def main():
CUDA_OK = torch.cuda.is_available()
args = parse()
dl = DataLoader()
train_iter, valid_iter = dl.load_translation(
data_path=args.data_path,
exts=('.' + args.src, '.' + args.tgt), # ('.zh', '.en')
batch_size=args.batch_size,
dl_save_path=args.dl_path)
args.n_src_words, args.n_tgt_words = len(dl.SRC.vocab), len(dl.TGT.vocab)
args.src_pdx, args.tgt_pdx = dl.src_padding_index, dl.tgt_padding_index
print(args)
model = build_model(args, cuda_ok=CUDA_OK)
trainer = Trainer(args,
model=model,
optimizer=torch.optim.Adam(model.parameters(),
lr=1e-3,
betas=(0.9, 0.98),
eps=1e-9),
criterion=nn.CrossEntropyLoss(ignore_index=args.tgt_pdx,
reduction='mean'),
cuda_ok=CUDA_OK)
trainer.train(train_iter,
valid_iter,
n_epochs=args.n_epochs,
save_path=args.ckpt_path)
def main(**kwargs):
# opt config
opt._parse(kwargs)
# Data Loader
data_loader = DLoader(opt)
train_loader, test_loader, test_video = data_loader.run()
# Train my model
model = Resnet2D(opt, train_loader, test_loader, test_video)
model.run()
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
# setup networks
Network = getattr(models, args.net)
model = Network(**args.net_params)
model = model.cuda()
#criterion = getattr(criterions, args.criterion)
criterion = F.cross_entropy
model_file = os.path.join(ckpts, 'model_last.tar')
checkpoint = torch.load(model_file)
model.load_state_dict(checkpoint['state_dict'])
valid_list = os.path.join(args.data_dir, 'file_list.txt')
Dataset = getattr(datasets, args.dataset)
repeat = False
valid_dir = args.data_dir
valid_set = Dataset(valid_list, root=valid_dir, for_train=False)
valid_loader = DataLoader(valid_set,
batch_size=1,
shuffle=False,
collate_fn=valid_set.collate,
num_workers=2,
pin_memory=True,
prefetch=repeat)
start = time.time()
model.eval()
with torch.no_grad():
scores = validate(valid_loader, model, criterion, out_dir,
valid_set.names)
msg = 'total time {:.4f} minutes'.format((time.time() - start) / 60)
logging.info(msg)
def __init__(self):
self._opt = TestOptions().parse()
assert len(self._opt.checkpoint_path) !=0 and os.path.exists(self._opt.checkpoint_path), "checkpoint_path does not exist."
df = DatasetFactory()
val_dataset = df.get_by_name(self._opt, 'Validation')
self.val_loader = DataLoader(val_dataset,
batch_size = self._opt.batch_size,
shuffle = False,
drop_last = False,
num_workers=self._opt.n_threads_test)
test_dataset = df.get_by_name(self._opt, 'Test')
self.test_loader = DataLoader(test_dataset,
batch_size = self._opt.batch_size,
shuffle = True,
drop_last = True,
num_workers=self._opt.n_threads_test)
self.num_classes = test_dataset.n_classes
self.model = ModelFactory().get_by_name(self._opt.model_name, self._opt, self.num_classes)
if self._opt.num_gpus > 1:
self.model = make_parallel(self.model, self.num_gpus)
def get_dataloader(cfg: object, mode: str) -> tuple:
"""Get dataloader function
This is function to get dataloaders.
Get dataset, then make dataloaders.
Args:
cfg: Config.
mode: Mode.
trainval: For trainning and validation.
test: For test.
Returns:
Tuple of dataloaders.
"""
log.info(f"Loading {cfg.data.dataset.name} dataset...")
dataset = get_dataset(cfg, mode)
sampler = get_sampler(cfg, mode, dataset)
if mode == "trainval":
train_dataloader = DataLoader(cfg,
dataset=dataset.train,
sampler=sampler.train)
val_dataloader = DataLoader(cfg,
dataset=dataset.val,
sampler=sampler.val)
dataloaders = (train_dataloader, val_dataloader)
elif mode == "test":
test_dataloader = DataLoader(cfg,
dataset=dataset.test,
sampler=sampler.test)
dataloaders = (test_dataloader)
log.info(f"Successfully loaded {cfg.data.dataset.name} dataset.")
return dataloaders
def get_dataloader(cfg: object, mode: str) -> tuple:
"""Get dataloader function
This is function to get dataloaders.
Get dataset, then make dataloaders.
Args:
cfg: Config.
mode: Mode.
trainval: For trainning and validation.
test: For test.
Returns:
Tuple of dataloaders.
"""
log.info(f"Loading {cfg.data.dataset.name} dataset...")
if mode == "trainval":
train_dataset, val_dataset = get_dataset(cfg, mode="trainval")
train_sampler = get_sampler(cfg, mode="train", dataset=train_dataset)
val_sampler = get_sampler(cfg, mode="val", dataset=val_dataset)
train_dataloader = DataLoader(cfg, dataset=train_dataset, sampler=train_sampler)
val_dataloader = DataLoader(cfg, dataset=val_dataset, sampler=val_sampler)
log.info(f"Successfully loaded {cfg.data.dataset.name} dataset.")
return train_dataloader, val_dataloader
elif mode == "test":
test_dataset = get_dataset(cfg, mode="test")
test_sampler = get_sampler(cfg, mode="test", dataset=test_dataset)
test_dataloader = DataLoader(cfg, dataset=test_dataset, sampler=test_sampler)
log.info(f"Successfully loaded {cfg.data.dataset.name} dataset.")
return test_dataloader
def __init__(self):
self._opt = TrainOptions().parse()
df = DatasetFactory()
train_dataset = df.get_by_name(self._opt, 'Train')
self.train_loader = DataLoader(train_dataset,
batch_size=self._opt.batch_size,
shuffle=True,
drop_last=True,
num_workers=self._opt.n_threads_train)
val_dataset = df.get_by_name(self._opt, 'Validation')
self.val_loader = DataLoader(val_dataset,
batch_size=self._opt.batch_size,
shuffle=False,
drop_last=False,
num_workers=self._opt.n_threads_test)
self.num_classes = train_dataset.n_classes
self.model = ModelFactory().get_by_name(self._opt.model_name,
self._opt, self.num_classes)
if self._opt.reg_func is not None:
self.model = set_model_regularization(self.model,
self._opt.reg_func,
self._opt.reg_layers,
self._opt.reg_bias)
if self._opt.num_gpus > 1:
self.model = make_parallel(self.model, self.num_gpus)
if self._opt.optimizer == 'Adam':
#self.optimizer = optimizers.Adam(learning_rate=self._opt.init_lr, #keras 2.3+
self.optimizer = optimizers.Adam(
lr=self._opt.init_lr, # keras 2.2.0
beta_1=self._opt.adam_b1,
beta_2=self._opt.adam_b2)
elif self._opt.optimizer == 'SGD':
self.optimizer = optimizers.SGD(lr=self._opt.init_lr,
momentum=self._opt.momentum,
nesterov=True)
self.callback_list = list()
def setup(self, bottom, top):
"""
img_root:以及图片 存放路径 train.txt,val.txt,test.txt存放路径
phase:train or val or test
batch_size:batch_size
random:是否shuffle
"""
params = eval(self.param_str)
self.img_root = params['img_root']
self.phase = params['phase']
self.random = params.get('randomize', True)
self.crop_size = params.get('crop_size', [48, 48, 48])
self.batch_size = params['batch_size']
if self.phase not in ['train', 'test', 'val']:
raise Exception("phase must be train or val or test")
# two tops: data and label
if len(top) != 2:
raise Exception("Need to define two tops: data and label.")
# data layers have no bottoms
if len(bottom) != 0:
raise Exception("Do not define a bottom.")
self.data = {}
top[0].reshape(self.batch_size, 1, *self.crop_size)
top[1].reshape(self.batch_size, 1, *self.crop_size)
# load indices for images and labels
split_f = '{}txtfiles/{}.txt'.format(self.img_root, self.phase)
self.indices = open(split_f, 'r').read().splitlines()
#self.dataset=Dataset(self.indices,augment=True,crop_size=self.crop_size,randn=15)
self.dataset = Dataset(self.indices,
augment=True,
crop_size=self.crop_size,
randn=7)
self.dataloader = iter(
DataLoader(self.dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=1,
drop_last=True))
#import ipdb;ipdb.set_trace()
self.idx = 0
self.load_image = load_ct
# make eval deterministic
if 'train' not in self.phase:
self.random = False
# randomization: seed and pick
if self.random:
random.shuffle(self.indices)
def main():
# setup environments and seeds
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
# setup networks
Network = getattr(models, args.net)
model = Network(**args.net_params)
model = model.cuda()
optimizer = getattr(torch.optim, args.opt)(model.parameters(),
**args.opt_params)
criterion = getattr(criterions, args.criterion)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_iter = checkpoint['iter']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optim_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# Data loading code
Dataset = getattr(datasets, args.dataset)
# The loader will get 1000 patches from 50 subjects for each sub epoch
# each subject sample 20 patches
train_list = os.path.join(args.data_dir, 'file_list.txt')
train_set = Dataset(train_list,
root=args.data_dir,
for_train=True,
sample_size=args.patch_per_sample)
num_iters = args.num_iters or len(train_set) * args.num_epochs
num_iters -= args.start_iter
train_sampler = SSampler(len(train_set), num_iters)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
collate_fn=train_set.collate,
sampler=train_sampler,
num_workers=args.workers,
pin_memory=True,
prefetch=False)
#repeat = False
#valid_dir = args.data_dir
#valid_list = os.path.join(args.data_dir, 'valid_list.txt')
#valid_set = Dataset(valid_list, root=args.data_dir, for_train=False)
#valid_loader = DataLoader(
# valid_set,
# batch_size=1, shuffle=False,
# collate_fn=valid_set.collate,
# num_workers=2, pin_memory=True, prefetch=repeat)
logging.info('-------------- New training session ----------------')
start = time.time()
enum_batches = len(train_set) / args.batch_size
args.schedule = {
int(k * enum_batches): v
for k, v in args.schedule.items()
}
args.save_freq = int(enum_batches * args.save_freq)
## this is 0? in their configuration file
#stop_class_balancing = int(args.stop_class_balancing * enum_batches)
#weight = None
losses = AverageMeter()
torch.set_grad_enabled(True)
for i, (data, label) in enumerate(train_loader, args.start_iter):
adjust_learning_rate(optimizer, i)
# look at dataset class
#weight = data.pop()
#if i < stop_class_balancing:
# alpha = float(i)/stop_class_balancing
# weight = alpha + (1.0 - alpha)*weight # alpha*y2 + (1.0 - alpha)*y1
# weight = weight.cuda(non_blocking=True)
#else:
# weight = None
#data = [d.cuda(non_blocking=True) for d in data]
for x1, x2, target in zip(*[d.split(args.batch_size) for d in data]):
x1, x2, target = [
t.cuda(non_blocking=True) for t in (x1, x2, target)
]
# compute output
output = model((x1, x2)) # nx5x9x9x9, target nx9x9x9
loss = criterion(output, target)
#loss = criterion(output, target, weight)
# measure accuracy and record loss
losses.update(loss.item(), target.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % args.save_freq == 0:
file_name = os.path.join(ckpts, 'model_iter_{}.tar'.format(i + 1))
torch.save(
{
'iter': i + 1,
'state_dict': model.state_dict(),
'optim_dict': optimizer.state_dict(),
}, file_name)
msg = 'Iter {0:}, Epoch {1:.4f}, Loss {2:.4f}'.format(
i + 1, (i + 1) / enum_batches, losses.avg)
logging.info(msg)
losses.reset()
file_name = os.path.join(ckpts, 'model_last.tar')
torch.save(
{
'iter': i + 1,
'state_dict': model.state_dict(),
'optim_dict': optimizer.state_dict(),
}, file_name)
msg = 'total time: {} minutes'.format((time.time() - start) / 60)
logging.info(msg)
Config.image_channels))
sample_outputs = network(sample_inputs, training=True)
network.summary()
if __name__ == '__main__':
# GPU settings
gpus = tf.config.list_physical_devices("GPU")
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# dataset
train_dataset = DetectionDataset()
train_data, train_size = train_dataset.generate_datatset()
data_loader = DataLoader()
steps_per_epoch = tf.math.ceil(train_size / Config.batch_size)
# model
centernet = CenterNet()
print_model_summary(centernet)
load_weights_from_epoch = Config.load_weights_from_epoch
if Config.load_weights_before_training:
centernet.load_weights(filepath=Config.save_model_dir +
"epoch-{}".format(load_weights_from_epoch))
print("Successfully load weights!")
else:
load_weights_from_epoch = -1
# optimizer
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
im2 = np.fliplr(im2)
angle = random.choice([0, 1, 2, 3])
im1 = np.rot90(im1, angle)
im2 = np.rot90(im2, angle)
return im1.copy(), im2.copy()
if __name__ == '__main__':
from config.config_lol_my_data_ps_pair_3 import cfg
train_set = TrainDataset(None, None, cfg=cfg)
print(len(train_set))
train_loader = DataLoader(train_set,
batch_size=64,
num_workers=16,
shuffle=False,
drop_last=True,
timeout=0)
valid_hr_file = '/workspace/nas_mengdongwei/dataset/div2k/div2k_valid_hr_paths.txt'
valid_lr_file = '/workspace/nas_mengdongwei/dataset/div2k/div2k_valid_lr_paths.txt'
test_set = TestDataset(valid_hr_file, valid_hr_file)
test_loader = DataLoader(test_set,
batch_size=1,
num_workers=16,
shuffle=False,
drop_last=True,
timeout=0)
for index, sample in enumerate(test_loader):
lr, hr, cubic = sample
print(hr.shape)
def train():
#开启GPU
gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# 读取数据
dataloader = DetectionDataset()
train_data, train_size = dataloader.generate_datatset(
mode="train") #获取txt文件中的string数据，返回的是batch_size的string，以及txt的大小
train_steps_per_epoch = tf.math.ceil(train_size / Config.batch_size)
#验证集
val_data, val_size = dataloader.generate_datatset(mode="val")
val_steps_per_epoch = tf.math.ceil(val_size / Config.batch_size)
data_loader = DataLoader() #创建一个class的大小数据加载
if os.path.exists(Config.log_dir):
# 清除summary目录下原有的东西
shutil.rmtree(Config.log_dir)
# 建立模型保存目录
if not os.path.exists(os.path.split(Config.save_model_path)[0]):
os.mkdir(os.path.split(Config.save_model_path)[0])
print(
'Total on {}, train on {} samples, val on {} samples with batch size {}.'
.format((train_size + val_size), train_size, val_size,
Config.batch_size))
# optimizer
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=1e-4,
decay_steps=train_steps_per_epoch * Config.learning_rate_decay_epochs,
decay_rate=0.96)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
# 创建模型结构
centernet = CenterNet()
print_model_summary(centernet)
try:
centernet.load_weights(filepath=Config.save_model_path)
print("Load weights...")
except:
print("load weights...")
# 定义模型评估指标
train_loss = tf.metrics.Mean(name='train_loss')
valid_loss = tf.metrics.Mean(name='valid_loss')
post_process = PostProcessing()
#设置保存最好模型的指标
best_test_loss = float('inf')
# 创建summary
summary_writer = tf.summary.create_file_writer(logdir=Config.log_dir)
#训练
for epoch in range(1, Config.epochs + 1):
train_loss.reset_states()
valid_loss.reset_states()
#处理训练集数据
for step, batch_data in enumerate(train_data):
step_start_time = time.time()
images, labels = data_loader.read_batch_data(
batch_data
) # 返回的是图片image,以及标签信息[batch, max_boxes_per_image, xmin, ymin, xmax, ymax, class_id]
with tf.GradientTape() as tape:
# 得到预测
pred = centernet(images, training=True)
# 计算损失
loss_value = post_process.training_procedure(
batch_labels=labels, pred=pred)
# 反向传播梯度下降
# model.trainable_variables代表把loss反向传播到每个可以训练的变量中
gradients = tape.gradient(target=loss_value,
sources=centernet.trainable_variables)
# 将每个节点的误差梯度gradients，用于更新该节点的可训练变量值
# zip是把梯度和可训练变量值打包成元组
optimizer.apply_gradients(
grads_and_vars=zip(gradients, centernet.trainable_variables))
# 更新train_loss
train_loss.update_state(values=loss_value)
step_end_time = time.time()
print("Epoch: {}/{}, step: {}/{}, loss: {}, time_cost: {:.3f}s".
format(epoch, Config.epochs, step, train_steps_per_epoch,
train_loss.result(), step_end_time - step_start_time))
with summary_writer.as_default():
tf.summary.scalar(
"steps_perbatch_train_loss",
train_loss.result(),
step=tf.cast(((epoch - 1) * train_steps_per_epoch + step),
tf.int64))
# 计算验证集
for step, batch_data in enumerate(val_data):
step_start_time = time.time()
images, labels = data_loader.read_batch_data(
batch_data
) # 返回的是图片image,以及标签信息[batch, max_boxes_per_image, xmin, ymin, xmax, ymax, class_id]
# 得到预测，不training
pred = centernet(images)
# 计算损失
loss_value = post_process.training_procedure(batch_labels=labels,
pred=pred)
# 更新valid_loss
valid_loss.update_state(loss_value)
step_end_time = time.time()
print(
"--------Epoch: {}/{}, step: {}/{}, loss: {}, time_cost: {:.3f}s"
.format(epoch, Config.epochs, step, val_steps_per_epoch,
valid_loss.result(), step_end_time - step_start_time))
with summary_writer.as_default():
tf.summary.scalar("steps_perbatch_val_loss",
valid_loss.result(),
step=tf.cast(
(epoch - 1) * val_steps_per_epoch + step,
tf.int64))
# 保存到tensorboard里
with summary_writer.as_default():
tf.summary.scalar("train_loss",
train_loss.result(),
step=optimizer.iterations)
tf.summary.scalar('valid_loss',
valid_loss.result(),
step=optimizer.iterations)
# 只保存最好模型
if valid_loss.result() < best_test_loss:
best_test_loss = valid_loss.result()
centernet.save_weights(Config.save_model_path, save_format="tf")
print("Update model's weights")
def main():
USER_DATA_ROOT = '../user_data'
if len(args.store_name) == 0:
args.store_name = '_'.join([
'optim:{}'.format(args.optim),
'batch_size:{}'.format(args.batch_size),
'hidden_units:{}'.format(args.hidden_units)
])
setattr(args, 'save_root', os.path.join(USER_DATA_ROOT, args.store_name))
print("save experiment to :{}".format(args.save_root))
check_rootfolders()
num_class = 53
setattr(args, 'num_class', num_class)
pos_weight = torch.ones(
[num_class]) * 25 # here we approximately set the pos weight as 25
criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
########################### Create the classifier ###################
model = MLP_RNN(args.hidden_units, args.num_class)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total Params: {}".format(pytorch_total_params))
if args.gpus is not None:
if len(args.gpus) != 1:
model = nn.DataParallel(model)
model.cuda()
pos_weight = pos_weight.cuda()
if args.optim == 'SGD':
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
train_loader, val_loader = DataLoader(
args.batch_size,
lmdb_file=args.lmdb,
val_ratio=args.val_ratio,
train_num_workers=args.workers,
val_num_workers=args.workers).create_dataloaders()
log = open(
os.path.join(args.save_root, args.root_log,
'{}.txt'.format(args.store_name)), 'w')
best_loss = 1000
val_accum_epochs = 0
best_acc = 0
for epoch in range(args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
train(train_loader, model, criterion, optimizer, epoch, log)
torch.cuda.empty_cache()
if val_loader is None:
# save every epoch model
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
},
True,
False,
filename='MLP_RNN_{}'.format(epoch))
elif (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
loss_val, acc_val = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log)
is_best_loss = loss_val < best_loss
best_loss = min(loss_val, best_loss)
is_best_acc = acc_val > best_acc
best_acc = max(acc_val, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
},
is_best_loss,
is_best_acc,
filename='MLP_RNN')
if not is_best_acc:
val_accum_epochs += 1
else:
val_accum_epochs = 0
if val_accum_epochs >= args.early_stop:
print("validation acc did not improve over {} epochs, stop".
format(args.early_stop))
break
import argparse
import os
import numpy as np
import cv2
import tensorflow as tf
from configuration import Config
from data.dataloader import DetectionDataset, DataLoader
def print_model_summary(network):
sample_inputs = tf.random.normal(shape=(Config.batch_size,
Config.get_image_size()[0],
Config.get_image_size()[1],
Config.image_channels))
sample_outputs = network(sample_inputs, training=True)
network.summary()
if __name__ == "__main__":
gpu = tf.config.list_physical_devices("GPU: 0")
#get MOT dataset
train_dataset = DetectionDataset()
train_data, train_size = train_dataset.generate_datatset()
data_loader = DataLoader()
steps_per_epoch = tf.math.ceil(train_size / Config.batch_size)
centernet = CenterNet()
print_model_summary(centernet)
