default='parametric',
choices=['euclidian', 'cosine', 'parametric'])
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args() ###############################
args.name = f'DTN_hallu{args.hallu_m}_shot{args.shot}_trainway{args.train_way}'+\
f'_validway{args.valid_way}_{args.distance}'
wandb.init(config=args, project='dlcv_proto_net', name=args.name)
# Image transform
train_trans, valid_trans = get_transform()
# Base Train data
base_train_set = MiniImageNet_Dataset('../hw4_data/train/', train_trans)
base_train_loader = DataLoader(base_train_set,
batch_size=64,
num_workers=6,
shuffle=True)
# Train data
train_set = MiniImageNet_Dataset('../hw4_data/train/', train_trans)
train_sampler = CategoriesSampler(train_set.label,
n_batch=args.n_batch,
n_ways=args.train_way,
n_shot=args.shot + args.query)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=6,
worker_init_fn=worker_init_fn)
# Valid data
valid_set = MiniImageNet_Dataset('../hw4_data/val/', valid_trans)
if __name__ == '__main__':
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu #0:1080ti 1:1070
warnings.filterwarnings("ignore")
## load dataset
train_dataset = VCTK_Dataset('preprocess/vctk.h5',
'preprocess/sample_segments/train_samples',
seg_len=128,
mode='train')
valid_dataset = VCTK_Dataset('preprocess/vctk.h5',
'preprocess/sample_segments/valid_samples',
seg_len=128,
mode='test')
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
#num_workers = args.num_workers,
shuffle=True)
valid_dataloader = DataLoader(
valid_dataset,
batch_size=args.batch_size * 4,
)
#num_workers = args.num_workers)
## train
train(args, train_dataloader, valid_dataloader)
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
root = '../hw3_data/digits/'
# dataset
source, target = args.source, args.target
train_source_dataset = Digits_Dataset(root + f'{source}/train', source, t0)
train_target_dataset = Digits_Dataset(root + f'{target}/train', target, t0)
valid_source_dataset = Digits_Dataset(root + f'{source}/test', source, t1)
valid_target_dataset = Digits_Dataset(root + f'{target}/test', target, t1)
# dataloaders
train_source_dataloader = DataLoader(
train_source_dataset,
batch_size=args.bsize,
num_workers=args.num_workers,
collate_fn=train_source_dataset.collate_fn,
shuffle=True)
train_target_dataloader = DataLoader(
train_target_dataset,
batch_size=args.bsize,
num_workers=args.num_workers,
collate_fn=train_target_dataset.collate_fn,
shuffle=True)
valid_source_dataloader = DataLoader(valid_source_dataset,
batch_size=args.bsize * 4,
num_workers=args.num_workers)
valid_target_dataloader = DataLoader(valid_target_dataset,
batch_size=args.bsize * 4,
num_workers=args.num_workers)
def build_model(self):
""" DataLoader """
train_transform = [
transforms.RandomHorizontalFlip(),
transforms.Resize((self.img_size + 30, self.img_size + 30)),
transforms.RandomCrop(self.img_size),
transforms.ToArray(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
transforms.ToTensor()
]
test_transform = [
transforms.Resize((self.img_size, self.img_size)),
transforms.ToArray(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
transforms.ToTensor()
]
self.trainA = os.path.join('dataset', self.dataset, 'trainA')
self.trainB = os.path.join('dataset', self.dataset, 'trainB')
self.testA = os.path.join('dataset', self.dataset, 'testA')
self.testB = os.path.join('dataset', self.dataset, 'testB')
self.trainA_loader = DataLoader(self.trainA,
batch_size=self.batch_size,
transforms=train_transform,
shuffle=True)
self.trainB_loader = DataLoader(self.trainB,
batch_size=self.batch_size,
transforms=train_transform,
shuffle=True)
self.testA_loader = DataLoader(self.testA,
batch_size=1,
transforms=test_transform,
shuffle=False)
self.testB_loader = DataLoader(self.testB,
batch_size=1,
transforms=test_transform,
shuffle=False)
""" Define Generator, Discriminator """
self.genA2B = ResnetGenerator(input_nc=3,
output_nc=3,
ngf=self.ch,
n_blocks=self.n_res,
img_size=self.img_size,
light=self.light)
self.genB2A = ResnetGenerator(input_nc=3,
output_nc=3,
ngf=self.ch,
n_blocks=self.n_res,
img_size=self.img_size,
light=self.light)
self.disGA = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disGB = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disLA = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
self.disLB = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
""" Define Loss """
self.L1_loss = dygraph.L1Loss()
self.MSE_loss = layers.mse_loss
self.BCELoss = bce_loss
# BCELoss should be called with Normalize=True, use seperately
""" Trainer """
self.G_optim = optimizer.Adam(learning_rate=self.lr,
beta1=0.5,
beta2=0.999,
parameter_list=self.genA2B.parameters() +
self.genB2A.parameters())
self.D_optim = optimizer.Adam(learning_rate=self.lr,
beta1=0.5,
beta2=0.999,
parameter_list=self.disGA.parameters() +
self.disLB.parameters())
default='cuda:0',
help='cpu or cuda:0 or cuda:1')
args = parser.parse_args() if string is None else parser.parse_args(string)
return args
if __name__ == '__main__':
args = parse_args()
wandb.init(config=args, project='dlcv_gan_face')
transform = transforms.Compose([
transforms.Resize(args.img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5] * 3, [0.5] * 3)
])
train_dataset = Face_Dataset('../hw3_data/face/train', transform)
valid_dataset = Face_Dataset('../hw3_data/face/test', transform)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch,
shuffle=True,
num_workers=args.num_workers)
valid_dataloader = DataLoader(valid_dataset,
batch_size=args.batch,
num_workers=args.num_workers)
train(args, train_dataloader, valid_dataloader)
import copy
# from depth_CPM import CPM3D
from model import FSRCNN
import torch.utils.data as data
from torch import nn
from train import train_model
from tensorboardX import SummaryWriter
from dataset import DataLoader
from torch.optim.lr_scheduler import ExponentialLR
import torch
import torch.backends.cudnn as cudnn
#training code
if args.phase == 'train':
dataloaders = data.DataLoader(DataLoader(args),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
device = torch.device("cuda:0" if (
torch.cuda.is_available() and args.ngpu > 0) else "cpu")
print("constructing model ....")
model = FSRCNN()
model = nn.DataParallel(model.to(device), gpuids)
if args.resume:
model.load_state_dict(torch.load(args.model_path))
def run_experiment(args):
train_distortion, val_distortion = None, None
if args.distortion:
train_distortion = Distorter(loc=0,
sd=args.distortion_sd,
seed=args.seed)
val_distortion = Distorter(loc=0,
sd=args.distortion_sd,
seed=args.seed + 1)
train_params = dict(
distortion=train_distortion,
variable_binning=args.variable_binning,
start=args.start,
varying_start_value=args.varying_start_value,
n_agents=args.n_agents,
timesteps=args.timesteps,
compute_fiv=args.compute_frequency_increment_values,
)
train_loader = DataLoader(
train_params,
batch_size=args.batch_size,
seed=args.seed,
distortion=args.distortion,
n_sims=args.n_sims,
train=True,
n_workers=args.n_workers,
normalize_samples=args.normalize_samples,
)
val_params = dict(
distortion=val_distortion,
variable_binning=args.variable_binning,
start=args.start,
varying_start_value=args.varying_start_value,
n_agents=args.n_agents,
timesteps=args.timesteps,
compute_fiv=args.compute_frequency_increment_values,
)
val_loader = DataLoader(
val_params,
batch_size=args.batch_size,
seed=args.seed + 1,
distortion=args.distortion,
n_sims=int(args.val_size * args.n_sims),
train=False,
n_workers=args.n_workers,
normalize_samples=args.normalize_samples,
)
if args.cuda and torch.cuda.is_available():
device = torch.device("cuda")
print("Using GPU")
else:
device = torch.device("cpu")
if args.model == "FCN":
model = FCN(1, 1)
elif args.model == "LSTMFCN":
model = LSTMFCN(args.hidden_size, 1, args.num_layers, 1, args.dropout,
args.rnn_dropout, args.bidirectional)
elif args.model == "INCEPTION":
model = InceptionTime(1, 1)
else:
model = ResNet(1)
model = model.to(device)
trainer = Trainer(model, train_loader, val_loader, device=device)
trainer.fit(args.n_epochs,
learning_rate=args.learning_rate,
lr_patience=args.learning_rate_patience,
early_stop_patience=args.early_stop_patience,
evaluation_maximum=args.evaluation_maximum)
run_id = str(uuid.uuid1())[:8] if args.outfile is None else args.outfile
trainer.model.eval()
trainer.save_model(run_id)
if args.test:
print("Evaluating the model...")
if args.test_samples is not None:
print("Using precomputed test-samples...")
else:
print("Generating test samples...")
test_samples = args.test_samples if args.test_samples is not None else generate_test_samples(
args.timesteps, args.n_agents, args.n_workers)
nn_results = test_model(model,
test_samples,
args.timesteps,
args.n_agents,
device=device,
normalize_samples=args.normalize_samples)
nn_scores = evaluate_results(nn_results, prefix="NN_")
return run_id, nn_results, nn_scores
def language_smoothing_experiment():
import pandas as pd
# create boost_results pandas dataframe with colums: boost_percent, cross_entropy, dataset_name
boost_results_df = pd.DataFrame(
columns=["boost_percents", "cross_entropies", "dataset_name"])
# create boost_results pandas dataframe with colums: discount_percent, cross_entropy, dataset_name
discount_results_df = pd.DataFrame(
columns=["discount_percents", "cross_entropies", "dataset_name"])
for dataset_name in ["TEXTCZ1", "TEXTEN1"]:
print(f"---Language smoothing experiment on {dataset_name} dataset---")
train_data = DataLoader(f"datasets/{dataset_name}_train.txt")
heldout_data = DataLoader(f"datasets/{dataset_name}_heldout.txt")
test_data = DataLoader(f"datasets/{dataset_name}_test.txt")
print("Fitting the training data...")
model = LinearInterpolationModel(train_data.get_clean_data(), 3)
print("Fitting the lambdas on the TRAINING data...")
# model.fit_lambdas(train_data.get_clean_data())
print(
f"Test cross-entropy: {model.cross_entropy(test_data.get_clean_data())}\n\n"
)
print("Now fitting the lambdas (correctly) on the HELDOUT data...")
model.fit_lambdas(heldout_data.get_clean_data())
print(
f"Test cross-entropy: {model.cross_entropy(test_data.get_clean_data())}\n\n"
)
boost_results = boost_lambdas_experiment(model, test_data,
dataset_name)
boost_results[
"dataset_name"] = "Czech" if dataset_name == "TEXTCZ1" else "English"
boost_results_df = boost_results_df.append(pd.DataFrame(boost_results),
ignore_index=True)
discount_results = decrease_lambdas_experiment(model, test_data,
dataset_name)
discount_results[
"dataset_name"] = "Czech" if dataset_name == "TEXTCZ1" else "English"
discount_results_df = discount_results_df.append(
pd.DataFrame(discount_results), ignore_index=True)
# plot with seaborn
import seaborn as sns
sns.set(style="darkgrid")
# plot boost_results
plt.figure(figsize=(9, 6))
sns.lineplot(x="boost_percents",
y="cross_entropies",
data=boost_results_df,
hue="dataset_name",
marker="o",
legend="brief")
plt.title("Boosting trigram-lambda experiment")
plt.xlabel("Boost percentage")
plt.ylabel("Cross-entropy")
plt.savefig("results/boost_experiment.png")
# plot discount_results
plt.figure(figsize=(9, 6))
sns.lineplot(x="discount_percents",
y="cross_entropies",
data=discount_results_df,
hue="dataset_name",
marker="o",
legend="brief")
plt.title("Discounting trigram-lambda experiment")
plt.xlabel("Discount percentage")
plt.ylabel("Cross-entropy")
plt.savefig("results/discount_experiment.png")
def train_style(model_prefix, alpha=0.5, size=256, batch_size=4, tv_weight=1e-4, max_epoch=1000, lr=1e-4,
style_layer = ['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1'], content_layer = 'relu4_1'):
desc_executor, gene_executor, gram_executors = init_executor(batch_size, style_layer, content_layer)
tv_grad_executor = get_tv_grad_executor(desc_executor.arg_dict['data'], mx.gpu(), tv_weight)
optimizer = mx.optimizer.Adam(learning_rate=lr, wd=5e-5)
optim_states = []
for i, var in enumerate(gene_executor.grad_dict):
if var != 'data':
optim_states.append(optimizer.create_state(i, gene_executor.arg_dict[var])) # allocate memory
else:
optim_states.append([])
target_grams = [mx.nd.zeros(x.outputs[0].shape, mx.gpu()) for x in gram_executors] #[64*64, 128*128, 256*256, 512*512]
gram_diff = [mx.nd.empty(x.outputs[0].shape, mx.gpu()) for x in gram_executors]
gram_grad = [mx.nd.empty(x.shape, mx.gpu()) for x in desc_executor.outputs[:len(style_layer)]]
content_content = mx.nd.empty(desc_executor.outputs[-1].shape, mx.gpu())
style_content = mx.nd.empty(content_content.shape, mx.gpu())
content_grad = mx.nd.empty(content_content.shape, mx.gpu())
# target_style = [mx.nd.empty(desc_executor.outputs[i].shape, mx.gpu()) for i in range(len(style_layer))]
style_grad = [mx.nd.empty(x.shape, mx.gpu()) for x in desc_executor.outputs[:len(style_layer)]]
content_loader = DataLoader(CONTENTPATH, batch_size=batch_size)
style_loader = DataLoader(STYLEPATH, batch_size=batch_size)
for epoch in range(max_epoch):
content_loader.next().copyto(desc_executor.arg_dict['data'])
desc_executor.forward()
content_content[:] = desc_executor.outputs[-1]
style_loader.next().copyto(desc_executor.arg_dict['data'])
desc_executor.forward()
style_content[:] = desc_executor.outputs[-1]
target_content = adaInstanceNorm(content_content, style_content)
for j in range(len(style_layer)):
desc_executor.outputs[j].copyto(gram_executors[j].arg_dict['gram_data'])
gram_executors[j].forward()
target_grams[j][:] = gram_executors[j].outputs[0]
target_grams[j][:] /= 1
target_content.copyto(gene_executor.arg_dict['data'])
gene_executor.forward(is_train=True)
generate_imgs = [postprocess_img(img.asnumpy()) for img in gene_executor.outputs[0]]
generate_imgs = [preprocess_img(img) for img in generate_imgs]
for i in range(batch_size):
desc_executor.arg_dict['data'][i:i+1] = generate_imgs[i] #copy numpy to mxnet.ndarray
tv_grad_executor.forward()
desc_executor.forward(is_train=True)
loss = [0] * len(desc_executor.outputs)
for j in range(len(style_layer)):
desc_executor.outputs[j].copyto(gram_executors[j].arg_dict['gram_data'])
gram_executors[j].forward(is_train=True)
gram_diff[j] = gram_executors[j].outputs[0] - target_grams[j]
gram_executors[j].backward(gram_diff[j])
gram_grad[j][:] = gram_executors[j].grad_dict['gram_data'] / batch_size
loss[j] += np.sum(np.square(gram_diff[j].asnumpy())) / batch_size
dec_gen_feat = desc_executor.outputs[-1]
layer_size = np.prod(dec_gen_feat.shape)
content_diff = dec_gen_feat - target_content
# loss[-1] += alpha * np.sum(np.mean(np.square(content_diff.asnumpy()), axis=(2,3))) / batch_size
loss[-1] += alpha * np.sum(np.square(content_diff.asnumpy()/np.sqrt(layer_size))) / batch_size
content_grad = alpha * content_diff / batch_size / layer_size
if epoch % 20 == 0:
print epoch, 'loss', sum(loss), np.array(loss)
desc_executor.backward(style_grad+[content_grad])
gene_executor.backward(desc_executor.grad_dict['data']+tv_grad_executor.outputs[0])
for i, var in enumerate(gene_executor.grad_dict):
if var != 'data':
optimizer.update(i, gene_executor.arg_dict[var], gene_executor.grad_dict[var], optim_states[i]) #update parameter
if epoch % 500 == 499:
mx.nd.save('%s_args.nd'%model_prefix, gene_executor.arg_dict)
mx.nd.save('%s_auxs.nd'%model_prefix, gene_executor.aux_dict)
mx.nd.save('%s_args.nd'%model_prefix, gene_executor.arg_dict)
mx.nd.save('%s_auxs.nd'%model_prefix, gene_executor.aux_dict)
def main(args):
# Get device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Define model
model = nn.DataParallel(LightSpeech()).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of LightSpeech Parameters:', num_param)
# Get dataset
dataset = LightSpeechDataset()
# Optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=hp.learning_rate,
weight_decay=hp.weight_decay)
# Criterion
criterion = LigthSpeechLoss()
# Load checkpoint if exists
try:
checkpoint = torch.load(
os.path.join(hp.checkpoint_path,
'checkpoint_%d.pth.tar' % args.restore_step))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step %d---\n" % args.restore_step)
except:
print("\n---Start New Training---\n")
if not os.path.exists(hp.checkpoint_path):
os.mkdir(hp.checkpoint_path)
# Init logger
if not os.path.exists(hp.logger_path):
os.mkdir(hp.logger_path)
# Define Some Information
Time = np.array([])
Start = time.clock()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
training_loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=collate_fn,
drop_last=True,
num_workers=0)
total_step = hp.epochs * len(training_loader) * hp.batch_size
for i, batchs in enumerate(training_loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.clock()
current_step = i * hp.batch_size + j + args.restore_step + \
epoch * len(training_loader)*hp.batch_size + 1
# Init
optimizer.zero_grad()
# Get Data
character = torch.from_numpy(
data_of_batch["text"]).long().to(device)
# mel_gt_target = torch.from_numpy(
# data_of_batch["mel_gt_target"]).float().to(device)
mel_tac2_target = torch.from_numpy(
data_of_batch["mel_tac2_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
cemb = torch.from_numpy(
data_of_batch["cemb"]).float().to(device)
input_lengths = torch.from_numpy(
data_of_batch["length_text"]).long().to(device)
output_lengths = torch.from_numpy(
data_of_batch["length_mel"]).long().to(device)
max_c_len = max(input_lengths).item()
max_mel_len = max(output_lengths).item()
# Forward
mel, padd_predicted, cemb_out = model(character, input_lengths,
output_lengths,
max_c_len, max_mel_len,
D)
# Cal Loss
mel_loss, similarity_loss, duration_loss = criterion(
mel, padd_predicted, cemb_out, mel_tac2_target, D, cemb)
total_loss = mel_loss + similarity_loss + duration_loss
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
s_l = similarity_loss.item()
d_l = duration_loss.item()
with open(os.path.join("logger", "total_loss.txt"),
"a") as f_total_loss:
f_total_loss.write(str(t_l) + "\n")
with open(os.path.join("logger", "mel_loss.txt"),
"a") as f_mel_loss:
f_mel_loss.write(str(m_l) + "\n")
with open(os.path.join("logger", "similarity_loss.txt"),
"a") as f_s_loss:
f_s_loss.write(str(s_l) + "\n")
with open(os.path.join("logger", "duration_loss.txt"),
"a") as f_d_loss:
f_d_loss.write(str(d_l) + "\n")
# Backward
total_loss.backward()
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(), 1.)
# Update weights
optimizer.step()
# Print
if current_step % hp.log_step == 0:
Now = time.clock()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
str2 = "Mel Loss: {:.4f}, Duration Loss: {:.4f}, Similarity Loss: {:.4f};".format(
m_l, d_l, s_l)
str3 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now - Start),
(total_step - current_step) * np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
with open(os.path.join("logger", "logger.txt"),
"a") as f_logger:
f_logger.write(str1 + "\n")
f_logger.write(str2 + "\n")
f_logger.write(str3 + "\n")
f_logger.write("\n")
if current_step % hp.save_step == 0:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(hp.checkpoint_path,
'checkpoint_%d.pth.tar' % current_step))
print("save model at step %d ..." % current_step)
end_time = time.clock()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
ITORS = 100
# 加载字典
vocab = Vocab(vocab_path)
char_vocab = CharVocab(char_vocab_path)
entity_vocab = EntityVocab(entity_vocab_path)
# 创建数据集
train_data_set = DataSet(path=train_data_path,
vocab=vocab,
entity_vocab=entity_vocab,
entity_padding_len=max_sent_length)
test_data_set = DataSet(path=test_data_path,
vocab=vocab,
entity_vocab=entity_vocab,
entity_padding_len=max_sent_length)
# 创建加载器
train_data_loader = DataLoader(train_data_set, shuffle=True, batch_size=BATCH_SIZE)
test_data_loader = DataLoader(test_data_set, shuffle=True, batch_size=BATCH_SIZE)
# 是否用GPU
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
# 模型初始化
model = Model(vocab=vocab,
char_vocab=char_vocab,
entity_vocab=entity_vocab,
char_embedding_dim=char_embedding_dim,
word_embedding_dim=word_embedding_dim,
hidden_size=hidden_size,
device=device,
kernel_n=kernel_n, # 卷积核长度
def sentence_weight_examine(sentence_idx):
dataset = SimpleDataset(validation_percentage=validation_percentage,
dataset_name=dataset_name)
loader = DataLoader(data='valid',
simple_dataset=dataset,
dataset_name='sem-2016',
padding=False)
# for idx, sentence in enumerate(dataset.valid_original_sentence):
# print(idx)
# print(sentence)
# print(dataset.valid_data[idx][0])
# print(loader[idx][1])
# f, ax = plt.subplots(figsize=(9, 6))
# flights = flights_long.pivot("month", "year", "passengers")
# print(type(flights))
# exit()
item = loader[sentence_idx][0]
preprocessed_sentence = dataset.valid_data[sentence_idx][0]
orig_sentence = dataset.valid_original_sentence[sentence_idx]
translator = str.maketrans(string.punctuation,
' ' * len(string.punctuation))
orig_sentence = orig_sentence.translate(translator)
orig_sentence = orig_sentence.split()
weights, existence = get_sentence_weights('./topic-attention', item)
topics = []
for i in range(11):
topics.append(str(i + 1))
stopwords_english = set(stopwords.words('english'))
attention_weights = []
words = []
for idx, weight in enumerate(weights):
weight = list(weight.squeeze(0).squeeze(-1).detach().numpy())
temp = []
for word in orig_sentence:
words.append(word)
if word.lower() in stopwords_english:
temp.append(float(0.0))
else:
temp.append(
float(weight[preprocessed_sentence.index(word.lower())]))
attention_weights.append(np.array(temp))
attention_weights = np.array(attention_weights)
attention_weights = attention_weights.transpose()
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(attention_weights,
annot=True,
fmt=".2f",
ax=ax,
xticklabels=topics,
yticklabels=orig_sentence,
cmap="YlGnBu",
cbar_kws={'label': 'Attention Weights'})
plt.xlabel('Topics')
plt.ylabel('Sentence')
plt.savefig('./attention_heatmap/valid_sentence_#' + str(sentence_idx) +
'_attention_weights')
topic_probs = []
for idx in range(len(existence)):
prob = float(existence[idx])
topic_probs.append(prob)
topic_probs = [np.array(topic_probs)]
topic_probs = np.array(topic_probs).transpose()
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(topic_probs,
annot=True,
fmt=".2f",
ax=ax,
xticklabels=['Topic Probabilities'],
yticklabels=topics,
cmap="YlGnBu")
plt.savefig('./attention_heatmap/valid_sentence_#' + str(sentence_idx) +
'_topic_probs')
import tensorflow.keras as keras import tensorflow as tf from method import generator, discriminator, CallBackFun import os from tools import copy_code config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.enable_eager_execution(config) os.environ["CUDA_VISIBLE_DEVICES"] = "3" experiment_path = '/export/project/SAVE_PATH/' train_folder = 'apr28-photo2ce-128-par1' copy_code(src='/export/project/CODE_PATH/', dst=experiment_path + train_folder + '/') train_data = DataLoader(path_x='../cezanne2photo/trainB/', path_y='../cezanne2photo/trainA/', is_pair=False) test_data = DataLoader(path_x='../cezanne2photo/testB/', path_y='../cezanne2photo/testA/', is_pair=False) # Different lr for generative network and discriminate network optimizer_dis = tf.train.AdamOptimizer(1e-6) optimizer_gen = tf.train.AdamOptimizer(1e-4) loss_fn_mse = keras.losses.MeanSquaredError() loss_fn_mae = keras.losses.MeanAbsoluteError() gen_xy = generator(input_shape=[128, 128, 3]) gen_yx = generator(input_shape=[128, 128, 3]) dis = discriminator(input_shape=[128, 128, 3]) # Print Network Information
m = GBDTMulti(LIB, out_dim=meta['out'], params=p)
x_train, y_train, x_test, y_test = data
m.set_data((x_train, y_train), (x_test, y_test))
t = time.time()
m.train(ROUND)
t = time.time() - t
if args.time == 1:
print("Average time: {:.3f}".format(t / ROUND))
else:
print("Total time: {:.3f}".format(t))
del m
if __name__ == '__main__':
m = DataLoader()
if args.data == 'mnist_reg':
data, meta = m.get('mnist_reg', True, 10000)
for k in [4, 8, 16]:
regression(data, meta, 7, 0.1, k, True)
regression(data, meta, 7, 0.1, k, False)
elif args.data == 'nus-wide':
data, meta = m.get('nus-wide', True, 10000)
for k in [8, 16, 32, 64]:
regression(data, meta, 8, 0.1, k, True)
regression(data, meta, 8, 0.1, k, False)
elif args.data == 'Caltech101':
data, meta = m.get('Caltech101', False, 10000)
for k in [8, 16, 32, 64]:
classification(data, meta, 8, 0.1, k, True)
classification(data, meta, 8, 0.1, k, False)
test_acc_stdev = np.std(test_acc_list)
if print_result:
print("K-fold: {}, Kernel: {}".format(self.loader.get_n_splits(), self.gpc_dict[0].kernel))
print("Train Acc: {} (+/- {})".format(train_acc_avg, train_acc_stdev))
print("Test Acc: {} (+/- {})".format(test_acc_avg, test_acc_stdev))
print("=" * 10)
return train_acc_list, test_acc_list
if __name__ == '__main__':
now_fold = 0
data_loader = DataLoader('{}.tsv'.format(FILE_NAME))
data_loader.transform_x(decomposition.PCA, n_components=80)
kernel_list = [
Matern(0.5) + Matern(1.5) + Matern(nu=2.5),
random() * Matern(0.5) + random() * Matern(1.5) + random() * Matern(nu=2.5),
random() * Matern(0.5) + random() * Matern(1.5) + random() * Matern(nu=2.5),
random() * Matern(0.5) + random() * Matern(1.5) + random() * Matern(nu=2.5),
]
results = []
for now_kernel in kernel_list:
try:
gp_classifier = GPC(
kernel=now_kernel,
loader=data_loader,
def validate(test_img_files_path, test_target_files_path, category_list,
split_size, batch_size, load_size, model, cell_dim, num_boxes,
num_classes, device, iou_threshold_nms, iou_threshold_map,
threshold, use_nms):
"""
Uses the test dataset to validate the performance of the model. Calculates
the mean Average Precision (mAP) for object detection.
Parameters:
test_img_files_path (str): System path to the image directory containing
the test dataset images.
test_target_files_path (str): System path to the json file containing the
ground-truth labels for the test dataset.
category_list (list): A list containing all classes which should be detected.
split_size (int): Size of the grid which is applied to the image.
batch_size (int): Batch size.
load_size (int): Amount of batches which are loaded in one function call.
model (): The YOLOv1-model.
cell_dim (int): The dimension of a single cell.
num_boxes (int): Amount of bounding boxes which are being predicted by
the model.
num_classes (int): Amount of classes which are being predicted.
device (): Device which is used for training and testing the model.
iou_threshold_nms (float): Threshold for the IoU between the predicted boxes
and the ground-truth boxes for non maximum suppression.
iou_threshold_map (float): Threshold for the IoU between the predicted boxes
and the ground-truth boxes for mean average precision.
threshold (float): Threshold for the confidence score of predicted
bounding boxes.
use_nms (bool): Specifies if non max suppression should be applied to the
bounding box predictions.
"""
model.eval()
print("DATA IS BEING LOADED FOR VALIDATION")
print("")
# Initialize the DataLoader for the test dataset
data = DataLoader(test_img_files_path, test_target_files_path,
category_list, split_size, batch_size, load_size)
data.LoadFiles()
# Here will all predicted and ground-truth bounding boxes for the whole test
# dataset be stored. These two lists will be finally used for evaluation.
# Every element of the list will have the following form:
# [image index, class prediction, confidence score, x1, y1, x2, y2]
# Every element of the list represents a single bounding box.
all_pred_boxes = []
all_target_boxes = []
train_idx = 0 # Tracks the sample index for each image in the test dataset
# This while loop is used to fill the two lists all_pred_boxes and all_target_boxes.
while len(data.img_files) > 0:
print("LOADING NEW VALIDATION BATCHES")
print("Remaining validation files:" + str(len(data.img_files)))
print("")
data.LoadData()
for batch_idx, (img_data, target_data) in enumerate(data.data):
img_data = img_data.to(device)
target_data = target_data.to(device)
with torch.no_grad():
predictions = model(img_data)
print('Extracting bounding boxes')
print('Batch: {}/{} ({:.0f}%)'.format(batch_idx + 1, len(
data.data), (batch_idx + 1) / len(data.data) * 100.))
print('')
pred_boxes = extract_boxes(predictions, num_classes, num_boxes,
cell_dim, threshold)
target_boxes = extract_boxes(target_data, num_classes, 1, cell_dim,
threshold)
for sample_idx in range(len(pred_boxes)):
if use_nms:
# Applies non max suppression to the bounding box predictions
nms_boxes = non_max_suppression(pred_boxes[sample_idx],
iou_threshold_nms)
else:
# Use the same list without changing anything
nms_boxes = pred_boxes[sample_idx]
for nms_box in nms_boxes:
all_pred_boxes.append([train_idx] + nms_box)
for box in target_boxes[sample_idx]:
all_target_boxes.append([train_idx] + box)
train_idx += 1
pred = {
0: 0,
1: 0,
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 0,
8: 0,
9: 0,
10: 0,
11: 0,
12: 0,
13: 0
}
for prediction in all_pred_boxes:
cls_idx = prediction[1]
pred[cls_idx] += 1
print(pred)
pred = {
0: 0,
1: 0,
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 0,
8: 0,
9: 0,
10: 0,
11: 0,
12: 0,
13: 0
}
for prediction in all_target_boxes:
cls_idx = prediction[1]
pred[cls_idx] += 1
print(pred)
"""
from config import *
import copy
from model import G
import torch.utils.data as data
from torch import nn
from train import train_model
from tensorboardX import SummaryWriter
from dataset import DataLoader
from torch.optim.lr_scheduler import ExponentialLR
import torch
import torch.backends.cudnn as cudnn
#training code
if args.phase=='train':
dataloaders = data.DataLoader(DataLoader(args), batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
device = torch.device("cuda:0")
print("constructing model ....")
model = G(args.nframes)
model = nn.DataParallel(model.to(device), gpuids)
if args.resume:
ckpt = torch.load(args.model_path)
new_ckpt = {}
for key in ckpt:
if not key.startswith('module'):
new_key = 'module.' + key
else:
model.restore(str(hp.load_weight_file))
optimizer = optim.Adam(model.parameters())
current_step = model.get_step()
stft = MelSTFT(filter_length=hp.n_fft,
hop_length=hp.hop_length,
win_length=hp.win_length,
n_mel_channels=hp.n_mels,
sampling_rate=hp.sampling_rate,
mel_fmin=hp.min_f,
mel_fmax=hp.max_f).to(device=hp.device)
train_set = DataLoader(q_size=hp.q_size,
n_loading_threads=hp.n_loading_threads,
stft=stft,
redundancy=hp.redundancy,
device=hp.device)
# Iterate on training session number:
for session in hp.schedule:
r, max_step, batch_size = session
if current_step < max_step:
model.set_r(r)
train_set.set_state(batch_size, r)
training_steps = max_step - current_step
simple_table([(f'Steps with r={r}',
str(training_steps // 1000) + 'k Steps'),
('Batch Size', batch_size),
('Outputs/Step (r)', model.get_r())])
'early_stop': 25, 'one_side': True, 'verbose': False, 'hist_cache': 48,
'min_samples': 8}
x_train, y_train, x_test, y_test = data
m = GBDTSingle(LIB, out_dim=1, params=p)
preds = np.zeros_like(y_test)
m.set_data((x_train, None), (x_test, None))
for i in range(meta['out']):
yy_train = np.ascontiguousarray(y_train[:, i])
yy_test = np.ascontiguousarray(y_test[:, i])
m._set_label(yy_train, True)
m._set_label(yy_test, False)
m.train(ROUND)
_ = m.predict(x_test)
preds[:, i] = _
m.reset()
np.save("result/gbdt", preds)
if __name__ == '__main__':
m = DataLoader()
data, meta = m.get(args.data, False, args.N)
if args.mode == 'gbdtmo':
train_gbdt_multi(data, meta)
elif args.mode == 'gbdtso':
train_gbdt_single(data, meta)
elif args.mode == 'lightgbm':
train_lightgbm(data, meta)
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from dataset import Dataset
from dataset import DataLoader
train_dataset = Dataset('data')
loader = DataLoader(train_dataset)
for batch_inputs, batch_outputs, masks, lengths in loader:
loader.print_batch(batch_inputs)
print(lengths)
print(masks)
args.name = f'shot{args.shot}_trainway{args.train_way}_validway{args.valid_way}'+\
f'_{args.distance}_epochs{args.epochs}'
wandb.init(config=args, project='dlcv_proto_net', name=args.name)
# Image transform
train_trans, valid_trans = get_transform()
# Train data
train_set = MiniImageNet_Dataset('../hw4_data/train/', train_trans)
train_sampler = CategoriesSampler(train_set.label,
n_batch=args.n_batch,
n_ways=args.train_way,
n_shot=args.shot + args.query)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=6,
worker_init_fn=worker_init_fn)
# Valid data
valid_set = MiniImageNet_Dataset('../hw4_data/val/', valid_trans)
valid_sampler = CategoriesSampler(valid_set.label,
n_batch=args.n_batch,
n_ways=args.valid_way,
n_shot=args.shot + args.query)
valid_loader = DataLoader(valid_set,
batch_sampler=valid_sampler,
num_workers=6,
worker_init_fn=worker_init_fn)
# model
model = Conv4().to(args.device)
file_handler = logging.FileHandler('%s/record.txt' % (log_dir))
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(opt)
# Data Loading ###################################################################################################################
log_string('Load dataset {} ...'.format(opt.dataset))
Data_path = {
"ModelNet": '/eva_data/psa/datasets/PointNet/ModelNet40_pcd/',
"ShapeNet": '/eva_data/psa/datasets/MSN_PointNet/ShapeNetCore.v1'
}
TRAIN_DATASET = DataLoader(root=Data_path[opt.dataset],
dataset=opt.dataset,
sparsify_mode=opt.sparsify_mode,
npoint=opt.num_point,
split='train')
TEST_DATASET = DataLoader(root=Data_path[opt.dataset],
dataset=opt.dataset,
sparsify_mode=opt.sparsify_mode,
npoint=opt.num_point,
split='test')
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET,
batch_size=opt.batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET,
batch_size=opt.batch_size,
shuffle=False,
if __name__ == '__main__':
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
## load tokenizer
#tokenizer = BertTokenizer.from_pretrained(pretrained_weights)#, do_lower_case=True)
tokenizer = BertJapaneseTokenizer.from_pretrained(
pretrained_weights) #, do_lower_case=True)
## load dataset
train_dataset = Cinnamon_Dataset(f'{args.cinnamon_data_path}/train/',
tokenizer, args.delta)
valid_dataset = Cinnamon_Dataset(f'{args.cinnamon_data_path}/dev/',
tokenizer, args.delta)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=train_dataset.collate_fn,
shuffle=True)
valid_dataloader = DataLoader(valid_dataset,
batch_size=args.batch_size * 4,
num_workers=args.num_workers,
collate_fn=valid_dataset.collate_fn)
## train
train(args, train_dataloader, valid_dataloader)
def main(args):
# Get device
# device = torch.device('cuda'if torch.cuda.is_available()else 'cpu')
device = 'cuda'
# Define model
model = FastSpeech().to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech Parameters:', num_param)
current_time = time.strftime("%Y-%m-%dT%H:%M", time.localtime())
writer = SummaryWriter(log_dir='log/' + current_time)
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.9, 0.98),
eps=1e-9)
# Load checkpoint if exists
try:
checkpoint_in = open(
os.path.join(hp.checkpoint_path, 'checkpoint.txt'), 'r')
args.restore_step = int(checkpoint_in.readline().strip())
checkpoint_in.close()
checkpoint = torch.load(
os.path.join(hp.checkpoint_path,
'checkpoint_%08d.pth' % args.restore_step))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step %d---\n" % args.restore_step)
except:
print("\n---Start New Training---\n")
if not os.path.exists(hp.checkpoint_path):
os.mkdir(hp.checkpoint_path)
# Get dataset
dataset = FastSpeechDataset()
# Optimizer and loss
scheduled_optim = ScheduledOptim(optimizer, hp.d_model, hp.n_warm_up_step,
args.restore_step)
fastspeech_loss = FastSpeechLoss().to(device)
print("Defined Optimizer and Loss Function.")
# Init logger
if not os.path.exists(hp.logger_path):
os.mkdir(hp.logger_path)
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
t_l = 0.0
for epoch in range(hp.epochs):
# Get Training Loader
training_loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=collate_fn,
drop_last=True,
num_workers=0)
total_step = hp.epochs * len(training_loader) * hp.batch_size
for i, batchs in enumerate(training_loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i * hp.batch_size + j + args.restore_step + \
epoch * len(training_loader)*hp.batch_size + 1
# Init
scheduled_optim.zero_grad()
# Get Data
condition1 = torch.from_numpy(
data_of_batch["condition1"]).long().to(device)
condition2 = torch.from_numpy(
data_of_batch["condition2"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).long().to(device)
norm_f0 = torch.from_numpy(
data_of_batch["norm_f0"]).long().to(device)
mel_in = torch.from_numpy(
data_of_batch["mel_in"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
mel_pos = torch.from_numpy(
data_of_batch["mel_pos"]).long().to(device)
src_pos = torch.from_numpy(
data_of_batch["src_pos"]).long().to(device)
lens = data_of_batch["lens"]
max_mel_len = data_of_batch["mel_max_len"]
# print(condition1,condition2)
# Forward
mel_output = model(src_seq1=condition1,
src_seq2=condition2,
mel_in=mel_in,
src_pos=src_pos,
mel_pos=mel_pos,
mel_max_length=max_mel_len,
length_target=D)
# print(mel_target.size())
# print(mel_output)
# print(mel_postnet_output)
# Cal Loss
# mel_loss, mel_postnet_loss= fastspeech_loss(mel_output, mel_postnet_output,mel_target,)
# print(mel_output.shape,mel_target.shape)
Loss = torch.nn.CrossEntropyLoss()
predict = mel_output.transpose(1, 2)
target1 = mel_target.long().squeeze()
target2 = norm_f0.long().squeeze()
target = ((target1 + target2) / 2).long().squeeze()
# print(predict.shape,target.shape)
# print(target.float().mean())
losses = []
# print(lens,target)
for index in range(predict.shape[0]):
# print(predict[i,:,:lens[i]].shape,target[i,:lens[i]].shape)
losses.append(
Loss(predict[index, :, :lens[index]].transpose(0, 1),
target[index, :lens[index]]).unsqueeze(0))
# losses.append(0.5*Loss(predict[index,:,:lens[index]].transpose(0,1),target2[index,:lens[index]]).unsqueeze(0))
total_loss = torch.cat(losses).mean()
t_l += total_loss.item()
# assert np.isnan(t_l)==False
with open(os.path.join("logger", "total_loss.txt"),
"a") as f_total_loss:
f_total_loss.write(str(t_l) + "\n")
# Backward
if not np.isnan(t_l):
total_loss.backward()
else:
print(condition1, condition2, D)
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
# Update weights
if args.frozen_learning_rate:
scheduled_optim.step_and_update_lr_frozen(
args.learning_rate_frozen)
else:
scheduled_optim.step_and_update_lr()
# Print
if current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch[{}/{}] Step[{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
str2 = "Loss:{:.4f} ".format(t_l / hp.log_step)
str3 = "LR:{:.6f}".format(
scheduled_optim.get_learning_rate())
str4 = "T: {:.1f}s ETR:{:.1f}s.".format(
(Now - Start),
(total_step - current_step) * np.mean(Time))
print('\r' + str1 + ' ' + str2 + ' ' + str3 + ' ' + str4,
end='')
writer.add_scalar('loss', t_l / hp.log_step, current_step)
writer.add_scalar('lreaning rate',
scheduled_optim.get_learning_rate(),
current_step)
if hp.gpu_log_step != -1 and current_step % hp.gpu_log_step == 0:
os.system('nvidia-smi')
with open(os.path.join("logger", "logger.txt"),
"a") as f_logger:
f_logger.write(str1 + "\n")
f_logger.write(str2 + "\n")
f_logger.write(str3 + "\n")
f_logger.write(str4 + "\n")
f_logger.write("\n")
t_l = 0.0
if current_step % hp.fig_step == 0 or current_step == 20:
f = plt.figure()
plt.matshow(mel_output[0].cpu().detach().numpy())
plt.savefig('out_predicted.png')
plt.matshow(
F.softmax(predict, dim=1).transpose(
1, 2)[0].cpu().detach().numpy())
plt.savefig('out_predicted_softmax.png')
writer.add_figure('predict', f, current_step)
plt.cla()
f = plt.figure(figsize=(8, 6))
# plt.matshow(mel_target[0].cpu().detach().numpy())
# x=np.arange(mel_target.shape[1])
# y=sample_from_discretized_mix_logistic(mel_output.transpose(1,2)).cpu().detach().numpy()[0]
# plt.plot(x,y)
sample = []
p = F.softmax(predict, dim=1).transpose(
1, 2)[0].detach().cpu().numpy()
for index in range(p.shape[0]):
sample.append(np.random.choice(200, 1, p=p[index]))
sample = np.array(sample)
plt.plot(np.arange(sample.shape[0]),
sample,
color='grey',
linewidth='1')
for index in range(D.shape[1]):
x = np.arange(D[0][index].cpu().numpy()
) + D[0][:index].cpu().numpy().sum()
y = np.arange(D[0][index].detach().cpu().numpy())
if condition2[0][index].cpu().numpy() != 0:
y.fill(
(condition2[0][index].cpu().numpy() - 40.0) *
5)
plt.plot(x, y, color='blue')
plt.plot(np.arange(target.shape[1]),
target[0].squeeze().detach().cpu().numpy(),
color='red',
linewidth='1')
plt.savefig('out_target.png', dpi=300)
writer.add_figure('target', f, current_step)
plt.cla()
plt.close("all")
if current_step % (hp.save_step) == 0:
print("save model at step %d ..." % current_step, end='')
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(hp.checkpoint_path,
'checkpoint_%08d.pth' % current_step))
checkpoint_out = open(
os.path.join(hp.checkpoint_path, 'checkpoint.txt'),
'w')
checkpoint_out.write(str(current_step))
checkpoint_out.close()
# os.system('python savefig.py')
print('save completed')
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
def inference(args):
if args.target=='mnistm':
args.source = 'usps'
elif args.target=='usps':
args.source = 'svhn'
elif args.target=='svhn':
args.source = 'mnistm'
else:
raise NotImplementedError(f"{args.target}: not implemented!")
size = args.img_size
t1 = transforms.Compose([
transforms.Resize(size),
transforms.Grayscale(3),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
valid_target_dataset = Digits_Dataset_Test(args.dataset_path, t1)
valid_target_dataloader = DataLoader(valid_target_dataset,
batch_size=512,
num_workers=6)
load = torch.load(
f"./p3/result/3_2/{args.source}2{args.target}/best_model.pth",
map_location='cpu')
feature_extractor = FeatureExtractor()
feature_extractor.load_state_dict(load['F'])
feature_extractor.cuda()
feature_extractor.eval()
label_predictor = LabelPredictor()
label_predictor.load_state_dict(load['C'])
label_predictor.cuda()
label_predictor.eval()
out_preds = []
out_fnames = []
count=0
for i,(imgs, fnames) in enumerate(valid_target_dataloader):
bsize = imgs.size(0)
imgs = imgs.cuda()
features = feature_extractor(imgs)
class_output = label_predictor(features)
_, preds = class_output.max(1)
preds = preds.detach().cpu()
out_preds.append(preds)
out_fnames += fnames
count+=bsize
print(f"\t [{count}/{len(valid_target_dataloader.dataset)}]",
end=" \r")
out_preds = torch.cat(out_preds)
out_preds = out_preds.cpu().numpy()
d = {'image_name':out_fnames, 'label':out_preds}
df = pd.DataFrame(data=d)
df = df.sort_values('image_name')
df.to_csv(args.out_csv, index=False)
print(f' [Info] finish predicting {args.dataset_path}')
encoding='utf8')
# convert results to submission format
convert(f'{args.save_result_path}/result.csv',
f'{args.save_result_path}/submission.csv')
# score
if args.score:
s = score(args.ref_file, f'{args.save_result_path}/submission.csv')
print('\t[Info] score:', s)
if __name__ == '__main__':
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
tokenizer = BertJapaneseTokenizer.from_pretrained(
pretrained_weights) #, do_lower_case=True)
dataset = Cinnamon_Dataset_Testing(
f'/media/D/ADL2020-SPRING/project/cinnamon/{args.dev_or_test}/',
tokenizer, args.delta)
dataloader = DataLoader(dataset,
batch_size=1,
collate_fn=dataset.collate_fn,
shuffle=False)
inference(args, tokenizer, dataloader)
def train(num_gpus, rank, group_name, output_directory, log_directory,
checkpoint_path):
# Get device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.manual_seed(hp.seed)
torch.cuda.manual_seed(hp.seed)
#=====START: ADDED FOR DISTRIBUTED======
if num_gpus > 1:
init_distributed(rank, num_gpus, group_name, **dist_config)
#=====END: ADDED FOR DISTRIBUTED======
criterion = WaveGlowLoss(hp.sigma)
model = WaveGlow().cuda()
#=====START: ADDED FOR DISTRIBUTED======
if num_gpus > 1:
model = apply_gradient_allreduce(model)
#=====END: ADDED FOR DISTRIBUTED======
learning_rate = hp.learning_rate
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
if hp.fp16_run:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Load checkpoint if one exists
iteration = 0
if checkpoint_path:
model, optimizer, iteration = load_checkpoint(checkpoint_path, model,
optimizer)
iteration += 1 # next iteration is iteration + 1
# Get dataset
dataset = FastSpeechDataset()
# Get training loader
print("Get Training Loader")
training_loader = DataLoader(dataset,
batch_size=hp.batch_size,
shuffle=True,
collate_fn=collate_fn,
drop_last=True,
num_workers=0)
if rank == 0:
if not os.path.isdir(output_directory):
os.makedirs(output_directory)
os.chmod(output_directory, 0o775)
print("output directory", output_directory)
if hp.with_tensorboard and rank == 0:
logger = prepare_directories_and_logger(output_directory,
log_directory)
model = model.train()
epoch_offset = max(0, int(iteration / len(training_loader)))
beta = hp.batch_size
print("Total Epochs: {}".format(hp.epochs))
print("Batch Size: {}".format(hp.batch_size))
# ================ MAIN TRAINNIG LOOP! ===================
for epoch in range(epoch_offset, hp.epochs):
print("Epoch: {}".format(epoch))
for i, data_of_batch in enumerate(training_loader):
model.zero_grad()
if not hp.pre_target:
# Prepare Data
src_seq = data_of_batch["texts"]
src_pos = data_of_batch["pos"]
src_seq = torch.from_numpy(src_seq).long().to(device)
src_pos = torch.from_numpy(src_pos).long().to(device)
alignment_target = get_alignment(src_seq,
tacotron2).float().to(device)
# For Data Parallel
else:
# Prepare Data
audio = data_of_batch["audios"]
src_seq = data_of_batch["texts"]
src_pos = data_of_batch["pos"]
mel_tgt = data_of_batch["mels"]
alignment_target = data_of_batch["alignment"]
audio = torch.cat(audio).to(device)
src_seq = torch.from_numpy(src_seq).long().to(device)
src_pos = torch.from_numpy(src_pos).long().to(device)
alignment_target = torch.from_numpy(
alignment_target).float().to(device)
mel_tgt = torch.from_numpy(mel_tgt).float().to(device)
# For Data Parallel
mel_max_length = mel_tgt.size(1)
outputs = model(src_seq, src_pos, audio, mel_max_length,
alignment_target)
_, _, _, duration_predictor = outputs
max_like, dur_loss = criterion(outputs, alignment_target)
# mel_loss = criterion(outputs, alignment_target, mel_tgt)
loss = max_like + dur_loss
loss.backward()
reduced_loss = loss.item()
print('{}:\t{:.9f}'.format(iteration, reduced_loss))
#grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hp.grad_clip_thresh)a
'''if iteration % 8 == 0:
optimizer.step()
model.zero_grad()'''
optimizer.step()
if hp.with_tensorboard and rank == 0:
logger.log_training(reduced_loss, dur_loss, learning_rate,
iteration)
if (iteration % hp.save_step == 0):
if rank == 0:
# logger.log_alignment(model, mel_predict, iteration)
checkpoint_path = "{}/TTSglow_{}".format(
output_directory, iteration)
save_checkpoint(model, optimizer, learning_rate, iteration,
checkpoint_path)
iteration += 1
def cnn_train(net, user_opts, is_training=True):
opts = dict()
opts['expDir'] = 'Exp'
opts['continue'] = -1
opts['batchSize'] = 10
opts['numSubBatches'] = 1
opts['train'] = []
opts['val'] = []
opts['gpu'] = []
opts['numEpochs'] = 5
opts['learningRate'] = 0.001
opts['weightDecay'] = 0.0005
opts['momentum'] = 0.9
opts['randomSeed'] = 0
opts['folder_map'] = []
opts['test_data_idx'] = []
opts['train_data_idx'] = []
opts['plotStatistics'] = True
opts['silent'] = False
opts = arg_parse(opts, user_opts)
if 'data_loader_train' not in opts or 'data_loader_val' not in opts:
opts['data_loader_train'] = DataLoader(opts['train_data_idx'],
opts['teethRegionDetector'])
opts['data_loader_val'] = DataLoader(opts['test_data_idx'],
opts['teethRegionDetector'])
if not os.path.isdir(opts['expDir']):
os.mkdir(opts['expDir'])
evaluate_mode = False
def model_path(ep):
return os.path.join(opts['expDir'], 'net-epoch-' + str(ep),
'model.ckpt')
def model_folder_path(ep):
return os.path.join(opts['expDir'], 'net-epoch-' + str(ep))
model_figure_path = os.path.join(opts['expDir'], 'net-train.pdf')
fig = plt.gcf()
fig.show()
fig.canvas.draw()
sess = tf.Session(config=net.config)
if opts['continue'] is not None:
prev_pos_1 = max(
0, min(opts['continue'], find_last_checkpoint(opts['expDir'])))
else:
prev_pos_1 = max(0, find_last_checkpoint(opts['expDir']))
start_1 = prev_pos_1 + 1
if prev_pos_1 >= 1:
print('Resuming by loading epoch', str(prev_pos_1))
stats, sess = load_state(model_folder_path(prev_pos_1),
model_path(prev_pos_1), net.saver, sess)
if sess is None or stats is None:
sess.run(tf.global_variables_initializer())
stats = dict()
stats['train'] = []
stats['val'] = []
print('Failed to load. Starting with epoch ', str(start_1), '\n')
else:
print('Continuing at epoch ', str(start_1), '\n')
else:
sess.run(tf.global_variables_initializer())
if net.load_pre_trained_model_func is not None:
net.load_pre_trained_model_func(sess)
stats = dict()
stats['train'] = []
stats['val'] = []
print('Starting at epoch ', str(start_1), '\n')
state = dict()
opts['session'] = sess
timer = Timer()
for ep in range(start_1 - 1, opts['numEpochs']):
# Set the random seed based on the epoch and opts.randomSeed.
# This is important for reproducibility, including when training
# is restarted from a checkpoint.
epoch = ep + 1
print(' Epoch # ' + str(epoch))
random.seed(epoch + opts['randomSeed'])
# Train for one epoch
params = opts
params['epoch'] = epoch
if is_training:
[net, state] = process_epoch(net, state, params, timer, 'train')
[net, state] = process_epoch(net, state, params, timer, 'val')
else:
[net, state] = process_epoch(net,
state,
params,
timer,
'val',
training=False)
if not evaluate_mode:
save_state(model_folder_path(epoch), model_path(epoch), net, state,
opts['silent'])
last_stats = state['stats']
stats['train'].append(last_stats['train'])
stats['val'].append(last_stats['val'])
save_stats(model_folder_path(epoch), stats)
if opts['plotStatistics']:
plot_stats(fig, stats, model_figure_path)
plt.close(fig)
return net, sess, stats
def main(args):
# Get device
device = torch.device('cuda'if torch.cuda.is_available()else 'cpu')
# Define model
model = nn.DataParallel(FastSpeech()).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech Parameters:', num_param)
# Get dataset
dataset = FastSpeechDataset()
# Optimizer and loss
optimizer = torch.optim.Adam(
model.parameters(), betas=(0.9, 0.98), eps=1e-9)
scheduled_optim = ScheduledOptim(optimizer,
hp.d_model,
hp.n_warm_up_step,
args.restore_step)
fastspeech_loss = FastSpeechLoss().to(device)
print("Defined Optimizer and Loss Function.")
# Load checkpoint if exists
try:
checkpoint = torch.load(os.path.join(
hp.checkpoint_path, 'checkpoint_%d.pth.tar' % args.restore_step))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step %d---\n" % args.restore_step)
except:
print("\n---Start New Training---\n")
if not os.path.exists(hp.checkpoint_path):
os.mkdir(hp.checkpoint_path)
# Init logger
if not os.path.exists(hp.logger_path):
os.mkdir(hp.logger_path)
# Define Some Information
Time = np.array([])
Start = time.clock()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
training_loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=collate_fn,
drop_last=True,
num_workers=0)
total_step = hp.epochs * len(training_loader) * hp.batch_size
for i, batchs in enumerate(training_loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.clock()
current_step = i * hp.batch_size + j + args.restore_step + \
epoch * len(training_loader)*hp.batch_size + 1
# Init
scheduled_optim.zero_grad()
# Get Data
character = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).int().to(device)
mel_pos = torch.from_numpy(
data_of_batch["mel_pos"]).long().to(device)
src_pos = torch.from_numpy(
data_of_batch["src_pos"]).long().to(device)
max_mel_len = data_of_batch["mel_max_len"]
# Forward
# 注意：此处训练时没有将标准的时长送入decoder，还是将length regular输出的时长送了进去，和论文的叙述不符
mel_output, mel_postnet_output, duration_predictor_output = model(character,
src_pos,
mel_pos=mel_pos,
mel_max_length=max_mel_len,
length_target=D)
# print(mel_target.size())
# print(mel_output.size())
# Cal Loss
mel_loss, mel_postnet_loss, duration_loss = fastspeech_loss(mel_output,
mel_postnet_output,
duration_predictor_output,
mel_target,
D)
total_loss = mel_loss + mel_postnet_loss + duration_loss
# 三种loss的比例是1：1：1.
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = duration_loss.item()
with open(os.path.join("logger", "total_loss.txt"), "a") as f_total_loss:
f_total_loss.write(str(t_l)+"\n")
with open(os.path.join("logger", "mel_loss.txt"), "a") as f_mel_loss:
f_mel_loss.write(str(m_l)+"\n")
with open(os.path.join("logger", "mel_postnet_loss.txt"), "a") as f_mel_postnet_loss:
f_mel_postnet_loss.write(str(m_p_l)+"\n")
with open(os.path.join("logger", "duration_loss.txt"), "a") as f_d_loss:
f_d_loss.write(str(d_l)+"\n")
# Backward
total_loss.backward()
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(
model.parameters(), hp.grad_clip_thresh)
# Update weights
if args.frozen_learning_rate:
scheduled_optim.step_and_update_lr_frozen(
args.learning_rate_frozen)
else:
scheduled_optim.step_and_update_lr()
# Print
if current_step % hp.log_step == 0:
Now = time.clock()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch+1, hp.epochs, current_step, total_step)
str2 = "Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f};".format(
m_l, m_p_l, d_l)
str3 = "Current Learning Rate is {:.6f}.".format(
scheduled_optim.get_learning_rate())
str4 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now-Start), (total_step-current_step)*np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
print(str4)
with open(os.path.join("logger", "logger.txt"), "a") as f_logger:
f_logger.write(str1 + "\n")
f_logger.write(str2 + "\n")
f_logger.write(str3 + "\n")
f_logger.write(str4 + "\n")
f_logger.write("\n")
if current_step % hp.save_step == 0:
torch.save({'model': model.state_dict(), 'optimizer': optimizer.state_dict(
)}, os.path.join(hp.checkpoint_path, 'checkpoint_%d.pth.tar' % current_step))
print("save model at step %d ..." % current_step)
end_time = time.clock()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(
Time, [i for i in range(len(Time))], axis=None)
Time = np.append(Time, temp_value)
x_train, y_train, x_test, y_test = data
m.set_data((x_train, y_train), (x_test, y_test))
m.train(ROUND)
def train_gbdt_single(data, meta):
depth = cfg.Depth[args.mode][args.data]
lr = cfg.Learning_rate[args.mode][args.data]
p = {'max_depth': depth, 'max_leaves': int(0.75 * 2 ** depth), 'topk': 0, 'loss': b"ce_column",
'gamma': GAMMA, 'num_threads': num_threads, 'max_bins': meta['bin'], 'lr': lr, 'reg_l2': 1.0,
'early_stop': 25, 'one_side': True, 'verbose': True,
'min_samples': min_samples}
x_train, y_train, x_test, y_test = data
m = GBDTSingle(LIB, out_dim=meta['out'], params=p)
m.set_data((x_train, y_train), (x_test, y_test))
m.train_multi(ROUND)
if __name__ == '__main__':
m = DataLoader()
data, meta = m.get(args.data, False, args.N)
if args.mode == 'gbdtmo':
train_gbdt_multi(data, meta)
elif args.mode == 'gbdtso':
train_gbdt_single(data, meta)
elif args.mode == 'lightgbm':
train_lightgbm(data, meta)
