def test_stochastic_binary_inference_mode(alpha, test_values, expected_values):
K.set_learning_phase(0)
x = K.placeholder(ndim=2)
q = stochastic_binary(alpha)
f = K.function([x], [q(x)])
result = f([test_values])[0]
assert_allclose(result, expected_values, rtol=1e-05)
def test_stochastic_binary():
np.random.seed(42)
K.set_learning_phase(1)
x = np.random.uniform(-0.01, 0.01, size=10)
x = np.sort(x)
s = stochastic_binary(alpha="auto_po2")
ty = np.zeros_like(s)
ts = 0.0
n = 1000
for _ in range(n):
y = K.eval(s(K.constant(x)))
scale = K.eval(s.scale)[0]
ts = ts + scale
ty = ty + (y / scale)
result = (ty / n).astype(np.float32)
scale = np.array([ts / n])
expected = np.array(
[-1., -1., -1., -0.852, 0.782, 0.768, 0.97, 0.978, 1.0,
1.0]).astype(np.float32)
expected_scale = np.array([0.003906])
assert_allclose(result, expected, atol=0.1)
assert_allclose(scale, expected_scale, rtol=0.1)
def test_stochastic_binary():
np.random.seed(42)
K.set_learning_phase(1)
x = np.random.uniform(-0.01, 0.01, size=10)
x = np.sort(x)
# Adding a dimension to have a common channel axis for quantization. This is
# to cope with a bug fix in "_get_scale" without changing the test cases.
x = np.expand_dims(x, axis=1)
s = stochastic_binary(alpha="auto_po2")
ty = np.zeros_like(s)
ts = 0.0
n = 1000
for _ in range(n):
y = K.eval(s(K.constant(x)))
scale = K.eval(s.scale)[0]
ts = ts + scale
ty = ty + (y / scale)
# Perform squeezing to remove the common channel axis.
result = (ty/n).astype(np.float32)
result = np.squeeze(result)
scale = np.array([ts/n])
scale = np.squeeze(scale)
expected = np.array(
[-1., -1., -1., -0.852, 0.782, 0.768, 0.97, 0.978, 1.0, 1.0]
).astype(np.float32)
expected_scale = np.array([0.003906])
assert_allclose(result, expected, atol=0.1)
assert_allclose(scale, expected_scale, rtol=0.1)
def main():
# check the mean value of samples from stochastic_rounding for po2
np.random.seed(42)
count = 100000
val = 42
a = K.constant([val] * count)
b = quantized_po2(use_stochastic_rounding=True)(a)
res = np.sum(K.eval(b)) / count
print(res, "should be close to ", val)
b = quantized_relu_po2(use_stochastic_rounding=True)(a)
res = np.sum(K.eval(b)) / count
print(res, "should be close to ", val)
a = K.constant([-1] * count)
b = quantized_relu_po2(use_stochastic_rounding=True)(a)
res = np.sum(K.eval(b)) / count
print(res, "should be all ", 0)
# non-stochastic rounding quantizer.
a = K.constant([-3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0])
a = K.constant([0.194336])
print(" a =", K.eval(a).astype(np.float16))
print("qa =", K.eval(quantized_relu(6,2)(a)).astype(np.float16))
print("ss =", K.eval(smooth_sigmoid(a)).astype(np.float16))
print("hs =", K.eval(hard_sigmoid(a)).astype(np.float16))
print("ht =", K.eval(hard_tanh(a)).astype(np.float16))
print("st =", K.eval(smooth_tanh(a)).astype(np.float16))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print("qb_111 =", K.eval(quantized_bits(1,1,1)(c)).astype(np.float16))
print("qb_210 =", K.eval(quantized_bits(2,1,0)(c)).astype(np.float16))
print("qb_211 =", K.eval(quantized_bits(2,1,1)(c)).astype(np.float16))
print("qb_300 =", K.eval(quantized_bits(3,0,0)(c)).astype(np.float16))
print("qb_301 =", K.eval(quantized_bits(3,0,1)(c)).astype(np.float16))
c_1000 = K.constant(np.array([list(K.eval(c))] * 1000))
b = np.sum(K.eval(bernoulli()(c_1000)).astype(np.int32), axis=0) / 1000.0
print(" hs =", K.eval(hard_sigmoid(c)).astype(np.float16))
print(" b_all =", b.astype(np.float16))
T = 0.0
t = K.eval(stochastic_ternary(alpha="auto")(c_1000))
for i in range(10):
print("stochastic_ternary({}) =".format(i), t[i])
print(" st_all =", np.round(
np.sum(t.astype(np.float32), axis=0).astype(np.float16) /
1000.0, 2).astype(np.float16))
print(" ternary =", K.eval(ternary(threshold=0.5)(c)).astype(np.int32))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print(" b_10 =", K.eval(binary(1)(c)).astype(np.float16))
print("qr_10 =", K.eval(quantized_relu(1,0)(c)).astype(np.float16))
print("qr_11 =", K.eval(quantized_relu(1,1)(c)).astype(np.float16))
print("qr_20 =", K.eval(quantized_relu(2,0)(c)).astype(np.float16))
print("qr_21 =", K.eval(quantized_relu(2,1)(c)).astype(np.float16))
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("smooth"); print("with smooth sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("real"); print("with real sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("hard")
print(" c =", K.eval(c).astype(np.float16))
print("q2_31 =", K.eval(quantized_po2(3,1)(c)).astype(np.float16))
print("q2_32 =", K.eval(quantized_po2(3,2)(c)).astype(np.float16))
print("qr2_21 =", K.eval(quantized_relu_po2(2,1)(c)).astype(np.float16))
print("qr2_22 =", K.eval(quantized_relu_po2(2,2)(c)).astype(np.float16))
print("qr2_44 =", K.eval(quantized_relu_po2(4,1)(c)).astype(np.float16))
# stochastic rounding
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print("q2_32_2 =", K.eval(quantized_relu_po2(32,2)(c)).astype(np.float16))
b = K.eval(stochastic_binary()(c_1000)).astype(np.int32)
for i in range(5):
print("sbinary({}) =".format(i), b[i])
print("sbinary =", np.round(np.sum(b, axis=0) / 1000.0, 2).astype(np.float16))
print(" binary =", K.eval(binary()(c)).astype(np.int32))
print(" c =", K.eval(c).astype(np.float16))
for i in range(10):
print(" s_bin({}) =".format(i),
K.eval(binary(use_stochastic_rounding=1)(c)).astype(np.int32))
for i in range(10):
print(" s_po2({}) =".format(i),
K.eval(quantized_po2(use_stochastic_rounding=1)(c)).astype(np.int32))
for i in range(10):
print(
" s_relu_po2({}) =".format(i),
K.eval(quantized_relu_po2(use_stochastic_rounding=1)(c)).astype(
np.int32))
def main():
np.random.seed(42)
a = K.constant([-3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0])
a = K.constant([0.194336])
print(" a =", K.eval(a).astype(np.float16))
print("qa =", K.eval(quantized_relu(6,2)(a)).astype(np.float16))
print("ss =", K.eval(smooth_sigmoid(a)).astype(np.float16))
print("hs =", K.eval(hard_sigmoid(a)).astype(np.float16))
print("ht =", K.eval(hard_tanh(a)).astype(np.float16))
print("st =", K.eval(smooth_tanh(a)).astype(np.float16))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print("qb_111 =", K.eval(quantized_bits(1,1,1)(c)).astype(np.float16))
print("qb_210 =", K.eval(quantized_bits(2,1,0)(c)).astype(np.float16))
print("qb_211 =", K.eval(quantized_bits(2,1,1)(c)).astype(np.float16))
print("qb_300 =", K.eval(quantized_bits(3,0,0)(c)).astype(np.float16))
print("qb_301 =", K.eval(quantized_bits(3,0,1)(c)).astype(np.float16))
c_1000 = K.constant(np.array([list(K.eval(c))] * 1000))
b = np.sum(K.eval(bernoulli()(c_1000)).astype(np.int32), axis=0) / 1000.0
print(" hs =", K.eval(hard_sigmoid(c)).astype(np.float16))
print(" b_all =", b.astype(np.float16))
T = 0.0
t = K.eval(stochastic_ternary(threshold=T)(c_1000)).astype(np.int32)
for i in range(10):
print("sternary({}) =".format(i), t[i])
print(" st_all =", np.round(
np.sum(t.astype(np.float32), axis=0).astype(np.float16) /
1000.0, 2).astype(np.float16))
print(" ternary =", K.eval(ternary(threshold=0.5)(c)).astype(np.int32))
b = K.eval(stochastic_binary()(c_1000)).astype(np.int32)
for i in range(5):
print("sbinary({}) =".format(i), b[i])
print("sbinary =", np.round(np.sum(b, axis=0) / 1000.0, 2).astype(np.float16))
print(" binary =", K.eval(binary()(c)).astype(np.int32))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print(" b_10 =", K.eval(binary(1)(c)).astype(np.float16))
print("qr_10 =", K.eval(quantized_relu(1,0)(c)).astype(np.float16))
print("qr_11 =", K.eval(quantized_relu(1,1)(c)).astype(np.float16))
print("qr_20 =", K.eval(quantized_relu(2,0)(c)).astype(np.float16))
print("qr_21 =", K.eval(quantized_relu(2,1)(c)).astype(np.float16))
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("smooth"); print("with smooth sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("real"); print("with real sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("hard")
print(" c =", K.eval(c).astype(np.float16))
print("q2_31 =", K.eval(quantized_po2(3,1)(c)).astype(np.float16))
print("q2_32 =", K.eval(quantized_po2(3,2)(c)).astype(np.float16))
print("qr2_21 =", K.eval(quantized_relu_po2(2,1)(c)).astype(np.float16))
print("qr2_22 =", K.eval(quantized_relu_po2(2,2)(c)).astype(np.float16))
print("qr2_44 =", K.eval(quantized_relu_po2(4,1)(c)).astype(np.float16))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
print("q2_32_2 =", K.eval(quantized_relu_po2(32,2)(c)).astype(np.float16))
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
print(str(w[-1].message))