commit f733e0375f62bbecbbf87045e4e40afad858b318
author Manuel Bottini <manuel.bottini@arm.com> Wed May 19 16:15:36 2021 +0100
committer Manuel Bottini <manuel.bottini@arm.com> Wed May 26 13:59:44 2021 +0000
tree 272873e8a5c0381f0ff6b1ead7892d65d7a7f354
parent d89e2faa60d148f3c04e57032a28f1065a1be0e8

DirectConvolutionLayer create image failure

Add implicit padding test on weights before the configure
Fix problem of considering left padding of weights when using cl image

Resolves: COMPMID-4493

Change-Id: I141d2de68e8bdfcbd6f18209db4f29fcc05305a1
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5689
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

tests/validation/fixtures/DirectConvolutionLayerFixture.h

diff --git a/tests/validation/fixtures/DirectConvolutionLayerFixture.h b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
index d21a2e7..614aa20 100644
--- a/tests/validation/fixtures/DirectConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
@@ -162,6 +162,9 @@
         TensorType bias    = create_tensor<TensorType>(bias_shape, bias_data_type, 1, quantization_info);
         TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, quantization_info, data_layout);
 
+        add_padding_x({ &src, &bias, &dst }, data_layout);
+        add_padding_x({ &weights }, data_layout, input_shape[0] % 4 == 0); // Don't add left padding if cl image will be used
+
         // Create and configure function
         FunctionType conv;
         conv.configure(&src, &weights, &bias, &dst, info, act_info);
@@ -171,8 +174,6 @@
         ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
 
-        add_padding_x({ &src, &bias, &dst }, data_layout);
-
         // Allocate tensors
         src.allocator()->allocate();
         weights.allocator()->allocate();