diff options
Diffstat (limited to 'src/gpu/cl/operators/ClConv2d.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClConv2d.cpp | 32 |
1 files changed, 13 insertions, 19 deletions
diff --git a/src/gpu/cl/operators/ClConv2d.cpp b/src/gpu/cl/operators/ClConv2d.cpp index 51248d4a7a..eb9475ccaa 100644 --- a/src/gpu/cl/operators/ClConv2d.cpp +++ b/src/gpu/cl/operators/ClConv2d.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021-2022 Arm Limited. + * Copyright (c) 2021-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -90,7 +90,6 @@ void ClConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *s case ConvolutionMethod::WINOGRAD: { ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1); - ARM_COMPUTE_ERROR_ON(conv2d_info.post_ops.size() > 0); auto f = std::make_unique<ClWinogradConv2d>(); f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math); _operator = std::move(f); @@ -99,7 +98,6 @@ void ClConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *s case ConvolutionMethod::DIRECT: { ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1); - ARM_COMPUTE_ERROR_ON(conv2d_info.post_ops.size() > 0); auto f = std::make_unique<ClDirectConv2d>(); f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info); _operator = std::move(f); @@ -108,7 +106,6 @@ void ClConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *s case ConvolutionMethod::INDIRECT: { ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1); - ARM_COMPUTE_ERROR_ON(conv2d_info.post_ops.size() > 0); auto f = std::make_unique<ClIndirectConv2d>(); f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info); _operator = std::move(f); @@ -142,7 +139,6 @@ Status ClConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, co { //Validate Winograd ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClWinogradConv2d is not supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClWinogradConv2d does not support PostOps"); ARM_COMPUTE_RETURN_ON_ERROR(ClWinogradConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math)); break; } @@ -150,7 +146,6 @@ Status ClConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, co { // Validate direct convolution layer ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClDirectConv2d is not supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClDirectConv2d does not support PostOps"); ARM_COMPUTE_RETURN_ON_ERROR(ClDirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); break; } @@ -158,7 +153,6 @@ Status ClConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, co { // Validate indirect convolution layer ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClIndirectConv2d is not supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClIndirectConv2d does not support PostOps"); ARM_COMPUTE_RETURN_ON_ERROR(ClIndirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); break; } @@ -271,17 +265,17 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const if(is_data_type_float(src->data_type())) { // Get dst shape - TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); - const bool is_large_kernel_sz = (weights->dimension(idx_w) >= kernel_sz_direct_conv_thr) && (weights->dimension(idx_h) >= kernel_sz_direct_conv_thr); - const bool is_ifm_ge_8 = src->dimension(idx_c) >= 8; - const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16; - const bool is_ofm_lte_8 = weights->dimension(3U) <= 8; - const bool is_ofm_lt_64 = weights->dimension(3U) < 64; - const bool workload_gte_8192 = (output_shape[0] * output_shape[1] * output_shape[2]) / 16 >= 8192; - const bool is_ifm_gt_ofm = src->dimension(idx_c) > weights->dimension(3U); - const bool is_m_one = output_shape[1] * output_shape[2] == 1; - const bool is_unit_stride = (conv2d_info.conv_info.stride().first == 1) && (conv2d_info.conv_info.stride().second == 1); - const int32_t kernel_sz = weights->dimension(idx_w) * weights->dimension(idx_h); + TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); + const bool is_large_kernel_sz = (weights->dimension(idx_w) >= kernel_sz_direct_conv_thr) && (weights->dimension(idx_h) >= kernel_sz_direct_conv_thr); + const bool is_ifm_ge_8 = src->dimension(idx_c) >= 8; + const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16; + const bool is_ofm_lte_8 = weights->dimension(3U) <= 8; + const bool is_ofm_lt_64 = weights->dimension(3U) < 64; + const bool workload_gte_8192 = (output_shape[0] * output_shape[1] * output_shape[2]) / 16 >= 8192; + const bool is_ifm_gt_ofm = src->dimension(idx_c) > weights->dimension(3U); + const bool is_m_one = output_shape[1] * output_shape[2] == 1; + const bool is_unit_stride = (conv2d_info.conv_info.stride().first == 1) && (conv2d_info.conv_info.stride().second == 1); + const int32_t kernel_sz = weights->dimension(idx_w) * weights->dimension(idx_h); // Run Winograd if valid and IFM >= 8 if(is_wino_valid && is_ifm_ge_8) @@ -330,7 +324,7 @@ ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const { const bool is_kernel_sz_odd = kernel_sz % 2; const bool is_g77 = gpu_target == GPUTarget::G77; - preferred_conv_method = (kernel_sz > 1) && (kernel_sz <= 81) && is_kernel_sz_odd && is_g77? ConvolutionMethod::INDIRECT : ConvolutionMethod::DIRECT; + preferred_conv_method = (kernel_sz > 1) && (kernel_sz <= 81) && is_kernel_sz_odd && is_g77 ? ConvolutionMethod::INDIRECT : ConvolutionMethod::DIRECT; } // Direct/indirect convolution used for the first layer of the network |