def gru(_inputs, initial_state, *parameters):
# _inputs: a list with length num_steps,
# corresponding element: batch_size * input_dim matrix
H = initial_state
[W_xz, W_hz, b_z,
W_xr, W_hr, b_r,
W_xh, W_hh, b_h,
W_hy, b_y] = parameters
_outputs = []
for X in _inputs:
# compute update gate from input and last/initial hidden state
update_gate = nd.sigmoid(nd.dot(X, W_xz) + nd.dot(H, W_hz) + b_z)
# compute reset gate from input and last/initial hidden state
reset_gate = nd.sigmoid(nd.dot(X, W_xr) + nd.dot(H, W_hr) + b_r)
# compute candidate hidden state from input, reset gate and last/initial hidden state
H_candidate = nd.tanh(nd.dot(X, W_xh) + reset_gate * nd.dot(H, W_hh) + b_h)
# compute hidden state from candidate hidden state and last hidden state
H = update_gate * H + (1 - update_gate) * H_candidate
# compute output from hidden state
Y = nd.dot(H, W_hy) + b_y
_outputs.append(Y)
return _outputs, H
def yolo2_forward(x, num_class, anchor_scales):
"""Transpose/reshape/organize convolution outputs."""
stride = num_class + 5
# transpose and reshape, 4th dim is the number of anchors
x = x.transpose((0, 2, 3, 1))
x = x.reshape((0, 0, 0, -1, stride))
# now x is (batch, m, n, stride), stride = num_class + 1(object score) + 4(coordinates)
# class probs
cls_pred = x.slice_axis(begin=0, end=num_class, axis=-1)
# object score
score_pred = x.slice_axis(begin=num_class, end=num_class + 1, axis=-1)
score = nd.sigmoid(score_pred)
# center prediction, in range(0, 1) for each grid
xy_pred = x.slice_axis(begin=num_class + 1, end=num_class + 3, axis=-1)
xy = nd.sigmoid(xy_pred)
# width/height prediction
wh = x.slice_axis(begin=num_class + 3, end=num_class + 5, axis=-1)
# convert x, y to positions relative to image
x, y = transform_center(xy)
# convert w, h to width/height relative to image
w, h = transform_size(wh, anchor_scales)
# cid is the argmax channel
cid = nd.argmax(cls_pred, axis=-1, keepdims=True)
# convert to corner format boxes
half_w = w / 2
half_h = h / 2
left = nd.clip(x - half_w, 0, 1)
top = nd.clip(y - half_h, 0, 1)
right = nd.clip(x + half_w, 0, 1)
bottom = nd.clip(y + half_h, 0, 1)
output = nd.concat(*[cid, score, left, top, right, bottom],
dim=4) # 为什么left和top有很多0？
return output, cls_pred, score, nd.concat(*[xy, wh], dim=4)
def class_and_score_forward(x):
class_part = nd.slice_axis(x,begin=0,end=3,axis=-1)
concentration_part = nd.slice_axis(x,begin=3,end=5,axis=-1)
class_part = nd.sigmoid(class_part)
concentration_part = nd.sigmoid(concentration_part)
return class_part,concentration_part
def lstm_rnn(inputs, state_h, state_c, *params):
'''
:param inputs: 输入
:param state_h: 上一时刻的输出
:param state_c: 上一时刻的状态
:param params: 参数对
:return: 输出
输入门：It=σ(Xt*Wxi+Ht−1*Whi+bi)
遗忘门：Ft=σ(Xt*Wxf+Ht−1*Whf+bf)
输出门：Ot=σ(Xt*Wxo+Ht−1*Who+bo)
输入状态：I_state=tanh(Xt*Wxc+Ht−1*Whc+bc)
输出：Y=Why*Ht-1+by
'''
[
W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c,
W_hy, b_y
] = params
H = state_h # 与输入组成一个输入门状态，控制有多少新输入补充到最新记忆里
C = state_c # 记录这一时刻的状态，传给下一时刻
outputs = []
for X in inputs:
I = nd.sigmoid(nd.dot(X, W_xi) + nd.dot(H, W_hi) + b_i) # 输入门,就是
C_tilda = nd.tanh(nd.dot(X, W_xc) + nd.dot(H, W_hc) +
b_c) # 输入门状态用来控制有多少输入信息补充到最新的记忆
F = nd.sigmoid(nd.dot(X, W_xf) + nd.dot(H, W_hf) +
b_f) # 遗忘门，控制上一刻有多少信息被遗忘
O = nd.sigmoid(nd.dot(X, W_xo) + nd.dot(H, W_ho) + b_o) # 输出门
C = F * C + C_tilda * I # 更新作为下一刻的状态
H = O * C.tanh()
Y = nd.dot(H, W_hy) + b_y # 这一刻的输出作为下一刻的输入
outputs.append(Y)
return (outputs, H, C)
def forward_single(self, feature, data, begin_state):
""" unroll one step
Parameters
----------
feature: a NDArray with shape [n, d].
data: a NDArray with shape [n, b, d].
begin_state: a NDArray with shape [n, b, d].
Returns
-------
output: ouptut of the cell, which is a NDArray with shape [n, b, d]
states: a list of hidden states (list of hidden units with shape [n, b, d]) of RNNs.
"""
if begin_state is None:
num_nodes, batch_size, _ = data.shape
begin_state = [nd.zeros((num_nodes, batch_size, self.hidden_size), ctx=feature.context)]
prev_state = begin_state[0]
data_and_state = nd.concat(data, prev_state, dim=-1)
z = nd.sigmoid(self.dense_z(feature, data_and_state))
r = nd.sigmoid(self.dense_r(feature, data_and_state))
state = z * prev_state + (1 - z) * nd.tanh(self.dense_i2h(feature, data) + self.dense_h2h(feature, r * prev_state))
return state, [state]
def nodeforward(self, x, cs, hs, ctx):
x = nd.reshape(x, (self.dim_h, ))
_Ui = nd.zeros((self.dim_h, ), ctx=ctx)
_Uo = nd.zeros((self.dim_h, ), ctx=ctx)
_Uu = nd.zeros((self.dim_h, ), ctx=ctx)
_Uf = [nd.zeros((self.dim_h, ), ctx=ctx) for i in range(len(cs))]
for idx in range(len(cs)):
_Ui = nd.add(_Ui, nd.dot(self.Uis[idx].data(), hs[idx]))
_Uo = nd.add(_Uo, nd.dot(self.Uos[idx].data(), hs[idx]))
_Uu = nd.add(_Uu, nd.dot(self.Uus[idx].data(), hs[idx]))
for j in range(len(cs)):
_Uf[idx] = nd.add(_Uf[idx],
nd.dot(self.Ufs[idx][j].data(), hs[j]))
i = nd.sigmoid(
nd.add(nd.add(nd.dot(self.Wi.data(), x), _Ui), self.bi.data()))
o = nd.sigmoid(
nd.add(nd.add(nd.dot(self.Wo.data(), x), _Uo), self.bo.data()))
f = [
nd.sigmoid(
nd.add(nd.add(nd.dot(self.Wf.data(), x), _Uf[idx]),
self.bf.data())) for idx in range(len(cs))
]
u = nd.tanh(
nd.add(nd.add(nd.dot(self.Wu.data(), x), _Uu), self.bu.data()))
c = nd.zeros((self.dim_h, ), ctx=ctx)
for idx in range(len(cs)):
c = nd.add(c, nd.multiply(f[idx], cs[idx]))
c = nd.add(nd.multiply(i, u), c)
h = nd.multiply(o, nd.tanh(c))
return c, h
def lstm(_inputs, initial_state_h, initial_state_c, *parameters):
# _inputs: a list with length num_steps,
# corresponding element: batch_size * input_dim matrix
H = initial_state_h # hidden state
C = initial_state_c # memory cell
[W_xi, W_hi, b_i,
W_xf, W_hf, b_f,
W_xo, W_ho, b_o,
W_xc, W_hc, b_c,
W_hy, b_y] = parameters
_outputs = []
for X in _inputs:
# compute INPUT gate from input and last/initial hidden state
input_gate = nd.sigmoid(nd.dot(X, W_xi) + nd.dot(H, W_hi) + b_i)
# compute FORGET gate from input and last/initial hidden state
forget_gate = nd.sigmoid(nd.dot(X, W_xf) + nd.dot(H, W_hf) + b_f)
# compute OUTPUT gate from input and last/initial hidden state
output_gate = nd.sigmoid(nd.dot(X, W_xo) + nd.dot(H, W_ho) + b_o)
# compute memory cell candidate from input and last/initial hidden state
memory_cell_candidate = nd.tanh(nd.dot(X, W_xc) + nd.dot(H, W_hc) + b_c)
# compute memory cell from last memory cell and memory cell candidate
C = forget_gate * C + input_gate * memory_cell_candidate
# compute hidden state from output gate and memory cell
H = output_gate * nd.tanh(C)
# compute output from hidden state
Y = nd.dot(H, W_hy) + b_y
_outputs.append(Y)
return _outputs, H, C
def accumulate_grad2_ucd(self, dat, min_mcmc=1, max_mcmc=100):
# Initial value for Gibbs sampling
N = dat.shape[0]
ind = np.random.choice(N, 1)[0]
v0 = dat[ind, :]
# Gibbs samples
samp = UnbiasedRBMSampler(self.w, self.b, self.c, ctx=self.ctx)
vhist, vchist, disc = samp.sample(v0,
min_steps=min_mcmc,
max_steps=max_mcmc)
burnin = min_mcmc - 1
tau = vchist.shape[0]
remain = tau - burnin
vk = vhist[burnin, :]
hk_mean = nd.sigmoid(nd.dot(self.w.T, vk) + self.c)
hhist_mean = nd.sigmoid(nd.dot(vhist[-remain:, :], self.w) + self.c)
hchist_mean = nd.sigmoid(nd.dot(vchist[-remain:, :], self.w) + self.c)
# Second term
self.db2 += vk + nd.sum(vhist[-remain:, :], axis=0) -\
nd.sum(vchist[-remain:, :], axis=0)
self.dc2 += hk_mean + nd.sum(hhist_mean, axis=0) -\
nd.sum(hchist_mean, axis=0)
self.dw2 += nd.dot(vk.reshape(-1, 1), hk_mean.reshape(1, -1)) +\
nd.dot(vhist[-remain:, :].T, hhist_mean) -\
nd.dot(vchist[-remain:, :].T, hchist_mean)
return tau, disc
def train(train_data,
test_data,
net,
loss,
trainer,
ctx,
num_epochs,
best_score=0.78):
print("Start training on ", ctx)
if isinstance(ctx, mx.Context):
ctx = [ctx]
best_ths = 0.5
for epoch in range(num_epochs):
train_loss, train_acc, n, m = 0.0, 0.0, 0.0, 0.0
train_data.reset()
start = time()
for i, batch in enumerate(train_data):
data, label, batch_size = get_batch(batch, ctx)
losses = []
with autograd.record():
outputs = [net(X).reshape(-1) for X in data]
losses = [loss(yhat, y) for yhat, y in zip(outputs, label)]
for l in losses:
l.backward()
train_acc += sum([((nd.sigmoid(yhat) > 0.5) == y).sum().asscalar()
for yhat, y in zip(outputs, label)])
train_loss += sum([l.sum().asscalar() for l in losses])
n += batch_size
m += sum([y.size for y in label])
trainer.step(batch_size)
print("Epoch %d. Loss: %.5f, Train acc %.5f, Time %.1f sec" %
(epoch, train_loss / n, train_acc / m, time() - start))
test_data.reset()
for i, batch in enumerate(test_data):
data = batch.data[0]
label = batch.label[0]
data = data.as_in_context(ctx[0])
label = label.as_in_context(ctx[0])
yhat = net(data).reshape(-1)
pred = nd.sigmoid(yhat)
nd.waitall()
if (i == 0):
cpl_pred = pred
cpl_lable = label
else:
cpl_pred = nd.concat(cpl_pred, pred, dim=0)
cpl_lable = nd.concat(cpl_lable, label, dim=0)
pred = cpl_pred.asnumpy()
label = cpl_lable.asnumpy()
scores, ths = score_plt(pred, label)
print('score=', max(scores))
print('threshold=', scores.index(max(scores)))
plt.plot(ths, scores, color='green')
plt.show()
def gru_rnn(inputs, h, temperature=1.0):
outputs = []
for X in inputs:
z = nd.sigmoid(nd.dot(X, Wxz) + nd.dot(h, Whz) + bz)
r = nd.sigmoid(nd.dot(X, Wxr) + nd.dot(h, Whr) + br)
g = nd.tanh(nd.dot(X, Wxh) + nd.dot(r * h, Whh) + bh)
h = z * h + (1 - z) * g
yhat_linear = nd.dot(h, Why) + by
yhat = softmax(yhat_linear, temperature=temperature)
outputs.append(yhat)
return (outputs, h)
def gru_rnn(inputs, h, temperature=1.0):
outputs = []
for X in inputs:
z = nd.sigmoid(nd.dot(X, Wxz) + nd.dot(h, Whz) + bz)
r = nd.sigmoid(nd.dot(X, Wxr) + nd.dot(h, Whr) + br)
g = nd.tanh(nd.dot(X, Wxh) + nd.dot(r * h, Whh) + bh)
h = z * h + (1 - z) * g
yhat_linear = nd.dot(h, Why) + by
yhat = softmax(yhat_linear, temperature=temperature)
outputs.append(yhat)
return (outputs, h)
def gru(inputs, state, params):
W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params
H = state
outputs = []
for X in inputs:
Z = nd.sigmoid(nd.dot(X, W_xz) + nd.dot(H, W_hz) + b_z)
R = nd.sigmoid(nd.dot(X, W_xr) + nd.dot(H, W_hr) + b_r)
H_ = nd.tanh(nd.dot(X, W_xh) + R * nd.dot(H, W_hh) + b_h)
H = Z * H + (1 - Z) * H_
Y = nd.dot(H, W_hq) + b_q
outputs.append(Y)
return outputs, H
def forward(self, x):
if self.dependent_G:
g = nd.sigmoid(nd.dot(x, self.G.data()))
else:
g = nd.sigmoid(self.G.data())
W0 = nd.tanh(self.W0_hat.data()) * nd.sigmoid(self.M0_hat.data())
W1 = nd.tanh(self.W1_hat.data()) * nd.sigmoid(self.M1_hat.data())
a = nd.dot(x, W0)
m = nd.exp(nd.dot(nd.log(nd.abs(x) + 1e-10), W1))
y = g * a + (1 - g) * m
return y
def lstm(inputs, state, params):
W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q = params
H, C = state
outputs = []
for X in inputs:
I = nd.sigmoid(nd.dot(X, W_xi) + nd.dot(H, W_hi) + b_i)
F = nd.sigmoid(nd.dot(X, W_xf) + nd.dot(H, W_hf) + b_f)
O = nd.sigmoid(nd.dot(X, W_xo) + nd.dot(H, W_ho) + b_o)
C_ = nd.tanh(nd.dot(X, W_xc) + nd.dot(H, W_hc) + b_c)
C = F * C + I * C_
H = O * nd.tanh(C)
Y = nd.dot(H, W_hq) + b_q
outputs.append(Y)
return outputs, (H, C)
def predict_LP(self, batch_out):
batch_out = self.slice_out(batch_out)
out = nd.concat(nd.sigmoid(batch_out[0]), *batch_out[1:], dim=-1)[0]
# (10L, 16L, 10L)
best_index = out[:, :, 0].reshape(-1).argmax(axis=0)
out = out.reshape((-1, 10))
pred = out[best_index].reshape(-1) # best out
pred[1:4] *= 1000
for i in range(3):
p = (nd.sigmoid(pred[i + 4]) - 0.5) * 2 * self.LP_r_max[i]
pred[i + 4] = p * math.pi / 180.
return pred.asnumpy()
def gru_rnn(inputs, H, *params):
# inputs: num_steps 个尺寸为 batch_size * vocab_size 矩阵
# H: 尺寸为 batch_size * hidden_dim 矩阵
# outputs: num_steps 个尺寸为 batch_size * vocab_size 矩阵
W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hy, b_y = params
outputs = []
for X in inputs:
Z = nd.sigmoid(nd.dot(X, W_xz) + nd.dot(H, W_hz) + b_z)
R = nd.sigmoid(nd.dot(X, W_xr) + nd.dot(H, W_hr) + b_r)
H_tilda = nd.tanh(nd.dot(X, W_xh) + R * nd.dot(H, W_hh) + b_h)
H = Z * H + (1 - Z) * H_tilda
Y = nd.dot(H, W_hy) + b_y
outputs.append(Y)
return (outputs, H)
def gru(self, inputs, state):
W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = self.params
H = state
outputs = []
for X in inputs:
Z = nd.sigmoid(nd.dot(X, W_xz) + nd.dot(H, W_hz) + b_z)
R = nd.sigmoid(nd.dot(X, W_xr) + nd.dot(H, W_hr) + b_r)
H_tilda = nd.tanh(nd.dot(R * H, W_hh) + nd.dot(X, W_xh) + b_h)
H = Z * H + (1 - Z) * H_tilda
Y = nd.dot(H, W_hq) + b_q
outputs.append(Y)
return outputs, H
def forward(self, x=0):
if (mx.autograd.is_training()):
u = nd.random.uniform(0, 1)
s = nd.log(u) - nd.log(1 - u) + self._qz_loga.data()
if (self._temperature == 0):
s = nd.sign(s)
else:
s = nd.sigmoid(s / self._temperature)
else:
s = nd.sigmoid(self._qz_loga.data())
s = s * (self._limit_hi - self._limit_lo) + self._limit_lo
return nd.minimum(1, nd.maximum(s, 0))
def lstm(inputs, state, params):
# inputs和outputs皆为num_steps个形状为(batch_size, vocab_size)的矩阵
[ W_xi,W_hi,b_i,W_xf,W_hf,b_f,W_xo,W_ho,b_o,W_xc,W_hc,b_c ,W_hq,b_q] = params
(H,C) = state
outputs = []
for X in inputs:
I = nd.sigmoid(nd.dot(X,W_xi)+nd.dot(H,W_hi)+b_i)
F = nd.sigmoid(nd.dot(X,W_xf)+nd.dot(H,W_hf)+b_f)
O = nd.sigmoid(nd.dot(X,W_xo)+nd.dot(H,W_ho)+b_o)
C_tilda = nd.tanh(nd.dot(X,W_xc)+nd.dot(H,W_hc)+b_c)
C=F*C+I*C_tilda
H=C.tanh()*O
Y=nd.dot(H,W_hq)+b_q
outputs.append(Y)
return outputs, (H,C)
def volume_render_radiance_field(radiance_field, depth_values, ray_directions, radiance_field_noise_std=0.0,
white_background=False):
# TESTED
one_e_10 = nd.array([1e10], dtype=ray_directions.dtype, ctx=ray_directions.context).broadcast_to(depth_values[..., :1].shape)
dists = nd.concat(*[depth_values[..., 1:] - depth_values[..., :-1], one_e_10], dim=-1)
dists = dists * ray_directions[..., None, :].norm(ord=2, axis=-1)
rgb = nd.sigmoid(radiance_field[..., :3])
noise = 0.0
if radiance_field_noise_std > 0.0:
noise = nd.random.normal(0.0, 1.0, shape=radiance_field[..., 3].shape,
dtype=radiance_field.dtype, ctx=radiance_field.context)
noise = noise * radiance_field_noise_std
sigma_a = nd.relu(radiance_field[..., 3] + noise)
alpha = 1.0 - nd.exp(-sigma_a * dists)
weights = alpha * cumprod_exclusive_gluon(1.0 - alpha + 1e-10)
rgb_map = weights[..., None] * rgb
rgb_map = rgb_map.sum(axis=-2)
depth_map = weights * depth_values
depth_map = depth_map.sum(axis=-1)
# depth_map = (weights * depth_values).sum(dim=-1)
acc_map = weights.sum(axis=-1)
disp_map = 1.0 / nd.maximum(1e-10 * nd.ones_like(depth_map), depth_map / acc_map)
if white_background:
rgb_map = rgb_map + (1.0 - acc_map[..., None])
return rgb_map, disp_map, acc_map, weights, depth_map
def plot_attention(net, n_samples=10, mean=False):
from matplotlib import pyplot as plt
import seaborn as sns
sns.set()
idx = np.random.choice(np.arange(len(va_x)), size=n_samples, replace=False)
_dat = [va_x[i] for i in idx]
w_idx = []
word = [[idx2word[x] for x in y] for y in _dat]
original_txt = [va_origin[i] for i in idx]
out, att = net(nd.array(_dat, ctx=context))
print('attention shape = {}'.format(att.shape))
_a = []
_w = []
for x, y, z in zip(word, att, original_txt):
_idx = [i for i, _x in enumerate(x) if _x is not 'PAD']
_w.append(np.array([x[i] for i in _idx]))
_a.append(np.array([y[i].asnumpy() for i in _idx]))
_label = [va_y[i] for i in idx]
_pred = (nd.sigmoid(out) > .5).asnumpy()
fig, axes = plt.subplots(np.int(np.ceil(n_samples / 4)),
4,
sharex=False,
sharey=True)
plt.subplots_adjust(hspace=1)
if mean == True:
fig.set_size_inches(20, 4)
plt.subplots_adjust(hspace=5)
else:
fig.set_size_inches(20, 20)
plt.subplots_adjust(hspace=1)
cbar_ax = fig.add_axes([.91, .3, .04, .4])
def cal_normalized_tp_pos_fp_neg(output, target, nclass, score_thresh):
"""mIoU"""
# inputs are NDarray, output 4D, target 3D
# the category 0 is ignored class, typically for background / boundary
mini = 1
maxi = 1 # nclass
nbins = 1 # nclass
predict = (nd.sigmoid(output).asnumpy() > score_thresh).astype(
'int64') # P
# predict = (output.asnumpy() > 0).astype('int64') # P
if len(target.shape) == 3:
target = nd.expand_dims(target, axis=1).asnumpy().astype('int64') # T
elif len(target.shape) == 4:
target = target.asnumpy().astype('int64') # T
else:
raise ValueError("Unknown target dimension")
intersection = predict * (predict == target) # TP
tp = intersection.sum()
fp = (predict * (predict != target)).sum() # FP
tn = ((1 - predict) * (predict == target)).sum() # TN
fn = ((predict != target) * (1 - predict)).sum() # FN
pos = tp + fn
neg = fp + tn
return tp, pos, fp, neg
def forward(self, S_a, S_b):
M_a = self.maxpooling(S_a).flatten()
M_b = self.maxpooling(S_b).flatten()
gate_rate = nd.sigmoid(nd.dot(M_a, self.W_a.data()) + \
nd.dot(M_b, self.W_b.data()) + self.b.data())
gated_output = gate_rate * M_a + (1 - gate_rate) * M_b
return gated_output
def predict_LP(self, LP_batch_out):
# LP_batch_out = self.fp16_2_fp32(LP_batch_out)
LP_batch_out = self.merge_and_slice(LP_batch_out, self.LP_slice_point)
LP_score = nd.sigmoid(LP_batch_out[0])
LP_pose_xy = LP_batch_out[1]
LP_pose_z = LP_batch_out[2]
LP_pose_r = LP_batch_out[3]
LP_batch_out = nd.concat(
LP_score, LP_pose_xy, LP_pose_z, LP_pose_r, dim=-1)
LP_batch_out = nd.split(LP_batch_out, axis=0, num_outputs=len(LP_batch_out))
LP_batch_pred = []
for i, out in enumerate(LP_batch_out):
best_index = LP_score[i].reshape(-1).argmax(axis=0)
out = out.reshape((-1, 7))
pred = out[best_index][0] # best out
pred[1:7] = self.LP_pose_activation(pred[1:7])
LP_batch_pred.append(nd.expand_dims(pred, axis=0))
LP_batch_pred = nd.concat(*LP_batch_pred, dim=0)
return LP_batch_pred.asnumpy()
def forward(self, x, x_mask=None):
N, T, D = tuple(x.shape) # bs, sl, vec
bs, sl, vec = tuple(x.shape)
direct_mask = get_direct_mask(bs, sl, self.direction)
#x_mask_tile = x_mask.expand_dims(1)
#mask = np.logical_and(direct_mask, x_mask_tile).astype(float)
mask = direct_mask.astype('float32')
x_map = self.linear1(x) # bs, sl, vec
#x_map_tile = x_map.expand_dims(1) #
x_map_tile = nd.tile(x_map.expand_dims(1),
(1, sl, 1, 1)) # bs, sl, sl, vec
x_map_drop = self.dropout(x_map)
dependent = self.linear2(x_map_drop)
dependent_etd = dependent.expand_dims(1)
head = self.linear3(x_map_drop)
head_etd = head.expand_dims(2)
loggits = scaled_tanh(dependent_etd + head_etd + self.f_bias, 5.0)
loggits_masked = exp_mask_for_tensor(loggits, mask)
attn_score = nd.softmax(loggits_masked, 2)
attn_score = mask_for_tensor(attn_score, mask)
attn_result = (attn_score * x_map_tile).nansum(2)
fusion_gate = nd.sigmoid(
self.linear4(x_map) + self.linear5(attn_result) + self.o_bias)
output = fusion_gate * x_map + (1 - fusion_gate) * attn_result
return output
def get_pred(net, loss, idx2word, iterator, context):
pred_sa = []
label_sa = []
va_text = []
iterator.reset()
for i, batch in enumerate(iterator):
if i % 100 == 0:
print('i = {}'.format(i))
data = batch.data[0].as_in_context(context)
label = batch.data[1].as_in_context(context)
output, _ = net(data)
L = loss(output, label)
pred = (nd.sigmoid(output) > 0.5).reshape((-1, ))
pred_sa.extend(pred.asnumpy())
label_sa.extend(label.asnumpy())
va_text.extend([
' '.join([
idx2word[np.int(x)] for x in y.asnumpy()
if idx2word[np.int(x)] is not 'PAD'
]) for y in data
])
pred_sa_pd = pd.DataFrame(pred_sa, columns=['pred_sa'])
label_pd = pd.DataFrame(label_sa, columns=['label'])
text_pd = pd.DataFrame(va_text, columns=['text'])
res = pd.concat([text_pd, pred_sa_pd, label_pd], axis=1)
return res
def compute(self, input):
"""Compute the output of the neural net given the input.
The input has to be a ndarray of shape input_size or (input_size, N) where N is the batch_size."""
if len(input.shape) == 1:
input = input.reshape((input.shape[0], 1))
X = input[0]
Y = input[1]
for i in range(self.n):
_X = nd.cos(self.thetas[i]) * X + nd.sin(
self.thetas[i]) * Y # Sym / (cos(O)u_x + sin(O)u_y)
Y = -nd.sin(self.thetas[i]) * X + nd.cos(self.thetas[i]) * Y
X = nd.abs(_X + self.biases[i]) - self.biases[i]
if (False): # optimize
_X = nd.cos(self.thetas[i]) * X - nd.sin(
self.thetas[i]) * Y # Sym / (cos(O)u_x + sin(O)u_y)
Y = nd.sin(self.thetas[i]) * X + nd.cos(self.thetas[i]) * Y
X = _X
_X = nd.cos(self.thetas[self.n]) * X + nd.sin(self.thetas[self.n]) * Y
Y = -nd.sin(self.thetas[self.n]) * X + nd.cos(self.thetas[self.n]) * Y
return nd.sigmoid(Y - _X)
def check_tbox(image, label):
plt.clf()
rgb_mean = RGB_MEAN.as_in_context(image.context)
rgb_std = RGB_STD.as_in_context(image.context)
assert label.shape == (1, 5), \
"shape of label expected [1, 5], but given {}".format(label.shape)
assert image.shape == (3, 256, 256), \
"shape of image expected [3, 256, 256], given {}".format(image.shape)
scores_tmp = nd.zeros((1, 16, 16, 3, 1))
label = label.expand_dims(axis=0)
tid, tscore, tbox, _ = yolo2_target(scores_tmp, label, anchor_scales)
t_xy = tbox.slice_axis(begin=0, end=2, axis=-1)
t_wh = tbox.slice_axis(begin=2, end=4, axis=-1)
xy = nd.sigmoid(t_xy)
x, y = transform_center(xy)
w, h = transform_size(t_wh, anchor_scales)
left = nd.clip(x - w / 2, 0, 1)
top = nd.clip(y - h / 2, 0, 1)
right = nd.clip(x + w / 2, 0, 1)
bottom = nd.clip(y + h / 2, 0, 1)
output = nd.concat(*[tid, tscore, left, top, right, bottom], dim=-1)
out = nd.contrib.box_nms(output.reshape((0, -1, 6)))
out = out.asnumpy()
box = out[0][0][2:6] * np.array([image.shape[1], image.shape[2]] * 2)
rect = box_to_rect(nd.array(box), 'green', 2)
image = image.transpose((1, 2, 0))
i0 = (image * rgb_std + rgb_mean).asnumpy()
i0 = i0.clip(0, 255) / 255.
plt.imshow(i0)
plt.gca().add_patch(rect)
plt.show()
#plt.savefig('check_tbox.jpg')
return box
def Route(self, x):
# print x.context
# b_mat = nd.repeat(self.b_mat.data(), repeats=x.shape[0], axis=0)#nd.stop_gradient(nd.repeat(self.b_mat.data(), repeats=x.shape[0], axis=0))
b_mat = nd.zeros((x.shape[0], 1, self.num_cap, self.num_locations),
ctx=x.context)
x_expand = nd.expand_dims(nd.expand_dims(x, axis=2), 2)
w_expand = nd.repeat(nd.expand_dims(self.w_ij.data(x.context), axis=0),
repeats=x.shape[0],
axis=0)
u_ = w_expand * x_expand
u = nd.sum(u_, axis=1)
# u_ = nd.square(w_expand - x_expand)
# u = -nd.sum(u_, axis = 1)
u_no_gradient = nd.stop_gradient(u)
for i in range(self.route_num):
# c_mat = nd.softmax(b_mat, axis=2)
c_mat = nd.sigmoid(b_mat)
if i == self.route_num - 1:
s = nd.sum(u * c_mat, axis=-1)
else:
s = nd.sum(u_no_gradient * c_mat, axis=-1)
v = squash(s, 1)
if i != self.route_num - 1:
v1 = nd.expand_dims(v, axis=-1)
update_term = nd.sum(u_no_gradient * v1, axis=1, keepdims=True)
b_mat = b_mat + update_term
# b_mat = update_term
# else:
# v = s
return v
def mask_loss(self, mask_pred, mask_eoc, mask_target, matches, bt_target):
samples = matches >= 0
pos_num = samples.sum(axis=-1).asnumpy().astype('int')
rank = (-matches).argsort(axis=-1)
# pos_bboxes = []
# pos_masks = []
# mask_preds = []
losses = []
for i in range(mask_pred.shape[0]):
if pos_num[i] == 0:
losses.append(nd.zeros(shape=(1, ), ctx=mask_pred.context))
continue
idx = rank[i, :pos_num[i]]
pos_bboxe = nd.take(bt_target[i], idx)
area = (pos_bboxe[:, 3] - pos_bboxe[:, 1]) * (pos_bboxe[:, 2] -
pos_bboxe[:, 0])
weight = self.gt_weidth * self.gt_height / area
mask_gt = mask_target[i, matches[i, idx], :, :]
mask_preds = nd.sigmoid(
nd.dot(nd.take(mask_eoc[i], idx), mask_pred[i]))
_, h, w = mask_preds.shape
mask_preds = self.crop(pos_bboxe, h, w, mask_preds)
loss = self.SBCELoss(mask_preds, mask_gt) * weight
# loss = 0.5 * nd.square(mask_gt - mask_preds) / (mask_gt.shape[0]*mask_gt.shape[1]*mask_gt.shape[2])
losses.append(nd.mean(loss))
return nd.concat(*losses, dim=0)
def forward(self, inputs, state):
""" forward function """
h, = state
outputs = []
for x in inputs:
z = nd.sigmoid(
nd.dot(x, self.w_xz) + nd.dot(h, self.w_hz) + self.b_z)
r = nd.sigmoid(
nd.dot(x, self.w_xr) + nd.dot(h, self.w_hr) + self.b_r)
h_tilda = nd.tanh(
nd.dot(x, self.w_xh) + nd.dot(h, self.w_hh) + self.b_h)
h = z * h + (1 - z) * h_tilda
y = nd.dot(h, self.w_hq) + self.b_q
outputs.append(y)
y_hat = nd.concat(*outputs, dim=0)
return y_hat, (h, )
def forward(self, current, previous, doc_encode):
"""[summary]
Args:
current ([type]): h_j (batch_size, sentence_hidden_size * 2)
previous ([type]): s_j (batch_size, sentence_hidden_size * 2)
doc_encode ([type]): d (batch_size, ndoc_dims)
"""
# content: (batch_size, 1)
content = self.content_encoder(current)
# salience: (batch_size, sentence_hidden_size * 2)
salience = self.salience_encoder(doc_encode)
salience = current * salience
# salience: (batch_size,)
salience = nd.sum_axis(salience, -1)
# salience: (batch_size, 1)
salience = nd.expand_dims(salience, -1)
# novelty: (bathc_size, sentence_hidden_size * 2)
novelty = self.novelty_encoder(nd.tanh(previous))
novelty = current * novelty
# salience: (batch_size,)
novelty = nd.sum_axis(novelty, -1)
# salience: (batch_size, 1)
novelty = nd.expand_dims(novelty, -1)
# P: (batch_size, 1)
P = nd.sigmoid(content + salience - novelty)
return P
def forward(self, x):
x = self.layer(x)
# Note that the loss function has the sigmoid operation for better numerical stability. When
# doing inference, we need to add the sigmoid function to the model.
if not autograd.is_training():
x = nd.sigmoid(x)
return x
def lstm_rnn(inputs, h, c, temperature=1.0):
outputs = []
for X in inputs:
g = nd.tanh(nd.dot(X, Wxg) + nd.dot(h, Whg) + bg)
i = nd.sigmoid(nd.dot(X, Wxi) + nd.dot(h, Whi) + bi)
f = nd.sigmoid(nd.dot(X, Wxf) + nd.dot(h, Whf) + bf)
o = nd.sigmoid(nd.dot(X, Wxo) + nd.dot(h, Who) + bo)
#######################
#
#######################
c = f * c + i * g
h = o * nd.tanh(c)
#######################
#
#######################
yhat_linear = nd.dot(h, Why) + by
yhat = softmax(yhat_linear, temperature=temperature)
outputs.append(yhat)
return (outputs, h, c)
