def train_adaptive(rank,
machine,
max_beam_size,
lr,
shared_model,
counter,
lock,
optimizer,
eval_data_X, eval_data_Y, index2word, index2label,
suffix, result_path, decode_method, beam_size,
reward_coef_fscore, reward_coef_beam_size,
f_score_index_begin,
args,
):
torch.manual_seed(123 + rank)
# create adative model
model = AdaptiveActorCritic(max_beam_size=max_beam_size, action_space=3)
if optimizer is None:
optimizer = optim.Adam(shared_model.parameters(), lr=lr)
model.train()
batch_num = len(eval_data_X)
instance_num = 0
beam_size_seqs = []
for batch in eval_data_X:
instance_num += len(batch)
for epoch in range(1, args.n_epochs + 1):
print("Epoch: {} of training process {}".format(epoch, rank))
desc = result_path + '_process_' + str(rank) + '_' + str(epoch) + '_'
if result_path:
f_sen = open(os.path.join(args.logdir,
desc + "sen_" + suffix + ".txt"), 'w')
f_pred = open(os.path.join(args.logdir,
desc + "pred_" + suffix + ".txt"), 'w')
f_label = open(os.path.join(args.logdir,
desc + "label_" + suffix + ".txt"), 'w')
f_result_processed = \
open(os.path.join(args.logdir,
desc + "result_processed_" + suffix + ".txt"), 'w')
f_beam_size = \
open(os.path.join(args.logdir,
desc + 'beam_size_' + suffix + ".txt"), 'w')
true_pos_count = 0
pred_pos_count = 0
true_pred_pos_count = 0
# shuffle
batch_idx_list = range(batch_num)
batch_idx_list = np.random.permutation(batch_idx_list)
for batch_idx in batch_idx_list:
sen = eval_data_X[batch_idx]
label = eval_data_Y[batch_idx]
current_batch_size = len(sen)
current_sen_len = len(sen[0])
# DEBUG
# print(batch_idx, current_sen_len)
if current_sen_len < 3: # ignore sentence having tiny length
continue
sen_var = Variable(torch.LongTensor(sen))
label_var = Variable(torch.LongTensor(label))
if machine.gpu:
sen_var = sen_var.cuda()
label_var = label_var.cuda()
# Initialize the hidden and cell states
# The axes semantics are
# (num_layers * num_directions, batch_size, hidden_size)
# So 1 for single-directional LSTM encoder,
# 2 for bi-directional LSTM encoder.
init_enc_hidden = Variable(
torch.zeros((2, current_batch_size, machine.hidden_dim)))
init_enc_cell = Variable(
torch.zeros((2, current_batch_size, machine.hidden_dim)))
if machine.gpu:
init_enc_hidden = init_enc_hidden.cuda()
init_enc_cell = init_enc_cell.cuda()
enc_hidden_seq, (enc_hidden_out, enc_cell_out) = machine.encode(sen_var,
init_enc_hidden,
init_enc_cell)
# The semantics of enc_hidden_out is (num_layers * num_directions,
# batch, hidden_size), and it is "tensor containing the hidden state
# for t = seq_len".
#
# Here we use a linear layer to transform the two-directions of the dec_hidden_out's into a single hidden_dim vector, to use as the input of the decoder
init_dec_hidden = machine.enc2dec_hidden(
torch.cat([enc_hidden_out[0], enc_hidden_out[1]], dim=1))
init_dec_cell = machine.enc2dec_cell(
torch.cat([enc_cell_out[0], enc_cell_out[1]], dim=1))
# ===================================
if decode_method == "adaptive":
# the input argument "beam_size" serves as initial_beam_size here
# TODO: implement this here
label_pred_seq, accum_logP_pred_seq, logP_pred_seq, \
attention_pred_seq, episode, sen_beam_size_seq = \
decode_one_sentence_adaptive_rl(machine,
current_sen_len, init_dec_hidden, init_dec_cell, enc_hidden_seq,
beam_size, max_beam_size, model, shared_model, reward_coef_fscore,
reward_coef_beam_size, label_var, f_score_index_begin, counter, lock,
optimizer, args)
else:
raise Exception("Not implemented!")
# ===================================
# update beam seq
beam_size_seqs += sen_beam_size_seq
### Debugging...
# print("input sentence =", sen)
# print("true label =", label)
# print("predicted label =", label_pred_seq)
# print("episode =", episode)
for label_index in range(f_score_index_begin, machine.label_size):
true_pos = (label_var == label_index)
true_pos_count += true_pos.float().sum()
pred_pos = (label_pred_seq == label_index)
pred_pos_count += pred_pos.float().sum()
true_pred_pos = true_pos & pred_pos
true_pred_pos_count += true_pred_pos.float().sum()
# Write result into file
if result_path:
if machine.gpu:
label_pred_seq = label_pred_seq.cpu()
label_pred_seq = label_pred_seq.data.numpy().tolist()
# Here label_pred_seq.shape = (batch size, sen len)
# sen, label, label_pred_seq are list of lists,
# thus I would like to flatten them for iterating easier
sen = list(itertools.chain.from_iterable(sen))
label = list(itertools.chain.from_iterable(label))
label_pred_seq = list(itertools.chain.from_iterable(label_pred_seq))
assert len(sen) == len(label) and len(label) == len(label_pred_seq)
for i in range(len(sen)):
f_sen.write(str(sen[i]) + '\n')
f_label.write(str(label[i]) + '\n')
f_pred.write(str(label_pred_seq[i]) + '\n')
# clean version (does not print <PAD>, print a newline instead of <EOS>)
# if sen[i] != 0 and sen[i] != 2: # not <PAD> and not <EOS>
# if sen[i] != 0: # not <PAD>
result_sen = index2word[sen[i]]
result_label = index2label[label[i]]
result_pred = index2label[label_pred_seq[i]]
f_result_processed.write(
"%s %s %s\n" % (result_sen, result_label, result_pred))
f_sen.flush()
f_label.flush()
f_pred.flush()
f_result_processed.flush()
if decode_method == "adaptive":
beam_size_seq_str = ' '.join(map(str, sen_beam_size_seq))
f_beam_size.write(beam_size_seq_str + '\n')
f_beam_size.flush()
# End for batch_idx
if machine.gpu:
true_pos_count = true_pos_count.cpu()
pred_pos_count = pred_pos_count.cpu()
true_pred_pos_count = true_pred_pos_count.cpu()
true_pos_count = true_pos_count.data.numpy()[0]
pred_pos_count = pred_pos_count.data.numpy()[0]
true_pred_pos_count = true_pred_pos_count.data.numpy()[0]
precision = true_pred_pos_count / pred_pos_count if pred_pos_count > 0 else 0
recall = true_pred_pos_count / true_pos_count if true_pos_count > 0 else 0
fscore = 2 / (1 / precision + 1 / recall) if (
precision > 0 and recall > 0) else 0
fscore = fscore * 100
if result_path:
f_sen.close()
f_pred.close()
f_label.close()
f_result_processed.close()
f_beam_size.close()
avg_beam_sizes = sum(beam_size_seqs) / float(len(beam_size_seqs))
print("Epoch {} of process {}: Avg beam size: {}".format(epoch,
rank,
avg_beam_sizes))
print("Epoch {} of process {}: Avg Fscore = {}".format(epoch,
rank,
fscore))
def train_adaptive(rank, machine, max_beam_size, shared_model, optimizer,
data_X, data_Y, index2word, index2label, suffix,
decode_method, beam_size, reward_coef_fscore,
reward_coef_beam_size, f_score_index_begin, args):
torch.manual_seed(123 + rank)
logfile = open(os.path.join(args.logdir, "log_" + str(rank) + ".txt"),
"w+")
# create adaptive model
model = AdaptiveActorCritic(max_beam_size=max_beam_size, action_space=3)
# If a shared_optimizer is not passed in
if optimizer is None:
optimizer = optim.Adam(shared_model.parameters(), lr=args.lr)
# torch.nn.modules.module:
# Sets the module in training mode
model.train()
batch_num = len(data_X)
for epoch in range(0, args.n_epochs):
reward_list = []
# shuffle
batch_idx_list = range(batch_num)
batch_idx_list = np.random.permutation(batch_idx_list)
time_begin = time.time()
for batch_idx in batch_idx_list:
sen = data_X[batch_idx]
label = data_Y[batch_idx]
current_batch_size = len(sen)
current_sen_len = len(sen[0])
# DEBUG
# print(batch_idx, current_sen_len)
if current_sen_len < 3: # ignore sentence having tiny length
continue
sen_var = Variable(torch.LongTensor(sen))
label_var = Variable(torch.LongTensor(label))
if machine.gpu:
sen_var = sen_var.cuda()
label_var = label_var.cuda()
# Initialize the hidden and cell states
# The axes semantics are
# (num_layers * num_directions, batch_size, hidden_size)
# So 1 for single-directional LSTM encoder,
# 2 for bi-directional LSTM encoder.
init_enc_hidden = Variable(
torch.zeros((2, current_batch_size, machine.hidden_dim)))
init_enc_cell = Variable(
torch.zeros((2, current_batch_size, machine.hidden_dim)))
if machine.gpu:
init_enc_hidden = init_enc_hidden.cuda()
init_enc_cell = init_enc_cell.cuda()
enc_hidden_seq, (enc_hidden_out, enc_cell_out) = machine.encode(
sen_var, init_enc_hidden, init_enc_cell)
# The semantics of enc_hidden_out is (num_layers * num_directions,
# batch, hidden_size), and it is "tensor containing the hidden state
# for t = seq_len".
#
# Here we use a linear layer to transform the two-directions of the dec_hidden_out's into a single hidden_dim vector, to use as the input of the decoder
init_dec_hidden = machine.enc2dec_hidden(
torch.cat([enc_hidden_out[0], enc_hidden_out[1]], dim=1))
init_dec_cell = machine.enc2dec_cell(
torch.cat([enc_cell_out[0], enc_cell_out[1]], dim=1))
# ===================================
if decode_method == "adaptive":
# the input argument "beam_size" serves as initial_beam_size here
# TODO: implement this here
label_pred_seq, accum_logP_pred_seq, logP_pred_seq, \
attention_pred_seq, episode, sen_beam_size_seq, total_reward = \
decode_one_sentence_adaptive_rl(machine,
current_sen_len, init_dec_hidden, init_dec_cell, enc_hidden_seq,
beam_size, max_beam_size, model, shared_model, reward_coef_fscore,
reward_coef_beam_size, label_var, f_score_index_begin,
optimizer, args)
reward_list.append(total_reward)
else:
raise Exception("Not implemented!")
# ===================================
# update beam seq
#beam_size_seqs.append(sen_beam_size_seq)
### Debugging...
# print("input sentence =", sen)
# print("true label =", label)
# print("predicted label =", label_pred_seq)
# print("episode =", episode)
# End for batch_idx
time_end = time.time()
time_used = time_end - time_begin
reward_list = np.array(reward_list)
reward_mean = np.mean(reward_list)
reward_std = np.std(reward_list)
log_msg = "%d\t%f\t%f\t%f" % (epoch, reward_mean, reward_std,
time_used)
print(log_msg)
#print(log_msg, file=logfile, flush=True)
# Save shared model and (supposedly) shared optimizer
# Purposely possibly over-writing other threads' model for the same epoch
checkpoint_filename = os.path.join(args.logdir,
"ckpt_" + str(epoch) + ".pth")
torch.save(
{
'epoch': epoch,
'state_dict': shared_model.state_dict(),
'optimizer': optimizer.state_dict()
}, checkpoint_filename)
# End for epoch
logfile.close()