def test_other(self, dtype, input, sigma):
theano.config.compute_test_value = 'ignore'
sigma_theano = theano.shared(sigma)
window_radius = int(sigma * 4)
if input == 'pixel':
test_data = np.ones((100, 100))
test_data[50, 50] = 2
elif input == 'random':
test_data = 10 * np.ones((100, 100))
test_data += np.random.randn(100, 100)
else:
raise ValueError(input)
test_data = test_data.astype(dtype)
data = T.tensor3('data', dtype=dtype)
data.tag.test_value = test_data[np.newaxis, :, :]
print(data.dtype)
blur = gaussian_filter(data, sigma_theano, window_radius)
f = theano.function([data], blur)
out = f(test_data[np.newaxis, :, :])[0, :, :]
scipy_out = scipy_filter(test_data, sigma, mode='nearest')
if dtype == 'float32':
rtol = 5e-6
else:
rtol = 1e-7
np.testing.assert_allclose(out, scipy_out, rtol=rtol)
def check_other(self, dtype, input, sigma):
theano.config.compute_test_value = 'ignore'
sigma_theano = theano.shared(sigma)
window_radius = int(sigma*4)
if input == 'pixel':
test_data = np.ones((100, 100))
test_data[50, 50] = 2
elif input == 'random':
test_data = 10*np.ones((100, 100))
test_data += np.random.randn(100, 100)
else:
raise ValueError(input)
test_data = test_data.astype(dtype)
data = T.tensor3('data', dtype=dtype)
data.tag.test_value = test_data[np.newaxis, :, :]
print(data.dtype)
blur = gaussian_filter(data, sigma_theano, window_radius)
f = theano.function([data], blur)
out = f(test_data[np.newaxis, :, :])[0, :, :]
scipy_out = scipy_filter(test_data, sigma, mode='nearest')
if dtype == 'float32':
rtol = 1e-6
else:
rtol = 1e-7
np.testing.assert_allclose(out, scipy_out, rtol=rtol)
def test_blur_ones(self):
sigma = theano.shared(20.0)
window_radius = 20 * 4
data = T.tensor3('data', dtype='float64')
data.tag.test_value = np.zeros((1, 10, 10))
blur = gaussian_filter(data, sigma, window_radius)
f = theano.function([data], blur)
test_data = np.ones((1000, 1000))
out = f(test_data[np.newaxis, :, :])[0, :, :]
np.testing.assert_allclose(out, 1)
def test_blur_ones(self):
sigma = theano.shared(20.0)
window_radius = 20*4
data = T.tensor3('data', dtype='float64')
data.tag.test_value = np.zeros((1, 10, 10))
blur = gaussian_filter(data, sigma, window_radius)
f = theano.function([data], blur)
test_data = np.ones((1000, 1000))
out = f(test_data[np.newaxis, :, :])[0, :, :]
np.testing.assert_allclose(out, 1)
