class Generator(object):
'''
generator for testing
'''
def __init__(self):
self.tool = Tool(hparams.sens_num, hparams.key_len, hparams.sen_len,
hparams.poem_len, 0.0)
self.tool.load_dic(hparams.vocab_path, hparams.ivocab_path)
vocab_size = self.tool.get_vocab_size()
print("vocabulary size: %d" % (vocab_size))
PAD_ID = self.tool.get_PAD_ID()
B_ID = self.tool.get_B_ID()
assert vocab_size > 0 and PAD_ID >= 0 and B_ID >= 0
self.hps = hparams._replace(vocab_size=vocab_size,
pad_idx=PAD_ID,
bos_idx=B_ID)
# load model
model = MixPoetAUS(self.hps)
# load trained model
utils.restore_checkpoint(self.hps.model_dir, device, model)
self.model = model.to(device)
self.model.eval()
#utils.print_parameter_list(self.model)
# load poetry filter
print("loading poetry filter...")
self.filter = PoetryFilter(self.tool.get_vocab(),
self.tool.get_ivocab(), self.hps.data_dir)
print("--------------------------")
def generate_one(self,
keyword,
length,
factor_label1,
factor_label2,
beam_size=20,
verbose=1,
manu=False):
'''
generate one poem according to the inputs:
keyword: a topic word
length: the length of each line, 5 or 7
factor_label: label for the two factors, when factor_label = -1,
the model infers an appropriate class in terms of the keyword
verbose: 0, 1, 2, 3
'''
assert length == 5 or length == 7
key_state = self.get_key_state(keyword)
# infer labels when factor_label = -1
if factor_label1 == -1:
factor_label1 = self.get_inferred_factor(key_state, 1)
if verbose >= 1:
print("inferred label1: %d" % (factor_label1[0].item()))
else:
factor_label1 = torch.tensor([factor_label1],
dtype=torch.long,
device=device)
if factor_label2 == -1:
factor_label2 = self.get_inferred_factor(key_state, 2)
if verbose >= 1:
print("inferred label2: %d" % (factor_label2[0].item()))
else:
factor_label2 = torch.tensor([factor_label2],
dtype=torch.long,
device=device)
# get decoder initial state
dec_init_state = self.get_dec_init_state(key_state, factor_label1,
factor_label2, length)
context = torch.zeros((1, self.hps.context_size),
dtype=torch.float,
device=device) # (B, context_size)
# initialize beam pool
beam_pool = PoetryBeam(beam_size, length, self.tool.get_B_ID(),
self.tool.get_E_ID(), self.tool.get_UNK_ID(),
self.filter.get_level_cids(),
self.filter.get_oblique_cids())
# beam search
poem = []
self.filter.reset(length, verbose)
for step in range(0, self.hps.sens_num):
# generate each line
if verbose >= 1:
print("\ngenerating step: %d" % (step))
# get the rhythm pattern and rhyme id of te current line
_, rhythms, rhyme = self.filter.get_pattern(step)
#
pos_tensor = self.tool.pos2tensor(step)
pos_tensor = pos_tensor.to(device)
init_state = torch.cat(
[dec_init_state.clone().detach(), pos_tensor], dim=-1)
# reset beam pool
beam_pool.reset(init_state, rhythms, rhyme,
self.filter.get_rhyme_cids(rhyme),
self.filter.get_repetitive_ids())
candidates, costs, states = self.beam_search(
beam_pool, length, context)
lines = [self.tool.idxes2line(idxes) for idxes in candidates]
lines, costs, states = self.filter.filter_illformed(
lines, costs, states, step)
if len(lines) == 0:
return "", "generation failed!"
which = 0
if manu:
for i, (line, cost) in enumerate(zip(lines, costs)):
print("%d, %s, %.2f" % (i, line, cost))
which = int(input("select sentence>"))
line = lines[which]
poem.append(line)
# the first line determin the rhythm pattern of the poem
if step == 0:
self.filter.set_pattern(line)
# when the first line doesn't rhyme, we can determin the rhyme
# in terms of the second line
if step == 1 and self.filter.get_rhyme() == -1:
self.filter.set_rhyme(line)
# set repetitive chars
self.filter.add_repetitive(self.tool.line2idxes(line))
# update the context vector
context = self.update_context(context, states[which], beam_size,
length)
return poem, "ok"
# ------------------------------------
def beam_search(self, beam_pool, trg_len, ori_context):
# current size of beam candidates in the beam pool
n_samples = beam_pool.uncompleted_num()
# (1, context_size) -> (B, context_size)
context = ori_context.repeat(n_samples, 1)
for k in range(0, trg_len + 10):
#print ("beam search position %d" % (k))
inps, states = beam_pool.get_beam_tails()
logits, new_states = self.do_dec_step(inps, states,
context[:n_samples, :])
beam_pool.advance(logits, new_states, k)
n_samples = beam_pool.uncompleted_num()
if n_samples == 0:
break
candidates, costs, dec_states = beam_pool.get_search_results()
return candidates, costs, dec_states
# ---------------------------
def get_key_state(self, keyword):
#print (keyword)
key_tensor = self.tool.keys2tensor([keyword])
#print (key_tensor)
return self.model.compute_key_state(key_tensor.to(device))
def get_inferred_factor(self, key_state, factor_id):
assert factor_id == 1 or factor_id == 2
return self.model.compute_inferred_label(key_state, factor_id)
def get_dec_init_state(self, key_state, label1, label2, length):
state = self.model.compute_dec_init_state(key_state, label1, label2)
# length tensor
len_tensor = self.tool.lengths2tensor([length]).to(device)
dec_init_state = torch.cat([state, len_tensor], dim=-1) # (1, H)
return dec_init_state
def do_dec_step(self, inps, states, context):
logits, new_state = self.model.dec_step(inps, states, context)
return logits, new_state
def update_context(self, old_context, ori_states, beam_size, length):
# update the context vector
# old_context: (1, context_size)
# states: (1, H) * L
H = ori_states[0].size(1)
states = ori_states[1:length + 2] + [
torch.zeros_like(ori_states[0], device=device)
] * (7 - length)
states = [state.view(H, 1) for state in states]
states = torch.cat(states, dim=1).unsqueeze(0) # (1, H, L)
context = self.model.layers['context'](old_context, states)
return context
class Generator(object):
'''
generator for testing
'''
def __init__(self, hps, device):
self.tool = Tool(hps.sens_num, hps.sen_len, hps.key_len,
hps.topic_slots, 0.0)
self.tool.load_dic(hps.vocab_path, hps.ivocab_path)
vocab_size = self.tool.get_vocab_size()
print("vocabulary size: %d" % (vocab_size))
PAD_ID = self.tool.get_PAD_ID()
B_ID = self.tool.get_B_ID()
assert vocab_size > 0 and PAD_ID >= 0 and B_ID >= 0
self.hps = hps._replace(vocab_size=vocab_size,
pad_idx=PAD_ID,
bos_idx=B_ID)
self.device = device
# load model
model = WorkingMemoryModel(self.hps, device)
# load trained model
utils.restore_checkpoint(self.hps.model_dir, device, model)
self.model = model.to(device)
self.model.eval()
null_idxes = self.tool.load_function_tokens(self.hps.data_dir +
"fchars.txt").to(
self.device)
self.model.set_null_idxes(null_idxes)
self.model.set_tau(hps.min_tau)
# load poetry filter
print("loading poetry filter...")
self.filter = PoetryFilter(self.tool.get_vocab(),
self.tool.get_ivocab(), self.hps.data_dir)
self.visual_tool = Visualization(hps.topic_slots, hps.his_mem_slots,
"../log/")
print("--------------------------")
def generate_one(self,
keywords,
pattern,
beam_size=20,
verbose=1,
manu=False,
visual=0):
'''
generate one poem according to the inputs:
keyword: a list of topic words, at most key_slots
pattern: genre pattern of the poem to be generated
verbose: 0, 1, 2, 3
visual: 0, 1, 2
'''
key_inps = self.tool.keywords2tensor([keywords] * beam_size)
key_inps = [key.to(self.device) for key in key_inps]
if visual > 0:
self.visual_tool.reset(keywords)
# inps is a pseudo tensor with pad symbols for generating the first line
inps, all_ph_inps, all_len_inps, all_lengths = self.tool.patterns2tensor(
[pattern] * beam_size)
inps = inps.to(self.device)
all_ph_inps = [ph_inps.to(self.device) for ph_inps in all_ph_inps]
all_len_inps = [len_inps.to(self.device) for len_inps in all_len_inps]
# for quatrains, all lines in a poem share the same length
length = all_lengths[0]
# initialize beam pool
beam_pool = PoetryBeam(self.device, beam_size, length,
self.tool.get_B_ID(), self.tool.get_E_ID(),
self.tool.get_UNK_ID(),
self.filter.get_level_cids(),
self.filter.get_oblique_cids())
self.filter.reset(length, verbose)
with torch.no_grad():
topic_mem, topic_mask, history_mem, history_mask,\
global_trace, topic_trace, key_init_state = self.model.initialize_mems(key_inps)
# beam search
poem = []
for step in range(0, self.hps.sens_num):
# generate each line
if verbose >= 1:
print("\ngenerating step: %d" % (step))
if step > 0:
key_init_state = None
candidates, costs, states, read_aligns, local_mem, local_mask = self.beam_search(
beam_pool, length, inps, all_ph_inps[step], pattern[step],
all_len_inps[step], key_init_state, history_mem, history_mask,
topic_mem, topic_mask, global_trace, topic_trace)
lines = [self.tool.idxes2line(idxes) for idxes in candidates]
lines, costs, states, read_aligns = self.filter.filter_illformed(
lines, costs, states, read_aligns, pattern[step][-1])
if len(lines) == 0:
return [], "line {} generation failed!".format(step)
which = 0
if manu:
for i, (line, cost) in enumerate(zip(lines, costs)):
print("%d, %s, %.2f" % (i, line, cost))
which = int(input("select sentence>"))
line = lines[which]
poem.append(line)
# set repetitive chars
self.filter.add_repetitive(self.tool.line2idxes(line))
# ---------------------------------------
# write into history memory
write_log = None
if step >= 1:
with torch.no_grad():
history_mem, write_log = self.update_history_mem(
history_mem, local_mem, local_mask, global_trace)
history_mask = history_mem.abs().sum(-1).eq(
0) # (B, mem_slots)
with torch.no_grad():
# update global trace
global_trace = self.update_glocal_trace(
global_trace, states[which], length)
# update topic trace
topic_trace = self.update_topic_trace(topic_trace, topic_mem,
read_aligns[which])
# build inps
inps = self.tool.line2tensor(line, beam_size).to(self.device)
if visual > 0:
# show visualization of memory reading
self.visual_tool.add_gen_line(line)
self.visual_tool.draw(read_aligns[which], write_log, step,
visual)
return poem, "ok"
# ------------------------------------
def beam_search(self, beam_pool, trg_len, inputs, phs, ph_labels, lens,
key_init_state, history_mem, history_mask, topic_mem,
topic_mask, global_trace, topic_trace):
local_mem, local_mask, init_state = \
self.model.computer_local_memory(inputs, key_init_state is None)
if key_init_state is not None:
init_state = key_init_state
# reset beam pool
if 1 <= ph_labels[-1] <= 30:
rhyme = ph_labels[-1]
else:
rhyme = -1
beam_pool.reset(init_state[0, :].unsqueeze(0), ph_labels + [0] * 10,
self.filter.get_rhyme_cids(rhyme),
self.filter.get_repetitive_ids())
# current size of beam candidates in the beam pool
n_samples = beam_pool.uncompleted_num()
total_mask = torch.cat([topic_mask, history_mask, local_mask], dim=1)
total_mem = torch.cat([topic_mem, history_mem, local_mem], dim=1)
for k in range(0, trg_len + 5):
inp, state = beam_pool.get_beam_tails()
if k <= trg_len:
ph_inp = phs[0:n_samples, k]
len_inp = lens[0:n_samples, k]
else:
ph_inp = torch.zeros(n_samples,
dtype=torch.long,
device=self.device)
len_inp = torch.zeros(n_samples,
dtype=torch.long,
device=self.device)
with torch.no_grad():
logit, new_state, read_align = self.model.dec_step(
inp, state, ph_inp, len_inp, total_mem[0:n_samples, :, :],
total_mask[0:n_samples, :], global_trace[0:n_samples, :],
topic_trace[0:n_samples, :])
beam_pool.advance(logit, new_state, read_align, k)
n_samples = beam_pool.uncompleted_num()
if n_samples == 0:
break
candidates, costs, dec_states, read_aligns = beam_pool.get_search_results(
)
return candidates, costs, dec_states, read_aligns, local_mem, local_mask
# ---------------
def update_history_mem(self, history_mem, local_mem, local_mask,
global_trace):
new_history_mem, write_log = self.model.layers['memory_write'](
history_mem, local_mem, 1.0 - local_mask.float(), global_trace,
self.model.null_mem)
return new_history_mem, write_log
def update_glocal_trace(self, global_trace, dec_states, length):
# dec_states: (1, H) * L_gen
batch_size = global_trace.size(0)
# (1, H) -> (B, H, 1)
states = [
state.unsqueeze(2).repeat(batch_size, 1, 1) for state in dec_states
]
l = len(states)
mask = torch.zeros(batch_size,
l,
dtype=torch.float,
device=self.device)
mask[:, 0:length + 1] = 1.0
# update global trace vector
new_global_trace = self.model.update_global_trace(
global_trace, states, mask)
return new_global_trace
def update_topic_trace(self, topic_trace, topic_mem, read_align):
# read_align: (1, 1, mem_slots) * L_gen
batch_size = topic_trace.size(0)
# concat_aligns: (B, L_gen, mem_slots)
concat_aligns = torch.cat(read_align, dim=1).repeat(batch_size, 1, 1)
new_topic_trace = self.model.update_topic_trace(
topic_trace, topic_mem, concat_aligns)
return new_topic_trace
