[WebNN EP] Support axes and fix some validation for Resize (microsoft…

…#21952)

- Supports arbitrary axes for Resize opset 18+
- Check all inputs and attributes more carefully

---------

Co-authored-by: Dwayne Robinson <fdwr@hotmail.com>

js/web/docs/webnn-operators.md

@@ -78,7 +78,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | ReduceSumSquare | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceSumSquare | ✓ | ✓ | Input 'axes' if present should be a constant |
 | Relu | ai.onnx(7-12, 13, 14+) | relu | ✓ | ✓ | |
 | Reshape | ai.onnx(7-12, 13, 14-18, 19-20, 21+) | reshape | ✓ | ✓ | Input 'shape' should be a constant, 0 dimension value in 'shape' is not supported |
-| Resize | ai.onnx(11-12, 13-17, 18, 19+) | resample2d | ✓ | ✓ | Only supports 4-D input, exclude_outside != 0, input 'scales' and 'sizes' if present must be a constant, 'linear' and 'nearest' modes |
+| Resize | ai.onnx(11-12, 13-17, 18, 19+) | resample2d | ✓ | ✓ | Only supports 4-D input, antialias == 0, coordinate_transformation_mode == 'half_pixel', exclude_outside == 0, keep_aspect_ratio_policy == 'stretch', 'linear' and 'nearest' modes, input 'scales' and 'sizes' if present must be a constant |
 | Shape | ai.onnx(7-12, 13-14, 15-18, 19-20, 21+) | slice | ✓ | ✓ | |
 | Sigmoid | ai.onnx(7-12, 13+) | sigmoid | ✓ | ✓ | |
 | Softplus | ai.onnx(7+) | softplus | ✓ | ✓ | |

onnxruntime/core/providers/webnn/builders/helper.h

@@ -36,6 +36,31 @@ WebnnDeviceType DeviceTypeFromString(const std::string_view& device_type);
 // Collects all the initializer tensors in the subGraph and its ancestor graphs.
 InitializedTensorSet CollectAllInitializedTensors(const GraphViewer& graph_viewer);
 
+inline std::vector<int64_t> convertAxesFromNCHWtoNHWC(const std::vector<int64_t>& axes) {
+  constexpr std::array<int64_t, 4> nchw_to_nhwc = {0, 3, 1, 2};
+  std::vector<int64_t> new_axes;
+  new_axes.reserve(axes.size());
+  for (int64_t axis : axes) {
+    if (axis >= nchw_to_nhwc.size()) {
+      ORT_THROW("Invalid axis value: ", axis);
+    }
+    new_axes.push_back(nchw_to_nhwc[static_cast<size_t>(axis)]);
+  }
+  return new_axes;
+}
+
+inline std::vector<int64_t> HandleNegativeAxes(const std::vector<int64_t>& axes, size_t input_size) {
+  std::vector<int64_t> new_axes(axes.size());
+  for (size_t i = 0; i < axes.size(); ++i) {
+    new_axes[i] = HandleNegativeAxis(axes[i], input_size);
+  }
+  return new_axes;
+}
+
+inline std::vector<int64_t> GetResolvedAxes(const NodeAttrHelper& helper, size_t input_size) {
+  return HandleNegativeAxes(helper.Get("axes", std::vector<int64_t>{}), input_size);
+}
+
 bool GetShape(const NodeArg& node_arg, std::vector<int64_t>& shape, const logging::Logger& logger);
 
 template <typename T>
@@ -144,6 +169,17 @@ inline bool ReadScalarTensorData(const onnx::TensorProto& tensor, emscripten::va
   return true;
 }
 
+inline bool IsEmptyTensor(const InitializedTensorSet& initializers, const std::string& name) {
+  if (name.empty() || !Contains(initializers, name)) {
+    return true;
+  }
+
+  const auto& tensor = *initializers.at(name);
+  const auto dims = tensor.dims();
+  // An empty tensor contains a 0 in the dimensions list.
+  return std::any_of(dims.begin(), dims.end(), [](auto d) { return d == 0; });
+}
+
 bool IsInputSupported(const NodeArg& node_arg, const std::string& parent_name, const logging::Logger& logger);
 
 // Get a list of groups of supported nodes, each group represents a subgraph supported by WebNN EP.