def modelDeploy(args, model, optimizer, scheduler, logger):
if args.num_gpus >= 1:
from torch.nn.parallel import DataParallel
model = DataParallel(model)
model = model.cuda()
if torch.backends.cudnn.is_available():
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
cudnn.deterministic = True
trainData = {'epoch': 0, 'loss': [], 'miou': [], 'val': [], 'bestMiou': 0}
if args.resume:
if os.path.isfile(args.resume):
logger.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume,
map_location=torch.device('cpu'))
# model&optimizer
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
# stop point
trainData = checkpoint['trainData']
for i in range(trainData['epoch']):
scheduler.step()
# print(trainData)
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, trainData['epoch']))
else:
logger.error("=> no checkpoint found at '{}'".format(args.resume))
assert False, "=> no checkpoint found at '{}'".format(args.resume)
if args.finetune:
if os.path.isfile(args.finetune):
logger.info("=> finetuning checkpoint '{}'".format(args.finetune))
state_all = torch.load(args.finetune, map_location='cpu')['model']
state_clip = {} # only use backbone parameters
# print(model.state_dict().keys())
for k, v in state_all.items():
state_clip[k] = v
# print(state_clip.keys())
model.load_state_dict(state_clip, strict=False)
else:
logger.warning("finetune is not a file.")
pass
if args.freeze_bn:
logger.warning('Freezing batch normalization layers')
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
m.weight.requires_grad = False
m.bias.requires_grad = False
return model, trainData
def load_reid_model():
model = DataParallel(Model())
ckpt = '/home/honglongcai/Github/PretrainedModel/model_410.pt'
model.load_state_dict(torch.load(ckpt, map_location='cuda'))
logger.info('Load ReID model from {}'.format(ckpt))
model = model.cuda()
model.eval()
return model
class TestProcess:
def __init__(self):
self.net = ET_Net()
if (ARGS['gpu']):
self.net = DataParallel(module=self.net.cuda())
self.net.load_state_dict(torch.load(ARGS['weight']))
self.test_dataset = get_dataset(dataset_name=ARGS['dataset'], part='test')
def predict(self):
start = time.time()
self.net.eval()
test_dataloader = DataLoader(self.test_dataset, batch_size=1) # only support batch size = 1
os.makedirs(ARGS['prediction_save_folder'], exist_ok=True)
for items in test_dataloader:
images, mask, filename = items['image'], items['mask'], items['filename']
images = images.float()
mask = mask.long()
print('image shape:', images.size())
image_patches, big_h, big_w = get_test_patches(images, ARGS['crop_size'], ARGS['stride_size'])
test_patch_dataloader = DataLoader(image_patches, batch_size=ARGS['batch_size'], shuffle=False, drop_last=False)
test_results = []
print('Number of batches for testing:', len(test_patch_dataloader))
for patches in test_patch_dataloader:
if ARGS['gpu']:
patches = patches.cuda()
with torch.no_grad():
result_patches_edge, result_patches = self.net(patches)
test_results.append(result_patches.cpu())
test_results = torch.cat(test_results, dim=0)
# merge
test_results = recompone_overlap(test_results, ARGS['crop_size'], ARGS['stride_size'], big_h, big_w)
test_results = test_results[:, 1, :images.size(2), :images.size(3)] * mask
test_results = Image.fromarray(test_results[0].numpy())
test_results.save(os.path.join(ARGS['prediction_save_folder'], filename[0]))
print(f'Finish prediction for {filename[0]}')
finish = time.time()
print('Predicting time consumed: {:.2f}s'.format(finish - start))
class BaseInferencer(object):
def __init__(self,
model,
images_path,
labels_path,
patient_ids,
sample_shape,
checkpoint_restore,
inference_dir,
use_gpu=False,
gpu_ids=None):
# model settings
self.model = model
# data settings
assert len(images_path) == len(labels_path)
self.images_path = images_path
self.labels_path = labels_path
self.patient_ids = patient_ids
self.length = len(images_path)
self.sample_shape = sample_shape
self.inference_dir = inference_dir
# gpu settings
self.use_gpu = use_gpu
if use_gpu and torch.cuda.device_count() > 0:
self.model.cuda()
if gpu_ids is not None:
if len(gpu_ids) > 1:
self.multi_gpu = True
self.model = DataParallel(model, gpu_ids)
else:
self.multi_gpu = False
else:
if torch.cuda.device_count() > 1:
self.multi_gpu = True
self.model = DataParallel(model)
else:
self.multi_gpu = False
else:
self.multi_gpu = False
self.model = self.model.cpu()
self.model.load_state_dict(torch.load(checkpoint_restore))
def inference(self):
self.model.eval()
logging.info('*' * 80)
logging.info('start inference loop')
logging.info('%d patients need to be inference ' % self.length)
for index in range(self.length):
logging.info('start inference %d-th patient' % (index + 1))
self.__inference__(index)
logging.info('*' * 80)
logging.info('inference patient: %d' % self.length)
logging.info('inference result saved in: %s' % self.inference_dir)
def __inference__(self, index):
"""
:rtype: object
"""
pass
class BaseEvaluation(object):
def __init__(self,
model,
metrics,
images_path,
labels_path,
sample_shape,
checkpoint_restore,
use_gpu=False,
gpu_ids=None):
# model settings
self.model = model
# metrics settings
assert type(metrics) == dict
self.metrics = metrics
# data settings
assert len(images_path) == len(labels_path)
self.images_path = images_path
self.labels_path = labels_path
self.length = len(images_path)
self.sample_shape = sample_shape
# gpu settings
self.use_gpu = use_gpu
if use_gpu and torch.cuda.device_count() > 0:
self.model.cuda()
if gpu_ids is not None:
if len(gpu_ids) > 1:
self.multi_gpu = True
self.model = DataParallel(model, gpu_ids)
else:
self.multi_gpu = False
else:
if torch.cuda.device_count() > 1:
self.multi_gpu = True
self.model = DataParallel(model)
else:
self.multi_gpu = False
else:
self.multi_gpu = False
self.model = self.model.cpu()
self.model.load_state_dict(torch.load(checkpoint_restore))
def load_data(self, index):
"""
:rtype: image -> nd-array, label -> nd-array
"""
pass
def eval_one_patient(self, image, label):
"""
:rtype: metrics -> dict
"""
pass
def eval(self):
self.model.eval()
logging.info('*' * 80)
logging.info('start evaluation loop')
logging.info('%d patients need to be evaluated ' % self.length)
result = dict()
for index in range(self.length):
logging.info('start evaluation %d-th patient' % (index + 1))
image, label = self.load_data(index)
with torch.no_grad():
metrics = self.eval_one_patient(image, label)
for key in metrics.keys():
if key not in result.keys():
result[key] = list()
result[key].append(metrics[key])
logging.info('evaluation metrics result: %s' % str(metrics))
mean_result = dict()
for key in result.keys():
mean_result[key] = np.mean(result[key])
logging.info('*' * 80)
logging.info('evaluation report: ')
logging.info('evaluation patient: %d' % self.length)
logging.info('evaluation metrics %s' % str(mean_result))
class BaseTrainer(object):
def __init__(self,
epochs,
model,
train_dataloader,
train_loss_func,
train_metrics_func,
optimizer,
log_dir,
checkpoint_dir,
checkpoint_frequency,
checkpoint_restore=None,
val_dataloader=None,
val_metrics_func=None,
lr_scheduler=None,
lr_reduce_metric=None,
use_gpu=False,
gpu_ids=None):
# train settings
self.epochs = epochs
self.model = model
self.train_dataloader = train_dataloader
self.train_loss_func = train_loss_func
self.train_metrics_func = train_metrics_func
self.optimizer = optimizer
self.checkpoint_dir = checkpoint_dir
self.checkpoint_frequency = checkpoint_frequency
self.writer = SummaryWriter(logdir=log_dir)
# validation settings
if val_dataloader is not None:
self.validation = True
self.val_dataloader = val_dataloader
self.val_metrics_func = val_metrics_func
else:
self.validation = False
# lr scheduler settings
if lr_scheduler is not None:
self.lr_schedule = True
self.lr_scheduler = lr_scheduler
if isinstance(lr_scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_reduce_metric = lr_reduce_metric
else:
self.lr_schedule = False
# multi-gpu settings
self.use_gpu = use_gpu
gpu_visible = list()
for index in range(len(gpu_ids)):
gpu_visible.append(str(gpu_ids[index]))
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_visible)
if use_gpu and torch.cuda.device_count() > 0:
self.model.cuda()
if gpu_ids is not None:
if len(gpu_ids) > 1:
self.multi_gpu = True
self.model = DataParallel(model, gpu_ids)
else:
self.multi_gpu = False
else:
if torch.cuda.device_count() > 1:
self.multi_gpu = True
self.model = DataParallel(model)
else:
self.multi_gpu = False
else:
self.multi_gpu = False
self.device = torch.device('cpu')
self.model = self.model.cpu()
# checkpoint settings
if checkpoint_restore is not None:
self.model.load_state_dict(torch.load(checkpoint_restore))
def train(self):
for epoch in range(1, self.epochs + 1):
logging.info('*' * 80)
logging.info('start epoch %d training loop' % epoch)
# train
self.model.train()
loss, metrics = self.train_epochs(epoch)
self.writer.add_scalar('train_loss', loss, epoch)
for key in metrics.keys():
self.writer.add_scalar(key, metrics[key], epoch)
if self.lr_schedule:
if isinstance(self.lr_scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_scheduler.step(loss[self.lr_reduce_metric])
else:
self.lr_scheduler.step()
logging.info('train loss result: %s' % str(loss))
logging.info('train metrics result: %s' % str(metrics))
# validation
if self.validation:
logging.info('validation start ... ')
self.model.eval()
loss, metrics = self.val_epochs(epoch)
self.writer.add_scalar('val_loss', loss, epoch)
for key in metrics.keys():
self.writer.add_scalar(key, metrics[key], epoch)
logging.info('validation loss result: %s' % str(loss))
logging.info('validation metrics result: %s' % str(metrics))
# model checkpoint
if epoch % self.checkpoint_frequency == 0:
logging.info('saving model...')
checkpoint_name = 'checkpoint_%d.pth' % epoch
if self.multi_gpu:
torch.save(
self.model.module.state_dict(),
os.path.join(self.checkpoint_dir, checkpoint_name))
else:
torch.save(
self.model.state_dict(),
os.path.join(self.checkpoint_dir, checkpoint_name))
logging.info('model have saved for epoch_%d ' % epoch)
else:
logging.info('saving model skipped.')
def train_epochs(self, epoch) -> (dict, dict):
"""
:rtype: loss -> dict , metrics -> dict
"""
pass
def val_epochs(self, epoch) -> (dict, dict):
"""
:rtype: loss -> dict , metrics -> dict
"""
pass
'params': IDE.classifier.parameters(),
'lr': 0.01
}],
momentum=0.9,
weight_decay=5e-4,
nesterov=True)
# Decay LR by a factor of 0.1 every 20 epochs (20 epochs for market and 30 epochs for duke)
scheduler_IDE = lr_scheduler.StepLR(IDE_optimizer, step_size=10, gamma=0.1)
## load checkpoint
ckpt_dir = './checkpoints/espgan_m2d_lam5/'
utils.mkdir(ckpt_dir)
try:
ckpt = utils.load_checkpoint(ckpt_dir, map_location=torch.device('cpu'))
start_epoch = ckpt['epoch']
Da.load_state_dict(ckpt['Da'])
Db.load_state_dict(ckpt['Db'])
Ga.load_state_dict(ckpt['Ga'])
Gb.load_state_dict(ckpt['Gb'])
IDE.load_state_dict(ckpt['IDE'])
da_optimizer.load_state_dict(ckpt['da_optimizer'])
db_optimizer.load_state_dict(ckpt['db_optimizer'])
ga_optimizer.load_state_dict(ckpt['ga_optimizer'])
gb_optimizer.load_state_dict(ckpt['gb_optimizer'])
IDE_optimizer.load_state_dict(ckpt['IDE_optimizer'])
except:
start_epoch = 0
print('Training form zero')
## run
class SSLOnlineEvaluator(Callback): # pragma: no cover
"""Attaches a MLP for fine-tuning using the standard self-supervised protocol.
Example::
# your datamodule must have 2 attributes
dm = DataModule()
dm.num_classes = ... # the num of classes in the datamodule
dm.name = ... # name of the datamodule (e.g. ImageNet, STL10, CIFAR10)
# your model must have 1 attribute
model = Model()
model.z_dim = ... # the representation dim
online_eval = SSLOnlineEvaluator(
z_dim=model.z_dim
)
"""
def __init__(
self,
z_dim: int,
drop_p: float = 0.2,
hidden_dim: Optional[int] = None,
num_classes: Optional[int] = None,
dataset: Optional[str] = None,
):
"""
Args:
z_dim: Representation dimension
drop_p: Dropout probability
hidden_dim: Hidden dimension for the fine-tune MLP
"""
super().__init__()
self.z_dim = z_dim
self.hidden_dim = hidden_dim
self.drop_p = drop_p
self.optimizer: Optional[Optimizer] = None
self.online_evaluator: Optional[SSLEvaluator] = None
self.num_classes: Optional[int] = None
self.dataset: Optional[str] = None
self.num_classes: Optional[int] = num_classes
self.dataset: Optional[str] = dataset
self._recovered_callback_state: Optional[Dict[str, Any]] = None
def setup(self,
trainer: Trainer,
pl_module: LightningModule,
stage: Optional[str] = None) -> None:
if self.num_classes is None:
self.num_classes = trainer.datamodule.num_classes
if self.dataset is None:
self.dataset = trainer.datamodule.name
def on_pretrain_routine_start(self, trainer: Trainer,
pl_module: LightningModule) -> None:
# must move to device after setup, as during setup, pl_module is still on cpu
self.online_evaluator = SSLEvaluator(
n_input=self.z_dim,
n_classes=self.num_classes,
p=self.drop_p,
n_hidden=self.hidden_dim,
).to(pl_module.device)
# switch fo PL compatibility reasons
accel = (trainer.accelerator_connector if hasattr(
trainer, "accelerator_connector") else
trainer._accelerator_connector)
if accel.is_distributed:
if accel.use_ddp:
from torch.nn.parallel import DistributedDataParallel as DDP
self.online_evaluator = DDP(self.online_evaluator,
device_ids=[pl_module.device])
elif accel.use_dp:
from torch.nn.parallel import DataParallel as DP
self.online_evaluator = DP(self.online_evaluator,
device_ids=[pl_module.device])
else:
rank_zero_warn(
"Does not support this type of distributed accelerator. The online evaluator will not sync."
)
self.optimizer = torch.optim.Adam(self.online_evaluator.parameters(),
lr=1e-4)
if self._recovered_callback_state is not None:
self.online_evaluator.load_state_dict(
self._recovered_callback_state["state_dict"])
self.optimizer.load_state_dict(
self._recovered_callback_state["optimizer_state"])
def to_device(self, batch: Sequence,
device: Union[str, torch.device]) -> Tuple[Tensor, Tensor]:
# get the labeled batch
if self.dataset == "stl10":
labeled_batch = batch[1]
batch = labeled_batch
inputs, y = batch
# last input is for online eval
x = inputs[-1]
x = x.to(device)
y = y.to(device)
return x, y
def shared_step(
self,
pl_module: LightningModule,
batch: Sequence,
):
with torch.no_grad():
with set_training(pl_module, False):
x, y = self.to_device(batch, pl_module.device)
representations = pl_module(x).flatten(start_dim=1)
# forward pass
mlp_logits = self.online_evaluator(
representations) # type: ignore[operator]
mlp_loss = F.cross_entropy(mlp_logits, y)
acc = accuracy(mlp_logits.softmax(-1), y)
return acc, mlp_loss
def on_train_batch_end(
self,
trainer: Trainer,
pl_module: LightningModule,
outputs: Sequence,
batch: Sequence,
batch_idx: int,
dataloader_idx: int,
) -> None:
train_acc, mlp_loss = self.shared_step(pl_module, batch)
# update finetune weights
mlp_loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
pl_module.log("online_train_acc",
train_acc,
on_step=True,
on_epoch=False)
pl_module.log("online_train_loss",
mlp_loss,
on_step=True,
on_epoch=False)
def on_validation_batch_end(
self,
trainer: Trainer,
pl_module: LightningModule,
outputs: Sequence,
batch: Sequence,
batch_idx: int,
dataloader_idx: int,
) -> None:
val_acc, mlp_loss = self.shared_step(pl_module, batch)
pl_module.log("online_val_acc",
val_acc,
on_step=False,
on_epoch=True,
sync_dist=True)
pl_module.log("online_val_loss",
mlp_loss,
on_step=False,
on_epoch=True,
sync_dist=True)
def on_save_checkpoint(self, trainer: Trainer, pl_module: LightningModule,
checkpoint: Dict[str, Any]) -> dict:
return {
"state_dict": self.online_evaluator.state_dict(),
"optimizer_state": self.optimizer.state_dict()
}
def on_load_checkpoint(self, trainer: Trainer, pl_module: LightningModule,
callback_state: Dict[str, Any]) -> None:
self._recovered_callback_state = callback_state
def main():
global args, best_prec1
args = parser.parse_args()
# Read list of training and validation data
listfiles_train, labels_train = read_lists(TRAIN_OUT)
listfiles_val, labels_val = read_lists(VAL_OUT)
listfiles_test, labels_test = read_lists(TEST_OUT)
dataset_train = Dataset(listfiles_train,
labels_train,
subtract_mean=False,
V=12)
dataset_val = Dataset(listfiles_val, labels_val, subtract_mean=False, V=12)
dataset_test = Dataset(listfiles_test,
labels_test,
subtract_mean=False,
V=12)
# shuffle data
dataset_train.shuffle()
dataset_val.shuffle()
dataset_test.shuffle()
tra_data_size, val_data_size, test_data_size = dataset_train.size(
), dataset_val.size(), dataset_test.size()
print 'training size:', tra_data_size
print 'validation size:', val_data_size
print 'testing size:', test_data_size
batch_size = args.b
print("batch_size is :" + str(batch_size))
learning_rate = args.lr
print("learning_rate is :" + str(learning_rate))
num_cuda = cuda.device_count()
print("number of GPUs have been detected:" + str(num_cuda))
# creat model
print("model building...")
mvcnn = DataParallel(modelnet40_Alex(num_cuda, batch_size))
#mvcnn = modelnet40(num_cuda, batch_size, multi_gpu = False)
mvcnn.cuda()
# 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_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
mvcnn.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
#print(mvcnn)
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.Adadelta(mvcnn.parameters(), weight_decay=1e-4)
# evaluate performance only
if args.evaluate:
print 'testing mode ------------------'
validate(dataset_test, mvcnn, criterion, optimizer, batch_size)
return
print 'training mode ------------------'
for epoch in xrange(args.start_epoch, args.epochs):
print('epoch:', epoch)
#adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(dataset_train, mvcnn, criterion, optimizer, epoch, batch_size)
# evaluate on validation set
prec1 = validate(dataset_val, mvcnn, criterion, optimizer, batch_size)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
if is_best:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': mvcnn.state_dict(),
'best_prec1': best_prec1,
}, is_best, epoch)
elif epoch % 5 is 0:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': mvcnn.state_dict(),
'best_prec1': best_prec1,
}, is_best, epoch)
# Train or Test
if not args.demo:
avg_tool = CumulativeAverager()
vloss, is_best = torch.tensor(float(np.inf)), None
if args.load_from is not None:
if os.path.isfile(args.load_from):
log_str = add_to_log("=> loading checkpoint '{}'".format(args.load_from))
checkpoint = torch.load(args.load_from)
start = checkpoint['epoch']
vloss = checkpoint['best_val_loss']
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
args.lr = checkpoint['learning_rate']
log_str = add_to_log("=> loaded checkpoint '{}' (epoch {})"
.format(args.load_from, checkpoint['epoch']))
else:
log_str = add_to_log("=> no checkpoint found at '{}'".format(args.load_from))
else:
start = 0
for epoch in range(start, args.epochs):
train(epoch, losstype=losstype)
val_loss = validate(losstype=losstype).cpu()
scheduler.step(val_loss)
def main(args):
crop_size = args.crop_size
assert isinstance(crop_size, tuple)
print_info_message(
'Running Model at image resolution {}x{} with batch size {}'.format(
crop_size[0], crop_size[1], args.batch_size))
if not os.path.isdir(args.savedir):
os.makedirs(args.savedir)
num_gpus = torch.cuda.device_count()
device = 'cuda' if num_gpus > 0 else 'cpu'
print('device : ' + device)
# Get a summary writer for tensorboard
writer = SummaryWriter(log_dir=args.savedir,
comment='Training and Validation logs')
#
# Training the model with 13 classes of CamVid dataset
# TODO: This process should be done only if specified
#
if not args.finetune:
train_dataset, val_dataset, class_wts, seg_classes, color_encoding = import_dataset(
label_conversion=False) # 13 classes
args.use_depth = False # 'use_depth' is always false for camvid
print_info_message('Training samples: {}'.format(len(train_dataset)))
print_info_message('Validation samples: {}'.format(len(val_dataset)))
# Import model
if args.model == 'espnetv2':
from model.segmentation.espnetv2 import espnetv2_seg
args.classes = seg_classes
model = espnetv2_seg(args)
elif args.model == 'espdnet':
from model.segmentation.espdnet import espdnet_seg
args.classes = seg_classes
print("Trainable fusion : {}".format(args.trainable_fusion))
print("Segmentation classes : {}".format(seg_classes))
model = espdnet_seg(args)
elif args.model == 'espdnetue':
from model.segmentation.espdnet_ue import espdnetue_seg2
args.classes = seg_classes
print("Trainable fusion : {}".format(args.trainable_fusion))
("Segmentation classes : {}".format(seg_classes))
print(args.weights)
model = espdnetue_seg2(args, False, fix_pyr_plane_proj=True)
else:
print_error_message('Arch: {} not yet supported'.format(
args.model))
exit(-1)
# Freeze batch normalization layers?
if args.freeze_bn:
freeze_bn_layer(model)
# Set learning rates
train_params = [{
'params': model.get_basenet_params(),
'lr': args.lr
}, {
'params': model.get_segment_params(),
'lr': args.lr * args.lr_mult
}]
# Define an optimizer
optimizer = optim.SGD(train_params,
lr=args.lr * args.lr_mult,
momentum=args.momentum,
weight_decay=args.weight_decay)
# Compute the FLOPs and the number of parameters, and display it
num_params, flops = show_network_stats(model, crop_size)
try:
writer.add_graph(model,
input_to_model=torch.Tensor(
1, 3, crop_size[0], crop_size[1]))
except:
print_log_message(
"Not able to generate the graph. Likely because your model is not supported by ONNX"
)
#criterion = nn.CrossEntropyLoss(weight=class_wts, reduction='none', ignore_index=args.ignore_idx)
criterion = SegmentationLoss(n_classes=seg_classes,
loss_type=args.loss_type,
device=device,
ignore_idx=args.ignore_idx,
class_wts=class_wts.to(device))
nid_loss = NIDLoss(image_bin=32,
label_bin=seg_classes) if args.use_nid else None
if num_gpus >= 1:
if num_gpus == 1:
# for a single GPU, we do not need DataParallel wrapper for Criteria.
# So, falling back to its internal wrapper
from torch.nn.parallel import DataParallel
model = DataParallel(model)
model = model.cuda()
criterion = criterion.cuda()
if args.use_nid:
nid_loss.cuda()
else:
from utilities.parallel_wrapper import DataParallelModel, DataParallelCriteria
model = DataParallelModel(model)
model = model.cuda()
criterion = DataParallelCriteria(criterion)
criterion = criterion.cuda()
if args.use_nid:
nid_loss = DataParallelCriteria(nid_loss)
nid_loss = nid_loss.cuda()
if torch.backends.cudnn.is_available():
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
cudnn.deterministic = True
# Get data loaders for training and validation data
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.workers)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=20,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
# Get a learning rate scheduler
lr_scheduler = get_lr_scheduler(args.scheduler)
write_stats_to_json(num_params, flops)
extra_info_ckpt = '{}_{}_{}'.format(args.model, args.s, crop_size[0])
#
# Main training loop of 13 classes
#
start_epoch = 0
best_miou = 0.0
for epoch in range(start_epoch, args.epochs):
lr_base = lr_scheduler.step(epoch)
# set the optimizer with the learning rate
# This can be done inside the MyLRScheduler
lr_seg = lr_base * args.lr_mult
optimizer.param_groups[0]['lr'] = lr_base
optimizer.param_groups[1]['lr'] = lr_seg
print_info_message(
'Running epoch {} with learning rates: base_net {:.6f}, segment_net {:.6f}'
.format(epoch, lr_base, lr_seg))
# Use different training functions for espdnetue
if args.model == 'espdnetue':
from utilities.train_eval_seg import train_seg_ue as train
from utilities.train_eval_seg import val_seg_ue as val
else:
from utilities.train_eval_seg import train_seg as train
from utilities.train_eval_seg import val_seg as val
miou_train, train_loss = train(model,
train_loader,
optimizer,
criterion,
seg_classes,
epoch,
device=device,
use_depth=args.use_depth,
add_criterion=nid_loss)
miou_val, val_loss = val(model,
val_loader,
criterion,
seg_classes,
device=device,
use_depth=args.use_depth,
add_criterion=nid_loss)
batch_train = iter(train_loader).next()
batch = iter(val_loader).next()
in_training_visualization_img(
model,
images=batch_train[0].to(device=device),
labels=batch_train[1].to(device=device),
class_encoding=color_encoding,
writer=writer,
epoch=epoch,
data='Segmentation/train',
device=device)
in_training_visualization_img(model,
images=batch[0].to(device=device),
labels=batch[1].to(device=device),
class_encoding=color_encoding,
writer=writer,
epoch=epoch,
data='Segmentation/val',
device=device)
# remember best miou and save checkpoint
is_best = miou_val > best_miou
best_miou = max(miou_val, best_miou)
weights_dict = model.module.state_dict(
) if device == 'cuda' else model.state_dict()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.model,
'state_dict': weights_dict,
'best_miou': best_miou,
'optimizer': optimizer.state_dict(),
}, is_best, args.savedir, extra_info_ckpt)
writer.add_scalar('Segmentation/LR/base', round(lr_base, 6), epoch)
writer.add_scalar('Segmentation/LR/seg', round(lr_seg, 6), epoch)
writer.add_scalar('Segmentation/Loss/train', train_loss, epoch)
writer.add_scalar('Segmentation/Loss/val', val_loss, epoch)
writer.add_scalar('Segmentation/mIOU/train', miou_train, epoch)
writer.add_scalar('Segmentation/mIOU/val', miou_val, epoch)
writer.add_scalar('Segmentation/Complexity/Flops', best_miou,
math.ceil(flops))
writer.add_scalar('Segmentation/Complexity/Params', best_miou,
math.ceil(num_params))
# Save the pretrained weights
model_dict = copy.deepcopy(model.state_dict())
del model
torch.cuda.empty_cache()
#
# Finetuning with 4 classes
#
args.ignore_idx = 4
train_dataset, val_dataset, class_wts, seg_classes, color_encoding = import_dataset(
label_conversion=True) # 5 classes
print_info_message('Training samples: {}'.format(len(train_dataset)))
print_info_message('Validation samples: {}'.format(len(val_dataset)))
#set_parameters_for_finetuning()
# Import model
if args.model == 'espnetv2':
from model.segmentation.espnetv2 import espnetv2_seg
args.classes = seg_classes
model = espnetv2_seg(args)
elif args.model == 'espdnet':
from model.segmentation.espdnet import espdnet_seg
args.classes = seg_classes
print("Trainable fusion : {}".format(args.trainable_fusion))
print("Segmentation classes : {}".format(seg_classes))
model = espdnet_seg(args)
elif args.model == 'espdnetue':
from model.segmentation.espdnet_ue import espdnetue_seg2
args.classes = seg_classes
print("Trainable fusion : {}".format(args.trainable_fusion))
print("Segmentation classes : {}".format(seg_classes))
print(args.weights)
model = espdnetue_seg2(args, args.finetune, fix_pyr_plane_proj=True)
else:
print_error_message('Arch: {} not yet supported'.format(args.model))
exit(-1)
if not args.finetune:
new_model_dict = model.state_dict()
# for k, v in model_dict.items():
# if k.lstrip('module.') in new_model_dict:
# print('In:{}'.format(k.lstrip('module.')))
# else:
# print('Not In:{}'.format(k.lstrip('module.')))
overlap_dict = {
k.replace('module.', ''): v
for k, v in model_dict.items()
if k.replace('module.', '') in new_model_dict
and new_model_dict[k.replace('module.', '')].size() == v.size()
}
no_overlap_dict = {
k.replace('module.', ''): v
for k, v in new_model_dict.items()
if k.replace('module.', '') not in new_model_dict
or new_model_dict[k.replace('module.', '')].size() != v.size()
}
print(no_overlap_dict.keys())
new_model_dict.update(overlap_dict)
model.load_state_dict(new_model_dict)
output = model(torch.ones(1, 3, 288, 480))
print(output[0].size())
print(seg_classes)
print(class_wts.size())
#print(model_dict.keys())
#print(new_model_dict.keys())
criterion = SegmentationLoss(n_classes=seg_classes,
loss_type=args.loss_type,
device=device,
ignore_idx=args.ignore_idx,
class_wts=class_wts.to(device))
nid_loss = NIDLoss(image_bin=32,
label_bin=seg_classes) if args.use_nid else None
# Set learning rates
args.lr /= 100
train_params = [{
'params': model.get_basenet_params(),
'lr': args.lr
}, {
'params': model.get_segment_params(),
'lr': args.lr * args.lr_mult
}]
# Define an optimizer
optimizer = optim.SGD(train_params,
lr=args.lr * args.lr_mult,
momentum=args.momentum,
weight_decay=args.weight_decay)
if num_gpus >= 1:
if num_gpus == 1:
# for a single GPU, we do not need DataParallel wrapper for Criteria.
# So, falling back to its internal wrapper
from torch.nn.parallel import DataParallel
model = DataParallel(model)
model = model.cuda()
criterion = criterion.cuda()
if args.use_nid:
nid_loss.cuda()
else:
from utilities.parallel_wrapper import DataParallelModel, DataParallelCriteria
model = DataParallelModel(model)
model = model.cuda()
criterion = DataParallelCriteria(criterion)
criterion = criterion.cuda()
if args.use_nid:
nid_loss = DataParallelCriteria(nid_loss)
nid_loss = nid_loss.cuda()
if torch.backends.cudnn.is_available():
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
cudnn.deterministic = True
# Get data loaders for training and validation data
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.workers)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=20,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
# Get a learning rate scheduler
args.epochs = 50
lr_scheduler = get_lr_scheduler(args.scheduler)
# Compute the FLOPs and the number of parameters, and display it
num_params, flops = show_network_stats(model, crop_size)
write_stats_to_json(num_params, flops)
extra_info_ckpt = '{}_{}_{}_{}'.format(args.model, seg_classes, args.s,
crop_size[0])
#
# Main training loop of 13 classes
#
start_epoch = 0
best_miou = 0.0
for epoch in range(start_epoch, args.epochs):
lr_base = lr_scheduler.step(epoch)
# set the optimizer with the learning rate
# This can be done inside the MyLRScheduler
lr_seg = lr_base * args.lr_mult
optimizer.param_groups[0]['lr'] = lr_base
optimizer.param_groups[1]['lr'] = lr_seg
print_info_message(
'Running epoch {} with learning rates: base_net {:.6f}, segment_net {:.6f}'
.format(epoch, lr_base, lr_seg))
# Use different training functions for espdnetue
if args.model == 'espdnetue':
from utilities.train_eval_seg import train_seg_ue as train
from utilities.train_eval_seg import val_seg_ue as val
else:
from utilities.train_eval_seg import train_seg as train
from utilities.train_eval_seg import val_seg as val
miou_train, train_loss = train(model,
train_loader,
optimizer,
criterion,
seg_classes,
epoch,
device=device,
use_depth=args.use_depth,
add_criterion=nid_loss)
miou_val, val_loss = val(model,
val_loader,
criterion,
seg_classes,
device=device,
use_depth=args.use_depth,
add_criterion=nid_loss)
batch_train = iter(train_loader).next()
batch = iter(val_loader).next()
in_training_visualization_img(model,
images=batch_train[0].to(device=device),
labels=batch_train[1].to(device=device),
class_encoding=color_encoding,
writer=writer,
epoch=epoch,
data='SegmentationConv/train',
device=device)
in_training_visualization_img(model,
images=batch[0].to(device=device),
labels=batch[1].to(device=device),
class_encoding=color_encoding,
writer=writer,
epoch=epoch,
data='SegmentationConv/val',
device=device)
# remember best miou and save checkpoint
is_best = miou_val > best_miou
best_miou = max(miou_val, best_miou)
weights_dict = model.module.state_dict(
) if device == 'cuda' else model.state_dict()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.model,
'state_dict': weights_dict,
'best_miou': best_miou,
'optimizer': optimizer.state_dict(),
}, is_best, args.savedir, extra_info_ckpt)
writer.add_scalar('SegmentationConv/LR/base', round(lr_base, 6), epoch)
writer.add_scalar('SegmentationConv/LR/seg', round(lr_seg, 6), epoch)
writer.add_scalar('SegmentationConv/Loss/train', train_loss, epoch)
writer.add_scalar('SegmentationConv/Loss/val', val_loss, epoch)
writer.add_scalar('SegmentationConv/mIOU/train', miou_train, epoch)
writer.add_scalar('SegmentationConv/mIOU/val', miou_val, epoch)
writer.add_scalar('SegmentationConv/Complexity/Flops', best_miou,
math.ceil(flops))
writer.add_scalar('SegmentationConv/Complexity/Params', best_miou,
math.ceil(num_params))
writer.close()
class GetFeature(object):
"""Extract features
Arguments
model_weight_file: pre-trained model
sys_device_ids: cpu/gpu
"""
def __init__(self, model_weight_file, sys_device_ids=''):
if len(sys_device_ids) > 0:
os.environ['CUDA_VISIBLE_DEVICES'] = sys_device_ids
self.sys_device_ids = sys_device_ids
self.model = DataParallel(Model())
if torch.cuda.is_available() and self.sys_device_ids != '':
device = torch.device('cuda')
else:
device = torch.device('cpu')
self.model.load_state_dict(torch.load(model_weight_file,
map_location=device))
self.model.to(device)
self.model.eval()
def __call__(self, photo_path=None, batch_size=1):
"""
get global feature and local feature
:param photo_path : either photo directory or a single image
:param batch_size : useful only when photo_path is a directory
:return: feature: numpy array, dim = num_images * 2048,
photo_name: a list, len = num_images
"""
'''
if photo_dir is None and photo is None:
raise self.InputError('Error: both photo_path '
'and images is None.')
if photo_dir and photo:
raise self.InputError('Error: only need one argument, '
'either photo_path or images.')
'''
# input is a directory
if os.path.isdir(photo_path):
dataset = Data(photo_path, self._img_process)
data_loader = DataLoader(dataset, batch_size=batch_size,
num_workers=8)
features = torch.FloatTensor()
photos = []
for batch, (images, names) in enumerate(data_loader):
images = images.float()
if torch.cuda.is_available() and self.sys_device_ids != '':
images = images.to('cuda')
feature = self.model(images).data.cpu()
features = torch.cat((features, feature), 0)
photos = photos + list(names)
if batch % 10 == 0:
print('processing batch: {}'.format(batch))
features = features.numpy()
features = features/np.linalg.norm(features, axis=1,
keepdims=True)
return features, photos
# input is a single image
else:
photo_name = photo_path.split('/')[-1]
img = Image.open(photo_path)
image = self._img_process(img)
image = np.expand_dims(image, axis=0)
image = torch.from_numpy(image).float()
feature = self.model(image).data.numpy()
feature = feature/np.linalg.norm(feature, axis=1,
keepdims=True)
return feature, [photo_name]
def _img_process(self, img):
img = img.resize((128, 384), resample=3)
img = np.asarray(img)
img = img[:, :, :3]
img = img.astype(float)
img = img / 255
im_mean = np.array([0.485, 0.456, 0.406])
im_std = np.array([0.229, 0.224, 0.225])
img = img - im_mean
img = img / im_std
img = np.transpose(img, (2, 0, 1))
return img
def main(args):
if not os.path.exists(args.outputs_dir):
os.makedirs(args.outputs_dir)
print("===> Loading datasets")
data_set = EvalDataset(
args.test_lr,
n_frames=args.n_frames,
interval_list=args.interval_list,
)
eval_loader = DataLoader(data_set,
batch_size=args.batch_size,
num_workers=args.workers)
#### random seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.benchmark = True
#cudnn.deterministic = True
print("===> Building model")
#### create model
model = EDVR_arch.EDVR(nf=args.nf,
nframes=args.n_frames,
groups=args.groups,
front_RBs=args.front_RBs,
back_RBs=args.back_RBs,
center=args.center,
predeblur=args.predeblur,
HR_in=args.HR_in,
w_TSA=args.w_TSA)
print("===> Setting GPU")
gups = args.gpus if args.gpus != 0 else torch.cuda.device_count()
device_ids = list(range(gups))
model = DataParallel(model, device_ids=device_ids)
model = model.cuda()
# print(model)
# optionally resume from a checkpoint
if args.resume:
if os.path.isdir(args.resume):
# 获取目录中最后一个
pth_list = sorted(glob(os.path.join(args.resume, '*.pth')))
if len(pth_list) > 0:
args.resume = pth_list[-1]
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
state_dict = checkpoint['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
namekey = 'module.' + k # remove `module.`
new_state_dict[namekey] = v
model.load_state_dict(new_state_dict)
#### training
print("===> Eval")
model.eval()
with tqdm(total=(len(data_set) - len(data_set) % args.batch_size)) as t:
for data in eval_loader:
data_x = data['LRs'].cuda()
names = data['files']
with torch.no_grad():
outputs = model(data_x).data.float().cpu()
outputs = outputs * 255.
outputs = outputs.clamp_(0, 255).numpy()
for img, file in zip(outputs, names):
img = np.transpose(img[[2, 1, 0], :, :], (1, 2, 0))
img = img.round()
arr = file.split('/')
dst_dir = os.path.join(args.outputs_dir, arr[-2])
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
dst_name = os.path.join(dst_dir, arr[-1])
cv2.imwrite(dst_name, img)
t.update(len(names))
class Im2latex(BaseAgent):
def __init__(self, cfg):
super().__init__(cfg)
self.device = get_device()
cfg.device = self.device
self.cfg = cfg
# dataset
train_dataset = Im2LatexDataset(cfg, mode="train")
self.id2token = train_dataset.id2token
self.token2id = train_dataset.token2id
collate = custom_collate(self.token2id, cfg.max_len)
self.train_loader = DataLoader(train_dataset,
batch_size=cfg.bs,
shuffle=cfg.data_shuffle,
num_workers=cfg.num_w,
collate_fn=collate,
drop_last=True)
if cfg.valid_img_path != "":
valid_dataset = Im2LatexDataset(cfg,
mode="valid",
vocab={
'id2token': self.id2token,
'token2id': self.token2id
})
self.valid_loader = DataLoader(valid_dataset,
batch_size=cfg.bs //
cfg.beam_search_k,
shuffle=cfg.data_shuffle,
num_workers=cfg.num_w,
collate_fn=collate,
drop_last=True)
# define models
self.model = Im2LatexModel(cfg) # fill the parameters
# weight initialization setting
for name, param in self.model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
torch.nn.init.constant_(param, 0.0)
elif 'weight' in name:
torch.nn.init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
self.model = DataParallel(self.model)
# define criterion
self.criterion = cal_loss
self.optimizer = torch.optim.Adam(params=self.model.parameters(),
lr=cfg.lr,
betas=(cfg.adam_beta_1,
cfg.adam_beta_2))
milestones = cfg.milestones
self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer,
milestones,
gamma=cfg.gamma,
verbose=True)
# initialize counter
self.current_epoch = 1
self.current_iteration = 1
self.best_metric = 100
self.best_info = ''
# set the manual seed for torch
torch.cuda.manual_seed_all(self.cfg.seed)
if self.cfg.cuda:
self.model = self.model.to(self.device)
self.logger.info("Program will run on *****GPU-CUDA***** ")
else:
self.logger.info("Program will run on *****CPU*****\n")
# Model Loading from cfg if not found start from scratch.
self.exp_dir = os.path.join('./experiments', cfg.exp_name)
self.load_checkpoint(cfg.checkpoint_filename)
# Summary Writer
self.summary_writer = SummaryWriter(
log_dir=os.path.join(self.exp_dir, 'summaries'))
def load_checkpoint(self, file_name):
"""
Latest checkpoint loader
:param file_name: name of the checkpoint file
:return:
"""
try:
self.logger.info("Loading checkpoint '{}'".format(file_name))
checkpoint = torch.load(file_name, map_location=self.device)
self.current_epoch = checkpoint['epoch']
self.current_iteration = checkpoint['iteration']
self.model.load_state_dict(checkpoint['model'], strict=False)
self.optimizer.load_state_dict(checkpoint['optimizer'])
info = "Checkpoint loaded successfully from "
self.logger.info(
info + "'{}' at (epoch {}) at (iteration {})\n".format(
file_name, checkpoint['epoch'], checkpoint['iteration']))
except OSError as e:
self.logger.info("Checkpoint not found in '{}'.".format(file_name))
self.logger.info("**First time to train**")
def save_checkpoint(self, file_name="checkpoint.pth", is_best=False):
"""
Checkpoint saver
:param file_name: name of the checkpoint file
:param is_best: boolean flag to indicate whether current
checkpoint's accuracy is the best so far
:return:
"""
state = {
'epoch': self.current_epoch,
'iteration': self.current_iteration,
'model': self.model.state_dict(),
'vocab': self.id2token,
'optimizer': self.optimizer.state_dict()
}
# save the state
checkpoint_dir = os.path.join(self.exp_dir, 'checkpoints')
if is_best:
torch.save(state, os.path.join(checkpoint_dir, 'best.pt'))
self.best_info = 'best: e{}_i{}'.format(self.current_epoch,
self.current_iteration)
else:
file_name = "e{}-i{}.pt".format(self.current_epoch,
self.current_iteration)
torch.save(state, os.path.join(checkpoint_dir, file_name))
def run(self):
"""
The main operator
:return:
"""
try:
if self.cfg.mode == 'train':
self.train()
elif self.cfg.mode == 'predict':
self.predict()
except KeyboardInterrupt:
self.logger.info("You have entered CTRL+C.. Wait to finalize")
def train(self):
"""
Main training loop
:return:
"""
prev_perplexity = 0
for e in range(self.current_epoch, self.cfg.epochs + 1):
this_perplexity = self.train_one_epoch()
self.save_checkpoint()
self.scheduler.step()
self.current_epoch += 1
if self.cfg.valid_img_path:
self.validate()
def train_one_epoch(self):
"""
One epoch of training
:return:
"""
tqdm_bar = tqdm(enumerate(self.train_loader, 1),
total=len(self.train_loader))
self.model.train()
last_avg_perplexity, avg_perplexity = 0, 0
for i, (imgs, tgt) in tqdm_bar:
imgs = imgs.float().to(self.device)
tgt = tgt.long().to(self.device)
# [B, MAXLEN, VOCABSIZE]
logits = self.model(imgs, tgt, is_train=True)
loss = self.criterion(logits, tgt)
avg_perplexity += loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.cfg.grad_clip)
self.optimizer.step()
self.current_iteration += 1
# logging
if i % self.cfg.log_freq == 0:
avg_perplexity = avg_perplexity / self.cfg.log_freq
self.summary_writer.add_scalar(
'perplexity/train',
avg_perplexity,
global_step=self.current_iteration)
self.summary_writer.add_scalar(
'lr',
self.scheduler.get_last_lr(),
global_step=self.current_iteration)
tqdm_bar.set_description("e{} | avg_perplexity: {:.3f}".format(
self.current_epoch, avg_perplexity))
# save if best
if avg_perplexity < self.best_metric:
self.save_checkpoint(is_best=True)
self.best_metric = avg_perplexity
last_avg_perplexity = avg_perplexity
avg_perplexity = 0
mask = (tgt[0] != 2)
pred = str(logits[0].argmax(1)[mask].cpu().detach().tolist())
gt = str(tgt[0][mask].cpu().tolist())
self.summary_writer.add_text('example/train',
pred + ' \n' + gt,
global_step=self.current_iteration)
return last_avg_perplexity
def validate(self):
"""
One cycle of model validation
:return:
"""
tqdm_bar = tqdm(enumerate(self.valid_loader, 1),
total=len(self.valid_loader))
self.model.eval()
acc = 0
with torch.no_grad():
for i, (imgs, tgt) in tqdm_bar:
imgs = imgs.to(self.device).float()
tgt = tgt.to(self.device).long()
logits = self.model(
imgs, is_train=False).long() # [B, MAXLEN, VOCABSIZE]
# mask = (tgt == 2)
# tgt[mask] = 1
# logits[mask] = 1
acc += torch.all(tgt == logits, dim=1).sum() / imgs.size(0)
# print('t', tgt)
# print('l', logits)
tqdm_bar.set_description('acc {:.4f}'.format(acc / i))
if i % self.cfg.log_freq == 0:
self.summary_writer.add_scalar(
'accuracy/valid',
acc.item() / i,
global_step=self.current_iteration)
def predict(self):
"""
get predict results
:return:
"""
from torchvision import transforms
from pathlib import Path
from PIL import Image
from time import time
self.model.eval()
transform = transforms.ToTensor()
image_path = Path(self.cfg.test_img_path)
t = time()
with torch.no_grad():
images = []
imgPath = list(image_path.glob('*.jpg')) + list(
image_path.glob('*.png'))
for i, img in enumerate(imgPath):
print(i, ':', img)
img = Image.open(img)
img = transform(img)
images.append(img)
images = torch.stack(images, dim=0)
out = self.model(images) # [B, max_len, vocab_size]
# out = out.argmax(2)
for i, output in enumerate(out):
print(
i, ' '.join([
self.id2token[out.item()] for out in output
if out.item() != 1
]))
print(time() - t)
def finalize(self):
"""
Finalizes all the operations of the 2 Main classes of the process,
the operator and the data loader
:return:
"""
print(self.best_info)
pass
class SRGANModel(BaseModel):
def __init__(self, opt, dataset=None):
super(SRGANModel, self).__init__(opt)
if dataset:
self.cri_text = True
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if opt['dist']:
self.netG = DistributedDataParallel(
self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
if opt['dist']:
self.netD = DistributedDataParallel(
self.netD, device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError(
'Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt,
use_bn=False).to(self.device)
if opt['dist']:
pass # do not need to use DistributedDataParallel for netF
else:
self.netF = DataParallel(self.netF)
if self.cri_text:
from lib.models.model_builder import ModelBuilder
self.netT = ModelBuilder(
arch="ResNet_ASTER",
rec_num_classes=dataset.rec_num_classes,
sDim=512,
attDim=512,
max_len_labels=100,
eos=dataset.char2id[dataset.EOS],
STN_ON=True).to(self.device)
self.netT = DataParallel(self.netT)
self.netT.eval()
from lib.util.serialization import load_checkpoint
checkpoint = load_checkpoint(train_opt['text_model'])
self.netT.load_state_dict(checkpoint['state_dict'])
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0,
0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt[
'D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt[
'D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt[
'weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(
): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning(
'Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params,
lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1_G'],
train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt[
'weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'],
train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(
optimizer,
train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer,
train_opt['T_period'],
eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights']))
else:
raise NotImplementedError(
'MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def feed_data(self, data, need_GT=True):
self.var_L = data['LQ'].to(self.device) # LQ
if need_GT:
self.var_H = data['GT'].to(self.device) # GT
input_ref = data['ref'] if 'ref' in data else data['GT']
self.var_ref = input_ref.to(self.device)
def optimize_parameters(self, step, text_input=None):
# G
for p in self.netD.parameters():
p.requires_grad = False
self.optimizer_G.zero_grad()
self.fake_H = self.netG(self.var_L)
l_g_total = 0
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix: # pixel loss
l_g_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.var_H)
l_g_total += l_g_pix
if self.cri_fea: # feature loss
real_fea = self.netF(self.var_H).detach()
fake_fea = self.netF(self.fake_H)
l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
l_g_total += l_g_fea
if self.cri_text:
_, label, length = text_input
input_dict = {}
input_dict['images'] = self.fake_H
input_dict['rec_target'] = label
input_dict['rec_length'] = length
output_dict = self.netT(input_dict)
l_g_total += output_dict['losses']['loss_rec'].mean(dim=0)
if self.opt['train']['gan_type'] == 'gan':
pred_g_fake = self.netD(self.fake_H)
l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
elif self.opt['train']['gan_type'] == 'ragan':
pred_d_real = self.netD(self.var_ref).detach()
pred_g_fake = self.netD(self.fake_H)
l_g_gan = self.l_gan_w * (
self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False)
+ self.cri_gan(pred_g_fake - torch.mean(pred_d_real),
True)) / 2
l_g_total += l_g_gan
l_g_total.backward()
self.optimizer_G.step()
# D
for p in self.netD.parameters():
p.requires_grad = True
self.optimizer_D.zero_grad()
if self.opt['train']['gan_type'] == 'gan':
# need to forward and backward separately, since batch norm statistics differ
# real
pred_d_real = self.netD(self.var_ref)
l_d_real = self.cri_gan(pred_d_real, True)
l_d_real.backward()
# fake
pred_d_fake = self.netD(
self.fake_H.detach()) # detach to avoid BP to G
l_d_fake = self.cri_gan(pred_d_fake, False)
l_d_fake.backward()
elif self.opt['train']['gan_type'] == 'ragan':
# pred_d_real = self.netD(self.var_ref)
# pred_d_fake = self.netD(self.fake_H.detach()) # detach to avoid BP to G
# l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
# l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
# l_d_total = (l_d_real + l_d_fake) / 2
# l_d_total.backward()
pred_d_fake = self.netD(self.fake_H.detach()).detach()
pred_d_real = self.netD(self.var_ref)
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake),
True) * 0.5
l_d_real.backward()
pred_d_fake = self.netD(self.fake_H.detach())
l_d_fake = self.cri_gan(
pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5
l_d_fake.backward()
self.optimizer_D.step()
# set log
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix:
self.log_dict['l_g_pix'] = l_g_pix.item()
if self.cri_fea:
self.log_dict['l_g_fea'] = l_g_fea.item()
self.log_dict['l_g_gan'] = l_g_gan.item()
self.log_dict['l_d_real'] = l_d_real.item()
self.log_dict['l_d_fake'] = l_d_fake.item()
self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
def test(self):
self.netG.eval()
with torch.no_grad():
self.fake_H = self.netG(self.var_L)
self.netG.train()
def get_current_log(self):
return self.log_dict
def get_current_visuals(self, need_GT=True):
out_dict = OrderedDict()
out_dict['LQ'] = self.var_L.detach()[0].float().cpu()
out_dict['rlt'] = self.fake_H.detach()[0].float().cpu()
if need_GT:
out_dict['GT'] = self.var_H.detach()[0].float().cpu()
return out_dict
def print_network(self):
# Generator
s, n = self.get_network_description(self.netG)
if isinstance(self.netG, nn.DataParallel) or isinstance(
self.netG, DistributedDataParallel):
net_struc_str = '{} - {}'.format(
self.netG.__class__.__name__,
self.netG.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netG.__class__.__name__)
if self.rank <= 0:
logger.info(
'Network G structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.is_train:
# Discriminator
s, n = self.get_network_description(self.netD)
if isinstance(self.netD, nn.DataParallel) or isinstance(
self.netD, DistributedDataParallel):
net_struc_str = '{} - {}'.format(
self.netD.__class__.__name__,
self.netD.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netD.__class__.__name__)
if self.rank <= 0:
logger.info(
'Network D structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.cri_fea: # F, Perceptual Network
s, n = self.get_network_description(self.netF)
if isinstance(self.netF, nn.DataParallel) or isinstance(
self.netF, DistributedDataParallel):
net_struc_str = '{} - {}'.format(
self.netF.__class__.__name__,
self.netF.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netF.__class__.__name__)
if self.rank <= 0:
logger.info(
'Network F structure: {}, with parameters: {:,d}'.
format(net_struc_str, n))
logger.info(s)
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG,
self.opt['path']['strict_load'])
load_path_D = self.opt['path']['pretrain_model_D']
if self.opt['is_train'] and load_path_D is not None:
logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD,
self.opt['path']['strict_load'])
def save(self, iter_step):
self.save_network(self.netG, 'G', iter_step)
self.save_network(self.netD, 'D', iter_step)
class VisualizeProcess:
def __init__(self):
self.net = ET_Net()
if (ARGS['gpu']):
self.net = DataParallel(module=self.net.cuda())
self.net.load_state_dict(torch.load(ARGS['weight']))
self.train_dataset = get_dataset(dataset_name=ARGS['dataset'],
part='train')
self.val_dataset = get_dataset(dataset_name=ARGS['dataset'],
part='val')
def visualize(self):
start = time.time()
self.net.eval()
val_batch_size = min(ARGS['batch_size'], len(self.val_dataset))
val_dataloader = DataLoader(self.val_dataset,
batch_size=val_batch_size)
for batch_index, items in enumerate(val_dataloader):
images, labels, edges = items['image'], items['label'], items[
'edge']
images = images.float()
labels = labels.long()
edges = edges.long()
if ARGS['gpu']:
labels = labels.cuda()
images = images.cuda()
edges = edges.cuda()
print('image shape:', images.size())
with torch.no_grad():
outputs_edge, outputs = self.net(images)
pred = torch.max(outputs, dim=1)[1]
iou = torch.sum(pred[0] & labels[0]) / (
torch.sum(pred[0] | labels[0]) + 1e-6)
mean = torch.FloatTensor([123.68, 116.779, 103.939]).reshape(
(3, 1, 1)) / 255.
images = images + mean.cuda()
# images *= 255.
print('pred min: ', pred[0].min(), ' max: ', pred[0].max())
print('label min:', labels[0].min(), ' max: ', labels[0].max())
print('edge min:', edges[0].min(), ' max: ', edges[0].max())
print('output edge min:', outputs_edge[0].min(), ' max: ',
outputs_edge[0].max())
print('IoU:', iou)
print('Intersect num:', torch.sum(pred[0] & labels[0]))
print('Union num:', torch.sum(pred[0] | labels[0]))
plt.subplot(221)
plt.imshow(images[0].cpu().numpy().transpose(
(1, 2, 0))), plt.axis('off')
plt.subplot(222)
plt.imshow(labels[0].cpu().numpy(), cmap='gray'), plt.axis('off')
plt.subplot(223)
# plt.imshow(pred[0].cpu().numpy(), cmap='gray'), plt.axis('off')
plt.imshow(outputs[0, 1].cpu().numpy(),
cmap='gray'), plt.axis('off')
plt.subplot(224)
plt.imshow(outputs_edge[0, 1].cpu().numpy(),
cmap='gray'), plt.axis('off')
plt.show()
# update training loss for each iteration
# self.writer.add_scalar('Train/loss', loss.item(), n_iter)
finish = time.time()
print('validating time consumed: {:.2f}s'.format(finish - start))
class BaseEngine(object):
def __init__(self, args):
self._make_dataset(args)
self._make_model(args)
tc.manual_seed(args.seed)
if args.cuda and tc.cuda.is_available():
tc.cuda.manual_seed_all(args.seed)
if tc.cuda.device_count() > 1:
self.batch_size = args.batch_size * tc.cuda.device_count()
self.model = DataParallel(self.model)
else:
self.batch_size = args.batch_size
self.model = self.model.cuda()
else:
self.batch_size = args.batch_size
self._make_optimizer(args)
self._make_loss(args)
self._make_metric(args)
self.num_training_samples = args.num_training_samples
self.tag = args.tag or 'default'
self.dump_dir = get_dir(args.dump_dir)
self.train_logger = get_logger('train.{}.{}'.format(
self.__class__.__name__, self.tag))
def _make_dataset(self, args):
raise NotImplementedError
def _make_model(self, args):
raise NotImplementedError
def _make_optimizer(self, args):
raise NotImplementedError
def _make_loss(self, args):
raise NotImplementedError
def _make_metric(self, args):
raise NotImplementedError
def dump(self, epoch, model=True, optimizer=True, decayer=True):
state = {'epoch': epoch}
if model:
state['model'] = self.model.state_dict()
if optimizer:
state['optimizer'] = self.optimizer.state_dict()
if decayer and (getattr(self, 'decayer', None) is not None):
state['decayer'] = self.decayer.state_dict()
tc.save(state,
os.path.join(self.dump_dir, 'state_{}.pkl'.format(self.tag)))
self.train_logger.info('Checkpoint {} dumped'.format(self.tag))
def load(self, model=True, optimizer=True, decayer=True):
try:
state = tc.load(
os.path.join(self.dump_dir, 'state_{}.pkl'.format(self.tag)))
except FileNotFoundError:
return 0
if model and (state.get('model') is not None):
self.model.load_state_dict(state['model'])
if optimizer and (state.get('optimizer') is not None):
self.optimizer.load_state_dict(state['optimizer'])
if decayer and (state.get('decayer') is not None) and (getattr(
self, 'decayer', None) is not None):
self.decayer.load_state_dict(state['decayer'])
return state['epoch']
def eval(self):
raise NotImplementedError
def test(self):
raise NotImplementedError
def train(self, num_epochs, resume=False):
raise NotImplementedError
class Trainer():
def __init__(self, config, debug=False):
self.config = config
self.epoch = 0
self.iteration = 0
if debug:
self.config['trainer']['save_freq'] = 5
self.config['trainer']['valid_freq'] = 5
self.config['trainer']['iterations'] = 5
# setup data set and data loader
self.train_dataset = AVEDataset(config['data_loader'], split='train')
self.train_sampler = None
self.train_args = config['trainer']
self.train_loader = DataLoader(
self.train_dataset,
batch_size=self.train_args['batch_size'],
shuffle=True,
num_workers=self.train_args['num_workers'],
pin_memory=True)
# set loss functions
self.adversarial_loss = AdversarialLoss(
type=self.config['losses']['GAN_LOSS'])
self.adversarial_loss = self.adversarial_loss.to(self.config['device'])
self.l1_loss = nn.L1Loss()
# setup models including generator and discriminator
net = importlib.import_module('model.' + config['model'])
self.netG = net.InpaintGenerator()
self.netG = self.netG.to(self.config['device'])
self.netD = net.Discriminator(
in_channels=3, use_sigmoid=config['losses']['GAN_LOSS'] != 'hinge')
self.netD = self.netD.to(self.config['device'])
self.optimG = torch.optim.Adam(self.netG.parameters(),
lr=config['trainer']['lr'],
betas=(self.config['trainer']['beta1'],
self.config['trainer']['beta2']))
self.optimD = torch.optim.Adam(self.netD.parameters(),
lr=config['trainer']['lr'],
betas=(self.config['trainer']['beta1'],
self.config['trainer']['beta2']))
self.load()
if config['distributed']:
self.netG = DataParallel(self.netG)
self.netD = DataParallel(self.netD)
# set summary writer
self.dis_writer = None
self.gen_writer = None
self.summary = {}
if self.config['global_rank'] == 0 or (not config['distributed']):
self.dis_writer = SummaryWriter(
os.path.join(config['save_dir'], 'dis'))
self.gen_writer = SummaryWriter(
os.path.join(config['save_dir'], 'gen'))
# get current learning rate
def get_lr(self):
return self.optimG.param_groups[0]['lr']
# learning rate scheduler, step
def adjust_learning_rate(self):
decay = 0.1**(
min(self.iteration, self.config['trainer']['niter_steady']) //
self.config['trainer']['niter'])
new_lr = self.config['trainer']['lr'] * decay
if new_lr != self.get_lr():
for param_group in self.optimG.param_groups:
param_group['lr'] = new_lr
for param_group in self.optimD.param_groups:
param_group['lr'] = new_lr
# add summary
def add_summary(self, writer, name, val):
if name not in self.summary:
self.summary[name] = 0
self.summary[name] += val
if writer is not None and self.iteration % 100 == 0:
writer.add_scalar(name, self.summary[name] / 100, self.iteration)
self.summary[name] = 0
# add image
def add_images(self, writer, input_image, output_image, gt_image):
if writer is not None and self.iteration % 100 == 0:
b, t, c, h, w = input_image.size()
input_image = input_image.view(b * t, c, h, w)
output_image = output_image.view(b * t, c, h, w)
gt_image = gt_image.view(b * t, c, h, w)
writer.add_image("input/input_image",
make_grid((input_image + 1) / 2, t),
self.iteration)
writer.add_image("output/output_image",
make_grid((output_image + 1) / 2, t),
self.iteration)
writer.add_image("output/gt_image", make_grid((gt_image + 1) / 2,
t), self.iteration)
# load netG and netD
def load(self):
model_path = self.config['save_dir']
if os.path.isfile(os.path.join(model_path, 'latest.ckpt')):
latest_epoch = open(os.path.join(model_path, 'latest.ckpt'),
'r').read().splitlines()[-1]
else:
ckpts = [
os.path.basename(i).split('.pth')[0]
for i in glob.glob(os.path.join(model_path, '*.pth'))
]
ckpts.sort()
latest_epoch = ckpts[-1] if len(ckpts) > 0 else None
if latest_epoch is not None:
gen_path = os.path.join(
model_path, 'gen_{}.pth'.format(str(latest_epoch).zfill(5)))
dis_path = os.path.join(
model_path, 'dis_{}.pth'.format(str(latest_epoch).zfill(5)))
opt_path = os.path.join(
model_path, 'opt_{}.pth'.format(str(latest_epoch).zfill(5)))
if self.config['global_rank'] == 0:
print('Loading model from {}...'.format(gen_path))
data = torch.load(gen_path, map_location=self.config['device'])
self.netG.load_state_dict(data['netG'])
data = torch.load(dis_path, map_location=self.config['device'])
self.netD.load_state_dict(data['netD'])
data = torch.load(opt_path, map_location=self.config['device'])
self.optimG.load_state_dict(data['optimG'])
self.optimD.load_state_dict(data['optimD'])
self.epoch = data['epoch']
self.iteration = data['iteration']
else:
if self.config['global_rank'] == 0:
print(
'Warnning: There is no trained model found. An initialized model will be used.'
)
# save parameters every eval_epoch
def save(self, it):
if self.config['global_rank'] == 0:
gen_path = os.path.join(self.config['save_dir'],
'gen_{}.pth'.format(str(it).zfill(5)))
dis_path = os.path.join(self.config['save_dir'],
'dis_{}.pth'.format(str(it).zfill(5)))
opt_path = os.path.join(self.config['save_dir'],
'opt_{}.pth'.format(str(it).zfill(5)))
print('\nsaving model to {} ...'.format(gen_path))
if isinstance(self.netG, torch.nn.DataParallel) or isinstance(
self.netG, DDP):
netG = self.netG.module
netD = self.netD.module
else:
netG = self.netG
netD = self.netD
torch.save({'netG': netG.state_dict()}, gen_path)
torch.save({'netD': netD.state_dict()}, dis_path)
torch.save(
{
'epoch': self.epoch,
'iteration': self.iteration,
'optimG': self.optimG.state_dict(),
'optimD': self.optimD.state_dict()
}, opt_path)
os.system('echo {} > {}'.format(
str(it).zfill(5),
os.path.join(self.config['save_dir'], 'latest.ckpt')))
# train entry
def train(self):
pbar = range(int(self.train_args['iterations']))
if self.config['global_rank'] == 0:
pbar = tqdm(pbar,
initial=self.iteration,
dynamic_ncols=True,
smoothing=0.01)
while True:
self.epoch += 1
# if self.config['distributed']:
# self.train_sampler.set_epoch(self.epoch)
self._train_epoch(pbar)
if self.iteration > self.train_args['iterations']:
break
print('\nEnd training....')
# process input and calculate loss every training epoch
def _train_epoch(self, pbar):
device = self.config['device']
for frames, masks in self.train_loader:
self.adjust_learning_rate()
self.iteration += 1
frames, masks = frames.to(device), masks.to(device)
b, t, c, h, w = frames.size()
masked_frame = (frames * (1 - masks).float())
pred_img = self.netG(masked_frame, masks)
frames = frames.view(b * t, c, h, w)
masks = masks.view(b * t, 1, h, w)
comp_img = frames * (1. - masks) + masks * pred_img
gen_loss = 0
dis_loss = 0
# discriminator adversarial loss
real_vid_feat = self.netD(frames)
fake_vid_feat = self.netD(comp_img.detach())
dis_real_loss = self.adversarial_loss(real_vid_feat, True, True)
dis_fake_loss = self.adversarial_loss(fake_vid_feat, False, True)
dis_loss += (dis_real_loss + dis_fake_loss) / 2
self.add_summary(self.dis_writer, 'loss/dis_vid_fake',
dis_fake_loss.item())
self.add_summary(self.dis_writer, 'loss/dis_vid_real',
dis_real_loss.item())
self.optimD.zero_grad()
dis_loss.backward()
self.optimD.step()
# generator adversarial loss
gen_vid_feat = self.netD(comp_img)
gan_loss = self.adversarial_loss(gen_vid_feat, True, False)
gan_loss = gan_loss * self.config['losses']['adversarial_weight']
gen_loss += gan_loss
self.add_summary(self.gen_writer, 'loss/gan_loss', gan_loss.item())
# generator l1 loss
hole_loss = self.l1_loss(pred_img * masks, frames * masks)
hole_loss = hole_loss / torch.mean(
masks) * self.config['losses']['hole_weight']
gen_loss += hole_loss
self.add_summary(self.gen_writer, 'loss/hole_loss',
hole_loss.item())
valid_loss = self.l1_loss(pred_img * (1 - masks),
frames * (1 - masks))
valid_loss = valid_loss / torch.mean(
1 - masks) * self.config['losses']['valid_weight']
gen_loss += valid_loss
self.add_summary(self.gen_writer, 'loss/valid_loss',
valid_loss.item())
self.optimG.zero_grad()
gen_loss.backward()
self.optimG.step()
self.add_images(self.gen_writer,
masked_frame.cpu().detach(),
comp_img.cpu().detach(),
frames.cpu().detach())
# console logs
if self.config['global_rank'] == 0:
pbar.update(1)
pbar.set_description((
f"d: {dis_loss.item():.3f}; g: {gan_loss.item():+.3f}; "
f"hole: {hole_loss.item():.3f}; valid: {valid_loss.item():.3f}"
))
# saving models
if self.iteration % self.train_args['save_freq'] == 0:
self.save(int(self.iteration // self.train_args['save_freq']))
if self.iteration > self.train_args['iterations']:
break
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.freeze()
viewer = Visualizer(cfg.OUTPUT_DIR)
#Model
model = build_model(cfg)
model = DataParallel(model).cuda()
if cfg.MODEL.WEIGHT !="":
model.module.backbone.load_state_dict(torch.load(cfg.MODEL.WEIGHT))
#freeze backbone
for key,val in model.module.backbone.named_parameters():
val.requires_grad = False
batch_time = AverageMeter()
data_time = AverageMeter()
#optimizer
optimizer = getattr(torch.optim,cfg.SOLVER.OPTIM)(model.parameters(),lr = cfg.SOLVER.BASE_LR,weight_decay=cfg.SOLVER.WEIGHT_DECAY)
lr_sche = torch.optim.lr_scheduler.MultiStepLR(optimizer,cfg.SOLVER.STEPS,gamma= cfg.SOLVER.GAMMA)
#dataset
datasets = make_dataset(cfg,is_train=False)
dataloaders = make_dataloaders(cfg,datasets,False)
iter_epoch = (cfg.SOLVER.MAX_ITER)//len(dataloaders[0])+1
if not os.path.exists(cfg.OUTPUT_DIR):
os.mkdir(cfg.OUTPUT_DIR)
ite = 0
batch_it = [i *cfg.SOLVER.IMS_PER_BATCH for i in range(1,4)]
# start time
start = time.time()
inference_list = ['resnet18_14.pth','resnet18_13.pth','resnet18_12.pth','resnet18_11.pth','resnet18_10.pth']
for inference_weight in inference_list:
model.load_state_dict(torch.load(os.path.join(resume_dir,inference_weight)))
model.eval()
total_count = 0
one_count = 0
two_count = 0
three_count = 0
one_number = 0
two_number = 0
three_number = 0
for dataloader in dataloaders:
for imgs,labels,types in tqdm.tqdm(dataloader,desc="dataloader:"):
types = np.asarray(types)
lr_sche.step()
data_time.update(time.time() - start)
inputs = torch.cat([imgs[0].cuda(),imgs[1].cuda(),imgs[2].cuda()],dim=0)
with torch.no_grad():
features = model(inputs)
acc,batch_loss = loss_opts.batch_triple_loss_acc(features,labels,types,size_average=True)
print(batch_loss)
xxx
total_count+= batch_loss.shape[0]-acc
ONE_CLASS = (batch_loss[np.nonzero(types=='ONE_CLASS_TRIPLET')[0]])
TWO_CLASS = (batch_loss[np.nonzero(types=='TWO_CLASS_TRIPLET')[0]])
THREE_CLASS = (batch_loss[np.nonzero(types=='THREE_CLASS_TRIPLET')[0]])
one_count += ONE_CLASS.shape[0] - torch.nonzero(ONE_CLASS).shape[0]
two_count += TWO_CLASS.shape[0] - torch.nonzero(TWO_CLASS).shape[0]
three_count += THREE_CLASS.shape[0] - torch.nonzero(THREE_CLASS).shape[0]
one_number+=ONE_CLASS.shape[0]
two_number+=TWO_CLASS.shape[0]
three_number+=THREE_CLASS.shape[0]
# viewer.line("train/loss",loss.item()*100,ite)
print(inference_weight,total_count/(one_number+two_number+three_number),one_count/one_number,two_count/two_number,three_count/three_number)
class CalculateMetricProcess:
def __init__(self):
self.net = ET_Net()
if (ARGS['gpu']):
self.net = DataParallel(module=self.net.cuda())
self.net.load_state_dict(torch.load(ARGS['weight']))
self.metric_dataset = get_dataset(dataset_name=ARGS['dataset'],
part='metric')
def predict(self):
start = time.time()
self.net.eval()
metric_dataloader = DataLoader(
self.metric_dataset, batch_size=1) # only support batch size = 1
os.makedirs(ARGS['prediction_save_folder'], exist_ok=True)
y_true = []
y_pred = []
for items in metric_dataloader:
images, labels, mask = items['image'], items['label'], items[
'mask']
images = images.float()
print('image shape:', images.size())
image_patches, big_h, big_w = get_test_patches(
images, ARGS['crop_size'], ARGS['stride_size'])
test_patch_dataloader = DataLoader(image_patches,
batch_size=ARGS['batch_size'],
shuffle=False,
drop_last=False)
test_results = []
print('Number of batches for testing:', len(test_patch_dataloader))
for patches in test_patch_dataloader:
if ARGS['gpu']:
patches = patches.cuda()
with torch.no_grad():
result_patches_edge, result_patches = self.net(patches)
test_results.append(result_patches.cpu())
test_results = torch.cat(test_results, dim=0)
# merge
test_results = recompone_overlap(test_results, ARGS['crop_size'],
ARGS['stride_size'], big_h, big_w)
test_results = test_results[:, 1, :images.size(2), :images.size(3)]
y_pred.append(test_results[mask == 1].reshape(-1))
y_true.append(labels[mask == 1].reshape(-1))
y_pred = torch.cat(y_pred).numpy()
y_true = torch.cat(y_true).numpy()
calc_metrics(y_pred, y_true)
finish = time.time()
print('Calculating metric time consumed: {:.2f}s'.format(finish -
start))
def main(args):
print("===> Loading datasets")
data_set = DatasetLoader(args.data_lr,
args.data_hr,
size_w=args.size_w,
size_h=args.size_h,
scale=args.scale,
n_frames=args.n_frames,
interval_list=args.interval_list,
border_mode=args.border_mode,
random_reverse=args.random_reverse)
train_loader = DataLoader(data_set,
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=True,
pin_memory=False,
drop_last=True)
#### random seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.benchmark = True
#cudnn.deterministic = True
print("===> Building model")
#### create model
model = EDVR_arch.EDVR(nf=args.nf,
nframes=args.n_frames,
groups=args.groups,
front_RBs=args.front_RBs,
back_RBs=args.back_RBs,
center=args.center,
predeblur=args.predeblur,
HR_in=args.HR_in,
w_TSA=args.w_TSA)
criterion = CharbonnierLoss()
print("===> Setting GPU")
gups = args.gpus if args.gpus != 0 else torch.cuda.device_count()
device_ids = list(range(gups))
model = DataParallel(model, device_ids=device_ids)
model = model.cuda()
criterion = criterion.cuda()
# print(model)
start_epoch = args.start_epoch
# optionally resume from a checkpoint
if args.resume:
if os.path.isdir(args.resume):
# 获取目录中最后一个
pth_list = sorted(glob(os.path.join(args.resume, '*.pth')))
if len(pth_list) > 0:
args.resume = pth_list[-1]
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch'] + 1
state_dict = checkpoint['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
namekey = 'module.' + k # remove `module.`
new_state_dict[namekey] = v
model.load_state_dict(new_state_dict)
# 如果文件中有lr，则不用启动参数
args.lr = checkpoint.get('lr', args.lr)
# 如果设置了 start_epoch 则不用checkpoint中的epoch参数
start_epoch = args.start_epoch if args.start_epoch != 0 else start_epoch
#如果use_current_lr大于0 测代替作为lr
args.lr = args.use_current_lr if args.use_current_lr > 0 else args.lr
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
weight_decay=args.weight_decay,
betas=(args.beta1, args.beta2),
eps=1e-8)
#### training
print("===> Training")
for epoch in range(start_epoch, args.epochs):
adjust_lr(optimizer, epoch)
if args.use_tqdm == 1:
losses, psnrs = one_epoch_train_tqdm(
model, optimizer, criterion, len(data_set), train_loader,
epoch, args.epochs, args.batch_size,
optimizer.param_groups[0]["lr"])
else:
losses, psnrs = one_epoch_train_logger(
model, optimizer, criterion, len(data_set), train_loader,
epoch, args.epochs, args.batch_size,
optimizer.param_groups[0]["lr"])
# save model
# if epoch %9 != 0:
# continue
model_out_path = os.path.join(
args.checkpoint, "model_epoch_%04d_edvr_loss_%.3f_psnr_%.3f.pth" %
(epoch, losses.avg, psnrs.avg))
if not os.path.exists(args.checkpoint):
os.makedirs(args.checkpoint)
torch.save(
{
'state_dict': model.module.state_dict(),
"epoch": epoch,
'lr': optimizer.param_groups[0]["lr"]
}, model_out_path)
def create_and_test_triplet_network(batch_triplet_indices_loader,
experiment_name,
path_to_emb_net,
unseen_triplets,
dataset_name,
model_name,
logger,
test_n,
n,
dim,
layers,
learning_rate=5e-2,
epochs=20,
hl_size=100):
"""
Description: Constructs the OENN network, defines an optimizer and trains the network on the data w.r.t triplet loss.
:param model_name:
:param dataset_name:
:param test_n:
:param path_to_emb_net: Data loader object. Gives triplet indices in batches.
:param n: # points
:param dim: # features/ dimensions
:param layers: # layers
:param learning_rate: learning rate of optimizer.
:param epochs: # epochs
:param hl_size: # width of the hidden layer
:param unseen_triplets: #TODO
:param logger: # for logging
:return:
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
digits = int(math.ceil(math.log2(n)))
# Define train model
emb_net_train = define_model(model_name=model_name,
digits=digits,
hl_size=hl_size,
dim=dim,
layers=layers)
emb_net_train = emb_net_train.to(device)
for param in emb_net_train.parameters():
param.requires_grad = False
if torch.cuda.device_count() > 1:
emb_net_train = DataParallel(emb_net_train)
print('multi-gpu')
checkpoint = torch.load(path_to_emb_net)['model_state_dict']
key_word = list(checkpoint.keys())[0].split('.')[0]
if key_word == 'module':
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
emb_net_train.load_state_dict(new_state_dict)
else:
emb_net_train.load_state_dict(checkpoint)
emb_net_train.eval()
# Define test model
emb_net_test = define_model(model_name=model_name,
digits=digits,
hl_size=hl_size,
dim=dim,
layers=layers)
emb_net_test = emb_net_test.to(device)
if torch.cuda.device_count() > 1:
emb_net_test = DataParallel(emb_net_test)
print('multi-gpu')
# Optimizer
optimizer = torch.optim.Adam(emb_net_test.parameters(), lr=learning_rate)
criterion = nn.TripletMarginLoss(margin=1, p=2)
criterion = criterion.to(device)
logger.info('#### Dataset Selection #### \n')
logger.info('dataset:', dataset_name)
logger.info('#### Network and learning parameters #### \n')
logger.info('------------------------------------------ \n')
logger.info('Model Name: ' + model_name + '\n')
logger.info('Number of hidden layers: ' + str(layers) + '\n')
logger.info('Hidden layer width: ' + str(hl_size) + '\n')
logger.info('Embedding dimension: ' + str(dim) + '\n')
logger.info('Learning rate: ' + str(learning_rate) + '\n')
logger.info('Number of epochs: ' + str(epochs) + '\n')
logger.info(' #### Training begins #### \n')
logger.info('---------------------------\n')
digits = int(math.ceil(math.log2(n)))
bin_array = data_utils.get_binary_array(n, digits)
trip_data = torch.tensor(bin_array[unseen_triplets])
trip = trip_data.squeeze().to(device).float()
# Training begins
train_time = 0
for ep in range(epochs):
# Epoch is one pass over the dataset
epoch_loss = 0
for batch_ind, trips in enumerate(batch_triplet_indices_loader):
sys.stdout.flush()
trip = trips.squeeze().to(device).float()
# Training time
begin_train_time = time.time()
# Forward pass
embedded_a = emb_net_test(trip[:, :digits])
embedded_p = emb_net_train(trip[:, digits:2 * digits])
embedded_n = emb_net_train(trip[:, 2 * digits:])
# Compute loss
loss = criterion(embedded_a, embedded_p, embedded_n).to(device)
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
# End of training
end_train_time = time.time()
if batch_ind % 50 == 0:
logger.info('Epoch: ' + str(ep) + ' Mini batch: ' +
str(batch_ind) + '/' +
str(len(batch_triplet_indices_loader)) +
' Loss: ' + str(loss.item()))
sys.stdout.flush() # Prints faster to the out file
epoch_loss += loss.item()
train_time = train_time + end_train_time - begin_train_time
# Log
logger.info('Epoch ' + str(ep) + ' - Average Epoch Loss: ' +
str(epoch_loss / len(batch_triplet_indices_loader)) +
' Training time ' + str(train_time))
sys.stdout.flush() # Prints faster to the out file
# Saving the results
logger.info('Saving the models and the results')
sys.stdout.flush() # Prints faster to the out file
os.makedirs('test_checkpoints', mode=0o777, exist_ok=True)
model_path = 'test_checkpoints/' + \
experiment_name + \
'.pt'
torch.save(
{
'epochs': ep,
'model_state_dict': emb_net_test.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss:': epoch_loss,
}, model_path)
# Compute the embedding of the data points.
bin_array_test = data_utils.get_binary_array(test_n, digits)
test_embeddings = emb_net_test(
torch.Tensor(bin_array_test).cuda().float()).cpu().detach().numpy()
train_embeddings = emb_net_train(
torch.Tensor(bin_array).cuda().float()).cpu().detach().numpy()
unseen_triplet_error, _ = data_utils.triplet_error_unseen(
test_embeddings, train_embeddings, unseen_triplets)
logger.info('Unseen triplet error is ' + str(unseen_triplet_error))
return unseen_triplet_error
class TrainValProcess():
def __init__(self):
self.net = ET_Net()
if (ARGS['weight']):
self.net.load_state_dict(torch.load(ARGS['weight']))
else:
self.net.load_encoder_weight()
if (ARGS['gpu']):
self.net = DataParallel(module=self.net.cuda())
self.train_dataset = get_dataset(dataset_name=ARGS['dataset'],
part='train')
self.val_dataset = get_dataset(dataset_name=ARGS['dataset'],
part='val')
self.optimizer = Adam(self.net.parameters(), lr=ARGS['lr'])
# Use / to get an approximate result, // to get an accurate result
total_iters = len(
self.train_dataset) // ARGS['batch_size'] * ARGS['num_epochs']
self.lr_scheduler = LambdaLR(
self.optimizer,
lr_lambda=lambda iter:
(1 - iter / total_iters)**ARGS['scheduler_power'])
self.writer = SummaryWriter()
def train(self, epoch):
start = time.time()
self.net.train()
train_dataloader = DataLoader(self.train_dataset,
batch_size=ARGS['batch_size'],
shuffle=False)
epoch_loss = 0.
for batch_index, items in enumerate(train_dataloader):
images, labels, edges = items['image'], items['label'], items[
'edge']
images = images.float()
labels = labels.long()
edges = edges.long()
if ARGS['gpu']:
labels = labels.cuda()
images = images.cuda()
edges = edges.cuda()
self.optimizer.zero_grad()
outputs_edge, outputs = self.net(images)
# print('output edge min:', outputs_edge[0, 1].min(), ' max: ', outputs_edge[0, 1].max())
# plt.imshow(outputs_edge[0, 1].detach().cpu().numpy() * 255, cmap='gray')
# plt.show()
loss_edge = lovasz_softmax(outputs_edge,
edges) # Lovasz-Softmax loss
loss_seg = lovasz_softmax(outputs, labels) #
loss = ARGS['combine_alpha'] * loss_seg + (
1 - ARGS['combine_alpha']) * loss_edge
loss.backward()
self.optimizer.step()
self.lr_scheduler.step()
n_iter = (epoch - 1) * len(train_dataloader) + batch_index + 1
pred = torch.max(outputs, dim=1)[1]
iou = torch.sum(pred & labels) / (torch.sum(pred | labels) + 1e-6)
# print('edge min:', edges.min(), ' max: ', edges.max())
# print('output edge min:', outputs_edge.min(), ' max: ', outputs_edge.max())
print(
'Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tL_edge: {:0.4f}\tL_seg: {:0.4f}\tL_all: {:0.4f}\tIoU: {:0.4f}\tLR: {:0.4f}'
.format(loss_edge.item(),
loss_seg.item(),
loss.item(),
iou.item(),
self.optimizer.param_groups[0]['lr'],
epoch=epoch,
trained_samples=batch_index * ARGS['batch_size'],
total_samples=len(train_dataloader.dataset)))
epoch_loss += loss.item()
# update training loss for each iteration
# self.writer.add_scalar('Train/loss', loss.item(), n_iter)
for name, param in self.net.named_parameters():
layer, attr = os.path.splitext(name)
attr = attr[1:]
self.writer.add_histogram("{}/{}".format(layer, attr), param,
epoch)
epoch_loss /= len(train_dataloader)
self.writer.add_scalar('Train/loss', epoch_loss, epoch)
finish = time.time()
print('epoch {} training time consumed: {:.2f}s'.format(
epoch, finish - start))
def validate(self, epoch):
start = time.time()
self.net.eval()
val_batch_size = min(ARGS['batch_size'], len(self.val_dataset))
val_dataloader = DataLoader(self.val_dataset,
batch_size=val_batch_size)
epoch_loss = 0.
for batch_index, items in enumerate(val_dataloader):
images, labels, edges = items['image'], items['label'], items[
'edge']
# print('label min:', labels[0].min(), ' max: ', labels[0].max())
# print('edge min:', labels[0].min(), ' max: ', labels[0].max())
if ARGS['gpu']:
labels = labels.cuda()
images = images.cuda()
edges = edges.cuda()
print('image shape:', images.size())
with torch.no_grad():
outputs_edge, outputs = self.net(images)
loss_edge = lovasz_softmax(outputs_edge,
edges) # Lovasz-Softmax loss
loss_seg = lovasz_softmax(outputs, labels) #
loss = ARGS['combine_alpha'] * loss_seg + (
1 - ARGS['combine_alpha']) * loss_edge
pred = torch.max(outputs, dim=1)[1]
iou = torch.sum(pred & labels) / (torch.sum(pred | labels) + 1e-6)
print(
'Validating Epoch: {epoch} [{val_samples}/{total_samples}]\tLoss: {:0.4f}\tIoU: {:0.4f}'
.format(loss.item(),
iou.item(),
epoch=epoch,
val_samples=batch_index * val_batch_size,
total_samples=len(val_dataloader.dataset)))
epoch_loss += loss
# update training loss for each iteration
# self.writer.add_scalar('Train/loss', loss.item(), n_iter)
epoch_loss /= len(val_dataloader)
self.writer.add_scalar('Val/loss', epoch_loss, epoch)
finish = time.time()
print('epoch {} training time consumed: {:.2f}s'.format(
epoch, finish - start))
def train_val(self):
print('Begin training and validating:')
for epoch in range(ARGS['num_epochs']):
self.train(epoch)
self.validate(epoch)
self.net.state_dict()
print(f'Finish training and validating epoch #{epoch+1}')
if (epoch + 1) % ARGS['epoch_save'] == 0:
os.makedirs(ARGS['weight_save_folder'], exist_ok=True)
torch.save(
self.net.state_dict(),
os.path.join(ARGS['weight_save_folder'],
f'epoch_{epoch+1}.pth'))
print(f'Model saved for epoch #{epoch+1}.')
print('Finish training and validating.')