def epoch_predict(env, args, model, loader):
"""Predict in one epoch"""
model.eval()
arcs, rels, probs = [], [], []
for words, feats in loader():
# ignore the first token of each sentence
tmp_words = layers.pad(words[:, 1:],
paddings=[0, 0, 1, 0],
pad_value=args.pad_index)
mask = tmp_words != args.pad_index
lens = nn.reduce_sum(mask, -1)
s_arc, s_rel = model(words, feats)
arc_preds, rel_preds = decode(args, s_arc, s_rel, mask)
arcs.extend(
layers.split(nn.masked_select(arc_preds, mask),
lens.numpy().tolist()))
rels.extend(
layers.split(nn.masked_select(rel_preds, mask),
lens.numpy().tolist()))
if args.prob:
arc_probs = nn.index_sample(layers.softmax(s_arc, -1),
layers.unsqueeze(arc_preds, -1))
probs.extend(
layers.split(
nn.masked_select(layers.squeeze(arc_probs, axes=[-1]),
mask),
lens.numpy().tolist()))
arcs = [seq.numpy().tolist() for seq in arcs]
rels = [env.REL.vocab[seq.numpy().tolist()] for seq in rels]
probs = [[round(p, 3) for p in seq.numpy().tolist()] for seq in probs]
return arcs, rels, probs
def forward(self, input, pre_encode_hidden):
#print('Im here!')
#print(input.shape)
pre_hidden, encode_hidden = layers.split(pre_encode_hidden,
num_or_sections=[self._hiden_size, self._encode_hiden_size],
dim=1)
concat_input_hidden = layers.concat([input, pre_hidden, encode_hidden], 1)
gate_input = layers.matmul(x=concat_input_hidden, y=self._gate_weight)
gate_input = layers.elementwise_add(gate_input, self._gate_bias)
gate_input = self._gate_activation(gate_input)
r, u = layers.split(gate_input, num_or_sections=2, dim=1)
r_hidden = r * pre_hidden
candidate = layers.matmul(
layers.concat([input, r_hidden, encode_hidden], 1), self._candidate_weight)
candidate = layers.elementwise_add(candidate, self._candidate_bias)
c = self._activation(candidate)
new_hidden = u * pre_hidden + (1 - u) * c
return new_hidden
def forward(self, x, y, **kargs):
"""
Adaptive Normalization forward.
Args:
x (N x C1 x *): input,
y (N x C2): Conditional information.
Returns:
out (N x c1 x *): output
"""
residual_dim = len(x.shape) - len(y.shape)
if self.projection:
if self.separate_projection:
gamma = self.fc_gamma(y)
beta = self.fc_beta(y)
for _ in range(residual_dim):
gamma = L.unsqueeze(gamma, -1)
beta = L.unsqueeze(beta, -1)
else:
y = self.fc(x)
for _ in range(residual_dim):
y = L.unsqueeze(y, -1)
gamma, beta = L.split(y, num_or_sections=2, dim=1)
else:
for _ in range(residual_dim):
y = L.unsqueeze(y, -1)
gamma, beta = L.split(y, 2, 1)
x = self.norm(x) if self.norm is not None else x
out = x * (1 + gamma) + beta
return out
def seq2seq_api_rnn(input_embedding,
len=3,
init_hiddens=None,
init_cells=None):
class EncoderCell(layers.RNNCell):
def __init__(self,
num_layers,
hidden_size,
dropout_prob=0.,
forget_bias=0.):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(
layers.LSTMCell(
hidden_size,
forget_bias=forget_bias,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.
UniformInitializer(low=-init_scale,
high=init_scale))))
def call(self, step_input, states):
new_states = []
for i in range(self.num_layers):
out, new_state = self.lstm_cells[i](step_input, states[i])
step_input = layers.dropout(
out,
self.dropout_prob,
dropout_implementation='upscale_in_train'
) if self.dropout_prob > 0 else out
new_states.append(new_state)
return step_input, new_states
cell = EncoderCell(num_layers, hidden_size, dropout)
output, new_states = layers.rnn(
cell,
inputs=input_embedding,
initial_states=[[hidden, cell] for hidden, cell in zip([
layers.reshape(init_hidden, shape=[-1, hidden_size])
for init_hidden in layers.split(
init_hiddens, num_or_sections=num_layers, dim=0)
], [
layers.reshape(init_cell, shape=[-1, hidden_size])
for init_cell in layers.split(
init_cells, num_or_sections=num_layers, dim=0)
])],
time_major=False)
last_hidden = layers.stack([hidden for hidden, _ in new_states], 0)
last_cell = layers.stack([cell for _, cell in new_states], 0)
return output, last_hidden, last_cell
def forward(self, z, condition=None):
"""Transform a random noise sampled from a standard Gaussian distribution into sample from the target distribution. And output the mean and log standard deviation of the output distribution.
Args:
z (Variable): shape(B, T), random noise sampled from a standard gaussian disribution.
condition (Variable, optional): shape(B, F, T), dtype float, the upsampled condition. Defaults to None.
Returns:
(z, out_mu, out_log_std)
z (Variable): shape(B, T), dtype float, transformed noise, it is the synthesized waveform.
out_mu (Variable): shape(B, T), dtype float, means of the output distributions.
out_log_std (Variable): shape(B, T), dtype float, log standard deviations of the output distributions.
"""
for i, flow in enumerate(self.flows):
theta = flow(z, condition) # w, mu, log_std [0: T]
w, mu, log_std = F.split(theta, 3, dim=-1) # (B, T, 1) for each
mu = F.squeeze(mu, [-1]) #[0: T]
log_std = F.squeeze(log_std, [-1]) #[0: T]
z = z * F.exp(log_std) + mu #[0: T]
if i == 0:
out_mu = mu
out_log_std = log_std
else:
out_mu = out_mu * F.exp(log_std) + mu
out_log_std += log_std
return z, out_mu, out_log_std
def add_input(self, x_t, speaker_embed=None):
"""
Takes a step of inputs and return a step of outputs. It works similarily with the `forward` method, but in a `step-in-step-out` fashion.
Args:
x_t (Variable): shape(B, C_in, T=1), dtype float32, the input of Conv1DGLU layer, where B means batch_size, C_in means the input channels.
speaker_embed (Variable): Shape(B, C_sp), dtype float32, speaker embed, where C_sp means speaker embedding size.
Returns:
x (Variable): shape(B, C_out), the output of Conv1DGLU, where C_out means the `num_filter`.
"""
residual = x_t
x_t = F.dropout(x_t,
self.dropout,
dropout_implementation="upscale_in_train")
x_t = self.conv.add_input(x_t)
content_t, gate_t = F.split(x_t, num_or_sections=2, dim=1)
if speaker_embed is not None:
sp = F.softsign(self.fc(speaker_embed))
content_t = F.elementwise_add(content_t, sp, axis=0)
# glu
x_t = F.sigmoid(gate_t) * content_t
if self.residual:
x_t = F.scale(x_t + residual, np.sqrt(0.5))
return x_t
def forward(self, x, condition=None):
"""Conv1D gated-tanh Block.
Args:
x (Variable): shape(B, C_res, T), the input. (B stands for batch_size, C_res stands for residual channels, T stands for time steps.) dtype float32.
condition (Variable, optional): shape(B, C_cond, T), the condition, it has been upsampled in time steps, so it has the same time steps as the input does.(C_cond stands for the condition's channels). Defaults to None.
Returns:
(residual, skip_connection)
residual (Variable): shape(B, C_res, T), the residual, which is used as the input to the next layer of ResidualBlock.
skip_connection (Variable): shape(B, C_res, T), the skip connection. This output is accumulated with that of other ResidualBlocks.
"""
time_steps = x.shape[-1]
h = x
# dilated conv
h = self.conv(h)
if h.shape[-1] != time_steps:
h = h[:, :, :time_steps]
# condition
if condition is not None:
h += self.condition_proj(condition)
# gated tanh
content, gate = F.split(h, 2, dim=1)
z = F.sigmoid(gate) * F.tanh(content)
# projection
residual = F.scale(z + x, math.sqrt(.5))
skip_connection = z
return residual, skip_connection
def forward(self, x, speaker_embed=None):
"""
Args:
x (Variable): shape(B, C_in, T), dtype float32, the input of Conv1DGLU layer, where B means batch_size, C_in means the input channels T means input time steps.
speaker_embed (Variable): shape(B, C_sp), dtype float32, speaker embed, where C_sp means speaker embedding size.
Returns:
x (Variable): shape(B, C_out, T), the output of Conv1DGLU, where
C_out means the `num_filters`.
"""
residual = x
x = F.dropout(x,
self.dropout,
dropout_implementation="upscale_in_train")
x = self.conv(x)
content, gate = F.split(x, num_or_sections=2, dim=1)
if speaker_embed is not None:
sp = F.softsign(self.fc(speaker_embed))
content = F.elementwise_add(content, sp, axis=0)
# glu
x = F.sigmoid(gate) * content
if self.residual:
x = F.scale(x + residual, np.sqrt(0.5))
return x
def forward(self, input, bias=None, padding=None):
"""
input: input feature (B, T, C)
padding: only used when using causal conv, we pad mannually
"""
input_dropped = F.dropout(input,
1. - self.keep_prob,
dropout_implementation="upscale_in_train")
if self.causal:
assert padding is not None
input_dropped = F.concat([padding, input_dropped], axis=1)
hidden = self.conv(input_dropped)
if self.has_bias:
assert bias is not None
transformed_bias = F.softsign(self.bias_affine(bias))
hidden_embedded = hidden + F.unsqueeze(transformed_bias, [1])
else:
hidden_embedded = hidden
# glu
content, gate = F.split(hidden, num_or_sections=2, dim=-1)
content = hidden_embedded[:, :, :self.in_channel]
hidden = F.sigmoid(gate) * content
# # residual
hidden = F.scale(input + hidden, math.sqrt(0.5))
return hidden
def add_input(self, x, condition=None):
"""Add a step input. This method works similarily with `forward` but in a `step-in-step-out` fashion.
Args:
x (Variable): shape(B, C_res, T=1), input for a step, dtype float32.
condition (Variable, optional): shape(B, C_cond, T=1). condition for a step, dtype float32. Defaults to None.
Returns:
(residual, skip_connection)
residual (Variable): shape(B, C_res, T=1), the residual for a step, which is used as the input to the next layer of ResidualBlock.
skip_connection (Variable): shape(B, C_res, T=1), the skip connection for a step. This output is accumulated with that of other ResidualBlocks.
"""
h = x
# dilated conv
h = self.conv.add_input(h)
# condition
if condition is not None:
h += self.condition_proj(condition)
# gated tanh
content, gate = F.split(h, 2, dim=1)
z = F.sigmoid(gate) * F.tanh(content)
# projection
residual = F.scale(z + x, np.sqrt(0.5))
skip_connection = z
return residual, skip_connection
def sample_from_mog(self, y):
"""Sample from the output distribution where the output distribution is a mixture of Gaussians.
Args:
y (Variable): shape(B, T, C_output), dtype float32, the parameterd of the output distribution. It is the concatenation of 3 parts, the logits of every distribution, the mean of each distribution and the log standard deviation of each distribution. Each part's shape is (B, T, n_mixture), where `n_mixture` means the number of Gaussians in the mixture.
Returns:
Variable: shape(B, T), waveform sampled from the output distribution.
"""
batch_size, time_steps, output_dim = y.shape
n_mixture = output_dim // 3
w, mu, log_std = F.split(y, 3, dim=-1)
reshaped_w = F.reshape(w, (batch_size * time_steps, n_mixture))
prob_ids = F.sampling_id(F.softmax(reshaped_w))
prob_ids = F.reshape(prob_ids, (batch_size, time_steps))
prob_ids = prob_ids.numpy()
index = np.array([[[b, t, prob_ids[b, t]] for t in range(time_steps)]
for b in range(batch_size)]).astype("int32")
index_var = dg.to_variable(index)
mu_ = F.gather_nd(mu, index_var)
log_std_ = F.gather_nd(log_std, index_var)
dist = D.Normal(mu_, F.exp(log_std_))
samples = dist.sample(shape=[])
samples = F.clip(samples, min=-1., max=1.)
return samples
def forward(self, x, *cond_inputs, norm_weights=(None, None), **kwargs):
"""
Spatially Adaptive Normalization (SPADE) forward.
"""
output = self.norm(x)
for i in range(len(cond_inputs)):
if cond_inputs[i] is None:
continue
if type(cond_inputs[i]) == list:
cond_input, mask = cond_inputs[i]
mask = L.image_resize(mask, size=x.shape[2:], resample='BILINEAR', align_corners=False)
else:
cond_input = cond_inputs[i]
mask = None
label_map = L.image_resize(cond_input, x.shape[2:])
if norm_weights is None or norm_weights[0] is None or i != 0:
affine_params = self.mlps[i](label_map)
else:
affine_params = self.mlps[i](label_map, conv_weights=norm_weights)
gamma, beta = L.split(affine_params, 2, 1)
if mask is not None:
gamma = gamma * (1 - mask)
beta = beta * (1 - mask)
output = output * (1 + gamma) + beta
return output
def forward(self, input, class_id, input_class_emb=False):
if isinstance(input, list):
codes = [input[0]]
codes += [
input[2 * i + 1:2 * i + 3] for i in range(len(input) // 2)
]
else:
codes = layers.split(input, self.num_split, 1)
if not input_class_emb:
class_emb = self.embed_y(class_id) # 128
else:
class_emb = class_id
out = self.noise_fc(codes[0])
out = layers.transpose(layers.reshape(out, (out.shape[0], 4, 4, -1)),
(0, 3, 1, 2))
for i, (code, gblock) in enumerate(zip(codes[1:], self.blocks)):
if isinstance(input, list):
condition = [layers.concat([c, class_emb], 1) for c in code]
else:
condition = layers.concat([code, class_emb], 1)
out = gblock(out, condition)
out = self.output_layer_bn(out)
out = layers.relu(out)
out = self.output_layer_conv(out)
return (layers.tanh(out) + 1) / 2
def forward(self, input, pre_hidden):
xu_t, xr_t, xc_t = layers.split(input, num_or_sections=3, dim=-1)
gate_input = layers.matmul(x=pre_hidden, y=self._gate_weight)
gate_input = layers.elementwise_add(gate_input, self._gate_bias)
hu_t, hr_t = layers.split(gate_input, num_or_sections=2, dim=-1)
u_add = layers.elementwise_add(xu_t, hu_t)
r_add = layers.elementwise_add(xr_t, hr_t)
u = self._gate_activation(u_add)
r = self._gate_activation(r_add)
r_hidden = r * pre_hidden
candidate = layers.matmul(r_hidden, self._candidate_weight)
candidate = layers.elementwise_add(xc_t, candidate)
candidate = layers.elementwise_add(candidate, self._candidate_bias)
c = self._activation(candidate)
new_hidden = (1 - u) * pre_hidden + u * c
return new_hidden
def gru_step(self, input, hidden, mask=None):
""" gru step """
hidden_array = []
for i in range(self.num_layers):
hidden_temp = layers.slice(hidden,
axes=[0],
starts=[i],
ends=[i + 1])
hidden_temp = layers.reshape(hidden_temp,
shape=[-1, self.hidden_size])
hidden_array.append(hidden_temp)
last_hidden_array = []
for k in range(self.num_layers):
trans_input = layers.matmul(input, self.weight_input_array[k])
trans_input += self.bias_input_array[k]
trans_hidden = layers.matmul(hidden_array[k],
self.weight_hidden_array[k])
trans_hidden += self.bias_hidden_array[k]
input_array = layers.split(trans_input, num_or_sections=3, dim=-1)
trans_array = layers.split(trans_hidden, num_or_sections=3, dim=-1)
reset_gate = layers.sigmoid(input_array[0] + trans_array[0])
input_gate = layers.sigmoid(input_array[1] + trans_array[1])
new_gate = layers.tanh(input_array[2] +
reset_gate * trans_array[2])
new_hidden = new_gate + input_gate * (hidden_array[k] - new_gate)
if mask:
neg_mask = layers.fill_constant_batch_size_like(
input=mask, shape=[1], value=1.0, dtype='float32') - mask
new_hidden = new_hidden * mask + hidden_array[k] * neg_mask
last_hidden_array.append(new_hidden)
input = new_hidden
if self.dropout and self.dropout > 0.0:
input = layers.dropout(input, dropout_prob=self.dropout)
last_hidden = layers.concat(last_hidden_array, 0)
last_hidden = layers.reshape(
last_hidden, shape=[self.num_layers, -1, self.hidden_size])
return input, last_hidden
def forward(self, audio, mel, audio_start, clip_kl=True):
"""Compute loss of Clarinet model.
Args:
audio (Variable): shape(B, T_audio), dtype flaot32, ground truth waveform.
mel (Variable): shape(B, F, T_mel), dtype flaot32, condition(mel spectrogram here).
audio_start (Variable): shape(B, ), dtype int64, audio starts positions.
clip_kl (bool, optional): whether to clip kl_loss by maximum=100. Defaults to True.
Returns:
Dict(str, Variable)
loss (Variable): shape(1, ), dtype flaot32, total loss.
kl (Variable): shape(1, ), dtype flaot32, kl divergence between the teacher's output distribution and student's output distribution.
regularization (Variable): shape(1, ), dtype flaot32, a regularization term of the KL divergence.
spectrogram_frame_loss (Variable): shape(1, ), dytpe: float, stft loss, the L1-distance of the magnitudes of the spectrograms of the ground truth waveform and synthesized waveform.
"""
batch_size, audio_length = audio.shape # audio clip's length
z = F.gaussian_random(audio.shape)
condition = self.encoder(mel) # (B, C, T)
condition_slice = crop(condition, audio_start, audio_length)
x, s_means, s_scales = self.student(z, condition_slice) # all [0: T]
s_means = s_means[:, 1:] # (B, T-1), time steps [1: T]
s_scales = s_scales[:, 1:] # (B, T-1), time steps [1: T]
s_clipped_scales = F.clip(s_scales, self.min_log_scale, 100.)
# teacher outputs single gaussian
y = self.teacher(x[:, :-1], condition_slice[:, :, 1:])
_, t_means, t_scales = F.split(y, 3, -1) # time steps [1: T]
t_means = F.squeeze(t_means, [-1]) # (B, T-1), time steps [1: T]
t_scales = F.squeeze(t_scales, [-1]) # (B, T-1), time steps [1: T]
t_clipped_scales = F.clip(t_scales, self.min_log_scale, 100.)
s_distribution = D.Normal(s_means, F.exp(s_clipped_scales))
t_distribution = D.Normal(t_means, F.exp(t_clipped_scales))
# kl divergence loss, so we only need to sample once? no MC
kl = s_distribution.kl_divergence(t_distribution)
if clip_kl:
kl = F.clip(kl, -100., 10.)
# context size dropped
kl = F.reduce_mean(kl[:, self.teacher.context_size:])
# major diff here
regularization = F.mse_loss(t_scales[:, self.teacher.context_size:],
s_scales[:, self.teacher.context_size:])
# introduce information from real target
spectrogram_frame_loss = F.mse_loss(self.stft.magnitude(audio),
self.stft.magnitude(x))
loss = kl + self.lmd * regularization + spectrogram_frame_loss
loss_dict = {
"loss": loss,
"kl_divergence": kl,
"regularization": regularization,
"stft_loss": spectrogram_frame_loss
}
return loss_dict
def forward(self, x):
"""Forward network"""
mask = layers.reduce_any(x != self.pad_index, -1)
lens = nn.reduce_sum(mask, -1)
masked_x = nn.masked_select(x, mask)
h, _ = self.transformer(masked_x)
feat_embed = nn.pad_sequence_paddle(
layers.split(h, lens.numpy().tolist(), dim=0), self.pad_index)
return feat_embed
def _build_distribution(self, enc_final_state=None):
enc_hidden = [
layers.concat(state, axis=-1) for state in enc_final_state
]
enc_hidden = layers.concat(enc_hidden, axis=-1)
z_mean_log_var = layers.fc(input=enc_hidden,
size=self.latent_size * 2,
name='fc_dist')
z_mean, z_log_var = layers.split(z_mean_log_var, 2, -1)
return z_mean, z_log_var
def epoch_predict(env, args, model, loader):
"""Predict in one epoch"""
connections, deprels, probabilities = [], [], []
pad_index = args.pad_index
bos_index = args.bos_index
eos_index = args.eos_index
for batch, inputs in enumerate(loader(), start=1):
if args.encoding_model.startswith("ernie"):
words = inputs[0]
connection_prob, deprel_prob, words = model(words)
else:
words, feats = inputs
connection_prob, deprel_prob, words = model(words, feats)
mask = layers.logical_and(
layers.logical_and(words != pad_index, words != bos_index),
words != eos_index,
)
lens = nn.reduce_sum(mask, -1)
connection_predicts, deprel_predicts = decode(args, connection_prob,
deprel_prob, mask)
connections.extend(
layers.split(nn.masked_select(connection_predicts, mask),
lens.numpy().tolist()))
deprels.extend(
layers.split(nn.masked_select(deprel_predicts, mask),
lens.numpy().tolist()))
if args.prob:
arc_probs = nn.index_sample(
layers.softmax(connection_prob, -1),
layers.unsqueeze(connection_predicts, -1))
probabilities.extend(
layers.split(
nn.masked_select(layers.squeeze(arc_probs, axes=[-1]),
mask),
lens.numpy().tolist(),
))
connections = [seq.numpy().tolist() for seq in connections]
deprels = [env.REL.vocab[seq.numpy().tolist()] for seq in deprels]
probabilities = [[round(p, 3) for p in seq.numpy().tolist()]
for seq in probabilities]
return connections, deprels, probabilities
def forward(self, x):
"""Forward network"""
mask = layers.reduce_any(x != self.pad_index, -1)
lens = nn.reduce_sum(mask, -1)
masked_x = nn.masked_select(x, mask)
char_mask = masked_x != self.pad_index
emb = self.embed(masked_x)
_, (h, _) = self.lstm(emb, char_mask, self.pad_index)
h = layers.concat(layers.unstack(h), axis=-1)
feat_embed = nn.pad_sequence_paddle(
layers.split(h, lens.numpy().tolist(), dim=0), self.pad_index)
return feat_embed
def flat_words(self, words):
pad_index = self.args.pad_index
lens = nn.reduce_sum(words != pad_index, dim=-1)
position = layers.cumsum(lens + layers.cast((lens == 0), "int32"),
axis=1) - 1
flat_words = nn.masked_select(words, words != pad_index)
flat_words = nn.pad_sequence_paddle(
layers.split(flat_words,
layers.reduce_sum(lens, -1).numpy().tolist(),
pad_index))
max_len = flat_words.shape[1]
position = nn.mask_fill(position, position >= max_len, max_len - 1)
return flat_words, position
def pad_packed_sequence(self, x, batch_sizes, unsorted_indices):
"""Pads a packed sequences."""
h_size = x.shape[1]
split_x = layers.split(x, batch_sizes, dim=0)
max_bs = batch_sizes[0]
step_embs = []
for step, cur_bs in enumerate(batch_sizes):
pad_emb = layers.zeros(shape=(max_bs - cur_bs, h_size),
dtype=x.dtype)
step_emb = layers.concat(input=(split_x[step], pad_emb))
step_embs.append(step_emb)
new_x = layers.stack(step_embs, axis=1)
new_x = layers.index_select(new_x, unsorted_indices)
return new_x
def forward(self, input, pre_hidden, pre_cell):
concat_input_hidden = layers.concat([input, pre_hidden], 1)
gate_input = layers.matmul(x=concat_input_hidden, y=self._weight)
gate_input = layers.elementwise_add(gate_input, self._bias)
i, j, f, o = layers.split(gate_input, num_or_sections=4, dim=-1)
new_cell = layers.elementwise_add(
layers.elementwise_mul(
pre_cell,
layers.sigmoid(layers.elementwise_add(f, self._forget_bias))),
layers.elementwise_mul(layers.sigmoid(i), layers.tanh(j)))
new_hidden = layers.tanh(new_cell) * layers.sigmoid(o)
return new_hidden, new_cell
def __call__(self, x):
bond_feature = get_bond_feature_dims()
bond_input = L.split(x, num_or_sections=len(bond_feature), dim=-1)
outputs = None
count = 0
for _x, _bond_input_dim in zip(bond_input, bond_feature):
count += 1
emb = L.embedding(_x,
size=(_bond_input_dim, self.emb_dim),
param_attr=F.ParamAttr(name=self.name +
'_bond_feat_%s' % count))
if outputs is None:
outputs = emb
else:
outputs = outputs + emb
return outputs
def for_rnn_net(self):
x = layers.data(shape=[BATCH_SIZE, SEQ_LEN, INPUT_DIM],
dtype="float32",
name="x",
append_batch_size=False)
split_x = layers.split(x, num_or_sections=SEQ_LEN, dim=1)
h_pre = fluid.layers.zeros(shape=[BATCH_SIZE, 1, INPUT_DIM],
dtype="float32")
for i in range(SEQ_LEN):
x_t = split_x[i]
h = layers.scale(x=layers.elementwise_add(x=h_pre, y=x_t),
scale=self.scale)
h_pre = h
return layers.mean(h_pre)
def forward(self, x, *cond_inputs, **kwargs):
output = self.norm(x) if self.norm is not None else x
for i in range(len(cond_inputs)):
if cond_inputs[i] is None:
continue
label_map = L.image_resize(cond_inputs[i], out_shape=x.shape[2:], resample='NEAREST')
if self.separate_projection:
hidden = self.mlps[i](label_map)
gamma = self.gammas[i](hidden)
beta = self.betas[i](hidden)
else:
affine_params = self.mlps[i](label_map)
gamma, beta = L.split(affine_params, 2, 1)
output = output * (1 + gamma) + beta
return output
def forward(self, input, state):
#logging.info("input shape: {}".format(input.shape))
pre_hidden, pre_cell = state
#logging.info("pre hidden shape: {}".format(pre_hidden.shape))
#logging.info("pre cell shape: {}".format(pre_cell.shape))
# i,f,c,o 四个值均有Wx+Wh+b 即W(x+h)+b
# 因此：
# 实际相乘为[x, b]·W+b
# x,b 横向相连, shape为[batch_size, input_size+hidden_size]
# W的shape为[input_size+hidden_size, 4*hidden_size]
# b的shape为[4*hidden_size,]
# 横向连接
# shape: [batch_size, input_size+hidden_size]
concat_input_hidden = L.concat([input, pre_hidden], axis=1)
#logging.info("x concat h shape: {}".format(concat_input_hidden.shape))
# 计算Wx+Wh+b
# shape: [batch_size, 4*hidden_size]
gate_input = L.matmul(x=concat_input_hidden, y=self._weight)
#logging.info("[x, b]·W shape: {}".format(gate_input.shape))
# shape: [batch_size, 4*hidden_size]
gate_input = L.elementwise_add(gate_input, self._bias)
#logging.info("[x, b]·W+b shape: {}".format(gate_input.shape))
# i,f,c,o四值按最后一维分开 因此每个的最后一维都是hidden_size
i, f, c, o = L.split(gate_input, num_or_sections=4, dim=-1)
# new_c = pre_c·sigmoid(f+forget_bias) + sigmoid(i)·tanh(c)
# shape: [batch_size, hidden_size]
new_cell = L.elementwise_add(
L.elementwise_mul(
pre_cell,
L.sigmoid(L.elementwise_add(f, self._forget_bias))),
L.elementwise_mul(L.sigmoid(i), L.tanh(c))
)
#logging.info("new_cell shape: {}".format(new_cell.shape))
# new_h = tanh(new_c)*sigmoid(o)
# shape: [batch_size, hidden_size]
new_hidden = L.tanh(new_cell) * L.sigmoid(o)
#logging.info("new_hidden shape: {}".format(new_hidden.shape))
return new_hidden, [new_hidden, new_cell]
def pairwise_hinge(self):
"""pairwise model"""
poi_repr = L.split(self.poi_repr, 2, dim=0)
pos_repr, neg_repr = poi_repr
pos_pred = L.cos_sim(self.query_repr, pos_repr)
neg_pred = L.cos_sim(self.query_repr, neg_repr)
mode = 'hinge_loss'
# log(1 + e-z), max(0, 1 - z)
if 'hinge_loss' == mode:
theta_z = L.relu(1 + neg_pred - pos_pred)
elif 'logistic_loss' == mode:
theta_z = L.log(1 + L.exp(neg_pred - pos_pred))
self.loss = L.reduce_mean(theta_z)
pos_cnt = L.reduce_sum(L.cast(L.greater_than(pos_pred, neg_pred), dtype="float32"))
neg_cnt = L.reduce_sum(L.cast(L.less_than(pos_pred, neg_pred), dtype="float32"))
self.order = pos_cnt / (1e-5 + neg_cnt)
self.metrics = [self.loss, self.order]
def weight_layers(lm_embeddings, name="", l2_coef=0.0):
'''
Weight the layers of a biLM with trainable scalar weights to
compute ELMo representations.
Input:
lm_embeddings(list): representations of 2 layers from biLM.
name = a string prefix used for the trainable variable names
l2_coef: the l2 regularization coefficient $\lambda$.
Pass None or 0.0 for no regularization.
Output:
weighted_lm_layers: weighted embeddings form biLM
'''
n_lm_layers = len(lm_embeddings)
W = layers.create_parameter(
[
n_lm_layers,
],
dtype="float32",
name=name + "ELMo_w",
attr=fluid.ParamAttr(name=name + "ELMo_w",
initializer=fluid.initializer.Constant(0.0),
regularizer=fluid.regularizer.L2Decay(l2_coef)))
normed_weights = layers.softmax(W + 1.0 / n_lm_layers)
splited_normed_weights = layers.split(normed_weights, n_lm_layers, dim=0)
# compute the weighted, normalized LM activations
pieces = []
for w, t in zip(splited_normed_weights, lm_embeddings):
pieces.append(t * w)
sum_pieces = layers.sums(pieces)
# scale the weighted sum by gamma
gamma = layers.create_parameter(
[1],
dtype="float32",
name=name + "ELMo_gamma",
attr=fluid.ParamAttr(name=name + "ELMo_gamma",
initializer=fluid.initializer.Constant(1.0)))
weighted_lm_layers = sum_pieces * gamma
return weighted_lm_layers
def forward(self, input, pre_hidden):
concat_input_hidden = layers.concat([input, pre_hidden], 1)
gate_input = layers.matmul(x=concat_input_hidden, y=self._gate_weight)
gate_input = layers.elementwise_add(gate_input, self._gate_bias)
gate_input = self._gate_activation(gate_input)
r, u = layers.split(gate_input, num_or_sections=2, dim=1)
r_hidden = r * pre_hidden
candidate = layers.matmul(layers.concat([input, r_hidden], 1),
self._candidate_weight)
candidate = layers.elementwise_add(candidate, self._candidate_bias)
c = self._activation(candidate)
new_hidden = u * pre_hidden + (1 - u) * c
return new_hidden
