From a5622fee575a238ecae9b70ff079d2f7a2f903a6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 27 May 2020 11:18:31 -0700
Subject: [PATCH] Fix bugs in quantized PRELU operations.

PiperOrigin-RevId: 313426576
Change-Id: Ifb53ef0add80b5793e428fbfacbbea779bc9ae63
---
 tensorflow/lite/micro/kernels/prelu.cc      |  5 +++--
 tensorflow/lite/micro/kernels/prelu_test.cc | 22 ++++++++++-----------
 2 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/tensorflow/lite/micro/kernels/prelu.cc b/tensorflow/lite/micro/kernels/prelu.cc
index 801181abba4..921aa208ea2 100644
--- a/tensorflow/lite/micro/kernels/prelu.cc
+++ b/tensorflow/lite/micro/kernels/prelu.cc
@@ -68,8 +68,9 @@ TfLiteStatus PreluEval(TfLiteContext* context, TfLiteNode* node) {
   int output_shift_1 = 0;
   int32_t output_multiplier_2 = 0;
   int output_shift_2 = 0;
-  if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) {
-    double real_multiplier_1 = static_cast<double>(input->params.scale) *
+  if (output->type == kTfLiteInt8 || output->type == kTfLiteUInt8 ||
+      output->type == kTfLiteInt16) {
+    double real_multiplier_1 = static_cast<double>(input->params.scale) /
                                static_cast<double>(output->params.scale);
     double real_multiplier_2 = static_cast<double>(input->params.scale) *
                                static_cast<double>(alpha->params.scale) /
diff --git a/tensorflow/lite/micro/kernels/prelu_test.cc b/tensorflow/lite/micro/kernels/prelu_test.cc
index 66c0a609e8a..4199ae69689 100644
--- a/tensorflow/lite/micro/kernels/prelu_test.cc
+++ b/tensorflow/lite/micro/kernels/prelu_test.cc
@@ -156,14 +156,14 @@ TF_LITE_MICRO_TESTS_BEGIN
 TF_LITE_MICRO_TEST(FloatPreluActivationsOpTest) {
   const int output_dims_count = 12;
   float output_data[output_dims_count];
-  tflite::testing::TestPreluFloat({1, 2, 2, 3},  // input shape
+  tflite::testing::TestPreluFloat({3, 2, 2, 3},  // input shape
                                   {
                                       0.0f, 0.0f, 0.0f,     // Row 1, Column 1
                                       1.0f, 1.0f, 1.0f,     // Row 1, Column 2
                                       -1.0f, -1.0f, -1.0f,  // Row 2, Column 1
                                       -2.0f, -2.0f, -2.0f,  // Row 1, Column 2
                                   },
-                                  {1, 1, 1, 3},        // alpha shape
+                                  {3, 1, 1, 3},        // alpha shape
                                   {0.0f, 1.0f, 2.0f},  // alpha values
                                   {
                                       0.0f, 0.0f, 0.0f,    // Row 1, Column 1
@@ -171,26 +171,26 @@ TF_LITE_MICRO_TEST(FloatPreluActivationsOpTest) {
                                       0.0f, -1.0f, -2.0f,  // Row 2, Column 1
                                       0.0f, -2.0f, -4.0f,  // Row 1, Column 2
                                   },
-                                  {1, 2, 2, 3},  // output shape
+                                  {3, 2, 2, 3},  // output shape
                                   output_data);
 }
 
 TF_LITE_MICRO_TEST(QuantizedUint8PreluActivationsOpTest) {
   using tflite::testing::F2Q;
-  const float kMin = -1;
-  const float kMax = 127.f / 128.f;
+  const float kMin = -4;
+  const float kMax = 127.f / 32.f;
   const float kAlphaMin = -0.5f;
   const float kAlphaMax = 0.5f;
   const int output_dims_count = 12;
   uint8_t output_data[output_dims_count];
   tflite::testing::TestPreluQuantized(
-      {1, 2, 2, 3},  // input shape
+      {3, 2, 2, 3},  // input shape
       {F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax),
        F2Q(0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax),
        F2Q(-1.0f, kMin, kMax), F2Q(-1.0f, kMin, kMax), F2Q(-1.0f, kMin, kMax),
        F2Q(-0.25f, kMin, kMax), F2Q(-0.25f, kMin, kMax),
        F2Q(-0.25f, kMin, kMax)},
-      kMin, kMax, {1, 1, 1, 3},  // alpha shape
+      kMin, kMax, {3, 1, 1, 3},  // alpha shape
       {F2Q(0.0f, kMin, kMax), F2Q(0.5f, kMin, kMax), F2Q(-0.5f, kMin, kMax)},
       kMin, kMax,
       {F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax),
@@ -198,7 +198,7 @@ TF_LITE_MICRO_TEST(QuantizedUint8PreluActivationsOpTest) {
        F2Q(0.0f, kMin, kMax), F2Q(-0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax),
        F2Q(0.0f, kMin, kMax), F2Q(-0.125f, kMin, kMax),
        F2Q(0.125f, kMin, kMax)},
-      {1, 2, 2, 3},  // output shape
+      {3, 2, 2, 3},  // output shape
       kMin, kMax, output_data);
 }
 
@@ -211,13 +211,13 @@ TF_LITE_MICRO_TEST(QuantizedInt8PreluActivationsOpTest) {
   const int output_dims_count = 12;
   int8_t output_data[output_dims_count];
   tflite::testing::TestPreluQuantized(
-      {1, 2, 2, 3},  // input shape
+      {3, 2, 2, 3},  // input shape
       {F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax),
        F2QS(0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax),
        F2QS(-1.0f, kMin, kMax), F2QS(-1.0f, kMin, kMax),
        F2QS(-1.0f, kMin, kMax), F2QS(-0.25f, kMin, kMax),
        F2QS(-0.25f, kMin, kMax), F2QS(-0.25f, kMin, kMax)},
-      kMin, kMax, {1, 1, 1, 3},  // alpha shape
+      kMin, kMax, {3, 1, 1, 3},  // alpha shape
       {F2QS(0.0f, kMin, kMax), F2QS(0.5f, kMin, kMax), F2QS(-0.5f, kMin, kMax)},
       kMin, kMax,
       {F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax),
@@ -225,7 +225,7 @@ TF_LITE_MICRO_TEST(QuantizedInt8PreluActivationsOpTest) {
        F2QS(0.0f, kMin, kMax), F2QS(-0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax),
        F2QS(0.0f, kMin, kMax), F2QS(-0.125f, kMin, kMax),
        F2QS(0.125f, kMin, kMax)},
-      {1, 2, 2, 3},  // output shape
+      {3, 2, 2, 3},  // output shape
       kMin, kMax, output_data);
 }
 TF_LITE_MICRO_TESTS_END