Fix Pooling Layer Bug when input is 1xN size

Return error in pooling layer when any calculated output dimension is less than 1.

Simplify use of pooling layer output dimension values in
CpuPoolingKernel.cpp.

Remove some invalid tests in cpu/gpu pooling layers.

Resolves COMPMID-4358.

Signed-off-by: Freddie Liardet <frederick.liardet@arm.com>
Change-Id: If8f8ffec579d3eca1c27a45e5b0b684a77103cff
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5559
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 16 insertions, 14 deletions

diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 8e49c068af..d0dc202f91 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -759,25 +759,27 @@ inline TensorShape compute_min_max_shape(const ITensorInfo *input)
  */
 inline TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo pool_info)
 {
-    unsigned int pooled_w = 0;
-    unsigned int pooled_h = 0;
+    int pooled_w = 0;
+    int pooled_h = 0;
 
     TensorShape output_shape{ input.tensor_shape() };
 
-    const bool         is_global_pooling = pool_info.is_global_pooling;
-    const unsigned int idx_width         = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
-    const unsigned int idx_height        = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
-    const unsigned int pool_size_x       = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size.width;
-    const unsigned int pool_size_y       = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size.height;
+    const bool is_global_pooling = pool_info.is_global_pooling;
+    const int  idx_width         = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
+    const int  idx_height        = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
+    const int  input_width       = input.tensor_shape()[idx_width];
+    const int  input_height      = input.tensor_shape()[idx_height];
+    const int  pool_size_x       = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size.width;
+    const int  pool_size_y       = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size.height;
 
-    std::tie(pooled_w, pooled_h) = scaled_dimensions(output_shape[idx_width],
-                                                     output_shape[idx_height],
-                                                     pool_size_x,
-                                                     pool_size_y,
-                                                     pool_info.pad_stride_info);
+    std::tie(pooled_w, pooled_h) = scaled_dimensions_signed(input_width, input_height,
+                                                            pool_size_x, pool_size_y,
+                                                            pool_info.pad_stride_info);
 
-    output_shape.set(idx_width, pooled_w);
-    output_shape.set(idx_height, pooled_h);
+    ARM_COMPUTE_ERROR_ON_MSG((pooled_w < 1 || pooled_h < 1), "Calculated output dimension size is invalid");
+
+    output_shape.set(idx_width, static_cast<size_t>(pooled_w));
+    output_shape.set(idx_height, static_cast<size_t>(pooled_h));
 
     return output_shape;
 }