diff options
Diffstat (limited to 'src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp | 19 |
1 files changed, 8 insertions, 11 deletions
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index ace3379618..f1d2924c92 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -62,7 +62,7 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); if(biases != nullptr) @@ -77,7 +77,6 @@ Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, co if((output != nullptr) && (output->total_size() != 0)) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); CLWeightsReshapeKernel::validate(weights, biases, output); } @@ -233,7 +232,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * shape_im2col.set(1, conv_w * conv_h); // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo im2col_reshaped_info(shape_im2col, 1, data_type, input->info()->fixed_point_position()); + TensorInfo im2col_reshaped_info(shape_im2col, 1, data_type); im2col_reshaped_info.set_quantization_info(input->info()->quantization_info()); _im2col_output.allocator()->init(im2col_reshaped_info); _memory_group.manage(&_im2col_output); @@ -257,7 +256,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type; // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position()); + TensorInfo info_gemm(shape_gemm, 1, gemm_data_type); info_gemm.set_quantization_info(output->info()->quantization_info()); _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); @@ -326,10 +325,9 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8 && input->data_layout() == DataLayout::NHWC, "NHWC is unsupported for QASYMM8!"); @@ -369,7 +367,6 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } @@ -395,7 +392,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI // Output tensor auto inizialitation if not yet initialized ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr)); - weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, append_bias), 1, data_type, weights->fixed_point_position()); + weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, append_bias), 1, data_type); weights_to_use = &weights_reshaped_info; if(!skip_im2col) @@ -408,7 +405,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI } shape_im2col.set(0, mat_weights_rows); shape_im2col.set(1, conv_w * conv_h); - im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type, input->fixed_point_position()); + im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type); im2col_reshaped_info.set_quantization_info(input->quantization_info()); ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation)); gemm_input_to_use = &im2col_reshaped_info; @@ -422,7 +419,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI shape_gemm.set(1, conv_w * conv_h); const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type; // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. - info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type, input->fixed_point_position()); + info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type); info_gemm.set_quantization_info(output->quantization_info()); gemm_output_to_use = &info_gemm; } @@ -436,7 +433,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); if(!is_nhwc) { - tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8, input->fixed_point_position()); + tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8); tmp_info.set_quantization_info(output->quantization_info()); gemm_output_staged_to_use = &tmp_info; } |