def test_smoke_no_transform(self, device):
x_data = torch.rand(1, 2, 3, 4).to(device)
batch_prob = torch.rand(1) < 0.5
start_points = torch.rand(1, 4, 2).to(device)
end_points = torch.rand(1, 4, 2).to(device)
params = dict(batch_prob=batch_prob,
start_points=start_points,
end_points=end_points)
out_data = F.apply_perspective(x_data, params, return_transform=False)
assert out_data.shape == x_data.shape
def test_smoke(self, device):
x_data = torch.rand(1, 2, 3, 4).to(device)
batch_prob = torch.rand(1) < 0.5
start_points = torch.rand(1, 4, 2).to(device)
end_points = torch.rand(1, 4, 2).to(device)
params = dict(batch_prob=batch_prob,
start_points=start_points,
end_points=end_points,
interpolation=torch.tensor(1))
out_data = F.apply_perspective(x_data, params)
assert out_data.shape == x_data.shape
def test_smoke_transform(self, device):
x_data = torch.rand(1, 2, 3, 4).to(device)
batch_prob = torch.rand(1) < 0.5
start_points = torch.rand(1, 4, 2).to(device)
end_points = torch.rand(1, 4, 2).to(device)
params = dict(batch_prob=batch_prob,
start_points=start_points,
end_points=end_points)
out_data = F.apply_perspective(x_data, params, return_transform=True)
assert isinstance(out_data, tuple)
assert len(out_data) == 2
assert out_data[0].shape == x_data.shape
assert out_data[1].shape == (1, 3, 3)