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import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from torchmetrics.functional import dice, jaccard_index, accuracy
from segmentation_models_pytorch.losses import DiceLoss, TverskyLoss, FocalLoss
from src.medicalDataLoader import MedicalImageDataset
from src.utils import getTargetSegmentation, plot_img
from UNET_perso import UNET
## Parameters & Hyperparameters ##
EPOCHS = 2
BATCH_SIZE_TRAIN = 8
BATCH_SIZE_VAL = 8
LR = 1e-3
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.cuda.empty_cache()
## Model ##
model = UNET(in_channels=1, out_channels=4).to(DEVICE)
## Loss ##
lossCE = nn.CrossEntropyLoss().to(DEVICE)
lossDice = DiceLoss(mode='multiclass').to(DEVICE)
## optimizer ##
#optimizer = torch.optim.Adam(model.parameters(), lr=LR)
optimizer = torch.optim.NAdam(model.parameters(), lr=LR)
transform = transforms.Compose([transforms.ToTensor()])
ROOT_DIR = './Data'
train_set = MedicalImageDataset('train',
ROOT_DIR,
transform=transform,
mask_transform=transform,
augment=True,
equalize=False)
train_loader = DataLoader(train_set,
batch_size=BATCH_SIZE_TRAIN,
shuffle=True)
val_set = MedicalImageDataset('val',
ROOT_DIR,
transform=transform,
mask_transform=transform,
equalize=False)
val_loader = DataLoader(val_set,
batch_size=BATCH_SIZE_VAL,
shuffle=False)
test_set = MedicalImageDataset('test',
ROOT_DIR,
transform=transform,
mask_transform=transform,
equalize=False)
test_loader = DataLoader(test_set,
batch_size=BATCH_SIZE_VAL,
shuffle=False)
def train(dataLoader, model, optimizer, epoch, loss_fn1, loss_fn2=None):
print(f'~~~ train for epoch {epoch} ~~~')
model.train()
loop = tqdm(dataLoader)
train_loss = 0
for i, (img, labels, name) in enumerate(loop):
#if torch.cuda.is_available():
labels = getTargetSegmentation(labels)
img, labels = img.to(DEVICE), labels.to(DEVICE)
yPred = model(img)
if loss_fn2!=None:
loss = 0.5*loss_fn1(yPred, labels) + 0.5*loss_fn2(yPred, labels)
else : loss = loss_fn1(yPred, labels)
train_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
loop.set_postfix(loss=loss.item()/len(dataLoader))
print('total train loss : {:.4f}\n'.format(train_loss/len(dataLoader.dataset)))
return model, train_loss/len(dataLoader.dataset)
def test(dataLoader, model, loss_fn, epoch):
print(f'~~~ validation for epoch {epoch} ~~~')
model.eval()
size = len(dataLoader)
loop = tqdm(dataLoader)
test_loss = 0
Acc = 0
Dsc1, Dsc2, Dsc3 = 0, 0, 0
IOU1, IOU2, IOU3 = 0, 0, 0
for i, (img, labels, name) in enumerate(loop):
#if torch.cuda.is_available():
labels = getTargetSegmentation(labels)
img, labels = img.to(DEVICE), labels.to(DEVICE)
yPred = model(img)
loss = loss_fn(yPred, labels)
test_loss += loss.item()
loop.set_postfix(loss=loss.item()/len(dataLoader))
Dsc = dice(yPred, labels, average='none', num_classes=4).cpu()
IOU = jaccard_index(yPred, labels, task='multiclass', average='none', num_classes=4).cpu()
Dsc1 += Dsc[1]
Dsc2 += Dsc[2]
Dsc3 += Dsc[3]
IOU1 += IOU[1]
IOU2 += IOU[2]
IOU3 += IOU[3]
print('total test loss : {:.4f}\nDice score 1 : {:.4f} | Dice score 2 : {:.4f} | Dice score 3 : {:.4f}\nIOU 1 : {:.4f} | IOU 2 : {:.4f} | IOU 3 : {:.4f}\n'.format(test_loss/size, Dsc1/size, Dsc2/size, Dsc3/size, IOU1/size, IOU2/size, IOU3/size))
return test_loss/size, Dsc1/size, Dsc2/size, Dsc3/size, IOU1/size, IOU2/size, IOU3/size
def main(train_loader, test_loader, model, optimizer, loss1, loss2):
train_loss_lst, test_loss_lst = [], []
Dsc1_lst, Dsc2_lst, Dsc3_lst = [], [], []
IOU1_lst, IOU2_lst, IOU3_lst = [], [], []
for i in range(EPOCHS):
model, train_loss = train(train_loader, model, optimizer, i+1, loss_fn1=loss1, loss_fn2=loss2)
test_loss, Dsc1, Dsc2, Dsc3, IOU1, IOU2, IOU3 = test(test_loader, model, loss1, i+1)
train_loss_lst.append(train_loss)
test_loss_lst.append(test_loss)
Dsc1_lst.append(Dsc1)
Dsc2_lst.append(Dsc2)
Dsc3_lst.append(Dsc3)
IOU1_lst.append(IOU1)
IOU2_lst.append(IOU2)
IOU3_lst.append(IOU3)
return model
if __name__=='__main__':
mdoel = main(train_loader, test_loader, model, optimizer, loss1=lossCE, loss2=lossDice)
plot_img(test_loader, 8, model) |