def test_equal_numpy_qshift1():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
ref = np_coldfilt(mandrill.T, ha, hb).T
y_op = rowdfilt(mandrill_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift1():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
ref = np_coldfilt(mandrill.T, ha, hb).T
y_op = rowdfilt(mandrill_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift2():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
im = mandrill[:508, :504]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_coldfilt(im.T, ha, hb).T
y_op = rowdfilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift2():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
im = mandrill[:508, :504]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_coldfilt(im.T, ha, hb).T
y_op = rowdfilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_small_in():
ha = qshift('qshift_b')[0]
hb = qshift('qshift_b')[1]
im = mandrill[0:4,0:4]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_coldfilt(im, ha, hb)
y_op = coldfilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def setup():
global barbara, barbara_t
global bshape, bshape_half
global ref_rowdfilt, ch
py3nvml.grab_gpus(1, gpu_fraction=0.5, env_set_ok=True)
barbara = datasets.barbara()
barbara = (barbara / barbara.max()).astype('float32')
barbara = barbara.transpose([2, 0, 1])
bshape = list(barbara.shape)
bshape_half = bshape[:]
bshape_half[2] //= 2
barbara_t = torch.unsqueeze(torch.tensor(barbara, dtype=torch.float32),
dim=0).to(dev)
ch = barbara_t.shape[1]
# Some useful functions
ref_rowdfilt = lambda x, ha, hb: np.stack(
[np_coldfilt(s.T, ha, hb).T for s in x], axis=0)
