COMPMID-1167: Validation for NEDepthwiseConvolutionLayer
Change-Id: I9689e1a0627dc015dd2ce98417e4c97bb55581bb
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/131327
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/NEON/DepthwiseConvolutionLayer.cpp b/tests/validation/NEON/DepthwiseConvolutionLayer.cpp
index b1cc491..956fd74 100644
--- a/tests/validation/NEON/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/NEON/DepthwiseConvolutionLayer.cpp
@@ -54,57 +54,140 @@
TEST_SUITE(NEON)
TEST_SUITE(DepthwiseConvLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallDepthwiseConvolutionLayerDataset3x3(),
- datasets::LargeDepthwiseConvolutionLayerDataset3x3()),
- depth_multipliers),
- framework::dataset::make("DataType", DataType::F32)),
- input_shape, kernel_size, info, depth_multiplier, data_type)
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(32U, 18U, 3U), 1, DataType::F32), // Mismatching input feature maps
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Unsupported weights dimensions
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Mismatching depth multiplier
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid stride
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases size
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases dimensions
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
+ }),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ })),
+ framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ })),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ })),
+ framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(4, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ })),
+ framework::dataset::make("DepthMultiplier", { 1,
+ 1,
+ 1,
+ 3,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })),
+ input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, expected)
{
- // Get shapes
- TensorShape weights_shape(kernel_size.width, kernel_size.height);
-
- const TensorInfo in_info(input_shape, 1, data_type);
- const TensorInfo we_info(weights_shape, 1, data_type);
- const TensorShape output_shape = compute_depthwise_convolution_shape(in_info, we_info, info, depth_multiplier);
-
- weights_shape.set(2, output_shape.z());
-
- // Create tensors
- Tensor src = create_tensor<Tensor>(input_shape, data_type);
- Tensor dst = create_tensor<Tensor>(output_shape, data_type);
- Tensor weights = create_tensor<Tensor>(weights_shape, data_type);
- const TensorShape bias_shape(weights_shape[2]);
- Tensor bias = create_tensor<Tensor>(bias_shape, data_type);
-
- ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
-
- // Create and configure function
- NEDepthwiseConvolutionLayer3x3 depthwise_layer;
- depthwise_layer.configure(&src, &weights, &bias, &dst, info, depth_multiplier);
-
- // Validate valid region
- const ValidRegion input_valid_region = shape_to_valid_region(input_shape);
- const ValidRegion output_valid_region = shape_to_valid_region(output_shape);
- const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape);
- const ValidRegion bias_valid_region = shape_to_valid_region(bias_shape);
-
- validate(src.info()->valid_region(), input_valid_region);
- validate(dst.info()->valid_region(), output_valid_region);
- validate(weights.info()->valid_region(), weights_valid_region);
- validate(bias.info()->valid_region(), bias_valid_region);
-
- // Validate padding
- bool is_optimized_run = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input_shape, info, data_type, depth_multiplier, DataLayout::NCHW);
- const int step_non_opt_dwc = 16 >> info.stride().first;
- const int step_bias_add = 16 / src.info()->element_size();
- const int step = is_optimized_run ? step_bias_add : std::max(step_non_opt_dwc, step_bias_add);
- const PaddingSize padding = PaddingCalculator(output_shape.x(), step).required_padding();
- validate(dst.info()->padding(), padding);
+ bool is_valid = bool(NEDepthwiseConvolutionLayer3x3::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier));
+ ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
+DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(27U, 13U, 3U), 1, DataType::F32), // Mismatching input feature maps
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching depth multiplier
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 8U), 1, DataType::QASYMM8),
+ }),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8),
+ })),
+ framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(24U), 1, DataType::S32),
+ })),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 11U, 24U), 1, DataType::QASYMM8),
+ })),
+ framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 1, 0),
+ })),
+ framework::dataset::make("DepthMultiplier", { 1,
+ 1,
+ 3,
+ 1,
+ 1,
+ 1,
+ 2,
+ 3,
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, true, true })),
+ input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, expected)
+{
+ bool is_valid = bool(NEDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier));
+ ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
TEST_SUITE(Float)
TEST_SUITE(F32)
TEST_SUITE(Generic)