def link_loss(self, input, target, neighbors=8):
batch_size = input.shape[0]
self.pos_link_weight = (target
== 1).astype('float32') * nd.broadcast_axes(
nd.expand_dims(self.pixel_weight, axis=1),
axis=1,
size=neighbors) # (2, 8, 256, 256)
self.neg_link_weight = (target
== 0).astype('float32') * nd.broadcast_axes(
nd.expand_dims(self.pixel_weight, axis=1),
axis=1,
size=neighbors)
sum_pos_link_weight = nd.sum(nd.reshape(self.pos_link_weight,
(batch_size, -1)),
axis=1)
sum_neg_link_weight = nd.sum(nd.reshape(self.neg_link_weight,
(batch_size, -1)),
axis=1)
self.link_cross_entropy = []
for i in range(neighbors):
assert input.shape[1] == 16
this_input = input[:, [2 * i, 2 * i + 1]]
this_target = target[:, i]
self.link_cross_entropy.append(
self.link_cross_entropy_layer(this_input, this_target)[1])
self.link_cross_entropy = nd.concat(*self.link_cross_entropy,
dim=1) # (2, 8, 256, 256)
loss_link_pos = []
loss_link_neg = []
ctx = try_gpu()
for i in range(batch_size):
if sum_pos_link_weight[i].asscalar() == 0:
loss_link_pos_temp = nd.zeros(self.pos_link_weight[0].shape,
ctx, 'float32')
loss_link_pos.append(nd.expand_dims(loss_link_pos_temp,
axis=0))
else:
loss_link_pos_temp = self.pos_link_weight[
i] * self.link_cross_entropy[i] / sum_pos_link_weight[i]
loss_link_pos.append(nd.expand_dims(loss_link_pos_temp,
axis=0))
if sum_neg_link_weight[i].asscalar() == 0:
loss_link_neg_temp = nd.zeros(self.neg_link_weight[0].shape,
ctx, 'float32')
loss_link_neg.append(nd.expand_dims(loss_link_neg_temp,
axis=0))
else:
loss_link_neg_temp = self.neg_link_weight[
i] * self.link_cross_entropy[i] / sum_neg_link_weight[
i] # (8, 256, 256)
loss_link_neg.append(nd.expand_dims(loss_link_neg_temp,
axis=0))
loss_link_pos = nd.concat(*loss_link_pos, dim=0)
loss_link_neg = nd.concat(*loss_link_neg, dim=0) # (2, 8, 256, 256)
loss_link_pos = nd.sum(nd.reshape(loss_link_pos, (batch_size, -1)),
axis=1)
loss_link_neg = nd.sum(nd.reshape(loss_link_neg, (batch_size, -1)),
axis=1)
return nd.mean(loss_link_pos), nd.mean(loss_link_neg)
def getSelfMask(q_seq):
batch_size, seq_len = q_seq.shape
mask_matrix = np.ones(shape=(seq_len, seq_len), dtype=np.float)
mask = np.tril(mask_matrix, k=0)
mask = nd.expand_dims(nd.array(mask, ctx=ghp.ctx), axis=0)
mask = nd.broadcast_axes(mask, axis=0, size=batch_size)
return mask
def _get_self_tril_mask(self, dec_idx):
batch_size, seq_len = dec_idx.shape
mask_matrix = np.ones(shape=(seq_len, seq_len))
mask = np.tril(mask_matrix, k=0)
mask = nd.expand_dims(nd.array(mask, ctx=self._ctx), axis=0)
mask = nd.broadcast_axes(mask, axis=0, size=batch_size)
return mask
def eval(en_bert, mt_model, en_vocab, ch_vocab, dev_dataiter, logger, ctx):
references = []
hypothesis = []
score = 0
chencherry = SmoothingFunction()
for trans, _, label, trans_valid_len, label_valid_len in tqdm(
dev_dataiter):
trans = trans.as_in_context(ctx)
trans_valid_len = trans_valid_len.as_in_context(ctx)
batch_size = trans.shape[0]
trans_token_type = nd.zeros_like(trans)
en_bert_outputs = en_bert(trans, trans_token_type, trans_valid_len)
ch_sentences = [BOS]
aim = ch_vocab[ch_sentences]
aim = nd.array([aim], ctx=ctx)
aim = nd.broadcast_axes(aim, axis=0, size=batch_size)
for n in range(0, args.max_ch_len):
mt_outputs = mt_model(en_bert_outputs, trans, aim)
predicts = nd.argmax(nd.softmax(mt_outputs, axis=-1), axis=-1)
final_predict = predicts[:, -1:]
aim = nd.concat(aim, final_predict, dim=1)
label = label.asnumpy().tolist()
predict_valid_len = nd.sum(nd.not_equal(
predicts, ch_vocab(ch_vocab.padding_token)),
axis=-1).asnumpy().tolist()
predicts = aim[:, 1:].asnumpy().tolist()
label_valid_len = label_valid_len.asnumpy().tolist()
for refer, hypoth, l_v_len, p_v_len in zip(label, predicts,
label_valid_len,
predict_valid_len):
l_v_len = int(l_v_len)
p_v_len = int(p_v_len)
refer = refer[:l_v_len]
refer_str = [ch_vocab.idx_to_token[int(idx)] for idx in refer]
hypoth_str = [ch_vocab.idx_to_token[int(idx)] for idx in hypoth]
hypoth_str_valid = []
for token in hypoth_str:
if token == EOS:
hypoth_str_valid.append(token)
break
hypoth_str_valid.append(token)
references.append(refer_str)
hypothesis.append(hypoth_str_valid)
for refer, hypoth in zip(references, hypothesis):
score += sentence_bleu([refer],
hypoth,
smoothing_function=chencherry.method1)
logger.info("dev sample:")
logger.info("refer :{}".format(" ".join(references[0]).replace(
EOS, "[EOS]").replace(ch_vocab.padding_token, "")))
logger.info("hypoth:{}".format(" ".join(hypothesis[0]).replace(
EOS, "[EOS]")))
return score / len(references)
def forward(self, src_idx, tgt_idx):
# compute encoder mask
key_mask = self._get_key_mask(src_idx,
src_idx,
pad_idx=self.src_pad_idx)
src_non_pad_mask = self._get_non_pad_mask(src_idx,
pad_idx=self.src_pad_idx)
# compute decoder mask
self_tril_mask = self._get_self_tril_mask(tgt_idx)
self_key_mask = self._get_key_mask(tgt_idx,
tgt_idx,
pad_idx=self.tgt_pad_idx)
self_att_mask = nd.greater((self_key_mask + self_tril_mask), 1)
context_att_mask = self._get_key_mask(src_idx,
tgt_idx,
pad_idx=self.src_pad_idx)
tgt_non_pad_mask = self._get_non_pad_mask(tgt_idx,
pad_idx=self.tgt_pad_idx)
# Encoder
position = nd.array(self._position_encoding_init(
src_idx.shape[1], self._model_dim),
ctx=src_idx.context)
position = nd.expand_dims(position, axis=0)
position = nd.broadcast_axes(position, axis=0, size=tgt_idx.shape[0])
position = position * src_non_pad_mask
src_emb = self.embedding(src_idx)
enc_output = self.encoder(src_emb, position, key_mask,
src_non_pad_mask)
# Decoder
position = nd.array(self._position_encoding_init(
tgt_idx.shape[1], self._model_dim),
ctx=src_idx.context)
position = nd.expand_dims(position, axis=0)
position = nd.broadcast_axes(position, axis=0, size=tgt_idx.shape[0])
position = position * tgt_non_pad_mask
tgt_emb = self.embedding(tgt_idx)
outputs = self.decoder(enc_output, tgt_emb, position, self_att_mask,
context_att_mask, tgt_non_pad_mask)
outputs = self.linear(outputs)
return outputs
def getMask(q_seq, k_seq):
# q_seq shape : (batch_size, q_seq_len)
# k_seq shape : (batch_size, k_seq_len)
q_len = q_seq.shape[1]
pad_mask = nd.not_equal(k_seq, 0)
pad_mask = nd.expand_dims(pad_mask, axis=1)
pad_mask = nd.broadcast_axes(pad_mask, axis=1, size=q_len)
return pad_mask
def _get_key_mask(self, enc_idx, dec_idx, pad_idx=None):
seq_len = dec_idx.shape[1]
if pad_idx:
pad_mask = nd.not_equal(enc_idx, pad_idx)
else:
pad_mask = nd.not_equal(enc_idx, 0)
pad_mask = nd.expand_dims(pad_mask, axis=1)
pad_mask = nd.broadcast_axes(pad_mask, axis=1, size=seq_len)
return pad_mask
def batch_process(seq, ctx):
seq = np.array(seq)
aligned_seq = np.zeros(
(max_sequence_length - 2 * region_radius, batch_size, region_size))
for i in range(region_radius, max_sequence_length - region_radius):
aligned_seq[i - region_radius] = seq[:, i - region_radius:i -
region_radius + region_size]
aligned_seq = nd.array(aligned_seq, ctx)
batch_sequence = nd.array(seq, ctx)
trimed_seq = batch_sequence[:, region_radius:max_sequence_length -
region_radius]
mask = nd.broadcast_axes(nd.greater(trimed_seq, 0).reshape(
(batch_size, -1, 1)),
axis=2,
size=128)
return aligned_seq, nd.array(trimed_seq, ctx), mask
def batch_process(seq, isContextWord, ctx):
seq = np.array(seq)
aligned_seq = np.zeros(
(max_sequence_length - 2 * region_radius, batch_size, region_size))
for i in range(region_radius, max_sequence_length - region_radius):
aligned_seq[i - region_radius] = seq[:, i - region_radius:i -
region_radius + region_size]
if isContextWord:
unit_id_bias = np.array([i * vocab_size for i in range(region_size)])
aligned_seq = aligned_seq.transpose((1, 0, 2)) + unit_id_bias
aligned_seq = nd.array(aligned_seq, ctx)
batch_sequence = nd.array(seq, ctx)
trimed_seq = batch_sequence[:, region_radius:max_sequence_length -
region_radius]
mask = nd.broadcast_axes(nd.greater(trimed_seq, 0).reshape(
(batch_size, -1, 1)),
axis=2,
size=128)
return aligned_seq, nd.array(trimed_seq, ctx), mask
def forward(self, en_bert_output, en_idx, ch_idx):
self_tril_mask = self._get_self_tril_mask(ch_idx)
self_key_mask = self._get_key_mask(ch_idx,
ch_idx,
pad_idx=self.ch_pad_idx)
self_att_mask = nd.greater((self_key_mask + self_tril_mask), 1)
context_att_mask = self._get_key_mask(en_idx,
ch_idx,
pad_idx=self.en_pad_idx)
non_pad_mask = self._get_non_pad_mask(ch_idx, pad_idx=self.ch_pad_idx)
position = nd.array(self._position_encoding_init(
ch_idx.shape[1], self._model_dim),
ctx=self._ctx)
position = nd.expand_dims(position, axis=0)
position = nd.broadcast_axes(position, axis=0, size=ch_idx.shape[0])
position = position * non_pad_mask
ch_emb = self.ch_embedding(ch_idx)
outputs = self.decoder(en_bert_output, ch_emb, position, self_att_mask,
context_att_mask, non_pad_mask)
outputs = self.linear(outputs)
return outputs
def translate(args):
gpu_idx = args.gpu
if not gpu_idx:
ctx = mx.cpu()
else:
ctx = mx.gpu(gpu_idx - 1)
en_bert, en_vocab = gluonnlp.model.get_model(
args.bert_model,
dataset_name=args.en_bert_dataset,
pretrained=True,
ctx=ctx,
use_pooler=False,
use_decoder=False,
use_classifier=False)
_, ch_vocab = gluonnlp.model.get_model(args.bert_model,
dataset_name=args.ch_bert_dataset,
pretrained=True,
ctx=ctx,
use_pooler=False,
use_decoder=False,
use_classifier=False)
mt_model = MTModel_Hybird(en_vocab=en_vocab,
ch_vocab=ch_vocab,
embedding_dim=args.mt_emb_dim,
model_dim=args.mt_model_dim,
head_num=args.mt_head_num,
layer_num=args.mt_layer_num,
ffn_dim=args.mt_ffn_dim,
dropout=args.mt_dropout,
att_dropout=args.mt_att_dropout,
ffn_dropout=args.mt_ffn_dropout,
ctx=ctx)
en_bert.load_parameters(args.en_bert_model_params_path, ctx=ctx)
mt_model.load_parameters(args.mt_model_params_path, ctx=ctx)
en_bert_tokenzier = BERTTokenizer(en_vocab)
ch_bert_tokenzier = BERTTokenizer(ch_vocab)
while True:
trans = input("input:")
trans = en_bert_tokenzier(trans)
trans = [en_vocab.cls_token] + \
trans + [en_vocab.sep_token]
trans_valid_len = len(trans)
if args.max_en_len and len(trans) > args.max_en_len:
trans = trans[0:args.max_en_len]
aim = [BOS]
trans = en_vocab[trans]
aim = ch_vocab[aim]
aim = nd.array([aim], ctx=ctx)
trans = nd.array([trans], ctx=ctx)
trans_valid_len = nd.array([trans_valid_len], ctx=ctx)
trans_token_types = nd.zeros_like(trans)
batch_size = 1
beam_size = 6
en_bert_outputs = en_bert(trans, trans_token_types, trans_valid_len)
mt_outputs = mt_model(en_bert_outputs, trans, aim)
en_bert_outputs = nd.broadcast_axes(en_bert_outputs,
axis=0,
size=beam_size)
trans = nd.broadcast_axes(trans, axis=0, size=beam_size)
targets = None
for n in range(0, args.max_ch_len):
aim, targets = beam_search(mt_outputs[:, n, :],
targets=targets,
max_seq_len=args.max_ch_len,
ctx=ctx,
beam_width=beam_size)
mt_outputs = mt_model(en_bert_outputs, trans, aim)
predict = aim.asnumpy().tolist()
predict_strs = []
for pred in predict:
predict_token = [ch_vocab.idx_to_token[int(idx)] for idx in pred]
predict_str = ""
sub_token = []
for token in predict_token:
# if token in ["[CLS]", EOS, "[SEP]"]:
# continue
if len(sub_token) == 0:
sub_token.append(token)
elif token[:2] != "##" and len(sub_token) != 0:
predict_str += "".join(sub_token) + " "
sub_token = []
sub_token.append(token)
else:
if token[:2] == "##":
token = token.replace("##", "")
sub_token.append(token)
if token == EOS:
if len(sub_token) != 0:
predict_str += "".join(sub_token) + " "
break
predict_strs.append(
predict_str.replace("[SEP]", "").replace("[CLS]",
"").replace(EOS, ""))
for predict_str in predict_strs:
print(predict_str)
def translate(args):
gpu_idx = args.gpu
if not gpu_idx:
ctx = mx.cpu()
else:
ctx = mx.gpu(gpu_idx - 1)
src_bert, src_vocab = gluonnlp.model.get_model(args.bert_model,
dataset_name=args.src_bert_dataset,
pretrained=True,
ctx=ctx,
use_pooler=False,
use_decoder=False,
use_classifier=False)
_, tgt_vocab = gluonnlp.model.get_model(args.bert_model,
dataset_name=args.tgt_bert_dataset,
pretrained=True,
ctx=ctx,
use_pooler=False,
use_decoder=False,
use_classifier=False)
mt_model = MTModel_Hybird(src_vocab=src_vocab,
tgt_vocab=tgt_vocab,
embedding_dim=args.mt_emb_dim,
model_dim=args.mt_model_dim,
head_num=args.mt_head_num,
layer_num=args.mt_layer_num,
ffn_dim=args.mt_ffn_dim,
dropout=args.mt_dropout,
att_dropout=args.mt_att_dropout,
ffn_dropout=args.mt_ffn_dropout,
ctx=ctx)
src_bert.load_parameters(args.bert_model_params_path, ctx=ctx)
mt_model.load_parameters(args.mt_model_params_path, ctx=ctx)
src_bert_tokenzier = BERTTokenizer(src_vocab)
tgt_bert_tokenzier = BERTTokenizer(tgt_vocab)
while True:
src = input("input:")
src = src_bert_tokenzier(src)
src = [src_vocab.cls_token] + \
src + [src_vocab.sep_token]
src_valid_len = len(src)
if args.max_src_len and len(src) > args.max_src_len:
src = src[0:args.max_src_len]
tgt = [BOS]
src = src_vocab[src]
tgt = tgt_vocab[tgt]
tgt = nd.array([tgt], ctx=ctx)
src = nd.array([src], ctx=ctx)
src_valid_len = nd.array([src_valid_len], ctx=ctx)
src_token_types = nd.zeros_like(src)
beam_size = 6
src_bert_outputs = src_bert(src, src_token_types, src_valid_len)
mt_outputs = mt_model(src_bert_outputs, src, tgt)
src_bert_outputs = nd.broadcast_axes(
src_bert_outputs, axis=0, size=beam_size)
src = nd.broadcast_axes(src, axis=0, size=beam_size)
targets = None
for n in range(0, args.max_tgt_len):
tgt, targets = beam_search(
mt_outputs[:, n, :], targets=targets, max_seq_len=args.max_tgt_len, ctx=ctx, beam_width=beam_size)
mt_outputs = mt_model(src_bert_outputs, src, tgt)
predict = tgt.asnumpy().tolist()
predict_strs = []
for pred in predict:
predict_token = [tgt_vocab.idx_to_token[int(idx)] for idx in pred]
predict_str = ""
sub_token = []
for token in predict_token:
# if token in ["[CLS]", EOS, "[SEP]"]:
# continue
if len(sub_token) == 0:
sub_token.append(token)
elif token[:2] != "##" and len(sub_token) != 0:
predict_str += "".join(sub_token) + " "
sub_token = []
sub_token.append(token)
else:
if token[:2] == "##":
token = token.replace("##", "")
sub_token.append(token)
if token == EOS:
if len(sub_token) != 0:
predict_str += "".join(sub_token) + " "
break
predict_strs.append(predict_str.replace(
"[SEP]", "").replace("[CLS]", "").replace(EOS, ""))
for predict_str in predict_strs:
print(predict_str)
