commit d8539b2460304799e43e403f15e7723834e35503
author Georgios Pinitas <georgios.pinitas@arm.com> Mon Jul 03 16:35:17 2017 +0100
committer Anthony Barbier <anthony.barbier@arm.com> Mon Sep 17 14:15:39 2018 +0100
tree 6f50eb6f00ac2cee03c663e064b7b8e139dab323
parent 09004ca6a259f20e2fe31844ee5a63cc80e56dfd

COMPMID-417: Fix neon_cnn example.

Add proper stride and paddding info to avoid incorrect tensor shapes.

Change-Id: Ib906e43cf4ef09364acfb03201e765642dbc25af
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/79555
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

examples/neon_cnn.cpp

diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
index 952ae4d..238f057 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -143,22 +143,22 @@
     /* [Configure functions] */
 
     // in:32x32x1: 5x5 convolution, 8 output features maps (OFM)
-    conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo());
+    conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */));
 
     // in:32x32x8, out:32x32x8, Activation function: relu
     act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
     // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
-    pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2));
+    pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
 
     // in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
-    conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo());
+    conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */));
 
     // in:16x16x16, out:16x16x16, Activation function: relu
     act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
     // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
-    pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2));
+    pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
 
     // in:8x8x16, out:128
     fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0);