Fix tests, add depth_multiplier value check in DEPTHWISE_CONV_2D.
depth_multiplier value was not validated in the
depthwiseConvPrepare method. Newly added tests for the per-channel
quantized convolution had wrong output channel count for their
depth_multiplier value, which was not checked.
+ Minor fix in CpuExecutor, removed use of a filter mOperand field
Bug: 122046844
Test: NeuralNetworksTest_static
Change-Id: I95a187c48dcdb5a8c831557aac35da884680e2c0
Merged-In: I95a187c48dcdb5a8c831557aac35da884680e2c0
(cherry picked from commit 39520cb9792e29efbc35d4d55bae3a43845ca78d)
diff --git a/common/CpuExecutor.cpp b/common/CpuExecutor.cpp
index 19e825a..2066a62 100644
--- a/common/CpuExecutor.cpp
+++ b/common/CpuExecutor.cpp
@@ -672,7 +672,6 @@
}
const RunTimeOperandInfo& input = mOperands[ins[0]];
const RunTimeOperandInfo& filter = mOperands[ins[1]];
- const Operand& filterOperand = mModel->operands[ins[1]];
const RunTimeOperandInfo& bias = mOperands[ins[2]];
int32_t padding_left, padding_right;
@@ -735,7 +734,7 @@
if (!depthwiseConvPrepare(input_tmp.shape(), filter.shape(), bias.shape(), padding_left,
padding_right, padding_top, padding_bottom, stride_width,
- stride_height, &outShape) ||
+ stride_height, depth_multiplier, &outShape) ||
!setInfoAndAllocateIfNeeded(&output_tmp, outShape)) {
success = false;
break;
@@ -761,7 +760,7 @@
success = depthwiseConvQuant8PerChannel(
reinterpret_cast<const uint8_t*>(input_tmp.buffer), input_tmp.shape(),
reinterpret_cast<const int8_t*>(filter.buffer), filter.shape(),
- filterOperand.extraParams.channelQuant().scales.data(),
+ filter.extraParams.channelQuant().scales.data(),
reinterpret_cast<const int32_t*>(bias.buffer), bias.shape(),
padding_left, padding_right, padding_top, padding_bottom, stride_width,
stride_height, depth_multiplier, activation,
diff --git a/common/OperationsUtils.cpp b/common/OperationsUtils.cpp
index 72edd2a..640c9d8 100644
--- a/common/OperationsUtils.cpp
+++ b/common/OperationsUtils.cpp
@@ -360,13 +360,10 @@
return true;
}
-bool depthwiseConvPrepare(const Shape& input,
- const Shape& filter,
- const Shape& bias,
- int32_t padding_left, int32_t padding_right,
- int32_t padding_top, int32_t padding_bottom,
- int32_t stride_width, int32_t stride_height,
- Shape* output) {
+bool depthwiseConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
+ int32_t padding_left, int32_t padding_right, int32_t padding_top,
+ int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
+ int32_t depth_multiplier, Shape* output) {
if (filter.type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
NN_OPS_CHECK(input.type == OperandType::TENSOR_QUANT8_ASYMM);
} else {
@@ -384,12 +381,15 @@
NN_OPS_CHECK(getSizeOfDimension(filter, 3) == getSizeOfDimension(bias, 0));
uint32_t channels_out = getSizeOfDimension(filter, 3);
+ uint32_t channels_in = getSizeOfDimension(input, 3);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t filterWidth = getSizeOfDimension(filter, 2);
uint32_t filterHeight = getSizeOfDimension(filter, 1);
uint32_t batches = getSizeOfDimension(input, 0);
+ NN_OPS_CHECK(depth_multiplier * channels_in == channels_out);
+
uint32_t outWidth = computeOutSize(width, filterWidth, stride_width,
padding_left, padding_right);
uint32_t outHeight = computeOutSize(height, filterHeight, stride_height,
diff --git a/common/include/OperationsUtils.h b/common/include/OperationsUtils.h
index 77e1a64..c62f6e4 100644
--- a/common/include/OperationsUtils.h
+++ b/common/include/OperationsUtils.h
@@ -341,13 +341,10 @@
bool quantizePrepare(const Shape& input, Shape* output);
-bool depthwiseConvPrepare(const Shape& input,
- const Shape& filter,
- const Shape& bias,
- int32_t padding_left, int32_t padding_right,
- int32_t padding_top, int32_t padding_bottom,
- int32_t stride_width, int32_t stride_height,
- Shape* output);
+bool depthwiseConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
+ int32_t padding_left, int32_t padding_right, int32_t padding_top,
+ int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
+ int32_t depth_multiplier, Shape* output);
bool convPrepare(const Shape& input,
const Shape& filter,
diff --git a/runtime/test/generated/examples/depthwise_conv2d_per_channel.example.cpp b/runtime/test/generated/examples/depthwise_conv2d_per_channel.example.cpp
index a451d5d..b25a244 100644
--- a/runtime/test/generated/examples/depthwise_conv2d_per_channel.example.cpp
+++ b/runtime/test/generated/examples/depthwise_conv2d_per_channel.example.cpp
@@ -119,7 +119,7 @@
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
- {{0, {132, 130, 128, 128, 132, 130, 129, 128}}},
+ {{0, {132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131}}},
// int -> QUANT16_SYMM map
{},
// int -> FLOAT16 map
@@ -145,7 +145,7 @@
// int -> FLOAT32 map
{},
// int -> INT32 map
- {{2, {4, 4}}},
+ {{2, {4, 4, 4, 4}}},
// int -> QUANT8_ASYMM map
{{0, {129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130}}},
// int -> QUANT16_SYMM map
@@ -155,7 +155,7 @@
// int -> BOOL8 map
{},
// int -> QUANT8_SYMM_PER_CHANNEL map
- {{1, {1, 1, 1, 1, 1, 1, 1, 1}}},
+ {{1, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}},
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
@@ -164,7 +164,7 @@
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
- {{0, {132, 130, 128, 128, 132, 130, 129, 128}}},
+ {{0, {132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131}}},
// int -> QUANT16_SYMM map
{},
// int -> FLOAT16 map
@@ -209,7 +209,7 @@
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
- {{0, {132, 130, 128, 128, 132, 130, 129, 128}}},
+ {{0, {132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131}}},
// int -> QUANT16_SYMM map
{},
// int -> FLOAT16 map
@@ -235,7 +235,7 @@
// int -> FLOAT32 map
{},
// int -> INT32 map
- {{2, {4, 4}}},
+ {{2, {4, 4, 4, 4}}},
// int -> QUANT8_ASYMM map
{{0, {129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130, 129, 130}}},
// int -> QUANT16_SYMM map
@@ -245,7 +245,7 @@
// int -> BOOL8 map
{},
// int -> QUANT8_SYMM_PER_CHANNEL map
- {{1, {1, 1, 1, 1, 1, 1, 1, 1}}},
+ {{1, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}},
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
@@ -254,7 +254,7 @@
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
- {{0, {132, 130, 128, 128, 132, 130, 129, 128}}},
+ {{0, {132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131, 132, 130, 134, 131}}},
// int -> QUANT16_SYMM map
{},
// int -> FLOAT16 map
@@ -299,7 +299,7 @@
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
- {{0, {132, 128, 132, 129, 130, 128, 130, 128}}},
+ {{0, {132, 132, 132, 132, 130, 130, 130, 130, 134, 134, 134, 134, 131, 131, 131, 131}}},
// int -> QUANT16_SYMM map
{},
// int -> FLOAT16 map
@@ -325,7 +325,7 @@
// int -> FLOAT32 map
{},
// int -> INT32 map
- {{2, {4, 4}}},
+ {{2, {4, 4, 4, 4}}},
// int -> QUANT8_ASYMM map
{{0, {129, 129, 129, 129, 129, 129, 129, 129, 129, 130, 130, 130, 130, 130, 130, 130, 130, 130}}},
// int -> QUANT16_SYMM map
@@ -335,7 +335,7 @@
// int -> BOOL8 map
{},
// int -> QUANT8_SYMM_PER_CHANNEL map
- {{1, {1, 1, 1, 1, 1, 1, 1, 1}}},
+ {{1, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}},
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
@@ -344,7 +344,7 @@
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
- {{0, {132, 128, 132, 129, 130, 128, 130, 128}}},
+ {{0, {132, 132, 132, 132, 130, 130, 130, 130, 134, 134, 134, 134, 131, 131, 131, 131}}},
// int -> QUANT16_SYMM map
{},
// int -> FLOAT16 map
diff --git a/runtime/test/generated/models/depthwise_conv2d_per_channel.model.cpp b/runtime/test/generated/models/depthwise_conv2d_per_channel.model.cpp
index 092bd87..d7a7447 100644
--- a/runtime/test/generated/models/depthwise_conv2d_per_channel.model.cpp
+++ b/runtime/test/generated/models/depthwise_conv2d_per_channel.model.cpp
@@ -55,13 +55,13 @@
void CreateModel_same_weight_as_input(Model *model) {
OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0);
- OperandType type10(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({0.5f, 0.5f},3));
+ OperandType type11(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({0.5f, 0.5f},3));
OperandType type2(Type::TENSOR_INT32, {2});
OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 1.0f, 0);
OperandType type4(Type::INT32, {});
// Phase 1, operands
auto op1 = model->addOperand(&type0);
- auto op2 = model->addOperand(&type10);
+ auto op2 = model->addOperand(&type11);
auto op3 = model->addOperand(&type2);
auto param = model->addOperand(&type4);
auto param1 = model->addOperand(&type4);
@@ -103,15 +103,15 @@
}
void CreateModel_different(Model *model) {
- OperandType type2(Type::TENSOR_INT32, {2});
OperandType type4(Type::INT32, {});
OperandType type5(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 2}, 0.5f, 128);
- OperandType type6(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f},3));
- OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 1.0f, 128);
+ OperandType type6(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 4}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f, 1.0f, 0.5f},3));
+ OperandType type7(Type::TENSOR_INT32, {4});
+ OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 1.0f, 128);
// Phase 1, operands
auto op11 = model->addOperand(&type5);
auto op21 = model->addOperand(&type6);
- auto op31 = model->addOperand(&type2);
+ auto op31 = model->addOperand(&type7);
auto param8 = model->addOperand(&type4);
auto param9 = model->addOperand(&type4);
auto param10 = model->addOperand(&type4);
@@ -120,12 +120,12 @@
auto param13 = model->addOperand(&type4);
auto param14 = model->addOperand(&type4);
auto param15 = model->addOperand(&type4);
- auto op41 = model->addOperand(&type7);
+ auto op41 = model->addOperand(&type8);
// Phase 2, operations
- static int8_t op21_init[] = {1, 1, 1, 1, 1, 1, 1, 1};
- model->setOperandValue(op21, op21_init, sizeof(int8_t) * 8);
- static int32_t op31_init[] = {4, 4};
- model->setOperandValue(op31, op31_init, sizeof(int32_t) * 2);
+ static int8_t op21_init[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+ model->setOperandValue(op21, op21_init, sizeof(int8_t) * 16);
+ static int32_t op31_init[] = {4, 4, 4, 4};
+ model->setOperandValue(op31, op31_init, sizeof(int32_t) * 4);
static int32_t param8_init[] = {0};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static int32_t param9_init[] = {0};
@@ -156,15 +156,15 @@
}
void CreateModel_different_weight_as_input(Model *model) {
- OperandType type11(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f},3));
- OperandType type2(Type::TENSOR_INT32, {2});
+ OperandType type12(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 4}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f, 1.0f, 0.5f},3));
OperandType type4(Type::INT32, {});
OperandType type5(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 2}, 0.5f, 128);
- OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 1.0f, 128);
+ OperandType type7(Type::TENSOR_INT32, {4});
+ OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 1.0f, 128);
// Phase 1, operands
auto op11 = model->addOperand(&type5);
- auto op21 = model->addOperand(&type11);
- auto op31 = model->addOperand(&type2);
+ auto op21 = model->addOperand(&type12);
+ auto op31 = model->addOperand(&type7);
auto param8 = model->addOperand(&type4);
auto param9 = model->addOperand(&type4);
auto param10 = model->addOperand(&type4);
@@ -173,7 +173,7 @@
auto param13 = model->addOperand(&type4);
auto param14 = model->addOperand(&type4);
auto param15 = model->addOperand(&type4);
- auto op41 = model->addOperand(&type7);
+ auto op41 = model->addOperand(&type8);
// Phase 2, operations
static int32_t param8_init[] = {0};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
@@ -205,16 +205,16 @@
}
void CreateModel_layout_nhwc(Model *model) {
- OperandType type2(Type::TENSOR_INT32, {2});
+ OperandType type10(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 4}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f, 1.0f, 0.5f},3));
OperandType type4(Type::INT32, {});
OperandType type5(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 2}, 0.5f, 128);
- OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 1.0f, 128);
- OperandType type8(Type::BOOL, {});
- OperandType type9(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f},3));
+ OperandType type7(Type::TENSOR_INT32, {4});
+ OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 1.0f, 128);
+ OperandType type9(Type::BOOL, {});
// Phase 1, operands
auto op12 = model->addOperand(&type5);
- auto op22 = model->addOperand(&type9);
- auto op32 = model->addOperand(&type2);
+ auto op22 = model->addOperand(&type10);
+ auto op32 = model->addOperand(&type7);
auto param16 = model->addOperand(&type4);
auto param17 = model->addOperand(&type4);
auto param18 = model->addOperand(&type4);
@@ -223,13 +223,13 @@
auto param21 = model->addOperand(&type4);
auto param22 = model->addOperand(&type4);
auto param23 = model->addOperand(&type4);
- auto layout = model->addOperand(&type8);
- auto op42 = model->addOperand(&type7);
+ auto layout = model->addOperand(&type9);
+ auto op42 = model->addOperand(&type8);
// Phase 2, operations
- static int8_t op22_init[] = {1, 1, 1, 1, 1, 1, 1, 1};
- model->setOperandValue(op22, op22_init, sizeof(int8_t) * 8);
- static int32_t op32_init[] = {4, 4};
- model->setOperandValue(op32, op32_init, sizeof(int32_t) * 2);
+ static int8_t op22_init[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+ model->setOperandValue(op22, op22_init, sizeof(int8_t) * 16);
+ static int32_t op32_init[] = {4, 4, 4, 4};
+ model->setOperandValue(op32, op32_init, sizeof(int32_t) * 4);
static int32_t param16_init[] = {0};
model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
static int32_t param17_init[] = {0};
@@ -262,16 +262,16 @@
}
void CreateModel_layout_nhwc_weight_as_input(Model *model) {
- OperandType type12(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f},3));
- OperandType type2(Type::TENSOR_INT32, {2});
+ OperandType type13(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 4}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f, 1.0f, 0.5f},3));
OperandType type4(Type::INT32, {});
OperandType type5(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 2}, 0.5f, 128);
- OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 1.0f, 128);
- OperandType type8(Type::BOOL, {});
+ OperandType type7(Type::TENSOR_INT32, {4});
+ OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 1.0f, 128);
+ OperandType type9(Type::BOOL, {});
// Phase 1, operands
auto op12 = model->addOperand(&type5);
- auto op22 = model->addOperand(&type12);
- auto op32 = model->addOperand(&type2);
+ auto op22 = model->addOperand(&type13);
+ auto op32 = model->addOperand(&type7);
auto param16 = model->addOperand(&type4);
auto param17 = model->addOperand(&type4);
auto param18 = model->addOperand(&type4);
@@ -280,8 +280,8 @@
auto param21 = model->addOperand(&type4);
auto param22 = model->addOperand(&type4);
auto param23 = model->addOperand(&type4);
- auto layout = model->addOperand(&type8);
- auto op42 = model->addOperand(&type7);
+ auto layout = model->addOperand(&type9);
+ auto op42 = model->addOperand(&type8);
// Phase 2, operations
static int32_t param16_init[] = {0};
model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
@@ -315,16 +315,16 @@
}
void CreateModel_layout_nchw(Model *model) {
- OperandType type13(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5f, 128);
- OperandType type2(Type::TENSOR_INT32, {2});
+ OperandType type10(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 4}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f, 1.0f, 0.5f},3));
+ OperandType type14(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5f, 128);
+ OperandType type15(Type::TENSOR_QUANT8_ASYMM, {1, 4, 2, 2}, 1.0f, 128);
OperandType type4(Type::INT32, {});
- OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 1.0f, 128);
- OperandType type8(Type::BOOL, {});
- OperandType type9(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f},3));
+ OperandType type7(Type::TENSOR_INT32, {4});
+ OperandType type9(Type::BOOL, {});
// Phase 1, operands
- auto op12 = model->addOperand(&type13);
- auto op22 = model->addOperand(&type9);
- auto op32 = model->addOperand(&type2);
+ auto op12 = model->addOperand(&type14);
+ auto op22 = model->addOperand(&type10);
+ auto op32 = model->addOperand(&type7);
auto param16 = model->addOperand(&type4);
auto param17 = model->addOperand(&type4);
auto param18 = model->addOperand(&type4);
@@ -333,13 +333,13 @@
auto param21 = model->addOperand(&type4);
auto param22 = model->addOperand(&type4);
auto param23 = model->addOperand(&type4);
- auto layout = model->addOperand(&type8);
- auto op42 = model->addOperand(&type7);
+ auto layout = model->addOperand(&type9);
+ auto op42 = model->addOperand(&type15);
// Phase 2, operations
- static int8_t op22_init[] = {1, 1, 1, 1, 1, 1, 1, 1};
- model->setOperandValue(op22, op22_init, sizeof(int8_t) * 8);
- static int32_t op32_init[] = {4, 4};
- model->setOperandValue(op32, op32_init, sizeof(int32_t) * 2);
+ static int8_t op22_init[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+ model->setOperandValue(op22, op22_init, sizeof(int8_t) * 16);
+ static int32_t op32_init[] = {4, 4, 4, 4};
+ model->setOperandValue(op32, op32_init, sizeof(int32_t) * 4);
static int32_t param16_init[] = {0};
model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
static int32_t param17_init[] = {0};
@@ -372,16 +372,16 @@
}
void CreateModel_layout_nchw_weight_as_input(Model *model) {
- OperandType type13(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5f, 128);
- OperandType type14(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 2}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f},3));
- OperandType type2(Type::TENSOR_INT32, {2});
+ OperandType type14(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5f, 128);
+ OperandType type15(Type::TENSOR_QUANT8_ASYMM, {1, 4, 2, 2}, 1.0f, 128);
+ OperandType type16(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {1, 2, 2, 4}, 0.0f, 0, SymmPerChannelQuantParams({1.0f, 0.5f, 1.0f, 0.5f},3));
OperandType type4(Type::INT32, {});
- OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 1.0f, 128);
- OperandType type8(Type::BOOL, {});
+ OperandType type7(Type::TENSOR_INT32, {4});
+ OperandType type9(Type::BOOL, {});
// Phase 1, operands
- auto op12 = model->addOperand(&type13);
- auto op22 = model->addOperand(&type14);
- auto op32 = model->addOperand(&type2);
+ auto op12 = model->addOperand(&type14);
+ auto op22 = model->addOperand(&type16);
+ auto op32 = model->addOperand(&type7);
auto param16 = model->addOperand(&type4);
auto param17 = model->addOperand(&type4);
auto param18 = model->addOperand(&type4);
@@ -390,8 +390,8 @@
auto param21 = model->addOperand(&type4);
auto param22 = model->addOperand(&type4);
auto param23 = model->addOperand(&type4);
- auto layout = model->addOperand(&type8);
- auto op42 = model->addOperand(&type7);
+ auto layout = model->addOperand(&type9);
+ auto op42 = model->addOperand(&type15);
// Phase 2, operations
static int32_t param16_init[] = {0};
model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
diff --git a/runtime/test/generated/vts_models/depthwise_conv2d_per_channel.model.cpp b/runtime/test/generated/vts_models/depthwise_conv2d_per_channel.model.cpp
index 877a360..484f86c 100644
--- a/runtime/test/generated/vts_models/depthwise_conv2d_per_channel.model.cpp
+++ b/runtime/test/generated/vts_models/depthwise_conv2d_per_channel.model.cpp
@@ -308,58 +308,22 @@
},
{
.type = OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 0, .length = 8},
+ .location = {.poolIndex = 0, .offset = 0, .length = 16},
.extraParams = std::move(extraParams1),
},
{
.type = OperandType::TENSOR_INT32,
- .dimensions = {2},
+ .dimensions = {4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 8, .length = 8},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 16, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 20, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 24, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 28, .length = 4},
+ .location = {.poolIndex = 0, .offset = 16, .length = 16},
},
{
.type = OperandType::INT32,
@@ -398,8 +362,44 @@
.location = {.poolIndex = 0, .offset = 44, .length = 4},
},
{
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 48, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 52, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 56, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 60, .length = 4},
+ },
+ {
.type = OperandType::TENSOR_QUANT8_ASYMM,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 128,
@@ -419,7 +419,7 @@
const std::vector<uint32_t> inputIndexes = {0};
const std::vector<uint32_t> outputIndexes = {11};
std::vector<uint8_t> operandValues = {
- 1, 1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0
};
const std::vector<hidl_memory> pools = {};
@@ -454,7 +454,7 @@
},
{
.type = OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
@@ -464,7 +464,7 @@
},
{
.type = OperandType::TENSOR_INT32,
- .dimensions = {2},
+ .dimensions = {4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
@@ -545,7 +545,7 @@
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 128,
@@ -600,58 +600,22 @@
},
{
.type = OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 0, .length = 8},
+ .location = {.poolIndex = 0, .offset = 0, .length = 16},
.extraParams = std::move(extraParams1),
},
{
.type = OperandType::TENSOR_INT32,
- .dimensions = {2},
+ .dimensions = {4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 8, .length = 8},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 16, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 20, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 24, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 28, .length = 4},
+ .location = {.poolIndex = 0, .offset = 16, .length = 16},
},
{
.type = OperandType::INT32,
@@ -690,17 +654,53 @@
.location = {.poolIndex = 0, .offset = 44, .length = 4},
},
{
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 48, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 52, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 56, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 60, .length = 4},
+ },
+ {
.type = OperandType::BOOL,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 48, .length = 1},
+ .location = {.poolIndex = 0, .offset = 64, .length = 1},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 128,
@@ -720,7 +720,7 @@
const std::vector<uint32_t> inputIndexes = {0};
const std::vector<uint32_t> outputIndexes = {12};
std::vector<uint8_t> operandValues = {
- 1, 1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0
};
const std::vector<hidl_memory> pools = {};
@@ -755,7 +755,7 @@
},
{
.type = OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
@@ -765,7 +765,7 @@
},
{
.type = OperandType::TENSOR_INT32,
- .dimensions = {2},
+ .dimensions = {4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
@@ -855,7 +855,7 @@
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 128,
@@ -910,58 +910,22 @@
},
{
.type = OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 0, .length = 8},
+ .location = {.poolIndex = 0, .offset = 0, .length = 16},
.extraParams = std::move(extraParams1),
},
{
.type = OperandType::TENSOR_INT32,
- .dimensions = {2},
+ .dimensions = {4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 8, .length = 8},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 16, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 20, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 24, .length = 4},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 28, .length = 4},
+ .location = {.poolIndex = 0, .offset = 16, .length = 16},
},
{
.type = OperandType::INT32,
@@ -1000,17 +964,53 @@
.location = {.poolIndex = 0, .offset = 44, .length = 4},
},
{
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 48, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 52, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 56, .length = 4},
+ },
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::CONSTANT_COPY,
+ .location = {.poolIndex = 0, .offset = 60, .length = 4},
+ },
+ {
.type = OperandType::BOOL,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 48, .length = 1},
+ .location = {.poolIndex = 0, .offset = 64, .length = 1},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 4, 2, 2},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 128,
@@ -1030,7 +1030,7 @@
const std::vector<uint32_t> inputIndexes = {0};
const std::vector<uint32_t> outputIndexes = {12};
std::vector<uint8_t> operandValues = {
- 1, 1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1
};
const std::vector<hidl_memory> pools = {};
@@ -1065,7 +1065,7 @@
},
{
.type = OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 2, 2, 4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
@@ -1075,7 +1075,7 @@
},
{
.type = OperandType::TENSOR_INT32,
- .dimensions = {2},
+ .dimensions = {4},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
@@ -1165,7 +1165,7 @@
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
- .dimensions = {1, 2, 2, 2},
+ .dimensions = {1, 4, 2, 2},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 128,
diff --git a/runtime/test/specs/V1_2/depthwise_conv2d_per_channel.mod.py b/runtime/test/specs/V1_2/depthwise_conv2d_per_channel.mod.py
index 152a808..2df79ee 100644
--- a/runtime/test/specs/V1_2/depthwise_conv2d_per_channel.mod.py
+++ b/runtime/test/specs/V1_2/depthwise_conv2d_per_channel.mod.py
@@ -31,17 +31,18 @@
# TEST 2: Different scales, zeroPoint=128
i2 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 3, 3, 2}, 0.5f, 128")
-f2 = Parameter("op2", "TENSOR_QUANT8_SYMM_PER_CHANNEL", "{1, 2, 2, 2}, 0.0f, 0",
- [1, 1, 1, 1, 1, 1, 1, 1],
- extraParams = SymmPerChannelQuantParams(channelDim=3, scales=[1.0, 0.5]))
-b2 = Parameter("op3", "TENSOR_INT32", "{2}", [4, 4])
-o2 = Output("op4", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 1.f, 128")
+f2 = Parameter("op2", "TENSOR_QUANT8_SYMM_PER_CHANNEL", "{1, 2, 2, 4}, 0.0f, 0",
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ extraParams = SymmPerChannelQuantParams(channelDim=3, scales=[1.0, 0.5, 1.0, 0.5]))
+b2 = Parameter("op3", "TENSOR_INT32", "{4}", [4, 4, 4, 4])
+o2 = Output("op4", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 4}, 1.f, 128")
Model("different").Operation("DEPTHWISE_CONV_2D", i2, f2, b2, 0, 0, 0, 0, 1, 1, 2, 0).To(o2)
# Instantiate an example
Example({
i2: [129, 130] * 9,
- o2: [132, 130, 128, 128, 132, 130, 129, 128],
+ o2: [132, 130, 134, 131, 132, 130, 134, 131,
+ 132, 130, 134, 131, 132, 130, 134, 131],
}).AddInput(f2, b2)
@@ -49,15 +50,16 @@
# TEST 3: With layout param
i3 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 3, 3, 2}, 0.5f, 128")
-f3 = Parameter("op2", "TENSOR_QUANT8_SYMM_PER_CHANNEL", "{1, 2, 2, 2}, 0.0f, 0",
- [1, 1, 1, 1, 1, 1, 1, 1],
- extraParams = SymmPerChannelQuantParams(channelDim=3, scales=[1.0, 0.5]))
-b3 = Parameter("op3", "TENSOR_INT32", "{2}", [4, 4])
-o3 = Output("op4", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 1.f, 128")
+f3 = Parameter("op2", "TENSOR_QUANT8_SYMM_PER_CHANNEL", "{1, 2, 2, 4}, 0.0f, 0",
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ extraParams = SymmPerChannelQuantParams(channelDim=3, scales=[1.0, 0.5, 1.0, 0.5]))
+b3 = Parameter("op3", "TENSOR_INT32", "{4}", [4, 4, 4, 4])
+o3 = Output("op4", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 4}, 1.f, 128")
Model("layout").Operation("DEPTHWISE_CONV_2D", i3, f3, b3, 0, 0, 0, 0, 1, 1, 2, 0, layout).To(o3)
# Instantiate an example
Example({
i3: [129, 130] * 9,
- o3: [132, 130, 128, 128, 132, 130, 129, 128],
+ o3: [132, 130, 134, 131, 132, 130, 134, 131,
+ 132, 130, 134, 131, 132, 130, 134, 131],
}).AddNchw(i3, o3, layout).AddInput(f3, b3)
