Add GridSample test with data for failing unit test

onnxruntime/core/providers/coreml/mlprogram_test_scripts/gridsample_test.py

@@ -0,0 +1,55 @@
+import numpy as np
+import coremltools as ct
+from coremltools.converters.mil import Builder as mb
+
+target = ct.target.iOS15
+
+x_shape = (2, 2, 3, 2)
+grid_shape = (2, 3, 2, 2)
+
+@mb.program(input_specs=[mb.TensorSpec(shape=x_shape),
+                         mb.TensorSpec(shape=grid_shape)],
+            opset_version=target)
+def prog(x, grid):
+    sampling = mb.const(name="sampling_mode", val="bilinear")
+    padding_mode = mb.const(name="pmode", val="reflection")
+    pad = mb.const(name="pval", val=np.float32(0))
+    coord_mode = mb.const(name="coord_mode", val="normalized_minus_one_to_one")
+    align_corners = mb.const(name="align_corners", val=False)
+    z = mb.resample(x=x, coordinates=grid, sampling_mode=sampling,
+                    padding_mode=padding_mode, padding_value=pad, coordinates_mode=coord_mode,
+                    align_corners=align_corners)
+
+    return z
+
+# print(prog)
+
+# Convert to ML program
+m = ct.convert(prog, minimum_deployment_target=target, compute_precision=ct.precision.FLOAT32)
+
+# spec = m.get_spec()
+# print(spec)
+
+m.save("GridSample.mlpackage")
+# construct MLModel with compute_units=ComputeUnit.CPU and run predict
+m_cpu = ct.models.MLModel('GridSample.mlpackage', compute_units=ct.ComputeUnit.CPU_ONLY)
+m_all = ct.models.MLModel('GridSample.mlpackage', compute_units=ct.ComputeUnit.ALL)
+
+# GridSampleTest.test_grid_sample_20_4D_bilinear_reflection_no_align_corners
+# ORT produces different output for this test. ORT output is generated by pytorch
+x = np.array([-0.173652, -1.513725, -0.704586, -1.952375, -0.699404, -0.806298,
+              1.640852, -0.138969, -0.695411, -1.352111, 0.568797, -0.564294,
+              -0.056468, 0.641604, -0.438370, 0.450167, -1.091401, 1.669729,
+              -0.908544, 0.244467, 0.172109, 1.156741, -0.617128, 1.155460]
+).astype(np.float32).reshape(x_shape)
+
+grid = np.array([
+  0.252250, -0.151452, 0.824706, -0.588292, -0.591147, -0.155082,
+  -0.732938, 0.457493, -0.439559, 0.492330, 0.696447, 0.700722,
+  -0.220298, 0.654884, -0.635434, -1.195619, -0.114204, -0.870080,
+  -0.929674, 0.305035, 1.025429, -0.472240, -0.067881, -0.869393]
+).astype(np.float32).reshape(grid_shape)
+
+
+print(m_cpu.predict({'x': x, 'grid': grid}))
+print(m_all.predict({'x': x, 'grid': grid}))