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100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import unittest
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import mlx.core as mx
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import mlx.nn as nn
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import mlx_tests
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import numpy as np
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try:
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import torch
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import torch.nn.functional as F
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has_torch = True
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except ImportError as e:
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has_torch = False
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class TestUpsample(mlx_tests.MLXTestCase):
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@unittest.skipIf(not has_torch, "requires Torch")
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def test_torch_upsample(self):
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def run_upsample(
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N,
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C,
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idim,
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scale_factor,
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mode,
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align_corner,
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dtype="float32",
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atol=1e-5,
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):
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with self.subTest(
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N=N,
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C=C,
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idim=idim,
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scale_factor=scale_factor,
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mode=mode,
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align_corner=align_corner,
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):
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np_dtype = getattr(np, dtype)
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np.random.seed(0)
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iH, iW = idim
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in_np = np.random.normal(-1.0, 1.0, (N, iH, iW, C)).astype(np_dtype)
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in_mx = mx.array(in_np)
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in_pt = torch.from_numpy(in_np.transpose(0, 3, 1, 2)).to("cpu")
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out_mx = nn.Upsample(
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scale_factor=scale_factor,
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mode=mode,
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align_corners=align_corner,
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)(in_mx)
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mode_pt = {
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"linear": "bilinear",
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"cubic": "bicubic",
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}[mode]
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out_pt = F.interpolate(
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in_pt,
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scale_factor=scale_factor,
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mode=mode_pt,
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align_corners=align_corner,
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)
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out_pt = torch.permute(out_pt, (0, 2, 3, 1)).numpy(force=True)
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self.assertEqual(out_pt.shape, out_mx.shape)
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self.assertTrue(np.allclose(out_pt, out_mx, atol=atol))
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for dtype in ("float32",):
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for N, C in ((1, 1), (2, 3)):
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# only test cases in which target sizes are intergers
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# if not, there will be numerical difference between mlx
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# and torch due to different indices selection.
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for idim, scale_factor in (
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((2, 2), (1.0, 1.0)),
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((2, 2), (1.5, 1.5)),
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((2, 2), (2.0, 2.0)),
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((4, 4), (0.5, 0.5)),
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((7, 7), (2.0, 2.0)),
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((10, 10), (0.2, 0.2)),
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((11, 21), (3.0, 3.0)),
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((11, 21), (3.0, 2.0)),
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):
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# only test linear and cubic interpolation
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# there will be numerical difference in nearest
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# due to different indices selection.
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for mode in ("cubic", "linear"):
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for align_corner in (False, True):
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run_upsample(
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N,
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C,
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idim,
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scale_factor,
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mode,
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align_corner,
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dtype=dtype,
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)
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if __name__ == "__main__":
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unittest.main()
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