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ConvTranspose2d Operator (#91)
Add ConvTranspose2d operator
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‎src/operators/ai.onnx/ConvTranspose/11/execute_operator__ai_onnx__convtranspose__11__T_tensor_float.c‎

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//this file was generated by ../../../../../../scripts/onnx_generator/OperatorTemplate.py
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#include "operator__ai_onnx__convtranspose__11.h"
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#include "tracing.h"
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#include "utils.h"
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//transformes a 3d pos into a 1d flat array pos
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static inline int calcArrayPos3D(int x, int y, int outputChannel, int width, int height) {
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return outputChannel * height * width + y * width + x;
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}
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//transformes a 4d pos into a 1d flat array pos
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static inline int calcArrayPos4D(int x, int y, int outputChannel, int intputChannel, int width, int height, int nOfOutputChannels) {
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return intputChannel * nOfOutputChannels *height * width
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+ outputChannel * height * width
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+ y * width
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+ x;
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}
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operator_status
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execute_operator__ai_onnx__convtranspose__11__T_tensor_float(
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node_context *ctx
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)
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{
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TRACE_ENTRY(1);
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TRACE_NODE(2, true, ctx->onnx_node);
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/* UNCOMMENT AS NEEDED */
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Onnx__TensorProto *i_X = searchInputByName(ctx, 0);
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Onnx__TensorProto *i_W = searchInputByName(ctx, 1);
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Onnx__TensorProto *i_B = searchInputByName(ctx, 2);
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TRACE_TENSOR(2, true, i_X);
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TRACE_TENSOR(2, true, i_W);
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TRACE_TENSOR(2, B, i_B);
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context_operator__ai_onnx__convtranspose__11 *op_ctx = ctx->executer_context;
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// size is not needed, because this operator works for one fixed size only
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// char* auto_pad = op_ctx->auto_pad;
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// size_t n_dilations = op_ctx->n_dilations;
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int64_t* dilations = op_ctx->dilations;
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// int64_t group = op_ctx->group;
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// size_t n_kernel_shape = op_ctx->n_kernel_shape;
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// int64_t* kernel_shape = op_ctx->kernel_shape;
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// size_t n_output_padding = op_ctx->n_output_padding;
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// int64_t* output_padding = op_ctx->output_padding;
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// size_t n_output_shape = op_ctx->n_output_shape;
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// int64_t* output_shape = op_ctx->output_shape;
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// size_t n_pads = op_ctx->n_pads;
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int64_t* pads = op_ctx->pads;
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// size_t n_strides = op_ctx->n_strides;
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int64_t* strides = op_ctx->strides;
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// TRACE_VAR(2, true, auto_pad, "\"%s\"");
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TRACE_ARRAY(2, true, dilations, , n_dilations, "%" PRId64);
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// TRACE_VAR(2, true, group, "%" PRId64);
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// TRACE_ARRAY(2, true, kernel_shape, , n_kernel_shape, "%" PRId64);
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// TRACE_ARRAY(2, true, output_padding, , n_output_padding, "%" PRId64);
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// TRACE_ARRAY(2, true, output_shape, , n_output_shape, "%" PRId64);
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TRACE_ARRAY(2, true, pads, , n_pads, "%" PRId64);
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TRACE_ARRAY(2, true, strides, , n_strides, "%" PRId64);
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Onnx__TensorProto *o_Y = searchOutputByName(ctx, 0);
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// TRACE_TENSOR(2, true, o_Y);
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/* DO CALCULATION HERE */
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const int inputSizeX = i_X->dims[3];
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const int inputSizeY = i_X->dims[2];
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const int inputChannels = i_X->dims[1];
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const float *input = i_X->float_data;
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const float *weights = i_W->float_data;
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const int kernelSizeX = i_W->dims[3];
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const int kernelSizeY = i_W->dims[2];
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const int outputChannels = i_W->dims[1];
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const int strideX = strides[1];
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const int strideY = strides[0];
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const int dilationsX = dilations[1];
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const int dilationsY = dilations[0];
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const int padStartY = pads[0];
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const int padStartX = pads[1];
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//not used because outputSize is used to test for the padEnd
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//const int padEndY = pads[2];
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//const int padEndX = pads[3];
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const int outputSizeX = o_Y->dims[2];
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const int outputSizeY = o_Y->dims[3];
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float* output = o_Y->float_data;
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//fill with bias
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for(int c=0; c<outputChannels; c++) {
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float bias = i_B?i_B->float_data[c]:0;
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for(int y=0; y<outputSizeY; y++) {
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for(int x=0; x<outputSizeX; x++) {
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output[calcArrayPos3D(x,y,c,outputSizeX, outputSizeY)] = bias;
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}
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}
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}
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//actual transpose convolution
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for(int i=0; i < inputChannels; i++) {
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for(int c=0; c<outputChannels; c++) {
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for(int inputPosY=0; inputPosY<inputSizeY; inputPosY++) {
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for(int inputPosX=0; inputPosX<inputSizeX; inputPosX++) {
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float _input = input[calcArrayPos3D(inputPosX, inputPosY, i, inputSizeX, inputSizeY)];
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for(int kernelPosX=0; kernelPosX<kernelSizeX; kernelPosX++) {
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int x = inputPosX*strideX+kernelPosX*dilationsX - padStartX;
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if(x < 0 || x >= outputSizeX) {
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continue;
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}
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for(int kernelPosY=0; kernelPosY<kernelSizeY; kernelPosY++) {
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int y = inputPosY*strideY+kernelPosY*dilationsY - padStartY;
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if(y < 0 || y >= outputSizeY) {
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continue;
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}
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const float _weight = weights[calcArrayPos4D(kernelPosX, kernelPosY, c, i, kernelSizeX, kernelSizeY, outputChannels)];
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output[calcArrayPos3D(x, y, c, outputSizeX, outputSizeY)] += _input * _weight;
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}
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}
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}
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}
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}
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}
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TRACE_EXIT(1);
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/* CHANGE RETURN CODE IF THIS EXECUTER IS VALID */
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return OP_OK;
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}

‎src/operators/ai.onnx/ConvTranspose/11/free_operator__ai_onnx__convtranspose__11.c‎

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//this file was generated by ../../../../../../scripts/onnx_generator/OperatorTemplate.py
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#include "operator__ai_onnx__convtranspose__11.h"
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#include "tracing.h"
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#include "utils.h"
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void
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free_operator__ai_onnx__convtranspose__11(
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node_context *ctx
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)
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{
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TRACE_ENTRY(1);
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TRACE_NODE(2, true, ctx->onnx_node);
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/* UNCOMMENT AS NEEDED */
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// Onnx__TensorProto *i_X = searchInputByName(ctx, 0);
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// Onnx__TensorProto *i_W = searchInputByName(ctx, 1);
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// Onnx__TensorProto *i_B = searchInputByName(ctx, 2);
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// TRACE_TENSOR(2, true, i_X);
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// TRACE_TENSOR(2, true, i_W);
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// TRACE_TENSOR(2, B, i_B);
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// Onnx__AttributeProto *a_auto_pad = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"auto_pad");
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// Onnx__AttributeProto *a_dilations = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"dilations");
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// Onnx__AttributeProto *a_group = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"group");
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// Onnx__AttributeProto *a_kernel_shape = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"kernel_shape");
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// Onnx__AttributeProto *a_output_padding = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"output_padding");
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// Onnx__AttributeProto *a_output_shape = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"output_shape");
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// Onnx__AttributeProto *a_pads = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"pads");
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// Onnx__AttributeProto *a_strides = searchAttributeNyName(ctx->onnx_node->n_attribute,ctx->onnx_node->attribute,"strides");
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// TRACE_ATTRIBUTE(2, a_auto_pad, a_auto_pad);
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// TRACE_ATTRIBUTE(2, a_dilations, a_dilations);
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// TRACE_ATTRIBUTE(2, a_group, a_group);
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// TRACE_ATTRIBUTE(2, a_kernel_shape, a_kernel_shape);
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// TRACE_ATTRIBUTE(2, a_output_padding, a_output_padding);
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// TRACE_ATTRIBUTE(2, a_output_shape, a_output_shape);
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// TRACE_ATTRIBUTE(2, a_pads, a_pads);
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// TRACE_ATTRIBUTE(2, a_strides, a_strides);
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Onnx__TensorProto *o_Y = searchOutputByName(ctx, 0);
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// TRACE_TENSOR(2, true, o_Y);
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/* FREE CONTEXT HERE IF NEEDED */
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context_operator__ai_onnx__convtranspose__11 *op_ctx = ctx->executer_context;
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// TRACE_VAR(2, true, op_ctx->auto_pad, "\"%s\"");
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TRACE_ARRAY(2, true, op_ctx->dilations, , op_ctx->n_dilations, "%" PRId64);
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// TRACE_VAR(2, true, op_ctx->group, "%" PRId64);
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// TRACE_ARRAY(2, true, op_ctx->kernel_shape, , op_ctx->n_kernel_shape, "%" PRId64);
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// TRACE_ARRAY(2, true, op_ctx->output_padding, , op_ctx->n_output_padding, "%" PRId64);
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// TRACE_ARRAY(2, true, op_ctx->output_shape, , op_ctx->n_output_shape, "%" PRId64);
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TRACE_ARRAY(2, true, op_ctx->pads, , op_ctx->n_pads, "%" PRId64);
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TRACE_ARRAY(2, true, op_ctx->strides, , op_ctx->n_strides, "%" PRId64);
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// free(op_ctx->auto_pad);
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free(op_ctx->dilations);
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// free(op_ctx->kernel_shape);
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// free(op_ctx->output_padding);
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// free(op_ctx->output_shape);
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free(op_ctx->pads);
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free(op_ctx->strides);
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free(op_ctx);
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/* FREE OUTPUT DATA_TYPE AND SHAPE HERE */
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/* DO NOT FREE THE TENSOR ITSELF */
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// freeTensorData(o_Y);
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free(o_Y->dims);
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TRACE_EXIT(1);
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}

‎src/operators/ai.onnx/ConvTranspose/11/info_operator__ai_onnx__convtranspose__11.c‎

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//this file was generated by ../../../../../../scripts/onnx_generator/OperatorInfo.py
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#include "operators/operator_info.h"
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#include "operator__ai_onnx__convtranspose__11.h"
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/* attributes */
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static
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operator_info_attribute
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attributes[] = {
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{
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.name = "auto_pad",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__STRING
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},
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{
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.name = "dilations",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INTS
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},
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{
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.name = "group",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INT
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},
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{
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.name = "kernel_shape",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INTS
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},
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{
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.name = "output_padding",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INTS
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},
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{
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.name = "output_shape",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INTS
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},
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{
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.name = "pads",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INTS
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},
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{
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.name = "strides",
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.optional = true,
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.type = ONNX__ATTRIBUTE_PROTO__ATTRIBUTE_TYPE__INTS
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}
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};
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/* input tensors */
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static
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uint32_t
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input_tensor_type_X[] = {
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ONNX__TENSOR_PROTO__DATA_TYPE__DOUBLE,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT16
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};
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static
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uint32_t
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input_tensor_type_W[] = {
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ONNX__TENSOR_PROTO__DATA_TYPE__DOUBLE,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT16
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};
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static
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uint32_t
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input_tensor_type_B[] = {
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ONNX__TENSOR_PROTO__DATA_TYPE__DOUBLE,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT16
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};
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static
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operator_info_tensor
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inputs[] = {
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{
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.name = "X",
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.optional = false,
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.variadic = false,
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.homogeneous = true,
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.constraint = "T",
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.n_types = 3,
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.types = input_tensor_type_X
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},
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{
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.name = "W",
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.optional = false,
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.variadic = false,
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.homogeneous = true,
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.constraint = "T",
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.n_types = 3,
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.types = input_tensor_type_W
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},
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{
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.name = "B",
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.optional = true,
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.variadic = true,
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.homogeneous = true,
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.constraint = "T",
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.n_types = 3,
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.types = input_tensor_type_B
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}
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};
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/* output tensors */
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static
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uint32_t
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output_tensor_type_Y[] = {
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ONNX__TENSOR_PROTO__DATA_TYPE__DOUBLE,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT,
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ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT16
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};
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static
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operator_info_tensor
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outputs[] = {
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{
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.name = "Y",
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.optional = false,
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.variadic = false,
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.homogeneous = true,
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.constraint = "T",
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.n_types = 3,
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.types = output_tensor_type_Y
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}
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};
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/* constraints */
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static
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operator_info_constraint
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constraints[] = {
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{ "T" }
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};
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/* operator info */
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operator_info
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info_operator__ai_onnx__convtranspose__11 = {
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.name = "ConvTranspose",
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.range_input = { 2, 3 },
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.range_output = { 1, 1 },
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.n_attribute = 8,
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.attribute = attributes,
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.n_input = 3,
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.input = inputs,
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.n_output = 1,
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.output = outputs,
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.n_constraint = 1,
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.constraint = constraints
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};

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