def test_initialize_stripe(self):
"""Test that we can initialize a stripe pattern"""
stripes = patterns.StripePattern(WHITE, BLACK)
self.assertEqual(stripes.color_a, WHITE)
self.assertEqual(stripes.color_b, BLACK)
self.assertEqual(stripes.transform, transforms.Identity(4))
def test_default_view(self):
"""Test that the view transform for the default view is identity"""
from_point = points.Point(0, 0, 0)
to_point = points.Point(0, 0, -1)
up = vectors.Vector(0, 1, 0)
result = transforms.ViewTransform(from_point, to_point, up)
self.assertEqual(result, transforms.Identity(4))
def __init__(self, material: Union[materials.Material, None] = None):
self.id = uuid.uuid4()
self.set_transform(transforms.Identity(4))
if material is None:
self.set_material(materials.Material())
else:
self.set_material(material)
def test_identity_matrix_mult(self):
"""Test we can identify the identity matrix by a matrix and tuple"""
ident = transforms.Identity(3)
M = matrices.Matrix(3, 3)
M.set_row(0, [1, 2, 3])
M.set_row(1, [3, 2, 1])
M.set_row(2, [2, 4, 6])
M2 = M * ident
self.assertEqual(M2, M)
t = tuples.Tuple(["a", "b", "c"], 1, 2, 3)
t2 = ident * t
self.assertEqual(t2, t)
def __init__(self, hsize: int, vsize: int, field_of_view: float) -> None:
self.hsize = hsize
self.vsize = vsize
self.field_of_view = field_of_view
half_view = math.tan(field_of_view / 2)
aspect = hsize / vsize
if aspect >= 1:
self.half_width = half_view
self.half_height = half_view / aspect
else:
self.half_width = half_view * aspect
self.half_height = half_view
self.pixel_size = self.half_width * 2 / self.hsize
self.transform = transforms.Identity(4)
def main(epochs, batch_size, learn_angle, angle_lr):
transform = torchvision.transforms.Compose([
transforms.RandomRotation(30),
transforms.Identity() if learn_angle else transforms.Free(),
transforms.ToTensor(),
transforms.Normalize()
])
set_train = dataset.MNIST(root='./data',
train=True,
download=True,
transform=transform)
set_test = dataset.MNIST(root='./data',
train=False,
download=True,
transform=transform)
loader_train = torch.utils.data.DataLoader(set_train,
batch_size=batch_size,
shuffle=True,
num_workers=2)
loader_test = torch.utils.data.DataLoader(set_test,
batch_size=batch_size,
shuffle=False,
num_workers=2)
nn = model.Net(learn_angle)
if torch.cuda.is_available():
nn.cuda()
optimizer = optim.SGD(nn.parameters(), lr=0.001, momentum=0.9)
VIEW_INTERVAL = 100
for epoch in range(epochs):
acc_loss = 0.0
running_loss = 0.0
for i, sample in enumerate(loader_train):
x, y = pack(sample, learn_angle)
optimizer.zero_grad()
y_pred = nn(x)
loss = model.loss(y_pred, y, angle_lr)
loss.backward()
optimizer.step()
# report loss
acc_loss += loss.item()
if i % VIEW_INTERVAL == VIEW_INTERVAL - 1:
running_loss = acc_loss / VIEW_INTERVAL
click.secho(
f"\rEpoch {epoch+1}, iteration {i+1}; "
f"loss: {(running_loss):.3f}; ",
err=True,
nl=False)
acc_loss = 0.0
# testing
count_correct = 0
count_total = 0
for sample in loader_test:
x, labels = pack(sample, learn_angle)
y_pred = nn(Variable(x))
if learn_angle:
labels = labels[0]
y_pred = y_pred[0]
_, labels_pred = torch.max(y_pred.data, 1)
c = (labels_pred == labels).squeeze()
count_correct += c.sum().item()
count_total += len(c)
click.secho(
f"\rEpoch {epoch+1}; loss: {(running_loss):.3f}; "
f"Test Acc: {100.0 * count_correct / count_total :.2f}%",
err=True,
nl=False)
running_loss = 0
click.secho('', err=True)
def test_identity_matrix_transpose(self):
"""Test that the transpose of the identity matrix is identity"""
ident = transforms.Identity(5)
self.assertEqual(ident.transpose(), ident)
def __init__(self, array, transform=transforms.Identity()):
Parentable.__init__(self)
self._array = np.asarray(np.atleast_1d(array), dtype=np.float64)
self.transform = transform
self.prior = None
self.fixed = False
def __init__(self):
self.set_transform(transforms.Identity(4))
def test_default_transform(self):
"""Test that we get the identity matrix as the default transform"""
s = shapes.Sphere()
self.assertEqual(s.transform, transforms.Identity(4))
