def test_backward(self):
x = chainer.Variable(numpy.array([1]))
y1 = F.identity(x)
y2 = F.identity(x)
z = y1 + y2
z.grad = numpy.array([1])
z.backward(retain_grad=True)
self.assertEqual(y1.grad[0], 1)
self.assertEqual(y2.grad[0], 1)
self.assertEqual(x.grad[0], 2)
def test_backward(self):
x = chainer.Variable(numpy.array([1]))
y1 = F.identity(x)
y2 = F.identity(x)
z = y1 + y2
z.grad = numpy.array([1])
z.backward(retain_grad=True)
self.assertEqual(y1.grad[0], 1)
self.assertEqual(y2.grad[0], 1)
self.assertEqual(x.grad[0], 2)
def __call__(self, x, t):
self.clear()
h = F.relu(self.bn1(self.conv1(x), test=not self.train))
h = self.res2(h, self.train)
h = F.max_pooling_2d(h, 2, stride=2)
h = self.res3(h, self.train)
h = self.res4(h, self.train)
inter = F.identity(h)
h = self.hg1(h, self.train)
# Residual layers at output resolution
h = self.res5(h, self.train)
h = F.relu(self.bn6(self.conv6(h), test=not self.train))
ll_ = self.conv7(h)
# Predicted heatmaps
tmpOut = self.inter_conv1(h)
tmpOut_ = self.inter_conv2(tmpOut)
h = add(ll_, tmpOut_, inter)
h = self.hg2(h, self.train)
h = self.res8(h, self.train)
h = F.relu(self.bn9(self.conv9(h), test=not self.train))
h = self.final_conv1(h)
h = F.concat((tmpOut, h))
t = F.concat((t, t))
self.loss = F.mean_squared_error(h,t)
if self.train:
return self.loss
else:
self.pred = h
return self.pred
def test_raise(self):
x = np.array([1], np.float32)
x = chainer.Variable(x)
y = F.identity(x)
y.grad = np.array([np.nan], np.float32)
with self.assertRaises(RuntimeError):
y.backward()
def test_raise(self):
x = np.array([1], np.float32)
x = chainer.Variable(x)
y = F.identity(x)
y.grad = np.array([np.nan], np.float32)
with self.assertRaises(RuntimeError):
y.backward()
def test_raise_double_backprop_2(self):
x = chainer.Variable(np.empty(1, np.float32))
z = F.identity(x) # new style
y = IdentityFunction()(z) # old style
y.backward()
with self.assertRaises(RuntimeError):
x.grad_var.backward()
def test_raise_double_backprop_2(self):
x = chainer.Variable(np.empty(1, np.float32))
z = F.identity(x) # new style
y = IdentityFunction()(z) # old style
y.backward()
with self.assertRaises(RuntimeError):
x.grad_var.backward()
def test_default_backward(self):
x = chainer.Variable(np.empty(1, np.float32))
y = F.identity(x)
y.backward()
self.assertIsNone(x.grad_var.creator)
x.grad_var.backward()
self.assertIsNone(y.grad_var.grad_var)
def __call__(self, x):
if self.resize_identity:
identity = self.identity_conv(x)
else:
identity = F.identity(x)
x = self.body(x)
x = x + identity
return x
def house_transform(self,z):
vec_t = self.qh_vec_0
for i in range(self.num_trans):
vec_t = F.identity(self.qlin_h_vec_t(vec_t))
vec_t_product = F.matmul(vec_t, vec_t, transb=True)
vec_t_norm_sqr = F.tile(F.sum(F.square(vec_t)), (z.shape[0], z.shape[1]))
z = z - 2*F.matmul(vec_t_product, z)/vec_t_norm_sqr
return z
def crop(inputs, outsize, offset):
x = F.identity(inputs)
crop_axis = [i!=j for i, j in zip(inputs.data.shape, outsize)]
i = 0
for index, tf in enumerate(crop_axis):
if tf:
_, x, _ = F.split_axis(x, [offset[i], offset[i] + outsize[index]], index)
i += 1
return x
def crop(inputs, outsize, offset):
x = F.identity(inputs)
crop_axis = [i!=j for i, j in zip(inputs.data.shape, outsize)]
i = 0
for index, tf in enumerate(crop_axis):
if tf:
_, x, _ = F.split_axis(x, [offset[i], offset[i] + outsize[index]], index)
i += 1
return x
def test_forward(self):
xs = (Variable(numpy.array([0])), Variable(numpy.array([0])), Variable(numpy.array([0])))
xs[0].rank = 1
xs[1].rank = 3
xs[2].rank = 2
ys = identity(*xs)
self.assertEqual(len(ys), len(xs))
for y in ys:
# rank is (maximum rank in xs) + 2, since Function call
# automatically inserts Split function.
self.assertEqual(y.rank, 5)
def test_forward(self):
xs = (Variable(numpy.array([0])), Variable(numpy.array([0])),
Variable(numpy.array([0])))
xs[0].rank = 1
xs[1].rank = 3
xs[2].rank = 2
ys = identity(*xs)
self.assertEqual(len(ys), len(xs))
for y in ys:
# rank is (maximum rank in xs) + 2, since Function call
# automatically inserts Split function.
self.assertEqual(y.rank, 5)
def __call__(self, x):
x, t, l = x
reshape = (1, x.shape[1]) + (1,) * (x.ndim - 2)
if chainer.config.train:
# batch norm
mean = F.mean(x, axis=(0,) + tuple(range(2, x.ndim)))
x = x - F.broadcast_to(
F.reshape(mean, reshape),
x.shape)
var = F.mean(x ** 2, axis=(0,) + tuple(range(2, x.ndim)))
m = x.size // self.gamma.size
adjust = m / max(m - 1., 1.) # unbiased estimation
self.avg_mean *= self.decay
self.avg_mean += (1 - self.decay) * mean.array
self.avg_var *= self.decay
self.avg_var += (1 - self.decay) * adjust * var.array
else:
mean = self.avg_mean
var = self.avg_var
x = x - F.broadcast_to(F.reshape(mean, reshape), x.shape)
z0 = F.identity(self.gamma) / F.sqrt(var + self.eps)
z = F.reshape(z0, reshape)
x = x * F.broadcast_to(z, x.shape) + F.broadcast_to(
F.reshape(self.beta, reshape), x.shape)
# calculate Lipschitz constant
if getattr(chainer.config, 'lmt', False):
if getattr(chainer.config, 'exact', False):
l = l * F.reshape(F.max(F.absolute(z0)), (1,))
else:
normalize(self.u.array)
perturb(self.u.array, 1e-2, self.xp)
u = self.u * z0
l = l * l2_norm(u)
return x, t, l
def __call__(self, x):
if not self._residual:
h = x
for i in range(self._ninner):
h = self['conv_%d' % i](h)
if self._norm_param is not None \
and _n_spatial_unit(h) != 1: # NOTE: if spatial unit is 1, activations could be always
h = self['conv_norm_%d' % i](
h) # zeroed by the batch normalization
h = self._activation(h)
return h
else:
h = x
for i in range(self._ninner):
h = self['conv_%d' % i](h)
if self._norm_param is not None \
and _n_spatial_unit(h) != 1:
h = self['conv_norm_%d' % i](h)
if i == 0:
g = F.identity(h) # TODO: order should be checked
if i != (self._ninner - 1):
h = self._activation(h)
return self._activation(g + h)
def forward(self, x, x_org, feed_previous=False):
bsize = len(x)
x_emb = self.embedding(x)
x_emb = normalizing(x_emb, 1)
x_emb = F.expand_dims(x_emb, 1)
H = self.conv_encoder(x_emb)
H_mean, H_log_sigma_sq = self.vae_classifier(H)
mu = self.xp.zeros((bsize, self.ef_dim), np.float32)
ln_sigma = self.xp.ones((bsize, self.ef_dim), np.float32)
eps = F.gaussian(mu, ln_sigma) # N(0, 1)
H_dec = H_mean + eps * F.sqrt(F.exp(H_log_sigma_sq))
H_dec2 = F.identity(H_dec)
# moddel: cnn_rnn
loss, rec_sent_1, _ = self.lstm_decoder(H_dec2,
x_org,
feed_previous=feed_previous)
_, rec_sent_2, _ = self.lstm_decoder(H_dec2, x_org, feed_previous=True)
# KL loss
kl_loss = F.mean(-0.5 * F.mean(1 + H_log_sigma_sq \
- F.square(H_mean) \
- F.exp(H_log_sigma_sq), axis=1))
loss += kl_loss
chainer.report({'loss': loss.data, 'kl_loss': kl_loss.data}, self)
return loss, rec_sent_1, rec_sent_2
def test_int(self):
x = np.array([1], np.int)
x = chainer.Variable(x)
y = F.identity(x)
y.grad = np.array([0], np.int)
y.backward()
def __call__(self, x):
x = F.identity(x)
return self.ops(*([x] + self.args))
def predict(self, x):
h1 = F.identity(self.l1(x))
h2 = F.identity(self.l2(h1))
return self.l3(h2)
def __call__(self, x_data):
with using_config("train", False):
h = self.forward(x_data)
return F.identity(h)
def Q_func(self, x):
h1 = F.leaky_relu(self.L1(x))
h2 = F.leaky_relu(self.L2(h1))
h3 = F.leaky_relu(self.L3(h2))
return F.identity(self.Q_value(h3))
def empty(x):
xp = backend.get_array_module(x)
s0, _, s2, s3 = x.shape
return F.identity(xp.empty((s0, 0, s2, s3), dtype=np.float32))
def __call__(self, x):
store_activations = {}
# down convolution
for i in range(1, self.n_layers + 1):
if i == 1:
h = F.identity(x)
else:
h = F.max_pooling_nd(h, 2, stride=2)
h = self['down_unet_block_%d' % (i)](h)
h = self.down_conv_dropout(h)
store_activations['down_unet_block_%d' % (i)] = h
del h # clear hidden layer
# up convolution
for i in range(self.n_layers - 1, 0, -1):
if i == self.n_layers - 1:
h = store_activations['down_unet_block_%d' % (i + 1)]
del store_activations['down_unet_block_%d' % (i + 1)] # clear
else:
h = h
h = self['deconv_%d' % i](h)
if self.batch_norm:
h = self['bn_deconv_%d' % i](h)
h = self.up_conv_activate_function(h)
down_conv = store_activations['down_unet_block_%d' % (i)]
del store_activations['down_unet_block_%d' % (i)] # clear
if self.n_dims == 2:
h = F.concat([
h[:, :, 0:down_conv.shape[2], 0:down_conv.shape[3]],
down_conv
]) # fuse layer
elif self.n_dims == 3:
h = F.concat([
h[:, :, 0:down_conv.shape[2], 0:down_conv.shape[3],
0:down_conv.shape[4]], down_conv
]) # fuse layer
del down_conv
h = self['up_unet_block_%d' % i](h)
h = self.up_conv_dropout(h)
if i == 1:
o = self['up_conv%d_3' % i](h)
if self.n_dims == 2:
score = o[:, :, 0:x.shape[2], 0:x.shape[3]]
elif self.n_dims == 3:
score = o[:, :, 0:x.shape[2], 0:x.shape[3], 0:x.shape[4]]
self.score = score
del h, o # clear hidden layer
return self.score
def __call__(self, x):
x = F.identity(x)
return self.bn(x)
def test_int(self):
x = np.array([1], np.int)
x = chainer.Variable(x)
y = F.identity(x)
y.grad = np.array([0], np.int)
y.backward()
def __call__(self, x):
x = F.identity(x)
self.args[self.input_argname] = x
return self.ops(**self.args)
