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 __init__( # TODO move parameters to config file
self,
pset,
batch_size=64,
max_size=100,
vocab_inp_size=32,
vocab_tar_size=32,
embedding_dim=64,
units=128,
hidden_size=128,
alpha=0.1,
epochs=200,
epoch_decay=1,
min_epochs=10,
verbose=True):
self.alpha = alpha
self.batch_size = batch_size
self.max_size = max_size
self.epochs = epochs
self.epoch_decay = epoch_decay
self.min_epochs = min_epochs
self.train_steps = 0
self.verbose = verbose
self.enc = Encoder(vocab_inp_size, embedding_dim, units, batch_size)
self.dec = Decoder(vocab_inp_size, vocab_tar_size, embedding_dim,
units, batch_size)
self.surrogate = Surrogate(hidden_size)
self.population = Population(pset, max_size, batch_size)
self.prob = 0.5
self.optimizer = tf.keras.optimizers.Adam()
self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False, reduction='none')
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 __init__(self, opt, vocabs):
super(S2SModel, self).__init__()
self.opt = opt
self.vocabs = vocabs
self.encoder = Encoder(vocabs, opt)
self.decoder = Decoder(vocabs, opt)
self.generator = ProdGenerator(self.opt.decoder_rnn_size, vocabs,
self.opt)
def __init__(self,
args,
vocab,
n_dim,
image_dim,
layers,
dropout,
num_choice=5):
super().__init__()
print("Model name: DA, 1 layer, fixed subspaces")
self.vocab = vocab
self.encoder = Encoder(args, vocab, n_dim, image_dim, layers, dropout,
num_choice).cuda()
self.decoder = Decoder(args, vocab, n_dim, image_dim, layers, dropout,
num_choice).cuda()
def __init__(self,
num_layers,
d_model,
num_heads,
dff,
input_vocab_size,
target_vocab_size,
dropout=0.1):
super(Transformer, self).__init__()
self.encoder = Encoder(num_layers, d_model, num_heads, dff,
input_vocab_size, dropout)
self.decoder = Decoder(num_layers, d_model, num_heads, dff,
target_vocab_size, dropout)
self.final_layer = tf.keras.layers.Dense(target_vocab_size)
def __init__(self,
num_encoder_layers: int = 6,
num_decoder_layers: int = 6,
dim_embedding: int = 512,
num_heads: int = 6,
dim_feedfordward: int = 512,
dropout: float = 0.1,
activation: nn.Module = nn.ReLU()):
super().__init__()
self.encoder = Encoder(num_layers=num_encoder_layers,
dim_embedding=dim_embedding,
num_heads=num_heads,
dim_feedfordward=dim_feedfordward,
dropout=dropout)
self.decoder = Decoder(num_layers=num_decoder_layers,
dim_embedding=dim_embedding,
num_heads=num_heads,
dim_feedfordward=dim_feedfordward,
dropout=dropout)
self.criterion = nn.CrossEntropyLoss()
def __init__(self):
super(Model, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder()
self.embeds = nn.Embedding(config.vocab_size, config.emb_dim)
init_wt.init_wt_normal(self.embeds.weight)
self.encoder = get_cuda(self.encoder)
self.decoder = get_cuda(self.decoder)
self.embeds = get_cuda(self.embeds)
# if __name__ == '__main__':
#
# my_model = Model()
# my_model_paramters = my_model.parameters()
#
# print(my_model_paramters)
# my_model_paramters_group = list(my_model_paramters)
# print(my_model_paramters_group)
def __init__(self,
args,
vocab,
n_dim,
image_dim,
layers,
dropout,
num_choice=5):
super().__init__()
print("Model name: DA")
self.vocab = vocab
self.encoder = Encoder(args, vocab, n_dim, image_dim, layers, dropout,
num_choice).cuda()
#self.encoder = TransformerEncoder(args, vocab, n_dim, image_dim, layers, dropout, num_choice).cuda()
#self.encoder = DAEncoder(args, vocab, n_dim, image_dim, layers, dropout, num_choice).cuda()
#self.encoder = MHEncoder(args, vocab, n_dim, image_dim, layers, dropout, num_choice).cuda()
##self.encoder = HierarchicalDA(args, vocab, n_dim, image_dim, layers, dropout, num_choice).cuda()
#self.decoder = Disc(args, vocab, n_dim, image_dim, layers, dropout, num_choice)
#self.decoder = SumDisc(args, vocab, n_dim, image_dim, layers, dropout, num_choice)
self.decoder = Decoder(args, vocab, n_dim, image_dim, layers, dropout,
num_choice).cuda()
def make_model(src_vocab,
tar_vocab,
N=6,
d_model=512,
d_ff=2014,
h=8,
dropout=0.1):
c = copy.deepcopy
attn = MultiHeadedAttention(h, d_model)
ff = PositionwiseFeedForward(d_model, d_ff, dropout)
position = PositionalEncoding(d_model, dropout)
model = GeneralEncoderDecoder(
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),
nn.Sequential(Embedding(d_model, src_vocab), c(position)),
nn.Sequential(Embedding(d_model, tar_vocab), c(position)),
Generator(d_model, tar_vocab))
# 随机初始化参数，这非常重要
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return model
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=256,
shuffle=True,
num_workers=2)
testset = torchvision.datasets.ImageFolder(root='./data/Test',
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=200,
shuffle=False,
num_workers=2)
# 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'])
def main():
# parameters
learning_rate = 0.01
num_epochs = 50
batch_size = 250
feature_size = 2048
test_index = 2
# create the save log file
print("Create the directory")
if not os.path.exists("./save"):
os.makedirs("./save")
if not os.path.exists("./logfile"):
os.makedirs("./logfile")
if not os.path.exists("./logfile/MTL"):
os.makedirs("./logfile/MTL")
# load my Dataset
type = ["infograph", "quickdraw", "sketch", "real", "test"]
print("training set : %s ,%s, %s" % (type[0], type[1], type[3]))
print("testing set : %s" % (type[2]))
inf_train_dataset = Dataset.Dataset(mode="train", type=type[0])
inf_train_loader = DataLoader(inf_train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
qdr_train_dataset = Dataset.Dataset(mode="train", type=type[1])
qdr_train_loader = DataLoader(qdr_train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
skt_train_dataset = Dataset.Dataset(mode="train", type=type[2])
skt_train_loader = DataLoader(skt_train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
rel_train_dataset = Dataset.Dataset(mode="train", type=type[3])
rel_train_loader = DataLoader(rel_train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dataset = Dataset.Dataset(mode="test", type=type[0])
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
print('the source dataset has %d size.' % (len(inf_train_dataset)))
print('the target dataset has %d size.' % (len(test_dataset)))
print('the batch_size is %d' % (batch_size))
# Pre-train models
encoder = Encoder()
classifier = Classifier(feature_size)
domain_classifier = Domain_classifier(feature_size, number_of_domain)
# GPU enable
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print('Device used:', device)
if torch.cuda.is_available():
encoder = encoder.to(device)
domain_classifier = domain_classifier.to(device)
classifier = classifier.to(device)
# setup optimizer
optimizer_encoder = optim.Adam(encoder.parameters(),
weight_decay=1e-4,
lr=learning_rate)
optimizer_domain_classifier = optim.Adam(domain_classifier.parameters(),
weight_decay=1e-4,
lr=learning_rate)
optimizer_classifier = optim.Adam(classifier.parameters(),
weight_decay=1e-4,
lr=learning_rate)
print("Starting training...")
for epoch in range(num_epochs):
print("Epoch:", epoch + 1)
train_loader = [
inf_train_loader, qdr_train_loader, skt_train_loader,
rel_train_loader
]
domain_labels = torch.LongTensor([[0 for i in range(batch_size)],
[1 for i in range(batch_size)],
[2 for i in range(batch_size)],
[3 for i in range(batch_size)]])
mtl_criterion = nn.CrossEntropyLoss()
moe_criterion = nn.CrossEntropyLoss()
encoder.train()
domain_classifier.train()
classifier.train()
epoch_D_loss = 0.0
epoch_C_loss = 0.0
sum_trg_acc = 0.0
sum_label_acc = 0.0
sum_test_acc = 0.0
for index, (inf, qdr, skt, rel, test) in enumerate(
zip(train_loader[0], train_loader[1], train_loader[2],
train_loader[3], test_loader)):
optimizer_encoder.zero_grad()
optimizer_classifier.zero_grad()
optimizer_domain_classifier.zero_grad()
# colculate the lambda_
p = (index + len(train_loader[0]) * epoch) / (
len(train_loader[0]) * num_epochs)
lambda_ = 2.0 / (1. + np.exp(-10 * p)) - 1.0
s1_imgs, s1_labels = inf
s2_imgs, s2_labels = qdr
s3_imgs, s3_labels = rel
t1_imgs, _ = skt
s1_imgs = Variable(s1_imgs).to(device)
s1_labels = Variable(s1_labels).to(device)
s2_imgs = Variable(s2_imgs).to(device)
s2_labels = Variable(s2_labels).to(device)
s3_imgs = Variable(s3_imgs).to(device)
s3_labels = Variable(s3_labels).to(device)
t1_imgs = Variable(t1_imgs).to(device)
s1_feature = encoder(s1_imgs)
#t1_feature = encoder(t1_imgs)
# Testing
test_imgs, test_labels = test
test_imgs = Variable(test_imgs).to(device)
test_labels = Variable(test_labels).to(device)
test_feature = encoder(test_imgs)
test_output = classifier(test_feature)
test_preds = test_output.argmax(1).cpu()
test_acc = np.mean((test_preds == test_labels.cpu()).numpy())
# Classifier network
s1_output = classifier(s1_feature)
s1_preds = s1_output.argmax(1).cpu()
s1_acc = np.mean((s1_preds == s1_labels.cpu()).numpy())
s1_c_loss = mtl_criterion(s1_output, s1_labels)
C_loss = s1_c_loss
# Domain_classifier network with source domain
#domain_labels = Variable(domain_labels).to(device)
#s1_domain_output = domain_classifier(s1_feature,lambda_)
#s1_domain_preds = s1_domain_output.argmax(1).cpu()
#if index == 10:
# print(s1_domain_preds)
#s1_domain_acc = np.mean((s1_domain_preds == 0).numpy())
#print(s1_domain_output.shape)
#print(s1_domain_output[0])
#s1_d_loss = moe_criterion(s1_domain_output,domain_labels[0])
#D_loss_src = s1_d_loss
#print(D_loss_src.item())
# Domain_classifier network with target domain
#t1_domain_output = domain_classifier(t1_feature,lambda_)
#t1_domain_preds = t1_domain_output.argmax(1).cpu()
#t1_domain_acc = np.mean((t1_domain_preds == 3).numpy())
#t1_d_loss = moe_criterion(t1_domain_output,domain_labels[3])
#D_loss = D_loss_src + t1_d_loss
loss = C_loss
D_loss = 0
#epoch_D_loss += D_loss.item()
epoch_C_loss += C_loss.item()
#sum_trg_acc += t1_domain_acc
#D_src_acc = (s1_domain_acc + s2_domain_acc + s3_domain_acc)/3.
loss.backward()
optimizer_encoder.step()
optimizer_classifier.step()
optimizer_domain_classifier.step()
if (index + 1) % 10 == 0:
print(
'Iter [%d/%d] loss %.4f , D_loss %.4f ,Acc %.4f ,Test Acc: %.4f'
% (index + 1, len(train_loader[0]), loss.item(), D_loss,
s1_acc, test_acc))
test_acc = 0.
test_loss = 0.
encoder.eval()
domain_classifier.eval()
classifier.eval()
for index, (imgs, labels) in enumerate(test_loader):
output_list = []
loss_mtl = []
imgs = Variable(imgs).to(device)
labels = Variable(labels).to(device)
hidden = encoder(imgs)
output = classifier(hidden)
preds = output.argmax(1).cpu()
s1_acc = np.mean((preds == labels.cpu()).numpy())
"""
for sthi in classifiers:
output = sthi(hidden)
output_list.append(output.cpu())
loss = mtl_criterion(output, labels)
loss_mtl.append(loss)
output = torch.FloatTensor(np.array(output_list).sum(0))
preds = output.argmax(1).cpu()
s1_preds = output_list[0].argmax(1).cpu()
s2_preds = output_list[1].argmax(1).cpu()
s3_preds = output_list[2].argmax(1).cpu()
acc = np.mean((preds == labels.cpu()).numpy())
s1_acc = np.mean((s1_preds == labels.cpu()).numpy())
s2_acc = np.mean((s2_preds == labels.cpu()).numpy())
s3_acc = np.mean((s3_preds == labels.cpu()).numpy())
if index == 0:
print(acc)
loss_mtl = sum(loss_mtl)
loss = loss_mtl
test_acc += acc
test_loss += loss.item()
"""
#print('Testing: loss %.4f,Acc %.4f ,s1 %.4f,s2 %.4f,s3 %.4f' %(test_loss/len(test_loader),test_acc/len(test_loader),s1_acc,s2_acc,s3_acc))
return 0
return tf.reduce_mean(loss_)
def main():
pass
if __name__ == "__main__":
BATCH_SIZE = 64
vocab_inp_size = 32
vocab_tar_size = 32
embedding_dim = 64
units = 128
# Encoder
encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
# Decoder
decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')
@tf.function
def train_step(inp, targ, enc_hidden, enc_cell):
loss = 0
with tf.GradientTape() as tape:
enc_output, enc_hidden, enc_cell = encoder(inp,
[enc_hidden, enc_cell])
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
}])
def create_model_code_retrieval(opt, dataset, all_dict):
def _init_param(opt, model):
for p in model.parameters():
p.data.uniform_(-opt.param_init, opt.param_init)
dict_code, dict_comment = all_dict[0], all_dict[1]
if opt.dataset_type == "c":
dict_leaves = all_dict[2]
else:
dict_leaves = dict_code
if opt.modal_type == "seq8tree8cfg9selfattn":
seq_encoder = RetrievalCodeEncoderWrapper(opt, Encoder(opt, dict_code),
"seq9coattn")
tree_encoder = RetrievalCodeEncoderWrapper(
opt, TreeEncoder_TreeLSTM_dgl(opt, dict_leaves), "tree9coattn")
if opt.use_outmlp3:
cfg_encoder = RetrievalCodeEncoderWrapper(
opt,
CFGEncoder_GGNN(opt, dataset.new_annotation_dim,
dataset.new_n_edge_types, dataset.new_n_node),
"cfg")
else:
cfg_encoder = RetrievalCodeEncoderWrapper(
opt,
CFGEncoder_GGNN(opt, dataset.new_annotation_dim,
dataset.new_n_edge_types, dataset.new_n_node),
"cfg9coattn")
code_encoder = RetrievalCodeEncoderWrapper(
opt, (seq_encoder, tree_encoder, cfg_encoder), opt.modal_type)
comment_encoder = RetrievalCommentEncoderWrapper(
opt, Encoder(opt, dict_comment))
_init_param(opt, seq_encoder)
_init_param(opt, tree_encoder)
_init_param(opt, comment_encoder)
if opt.modal_type == "seq8tree8cfg9selfattn":
model = ModelCodeRetrieval(code_encoder, comment_encoder, opt)
print("model.state_dict().keys(): \n ", model.state_dict().keys())
if opt.model_from:
if os.path.exists(opt.model_from):
print("Loading from checkpoint at %s" % opt.model_from)
checkpoint = torch.load(opt.model_from,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint)
else:
print("not load pt file")
print("create_model_code_retrieval, opt.gpus: ", opt.gpus)
if opt.gpus:
model.cuda()
print("model.cuda() ok")
gpu_list = [int(k) for k in opt.gpus.split(",")]
gpu_list = list(range(len(gpu_list)))
if len(gpu_list) > 1:
model = torch.nn.DataParallel(model, device_ids=gpu_list)
print("DataParallel ok , gpu_list: ", gpu_list)
return model
import tensorflow as tf
from model.Attention import Attention
from model.Decoder import Decoder
from model.Encoder import Encoder
if __name__ == "__main__":
BATCH_SIZE = 64
vocab_inp_size = 32
vocab_tar_size = 32
embedding_dim = 256
units = 1024
# Encoder
encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
example_input_batch = tf.random.uniform(shape=(64, 16),
minval=0,
maxval=31,
dtype=tf.int64)
example_target_batch = tf.random.uniform(shape=(64, 11),
minval=0,
maxval=31,
dtype=tf.int64)
print(example_input_batch.shape, example_target_batch.shape)
# sample input
sample_hidden = encoder.initialize_hidden_state()
sample_cell = encoder.initialize_cell_state()
sample_output, sample_hidden, cell_hidden = encoder(
example_input_batch, [sample_hidden, sample_cell])
print(
class NeoOriginal:
def __init__( # TODO move parameters to config file
self,
pset,
batch_size=64,
max_size=100,
vocab_inp_size=32,
vocab_tar_size=32,
embedding_dim=64,
units=128,
hidden_size=128,
alpha=0.1,
epochs=200,
epoch_decay=1,
min_epochs=10,
verbose=True):
self.alpha = alpha
self.batch_size = batch_size
self.max_size = max_size
self.epochs = epochs
self.epoch_decay = epoch_decay
self.min_epochs = min_epochs
self.train_steps = 0
self.verbose = verbose
self.enc = Encoder(vocab_inp_size, embedding_dim, units, batch_size)
self.dec = Decoder(vocab_inp_size, vocab_tar_size, embedding_dim,
units, batch_size)
self.surrogate = Surrogate(hidden_size)
self.population = Population(pset, max_size, batch_size)
self.prob = 0.5
self.optimizer = tf.keras.optimizers.Adam()
self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False, reduction='none')
def save_models(self):
self.enc.save_weights("model/weights/encoder/enc_{}".format(
self.train_steps),
save_format="tf")
self.dec.save_weights("model/weights/decoder/dec_{}".format(
self.train_steps),
save_format="tf")
self.surrogate.save_weights(
"model/weights/surrogate/surrogate_{}".format(self.train_steps),
save_format="tf")
def load_models(self, train_steps):
self.enc.load_weights(
"model/weights/encoder/enc_{}".format(train_steps))
self.dec.load_weights(
"model/weights/decoder/dec_{}".format(train_steps))
self.surrogate.load_weights(
"model/weights/surrogate/surrogate_{}".format(train_steps))
@tf.function
def train_step(self, inp, targ, targ_surrogate, enc_hidden, enc_cell):
autoencoder_loss = 0
with tf.GradientTape(persistent=True) as tape:
enc_output, enc_hidden, enc_cell = self.enc(
inp, [enc_hidden, enc_cell])
surrogate_output = self.surrogate(enc_hidden)
surrogate_loss = self.surrogate_loss_function(
targ_surrogate, surrogate_output)
dec_hidden = enc_hidden
dec_cell = enc_cell
context = tf.zeros(shape=[len(dec_hidden), 1, dec_hidden.shape[1]])
dec_input = tf.expand_dims([1] * len(inp), 1)
for t in range(1, self.max_size):
initial_state = [dec_hidden, dec_cell]
predictions, context, [dec_hidden, dec_cell
], _ = self.dec(dec_input, context,
enc_output,
initial_state)
autoencoder_loss += self.autoencoder_loss_function(
targ[:, t], predictions)
# Probabilistic teacher forcing
# (feeding the target as the next input)
if tf.random.uniform(shape=[], maxval=1,
dtype=tf.float32) > self.prob:
dec_input = tf.expand_dims(targ[:, t], 1)
else:
pred_token = tf.argmax(predictions,
axis=1,
output_type=tf.dtypes.int32)
dec_input = tf.expand_dims(pred_token, 1)
loss = autoencoder_loss + self.alpha * surrogate_loss
ae_loss_per_token = autoencoder_loss / int(targ.shape[1])
batch_loss = ae_loss_per_token + self.alpha * surrogate_loss
batch_ae_loss = (autoencoder_loss / int(targ.shape[1]))
batch_surrogate_loss = surrogate_loss
gradients, variables = self.backward(loss, tape)
self.optimize(gradients, variables)
return batch_loss, batch_ae_loss, batch_surrogate_loss
def backward(self, loss, tape):
variables = \
self.enc.trainable_variables + self.dec.trainable_variables \
+ self.surrogate.trainable_variables
gradients = tape.gradient(loss, variables)
return gradients, variables
def optimize(self, gradients, variables):
self.optimizer.apply_gradients(zip(gradients, variables))
def surrogate_breed(self, output, latent, tape):
gradients = tape.gradient(output, latent)
return gradients
def update_latent(self, latent, gradients, eta):
latent += eta * gradients
return latent
def autoencoder_loss_function(self, real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = self.loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_mean(loss_)
def surrogate_loss_function(self, real, pred):
loss_ = tf.keras.losses.mean_squared_error(real, pred)
return tf.reduce_mean(loss_)
def __train(self):
for epoch in range(self.epochs):
self.epoch = epoch
start = time.time()
total_loss = 0
total_ae_loss = 0
total_surrogate_loss = 0
data_generator = self.population()
for (batch, (inp, targ,
targ_surrogate)) in enumerate(data_generator):
enc_hidden = self.enc.initialize_hidden_state(
batch_sz=len(inp))
enc_cell = self.enc.initialize_cell_state(batch_sz=len(inp))
batch_loss, batch_ae_loss, batch_surr_loss = self.train_step(
inp, targ, targ_surrogate, enc_hidden, enc_cell)
total_loss += batch_loss
total_ae_loss += batch_ae_loss
total_surrogate_loss += batch_surr_loss
if False and self.verbose:
print(f'Epoch {epoch + 1} Batch {batch} '
f'Loss {batch_loss.numpy():.4f}')
if self.verbose and ((epoch + 1) % 10 == 0 or epoch == 0):
epoch_loss = total_loss / self.population.steps_per_epoch
ae_loss = total_ae_loss / self.population.steps_per_epoch
surrogate_loss = \
total_surrogate_loss / self.population.steps_per_epoch
epoch_time = time.time() - start
print(f'Epoch {epoch + 1} Loss {epoch_loss:.6f} AE_loss '
f'{ae_loss:.6f} Surrogate_loss '
f'{surrogate_loss:.6f} Time: {epoch_time:.3f}')
# decrease number of epochs, but don't go below self.min_epochs
self.epochs = max(self.epochs - self.epoch_decay, self.min_epochs)
def _gen_children(self,
candidates,
enc_output,
enc_hidden,
enc_cell,
max_eta=1000):
children = []
eta = 0
enc_mask = enc_output._keras_mask
last_copy_ind = len(candidates)
while eta < max_eta:
eta += 1
start = time.time()
new_children = self._gen_decoded(eta, enc_output, enc_hidden,
enc_cell, enc_mask).numpy()
new_children = self.cut_seq(new_children, end_token=2)
new_ind, copy_ind = self.find_new(new_children, candidates)
if len(copy_ind) < last_copy_ind:
last_copy_ind = len(copy_ind)
print("Eta {} Not-changed {} Time: {:.3f}".format(
eta, len(copy_ind),
time.time() - start))
for i in new_ind:
children.append(new_children[i])
if len(copy_ind) < 1:
break
enc_output = tf.gather(enc_output, copy_ind)
enc_mask = tf.gather(enc_mask, copy_ind)
enc_hidden = tf.gather(enc_hidden, copy_ind)
enc_cell = tf.gather(enc_cell, copy_ind)
candidates = tf.gather(candidates, copy_ind)
if eta == max_eta:
print("Maximal value of eta reached - breed stopped")
for i in copy_ind:
children.append(new_children[i])
return children
def _gen_decoded(self, eta, enc_output, enc_hidden, enc_cell, enc_mask):
with tf.GradientTape(persistent=True,
watch_accessed_variables=False) as tape:
tape.watch(enc_hidden)
surrogate_output = self.surrogate(enc_hidden)
gradients = self.surrogate_breed(surrogate_output, enc_hidden, tape)
dec_hidden = self.update_latent(enc_hidden, gradients, eta=eta)
dec_cell = enc_cell
context = tf.zeros(shape=[len(dec_hidden), 1, dec_hidden.shape[1]])
dec_input = tf.expand_dims([1] * len(enc_hidden), 1)
child = dec_input
for _ in range(1, self.max_size - 1):
initial_state = [dec_hidden, dec_cell]
predictions, context, [dec_hidden, dec_cell
], _ = self.dec(dec_input, context,
enc_output, initial_state,
enc_mask)
dec_input = tf.expand_dims(
tf.argmax(predictions, axis=1, output_type=tf.dtypes.int32), 1)
child = tf.concat([child, dec_input], axis=1)
stop_tokens = tf.expand_dims([2] * len(enc_hidden), 1)
child = tf.concat([child, stop_tokens], axis=1)
return child
def cut_seq(self, seq, end_token=2):
ind = (seq == end_token).argmax(1)
res = []
tree_max = []
for d, i in zip(seq, ind):
repaired_tree = create_expression_tree(d[:i + 1][1:-1])
repaired_seq = [i.data for i in repaired_tree.preorder()
][-(self.max_size - 2):]
tree_max.append(len(repaired_seq) == self.max_size - 2)
repaired_seq = [1] + repaired_seq + [2]
res.append(np.pad(repaired_seq, (0, self.max_size - i - 1)))
return res
def find_new(self, seq, candidates):
new_ind = []
copy_ind = []
n = False
cp = False
for i, (s, c) in enumerate(zip(seq, candidates)):
if not np.array_equal(s, c):
if not n:
n = True
new_ind.append(i)
else:
if not cp:
cp = True
copy_ind.append(i)
return new_ind, copy_ind
def _gen_latent(self, candidates):
enc_hidden = self.enc.initialize_hidden_state(batch_sz=len(candidates))
enc_cell = self.enc.initialize_cell_state(batch_sz=len(candidates))
enc_output, enc_hidden, enc_cell = self.enc(candidates,
[enc_hidden, enc_cell])
return enc_output, enc_hidden, enc_cell
def update(self):
print("Training")
self.enc.train()
self.dec.train()
self.__train()
self.save_models()
self.train_steps += 1
def breed(self):
print("Breed")
self.dec.eval()
data_generator = self.population(
batch_size=len(self.population.samples))
tokenized_pop = []
for (batch, (inp, _, _)) in enumerate(data_generator):
enc_output, enc_hidden, enc_cell = self._gen_latent(inp)
tokenized_pop += (self._gen_children(inp, enc_output, enc_hidden,
enc_cell))
pop_expressions = [
self.population.tokenizer.reproduce_expression(tp)
for tp in tokenized_pop
]
offspring = [deap.creator.Individual(pe) for pe in pop_expressions]
return offspring
def main():
#create tensorboard summary writer
writer = SummaryWriter(args.experiment_id)
#[TODO] may need to resize input image
cudnn.enabled = True
#create model: Encoder
model_encoder = Encoder()
model_encoder.train()
model_encoder.cuda(args.gpu)
optimizer_encoder = optim.Adam(model_encoder.parameters(), lr=args.learning_rate, betas=(0.95, 0.99))
optimizer_encoder.zero_grad()
#create model: Decoder
model_decoder = Decoder()
model_decoder.train()
model_decoder.cuda(args.gpu)
optimizer_decoder = optim.Adam(model_decoder.parameters(), lr=args.learning_rate, betas=(0.95, 0.99))
optimizer_decoder.zero_grad()
l2loss = nn.MSELoss()
#load data
for i in range(1, 360002, 30000):
train_data, valid_data = get_data(i)
for e in range(1, args.epoch + 1):
train_loss_value = 0
validation_loss_value = 0
for j in range(0, int(args.train_size/4), args.batch_size):
optimizer_decoder.zero_grad()
optimizer_decoder.zero_grad()
image = Variable(torch.tensor(train_data[j: j + args.batch_size, :, :])).cuda(args.gpu)
latent = model_encoder(image)
img_recon = model_decoder(latent)
img_recon = F.interpolate(img_recon, size=image.shape[2:], mode='bilinear', align_corners=True)
loss = l2loss(img_recon, image)
train_loss_value += loss.data.cpu().numpy() / args.batch_size
loss.backward()
optimizer_decoder.step()
optimizer_encoder.step()
print("data load: {:8d}".format(i))
print("epoch: {:8d}".format(e))
print("train_loss: {:08.6f}".format(train_loss_value / (args.train_size / args.batch_size)))
for j in range(0,int(args.validation_size/4), args.batch_size):
model_encoder.eval()
model_decoder.eval()
image = Variable(torch.tensor(valid_data[j: j + args.batch_size, :, :])).cuda(args.gpu)
latent = model_encoder(image)
img_recon = model_decoder(latent)
img_1 = img_recon[0][0]
img = image[0][0]
img_recon = F.interpolate(img_recon, size=image.shape[2:], mode='bilinear', align_corners=True)
save_image(img_1, args.image_dir + '/fake' + str(i) + "_" + str(j) + ".png")
save_image(img, args.image_dir + '/real' + str(i) + "_" + str(j) + ".png")
image = Variable(torch.tensor(train_data[j: j + args.batch_size, :, :, :])).cuda(args.gpu)
loss = l2loss(img_recon, image)
validation_loss_value += loss.data.cpu().numpy() / args.batch_size
model_encoder.train()
model_decoder.train()
print("train_loss: {:08.6f}".format(validation_loss_value / (args.validation_size / args.batch_size)))
torch.save({'encoder_state_dict': model_encoder.state_dict()}, osp.join(args.checkpoint_dir, 'AE_encoder.pth'))
torch.save({'decoder_state_dict': model_decoder.state_dict()}, osp.join(args.checkpoint_dir, 'AE_decoder.pth'))
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
