def channel(self, zMean):
batch = K.shape(zMean)[0]
# Generate Laplace r.v.
# Source: https://en.wikipedia.org/wiki/Laplace_distribution#Generating_random_variables_according_to_the_Laplace_distribution
# Code: https://stackoverflow.com/questions/56691436/how-can-one-add-laplacian-noise-to-a-tensor-in-keras
u = K.random_uniform((batch, self.latent_dim), minval=-0.5, maxval=0.5)
epsilon = K.sign(u) * K.log(1 - 2 * K.abs(u) + K.epsilon())
return zMean + self.n0 / self.latent_dim * epsilon
def call(self, inputs, weights_mode=False):
image, noise_std = inputs
threshold = self.alpha * noise_std
if not weights_mode:
threshold = tf.expand_dims(threshold, axis=1)
threshold = tf.expand_dims(threshold, axis=1)
input_sign = K.sign(image)
soft_thresh_unsigned = ReLU()(input_sign * image - threshold)
soft_thresh = soft_thresh_unsigned * input_sign
return soft_thresh
def mean_percentage_error(y_true, y_pred):
"""
Bla bla bla
"""
y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)
MPE_model = K.mean(
(y_true - y_pred) /
(K.sign(y_true) * K.clip(K.abs(y_true), K.epsilon(), None)))
return 100. * K.abs(MPE_model)
def off_loss(y_true,y_pred):
mask = K.batch_flatten(K.sign(y_true[...,4]))
N = K.sum(mask)
offy_pred = K.batch_flatten(y_pred[...,2])
offx_pred = K.batch_flatten(y_pred[...,3])
offy_true = K.batch_flatten(y_true[...,2])
offx_true = K.batch_flatten(y_true[...,3])
offloss = K.abs(offx_pred*mask-offx_true) + K.abs(offy_pred*mask-offy_true)
offloss = K.sum(offloss)/N
return offloss
def mask_rs(x):
y = K.sign(tf.keras.activations.relu(x))
scalefactor = tf.compat.v1.size(y, out_type=tf.dtypes.float32) / (
1 + tf.math.count_nonzero(y, dtype=tf.dtypes.float32))
y *= scalefactor
def grad(dy):
return dy
return y, grad
def closs(y_true, y_pred):
mask = K.batch_flatten(K.sign(y_true[..., 6]))
N = K.sum(mask)
heatmaps = K.batch_flatten(y_true[..., 0])
cls_pred = K.batch_flatten(y_pred[..., 1])
cls_pred = K.clip(cls_pred, 1e-7, 1 - 1e-7)
cls_true = K.batch_flatten(y_true[..., 1])
cls_loss = K.sum(
focal_loss(gamma1, gamma2, cls_true, cls_pred, heatmaps)) / N
return cls_loss
def flip(x):
# y = K.sign(tf.keras.activations.relu(tf.keras.activations.tanh(x)))
# y = K.sign(tf.keras.activations.relu(x))
# y = K.sign(tf.keras.activations.tanh(x))
y = K.sign(x)
def grad(dy):
return dy
return y, grad
def channel(self, zMean):
batch = K.shape(zMean)[0]
# Generate Laplace r.v.
# Source: https://en.wikipedia.org/wiki/Laplace_distribution#Generating_random_variables_according_to_the_Laplace_distribution
# Code: https://stackoverflow.com/questions/56691436/how-can-one-add-laplacian-noise-to-a-tensor-in-keras
u = K.random_uniform((batch, self.latent_dim), minval=-0.5, maxval=0.5)
epsilon = K.sign(u) * K.log(1 - 2 * K.abs(u) + K.epsilon())
# Because BatchNormalization produces z vector with signal power 'latentDim',
# we should not scale the noise power here.
return zMean + np.sqrt(self.train_noisepow) * epsilon
def sloss(y_true, y_pred):
mask = K.batch_flatten(K.sign(y_true[..., 6]))
N = K.sum(mask)
sizey_true = K.batch_flatten(y_true[..., 4])
sizey_pred = K.batch_flatten(y_pred[..., 4])
sizex_true = K.batch_flatten(y_true[..., 5])
sizex_pred = K.batch_flatten(y_pred[..., 5])
size_loss = K.sum(
K.abs(sizex_pred * mask - sizex_true) +
K.abs(sizey_pred * mask - sizey_true)) / N
return size_loss
def all_loss(self, y_true, y_pred):
mask = K.sign(y_true[..., 2 * self.__category_n + 2])
N = K.sum(mask)
alpha = 2.
beta = 4.
heat_loss = self.__calculate_heatmap_loss(y_true, y_pred, alpha, beta)
offset_loss = self.__calculate_offset_loss(y_true, y_pred, mask)
size_loss = self.__calculate_size_loss(y_true, y_pred, mask)
return (heat_loss + 1.0 * offset_loss + 5.0 * size_loss) / N
def continuous_dice_coef(y_true, y_pred, smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
# this is a workaround to allow a boolean check for continuous dice
if tf.cond(tf.greater(intersection, 0.), lambda: 1, lambda: 0) == 1:
c = K.sum(y_true_f * y_pred_f) / K.sum(y_true_f * K.sign(y_pred_f))
else:
c = 1
continuous_union = c * K.sum(y_true_f) + K.sum(y_pred_f)
return (2. * intersection + smooth) / (continuous_union + smooth)
def call(self, x):
# Apply sigmoid function to the params
x = K.sigmoid(self.image_params) * K.sign(K.abs(x) + 1)
# [0,1] -> [0, 255]
x = 255 * x
# RGB -> BGR
x = x[:, :, :, ::-1]
# Convert to caffe color scale
mean = K.variable(
np.array([103.939, 116.779, 123.68],
np.float32).reshape(1, 1, 1, 3))
return x - mean
def call(self, Inputs):
Reshaped_Inputs = K.reshape(Inputs, [
-1,
Inputs.get_shape().as_list()[1] * Inputs.get_shape().as_list()[2],
Inputs.get_shape().as_list()[3]
])
Bilinear_Pooling = K.batch_dot(Reshaped_Inputs,
Reshaped_Inputs,
axes=[1, 1])
Signed_Sqrt = K.sign(Bilinear_Pooling) * K.sqrt(
K.abs(Bilinear_Pooling) + 1e-9)
return K.l2_normalize(Signed_Sqrt, axis=-1)
def cdc_of_true_positive(y_true, y_pred, smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
# this is a workaround to allow a boolean check for continuous dice
if tf.cond(tf.greater(intersection, 0.), lambda: 1, lambda: 0) == 1:
c = K.sum(y_true_f * y_pred_f) / K.sum(y_true_f * K.sign(y_pred_f))
else:
c = 1
# this differs here from normal dice in the second term of the union
continuous_union = c * K.sum(y_true_f) + K.sum(intersection)
return (2. * intersection + smooth) / (continuous_union + smooth)
def heaviside(x):
""" Définis la fonction de Heaviside qui n'est pas défini
par défaut dans keras. A utiliser comme fonction
d'activiation lors de la définition d'une couche par exemple
modele.add(Dense(4,activation=heaviside))
Attention il n'y a pas de guillemet ici.
Astuce de la formule : H(x) = 1/2 (1+|x|) """
# return (1+x/K.abs(x))/2
# return (1+K.sign(x))/2
z = K.zeros_like(x)
return 1 - K.maximum(z, K.sign(-x))
def call(self, inputs):
output = K.dot(inputs, self.kernel)
if self.activation is not None:
output = self.activation(output)
b = 0.5 * K.sign(output) + 1
Sw = b - self.mw
Sb = b - self.mb
F = Sw - Sb + \
1e3 * ((b - 0.5) - output)**2 + \
1e3 * ((b - 0.5))**2 - \
1e6 * ((b - K.mean(output, axis = -1))**2)
self.add_loss(F)
return b
def mask_rs(x):
y = K.sign(tf.keras.activations.relu(x))
# some papers (arXiv:1905.01067v4 and arXiv:1911.13299v1) do a
# rescaling of the weights/masks while backpropagating, we can do it here as well
scalefactor = tf.compat.v1.size(y, out_type=tf.dtypes.float32) / (
1 + tf.math.count_nonzero(y, dtype=tf.dtypes.float32))
y *= scalefactor
def grad(dy):
return dy
return y, grad
def call(self, inputs):
binary_kernel = self.kernel + K.stop_gradient(
K.sign(self.kernel) - self.kernel)
binary_kernel = binary_kernel + K.stop_gradient(
binary_kernel * self.multiplier - binary_kernel)
outputs = K.conv2d(inputs,
binary_kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
return outputs
def false_positive_continuous_dice_coef_loss(y_true, y_pred, smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
# this is a workaround to allow a boolean check for continuous dice
if tf.cond(tf.greater(intersection, 0.), lambda: 1, lambda: 0) == 1:
c = K.sum(y_true_f * y_pred_f) / K.sum(y_true_f * K.sign(y_pred_f))
else:
c = 1
continuous_union = c * K.sum(y_true_f) + K.sum(y_pred_f)
false_positive_augmented_union = continuous_union - intersection
return -(2. * intersection + smooth) / (false_positive_augmented_union +
smooth) + 1
def rank_loss(y_true, y_pred):
y_true_p1 = y_true[:, 0:window_size]
y_true_p2 = y_true[:, window_size:]
y_true_p1 = K.reshape(y_true_p1, [-1, window_size])
y_true_p2 = K.reshape(y_true_p2, [-1, window_size])
y_pred = K.reshape(y_pred, [-1, window_size])
R = tf.matmul(y_true_p2, K.transpose(y_true_p1))
P = tf.matmul(y_true_p2, K.transpose(y_pred))
diag_P = tf.linalg.tensor_diag_part(P)
temp = -K.sign(P - K.reshape(diag_P, [-1, 1])) * R
loss = K.sum(temp) / K.sum(R * 1.0)
return loss
def update_neurons(self):
"""Update neurons according to activation function."""
# Update membrane potentials.
new_mem = self.get_new_mem()
# Generate spikes.
output_spikes = self.get_spikes(new_mem)
if self.spiketrain is not None:
self.add_update([(self.spiketrain,
self.time * k.sign(output_spikes))])
# Compute post-synaptic potential.
psp = self.get_psp(output_spikes)
return psp
def mask_flip(x):
# y = K.sign(tf.keras.activations.relu(tf.keras.activations.tanh(x)))
# y = K.sign(tf.keras.activations.relu(x))
# y = K.sign(tf.keras.activations.tanh(x))
# y = K.sign(x)
a = 0.005
y = K.sign(
tf.keras.activations.relu(x - a) - tf.keras.activations.relu(-x - a))
# y = K.sign(tf.keras.activations.relu(tf.keras.activations.tanh(x) - a) - tf.keras.activations.relu(-tf.keras.activations.tanh(x) - a))
# scalefactor = tf.compat.v1.size(y, out_type=tf.dtypes.float32) / (1+tf.math.count_nonzero(y, dtype=tf.dtypes.float32))
# y *= scalefactor
def grad(dy):
return dy
return y, grad
def calculate_binary_weights(conv_layer, M):
'''
conv_layer: original layer's W
'''
mean = BK.mean(BK.reshape(conv_layer, shape=(-1, )), axis=0)
variance = BK.var(BK.reshape(conv_layer, shape=(-1, )), axis=0)
shifted_stddev = -1 + np.array(range(M)) * (2 / (M - 1))
shifted_stddev = BK.constant(shifted_stddev,
dtype="float32",
name="shifted_stddev")
shifted_stddev *= BK.sqrt(variance)
shifted_stddev = BK.reshape(shifted_stddev,
shape=[M] + [1] * len(conv_layer.get_shape()))
binary_weights = conv_layer - mean
binary_weights = BK.tile(BK.expand_dims(binary_weights, 0),
n=[M] + [1] * len(conv_layer.get_shape()))
binary_weights = BK.sign(binary_weights + shifted_stddev)
return binary_weights
def binary_focal_loss_fixed(y_true, y_pred):
pt_1 = tf.where(tf.equal(y_true[..., 0], 1), y_pred[..., 0],
tf.ones_like(y_pred[..., 0]))
pt_0 = tf.where(tf.equal(y_true[..., 0], 0), y_pred[..., 0],
tf.zeros_like(y_pred[..., 0]))
epsilon = K.epsilon()
# clip to prevent NaN's and Inf's
pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)
pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)
loss = -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \
-K.sum((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
mask = K.sign(y_true[..., 0] - 1 + K.epsilon())
mask = K.clip(mask, 0, 1)
N = K.sum(mask) + 1
return loss / N
def call(self, inputs):
inputs, weights = inputs
weights = weights/tf.reduce_sum(weights)
weights_expand = tf.expand_dims(weights, axis=1)
mean, variance = tf.nn.weighted_moments(inputs, [0], weights_expand)
counter = K.update_add(self.counter, K.ones_like(self.counter))
init = K.sign(counter-K.ones_like(counter))
mean = K.update(self.mean, init*self.mean+(1.0-init)*mean)
variance = K.update(self.variance, init*self.variance+(1.0-init)*variance)
mean_expand = tf.expand_dims(mean, axis=0)
variance_expand = tf.expand_dims(variance, axis=0)
outputs = (inputs-mean_expand)/tf.sqrt(variance_expand+self.epsilon)
return outputs
def off_loss(y_true, y_pred):
mask = K.batch_flatten(K.sign(y_true[..., 6]))
N = K.sum(mask)
offy_pred = K.batch_flatten(y_pred[..., 2])
offx_pred = K.batch_flatten(y_pred[..., 3])
offy_true = K.batch_flatten(y_true[..., 2])
offx_true = K.batch_flatten(y_true[..., 3])
offloss1 = K.abs(offx_pred * mask - offx_true) + K.abs(offy_pred * mask -
offy_true)
offloss1 = K.sum(offloss1) / N
offh_pred = K.batch_flatten(y_pred[..., 4])
offw_pred = K.batch_flatten(y_pred[..., 5])
offh_true = K.batch_flatten(y_true[..., 4])
offw_true = K.batch_flatten(y_true[..., 5])
offloss2 = K.abs(offw_pred * mask - offw_true) + K.abs(offh_pred * mask -
offh_true)
offloss2 = K.sum(offloss2) / N
offloss = (offloss1 + 2 * offloss2)
return offloss
def get_gradients(self, tape, loss, var_list, grad_loss=None):
"""Called in `minimize` to compute gradients from loss."""
grads = tape.gradient(loss, var_list, grad_loss)
if not (hasattr(self.e2efs_layer, 'regularization_loss')):
return list(zip(grads, var_list))
with tf.GradientTape() as e2efs_tape:
e2efs_loss = self.e2efs_layer.regularization_func(self.e2efs_layer.kernel)
e2efs_grad = grads[0]
e2efs_regularizer_grad = e2efs_tape.gradient(e2efs_loss, [self.e2efs_layer.kernel])[0]
# tf.print(e2efs_regularizer_grad)
e2efs_regularizer_grad_corrected = e2efs_regularizer_grad / (tf.norm(e2efs_regularizer_grad) + K.epsilon())
e2efs_grad_corrected = e2efs_grad / (tf.norm(e2efs_grad) + K.epsilon())
combined_e2efs_grad = (1. - self.e2efs_layer.moving_factor) * e2efs_grad_corrected + \
self.e2efs_layer.moving_factor * e2efs_regularizer_grad_corrected
combined_e2efs_grad = K.sign(
self.e2efs_layer.moving_factor) * K.minimum(self.th, K.max(
K.abs(combined_e2efs_grad))) * combined_e2efs_grad / K.max(
K.abs(combined_e2efs_grad) + K.epsilon())
grads[0] = combined_e2efs_grad
return list(zip(grads, var_list))
def final_loss(y_true, y_pred):
mask = K.batch_flatten(K.sign(y_true[..., 6]))
N = K.sum(mask)
heatmaps = K.batch_flatten(y_true[..., 0])
cls_pred = K.batch_flatten(y_pred[..., 1])
cls_pred = K.clip(cls_pred, 1e-7, 1 - 1e-7)
cls_true = K.batch_flatten(y_true[..., 1])
cls_loss = K.sum(
focal_loss(gamma1, gamma2, cls_true, cls_pred, heatmaps)) / N
offy_pred = K.batch_flatten(y_pred[..., 2])
offx_pred = K.batch_flatten(y_pred[..., 3])
offy_true = K.batch_flatten(y_true[..., 2])
offx_true = K.batch_flatten(y_true[..., 3])
offloss1 = K.abs(offx_pred * mask -
offx_true) + K.abs(offy_pred * mask - offy_true)
offloss1 = K.sum(offloss1) / N
offh_pred = K.batch_flatten(y_pred[..., 4])
offw_pred = K.batch_flatten(y_pred[..., 5])
offh_true = K.batch_flatten(y_true[..., 4])
offw_true = K.batch_flatten(y_true[..., 5])
offloss2 = K.abs(offw_pred * mask -
offw_true) + K.abs(offh_pred * mask - offh_true)
offloss2 = K.sum(offloss2) / N
offloss = (offloss1 + 2 * offloss2)
sizey_true = K.batch_flatten(y_true[..., 6])
sizey_pred = K.batch_flatten(y_pred[..., 6])
sizex_true = K.batch_flatten(y_true[..., 7])
sizex_pred = K.batch_flatten(y_pred[..., 7])
size_loss = K.sum(
K.abs(sizex_pred * mask - sizex_true) +
K.abs(sizey_pred * mask - sizey_true)) / N
loss = (1 * cls_loss + 0.8 * offloss + 1 * size_loss)
return loss
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [state_ops.assign_add(self.iterations, 1)]
lr = self.lr
if self.initial_decay > 0:
lr = lr * ( # pylint: disable=g-no-augmented-assignment
1. / (1. + self.decay * math_ops.cast(self.iterations,
K.dtype(self.decay))))
t = math_ops.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (
K.sqrt(1. - math_ops.pow(self.beta_2, t)) /
(1. - math_ops.pow(self.beta_1, t)))
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
#v_t = (self.beta_2 * v) + (1. - self.beta_2) * math_ops.square(g) # from amsgrad
v_t = v - (1-self.beta_2)*K.sign(v-math_ops.square(g))*math_ops.square(g)
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(state_ops.assign(m, m_t))
self.updates.append(state_ops.assign(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(state_ops.assign(p, new_p))
return self.updates
def call(self, inputs):
#***************************************************************************************************
#Binary layer as in https://arxiv.org/abs/1802.08530
#M. D. McDonnell, Training wide residual networks for deployment using a single bit for each weight
#ICLR, 2018
#
#This code sets the full precsion weights to binary for forward and bacjkward propagation
#but enables gradients to update the full precision weights that ar used only during training
#
binary_kernel = self.kernel + K.stop_gradient(
K.sign(self.kernel) - self.kernel)
binary_kernel = binary_kernel + K.stop_gradient(
binary_kernel * self.multiplier - binary_kernel)
#***************************************************************************************************
outputs = K.conv2d(inputs,
binary_kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
return outputs
