mirror of
https://github.com/ml-explore/mlx.git
synced 2025-10-19 00:04:41 +08:00
Upsample2d (#414)
Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com> Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
This commit is contained in:

committed by
GitHub

parent
d729a1991b
commit
22364c40b7
@@ -1,4 +1,4 @@
|
||||
# Copyright © 2023 Apple Inc.
|
||||
# Copyright © 2023-2024 Apple Inc.
|
||||
|
||||
import os
|
||||
import tempfile
|
||||
@@ -8,7 +8,7 @@ import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
import mlx_tests
|
||||
import numpy as np
|
||||
from mlx.utils import tree_flatten, tree_map, tree_unflatten
|
||||
from mlx.utils import tree_flatten, tree_map
|
||||
|
||||
|
||||
class TestBase(mlx_tests.MLXTestCase):
|
||||
@@ -905,6 +905,228 @@ class TestLayers(mlx_tests.MLXTestCase):
|
||||
self.assertTrue(y.shape, x.shape)
|
||||
self.assertTrue(y.dtype, mx.float16)
|
||||
|
||||
def test_upsample(self):
|
||||
b, h, w, c = 1, 2, 2, 1
|
||||
scale_factor = 2
|
||||
upsample_nearest = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="nearest", align_corners=True
|
||||
)
|
||||
upsample_bilinear = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="linear", align_corners=True
|
||||
)
|
||||
upsample_nearest = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="nearest", align_corners=True
|
||||
)
|
||||
upsample_bilinear_no_align_corners = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="linear", align_corners=False
|
||||
)
|
||||
upsample_nearest_no_align_corners = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="nearest", align_corners=False
|
||||
)
|
||||
# Test single feature map, align corners
|
||||
x = mx.arange(b * h * w * c).reshape((b, c, h, w)).transpose((0, 2, 3, 1))
|
||||
expected_nearest = mx.array(
|
||||
[[[[0, 0, 1, 1], [0, 0, 1, 1], [2, 2, 3, 3], [2, 2, 3, 3]]]]
|
||||
).transpose((0, 2, 3, 1))
|
||||
expected_bilinear = mx.array(
|
||||
[
|
||||
[
|
||||
[
|
||||
[0, 0.333333, 0.666667, 1],
|
||||
[0.666667, 1, 1.33333, 1.66667],
|
||||
[1.33333, 1.66667, 2, 2.33333],
|
||||
[2, 2.33333, 2.66667, 3],
|
||||
]
|
||||
]
|
||||
]
|
||||
).transpose((0, 2, 3, 1))
|
||||
# Test single feature map, no align corners
|
||||
x = (
|
||||
mx.arange(1, b * h * w * c + 1)
|
||||
.reshape((b, c, h, w))
|
||||
.transpose((0, 2, 3, 1))
|
||||
)
|
||||
expected_bilinear_no_align_corners = mx.array(
|
||||
[
|
||||
[
|
||||
[
|
||||
[1.0000, 1.2500, 1.7500, 2.0000],
|
||||
[1.5000, 1.7500, 2.2500, 2.5000],
|
||||
[2.5000, 2.7500, 3.2500, 3.5000],
|
||||
[3.0000, 3.2500, 3.7500, 4.0000],
|
||||
]
|
||||
]
|
||||
]
|
||||
).transpose((0, 2, 3, 1))
|
||||
expected_nearest_no_align_corners = mx.array(
|
||||
[[[[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]]]]
|
||||
).transpose((0, 2, 3, 1))
|
||||
self.assertTrue(
|
||||
np.allclose(
|
||||
upsample_nearest_no_align_corners(x), expected_nearest_no_align_corners
|
||||
)
|
||||
)
|
||||
self.assertTrue(
|
||||
np.allclose(
|
||||
upsample_bilinear_no_align_corners(x),
|
||||
expected_bilinear_no_align_corners,
|
||||
)
|
||||
)
|
||||
|
||||
# Test a more complex batch
|
||||
b, h, w, c = 2, 3, 3, 2
|
||||
scale_factor = 2
|
||||
x = mx.arange((b * h * w * c)).reshape((b, c, h, w)).transpose((0, 2, 3, 1))
|
||||
|
||||
upsample_nearest = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="nearest", align_corners=True
|
||||
)
|
||||
upsample_bilinear = nn.Upsample(
|
||||
scale_factor=scale_factor, mode="linear", align_corners=True
|
||||
)
|
||||
|
||||
expected_nearest = mx.array(
|
||||
[
|
||||
[
|
||||
[
|
||||
[0.0, 0.0, 1.0, 1.0, 2.0, 2.0],
|
||||
[0.0, 0.0, 1.0, 1.0, 2.0, 2.0],
|
||||
[3.0, 3.0, 4.0, 4.0, 5.0, 5.0],
|
||||
[3.0, 3.0, 4.0, 4.0, 5.0, 5.0],
|
||||
[6.0, 6.0, 7.0, 7.0, 8.0, 8.0],
|
||||
[6.0, 6.0, 7.0, 7.0, 8.0, 8.0],
|
||||
],
|
||||
[
|
||||
[9.0, 9.0, 10.0, 10.0, 11.0, 11.0],
|
||||
[9.0, 9.0, 10.0, 10.0, 11.0, 11.0],
|
||||
[12.0, 12.0, 13.0, 13.0, 14.0, 14.0],
|
||||
[12.0, 12.0, 13.0, 13.0, 14.0, 14.0],
|
||||
[15.0, 15.0, 16.0, 16.0, 17.0, 17.0],
|
||||
[15.0, 15.0, 16.0, 16.0, 17.0, 17.0],
|
||||
],
|
||||
],
|
||||
[
|
||||
[
|
||||
[18.0, 18.0, 19.0, 19.0, 20.0, 20.0],
|
||||
[18.0, 18.0, 19.0, 19.0, 20.0, 20.0],
|
||||
[21.0, 21.0, 22.0, 22.0, 23.0, 23.0],
|
||||
[21.0, 21.0, 22.0, 22.0, 23.0, 23.0],
|
||||
[24.0, 24.0, 25.0, 25.0, 26.0, 26.0],
|
||||
[24.0, 24.0, 25.0, 25.0, 26.0, 26.0],
|
||||
],
|
||||
[
|
||||
[27.0, 27.0, 28.0, 28.0, 29.0, 29.0],
|
||||
[27.0, 27.0, 28.0, 28.0, 29.0, 29.0],
|
||||
[30.0, 30.0, 31.0, 31.0, 32.0, 32.0],
|
||||
[30.0, 30.0, 31.0, 31.0, 32.0, 32.0],
|
||||
[33.0, 33.0, 34.0, 34.0, 35.0, 35.0],
|
||||
[33.0, 33.0, 34.0, 34.0, 35.0, 35.0],
|
||||
],
|
||||
],
|
||||
]
|
||||
).transpose((0, 2, 3, 1))
|
||||
expected_bilinear = mx.array(
|
||||
[
|
||||
[
|
||||
[
|
||||
[0.0, 0.4, 0.8, 1.2, 1.6, 2.0],
|
||||
[1.2, 1.6, 2.0, 2.4, 2.8, 3.2],
|
||||
[2.4, 2.8, 3.2, 3.6, 4.0, 4.4],
|
||||
[3.6, 4.0, 4.4, 4.8, 5.2, 5.6],
|
||||
[4.8, 5.2, 5.6, 6.0, 6.4, 6.8],
|
||||
[6.0, 6.4, 6.8, 7.2, 7.6, 8.0],
|
||||
],
|
||||
[
|
||||
[9.0, 9.4, 9.8, 10.2, 10.6, 11.0],
|
||||
[10.2, 10.6, 11.0, 11.4, 11.8, 12.2],
|
||||
[11.4, 11.8, 12.2, 12.6, 13.0, 13.4],
|
||||
[12.6, 13.0, 13.4, 13.8, 14.2, 14.6],
|
||||
[13.8, 14.2, 14.6, 15.0, 15.4, 15.8],
|
||||
[15.0, 15.4, 15.8, 16.2, 16.6, 17.0],
|
||||
],
|
||||
],
|
||||
[
|
||||
[
|
||||
[18.0, 18.4, 18.8, 19.2, 19.6, 20.0],
|
||||
[19.2, 19.6, 20.0, 20.4, 20.8, 21.2],
|
||||
[20.4, 20.8, 21.2, 21.6, 22.0, 22.4],
|
||||
[21.6, 22.0, 22.4, 22.8, 23.2, 23.6],
|
||||
[22.8, 23.2, 23.6, 24.0, 24.4, 24.8],
|
||||
[24.0, 24.4, 24.8, 25.2, 25.6, 26.0],
|
||||
],
|
||||
[
|
||||
[27.0, 27.4, 27.8, 28.2, 28.6, 29.0],
|
||||
[28.2, 28.6, 29.0, 29.4, 29.8, 30.2],
|
||||
[29.4, 29.8, 30.2, 30.6, 31.0, 31.4],
|
||||
[30.6, 31.0, 31.4, 31.8, 32.2, 32.6],
|
||||
[31.8, 32.2, 32.6, 33.0, 33.4, 33.8],
|
||||
[33.0, 33.4, 33.8, 34.2, 34.6, 35.0],
|
||||
],
|
||||
],
|
||||
]
|
||||
).transpose((0, 2, 3, 1))
|
||||
self.assertTrue(np.allclose(upsample_nearest(x), expected_nearest))
|
||||
self.assertTrue(np.allclose(upsample_bilinear(x), expected_bilinear))
|
||||
|
||||
# Test different height and width scale_factor
|
||||
b, h, w, c = 1, 2, 2, 2
|
||||
x = mx.arange(b * h * w * c).reshape((b, c, h, w)).transpose((0, 2, 3, 1))
|
||||
upsample_nearest = nn.Upsample(
|
||||
scale_factor=(2, 3), mode="nearest", align_corners=True
|
||||
)
|
||||
upsample_bilinear = nn.Upsample(
|
||||
scale_factor=(2, 3), mode="linear", align_corners=True
|
||||
)
|
||||
|
||||
expected_nearest = mx.array(
|
||||
[
|
||||
[
|
||||
[
|
||||
[0, 0, 0, 1, 1, 1],
|
||||
[0, 0, 0, 1, 1, 1],
|
||||
[2, 2, 2, 3, 3, 3],
|
||||
[2, 2, 2, 3, 3, 3],
|
||||
],
|
||||
[
|
||||
[4, 4, 4, 5, 5, 5],
|
||||
[4, 4, 4, 5, 5, 5],
|
||||
[6, 6, 6, 7, 7, 7],
|
||||
[6, 6, 6, 7, 7, 7],
|
||||
],
|
||||
]
|
||||
]
|
||||
).transpose((0, 2, 3, 1))
|
||||
expected_bilinear = mx.array(
|
||||
[
|
||||
[
|
||||
[
|
||||
[0, 0.2, 0.4, 0.6, 0.8, 1],
|
||||
[0.666667, 0.866667, 1.06667, 1.26667, 1.46667, 1.66667],
|
||||
[1.33333, 1.53333, 1.73333, 1.93333, 2.13333, 2.33333],
|
||||
[2, 2.2, 2.4, 2.6, 2.8, 3],
|
||||
],
|
||||
[
|
||||
[4, 4.2, 4.4, 4.6, 4.8, 5],
|
||||
[4.66667, 4.86667, 5.06667, 5.26667, 5.46667, 5.66667],
|
||||
[5.33333, 5.53333, 5.73333, 5.93333, 6.13333, 6.33333],
|
||||
[6, 6.2, 6.4, 6.6, 6.8, 7],
|
||||
],
|
||||
]
|
||||
]
|
||||
).transpose((0, 2, 3, 1))
|
||||
self.assertTrue(np.allclose(upsample_nearest(x), expected_nearest))
|
||||
self.assertTrue(np.allclose(upsample_bilinear(x), expected_bilinear))
|
||||
|
||||
# Test repr
|
||||
self.assertEqual(
|
||||
str(nn.Upsample(scale_factor=2)),
|
||||
"Upsample(scale_factor=2.0, mode='nearest', align_corners=False)",
|
||||
)
|
||||
self.assertEqual(
|
||||
str(nn.Upsample(scale_factor=(2, 3))),
|
||||
"Upsample(scale_factor=(2.0, 3.0), mode='nearest', align_corners=False)",
|
||||
)
|
||||
|
||||
def test_pooling(self):
|
||||
# Test 1d pooling
|
||||
x = mx.array(
|
||||
|
Reference in New Issue
Block a user