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# Copyright © 2023-2024 Apple Inc.
import unittest
import mlx.core as mx
import mlx.nn as nn
import mlx_tests
import numpy as np
try:
import torch
import torch.nn.functional as F
has_torch = True
except ImportError as e:
has_torch = False
class TestUpsample(mlx_tests.MLXTestCase):
@unittest.skipIf(not has_torch, "requires Torch")
def test_torch_upsample(self):
def run_upsample(
N,
C,
idim,
scale_factor,
mode,
align_corner,
dtype="float32",
atol=1e-5,
):
with self.subTest(
N=N,
C=C,
idim=idim,
scale_factor=scale_factor,
mode=mode,
align_corner=align_corner,
):
np_dtype = getattr(np, dtype)
np.random.seed(0)
iH, iW = idim
in_np = np.random.normal(-1.0, 1.0, (N, iH, iW, C)).astype(np_dtype)
in_mx = mx.array(in_np)
in_pt = torch.from_numpy(in_np.transpose(0, 3, 1, 2)).to("cpu")
out_mx = nn.Upsample(
scale_factor=scale_factor,
mode=mode,
align_corners=align_corner,
)(in_mx)
mode_pt = {
"nearest": "nearest",
"linear": "bilinear",
"cubic": "bicubic",
}[mode]
out_pt = F.interpolate(
in_pt,
scale_factor=scale_factor,
mode=mode_pt,
align_corners=align_corner if mode != "nearest" else None,
)
out_pt = torch.permute(out_pt, (0, 2, 3, 1)).numpy(force=True)
self.assertEqual(out_pt.shape, out_mx.shape)
self.assertTrue(np.allclose(out_pt, out_mx, atol=atol))
for dtype in ("float32",):
for N, C in ((1, 1), (2, 3)):
# only test cases in which target sizes are intergers
# if not, there will be numerical difference between mlx
# and torch due to different indices selection.
for idim, scale_factor in (
((2, 2), (1.0, 1.0)),
((2, 2), (1.5, 1.5)),
((2, 2), (2.0, 2.0)),
((4, 4), (0.5, 0.5)),
((7, 7), (2.0, 2.0)),
((10, 10), (0.2, 0.2)),
((10, 10), (0.3, 0.3)),
((11, 21), (3.0, 3.0)),
((11, 21), (3.0, 2.0)),
):
for mode in ("cubic", "linear", "nearest"):
for align_corner in (False, True):
if mode == "nearest" and align_corner:
continue
run_upsample(
N,
C,
idim,
scale_factor,
mode,
align_corner,
dtype=dtype,
)
if __name__ == "__main__":
mlx_tests.MLXTestRunner()
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