def test_double_s(self):
vec_n = tf.constant([1.0, 1.5, 3.5, 1.0])
vec_d = tf.constant([1e-6, 1.33e-6])
ts0, rs0 = vtmm.tmm_rt('s', self.omega, self.kx, vec_n, vec_d)
ts1, rs1 = calc_rt_pytmm('s', self.omega, self.kx, vec_n, vec_d)
self.assertLessEqual(np.linalg.norm(ts0.numpy() - ts1.numpy()), TOL)
self.assertLessEqual(np.linalg.norm(rs0.numpy() - rs1.numpy()), TOL)
def test_double_p(self):
vec_n = tf.constant([1.0, 1.5, 3.5, 1.0])
vec_d = tf.constant([1e-6, 1.33e-6])
tp0, rp0 = vtmm.tmm_rt('p', self.omega, self.kx, vec_n, vec_d)
tp1, rp1 = calc_rt_pytmm('p', self.omega, self.kx, vec_n, vec_d)
self.assertLessEqual(np.linalg.norm(tp0.numpy() - tp1.numpy()), TOL)
self.assertLessEqual(np.linalg.norm(rp0.numpy() - rp1.numpy()), TOL)
def test_benchmark_medium(self):
vec_n = tf.constant([1.0, 1.5, 3.5, 1.5, 1.0])
vec_d = tf.constant([1e-6, 1e-6, 1e-6])
print("Medium stack benchmark")
t1 = timeit.timeit(
lambda: vtmm.tmm_rt('p', self.omega, self.kx, vec_n, vec_d),
number=NUM)
print("vtmm: %.4f s" % t1)
t2 = timeit.timeit(
lambda: calc_rt_pytmm('p', self.omega, self.kx, vec_n, vec_d),
number=NUM)
print("tmm: %.4f s" % t2)
def test_benchmark_single(self):
vec_n = tf.constant([1.0, 3.5, 1.0])
vec_d = tf.constant([1e-6])
print("Single omega / kx benchmark")
t1 = timeit.timeit(lambda: vtmm.tmm_rt('p', self.omega[0:1], self.kx[
0:1], vec_n, vec_d),
number=NUM)
print("vtmm: %.4f s" % t1)
t2 = timeit.timeit(lambda: calc_rt_pytmm('p', self.omega[0:1], self.kx[
0:1], vec_n, vec_d),
number=NUM)
print("tmm: %.4f s" % t2)
def test_benchmark_large(self):
vec_n = tf.constant([1.0, 1.5, 3.5, 1.5, 2.5, 3.0, 1.5, 2, 3, 1.0])
vec_d = tf.constant(
[1e-6, 1.33e-6, 1e-6, 1e-6, 2e-6, 1e-5, 1.25e-6, 1e-6])
print("Large stack benchmark")
t1 = timeit.timeit(
lambda: vtmm.tmm_rt('p', self.omega, self.kx, vec_n, vec_d),
number=NUM)
print("vtmm: %.4f s" % t1)
t2 = timeit.timeit(
lambda: calc_rt_pytmm('p', self.omega, self.kx, vec_n, vec_d),
number=NUM)
print("tmm: %.4f s" % t2)
def test_benchmark(self):
vec_n = tf.constant([1.0, 1.5, 3.5, 1.5, 2.5, 3.0, 1.5, 2, 3, 1.0])
vec_d = tf.constant(
[1e-6, 1.33e-6, 1e-6, 1e-6, 2e-6, 1e-5, 1.25e-6, 1e-6])
print("Running benchmark")
print("----------------------")
t1 = timeit.timeit(
lambda: vtmm.tmm_rt('p', self.omega, self.kx, vec_n, vec_d),
number=50)
print("Tensorflow (vectorized implementation): %.3f" % t1)
t2 = timeit.timeit(
lambda: calc_rt_pytmm('p', self.omega, self.kx, vec_n, vec_d),
number=50)
print("TMM (unvectorized implementation): %.3f" % t2)
