Lint

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

@@ -1,27 +1,33 @@
-import numpy as np
 import coremltools as ct
+import numpy as np
 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)
+
+@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)
+    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
@@ -32,24 +38,77 @@ def prog(x, grid):
 
 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)
+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}))
+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}))