# Restore model
if resume:
model_path = output_dir.joinpath(f'model_tmp.pth')
logger.info(f'Resume from {model_path}')
param = torch.load(model_path)
model.load_state_dict(param)
del param
opt_path = output_dir.joinpath(f'opt_tmp.pth')
param = torch.load(opt_path)
optimizer.load_state_dict(param)
del param
#Validation
valid_losses = []
valid_ious = []
model.eval()
with torch.no_grad():
with tqdm(valid_loader) as _tqdm:
for batched in _tqdm:
images, labels = batched
if fp16:
images = images.half()
images, labels = images.to(device), labels.to(device)
preds = model.tta(images, net_type=net_type)
if fp16:
loss = loss_fn(preds.float(), labels)
else:
loss = loss_fn(preds, labels)
preds_np = preds.detach().cpu().numpy()
labels_np = labels.detach().cpu().numpy()
def process(config_path):
gc.collect()
torch.cuda.empty_cache()
config = yaml.load(open(config_path))
net_config = config['Net']
data_config = config['Data']
train_config = config['Train']
loss_config = config['Loss']
opt_config = config['Optimizer']
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
t_max = opt_config['t_max']
# Collect training parameters
max_epoch = train_config['max_epoch']
batch_size = train_config['batch_size']
fp16 = train_config['fp16']
resume = train_config['resume']
pretrained_path = train_config['pretrained_path']
freeze_enabled = train_config['freeze']
seed_enabled = train_config['seed']
#########################################
# Deterministic training
if seed_enabled:
seed = 100
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed=seed)
import random
random.seed(a=100)
#########################################
# Network
if 'unet' in net_config['dec_type']:
net_type = 'unet'
model = EncoderDecoderNet(**net_config)
else:
net_type = 'deeplab'
net_config['output_channels'] = 19
model = SPPNet(**net_config)
dataset = data_config['dataset']
if dataset == 'deepglobe-dynamic':
from dataset.deepglobe_dynamic import DeepGlobeDatasetDynamic as Dataset
net_config['output_channels'] = 7
classes = np.arange(0, 7)
else:
raise NotImplementedError
del data_config['dataset']
modelname = config_path.stem
timestamp = datetime.timestamp(datetime.now())
print("timestamp =", datetime.fromtimestamp(timestamp))
output_dir = Path(os.path.join(ROOT_DIR, f'model/{modelname}_{datetime.fromtimestamp(timestamp)}') )
output_dir.mkdir(exist_ok=True)
log_dir = Path(os.path.join(ROOT_DIR, f'logs/{modelname}_{datetime.fromtimestamp(timestamp)}') )
log_dir.mkdir(exist_ok=True)
dataset_dir= '/home/sfoucher/DEV/pytorch-segmentation/data/deepglobe_as_pascalvoc/VOCdevkit/VOC2012'
logger = debug_logger(log_dir)
logger.debug(config)
logger.info(f'Device: {device}')
logger.info(f'Max Epoch: {max_epoch}')
# Loss
loss_fn = MultiClassCriterion(**loss_config).to(device)
params = model.parameters()
optimizer, scheduler = create_optimizer(params, **opt_config)
# history
if resume:
with open(log_dir.joinpath('history.pkl'), 'rb') as f:
history_dict = pickle.load(f)
best_metrics = history_dict['best_metrics']
loss_history = history_dict['loss']
iou_history = history_dict['iou']
start_epoch = len(iou_history)
for _ in range(start_epoch):
scheduler.step()
else:
start_epoch = 0
best_metrics = 0
loss_history = []
iou_history = []
affine_augmenter = albu.Compose([albu.HorizontalFlip(p=.5),albu.VerticalFlip(p=.5)
# Rotate(5, p=.5)
])
# image_augmenter = albu.Compose([albu.GaussNoise(p=.5),
# albu.RandomBrightnessContrast(p=.5)])
image_augmenter = None
# This has been put in the loop for the dynamic training
"""
# Dataset
train_dataset = Dataset(affine_augmenter=affine_augmenter, image_augmenter=image_augmenter,
net_type=net_type, **data_config)
valid_dataset = Dataset(split='valid', net_type=net_type, **data_config)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4,
pin_memory=True, drop_last=True)
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
"""
# Pretrained model
if pretrained_path:
logger.info(f'Resume from {pretrained_path}')
param = torch.load(pretrained_path)
model.load_state_dict(param)
model.logits = torch.nn.Conv2d(256, net_config['output_channels'], 1)
del param
# To device
model = model.to(device)
#########################################
if freeze_enabled:
# Code de Rémi
# Freeze layers
for param_index in range(int((len(optimizer.param_groups[0]['params']))*0.5)):
optimizer.param_groups[0]['params'][param_index].requires_grad = False
#########################################
params_to_update = model.parameters()
print("Params to learn:")
if freeze_enabled:
params_to_update = []
for name,param in model.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t",name)
optimizer, scheduler = create_optimizer(params_to_update, **opt_config)
# fp16
if fp16:
# I only took the necessary files because I don't need the C backend of apex,
# which is broken and can't be installed
# from apex import fp16_utils
from utils.apex.apex.fp16_utils.fp16util import BN_convert_float
from utils.apex.apex.fp16_utils.fp16_optimizer import FP16_Optimizer
# model = fp16_utils.BN_convert_float(model.half())
model = BN_convert_float(model.half())
# optimizer = fp16_utils.FP16_Optimizer(optimizer, verbose=False, dynamic_loss_scale=True)
optimizer = FP16_Optimizer(optimizer, verbose=False, dynamic_loss_scale=True)
logger.info('Apply fp16')
# Restore model
if resume:
model_path = output_dir.joinpath(f'model_tmp.pth')
logger.info(f'Resume from {model_path}')
param = torch.load(model_path)
model.load_state_dict(param)
del param
opt_path = output_dir.joinpath(f'opt_tmp.pth')
param = torch.load(opt_path)
optimizer.load_state_dict(param)
del param
i_iter = 0
ma_loss= 0
ma_iou= 0
# Train
for i_epoch in range(start_epoch, max_epoch):
logger.info(f'Epoch: {i_epoch}')
logger.info(f'Learning rate: {optimizer.param_groups[0]["lr"]}')
train_losses = []
train_ious = []
model.train()
# Initialize randomized but balanced datasets
train_dataset = Dataset(base_dir = dataset_dir,
affine_augmenter=affine_augmenter, image_augmenter=image_augmenter,
net_type=net_type, **data_config)
valid_dataset = Dataset(base_dir = dataset_dir,
split='valid', net_type=net_type, **data_config)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4,
pin_memory=True, drop_last=True)
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
with tqdm(train_loader) as _tqdm:
for i, batched in enumerate(_tqdm):
images, labels = batched
if fp16:
images = images.half()
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
preds = model(images)
if net_type == 'deeplab':
preds = F.interpolate(preds, size=labels.shape[1:], mode='bilinear', align_corners=True)
if fp16:
loss = loss_fn(preds.float(), labels)
else:
loss = loss_fn(preds, labels)
preds_np = preds.detach().cpu().numpy()
labels_np = labels.detach().cpu().numpy()
iou = compute_iou_batch(np.argmax(preds_np, axis=1), labels_np, classes)
_tqdm.set_postfix(OrderedDict(seg_loss=f'{loss.item():.5f}', iou=f'{iou:.3f}'))
train_losses.append(loss.item())
train_ious.append(iou)
ma_loss= 0.01*loss.item() + 0.99 * ma_loss
ma_iou= 0.01*iou + 0.99 * ma_iou
plotter.plot('loss', 'train', 'iteration Loss', i_iter, loss.item())
plotter.plot('iou', 'train', 'iteration iou', i_iter, iou)
plotter.plot('loss', 'ma_loss', 'iteration Loss', i_iter, ma_loss)
plotter.plot('iou', 'ma_iou', 'iteration iou', i_iter, ma_iou)
if fp16:
optimizer.backward(loss)
else:
loss.backward()
optimizer.step()
i_iter += 1
scheduler.step()
train_loss = np.mean(train_losses)
train_iou = np.nanmean(train_ious)
logger.info(f'train loss: {train_loss}')
logger.info(f'train iou: {train_iou}')
plotter.plot('loss-epoch', 'train', 'iteration Loss', i_epoch, train_loss)
plotter.plot('iou-epoch', 'train', 'iteration iou', i_epoch, train_iou)
torch.save(model.state_dict(), output_dir.joinpath('model_tmp.pth'))
torch.save(optimizer.state_dict(), output_dir.joinpath('opt_tmp.pth'))
valid_losses = []
valid_ious = []
model.eval()
with torch.no_grad():
with tqdm(valid_loader) as _tqdm:
for batched in _tqdm:
images, labels = batched
if fp16:
images = images.half()
images, labels = images.to(device), labels.to(device)
preds = model.tta(images, net_type=net_type)
if fp16:
loss = loss_fn(preds.float(), labels)
else:
loss = loss_fn(preds, labels)
preds_np = preds.detach().cpu().numpy()
labels_np = labels.detach().cpu().numpy()
# I changed a parameter in the compute_iou method to prevent it from yielding nans
iou = compute_iou_batch(np.argmax(preds_np, axis=1), labels_np, classes)
_tqdm.set_postfix(OrderedDict(seg_loss=f'{loss.item():.5f}', iou=f'{iou:.3f}'))
valid_losses.append(loss.item())
valid_ious.append(iou)
valid_loss = np.mean(valid_losses)
valid_iou = np.mean(valid_ious)
logger.info(f'valid seg loss: {valid_loss}')
logger.info(f'valid iou: {valid_iou}')
plotter.plot('loss-epoch', 'valid', 'iteration Loss', i_epoch, valid_loss)
plotter.plot('iou-epoch', 'valid', 'iteration iou', i_epoch, valid_iou)
if best_metrics < valid_iou:
best_metrics = valid_iou
logger.info('Best Model!')
torch.save(model.state_dict(), output_dir.joinpath('model.pth'))
torch.save(optimizer.state_dict(), output_dir.joinpath('opt.pth'))
loss_history.append([train_loss, valid_loss])
iou_history.append([train_iou, valid_iou])
history_ploter(loss_history, log_dir.joinpath('loss.png'))
history_ploter(iou_history, log_dir.joinpath('iou.png'))
history_dict = {'loss': loss_history,
'iou': iou_history,
'best_metrics': best_metrics}
with open(log_dir.joinpath('history.pkl'), 'wb') as f:
pickle.dump(history_dict, f)