data(), w131.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op10); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #10" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op10, xnn_delete_operator); + + xnn_operator_t op11 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op11); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #11" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op11, xnn_delete_operator); + + xnn_operator_t op12 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 24 /* input channels per group */, + 72 /* output_channels_per_group */, + 24 /* input pixel stride */, + 72 /* output pixel stride */, + w132.data(), w133.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op12); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #12" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op12, xnn_delete_operator); + + xnn_operator_t op13 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 2 /* top padding */, 2 /* right padding */, + 2 /* bottom padding */, 2 /* left padding */, + 5 /* kernel height */, 5 /* kernel width */, + 2 /* subsampling height */, 2 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 72 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 72 /* input pixel stride */, + 72 /* output pixel stride */, + w134.data(), w135.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op13); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #13" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op13, xnn_delete_operator); + + xnn_operator_t op14 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 72 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op14); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #14" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op14, xnn_delete_operator); + + xnn_operator_t op15 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 72 /* input channels per group */, + 24 /* output_channels_per_group */, + 72 /* input pixel stride */, + 24 /* output pixel stride */, + w136.data(), w137.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op15); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #15" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op15, xnn_delete_operator); + + xnn_operator_t op16 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 24 /* input channels per group */, + 72 /* output_channels_per_group */, + 24 /* input pixel stride */, + 72 /* output pixel stride */, + w138.data(), w139.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op16); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #16" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op16, xnn_delete_operator); + + xnn_operator_t op17 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op17); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #17" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op17, xnn_delete_operator); + + xnn_operator_t op18 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 72 /* input channels per group */, + 40 /* output_channels_per_group */, + 72 /* input pixel stride */, + 40 /* output pixel stride */, + w140.data(), w141.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op18); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #18" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op18, xnn_delete_operator); + + xnn_operator_t op19 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 40 /* input channels per group */, + 120 /* output_channels_per_group */, + 40 /* input pixel stride */, + 120 /* output pixel stride */, + w142.data(), w143.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op19); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #19" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op19, xnn_delete_operator); + + xnn_operator_t op20 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 2 /* top padding */, 2 /* right padding */, + 2 /* bottom padding */, 2 /* left padding */, + 5 /* kernel height */, 5 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 120 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 120 /* input pixel stride */, + 120 /* output pixel stride */, + w144.data(), w145.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op20); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #20" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op20, xnn_delete_operator); + + xnn_operator_t op21 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 120 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op21); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #21" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op21, xnn_delete_operator); + + xnn_operator_t op22 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 120 /* input channels per group */, + 32 /* output_channels_per_group */, + 120 /* input pixel stride */, + 32 /* output pixel stride */, + w146.data(), w147.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op22); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #22" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op22, xnn_delete_operator); + + xnn_operator_t op23 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 32 /* input channels per group */, + 120 /* output_channels_per_group */, + 32 /* input pixel stride */, + 120 /* output pixel stride */, + w148.data(), w149.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op23); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #23" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op23, xnn_delete_operator); + + xnn_operator_t op24 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op24); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #24" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op24, xnn_delete_operator); + + xnn_operator_t op25 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 120 /* input channels per group */, + 40 /* output_channels_per_group */, + 120 /* input pixel stride */, + 40 /* output pixel stride */, + w150.data(), w151.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op25); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #25" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op25, xnn_delete_operator); + + xnn_operator_t op26 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op26); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #26" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op26, xnn_delete_operator); + + xnn_operator_t op27 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 40 /* input channels per group */, + 120 /* output_channels_per_group */, + 40 /* input pixel stride */, + 120 /* output pixel stride */, + w152.data(), w153.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op27); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #27" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op27, xnn_delete_operator); + + xnn_operator_t op28 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 2 /* top padding */, 2 /* right padding */, + 2 /* bottom padding */, 2 /* left padding */, + 5 /* kernel height */, 5 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 120 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 120 /* input pixel stride */, + 120 /* output pixel stride */, + w154.data(), w155.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op28); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #28" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op28, xnn_delete_operator); + + xnn_operator_t op29 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 120 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op29); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #29" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op29, xnn_delete_operator); + + xnn_operator_t op30 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 120 /* input channels per group */, + 32 /* output_channels_per_group */, + 120 /* input pixel stride */, + 32 /* output pixel stride */, + w156.data(), w157.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op30); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #30" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op30, xnn_delete_operator); + + xnn_operator_t op31 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 32 /* input channels per group */, + 120 /* output_channels_per_group */, + 32 /* input pixel stride */, + 120 /* output pixel stride */, + w158.data(), w159.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op31); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #31" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op31, xnn_delete_operator); + + xnn_operator_t op32 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op32); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #32" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op32, xnn_delete_operator); + + xnn_operator_t op33 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 120 /* input channels per group */, + 40 /* output_channels_per_group */, + 120 /* input pixel stride */, + 40 /* output pixel stride */, + w160.data(), w161.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op33); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #33" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op33, xnn_delete_operator); + + xnn_operator_t op34 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op34); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #34" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op34, xnn_delete_operator); + + xnn_operator_t op35 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 40 /* input channels per group */, + 240 /* output_channels_per_group */, + 40 /* input pixel stride */, + 240 /* output pixel stride */, + w162.data(), w163.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op35); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #35" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op35, xnn_delete_operator); + + xnn_operator_t op36 = nullptr; + status = xnn_create_hardswish_nc_f32( + 240 /* channels */, + 240 /* input stride */, + 240 /* output stride */, + 0 /* flags */, + &op36); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #36" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op36, xnn_delete_operator); + + xnn_operator_t op37 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 2 /* subsampling height */, 2 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 240 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 240 /* input pixel stride */, + 240 /* output pixel stride */, + w164.data(), w165.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op37); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #37" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op37, xnn_delete_operator); + + xnn_operator_t op38 = nullptr; + status = xnn_create_hardswish_nc_f32( + 240 /* channels */, + 240 /* input stride */, + 240 /* output stride */, + 0 /* flags */, + &op38); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #38" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op38, xnn_delete_operator); + + xnn_operator_t op39 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 240 /* input channels per group */, + 80 /* output_channels_per_group */, + 240 /* input pixel stride */, + 80 /* output pixel stride */, + w166.data(), w167.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op39); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #39" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op39, xnn_delete_operator); + + xnn_operator_t op40 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 80 /* input channels per group */, + 200 /* output_channels_per_group */, + 80 /* input pixel stride */, + 200 /* output pixel stride */, + w168.data(), w169.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op40); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #40" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op40, xnn_delete_operator); + + xnn_operator_t op41 = nullptr; + status = xnn_create_hardswish_nc_f32( + 200 /* channels */, + 200 /* input stride */, + 200 /* output stride */, + 0 /* flags */, + &op41); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #41" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op41, xnn_delete_operator); + + xnn_operator_t op42 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 200 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 200 /* input pixel stride */, + 200 /* output pixel stride */, + w170.data(), w171.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op42); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #42" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op42, xnn_delete_operator); + + xnn_operator_t op43 = nullptr; + status = xnn_create_hardswish_nc_f32( + 200 /* channels */, + 200 /* input stride */, + 200 /* output stride */, + 0 /* flags */, + &op43); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #43" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op43, xnn_delete_operator); + + xnn_operator_t op44 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 200 /* input channels per group */, + 80 /* output_channels_per_group */, + 200 /* input pixel stride */, + 80 /* output pixel stride */, + w172.data(), w173.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op44); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #44" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op44, xnn_delete_operator); + + xnn_operator_t op45 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op45); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #45" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op45, xnn_delete_operator); + + xnn_operator_t op46 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 80 /* input channels per group */, + 184 /* output_channels_per_group */, + 80 /* input pixel stride */, + 184 /* output pixel stride */, + w174.data(), w175.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op46); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #46" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op46, xnn_delete_operator); + + xnn_operator_t op47 = nullptr; + status = xnn_create_hardswish_nc_f32( + 184 /* channels */, + 184 /* input stride */, + 184 /* output stride */, + 0 /* flags */, + &op47); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #47" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op47, xnn_delete_operator); + + xnn_operator_t op48 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 184 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 184 /* input pixel stride */, + 184 /* output pixel stride */, + w176.data(), w177.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op48); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #48" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op48, xnn_delete_operator); + + xnn_operator_t op49 = nullptr; + status = xnn_create_hardswish_nc_f32( + 184 /* channels */, + 184 /* input stride */, + 184 /* output stride */, + 0 /* flags */, + &op49); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #49" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op49, xnn_delete_operator); + + xnn_operator_t op50 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 184 /* input channels per group */, + 80 /* output_channels_per_group */, + 184 /* input pixel stride */, + 80 /* output pixel stride */, + w178.data(), w179.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op50); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #50" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op50, xnn_delete_operator); + + xnn_operator_t op51 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op51); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #51" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op51, xnn_delete_operator); + + xnn_operator_t op52 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 80 /* input channels per group */, + 184 /* output_channels_per_group */, + 80 /* input pixel stride */, + 184 /* output pixel stride */, + w180.data(), w181.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op52); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #52" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op52, xnn_delete_operator); + + xnn_operator_t op53 = nullptr; + status = xnn_create_hardswish_nc_f32( + 184 /* channels */, + 184 /* input stride */, + 184 /* output stride */, + 0 /* flags */, + &op53); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #53" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op53, xnn_delete_operator); + + xnn_operator_t op54 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 184 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 184 /* input pixel stride */, + 184 /* output pixel stride */, + w182.data(), w183.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op54); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #54" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op54, xnn_delete_operator); + + xnn_operator_t op55 = nullptr; + status = xnn_create_hardswish_nc_f32( + 184 /* channels */, + 184 /* input stride */, + 184 /* output stride */, + 0 /* flags */, + &op55); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #55" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op55, xnn_delete_operator); + + xnn_operator_t op56 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 184 /* input channels per group */, + 80 /* output_channels_per_group */, + 184 /* input pixel stride */, + 80 /* output pixel stride */, + w184.data(), w185.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op56); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #56" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op56, xnn_delete_operator); + + xnn_operator_t op57 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op57); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #57" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op57, xnn_delete_operator); + + xnn_operator_t op58 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 80 /* input channels per group */, + 480 /* output_channels_per_group */, + 80 /* input pixel stride */, + 480 /* output pixel stride */, + w186.data(), w187.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op58); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #58" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op58, xnn_delete_operator); + + xnn_operator_t op59 = nullptr; + status = xnn_create_hardswish_nc_f32( + 480 /* channels */, + 480 /* input stride */, + 480 /* output stride */, + 0 /* flags */, + &op59); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #59" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op59, xnn_delete_operator); + + xnn_operator_t op60 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 480 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 480 /* input pixel stride */, + 480 /* output pixel stride */, + w188.data(), w189.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op60); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #60" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op60, xnn_delete_operator); + + xnn_operator_t op61 = nullptr; + status = xnn_create_hardswish_nc_f32( + 480 /* channels */, + 480 /* input stride */, + 480 /* output stride */, + 0 /* flags */, + &op61); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #61" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op61, xnn_delete_operator); + + xnn_operator_t op62 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 480 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op62); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #62" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op62, xnn_delete_operator); + + xnn_operator_t op63 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 480 /* input channels per group */, + 120 /* output_channels_per_group */, + 480 /* input pixel stride */, + 120 /* output pixel stride */, + w190.data(), w191.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op63); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #63" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op63, xnn_delete_operator); + + xnn_operator_t op64 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 120 /* input channels per group */, + 480 /* output_channels_per_group */, + 120 /* input pixel stride */, + 480 /* output pixel stride */, + w192.data(), w193.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op64); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #64" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op64, xnn_delete_operator); + + xnn_operator_t op65 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op65); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #65" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op65, xnn_delete_operator); + + xnn_operator_t op66 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 480 /* input channels per group */, + 112 /* output_channels_per_group */, + 480 /* input pixel stride */, + 112 /* output pixel stride */, + w194.data(), w195.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op66); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #66" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op66, xnn_delete_operator); + + xnn_operator_t op67 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 112 /* input channels per group */, + 672 /* output_channels_per_group */, + 112 /* input pixel stride */, + 672 /* output pixel stride */, + w196.data(), w197.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op67); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #67" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op67, xnn_delete_operator); + + xnn_operator_t op68 = nullptr; + status = xnn_create_hardswish_nc_f32( + 672 /* channels */, + 672 /* input stride */, + 672 /* output stride */, + 0 /* flags */, + &op68); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #68" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op68, xnn_delete_operator); + + xnn_operator_t op69 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 1 /* top padding */, 1 /* right padding */, + 1 /* bottom padding */, 1 /* left padding */, + 3 /* kernel height */, 3 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 672 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 672 /* input pixel stride */, + 672 /* output pixel stride */, + w198.data(), w199.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op69); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #69" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op69, xnn_delete_operator); + + xnn_operator_t op70 = nullptr; + status = xnn_create_hardswish_nc_f32( + 672 /* channels */, + 672 /* input stride */, + 672 /* output stride */, + 0 /* flags */, + &op70); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #70" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op70, xnn_delete_operator); + + xnn_operator_t op71 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 672 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op71); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #71" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op71, xnn_delete_operator); + + xnn_operator_t op72 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 672 /* input channels per group */, + 168 /* output_channels_per_group */, + 672 /* input pixel stride */, + 168 /* output pixel stride */, + w200.data(), w201.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op72); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #72" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op72, xnn_delete_operator); + + xnn_operator_t op73 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 168 /* input channels per group */, + 672 /* output_channels_per_group */, + 168 /* input pixel stride */, + 672 /* output pixel stride */, + w202.data(), w203.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op73); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #73" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op73, xnn_delete_operator); + + xnn_operator_t op74 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op74); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #74" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op74, xnn_delete_operator); + + xnn_operator_t op75 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 672 /* input channels per group */, + 112 /* output_channels_per_group */, + 672 /* input pixel stride */, + 112 /* output pixel stride */, + w204.data(), w205.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op75); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #75" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op75, xnn_delete_operator); + + xnn_operator_t op76 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op76); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #76" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op76, xnn_delete_operator); + + xnn_operator_t op77 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 112 /* input channels per group */, + 672 /* output_channels_per_group */, + 112 /* input pixel stride */, + 672 /* output pixel stride */, + w206.data(), w207.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op77); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #77" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op77, xnn_delete_operator); + + xnn_operator_t op78 = nullptr; + status = xnn_create_hardswish_nc_f32( + 672 /* channels */, + 672 /* input stride */, + 672 /* output stride */, + 0 /* flags */, + &op78); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #78" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op78, xnn_delete_operator); + + xnn_operator_t op79 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 2 /* top padding */, 2 /* right padding */, + 2 /* bottom padding */, 2 /* left padding */, + 5 /* kernel height */, 5 /* kernel width */, + 2 /* subsampling height */, 2 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 672 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 672 /* input pixel stride */, + 672 /* output pixel stride */, + w208.data(), w209.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op79); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #79" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op79, xnn_delete_operator); + + xnn_operator_t op80 = nullptr; + status = xnn_create_hardswish_nc_f32( + 672 /* channels */, + 672 /* input stride */, + 672 /* output stride */, + 0 /* flags */, + &op80); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #80" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op80, xnn_delete_operator); + + xnn_operator_t op81 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 672 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op81); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #81" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op81, xnn_delete_operator); + + xnn_operator_t op82 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 672 /* input channels per group */, + 168 /* output_channels_per_group */, + 672 /* input pixel stride */, + 168 /* output pixel stride */, + w210.data(), w211.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op82); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #82" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op82, xnn_delete_operator); + + xnn_operator_t op83 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 168 /* input channels per group */, + 672 /* output_channels_per_group */, + 168 /* input pixel stride */, + 672 /* output pixel stride */, + w212.data(), w213.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op83); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #83" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op83, xnn_delete_operator); + + xnn_operator_t op84 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op84); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #84" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op84, xnn_delete_operator); + + xnn_operator_t op85 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 672 /* input channels per group */, + 160 /* output_channels_per_group */, + 672 /* input pixel stride */, + 160 /* output pixel stride */, + w214.data(), w215.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op85); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #85" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op85, xnn_delete_operator); + + xnn_operator_t op86 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 160 /* input channels per group */, + 960 /* output_channels_per_group */, + 160 /* input pixel stride */, + 960 /* output pixel stride */, + w216.data(), w217.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op86); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #86" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op86, xnn_delete_operator); + + xnn_operator_t op87 = nullptr; + status = xnn_create_hardswish_nc_f32( + 960 /* channels */, + 960 /* input stride */, + 960 /* output stride */, + 0 /* flags */, + &op87); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #87" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op87, xnn_delete_operator); + + xnn_operator_t op88 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 2 /* top padding */, 2 /* right padding */, + 2 /* bottom padding */, 2 /* left padding */, + 5 /* kernel height */, 5 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 960 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 960 /* input pixel stride */, + 960 /* output pixel stride */, + w218.data(), w219.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op88); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #88" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op88, xnn_delete_operator); + + xnn_operator_t op89 = nullptr; + status = xnn_create_hardswish_nc_f32( + 960 /* channels */, + 960 /* input stride */, + 960 /* output stride */, + 0 /* flags */, + &op89); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #89" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op89, xnn_delete_operator); + + xnn_operator_t op90 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 960 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op90); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #90" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op90, xnn_delete_operator); + + xnn_operator_t op91 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 240 /* output_channels_per_group */, + 960 /* input pixel stride */, + 240 /* output pixel stride */, + w220.data(), w221.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op91); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #91" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op91, xnn_delete_operator); + + xnn_operator_t op92 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 240 /* input channels per group */, + 960 /* output_channels_per_group */, + 240 /* input pixel stride */, + 960 /* output pixel stride */, + w222.data(), w223.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op92); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #92" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op92, xnn_delete_operator); + + xnn_operator_t op93 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op93); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #93" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op93, xnn_delete_operator); + + xnn_operator_t op94 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 160 /* output_channels_per_group */, + 960 /* input pixel stride */, + 160 /* output pixel stride */, + w224.data(), w225.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op94); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #94" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op94, xnn_delete_operator); + + xnn_operator_t op95 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op95); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #95" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op95, xnn_delete_operator); + + xnn_operator_t op96 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 160 /* input channels per group */, + 960 /* output_channels_per_group */, + 160 /* input pixel stride */, + 960 /* output pixel stride */, + w226.data(), w227.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op96); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #96" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op96, xnn_delete_operator); + + xnn_operator_t op97 = nullptr; + status = xnn_create_hardswish_nc_f32( + 960 /* channels */, + 960 /* input stride */, + 960 /* output stride */, + 0 /* flags */, + &op97); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #97" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op97, xnn_delete_operator); + + xnn_operator_t op98 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 2 /* top padding */, 2 /* right padding */, + 2 /* bottom padding */, 2 /* left padding */, + 5 /* kernel height */, 5 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 960 /* groups */, + 1 /* input channels per group */, + 1 /* output_channels_per_group */, + 960 /* input pixel stride */, + 960 /* output pixel stride */, + w228.data(), w229.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op98); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #98" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op98, xnn_delete_operator); + + xnn_operator_t op99 = nullptr; + status = xnn_create_hardswish_nc_f32( + 960 /* channels */, + 960 /* input stride */, + 960 /* output stride */, + 0 /* flags */, + &op99); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #99" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op99, xnn_delete_operator); + + xnn_operator_t op100 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 960 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op100); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #100" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op100, xnn_delete_operator); + + xnn_operator_t op101 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 240 /* output_channels_per_group */, + 960 /* input pixel stride */, + 240 /* output pixel stride */, + w230.data(), w231.data(), + 0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op101); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #101" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op101, xnn_delete_operator); + + xnn_operator_t op102 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 240 /* input channels per group */, + 960 /* output_channels_per_group */, + 240 /* input pixel stride */, + 960 /* output pixel stride */, + w232.data(), w233.data(), + 0.0f /* output min */, +0x1.00014Fp+0 /* output max */, + 0 /* flags */, + &op102); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #102" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op102, xnn_delete_operator); + + xnn_operator_t op103 = nullptr; + status = xnn_create_multiply_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op103); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #103" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op103, xnn_delete_operator); + + xnn_operator_t op104 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 160 /* output_channels_per_group */, + 960 /* input pixel stride */, + 160 /* output pixel stride */, + w234.data(), w235.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op104); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #104" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op104, xnn_delete_operator); + + xnn_operator_t op105 = nullptr; + status = xnn_create_add_nd_f32( + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op105); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #105" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op105, xnn_delete_operator); + + xnn_operator_t op106 = nullptr; + status = xnn_create_convolution2d_nchw_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 160 /* input channels per group */, + 960 /* output_channels_per_group */, + 160 /* input pixel stride */, + 960 /* output pixel stride */, + w236.data(), w237.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op106); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #106" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op106, xnn_delete_operator); + + xnn_operator_t op107 = nullptr; + status = xnn_create_hardswish_nc_f32( + 960 /* channels */, + 960 /* input stride */, + 960 /* output stride */, + 0 /* flags */, + &op107); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #107" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op107, xnn_delete_operator); + + xnn_operator_t op108 = nullptr; + status = xnn_create_global_average_pooling_ncw_f32( + 960 /* channels */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op108); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #108" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op108, xnn_delete_operator); + + xnn_operator_t op109 = nullptr; + status = xnn_create_convolution2d_nhwc_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 960 /* input channels per group */, + 1280 /* output_channels_per_group */, + 960 /* input pixel stride */, + 1280 /* output pixel stride */, + w238.data(), w239.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op109); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #109" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op109, xnn_delete_operator); + + xnn_operator_t op110 = nullptr; + status = xnn_create_hardswish_nc_f32( + 1280 /* channels */, + 1280 /* input stride */, + 1280 /* output stride */, + 0 /* flags */, + &op110); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #110" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op110, xnn_delete_operator); + + xnn_operator_t op111 = nullptr; + status = xnn_create_global_average_pooling_nwc_f32( + 1280 /* channels */, 1280 /* input stride */, 1280 /* output stride */, + -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(), + 0 /* flags */, + &op111); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #111" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op111, xnn_delete_operator); + + xnn_operator_t op112 = nullptr; + status = xnn_create_convolution2d_nhwc_f32( + 0 /* top padding */, 0 /* right padding */, + 0 /* bottom padding */, 0 /* left padding */, + 1 /* kernel height */, 1 /* kernel width */, + 1 /* subsampling height */, 1 /* subsampling width */, + 1 /* dilation_height */, 1 /* dilation_width */, + 1 /* groups */, + 1280 /* input channels per group */, + 1001 /* output_channels_per_group */, + 1280 /* input pixel stride */, + 1001 /* output pixel stride */, + w240.data(), w241.data(), + -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */, + 0 /* flags */, + &op112); + if (status != xnn_status_success) { + std::cerr << "failed to create operation #112" << std::endl; + return ExecutionPlan(); + } + operators.emplace_back(op112, xnn_delete_operator); + + + + status = xnn_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_convolution2d_nchw_f32( + op3, + 1 /* batch size */, 112 /* input height */, 112 /* input 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(); + } + + { + const size_t a_shape[] = { 1, 16, 112, 112 }; + const size_t b_shape[] = { 1, 16, 112, 112 }; + status = xnn_setup_add_nd_f32( + op4, + 4, a_shape, 4, b_shape, + v4.data() /* a */, v2.data() /* b */, 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 */, 112 /* input height */, 112 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op6, + 1 /* batch size */, 112 /* input height */, 112 /* input width */, + v6.data() /* input */, 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 */, 56 /* input height */, 56 /* 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(); + } + + { + const size_t a_shape[] = { 1, 24, 56, 56 }; + const size_t b_shape[] = { 1, 24, 56, 56 }; + status = xnn_setup_add_nd_f32( + op11, + 4, a_shape, 4, b_shape, + v11.data() /* a */, v8.data() /* b */, 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 */, 56 /* input height */, 56 /* 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 */, 56 /* input height */, 56 /* 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(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op14, + 1 /* batch size */, 784 /* width */, + v14.data() /* input */, 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 */, 1 /* input height */, 1 /* 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_convolution2d_nchw_f32( + op16, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v16.data() /* input */, v17.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #16" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 72, 28, 28 }; + const size_t b_shape[] = { 1, 72, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op17, + 4, a_shape, 4, b_shape, + v14.data() /* a */, v17.data() /* b */, v18.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #17" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op18, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + 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_convolution2d_nchw_f32( + op19, + 1 /* batch size */, 28 /* input height */, 28 /* input 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 */, 28 /* input height */, 28 /* 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_global_average_pooling_ncw_f32( + op21, + 1 /* batch size */, 784 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op22, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v22.data() /* input */, 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 */, 1 /* input height */, 1 /* 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(); + } + + { + const size_t a_shape[] = { 1, 120, 28, 28 }; + const size_t b_shape[] = { 1, 120, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op24, + 4, a_shape, 4, b_shape, + v21.data() /* a */, v24.data() /* b */, v25.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #24" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op25, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + v25.data() /* input */, v26.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #25" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 40, 28, 28 }; + const size_t b_shape[] = { 1, 40, 28, 28 }; + status = xnn_setup_add_nd_f32( + op26, + 4, a_shape, 4, b_shape, + v26.data() /* a */, v19.data() /* b */, v27.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #26" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op27, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + 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_convolution2d_nchw_f32( + op28, + 1 /* batch size */, 28 /* input height */, 28 /* input 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_global_average_pooling_ncw_f32( + op29, + 1 /* batch size */, 784 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op31, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v31.data() /* input */, v32.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #31" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 120, 28, 28 }; + const size_t b_shape[] = { 1, 120, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op32, + 4, a_shape, 4, b_shape, + v29.data() /* a */, v32.data() /* b */, v33.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #32" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op33, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + v33.data() /* input */, v34.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #33" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 40, 28, 28 }; + const size_t b_shape[] = { 1, 40, 28, 28 }; + status = xnn_setup_add_nd_f32( + op34, + 4, a_shape, 4, b_shape, + v34.data() /* a */, v27.data() /* b */, v35.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #34" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op35, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + 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_hardswish_nc_f32( + op36, + 784 /* batch size */, + 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_convolution2d_nchw_f32( + op37, + 1 /* batch size */, 28 /* input height */, 28 /* input width */, + 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_hardswish_nc_f32( + op38, + 196 /* batch size */, + 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 */, 14 /* input height */, 14 /* 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 */, 14 /* input height */, 14 /* 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(); + } + + status = xnn_setup_hardswish_nc_f32( + op41, + 196 /* batch size */, + v41.data() /* input */, 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(); + } + + status = xnn_setup_hardswish_nc_f32( + op43, + 196 /* batch size */, + v43.data() /* input */, 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(); + } + + { + const size_t a_shape[] = { 1, 80, 14, 14 }; + const size_t b_shape[] = { 1, 80, 14, 14 }; + status = xnn_setup_add_nd_f32( + op45, + 4, a_shape, 4, b_shape, + v45.data() /* a */, v40.data() /* b */, 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_convolution2d_nchw_f32( + op48, + 1 /* batch size */, 14 /* input height */, 14 /* input 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_hardswish_nc_f32( + op49, + 196 /* batch size */, + 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 */, 14 /* input height */, 14 /* 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, 80, 14, 14 }; + const size_t b_shape[] = { 1, 80, 14, 14 }; + status = xnn_setup_add_nd_f32( + op51, + 4, a_shape, 4, b_shape, + v51.data() /* a */, v46.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_hardswish_nc_f32( + op53, + 196 /* batch size */, + 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_convolution2d_nchw_f32( + op54, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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_hardswish_nc_f32( + op55, + 196 /* batch size */, + 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_convolution2d_nchw_f32( + op56, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v56.data() /* input */, v57.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #56" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 80, 14, 14 }; + const size_t b_shape[] = { 1, 80, 14, 14 }; + status = xnn_setup_add_nd_f32( + op57, + 4, a_shape, 4, b_shape, + v57.data() /* a */, v52.data() /* b */, 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 */, 14 /* input height */, 14 /* 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_hardswish_nc_f32( + op59, + 196 /* batch size */, + v59.data() /* input */, v60.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #59" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op60, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v60.data() /* input */, v61.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #60" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_hardswish_nc_f32( + op61, + 196 /* batch size */, + v61.data() /* input */, v62.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #61" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op62, + 1 /* batch size */, 196 /* width */, + v62.data() /* input */, 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 */, 1 /* input height */, 1 /* 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_convolution2d_nchw_f32( + op64, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v64.data() /* input */, v65.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #64" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 480, 14, 14 }; + const size_t b_shape[] = { 1, 480, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op65, + 4, a_shape, 4, b_shape, + v62.data() /* a */, v65.data() /* b */, v66.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #65" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op66, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + 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_convolution2d_nchw_f32( + op67, + 1 /* batch size */, 14 /* input height */, 14 /* input 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_hardswish_nc_f32( + op68, + 196 /* batch size */, + 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 */, 14 /* input height */, 14 /* 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(); + } + + status = xnn_setup_hardswish_nc_f32( + op70, + 196 /* batch size */, + v70.data() /* input */, v71.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #70" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op71, + 1 /* batch size */, 196 /* 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 */, 1 /* input height */, 1 /* 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_convolution2d_nchw_f32( + op73, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v73.data() /* input */, v74.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #73" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 672, 14, 14 }; + const size_t b_shape[] = { 1, 672, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op74, + 4, a_shape, 4, b_shape, + v71.data() /* a */, v74.data() /* b */, v75.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #74" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op75, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v75.data() /* input */, v76.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #75" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 112, 14, 14 }; + const size_t b_shape[] = { 1, 112, 14, 14 }; + status = xnn_setup_add_nd_f32( + op76, + 4, a_shape, 4, b_shape, + v76.data() /* a */, v67.data() /* b */, 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 */, 14 /* input height */, 14 /* 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_hardswish_nc_f32( + op78, + 196 /* batch size */, + v78.data() /* input */, v79.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #78" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op79, + 1 /* batch size */, 14 /* input height */, 14 /* input width */, + v79.data() /* input */, v80.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #79" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_hardswish_nc_f32( + op80, + 49 /* batch size */, + v80.data() /* input */, v81.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #80" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op81, + 1 /* batch size */, 49 /* width */, + v81.data() /* input */, 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 */, 1 /* input height */, 1 /* 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_convolution2d_nchw_f32( + op83, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v83.data() /* input */, v84.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #83" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 672, 7, 7 }; + const size_t b_shape[] = { 1, 672, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op84, + 4, a_shape, 4, b_shape, + v81.data() /* a */, v84.data() /* b */, v85.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #84" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op85, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + 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_convolution2d_nchw_f32( + op86, + 1 /* batch size */, 7 /* input height */, 7 /* input 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_hardswish_nc_f32( + op87, + 49 /* batch size */, + 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 */, 7 /* input height */, 7 /* 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(); + } + + status = xnn_setup_hardswish_nc_f32( + op89, + 49 /* batch size */, + v89.data() /* input */, v90.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #89" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op90, + 1 /* batch size */, 49 /* 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(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op91, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v91.data() /* input */, 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 */, 1 /* input height */, 1 /* 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(); + } + + { + const size_t a_shape[] = { 1, 960, 7, 7 }; + const size_t b_shape[] = { 1, 960, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op93, + 4, a_shape, 4, b_shape, + v90.data() /* a */, v93.data() /* b */, v94.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #93" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op94, + 1 /* batch size */, 7 /* input height */, 7 /* input 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(); + } + + { + const size_t a_shape[] = { 1, 160, 7, 7 }; + const size_t b_shape[] = { 1, 160, 7, 7 }; + status = xnn_setup_add_nd_f32( + op95, + 4, a_shape, 4, b_shape, + v95.data() /* a */, v86.data() /* b */, v96.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #95" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op96, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + 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_hardswish_nc_f32( + op97, + 49 /* batch size */, + 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_nchw_f32( + op98, + 1 /* batch size */, 7 /* input height */, 7 /* 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(); + } + + status = xnn_setup_hardswish_nc_f32( + op99, + 49 /* batch size */, + v99.data() /* input */, v100.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #99" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op100, + 1 /* batch size */, 49 /* width */, + v100.data() /* input */, v101.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #100" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op101, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v101.data() /* input */, v102.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #101" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op102, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v102.data() /* input */, v103.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #102" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 960, 7, 7 }; + const size_t b_shape[] = { 1, 960, 1, 1 }; + status = xnn_setup_multiply_nd_f32( + op103, + 4, a_shape, 4, b_shape, + v100.data() /* a */, v103.data() /* b */, v104.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #103" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op104, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + v104.data() /* input */, v105.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #104" << std::endl; + return ExecutionPlan(); + } + + { + const size_t a_shape[] = { 1, 160, 7, 7 }; + const size_t b_shape[] = { 1, 160, 7, 7 }; + status = xnn_setup_add_nd_f32( + op105, + 4, a_shape, 4, b_shape, + v105.data() /* a */, v96.data() /* b */, v106.data() /* output */, + threadpool /* threadpool */); + } + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #105" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nchw_f32( + op106, + 1 /* batch size */, 7 /* input height */, 7 /* input width */, + v106.data() /* input */, v107.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #106" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_hardswish_nc_f32( + op107, + 49 /* batch size */, + v107.data() /* input */, v108.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #107" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_ncw_f32( + op108, + 1 /* batch size */, 49 /* width */, + v108.data() /* input */, v109.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #108" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nhwc_f32( + op109, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v109.data() /* input */, v110.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #109" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_hardswish_nc_f32( + op110, + 1 /* batch size */, + v110.data() /* input */, v111.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #110" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_global_average_pooling_nwc_f32( + op111, + 1 /* batch size */, 1 /* width */, + v111.data() /* input */, v112.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #111" << std::endl; + return ExecutionPlan(); + } + + status = xnn_setup_convolution2d_nhwc_f32( + op112, + 1 /* batch size */, 1 /* input height */, 1 /* input width */, + v112.data() /* input */, v113.data() /* output */, + threadpool /* threadpool */); + if (status != xnn_status_success) { + std::cerr << "failed to setup operation #112" << std::endl; + return ExecutionPlan(); + } + + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wpessimizing-move" + return operators; + #pragma clang diagnostic pop +} + +} // namespace models diff --git a/models/fp32-sparse-mobilenet-v3-small.cc b/models/fp32-sparse-mobilenet-v3-small.cc new file mode 100644 index 000000000..7d1260cdf --- /dev/null +++ b/models/fp32-sparse-mobilenet-v3-small.cc @@ -0,0 +1,3316 @@ +// 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 diff --git a/models/models.h b/models/models.h index b6a0cfa3b..7306f593e 100644 --- a/models/models.h +++ b/models/models.h @@ -20,6 +20,11 @@ ExecutionPlan FP32MobileNetV2(pthreadpool_t threadpool); ExecutionPlan FP32MobileNetV3Large(pthreadpool_t threadpool); ExecutionPlan FP32MobileNetV3Small(pthreadpool_t threadpool); +ExecutionPlan FP32SparseMobileNetV1(float sparsity, pthreadpool_t threadpool); +ExecutionPlan FP32SparseMobileNetV2(float sparsity, pthreadpool_t threadpool); +ExecutionPlan FP32SparseMobileNetV3Large(float sparsity, pthreadpool_t threadpool); +ExecutionPlan FP32SparseMobileNetV3Small(float sparsity, pthreadpool_t threadpool); + ExecutionPlan FP16MobileNetV1(pthreadpool_t threadpool); ExecutionPlan FP16MobileNetV2(pthreadpool_t threadpool); ExecutionPlan FP16MobileNetV3Large(pthreadpool_t threadpool); |