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Unverified Commit 4c0fdc61 authored by Saswat Das's avatar Saswat Das Committed by GitHub
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Port test_adjust_xxx in test_transforms to pytest (#3950)

parent 31d53367
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test/test_transforms.py
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...@@ -10,6 +10,7 @@ import unittest ...@@ -10,6 +10,7 @@ import unittest
import math import math
import random import random
import numpy as np import numpy as np
import pytest
from PIL import Image from PIL import Image
try: try:
import accimage import accimage
...@@ -1231,195 +1232,6 @@ class Tester(unittest.TestCase): ...@@ -1231,195 +1232,6 @@ class Tester(unittest.TestCase):
torch.testing.assert_close(target, result1) torch.testing.assert_close(target, result1)
torch.testing.assert_close(target, result2) torch.testing.assert_close(target, result2)
def test_adjust_brightness(self):
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_brightness(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_brightness(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [0, 2, 6, 27, 67, 113, 18, 4, 117, 45, 127, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_brightness(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 10, 26, 108, 255, 255, 74, 16, 255, 180, 255, 2]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjust_contrast(self):
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_contrast(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_contrast(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [43, 45, 49, 70, 110, 156, 61, 47, 160, 88, 170, 43]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_contrast(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 0, 0, 22, 184, 255, 0, 0, 255, 94, 255, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
@unittest.skipIf(Image.__version__ >= '7', "Temporarily disabled")
def test_adjust_saturation(self):
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_saturation(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_saturation(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [2, 4, 8, 87, 128, 173, 39, 25, 138, 133, 215, 88]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_saturation(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 6, 22, 0, 149, 255, 32, 0, 255, 4, 255, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjust_hue(self):
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
with self.assertRaises(ValueError):
F.adjust_hue(x_pil, -0.7)
F.adjust_hue(x_pil, 1)
# test 0: almost same as x_data but not exact.
# probably because hsv <-> rgb floating point ops
y_pil = F.adjust_hue(x_pil, 0)
y_np = np.array(y_pil)
y_ans = [0, 5, 13, 54, 139, 226, 35, 8, 234, 91, 255, 1]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 1
y_pil = F.adjust_hue(x_pil, 0.25)
y_np = np.array(y_pil)
y_ans = [13, 0, 12, 224, 54, 226, 234, 8, 99, 1, 222, 255]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_hue(x_pil, -0.25)
y_np = np.array(y_pil)
y_ans = [0, 13, 2, 54, 226, 58, 8, 234, 152, 255, 43, 1]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjust_sharpness(self):
x_shape = [4, 4, 3]
x_data = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 0,
0, 65, 108, 101, 120, 97, 110, 100, 101, 114, 32, 86, 114, 121, 110, 105,
111, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_sharpness(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_sharpness(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 30,
30, 74, 103, 96, 114, 97, 110, 100, 101, 114, 32, 81, 103, 108, 102, 101,
107, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_sharpness(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 0,
0, 46, 118, 111, 132, 97, 110, 100, 101, 114, 32, 95, 135, 146, 126, 112,
119, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 3
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
x_th = torch.tensor(x_np.transpose(2, 0, 1))
y_pil = F.adjust_sharpness(x_pil, 2)
y_np = np.array(y_pil).transpose(2, 0, 1)
y_th = F.adjust_sharpness(x_th, 2)
torch.testing.assert_close(y_np, y_th.numpy())
def test_adjust_gamma(self):
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_gamma(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_gamma(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [0, 35, 57, 117, 186, 241, 97, 45, 245, 152, 255, 16]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_gamma(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 0, 0, 11, 71, 201, 5, 0, 215, 31, 255, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjusts_L_mode(self):
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_rgb = Image.fromarray(x_np, mode='RGB')
x_l = x_rgb.convert('L')
self.assertEqual(F.adjust_brightness(x_l, 2).mode, 'L')
self.assertEqual(F.adjust_saturation(x_l, 2).mode, 'L')
self.assertEqual(F.adjust_contrast(x_l, 2).mode, 'L')
self.assertEqual(F.adjust_hue(x_l, 0.4).mode, 'L')
self.assertEqual(F.adjust_sharpness(x_l, 2).mode, 'L')
self.assertEqual(F.adjust_gamma(x_l, 0.5).mode, 'L')
def test_color_jitter(self): def test_color_jitter(self):
color_jitter = transforms.ColorJitter(2, 2, 2, 0.1) color_jitter = transforms.ColorJitter(2, 2, 2, 0.1)
...@@ -2019,5 +1831,201 @@ class Tester(unittest.TestCase): ...@@ -2019,5 +1831,201 @@ class Tester(unittest.TestCase):
t.__repr__() t.__repr__()
def test_adjust_brightness():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_brightness(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_brightness(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [0, 2, 6, 27, 67, 113, 18, 4, 117, 45, 127, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_brightness(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 10, 26, 108, 255, 255, 74, 16, 255, 180, 255, 2]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjust_contrast():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_contrast(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_contrast(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [43, 45, 49, 70, 110, 156, 61, 47, 160, 88, 170, 43]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_contrast(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 0, 0, 22, 184, 255, 0, 0, 255, 94, 255, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
@pytest.mark.skipif(Image.__version__ >= '7', reason="Temporarily disabled")
def test_adjust_saturation():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_saturation(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_saturation(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [2, 4, 8, 87, 128, 173, 39, 25, 138, 133, 215, 88]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_saturation(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 6, 22, 0, 149, 255, 32, 0, 255, 4, 255, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjust_hue():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
with pytest.raises(ValueError):
F.adjust_hue(x_pil, -0.7)
F.adjust_hue(x_pil, 1)
# test 0: almost same as x_data but not exact.
# probably because hsv <-> rgb floating point ops
y_pil = F.adjust_hue(x_pil, 0)
y_np = np.array(y_pil)
y_ans = [0, 5, 13, 54, 139, 226, 35, 8, 234, 91, 255, 1]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 1
y_pil = F.adjust_hue(x_pil, 0.25)
y_np = np.array(y_pil)
y_ans = [13, 0, 12, 224, 54, 226, 234, 8, 99, 1, 222, 255]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_hue(x_pil, -0.25)
y_np = np.array(y_pil)
y_ans = [0, 13, 2, 54, 226, 58, 8, 234, 152, 255, 43, 1]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjust_sharpness():
x_shape = [4, 4, 3]
x_data = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 0,
0, 65, 108, 101, 120, 97, 110, 100, 101, 114, 32, 86, 114, 121, 110, 105,
111, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_sharpness(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_sharpness(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 30,
30, 74, 103, 96, 114, 97, 110, 100, 101, 114, 32, 81, 103, 108, 102, 101,
107, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_sharpness(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 0,
0, 46, 118, 111, 132, 97, 110, 100, 101, 114, 32, 95, 135, 146, 126, 112,
119, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 3
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
x_th = torch.tensor(x_np.transpose(2, 0, 1))
y_pil = F.adjust_sharpness(x_pil, 2)
y_np = np.array(y_pil).transpose(2, 0, 1)
y_th = F.adjust_sharpness(x_th, 2)
torch.testing.assert_close(y_np, y_th.numpy())
def test_adjust_gamma():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
# test 0
y_pil = F.adjust_gamma(x_pil, 1)
y_np = np.array(y_pil)
torch.testing.assert_close(y_np, x_np)
# test 1
y_pil = F.adjust_gamma(x_pil, 0.5)
y_np = np.array(y_pil)
y_ans = [0, 35, 57, 117, 186, 241, 97, 45, 245, 152, 255, 16]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
# test 2
y_pil = F.adjust_gamma(x_pil, 2)
y_np = np.array(y_pil)
y_ans = [0, 0, 0, 11, 71, 201, 5, 0, 215, 31, 255, 0]
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
torch.testing.assert_close(y_np, y_ans)
def test_adjusts_L_mode():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_rgb = Image.fromarray(x_np, mode='RGB')
x_l = x_rgb.convert('L')
assert F.adjust_brightness(x_l, 2).mode == 'L'
assert F.adjust_saturation(x_l, 2).mode == 'L'
assert F.adjust_contrast(x_l, 2).mode == 'L'
assert F.adjust_hue(x_l, 0.4).mode == 'L'
assert F.adjust_sharpness(x_l, 2).mode == 'L'
assert F.adjust_gamma(x_l, 0.5).mode == 'L'
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
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