def preprocess(image, model_name):
if model_name == "mv2":
image /= 128.
image = transforms.resize(image, (224, 224))
elif model_name == "vgg16":
image = vgg.prepare(image, size=(224, 224))
elif model_name == "resnet50":
image = resnet.prepare(image, size=(224, 224))
else:
raise Exception("illegal model")
return image
def test_prepare(self):
x1 = numpy.random.uniform(0, 255, (320, 240, 3)).astype(numpy.uint8)
x2 = numpy.random.uniform(0, 255, (320, 240)).astype(numpy.uint8)
x3 = numpy.random.uniform(0, 255, (160, 120, 3)).astype(self.dtype)
x4 = numpy.random.uniform(0, 255, (1, 160, 120)).astype(self.dtype)
x5 = numpy.random.uniform(0, 255, (3, 160, 120)).astype(numpy.uint8)
y1 = vgg.prepare(x1)
assert y1.shape == (3, 224, 224)
assert y1.dtype == self.dtype
y2 = vgg.prepare(x2)
assert y2.shape == (3, 224, 224)
assert y2.dtype == self.dtype
y3 = vgg.prepare(x3, size=None)
assert y3.shape == (3, 160, 120)
assert y3.dtype == self.dtype
y4 = vgg.prepare(x4)
assert y4.shape == (3, 224, 224)
assert y4.dtype == self.dtype
y5 = vgg.prepare(x5, size=None)
assert y5.shape == (3, 160, 120)
assert y5.dtype == self.dtype
def test_prepare(self):
x1 = numpy.random.uniform(0, 255, (320, 240, 3)).astype(numpy.uint8)
x2 = numpy.random.uniform(0, 255, (320, 240)).astype(numpy.uint8)
x3 = numpy.random.uniform(0, 255, (160, 120, 3)).astype(numpy.float32)
x4 = numpy.random.uniform(0, 255, (1, 160, 120)).astype(numpy.float32)
x5 = numpy.random.uniform(0, 255, (3, 160, 120)).astype(numpy.uint8)
y1 = vgg.prepare(x1)
self.assertEqual(y1.shape, (3, 224, 224))
self.assertEqual(y1.dtype, numpy.float32)
y2 = vgg.prepare(x2)
self.assertEqual(y2.shape, (3, 224, 224))
self.assertEqual(y2.dtype, numpy.float32)
y3 = vgg.prepare(x3, size=None)
self.assertEqual(y3.shape, (3, 160, 120))
self.assertEqual(y3.dtype, numpy.float32)
y4 = vgg.prepare(x4)
self.assertEqual(y4.shape, (3, 224, 224))
self.assertEqual(y4.dtype, numpy.float32)
y5 = vgg.prepare(x5, size=None)
self.assertEqual(y5.shape, (3, 160, 120))
self.assertEqual(y5.dtype, numpy.float32)
def test_prepare(self):
x1 = numpy.random.uniform(0, 255, (320, 240, 3)).astype(numpy.uint8)
x2 = numpy.random.uniform(0, 255, (320, 240)).astype(numpy.uint8)
x3 = numpy.random.uniform(0, 255, (160, 120, 3)).astype(self.dtype)
x4 = numpy.random.uniform(0, 255, (1, 160, 120)).astype(self.dtype)
x5 = numpy.random.uniform(0, 255, (3, 160, 120)).astype(numpy.uint8)
y1 = vgg.prepare(x1)
assert y1.shape == (3, 224, 224)
assert y1.dtype == self.dtype
y2 = vgg.prepare(x2)
assert y2.shape == (3, 224, 224)
assert y2.dtype == self.dtype
y3 = vgg.prepare(x3, size=None)
assert y3.shape == (3, 160, 120)
assert y3.dtype == self.dtype
y4 = vgg.prepare(x4)
assert y4.shape == (3, 224, 224)
assert y4.dtype == self.dtype
y5 = vgg.prepare(x5, size=None)
assert y5.shape == (3, 160, 120)
assert y5.dtype == self.dtype
def transform(in_data):
x_a, x_p, x_n = in_data
return prepare(x_a), prepare(x_p), prepare(x_n)