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Use integer division in tests/test_transforms for array slice indices + various PEP-8 fixes #51

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Jan 31, 2017
Merged

Use integer division in tests/test_transforms for array slice indices + various PEP-8 fixes #51

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39 changes: 19 additions & 20 deletions test/test_transforms.py
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Original file line number Diff line number Diff line change
@@ -1,29 +1,28 @@
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
import unittest
import random


class Tester(unittest.TestCase):
def test_crop(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
oheight = random.randint(5, (height - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2

img = torch.ones(3, height, width)
oh1 = (height - oheight) / 2
ow1 = (width - owidth) / 2
imgnarrow = img[:, oh1 :oh1 + oheight, ow1 :ow1 + owidth]
oh1 = (height - oheight) // 2
ow1 = (width - owidth) // 2
imgnarrow = img[:, oh1:oh1 + oheight, ow1:ow1 + owidth]
imgnarrow.fill_(0)
result = transforms.Compose([
transforms.ToPILImage(),
transforms.CenterCrop((oheight, owidth)),
transforms.ToTensor(),
])(img)
assert result.sum() == 0, "height: " + str(height) + " width: " \
+ str( width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
oheight += 1
owidth += 1
result = transforms.Compose([
Expand All @@ -33,25 +32,25 @@ def test_crop(self):
])(img)
sum1 = result.sum()
assert sum1 > 1, "height: " + str(height) + " width: " \
+ str( width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
oheight += 1
owidth += 1
owidth += 1
result = transforms.Compose([
transforms.ToPILImage(),
transforms.CenterCrop((oheight, owidth)),
transforms.ToTensor(),
])(img)
sum2 = result.sum()
assert sum2 > 0, "height: " + str(height) + " width: " \
+ str( width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
assert sum2 > sum1, "height: " + str(height) + " width: " \
+ str( width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)

def test_scale(self):
height = random.randint(24, 32) * 2
width = random.randint(24, 32) * 2
osize = random.randint(5, 12) * 2

img = torch.ones(3, height, width)
result = transforms.Compose([
transforms.ToPILImage(),
Expand All @@ -63,15 +62,15 @@ def test_scale(self):
# print result.size()
assert osize in result.size()
if height < width:
assert result.size(1) <= result.size(2)
assert result.size(1) <= result.size(2)
elif width < height:
assert result.size(1) >= result.size(2)

def test_random_crop(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
oheight = random.randint(5, (height - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
img = torch.ones(3, height, width)
result = transforms.Compose([
transforms.ToPILImage(),
Expand Down Expand Up @@ -100,20 +99,20 @@ def test_pad(self):
transforms.Pad(padding),
transforms.ToTensor(),
])(img)
assert result.size(1) == height + 2*padding
assert result.size(2) == width + 2*padding
assert result.size(1) == height + 2 * padding
assert result.size(2) == width + 2 * padding

def test_lambda(self):
trans = transforms.Lambda(lambda x: x.add(10))
x = torch.randn(10)
y = trans(x)
assert(y.equal(torch.add(x, 10)))
assert (y.equal(torch.add(x, 10)))

trans = transforms.Lambda(lambda x: x.add_(10))
x = torch.randn(10)
y = trans(x)
assert(y.equal(x))
assert (y.equal(x))


if __name__ == '__main__':
unittest.main()