def forward(self, bottom, top): self.label = cp.asarray(copy.deepcopy(bottom[1].data),cp.uint8) prob = cp.asarray(copy.deepcopy(bottom[0].data),cp.float64) prob = cp.subtract(prob,cp.max(prob,axis=1)[:,cp.newaxis,...]) prob = cp.exp(prob) self.softmax = cp.divide(prob,cp.sum(prob,axis=1)[:,cp.newaxis,...]) ## mask self.weight_mask = cp.ones_like(self.label, cp.float64) for weight_id in self.weight_dic: self.weight_mask[self.label == weight_id] = self.weight_dic[weight_id] if self.has_ignore_label: self.weight_mask[self.label == self.ignore_label] = 0 # self.weight_mask[self.label == 0] = 0.3 # self.weight_mask[self.label == 1] = 0.25 # self.weight_mask[self.label == 2] = 5 # self.weight_mask[self.label == 4] = 2 self.label[self.label == 3] = 2 compute_count = self.weight_mask[self.weight_mask != 0].size ## nomalize mask self.weight_mask = cp.divide(self.weight_mask, cp.divide(cp.sum(self.weight_mask), compute_count)) ## compute loss prob_compute_matrix = copy.deepcopy(self.softmax[self.index_0,self.label,self.index_2,self.index_3]) prob_compute_matrix[prob_compute_matrix < (1e-10)] = 1e-10 loss = - cp.divide(cp.sum(cp.multiply(cp.log(prob_compute_matrix),self.weight_mask)),compute_count) loss = cp.asnumpy(loss) top[0].data[...] = loss
def linear_model_backward(AL, Y, caches):
grads = OrderedDict()
L = len(caches) # the number of layers
m = AL.shape[1]
Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
# Initializing the backpropagation
dAL = -(cp.divide(Y, AL) - cp.divide(1 - Y, 1 - AL))
# Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches". Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
current_cache = caches[L - 1]
grads["dA" + str(L - 1)], grads["dW" + str(L)], grads[
"db" + str(L)] = linear_activation_backward(dAL,
current_cache,
activation="sigmoid")
for l in reversed(range(L - 1)):
# lth layer: (RELU -> LINEAR) gradients.
current_cache = caches[l]
dA_prev_temp, dW_temp, db_temp = linear_activation_backward(
grads["dA" + str(l + 1)], current_cache, activation="relu")
grads["dA" + str(l)] = dA_prev_temp
grads["dW" + str(l + 1)] = dW_temp
grads["db" + str(l + 1)] = db_temp
return grads
def update_pair(self, i, k):
cp.subtract(self.mass_r_arrayg[i, :],
self.mass_r_arrayg[i + 1:, :],
out=self.relative_r[:k])
cp.multiply(self.relative_r[:k],
self.relative_r[:k],
out=self.distance_sqv[:k])
# np.add.reduce(self.distance_sqv[:k], axis = 1, out = self.distance_sq[:k])
cp.sum(self.distance_sqv[:k], axis=1, out=self.distance_sq[:k])
cp.sqrt(self.distance_sq[:k], out=self.distance_inv[:k])
cp.divide(1.0, self.distance_inv[:k], out=self.distance_inv[:k])
cp.multiply(self.relative_r[:k],
self.distance_inv[:k].reshape(k, 1),
out=self.relative_r[:k])
cp.divide(self._G, self.distance_sq[:k], out=self.a_factor[:k])
cp.multiply(self.a_factor[:k],
self.mass_m_array[i + 1:],
out=self.a1[:k])
cp.multiply(self.a_factor[:k], self.mass_m_array[i], out=self.a2[:k])
cp.multiply(self.relative_r[:k],
self.a1[:k].reshape(k, 1),
out=self.a1r[:k])
# np.add.reduce(self.a1r[:k], axis = 0, out = self.a1v)
cp.sum(self.a1r[:k], axis=0, out=self.a1v)
cp.subtract(self.mass_a_arrayg[i, :],
self.a1v,
out=self.mass_a_arrayg[i, :])
cp.multiply(self.relative_r[:k],
self.a2[:k].reshape(k, 1),
out=self.a2r[:k])
cp.add(self.mass_a_arrayg[i + 1:, :],
self.a2r[:k],
out=self.mass_a_arrayg[i + 1:, :])
def backward(yhat, y, memory, param_values, arch):
gradsVals = {}
# y = y.reshape(yhat.shape)
if usegpu == True:
yhat = np.asarray(yhat)
y = np.asarray(y)
daprev = -(np.divide(y, yhat) - np.divide(1 - y, 1 - yhat))
for layer_idx_prev, layer in reversed(list(enumerate(arch))):
layer_idx = layer_idx_prev + 1
activCurr = layer["activation"]
da_curr = daprev
aprev = memory["A" + str(layer_idx_prev)]
z_curr = memory["Z" + str(layer_idx)]
w_curr = param_values["W" + str(layer_idx)]
b_curr = param_values["b" + str(layer_idx)]
daprev, dw_curr, db_curr = singleBackward(da_curr, w_curr, b_curr,
z_curr, aprev, activCurr)
gradsVals["dW" + str(layer_idx)] = dw_curr
gradsVals["db" + str(layer_idx)] = db_curr
return gradsVals
def cost(AL, Y, mode = 'SEL'):
'''
Parameters
----------
AL : cp.array(out_dim, examples)
Final layer output.
Y : cp.array(out_dim, examples)
Expected output.
mode : string, optional
Type of cost computation. The default is 'SEL'.
Returns
-------
cost : cp.array(out_dim, 1)
Cost output (cost per features).
dAL : cp.array(out_dim, examples)
Cost derivates function.
'''
m = Y.shape[1]
if mode == 'XC':
AL = cp.clip(AL, 1e-15, 1-1e-15)
cost = -(1/m)*cp.sum((Y*cp.log(AL)+((1-Y)*cp.log(1-AL))), axis = 1)
dAL = - (cp.divide(Y, AL) - cp.divide(1 - Y, 1 - AL))
elif mode == 'SEL':
cost = (1/(2*m))*cp.sum((AL - Y)**2, axis = 1)
dAL = AL - Y
cost = cp.squeeze(cost)
return cost, dAL
def GlobalReg(X, T, sigma2, outliers):
"""
:params:
:return
"""
[N, D] = X.shape
M = T.shape[0]
# Calculate P matrix
# Nominator of P
P_num = cp.sum((X[None, :, :] - T[:, None, :])**2, axis=2)
P_num = cp.exp(-P_num / (2 * sigma2))
# Denominator of P
P_den = cp.sum(P_num, axis=0)
P_den = cp.tile(P_den, (M, 1))
P_den[P_den == 0] = 2.220446049250313e-16
c = ((((2 * cp.pi * sigma2)**D / 2) * (outliers / (1 - outliers))) *
(M / N))
P_den += c
P = cp.divide(P_num, P_den)
P1 = cp.sum(P, axis=1)
Pt1 = cp.sum(P, axis=0)
c1 = c * cp.ones(N)
K1 = cp.dot(cp.transpose(P_num), cp.ones(M))
a = cp.tile(cp.divide(1, K1 + c1).reshape(N, 1), D)
Px = cp.dot(P_num, (cp.multiply(a, X)))
return P1, Pt1, Px
def _calc_offset(offset_map, region, parent_y, parent_x):
y0, y1, x0, x1 = region
x_area, y_area = offset_map[:, y0:y1, x0:x1]
x_vec = np.power(np.subtract(np.arange(x0, x1), parent_x), 2)
y_vec = np.power(np.subtract(np.arange(y0, y1), parent_y), 2)
xv, yv = np.meshgrid(x_vec, y_vec)
dist = np.sqrt(xv + yv) # sqrt(y^2 + x^2)
xv = np.divide(xv, dist) # normalize x
yv = np.divide(yv, dist) # normalize y
offset_map[0, y0:y1, x0:x1] = np.maximum(x_area, xv)
offset_map[1, y0:y1, x0:x1] = np.maximum(y_area, yv)
def forward(self, bottom, top): self.label = cp.asarray(copy.deepcopy(bottom[1].data),cp.uint8) prob = cp.asarray(copy.deepcopy(bottom[0].data),cp.float64) prob = cp.subtract(prob,cp.max(prob,axis=1)[:,cp.newaxis,...]) prob = cp.exp(prob) self.softmax = cp.divide(prob,cp.sum(prob,axis=1)[:,cp.newaxis,...]) ## mask self.weight_mask = cp.ones_like(self.label, cp.float64) for weight_id in self.weight_dic: self.weight_mask[self.label == weight_id] = self.weight_dic[weight_id] if self.has_ignore_label: self.weight_mask[self.label == self.ignore_label] = 0 # num_total = 15422668800 # empty_num = 3679002314 # road_num = 10565335603 # ped_num = 99066996 # car_num = 995347874 #self.label[self.label == 3] = 2 # w_empty = float((num_total-empty_num)/num_total) # w_road = float((num_total-road_num)/num_total) # w_ped = float((num_total-ped_num)/num_total) # w_car = float((num_total-car_num)/num_total) # print(w_empty) # print(w_road) # print(w_ped) # print(w_car) # empty:0.3 # road:0.25 self.weight_mask[self.label == 0] = 0.3 self.weight_mask[self.label == 1] = 0.25 self.weight_mask[self.label == 3] = 0.5 # self.weight_mask[self.label == 2] = w_ped # self.weight_mask[self.label == 4] = w_car compute_count = self.weight_mask[self.weight_mask != 0].size ## nomalize mask self.weight_mask = cp.divide(self.weight_mask, cp.divide(cp.sum(self.weight_mask), compute_count)) ## compute loss prob_compute_matrix = copy.deepcopy(self.softmax[self.index_0,self.label,self.index_2,self.index_3]) prob_compute_matrix[prob_compute_matrix < (1e-10)] = 1e-10 loss = - cp.divide(cp.sum(cp.multiply(cp.log(prob_compute_matrix),self.weight_mask)),compute_count) loss = cp.asnumpy(loss) top[0].data[...] = loss
def imgmodpha_gpu(img):
# ok
#return gpu
img = img
[xsize, ysize] = img.shape
cp.cuda.Device(0).use()
img = cp.array(img, dtype='<f4')
sp = cp.divide(cp.divide(cp.fft.fftshift(cp.fft.fft2(img)), xsize), ysize)
modul = cp.abs(sp)
phase = cp.angle(sp)
return modul, phase
def run_cupy(lat2, lon2):
import cupy as cp
# Allocate temporary arrays
size = len(lat2)
a = cp.empty(size, dtype='float64')
dlat = cp.empty(size, dtype='float64')
dlon = cp.empty(size, dtype='float64')
# Transfer inputs to the GPU
lat2 = cp.array(lat2)
lon2 = cp.array(lon2)
# Begin computation
lat1 = 0.70984286
lon1 = 1.23892197
MILES_CONST = 3959.0
cp.subtract(lat2, lat1, out=dlat)
cp.subtract(lon2, lon1, out=dlon)
# dlat = sin(dlat / 2.0) ** 2.0
cp.divide(dlat, 2.0, out=dlat)
cp.sin(dlat, out=dlat)
cp.multiply(dlat, dlat, out=dlat)
# a = cos(lat1) * cos(lat2)
lat1_cos = math.cos(lat1)
cp.cos(lat2, out=a)
cp.multiply(a, lat1_cos, out=a)
# a = a + sin(dlon / 2.0) ** 2.0
cp.divide(dlon, 2.0, out=dlon)
cp.sin(dlon, out=dlon)
cp.multiply(dlon, dlon, out=dlon)
cp.multiply(a, dlon, out=a)
cp.add(dlat, a, out=a)
c = a
cp.sqrt(a, out=a)
cp.arcsin(a, out=a)
cp.multiply(a, 2.0, out=c)
mi = c
cp.multiply(c, MILES_CONST, out=mi)
# Transfer outputs back to CPU
a = cp.asnumpy(a)
return a
def ruzicka_mat(matrix_a, vector_new):
matrix_a *= cp.arange(1023, -1, -1, dtype=cp.uint16)
min_up = cp.minimum(cp.array(matrix_a), vector_new)
max_down = cp.maximum(cp.array(matrix_a), vector_new)
numerator = cp.sum(min_up, axis=1)
denominator = cp.sum(max_down, axis=1)
return cp.asnumpy(cp.divide(numerator, denominator))
def _divide_nonzero(array1, array2, cval=1e-10):
"""
Divides two arrays.
Denominator is set to small value where zero to avoid ZeroDivisionError and
return finite float array.
Parameters
----------
array1 : (N, ..., M) ndarray
Array 1 in the enumerator.
array2 : (N, ..., M) ndarray
Array 2 in the denominator.
cval : float, optional
Value used to replace zero entries in the denominator.
Returns
-------
array : (N, ..., M) ndarray
Quotient of the array division.
"""
# Copy denominator
denominator = cp.copy(array2)
# Set zero entries of denominator to small value
denominator[denominator == 0] = cval
# Return quotient
return cp.divide(array1, denominator)
def apply_kernel(moments,
kernel,
num_moments,
num_vecs,
extra_points=1,
precision=32):
"""
Parameters
----------
Apply a given kernel in a given array of moments.
Return the cosine transform of type III.
"""
num_points = extra_points + num_moments
if kernel is not None:
moments = moments * kernel
mu_ext = cp.zeros(num_points, dtype=moments.dtype)
mu_ext[0:num_moments] = moments
smooth_moments = dctIII(mu_ext, precision)
points = cp.arange(0, num_points)
ek = cp.cos(cp.pi * (points + 0.5) / num_points)
gk = cp.pi * cp.sqrt(1. - ek**2)
rho = cp.divide(smooth_moments, gk)
return ek, rho
def initialize(m, n, d):
W = cp.random.normal(size=(m, d))
y = cp.random.normal(size=n)
a = 2*cp.around(cp.random.uniform(size=m))-cp.ones(m)
x_tmp = cp.random.uniform(size=(n, d))
x = cp.divide(x_tmp, cp.linalg.norm(x_tmp))
return x, W, a, y
def gradient(self, x0, y_true):
def func(a, t, params, A, function, bT, x, division):
index = int(t * (division - 1))
return cp.multiply(
-1.,
cp.add(
cp.dot(a, params[1][index]),
cp.dot(
cp.multiply(
bT,
cp.multiply(params[0][index],
function(cp.dot(x[index], A.T)))), A)))
n_data = len(x0)
y_pred = self(x0)
aT = cp.zeros_like(x0, dtype=cp.float32)
bT = cp.divide(cp.subtract(y_pred, y_true), n_data)
a = euler(func,
aT,
self.t[::-1],
args=(self.params, self.A, self.d_function, bT, self.x,
self.division))
g_alpha = cp.sum(
cp.multiply(bT, self.function(cp.dot(self.x, self.A.T))), 1)
g_beta = cp.einsum("ilj,ilk->ijk", a[::-1], self.x)
g_gamma = cp.sum(a[::-1], 1)
return (g_alpha, g_beta, g_gamma)
def getMix_tau(self):
ones = cp.ones((H, W))[self.mask]
v1 = Eta_n / self.rho
v2 = Eta_n * M / self.rho
mix_v = cp.divide(2 * v1 * v2, (v1 * (ones - self.psi[self.mask]) + v2 * (ones + self.psi[self.mask])))
mix_tau = 3 * mix_v + 0.5
#print(mix_tau.mean())
return mix_tau
def divide(numerator, denominator):
"""divide a tensor by another
Args:
tensor1 (ndarray): numerator tensor.
tensor2 (ndarray): denominator tensor.
"""
return cp.divide(numerator, denominator)
def _forward(self, inputs, is_training=True):
shiftx = inputs - cp.max(inputs)
outputs = cp.divide(cp.exp(shiftx),
cp.sum(cp.exp(shiftx), axis=-1, keepdims=True))
del inputs
if is_training:
self.outputs = outputs
return outputs
def ruzicka_vec(vector_old, vector_new):
vector_old_cp = cp.array(vector_old) * cp.arange(
1023, -1, -1, dtype=cp.uint16)
min_up = cp.minimum(vector_old_cp, vector_new)
max_down = cp.maximum(vector_old_cp, vector_new)
numerator = cp.sum(min_up, axis=1)
denominator = cp.sum(max_down, axis=1)
return cp.asnumpy(cp.divide(numerator, denominator))
def divide(numerator, denominator, dtype=None):
"""divide a tensor by another
Args:
tensor1 (ndarray): numerator tensor.
tensor2 (ndarray): denominator tensor.
dtype (dtype): type of the returned tensor.
"""
return cp.divide(numerator, denominator, dtype=dtype)
def pulse_compression(x, template, normalize=False, window=None, nfft=None):
"""
Pulse Compression is used to increase the range resolution and SNR
by performing matched filtering of the transmitted pulse (template)
with the received signal (x)
Parameters
----------
x : ndarray
Received signal, assume 2D array with [num_pulses, sample_per_pulse]
template : ndarray
Transmitted signal, assume 1D array
normalize : bool
Normalize transmitted signal
window : array_like, callable, string, float, or tuple, optional
Specifies the window applied to the signal in the Fourier
domain.
nfft : int, size of FFT for pulse compression. Default is number of
samples per pulse
Returns
-------
compressedIQ : ndarray
Pulse compressed output
"""
[num_pulses, samples_per_pulse] = x.shape
if nfft is None:
nfft = samples_per_pulse
if window is not None:
Nx = len(template)
if callable(window):
W = window(cp.fft.fftfreq(Nx))
elif isinstance(window, cp.ndarray):
if window.shape != (Nx, ):
raise ValueError("window must have the same length as data")
W = window
else:
W = get_window(window, Nx, False)
template = cp.multiply(template, W)
if normalize is True:
template = cp.divide(template, cp.linalg.norm(template))
fft_x = cp.fft.fft(x, nfft)
fft_template = cp.conj(cp.tile(cp.fft.fft(template, nfft),
(num_pulses, 1)))
compressedIQ = cp.fft.ifft(cp.multiply(fft_x, fft_template), nfft)
return compressedIQ
def calculate_survival_chance(self):
# 首先获取适应度
scores = self.scores
# 升序排列，数字越高，对应的适应度越高
order = cp.argsort(cp.argsort(scores))
# probs[i] 是第 i 个个体被选入下一代的概率
self.probs = cp.divide(order, cp.sum(order))
def backward(self, top, propagate_down, bottom): if propagate_down[0]: bottom_diff = copy.deepcopy(self.softmax) bottom_diff[self.index_0,self.label,self.index_2,self.index_3] -=1 bottom_diff = cp.multiply(bottom_diff,self.weight_mask) bottom_diff = cp.divide(bottom_diff,self.weight_mask[self.weight_mask != 0].size) bottom_diff = cp.asnumpy(bottom_diff) bottom[0].diff[...]=bottom_diff
def __estimate_one_step(self, threshold: int):
"""
Estimate the solution having a given threshold value and keeping only the singular values above these threshold.
Values smaller than numerical zero are always discarded.
:param threshold: Value specifying the smallest singular value (sorted in descending order) to keep. All singular
values smaller than given are discarded.
:type threshold: int
"""
self.current = cp.matmul(self.__V.T[:, :threshold],
cp.matmul(cp.diag(cp.divide(1, self.__D[:threshold])),
cp.matmul(self.__U[:, :threshold].T, self.q_estimator)))
def step(self, model, step_num):
# expected signal
self.y_expected = signal.get_signal_gpu(model.solution,
model.detector_geometry)
# multiplication
self.mult = cp.divide(self.w_det, self.y_expected)
self.mult = cp.where(cp.isnan(self.mult), 0, self.mult)
self.mult = cp.sum(self.mult, axis=-1)
self.mult /= self.wi
# find delta
model.solution = model.solution * self.mult
def run_gpu(X, eps, min_samples):
# Transfer inputs to GPU
X = cp.array(X)
# Begin computation
t0 = time.time()
mean = cp.mean(X, axis=0)
std = cp.std(X, axis=0)
cp.subtract(X, mean, out=X)
cp.divide(X, std, out=X)
print('Preprocessing:', time.time() - t0)
# Run DBSCAN
db = cuml.DBSCAN(eps=eps, min_samples=min_samples)
db = db.fit(X)
labels = db.labels_
# Transfer outputs to CPU
# labels = labels.to_pandas().to_numpy()
labels = cp.asnumpy(labels)
return labels
def divide(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.divide <numpy.divide>`.
See its docstring for more information.
"""
if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in divide")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
return Array._new(np.divide(x1._array, x2._array))
def model_backward(AL, Y, caches):
grads = {}
L = len(caches)
Y = Y.reshape(AL.shape)
# Initializing the backpropagation
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
# Backprop first layer
L_cache = caches[L-1]
grads["dA" + str(L-1)], grads["dW" + str(L)], grads["db" + str(L)] = layer_backward(dAL, L_cache, activation = 'sigmoid')
# Backprop layers L-1..1
for l in reversed(range(1, L)):
l_cache = caches[l - 1]
dA_prev_temp, dW_temp, db_temp = layer_backward(grads["dA" + str(l)], l_cache, activation = 'relu')
grads["dA" + str(l - 1)] = dA_prev_temp
grads["dW" + str(l)] = dW_temp
grads["db" + str(l)] = db_temp
return grads
def step(self, model, step_num):
#expected signal
for i in range(self.y_len):
tmp = cp.multiply(model.solution, model.detector_geometry[i])
self.y_expected[i] = cp.sum(tmp)
# multiplication
self.mult = cp.divide(self.w_det, self.y_expected)
self.mult = cp.where(cp.isnan(self.mult), 0, self.mult)
self.mult = cp.sum(self.mult, axis=-1)
self.mult /= self.wi
# find delta
model.solution = model.solution * self.mult
def __call__(self, params, g_params):
new_params, new_v = zip(
*[(cp.subtract(
param,
cp.multiply(
cp.divide(
self.rate,
cp.sqrt(
cp.add(cp.add(v, cp.square(g_param)),
self.eps).astype(cp.float32))), g_param)),
cp.add(v, cp.square(g_param)))
for param, g_param, v in zip(params, g_params, self.v)])
self.v = new_v
return new_params
