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[WebNN EP] Infer the layout via ONNX domain for Resize (#18871)

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Previously we added EP specific logic into generic core code to restrict
Resize for WebNN EP at
https://github.com/microsoft/onnxruntime/pull/18687 which does not scale
and make sense.

This PR reverts the change in
https://github.com/microsoft/onnxruntime/pull/18687 and uses ONNX domain
infomation to infer the layout infomation during layout transformation.
This commit is contained in:
Wanming Lin 2023-12-22 03:30:29 +08:00 committed by GitHub
parent 8507c06f8e
commit 1b64d30963
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2 changed files with 18 additions and 11 deletions
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3
onnxruntime/core/optimizer/layout_transformation/layout_transformation.cc
View file

@ -162,8 +162,7 @@ Status TransformLayoutForEP(Graph& graph, bool& modified, const IExecutionProvid
// Except for resize and convolution ops, all the other layout sensitive ops only require layout transformation
// for 0th input and output. For resize, add the other relevant inputs which need conversion. For Conv - layout
// transformer only converts layout for 0th input, weights should be handled by every EP.
// For resize in WebNN EP, we don't want to convert all the inputs except the 0th input.
if (node->OpType() == "Resize" && node->GetExecutionProviderType() != kWebNNExecutionProvider) {
if (node->OpType() == "Resize") {
// Older versions of resize have a bug where ROI and Scales cannot be made empty inputs. To handle this case,
// we need to jump a few extra hoops to make sure its inputs are correctly handled.
//

26
onnxruntime/core/providers/webnn/builders/impl/resize_op_builder.cc
View file

@ -120,10 +120,14 @@ Status ResizeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
std::vector<float> scales_hw;
std::vector<int32_t> sizes_hw;
std::vector<int32_t> axes;
const bool isNhwc = model_builder.GetPreferredLayout() == DataLayout::NHWC;
const bool is_nhwc = model_builder.GetPreferredLayout() == DataLayout::NHWC;
if (input_defs.size() == 3) { // Use scales.
ORT_RETURN_IF_NOT(GetResizeScales(initializers, node, scales, logger), "Error getting resize scales");
scales_hw = {scales[2], scales[3]};
if (is_nhwc) {
scales_hw = {scales[1], scales[2]};
} else {
scales_hw = {scales[2], scales[3]};
}
options.set("scales", emscripten::val::array(scales_hw));
} else { // We already checked number of inputs in IsOpSupportedImpl.
std::vector<int64_t> output_sizes;
@ -132,11 +136,15 @@ Status ResizeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
std::transform(output_sizes.cbegin(), output_sizes.cend(),
std::back_inserter(sizes),
[](int64_t dim) -> int32_t { return SafeInt<int32_t>(dim); });
sizes_hw = {sizes[2], sizes[3]};
if (is_nhwc) {
sizes_hw = {sizes[1], sizes[2]};
} else {
sizes_hw = {sizes[2], sizes[3]};
}
options.set("sizes", emscripten::val::array(sizes_hw));
}
if (isNhwc) {
if (is_nhwc) {
axes = {1, 2};
} else {
axes = {2, 3};
@ -203,6 +211,7 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers
return false;
}
const bool is_nhwc = node.Domain() == kMSInternalNHWCDomain;
// We want to check if the scales or sizes are not trying to resize on N/C channels here.
if (input_defs.size() == 3) { // We are using scales.
std::vector<float> scales;
@ -210,7 +219,7 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers
return false;
float scale_n = scales[0];
float scale_c = scales[1];
float scale_c = is_nhwc ? scales[3] : scales[1];
if (scale_n != 1.0f || scale_c != 1.0f) {
LOGS(logger, VERBOSE) << "Scales of N/C channel should be 1"
<< "Resize of N/C channels are not supported"
@ -220,8 +229,8 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers
// For now we only support upscale, so the scale_h and scale_w should be an integer >= 1.
// TODO support ResizeBilinear.
float scale_h = scales[2];
float scale_w = scales[3];
float scale_h = is_nhwc ? scales[1] : scales[2];
float scale_w = is_nhwc ? scales[2] : scales[3];
// Onnx spec requires scale to be a positive float, so we are not checking that here.
if (roundf(scale_h) != scale_h) {
@ -239,9 +248,8 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers
if (!GetResizeOutputSizes(initializers, node, output_sizes, logger))
return false;
bool is_NHWC = input_shape[3] == output_sizes[3];
auto output_size_n = output_sizes[0];
const int c_idx = is_NHWC ? 3 : 1;
const int c_idx = is_nhwc ? 3 : 1;
if (output_size_n != input_shape[0] || output_sizes[c_idx] != input_shape[c_idx]) {
LOGS(logger, VERBOSE) << "Output sizes of N/C chanel should match the input sizes, "
<< "Resize of N/C channels are not supported"