def main():
# get and process data
data = utils.DateData(2000)
print("Chinese time order: yy/mm/dd ", data.date_cn[:3],
"\nEnglish time order: dd/M/yyyy ", data.date_en[:3])
print("vocabularies: ", data.vocab)
print(
"x index sample: \n{}\n{}".format(data.idx2str(data.x[0]), data.x[0]),
"\ny index sample: \n{}\n{}".format(data.idx2str(data.y[0]),
data.y[0]))
model = Transformer(MODEL_DIM, MAX_LEN, N_LAYER, N_HEAD, data.num_word,
DROP_RATE)
# training
t0 = time.time()
for t in range(1000):
bx, by, seq_len = data.sample(64)
bx, by = utils.pad_zero(bx, max_len=MAX_LEN), utils.pad_zero(
by, max_len=MAX_LEN + 1)
loss = model.step(bx, by)
if t % 50 == 0:
logits = model(bx[:1], by[:1, :-1], False)[0].numpy()
t1 = time.time()
print(
"step: ",
t,
"| time: %.2f" % (t1 - t0),
"| loss: %.4f" % loss.numpy(),
"| target: ",
"".join([
data.i2v[i] for i in by[0, 1:] if i != data.v2i["<PAD>"]
]),
"| inference: ",
"".join([
data.i2v[i] for i in np.argmax(logits, axis=1)
if i != data.v2i["<PAD>"]
]),
)
t0 = t1
os.makedirs("./visual_helper/transformer", exist_ok=True)
model.save_weights("./visual_helper/transformer/model.ckpt")
with open("./visual_helper/transformer_v2i_i2v.pkl", "wb") as f:
pickle.dump({"v2i": data.v2i, "i2v": data.i2v}, f)
# prediction
src_seq = "02-11-30"
print("src: ", src_seq, "\nprediction: ",
model.translate(src_seq, data.v2i, data.i2v))
# save attention matrix for visualization
_ = model(bx[:1], by[:1, :-1], training=False)
data = {
"src": [data.i2v[i] for i in data.x[0]],
"tgt": [data.i2v[i] for i in data.y[0]],
"attentions": model.attentions
}
with open("./visual_helper/transformer_attention_matrix.pkl", "wb") as f:
pickle.dump(data, f)
def train(model, data, step):
# training
t0 = time.time()
for t in range(step):
bx, by, seq_len = data.sample(64)
bx, by = utils.pad_zero(bx, max_len=MAX_LEN), utils.pad_zero(
by, max_len=MAX_LEN + 1)
loss, logits = model.step(bx, by)
if t % 50 == 0:
logits = logits[0].numpy()
t1 = time.time()
print(
"step: ",
t,
"| time: %.2f" % (t1 - t0),
"| loss: %.4f" % loss.numpy(),
"| target: ",
"".join([data.i2v[i] for i in by[0, 1:10]]),
"| inference: ",
"".join([data.i2v[i] for i in np.argmax(logits, axis=1)[:10]]),
)
t0 = t1
os.makedirs("./visual/models/transformer", exist_ok=True)
model.save_weights("./visual/models/transformer/model.ckpt")
with open("./visual/tmp/transformer_v2i_i2v.pkl", "wb") as f:
pickle.dump({"v2i": data.v2i, "i2v": data.i2v}, f)
def translate(self, src, v2i, i2v):
src_pad = utils.pad_zero(src, self.max_len)
tgt = utils.pad_zero(
np.array([[
v2i["<GO>"],
] for _ in range(len(src))]), self.max_len + 1)
tgti = 0
x_embed = self.embed(src_pad)
encoded_z = self.encoder.call(x_embed,
False,
mask=self._pad_mask(src_pad))
while True:
y = tgt[:, :-1]
y_embed = self.embed(y)
decoded_z = self.decoder.call(
y_embed,
encoded_z,
False,
look_ahead_mask=self._look_ahead_mask(y),
pad_mask=self._pad_mask(y))
logit = self.o(decoded_z)[0, tgti, :].numpy()
idx = np.argmax(logit)
tgti += 1
tgt[0, tgti] = idx
if idx == v2i["<EOS>"] or tgti >= self.max_len:
break
return "".join([i2v[i] for i in tgt[0, 1:tgti]])
def get_date_url(self, dateobj):
qs = [('datef', utils.dateobj_to_str(dateobj, '-', reverse = True)), \
('selfday', utils.pad_zero(dateobj.day)), \
('selfmonth', utils.pad_zero(dateobj.month)), \
('selfyear', utils.pad_zero(dateobj.year)), \
('B1', 'Search') \
]
query = string.join(['%s=%s' % (q[0], q[1]) for q in qs], '&')
return self.courturl + 'dojqry.asp?' + query
def train(emb_dim=32, n_layer=3, n_head=4):
dataset = utils.DateData(4000)
print("Chinese time order: yy/mm/dd ", dataset.date_cn[:3],
"\nEnglish time order: dd/M/yyyy", dataset.date_en[:3])
print("Vocabularies: ", dataset.vocab)
print(
f"x index sample: \n{dataset.idx2str(dataset.x[0])}\n{dataset.x[0]}",
f"\ny index sample: \n{dataset.idx2str(dataset.y[0])}\n{dataset.y[0]}"
)
loader = DataLoader(dataset, batch_size=32, shuffle=True)
model = Transformer(n_vocab=dataset.num_word,
max_len=MAX_LEN,
n_layer=n_layer,
emb_dim=emb_dim,
n_head=n_head,
drop_rate=0.1,
padding_idx=0)
if torch.cuda.is_available():
print("GPU train avaliable")
device = torch.device("cuda")
model = model.cuda()
else:
device = torch.device("cpu")
model = model.cpu()
for i in range(100):
for batch_idx, batch in enumerate(loader):
bx, by, decoder_len = batch
bx, by = torch.from_numpy(utils.pad_zero(
bx, max_len=MAX_LEN)).type(
torch.LongTensor).to(device), torch.from_numpy(
utils.pad_zero(by, MAX_LEN + 1)).type(
torch.LongTensor).to(device)
loss, logits = model.step(bx, by)
if batch_idx % 50 == 0:
target = dataset.idx2str(by[0, 1:-1].cpu().data.numpy())
pred = model.translate(bx[0:1], dataset.v2i, dataset.i2v)
res = dataset.idx2str(pred[0].cpu().data.numpy())
src = dataset.idx2str(bx[0].cpu().data.numpy())
print(
"Epoch: ",
i,
"| t: ",
batch_idx,
"| loss: %.3f" % loss,
"| input: ",
src,
"| target: ",
target,
"| inference: ",
res,
)
def translate(self, src, v2i, i2v):
src_pad = utils.pad_zero(np.array([v2i[v] for v in src])[None, :], self.max_len)
tgt_seq = "<GO>"
tgt = utils.pad_zero(np.array([v2i[tgt_seq], ])[None, :], self.max_len + 1)
tgti = 0
while True:
logit = self.sess.run(self.logits, {self.tfx: src_pad, self.tfy: tgt, self.training: False})[0, tgti, :]
idx = np.argmax(logit)
tgti += 1
tgt[0, tgti] = idx
if idx == v2i["<EOS>"] or tgti >= self.max_len:
break
return "".join([i2v[i] for i in tgt[0, 1:tgti]])
def post_data(self, dateobj):
postdata = [('user_id', ''), ('find_val', ''), \
('cic_val', 'all'), ('val', 'CA'), \
('fromday', utils.pad_zero(dateobj.day)), \
('frommonth', utils.pad_zero(dateobj.month)), \
('fromyear', dateobj.year), \
('today', utils.pad_zero(dateobj.day)), \
('tomonth', utils.pad_zero(dateobj.month)), \
('toyear', dateobj.year), \
('submit', 'Go') \
]
return postdata
def translate(self, src, v2i, i2v):
self.eval()
device = next(self.parameters()).device
src_pad = src
# Initialize Decoder input by constructing a matrix M([n, self.max_len+1]) with initial value:
# M[n,0] = start token id
# M[n,:] = 0
target = torch.from_numpy(
utils.pad_zero(
np.array([[
v2i["<GO>"],
] for _ in range(len(src))]), self.max_len + 1)).to(device)
x_embed = self.embed(src_pad)
encoded_z = self.encoder(x_embed, False, mask=self._pad_mask(src_pad))
for i in range(0, self.max_len):
y = target[:, :-1]
y_embed = self.embed(y)
decoded_z = self.decoder(y_embed, encoded_z, False,
self._look_ahead_mask(y),
self._pad_mask(src_pad))
o = self.o(decoded_z)[:, i, :]
idx = o.argmax(dim=1).detach()
# Update the Decoder input, to predict for the next position.
target[:, i + 1] = idx
self.train()
return target
def translate(self, src, i2v, v2i):
src = tf.reshape(src, (-1, src.shape[-1]))
src_pad = utils.pad_zero(src, self.max_len)
tgt = utils.pad_zero(v2i["<GO>"] * tf.ones_like(src), self.max_len + 1)
tgti = 0
x_embed = self.embed(src_pad)
encoded_z = self.encoder(x_embed, mask=self._pad_mask(src_pad))
while True:
y = tgt[:, :-1]
y_embed = self.embed(y)
decoded_z = self.decoder((encoded_z, y_embed),
look_ahead_mask=self._look_ahead_mask(y),
pad_mask=self._pad_mask(src_pad))
logit = self.o(decoded_z)[:, tgti, :].numpy()
idx = np.argmax(logit, 1)
tgti += 1
tgt[:, tgti] = idx
if tgti >= self.max_len:
break
return [
"".join([i2v[i] for i in tgt[j, 1:tgti]]) for j in range(len(src))
]
def translate(self, src, v2i, i2v):
src_pad = utils.pad_zero(
np.array([v2i[v] for v in src])[None, :], self.max_len)
tgt = utils.pad_zero(
np.array([
v2i["<GO>"],
])[None, :], self.max_len + 1)
tgti = 0
x_embed = self.embed(src_pad)
encoded_z = self.encoder(x_embed, False, mask=self._pad_mask(src_pad))
while True:
y_embed = self.embed(tgt[:, :-1])
decoded_z = self.decoder(y_embed,
encoded_z,
False,
mask=self._look_ahead_mask())
logit = self.o(decoded_z)[0, tgti, :].numpy()
idx = np.argmax(logit)
tgti += 1
tgt[0, tgti] = idx
if idx == v2i["<EOS>"] or tgti >= self.max_len:
break
return "".join([i2v[i] for i in tgt[0, 1:tgti]])
def translate(self, src, v2i, i2v):
src_pad = utils.pad_zero(src, self.max_len)
tgt = utils.pad_zero(
np.array([[v2i["<GO>"], ] for _ in range(len(src))]), self.max_len+1)
tgti = 0
x_embed = self.embed(src_pad)
encoded_z = self.encoder.call(
x_embed, False, mask=self._pad_mask(src_pad))
# 用上一个预测生成的词去预测生成下一个词, 直到超出最大句子长度
while True:
y = tgt[:, :-1]
y_embed = self.embed(y)
decoded_z = self.decoder.call(
y_embed, encoded_z, False, yz_look_ahead_mask=self._look_ahead_mask(y), xz_pad_mask=self._pad_mask(src_pad))
# 将所有词都放在一起 decoder（即使有些词还没有被生成，还是空的）,
# 在 decoder 结束后, 只取出当前所要预测生成的那一个即可
logits = self.o(decoded_z)[:, tgti, :].numpy()
idx = np.argmax(logits, axis=1)
tgti += 1
# 将预测得到的词的索引作为结果保存起来
tgt[:, tgti] = idx
if tgti >= self.max_len:
break
return ["".join([i2v[i] for i in tgt[j, 1:tgti]]) for j in range(len(src))]
def get_post_data(self, tags, dateobj):
postdata = []
today = datetime.date.today()
for tag in tags:
name = None
value = None
if tag.name == 'input':
name = tag.get('name')
value = tag.get('value')
t = tag.get('type')
if t == 'image':
continue
if name in['btnGono', 'btnOfficeName', 'btnreset']:
continue
elif tag.name == 'select':
name = tag.get('name')
if name == 'ddloffcode':
value = '0'
elif name == 'ddlcatcode':
value = '0'
elif name == 'ddldeptcode':
value = 'All Department'
elif name == 'ddlgodatefrom' or name == 'ddlgodateto':
value = utils.pad_zero(dateobj.day)
elif name == 'ddlgomonfrom' or name == 'ddlgomonto':
value = utils.pad_zero(dateobj.month)
elif name == 'ddlgoyearfrom' or name == 'ddlgoyearto':
value = utils.pad_zero(dateobj.year)
elif name == 'ddlfromdate_day' or name == 'ddlfromdate_mon':
value = '01'
elif name == 'ddlfromdate_year':
value = '2013'
elif name == 'ddltodate_date':
value = utils.pad_zero(today.day)
elif name == 'ddltodate_mon':
value = utils.pad_zero(today.month)
elif name == 'ddltodate_year':
value = utils.pad_zero(today.year)
if name:
if value == None:
value = u''
postdata.append((name, value))
return postdata
def date_in_form(self, dateobj):
return [
("jday", utils.pad_zero(dateobj.day)),
("jmonth", utils.pad_zero(dateobj.month)),
("jyear", utils.pad_zero(dateobj.year)),
]
def date_in_form(self, dateobj):
return [('jday', utils.pad_zero(dateobj.day)), \
('jmonth', utils.pad_zero(dateobj.month)), \
('jyear', utils.pad_zero(dateobj.year)) \
]
def get_post_data(self, dateobj):
return [('bsubmit', 'Submit'), ('select', utils.pad_zero(dateobj.day)),\
('select2', utils.pad_zero(dateobj.month)), \
('select3', utils.pad_zero(dateobj.year % 2000))]
def load_data(data, size):
x, y, seq_len = data.sample(size)
x = utils.pad_zero(x, MAX_LEN)
y = utils.pad_zero(y, MAX_LEN + 1)
return (x, y[:, :-1]), y[:, 1:]
def get_date_string(self, dateobj):
return '%s/%s/%s' % (utils.pad_zero(dateobj.day), \
calendar.month_abbr[dateobj.month].lower(), \
utils.pad_zero(dateobj.year))
def date_in_form(self, dateobj):
return '%s/%s/%s' % (utils.pad_zero(dateobj.day), \
utils.pad_zero(dateobj.month), \
utils.pad_zero(dateobj.year))
def date_in_form(self, dateobj):
return [('jday', utils.pad_zero(dateobj.day)), \
('jmonth', utils.pad_zero(dateobj.month)), \
('jyear', utils.pad_zero(dateobj.year)) \
]
def date_in_form(self, dateobj):
return [('juddt', '%s/%s/%s' % (utils.pad_zero(dateobj.day), \
utils.pad_zero(dateobj.month), \
utils.pad_zero(dateobj.year)) \
)]
def download_oneday(self, relpath, dateobj):
dls = []
if dateobj >= self.flip_date1:
if dateobj >= self.flip_date2:
datestr = '%d-%d-%d' % (dateobj.day, dateobj.month,
dateobj.year)
else:
datestr = '%s-%s-%d' % (utils.pad_zero(
dateobj.day), utils.pad_zero(dateobj.month), dateobj.year)
mainhref = 'Contents-(%s).pdf' % datestr
else:
datestr = utils.dateobj_to_str(dateobj, '', reverse=True)
mainhref = 'Contents(%s-%s-%s).pdf' % (utils.pad_zero(
dateobj.day), utils.pad_zero(
dateobj.month), utils.pad_zero(dateobj.year % 100))
dateurl = self.baseurl % datestr
docurl = urllib.basejoin(dateurl, mainhref)
mainmeta = utils.MetaInfo()
mainmeta.set_date(dateobj)
mainmeta.set_url(self.url_fix(docurl))
response = self.download_url(docurl)
if not response or not response.webpage or response.error:
return dls
mainrelurl = os.path.join(relpath, 'main')
updated = False
if self.storage_manager.save_rawdoc(self.name, mainrelurl,
response.srvresponse,
response.webpage):
self.logger.info(u'Saved rawfile %s' % mainrelurl)
updated = True
page_type = self.get_file_extension(response.webpage)
if page_type != 'pdf':
self.logger.warn(
'Got a non-pdf page and we can\'t handle it for datte %s',
dateobj)
return dls
links = []
linknames = []
hrefs = utils.extract_links_from_pdf(StringIO(response.webpage))
for href in hrefs:
reobj = re.search('(?P<num>Part-\w+)', href)
if reobj:
partnum = reobj.groupdict()['num']
else:
partnum = '%s' % href
reobj = re.search('.pdf$', partnum)
if partnum:
partnum = partnum[:reobj.start()]
relurl = os.path.join(relpath, partnum)
docurl = urllib.basejoin(dateurl, href)
metainfo = utils.MetaInfo()
metainfo.set_date(dateobj)
metainfo['partnum'] = partnum
links.append(relurl)
linknames.append(partnum)
if self.save_gazette(relurl, docurl, metainfo):
dls.append(relurl)
mainmeta['links'] = links
mainmeta['linknames'] = linknames
if self.storage_manager.save_metainfo(self.name, mainrelurl, mainmeta):
updated = True
self.logger.info(u'Saved metainfo %s' % mainrelurl)
if updated:
dls.append(mainrelurl)
return dls
def get_date_string(self, dateobj):
return '%s/%s/%s' % (utils.pad_zero(dateobj.day), \
calendar.month_abbr[dateobj.month].lower(), \
utils.pad_zero(dateobj.year))
break
return "".join([i2v[i] for i in tgt[0, 1:tgti]])
# get and process data
vocab, x, y, v2i, i2v, date_cn, date_en = utils.get_date_data()
print("Chinese time order: ", date_cn[:3], "\nEnglish time order: ", date_en[:3])
print("vocabularies: ", vocab)
print("x index sample: \n", x[:2], "\ny index sample: \n", y[:2])
model = Transformer(MODEL_DIM, MAX_LEN, N_LAYER, N_HEAD, len(vocab), DROP_RATE)
# training
t0 = time.time()
for t in range(1000):
bi = np.random.randint(0, len(x), size=64)
bx, by = utils.pad_zero(x[bi], max_len=MAX_LEN), utils.pad_zero(y[bi], max_len=MAX_LEN+1)
_, loss_ = model.sess.run([model.train_op, model.loss], {model.tfx: bx, model.tfy: by, model.training: True})
if t % 50 == 0:
logits_ = model.sess.run(model.logits, {model.tfx: bx[:1, :], model.tfy: by[:1, :], model.training: False})
t1 = time.time()
print(
"step: ", t,
"| time: %.2f" % (t1-t0),
"| loss: %.3f" % loss_,
"| target: ", "".join([i2v[i] for i in by[0, 1:] if i != v2i["<PAD>"]]),
"| inference: ", "".join([i2v[i] for i in np.argmax(logits_[0], axis=1) if i != v2i["<PAD>"]]),
)
t0 = t1
# prediction
src_seq = "02-11-30"
# forward path - joint demosaicing & denoising in action
for t in range(num_steps):
# get the parameters
c = np.reshape(Theta[t,:Nk*(Ks**2*Kd-1)], (Nk, (Ks**2*Kd-1)))
normalized_c = c / np.sqrt(np.sum(c**2, axis=0))
k = B.dot(normalized_c)
k = np.reshape(k.T, (Nk, Ks, Ks, Kd))
w = np.reshape(Theta[t,Nk*(Ks**2*Kd-1):-1], (Nk, Nw))
l = np.reshape(Theta[t,-1], (1,))
tmp = np.zeros((Kd, u_t[t,0].shape[0]+2*padding, u_t[t,0].shape[1]+2*padding))
for i in range(Nk):
conv_k_sum = np.zeros((u_t[t,0].shape[0], u_t[t,0].shape[1]))
for d in range(Kd):
ki = k[i,:,:,d]
u_conv_k = pad_zero(u_t[t,d], padding, padding)
conv_k_sum += conv2(u_conv_k,ki,'valid')
phi_u = RBFforward(conv_k_sum, w[i,:])
for d in range(Kd):
ki = k[i,:,:,d]
tmp[d,:,:] += conv2(phi_u,ki[::-1,::-1],'full')
u_t[t+1] = np.clip(u_t[t] - crop_zero(tmp, padding, padding) - l*bwd_bayer(fwd_bayer(u_t[t]) - f), 0.0,255.0)
print '.',
#Evaluate
print "\nTest image: %d" % data_config['indices'][example]
#get the result
result = u_t[num_steps]
plt.figure(1)
plt.imshow(swapimdims_3HW_HW3(result).astype('uint8'), interpolation="none")
plt.show()
