Fix bug in parse_resize with nonstandard input shapes

src/onnx/parse_resize.cpp

@@ -534,7 +534,7 @@ struct parse_resize : op_parser<parse_resize>
             }
         });
 
-        auto ind = calc_neighbor_points(vvv_ind, in_s, out_s, resized_m);
+        auto ind = calc_neighbor_points(vvv_ind, in_s.as_standard(), out_s, resized_m);
 
         auto dim_lens = out_lens;
         // indices matrix size grows 2x per resized-axis:

test/onnx/gen_onnx.py

@@ -13022,6 +13022,45 @@ def resize_with_same_inout_shapes_test():
 
     return ([node], [X], [Y], [sizes_tensor])
 
+
+@onnx_test()
+def resize_nhwc_test():
+    # Test resize with NHWC layout (non-standard shape)
+    # Original NCHW shape: [1, 3, 2, 2] 
+    # Transposed to NHWC: [1, 2, 2, 3]
+    scales = np.array([1.0, 2.0, 2.0, 1.0], dtype=np.float32)
+    scale_tensor = helper.make_tensor(name='scales',
+                                      data_type=TensorProto.FLOAT,
+                                      dims=scales.shape,
+                                      vals=scales.flatten().astype(np.float32))
+
+    X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 3, 2, 2])
+    TX = helper.make_tensor_value_info('TX', TensorProto.FLOAT, [1, 2, 2, 3])
+    TY = helper.make_tensor_value_info('TY', TensorProto.FLOAT, [1, 4, 4, 3])
+    Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 3, 4, 4])
+
+    # Transpose NCHW to NHWC
+    trn1 = onnx.helper.make_node('Transpose',
+                                 inputs=['X'],
+                                 outputs=['TX'],
+                                 perm=[0, 2, 3, 1])
+
+    # Resize in NHWC format
+    resize = onnx.helper.make_node('Resize',
+                                   inputs=['TX', '', 'scales'],
+                                   outputs=['TY'],
+                                   coordinate_transformation_mode='asymmetric',
+                                   mode='linear')
+
+    # Transpose back NHWC to NCHW
+    trn2 = onnx.helper.make_node('Transpose',
+                                 inputs=['TY'],
+                                 outputs=['Y'],
+                                 perm=[0, 3, 1, 2])
+
+    return ([trn1, resize, trn2], [X], [Y], [scale_tensor])
+
+
 @onnx_test()
 def reversesequence_4D_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 2, 2])

test/onnx/verify/resize_nhwc_test.cpp

@@ -0,0 +1,84 @@
+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2025 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#include <migraphx/register_target.hpp>
+#include <migraphx/verify.hpp>
+#include <onnx_test.hpp>
+
+TEST_CASE(resize_nhwc_test)
+{
+    migraphx::program p = read_onnx("resize_nhwc_test.onnx");
+    p.compile(migraphx::make_target("ref"));
+
+    // Input shape: [1, 3, 2, 2] (NCHW) - using smaller size for easier verification
+    migraphx::shape sx{migraphx::shape::float_type, {1, 3, 2, 2}};
+    // clang-format off
+    std::vector<float> dx = 
+    {
+        0.0f, 1.0f, 2.0f, 3.0f,    // Channel 0
+        4.0f, 5.0f, 6.0f, 7.0f,    // Channel 1
+        8.0f, 9.0f, 10.0f, 11.0f   // Channel 2
+    };
+    // clang-format on
+    migraphx::parameter_map pp;
+    pp["X"] = migraphx::argument(sx, dx.data());
+
+    auto result        = p.eval(pp).back();
+    auto result_vector = result.to_vector<float>();
+
+    // The output should be [1, 3, 4, 4] after:
+    // 1. Transpose to NHWC [1, 2, 2, 3]
+    // 2. Resize with scales [1.0, 2.0, 2.0, 1.0] -> [1, 4, 4, 3]
+    // 3. Transpose back to NCHW [1, 3, 4, 4]
+
+    // Expected output size
+    EXPECT(result_vector.size() == 1 * 3 * 4 * 4);
+
+    // Verify output shape
+    EXPECT(result.get_shape() == migraphx::shape{migraphx::shape::float_type, {1, 3, 4, 4}});
+
+    // Expected golden values for resize with asymmetric coordinate transformation
+    // and linear interpolation mode
+    // clang-format off
+    std::vector<float> gold = 
+    {
+        // Channel 0
+        0.0f,  0.5f,  1.0f,  1.0f,
+        1.0f,  1.5f,  2.0f,  2.0f,
+        2.0f,  2.5f,  3.0f,  3.0f,
+        2.0f,  2.5f,  3.0f,  3.0f,
+        // Channel 1
+        4.0f,  4.5f,  5.0f,  5.0f,
+        5.0f,  5.5f,  6.0f,  6.0f,
+        6.0f,  6.5f,  7.0f,  7.0f,
+        6.0f,  6.5f,  7.0f,  7.0f,
+        // Channel 2
+        8.0f,  8.5f,  9.0f,  9.0f,
+        9.0f,  9.5f,  10.0f, 10.0f,
+        10.0f, 10.5f, 11.0f, 11.0f,
+        10.0f, 10.5f, 11.0f, 11.0f
+    };
+    // clang-format on
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}