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import sys
import os, argparse, time, tqdm, random, cv2
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch import nn
from dataset import CustomDataset, postprocess_image, tensor_to_mat
from network import STRNet
from losses import TSDLoss, TRGLoss
random_seed = 123
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(random_seed)
random.seed(random_seed)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_path", default='dataset', help="data root path")
parser.add_argument("-e", "--num_epochs", default=100, type=int, help="num epochs")
parser.add_argument("-b", "--batch_size", default=16, type=int, help="batch size > 1")
parser.add_argument("-n", "--num_workers", default=8, type=int, help="num_workers for DataLoader")
parser.add_argument("-sn", "--show_num", default=4, type=int, help="show result images during training num")
args = parser.parse_args()
return args
def load_weights_from_directory(model, weight_path) -> int:
if weight_path.endswith('.pth'):
wp = weight_path
else:
wps = sorted(os.listdir(weight_path), key=lambda x: int(x.split('_')[0]))
if wps:
wp = wps[-1]
else:
return 0
print(f"Loading weights from {wp}...")
model.load_state_dict(torch.load(os.path.join(weight_path, wp)))
return int(wp.split('_')[0])
if __name__ == "__main__":
args = get_args()
### Path
model_path = "results"
weight_path = os.path.join(model_path, "weights")
show_path = os.path.join(model_path, "show")
os.makedirs(model_path, exist_ok=True)
os.makedirs(weight_path, exist_ok=True)
os.makedirs(show_path, exist_ok=True)
### Hyperparameters
epochs = args.num_epochs
batch_size = args.batch_size
if batch_size <= 1:
raise "Batch size should bigger than 1 for batch normalization"
num_workers = args.num_workers
show_num = args.show_num
### DataLoader
dataloader_params = {'batch_size': batch_size,
'shuffle': True,
'drop_last': True,
'num_workers': num_workers}
train_data = CustomDataset(args.data_path, set_name="train")
train_gen = DataLoader(train_data, **dataloader_params)
dataloader_params = {'batch_size': 1,
'shuffle': True,
'drop_last': False,
'num_workers': num_workers}
val_data = CustomDataset(args.data_path, set_name="val")
val_gen = DataLoader(val_data, **dataloader_params)
steps_per_epoch = len(train_gen)
### Model
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device}...")
model = STRNet().to(device)
# load best weight
initial_epoch = load_weights_from_directory(model, weight_path) + 1
print(f"Training start from epoch {initial_epoch}")
# Train Setting
model_optim = Adam(model.parameters(), 0.0001, (0.5, 0.9))
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discrim_optim = Adam(model.discrim.parameters(), 0.0004)
### Train
for epoch in range(initial_epoch, epochs):
## training
train_loss = []
train_discrim_loss = []
model.train()
pgbar = tqdm.tqdm(train_gen, total=len(train_gen))
pgbar.set_description(f"Epoch {epoch}/{epochs}")
for I, Itegt, Mm, Msgt in pgbar:
I, Itegt, Mm, Msgt = I.to(device), Itegt.to(device), Mm.to(device), Msgt.to(device)
# train model
Ms, Ite, Ms_, Ite_ = model.forward(I, Mm)
Ltsd = TSDLoss(Msgt, Ms, Ms_)
Ltrg = TRGLoss(Mm, Ms, Ms_, Itegt, Ite, Ite_)
Lgsn = -torch.mean(model.discrim(Mm, Ite_))
total_loss = Ltsd + Ltrg + Lgsn
model_optim.zero_grad()
total_loss.backward()
model_optim.step()
# train discriminator
Ms, Ite, Ms_, Ite_ = model.forward(I, Mm)
Ldsn = torch.mean(F.relu(1-model.discrim(Mm, Itegt))) + \
torch.mean(F.relu(1+model.discrim(Mm, Ite_)))
discrim_optim.zero_grad()
Ldsn.backward()
discrim_optim.step()
ltsd = Ltsd.detach().cpu().item()
ltrg = Ltrg.detach().cpu().item()
lgsn = Lgsn.detach().cpu().item()
train_loss.append(total_loss.detach().cpu().item())
train_discrim_loss.append(Ldsn.detach().cpu().item())
pgbar.set_postfix_str(f"total loss : {train_loss[-1]:.6f} ltsd : {ltsd:.6f} ltrg : {ltrg:.6f} lgsn : {lgsn:.6f} d_loss : {train_discrim_loss[-1]:.6f}")
train_loss = sum(train_loss)/len(train_loss)
## validation
val_loss = []
# will saved in show directory
result_images = []
model.eval()
pgbar = tqdm.tqdm(val_gen, total=len(val_gen))
pgbar.set_description("Validating...")
for I, Itegt, Mm, Msgt in pgbar:
I, Itegt, Mm, Msgt = I.to(device), Itegt.to(device), Mm.to(device), Msgt.to(device)
# train model
Ms, Ite, Ms_, Ite_ = model.forward(I, Mm)
Ltsd = TSDLoss(Msgt, Ms, Ms_)
Ltrg = TRGLoss(Mm, Ms, Ms_, Itegt, Ite, Ite_)
Lgsn = -torch.mean(model.discrim(Mm, Ite_))
total_loss = Ltsd + Ltrg + Lgsn
val_loss.append(total_loss.detach().cpu().item())
pgbar.set_postfix_str(f"loss : {sum(val_loss[-10:]) / len(val_loss[-10:]):.6f}")
if len(result_images) < args.show_num:
result_images.append([I.cpu(), Itegt.cpu(), Ite_.cpu(), Msgt.cpu(), Ms_.cpu()])
else:
break
val_loss = sum(val_loss) / len(val_loss)
## visualize
fig, axs = plt.subplots(args.show_num, 1, figsize=(5, 2*args.show_num))
fig.suptitle("Image, Gt, Gen, Stroke Gt, Stroke")
for i, (I, Itegt, Ite_, Msgt, Ms_) in enumerate(result_images):
I = postprocess_image(tensor_to_mat(I))[0]
Itegt = postprocess_image(tensor_to_mat(Itegt))[0]
Ite_ = postprocess_image(tensor_to_mat(Ite_))[0]
Msgt = postprocess_image(tensor_to_mat(Msgt))[0]
Ms_ = postprocess_image(tensor_to_mat(Ms_))[0]
Msgt = cv2.cvtColor(Msgt, cv2.COLOR_GRAY2BGR)
Ms_ = cv2.cvtColor(Ms_, cv2.COLOR_GRAY2BGR)
axs[i].imshow(np.hstack([I, Itegt, Ite_, Msgt, Ms_]))
axs[i].set_xticks([])
axs[i].set_yticks([])
fig.savefig(os.path.join(model_path, "show", f"epoch_{epoch}.png"))
plt.close()
print(f"train_loss : {train_loss}, val_loss : {val_loss}")
print()
time.sleep(0.2)
torch.save(model.state_dict(), os.path.join(weight_path, f"{epoch}_train_{train_loss}_val_{val_loss}.pth"))