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Diffstat (limited to 'tests/validation/fixtures/DeconvolutionLayerFixture.h')
-rw-r--r--tests/validation/fixtures/DeconvolutionLayerFixture.h134
1 files changed, 92 insertions, 42 deletions
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
index 57951c0f36..83170c413c 100644
--- a/tests/validation/fixtures/DeconvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 ARM Limited.
+ * Copyright (c) 2017-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -42,22 +42,24 @@ namespace validation
{
using namespace arm_compute::misc::shape_calculator;
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW>
class DeconvolutionLayerFixtureBase : public framework::Fixture
{
public:
using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value || std::is_same<typename std::decay<T>::type, int8_t>::value, int32_t, T >::type;
public:
- template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
- DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias)
+ DataType data_type, DataType weights_data_type, DataLayout data_layout,
+ QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, QuantizationInfo weights_quantization_info, bool add_bias)
{
- _data_type = data_type;
- _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
- _data_layout = data_layout;
- _input_quantization_info = input_quantization_info;
- _output_quantization_info = output_quantization_info;
+ _data_type = data_type;
+ _weights_data_type = weights_data_type;
+ _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
+ _data_layout = data_layout;
+ _input_quantization_info = input_quantization_info;
+ _output_quantization_info = output_quantization_info;
+ _weights_quantization_info = weights_quantization_info;
_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, add_bias);
_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, add_bias);
@@ -72,14 +74,34 @@ protected:
case DataType::QASYMM8:
{
std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
- std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
+ std::uniform_int_distribution<uint32_t> distribution(bounds.first, bounds.second);
library->fill(tensor, distribution, i);
break;
}
case DataType::QASYMM8_SIGNED:
{
std::pair<int, int> bounds = get_quantized_qasymm8_signed_bounds(tensor.quantization_info(), -1.0f, 1.0f);
- std::uniform_int_distribution<int8_t> distribution(bounds.first, bounds.second);
+ std::uniform_int_distribution<int32_t> distribution(bounds.first, bounds.second);
+ library->fill(tensor, distribution, i);
+ break;
+ }
+ case DataType::QSYMM8_PER_CHANNEL:
+ {
+ int min_bound = 128;
+ int max_bound = -127;
+ for(size_t i = 0; i < _input_quantization_info.scale().size(); i++)
+ {
+ std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f);
+ if(bounds.first < min_bound)
+ {
+ min_bound = bounds.first;
+ }
+ if(bounds.second > max_bound)
+ {
+ max_bound = bounds.second;
+ }
+ }
+ std::uniform_int_distribution<int32_t> distribution(min_bound, max_bound);
library->fill(tensor, distribution, i);
break;
}
@@ -90,9 +112,14 @@ protected:
break;
}
case DataType::F16:
+ {
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
+ library->fill(tensor, distribution, i);
+ break;
+ }
case DataType::F32:
{
- std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
break;
}
@@ -108,8 +135,7 @@ protected:
{
case DataType::S32:
{
- const int32_t value = static_cast<int32_t>(tensor.quantization_info().uniform().offset);
- library->fill_tensor_value(tensor, value);
+ library->fill_tensor_value(tensor, 0);
break;
}
case DataType::F16:
@@ -135,7 +161,7 @@ protected:
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _input_quantization_info, _data_layout);
- TensorType weights = create_tensor<TensorType>(weights_shape, _data_type, 1, _input_quantization_info, _data_layout);
+ TensorType weights = create_tensor<TensorType>(weights_shape, _weights_data_type, 1, _weights_quantization_info, _data_layout);
TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _input_quantization_info, _data_layout);
TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _output_quantization_info, _data_layout);
@@ -143,13 +169,13 @@ protected:
FunctionType conv;
conv.configure(&src, &weights, add_bias ? &bias : nullptr, &dst, info);
- ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_ASSERT(src.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(weights.info()->is_resizable());
if(add_bias)
{
- ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
}
- ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
// Allocate tensors
src.allocator()->allocate();
@@ -160,13 +186,13 @@ protected:
}
dst.allocator()->allocate();
- ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!weights.info()->is_resizable());
if(add_bias)
{
- ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
}
- ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
// Fill tensors
fill(AccessorType(src), 0);
@@ -178,7 +204,6 @@ protected:
// Compute DeconvolutionLayer function
conv.run();
-
return dst;
}
@@ -187,7 +212,7 @@ protected:
{
// Create reference
SimpleTensor<T> src{ input_shape, _data_type, 1, _input_quantization_info };
- SimpleTensor<T> weights{ weights_shape, _data_type, 1, _input_quantization_info };
+ SimpleTensor<TW> weights{ weights_shape, _weights_data_type, 1, _weights_quantization_info };
SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _input_quantization_info };
// Fill reference
@@ -202,28 +227,27 @@ protected:
{
fill_zeros(bias);
}
-
- return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, _output_quantization_info);
+ return reference::deconvolution_layer<T, TW>(src, weights, bias, output_shape, info, _output_quantization_info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _data_type{};
+ DataType _weights_data_type{};
DataType _bias_data_type{};
DataLayout _data_layout{};
QuantizationInfo _input_quantization_info{};
QuantizationInfo _output_quantization_info{};
+ QuantizationInfo _weights_quantization_info{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
-class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
+class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
{
public:
- template <typename...>
void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias)
{
- ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
@@ -231,20 +255,18 @@ public:
TensorInfo input_info(input_shape, 1, data_type);
TensorInfo weights_info(weights_shape, 1, data_type);
TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
- DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, QuantizationInfo(),
- QuantizationInfo(), add_bias);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout, QuantizationInfo(),
+ QuantizationInfo(), QuantizationInfo(), add_bias);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
-class DeconvolutionValidationAsymmFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
+class DeconvolutionValidationAsymmFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
{
public:
- template <typename...>
void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int pad_left, unsigned int pad_right, unsigned int pad_top,
unsigned int pad_bottom, unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias)
{
- ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo info(sx, sy, pad_left, pad_right, pad_top, pad_bottom, DimensionRoundingType::CEIL);
@@ -252,20 +274,18 @@ public:
TensorInfo input_info(input_shape, 1, data_type);
TensorInfo weights_info(weights_shape, 1, data_type);
TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
- DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, QuantizationInfo(),
- QuantizationInfo(), add_bias);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout, QuantizationInfo(),
+ QuantizationInfo(), QuantizationInfo(), add_bias);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
-class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
+class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
{
public:
- template <typename...>
void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias)
{
- ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
@@ -273,8 +293,38 @@ public:
TensorInfo input_info(input_shape, 1, data_type, input_quantization_info);
TensorInfo weights_info(weights_shape, 1, data_type, input_quantization_info);
TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
- DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, input_quantization_info,
- output_quantization_info, add_bias);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout,
+ input_quantization_info,
+ output_quantization_info, input_quantization_info, add_bias);
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW, unsigned int kernel_size_x, unsigned int kernel_size_y>
+class DeconvolutionValidationQuantizedPerChannelFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, TW>
+{
+public:
+ void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
+ unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias,
+ DataType weights_data_type)
+ {
+ const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
+ const TensorShape bias_shape(num_kernels);
+ const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
+ auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
+ TensorInfo input_info(input_shape, 1, data_type, input_quantization_info);
+ TensorInfo weights_info(weights_shape, 1, weights_data_type, input_quantization_info);
+ TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
+
+ std::vector<float> weights_scales{};
+ std::mt19937 gen(library->seed());
+ std::uniform_real_distribution<float> dis(0.01f, 1.f);
+ for(size_t i = 0; i < output_shape[2]; ++i)
+ {
+ weights_scales.push_back(dis(gen));
+ }
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, TW>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, weights_data_type, data_layout,
+ input_quantization_info,
+ output_quantization_info, QuantizationInfo(weights_scales), add_bias);
}
};
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-05 09:05:54 +0000