def test_instancenorm_perchannel_correctness():
# have each channel with a different average and std
x = np.random.normal(loc=5.0, scale=2.0, size=(10, 1, 4, 4))
y = np.random.normal(loc=10.0, scale=3.0, size=(10, 1, 4, 4))
z = np.random.normal(loc=-5.0, scale=5.0, size=(10, 1, 4, 4))
batch = np.append(x, y, axis=1)
batch = np.append(batch, z, axis=1)
# this model does not provide a normalization axis
model = Sequential()
norm = normalization.InstanceNormalization(axis=None,
input_shape=(3, 4, 4),
center=False,
scale=False)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
model.fit(batch, batch, epochs=4, verbose=0)
out = model.predict(batch)
# values will not be normalized per-channel
for instance in range(10):
for channel in range(3):
activations = out[instance, channel]
assert abs(activations.mean()) > 1e-2
assert abs(activations.std() - 1.0) > 1e-6
# but values are still normalized per-instance
activations = out[instance]
assert_allclose(activations.mean(), 0.0, atol=1e-1)
assert_allclose(activations.std(), 1.0, atol=1e-1)
# this model sets the channel as a normalization axis
model = Sequential()
norm = normalization.InstanceNormalization(axis=1,
input_shape=(3, 4, 4),
center=False,
scale=False)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
model.fit(batch, batch, epochs=4, verbose=0)
out = model.predict(batch)
# values are now normalized per-channel
for instance in range(10):
for channel in range(3):
activations = out[instance, channel]
assert_allclose(activations.mean(), 0.0, atol=1e-1)
assert_allclose(activations.std(), 1.0, atol=1e-1)
def test_instancenorm_perinstancecorrectness():
model = Sequential()
norm = normalization.InstanceNormalization(input_shape=(10, ))
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# bimodal distribution
z = np.random.normal(loc=5.0, scale=10.0, size=(2, 10))
y = np.random.normal(loc=-5.0, scale=17.0, size=(2, 10))
x = np.append(z, y)
x = np.reshape(x, (4, 10))
model.fit(x, x, epochs=4, batch_size=4, verbose=1)
out = model.predict(x)
out -= K.eval(norm.beta)
out /= K.eval(norm.gamma)
# verify that each instance in the batch is individually normalized
for i in range(4):
instance = out[i]
assert_allclose(instance.mean(), 0.0, atol=1e-1)
assert_allclose(instance.std(), 1.0, atol=1e-1)
# if each instance is normalized, so should the batch
assert_allclose(out.mean(), 0.0, atol=1e-1)
assert_allclose(out.std(), 1.0, atol=1e-1)
def test_instancenorm_training_argument():
bn1 = normalization.InstanceNormalization(input_shape=(10, ))
x1 = Input(shape=(10, ))
y1 = bn1(x1, training=True)
model1 = Model(x1, y1)
np.random.seed(123)
x = np.random.normal(loc=5.0, scale=10.0, size=(20, 10))
output_a = model1.predict(x)
model1.compile(loss='mse', optimizer='rmsprop')
model1.fit(x, x, epochs=1, verbose=0)
output_b = model1.predict(x)
assert np.abs(np.sum(output_a - output_b)) > 0.1
assert_allclose(output_b.mean(), 0.0, atol=1e-1)
assert_allclose(output_b.std(), 1.0, atol=1e-1)
bn2 = normalization.InstanceNormalization(input_shape=(10, ))
x2 = Input(shape=(10, ))
bn2(x2, training=False)
def test_instancenorm_correctness_rank2():
model = Sequential()
norm = normalization.InstanceNormalization(input_shape=(10, 1), axis=-1)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10, 1))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= K.eval(norm.beta)
out /= K.eval(norm.gamma)
assert_allclose(out.mean(), 0.0, atol=1e-1)
assert_allclose(out.std(), 1.0, atol=1e-1)
def test_instancenorm_convnet():
model = Sequential()
norm = normalization.InstanceNormalization(axis=1, input_shape=(3, 4, 4))
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= np.reshape(K.eval(norm.beta), (1, 3, 1, 1))
out /= np.reshape(K.eval(norm.gamma), (1, 3, 1, 1))
assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
def test_shared_instancenorm():
'''Test that a IN layer can be shared
across different data streams.
'''
# Test single layer reuse
bn = normalization.InstanceNormalization(input_shape=(10, ))
x1 = Input(shape=(10, ))
bn(x1)
x2 = Input(shape=(10, ))
y2 = bn(x2)
x = np.random.normal(loc=5.0, scale=10.0, size=(2, 10))
model = Model(x2, y2)
model.compile('sgd', 'mse')
model.train_on_batch(x, x)
# Test model-level reuse
x3 = Input(shape=(10, ))
y3 = model(x3)
new_model = Model(x3, y3)
new_model.compile('sgd', 'mse')
new_model.train_on_batch(x, x)
