Built-in end-to-end benchmark on sparse models
PiperOrigin-RevId: 361829000
diff --git a/BUILD.bazel b/BUILD.bazel
index d35b38f..1675b74 100644
--- a/BUILD.bazel
+++ b/BUILD.bazel
@@ -5333,6 +5333,18 @@
)
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(
+ 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(
+ 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(
+ 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 @@
":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 aa38990..9aed708 100755
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -4738,7 +4738,11 @@
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 246e32c..629e4c6 100644
--- a/bench/end2end.cc
+++ b/bench/end2end.cc
@@ -68,6 +68,30 @@
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(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 faad087..0a0caed 100644
--- a/emscripten.bzl
+++ b/emscripten.bzl
@@ -26,7 +26,7 @@
"-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 0000000..aeb285f
--- /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 0000000..10b6aba
--- /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 0000000..06eecf3
--- /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 0000000..7d1260c
--- /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 b6a0cfa..7306f59 100644
--- a/models/models.h
+++ b/models/models.h
@@ -20,6 +20,11 @@
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);
