def forward(self, x):
n_batch, n_atom, n_feature = x.shape
atom_repeat = functions.reshape(x, (n_batch, 1, n_atom, n_feature))
atom_repeat = functions.broadcast_to(
atom_repeat, (n_batch, n_atom, n_atom, n_feature))
atom_repeat = functions.reshape(atom_repeat,
(n_batch, n_atom * n_atom, n_feature))
atom_tile = functions.reshape(x, (n_batch, n_atom, 1, n_feature))
atom_tile = functions.broadcast_to(
atom_tile, (n_batch, n_atom, n_atom, n_feature))
atom_tile = functions.reshape(atom_tile,
(n_batch, n_atom * n_atom, n_feature))
pair_x0 = functions.concat((atom_tile, atom_repeat), axis=2)
pair_x0 = functions.reshape(pair_x0,
(n_batch * n_atom * n_atom, n_feature * 2))
for l in self.linear_layers:
pair_x0 = l(pair_x0)
pair_x0 = functions.relu(pair_x0)
pair_x0 = functions.reshape(pair_x0,
(n_batch, n_atom * n_atom, self.n_channel))
pair_x1 = functions.concat((atom_repeat, atom_tile), axis=2)
pair_x1 = functions.reshape(pair_x1,
(n_batch * n_atom * n_atom, n_feature * 2))
for l in self.linear_layers:
pair_x1 = l(pair_x1)
pair_x1 = functions.relu(pair_x1)
pair_x1 = functions.reshape(pair_x1,
(n_batch, n_atom * n_atom, self.n_channel))
return pair_x0 + pair_x1
def __call__(self, h, dist):
"""
Args:
h (numpy.ndarray): axis 0 represents minibatch index,
axis 1 represents atom_index and axis2 represents
feature dimension.
dist (numpy.ndarray): axis 0 represents minibatch index,
axis 1 and 2 represent distance between atoms.
"""
mb, atom, ch = h.shape
if ch != self.hidden_dim:
raise ValueError('h.shape[2] {} and hidden_dim {} must be same!'
.format(ch, self.hidden_dim))
embedlist = self.xp.arange(
self.num_rbf).astype('f') * self.radius_resolution
dist = functions.reshape(dist, (mb, atom, atom, 1))
dist = functions.broadcast_to(dist, (mb, atom, atom, self.num_rbf))
dist = functions.exp(- self.gamma * (dist - embedlist) ** 2)
dist = functions.reshape(dist, (-1, self.num_rbf))
dist = self.dense1(dist)
dist = functions.softplus(dist)
dist = self.dense2(dist)
dist = functions.softplus(dist)
dist = functions.reshape(dist, (mb, atom, atom, self.hidden_dim))
h = functions.reshape(h, (mb, atom, 1, self.hidden_dim))
h = functions.broadcast_to(h, (mb, atom, atom, self.hidden_dim))
h = functions.sum(h * dist, axis=1)
return h
def __call__(self, x):
# chainer requires explicit broadcast for avoiding latent bugs
u = F.mean(x, -1, keepdims=True)
u = F.broadcast_to(u, x.shape)
s = F.mean((x - u) ** 2, -1, keepdims=True)
s = F.broadcast_to(s, x.shape)
x = (x - u) / F.sqrt(s + self.e)
return F.bias(F.scale(x, self.g, axis=2), self.b, axis=2)
def cosine_similarity(x, y, eps=1e-6):
n1, n2, n3 = x.data.shape
_, m2, _ = y.data.shape
z = F.batch_matmul(x, y, transb=True)
x2 = F.broadcast_to(F.reshape(F.sum(x * x, axis=2), (n1, n2, 1)), (n1, n2, m2))
y2 = F.broadcast_to(F.reshape(F.sum(y * y, axis=2), (n1, 1, m2)), (n1, n2, m2))
z /= F.exp(F.log(x2 * y2 + eps) / 2)
return z
def norm_by_freq(self, freq):
word_embs = self.W
mean = F.sum(freq * word_embs, axis=0, keepdims=True)
mean = F.broadcast_to(mean, word_embs.shape)
var = F.sum(freq * ((word_embs - mean) ** 2), axis=0, keepdims=True)
var = F.broadcast_to(var, word_embs.shape)
stddev = F.sqrt(1e-6 + var)
word_embs_norm = (word_embs - mean) / stddev
return word_embs_norm
def compute_dists(self, obs):
mean_var = Variable(self.running_stat.mean.astype(np.float32))
std_var = Variable(self.running_stat.std.astype(np.float32))
obs = obs - F.broadcast_to(mean_var, obs.shape)
obs = obs / (F.broadcast_to(std_var, obs.shape) + 1e-8)
if self.clip is not None:
obs = F.clip(obs, -self.clip, self.clip)
return self.policy.compute_dists(obs)
def get_normalized_vector(d, xp=None):
shape = tuple(range(1, len(d.shape)))
if xp is not None:
d /= (1e-12 + xp.max(xp.abs(d), shape, keepdims=True))
d /= xp.sqrt(1e-6 + xp.sum(d ** 2, shape, keepdims=True))
else:
d_term = 1e-12 + F.max(F.absolute(d), shape, keepdims=True)
d /= F.broadcast_to(d_term, d.shape)
d_term = F.sqrt(1e-6 + F.sum(d ** 2, shape, keepdims=True))
d /= F.broadcast_to(d_term, d.shape)
return d
def query(self, u):
xp = cuda.get_array_module(u)
size = self.m.shape[1]
inds = xp.arange(size - 1, -1, -1, dtype=numpy.int32)
tm = self.TA(inds)
tc = self.TC(inds)
tm = F.broadcast_to(tm, self.m.shape)
tc = F.broadcast_to(tc, self.c.shape)
p = F.softmax(F.batch_matmul(self.m + tm, u))
o = F.batch_matmul(F.swapaxes(self.c + tc, 2, 1), p)
o = F.squeeze(o, -1)
u = o + u
return u
def __call__(self, x):
"""Applies the linear layer.
Args:
x (~chainer.Variable): Batch of input vectors.
Returns:
~chainer.Variable: Output of the linear layer.
"""
norm = F.batch_l2_norm_squared(self.W) ** 0.5
norm_broadcasted = F.broadcast_to(
F.expand_dims(norm, 1), self.W.data.shape)
g_broadcasted = F.broadcast_to(
F.expand_dims(self.g, 1), self.W.data.shape)
return F.linear(x, g_broadcasted * self.W / norm_broadcasted, self.b)
def query(self, u):
m = self.m
c = self.c
batch, size = m.data.shape[:2]
inds = chainer.Variable(xp.arange(size, dtype=numpy.int32)[::-1])
tm = self.TA(inds)
tc = self.TC(inds)
tm = F.broadcast_to(tm, (batch,) + tm.data.shape)
tc = F.broadcast_to(tc, (batch,) + tc.data.shape)
p = F.softmax(F.batch_matmul(m + tm, u))
o = F.batch_matmul(F.swapaxes(c + tc, 2, 1), p)
o = F.reshape(o, (batch, m.data.shape[2]))
u = o + u
return u
def perspective(vertices, angle=30.):
assert (vertices.ndim == 3)
xp = chainer.cuda.get_array_module(vertices)
if isinstance(angle, float) or isinstance(angle, int):
angle = chainer.Variable(xp.array(angle, 'float32'))
angle = angle / 180. * 3.1416
angle = cf.broadcast_to(angle[None], (vertices.shape[0],))
width = cf.tan(angle)
width = cf.broadcast_to(width[:, None], vertices.shape[:2])
z = vertices[:, :, 2]
x = vertices[:, :, 0] / z / width
y = vertices[:, :, 1] / z / width
vertices = cf.concat((x[:, :, None], y[:, :, None], z[:, :, None]), axis=2)
return vertices
def __call__(self, x, y):
"""
Parameters
-----------------
x: Variable
Feature of unlabeled samples.
y: Variable
Feature of unlabeled samples.
"""
g = F.broadcast_to(
F.gaussian(
np.array([0], dtype=np.float32),
np.array([np.exp(1)], dtype=np.float32)), x.shape)
x_g = x * g
y_g = y * g
x_g_norm = F.sum(x_g**2, axis=1)
y_g_norm = F.sum(y_g**2, axis=1)
x_g_y_g = F.linear(x_g, y_g)
x_g_norm, x_g_y_g, y_g_norm = \
F.broadcast(
*[x_g_norm,
x_g_y_g,
F.expand_dims(y_g_norm, 1)])
#F.exp(- (x_g_norm - 2 * x_g_y_g+ y_g_norm))
return F.exp(- x_g_norm + 2 * x_g_y_g - y_g_norm)
def __call__(self, x, context):
e = model.embed(context)
shape = e.data.shape
x = F.broadcast_to(x, (shape[0], shape[1]))
e = F.reshape(e, (shape[0] * shape[1], shape[2]))
x = F.reshape(x, (shape[0] * shape[1],))
return self.loss_func(e, x)
def ordinal_loss(y, mask):
xp = cuda.get_array_module(y.data)
volatile = y.volatile
b, c, n = y.data.shape
max_y = F.broadcast_to(F.max(y, axis=1, keepdims=True), y.data.shape)
y = y - max_y
sum_y = F.broadcast_to(F.expand_dims(F.sum(y, axis=1), 1), y.data.shape)
down_tri = np.tri(c, dtype=np.float32)
up_tri = down_tri.T
w1 = Variable(xp.asarray(down_tri.reshape(c, c, 1, 1)), volatile=volatile)
w2 = Variable(xp.asarray(up_tri.reshape(c, c, 1, 1)), volatile=volatile)
h = F.exp(F.expand_dims(y, -1))
h1 = F.convolution_2d(h, w1)
h1 = F.convolution_2d(F.log(h1), w1)
h2 = F.convolution_2d(h, w2)
h2 = F.convolution_2d(F.log(h2), w2)
h = F.reshape(h1 + h2, (b, c, n))
return F.sum((h - sum_y - y) * mask) / b
def __call__(self, x, contexts):
e = self.embed(contexts)
batch_size, n_context, n_units = e.shape
x = F.broadcast_to(x[:, None], (batch_size, n_context))
e = F.reshape(e, (batch_size * n_context, n_units))
x = F.reshape(x, (batch_size * n_context,))
loss = self.loss_func(e, x)
reporter.report({'loss': loss}, self)
return loss
def __call__(self, x, context):
e = self.embed(context)
shape = e.data.shape
x = F.broadcast_to(x[:, None], (shape[0], shape[1]))
e = F.reshape(e, (shape[0] * shape[1], shape[2]))
x = F.reshape(x, (shape[0] * shape[1],))
loss = self.loss_func(e, x)
reporter.report({'loss': loss}, self)
return loss
def _bias(x, y, axis=1):
x_shape = x.data.shape
y_shape = y.data.shape
assert x_shape[axis:axis + len(y_shape)] == y_shape
y1_shape = tuple([1] * axis + list(y_shape) +
[1] * (len(x_shape) - axis - len(y_shape)))
y1 = functions.reshape(y, y1_shape)
y2 = functions.broadcast_to(y1, x_shape)
return x + y2
def _calc_distmat(self, h):
bs = h.shape[0]
h_l2_2 = F.sum(h**2, axis=1)
H = F.broadcast_to(h_l2_2, (bs, bs))
H_t = F.transpose(H)
XX = F.linear(h, h)
return (H_t - 2*XX + H)
def __call__(self, x, context):
e = self.embed(context)
shape = e.shape
x = F.broadcast_to(x[:, None], (shape[0], shape[1]))
e = F.reshape(e, (shape[0] * shape[1], shape[2]))
x = F.reshape(x, (shape[0] * shape[1],))
loss = self.loss_func(e, x)
# shouldn't we divide loss by batch size?
reporter.report({'loss': loss}, self)
return loss
def __call__(self, embeded_x, m_prev, h_prev, x):
batch_size = embeded_x.shape[0]
lstm_in = self.W(embeded_x) + self.U(h_prev)
m_tmp, h_tmp = F.lstm(m_prev, lstm_in)
# flags if feeding previous output
feed_prev = F.broadcast_to(F.expand_dims(x.data != IGNORE_LABEL, -1),
(batch_size, self.hidden_size))
m = F.where(feed_prev, m_tmp, m_prev)
h = F.where(feed_prev, h_tmp, h_prev)
return m, h
def check_backward(self, data, grad):
x = chainer.Variable(data)
bx = functions.broadcast_to(x, self.out_shape)
func = bx.creator
f = lambda: func.forward((data,))
bx.grad = grad
bx.backward()
gx, = gradient_check.numerical_grad(f, (data,), (bx.grad,))
gradient_check.assert_allclose(gx, x.grad)
def grad_unbias_hook(optimizer):
for p in optimizer.target.params():
grad_data = p.grad
bs = grad_data.shape[0]
shape = grad_data.shape
grad = Variable(grad_data)
mean_grad = F.broadcast_to(F.sum(grad) / bs, shape)
grad_unbias = grad - mean_grad
p.grad = grad_unbias.data
def __call__(self, x, context):
x = F.broadcast_to(x[:, None], (context.shape[0], context.shape[1]))
x = F.reshape(x, (context.shape[0] * context.shape[1],))
context = context.reshape((context.shape[0] * context.shape[1]))
e = self.rnn.charRNN(context)
loss = self.loss_func(e, x)
reporter.report({'loss': loss}, self)
return loss
def setUp(self):
self.x1 = numpy.random.uniform(
.5, 1, (batch_size, m, k)).astype(numpy.float32)
self.x2 = numpy.random.uniform(
.5, 1, (k, n)).astype(numpy.float32)
self.gy = numpy.random.uniform(
-1, 1, (batch_size, m, n)).astype(numpy.float32)
self.op = lambda x, y: F.batch_matmul(
x, F.broadcast_to(F.expand_dims(y, 0), (batch_size, k, n)))
self.forward_answer = numpy.array([
numpy.dot(self.x1[i], self.x2)
for i in six.moves.range(batch_size)])
def look_at(vertices, eye, at=None, up=None):
"""
"Look at" transformation of vertices.
"""
assert (vertices.ndim == 3)
xp = chainer.cuda.get_array_module(vertices)
batch_size = vertices.shape[0]
if at is None:
at = xp.array([0, 0, 0], 'float32')
if up is None:
up = xp.array([0, 1, 0], 'float32')
if isinstance(eye, list) or isinstance(eye, tuple):
eye = xp.array(eye, 'float32')
if eye.ndim == 1:
eye = cf.tile(eye[None, :], (batch_size, 1))
if at.ndim == 1:
at = cf.tile(at[None, :], (batch_size, 1))
if up.ndim == 1:
up = cf.tile(up[None, :], (batch_size, 1))
# create new axes
z_axis = cf.normalize(at - eye)
x_axis = cf.normalize(neural_renderer.cross(up, z_axis))
y_axis = cf.normalize(neural_renderer.cross(z_axis, x_axis))
# create rotation matrix: [bs, 3, 3]
r = cf.concat((x_axis[:, None, :], y_axis[:, None, :], z_axis[:, None, :]), axis=1)
if r.shape[0] != vertices.shape[0]:
r = cf.broadcast_to(r, (vertices.shape[0], 3, 3))
# apply
# [bs, nv, 3] -> [bs, nv, 3] -> [bs, nv, 3]
if vertices.shape != eye.shape:
eye = cf.broadcast_to(eye[:, None, :], vertices.shape)
vertices = vertices - eye
vertices = cf.matmul(vertices, r, transb=True)
return vertices
def __call__(self, x):
q_z = self.encoder(x)
z = q_z.sample(self.k)
p_x = self.decoder(z)
p_z = self.prior()
reconstr = F.mean(p_x.log_prob(
F.broadcast_to(x[None, :], (self.k,) + x.shape)))
kl_penalty = F.mean(chainer.kl_divergence(q_z, p_z))
loss = - (reconstr - self.beta * kl_penalty)
reporter.report({'loss': loss}, self)
reporter.report({'reconstr': reconstr}, self)
reporter.report({'kl_penalty': kl_penalty}, self)
return loss
def __call__(self, y, m_prev, s_prev, h_forward, h_backword, enable, disable_value):
# m is memory cell of lstm, s is previous hidden output
# calculate attention
c = self._attention(h_forward, h_backword, s_prev, enable, disable_value)
# decode once
embeded_y = self.E(y)
batch_size = y.shape[0]
lstm_in = self.W(embeded_y) + self.U(s_prev) + self.C(c)
m_tmp, s_tmp = F.lstm(m_prev, lstm_in)
feed_prev = F.broadcast_to(F.expand_dims(y.data != IGNORE_LABEL, -1),
(batch_size, self.hidden_size))
m = F.where(feed_prev, m_tmp, m_prev)
s = F.where(feed_prev, s_tmp, s_prev)
t = self.U_o(s) + self.V_o(embeded_y) + self.C_o(c)
return self.W_o(t), m, s
def __call__(self, x):
"""Normalize input and scale it.
Args:
x (chainer.Variable): A variable holding 4-dimensional array.
Its :obj:`dtype` is :obj:`numpy.float32`.
Returns:
chainer.Variable:
The shape and :obj:`dtype` are same as those of input.
"""
x = F.normalize(x, eps=self.eps, axis=1)
scale = F.broadcast_to(self.scale[:, np.newaxis, np.newaxis], x.shape)
return x * scale
def __call__(self, x, context):
x = F.broadcast_to(x[:, None], (context.shape[0], context.shape[1]))
x = F.reshape(x, (context.shape[0] * context.shape[1],))
if args.subword == 'rnn':
context = context.reshape((context.shape[0] * context.shape[1]))
e = self.rnn.charRNN(context)
if args.subword == 'none':
e = self.embed(context)
e = F.reshape(e, (e.shape[0] * e.shape[1], e.shape[2]))
loss = self.loss_func(e, x)
reporter.report({'loss': loss}, self)
return loss
def metric(self, model, images, labels):
xp = cupy.get_array_module(images)
batchsize = len(images)
embeddings = model(images)
embeddings = F.reshape(embeddings, ((batchsize, -1)))
shape = embeddings.shape
metric = 0
for embedding, label in zip(embeddings, labels):
eculideans = F.sum(
(embeddings - F.broadcast_to(embedding,
(batchsize, shape[1])))**2,
axis=1)
ratios = -F.log_softmax(F.expand_dims(-eculideans, axis=0))[0]
metric += F.sum(ratios[xp.where(labels == label)])
chainer.report({'metric': metric}, model)
return metric
def _context(self, p, fb_mat, fbe_mat):
batch_size, source_length, _ = fb_mat.data.shape
# {pe,e}_mat: shape = [batch * srclen, atten]
pe_mat = F.reshape(
F.broadcast_to(
F.expand_dims(self.p_e(p), 1),
[batch_size, source_length, self.atten_size]),
[batch_size * source_length, self.atten_size])
e_mat = F.tanh(fbe_mat + pe_mat)
# a_mat: shape = [batch, srclen]
a_mat = F.softmax(F.reshape(self.e_a(e_mat), [batch_size, source_length]))
# q: shape = [batch, 2 * hidden]
q = F.reshape(
F.batch_matmul(a_mat, fb_mat, transa=True),
[batch_size, 2 * self.hidden_size])
return q
def maximum_entropy_mellowmax(values, omega=1., beta_min=-10, beta_max=10):
"""Maximum entropy mellowmax policy function.
This function provides a categorical distribution whose expectation matches
the one of mellowmax function while maximizing its entropy.
See: http://arxiv.org/abs/1612.05628
Args:
values (Variable or ndarray):
Input values. Mellowmax is taken along the second axis.
omega (float):
Parameter of mellowmax.
beta_min (float):
Minimum value of beta, used in Brent's algorithm.
beta_max (float):
Maximum value of beta, used in Brent's algorithm.
Returns:
outputs (Variable)
"""
xp = chainer.cuda.get_array_module(values)
mm = mellowmax(values, axis=1)
# Advantage: Q - mellowmax(Q)
batch_adv = values - F.broadcast_to(F.expand_dims(mm, 1), values.shape)
# Move data to CPU because we use Brent's algorithm in scipy
batch_adv = chainer.cuda.to_cpu(batch_adv.data)
batch_beta = np.empty(mm.shape, dtype=np.float32)
# Beta is computed as the root of this function
def f(y, adv):
return np.sum(np.exp(y * adv) * adv)
for idx in np.ndindex(mm.shape):
idx_full = idx[:1] + (slice(None), ) + idx[1:]
adv = batch_adv[idx_full]
try:
beta = scipy.optimize.brentq(f,
a=beta_min,
b=beta_max,
args=(adv, ))
except ValueError:
beta = 0
batch_beta[idx] = beta
return F.softmax(xp.expand_dims(xp.asarray(batch_beta), 1) * values)
def __call__(self, x, contexts):
# print('skipgram')
x = x.astype(np.int32)
e = self.embed(contexts)
batch_size, n_context, n_units = e.shape
x = F.broadcast_to(x[:, None], (batch_size, n_context))
e = F.reshape(e, (batch_size * n_context, n_units))
x = F.reshape(x, (batch_size * n_context, ))
# for i in range(7,len(self.embed.W.data)):
# if i in [121,207,602,603]:
# print(self.embed.W.data[i][0], end = ' ')
# print()
loss = self.loss_func(e, x)
reporter.report({'loss': loss}, self)
return loss
def compute_r(self, x, v):
resnet_in = cf.relu(self.conv1_1(x))
residual = cf.relu(self.conv1_res(resnet_in))
out = cf.relu(self.conv1_2(resnet_in))
out = cf.relu(self.conv1_3(out)) + residual
v = cf.reshape(v, (v.shape[0], v.shape[1], 1, 1))
broadcast_shape = (
out.shape[0],
v.shape[1],
) + out.shape[2:]
v = cf.broadcast_to(v, shape=broadcast_shape)
resnet_in = cf.concat((out, v), axis=1)
residual = cf.relu(self.conv2_res(resnet_in))
out = cf.relu(self.conv2_1(resnet_in))
out = cf.relu(self.conv2_2(out)) + residual
out = self.conv2_3(out)
return out
def compute_context(previous_state):
decoder_factor = F.broadcast_to(
F.reshape(self.s(previous_state),
(batch_size, 1, self.n_attention_units)),
(batch_size, max_length, self.n_attention_units))
attention = F.softmax(
F.reshape(
self.o(
F.reshape(F.tanh(encoder_factor + decoder_factor),
(batch_size * max_length,
self.n_attention_units))),
(batch_size, max_length)))
context = F.reshape(F.batch_matmul(attention, hxs, transa=True),
(batch_size, encoder_output_size))
return context
def metric(self, model, images, labels):
batchsize = len(images)
embeddings = model(images)
embeddings = F.reshape(embeddings, ((batchsize, -1)))
shape = embeddings.shape
metric = 0
for embedding in embeddings:
eculideans = F.sum(
(embeddings - F.broadcast_to(embedding,
(batchsize, shape[1])))**2,
axis=1)
ratios = -F.log_softmax(F.expand_dims(-eculideans, axis=0))[0]
weights = F.softmax(F.expand_dims(-eculideans, axis=0))[0]
metric += F.sum(ratios * weights)
chainer.report({'metric': metric}, model)
return metric
def forward(self, xs, h, c, mask):
batch_size = len(xs)
lens = [x.shape[0] for x in xs]
#max_len = max(lens)
max_len = self.sequence_length
#mask = (np.expand_dims(np.arange(max_len), 0) <
# np.expand_dims(lens, 1)).astype(np.float)
#h = np.zeros((batch_size, self.num_hidden), dtype=np.float32)
#c = np.zeros((batch_size, self.num_hidden), dtype=np.float32)
#h = self.initial_h
#c = self.initial_c
inputs = F.pad_sequence(xs)
x = None
input = None
gate = None
i = None
o = None
f = None
nc = None
nh = None
m = None
pmask = None
nmask = None
for time in range(max_len):
x = inputs[:, time]
input = F.concat((x, h), axis=1)
gate = self.l(input)
i = gate[:, 0:self.num_hidden]
o = gate[:, self.num_hidden:self.num_hidden * 2]
f = gate[:, self.num_hidden * 2:self.num_hidden * 3]
nc = gate[:, self.num_hidden * 3:self.num_hidden * 4]
#i, o, f, nc = F.split_axis(gate, 4, axis=1)
i = F.sigmoid(i)
o = F.sigmoid(o)
f = F.sigmoid(f)
nc = F.tanh(nc)
nc = f * c + i * nc
nh = o * F.tanh(nc)
m = mask[:, time]
pmask = F.reshape(m, (self.batch_size, ))
pmask = F.broadcast_to(F.expand_dims(pmask, axis=1),
(self.batch_size, self.num_hidden))
nmask = 1.0 - pmask
h = nh * pmask + h * nmask
return h
def __call__(self, x):
batchsize = x.shape[0]
# Compute q(z|x)
qmu, qln_var = self.encode(x)
kl_inst = gaussian_kl_divergence_inst(qmu, qln_var)
logp_inst = None
self.kl = F.sum(kl_inst) / batchsize
self.logp = 0
for j in xrange(self.num_zsamples):
# z ~ q(z|x)
z = F.gaussian(qmu, qln_var)
# Compute p(x|z)
pxz = self.decode(z)
logpxz = pxz(x)
if logp_inst is None:
logp_inst = logpxz
else:
logp_inst += logpxz
# Compute objective
batchsize = logpxz.shape[0]
self.logp += F.sum(logpxz) / batchsize
# Compute standard deviation
logp_inst /= self.num_zsamples
obj_inst = kl_inst - logp_inst
obj_inst_mean = F.sum(obj_inst) / batchsize
obj_c = obj_inst - F.broadcast_to(obj_inst_mean, obj_inst.shape)
obj_var = F.sum(obj_c * obj_c) / (batchsize - 1)
self.logp /= self.num_zsamples
self.obj = self.kl - self.logp
reporter.report(
{
'obj': self.obj,
'obj_var': obj_var,
'kl': self.kl,
'logp': self.logp
}, self)
return self.obj
def predict(self, x):
batchsize = len(x)
xs = [F.dropout(self.embed(Variable(doc)), ratio=0.5) for doc in x]
hy, cy, ys = self.bi_lstm(hx=None, cx=None, xs=xs)
# hy: bilstmの最終的な中間層の状態(2, batchsize, mid_size)
# cy: ??
# ys: 中間層の各状態のベクトルを保存してある(batchsize, lim, mid_size*2)
ys = [F.dropout(midvec, ratio=0.3) for midvec in ys]
concat_ys = F.concat(ys, axis=0) # (batchsize*lim, mid_size*2)
attn = F.dropout(self.l_attn(concat_ys),
ratio=0.25) # (batchsize*lim, 1)
split_attention = F.split_axis(attn,
np.cumsum([len(doc)
for doc in xs])[:-1],
axis=0) # (batchsize, lim, 1)
split_attention_pad = F.pad_sequence(split_attention, padding=-1024.0)
attn_softmax = F.softmax(split_attention_pad,
axis=1) # (batchsize, lim, 1)
ys_pad = F.pad_sequence(ys, length=None,
padding=0.0) # (batchsize, lim, mid_size*2)
# ys と attn_softmax の積を計算するためにshapeを揃える
ys_pad_reshape = F.reshape(
ys_pad, (-1, ys_pad.shape[-1])) # (batchsize*lim, mid_size*2)
attn_softmax_reshape = F.broadcast_to(
F.reshape(attn_softmax, (-1, attn_softmax.shape[-1])),
ys_pad_reshape.shape) # (batchsize*lim, mid_size*2)
attention_hidden = ys_pad_reshape * attn_softmax_reshape # 隠れ層 * 重みを計算 (batchsize*lim, mid_size*2)
attention_hidden_reshape = F.reshape(
attention_hidden,
(batchsize, -1,
attention_hidden.shape[-1])) # (batchsize*lim, mid_size*2)
result = F.sum(attention_hidden_reshape,
axis=1) # 隠れ層の重み付き和を計算(batchsize, mid_size*2)
if chainer.config.train:
return self.l3(F.dropout(result, ratio=0.2))
# return self.l3(result)
else:
attn_list = F.transpose(attn_softmax[0])[0]
return self.l3(result), attn_list.data
def evaluate_with_quantile_thresholds(taus):
assert taus.ndim == 2
assert taus.shape[0] == batch_size
n_taus = taus.shape[1]
phi_taus = self.phi(taus)
assert phi_taus.ndim == 3
assert phi_taus.shape == (batch_size, n_taus, hidden_size)
psi_x_b = F.broadcast_to(F.expand_dims(psi_x, axis=1),
phi_taus.shape)
h = psi_x_b * phi_taus
h = F.reshape(h, (-1, hidden_size))
assert h.shape == (batch_size * n_taus, hidden_size)
h = self.f(h)
assert h.ndim == 2
assert h.shape[0] == batch_size * n_taus
n_actions = h.shape[-1]
h = F.reshape(h, (batch_size, n_taus, n_actions))
return QuantileDiscreteActionValue(h)
def __call__(self, a_list):
e_list = []
self.empha = []
sum_e = Variable(np.array([[0]], dtype='float32'))
for a in a_list:
w = functions.tanh(self.pw(a))
e = functions.exp(self.we(w))
e_list.append(e)
sum_e += e
ZEROS = Variable(np.zeros((1, self.hidden_size), dtype='float32'))
aa = ZEROS
for a, e in zip(a_list, e_list):
e /= sum_e
self.empha.append(e)
aa += a * functions.broadcast_to(e, (1, self.hidden_size))
#aa += functions.reshape(functions.batch_matmul(a, e), (batch_size, self.hidden_size))
return aa
def forward_onestep(self, prev_h_g, prev_h_e, prev_c_e, x, v, r):
broadcast_shape = (
prev_h_e.shape[0],
v.shape[1],
) + prev_h_e.shape[2:]
v = cf.reshape(v, v.shape + (1, 1))
v = cf.broadcast_to(v, shape=broadcast_shape)
x = cf.relu(self.params.conv_x_1(x))
x = cf.relu(self.params.conv_x_2(x))
lstm_in = cf.concat((prev_h_e, prev_h_g, x, v, r), axis=1)
forget_gate = cf.sigmoid(self.params.lstm_f(lstm_in))
input_gate = cf.sigmoid(self.params.lstm_i(lstm_in))
next_c = forget_gate * prev_c_e + input_gate * cf.tanh(
self.params.lstm_tanh(lstm_in))
next_h = cf.sigmoid(self.params.lstm_o(lstm_in)) * cf.tanh(next_c)
return next_h, next_c
def compute_discriminative_loss(self, fact_vectors):
reason_pool = F.max(fact_vectors, axis=0) # dim of fact_vectors is (n_view_steps, batch_size, voc_size)
batch_size, voc_size = reason_pool.data.shape
loss = 0
for i_word in range(voc_size):
one_word_score = \
F.get_item(reason_pool, [range(batch_size), i_word])
one_word_score = F.expand_dims(one_word_score, axis=1)
broaded_one_word_score = \
F.broadcast_to(one_word_score, reason_pool.data.shape)
real_value_of_hinge = 1-(reason_pool-broaded_one_word_score)
zero_mat = Variable(self.xp.zeros_like( \
real_value_of_hinge.data, self.xp.float32), volatile='auto')
hinge_loss = F.where( \
real_value_of_hinge.data<0, zero_mat, real_value_of_hinge.data)
loss += F.sum(hinge_loss) - voc_size
loss /= (voc_size-1)*voc_size
return loss
def __call__(self, x):
if self.dr:
with chainer.using_config('train', True):
x = F.dropout(x, self.dr)
if self.gap:
x = F.sum(x, axis=(2,3))
N = x.shape[0]
#Below code copyed from https://github.com/pfnet-research/chainer-gan-lib/blob/master/minibatch_discrimination/net.py
feature = F.reshape(F.leaky_relu(x), (N, -1))
m = F.reshape(self.md(feature), (N, self.B * self.C, 1))
m0 = F.broadcast_to(m, (N, self.B * self.C, N))
m1 = F.transpose(m0, (2, 1, 0))
d = F.absolute(F.reshape(m0 - m1, (N, self.B, self.C, N)))
d = F.sum(F.exp(-F.sum(d, axis=2)), axis=2) - 1
h = F.concat([feature, d])
h = self.l(h)
return h
def forward(self, u):
B, C, H, W = u.shape
h1 = u.reshape((B, C, H * W))
h2 = self.context(u)
h2 = h2.reshape((B, H * W, 1))
h2 = F.softmax(h2)
z = F.batch_matmul(h1, h2)
x = F.relu(self.ln(self.down(z)))
x = self.up(x)
x = F.broadcast_to(x, (H, W, B, C))
x = x.transpose((2, 3, 0, 1))
return u + x
def lighting(faces,
textures,
intensity_ambient=0.5,
intensity_directional=0.5,
color_ambient=(1, 1, 1),
color_directional=(1, 1, 1),
direction=(0, 1, 0)):
xp = chainer.cuda.get_array_module(faces)
bs, nf = faces.shape[:2]
# arguments
if isinstance(color_ambient, tuple) or isinstance(color_ambient, list):
color_ambient = xp.array(color_ambient, 'float32')
if isinstance(color_directional, tuple) or isinstance(
color_directional, list):
color_directional = xp.array(color_directional, 'float32')
if isinstance(direction, tuple) or isinstance(direction, list):
direction = xp.array(direction, 'float32')
if color_ambient.ndim == 1:
color_ambient = cf.broadcast_to(color_ambient[None, :], (bs, 3))
if color_directional.ndim == 1:
color_directional = cf.broadcast_to(color_directional[None, :], (bs,
3))
if direction.ndim == 1:
direction = cf.broadcast_to(direction[None, :], (bs, 3))
# create light
light = xp.zeros((bs, nf, 3), 'float32')
# ambient light
if intensity_ambient != 0:
light = light + intensity_ambient * cf.broadcast_to(
color_ambient[:, None, :], light.shape)
# directional light
if intensity_directional != 0:
faces = faces.reshape((bs * nf, 3, 3))
v10 = faces[:, 0] - faces[:, 1]
v12 = faces[:, 2] - faces[:, 1]
normals = cf.normalize(cross(v10, v12))
normals = normals.reshape((bs, nf, 3))
if direction.ndim == 2:
direction = cf.broadcast_to(direction[:, None, :], normals.shape)
cos = cf.relu(cf.sum(normals * direction, axis=2))
light = (light + intensity_directional * cfmath.mul(
*cf.broadcast(color_directional[:, None, :], cos[:, :, None])))
# apply
light = cf.broadcast_to(light[:, :, None, None, None, :], textures.shape)
textures = textures * light
return textures
def __call__(self, x):
N = x.shape[0]
h = F.leaky_relu(self.bn1(self.conv1(x)), slope=0.2)
h = F.leaky_relu(self.bn2(self.conv2(h)), slope=0.2)
h = F.leaky_relu(self.bn3(self.conv3(h)), slope=0.2)
h = F.reshape(h, (x.shape[0], -1))
feature = F.reshape(self.l_hidden(h), (N, self.B, self.C, 1))
feature = F.broadcast_to(feature, (N, self.B, self.C, N))
feature_batch = F.transpose(feature, (3, 1, 2, 0))
feature = F.absolute(feature - feature_batch)
feature = F.exp(-F.sum(feature, axis=2))
feature = F.sum(feature, axis=2) - 1
h = F.concat([h, feature])
if self.use_feature_matching:
return h, self.lout(h)
else:
return self.lout(h)
def generate_2dmeshgrid(H, W, N, xp=np):
"""Generate 2d meshgrid.
Returns:
Array: Shape is (N, 3, H*W)
"""
global meshgrid
if meshgrid is None or meshgrid.shape[2] != H * W:
ys, xs = xp.meshgrid(xp.arange(0, H, dtype=np.float32),
xp.arange(0, W, dtype=np.float32),
indexing='ij',
copy=False)
meshgrid = xp.concatenate(
[xs[None], ys[None],
xp.ones(
(1, H, W), dtype=np.float32)], axis=0).reshape(1, 3, H * W)
meshgrid = F.broadcast_to(meshgrid, (N, 3, H * W))
return meshgrid
def rescale_adj(adj):
"""Normalize adjacency matrix
It ensures that activations are on a similar scale irrespective of
the number of neighbors
Args:
adj (:class:`chainer.Variable`, or :class:`numpy.ndarray` \
or :class:`cupy.ndarray`):
adjacency matrix
Returns:
:class:`chainer.Variable`: normalized adjacency matrix
"""
xp = cuda.get_array_module(adj)
num_neighbors = functions.sum(adj, axis=(1, 2))
base = xp.ones(num_neighbors.shape, dtype=xp.float32)
cond = num_neighbors.data != 0
num_neighbors_inv = 1 / functions.where(cond, num_neighbors, base)
return adj * functions.broadcast_to(num_neighbors_inv[:, None, None, :],
adj.shape)
def __call__(self, x):
xs = []
h = self.f0(x)
# tail
xs.append(self.t0(h))
xs.append(self.t1(F.max_pooling_2d(xs[0], 2)))
xs.append(self.t2(F.max_pooling_2d(xs[1], 2)))
b = self.t3(xs[2])
# body
xs[2] = self.b2(F.concat([xs[2], F.broadcast_to(b, xs[2].shape)]))
xs[1] = self.b1(F.concat([xs[1], upsample(xs[2], 2)]))
xs[0] = self.b0(F.concat([xs[0], upsample(xs[1], 2)]))
# head
xs[1] = self.h1(F.concat([xs[1], F.max_pooling_2d(xs[0], 2)]))
xs[2] = self.h2(F.concat([xs[2], F.max_pooling_2d(xs[1], 2)]))
h = self.h3(xs[2])
y = self.fin(h)
return y
def get_elbo(self, x, k=None, with_ll=False):
if not k:
k = self.k
q_z = self.encoder(x)
z = q_z.sample(k)
p_x = self.decoder(z, n_batch_axes=2)
p_z = self.prior()
reconstr = p_x.log_prob(F.broadcast_to(x[None, :], (k, ) + x.shape))
kl_penalty = q_z.log_prob(z) - p_z.log_prob(z)
elbo_k = reconstr - kl_penalty
elbo = F.mean(elbo_k)
if with_ll:
log_likelihood = F.mean(F.logsumexp(elbo_k, axis=0) - numpy.log(k))
return elbo, log_likelihood
else:
return elbo
def __call__(self, x):
batch_size, sentence_len = x.shape
n_class, n_embed = self.c.shape
e = self.embed(x)
ep = self.pad_sequence(e)
# Eq. (2)
c = F.broadcast_to(self.c, (batch_size, n_class, n_embed))
g = F.matmul(ep, c, transb=True)
norm_ep = self.xp.expand_dims(self.xp.linalg.norm(ep.data, axis=2),
axis=2)
norm_c = self.xp.expand_dims(self.xp.linalg.norm(c.data, axis=2),
axis=2)
denom = self.xp.matmul(norm_ep, self.xp.transpose(
norm_c, (0, 2, 1))) + 1e-10 # avoid zero division
g = g / denom
# Eq. (3)
g = self.make_ngram(
g) # (batch_size, sentence_len, window_size, n_class)
g = F.reshape(
g, (batch_size * sentence_len * n_class, self.n_window * 2 + 1))
u = F.relu(
self.attention(g)) # (batch_size * sentence_len * n_class, 1)
u = F.reshape(u, (batch_size, sentence_len, n_class))
# Eq. (4)
m = F.max(u, axis=2) # (batch_size, sentence_len)
# Eq. (5)
mask = (x == PAD).astype(
self.xp.float32) * -1024.0 # make attention-scores for PAD 0
m = m + mask
beta = F.softmax(m)
beta = F.expand_dims(beta, axis=1)
# Eq. (6)
z = F.reshape(F.matmul(beta, e),
(batch_size, n_embed)) # (batch_size, n_embed)
# f_2
h = F.dropout(F.relu(self.l1(z)), ratio=.8)
return self.l2(h)
def update_core(self):
train_iter = self.get_iterator('main')
batch = train_iter.next()
t_data = self.converter(batch, self.device)
B = t_data.shape[0]
y_data = self.func(B)
B, C, H, W = t_data.shape[:4]
y_data = F.broadcast_to(y_data, (B, C, H, W))
loss = compute_loss(y_data, t_data)
pos_model = self.models['position']
dir_model = self.models['direction']
reporter.report({
'main/loss': loss,
'camera_position/x': pos_model.camera_position[0],
'camera_position/y': pos_model.camera_position[1],
'camera_position/z': pos_model.camera_position[2],
'camera_direction/x': dir_model.camera_zaxis[0],
'camera_direction/y': dir_model.camera_zaxis[1],
'camera_direction/z': dir_model.camera_zaxis[2]
})
y_data = y_data.data
if self.device >= 0:
y_data = y_data.get()
cuda.get_device_from_id(self.device).synchronize()
img = y_data[0]
img = np.transpose(img, (1, 2, 0))
img = np.clip(img, 0, 1)
img = (img * 255).astype(np.uint8)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
save_progress_image(self.odir, self.count, img)
for o in self.optimizers.values():
o.target.cleargrads()
loss.backward()
for o in self.optimizers.values():
o.update()
self.count += 1
def attention(self, hOut, encOut, args): #TODO この関数も何か汚い
"""calc attention"""
if args.attention == 0: #アテンションをしない時hOutをそのまま返す
return hOut
#ターゲット側の下準備
hOut1 = self.attnIn(hOut) #[batch, Dim]
hOut2 = F.expand_dims(hOut1, axis=1) #[batch, 1, Dim]
hOut3 = F.broadcast_to(hOut2, (len(hOut2), len(encOut[0]), args.hiddenDim)) #[batch, max(enc_sentlen), Dim] 今encOutとhOut3は同じshapeのはず
aval = F.sum(encOut*hOut3, axis=2) #[batch, sentlen]
cAttn1 = F.softmax(aval) #[batch, max(enc_sentlen)] paddingで0のところはかなり小さい数字の確率で出てくる
cAttn2 = F.expand_dims(cAttn1, axis=1) #[batch, 1, max(enc_sentlen)]
cAttn3 = F.batch_matmul(cAttn2, encOut) #[batch, 1, Dim]
context = F.reshape(cAttn3, (len(encOut), len(encOut[0][0]))) #[batch, Dim] エンコーダコンテキストベクトルの完成
c1 = F.concat((hOut, context)) #[batch, Dim + Dim]
c2 = self.attnOut(c1) #[bathc, Dim]
return F.tanh(c2) #活性化
def __call__(self, inputs):
pos_x, pos_y, offset_x, ego_x, ego_y, pose_x, pose_y = self._prepare_input(
inputs)
batch_size, past_len, _ = pos_x.shape
h_pos = self.pos_encoder(pos_x)
h_pose = self.pose_encoder(pose_x)
h = F.concat((h_pos, h_pose), axis=1) # (B, C, 2)
h = self.inter(h)
h_pos = self.pos_decoder(h)
pred_y = self.last(h_pos) # (B, 10, C+6+28)
pred_y = F.swapaxes(pred_y, 1, 2)
pred_y = pred_y[:, :pos_y.shape[1], :]
loss = F.mean_squared_error(pred_y, pos_y)
pred_y = pred_y + F.broadcast_to(F.expand_dims(offset_x, 1),
pred_y.shape)
pred_y = cuda.to_cpu(pred_y.data) * self._std + self._mean
return loss, pred_y, None
def log_propensity_independent(self, x, action):
xp = cuda.get_array_module(action)
pred = self._predict(x)
final_action = action
if self.k > 0 and action.shape[1] < pred.shape[1]:
all_actions = F.broadcast_to(xp.arange(0, pred.shape[1],
dtype=action.data.dtype),
pred.shape)
inv_items = inverse_select_items_per_row(all_actions, action)
items = select_items_per_row(all_actions, action)
final_action = F.concat((items, inv_items), axis=1)
pred = select_items_per_row(pred, final_action)
results = F.log_softmax(pred)
if self.k > 0:
results = results[:, :self.k]
return results
def __call__(self, x, y=None):
h = x
h = self.block1(h)
h = self.block2(h)
h = self.block3(h)
if y is not None:
emb = self.l_y(y)
H, W = h.shape[2], h.shape[3]
emb = F.broadcast_to(
F.reshape(emb, (emb.shape[0], emb.shape[1], 1, 1)),
(emb.shape[0], emb.shape[1], H, W))
h = F.concat([h, emb], axis=1)
h = self.block4(h)
h = self.block5(h)
h = self.block6(h)
h = self.activation(h)
h = F.sum(h, axis=(2, 3)) # Global pooling
output = self.l7(h)
return output
def __call__(self, v, h, label):
v_t = self.vertical_conv_t(v)
v_s = self.vertical_conv_s(v)
to_vertical_t = self.v_to_h_conv_t(v_t)
to_vertical_s = self.v_to_h_conv_s(v_s)
# v_gate = self.vertical_gate_conv(v)
# label bias is added to both vertical and horizontal conv
# here we take only shape as it should be the same
label = F.broadcast_to(F.expand_dims(F.expand_dims(self.label(label), -1), -1), v_t.shape)
v_t, v_s = v_t + label, v_s + label
v = F.tanh(v_t) * F.sigmoid(v_s)
h_t = self.horizontal_conv_t(h)
h_s = self.horizontal_conv_s(h)
h_t, h_s = h_t + to_vertical_t + label, h_s + to_vertical_s + label
h = self.horizontal_output(F.tanh(h_t) * F.sigmoid(h_s))
return v, h
def get_initial_logits(self, mb_size=None):
if mb_size is None:
mb_size = self.mb_size
assert mb_size is not None
#print self.multi_target_signal.data.shape
previous_states = None
if self.multi_target_signal is not None:
previous_states = self.multi_target_signal
else:
previous_states = self.decoder_chain.gru.get_initial_states(
mb_size)
#print previous_states
prev_y = F.broadcast_to(self.decoder_chain.bos_embeding,
(mb_size, self.decoder_chain.Eo))
new_states, logits, attn = self.advance_one_step(
previous_states, prev_y)
return new_states, logits, attn
def compute_ctxt(previous_state, prev_word_embedding=None):
current_mb_size = previous_state.data.shape[0]
if current_mb_size < mb_size:
al_factor, _ = F.split_axis(precomputed_al_factor,
(current_mb_size, ), 0)
used_fb_concat, _ = F.split_axis(fb_concat,
(current_mb_size, ), 0)
if mask_length > 0:
used_concatenated_penalties = concatenated_penalties[:
current_mb_size]
else:
al_factor = precomputed_al_factor
used_fb_concat = fb_concat
if mask_length > 0:
used_concatenated_penalties = concatenated_penalties
state_al_factor = self.al_lin_s(previous_state)
#As suggested by Isao Goto
if prev_word_embedding is not None:
state_al_factor = state_al_factor + self.al_lin_y(
prev_word_embedding)
state_al_factor_bc = F.broadcast_to(
F.reshape(state_al_factor, (current_mb_size, 1, self.Ha)),
(current_mb_size, nb_elems, self.Ha))
a_coeffs = F.reshape(
self.al_lin_o(
F.reshape(F.tanh(state_al_factor_bc + al_factor),
(current_mb_size * nb_elems, self.Ha))),
(current_mb_size, nb_elems))
if mask_length > 0:
with cuda.get_device_from_array(used_concatenated_penalties):
a_coeffs = a_coeffs + used_concatenated_penalties # - 10000 * (1-used_concatenated_mask.data)
attn = F.softmax(a_coeffs)
ci = F.reshape(batch_matmul(attn, used_fb_concat, transa=True),
(current_mb_size, self.Hi))
return ci, attn