def compute_retrospective_loss(self, observed_arr, encoded_arr,
decoded_arr, re_encoded_arr):
'''
Compute retrospective loss.
Returns:
The tuple data.
- `np.ndarray` of delta.
- `np.ndarray` of losses of each batch.
- float of loss of all batch.
'''
if self.__output_neuron_count == self.__hidden_neuron_count:
target_arr = nd.broadcast_sub(
encoded_arr, nd.expand_dims(observed_arr.mean(axis=2), axis=2))
summary_delta_arr = nd.sqrt(nd.power(decoded_arr - target_arr, 2))
else:
# For each batch, draw a samples from the Uniform distribution.
if self.__output_neuron_count > self.__hidden_neuron_count:
all_dim_arr = np.arange(self.__output_neuron_count)
np.random.shuffle(all_dim_arr)
choiced_dim_arr = all_dim_arr[:self.__hidden_neuron_count]
target_arr = nd.broadcast_sub(
encoded_arr,
nd.expand_dims(observed_arr[:, :,
choiced_dim_arr].mean(axis=2),
axis=2))
summary_delta_arr = nd.sqrt(
nd.power(decoded_arr[:, :, choiced_dim_arr] - target_arr,
2))
else:
all_dim_arr = np.arange(self.__hidden_neuron_count)
np.random.shuffle(all_dim_arr)
choiced_dim_arr = all_dim_arr[:self.__output_neuron_count]
target_arr = nd.broadcast_sub(
encoded_arr[:, :, choiced_dim_arr],
nd.expand_dims(observed_arr.mean(axis=2), axis=2))
summary_delta_arr = nd.sqrt(
nd.power(decoded_arr - target_arr, 2))
match_delta_arr = None
for i in range(self.__batch_size):
arr = nd.sqrt(
nd.power(encoded_arr[i, -1] - re_encoded_arr[i, -1], 2))
if match_delta_arr is None:
match_delta_arr = nd.expand_dims(arr, axis=0)
else:
match_delta_arr = nd.concat(match_delta_arr,
nd.expand_dims(arr, axis=0),
dim=0)
delta_arr = summary_delta_arr + nd.expand_dims(
self.__retrospective_lambda * match_delta_arr, axis=1)
v = nd.norm(delta_arr)
if v > self.__grad_clip_threshold:
delta_arr = delta_arr * self.__grad_clip_threshold / v
loss = nd.mean(delta_arr, axis=0, exclude=True)
return loss
def batch_norm(X,
gamma,
beta,
is_training,
moving_mean,
moving_variance,
eps=1e-5,
moving_momentum=0.9):
assert len(X.shape) in (2, 4)
if len(X.shape) == 2:
mean = X.mean(axis=0)
variance = ((X - mean)**2).mean(axis=0)
else:
mean = X.mean(axis=(0, 2, 3), keepdims=True)
variance = ((X - mean)**2).mean(axis=(0, 2, 3), keepdims=True)
# 变形使得可以正确广播
moving_mean = moving_mean.reshape(mean.reshape)
moving_variance = moving_variance.reshape(mean.shape)
if is_training:
X_hat = (X - mean) / nd.sqrt(variance + eps)
# 更新全局的均值方差
moving_mean[:] = moving_momentum * moving_mean + (
1.0 - moving_momentum) * variance
else:
X_hat = (X - moving_mean) / nd.sqrt(moving_variance + eps)
# 拉升和偏移
return gamma.reshape(mean.shape) * X_hat + beta.reshape(mean.shape)
def batch_norm(X, gamma, beta, is_training, moving_mean, moving_variance,
eps = 1e-5, moving_momentum = 0.9):
assert len(X.shape) in (2, 4)
# 全连接: batch_size x feature
if len(X.shape) == 2:
# 每个输入维度在样本上的平均和方差
mean = X.mean(axis=0)
variance = ((X - mean)**2).mean(axis=0)
# 2D卷积: batch_size x channel x height x width
else:
# 对每个通道算均值和方差，需要保持4D形状使得可以正确的广播
mean = X.mean(axis=(0,2,3), keepdims=True)
variance = ((X - mean)**2).mean(axis=(0,2,3), keepdims=True)
# 变形使得可以正确的广播
moving_mean = moving_mean.reshape(mean.shape)
moving_variance = moving_variance.reshape(mean.shape)
# 均一化
if not is_training:
X_hat = (X - mean) / nd.sqrt(variance + eps)
#!!! 更新全局的均值和方差
moving_mean[:] = moving_momentum * moving_mean + (
1.0 - moving_momentum) * mean
moving_variance[:] = moving_momentum * moving_variance + (
1.0 - moving_momentum) * variance
else:
#!!! 测试阶段使用全局的均值和方差
X_hat = (X - moving_mean) / nd.sqrt(moving_variance + eps)
# 拉升和偏移
return gamma.reshape(mean.shape) * X_hat + beta.reshape(mean.shape)
def adadelta(params, sqrs, deltas, roh, batch_size):
eps_stable = 1e-5
for param, sqr, delta in zip(params, sqrs, deltas):
g = param.grad / batch_size
sqr[:] = roh * sqr + (1. - roh) * nd.square(g)
g_next = nd.sqrt(delta + eps_stable) / nd.sqrt(sqr + eps_stable) * g
delta[:] = roh * delta + (1. - roh) * g_next * g_next
param[:] -= g_next
def adadelta(params, sqrs, deltas, batch_size, rho):
eps_stable = 1e-5
for param, sqr, delta in zip(params, sqrs, deltas):
g = param.grad / batch_size
sqr[:] = rho * sqr + (1. - rho) * nd.square(g)
cur_delta = nd.sqrt(delta + eps_stable) / nd.sqrt(sqr + eps_stable) * g
delta[:] = rho * delta + (1. - rho) * cur_delta * cur_delta
param[:] -= cur_delta
def forward(self, X1, X2):
X1 = self.bimp(X1)
X2 = self.bimp(X2)
X1_norm = nd.sqrt(nd.sum(X1 * X1, axis=-1) + 1e-12)
X2_norm = nd.sqrt(nd.sum(X2 * X2, axis=-1) + 1e-12)
distance_cos = 1 - nd.sum(X1 * X2,
axis=-1) / (X1_norm * X2_norm + 1e-12)
return distance_cos
def update(self, index, weight, grad, state):
assert (isinstance(weight, NDArray))
assert (isinstance(grad, NDArray))
self._update_count(index)
lr = self._get_lr(index)
wd = self._get_wd(index)
t = self._index_update_count[index]
with bulk(self._bulk):
# preprocess grad
grad *= self.rescale_grad
if self.clip_gradient is not None:
grad = clip(grad, -self.clip_gradient, self.clip_gradient)
mean, var = state
mean *= self.beta1
mean += (1. - self.beta1) * grad
var *= self.beta2
var += (1. - self.beta2) * square(grad)
r1 = weight.norm()
if not self.bias_correction:
r1 = minimum(maximum(r1, self.lower_bound), self.upper_bound)
sqrt_var = sqrt(var)
sqrt_var += self.epsilon
g = mean / sqrt_var
g += wd * weight
else:
# apply bias correction
mean_hat = mean / (1. - power(self.beta1, t))
var_hat = var / (1. - power(self.beta2, t))
if self._eps_after_sqrt:
sqrt(var_hat, out=var_hat)
var_hat += self.epsilon
else:
var_hat += self.epsilon
sqrt(var_hat, out=var_hat)
mean_hat /= var_hat
mean_hat += wd * weight
g = mean_hat
r2 = g.norm()
# calculate lamb_trust_ratio
ratio = r1 / r2
# becomes NaN if ratio == NaN or 0, otherwise 0
nan_or_zero = 1 - ratio / ratio
r = where(nan_or_zero, ones_like(ratio), ratio)
lr *= r
# update weight
g *= lr
weight[:] -= g
def verify_instance_norm_rewrite(shp, eps):
# assert len(shp) == 4 # NCHW
assert len(shp) >= 3
vshp = (shp[1], )
data_np = np.random.uniform(size=shp)
gamma_np = np.random.uniform(size=vshp)
beta_np = np.random.uniform(size=vshp)
x = nd.array(data_np)
gamma = nd.array(gamma_np)
beta = nd.array(beta_np)
# org op
y = nd.InstanceNorm(x, gamma=gamma, beta=beta, eps=eps)
# rewrite op
axis = [i for i in range(len(shp)) if i != 1]
for i in axis:
gamma = nd.expand_dims(gamma, axis=i)
beta = nd.expand_dims(beta, axis=i)
n = np.product(shp[2:])
mean = nd.sum(x, axis=axis, keepdims=True) / n
dev = x - mean
var = nd.sum(dev * dev, axis=axis, keepdims=True) / n
std = nd.sqrt(var) + eps
frac = dev / std
z = frac * gamma + beta
# compare
assert z.shape == y.shape
zn, zp = get_norm(z)
yn, yp = get_norm(y)
rn = np.linalg.norm(zp - yp)
print(zn, yn, rn)
def goodness_of_function_optimizer_function(self):
for param, sqr in zip(self.__params, self.__sqrs):
g = param.grad / self.__batch_size
# 注意 这里不是 +=
sqr[:] = self.__gamma * sqr + (1. - self.__gamma) * nd.square(g)
div = self.__learning_rate * g / nd.sqrt(sqr + self.__eps_stable)
param[:] -= div
def TimeseriesFromPSD_nd(param_noise):
"""
GPU only
"""
(asd_pos, asd_neg, low_f, high_f, high_f_, size, fs, fmin, fmax) = param_noise
(*D_, N) = size
D = reduce(lambda x, y: x * y, D_)
# Gauss noise and its one-sided PSD without window
gauss_noise = 1* nd.random_normal(loc=0,scale=64,shape=(D, N), ctx=ctx)
_, xf_noise, psd_gauss = oneSidedPeriodogram_nd(gauss_noise, fs=8192)
psd_gauss = nd.array(psd_gauss, ctx = ctx, dtype='float64')
psd_twosided = nd.concat( # low positive
nd.zeros((D, low_f), ctx = ctx, dtype='float64'),
# high positive
psd_gauss[:, low_f:high_f] * asd_pos,
nd.zeros((D, high_f_), ctx = ctx, dtype='float64'),
nd.zeros((D, high_f_), ctx = ctx, dtype='float64'),
# high negative
psd_gauss[:, low_f:high_f][::-1] * asd_neg,
# low negative
nd.zeros((D, low_f), ctx = ctx, dtype='float64'), dim=1)
amplitude = nd.sqrt(psd_twosided *2 *fs*N )
epsilon_imag = nd.random_uniform(low=0, high=1, shape=(D,N),ctx=ctx,dtype='float64')*2*np.pi
re = nd.cos(epsilon_imag)*amplitude
im = nd.sin(epsilon_imag)*amplitude
temp = nd.zeros((D, N*2) , ctx=ctx)
temp[:,::2] = re
temp[:,1::2] = im
timeseries = mx.contrib.ndarray.ifft(temp)/N
return timeseries.reshape(size), psd_twosided
def merge(conv_w, gamma, beta, running_mean, running_var):
gamma_over_var = gamma / nd.sqrt(running_var + 1e-5)
gamma_over_var_expanded = nd.reshape(gamma_over_var,
(gamma_over_var.shape[0], 1, 1, 1))
new_w = gamma_over_var_expanded * nd.cast(conv_w, 'float32')
new_b = beta - running_mean * gamma_over_var
return new_w, new_b
def adagrad(params, sqrs, lr, batch_size):
eps_stable = 1e-7
for param, sqr in zip(params, sqrs):
g = param.grad / batch_size
sqr[:] += nd.square(g)
div = lr * g / nd.sqrt(sqr + eps_stable)
param[:] -= div
def verify_l2normalization_rewrite(shape, eps, mode):
assert len(shape) == 4 # NCHW
data_np = np.random.uniform(size=shape)
x = nd.array(data_np)
# org op
y = nd.L2Normalization(x, eps=eps, mode=mode)
# rewrite op
z = nd.broadcast_mul(x, x)
if mode == "channel":
axis = [1]
elif mode == "instance":
axis = [1, 2, 3]
elif mode == "spatial":
axis = [2, 3]
else:
assert "not valid `mode` type: %s" % mode
z = nd.sum(z, axis=axis)
eps_tensor = nd.array([eps])
z = nd.broadcast_add(z, eps_tensor)
z = nd.sqrt(z)
for i in axis:
z = nd.expand_dims(z, axis=i)
z = nd.repeat(z, repeats=shape[i], axis=i)
z = nd.broadcast_div(x, z)
print(z.shape)
return
# compare
assert z.shape == y.shape
zn, zp = get_norm(z)
yn, yp = get_norm(y)
rn = np.linalg.norm(zp - yp)
print(zn, yn, rn)
def rmsprop(params, sqrs, lr, gamma, batch_size):
eps_stable = 1e-8
for param, sqr in zip(params, sqrs):
g = param.grad / batch_size
sqr[:] = gamma * sqr + (1. - gamma) * nd.square(g)
div = lr * g / nd.sqrt(sqr + eps_stable)
param[:] -= div
def adagrad(params, sqrs, lr, batch_size):
eps_stable = 1e-7
for param, sqr in zip(params, sqrs):
g = param.grad / batch_size
sqr[:] = sqr + nd.square(g)
div = lr * g / (nd.sqrt(eps_stable + sqr))
param[:] -= div
def update(self):
self.state_step += 1
for idx, data in self.trace:
grad = data.grad
clr = self.args.lr
# clr = self.args.lr / (1 + (self.state_step - 1) * group['lr_decay'])
# the update is non-linear so indices must be unique
grad_indices = idx
grad_values = grad
grad_sum = (grad_values * grad_values).mean(1)
ctx = self.state_sum.context
if ctx != grad_indices.context:
grad_indices = grad_indices.as_in_context(ctx)
if ctx != grad_sum.context:
grad_sum = grad_sum.as_in_context(ctx)
self.state_sum[grad_indices] += grad_sum
std = self.state_sum[grad_indices] # _sparse_mask
std_values = nd.expand_dims(nd.sqrt(std) + 1e-10, 1)
if self.gpu >= 0:
std_values = std_values.as_in_context(mx.gpu(self.args.gpu))
tmp = -clr * grad_values / std_values
if tmp.context != ctx:
tmp = tmp.as_in_context(ctx)
# TODO(zhengda) the overhead is here.
self.emb[grad_indices] = mx.nd.take(self.emb, grad_indices) + tmp
self.trace = []
def update(self, index, weight, grad, state):
assert(isinstance(weight, NDArray))
assert(isinstance(grad, NDArray))
self._update_count(index)
lr = self._get_lr(index)
wd = self._get_wd(index)
t = self._index_update_count[index]
# preprocess grad
#grad = grad * self.rescale_grad + wd * weight
grad *= self.rescale_grad + wd * weight
if self.clip_gradient is not None:
grad = clip(grad, -self.clip_gradient, self.clip_gradient)
# warming momentum schedule
momentum_t = self.beta1 * (1. - 0.5 * (pow(0.96, t * self.schedule_decay)))
momentum_t_1 = self.beta1 * (1. - 0.5 * (pow(0.96, (t + 1) * self.schedule_decay)))
self.m_schedule = self.m_schedule * momentum_t
m_schedule_next = self.m_schedule * momentum_t_1
# update m_t and v_t
m_t, v_t = state
m_t[:] = self.beta1 * m_t + (1. - self.beta1) * grad
v_t[:] = self.beta2 * v_t + (1. - self.beta2) * grad * grad
grad_prime = grad / (1. - self.m_schedule)
m_t_prime = m_t / (1. - m_schedule_next)
v_t_prime = v_t / (1. - pow(self.beta2, t))
m_t_bar = (1. - momentum_t) * grad_prime + momentum_t_1 * m_t_prime
# update weight
weight[:] -= lr * m_t_bar / (sqrt(v_t_prime) + self.epsilon)
def update(self, index, weight, grad, state):
assert (isinstance(weight, NDArray))
assert (isinstance(grad, NDArray))
self._update_count(index)
lr = self._get_lr(index)
wd = self._get_wd(index)
is_sparse = grad.stype == 'row_sparse'
history = state
if is_sparse:
kwargs = {
'epsilon': self.float_stable_eps,
'rescale_grad': self.rescale_grad
}
if self.clip_gradient:
kwargs['clip_gradient'] = self.clip_gradient
sparse.adaalter_update(weight,
grad,
history,
out=weight,
lr=lr,
wd=wd,
**kwargs)
# raise NotImplementedError('AdaAlter has not been implemented for sparse nd')
else:
grad = grad * self.rescale_grad
if self.clip_gradient is not None:
grad = clip(grad, -self.clip_gradient, self.clip_gradient)
div = grad / sqrt(history + self.float_stable_eps)
weight[:] += (div + weight * wd) * -lr
history[:] += square(grad)
def verify_l2normalization_rewrite_tile(shape, eps, mode):
assert len(shape) == 4 # NCHW
data_np = np.random.uniform(size=shape)
x = nd.array(data_np)
# org op
y = nd.L2Normalization(x, eps, mode=mode)
# rewrite op
z = nd.broadcast_mul(x, x)
if mode == "channel":
axis = [1]
elif mode == "instance":
axis = [1, 2, 3]
elif mode == "spatial":
axis = [2, 3]
else:
assert "not valid `mode` type: %s" % mode
reps = tuple(
[shp if i in axis else 1 for i, shp in enumerate(list(shape))])
z = nd.sum(z, axis=axis, keepdims=True)
eps_tensor = nd.array([eps])
z = nd.sqrt(z)
z = nd.tile(z, reps=reps)
z = nd.broadcast_div(x, z)
# compare
assert z.shape == y.shape
zn, zp = get_norm(z)
yn, yp = get_norm(y)
rn = np.linalg.norm(zp - yp)
print(zn, yn, rn)
def grad_clipping(params, theta):
norm = nd.array([0.0], ctx)
for p in params:
norm += nd.sum(p.grad**2)
norm = nd.sqrt(norm).asscalar()
if norm > theta:
for p in params:
p.grad[:] *= theta / norm
def get_global_norm(arrays):
ctx = arrays[0].context
total_norm = nd.add_n(*[
nd.dot(x, x).as_in_context(ctx)
for x in (arr.reshape((-1, )) for arr in arrays)
])
total_norm = nd.sqrt(total_norm).asscalar()
return total_norm
def hybrid_forward(self, F, pred, label, sample_weight=None):
#label = _reshape_like(F, label, pred)
#loss = F.square(pred-label)
#loss = _apply_weighting(F, loss, self._weight/2, sample_weight)
loss = F.sqrt(F.square(pred - label))
#return F.mean(loss, axis=self._batch_axis, exclude=True)
return loss
def adam(params, vs, sqrs, lr, batch_size, t):
for param, v, sqr in zip(params, vs, sqrs):
current_v = beta1 * v + (1 - beta1) * param.grad
current_sqr = beta2 * sqr + (1 - beta2) * param.grad * param.grad
v[:] = current_v / (1 - beta1**t)
sqr[:] = current_sqr / (1 - beta2**t)
grad = v / nd.sqrt(sqr + eps_stable)
param[:] = param - (lr / batch_size) * grad
def grad_clipping(params, theta, ctx):
if theta is not None:
norm = nd.array([0.0], ctx)
for p in params:
norm += nd.sum(p.grad * p.grad)
norm = nd.sqrt(norm).asscalar()
if norm > theta:
for p in params:
p.grad[:] *= theta / norm
def batched_l2_dist(a, b):
a_squared = nd.power(nd.norm(a, axis=-1), 2)
b_squared = nd.power(nd.norm(b, axis=-1), 2)
squared_res = nd.add(nd.linalg_gemm(
a, nd.transpose(b, axes=(0, 2, 1)), nd.broadcast_axes(nd.expand_dims(b_squared, axis=-2), axis=1, size=a.shape[1]), alpha=-2
), nd.expand_dims(a_squared, axis=-1))
res = nd.sqrt(nd.clip(squared_res, 1e-30, np.finfo(np.float32).max))
return res
def grad_clipping(params, theta, ctx):
if theta is not None:
norm = nd.array([0.0], ctx)
for p in params:
norm += nd.sum(p.grad * p.grad)
norm = nd.sqrt(norm).asscalar()
if norm > theta:
for p in params:
p.grad[:] *= theta / norm
def goodness_of_function_optimizer_function(self):
for param, v, sqr in zip(self.__params, self.__vs, self.__sqrs):
g = param.grad / self.__batch_size
v[:] = self.__beta1 * v + (1 - self.__beta1) * g
sqr[:] = self.__beta2 * sqr + (1 - self.__beta2) * nd.square(g)
v_hat = v / (1 - self.__beta1**self.__t)
sqr_hat = sqr / (1 - self.__beta2**self.__t)
div = self.__learning_rate * v_hat / nd.sqrt(sqr_hat +
self.__eps_stable)
param[:] -= div
def grad_clipping(params, clipping_norm, ctx):
"""Gradient clipping."""
if clipping_norm is not None:
norm = nd.array([0.0], ctx)
for p in params:
norm += nd.sum(p.grad ** 2)
norm = nd.sqrt(norm).asscalar()
if norm > clipping_norm:
for p in params:
p.grad[:] *= clipping_norm / norm
def gradient_clipping(parameters, threshold, ctx):
if threshold is not None:
norm = nd.array([0.0], ctx)
for parameter in parameters:
norm += nd.sum(parameter.grad ** 2)
norm = nd.sqrt(norm).asscalar()
if norm > threshold:
for parameter in parameters:
parameter.grad[:] *= (threshold / norm)
def adam(params, lr, vals, sqrs, iter, batch_size, beta1=0.9, beta2=0.999):
eps_stable = 1e-8
for param, val, sqr in zip(params, vals, sqrs):
g = param.grad / batch_size
val[:] = beta1 * val + (1 - beta1) * g
sqr[:] = beta2 * sqr + (1 - beta2) * nd.square(g)
#val_next = val / (1 - nd.power(beta1, iter))
val_next = val / (1. - beta1**iter)
#sqr_next = sqr / (1. - nd.power(beta2, iter))
sqr_next = sqr / (1. - beta2**iter)
g_next = lr * val_next / (nd.sqrt(sqr_next) + eps_stable)
param[:] -= g_next
def _ratio_enum(anchor, ratios):
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = nd.round(nd.sqrt(size_ratios))
hs = nd.round(ws * ratios)
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def grad_clipping(params,theta,ctx):
if theta is not None:
norm = nd.array([0.0],ctx)
for p in params:
# print('grad_clipping:grad=',p.grad)
norm += nd.sum(p.grad ** 2)
# print('norm:',norm)
norm = nd.sqrt(norm).asscalar()
# print('grad_clipoing:norm=%f,theta=%f' %(norm,theta))
if norm > theta:
for p in params:
p.grad[:] *= theta / norm
