From 53a6ec5944132000e2c6779c04d722b3b2d2501c Mon Sep 17 00:00:00 2001 From: Xinghang Zhou Date: Tue, 14 Nov 2017 15:14:25 +0800 Subject: APPBROWSER-304,342: Add exclude padding support for OpenGL ES implementation and implement MaxPool operators Change-Id: Ie6ba36ff114feec2a21739dba11bbb60b76af443 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/113697 Tested-by: Jenkins Reviewed-by: Stephen Li Reviewed-by: Pablo Tello Reviewed-by: Anthony Barbier --- src/core/GLES_COMPUTE/cs_shaders/fill_border.cs | 46 ++- src/core/GLES_COMPUTE/cs_shaders/pooling_layer.cs | 174 ++++++++--- .../GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp | 319 ++++++++++++++------- 3 files changed, 380 insertions(+), 159 deletions(-) (limited to 'src/core/GLES_COMPUTE') diff --git a/src/core/GLES_COMPUTE/cs_shaders/fill_border.cs b/src/core/GLES_COMPUTE/cs_shaders/fill_border.cs index c64572b061..4f87b9242a 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/fill_border.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/fill_border.cs @@ -132,7 +132,7 @@ void main() ImageIterator buf_iter = CONVERT_TENSOR3D_TO_IMAGE_ITERATOR_NO_STEP(buf_attrs, buf_shift); // Update pointer to point to the starting point of the valid region - TENSOR_ITERATOR_ADVANCE_IN_BYTES(buf_iter, uint(start_pos_y) * buf_attrs.stride_y + uint(start_pos_x) * buf_attrs.stride_x); + TENSOR_ITERATOR_ADVANCE_IN_BYTES(buf_iter, start_pos_y * int(buf_attrs.stride_y) + start_pos_x * int(buf_attrs.stride_x)); int total_width = BORDER_SIZE_LEFT + int(width) + BORDER_SIZE_RIGHT; int gid0 = int(gl_GlobalInvocationID.x); @@ -158,12 +158,29 @@ void main() { if(pos % 2 == 0) { - STORE_PACK2_HALF(buf_ptr, offset, left_val.xx); + if(BORDER_SIZE_LEFT % 2 == 0) + { + STORE_PACK2_HALF(buf_ptr, offset, left_val.xx); + } + else + { + STORE_PACK2_HALF(buf_ptr, offset, left_val.yy); + } + i++; } } } // Handle right border - vec2 right_val = LOAD_UNPACK2_HALF(buf_ptr, IMAGE_OFFSET(buf_iter, int(width) - 1, gidH)); + vec2 right_val_origin = LOAD_UNPACK2_HALF(buf_ptr, IMAGE_OFFSET(buf_iter, int(width) - 1, gidH)); + vec2 right_val; + if((((BORDER_SIZE_LEFT + int(width)) % 2)) == 1) + { + right_val = vec2(right_val_origin.x, right_val_origin.x); + } + else + { + right_val = vec2(right_val_origin.y, right_val_origin.y); + } for(int i = 0; i < BORDER_SIZE_RIGHT; ++i) { uint offset = IMAGE_OFFSET(buf_iter, int(width) + i, gidH); @@ -173,7 +190,8 @@ void main() { if(pos % 2 == 0) { - STORE_PACK2_HALF(buf_ptr, offset, right_val.yy); + STORE_PACK2_HALF(buf_ptr, offset, right_val); + i++; } else { @@ -184,7 +202,8 @@ void main() { if(pos % 2 == 0) { - STORE_PACK2_HALF(buf_ptr, offset, right_val.yy); + STORE_PACK2_HALF(buf_ptr, offset, right_val); + i++; } } } @@ -208,7 +227,14 @@ void main() { if(gidW == (int(width) - 1)) { - STORE_PACK2_HALF(buf_ptr, offset, top_val.xx); + if(((BORDER_SIZE_LEFT + int(width)) % 2 == 1)) + { + STORE_PACK2_HALF(buf_ptr, offset, top_val.xx); + } + else + { + STORE_PACK2_HALF(buf_ptr, offset, top_val.yy); + } } else { @@ -229,6 +255,10 @@ void main() { STORE_PACK2_HALF(buf_ptr, offset, top_val.yy); } + else + { + STORE_PACK2_HALF(buf_ptr, offset, top_val.xx); + } } else { @@ -268,6 +298,10 @@ void main() { STORE_PACK2_HALF(buf_ptr, offset, bottom_val.yy); } + else + { + STORE_PACK2_HALF(buf_ptr, offset, bottom_val.xx); + } } else { diff --git a/src/core/GLES_COMPUTE/cs_shaders/pooling_layer.cs b/src/core/GLES_COMPUTE/cs_shaders/pooling_layer.cs index 401b002111..64767a7ef1 100644 --- a/src/core/GLES_COMPUTE/cs_shaders/pooling_layer.cs +++ b/src/core/GLES_COMPUTE/cs_shaders/pooling_layer.cs @@ -259,7 +259,7 @@ layout(std140) uniform shader_params POOL_OP(data001.xyzw, data001.xyzw, data201.xyzw); \ POOL_OP(data010.xyzw, data010.xyzw, data21.xyzw); \ POOL_OP(res.xyzw, vec4(data000.xw, data001.z, data010.y), vec4(data000.y, data001.xw, data010.z)); \ - POOL_OP(res.xyzw, res.xyzw, vec4(data000.z, data001.y data010.xw)) + POOL_OP(res.xyzw, res.xyzw, vec4(data000.z, data001.y, data010.xw)) float calculate_max(const int pool_size, Tensor3D src, const int upper_bound_w, const int upper_bound_h, const int pad_x, const int pad_y, const int stride_x, const int stride_y) { @@ -271,11 +271,11 @@ float calculate_max(const int pool_size, Tensor3D src, const int upper_bound_w, float data_max; data_max = LOAD4(src, tensor3D_offset(src, 0, 0, 0)); - for(int i = 0; (start_x + i) < end_x; ++i) + for(int i = 0; (start_y + i) < end_y; ++i) { - for(int j = 0; (start_y + j) < end_y; ++j) + for(int j = 0; (start_x + j) < end_x; ++j) { - float data = LOAD4(src, tensor3D_offset(src, i, j, 0)); + float data = LOAD4(src, tensor3D_offset(src, j, i, 0)); POOL_OP_float(data_max, data_max, data); } } @@ -308,6 +308,11 @@ float calculate_avg(const int pool_size, Tensor3D src, const int upper_bound_w, } } +#if defined(EXCLUDE_PADDING) + start_x = max(0, start_x); + start_y = max(0, start_y); +#endif /* defined(EXCLUDE_PADDING) */ + return data_total / float((end_y - start_y) * (end_x - start_x)); } @@ -460,6 +465,10 @@ void main(void) int start_y = int(gl_GlobalInvocationID.y) * STRIDE_Y - PAD_Y; ivec4 end_x = min((start_x + (ivec4(3))), (ivec4(MAX_WIDTH))); int end_y = min((start_y + 3), MAX_HEIGHT); +#if defined(EXCLUDE_PADDING) + start_x = max(ivec4(0), start_x); + start_y = max(0, start_y); +#endif /* defined(EXCLUDE_PADDING) */ res *= (vec4((1.f)) / vec4((ivec4(end_y - start_y)) * (end_x - start_x))); #endif /*POOL_AVG*/ @@ -606,12 +615,16 @@ void main(void) #if defined(POOL_AVG) || defined(POOL_L2) { // Divide by pool region in case of average pooling - int start_x = int(gl_GlobalInvocationID.x) * STRIDE_X - PAD_X; - int start_y = int(gl_GlobalInvocationID.y) * STRIDE_Y - PAD_Y; - int end_x = int(min(start_x + POOL_SIZE, MAX_WIDTH)); - int end_y = int(min(start_y + POOL_SIZE, MAX_HEIGHT)); - float res1 = float((end_y - start_y) * (end_x - start_x)); - res = DIV_OP(res, res1); + int start_x = int(gl_GlobalInvocationID.x) * STRIDE_X - PAD_X; + int start_y = int(gl_GlobalInvocationID.y) * STRIDE_Y - PAD_Y; + int end_x = int(min(start_x + POOL_SIZE, MAX_WIDTH)); + int end_y = int(min(start_y + POOL_SIZE, MAX_HEIGHT)); +#if defined(EXCLUDE_PADDING) + start_x = max(0, start_x); + start_y = max(0, start_y); +#endif /* defined(EXCLUDE_PADDING) */ + float res1 = float((end_y - start_y) * (end_x - start_x)); + res = DIV_OP(res, res1); } #endif /* defined(POOL_AVG) || defined(POOL_L2) */ @@ -867,7 +880,7 @@ layout(std140) uniform shader_params POOL_OP(data001.xyzw, data001.xyzw, data201.xyzw); \ POOL_OP(data010.xyzw, data010.xyzw, data21.xyzw); \ POOL_OP(res.xyzw, vec4(data000.xw, data001.z, data010.y), vec4(data000.y, data001.xw, data010.z)); \ - POOL_OP(res.xyzw, res.xyzw, vec4(data000.z, data001.y data010.xw)) + POOL_OP(res.xyzw, res.xyzw, vec4(data000.z, data001.y, data010.xw)) vec2 load_and_unpack(Tensor3D src, uint offset) { @@ -970,7 +983,7 @@ vec2 calculate_max(const int pool_size, Tensor3D src, const int upper_bound_w, c vec2 calculate_avg(const int pool_size, Tensor3D src, const int upper_bound_w, const int upper_bound_h, const int pad_x, const int pad_y, const int stride_x, const int stride_y) { - int start_x1 = int(gl_GlobalInvocationID.x) * stride_x - pad_x; + int start_x1 = (2 * int(gl_GlobalInvocationID.x)) * stride_x - pad_x; int start_y1 = int(gl_GlobalInvocationID.y) * stride_y - pad_y; int end_x1 = int(min(start_x1 + pool_size, upper_bound_w)); int end_y1 = int(min(start_y1 + pool_size, upper_bound_h)); @@ -1002,11 +1015,11 @@ vec2 calculate_avg(const int pool_size, Tensor3D src, const int upper_bound_w, c } //Calculate sum2 - if((start_x2 + j) < end_x2 && end_x1 < upper_bound_w) + if((start_x2 + j) < end_x2 && end_x1 <= upper_bound_w) { if((stride_x % 2) == 0) { - vec2 data2 = load_and_unpack(src, (tensor3D_offset_fp16(src, (j + stride_x + 1), i, 0) >> uint(2))); + vec2 data2 = load_and_unpack(src, (tensor3D_offset_fp16(src, (j + stride_x), i, 0) >> uint(2))); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data2 = POW2_OP(data2, 2); @@ -1040,6 +1053,13 @@ vec2 calculate_avg(const int pool_size, Tensor3D src, const int upper_bound_w, c } } } +#if defined(EXCLUDE_PADDING) + start_x1 = max(0, start_x1); + start_y1 = max(0, start_y1); + start_x2 = max(0, start_x2); + start_y2 = max(0, start_y2); +#endif /* defined(EXCLUDE_PADDING) */ + //Calculate average vec2 data_avg; data_avg.x = data_total1 / float((end_y1 - start_y1) * (end_x1 - start_x1)); @@ -1203,6 +1223,10 @@ void main(void) int start_y = int(gl_GlobalInvocationID.y) * STRIDE_Y - PAD_Y; ivec4 end_x = min((start_x + (ivec4(3))), (ivec4(MAX_WIDTH))); int end_y = min((start_y + 3), MAX_HEIGHT); +#if defined(EXCLUDE_PADDING) + start_x = max(ivec4(0), start_x); + start_y = max(0, start_y); +#endif /* defined(EXCLUDE_PADDING) */ res *= (vec4((1.f)) / vec4((ivec4(end_y - start_y)) * (end_x - start_x))); #endif /*POOL_AVG*/ @@ -1354,44 +1378,96 @@ void main(void) } } - for(int y = STRIDE_X; y < int(POOL_SIZE + STRIDE_X); y++) + for(int y = 0; y < int(POOL_SIZE); y++) { - int x1 = STRIDE_X; - for(; x1 <= (int(POOL_SIZE + STRIDE_X) - 8); x1 += 8) + if((STRIDE_X % 2) == 0) { - vec4 data2; - vec4 data3; - LOAD4_fp16(data2, src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2))); - LOAD4_fp16(data3, src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2)) + uint(2)); + int x1 = STRIDE_X; + for(; x1 <= (int(POOL_SIZE + STRIDE_X) - 8); x1 += 8) + { + vec4 data2; + vec4 data3; + LOAD4_fp16(data2, src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2))); + LOAD4_fp16(data3, src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2)) + uint(2)); #if defined(POOL_L2) - // Raise to power of 2 for L2 Pooling - data2 *= data2; - data3 *= data3; + // Raise to power of 2 for L2 Pooling + data2 *= data2; + data3 *= data3; #endif /* defined(POOL_L2) */ - POOL_OP(vdata01, vdata01, data2); - POOL_OP(vdata11, vdata11, data3); - } + POOL_OP(vdata01, vdata01, data2); + POOL_OP(vdata11, vdata11, data3); + } - // Leftover - for(; x1 < int(POOL_SIZE + STRIDE_X); x1 = x1 + 2) + // Leftover + for(; x1 < int(POOL_SIZE + STRIDE_X); x1 = x1 + 2) + { + vec2 data4middle; + data4middle = load_and_unpack(src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2))); +#if defined(POOL_L2) + // Raise to power of 2 for L2 Pooling + data4middle *= data4middle; +#endif /* defined(POOL_L2) */ + if((x1 + 1) >= int(POOL_SIZE + STRIDE_X)) + { + POOL_OP_float(sdata.y, sdata.y, data4middle.x); + } + else + { + float data4; + POOL_OP_float(data4, data4middle.x, data4middle.y); + POOL_OP_float(sdata.y, sdata.y, data4); + } + } + } + else { - vec2 data4middle; - data4middle = load_and_unpack(src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2))); + vec2 dataorigin2; + dataorigin2 = load_and_unpack(src, (tensor3D_offset_fp16(src, (STRIDE_X - 1), y, 0) >> uint(2))); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling - data4middle *= data4middle; + dataorigin2.y *= dataorigin2.y; #endif /* defined(POOL_L2) */ - if((x1 + 1) >= int(POOL_SIZE + STRIDE_X)) + POOL_OP_float(sdata.y, sdata.y, dataorigin2.y); + + int x1 = STRIDE_X + 1; + for(; x1 <= (int(POOL_SIZE + STRIDE_X) - 8); x1 += 8) { - POOL_OP_float(sdata.y, sdata.y, data4middle.x); + vec4 data2; + vec4 data3; + LOAD4_fp16(data2, src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2))); + LOAD4_fp16(data3, src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2)) + uint(2)); + +#if defined(POOL_L2) + // Raise to power of 2 for L2 Pooling + data2 *= data2; + data3 *= data3; +#endif /* defined(POOL_L2) */ + + POOL_OP(vdata01, vdata01, data2); + POOL_OP(vdata11, vdata11, data3); } - else + + // Leftover + for(; x1 < int(POOL_SIZE + STRIDE_X); x1 = x1 + 2) { - float data4; - POOL_OP_float(data4, data4middle.x, data4middle.y); - POOL_OP_float(sdata.y, sdata.y, data4); + vec2 data4middle; + data4middle = load_and_unpack(src, (tensor3D_offset_fp16(src, x1, y, 0) >> uint(2))); +#if defined(POOL_L2) + // Raise to power of 2 for L2 Pooling + data4middle *= data4middle; +#endif /* defined(POOL_L2) */ + if((x1 + 1) >= int(POOL_SIZE + STRIDE_X)) + { + POOL_OP_float(sdata.y, sdata.y, data4middle.x); + } + else + { + float data4; + POOL_OP_float(data4, data4middle.x, data4middle.y); + POOL_OP_float(sdata.y, sdata.y, data4); + } } } } @@ -1414,14 +1490,20 @@ void main(void) #if defined(POOL_AVG) || defined(POOL_L2) { // Divide by pool region in case of average pooling - int start_x1 = int(gl_GlobalInvocationID.x) * STRIDE_X - PAD_X; - int start_y1 = int(gl_GlobalInvocationID.y) * STRIDE_Y - PAD_Y; - int end_x1 = int(min(start_x1 + POOL_SIZE, MAX_WIDTH)); - int end_y1 = int(min(start_y1 + POOL_SIZE, MAX_HEIGHT)); - int start_x2 = start_x1 + STRIDE_X; - int start_y2 = start_y1; - int end_x2 = int(min(start_x2 + POOL_SIZE, MAX_WIDTH)); - int end_y2 = int(min(start_y2 + POOL_SIZE, MAX_HEIGHT)); + int start_x1 = (2 * int(gl_GlobalInvocationID.x)) * STRIDE_X - PAD_X; + int start_y1 = int(gl_GlobalInvocationID.y) * STRIDE_Y - PAD_Y; + int end_x1 = int(min(start_x1 + POOL_SIZE, MAX_WIDTH)); + int end_y1 = int(min(start_y1 + POOL_SIZE, MAX_HEIGHT)); + int start_x2 = start_x1 + STRIDE_X; + int start_y2 = start_y1; + int end_x2 = int(min(start_x2 + POOL_SIZE, MAX_WIDTH)); + int end_y2 = int(min(start_y2 + POOL_SIZE, MAX_HEIGHT)); +#if defined(EXCLUDE_PADDING) + start_x1 = max(0, start_x1); + start_y1 = max(0, start_y1); + start_x2 = max(0, start_x2); + start_y2 = max(0, start_y2); +#endif /* defined(EXCLUDE_PADDING) */ vec2 res1; res1.x = float((end_y1 - start_y1) * (end_x1 - start_x1)); res1.y = float((end_y2 - start_y2) * (end_x2 - start_x2)); diff --git a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp index 0b6ba583a3..6451db741d 100644 --- a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp +++ b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp @@ -40,6 +40,176 @@ using namespace arm_compute; +namespace +{ +// Internal window config info +using GCPoolingConfig = std::pair; //num_elems_processed_per_iteration, border_size + +void auto_init(const ITensorInfo *input, ITensorInfo *output, unsigned int pooled_w, unsigned int pooled_h) +{ + TensorShape output_shape{ input->tensor_shape() }; + output_shape.set(0, pooled_w); + output_shape.set(1, pooled_h); + + auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); +} + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type() == PoolingType::L2), + "Unsupported combination of parameters!"); + + const bool is_global_pooling = pool_info.is_global_pooling(); + const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size(); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()), + "Global pooling is supported only with rectangular inputs!"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size) || (pool_info.pad_stride_info().pad().second >= pool_size)), + "Invalid pool size and pool pad combination!"); + + // Checks performed when output is configured + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); + + unsigned int pooled_w = 0; + unsigned int pooled_h = 0; + std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0), + input->dimension(1), + pool_size, + pool_size, + pool_info.pad_stride_info()); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != pooled_w) || (output->dimension(1) != pooled_h), + "Invalid output pooling dimensions!"); + } + + return Status{}; +} + +std::tuple validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info) +{ + int pool_pad_x = 0; + int pool_pad_y = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + unsigned int pooled_w = 0; + unsigned int pooled_h = 0; + int pool_size = pool_info.pool_size(); + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); + std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); + std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); + + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Update pool size in case of global pooling + pool_size = pool_info.is_global_pooling() ? input->dimension(0) : pool_size; + + // Check output dimensions + std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0), + input->dimension(1), + pool_size, + pool_size, + pad_stride_info); + + auto_init(input, output, pooled_w, pooled_h); + + BorderSize border_size = BorderSize(pool_pad_y, pool_pad_x); + const DataType data_type = input->data_type(); + + const int input_width = input->dimension(0); + const int input_height = input->dimension(1); + + unsigned int num_elems_processed_per_iteration = 1; + + // Create kernel + if(pool_size == 3) + { + // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where + // each thread computes 4 output elements + const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type); + + int num_elems_read_per_iteration = pool_size; + + if(input->data_type() == DataType::F32) + { + if(is_pool3x3_stride_le3) + { + // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3 + num_elems_processed_per_iteration = 4; + num_elems_read_per_iteration = pool_size * (pool_stride_x + 1); + } + } + else + { + if(is_pool3x3_stride_le3) + { + num_elems_processed_per_iteration = 4; + } + else + { + num_elems_processed_per_iteration = 2; + } + } + + const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; + + border_size.right = std::max(upper_bound_w, pool_pad_x); + border_size.bottom = std::max(upper_bound_h, pool_pad_y); + } + else // Run general case + { + if(input->data_type() == DataType::F32) + { + num_elems_processed_per_iteration = 1; + } + else + { + num_elems_processed_per_iteration = 2; + } + + const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; + + border_size.right = std::max(upper_bound_w, pool_pad_x); + border_size.bottom = std::max(upper_bound_h, pool_pad_y); + } + // Configure kernel window + Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + + if(input->data_type() == DataType::F32) + { + AccessWindowStatic input_access(input, -pool_pad_x, -pool_pad_y, input_width + border_size.right, input_height + border_size.bottom); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + bool window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_tuple(err, win, GCPoolingConfig(num_elems_processed_per_iteration, border_size)); + } + else + { + // Calculate output right and bottom border + const int output_width = output->dimension(0); + const int output_height = output->dimension(1); + const int output_padding_right = ceil_to_multiple(output_width, num_elems_processed_per_iteration) - output_width; + const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height; + const int input_padding_right = ceil_to_multiple(input_width + 2 * border_size.right, num_elems_processed_per_iteration) - (input_width + 2 * border_size.right); + const int input_padding_bottom = ceil_to_multiple(input_height + 2 * border_size.bottom, 1) - (input_height + 2 * border_size.bottom); + + // Configure kernel window + AccessWindowStatic input_access(input, -pool_pad_x, -pool_pad_y, input_width + border_size.right + input_padding_right, input_height + border_size.bottom + input_padding_bottom); + AccessWindowStatic output_access(output, 0, 0, output_width + output_padding_right, output_height + output_padding_bottom); + bool window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_tuple(err, win, GCPoolingConfig(num_elems_processed_per_iteration, border_size)); + } +} +} // namespace + GCPoolingLayerKernel::GCPoolingLayerKernel() : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1) { @@ -52,54 +222,41 @@ BorderSize GCPoolingLayerKernel::border_size() const void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const PoolingLayerInfo &pool_info) { - int pool_pad_x = 0; - int pool_pad_y = 0; - int pool_stride_x = 0; - int pool_stride_y = 0; - unsigned int pooled_w = 0; - unsigned int pooled_h = 0; - const PoolingType pool_type = pool_info.pool_type(); - int pool_size = pool_info.pool_size(); - const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); - const bool is_global_pooling = pool_info.is_global_pooling(); + int pool_pad_x = 0; + int pool_pad_y = 0; + int pool_stride_x = 0; + int pool_stride_y = 0; + unsigned int pooled_w = 0; + unsigned int pooled_h = 0; + const PoolingType pool_type = pool_info.pool_type(); + int pool_size = pool_info.pool_size(); + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); + const bool exclude_padding = pool_info.exclude_padding(); std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_ERROR_ON(!is_global_pooling && (pool_pad_x >= pool_size || pool_pad_y >= pool_size)); - ARM_COMPUTE_ERROR_ON(is_global_pooling && (input->info()->tensor_shape().x() != input->info()->tensor_shape().y())); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Update pool size in case of global pooling - pool_size = is_global_pooling ? input->info()->dimension(0) : pool_size; + pool_size = pool_info.is_global_pooling() ? input->info()->dimension(0) : pool_size; // Check output dimensions std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), pool_size, pool_size, - pool_info.pad_stride_info()); - - // Output auto initialization if not yet initialized - { - TensorShape output_shape{ input->info()->tensor_shape() }; - output_shape.set(0, pooled_w); - output_shape.set(1, pooled_h); - - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); - } + pad_stride_info); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h)); + auto_init(input->info(), output->info(), pooled_w, pooled_h); - const int input_width = input->info()->dimension(0); - const int input_height = input->info()->dimension(1); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info)); // Set instance variables - _input = input; - _output = output; - _pool_info = pool_info; - _border_size = BorderSize(pool_pad_y, pool_pad_x); + _input = input; + _output = output; + _pool_info = pool_info; + + const DataType data_type = input->info()->data_type(); // Set build options std::set build_opts; @@ -114,10 +271,14 @@ void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, { build_opts.insert("#define DATA_TYPE_FP16"); } + if(exclude_padding) + { + build_opts.emplace("#define EXCLUDE_PADDING"); + } build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type))); build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x))); - build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + pool_pad_x))); - build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + pool_pad_y))); + build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_x)))); + build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_y)))); build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y))); build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x))); build_opts.emplace(("#define PAD_Y " + support::cpp11::to_string(pool_pad_y))); @@ -127,37 +288,7 @@ void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, { // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where // each thread computes 4 output elements - const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type()); - - int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size; - - if(input->info()->data_type() == DataType::F32) - { - if(is_pool3x3_stride_le3) - { - // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3 - _num_elems_processed_per_iteration = 4; - num_elements_read_per_iteration = pool_size * (pool_stride_x + 1); - } - } - else - { - num_elements_read_per_iteration = pool_size; - if(is_pool3x3_stride_le3) - { - _num_elems_processed_per_iteration = 4; - } - else - { - _num_elems_processed_per_iteration = 2; - } - } - - const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width; - const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; - - _border_size.right = std::max(upper_bound_w, pool_pad_x); - _border_size.bottom = std::max(upper_bound_h, pool_pad_y); + const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type); std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size); if(is_pool3x3_stride_le3) @@ -173,53 +304,27 @@ void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *output, } else // Run general case { - if(input->info()->data_type() == DataType::F32) - { - _num_elems_processed_per_iteration = 1; - } - else - { - _num_elems_processed_per_iteration = 2; - } - const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width; - const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; - - _border_size.right = std::max(upper_bound_w, pool_pad_x); - _border_size.bottom = std::max(upper_bound_h, pool_pad_y); - build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size))); build_opts.insert("#define POOLING_LAYER_N"); _kernel = static_cast(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts)); } + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info); + ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); + + IGCKernel::configure(std::get<1>(win_config)); + GCPoolingConfig pooling_config = std::get<2>(win_config); + _num_elems_processed_per_iteration = pooling_config.first; + _border_size = pooling_config.second; +} - Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration)); - - if(input->info()->data_type() == DataType::F32) - { - AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); - AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration); - update_window_and_padding(win, input_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); - } - else - { - // Calculate output right and bottom border - const int output_width = output->info()->dimension(0); - const int output_height = output->info()->dimension(1); - const int output_padding_right = ceil_to_multiple(output_width, _num_elems_processed_per_iteration) - output_width; - const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height; - const int input_padding_right = ceil_to_multiple(input_width + 2 * _border_size.right, _num_elems_processed_per_iteration) - (input_width + 2 * _border_size.right); - const int input_padding_bottom = ceil_to_multiple(input_height + 2 * _border_size.bottom, 1) - (input_height + 2 * _border_size.bottom); - - // Configure kernel window - AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right + input_padding_right, input_height + _border_size.bottom + input_padding_bottom); - AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom); - update_window_and_padding(win, input_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); - } +Status GCPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info)); + ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info))); - IGCKernel::configure(win); + return Status{}; } void GCPoolingLayerKernel::run(const Window &window) @@ -239,7 +344,7 @@ void GCPoolingLayerKernel::run(const Window &window) do { // Upsample input by pool size - Window in_slice(slice); + Window in_slice(slice); // NOLINT in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration)); in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y)); 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