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train_seg.py
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train_seg.py
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import torch
from torch.optim import SGD, Adam, lr_scheduler
from tqdm import tqdm
import math
from torch.cuda import amp
import torch
from utils.loss import BinaryDiceLoss
import torch.nn as nn
import yaml
from basemodel import TextDetector
import numpy as np
from datetime import datetime
from torchsummary import summary
import numexpr
import os
import shutil
os.environ['NUMEXPR_MAX_THREADS'] = str(numexpr.detect_number_of_cores())
from seg_dataset import create_dataloader
from utils.general import LOGGER, Loggers, CUDA, DEVICE
import random
torch.random.manual_seed(0)
random.seed(0)
np.random.seed(0)
def one_cycle(y1=0.0, y2=1.0, steps=100):
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
def eval_model(model: nn.Module, val_loader):
global DEVICE
loss_func = BinaryDiceLoss()
pbar = enumerate(val_loader)
nb = len(val_loader)
model.eval()
pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
pr = tp = gt = m_loss = 0
with torch.no_grad():
for i, (imgs, masks) in pbar:
imgs = imgs.to(DEVICE)
masks = masks.to(DEVICE)
pred = model(imgs)
imgs.detach_()
del imgs
tp += torch.mul(pred, masks).sum().detach_()
gt += masks.sum().detach_()
pr += pred.sum().detach_()
loss = loss_func(pred, masks)
m_loss = (m_loss * i + loss.detach()) / (i + 1)
masks.detach_()
del masks
recall = tp / gt
precision = tp / pr
return recall, precision, m_loss
def train(hyp):
with open(r'data/training_hyp.yaml', 'w', encoding='utf8') as f:
yaml.safe_dump(hyp, f)
start_epoch = 0
hyp_train, hyp_data, hyp_model, hyp_logger, hyp_resume = hyp['train'], hyp['data'], hyp['model'], hyp['logger'], hyp['resume']
epochs = hyp_train['epochs']
batch_size = hyp_train['batch_size']
model = TextDetector(**hyp_model)
if CUDA:
model.cuda()
params = model.seg_net.parameters()
if hyp_train['optimizer'] == 'adam':
optimizer = Adam(params, lr=hyp_train['lr0'], betas=(hyp_train['momentum'], 0.999), weight_decay=hyp_train['weight_decay']) # adjust beta1 to momentum
else:
optimizer = SGD(params, lr=hyp_train['lr0'], momentum=hyp_train['momentum'], nesterov=True, weight_decay=hyp_train['weight_decay'])
if hyp_train['linear_lr']:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp_train['lrf']) + hyp_train['lrf'] # linear
else:
lf = one_cycle(1, hyp_train['lrf'], epochs) # cosine 1->hyp['lrf']
scaler = amp.GradScaler(enabled=CUDA)
loss_func = BinaryDiceLoss()
# Scheduler
if hyp_train['linear_lr']:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp_train['lrf']) + hyp_train['lrf'] # linear
else:
lf = one_cycle(1, hyp_train['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
logger = None
if hyp_resume['resume_training']:
LOGGER.info(f'resume traning ... ')
ckpt = torch.load(hyp_resume['ckpt'], map_location=DEVICE)
model.seg_net.load_state_dict(ckpt['weights'])
optimizer.load_state_dict(ckpt['optimizer'])
scheduler.load_state_dict(ckpt['scheduler'])
scheduler.step()
start_epoch = ckpt['epoch'] + 1
hyp_logger['run_id'] = ckpt['run_id']
logger = Loggers(hyp)
else:
if hyp_logger['type'] == 'wandb':
logger = Loggers(hyp)
num_workers = 8
train_img_dir, train_mask_dir, imgsz, augment, aug_param = hyp_data['train_img_dir'], hyp_data['train_mask_dir'], hyp_data['imgsz'], hyp_data['augment'], hyp_data['aug_param']
val_img_dir, val_mask_dir = hyp_data['val_img_dir'], hyp_data['val_mask_dir']
train_dataset, train_loader = create_dataloader(train_img_dir, train_mask_dir, imgsz, batch_size, augment, aug_param, shuffle=True, workers=num_workers, cache=hyp_data['cache'])
val_dataset, val_loader = create_dataloader(val_img_dir, val_mask_dir, imgsz, 4, augment=False, shuffle=False, workers=num_workers, cache=hyp_data['cache'])
nb = len(train_loader)
nw = max(round(3 * nb), 700)
LOGGER.info(f'num training imgs: {len(train_dataset)}, num val imgs: {len(val_dataset)}')
eval_interval = hyp_train['eval_interval']
best_f1 = -1
best_val_loss = np.inf
accumulation_steps = hyp_train['accumulation_steps']
summary(model, (3, 640, 640), device=DEVICE)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train_mask()
train_dataset.initialize()
pbar = enumerate(train_loader)
pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
m_loss = 0
for i, (imgs, masks) in pbar:
pbar.set_description(f' training size: {train_dataset.img_size}')
# warm up
ni = i + nb * epoch
if ni <= nw:
xi = [0, nw] # x interp
for j, x in enumerate(optimizer.param_groups):
x['lr'] = np.interp(ni, xi, [hyp_train['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp_train['warmup_momentum'], hyp_train['momentum']])
imgs, masks = imgs.to(DEVICE), masks.to(DEVICE)
with amp.autocast():
preds = model(imgs)
imgs.detach_()
del imgs
loss = loss_func(preds, masks)
masks.detach_()
del masks
scaler.scale(loss).backward()
if i % accumulation_steps == 0:
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
m_loss = (m_loss * i + loss.detach()) / (i + 1)
if (epoch + 1) % eval_interval == 0:
recall, precision, eval_m_loss = eval_model(model, val_loader)
f1 = 2 * recall * precision / (recall + precision)
last_ckpt = {'epoch': epoch,
'best_f1': best_f1,
'weights': model.seg_net.state_dict(),
'best_val_loss': best_val_loss,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'run_id': logger.wandb.id if logger is not None else None,
'date': datetime.now().isoformat(),
'hyp': hyp}
torch.save(last_ckpt, 'data/unet_last.ckpt')
if best_f1 < f1:
best_f1 = f1
LOGGER.info(f'saving model at epoch {epoch}, best val f1: {best_f1}')
shutil.copy2('data/unet_last.ckpt', 'data/unet_best.ckpt')
LOGGER.info(f'epoch {epoch}/{epochs-1} loss: {m_loss} precision: {precision} recall: {recall}')
if logger is not None:
log_dict = {}
log_dict['train/lr'] = optimizer.param_groups[0]['lr']
log_dict['train/loss'] = m_loss
log_dict['eval/recall'] = recall
log_dict['eval/precision'] = precision
log_dict['eval/f1'] = f1
log_dict['eval/eval_m_loss'] = eval_m_loss
logger.on_train_epoch_end(epoch, log_dict)
scheduler.step()
pbar.close()
if __name__ == '__main__':
hyp_p = r'data/train_hyp.yaml'
with open(hyp_p, 'r', encoding='utf8') as f:
hyp = yaml.safe_load(f.read())
hyp['data']['train_img_dir'] = [r'../datasets/codat_manga_v3/images/train', r'../datasets/ComicErased/processed']
# hyp['data']['train_img_dir'] = [r'../datasets/codat_manga_v3/images/val']
hyp['data']['val_img_dir'] = [r'../datasets/codat_manga_v3/images/val']
hyp['data']['train_mask_dir'] = r'../datasets/ComicSegV2'
hyp['data']['val_mask_dir'] = r'../datasets/ComicSegV2'
hyp['data']['imgsz'] = 1024
hyp['data']['cache'] = False
hyp['data']['aug_param']['neg'] = 0.3
hyp['data']['aug_param']['size_range'] = [0.85, 1.1]
hyp['train']['lr0'] = 0.004
hyp['train']['lrf'] = 0.005
hyp['train']['weight_decay'] = 0.00002
hyp['train']['epochs'] = 120
hyp['train']['accumulation_steps'] = 4
hyp['train']['batch_size'] = 4
hyp['logger']['type'] = 'wandb'
# hyp['resume']['resume_training'] = True
# hyp['resume']['ckpt'] = 'data/unet_last.ckpt'
train(hyp)