🤖 Adds tensorflow lite micro lib

2024-07-22 13:46:43 +02:00
parent 2827b7c1b5
commit c9c6125462
174 changed files with 57470 additions and 0 deletions
@@ -0,0 +1,254 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/lite/kernels/internal/reference/pad.h"
+
+#include <string.h>
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/portable_tensor.h"
+#include "tensorflow/lite/kernels/internal/types.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+
+namespace tflite {
+namespace ops {
+namespace micro {
+namespace pad {
+namespace {
+
+struct OpData {
+  PadParams params;
+  int32_t output_zero_point;
+};
+
+}  // namespace
+
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+  return context->AllocatePersistentBuffer(context, sizeof(OpData));
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TFLITE_DCHECK(node->user_data != nullptr);
+  OpData* data = static_cast<OpData*>(node->user_data);
+
+  TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  const TfLiteTensor* input = GetInput(context, node, /*index=*/0);
+  TF_LITE_ENSURE(context, input != nullptr);
+  const TfLiteTensor* paddings = GetInput(context, node, /*index=*/1);
+  TF_LITE_ENSURE(context, paddings != nullptr);
+  const TfLiteTensor* constant_values =
+      NumInputs(node) == 3 ? GetInput(context, node, /*index=*/2) : nullptr;
+  TfLiteTensor* output = GetOutput(context, node, /*index=*/0);
+  TF_LITE_ENSURE(context, output != nullptr);
+
+  TF_LITE_ENSURE_EQ(context, input->type, output->type);
+
+  // Current implementations rely on the inputs being <= 4D.
+  TF_LITE_ENSURE(context, NumDimensions(input) <=
+                              reference_ops::PadKernelMaxDimensionCount());
+
+  if (constant_values != nullptr) {
+    TF_LITE_ENSURE_EQ(context, input->type, constant_values->type);
+    // Ensure that constant_values is a scalar.
+    TF_LITE_ENSURE_EQ(context, NumElements(constant_values), 1);
+  }
+
+  // There must be a pair of paddings for each output dimension.
+  TF_LITE_ENSURE_EQ(context, GetTensorShape(paddings).FlatSize(),
+                    output->dims->size * 2);
+
+  // On Micro, outputs must be properly sized by the converter.
+  // NOTE: This data is only available because the paddings buffer is stored in
+  // the flatbuffer:
+  TF_LITE_ENSURE(context, IsConstantTensor(paddings));
+  const int32_t* paddings_data = GetTensorData<int32_t>(paddings);
+  for (int i = 0; i < output->dims->size; i++) {
+    int output_dim = output->dims->data[i];
+    int expected_dim =
+        input->dims->data[i] + paddings_data[i * 2] + paddings_data[i * 2 + 1];
+    TF_LITE_ENSURE_EQ(context, output_dim, expected_dim);
+  }
+
+  // Calculate OpData:
+  data->params.resizing_category = ResizingCategory::kGenericResize;
+  const int paddings_total = GetTensorShape(paddings).FlatSize();
+  if (paddings_total == 8 && (paddings_data[0] == 0 && paddings_data[1] == 0) &&
+      (paddings_data[6] == 0 && paddings_data[7] == 0)) {
+    data->params.resizing_category = ResizingCategory::kImageStyle;
+  }
+
+  const int num_input_dimensions = NumDimensions(input);
+  data->params.left_padding_count = num_input_dimensions;
+  data->params.right_padding_count = num_input_dimensions;
+
+  for (int idx = num_input_dimensions - 1; idx >= 0; --idx) {
+    data->params.left_padding[idx] = paddings_data[idx * 2];
+    data->params.right_padding[idx] = paddings_data[idx * 2 + 1];
+  }
+
+  if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8) {
+    if (constant_values == nullptr) {
+      // Quantized Pad requires that 0 is represented in the quantized
+      // range.
+      if (input->type == kTfLiteUInt8) {
+        TF_LITE_ENSURE(context, output->params.zero_point >=
+                                    std::numeric_limits<uint8_t>::min());
+        TF_LITE_ENSURE(context, output->params.zero_point <=
+                                    std::numeric_limits<uint8_t>::max());
+      } else {
+        TF_LITE_ENSURE(context, output->params.zero_point >=
+                                    std::numeric_limits<int8_t>::min());
+        TF_LITE_ENSURE(context, output->params.zero_point <=
+                                    std::numeric_limits<int8_t>::max());
+      }
+    } else {
+      // Quantized Pad requires that 'constant_values' is represented in the
+      // same quantized range as the input and output tensors.
+      TF_LITE_ENSURE_EQ(context, output->params.zero_point,
+                        constant_values->params.zero_point);
+      TF_LITE_ENSURE_EQ(context, static_cast<double>(output->params.scale),
+                        static_cast<double>(constant_values->params.scale));
+    }
+    data->output_zero_point = output->params.zero_point;
+  }
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  TFLITE_DCHECK(node->user_data != nullptr);
+  const OpData* data = static_cast<const OpData*>(node->user_data);
+
+  const TfLiteEvalTensor* input =
+      tflite::micro::GetEvalInput(context, node, /*index=*/0);
+  const TfLiteEvalTensor* constant_values =
+      NumInputs(node) == 3
+          ? tflite::micro::GetEvalInput(context, node, /*index=*/2)
+          : nullptr;
+  TfLiteEvalTensor* output =
+      tflite::micro::GetEvalOutput(context, node, /*index=*/0);
+
+  switch (input->type) {
+    case kTfLiteFloat32: {
+      float pad_value =
+          constant_values == nullptr
+              ? 0.f
+              : *tflite::micro::GetTensorData<float>(constant_values);
+      if (data->params.resizing_category == ResizingCategory::kImageStyle) {
+        reference_ops::PadImageStyle(
+            data->params, tflite::micro::GetTensorShape(input),
+            tflite::micro::GetTensorData<float>(input), &pad_value,
+            tflite::micro::GetTensorShape(output),
+            tflite::micro::GetTensorData<float>(output));
+      } else {
+        reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
+                           tflite::micro::GetTensorData<float>(input),
+                           &pad_value, tflite::micro::GetTensorShape(output),
+                           tflite::micro::GetTensorData<float>(output));
+      }
+    } break;
+    case kTfLiteUInt8: {
+      uint8_t pad_value;
+      if (constant_values == nullptr) {
+        pad_value = static_cast<uint8_t>(data->output_zero_point);
+      } else {
+        pad_value = *tflite::micro::GetTensorData<uint8_t>(constant_values);
+      }
+      if (data->params.resizing_category == ResizingCategory::kImageStyle) {
+        reference_ops::PadImageStyle(
+            data->params, tflite::micro::GetTensorShape(input),
+            tflite::micro::GetTensorData<uint8_t>(input), &pad_value,
+            tflite::micro::GetTensorShape(output),
+            tflite::micro::GetTensorData<uint8_t>(output));
+      } else {
+        reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
+                           tflite::micro::GetTensorData<uint8_t>(input),
+                           &pad_value, tflite::micro::GetTensorShape(output),
+                           tflite::micro::GetTensorData<uint8_t>(output));
+      }
+    } break;
+    case kTfLiteInt8: {
+      int8_t pad_value;
+      if (constant_values == nullptr) {
+        pad_value = static_cast<uint8_t>(data->output_zero_point);
+      } else {
+        pad_value = *tflite::micro::GetTensorData<int8_t>(constant_values);
+      }
+      if (data->params.resizing_category == ResizingCategory::kImageStyle) {
+        reference_ops::PadImageStyle(
+            data->params, tflite::micro::GetTensorShape(input),
+            tflite::micro::GetTensorData<int8_t>(input), &pad_value,
+            tflite::micro::GetTensorShape(output),
+            tflite::micro::GetTensorData<int8_t>(output));
+      } else {
+        reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
+                           tflite::micro::GetTensorData<int8_t>(input),
+                           &pad_value, tflite::micro::GetTensorShape(output),
+                           tflite::micro::GetTensorData<int8_t>(output));
+      }
+    } break;
+    case kTfLiteInt32: {
+      int32_t pad_value =
+          constant_values == nullptr
+              ? 0
+              : *tflite::micro::GetTensorData<int32_t>(constant_values);
+      reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
+                         tflite::micro::GetTensorData<int32_t>(input),
+                         &pad_value, tflite::micro::GetTensorShape(output),
+                         tflite::micro::GetTensorData<int32_t>(output));
+    } break;
+    default:
+
+      TF_LITE_KERNEL_LOG(context, "Type %s not currently supported by Pad.",
+                         TfLiteTypeGetName(input->type));
+      return kTfLiteError;
+  }
+#undef TF_LITE_PAD
+  return kTfLiteOk;
+}
+
+}  // namespace pad
+
+TfLiteRegistration Register_PAD() {
+  return {/*init=*/pad::Init,
+          /*free=*/nullptr,
+          /*prepare=*/pad::Prepare,
+          /*invoke=*/pad::Eval,
+          /*profiling_string=*/nullptr,
+          /*builtin_code=*/0,
+          /*custom_name=*/nullptr,
+          /*version=*/0};
+}
+
+// Also register Pad as PadV2.
+TfLiteRegistration Register_PADV2() {
+  return {/*init=*/pad::Init,
+          /*free=*/nullptr,
+          /*prepare=*/pad::Prepare,
+          /*invoke=*/pad::Eval,
+          /*profiling_string=*/nullptr,
+          /*builtin_code=*/0,
+          /*custom_name=*/nullptr,
+          /*version=*/0};
+}
+
+}  // namespace micro
+}  // namespace ops
+}  // namespace tflite