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IDF release/v5.1 d3c99ed3b8
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‎esp32/dio_qspi/include/sdkconfig.h

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@@ -458,6 +458,7 @@
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#define CONFIG_ETH_USE_SPI_ETHERNET 1
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#define CONFIG_ETH_SPI_ETHERNET_DM9051 1
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#define CONFIG_ETH_SPI_ETHERNET_W5500 1
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#define CONFIG_ETH_SPI_ETHERNET_KSZ8851SNL 1
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#define CONFIG_ESP_EVENT_POST_FROM_ISR 1
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#define CONFIG_ESP_EVENT_POST_FROM_IRAM_ISR 1
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#define CONFIG_ESP_HTTP_CLIENT_ENABLE_HTTPS 1

‎esp32/include/esp-tflite-micro/signal/micro/kernels/pcan_flexbuffers_generated_data.h

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#ifndef SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_
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#define SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_
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extern const int g_gen_data_size_snr_shift_6_test;
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extern const unsigned char g_gen_data_snr_shift_6_test[];
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#endif // SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_

‎esp32/include/esp-tflite-micro/signal/src/pcan_argc_fixed.h

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/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H
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#define SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H
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#include <cstdint>
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#include "msb.h"
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#include "tensorflow/lite/kernels/internal/compatibility.h"
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namespace tflite {
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namespace tflm_signal {
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#define kPcanSnrBits 12
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#define kPcanOutputBits 6
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int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut);
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uint32_t PcanShrink(const uint32_t x);
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void ApplyPcanAutoGainControlFixed(const int16_t* gain_lut, int32_t snr_shift,
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const uint32_t* noise_estimate,
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uint32_t* filterbank_output,
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int num_channels);
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} // namespace tflm_signal
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} // namespace tflite
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#endif // SIGNAL_MICRO_KERNELS__PCAN_AGC_FIXED_H

‎esp32/include/esp-tflite-micro/tensorflow/lite/builtin_ops.h

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@@ -200,6 +200,23 @@ typedef enum {
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kTfLiteBuiltinStablehloBroadcastInDim = 170,
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kTfLiteBuiltinStablehloConvolution = 171,
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kTfLiteBuiltinStablehloSlice = 172,
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kTfLiteBuiltinStablehloCustomCall = 173,
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kTfLiteBuiltinStablehloReduce = 174,
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kTfLiteBuiltinStablehloAbs = 175,
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kTfLiteBuiltinStablehloAnd = 176,
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kTfLiteBuiltinStablehloCosine = 177,
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kTfLiteBuiltinStablehloExponential = 178,
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kTfLiteBuiltinStablehloFloor = 179,
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kTfLiteBuiltinStablehloLog = 180,
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kTfLiteBuiltinStablehloMinimum = 181,
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kTfLiteBuiltinStablehloNegate = 182,
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kTfLiteBuiltinStablehloOr = 183,
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kTfLiteBuiltinStablehloPower = 184,
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kTfLiteBuiltinStablehloRemainder = 185,
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kTfLiteBuiltinStablehloRsqrt = 186,
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kTfLiteBuiltinStablehloSelect = 187,
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kTfLiteBuiltinStablehloSubtract = 188,
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kTfLiteBuiltinStablehloTanh = 189,
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} TfLiteBuiltinOperator;
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#ifdef __cplusplus

‎esp32/include/esp-tflite-micro/tensorflow/lite/kernels/internal/common.h

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@@ -16,6 +16,9 @@ limitations under the License.
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
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#include <algorithm>
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#include <cstddef>
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#include "tensorflow/lite/kernels/internal/runtime_shape.h"
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#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
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#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK
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#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
@@ -35,6 +38,113 @@ namespace tflite {
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constexpr int kReverseShift = -1;
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template <int MAX_DIM = 6>
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inline void ReduceDimensionsForBroadcast(const RuntimeShape& input1_shape,
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const RuntimeShape& input2_shape,
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size_t* compressed_input1_stride,
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size_t* compressed_input2_stride,
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size_t* compressed_output_shape) {
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size_t num_compressed_dims = 0;
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size_t compressed_input1_shape[MAX_DIM];
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size_t compressed_input2_shape[MAX_DIM];
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std::fill(compressed_input1_shape, compressed_input1_shape + MAX_DIM, 1);
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std::fill(compressed_input2_shape, compressed_input2_shape + MAX_DIM, 1);
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std::fill(compressed_output_shape, compressed_output_shape + MAX_DIM, 1);
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bool broadcast_input1 = false;
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bool broadcast_input2 = false;
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bool first_nonunit = true;
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bool degenerate_shape = false;
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const size_t num_input1_dims = input1_shape.DimensionsCount();
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const size_t num_input2_dims = input2_shape.DimensionsCount();
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const int32_t* input1_dims = input1_shape.DimsData();
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const int32_t* input2_dims = input2_shape.DimsData();
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const size_t num_common_dims =
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(num_input1_dims < num_input2_dims) ? num_input1_dims : num_input2_dims;
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for (size_t i = 1; i <= num_common_dims; i++) {
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const size_t input1_dim = input1_dims[num_input1_dims - i];
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const size_t input2_dim = input2_dims[num_input2_dims - i];
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degenerate_shape |= input1_dim == 0;
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degenerate_shape |= input2_dim == 0;
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if (input1_dim == 1 && input2_dim == 1) {
69+
continue;
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}
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assert(!broadcast_input1 || !broadcast_input2);
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if (input1_dim == 1) {
74+
if (!broadcast_input1) {
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broadcast_input1 = true;
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broadcast_input2 = false;
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num_compressed_dims++;
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}
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compressed_input2_shape[num_compressed_dims - 1] *= input2_dim;
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compressed_output_shape[num_compressed_dims - 1] *= input2_dim;
81+
} else if (input2_dim == 1) {
82+
if (!broadcast_input2) {
83+
broadcast_input1 = false;
84+
broadcast_input2 = true;
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num_compressed_dims++;
86+
}
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compressed_input1_shape[num_compressed_dims - 1] *= input1_dim;
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compressed_output_shape[num_compressed_dims - 1] *= input1_dim;
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} else {
90+
TFLITE_DCHECK(input1_dim == input2_dim);
91+
if (broadcast_input1 || broadcast_input2 || first_nonunit) {
92+
broadcast_input1 = false;
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broadcast_input2 = false;
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num_compressed_dims++;
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}
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compressed_input1_shape[num_compressed_dims - 1] *= input1_dim;
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compressed_input2_shape[num_compressed_dims - 1] *= input1_dim;
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compressed_output_shape[num_compressed_dims - 1] *= input1_dim;
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}
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first_nonunit = false;
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}
102+
if (num_input1_dims > num_input2_dims) {
103+
if (!broadcast_input2) {
104+
num_compressed_dims++;
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}
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for (size_t i = 0; i < num_input1_dims - num_input2_dims; i++) {
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const size_t input1_dim = input1_dims[i];
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degenerate_shape |= input1_dim == 0;
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compressed_input1_shape[num_compressed_dims - 1] *= input1_dim;
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compressed_output_shape[num_compressed_dims - 1] *= input1_dim;
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}
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} else if (num_input2_dims > num_input1_dims) {
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if (!broadcast_input1) {
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num_compressed_dims++;
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}
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for (size_t i = 0; i < num_input2_dims - num_input1_dims; i++) {
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const size_t input2_dim = input2_dims[i];
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degenerate_shape |= input2_dim == 0;
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compressed_input2_shape[num_compressed_dims - 1] *= input2_dim;
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compressed_output_shape[num_compressed_dims - 1] *= input2_dim;
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}
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}
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num_compressed_dims = (num_compressed_dims > 1) ? num_compressed_dims : 1;
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// Early exit without setting up context if any shape dimension is zero.
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TFLITE_DCHECK(!degenerate_shape);
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int input1_stride = 1;
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int input2_stride = 1;
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for (int i = 0; i < MAX_DIM; ++i) {
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compressed_input1_stride[i] = input1_stride;
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input1_stride *= compressed_input1_shape[i];
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compressed_input2_stride[i] = input2_stride;
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input2_stride *= compressed_input2_shape[i];
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}
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for (int i = 0; i < MAX_DIM; ++i) {
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if (compressed_input1_shape[i] != compressed_input2_shape[i]) {
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if (compressed_input1_shape[i] == 1) {
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compressed_input1_stride[i] = 0;
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} else {
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TFLITE_DCHECK_EQ(compressed_input2_shape[i], 1);
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compressed_input2_stride[i] = 0;
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}
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}
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}
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}
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inline void GetActivationMinMax(FusedActivationFunctionType ac,
39149
float* output_activation_min,
40150
float* output_activation_max) {

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