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(self):
theano.config.floatX = 'float64'
data = T.matrix('data')
data.tag.test_value = np.random.randn(10, 10)
blur = Blur(data, sigma=20.0, window_radius=80)
tmp = np.random.randn(1000, 2000)
tmp += 10.0
out = blur.output.eval({data: tmp})
scipy_out = scipy_filter(tmp, 20.0, mode='nearest')
np.testing.assert_allclose(out, scipy_out)
def test_blur(self):
theano.config.floatX = 'float64'
data = T.matrix('data')
data.tag.test_value = np.random.randn(10, 10)
blur = Blur(data, sigma=20.0, window_radius = 80)
tmp = np.random.randn(1000, 2000)
tmp += 10.0
out = blur.output.eval({data: tmp})
scipy_out = scipy_filter(tmp, 20.0, mode='nearest')
np.testing.assert_allclose(out, scipy_out)
