def run(config):
train_dir = config.train.dir
model = get_model(config).cuda()
criterion = get_loss(config)
optimizer = get_optimizer(config, model.parameters())
checkpoint = utils.checkpoint.get_initial_checkpoint(config)
if checkpoint is not None:
last_epoch, step = utils.checkpoint.load_checkpoint(
model, optimizer, checkpoint)
else:
last_epoch, step = -1, -1
print('from checkpoint: {} last epoch:{}'.format(checkpoint, last_epoch))
scheduler = get_scheduler(config, optimizer, last_epoch)
# dataloaders = {split:get_dataloader(config, split, get_transform(config, split))
# for split in ['train', 'val']}
print(config.data)
dataloaders = {
'train': get_train_dataloader(config, get_transform(config)),
'val': get_valid_dataloaders(config)[0]
}
writer = SummaryWriter(train_dir)
train(config, model, dataloaders, criterion, optimizer, scheduler, writer,
last_epoch + 1)
def run(config, folds_dir, balanced):
model = get_model(config[MODEL_NAME], config[MODEL_PARAMS]).cuda()
criterion = get_loss(config[LOSS_NAME], config[LOSS_PARAMS])
optimizer = get_optimizer(config[OPTIM_NAME],
model.parameters(),
optimizer_params=config[OPTIM_PARAMS])
last_epoch = -1
scheduler = get_scheduler(config[SCHEDULER_NAME], optimizer, last_epoch,
config[SCHEDULER_PARAMS])
datasets = {
stage: CustomDataset(folds_dir, stage, config[FOLD_ID],
config[DATA_PREFIX], config[INPUT_SIZE])
for stage in ['train', 'test']
}
print('Loading sampler')
if balanced:
train_sampler = BalancedBatchSampler(datasets['train'])
else:
train_sampler = None
print('Sampler loaded')
dataloaders = {
stage: get_dataloader(datasets[stage], config[BATCH_SIZE],
train_sampler)
for stage in ['train', 'test']
}
writer = SummaryWriter(config[TRAIN_DIR])
train(config, model, dataloaders, criterion, optimizer, scheduler, writer,
last_epoch + 1)
def run(config_file):
config = load_config(config_file)
os.makedirs(config.work_dir, exist_ok=True)
save_config(config, config.work_dir + '/config.yml')
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
all_transforms = {}
all_transforms['train'] = get_transforms(config.transforms.train)
all_transforms['valid'] = get_transforms(config.transforms.test)
dataloaders = {
phase: make_loader(
data_folder=config.data.train_dir,
df_path=config.data.train_df_path,
phase=phase,
batch_size=config.train.batch_size,
num_workers=config.num_workers,
idx_fold=config.data.params.idx_fold,
transforms=all_transforms[phase],
num_classes=config.data.num_classes,
pseudo_label_path=config.train.pseudo_label_path,
task='cls'
)
for phase in ['train', 'valid']
}
# create model
model = CustomNet(config.model.encoder, config.data.num_classes)
# train setting
criterion = get_loss(config)
params = [
{'params': model.base_params(), 'lr': config.optimizer.params.encoder_lr},
{'params': model.fresh_params(), 'lr': config.optimizer.params.decoder_lr}
]
optimizer = get_optimizer(params, config)
scheduler = get_scheduler(optimizer, config)
# model runner
runner = SupervisedRunner(model=model)
callbacks = [MultiClassAccuracyCallback(threshold=0.5), F1ScoreCallback()]
if os.path.exists(config.work_dir + '/checkpoints/best.pth'):
callbacks.append(CheckpointCallback(resume=config.work_dir + '/checkpoints/best_full.pth'))
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=dataloaders,
logdir=config.work_dir,
num_epochs=config.train.num_epochs,
callbacks=callbacks,
verbose=True,
fp16=True,
)
def run(config):
model = get_model(config[MODEL_NAME], config[MODEL_PARAMS]).cuda()
criterion = get_loss(config[LOSS_NAME], config[LOSS_PARAMS])
optimizer = get_optimizer(config[OPTIM_NAME],
model.parameters(),
optimizer_params=config[OPTIM_PARAMS])
last_epoch = -1
scheduler = get_scheduler(config[SCHEDULER_NAME], optimizer, last_epoch,
config[SCHEDULER_PARAMS])
datasets = {
stage: CustomDataset(DATA_DIR, stage, config[FOLD_ID],
config[DATA_PREFIX], config[INPUT_SIZE])
for stage in ['train', 'test']
}
dataloaders = {
stage: get_dataloader(datasets[stage], config[BATCH_SIZE])
for stage in ['train', 'test']
}
writer = SummaryWriter(config[TRAIN_DIR])
clip_grad_value_(model.parameters(), 2.0)
train(config, model, dataloaders, criterion, optimizer, scheduler, writer,
last_epoch + 1)
def __init__(self, args, logger):
self.args = args
self.logger = logger
self.writer = SummaryWriter(args.log_dir)
cudnn.enabled = True
# set up model
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = get_aux_net(args.network.arch)(aux_classes=args.aux_classes + 1, classes=args.n_classes)
self.model = self.model.to(self.device)
wandb.watch(self.model)
if args.mode == 'train':
# set up optimizer, lr scheduler and loss functions
optimizer = get_optimizer(self.args.training.optimizer)
optimizer_params = {k: v for k, v in self.args.training.optimizer.items() if k != "name"}
self.optimizer = optimizer(self.model.parameters(), **optimizer_params)
self.scheduler = get_scheduler(self.optimizer, self.args.training.lr_scheduler)
self.class_loss_func = nn.CrossEntropyLoss()
self.start_iter = 0
# resume
if args.training.resume:
self.load(args.model_dir + '/' + args.training.resume)
cudnn.benchmark = True
elif args.mode == 'val':
self.load(os.path.join(args.model_dir, args.validation.model))
else:
self.load(os.path.join(args.model_dir, args.testing.model))
def run(config):
train_dir = config.train.dir
model = get_model(config, model_type).to(device)
print('The nubmer of parameters : %d' % count_parameters(model))
criterion = get_loss(config)
optimizer = get_optimizer(config, model)
checkpoint = utils.checkpoint.get_initial_checkpoint(config,
model_type=model_type)
if checkpoint is not None:
last_epoch, step = utils.checkpoint.load_checkpoint(
model, optimizer, checkpoint, model_type=model_type)
else:
last_epoch, step = -1, -1
print('from checkpoint: {} last epoch:{}'.format(checkpoint, last_epoch))
scheduler = get_scheduler(config, optimizer, last_epoch)
print(config.data)
dataloaders = {
'train': get_train_dataloader(config),
'val': get_valid_dataloader(config),
'test': get_test_dataloader(config)
}
writer = SummaryWriter(config.train[model_type + '_dir'])
visualizer = get_visualizer(config)
train(config, model, dataloaders, criterion, optimizer, scheduler, writer,
visualizer, last_epoch + 1)
def run(self):
# checkpoint
self.scheduler = get_scheduler(self.config, self.optimizer,
self.last_epoch)
self.model.train()
postfix_dic = {
'lr': 0.0,
'acc': 0.0,
'loss': 0.0,
}
if self.config.data.sampler == "weight":
self.train_weigh()
else:
for epoch in range(self.last_epoch, self.num_epochs):
self.train_single_epoch(epoch)
if epoch % 200 == 199:
save_checkpoint(self.config, self.model, self.optimizer,
self.optimizer_center, epoch, self.step)
self.scheduler.step()
if epoch > self.config.train.num_epochs:
break
def run(config):
teacher_model = get_model(config, 'teacher').to(device)
criterion = get_loss(config)
# for teacher
trainable_params = filter(lambda p: p.requires_grad,
teacher_model.parameters())
optimizer_t = get_optimizer(config, teacher_model.parameters())
checkpoint_t = utils.checkpoint.get_initial_checkpoint(
config, model_type='teacher')
if checkpoint_t is not None:
last_epoch_t, step_t = utils.checkpoint.load_checkpoint(
teacher_model, optimizer_t, checkpoint_t, model_type='teacher')
else:
last_epoch_t, step_t = -1, -1
print('teacher model from checkpoint: {} last epoch:{}'.format(
checkpoint_t, last_epoch_t))
scheduler_t = get_scheduler(config, optimizer_t, last_epoch_t)
print(config.data)
dataloaders = {
'train': get_train_dataloader(config),
'val': get_valid_dataloader(config),
'test': get_test_dataloader(config)
}
writer = SummaryWriter(config.train['teacher' + '_dir'])
visualizer = get_visualizer(config)
train(config, teacher_model, dataloaders, criterion, optimizer_t,
scheduler_t, writer, visualizer, last_epoch_t + 1)
def run(config):
train_dir = config.train.dir
task = get_task(config)
optimizer = get_optimizer(config, task.get_model().parameters())
checkpoint = utils.checkpoint.get_initial_checkpoint(config)
if checkpoint is not None:
last_epoch, step = utils.checkpoint.load_checkpoint(
task.get_model(), optimizer, checkpoint)
else:
last_epoch, step = -1, -1
print('from checkpoint: {} last epoch:{}'.format(checkpoint, last_epoch))
scheduler = get_scheduler(config, optimizer, last_epoch)
preprocess_opt = task.get_preprocess_opt()
dataloaders = {
split: get_dataloader(config, split,
get_transform(config, split, **preprocess_opt))
for split in ['train', 'dev']
}
writer = SummaryWriter(config.train.dir)
train(config, task, dataloaders, optimizer, scheduler, writer,
last_epoch + 1)
def run(config):
train_dir = config.train.dir
student_model = get_model(config, model_type).to(device)
criterion = get_loss(config)
trainable_params = filter(lambda p: p.requires_grad,
student_model.parameters())
optimizer = get_optimizer(config, trainable_params)
checkpoint = utils.checkpoint.get_initial_checkpoint(config,
model_type=model_type)
if checkpoint is not None:
last_epoch, step = utils.checkpoint.load_checkpoint(
student_model, optimizer, checkpoint, model_type=model_type)
else:
last_epoch, step = -1, -1
print('student model from checkpoint: {} last epoch:{}'.format(
checkpoint, last_epoch))
scheduler = get_scheduler(config, optimizer, last_epoch)
print(config.data)
dataloaders = {
'train': get_train_dataloader(config),
'val': get_valid_dataloader(config),
'test': get_test_dataloader(config)
}
writer = SummaryWriter(config.train['student' + '_dir'])
visualizer = get_visualizer(config)
train(config, student_model, dataloaders, criterion, optimizer, scheduler,
writer, visualizer, last_epoch + 1)
def _get_scheduler(self, optimizer):
scheduler = get_scheduler(
optimizer,
self.config['runner']['total_steps'],
self.config['scheduler']
)
self._load_weight(scheduler, 'Scheduler')
return scheduler
def __init__(self, config):
"""Initialize Trainer
Args:
config (dict): Configuration dictionary
"""
super(Trainer, self).__init__()
# Define multi-task setting
dataset = config['dataset']
dataset_name = dataset['dataset_name']
self.tasks_weighting = dataset['tasks_weighting']
self.tasks = [k for k, v in self.tasks_weighting.items()]
# Setup network
model_config = config['model']
self.model = get_module(model_config, dataset_name, self.tasks)
print('Model constructed for {}'.format(' '.join(self.tasks)))
if 'grouping' in model_config:
print('groups = {}'.format(model_config['grouping']['groups']))
print('grouping method = {}'.format(model_config['grouping']['method']))
self.model = update_module(config, self.model, self.tasks)
# Setup for a task-conditional setting
model_params = config['model']['parameters']
if 'common_mt_params' in model_params:
self.task_conditional = not model_params['common_mt_params']
else:
self.task_conditional = False
# Setup optimizers
optimizer_config = config['optimizer']
optimizer_cls = get_optimizer(optimizer_config['algorithm'])
model_params = get_params(self.model, optimizer_config['parameters']['lr'], len(self.tasks),
self.task_conditional, self.tasks)
self.optimizer = optimizer_cls(model_params, **optimizer_config['parameters'])
# Setup schedulers
scheduler_config = config['scheduler']
scheduler_cls = get_scheduler(scheduler_config['lr_policy'])
self.scheduler = scheduler_cls(self.optimizer, **scheduler_config['parameters'])
# Setup loss function
losses_config = config['loss']
self.criterions = get_loss_functions(self.tasks, losses_config)
# Initialise performance meters
self.best_val_loss = 1e9
self.train_loss = {}
self.val_loss = {}
for task in self.tasks:
self.train_loss[task] = get_running_meter()
self.val_loss[task] = get_running_meter()
# Initialize img logging for visualization
self.img_logging = get_img_logging(dataset_name, self.tasks)
self.pred_decoder = get_pred_decoder(dataset_name, self.tasks)
def _get_scheduler(self, optimizer):
scheduler = get_scheduler(optimizer,
self.config['runner']['total_steps'],
self.config['scheduler'])
init_scheduler = self.init_ckpt.get('Scheduler')
if init_scheduler:
print(
'[Runner] - Loading scheduler weights from the previous experiment'
)
scheduler.load_state_dict(init_scheduler)
return scheduler
def search_once(config, policy):
model = get_model(config).cuda()
criterion = get_loss(config)
optimizer = get_optimizer(config, model.parameters())
scheduler = get_scheduler(config, optimizer, -1)
transforms = {'train': get_transform(config, 'train', params={'policies': policy}),
'val': get_transform(config, 'val')}
dataloaders = {split:get_dataloader(config, split, transforms[split])
for split in ['train', 'val']}
score_dict = train(config, model, dataloaders, criterion, optimizer, scheduler, None, 0)
return score_dict['f1_mavg']
def run(config):
teacher_model = get_model(config, 'teacher').to(device)
student_model = get_model(config, 'student').to(device)
print('The nubmer of parameters : %d'%count_parameters(student_model))
criterion = get_loss(config)
# for teacher
optimizer_t = None
checkpoint_t = utils.checkpoint.get_initial_checkpoint(config,
model_type='teacher')
if checkpoint_t is not None:
last_epoch_t, step_t = utils.checkpoint.load_checkpoint(teacher_model,
optimizer_t, checkpoint_t, model_type='teacher')
else:
last_epoch_t, step_t = -1, -1
print('teacher model from checkpoint: {} last epoch:{}'.format(
checkpoint_t, last_epoch_t))
# for student
optimizer_s = get_optimizer(config, student_model)
checkpoint_s = utils.checkpoint.get_initial_checkpoint(config,
model_type='student')
if checkpoint_s is not None:
last_epoch_s, step_s = utils.checkpoint.load_checkpoint(student_model,
optimizer_s, checkpoint_s, model_type='student')
else:
last_epoch_s, step_s = -1, -1
print('student model from checkpoint: {} last epoch:{}'.format(
checkpoint_s, last_epoch_s))
scheduler_s = get_scheduler(config, optimizer_s, last_epoch_s)
print(config.data)
dataloaders = {'train':get_train_dataloader(config, get_transform(config)),
'val':get_valid_dataloader(config)}
#'test':get_test_dataloader(config)}
writer = SummaryWriter(config.train['student' + '_dir'])
visualizer = get_visualizer(config)
result = train(config, student_model, teacher_model, dataloaders,
criterion, optimizer_s, scheduler_s, writer,
visualizer, last_epoch_s+1)
print('best psnr : %.3f, best epoch: %d'%(result['best_psnr'], result['best_epoch']))
def run(config):
train_group_csv_dir = './data/group_csv/'
writer = SummaryWriter(config.train.dir)
train_filenames = list(glob.glob(os.path.join(train_group_csv_dir, 'data_train_group_*')))[1:]
for ti, train_file in tqdm.tqdm(enumerate(train_filenames)):
gi_tr = train_file.replace('data_train_group_', '')
gi_tr = gi_tr.split('/')[-1]
gi_tr = gi_tr.replace('.csv', '')
group_idx = int(gi_tr)
utils.prepare_train_directories(config, group_idx)
model = get_model(config, group_idx)
if torch.cuda.is_available():
model = model.cuda()
criterion = get_loss(config)
optimizer = get_optimizer(config, model.parameters())
checkpoint = utils.checkpoint.get_initial_checkpoint(config, group_idx)
if checkpoint is not None:
last_epoch, step = utils.checkpoint.load_checkpoint(model, optimizer, checkpoint)
else:
last_epoch, step = -1, -1
if last_epoch > config.train.num_epochs:
print('group -- ', str(group_idx), '-- index-', ti, ' ----已xl，跳过')
continue
print('from checkpoint: {} last epoch:{}'.format(checkpoint, last_epoch))
print('group -- ', str(group_idx), '-- index-', ti)
scheduler = get_scheduler(config, optimizer, last_epoch)
dataloaders = {split:get_dataloader(config, group_idx, split, get_transform(config, split))
for split in ['train', 'val']}
train(config,group_idx, model, dataloaders, criterion, optimizer, scheduler,
writer, last_epoch+1)
def __init__(self, cfg, writer, logger, use_pseudo_label=False, modal_num=3, multimodal_merger=multimodal_merger):
self.cfg = cfg
self.writer = writer
self.class_numbers = 19
self.logger = logger
cfg_model = cfg['model']
self.cfg_model = cfg_model
self.best_iou = -100
self.iter = 0
self.nets = []
self.split_gpu = 0
self.default_gpu = cfg['model']['default_gpu']
self.PredNet_Dir = None
self.valid_classes = cfg['training']['valid_classes']
self.G_train = True
self.cls_feature_weight = cfg['training']['cls_feature_weight']
self.use_pseudo_label = use_pseudo_label
self.modal_num = modal_num
# cluster vectors & cuda initialization
self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda()
self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda()
self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda()
self.class_threshold_group = torch.full([self.modal_num + 1, 19], 0.6).cuda()
self.disc_T = torch.FloatTensor([0.0]).cuda()
#self.metrics = CustomMetrics(self.class_numbers)
self.metrics = CustomMetrics(self.class_numbers, modal_num=self.modal_num, model=self)
# multimodal / multi-branch merger
self.multimodal_merger = multimodal_merger
bn = cfg_model['bn']
if bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
elif bn == 'bn':
BatchNorm = nn.BatchNorm2d
elif bn == 'gn':
BatchNorm = nn.GroupNorm
else:
raise NotImplementedError('batch norm choice {} is not implemented'.format(bn))
if True:
self.PredNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num
).cuda()
self.load_PredNet(cfg, writer, logger, dir=None, net=self.PredNet)
self.PredNet_DP = self.init_device(self.PredNet, gpu_id=self.default_gpu, whether_DP=True)
self.PredNet.eval()
self.PredNet_num = 0
self.PredDnet = FCDiscriminator(inplanes=19)
self.load_PredDnet(cfg, writer, logger, dir=None, net=self.PredDnet)
self.PredDnet_DP = self.init_device(self.PredDnet, gpu_id=self.default_gpu, whether_DP=True)
self.PredDnet.eval()
self.BaseNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num
)
logger.info('the backbone is {}'.format(cfg_model['basenet']['version']))
self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True)
self.nets.extend([self.BaseNet])
self.nets_DP = [self.BaseNet_DP]
# Discriminator
self.SOURCE_LABEL = 0
self.TARGET_LABEL = 1
self.DNets = []
self.DNets_DP = []
for _ in range(self.modal_num+1):
_net_d = FCDiscriminator(inplanes=19)
self.DNets.append(_net_d)
_net_d_DP = self.init_device(_net_d, gpu_id=self.default_gpu, whether_DP=True)
self.DNets_DP.append(_net_d_DP)
self.nets.extend(self.DNets)
self.nets_DP.extend(self.DNets_DP)
self.optimizers = []
self.schedulers = []
optimizer_cls = torch.optim.SGD
optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items()
if k != 'name'}
optimizer_cls_D = torch.optim.Adam
optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items()
if k != 'name'}
if False:
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params)
else:
self.BaseOpti = optimizer_cls(self.BaseNet.optim_parameters(cfg['training']['optimizer']['lr']), **optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.DiscOptis = []
for _d_net in self.DNets:
self.DiscOptis.append(
optimizer_cls_D(_d_net.parameters(), **optimizer_params_D)
)
self.optimizers.extend(self.DiscOptis)
self.schedulers = []
if False:
self.BaseSchedule = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule'])
self.schedulers.extend([self.BaseSchedule])
else:
"""BaseSchedule detail see FUNC: scheduler_step()"""
self.learning_rate = cfg['training']['optimizer']['lr']
self.gamma = cfg['training']['lr_schedule']['gamma']
self.num_steps = cfg['training']['lr_schedule']['max_iter']
self._BaseSchedule_nouse = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule'])
self.schedulers.extend([self._BaseSchedule_nouse])
self.DiscSchedules = []
for _disc_opt in self.DiscOptis:
self.DiscSchedules.append(
get_scheduler(_disc_opt, cfg['training']['lr_schedule'])
)
self.schedulers.extend(self.DiscSchedules)
self.setup(cfg, writer, logger)
self.adv_source_label = 0
self.adv_target_label = 1
self.bceloss = nn.BCEWithLogitsLoss(reduce=False)
self.loss_fn = get_loss_function(cfg)
pseudo_cfg = copy.deepcopy(cfg)
pseudo_cfg['training']['loss']['name'] = 'cross_entropy4d'
self.pseudo_loss_fn = get_loss_function(pseudo_cfg)
self.mseloss = nn.MSELoss()
self.l1loss = nn.L1Loss()
self.smoothloss = nn.SmoothL1Loss()
self.triplet_loss = nn.TripletMarginLoss()
self.kl_distance = nn.KLDivLoss(reduction='none')
def run() -> float:
np.random.seed(0)
model_dir = config.experiment_dir
logger.info('=' * 50)
# logger.info(f'hyperparameters: {params}')
train_loader, val_loader, test_loader, label_encoder = load_data(args.fold)
model = create_model()
optimizer = get_optimizer(config, model.parameters())
lr_scheduler = get_scheduler(config, optimizer)
lr_scheduler2 = get_scheduler(
config, optimizer) if config.scheduler2.name else None
criterion = get_loss(config)
if args.weights is None:
last_epoch = 0
logger.info(f'training will start from epoch {last_epoch+1}')
else:
last_checkpoint = torch.load(args.weights)
assert last_checkpoint['arch'] == config.model.arch
model.load_state_dict(last_checkpoint['state_dict'])
optimizer.load_state_dict(last_checkpoint['optimizer'])
logger.info(f'checkpoint {args.weights} was loaded.')
last_epoch = last_checkpoint['epoch']
logger.info(f'loaded the model from epoch {last_epoch}')
if args.lr_override != 0:
set_lr(optimizer, float(args.lr_override))
elif 'lr' in config.scheduler.params:
set_lr(optimizer, config.scheduler.params.lr)
if args.gen_predict:
print('inference mode')
generate_submission(val_loader, test_loader, model, label_encoder,
last_epoch, args.weights)
sys.exit(0)
if args.gen_features:
print('inference mode')
generate_features(test_loader, model, args.weights)
sys.exit(0)
best_score = 0.0
best_epoch = 0
last_lr = get_lr(optimizer)
best_model_path = args.weights
for epoch in range(last_epoch + 1, config.train.num_epochs + 1):
logger.info('-' * 50)
# if not is_scheduler_continuous(config.scheduler.name):
# # if we have just reduced LR, reload the best saved model
# lr = get_lr(optimizer)
# logger.info(f'learning rate {lr}')
#
# if lr < last_lr - 1e-10 and best_model_path is not None:
# last_checkpoint = torch.load(os.path.join(model_dir, best_model_path))
# assert(last_checkpoint['arch']==config.model.arch)
# model.load_state_dict(last_checkpoint['state_dict'])
# optimizer.load_state_dict(last_checkpoint['optimizer'])
# logger.info(f'checkpoint {best_model_path} was loaded.')
# set_lr(optimizer, lr)
# last_lr = lr
#
# if lr < config.train.min_lr * 1.01:
# logger.info('reached minimum LR, stopping')
# break
get_lr(optimizer)
train(train_loader, model, criterion, optimizer, epoch, lr_scheduler,
lr_scheduler2)
score = validate(val_loader, model, epoch)
if not is_scheduler_continuous(config.scheduler.name):
lr_scheduler.step(score)
if lr_scheduler2 and not is_scheduler_continuous(
config.scheduler.name):
lr_scheduler2.step(score)
is_best = score > best_score
best_score = max(score, best_score)
if is_best:
best_epoch = epoch
data_to_save = {
'epoch': epoch,
'arch': config.model.arch,
'state_dict': model.state_dict(),
'best_score': best_score,
'score': score,
'optimizer': optimizer.state_dict(),
'options': config
}
filename = config.version
if is_best:
best_model_path = f'{filename}_f{args.fold}_e{epoch:02d}_{score:.04f}.pth'
save_checkpoint(data_to_save, best_model_path, model_dir)
logger.info(f'best score: {best_score:.04f}')
return -best_score
def train(cfg, writer, logger):
# This statement must be declared before using pytorch
use_cuda = False
if cfg.get("cuda", None) is not None:
if cfg.get("cuda", None) != "all":
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.get("cuda", None)
use_cuda = torch.cuda.is_available()
# Setup random seed
seed = cfg["training"].get("seed", random.randint(1, 10000))
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# Setup Dataloader
train_loader, val_loader = get_loader(cfg)
# Setup Model
model = get_model(cfg)
# writer.add_graph(model, torch.rand([1, 3, 224, 224]))
if use_cuda and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model,
device_ids=list(
range(torch.cuda.device_count())))
# Setup optimizer, lr_scheduler and loss function
optimizer = get_optimizer(model.parameters(), cfg)
scheduler = get_scheduler(optimizer, cfg)
loss_fn = get_loss_fn(cfg)
# Setup Metrics
epochs = cfg["training"]["epochs"]
recorder = RecorderMeter(epochs)
start_epoch = 0
# save model parameters every <n> epochs
save_interval = cfg["training"]["save_interval"]
if use_cuda:
model.cuda()
loss_fn.cuda()
# Resume Trained Model
resume_path = os.path.join(writer.file_writer.get_logdir(),
cfg["training"]["resume"])
best_path = os.path.join(writer.file_writer.get_logdir(),
cfg["training"]["best_model"])
if cfg["training"]["resume"] is not None:
if os.path.isfile(resume_path):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
resume_path))
checkpoint = torch.load(resume_path)
state = checkpoint["state_dict"]
if torch.cuda.device_count() <= 1:
state = convert_state_dict(state)
model.load_state_dict(state)
optimizer.load_state_dict(checkpoint["optimizer"])
scheduler.load_state_dict(checkpoint["scheduler"])
start_epoch = checkpoint["epoch"]
recorder = checkpoint['recorder']
logger.info("Loaded checkpoint '{}' (epoch {})".format(
resume_path, checkpoint["epoch"]))
else:
logger.info("No checkpoint found at '{}'".format(resume_path))
epoch_time = AverageMeter()
for epoch in range(start_epoch, epochs):
start_time = time.time()
need_hour, need_mins, need_secs = convert_secs2time(epoch_time.avg *
(epochs - epoch))
need_time = '[Need: {:02d}:{:02d}:{:02d}]'.format(
need_hour, need_mins, need_secs)
logger.info(
'\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} [learning_rate={:8.6f}]'.
format(time_string(), epoch, epochs, need_time, optimizer.
param_groups[0]['lr']) + # scheduler.get_last_lr() >=1.4
' [Best : Accuracy={:.2f}]'.format(recorder.max_accuracy(False)))
train_acc, train_los = train_epoch(train_loader, model, loss_fn,
optimizer, use_cuda, logger)
val_acc, val_los = validate_epoch(val_loader, model, loss_fn, use_cuda,
logger)
scheduler.step()
is_best = recorder.update(epoch, train_los, train_acc, val_los,
val_acc)
if is_best or epoch % save_interval == 0 or epoch == epochs - 1: # save model (resume model and best model)
save_checkpoint(
{
'epoch': epoch + 1,
'recorder': recorder,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
}, is_best, best_path, resume_path)
for name, param in model.named_parameters(): # save histogram
writer.add_histogram(name,
param.clone().cpu().data.numpy(), epoch)
writer.add_scalar('Train/loss', train_los, epoch) # save curves
writer.add_scalar('Train/acc', train_acc, epoch)
writer.add_scalar('Val/loss', val_los, epoch)
writer.add_scalar('Val/acc', val_acc, epoch)
epoch_time.update(time.time() - start_time)
writer.close()
def train(cfg):
# Setup seeds
torch.manual_seed(cfg.get('seed', 1337))
torch.cuda.manual_seed(cfg.get('seed', 1337))
np.random.seed(cfg.get('seed', 1337))
random.seed(cfg.get('seed', 1337))
# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Augmentations
augmentations = cfg['training'].get('augmentations', None)
data_aug = get_composed_augmentations(augmentations)
# Setup Dataloader
data_loader = get_loader(cfg['data']['dataset'])
data_path = cfg['data']['path']
t_loader = data_loader(
data_path,
is_transform=True,
split=cfg['data']['train_split'],
#img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
augmentations=data_aug)
v_loader = data_loader(
data_path,
is_transform=True,
split=cfg['data']['val_split'],
#img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
)
n_classes = t_loader.n_classes
trainloader = data.DataLoader(t_loader,
batch_size=cfg['training']['batch_size'],
num_workers=cfg['training']['n_workers'],
shuffle=True)
valloader = data.DataLoader(v_loader,
batch_size=cfg['training']['batch_size'],
num_workers=cfg['training']['n_workers'])
# Setup Metrics
running_metrics_val = runningScore(n_classes)
# Setup Model
model = get_model(cfg['model'], n_classes).to(device)
model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
# Setup optimizer, lr_scheduler and loss function
optimizer_cls = get_optimizer(cfg)
optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items()
if k != 'name'}
optimizer = optimizer_cls(model.parameters(), **optimizer_params)
scheduler = get_scheduler(optimizer, cfg['training']['lr_schedule'])
loss_fn = get_loss_function(cfg)
start_iter = 0
if cfg['training']['resume'] is not None:
if os.path.isfile(cfg['training']['resume']):
checkpoint = torch.load(cfg['training']['resume'])
model.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
start_iter = checkpoint["epoch"]
print("=====>",
"Loaded checkpoint '{}' (iter {})".format(
cfg['training']['resume'], checkpoint["epoch"]
)
)
else:
print("=====>","No checkpoint found at '{}'".format(cfg['training']['resume']))
val_loss_meter = averageMeter()
time_meter = averageMeter()
best_iou = -100.0
i = start_iter
flag = True
while i <= cfg['training']['train_iters'] and flag:
for (images, labels) in trainloader:
i += 1
start_ts = time.time()
scheduler.step()
model.train()
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
optimizer.step()
time_meter.update(time.time() - start_ts)
if (i + 1) % cfg['training']['print_interval'] == 0:
fmt_str = "Iter [{:d}/{:d}] Loss: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(i + 1,
cfg['training']['train_iters'],
loss.item(),
time_meter.avg / cfg['training']['batch_size'])
print(print_str)
time_meter.reset()
if (i + 1) % cfg['training']['val_interval'] == 0 or \
(i + 1) == cfg['training']['train_iters']:
model.eval()
with torch.no_grad():
for i_val, (images_val, labels_val) in tqdm(enumerate(valloader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
val_loss = loss_fn(input=outputs, target=labels_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics_val.update(gt, pred)
val_loss_meter.update(val_loss.item())
print("Iter %d Loss: %.4f" % (i + 1, val_loss_meter.avg))
score, class_iou = running_metrics_val.get_scores()
for k, v in score.items():
print(k,':',v)
for k, v in class_iou.items():
print('{}: {}'.format(k, v))
val_loss_meter.reset()
running_metrics_val.reset()
if score["Mean IoU : \t"] >= best_iou:
best_iou = score["Mean IoU : \t"]
state = {
"epoch": i + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_iou": best_iou,
}
save_path = os.path.join('./checkpoint',
"{}_{}_best_model.pkl".format(
cfg['model']['arch'],
cfg['data']['dataset']))
print("saving···")
torch.save(state, save_path)
if (i + 1) == cfg['training']['train_iters']:
flag = False
break
def train(cfg, logger):
# Setup Seeds
torch.manual_seed(cfg.get("seed", 1337))
torch.cuda.manual_seed(cfg.get("seed", 1337))
np.random.seed(cfg.get("seed", 1337))
random.seed(cfg.get("seed", 1337))
# Setup Device
device = torch.device("cuda:{}".format(cfg["training"]["gpu_idx"])
if torch.cuda.is_available() else "cpu")
# Setup Augmentations
augmentations = cfg["training"].get("augmentations", None)
# Setup Dataloader
data_loader = get_loader(cfg["data"]["dataset"])
data_path = cfg["data"]["path"]
t_loader = data_loader(
data_path,
split=cfg["data"]["train_split"],
)
v_loader = data_loader(
data_path,
split=cfg["data"]["val_split"],
)
n_classes = t_loader.n_classes
n_val = len(v_loader.files['val'])
trainloader = data.DataLoader(
t_loader,
batch_size=cfg["training"]["batch_size"],
num_workers=cfg["training"]["n_workers"],
shuffle=True,
)
valloader = data.DataLoader(v_loader,
batch_size=cfg["training"]["batch_size"],
num_workers=cfg["training"]["n_workers"])
# Setup Metrics
running_metrics_val = runningScore(n_classes, n_val)
# Setup Model
model = get_model(cfg["model"], n_classes).to(device)
model = torch.nn.DataParallel(model,
device_ids=[cfg["training"]["gpu_idx"]])
# Setup Optimizer, lr_scheduler and Loss Function
optimizer_cls = get_optimizer(cfg)
optimizer_params = {
k: v
for k, v in cfg["training"]["optimizer"].items() if k != "name"
}
optimizer = optimizer_cls(model.parameters(), **optimizer_params)
logger.info("Using optimizer {}".format(optimizer))
scheduler = get_scheduler(optimizer, cfg["training"]["lr_schedule"])
loss_fn = get_loss_function(cfg)
logger.info("Using loss {}".format(loss_fn))
# Resume Trained Model
if cfg["training"]["resume"] is not None:
if os.path.isfile(cfg["training"]["resume"]):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
cfg["training"]["resume"]))
checkpoint = torch.load(cfg["training"]["resume"])
model.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
start_iter = checkpoint["epoch"]
logger.info("Loaded checkpoint '{}' (iter {})".format(
cfg["training"]["resume"], checkpoint["epoch"]))
else:
logger.info("No checkpoint found at '{}'".format(
cfg["training"]["resume"]))
# Start Training
val_loss_meter = averageMeter()
time_meter = averageMeter()
start_iter = 0
best_dice = -100.0
i = start_iter
flag = True
while i <= cfg["training"]["train_iters"] and flag:
for (images, labels, img_name) in trainloader:
i += 1
start_ts = time.time()
scheduler.step()
model.train()
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
optimizer.step()
time_meter.update(time.time() - start_ts)
# print train loss
if (i + 1) % cfg["training"]["print_interval"] == 0:
fmt_str = "Iter [{:d}/{:d}] Loss: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(
i + 1,
cfg["training"]["train_iters"],
loss.item(),
time_meter.avg / cfg["training"]["batch_size"],
)
print(print_str)
logger.info(print_str)
time_meter.reset()
# validation
if (i + 1) % cfg["training"]["val_interval"] == 0 or (
i + 1) == cfg["training"]["train_iters"]:
model.eval()
with torch.no_grad():
for i_val, (images_val, labels_val,
img_name_val) in tqdm(enumerate(valloader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
val_loss = loss_fn(input=outputs, target=labels_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics_val.update(gt, pred, i_val)
val_loss_meter.update(val_loss.item())
logger.info("Iter %d Loss: %.4f" % (i + 1, val_loss_meter.avg))
# print val metrics
score, class_dice = running_metrics_val.get_scores()
for k, v in score.items():
print(k, v)
logger.info("{}: {}".format(k, v))
for k, v in class_dice.items():
logger.info("{}: {}".format(k, v))
val_loss_meter.reset()
running_metrics_val.reset()
# save model
if score["Dice : \t"] >= best_dice:
best_dice = score["Dice : \t"]
state = {
"epoch": i + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_dice": best_dice,
}
save_path = os.path.join(
cfg["training"]["model_dir"],
"{}_{}.pkl".format(cfg["model"]["arch"],
cfg["data"]["dataset"]),
)
torch.save(state, save_path)
if (i + 1) == cfg["training"]["train_iters"]:
flag = False
break
def train(cfg, writer, logger):
# Setup seeds
torch.manual_seed(cfg.get("seed", 1337))
torch.cuda.manual_seed(cfg.get("seed", 1337))
np.random.seed(cfg.get("seed", 1337))
random.seed(cfg.get("seed", 1337))
# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Dataloader
trainloader = get_loader(cfg, "train")
valloader = get_loader(cfg, "val")
n_classes = cfg["data"]["n_classes"]
n_channels = cfg["data"]["channels"]
# Setup Metrics
running_metrics_val = runningScore(n_classes)
# Setup Model
model = get_model(cfg, n_classes, n_channels).to(device)
model = torch.nn.DataParallel(model,
device_ids=range(torch.cuda.device_count()))
# Setup optimizer, lr_scheduler and loss function
optimizer_cls = get_optimizer(cfg)
optimizer_params = {
k: v
for k, v in cfg["training"]["optimizer"].items() if k != "name"
}
optimizer = optimizer_cls(model.parameters(), **optimizer_params)
logger.info("Using optimizer {}".format(optimizer))
scheduler = get_scheduler(optimizer, cfg["training"]["lr_schedule"])
loss_fn = get_loss_function(cfg)
logger.info("Using loss {}".format(loss_fn))
start_iter = 0
if cfg["training"]["resume"] is not None:
if os.path.isfile(cfg["training"]["resume"]):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
cfg["training"]["resume"]))
checkpoint = torch.load(cfg["training"]["resume"])
model.module.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
start_iter = checkpoint["epoch"]
logger.info("Loaded checkpoint '{}' (iter {})".format(
cfg["training"]["resume"], checkpoint["epoch"]))
else:
logger.info("No checkpoint found at '{}'".format(
cfg["training"]["resume"]))
val_loss_meter = averageMeter()
time_meter = averageMeter()
best_iou = -100.0
i = start_iter
flag = True
# fig = plt.figure()
# plt.rcParams['xtick.major.pad'] = '15'
# fig.show()
# fig.canvas.draw()
while i <= cfg["training"]["train_iters"] and flag:
for (images, labels) in trainloader:
i += 1
start_ts = time.time()
model.train()
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
# plot_grad_flow(model.named_parameters(), fig)
# zero mean conv for layer 1 of dsm encoder
optimizer.step()
scheduler.step()
# m = model._modules['module'].encoderDSM._modules['0']._modules['0']
# model._modules['module'].encoderDSM._modules['0']._modules['0'].weight = m.weight - torch.mean(m.weight)
model = zero_mean(model, all=False)
time_meter.update(time.time() - start_ts)
if (i + 1) % cfg["training"]["print_interval"] == 0:
fmt_str = "Iter [{:d}/{:d}] Loss: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(
i + 1,
cfg["training"]["train_iters"],
loss.item(),
time_meter.avg / cfg["training"]["batch_size"],
)
print(print_str)
logger.info(print_str)
writer.add_scalar("loss/train_loss", loss.item(), i + 1)
time_meter.reset()
if (i + 1) % cfg["training"]["val_interval"] == 0 or (
i + 1) == cfg["training"]["train_iters"]:
model.eval()
with torch.no_grad():
for i_val, (images_val,
labels_val) in tqdm(enumerate(valloader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
val_loss = loss_fn(input=outputs, target=labels_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
# plt.imshow(v_loader.decode_segmap(gt[0,:,:]))
# plt.imshow(v_loader.decode_segmap(pred[0, :, :]))
running_metrics_val.update(gt, pred)
val_loss_meter.update(val_loss.item())
writer.add_scalar("loss/val_loss", val_loss_meter.avg, i + 1)
logger.info("Iter %d Loss: %.4f" % (i + 1, val_loss_meter.avg))
score, class_iou = running_metrics_val.get_scores()
for k, v in score.items():
#print(k, v)
logger.info("{}: {}".format(k, v))
writer.add_scalar("val_metrics/{}".format(k), v, i + 1)
for k, v in class_iou.items():
logger.info("{}: {}".format(k, v))
writer.add_scalar("val_metrics/cls_{}".format(k), v, i + 1)
val_loss_meter.reset()
running_metrics_val.reset()
if score["Mean IoU : \t"] >= best_iou:
best_iou = score["Mean IoU : \t"]
state = {
"epoch": i + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_iou": best_iou,
}
save_path = os.path.join(
writer.file_writer.get_logdir(),
"{}_{}_best_model.pkl".format(cfg["model"]["arch"],
cfg["data"]["dataset"]),
)
torch.save(state, save_path)
if (i + 1) == cfg["training"]["train_iters"]:
flag = False
break
def main():
# args = parse_args()
IMAGE_PATH = 'data/images/'
num_classes_1 = 168
num_classes_2 = 11
num_classes_3 = 7
stats = (0.0692, 0.2051)
train_df = pd.read_csv('data/train_with_folds.csv')
# train_df = train_df.set_index(['image_id'])
# train_df = train_df.drop(['grapheme'], axis=1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# Data Loaders
# df_train, df_val = train_test_split(train_df, test_size=0.2, random_state=2021)
# train_transform = get_transform(128)
train_transform = A.Compose([
A.CoarseDropout(max_holes=1, max_width=64, max_height=64, p=0.9),
A.ShiftScaleRotate(rotate_limit=5, p=0.9),
A.Normalize(mean=stats[0], std=stats[1], always_apply=True)
])
val_transform = A.Compose(
[A.Normalize(mean=stats[0], std=stats[1], always_apply=True)])
BATCH_SIZE = 50
folds = [{
'train': [1, 2, 3, 4],
'val': [0]
}, {
'train': [0, 2, 3, 4],
'val': [1]
}, {
'train': [1, 0, 3, 4],
'val': [2]
}, {
'train': [1, 2, 0, 4],
'val': [3]
}, {
'train': [1, 2, 3, 0],
'val': [4]
}]
# Loop over folds
for fld in range(1):
fld = 4
print(f'Train fold: {fld}')
train_loader = get_loader(train_df,
IMAGE_PATH,
folds=folds[fld]['train'],
batch_size=BATCH_SIZE,
workers=4,
shuffle=True,
transform=train_transform)
val_loader = get_loader(train_df,
IMAGE_PATH,
folds=folds[fld]['val'],
batch_size=BATCH_SIZE,
workers=4,
shuffle=False,
transform=val_transform)
# Build Model
model = load_model('seresnext50_32x4d', pretrained=True)
model = model.cuda()
# Optimizer
optimizer = get_optimizer(model, lr=.00016)
# Loss
criterion1 = get_criterion()
# Training
history = pd.DataFrame()
history2 = pd.DataFrame()
torch.cuda.empty_cache()
gc.collect()
best = 0
best2 = 1e10
n_epochs = 100
early_epoch = 0
# Scheduler
scheduler = get_scheduler(optimizer,
train_loader=train_loader,
epochs=n_epochs)
# print('Loading previous training...')
# state = torch.load('model.pth')
# model.load_state_dict(state['model_state'])
# best = state['kaggle']
# best2 = state['loss']
# print(f'Loaded model with kaggle score: {best}, loss: {best2}')
# optimizer.load_state_dict(state['opt_state'])
# scheduler.load_state_dict(state['scheduler_state'])
# early_epoch = state['epoch'] + 1
# print(f'Beginning at epoch {early_epoch}')
# print('')
for epoch in range(n_epochs - early_epoch):
epoch += early_epoch
torch.cuda.empty_cache()
gc.collect()
# ###################################################################
# ############## TRAINING ###########################################
# ###################################################################
model.train()
total_loss = 0
total_loss_1 = 0
total_loss_2 = 0
total_loss_3 = 0
# ratio = pow(.5,epoch/50)
# ratio = 0.7
ratio = 1.0
t = tqdm(train_loader)
for batch_idx, (img_batch, y_batch) in enumerate(t):
img_batch = img_batch.cuda().float()
y_batch = y_batch.cuda().long()
optimizer.zero_grad()
label1 = y_batch[:, 0]
label2 = y_batch[:, 1]
label3 = y_batch[:, 2]
rand = np.random.rand()
if rand < 0.5:
images, targets = mixup(img_batch, label1, label2, label3,
0.4)
output1, output2, output3 = model(images)
l1, l2, l3 = mixup_criterion(output1,
output2,
output3,
targets,
rate=ratio)
elif rand < 1:
images, targets = cutmix(img_batch, label1, label2, label3,
0.4)
output1, output2, output3 = model(images)
l1, l2, l3 = cutmix_criterion(output1,
output2,
output3,
targets,
rate=ratio)
# else:
# output1,output2,output3 = model(img_batch)
# l1, l2, l3 = criterion1(output1,output2,output3, y_batch)
loss = l1 * .4 + l2 * .3 + l3 * .3
total_loss += loss
total_loss_1 += l1 * .4
total_loss_2 += l2 * .3
total_loss_3 += l3 * .3
t.set_description(
f'Epoch {epoch+1}/{n_epochs}, LR: %6f, Ratio: %.4f, Loss: %.4f, Root loss: %.4f, Vowel loss: %.4f, Consonant loss: %.4f'
% (optimizer.state_dict()['param_groups'][0]['lr'], ratio,
total_loss / (batch_idx + 1), total_loss_1 /
(batch_idx + 1), total_loss_2 /
(batch_idx + 1), total_loss_3 / (batch_idx + 1)))
# t.set_description(f'Epoch {epoch}/{n_epochs}, LR: %6f, Loss: %.4f'%(optimizer.state_dict()['param_groups'][0]['lr'],total_loss/(batch_idx+1)))
if history is not None:
history.loc[epoch + batch_idx / len(train_loader),
'train_loss'] = loss.data.cpu().numpy()
history.loc[
epoch + batch_idx / len(train_loader),
'lr'] = optimizer.state_dict()['param_groups'][0]['lr']
loss.backward()
optimizer.step()
# if scheduler is not None:
# scheduler.step()
# ###################################################################
# ############## VALIDATION #########################################
# ###################################################################
model.eval()
loss = 0
preds_1 = []
preds_2 = []
preds_3 = []
tars_1 = []
tars_2 = []
tars_3 = []
with torch.no_grad():
for img_batch, y_batch in val_loader:
img_batch = img_batch.cuda().float()
y_batch = y_batch.cuda().long()
o1, o2, o3 = model(img_batch)
l1, l2, l3 = criterion1(o1, o2, o3, y_batch)
loss += l1 * .4 + l2 * .3 + l3 * .3
for j in range(len(o1)):
preds_1.append(torch.argmax(F.softmax(o1[j]), -1))
preds_2.append(torch.argmax(F.softmax(o2[j]), -1))
preds_3.append(torch.argmax(F.softmax(o3[j]), -1))
for i in y_batch:
tars_1.append(i[0].data.cpu().numpy())
tars_2.append(i[1].data.cpu().numpy())
tars_3.append(i[2].data.cpu().numpy())
preds_1 = [p.data.cpu().numpy() for p in preds_1]
preds_2 = [p.data.cpu().numpy() for p in preds_2]
preds_3 = [p.data.cpu().numpy() for p in preds_3]
preds_1 = np.array(preds_1).T.reshape(-1)
preds_2 = np.array(preds_2).T.reshape(-1)
preds_3 = np.array(preds_3).T.reshape(-1)
scores = []
scores.append(
sklearn.metrics.recall_score(tars_1, preds_1, average='macro'))
scores.append(
sklearn.metrics.recall_score(tars_2, preds_2, average='macro'))
scores.append(
sklearn.metrics.recall_score(tars_3, preds_3, average='macro'))
final_score = np.average(scores, weights=[2, 1, 1])
loss /= len(val_loader)
if history2 is not None:
history2.loc[epoch, 'val_loss'] = loss.cpu().numpy()
history2.loc[epoch, 'acc'] = final_score
history2.loc[epoch, 'root_acc'] = scores[0]
history2.loc[epoch, 'vowel_acc'] = scores[1]
history2.loc[epoch, 'consonant_acc'] = scores[2]
if scheduler is not None:
scheduler.step(final_score)
print(
f'Dev loss: %.4f, Kaggle: {final_score}, Root acc: {scores[0]}, Vowel acc: {scores[1]}, Consonant acc: {scores[2]}'
% (loss))
if epoch > 0:
history2['acc'].plot()
plt.savefig(f'epoch%03d_{fld}_acc.png' % (epoch + 1))
plt.clf()
if loss < best2:
best2 = loss
print(f'Saving best model... (loss)')
torch.save(
{
'epoch': epoch,
'loss': loss,
'kaggle': final_score,
'model_state': model.state_dict(),
'opt_state': optimizer.state_dict(),
'scheduler_state': scheduler.state_dict()
}, f'model-1_{fld}.pth')
if final_score > best:
best = final_score
print(f'Saving best model... (acc)')
torch.save(
{
'epoch': epoch,
'loss': loss,
'kaggle': final_score,
'model_state': model.state_dict(),
'opt_state': optimizer.state_dict(),
'scheduler_state': scheduler.state_dict()
}, f'model_{fld}.pth')
def __init__(self, config_path, run_dir):
self.config_path = coerce_to_path_and_check_exist(config_path)
self.run_dir = coerce_to_path_and_create_dir(run_dir)
self.logger = get_logger(self.run_dir, name="trainer")
self.print_and_log_info(
"Trainer initialisation: run directory is {}".format(run_dir))
shutil.copy(self.config_path, self.run_dir)
self.print_and_log_info("Config {} copied to run directory".format(
self.config_path))
with open(self.config_path) as fp:
cfg = yaml.load(fp, Loader=yaml.FullLoader)
if torch.cuda.is_available():
type_device = "cuda"
nb_device = torch.cuda.device_count()
# XXX: set to False when input image sizes are not fixed
torch.backends.cudnn.benchmark = cfg["training"].get(
"cudnn_benchmark", True)
else:
type_device = "cpu"
nb_device = None
self.device = torch.device(type_device)
self.print_and_log_info("Using {} device, nb_device is {}".format(
type_device, nb_device))
# Datasets and dataloaders
self.dataset_kwargs = cfg["dataset"]
self.dataset_name = self.dataset_kwargs.pop("name")
train_dataset = get_dataset(self.dataset_name)("train",
**self.dataset_kwargs)
val_dataset = get_dataset(self.dataset_name)("val",
**self.dataset_kwargs)
self.restricted_labels = sorted(
self.dataset_kwargs["restricted_labels"])
self.n_classes = len(self.restricted_labels) + 1
self.is_val_empty = len(val_dataset) == 0
self.print_and_log_info("Dataset {} instantiated with {}".format(
self.dataset_name, self.dataset_kwargs))
self.print_and_log_info(
"Found {} classes, {} train samples, {} val samples".format(
self.n_classes, len(train_dataset), len(val_dataset)))
self.batch_size = cfg["training"]["batch_size"]
self.n_workers = cfg["training"]["n_workers"]
self.train_loader = DataLoader(train_dataset,
batch_size=self.batch_size,
num_workers=self.n_workers,
shuffle=True)
self.val_loader = DataLoader(val_dataset,
batch_size=self.batch_size,
num_workers=self.n_workers)
self.print_and_log_info(
"Dataloaders instantiated with batch_size={} and n_workers={}".
format(self.batch_size, self.n_workers))
self.n_batches = len(self.train_loader)
self.n_iterations, self.n_epoches = cfg["training"].get(
"n_iterations"), cfg["training"].get("n_epoches")
assert not (self.n_iterations is not None
and self.n_epoches is not None)
if self.n_iterations is not None:
self.n_epoches = max(self.n_iterations // self.n_batches, 1)
else:
self.n_iterations = self.n_epoches * len(self.train_loader)
# Model
self.model_kwargs = cfg["model"]
self.model_name = self.model_kwargs.pop("name")
model = get_model(self.model_name)(self.n_classes,
**self.model_kwargs).to(self.device)
self.model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
self.print_and_log_info("Using model {} with kwargs {}".format(
self.model_name, self.model_kwargs))
self.print_and_log_info('Number of trainable parameters: {}'.format(
f'{count_parameters(self.model):,}'))
# Optimizer
optimizer_params = cfg["training"]["optimizer"] or {}
optimizer_name = optimizer_params.pop("name", None)
self.optimizer = get_optimizer(optimizer_name)(model.parameters(),
**optimizer_params)
self.print_and_log_info("Using optimizer {} with kwargs {}".format(
optimizer_name, optimizer_params))
# Scheduler
scheduler_params = cfg["training"].get("scheduler", {}) or {}
scheduler_name = scheduler_params.pop("name", None)
self.scheduler_update_range = scheduler_params.pop(
"update_range", "epoch")
assert self.scheduler_update_range in ["epoch", "batch"]
if scheduler_name == "multi_step" and isinstance(
scheduler_params["milestones"][0], float):
n_tot = self.n_epoches if self.scheduler_update_range == "epoch" else self.n_iterations
scheduler_params["milestones"] = [
round(m * n_tot) for m in scheduler_params["milestones"]
]
self.scheduler = get_scheduler(scheduler_name)(self.optimizer,
**scheduler_params)
self.cur_lr = -1
self.print_and_log_info("Using scheduler {} with parameters {}".format(
scheduler_name, scheduler_params))
# Loss
loss_name = cfg["training"]["loss"]
self.criterion = get_loss(loss_name)()
self.print_and_log_info("Using loss {}".format(self.criterion))
# Pretrained / Resume
checkpoint_path = cfg["training"].get("pretrained")
checkpoint_path_resume = cfg["training"].get("resume")
assert not (checkpoint_path is not None
and checkpoint_path_resume is not None)
if checkpoint_path is not None:
self.load_from_tag(checkpoint_path)
elif checkpoint_path_resume is not None:
self.load_from_tag(checkpoint_path_resume, resume=True)
else:
self.start_epoch, self.start_batch = 1, 1
# Train metrics
train_iter_interval = cfg["training"].get(
"train_stat_interval", self.n_epoches * self.n_batches // 200)
self.train_stat_interval = train_iter_interval
self.train_time = AverageMeter()
self.train_loss = AverageMeter()
self.train_metrics_path = self.run_dir / TRAIN_METRICS_FILE
with open(self.train_metrics_path, mode="w") as f:
f.write(
"iteration\tepoch\tbatch\ttrain_loss\ttrain_time_per_img\n")
# Val metrics
val_iter_interval = cfg["training"].get(
"val_stat_interval", self.n_epoches * self.n_batches // 100)
self.val_stat_interval = val_iter_interval
self.val_loss = AverageMeter()
self.val_metrics = RunningMetrics(self.restricted_labels)
self.val_current_score = None
self.val_metrics_path = self.run_dir / VAL_METRICS_FILE
with open(self.val_metrics_path, mode="w") as f:
f.write("iteration\tepoch\tbatch\tval_loss\t" +
"\t".join(self.val_metrics.names) + "\n")
def __init__(self, cfg, writer, logger):
# super(CustomModel, self).__init__()
self.cfg = cfg
self.writer = writer
self.class_numbers = 19
self.logger = logger
cfg_model = cfg['model']
self.cfg_model = cfg_model
self.best_iou = -100
self.iter = 0
self.nets = []
self.split_gpu = 0
self.default_gpu = cfg['model']['default_gpu']
self.PredNet_Dir = None
self.valid_classes = cfg['training']['valid_classes']
self.G_train = True
self.objective_vectors = np.zeros([19, 256])
self.objective_vectors_num = np.zeros([19])
self.objective_vectors_dis = np.zeros([19, 19])
self.class_threshold = np.zeros(self.class_numbers)
self.class_threshold = np.full([19], 0.95)
self.metrics = CustomMetrics(self.class_numbers)
self.cls_feature_weight = cfg['training']['cls_feature_weight']
bn = cfg_model['bn']
if bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
# elif bn == 'sync_abn':
# BatchNorm = InPlaceABNSync
elif bn == 'bn':
BatchNorm = nn.BatchNorm2d
# elif bn == 'abn':
# BatchNorm = InPlaceABN
elif bn == 'gn':
BatchNorm = nn.GroupNorm
else:
raise NotImplementedError(
'batch norm choice {} is not implemented'.format(bn))
self.PredNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
).cuda()
self.load_PredNet(cfg, writer, logger, dir=None, net=self.PredNet)
self.PredNet_DP = self.init_device(self.PredNet,
gpu_id=self.default_gpu,
whether_DP=True)
self.PredNet.eval()
self.PredNet_num = 0
self.BaseNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=False,
)
logger.info('the backbone is {}'.format(
cfg_model['basenet']['version']))
self.BaseNet_DP = self.init_device(self.BaseNet,
gpu_id=self.default_gpu,
whether_DP=True)
self.nets.extend([self.BaseNet])
self.nets_DP = [self.BaseNet_DP]
self.optimizers = []
self.schedulers = []
# optimizer_cls = get_optimizer(cfg)
optimizer_cls = torch.optim.SGD
optimizer_params = {
k: v
for k, v in cfg['training']['optimizer'].items() if k != 'name'
}
# optimizer_cls_D = torch.optim.SGD
# optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items()
# if k != 'name'}
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(),
**optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.BaseSchedule = get_scheduler(self.BaseOpti,
cfg['training']['lr_schedule'])
self.schedulers.extend([self.BaseSchedule])
self.setup(cfg, writer, logger)
self.adv_source_label = 0
self.adv_target_label = 1
self.bceloss = nn.BCEWithLogitsLoss(size_average=True)
self.loss_fn = get_loss_function(cfg)
self.mseloss = nn.MSELoss()
self.l1loss = nn.L1Loss()
self.smoothloss = nn.SmoothL1Loss()
self.triplet_loss = nn.TripletMarginLoss()
def run() -> float:
np.random.seed(0)
model_dir = config.experiment_dir
logger.info('=' * 50)
train_loader, val_loader, test_loader = load_data(args.fold)
logger.info(f'creating a model {config.model.arch}')
model = create_model(config, pretrained=args.weights is None).cuda()
criterion = get_loss(config)
if args.summary:
torchsummary.summary(model, (3, config.model.input_size, config.model.input_size))
if args.lr_finder:
optimizer = get_optimizer(config, model.parameters())
lr_finder(train_loader, model, criterion, optimizer)
sys.exit()
if args.weights is None and config.train.head_only_warmup:
logger.info('-' * 50)
logger.info(f'doing warmup for {config.train.warmup.steps} steps')
logger.info(f'max_lr will be {config.train.warmup.max_lr}')
optimizer = get_optimizer(config, model.parameters())
warmup_scheduler = get_warmup_scheduler(config, optimizer)
freeze_layers(model)
train_epoch(train_loader, model, criterion, optimizer, 0,
warmup_scheduler, None, config.train.warmup.steps)
unfreeze_layers(model)
if args.weights is None and config.train.enable_warmup:
logger.info('-' * 50)
logger.info(f'doing warmup for {config.train.warmup.steps} steps')
logger.info(f'max_lr will be {config.train.warmup.max_lr}')
optimizer = get_optimizer(config, model.parameters())
warmup_scheduler = get_warmup_scheduler(config, optimizer)
train_epoch(train_loader, model, criterion, optimizer, 0,
warmup_scheduler, None, config.train.warmup.steps)
optimizer = get_optimizer(config, model.parameters())
if args.weights is None:
last_epoch = -1
else:
last_checkpoint = torch.load(args.weights)
model_arch = last_checkpoint['arch'].replace('se_', 'se')
if model_arch != config.model.arch:
dprint(model_arch)
dprint(config.model.arch)
assert model_arch == config.model.arch
model.load_state_dict(last_checkpoint['state_dict'])
if 'optimizer' in last_checkpoint.keys():
optimizer.load_state_dict(last_checkpoint['optimizer'])
logger.info(f'checkpoint loaded: {args.weights}')
last_epoch = last_checkpoint['epoch'] if 'epoch' in last_checkpoint.keys() else 99
logger.info(f'loaded the model from epoch {last_epoch}')
if args.lr != 0:
set_lr(optimizer, float(args.lr))
elif 'lr' in config.optimizer.params:
set_lr(optimizer, config.optimizer.params.lr)
elif 'base_lr' in config.scheduler.params:
set_lr(optimizer, config.scheduler.params.base_lr)
if not args.cosine:
lr_scheduler = get_scheduler(config.scheduler, optimizer, last_epoch=
(last_epoch if config.scheduler.name != 'cyclic_lr' else -1))
assert config.scheduler2.name == ''
lr_scheduler2 = get_scheduler(config.scheduler2, optimizer, last_epoch=last_epoch) \
if config.scheduler2.name else None
else:
epoch_size = min(len(train_loader), config.train.max_steps_per_epoch) \
* config.train.batch_size
set_lr(optimizer, float(config.cosine.start_lr))
lr_scheduler = CosineLRWithRestarts(optimizer,
batch_size=config.train.batch_size,
epoch_size=epoch_size,
restart_period=config.cosine.period,
period_inc=config.cosine.period_inc,
max_period=config.cosine.max_period)
lr_scheduler2 = None
if args.predict_oof or args.predict_test:
print('inference mode')
assert args.weights is not None
if args.predict_oof:
gen_train_prediction(val_loader, model, last_epoch, args.weights)
else:
gen_test_prediction(test_loader, model, args.weights)
sys.exit()
logger.info(f'training will start from epoch {last_epoch + 1}')
best_score = 0.0
best_epoch = 0
last_lr = get_lr(optimizer)
best_model_path = args.weights
for epoch in range(last_epoch + 1, config.train.num_epochs):
logger.info('-' * 50)
if not is_scheduler_continuous(lr_scheduler) and lr_scheduler2 is None:
# if we have just reduced LR, reload the best saved model
lr = get_lr(optimizer)
if lr < last_lr - 1e-10 and best_model_path is not None:
logger.info(f'learning rate dropped: {lr}, reloading')
last_checkpoint = torch.load(best_model_path)
assert(last_checkpoint['arch']==config.model.arch)
model.load_state_dict(last_checkpoint['state_dict'])
optimizer.load_state_dict(last_checkpoint['optimizer'])
logger.info(f'checkpoint loaded: {best_model_path}')
set_lr(optimizer, lr)
last_lr = lr
if config.train.lr_decay_coeff != 0 and epoch in config.train.lr_decay_milestones:
n_cycles = config.train.lr_decay_milestones.index(epoch) + 1
total_coeff = config.train.lr_decay_coeff ** n_cycles
logger.info(f'artificial LR scheduler: made {n_cycles} cycles, decreasing LR by {total_coeff}')
set_lr(optimizer, config.scheduler.params.base_lr * total_coeff)
lr_scheduler = get_scheduler(config.scheduler, optimizer,
coeff=total_coeff, last_epoch=-1)
# (last_epoch if config.scheduler.name != 'cyclic_lr' else -1))
if isinstance(lr_scheduler, CosineLRWithRestarts):
restart = lr_scheduler.epoch_step()
if restart:
logger.info('cosine annealing restarted, resetting the best metric')
best_score = min(config.cosine.min_metric_val, best_score)
train_epoch(train_loader, model, criterion, optimizer, epoch,
lr_scheduler, lr_scheduler2, config.train.max_steps_per_epoch)
score, _, _ = validate(val_loader, model, epoch)
if type(lr_scheduler) == ReduceLROnPlateau:
lr_scheduler.step(metrics=score)
elif not is_scheduler_continuous(lr_scheduler):
lr_scheduler.step()
if type(lr_scheduler2) == ReduceLROnPlateau:
lr_scheduler2.step(metrics=score)
elif lr_scheduler2 and not is_scheduler_continuous(lr_scheduler2):
lr_scheduler2.step()
is_best = score > best_score
best_score = max(score, best_score)
if is_best:
best_epoch = epoch
if is_best:
best_model_path = os.path.join(model_dir,
f'{config.version}_f{args.fold}_e{epoch:02d}_{score:.04f}.pth')
data_to_save = {
'epoch': epoch,
'arch': config.model.arch,
'state_dict': model.state_dict(),
'score': score,
'optimizer': optimizer.state_dict(),
'config': config
}
torch.save(data_to_save, best_model_path)
logger.info(f'a snapshot was saved to {best_model_path}')
logger.info(f'best score: {best_score:.04f}')
return -best_score
def __init__(self, cfg):
"""Construct a Unet generator
Parameters in cfg:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
image of size 128x128 will become of size 1x1 # at the bottleneck
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
We construct the U-Net from the innermost layer to the outermost layer.
It is a recursive process.
"""
super(Unet, self).__init__()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
input_nc = cfg['model']['input_nc']
output_nc = cfg['model']['output_nc']
num_downs = cfg['model']['num_downs']
ngf = cfg['model']['ngf']
norm_layer = nn.BatchNorm2d if cfg['model'][
'norm_layer'] == 'batch' else nn.InstanceNorm2d
use_dropout = cfg['model']['use_dropout']
self.hook = []
# construct unet structure
unet_block = UnetSkipConnectionBlock(
ngf * 8,
ngf * 8,
input_nc=None,
submodule=None,
norm_layer=norm_layer,
innermost=True) # add the innermost layer
for i in range(num_downs -
5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(ngf * 8,
ngf * 8,
input_nc=None,
submodule=unet_block,
norm_layer=norm_layer,
use_dropout=use_dropout)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(ngf * 4,
ngf * 8,
input_nc=None,
submodule=unet_block,
norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf * 2,
ngf * 4,
input_nc=None,
submodule=unet_block,
norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf,
ngf * 2,
input_nc=None,
submodule=unet_block,
norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(
output_nc,
ngf,
input_nc=input_nc,
submodule=unet_block,
outermost=True) # add the outermost layer
self.inputsF = [
Hook(layer) for layer in list(self.modules())
if isinstance(layer, nn.Conv2d)
]
self.out = None
self.criterion = cross_entropy2d
self.loss = None
self.optimizer = get_optimizer(self.parameters(), cfg)
if cfg["training"]["lr_schedule"] is not None:
self.scheduler = get_scheduler(self.optimizer,
cfg["training"]["lr_schedule"])
def run(config_file, device_id, idx_fold):
os.environ['CUDA_VISIBLE_DEVICES'] = str(device_id)
print('info: use gpu No.{}'.format(device_id))
config = load_config(config_file)
# for n-folds loop
if config.data.params.idx_fold == -1:
config.data.params.idx_fold = idx_fold
config.work_dir = config.work_dir + '_fold{}'.format(idx_fold)
elif config.data.params.idx_fold == 0:
original_fold = int(config.work_dir.split('_fold')[1])
if original_fold == idx_fold:
raise Exception(
'if you specify fold 0, you should use train.py or resume from fold 1.'
)
config.data.params.idx_fold = idx_fold
config.work_dir = config.work_dir.split('_fold')[0] + '_fold{}'.format(
idx_fold)
else:
raise Exception('you should use train.py if idx_fold is specified.')
print('info: training for fold {}'.format(idx_fold))
if not os.path.exists(config.work_dir):
os.makedirs(config.work_dir, exist_ok=True)
all_transforms = {}
all_transforms['train'] = get_transforms(config.transforms.train)
all_transforms['valid'] = get_transforms(config.transforms.test)
dataloaders = {
phase: make_loader(
df_path=config.data.train_df_path,
data_dir=config.data.train_dir,
features=config.data.features,
phase=phase,
img_size=(config.data.height, config.data.width),
batch_size=config.train.batch_size,
num_workers=config.num_workers,
idx_fold=config.data.params.idx_fold,
transforms=all_transforms[phase],
horizontal_flip=config.train.horizontal_flip,
model_scale=config.data.model_scale,
debug=config.debug,
pseudo_path=config.data.pseudo_path,
)
for phase in ['train', 'valid']
}
# create segmentation model with pre trained encoder
num_features = len(config.data.features)
print('info: num_features =', num_features)
model = CenterNetFPN(
slug=config.model.encoder,
num_classes=num_features,
)
optimizer = get_optimizer(model, config)
scheduler = get_scheduler(optimizer, config)
# model runner
runner = SupervisedRunner(model=model, device=get_device())
# train setting
criterion, callbacks = get_criterion_and_callback(config)
if config.train.early_stop_patience > 0:
callbacks.append(
EarlyStoppingCallback(patience=config.train.early_stop_patience))
if config.train.accumulation_size > 0:
accumulation_steps = config.train.accumulation_size // config.train.batch_size
callbacks.extend(
[OptimizerCallback(accumulation_steps=accumulation_steps)])
# to resume from check points if exists
if os.path.exists(config.work_dir + '/checkpoints/last_full.pth'):
callbacks.append(
CheckpointCallback(resume=config.work_dir +
'/checkpoints/last_full.pth'))
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=dataloaders,
logdir=config.work_dir,
num_epochs=config.train.num_epochs,
main_metric=config.train.main_metric,
minimize_metric=config.train.minimize_metric,
callbacks=callbacks,
verbose=True,
fp16=config.train.fp16,
)
def run(config_file):
config = load_config(config_file)
#set up the environment flags for working with the KAGGLE GPU OR COLAB_GPU
if 'COLAB_GPU' in os.environ:
config.work_dir = '/content/drive/My Drive/kaggle_cloud/' + config.work_dir
elif 'KAGGLE_WORKING_DIR' in os.environ:
config.work_dir = '/kaggle/working/' + config.work_dir
print('working directory:', config.work_dir)
#save the configuration to the working dir
if not os.path.exists(config.work_dir):
os.makedirs(config.work_dir, exist_ok=True)
save_config(config, config.work_dir + '/config.yml')
#Enter the GPUS you have,
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
all_transforms = {}
all_transforms['train'] = get_transforms(config.transforms.train)
#our dataset has an explicit validation folder, use that later.
all_transforms['valid'] = get_transforms(config.transforms.test)
print("before rajat config", config.data.height, config.data.width)
#fetch the dataloaders we need
dataloaders = {
phase: make_loader(data_folder=config.data.train_dir,
df_path=config.data.train_df_path,
phase=phase,
img_size=(config.data.height, config.data.width),
batch_size=config.train.batch_size,
num_workers=config.num_workers,
idx_fold=config.data.params.idx_fold,
transforms=all_transforms[phase],
num_classes=config.data.num_classes,
pseudo_label_path=config.train.pseudo_label_path,
debug=config.debug)
for phase in ['train', 'valid']
}
#creating the segmentation model with pre-trained encoder
'''
dumping the parameters for smp library
encoder_name: str = "resnet34",
encoder_depth: int = 5,
encoder_weights: str = "imagenet",
decoder_use_batchnorm: bool = True,
decoder_channels: List[int] = (256, 128, 64, 32, 16),
decoder_attention_type: Optional[str] = None,
in_channels: int = 3,
classes: int = 1,
activation: Optional[Union[str, callable]] = None,
aux_params: Optional[dict] = None,
'''
model = getattr(smp, config.model.arch)(
encoder_name=config.model.encoder,
encoder_weights=config.model.pretrained,
classes=config.data.num_classes,
activation=None,
)
#fetch the loss
criterion = get_loss(config)
params = [
{
'params': model.decoder.parameters(),
'lr': config.optimizer.params.decoder_lr
},
{
'params': model.encoder.parameters(),
'lr': config.optimizer.params.encoder_lr
},
]
optimizer = get_optimizer(params, config)
scheduler = get_scheduler(optimizer, config)
'''
dumping the catalyst supervised runner
https://github.com/catalyst-team/catalyst/blob/master/catalyst/dl/runner/supervised.py
model (Model): Torch model object
device (Device): Torch device
input_key (str): Key in batch dict mapping for model input
output_key (str): Key in output dict model output
will be stored under
input_target_key (str): Key in batch dict mapping for target
'''
runner = SupervisedRunner(model=model, device=get_device())
#@pavel,srk,rajat,vladimir,pudae check the IOU and the Dice Callbacks
callbacks = [DiceCallback(), IouCallback()]
#adding patience
if config.train.early_stop_patience > 0:
callbacks.append(
EarlyStoppingCallback(patience=config.train.early_stop_patience))
#thanks for handling the distributed training
'''
we are gonna take zero_grad after accumulation accumulation_steps
'''
if config.train.accumulation_size > 0:
accumulation_steps = config.train.accumulation_size // config.train.batch_size
callbacks.extend([
CriterionCallback(),
OptimizerCallback(accumulation_steps=accumulation_steps)
])
# to resume from check points if exists
if os.path.exists(config.work_dir + '/checkpoints/best.pth'):
callbacks.append(
CheckpointCallback(resume=config.work_dir +
'/checkpoints/last_full.pth'))
'''
pudae добавь пожалуйста обратный вызов
https://arxiv.org/pdf/1710.09412.pdf
**srk adding the mixup callback
'''
if config.train.mixup:
callbacks.append(MixupCallback())
if config.train.cutmix:
callbacks.append(CutMixCallback())
'''@rajat implemented cutmix, a wieghed combination of cutout and mixup '''
callbacks.append(MixupCallback())
callbacks.append(CutMixCallback())
'''
rajat introducing training loop
https://github.com/catalyst-team/catalyst/blob/master/catalyst/dl/runner/supervised.py
take care of the nvidias fp16 precision
'''
print(config.work_dir)
print(config.train.minimize_metric)
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=dataloaders,
logdir=config.work_dir,
num_epochs=config.train.num_epochs,
main_metric=config.train.main_metric,
minimize_metric=config.train.minimize_metric,
callbacks=callbacks,
verbose=True,
fp16=False,
)
def run(config_file):
config = load_config(config_file)
if not os.path.exists(config.work_dir):
os.makedirs(config.work_dir, exist_ok=True)
save_config(config, config.work_dir + '/config.yml')
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
all_transforms = {}
all_transforms['train'] = get_transforms(config.transforms.train)
all_transforms['valid'] = get_transforms(config.transforms.test)
dataloaders = {
phase: make_loader(data_folder=config.data.train_dir,
df_path=config.data.train_df_path,
phase=phase,
batch_size=config.train.batch_size,
num_workers=config.num_workers,
idx_fold=config.data.params.idx_fold,
transforms=all_transforms[phase],
num_classes=config.data.num_classes,
pseudo_label_path=config.train.pseudo_label_path,
debug=config.debug)
for phase in ['train', 'valid']
}
# create segmentation model with pre trained encoder
model = getattr(smp, config.model.arch)(
encoder_name=config.model.encoder,
encoder_weights=config.model.pretrained,
classes=config.data.num_classes,
activation=None,
)
# train setting
criterion = get_loss(config)
params = [
{
'params': model.decoder.parameters(),
'lr': config.optimizer.params.decoder_lr
},
{
'params': model.encoder.parameters(),
'lr': config.optimizer.params.encoder_lr
},
]
optimizer = get_optimizer(params, config)
scheduler = get_scheduler(optimizer, config)
# model runner
runner = SupervisedRunner(model=model)
callbacks = [DiceCallback(), IouCallback()]
# to resume from check points if exists
if os.path.exists(config.work_dir + '/checkpoints/best.pth'):
callbacks.append(
CheckpointCallback(resume=config.work_dir +
'/checkpoints/best_full.pth'))
if config.train.mixup:
callbacks.append(MixupCallback())
if config.train.cutmix:
callbacks.append(CutMixCallback())
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=dataloaders,
logdir=config.work_dir,
num_epochs=config.train.num_epochs,
callbacks=callbacks,
verbose=True,
fp16=True,
)
