class Instructor:
def __init__(self, model_name: str, args):
self.model_name = model_name
self.args = args
self.encoder = Encoder(self.args.add_noise).to(self.args.device)
self.decoder = Decoder(self.args.upsample_mode).to(self.args.device)
self.pretrainDataset = None
self.pretrainDataloader = None
self.pretrainOptimizer = None
self.pretrainScheduler = None
self.RHO_tensor = None
self.pretrain_batch_cnt = 0
self.writer = None
self.svmDataset = None
self.svmDataloader = None
self.testDataset = None
self.testDataloader = None
self.svm = SVC(C=self.args.svm_c,
kernel=self.args.svm_ker,
verbose=True,
max_iter=self.args.svm_max_iter)
self.resnet = Resnet(use_pretrained=True,
num_classes=self.args.classes,
resnet_depth=self.args.resnet_depth,
dropout=self.args.resnet_dropout).to(
self.args.device)
self.resnetOptimizer = None
self.resnetScheduler = None
self.resnetLossFn = None
def _load_data_by_label(self, label: str) -> list:
ret = []
LABEL_PATH = os.path.join(self.args.TRAIN_PATH, label)
for dir_path, _, file_list in os.walk(LABEL_PATH, topdown=False):
for file_name in file_list:
file_path = os.path.join(dir_path, file_name)
img_np = imread(file_path)
img = img_np.copy()
img = img.tolist()
ret.append(img)
return ret
def _load_all_data(self):
all_data = []
all_labels = []
for label_id in range(0, self.args.classes):
expression = LabelEnum(label_id)
sub_data = self._load_data_by_label(expression.name)
sub_labels = [label_id] * len(sub_data)
all_data.extend(sub_data)
all_labels.extend(sub_labels)
return all_data, all_labels
def _load_test_data(self):
file_map = pd.read_csv(
os.path.join(self.args.RAW_PATH, 'submission.csv'))
test_data = []
img_names = []
for file_name in file_map['file_name']:
file_path = os.path.join(self.args.TEST_PATH, file_name)
img_np = imread(file_path)
img = img_np.copy()
img = img.tolist()
test_data.append(img)
img_names.append(file_name)
return test_data, img_names
def trainAutoEncoder(self):
self.writer = SummaryWriter(
os.path.join(self.args.LOG_PATH, self.model_name))
all_data, all_labels = self._load_all_data()
self.pretrainDataset = FERDataset(all_data,
labels=all_labels,
args=self.args)
self.pretrainDataloader = DataLoader(dataset=self.pretrainDataset,
batch_size=self.args.batch_size,
shuffle=True,
num_workers=self.args.num_workers)
self.pretrainOptimizer = torch.optim.Adam([{
'params':
self.encoder.parameters(),
'lr':
self.args.pretrain_lr
}, {
'params':
self.decoder.parameters(),
'lr':
self.args.pretrain_lr
}])
tot_steps = math.ceil(
len(self.pretrainDataloader) /
self.args.cumul_batch) * self.args.epochs
self.pretrainScheduler = get_linear_schedule_with_warmup(
self.pretrainOptimizer,
num_warmup_steps=0,
num_training_steps=tot_steps)
self.RHO_tensor = torch.tensor(
[self.args.rho for _ in range(self.args.embed_dim)],
dtype=torch.float).unsqueeze(0).to(self.args.device)
epochs = self.args.epochs
for epoch in range(1, epochs + 1):
print()
print(
"================ AutoEncoder Training Epoch {:}/{:} ================"
.format(epoch, epochs))
print(" ---- Start training ------>")
self.epochTrainAutoEncoder(epoch)
print()
self.writer.close()
def epochTrainAutoEncoder(self, epoch):
self.encoder.train()
self.decoder.train()
cumul_loss = 0
cumul_steps = 0
cumul_samples = 0
self.pretrainOptimizer.zero_grad()
cumulative_batch = 0
for idx, (images, labels) in enumerate(tqdm(self.pretrainDataloader)):
batch_size = images.shape[0]
images, labels = images.to(self.args.device), labels.to(
self.args.device)
embeds = self.encoder(images)
outputs = self.decoder(embeds)
loss = torch.nn.functional.mse_loss(outputs, images)
if self.args.use_sparse:
rho_hat = torch.mean(embeds, dim=0, keepdim=True)
sparse_penalty = self.args.regulizer_weight * torch.nn.functional.kl_div(
input=torch.nn.functional.log_softmax(rho_hat, dim=-1),
target=torch.nn.functional.softmax(self.RHO_tensor,
dim=-1))
loss = loss + sparse_penalty
loss_each = loss / self.args.cumul_batch
loss_each.backward()
cumulative_batch += 1
cumul_steps += 1
cumul_loss += loss.detach().cpu().item() * batch_size
cumul_samples += batch_size
if cumulative_batch >= self.args.cumul_batch:
torch.nn.utils.clip_grad_norm_(self.encoder.parameters(),
max_norm=self.args.max_norm)
torch.nn.utils.clip_grad_norm_(self.decoder.parameters(),
max_norm=self.args.max_norm)
self.pretrainOptimizer.step()
self.pretrainScheduler.step()
self.pretrainOptimizer.zero_grad()
cumulative_batch = 0
if cumul_steps >= self.args.disp_period or idx + 1 == len(
self.pretrainDataloader):
print(" -> cumul_steps={:} loss={:}".format(
cumul_steps, cumul_loss / cumul_samples))
self.pretrain_batch_cnt += 1
self.writer.add_scalar('batch-loss',
cumul_loss / cumul_samples,
global_step=self.pretrain_batch_cnt)
self.writer.add_scalar('encoder_lr',
self.pretrainOptimizer.state_dict()
['param_groups'][0]['lr'],
global_step=self.pretrain_batch_cnt)
self.writer.add_scalar('decoder_lr',
self.pretrainOptimizer.state_dict()
['param_groups'][1]['lr'],
global_step=self.pretrain_batch_cnt)
cumul_steps = 0
cumul_loss = 0
cumul_samples = 0
self.saveAutoEncoder(epoch)
def saveAutoEncoder(self, epoch):
encoderPath = os.path.join(
self.args.CKPT_PATH,
self.model_name + "--Encoder" + "--EPOCH-{:}".format(epoch))
decoderPath = os.path.join(
self.args.CKPT_PATH,
self.model_name + "--Decoder" + "--EPOCH-{:}".format(epoch))
print("-----------------------------------------------")
print(" -> Saving AutoEncoder {:} ......".format(self.model_name))
torch.save(self.encoder.state_dict(), encoderPath)
torch.save(self.decoder.state_dict(), decoderPath)
print(" -> Successfully saved AutoEncoder.")
print("-----------------------------------------------")
def generateAutoEncoderTestResultSamples(self, sample_cnt):
self.encoder.eval()
self.decoder.eval()
print(' -> Generating samples with AutoEncoder ...')
save_path = os.path.join(self.args.SAMPLE_PATH, self.model_name)
if not os.path.exists(save_path):
os.mkdir(save_path)
with torch.no_grad():
for dir_path, _, file_list in os.walk(self.args.TEST_PATH,
topdown=False):
sample_file_list = random.choices(file_list, k=sample_cnt)
for file_name in sample_file_list:
file_path = os.path.join(dir_path, file_name)
img_np = imread(file_path)
img = img_np.copy()
img = ToTensor()(img)
img = img.reshape(1, 1, 48, 48)
img = img.to(self.args.device)
embed = self.encoder(img)
out = self.decoder(embed).cpu()
out = out.reshape(1, 48, 48)
out_img = ToPILImage()(out)
out_img.save(os.path.join(save_path, file_name))
print(' -> Done sampling from AutoEncoder with test pictures.')
def loadAutoEncoder(self, epoch):
encoderPath = os.path.join(
self.args.CKPT_PATH,
self.model_name + "--Encoder" + "--EPOCH-{:}".format(epoch))
decoderPath = os.path.join(
self.args.CKPT_PATH,
self.model_name + "--Decoder" + "--EPOCH-{:}".format(epoch))
print("-----------------------------------------------")
print(" -> Loading AutoEncoder {:} ......".format(self.model_name))
self.encoder.load_state_dict(torch.load(encoderPath))
self.decoder.load_state_dict(torch.load(decoderPath))
print(" -> Successfully loaded AutoEncoder.")
print("-----------------------------------------------")
def generateExtractedFeatures(
self, data: torch.FloatTensor) -> torch.FloatTensor:
"""
:param data: (batch, channel, l, w)
:return: embed: (batch, embed_dim)
"""
with torch.no_grad():
data = data.to(self.args.device)
embed = self.encoder(data)
embed = embed.detach().cpu()
return embed
def trainSVM(self, load: bool):
svm_path = os.path.join(self.args.CKPT_PATH, self.model_name + '--svm')
self.loadAutoEncoder(self.args.epochs)
self.encoder.eval()
self.decoder.eval()
if load:
print(' -> Loaded from SVM trained model.')
self.svm = joblib.load(svm_path)
return
print()
print("================ SVM Training Starting ================")
all_data, all_labels = self._load_all_data()
all_length = len(all_data)
self.svmDataset = FERDataset(all_data,
labels=all_labels,
use_da=False,
args=self.args)
self.svmDataloader = DataLoader(dataset=self.svmDataset,
batch_size=self.args.batch_size,
shuffle=False,
num_workers=self.args.num_workers)
print(" -> Converting to extracted features ...")
cnt = 0
all_embeds = []
all_labels = []
for images, labels in self.svmDataloader:
cnt += 1
embeds = self.generateExtractedFeatures(images)
all_embeds.extend(embeds.tolist())
all_labels.extend(labels.reshape(-1).tolist())
print(' -> Start SVM fit ...')
self.svm.fit(X=all_embeds, y=all_labels)
# self.svm.fit(X=all_embeds[0:3], y=[0, 1, 2])
joblib.dump(self.svm, svm_path)
print(" -> Done training for SVM.")
def genTestResult(self, from_svm=True):
print()
print('-------------------------------------------------------')
print(' -> Generating test result for {:} ...'.format(
'SVM' if from_svm else 'Resnet'))
test_data, img_names = self._load_test_data()
test_length = len(test_data)
self.testDataset = FERDataset(test_data,
filenames=img_names,
use_da=False,
args=self.args)
self.testDataloader = DataLoader(dataset=self.testDataset,
batch_size=self.args.batch_size,
shuffle=False,
num_workers=self.args.num_workers)
str_preds = []
for images, filenames in self.testDataloader:
if from_svm:
embeds = self.generateExtractedFeatures(images)
preds = self.svm.predict(X=embeds)
else:
self.resnet.eval()
outs = self.resnet(
images.repeat(1, 3, 1, 1).to(self.args.device))
preds = outs.max(-1)[1].cpu().tolist()
str_preds.extend([LabelEnum(pred).name for pred in preds])
# generate submission
assert len(str_preds) == len(img_names)
submission = pd.DataFrame({'file_name': img_names, 'class': str_preds})
submission.to_csv(os.path.join(self.args.DATA_PATH, 'submission.csv'),
index=False,
index_label=False)
print(' -> Done generation of submission.csv with model {:}'.format(
self.model_name))
def epochTrainResnet(self, epoch):
self.resnet.train()
cumul_loss = 0
cumul_acc = 0
cumul_steps = 0
cumul_samples = 0
cumulative_batch = 0
self.resnetOptimizer.zero_grad()
for idx, (images, labels) in enumerate(tqdm(self.pretrainDataloader)):
batch_size = images.shape[0]
images, labels = images.to(self.args.device), labels.to(
self.args.device)
images += torch.randn(images.shape).to(
images.device) * self.args.add_noise
images = images.repeat(1, 3, 1, 1)
outs = self.resnet(images)
preds = outs.max(-1)[1].unsqueeze(dim=1)
cur_acc = (preds == labels).type(torch.int).sum().item()
loss = self.resnetLossFn(outs, labels.squeeze(dim=1))
loss_each = loss / self.args.cumul_batch
loss_each.backward()
cumulative_batch += 1
cumul_steps += 1
cumul_loss += loss.detach().cpu().item() * batch_size
cumul_acc += cur_acc
cumul_samples += batch_size
if cumulative_batch >= self.args.cumul_batch:
torch.nn.utils.clip_grad_norm_(self.resnet.parameters(),
max_norm=self.args.max_norm)
self.resnetOptimizer.step()
self.resnetScheduler.step()
self.resnetOptimizer.zero_grad()
cumulative_batch = 0
if cumul_steps >= self.args.disp_period or idx + 1 == len(
self.pretrainDataloader):
print(" -> cumul_steps={:} loss={:} acc={:}".format(
cumul_steps, cumul_loss / cumul_samples,
cumul_acc / cumul_samples))
self.pretrain_batch_cnt += 1
self.writer.add_scalar('batch-loss',
cumul_loss / cumul_samples,
global_step=self.pretrain_batch_cnt)
self.writer.add_scalar('batch-acc',
cumul_acc / cumul_samples,
global_step=self.pretrain_batch_cnt)
self.writer.add_scalar(
'resnet_lr',
self.resnetOptimizer.state_dict()['param_groups'][0]['lr'],
global_step=self.pretrain_batch_cnt)
cumul_steps = 0
cumul_loss = 0
cumul_acc = 0
cumul_samples = 0
if epoch % 10 == 0:
self.saveResnet(epoch)
def saveResnet(self, epoch):
resnetPath = os.path.join(
self.args.CKPT_PATH,
self.model_name + "--Resnet" + "--EPOCH-{:}".format(epoch))
print("-----------------------------------------------")
print(" -> Saving Resnet {:} ......".format(self.model_name))
torch.save(self.resnet.state_dict(), resnetPath)
print(" -> Successfully saved Resnet.")
print("-----------------------------------------------")
def loadResnet(self, epoch):
resnetPath = os.path.join(
self.args.CKPT_PATH,
self.model_name + "--Resnet" + "--EPOCH-{:}".format(epoch))
print("-----------------------------------------------")
print(" -> Loading Resnet {:} ......".format(self.model_name))
self.resnet.load_state_dict(torch.load(resnetPath))
print(" -> Successfully loaded Resnet.")
print("-----------------------------------------------")
def trainResnet(self):
self.writer = SummaryWriter(
os.path.join(self.args.LOG_PATH, self.model_name))
all_data, all_labels = self._load_all_data()
self.pretrainDataset = FERDataset(all_data,
labels=all_labels,
args=self.args)
self.pretrainDataloader = DataLoader(dataset=self.pretrainDataset,
batch_size=self.args.batch_size,
shuffle=True,
num_workers=self.args.num_workers)
self.resnetOptimizer = self.getResnetOptimizer()
tot_steps = math.ceil(
len(self.pretrainDataloader) /
self.args.cumul_batch) * self.args.epochs
self.resnetScheduler = get_linear_schedule_with_warmup(
self.resnetOptimizer,
num_warmup_steps=tot_steps * self.args.warmup_rate,
num_training_steps=tot_steps)
self.resnetLossFn = torch.nn.CrossEntropyLoss(
weight=torch.tensor([
9.40661861,
1.00104606,
0.56843877,
0.84912748,
1.02660468,
1.29337298,
0.82603942,
],
dtype=torch.float,
device=self.args.device))
epochs = self.args.epochs
for epoch in range(1, epochs + 1):
print()
print(
"================ Resnet Training Epoch {:}/{:} ================"
.format(epoch, epochs))
print(" ---- Start training ------>")
self.epochTrainResnet(epoch)
print()
self.writer.close()
def getResnetOptimizer(self):
if self.args.resnet_optim == 'SGD':
return torch.optim.SGD([{
'params': self.resnet.baseParameters(),
'lr': self.args.resnet_base_lr,
'weight_decay': self.args.weight_decay,
'momentum': self.args.resnet_momentum
}, {
'params': self.resnet.finetuneParameters(),
'lr': self.args.resnet_ft_lr,
'weight_decay': self.args.weight_decay,
'momentum': self.args.resnet_momentum
}],
lr=self.args.resnet_base_lr)
elif self.args.resnet_optim == 'Adam':
return torch.optim.Adam([{
'params': self.resnet.baseParameters(),
'lr': self.args.resnet_base_lr
}, {
'params': self.resnet.finetuneParameters(),
'lr': self.args.resnet_ft_lr,
'weight_decay': self.args.weight_decay
}])
# Mode
print('==> Building model..')
encoder = Encoder(mask=mask)
decoder = Decoder(mask=mask)
classifier = Classifier()
encoder = encoder.to(device)
decoder = decoder.to(device)
classifier = classifier.to(device)
if device == 'cuda':
cudnn.benchmark = True
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./checkpoint/ckpt_' + codir + '.t7')
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
classifier.load_state_dict(checkpoint['classifier'])
best_loss = checkpoint['loss']
start_epoch = checkpoint['epoch']
# # in the cloud
# for param in encoder.parameters():
# param.requires_grad = False
criterion1 = nn.MSELoss()
criterion2 = nn.CrossEntropyLoss()
optimizer1 = optim.SGD(encoder.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=9e-4)
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)
# 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, 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_responses = inputs['len_responses'] # [batch, seq]
sampled_latents = inputs['sampled_latents'] # [batch, latent_size]
len_decoder = id_responses.size(1) - 1
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]
# p(z|x)
_mu, _logvar = self.prior_net(x) # [batch, latent]
# p(z|x,y)
mu, logvar = self.recognize_net(x, y) # [batch, latent]
# 重参数化
z = mu + (0.5 * logvar).exp() * sampled_latents # [batch, latent]
# 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) # 解码器每一步的输入 seq-1个[1, batch, embed_size]
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
decoder_input = decoder_inputs[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(decoder_input, state)
assert output.squeeze().equal(state[0][-1])
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: # 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 = 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]
# p(z|x)
_mu, _logvar = self.prior_net(x) # [batch, latent]
# 重参数化
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 = self.embedding(first_input_id) # 解码器初始输入 [1, batch, embed_size]
else:
decoder_input = self.embedding(next_input_id) # [1, batch, embed_size]
# 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({'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)
def load_model(self, path):
r""" 载入模型 """
checkpoint = torch.load(path, map_location=torch.device('cpu'))
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
