mirror of https://github.com/arendst/Tasmota.git
Prepare restoration of Tensorflow driver for Core 3 (#21327)
* Fix compilation of conv.cc * fix compilation of depthwise_conv.cc
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@ -115,13 +115,13 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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if (input->type == kTfLiteInt8) {
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if (input->type == kTfLiteInt8) {
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data_dims_t input_dims = {
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data_dims_t input_dims = {
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.width = input_width, .height = input_height,
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.width = input_width, .height = input_height,
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.channels = input->dims->data[3], 1
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.channels = input->dims->data[3], .extra = 1
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};
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};
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data_dims_t output_dims = {
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data_dims_t output_dims = {
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.width = output_width, .height = output_height,
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.width = output_width, .height = output_height,
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.channels = output->dims->data[3], 1
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.channels = output->dims->data[3], .extra = 1
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};
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};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, .channels = 0, .extra = 0};
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conv_params_t conv_params = {
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conv_params_t conv_params = {
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.in_offset = 0, .out_offset = 0,
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.in_offset = 0, .out_offset = 0,
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.stride = {params.stride_width, params.stride_height},
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.stride = {params.stride_width, params.stride_height},
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@ -209,13 +209,13 @@ inline void EvalQuantizedPerChannel(
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data_dims_t input_dims = {
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data_dims_t input_dims = {
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.width = input_width, .height = input_height,
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.width = input_width, .height = input_height,
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.channels = input_depth, 1
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.channels = input_depth, .extra = 1
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};
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};
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data_dims_t output_dims = {
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data_dims_t output_dims = {
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.width = output_width, .height = output_height,
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.width = output_width, .height = output_height,
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.channels = output_depth, 1
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.channels = output_depth, .extra = 1
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};
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};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, .channels = 0, .extra = 0};
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conv_params_t conv_params = {
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conv_params_t conv_params = {
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.in_offset = input_offset, .out_offset = output_offset,
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.in_offset = input_offset, .out_offset = output_offset,
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.stride = {stride_width, stride_height},
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.stride = {stride_width, stride_height},
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@ -118,13 +118,13 @@ inline void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
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data_dims_t input_dims = {
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data_dims_t input_dims = {
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.width = input_width, .height = input_height,
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.width = input_width, .height = input_height,
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.channels = input_depth, 1
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.channels = input_depth, .extra = 1
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};
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};
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data_dims_t output_dims = {
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data_dims_t output_dims = {
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.width = output_width, .height = output_height,
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.width = output_width, .height = output_height,
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.channels = output_depth, 1
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.channels = output_depth, .extra = 1
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};
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};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, .channels = 0, .extra = 0};
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dw_conv_params_t conv_params = {
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dw_conv_params_t conv_params = {
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.in_offset = input_offset, .out_offset = output_offset,
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.in_offset = input_offset, .out_offset = output_offset,
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.ch_mult = depth_multiplier,
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.ch_mult = depth_multiplier,
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@ -227,13 +227,13 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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if (input->type == kTfLiteInt8) {
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if (input->type == kTfLiteInt8) {
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data_dims_t input_dims = {
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data_dims_t input_dims = {
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.width = input_width, .height = input_height,
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.width = input_width, .height = input_height,
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.channels = input->dims->data[3], 1
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.channels = input->dims->data[3], .extra = 1
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};
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};
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data_dims_t output_dims = {
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data_dims_t output_dims = {
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.width = output_width, .height = output_height,
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.width = output_width, .height = output_height,
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.channels = output->dims->data[3], 1
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.channels = output->dims->data[3], .extra = 1
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};
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};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
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data_dims_t filter_dims = {.width = filter_width, .height = filter_height, .channels = 0, .extra = 0};
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dw_conv_params_t conv_params = {
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dw_conv_params_t conv_params = {
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.in_offset = 0, .out_offset = 0,
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.in_offset = 0, .out_offset = 0,
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.ch_mult = params.depth_multiplier,
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.ch_mult = params.depth_multiplier,
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