Fix some CI issues.

Remove redudant test.

onnxruntime/core/providers/coreml/builders/impl/resize_op_builder.cc

@@ -1,7 +1,7 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
-#include <math.h>
+#include <cmath>
 
 #include "core/framework/tensorprotoutils.h"
 #include "core/optimizer/initializer.h"
@@ -192,6 +192,19 @@
                       "Error getting validated scales");
     num_scales = output_scales.size();
 
+    // special case linear downsample.
+    // the CoreML implementation seems to be flaky and gives different outputs on different OS versions.
+    // use bilinear_resize instead. we check in IsOpSupportedImpl that the downsample input is evenly
+    // divisible by the output size so there's no rounding involved.
+    if (is_linear && (output_scales[num_scales - 1] < 1.f || output_scales[num_scales - 2] < 1.f)) {
+      using_scales = false;
+      using_sizes = true;
+      num_sizes = num_scales;
+      output_sizes = input_shape;
+      // only the last to dims have their size changed
+      output_sizes[input_rank - 2] = static_cast<int64_t>(input_shape[input_rank - 2] * output_scales[num_scales - 2]);
+      output_sizes[input_rank - 1] = static_cast<int64_t>(input_shape[input_rank - 1] * output_scales[num_scales - 1]);
+    }
   } else {
     ORT_RETURN_IF_NOT(GetValidatedResizeSizes(graph_viewer, node, input_shape, axes, output_sizes, logger),
                       "Error getting validated sizes");
@@ -200,7 +213,7 @@
 
 #if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
 
     std::string_view coreml_op_type;
     if (using_scales) {
@@ -265,7 +278,7 @@
 
     AddOperationOutput(*op, *output_defs[0]);
     model_builder.AddOperation(std::move(op));
   } else
 #endif
   {
     std::unique_ptr<COREML_SPEC::NeuralNetworkLayer> layer = model_builder.CreateNNLayer(node);
@@ -308,6 +321,16 @@
     return false;
   }
 
+  // as we allow empty shapes in the checks done by BaseOpBuilder::HasSupportedInputs we explicitly check for an empty
+  // an empty input here to be consistent.
+  // this should never happen in a real model though as a dim with value 0 (i.e. no input data) would typically be a
+  // dynamic dimension where a previous step had no output (e.g. Loop of zero interations, NonZero with no matches,
+  // NonMaxSupression with no boxes).
+  if (std::find(input_shape.begin(), input_shape.end(), 0) != input_shape.end()) {
+    LOGS(logger, VERBOSE) << "Resize input shape has with dimension values of 0 which is not supported.";
+    return false;
+  }
+
   const auto input_rank = input_shape.size();
   if (input_params.create_mlprogram) {
     if (input_rank < 3 || input_rank > 5) {
@@ -405,13 +428,13 @@
         auto h_out = h_in * scale_h;
         auto w_out = w_in * scale_w;
 
-        if (std::fmodf(float(h_in), h_out) != 0.f) {
+        if (std::fmod(float(h_in), h_out) != 0.f) {
           LOGS(logger, VERBOSE) << "Resize: downsampling output height: " << h_out
                                 << " is not a factor of input height: " << h_in;
           return false;
         }
 
-        if (std::fmodf(float(w_in), w_out) != 0.f) {
+        if (std::fmod(float(w_in), w_out) != 0.f) {
           LOGS(logger, VERBOSE) << "Resize: downsampling output width: " << w_out
                                 << " is not a factor of input width: " << w_in;
           return false;

onnxruntime/core/providers/coreml/coreml_execution_provider.cc

@@ -27,6 +27,7 @@ CoreMLExecutionProvider::CoreMLExecutionProvider(uint32_t coreml_flags)
     : IExecutionProvider{onnxruntime::kCoreMLExecutionProvider},
       coreml_flags_(coreml_flags),
       coreml_version_(coreml::util::CoreMLVersion()) {
+  LOGS_DEFAULT(VERBOSE) << "CoreML version: " << coreml_version_;
   if (coreml_version_ < MINIMUM_COREML_VERSION) {
     LOGS_DEFAULT(ERROR) << "CoreML EP is not supported on this platform.";
   }

onnxruntime/core/providers/xnnpack/tensor/resize.cc

@@ -68,9 +68,29 @@
     InlinedVector<float> scale(4, 1.0F);
     if (scale_tensor) {
       const Initializer scale_val(*scale_tensor, node_unit.ModelPath());
-      if (scale_val.DataAsSpan<float>()[1] != 1.0F) {
+      const auto scales = scale_val.DataAsSpan<float>();
+      if (scales[1] != 1.0F) {
         break;
       }
+
+      // downsampling output seems to require the output size to be a factor of the input to match ONNX
+      if (scales[2] < 1.0f || scales[3] < 1.0f) {
+        // we also require input_shape to be known to check
+        int64_t h_in = x_shape->dim(2).dim_value();
+        int64_t w_in = x_shape->dim(3).dim_value();
+        if (h_in < 0 || w_in < 0) {
+          break;
+        }
+
+        float scale_h = scales[2];
+        float scale_w = scales[3];
+        float h_out = h_in * scale_h;
+        float w_out = w_in * scale_w;
+        if (std::fmod(float(h_in), h_out) != 0.f ||
+            std::fmod(float(w_in), w_out) != 0.f) {
+          break;
+        }
+      }
     }
 
     if (size_tensor) {

onnxruntime/test/providers/cpu/tensor/resize_op_test.cc

@@ -256,32 +256,6 @@ TEST(ResizeOpTest, ResizeOpLinearDownSampleTest_4DBilinear) {
   run_test(true);
 }
 
-// Primarily to test CoreML using upsample_bilinear when the input size is evenly divisible by the output size
-TEST(ResizeOpTest, ResizeOpLinearDownSampleTest_4DBilinear_EvenlyDivisible) {
-  OpTester test("Resize", 13);
-  std::vector<float> roi{};
-  std::vector<float> scales{1.0f, 1.0f, 0.5f, 0.5f};
-
-  test.AddAttribute("mode", "linear");
-
-  constexpr int64_t N = 1, C = 1, H = 2, W = 4;
-  std::vector<float> X = {
-      1.0f, 2.0f, 3.0f, 4.0f,
-      5.0f, 6.0f, 7.0f, 8.0f};
-
-  test.AddInput<float>("X", {N, C, H, W}, X);
-  test.AddInput<float>("roi", {0}, roi);
-  test.AddInput<float>("scales", {4}, scales, /*scales_in_initializer*/ true);
-
-  std::vector<float> Y = {3.5f, 5.5f};
-
-  test.AddOutput<float>("Y", {N, C, static_cast<int64_t>(H * scales[2]), static_cast<int64_t>(W * scales[3])}, Y);
-  // QNN: result diff
-  // TRT: Segmentation fault in A100
-  std::unordered_set<std::string> excluded_providers({kQnnExecutionProvider});
-  test.Run(OpTester::ExpectResult::kExpectSuccess, "", ExcludeTrtOnA100(excluded_providers));
-}
-
 TEST(ResizeOpTest, NhwcResizeOpLinearDownSampleTest_4DBilinear) {
   OpTester test("Resize", 13);
   std::vector<float> roi{};
@@ -358,11 +332,12 @@ TEST(ResizeOpTest, NhwcResizeOpLinearDownSampleTest_4DBilinear_int8) {
 // Since NNAPI(TFLite) only using the scale calculate using the input/output size
 // For the above test (ResizeOpLinearDownSampleTest_4DBilinear)
 // The output size is [1,1,2,4].*[1,1,0.6,0.6]=[1,1,1,2]
-// NNAPI will recaluclate the scales as the output size divided by input size
+// NNAPI will recalculate the scales as the output size divided by input size
 // scales = [1,1,1,2]./[1,1,2,4] = [1,1,0.5,0.5]
 // See:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/reference/reference_ops.h
 // So the result of the above example will be different than CPU EP
-// Add the following 2 tests to test with scales valid to NNAPI
+// Add the following 2 tests to test with scales valid to NNAPI.
+// CoreML also doesn't handle a scale that doesn't divide the input size evenly.
 TEST(ResizeOpTest, ResizeOpLinearDownSampleTest_4DBilinear1) {
   // To test NNAPI EP, we need the scales/sizes to be in initializers
   auto run_test = [](bool scales_in_initializer) {