# print epoch reconstruction loss
now = datetime.utcnow().strftime("%Y%m%d-%H:%M:%S")
print('[LOG TRAIN {}] epoch: {:04}/{:04}, reconstruction loss: {:.4f}'.
format(now, epoch + 1, num_epochs, epoch_reconstruction_loss))
print(
'[LOG TRAIN {}] epoch: {:04}/{:04}, discriminator loss: {:.4f}'.format(
now, epoch + 1, num_epochs, epoch_discriminator_loss))
print('[LOG TRAIN {}] epoch: {:04}/{:04}, generator loss: {:.4f}'.format(
now, epoch + 1, num_epochs, epoch_generator_loss))
# =================== save model snapshots to disk ============================
# save trained encoder model file to disk
now = datetime.utcnow().strftime("%Y%m%d-%H_%M_%S")
encoder_model_name = "{}_ep_{}_encoder_model.pth".format(now, (epoch + 1))
torch.save(encoder_train.state_dict(),
os.path.join("./models", encoder_model_name))
# save trained decoder model file to disk
decoder_model_name = "{}_ep_{}_decoder_model.pth".format(now, (epoch + 1))
torch.save(decoder_train.state_dict(),
os.path.join("./models", decoder_model_name))
# save trained discriminator model file to disk
decoder_model_name = "{}_ep_{}_discriminator_model.pth".format(
now, (epoch + 1))
torch.save(discriminator_train.state_dict(),
os.path.join("./models", decoder_model_name))
totalElapsedTime = time.time() - start
#save execution summary
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 定义嵌入层
self.embedding = Embedding(
config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id,
config.dropout)
self.affect_embedding = Embedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id, config.dropout)
self.affect_embedding.embedding.weight.requires_grad = False
# 编码器
self.encoder = Encoder(
config.encoder_decoder_cell_type, # rnn类型
config.embedding_size + config.affect_embedding_size, # 输入维度
config.encoder_decoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # rnn层数
config.encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# 解码器
self.decoder = Decoder(
config.encoder_decoder_cell_type, # rnn类型
config.embedding_size + config.affect_embedding_size +
config.encoder_decoder_output_size,
config.encoder_decoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # rnn层数
config.dropout) # dropout概率
# 输出层
self.projector = nn.Sequential(
nn.Linear(config.encoder_decoder_output_size, config.num_vocab),
nn.Softmax(-1))
def forward(self, inputs, inference=False, max_len=60, gpu=True):
if not inference: # 训练
id_posts = inputs['posts'] # [batch, seq]
len_posts = inputs['len_posts'] # [batch]
id_responses = inputs['responses'] # [batch, seq]
len_decoder = id_responses.size(1) - 1
embed_posts = torch.cat(
[self.embedding(id_posts),
self.affect_embedding(id_posts)], 2)
embed_responses = torch.cat([
self.embedding(id_responses),
self.affect_embedding(id_responses)
], 2)
# encoder_output: [seq, batch, dim]
# state: [layers, batch, dim]
_, state_encoder = self.encoder(embed_posts.transpose(0, 1),
len_posts)
if isinstance(state_encoder, tuple):
context = state_encoder[0][-1, :, :]
else:
context = state_encoder[-1, :, :]
context = context.unsqueeze(0) # [1, batch, dim]
# 解码器的输入为回复去掉end_id
decoder_inputs = embed_responses[:, :-1, :].transpose(
0, 1) # [seq-1, batch, embed_size]
decoder_inputs = decoder_inputs.split([1] * len_decoder, 0)
outputs = []
for idx in range(len_decoder):
if idx == 0:
state = state_encoder # 解码器初始状态
decoder_input = torch.cat([decoder_inputs[idx], context],
2)
else:
decoder_input = torch.cat([decoder_inputs[idx], context],
2)
# output: [1, batch, dim_out]
# state: [num_layer, batch, dim_out]
output, state = self.decoder(decoder_input, state)
outputs.append(output)
outputs = torch.cat(outputs,
0).transpose(0, 1) # [batch, seq-1, dim_out]
output_vocab = self.projector(outputs) # [batch, seq-1, num_vocab]
return output_vocab
else: # 测试
id_posts = inputs['posts'] # [batch, seq]
len_posts = inputs['len_posts'] # [batch]
batch_size = id_posts.size(0)
embed_posts = torch.cat(
[self.embedding(id_posts),
self.affect_embedding(id_posts)], 2)
# state: [layers, batch, dim]
_, state_encoder = self.encoder(embed_posts.transpose(0, 1),
len_posts)
if isinstance(state_encoder, tuple):
context = state_encoder[0][-1, :, :]
else:
context = state_encoder[-1, :, :]
context = context.unsqueeze(0) # [1, batch, dim]
done = torch.tensor([0] * batch_size).bool()
first_input_id = (torch.ones(
(1, batch_size)) * self.config.start_id).long()
if gpu:
done = done.cuda()
first_input_id = first_input_id.cuda()
outputs = []
for idx in range(max_len):
if idx == 0: # 第一个时间步
state = state_encoder # 解码器初始状态
decoder_input = torch.cat([
self.embedding(first_input_id),
self.affect_embedding(first_input_id), context
], 2)
else:
decoder_input = torch.cat([
self.embedding(next_input_id),
self.affect_embedding(next_input_id), context
], 2)
# output: [1, batch, dim_out]
# state: [num_layers, batch, dim_out]
output, state = self.decoder(decoder_input, state)
outputs.append(output)
vocab_prob = self.projector(output) # [1, batch, num_vocab]
next_input_id = torch.argmax(
vocab_prob, 2) # 选择概率最大的词作为下个时间步的输入 [1, batch]
_done = next_input_id.squeeze(
0) == self.config.end_id # 当前时间步完成解码的 [batch]
done = done | _done # 所有完成解码的
if done.sum() == batch_size: # 如果全部解码完成则提前停止
break
outputs = torch.cat(outputs,
0).transpose(0, 1) # [batch, seq, dim_out]
output_vocab = self.projector(outputs) # [batch, seq, num_vocab]
return output_vocab
# 统计参数
def print_parameters(self):
r""" 统计参数 """
total_num = 0 # 参数总数
for param in self.parameters():
num = 1
if param.requires_grad:
size = param.size()
for dim in size:
num *= dim
total_num += num
print(f"参数总数: {total_num}")
def save_model(self, epoch, global_step, path):
r""" 保存模型 """
torch.save(
{
'embedding': self.embedding.state_dict(),
'affect_embedding': self.affect_embedding.state_dict(),
'encoder': self.encoder.state_dict(),
'decoder': self.decoder.state_dict(),
'projector': self.projector.state_dict(),
'epoch': epoch,
'global_step': global_step
}, path)
def load_model(self, path):
r""" 载入模型 """
checkpoint = torch.load(path)
self.embedding.load_state_dict(checkpoint['embedding'])
self.affect_embedding.load_state_dict(checkpoint['affect_embedding'])
self.encoder.load_state_dict(checkpoint['encoder'])
self.decoder.load_state_dict(checkpoint['decoder'])
self.projector.load_state_dict(checkpoint['projector'])
epoch = checkpoint['epoch']
global_step = checkpoint['global_step']
return epoch, global_step
caption_train = caption[:-1]
encoder.zero_grad()
decoder.zero_grad()
encod_out = encoder(image)
decoder_output = decoder(encod_out, caption_train)
loss = criterion(decoder_output, caption)
loss.backward()
optimizer.step()
loss_list.append(loss)
toc = time.time()
avg_loss = torch.mean(torch.Tensor(loss_list))
file.write(str(avg_loss.item()))
file.write(',')
print('epoch %d avg_loss %f time %.2f mins' % (epoch, avg_loss,
(toc - tic) / 60))
if epoch % save_every == 0:
torch.save(
encoder.state_dict(),
os.path.join('../Model Training/',
'iter_%d_encoder.pkl' % (epoch)))
torch.save(
decoder.state_dict(),
os.path.join('../Model Training/',
'iter_%d_decoder.pkl' % (epoch)))
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 定义嵌入层
self.embedding = Embedding(
config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id, # pad_id
config.dropout)
# 情感嵌入层
self.affect_embedding = Embedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id, config.dropout)
self.affect_embedding.embedding.weight.requires_grad = False
# post编码器
self.post_encoder = Encoder(
config.post_encoder_cell_type, # rnn类型
config.embedding_size + config.affect_embedding_size, # 输入维度
config.post_encoder_output_size, # 输出维度
config.post_encoder_num_layers, # rnn层数
config.post_encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# response编码器
self.response_encoder = Encoder(
config.response_encoder_cell_type,
config.embedding_size + config.affect_embedding_size, # 输入维度
config.response_encoder_output_size, # 输出维度
config.response_encoder_num_layers, # rnn层数
config.response_encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# 先验网络
self.prior_net = PriorNet(
config.post_encoder_output_size, # post输入维度
config.latent_size, # 潜变量维度
config.dims_prior) # 隐藏层维度
# 识别网络
self.recognize_net = RecognizeNet(
config.post_encoder_output_size, # post输入维度
config.response_encoder_output_size, # response输入维度
config.latent_size, # 潜变量维度
config.dims_recognize) # 隐藏层维度
# 初始化解码器状态
self.prepare_state = PrepareState(
config.post_encoder_output_size + config.latent_size,
config.decoder_cell_type, config.decoder_output_size,
config.decoder_num_layers)
# 解码器
self.decoder = Decoder(
config.decoder_cell_type, # rnn类型
config.embedding_size + config.affect_embedding_size +
config.post_encoder_output_size,
config.decoder_output_size, # 输出维度
config.decoder_num_layers, # rnn层数
config.dropout) # dropout概率
# bow预测
self.bow_predictor = nn.Sequential(
nn.Linear(config.post_encoder_output_size + config.latent_size,
config.num_vocab), nn.Softmax(-1))
# 输出层
self.projector = nn.Sequential(
nn.Linear(config.decoder_output_size, config.num_vocab),
nn.Softmax(-1))
def forward(self,
inputs,
inference=False,
use_true=False,
max_len=60,
gpu=True):
if not inference: # 训练
if use_true: # 解码时使用真实值
id_posts = inputs['posts'] # [batch, seq]
len_posts = inputs['len_posts'] # [batch]
id_responses = inputs['responses'] # [batch, seq]
len_responses = inputs['len_responses'] # [batch, seq]
sampled_latents = inputs[
'sampled_latents'] # [batch, latent_size]
len_decoder = id_responses.size(1) - 1
embed_posts = torch.cat([
self.embedding(id_posts),
self.affect_embedding(id_posts)
], 2)
embed_responses = torch.cat([
self.embedding(id_responses),
self.affect_embedding(id_responses)
], 2)
# state: [layers, batch, dim]
_, state_posts = self.post_encoder(embed_posts.transpose(0, 1),
len_posts)
_, state_responses = self.response_encoder(
embed_responses.transpose(0, 1), len_responses)
if isinstance(state_posts, tuple):
state_posts = state_posts[0]
if isinstance(state_responses, tuple):
state_responses = state_responses[0]
x = state_posts[-1, :, :] # [batch, dim]
y = state_responses[-1, :, :] # [batch, dim]
_mu, _logvar = self.prior_net(x) # [batch, latent]
mu, logvar = self.recognize_net(x, y) # [batch, latent]
z = mu + (0.5 *
logvar).exp() * sampled_latents # [batch, latent]
bow_predict = self.bow_predictor(torch.cat(
[z, x], 1)) # [batch, num_vocab]
first_state = self.prepare_state(torch.cat(
[z, x], 1)) # [num_layer, batch, dim_out]
decoder_inputs = embed_responses[:, :-1, :].transpose(
0, 1) # [seq-1, batch, embed_size]
decoder_inputs = decoder_inputs.split(
[1] * len_decoder, 0) # seq-1个[1, batch, embed_size]
outputs = []
for idx in range(len_decoder):
if idx == 0:
state = first_state # 解码器初始状态
decoder_input = torch.cat(
[decoder_inputs[idx],
x.unsqueeze(0)], 2)
# output: [1, batch, dim_out]
# state: [num_layer, batch, dim_out]
output, state = self.decoder(decoder_input, state)
outputs.append(output)
outputs = torch.cat(outputs,
0).transpose(0,
1) # [batch, seq-1, dim_out]
output_vocab = self.projector(
outputs) # [batch, seq-1, num_vocab]
return output_vocab, bow_predict, _mu, _logvar, mu, logvar
else:
id_posts = inputs['posts'] # [batch, seq]
len_posts = inputs['len_posts'] # [batch]
id_responses = inputs['responses'] # [batch, seq]
len_responses = inputs['len_responses'] # [batch]
sampled_latents = inputs[
'sampled_latents'] # [batch, latent_size]
len_decoder = id_responses.size(1) - 1
batch_size = id_posts.size(0)
embed_posts = torch.cat([
self.embedding(id_posts),
self.affect_embedding(id_posts)
], 2)
embed_responses = torch.cat([
self.embedding(id_responses),
self.affect_embedding(id_responses)
], 2)
# state: [layers, batch, dim]
_, state_posts = self.post_encoder(embed_posts.transpose(0, 1),
len_posts)
_, state_responses = self.response_encoder(
embed_responses.transpose(0, 1), len_responses)
if isinstance(state_posts, tuple):
state_posts = state_posts[0]
if isinstance(state_responses, tuple):
state_responses = state_responses[0]
x = state_posts[-1, :, :] # [batch, dim]
y = state_responses[-1, :, :] # [batch, dim]
_mu, _logvar = self.prior_net(x) # [batch, latent]
mu, logvar = self.recognize_net(x, y) # [batch, latent]
z = mu + (0.5 *
logvar).exp() * sampled_latents # [batch, latent]
bow_predict = self.bow_predictor(torch.cat(
[z, x], 1)) # [batch, num_vocab]
first_state = self.prepare_state(torch.cat(
[z, x], 1)) # [num_layer, batch, dim_out]
first_input_id = (torch.ones(
(1, batch_size)) * self.config.start_id).long()
if gpu:
first_input_id = first_input_id.cuda()
outputs = []
for idx in range(len_decoder):
if idx == 0:
state = first_state
decoder_input = torch.cat([
self.embedding(first_input_id),
self.affect_embedding(first_input_id),
x.unsqueeze(0)
], 2)
else:
decoder_input = torch.cat([
self.embedding(next_input_id),
self.affect_embedding(next_input_id),
x.unsqueeze(0)
], 2)
output, state = self.decoder(decoder_input, state)
outputs.append(output)
vocab_prob = self.projector(
output) # [1, batch, num_vocab]
next_input_id = torch.argmax(
vocab_prob, 2) # 选择概率最大的词作为下个时间步的输入 [1, batch]
outputs = torch.cat(outputs,
0).transpose(0,
1) # [batch, seq-1, dim_out]
output_vocab = self.projector(
outputs) # [batch, seq-1, num_vocab]
return output_vocab, bow_predict, _mu, _logvar, mu, logvar
else: # 测试
id_posts = inputs['posts'] # [batch, seq]
len_posts = inputs['len_posts'] # [batch]
sampled_latents = inputs['sampled_latents'] # [batch, latent_size]
batch_size = id_posts.size(0)
embed_posts = torch.cat(
[self.embedding(id_posts),
self.affect_embedding(id_posts)], 2)
# state: [layers, batch, dim]
_, state_posts = self.post_encoder(embed_posts.transpose(0, 1),
len_posts)
if isinstance(state_posts, tuple): # 如果是lstm则取h
state_posts = state_posts[0] # [layers, batch, dim]
x = state_posts[-1, :, :] # 取最后一层 [batch, dim]
_mu, _logvar = self.prior_net(x) # [batch, latent]
z = _mu + (0.5 *
_logvar).exp() * sampled_latents # [batch, latent]
first_state = self.prepare_state(torch.cat(
[z, x], 1)) # [num_layer, batch, dim_out]
done = torch.tensor([0] * batch_size).bool()
first_input_id = (torch.ones(
(1, batch_size)) * self.config.start_id).long()
if gpu:
done = done.cuda()
first_input_id = first_input_id.cuda()
outputs = []
for idx in range(max_len):
if idx == 0: # 第一个时间步
state = first_state # 解码器初始状态
decoder_input = torch.cat([
self.embedding(first_input_id),
self.affect_embedding(first_input_id),
x.unsqueeze(0)
], 2)
else:
decoder_input = torch.cat([
self.embedding(next_input_id),
self.affect_embedding(next_input_id),
x.unsqueeze(0)
], 2)
# output: [1, batch, dim_out]
# state: [num_layers, batch, dim_out]
output, state = self.decoder(decoder_input, state)
outputs.append(output)
vocab_prob = self.projector(output) # [1, batch, num_vocab]
next_input_id = torch.argmax(
vocab_prob, 2) # 选择概率最大的词作为下个时间步的输入 [1, batch]
_done = next_input_id.squeeze(
0) == self.config.end_id # 当前时间步完成解码的 [batch]
done = done | _done # 所有完成解码的
if done.sum() == batch_size: # 如果全部解码完成则提前停止
break
outputs = torch.cat(outputs,
0).transpose(0, 1) # [batch, seq, dim_out]
output_vocab = self.projector(outputs) # [batch, seq, num_vocab]
return output_vocab, _, _mu, _logvar, None, None
def print_parameters(self):
r""" 统计参数 """
total_num = 0 # 参数总数
for param in self.parameters():
num = 1
if param.requires_grad:
size = param.size()
for dim in size:
num *= dim
total_num += num
print(f"参数总数: {total_num}")
def save_model(self, epoch, global_step, path):
r""" 保存模型 """
torch.save(
{
'affect_embedding': self.affect_embedding.state_dict(),
'embedding': self.embedding.state_dict(),
'post_encoder': self.post_encoder.state_dict(),
'response_encoder': self.response_encoder.state_dict(),
'prior_net': self.prior_net.state_dict(),
'recognize_net': self.recognize_net.state_dict(),
'prepare_state': self.prepare_state.state_dict(),
'decoder': self.decoder.state_dict(),
'projector': self.projector.state_dict(),
'bow_predictor': self.bow_predictor.state_dict(),
'epoch': epoch,
'global_step': global_step
}, path)
def load_model(self, path):
r""" 载入模型 """
checkpoint = torch.load(path)
self.affect_embedding.load_state_dict(checkpoint['affect_embedding'])
self.embedding.load_state_dict(checkpoint['embedding'])
self.post_encoder.load_state_dict(checkpoint['post_encoder'])
self.response_encoder.load_state_dict(checkpoint['response_encoder'])
self.prior_net.load_state_dict(checkpoint['prior_net'])
self.recognize_net.load_state_dict(checkpoint['recognize_net'])
self.prepare_state.load_state_dict(checkpoint['prepare_state'])
self.decoder.load_state_dict(checkpoint['decoder'])
self.projector.load_state_dict(checkpoint['projector'])
self.bow_predictor.load_state_dict(checkpoint['bow_predictor'])
epoch = checkpoint['epoch']
global_step = checkpoint['global_step']
return epoch, global_step
class Mem2SeqRunner(ExperimentRunnerBase):
def __init__(self, args):
super(Mem2SeqRunner, self).__init__(args)
# Model parameters
self.gru_size = 128
self.emb_size = 128
#TODO: Try hops 4 with task 3
self.hops = 3
self.dropout = 0.2
self.encoder = Encoder(self.hops, self.nwords, self.gru_size)
self.decoder = Decoder(self.emb_size, self.hops, self.gru_size,
self.nwords)
self.optim_enc = torch.optim.Adam(self.encoder.parameters(), lr=0.001)
self.optim_dec = torch.optim.Adam(self.decoder.parameters(), lr=0.001)
if self.loss_weighting:
self.optim_loss_weights = torch.optim.Adam([self.loss_weights],
lr=0.0001)
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optim_dec,
mode='max',
factor=0.5,
patience=1,
min_lr=0.0001,
verbose=True)
if self.use_cuda:
self.cross_entropy = self.cross_entropy.cuda()
self.encoder = self.encoder.cuda()
self.decoder = self.decoder.cuda()
if self.loss_weighting:
self.loss_weights = self.loss_weights.cuda()
def train_batch_wrapper(self, batch, new_epoch, clip_grads):
context = batch[0].transpose(0, 1)
responses = batch[1].transpose(0, 1)
index = batch[2].transpose(0, 1)
sentinel = batch[3].transpose(0, 1)
context_lengths = batch[4]
target_lengths = batch[5]
return self.train_batch(context, responses, index, sentinel, new_epoch,
context_lengths, target_lengths, clip_grads)
def train_batch(self, context, responses, index, sentinel, new_epoch,
context_lengths, target_lengths, clip_grads):
# (TODO): remove transpose
if new_epoch: # (TODO): Change this part
self.loss = 0
self.ploss = 0
self.vloss = 0
self.n = 1
context = context.type(self.TYPE)
responses = responses.type(self.TYPE)
index = index.type(self.TYPE)
sentinel = sentinel.type(self.TYPE)
self.optim_enc.zero_grad()
self.optim_dec.zero_grad()
if self.loss_weighting:
self.optim_loss_weights.zero_grad()
h = self.encoder(context.transpose(0, 1))
self.decoder.load_memory(context.transpose(0, 1))
y = torch.from_numpy(np.array([2] * context.size(1),
dtype=int)).type(self.TYPE)
y_len = 0
h = h.unsqueeze(0)
output_vocab = torch.zeros(max(target_lengths), context.size(1),
self.nwords)
output_ptr = torch.zeros(max(target_lengths), context.size(1),
context.size(0))
if self.use_cuda:
output_vocab = output_vocab.cuda()
output_ptr = output_ptr.cuda()
while y_len < responses.size(0): # TODO: Add EOS condition
p_ptr, p_vocab, h = self.decoder(context, y, h)
output_vocab[y_len] = p_vocab
output_ptr[y_len] = p_ptr
#TODO: Add teqacher forcing ratio
y = responses[y_len].type(self.TYPE)
y_len += 1
# print(loss)
mask_v = torch.ones(output_vocab.size())
mask_p = torch.ones(output_ptr.size())
if self.use_cuda:
mask_p = mask_p.cuda()
mask_v = mask_v.cuda()
for i in range(responses.size(1)):
mask_v[target_lengths[i]:, i, :] = 0
mask_p[target_lengths[i]:, i, :] = 0
loss_v = self.cross_entropy(
output_vocab.contiguous().view(-1, self.nwords),
responses.contiguous().view(-1))
loss_ptr = self.cross_entropy(
output_ptr.contiguous().view(-1, context.size(0)),
index.contiguous().view(-1))
if self.loss_weighting:
loss = loss_ptr/(2*self.loss_weights[0]*self.loss_weights[0]) + loss_v/(2*self.loss_weights[1]*self.loss_weights[1]) + \
torch.log(self.loss_weights[0] * self.loss_weights[1])
loss_ptr = loss_ptr / (2 * self.loss_weights[0] *
self.loss_weights[0])
loss_v = loss_v / (2 * self.loss_weights[1] * self.loss_weights[1])
else:
loss = loss_ptr + loss_v
loss.backward()
ec = torch.nn.utils.clip_grad_norm_(self.encoder.parameters(), 10.0)
dc = torch.nn.utils.clip_grad_norm_(self.decoder.parameters(), 10.0)
self.optim_enc.step()
self.optim_dec.step()
if self.loss_weighting:
self.optim_loss_weights.step()
self.loss += loss.item()
self.vloss += loss_v.item()
self.ploss += loss_ptr.item()
return loss.item(), loss_v.item(), loss_ptr.item()
def evaluate_batch(self,
batch_size,
input_batches,
input_lengths,
target_batches,
target_lengths,
target_index,
target_gate,
src_plain,
profile_memory=None):
# Set to not-training mode to disable dropout
self.encoder.train(False)
self.decoder.train(False)
# Run words through encoder
decoder_hidden = self.encoder(input_batches.transpose(0,
1)).unsqueeze(0)
self.decoder.load_memory(input_batches.transpose(0, 1))
# Prepare input and output variables
decoder_input = Variable(torch.LongTensor([2] * batch_size))
decoded_words = []
all_decoder_outputs_vocab = Variable(
torch.zeros(max(target_lengths), batch_size, self.nwords))
all_decoder_outputs_ptr = Variable(
torch.zeros(max(target_lengths), batch_size,
input_batches.size(0)))
# all_decoder_outputs_gate = Variable(torch.zeros(self.max_r, batch_size))
# Move new Variables to CUDA
if self.use_cuda:
all_decoder_outputs_vocab = all_decoder_outputs_vocab.cuda()
all_decoder_outputs_ptr = all_decoder_outputs_ptr.cuda()
# all_decoder_outputs_gate = all_decoder_outputs_gate.cuda()
decoder_input = decoder_input.cuda()
p = []
for elm in src_plain:
elm_temp = [word_triple[0] for word_triple in elm]
p.append(elm_temp)
self.from_whichs = []
acc_gate, acc_ptr, acc_vac = 0.0, 0.0, 0.0
# Run through decoder one time step at a time
for t in range(max(target_lengths)):
decoder_ptr, decoder_vacab, decoder_hidden = self.decoder(
input_batches, decoder_input, decoder_hidden)
all_decoder_outputs_vocab[t] = decoder_vacab
topv, topvi = decoder_vacab.data.topk(1)
all_decoder_outputs_ptr[t] = decoder_ptr
topp, toppi = decoder_ptr.data.topk(1)
top_ptr_i = torch.gather(input_batches[:, :, 0], 0,
Variable(toppi.view(1,
-1))).transpose(0, 1)
next_in = [
top_ptr_i[i].item() if
(toppi[i].item() < input_lengths[i] - 1) else topvi[i].item()
for i in range(batch_size)
]
# if next_in in self.kb_entry.keys():
# ptr_distr.append([next_in, decoder_vacab.data])
decoder_input = Variable(
torch.LongTensor(next_in)) # Chosen word is next input
if self.use_cuda: decoder_input = decoder_input.cuda()
temp = []
from_which = []
for i in range(batch_size):
if (toppi[i].item() < len(p[i]) - 1):
temp.append(p[i][toppi[i].item()])
from_which.append('p')
else:
if target_index[t][i] != toppi[i].item():
self.incorrect_sentinel += 1
ind = topvi[i].item()
if ind == 3:
temp.append('<eos>')
else:
temp.append(self.i2w[ind])
from_which.append('v')
decoded_words.append(temp)
self.from_whichs.append(from_which)
self.from_whichs = np.array(self.from_whichs)
loss_v = self.cross_entropy(
all_decoder_outputs_vocab.contiguous().view(-1, self.nwords),
target_batches.contiguous().view(-1))
loss_ptr = self.cross_entropy(
all_decoder_outputs_ptr.contiguous().view(-1,
input_batches.size(0)),
target_index.contiguous().view(-1))
if self.loss_weighting:
loss = loss_ptr/(2*self.loss_weights[0]*self.loss_weights[0]) + loss_v/(2*self.loss_weights[1]*self.loss_weights[1]) + \
torch.log(self.loss_weights[0] * self.loss_weights[1])
else:
loss = loss_ptr + loss_v
self.loss += loss.item()
self.vloss += loss_v.item()
self.ploss += loss_ptr.item()
self.n += 1
# Set back to training mode
self.encoder.train(True)
self.decoder.train(True)
return decoded_words, self.from_whichs # , acc_ptr, acc_vac
def save_models(self, path):
torch.save(self.encoder.state_dict(),
os.path.join(path, 'encoder.pth'))
torch.save(self.decoder.state_dict(),
os.path.join(path, 'decoder.pth'))
def load_models(self, path: str = '.'):
self.encoder.load_state_dict(
torch.load(os.path.join(path, 'encoder.pth')))
self.decoder.load_state_dict(
torch.load(os.path.join(path, 'decoder.pth')))
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 情感嵌入层
self.affect_embedding = AffectEmbedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id)
# 定义嵌入层
self.embedding = WordEmbedding(
config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id) # pad_id
# post编码器
self.post_encoder = Encoder(
config.post_encoder_cell_type, # rnn类型
config.embedding_size, # 输入维度
config.post_encoder_output_size, # 输出维度
config.post_encoder_num_layers, # rnn层数
config.post_encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# response编码器
self.response_encoder = Encoder(
config.response_encoder_cell_type,
config.embedding_size, # 输入维度
config.response_encoder_output_size, # 输出维度
config.response_encoder_num_layers, # rnn层数
config.response_encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# 先验网络
self.prior_net = PriorNet(
config.post_encoder_output_size, # post输入维度
config.latent_size, # 潜变量维度
config.dims_prior) # 隐藏层维度
# 识别网络
self.recognize_net = RecognizeNet(
config.post_encoder_output_size, # post输入维度
config.response_encoder_output_size, # response输入维度
config.latent_size, # 潜变量维度
config.dims_recognize) # 隐藏层维度
# 初始化解码器状态
self.prepare_state = PrepareState(
config.post_encoder_output_size + config.latent_size,
config.decoder_cell_type, config.decoder_output_size,
config.decoder_num_layers)
# 解码器
self.decoder = Decoder(
config.decoder_cell_type, # rnn类型
config.embedding_size, # 输入维度
config.decoder_output_size, # 输出维度
config.decoder_num_layers, # rnn层数
config.dropout) # dropout概率
# 输出层
self.projector = nn.Sequential(
nn.Linear(config.decoder_output_size, config.num_vocab),
nn.Softmax(-1))
def forward(
self,
input,
inference=False, # 是否测试
use_true=False,
max_len=60): # 解码的最大长度
if not inference: # 训练
if use_true:
id_posts = input['posts'] # [batch, seq]
len_posts = input['len_posts'] # [batch]
id_responses = input['responses'] # [batch, seq]
len_responses = input['len_responses'] # [batch, seq]
sampled_latents = input[
'sampled_latents'] # [batch, latent_size]
embed_posts = self.embedding(
id_posts) # [batch, seq, embed_size]
embed_responses = self.embedding(
id_responses) # [batch, seq, embed_size]
# 解码器的输入为回复去掉end_id
decoder_input = embed_responses[:, :-1, :].transpose(
0, 1) # [seq-1, batch, embed_size]
len_decoder = decoder_input.size()[0] # 解码长度 seq-1
decoder_input = decoder_input.split(
[1] * len_decoder,
0) # 解码器每一步的输入 seq-1个[1, batch, embed_size]
# state = [layers, batch, dim]
_, state_posts = self.post_encoder(embed_posts.transpose(0, 1),
len_posts)
_, state_responses = self.response_encoder(
embed_responses.transpose(0, 1), len_responses)
if isinstance(state_posts, tuple):
state_posts = state_posts[0]
if isinstance(state_responses, tuple):
state_responses = state_responses[0]
x = state_posts[-1, :, :] # [batch, dim]
y = state_responses[-1, :, :] # [batch, dim]
_mu, _logvar = self.prior_net(x) # [batch, latent]
mu, logvar = self.recognize_net(x, y) # [batch, latent]
z = mu + (0.5 *
logvar).exp() * sampled_latents # [batch, latent]
first_state = self.prepare_state(torch.cat(
[z, x], 1)) # [num_layer, batch, dim_out]
outputs = []
for idx in range(len_decoder):
if idx == 0:
state = first_state # 解码器初始状态
input = decoder_input[
idx] # 当前时间步输入 [1, batch, embed_size]
# output: [1, batch, dim_out]
# state: [num_layer, batch, dim_out]
output, state = self.decoder(input, state)
outputs.append(output)
outputs = torch.cat(outputs,
0).transpose(0,
1) # [batch, seq-1, dim_out]
output_vocab = self.projector(
outputs) # [batch, seq-1, num_vocab]
return output_vocab, _mu, _logvar, mu, logvar
else:
id_posts = input['posts'] # [batch, seq]
len_posts = input['len_posts'] # [batch]
id_responses = input['responses'] # [batch, seq]
len_responses = input['len_responses'] # [batch]
sampled_latents = input[
'sampled_latents'] # [batch, latent_size]
len_decoder = id_responses.size()[1] - 1
batch_size = id_posts.size()[0]
device = id_posts.device.type
embed_posts = self.embedding(
id_posts) # [batch, seq, embed_size]
embed_responses = self.embedding(
id_responses) # [batch, seq, embed_size]
# state = [layers, batch, dim]
_, state_posts = self.post_encoder(embed_posts.transpose(0, 1),
len_posts)
_, state_responses = self.response_encoder(
embed_responses.transpose(0, 1), len_responses)
if isinstance(state_posts, tuple):
state_posts = state_posts[0]
if isinstance(state_responses, tuple):
state_responses = state_responses[0]
x = state_posts[-1, :, :] # [batch, dim]
y = state_responses[-1, :, :] # [batch, dim]
_mu, _logvar = self.prior_net(x) # [batch, latent]
mu, logvar = self.recognize_net(x, y) # [batch, latent]
z = mu + (0.5 *
logvar).exp() * sampled_latents # [batch, latent]
first_state = self.prepare_state(torch.cat(
[z, x], 1)) # [num_layer, batch, dim_out]
first_input_id = (torch.ones(
(1, batch_size)) * self.config.start_id).long()
if device == 'cuda':
first_input_id = first_input_id.cuda()
outputs = []
for idx in range(len_decoder):
if idx == 0:
state = first_state
input = self.embedding(first_input_id)
else:
input = self.embedding(
next_input_id) # 当前时间步输入 [1, batch, embed_size]
output, state = self.decoder(input, state)
outputs.append(output)
vocab_prob = self.projector(
output) # [1, batch, num_vocab]
next_input_id = torch.argmax(
vocab_prob, 2) # 选择概率最大的词作为下个时间步的输入 [1, batch]
outputs = torch.cat(outputs,
0).transpose(0,
1) # [batch, seq-1, dim_out]
output_vocab = self.projector(
outputs) # [batch, seq-1, num_vocab]
return output_vocab, _mu, _logvar, mu, logvar
else: # 测试
id_posts = input['posts'] # [batch, seq]
len_posts = input['len_posts'] # [batch]
sampled_latents = input['sampled_latents'] # [batch, latent_size]
batch_size = id_posts.size()[0]
device = id_posts.device.type
embed_posts = self.embedding(id_posts) # [batch, seq, embed_size]
# state = [layers, batch, dim]
_, state_posts = self.post_encoder(embed_posts.transpose(0, 1),
len_posts)
if isinstance(state_posts, tuple): # 如果是lstm则取h
state_posts = state_posts[0] # [layers, batch, dim]
x = state_posts[-1, :, :] # 取最后一层 [batch, dim]
_mu, _logvar = self.prior_net(x) # [batch, latent]
z = _mu + (0.5 *
_logvar).exp() * sampled_latents # [batch, latent]
first_state = self.prepare_state(torch.cat(
[z, x], 1)) # [num_layer, batch, dim_out]
outputs = []
done = torch.BoolTensor([0] * batch_size)
first_input_id = (torch.ones(
(1, batch_size)) * self.config.start_id).long()
if device == 'cuda':
done = done.cuda()
first_input_id = first_input_id.cuda()
for idx in range(max_len):
if idx == 0: # 第一个时间步
state = first_state # 解码器初始状态
input = self.embedding(
first_input_id) # 解码器初始输入 [1, batch, embed_size]
# output: [1, batch, dim_out]
# state: [num_layers, batch, dim_out]
output, state = self.decoder(input, state)
outputs.append(output)
vocab_prob = self.projector(output) # [1, batch, num_vocab]
next_input_id = torch.argmax(
vocab_prob, 2) # 选择概率最大的词作为下个时间步的输入 [1, batch]
_done = next_input_id.squeeze(
0) == self.config.end_id # 当前时间步完成解码的 [batch]
done = done | _done # 所有完成解码的
if done.sum() == batch_size: # 如果全部解码完成则提前停止
break
else:
input = self.embedding(
next_input_id) # [1, batch, embed_size]
outputs = torch.cat(outputs,
0).transpose(0, 1) # [batch, seq, dim_out]
output_vocab = self.projector(outputs) # [batch, seq, num_vocab]
return output_vocab, _mu, _logvar, None, None
# 统计参数
def print_parameters(self):
def statistic_param(params):
total_num = 0 # 参数总数
for param in params:
num = 1
if param.requires_grad:
size = param.size()
for dim in size:
num *= dim
total_num += num
return total_num
print("嵌入层参数个数: %d" % statistic_param(self.embedding.parameters()))
print("post编码器参数个数: %d" %
statistic_param(self.post_encoder.parameters()))
print("response编码器参数个数: %d" %
statistic_param(self.response_encoder.parameters()))
print("先验网络参数个数: %d" % statistic_param(self.prior_net.parameters()))
print("识别网络参数个数: %d" %
statistic_param(self.recognize_net.parameters()))
print("解码器初始状态参数个数: %d" %
statistic_param(self.prepare_state.parameters()))
print("解码器参数个数: %d" % statistic_param(self.decoder.parameters()))
print("输出层参数个数: %d" % statistic_param(self.projector.parameters()))
print("参数总数: %d" % statistic_param(self.parameters()))
# 保存模型
def save_model(self, epoch, global_step, path):
torch.save(
{
'affect_embedding': self.affect_embedding.state_dict(),
'embedding': self.embedding.state_dict(),
'post_encoder': self.post_encoder.state_dict(),
'response_encoder': self.response_encoder.state_dict(),
'prior_net': self.prior_net.state_dict(),
'recognize_net': self.recognize_net.state_dict(),
'prepare_state': self.prepare_state.state_dict(),
'decoder': self.decoder.state_dict(),
'projector': self.projector.state_dict(),
'epoch': epoch,
'global_step': global_step
}, path)
# 载入模型
def load_model(self, path):
checkpoint = torch.load(path)
self.affect_embedding.load_state_dict(checkpoint['affect_embedding'])
self.embedding.load_state_dict(checkpoint['embedding'])
self.post_encoder.load_state_dict(checkpoint['post_encoder'])
self.response_encoder.load_state_dict(checkpoint['response_encoder'])
self.prior_net.load_state_dict(checkpoint['prior_net'])
self.recognize_net.load_state_dict(checkpoint['recognize_net'])
self.prepare_state.load_state_dict(checkpoint['prepare_state'])
self.decoder.load_state_dict(checkpoint['decoder'])
self.projector.load_state_dict(checkpoint['projector'])
epoch = checkpoint['epoch']
global_step = checkpoint['global_step']
return epoch, global_step
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 情感嵌入层
self.affect_embedding = AffectEmbedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id)
# 定义嵌入层
self.embedding = WordEmbedding(
config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id) # pad_id
# 情感编码器
self.affect_encoder = Encoder(
config.encoder_decoder_cell_type, # rnn类型
config.affect_embedding_size, # 输入维度
config.affect_encoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # 层数
config.encoder_bidirectional, # 是否双向
config.dropout)
# 编码器
self.encoder = Encoder(
config.encoder_decoder_cell_type, # rnn类型
config.embedding_size, # 输入维度
config.encoder_decoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # rnn层数
config.encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
self.attention = Attention(config.encoder_decoder_output_size,
config.affect_encoder_output_size,
config.attention_type,
config.attention_size)
self.prepare_state = PrepareState(
config.encoder_decoder_cell_type,
config.encoder_decoder_output_size +
config.affect_encoder_output_size,
config.encoder_decoder_output_size)
self.linear_prepare_input = nn.Linear(
config.embedding_size + config.affect_encoder_output_size +
config.attention_size, config.decoder_input_size)
# 解码器
self.decoder = Decoder(
config.encoder_decoder_cell_type, # rnn类型
config.decoder_input_size, # 输入维度
config.encoder_decoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # rnn层数
config.dropout) # dropout概率
# 输出层
self.projector = nn.Sequential(
nn.Linear(
config.encoder_decoder_output_size + config.attention_size,
config.num_vocab), nn.Softmax(-1))
def forward(
self,
input,
inference=False, # 是否测试
max_len=60): # 解码的最大长度
if not inference: # 训练
id_posts = input['posts'] # [batch, seq]
len_posts = input['len_posts'] # [batch]
id_responses = input['responses'] # [batch, seq]
batch_size = id_posts.size()[0]
device = id_posts.device.type
embed_posts = self.embedding(id_posts) # [batch, seq, embed_size]
embed_responses = self.embedding(
id_responses) # [batch, seq, embed_size]
affect_posts = self.affect_embedding(id_posts)
# 解码器的输入为回复去掉end_id
decoder_input = embed_responses[:, :-1, :].transpose(
0, 1) # [seq-1, batch, embed_size]
len_decoder = decoder_input.size()[0] # 解码长度 seq-1
decoder_input = decoder_input.split(
[1] * len_decoder, 0) # 解码器每一步的输入 seq-1个[1, batch, embed_size]
# state: [layers, batch, dim]
_, state_encoder = self.encoder(embed_posts.transpose(0, 1),
len_posts)
# output_affect: [seq, batch, dim]
output_affect, state_affect_encoder = self.affect_encoder(
affect_posts.transpose(0, 1), len_posts)
context_affect = output_affect[-1, :, :].unsqueeze(
0) # [1, batch, dim]
outputs = []
weights = []
init_attn = torch.zeros(
[1, batch_size, self.config.attention_size])
if device == 'cuda':
init_attn = init_attn.cuda()
for idx in range(len_decoder):
if idx == 0:
state = self.prepare_state(state_encoder,
state_affect_encoder) # 解码器初始状态
input = torch.cat(
[decoder_input[idx], context_affect, init_attn], 2) #
else:
input = torch.cat([
decoder_input[idx], context_affect,
attn.transpose(0, 1)
], 2) #
input = self.linear_prepare_input(input)
# output: [1, batch, dim_out]
# state: [num_layer, batch, dim_out]
output, state = self.decoder(input, state)
# attn: [batch, 1, attention_size]
# weights: [batch, 1, encoder_len]
attn, weight = self.attention(output.transpose(0, 1),
output_affect.transpose(0, 1))
outputs.append(torch.cat([output, attn.transpose(0, 1)], 2))
weights.append(weight)
outputs = torch.cat(outputs,
0).transpose(0, 1) # [batch, seq-1, dim_out]
weights = torch.cat(weights, 1) # [batch, seq-1, dim_out]
output_vocab = self.projector(outputs) # [batch, seq-1, num_vocab]
return output_vocab, weights
else: # 测试
id_posts = input['posts'] # [batch, seq]
len_posts = input['len_posts'] # [batch]
batch_size = id_posts.size()[0]
device = id_posts.device.type
embed_posts = self.embedding(id_posts) # [batch, seq, embed_size]
affect_posts = self.affect_embedding(id_posts)
# state: [layers, batch, dim]
_, state_encoder = self.encoder(embed_posts.transpose(0, 1),
len_posts)
output_affect, state_affect_encoder = self.affect_encoder(
affect_posts.transpose(0, 1), len_posts)
context_affect = output_affect[-1, :, :].unsqueeze(
0) # [1, batch, dim]
outputs = []
weights = []
done = torch.BoolTensor([0] * batch_size)
first_input_id = (torch.ones(
(1, batch_size)) * self.config.start_id).long()
init_attn = torch.zeros(
[1, batch_size, self.config.attention_size])
if device == 'cuda':
done = done.cuda()
first_input_id = first_input_id.cuda()
init_attn = init_attn.cuda()
for idx in range(max_len):
if idx == 0: # 第一个时间步
state = self.prepare_state(state_encoder,
state_affect_encoder) # 解码器初始状态
input = torch.cat([
self.embedding(first_input_id), context_affect,
init_attn
], 2) # 解码器初始输入 [1, batch, embed_size]
else:
input = torch.cat(
[input, context_affect,
attn.transpose(0, 1)], 2)
input = self.linear_prepare_input(input)
# output: [1, batch, dim_out]
# state: [num_layers, batch, dim_out]
output, state = self.decoder(input, state)
# attn: [batch, 1, attention_size]
# weights: [batch, 1, encoder_len]
attn, weight = self.attention(output.transpose(0, 1),
output_affect.transpose(0, 1))
outputs.append(torch.cat([output, attn.transpose(0, 1)], 2))
weights.append(weight)
vocab_prob = self.projector(
torch.cat([output, attn.transpose(0, 1)],
2)) # [1, batch, num_vocab]
next_input_id = torch.argmax(
vocab_prob, 2) # 选择概率最大的词作为下个时间步的输入 [1, batch]
_done = next_input_id.squeeze(
0) == self.config.end_id # 当前时间步完成解码的 [batch]
done = done | _done # 所有完成解码的
if done.sum() == batch_size: # 如果全部解码完成则提前停止
break
else:
input = self.embedding(
next_input_id) # [1, batch, embed_size]
outputs = torch.cat(outputs,
0).transpose(0, 1) # [batch, seq, dim_out]
weights = torch.cat(weights, 1)
output_vocab = self.projector(outputs) # [batch, seq, num_vocab]
return output_vocab, weights
# 统计参数
def print_parameters(self):
def statistic_param(params):
total_num = 0 # 参数总数
for param in params:
num = 1
if param.requires_grad:
size = param.size()
for dim in size:
num *= dim
total_num += num
return total_num
print("嵌入层参数个数: %d" % statistic_param(self.embedding.parameters()))
print("编码器参数个数: %d" % statistic_param(self.encoder.parameters()))
print("情感编码器参数个数: %d" %
statistic_param(self.affect_encoder.parameters()))
print("准备状态参数个数: %d" %
statistic_param(self.prepare_state.parameters()))
print("准备输入参数个数: %d" %
statistic_param(self.linear_prepare_input.parameters()))
print("注意力参数个数: %d" % statistic_param(self.attention.parameters()))
print("解码器参数个数: %d" % statistic_param(self.decoder.parameters()))
print("输出层参数个数: %d" % statistic_param(self.projector.parameters()))
print("参数总数: %d" % statistic_param(self.parameters()))
# 保存模型
def save_model(self, epoch, global_step, path):
torch.save(
{
'affect_embedding': self.affect_embedding.state_dict(),
'embedding': self.embedding.state_dict(),
'encoder': self.encoder.state_dict(),
'affect_encoder': self.affect_encoder.state_dict(),
'prepare_state': self.prepare_state.state_dict(),
'linear_prepare_input': self.linear_prepare_input.state_dict(),
'attention': self.attention.state_dict(),
'projector': self.projector.state_dict(),
'decoder': self.decoder.state_dict(),
'epoch': epoch,
'global_step': global_step
}, path)
# 载入模型
def load_model(self, path):
checkpoint = torch.load(path)
self.affect_embedding.load_state_dict(checkpoint['embedding'])
self.embedding.load_state_dict(checkpoint['embedding'])
self.encoder.load_state_dict(checkpoint['encoder'])
self.affect_encoder.load_state_dict(checkpoint['affect_encoder'])
self.prepare_state.load_state_dict(checkpoint['prepare_state'])
self.linear_prepare_input.load_state_dict(
checkpoint['linear_prepare_input'])
self.attention.load_state_dict(checkpoint['attention'])
self.decoder.load_state_dict(checkpoint['decoder'])
self.projector.load_state_dict(checkpoint['projector'])
epoch = checkpoint['epoch']
global_step = checkpoint['global_step']
return epoch, global_step
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 定义嵌入层
self.embedding = Embedding(
config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id,
config.dropout)
self.affect_embedding = Embedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id, config.dropout)
self.affect_embedding.embedding.weight.requires_grad = False
# 编码器
self.encoder = Encoder(
config.encoder_cell_type, # rnn类型
config.embedding_size + config.affect_embedding_size, # 输入维度
config.encoder_output_size, # 输出维度
config.encoder_num_layers, # rnn层数
config.encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# 输出层
self.classifier = nn.Sequential(
nn.Linear(config.encoder_output_size,
config.encoder_output_size // 2),
nn.Linear(config.encoder_output_size // 2,
config.num_classifications), nn.Softmax(-1))
def forward(self, inputs):
x = inputs['x'] # [batch, len]
len_x = inputs['len_x'] # [batch]
embed_x = torch.cat(
[self.embedding(x), self.affect_embedding(x)],
2) # [batch, len, embed]
# state: [layers, batch, dim]
_, state = self.encoder(embed_x.transpose(0, 1), len_x)
if isinstance(state, tuple):
state = state[0]
context = state[-1, :, :] # [batch, dim]
output = self.classifier(context) # [batch, 7]
return output
# 统计参数
def print_parameters(self):
r""" 统计参数 """
total_num = 0 # 参数总数
for param in self.parameters():
num = 1
if param.requires_grad:
size = param.size()
for dim in size:
num *= dim
total_num += num
print(f"参数总数: {total_num}")
def save_model(self, epoch, global_step, path):
r""" 保存模型 """
torch.save(
{
'embedding': self.embedding.state_dict(),
'affect_embedding': self.affect_embedding.state_dict(),
'encoder': self.encoder.state_dict(),
'classifier': self.classifier.state_dict(),
'epoch': epoch,
'global_step': global_step
}, path)
def load_model(self, path):
r""" 载入模型 """
checkpoint = torch.load(path)
self.embedding.load_state_dict(checkpoint['embedding'])
self.affect_embedding.load_state_dict(checkpoint['affect_embedding'])
self.encoder.load_state_dict(checkpoint['encoder'])
self.classifier.load_state_dict(checkpoint['classifier'])
epoch = checkpoint['epoch']
global_step = checkpoint['global_step']
return epoch, global_step
