def main():
config_path = Path(args.config_path)
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' if torch.cuda.is_available() else 'cpu')
n_class = net_config['n_class']
max_epoch = train_config['max_epoch']
batch_size = train_config['batch_size']
num_workers = train_config['num_workers']
test_every = train_config['test_every']
resume = train_config['resume']
pretrained_path = train_config['pretrained_path']
use_rank = train_config['use_rank']
use_bined = train_config['use_bined']
del train_config['use_rank']
del train_config['use_bined']
train_dir = data_config['train_dir']
val_dir = data_config['val_dir']
train_name = data_config['train_name']
val_name = data_config['val_name']
train_type = data_config['train_type']
val_type = data_config['val_type']
del data_config['train_dir']
del data_config['val_dir']
del data_config['train_name']
del data_config['val_name']
del data_config['train_type']
del data_config['val_type']
model = load_model(**net_config)
# To device
model = model.to(device)
modelname = config_path.stem
output_dir = Path('../model') / modelname
output_dir.mkdir(exist_ok=True)
log_dir = Path('../logs') / modelname
log_dir.mkdir(exist_ok=True)
logger = debug_logger(log_dir)
logger.debug(config)
logger.info(f'Device: {device}')
logger.info(f'Max Epoch: {max_epoch}')
loss_fn = Criterion(**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']
diff_history = history_dict['diff']
# start_epoch = len(diff_history)
start_epoch = 47
for _ in range(start_epoch):
scheduler.step()
else:
start_epoch = 0
best_metrics = float('inf')
loss_history = []
diff_history = []
# Dataset
affine_augmenter = albu.Compose([albu.GaussNoise(var_limit=(0,25),p=.2),
albu.GaussianBlur(3, p=0.2),
albu.JpegCompression(50, 100, p=0.2)])
image_augmenter = albu.Compose([
albu.OneOf([
albu.RandomBrightnessContrast(0.25,0.25),
albu.CLAHE(clip_limit=2),
albu.RandomGamma(),
], p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20,p=0.2),
albu.RGBShift(p=0.2),
])
# image_augmenter = None
train_dataset = laod_dataset(data_type=train_type, affine_augmenter=affine_augmenter, image_augmenter=image_augmenter,
base_dir=train_dir, filename=train_name, use_bined=use_bined, n_class=n_class, **data_config)
valid_dataset = laod_dataset(data_type=val_type, split='valid', base_dir=val_dir, filename=val_name,
use_bined=use_bined, n_class=n_class, **data_config)
# top_10 = len(train_dataset) // 10
# top_30 = len(train_dataset) // 3.33
# train_weights = [ 3 if idx<top_10 else 2 if idx<top_30 else 1 for idx in train_dataset.labels_sort_idx]
# train_sample = WeightedRandomSampler(train_weights, num_samples=len(train_dataset), replacement=True)
# train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sample, num_workers=num_workers,
# pin_memory=True, drop_last=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True)
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=num_workers, pin_memory=True)
if torch.cuda.is_available():
model = nn.DataParallel(model)
# Pretrained model
if pretrained_path:
logger.info(f'Load pretrained from {pretrained_path}')
param = torch.load(pretrained_path, map_location='cpu')
if "state_dict" in param:
model.load_state_dict(param['state_dict'], strict=False)
else:
model.load_state_dict(param)
del param
# Restore model
if resume:
print("[INFO] resume training.")
model_path = output_dir.joinpath(f'model_epoch_{start_epoch-1}.pth')
logger.info(f'Resume from {model_path}')
param = torch.load(model_path, map_location='cpu')
model.load_state_dict(param)
del param
opt_path = output_dir.joinpath(f'opt_epoch_{start_epoch-1}.pth')
param = torch.load(opt_path)
optimizer.load_state_dict(param)
del param
file_train_log = open("file_train_log.txt", "a")
file_val_log = open("file_val_log.txt", "a")
# 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_diffs = []
model.train()
with tqdm(train_loader) as _tqdm:
for batched in _tqdm:
optimizer.zero_grad()
if use_rank:
if use_bined:
img1, img2, lbl1, lbl2, labels, yaw_lbl1, pitch_lbl1, roll_lbl1, yaw_lbl2, pitch_lbl2, roll_lbl2 = batched
img1, img2, lbl1, lbl2, labels = img1.to(device),img2.to(device),lbl1.to(device),lbl2.to(device),labels.to(device)
yaw_lbl1, pitch_lbl1, roll_lbl1 = yaw_lbl1.to(device), pitch_lbl1.to(device), roll_lbl1.to(device)
yaw_lbl2, pitch_lbl2, roll_lbl2 = yaw_lbl2.to(device), pitch_lbl2.to(device), roll_lbl2.to(device)
preds1, y_pres1, p_pres1, r_pres1 = model(img1, True)
preds2, y_pres2, p_pres2, r_pres2 = model(img2, True)
pre_list = [preds1,preds2,y_pres1,p_pres1,r_pres1,y_pres2,p_pres2,r_pres2]
lbl_list = [lbl1,lbl2,yaw_lbl1,pitch_lbl1,roll_lbl1,yaw_lbl2,pitch_lbl2,roll_lbl2,labels]
loss = loss_fn(pre_list, lbl_list, use_bined=True)
else:
img1, img2, lbl1, lbl2, labels = batched
img1, img2, lbl1, lbl2, labels = img1.to(device),img2.to(device),lbl1.to(device),lbl2.to(device),labels.to(device)
preds1 = model(img1, False)
preds2 = model(img2, False)
loss = loss_fn([preds1,preds2], [lbl1,lbl2,labels], use_bined=False)
# print(f"Preds1: {preds1}")
# print(f"Preds2: {preds2}")
# print(f"lib1: {lbl1}")
# print(f"lib2: {lbl2}")
diff = calculate_diff(preds1, lbl1)
diff += calculate_diff(preds2, lbl2)
diff /= 2
# print(f"Diff: {diff}")
elif use_bined:
images, labels, yaw_labels, pitch_labels, roll_labels = batched
images, labels = images.to(device), labels.to(device)
yaw_labels, pitch_labels, roll_labels = yaw_labels.to(device), pitch_labels.to(device), roll_labels.to(device)
preds, y_pres, p_pres, r_pres = model(images, use_bined)
loss = loss_fn([preds, y_pres, p_pres, r_pres], [labels, yaw_labels, pitch_labels, roll_labels], use_bined)
diff = calculate_diff(preds, labels)
else:
images, labels = batched
images, labels = images.to(device), labels.to(device)
preds = model(images, use_bined)
loss = loss_fn([preds], [labels])
diff = calculate_diff(preds, labels, mean=True)
_tqdm.set_postfix(OrderedDict(loss=f'{loss.item():.3f}', mae=f'{diff:.1f}'))
train_losses.append(loss.item())
train_diffs.append(diff)
loss.backward()
optimizer.step()
scheduler.step()
train_loss = np.mean(train_losses)
train_diff = np.nanmean(train_diffs)
logger.info(f'train loss: {train_loss}')
logger.info(f'train diff: {train_diff}')
file_train_log.write(f"{train_loss},{train_diff}")
# torch.save(model.module.state_dict(), output_dir.joinpath(f'model_tmp_epoch_{i_epoch}.pth'))
# torch.save(optimizer.state_dict(), output_dir.joinpath(f'opt_tmp_{i_epoch}.pth'))
if (i_epoch + 1) % test_every == 0:
valid_losses = []
valid_diffs = []
model.eval()
with torch.no_grad():
with tqdm(valid_loader) as _tqdm:
for batched in _tqdm:
if use_bined:
images, labels, yaw_labels, pitch_labels, roll_labels = batched
images, labels = images.to(device), labels.to(device)
# yaw_labels, pitch_labels, roll_labels = yaw_labels.to(device), pitch_labels.to(device), roll_labels.to(device)
preds, y_pres, p_pres, r_pres = model(images, use_bined)
# loss = loss_fn([preds, y_pres, p_pres, r_pres], [labels, yaw_labels, pitch_labels, roll_labels])
diff = calculate_diff(preds, labels)
else:
images, labels = batched
images, labels = images.to(device), labels.to(device)
preds = model(images, use_bined)
# loss = loss_fn([preds], [labels])
diff = calculate_diff(preds, labels)
_tqdm.set_postfix(OrderedDict(mae=f'{diff:.2f}'))
# _tqdm.set_postfix(OrderedDict(loss=f'{loss.item():.3f}', d_y=f'{np.mean(diff[:,0]):.1f}', d_p=f'{np.mean(diff[:,1]):.1f}', d_r=f'{np.mean(diff[:,2]):.1f}'))
valid_losses.append(0)
valid_diffs.append(diff)
valid_loss = np.mean(valid_losses)
valid_diff = np.mean(valid_diffs)
logger.info(f'valid seg loss: {valid_loss}')
logger.info(f'valid diff: {valid_diff}')
file_val_log.write(f"{valid_loss},{valid_diff}")
if best_metrics >= valid_diff:
best_metrics = valid_diff
logger.info('Best Model!\n')
torch.save(model.state_dict(), output_dir.joinpath(f'model_epoch_{i_epoch}_{valid_diff}.pth'))
torch.save(optimizer.state_dict(), output_dir.joinpath(f'opt_epoch_{i_epoch}_{valid_diff}.pth'))
torch.save(model.state_dict(), output_dir.joinpath(f'model_epoch_{i_epoch}_{valid_diff}.pth'))
torch.save(optimizer.state_dict(), output_dir.joinpath(f'opt_epoch_{i_epoch}_{valid_diff}.pth'))
else:
valid_loss = None
valid_diff = None
loss_history.append([train_loss, valid_loss])
diff_history.append([train_diff, valid_diff])
history_ploter(loss_history, log_dir.joinpath('loss.png'))
history_ploter(diff_history, log_dir.joinpath('diff.png'))
history_dict = {'loss': loss_history,
'diff': diff_history,
'best_metrics': best_metrics}
with open(log_dir.joinpath('history.pkl'), 'wb') as f:
pickle.dump(history_dict, f)
file_train_log.close()
file_val_log.close()
pred_data_path = "E:\CSI2019\AASCE_stage4_Process\data/0_DataOriginal\Archive/"
pred_output_path = "E:\CSI2019\AASCE_stage4_Process\data_output/Archive/labels_mat/"
pred_dataset = PredORIDataset(data_path=pred_data_path)
pred_loader = DataLoader(pred_dataset, batch_size=1, shuffle=False)
# # pred_data_path = "E:\CSI2019\AASCE_stage4_Process\data\selected2/"
# pred_data_path = "E:\CSI2019\AASCE_stage4_Process\data\ppt_selected2/"
# pred_output_path = "E:\CSI2019\AASCE_stage4_Process\data\selected2/labels_mat/"
# pred_dataset = PredORIDataset(data_path=pred_data_path)
# pred_loader = DataLoader(pred_dataset, batch_size=1, shuffle=False)
dir_checkpoint = "E:\CSI2019\AASCE_stage4_Process/saved_files"
log_dir = Path(dir_checkpoint + 'logs')
log_dir.mkdir(exist_ok=True, parents=True)
logger = debug_logger(log_dir)
logger.info(f'Device: {device}')
# model = UNetWithResnet50Encoder(n_channels=1, n_classes=18).cuda()
# model = NestedUNet(n_channels=1, n_classes=20)
# model = SCSE_UNet(n_channels=1, n_classes=20)
# model = SCSENestedUNet(n_channels=1, n_classes=18)
# model = DilatedUNet(in_channels=1, classes=20)
# model = ResUNet(in_channel=1, n_classes=5)
# stage1_model_whole = ResUNet(in_channel=1, n_classes=2) #
# root_dir = "E:\CSI2019\AASCE_stage4_Process/"
# stage1_model_whole_load_dir = "saved_model/stage1_resunet_whole_line/CP200.pth"
# stage1_model_whole.load_state_dict(torch.load(os.path.join(root_dir, stage1_model_whole_load_dir)))
# logger.info('Stage1_Model loaded from {}'.format(stage1_model_whole_load_dir))
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)
