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 beam_search_step(state, logits, eos_id, beam_width, is_first_step,
length_penalty):
"""logits.shape == [B*W, V]"""
_, vocab_size = logits.shape
bsz, beam_width = state.log_probs.shape
onehot_eos = L.cast(F.one_hot(L.ones([1], 'int64') * eos_id, vocab_size),
'int64') #[1, V]
probs = L.log(L.softmax(logits)) #[B*W, V]
probs = mask_prob(probs, onehot_eos, state.finished) #[B*W, V]
allprobs = L.reshape(state.log_probs, [-1, 1]) + probs #[B*W, V]
not_finished = 1 - L.reshape(state.finished, [-1, 1]) #[B*W,1]
not_eos = 1 - onehot_eos
length_to_add = not_finished * not_eos #[B*W,V]
alllen = L.reshape(state.lengths, [-1, 1]) + length_to_add
allprobs = L.reshape(allprobs, [-1, beam_width * vocab_size])
alllen = L.reshape(alllen, [-1, beam_width * vocab_size])
allscore = hyp_score(allprobs, alllen, length_penalty)
if is_first_step:
allscore = L.reshape(
allscore,
[bsz, beam_width, -1])[:, 0, :] # first step only consiter beam 0
scores, idx = L.topk(allscore, k=beam_width) #[B, W]
next_beam_id = idx // vocab_size #[B, W]
next_word_id = idx % vocab_size
gather_idx = L.concat([L.where(idx != -1)[:, :1],
L.reshape(idx, [-1, 1])], 1)
next_probs = L.reshape(L.gather_nd(allprobs, gather_idx), idx.shape)
next_len = L.reshape(L.gather_nd(alllen, gather_idx), idx.shape)
gather_idx = L.concat(
[L.where(next_beam_id != -1)[:, :1],
L.reshape(next_beam_id, [-1, 1])], 1)
next_finished = L.reshape(
L.gather_nd(state.finished, gather_idx), state.finished.shape
) #[gather new beam state according to new beam id]
#log.debug(gather_idx.numpy())
#log.debug(state.finished.numpy())
#log.debug(next_finished.numpy())
next_finished += L.cast(next_word_id == eos_id, 'int64')
next_finished = L.cast(next_finished > 0, 'int64')
#log.debug(next_word_id.numpy())
#log.debug(next_beam_id.numpy())
next_state = BeamSearchState(log_probs=next_probs,
lengths=next_len,
finished=next_finished)
output = BeamSearchOutput(scores=scores,
predicted_ids=next_word_id,
beam_parent_ids=next_beam_id)
return output, next_state
def beam_search_step(state, logits, eos_id, beam_width, is_first_step,
length_penalty):
"""logits.shape == [B*W, V]"""
beam_size, vocab_size = logits.shape # as batch size=1 in this hub module. the first dim means bsz * beam_size equals beam_size
logits_np = logits.numpy()
for i in range(beam_size):
logits_np[i][17963] = 0 # make [UNK] prob = 0
logits = D.to_variable(logits_np)
bsz, beam_width = state.log_probs.shape
onehot_eos = L.cast(F.one_hot(L.ones([1], 'int64') * eos_id, vocab_size),
'int64') #[1, V]
probs = L.log(L.softmax(logits)) #[B*W, V]
probs = mask_prob(probs, onehot_eos, state.finished) #[B*W, V]
allprobs = L.reshape(state.log_probs, [-1, 1]) + probs #[B*W, V]
not_finished = 1 - L.reshape(state.finished, [-1, 1]) #[B*W,1]
not_eos = 1 - onehot_eos
length_to_add = not_finished * not_eos #[B*W,V]
alllen = L.reshape(state.lengths, [-1, 1]) + length_to_add
allprobs = L.reshape(allprobs, [-1, beam_width * vocab_size])
alllen = L.reshape(alllen, [-1, beam_width * vocab_size])
allscore = hyp_score(allprobs, alllen, length_penalty)
if is_first_step:
allscore = L.reshape(
allscore,
[bsz, beam_width, -1])[:, 0, :] # first step only consiter beam 0
scores, idx = L.topk(allscore, k=beam_width) #[B, W]
next_beam_id = idx // vocab_size #[B, W]
next_word_id = idx % vocab_size
gather_idx = L.concat([L.where(idx != -1)[:, :1],
L.reshape(idx, [-1, 1])], 1)
next_probs = L.reshape(L.gather_nd(allprobs, gather_idx), idx.shape)
next_len = L.reshape(L.gather_nd(alllen, gather_idx), idx.shape)
gather_idx = L.concat(
[L.where(next_beam_id != -1)[:, :1],
L.reshape(next_beam_id, [-1, 1])], 1)
next_finished = L.reshape(
L.gather_nd(state.finished, gather_idx), state.finished.shape
) #[gather new beam state according to new beam id]
next_finished += L.cast(next_word_id == eos_id, 'int64')
next_finished = L.cast(next_finished > 0, 'int64')
next_state = BeamSearchState(log_probs=next_probs,
lengths=next_len,
finished=next_finished)
output = BeamSearchOutput(scores=scores,
predicted_ids=next_word_id,
beam_parent_ids=next_beam_id)
return output, next_state
def fast_nms(self, boxes, scores, masks, max_num_detections=100):
iou_threshold = self.nms_thresh
top_k = self.top_k
# 同类方框根据得分降序排列
scores, idx = P.argsort(scores, axis=1, descending=True)
idx = idx[:, :top_k]
scores = scores[:, :top_k]
num_classes, num_dets = P.shape(idx)[0], P.shape(idx)[1]
idx = P.reshape(idx, (-1, ))
boxes = P.gather(boxes, idx)
boxes = P.reshape(boxes, (num_classes, num_dets, 4))
masks = P.gather(masks, idx)
masks = P.reshape(masks, (num_classes, num_dets, -1))
# 计算一个c×n×n的IOU矩阵，其中每个n×n矩阵表示对该类n个候选框，两两之间的IOU
iou = jaccard(boxes, boxes)
# 因为自己与自己的IOU=1，IOU(A,B)=IOU(B,A)，所以对上一步得到的IOU矩阵
# 进行一次处理。具体做法是将每一个通道，的对角线元素和下三角部分置为0
rows = P.range(0, num_dets, 1, 'int32')
cols = P.range(0, num_dets, 1, 'int32')
rows = P.expand(P.reshape(rows, (1, -1)), [num_dets, 1])
cols = P.expand(P.reshape(cols, (-1, 1)), [1, num_dets])
tri_mask = P.cast(rows > cols, 'float32')
tri_mask = P.expand(P.reshape(tri_mask, (1, num_dets, num_dets)),
[num_classes, 1, 1])
iou = tri_mask * iou
iou_max = P.reduce_max(iou, dim=1)
# Now just filter out the ones higher than the threshold
keep = P.where(iou_max <= iou_threshold)
# Assign each kept detection to its corresponding class
classes = P.range(0, num_classes, 1, 'int32')
classes = P.expand(P.reshape(classes, (-1, 1)), [1, num_dets])
classes = P.gather_nd(classes, keep)
boxes = P.gather_nd(boxes, keep)
masks = P.gather_nd(masks, keep)
scores = P.gather_nd(scores, keep)
# Only keep the top cfg.max_num_detections highest scores across all classes
scores, idx = P.argsort(scores, axis=0, descending=True)
idx = idx[:max_num_detections]
scores = scores[:max_num_detections]
classes = P.gather(classes, idx)
boxes = P.gather(boxes, idx)
masks = P.gather(masks, idx)
return boxes, masks, classes, scores
def fast_nms(boxes, scores, conf_thresh, nms_thresh, keep_top_k, nms_top_k):
'''
:param boxes: [?, 4]
:param scores: [80, ?]
'''
# 同类方框根据得分降序排列
scores, idx = P.argsort(scores, axis=1, descending=True)
idx = idx[:, :keep_top_k]
scores = scores[:, :keep_top_k]
num_classes, num_dets = P.shape(idx)[0], P.shape(idx)[1]
idx = P.reshape(idx, (-1, ))
boxes = P.gather(boxes, idx)
boxes = P.reshape(boxes, (num_classes, num_dets, 4))
# 计算一个c×n×n的IOU矩阵，其中每个n×n矩阵表示对该类n个候选框，两两之间的IOU
iou = _iou(boxes, boxes)
# 因为自己与自己的IOU=1，IOU(A,B)=IOU(B,A)，所以对上一步得到的IOU矩阵
# 进行一次处理。具体做法是将每一个通道，的对角线元素和下三角部分置为0
rows = P.range(0, num_dets, 1, 'int32')
cols = P.range(0, num_dets, 1, 'int32')
rows = P.expand(P.reshape(rows, (1, -1)), [num_dets, 1])
cols = P.expand(P.reshape(cols, (-1, 1)), [1, num_dets])
tri_mask = P.cast(rows > cols, 'float32')
tri_mask = P.expand(P.reshape(tri_mask, (1, num_dets, num_dets)),
[num_classes, 1, 1])
iou = tri_mask * iou
iou_max = P.reduce_max(iou, dim=1)
# 同一类别，n个框与“分数比它高的框”的最高iou超过nms_thresh的话，就丢弃。下标是0的框肯定被保留。
keep = P.where(iou_max <= nms_thresh)
# Assign each kept detection to its corresponding class
classes = P.range(0, num_classes, 1, 'int32')
classes = P.expand(P.reshape(classes, (-1, 1)), [1, num_dets])
classes = P.gather_nd(classes, keep)
boxes = P.gather_nd(boxes, keep)
scores = P.gather_nd(scores, keep)
# Only keep the top cfg.max_num_detections highest scores across all classes
scores, idx = P.argsort(scores, axis=0, descending=True)
idx = idx[:nms_top_k]
scores = scores[:nms_top_k]
classes = P.gather(classes, idx)
boxes = P.gather(boxes, idx)
return boxes, scores, classes
def _get_pooled_output(self, enc_out, idx=None, name="pooled"):
"""Get pooled output of the last output embedding in Transformer.
Args:
enc_out: the output embeddings of Transformer, shape is [batch_size, max_seq_len, hidden_size]
idx (optional): the selected indices in pooling operator, shape is [batch_size, 1] or [batch_size, 2].
name: a string, the name of the pooling layer.
Returns:
pooled_out: the pooled output embedding, shape is [batch_size, hidden_size].
"""
if idx is None:
feat = enc_out[:, 0]
elif len(idx.shape) == 2 and idx.shape[1] == 1:
enc_out = layers.squeeze(enc_out, [1])
feat = layers.gather(input=enc_out, index=idx)
elif len(idx.shape) == 2 and idx.shape[1] == 2:
feat = layers.gather_nd(input=enc_out, index=idx)
else:
raise ValueError(f"Invalid indices shape {idx.shape} is used")
pooled_out = layers.fc(
input=feat,
size=self.hidden_size,
act="tanh",
param_attr=fluid.ParamAttr(name=f"{name}_fc.w_0", initializer=self.param_initializer),
bias_attr=f"{name}_fc.b_0")
return pooled_out
def no_nms(bboxes,
scores,
score_threshold,
keep_top_k):
scores = L.transpose(scores, [1, 0])
inds = L.where(scores > score_threshold)
if len(inds) == 0:
return L.zeros((0, 6), 'float32') - 1.0
cate_scores = L.gather_nd(scores, inds)
cate_labels = inds[:, 1]
bboxes = L.gather(bboxes, inds[:, 0])
# sort and keep top keep_top_k
_, sort_inds = L.argsort(cate_scores, descending=True)
if keep_top_k > 0 and len(sort_inds) > keep_top_k:
sort_inds = sort_inds[:keep_top_k]
bboxes = L.gather(bboxes, sort_inds)
cate_scores = L.gather(cate_scores, sort_inds)
cate_labels = L.gather(cate_labels, sort_inds)
cate_scores = L.unsqueeze(cate_scores, 1)
cate_labels = L.unsqueeze(cate_labels, 1)
cate_labels = L.cast(cate_labels, 'float32')
pred = L.concat([cate_labels, cate_scores, bboxes], 1)
return pred
def masked_select(input, mask):
"""Select the input value according to the mask
Arags：
input: input matrix
mask: mask matrix
Returns:
output
>>> input
[
[1, 2, 3],
[4, 5, 6]
]
>>> mask
[
[True, True, False],
[True, False, False]
]
>>> masked_select(input, mask)
[1, 2, 4]
"""
select = layers.where(mask)
output = layers.gather_nd(input, select)
return output
def forward(self, src_ids, *args, **kwargs):
tgt_labels = kwargs.pop('tgt_labels', None)
tgt_pos = kwargs.pop('tgt_pos', None)
encode_only = kwargs.pop('encode_only', False)
_, encoded, info = ErnieModel.forward(self, src_ids, *args, **kwargs)
#log.debug('hidden_-1 %r'% L.reduce_mean(info['hiddens'][0]).numpy())
#log.debug('hidden_0 %r'% L.reduce_mean(info['hiddens'][1]).numpy())
if encode_only:
return None, None, info
elif tgt_labels is None:
encoded = self.mlm(encoded)
encoded = self.mlm_ln(encoded)
logits = L.matmul(encoded, self.word_emb.weight, transpose_y=True) + self.mlm_bias
output_ids = L.argmax(logits, -1)
return output_ids, logits, info
else:
encoded_2d = L.gather_nd(encoded, tgt_pos)
#log.debug('input shape %s' % repr(src_ids.shape))
#log.debug(L.gather_nd(src_ids, tgt_pos).numpy())
encoded_2d = self.mlm(encoded_2d)
encoded_2d = self.mlm_ln(encoded_2d)
logits_2d = L.matmul(encoded_2d, self.word_emb.weight, transpose_y=True) + self.mlm_bias
if len(tgt_labels.shape) == 1:
tgt_labels = L.reshape(tgt_labels, [-1, 1])
loss = L.reduce_mean(
L.softmax_with_cross_entropy(logits_2d, tgt_labels, soft_label=(tgt_labels.shape[-1] != 1))
)
return loss, logits_2d, info
def batch_scatter(ref, indices, updates, in_place=False, overwrite=False):
"""Scatter updates to ref, according to corrensponding index in indices
in each batch. Currently, it only support 2d Tensor.
Args:
ref (Variable): with shape [batch_size, ...]
indices (Variable): with shape [batch_size, 1]
updates (Variable): with shape [batch_size]
in_place (bool): if True, scatter result will be assign to ref. otherwise,
a new Tensor will be returned. Default is False.
overwrite (bool): if True, scatter will over write corrensponding elements.
Default is False.
Returns: TODO
Raises: NULL
Examples:
ref
[[1, 1, 1],
[1, 1, 1]]
indices
[[2], [1]]
updates
[2, 3]
return
[[1, 1, 2],
[1, 3, 1]]
"""
ref_dtype = ref.dtype
if ref_dtype not in PaddleVarType.floats:
ref_in = layers.cast(ref, dtype='float32')
else:
ref_in = ref
if updates.dtype != ref_in.dtype:
updates = layers.cast(updates, dtype=ref_in.dtype)
batch_size = layers.cast(layers.shape(ref_in)[0], dtype=indices.dtype)
zero = layers.fill_constant(shape=[1], dtype=indices.dtype, value=0)
one = layers.fill_constant(shape=[1], dtype=indices.dtype, value=1)
batch_indices = layers.unsqueeze(
layers.range(zero, batch_size, one, dtype=indices.dtype), [1])
coord = layers.concat([batch_indices, indices], axis=1)
if overwrite:
mask = layers.gather_nd(ref_in, coord)
mask = layers.elementwise_sub(layers.zeros_like(mask), mask)
ref_in = layers.scatter_nd_add(ref_in, coord, mask)
output = layers.scatter_nd_add(ref_in, coord, updates)
if ref_dtype not in PaddleVarType.floats:
output = layers.cast(output, dtype=ref_dtype)
if in_place:
layers.assign(output, ref)
return ref
else:
return output
def forward(self, src_ids, *args, **kwargs):
pooled, encoded = ErnieModel.forward(self, src_ids, *args, **kwargs)
encoded_2d = L.gather_nd(encoded, L.where(src_ids == mask_id))
encoded_2d = self.mlm(encoded_2d)
encoded_2d = self.mlm_ln(encoded_2d)
logits_2d = L.matmul(
encoded_2d, self.word_emb.weight, transpose_y=True) + self.mlm_bias
return logits_2d
def seq_gather(seq, idxs):
"""seq是[None, seq_len, s_size]的格式，
idxs是[None, 1]的格式，
在seq的第i个序列中选出第idxs[i]个向量，
最终输出[None, s_size]的向量。
"""
idxs = layers.cast(idxs, dtype="int32")
batch_idxs = layers.arange(0, seq.shape[0], dtype="int32")
batch_idxs = layers.unsqueeze(batch_idxs, 1)
idxs = layers.concat([batch_idxs, idxs], 1)
return layers.gather_nd(seq, idxs)
def _calc_logits(self, enc_out, tgt_idx=None, name=""):
"""Get the logits of generation task.
The network may share weight with token embeddings.
Args:
enc_out: the output embeddings of Transformer, shape is [batch_size, max_seq_len, hidden_size]
tgt_idx (optional): the indices of prediction tokens, shape is [num_predictions, 2].
Returns:
logits: the logits of prediction task, shape is [num_predictions, vocab_size].
"""
if tgt_idx is None:
seq_feat = layers.reshape(x=enc_out, shape=[-1, self.hidden_size])
elif len(tgt_idx.shape) == 2 and tgt_idx.shape[1] == 2:
seq_feat = layers.gather_nd(input=enc_out, index=tgt_idx)
else:
raise ValueError(f"Invalid indices shape {tgt_idx.shape} is used")
seq_trans_feat = layers.fc(
input=seq_feat,
size=self.emb_size,
act=self.hidden_act,
param_attr=fluid.ParamAttr(
name="mask_lm_trans_fc.w_0",
initializer=self.param_initializer),
bias_attr="mask_lm_trans_fc.b_0")
seq_trans_feat = pre_process_layer(
seq_trans_feat, self.post_cls_cmd, name="mask_lm_trans")
if self.weight_sharing:
logits = layers.matmul(
x=seq_trans_feat,
y=fluid.default_main_program().global_block().var(
name + self.token_emb_name),
transpose_y=True)
if self.cls_bias:
logits += layers.create_parameter(
shape=[self.vocab_size],
dtype=self.dtype,
attr=fluid.ParamAttr(name="mask_lm_out_fc.b_0"),
is_bias=True)
else:
seq_out_bias_attr = "mask_lm_out_fc.b_0" if self.cls_bias else False
logits = layers.fc(
input=seq_trans_feat,
size=self.vocab_size,
param_attr=fluid.ParamAttr(
name="mask_lm_out_fc.w_0",
initializer=self.param_initializer),
bias_attr=seq_out_bias_attr)
return logits
def _calc_bow_logits(self, enc_out, bow_idx):
"""Get the logits of BoW task.
The network may share weight with token embeddings.
Args:
enc_out: the output embeddings of Transformer, shape is [batch_size, max_seq_len, hidden_dim]
bow_idx: the indices of prediction tokens, shape is [num_predictions, 1] or [num_predictions, 2].
Returns:
logits: the logits of prediction task, shape is [num_predictions, vocab_size].
"""
if len(bow_idx.shape) == 2 and bow_idx.shape[1] == 1:
enc_out = layers.squeeze(enc_out, [1])
bow_feat = layers.gather(input=enc_out, index=bow_idx, overwrite=False)
elif len(bow_idx.shape) == 2 and bow_idx.shape[1] == 2:
bow_feat = layers.gather_nd(input=enc_out, index=bow_idx)
else:
raise ValueError(f"Invalid indices shape {bow_idx.shape} is used")
bow_trans_feat = layers.fc(
input=bow_feat,
size=self.emb_size,
act=self.hidden_act,
param_attr=fluid.ParamAttr(
name="bow_trans_fc.w_0",
initializer=self.param_initializer),
bias_attr="bow_trans_fc.b_0")
bow_trans_feat = pre_process_layer(
bow_trans_feat, self.post_cls_cmd, name="bow_trans")
if self.weight_sharing:
bow_logits = layers.matmul(
x=bow_trans_feat,
y=fluid.default_main_program().global_block().var(
self.token_emb_name),
transpose_y=True)
if self.cls_bias:
bow_logits += layers.create_parameter(
shape=[self.vocab_size],
dtype=self.dtype,
attr=fluid.ParamAttr(name="bow_out_fc.b_0"),
is_bias=True)
else:
bow_out_bias_attr = "bow_out_fc.b_0" if self.cls_bias else False
bow_logits = layers.fc(input=bow_trans_feat,
size=self.vocab_size,
param_attr=fluid.ParamAttr(
name="bow_out_fc.w_0",
initializer=self.param_initializer),
bias_attr=bow_out_bias_attr)
return bow_logits
def _birnn_encoder(self, inputs, input_len, name_lens, name_pos,
name_tok_len):
"""forward
Args:
inputs (Variable): shape=[batch_size, max_seq_len, hidden_size]
input_len (Variable): shape=[batch_size]
name_lens (Variable): shape=[batch_size]
name_pos (Variable): shape=[batch_size, max_name_len, max_tokens]
name_tok_len (Variable): shape=[batch_size, max_name_len]
Returns: TODO
Raises: NULL
"""
rnn_output, rnn_final_state = self._rnn_encoder.forward(
inputs, input_len)
max_name_len = name_pos.shape[1]
name_begin = name_pos[:, :, 0]
name_repr_mask = layers.sequence_mask(name_lens,
max_name_len,
dtype=name_tok_len.dtype)
len_delta = layers.elementwise_mul(name_tok_len - 1,
name_repr_mask,
axis=0)
name_end = name_begin + len_delta
if self._bidirectional:
name_fwd_repr_gathered = nn_utils.batch_gather_2d(
rnn_output, name_end)[:, :, :self._hidden_size]
name_bwd_repr_gathered = nn_utils.batch_gather_2d(
rnn_output, name_begin)[:, :, self._hidden_size:]
name_repr_gathered = layers.concat(
input=[name_fwd_repr_gathered, name_bwd_repr_gathered],
axis=-1)
new_hidden_size = self._hidden_size * 2
else:
name_repr_gathered = layers.gather_nd(rnn_output, name_end)
new_hidden_size = self._hidden_size
name_repr_tmp = layers.reshape(
name_repr_gathered, shape=[-1, max_name_len, new_hidden_size])
name_repr_mask = layers.cast(name_repr_mask, dtype=name_repr_tmp.dtype)
name_repr = layers.elementwise_mul(name_repr_tmp,
name_repr_mask,
axis=0)
return name_repr, None
def matrix_nms(bboxes,
scores,
score_threshold,
post_threshold,
nms_top_k,
keep_top_k,
use_gaussian=False,
gaussian_sigma=2.):
scores = L.transpose(scores, [1, 0])
inds = L.where(scores > score_threshold)
if len(inds) == 0:
return L.zeros((0, 6), 'float32') - 1.0
cate_scores = L.gather_nd(scores, inds)
cate_labels = inds[:, 1]
bboxes = L.gather(bboxes, inds[:, 0])
# sort and keep top nms_top_k
_, sort_inds = L.argsort(cate_scores, descending=True)
if nms_top_k > 0 and len(sort_inds) > nms_top_k:
sort_inds = sort_inds[:nms_top_k]
bboxes = L.gather(bboxes, sort_inds)
cate_scores = L.gather(cate_scores, sort_inds)
cate_labels = L.gather(cate_labels, sort_inds)
# Matrix NMS
kernel = 'gaussian' if use_gaussian else 'linear'
cate_scores = _matrix_nms(bboxes, cate_labels, cate_scores, kernel=kernel, sigma=gaussian_sigma)
# filter.
keep = L.where(cate_scores >= post_threshold)
if len(keep) == 0:
return L.zeros((0, 6), 'float32') - 1.0
bboxes = L.gather(bboxes, keep)
cate_scores = L.gather(cate_scores, keep)
cate_labels = L.gather(cate_labels, keep)
# sort and keep keep_top_k
_, sort_inds = L.argsort(cate_scores, descending=True)
if len(sort_inds) > keep_top_k:
sort_inds = sort_inds[:keep_top_k]
bboxes = L.gather(bboxes, sort_inds)
cate_scores = L.gather(cate_scores, sort_inds)
cate_labels = L.gather(cate_labels, sort_inds)
cate_scores = L.unsqueeze(cate_scores, 1)
cate_labels = L.unsqueeze(cate_labels, 1)
cate_labels = L.cast(cate_labels, 'float32')
pred = L.concat([cate_labels, cate_scores, bboxes], 1)
return pred
def batch_gather(var, indices):
"""Gather slices from var in each batch, according to corrensponding
index in indices. Currently, it only support 2d Tensor.
Args:
var (Variable): with shape [batch_size, ...]
indices (Variable): with shape [batch_size, 1] or [batch_size]
Returns: Variable with shape [batch_size]
Raises: NULL
Examples:
var
[[1, 2, 3],
[4, 5, 6]]
indices
[[2], [1]]
return
[[3], [5]]
"""
if len(indices.shape) >= 2 and indices.shape[-1] != 1:
raise ValueError(
'shape of indices error. it should be a 1-D layers, or a 2-D layers which '
'the 2nd dimension is 1. but got shape = %s' %
(str(indices.shape), ))
if len(indices.shape) == 1:
indices = layers.reshape(indices, shape=[-1, 1])
reshape_input = len(var.shape) == 1
if reshape_input:
var = PaddleFluidWrapper.reshape(var, shape=[-1, 1])
batch_size = layers.cast(layers.shape(indices)[0], dtype=indices.dtype)
zero = layers.fill_constant(shape=[1], dtype=indices.dtype, value=0)
one = layers.fill_constant(shape=[1], dtype=indices.dtype, value=1)
batch_indices = layers.unsqueeze(
layers.range(zero, batch_size, one, dtype=indices.dtype), [1])
coord = layers.concat([batch_indices, indices], axis=1)
coord.stop_gradient = True
output = layers.gather_nd(var, coord)
if reshape_input:
output = PaddleFluidWrapper.reshape(output, shape=[-1])
return output
def index_sample(x, index):
"""Select input value according to index
Arags：
input: input matrix
index: index matrix
Returns:
output
>>> input
[
[1, 2, 3],
[4, 5, 6]
]
>>> index
[
[1, 2],
[0, 1]
]
>>> index_sample(input, index)
[
[2, 3],
[4, 5]
]
"""
x_s = x.shape
dim = len(index.shape) - 1
assert x_s[:dim] == index.shape[:dim]
r_x = layers.reshape(x, shape=(-1, *x_s[dim:]))
index = layers.reshape(index, shape=(index.shape[0], index.shape[1], 1))
# generate arange index, shape like index
# arr_index = layers.arange(start=0, end=layers.cast(layers.shape(x)[0], ), dtype=index.dtype)
batch_size = layers.cast(layers.shape(index)[0], dtype=index.dtype)
zero = layers.fill_constant(shape=[1], dtype=index.dtype, value=0)
one = layers.fill_constant(shape=[1], dtype=index.dtype, value=1)
arr_index = layers.unsqueeze(
layers.range(zero, batch_size, one, dtype=index.dtype), [1, 2])
arr_index = layers.expand_as(arr_index, index)
# genrate new index
new_index = layers.concat([arr_index, index], -1)
new_index = layers.reshape(new_index, (-1, 2))
# get output
out = layers.gather_nd(r_x, new_index)
out = layers.reshape(out, (-1, x_s[-1] * 2))
return out
def batch_gather_2d(var, indices):
"""Gather slices from var in each batch, according to corrensponding
index in indices. Currently, it only support 2d Tensor.
Args:
var (Variable): with shape [batch_size, ...]
indices (Variable): with shape [batch_size, max_len]
Returns: Variable with shape [batch_size]
Raises: NULL
Examples:
var
[[1, 2, 3],
[4, 5, 6]]
indices
[[2, 0], [1, 2]]
return
[[3, 1], [5, 6]]
"""
if len(indices.shape) != 2:
raise ValueError('shape of indices error. it should be a 2-D layers. '
'but got shape = %s' % (str(indices.shape), ))
batch_size = layers.shape(indices)[0]
zero = layers.fill_constant(shape=[1], dtype=indices.dtype, value=0)
one = layers.fill_constant(shape=[1], dtype=indices.dtype, value=1)
end = layers.cast(batch_size, dtype=indices.dtype)
batch_indices_1d = layers.unsqueeze(
layers.range(zero, end, one, dtype=indices.dtype), [1])
seq_len = indices.shape[1]
batch_indices = layers.expand(batch_indices_1d, [1, seq_len])
coord_2d = layers.concat(
[layers.unsqueeze(batch_indices, [2]),
layers.unsqueeze(indices, [2])],
axis=2)
coord_2d.stop_gradient = True
coord_1d = layers.reshape(coord_2d, shape=[-1, 2])
output_1d = layers.gather_nd(var, coord_1d)
output_2d = layers.reshape(output_1d, [batch_size, seq_len, var.shape[-1]])
return output_2d
def forward(self, *args, **kwargs):
"""
Args
tgt_labels(`Variable` of shape [batch_size, seqlen] or [batch, seqlen, vocab_size]):
ground trouth target sequence id (hard label) or distribution (soft label)
tgt_pos(`Variable` of shape [n_targets, 2]):
index of tgt_labels in `src_ids`, can be obtained from `fluid.layers.where(src_ids==mask_id)`
encoder_only(Bool):
if set, will not return loss, logits_2d
Returns:
loss(`Variable` of shape []):
cross entropy loss mean over every target label. if `encode_only`, returns None.
logits(`Variable` of shape [n_targets, vocab_size]):
logits for every targets. if `encode_only`, returns None.
info(Dictionary): see `ErnieModel`
"""
tgt_labels = kwargs.pop('tgt_labels', None)
tgt_pos = kwargs.pop('tgt_pos', None)
encode_only = kwargs.pop('encode_only', False)
_, encoded, info = ErnieModel.forward(self, *args, **kwargs)
if encode_only:
return None, None, info
elif tgt_labels is None or tgt_pos is None:
encoded = self.mlm(encoded)
encoded = self.mlm_ln(encoded)
logits = L.matmul(encoded, self.word_emb.weight,
transpose_y=True) + self.mlm_bias
output_ids = L.argmax(logits, -1)
return output_ids, logits, info
else:
encoded_2d = L.gather_nd(encoded, tgt_pos)
encoded_2d = self.mlm(encoded_2d)
encoded_2d = self.mlm_ln(encoded_2d)
logits_2d = L.matmul(encoded_2d,
self.word_emb.weight,
transpose_y=True) + self.mlm_bias
if len(tgt_labels.shape) == 1:
tgt_labels = L.reshape(tgt_labels, [-1, 1])
loss = L.reduce_mean(
L.softmax_with_cross_entropy(
logits_2d,
tgt_labels,
soft_label=(tgt_labels.shape[-1] != 1)))
return loss, logits_2d, info
def index_sample(x, index):
"""Select input value according to index
Arags：
input: input matrix
index: index matrix
Returns:
output
>>> input
[
[1, 2, 3],
[4, 5, 6]
]
>>> index
[
[1, 2],
[0, 1]
]
>>> index_sample(input, index)
[
[2, 3],
[4, 5]
]
"""
x_s = x.shape
dim = len(index.shape) - 1
assert x_s[:dim] == index.shape[:dim]
r_x = layers.reshape(x, shape=(-1, *x_s[dim:]))
index = layers.reshape(index, shape=(len(r_x), -1, 1))
# generate arange index, shape like index
arr_index = layers.arange(start=0, end=len(index), dtype=index.dtype)
arr_index = layers.unsqueeze(arr_index, axes=[1, 2])
arr_index = layers.expand_as(arr_index, index)
# genrate new index
new_index = layers.concat((arr_index, index), -1)
new_index = layers.reshape(new_index, (-1, 2))
# get output
out = layers.gather_nd(r_x, new_index)
out = layers.reshape(out, (*x_s[:dim], -1))
return out
def build_and_run_program(place, batch_size, beam_size, stop_gradient=False):
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
np.random.seed(2)
x = layers.assign(
np.random.rand(batch_size, beam_size, 32).astype("float32"))
indices = fluid.data(shape=[None, beam_size], dtype="int64", name="indices")
step_idx = layers.fill_constant(
shape=[1], dtype="int64", value=0, force_cpu=True)
max_len = layers.fill_constant(
shape=[1], dtype="int64", value=10, force_cpu=True)
cond = layers.less_than(x=step_idx, y=max_len)
while_op = layers.While(cond)
scores = layers.array_write(x, step_idx)
with while_op.block():
bs = layers.cast(layers.shape(x)[0], "int64")
for _ in range(20):
bs = layers.cast(bs, 'int64')
bs.stop_gradient = stop_gradient
batch_pos = layers.expand(
layers.unsqueeze(
layers.range(
0, bs, 1, dtype=bs.dtype), [1]), [1, beam_size])
topk_coordinates = layers.stack([batch_pos, indices], axis=2)
topk_coordinates.stop_gradient = stop_gradient
score = layers.gather_nd(x, topk_coordinates)
layers.increment(x=step_idx, value=1.0, in_place=True)
layers.array_write(score, i=step_idx, array=scores)
length_cond = layers.less_than(x=step_idx, y=max_len)
layers.assign(length_cond, cond)
out = layers.tensor_array_to_tensor(scores, axis=0, use_stack=True)[0]
loss = layers.reduce_mean(out)
opt = fluid.optimizer.Adam(0.01)
opt.minimize(loss)
exe = fluid.Executor(place)
data = np.random.random_integers(
low=0, high=beam_size - 1, size=(batch_size, beam_size)).astype("int64")
loss_val, = exe.run(feed={"indices": data}, fetch_list=[loss])
return loss_val
def forward(self, indices, speaker_position_rate=None):
"""
Args:
indices (Variable): shape (B, T), dtype: int64, position
indices, where B means the batch size, T means the time steps.
speaker_position_rate (Variable | float, optional), position
rate. It can be a float point number or a Variable with
shape (1,), then this speaker_position_rate is used for every
example. It can also be a Variable with shape (B, ), which
contains a speaker position rate for each utterance.
Returns:
out (Variable): shape(B, T, C_pos), dtype float32, position embedding, where C_pos
means position embedding size.
"""
batch_size, time_steps = indices.shape
# convert speaker_position_rate to a Variable with shape(B, )
if isinstance(speaker_position_rate, float):
speaker_position_rate = dg.to_variable(
np.array([speaker_position_rate]).astype("float32"))
speaker_position_rate = F.expand(speaker_position_rate,
[batch_size])
elif isinstance(speaker_position_rate, fluid.framework.Variable) \
and list(speaker_position_rate.shape) == [1]:
speaker_position_rate = F.expand(speaker_position_rate,
[batch_size])
assert len(speaker_position_rate.shape) == 1 and \
list(speaker_position_rate.shape) == [batch_size]
weight = compute_position_embedding(self.weight,
speaker_position_rate) # (B, V, C)
# make indices for gather_nd
batch_id = F.expand(
F.unsqueeze(
F.range(
0, batch_size, 1, dtype="int64"), [1]), [1, time_steps])
# (B, T, 2)
gather_nd_id = F.stack([batch_id, indices], -1)
out = F.gather_nd(weight, gather_nd_id)
return out
def forward(self, *args, **kwargs):
"""
Args:
nsp_labels (optional, `Variable` of shape [batch_size]):
labels for `next sentence prediction` tasks
mlm_pos (optional, `Variable` of shape [n_mask, 2]):
index of mask_id in `src_ids`, can be obtained from `fluid.layers.where(src_ids==mask_id)`
labels (optional, `Variable` of shape [n_mask]):
labels for `mask language model` tasks, the original token indices in masked position in `src_ids`
Returns:
loss (`Variable` of shape []):
total_loss of `next sentence prediction` and `masked language model`
mlm_loss (`Variable` of shape []):
loss for `masked language model` task
nsp_loss (`Variable` of shape []):
loss for `next sentence prediction` task
"""
mlm_labels = kwargs.pop('labels')
mlm_pos = kwargs.pop('mlm_pos')
nsp_labels = kwargs.pop('nsp_labels')
pooled, encoded = super(ErnieModelForPretraining,
self).forward(*args, **kwargs)
if len(mlm_labels.shape) == 1:
mlm_labels = L.reshape(mlm_labels, [-1, 1])
if len(nsp_labels.shape) == 1:
nsp_labels = L.reshape(nsp_labels, [-1, 1])
nsp_loss = self.pooler_heads[0](pooled, nsp_labels)
encoded_2d = L.gather_nd(encoded, mlm_pos)
encoded_2d = self.mlm(encoded_2d)
encoded_2d = self.mlm_ln(encoded_2d)
logits_2d = L.matmul(
encoded_2d, self.word_emb.weight, transpose_y=True) + self.mlm_bias
mlm_loss = L.reduce_mean(
L.softmax_with_cross_entropy(logits_2d, mlm_labels))
total_loss = mlm_loss + nsp_loss
return total_loss, mlm_loss, nsp_loss
def net(self, inputs, is_infer=False):
if is_infer:
bs = self.evaluate_batch_size
else:
bs = self.train_batch_size
stdv = 1.0 / math.sqrt(self.hidden_size)
def embedding_layer(input,
table_name,
emb_dim,
initializer_instance=None):
emb = fluid.embedding(
input=input,
size=[self.dict_size, emb_dim],
param_attr=fluid.ParamAttr(
name=table_name, initializer=initializer_instance))
return emb
sparse_initializer = fluid.initializer.Uniform(low=-stdv, high=stdv)
items_emb = embedding_layer(inputs[0], "emb", self.hidden_size,
sparse_initializer)
pre_state = items_emb
for i in range(self.step):
pre_state = layers.reshape(
x=pre_state, shape=[bs, -1, self.hidden_size])
state_in = layers.fc(
input=pre_state,
name="state_in",
size=self.hidden_size,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, uniq_max, h]
state_out = layers.fc(
input=pre_state,
name="state_out",
size=self.hidden_size,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, uniq_max, h]
state_adj_in = layers.matmul(inputs[3],
state_in) # [batch_size, uniq_max, h]
state_adj_out = layers.matmul(
inputs[4], state_out) # [batch_size, uniq_max, h]
gru_input = layers.concat([state_adj_in, state_adj_out], axis=2)
gru_input = layers.reshape(
x=gru_input, shape=[-1, self.hidden_size * 2])
gru_fc = layers.fc(input=gru_input,
name="gru_fc",
size=3 * self.hidden_size,
bias_attr=False)
pre_state, _, _ = fluid.layers.gru_unit(
input=gru_fc,
hidden=layers.reshape(
x=pre_state, shape=[-1, self.hidden_size]),
size=3 * self.hidden_size)
final_state = layers.reshape(
pre_state, shape=[bs, -1, self.hidden_size])
seq = layers.gather_nd(final_state, inputs[1])
last = layers.gather_nd(final_state, inputs[2])
seq_fc = layers.fc(
input=seq,
name="seq_fc",
size=self.hidden_size,
bias_attr=False,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, seq_max, h]
last_fc = layers.fc(input=last,
name="last_fc",
size=self.hidden_size,
bias_attr=False,
act=None,
num_flatten_dims=1,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [bathc_size, h]
seq_fc_t = layers.transpose(
seq_fc, perm=[1, 0, 2]) # [seq_max, batch_size, h]
add = layers.elementwise_add(seq_fc_t,
last_fc) # [seq_max, batch_size, h]
b = layers.create_parameter(
shape=[self.hidden_size],
dtype='float32',
default_initializer=fluid.initializer.Constant(value=0.0)) # [h]
add = layers.elementwise_add(add, b) # [seq_max, batch_size, h]
add_sigmoid = layers.sigmoid(add) # [seq_max, batch_size, h]
add_sigmoid = layers.transpose(
add_sigmoid, perm=[1, 0, 2]) # [batch_size, seq_max, h]
weight = layers.fc(
input=add_sigmoid,
name="weight_fc",
size=1,
act=None,
num_flatten_dims=2,
bias_attr=False,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, seq_max, 1]
weight *= inputs[5]
weight_mask = layers.elementwise_mul(
seq, weight, axis=0) # [batch_size, seq_max, h]
global_attention = layers.reduce_sum(
weight_mask, dim=1) # [batch_size, h]
final_attention = layers.concat(
[global_attention, last], axis=1) # [batch_size, 2*h]
final_attention_fc = layers.fc(
input=final_attention,
name="final_attention_fc",
size=self.hidden_size,
bias_attr=False,
act=None,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, h]
# all_vocab = layers.create_global_var(
# shape=[items_num - 1],
# value=0,
# dtype="int64",
# persistable=True,
# name="all_vocab")
all_vocab = np.arange(1, self.dict_size).reshape((-1)).astype('int32')
all_vocab = fluid.layers.cast(
x=fluid.layers.assign(all_vocab), dtype='int64')
all_emb = fluid.embedding(
input=all_vocab,
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
size=[self.dict_size, self.hidden_size]) # [all_vocab, h]
logits = layers.matmul(
x=final_attention_fc, y=all_emb,
transpose_y=True) # [batch_size, all_vocab]
softmax = layers.softmax_with_cross_entropy(
logits=logits, label=inputs[6]) # [batch_size, 1]
self.loss = layers.reduce_mean(softmax) # [1]
self.acc = layers.accuracy(input=logits, label=inputs[6], k=20)
self._cost = self.loss
if is_infer:
self._infer_results['acc'] = self.acc
self._infer_results['loss'] = self.loss
return
self._metrics["LOSS"] = self.loss
self._metrics["train_acc"] = self.acc
def network(items_num, hidden_size, step, bs):
stdv = 1.0 / math.sqrt(hidden_size)
items = fluid.data(name="items", shape=[bs, -1],
dtype="int64") #[batch_size, uniq_max]
seq_index = fluid.data(name="seq_index", shape=[bs, -1, 2],
dtype="int32") #[batch_size, seq_max, 2]
last_index = fluid.data(name="last_index", shape=[bs, 2],
dtype="int32") #[batch_size, 2]
adj_in = fluid.data(name="adj_in", shape=[bs, -1, -1],
dtype="float32") #[batch_size, seq_max, seq_max]
adj_out = fluid.data(name="adj_out", shape=[bs, -1, -1],
dtype="float32") #[batch_size, seq_max, seq_max]
mask = fluid.data(name="mask", shape=[bs, -1, 1],
dtype="float32") #[batch_size, seq_max, 1]
label = fluid.data(name="label", shape=[bs, 1],
dtype="int64") #[batch_size, 1]
datas = [items, seq_index, last_index, adj_in, adj_out, mask, label]
py_reader = fluid.io.DataLoader.from_generator(capacity=256,
feed_list=datas,
iterable=False)
feed_datas = datas
items_emb = fluid.embedding(
input=items,
param_attr=fluid.ParamAttr(name="emb",
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
size=[items_num, hidden_size]) #[batch_size, uniq_max, h]
pre_state = items_emb
for i in range(step):
pre_state = layers.reshape(x=pre_state, shape=[bs, -1, hidden_size])
state_in = layers.fc(
input=pre_state,
name="state_in",
size=hidden_size,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(low=-stdv, high=stdv)),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) #[batch_size, uniq_max, h]
state_out = layers.fc(
input=pre_state,
name="state_out",
size=hidden_size,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(low=-stdv, high=stdv)),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) #[batch_size, uniq_max, h]
state_adj_in = layers.matmul(adj_in,
state_in) #[batch_size, uniq_max, h]
state_adj_out = layers.matmul(adj_out,
state_out) #[batch_size, uniq_max, h]
gru_input = layers.concat([state_adj_in, state_adj_out], axis=2)
gru_input = layers.reshape(x=gru_input, shape=[-1, hidden_size * 2])
gru_fc = layers.fc(input=gru_input,
name="gru_fc",
size=3 * hidden_size,
bias_attr=False)
pre_state, _, _ = fluid.layers.gru_unit(input=gru_fc,
hidden=layers.reshape(
x=pre_state,
shape=[-1, hidden_size]),
size=3 * hidden_size)
final_state = layers.reshape(pre_state, shape=[bs, -1, hidden_size])
seq = layers.gather_nd(final_state, seq_index)
last = layers.gather_nd(final_state, last_index)
seq_fc = layers.fc(
input=seq,
name="seq_fc",
size=hidden_size,
bias_attr=False,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) #[batch_size, seq_max, h]
last_fc = layers.fc(
input=last,
name="last_fc",
size=hidden_size,
bias_attr=False,
act=None,
num_flatten_dims=1,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) #[bathc_size, h]
seq_fc_t = layers.transpose(seq_fc, perm=[1, 0,
2]) #[seq_max, batch_size, h]
add = layers.elementwise_add(seq_fc_t, last_fc) #[seq_max, batch_size, h]
b = layers.create_parameter(
shape=[hidden_size],
dtype='float32',
default_initializer=fluid.initializer.Constant(value=0.0)) #[h]
add = layers.elementwise_add(add, b) #[seq_max, batch_size, h]
add_sigmoid = layers.sigmoid(add) #[seq_max, batch_size, h]
add_sigmoid = layers.transpose(add_sigmoid,
perm=[1, 0, 2]) #[batch_size, seq_max, h]
weight = layers.fc(
input=add_sigmoid,
name="weight_fc",
size=1,
act=None,
num_flatten_dims=2,
bias_attr=False,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) #[batch_size, seq_max, 1]
weight *= mask
weight_mask = layers.elementwise_mul(seq, weight,
axis=0) #[batch_size, seq_max, h]
global_attention = layers.reduce_sum(weight_mask, dim=1) #[batch_size, h]
final_attention = layers.concat([global_attention, last],
axis=1) #[batch_size, 2*h]
final_attention_fc = layers.fc(
input=final_attention,
name="final_attention_fc",
size=hidden_size,
bias_attr=False,
act=None,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) #[batch_size, h]
all_vocab = layers.create_global_var(shape=[items_num - 1],
value=0,
dtype="int64",
persistable=True,
name="all_vocab")
all_emb = fluid.embedding(input=all_vocab,
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
size=[items_num, hidden_size]) #[all_vocab, h]
logits = layers.matmul(x=final_attention_fc, y=all_emb,
transpose_y=True) #[batch_size, all_vocab]
softmax = layers.softmax_with_cross_entropy(logits=logits,
label=label) #[batch_size, 1]
loss = layers.reduce_mean(softmax) # [1]
acc = layers.accuracy(input=logits, label=label, k=50)
return loss, acc, py_reader, feed_datas, logits
def to_2d(t_3d):
t_2d = L.gather_nd(t_3d, pad_idx)
return t_2d
def gather(x, indices, batch_pos):
topk_coordinates = fluid.layers.stack([batch_pos, indices], axis=2)
return layers.gather_nd(x, topk_coordinates)
def __call__(self, kernel_preds, cls_preds, mask_protos,
batch_gt_objs_tensors, batch_gt_clss_tensors,
batch_gt_masks_tensors, batch_gt_pos_idx_tensors):
'''
:param kernel_preds: kernel_preds里每个元素形状是[N, 256, seg_num_grid, seg_num_grid], 每个格子的预测卷积核。 从 小感受野 到 大感受野。
:param cls_preds: cls_preds里每个元素形状是 [N, 80, seg_num_grid, seg_num_grid], 每个格子的预测概率，未进行sigmoid()激活。 从 小感受野 到 大感受野。
:param mask_protos: [bs, 256, s4, s4] 掩码原型
:param batch_gt_objs_tensors: 里每个元素形状是[N, seg_num_grid, seg_num_grid, 1], 每个格子的objness。 从 小感受野 到 大感受野。
:param batch_gt_clss_tensors: 里每个元素形状是[N, seg_num_grid, seg_num_grid, 80], 每个格子真实类别onehot。 从 小感受野 到 大感受野。
:param batch_gt_masks_tensors: 里每个元素形状是[N, -1, s4, s4], 真实掩码。 从 小感受野 到 大感受野。
:param batch_gt_pos_idx_tensors: 里每个元素形状是[N, -1, 3], 正样本的下标。 从 小感受野 到 大感受野。
:return:
'''
batch_size = self.batch_size
num_layers = len(kernel_preds)
# ================= 计算损失 =================
num_ins = 0. # 记录这一批图片的正样本个数
loss_clss, loss_masks = [], []
for bid in range(batch_size):
for lid in range(num_layers):
# ================ 掩码损失 ======================
mask_proto = mask_protos[bid] # [256, s4, s4] 这张图片产生的掩码原型。
kernel_pred = kernel_preds[lid][
bid] # [256, seg_num_grid, seg_num_grid] 格子预测的卷积核（yolact中的“掩码系数”）
kernel_pred = L.transpose(
kernel_pred, perm=[1, 2, 0]
) # [seg_num_grid, seg_num_grid, 256] 格子预测的卷积核（yolact中的“掩码系数”）
gt_objs = batch_gt_objs_tensors[lid][
bid] # [seg_num_grid, seg_num_grid, 1]
gt_masks = batch_gt_masks_tensors[lid][bid] # [-1, s4, s4]
pmidx = batch_gt_pos_idx_tensors[lid][bid] # [-1, 3]
gt_objs.stop_gradient = True
gt_masks.stop_gradient = True
pmidx.stop_gradient = True
idx_sum = L.reduce_sum(pmidx, dim=1)
keep = L.where(idx_sum > -1)
keep = L.reshape(keep, (-1, ))
keep.stop_gradient = True
pmidx = L.gather(pmidx, keep) # [M, 3]
yx_idx = pmidx[:, :2] # [M, 2]
m_idx = pmidx[:, 2] # [M, ]
yx_idx.stop_gradient = True
m_idx.stop_gradient = True
# 抽出来
gt_obj = L.gather_nd(gt_objs,
yx_idx) # [M, 1] 是否是真正的正样本。
pos_krn = L.gather_nd(kernel_pred,
yx_idx) # [M, 256] 正样本的卷积核（掩码系数）。
gt_mask = L.gather(gt_masks, m_idx) # [M, s4, s4] 真实掩码。
# 正样本数量
num_ins += L.reduce_sum(gt_obj)
# 生成预测掩码
mask_proto = L.transpose(mask_proto, perm=[1, 2,
0]) # [s4, s4, 256]
masks = L.matmul(mask_proto, pos_krn,
transpose_y=True) # [s4, s4, M]
masks = L.sigmoid(masks) # [s4, s4, M]
masks = L.transpose(masks, perm=[2, 0, 1]) # [M, s4, s4]
loss_mask = self.dice_loss(masks, gt_mask, gt_obj)
loss_masks.append(loss_mask)
# ================ 分类损失。sigmoid_focal_loss() ======================
gamma = self.loss_gamma
alpha = self.loss_alpha
pred_conf = cls_preds[lid][
bid] # [80, seg_num_grid, seg_num_grid] 未进行sigmoid()激活。
pred_conf = L.transpose(pred_conf, perm=[
1, 2, 0
]) # [seg_num_grid, seg_num_grid, 80] 未进行sigmoid()激活。
pred_conf = L.sigmoid(
pred_conf
) # [seg_num_grid, seg_num_grid, 80] 已进行sigmoid()激活。
gt_clss = batch_gt_clss_tensors[lid][
bid] # [seg_num_grid, seg_num_grid, 80] 真实类别onehot
gt_clss.stop_gradient = True
pos_loss = gt_clss * (0 - L.log(pred_conf + 1e-9)) * L.pow(
1 - pred_conf, gamma) * alpha
neg_loss = (
1.0 - gt_clss) * (0 - L.log(1 - pred_conf + 1e-9)) * L.pow(
pred_conf, gamma) * (1 - alpha)
focal_loss = pos_loss + neg_loss
focal_loss = L.reduce_sum(focal_loss, dim=[0, 1])
loss_clss.append(focal_loss)
loss_masks = L.concat(loss_masks, axis=0)
loss_masks = L.reduce_sum(loss_masks) * self.ins_loss_weight
loss_masks = loss_masks / L.elementwise_max(
L.ones((1, ), dtype='float32'), num_ins)
loss_clss = L.concat(loss_clss, axis=0)
loss_clss = L.reduce_sum(loss_clss) * self.clss_loss_weight
loss_clss = loss_clss / L.elementwise_max(
L.ones((1, ), dtype='float32'), num_ins)
loss_all = {"loss_masks": loss_masks, "loss_clss": loss_clss}
return loss_all
def __compute_graph_bias(q, graph_attn_mask, pos_win):
"""
:param q: (batch_size, n_heads, query_len, dim_per_head)
:param graph_attn_mask: (batch_size, n_head, key_s_len, key_s_len)
:param pos_win:
:return:
"""
# (batch_size, n_heads, query_len, dim_per_head)
pos_v = layers.fc(input=q,
size=d_value,
num_flatten_dims=3,
param_attr=fluid.ParamAttr(
name=name + '_pos_fc.w_0',
initializer=param_initializer),
bias_attr=name + '_pos_fc.b_0')
# (batch_size, n_heads, query_len, 1)
pos_s = layers.fc(input=layers.tanh(pos_v),
size=1,
num_flatten_dims=3,
param_attr=fluid.ParamAttr(
name=name + '_pos_score_fc.w_0',
initializer=param_initializer),
bias_attr=False)
# (batch_size, n_heads, query_len, 1)
pos = layers.sigmoid(pos_s) * (key_s_len - 1)
# (batch_size, n_heads, query_len, 1)
pos_up = layers.cast(layers.ceil(pos), dtype='int64')
# print("pos_up.shape = %s" % str(pos_up.shape))
pos_down = layers.cast(layers.floor(pos), dtype='int64')
# print("pos_down.shape = %s" % str(pos_down.shape))
batch_ind = layers.range(0, layers.cast(batch_size, dtype='int64'), 1,
'int64')
# print("batch_ind.shape = %s" % str(batch_ind.shape))
batch_ind = layers.unsqueeze(batch_ind,
axes=[1, 2, 3]) # (batch_size, 1, 1, 1)
batch_ind = layers.expand(
batch_ind, expand_times=[1, n_head, query_len,
1]) # (batch_size, n_heads, query_len, 1)
# print("batch_ind.shape = %s" % str(batch_ind.shape))
head_ind = layers.range(0, n_head, 1, 'int64')
# print("head_ind.shape = %s" % str(head_ind.shape))
head_ind = layers.unsqueeze(head_ind, axes=[0, 2,
3]) # (1, n_heads, 1, 1)
head_ind = layers.expand(head_ind,
expand_times=[batch_size, 1, query_len, 1])
# print("head_ind.shape = %s" % str(head_ind.shape))
query_ind = layers.range(0, layers.cast(query_len, dtype='int64'), 1,
'int64')
# print("query_ind.shape = %s" % str(query_ind.shape))
query_ind = layers.unsqueeze(query_ind,
axes=[0, 1, 3]) # (1, 1, query_len, 1)
query_ind = layers.expand(query_ind,
expand_times=[batch_size, n_head, 1, 1])
# print("query_ind.shape = %s" % str(query_ind.shape))
# (batch_size, n_heads, query_len, 4)
pos_up_ind = layers.concat(
input=[batch_ind, head_ind, query_ind, pos_up], axis=-1)
# print("pos_up_ind.shape = %s" % str(pos_up_ind.shape))
pos_up_ind.stop_gradient = True
pos_down_ind = layers.concat(
input=[batch_ind, head_ind, query_ind, pos_down], axis=-1)
# print("pos_down_ind.shape = %s" % str(pos_down_ind.shape))
pos_down_ind.stop_gradient = True
# (batch_size, n_heads, query_len, key_s_len, key_s_len)
graph_attn_mask = layers.unsqueeze(graph_attn_mask, axes=[2])
# print("graph_attn_mask.shape = %s" % str(graph_attn_mask.shape))
graph_attn_mask = layers.expand(graph_attn_mask,
expand_times=[1, 1, query_len, 1, 1])
# print("graph_attn_mask.shape = %s" % str(graph_attn_mask.shape))
# (batch_size, n_heads, query_len, key_s_len)
graph_attn_mask_up = layers.gather_nd(input=graph_attn_mask,
index=pos_up_ind)
graph_attn_mask_down = layers.gather_nd(input=graph_attn_mask,
index=pos_down_ind)
# print("graph_attn_mask_up.shape = %s" % str(graph_attn_mask_up.shape))
# print("graph_attn_mask_down.shape = %s" % str(graph_attn_mask_down.shape))
# print("pos_up.shape = %s" % str(pos_up.shape))
# print("pos_down.shape = %s" % str(pos_down.shape))
# linearly combine up and down (batch_size, n_heads, query_len, key_s_len)
graph_attn_mask_select = graph_attn_mask_up * (1.0 - (layers.cast(pos_up, dtype='float32') - pos)) + \
graph_attn_mask_down * (1.0 - (pos - layers.cast(pos_down, dtype='float32')))
# print("graph_attn_mask_select.shape = %s" % str(graph_attn_mask_select.shape))
# re-weight the attention score with gaussian weights
gaussian_w = (
-0.5 * graph_attn_mask_select * graph_attn_mask_select) / (
(0.5 * pos_win)**2) # [batch, n_heads, query_len, key_s_len]
# print("gaussian_w.shape = %s" % str(gaussian_w.shape))
return gaussian_w