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 | import os
 import re
 
 from sklearn.model_selection import train_test_split
 
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
 from torchvision.models.segmentation import fcn_resnet50
 from torchvision.models.segmentation import FCN_ResNet50_Weights
 
 from tqdm import tqdm
 
 import torch
 
 import random
 import numpy as np
 
 import hashlib
 
 import dataset
 
 from sklearn.metrics import confusion_matrix
 
 random_state = 42
 random.seed(random_state); np.random.seed(random_state); torch.manual_seed(random_state)
 if torch.cuda.is_available():
 torch.cuda.manual_seed_all(random_state)
 
 batch_size = 8
 
 if __name__ == '__main__':
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 print(f'device:{device}')
 
 img_dir = 'FracAtlas/images/Fractured'
 mask_dir = 'FracAtlas/masks'
 
 img_mask_names = sorted(os.listdir(img_dir))
 
 X_train_val, X_test, y_train_val, y_test = train_test_split(img_mask_names, img_mask_names, test_size=0.2, random_state=random_state)
 X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=random_state)
 
 train_dataset = dataset.FracAtlasDataset(img_dir, mask_dir, X_train, dataset.get_transform(train=True))
 val_dataset = dataset.FracAtlasDataset(img_dir, mask_dir, X_val, dataset.get_transform(train=False))
 test_dataset = dataset.FracAtlasDataset(img_dir, mask_dir, X_test, dataset.get_transform(train=False))
 
 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True, persistent_workers=True, collate_fn=train_dataset.collate_fn)
 val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True, persistent_workers=True, collate_fn=val_dataset.collate_fn)
 test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True, persistent_workers=True, collate_fn=test_dataset.collate_fn)
 
 train_dataset_hash=hashlib.md5(''.join(X_train).encode('utf-8')).hexdigest()
 val_dataset_hash=hashlib.md5(''.join(X_val).encode('utf-8')).hexdigest()
 test_dataset_hash=hashlib.md5(''.join(X_test).encode('utf-8')).hexdigest()
 print(f'train_dataset_hash:{train_dataset_hash},val_dataset_hash:{val_dataset_hash},test_dataset_hash:{test_dataset_hash}')
 
 model = fcn_resnet50(weights=FCN_ResNet50_Weights.DEFAULT, aux_loss=True)
 
 model.classifier[4] = nn.Conv2d(512, 2, kernel_size=1, stride=1)
 model.aux_classifier[4] = nn.Conv2d(256, 2, kernel_size=1, stride=1)
 
 save_epoch=10
 
 
 saved_models = os.listdir(f'.{os.sep}models{os.sep}')
 saved_models = [model for model in saved_models if model.startswith(f'model_{train_dataset_hash}_on_{val_dataset_hash}_epoch')]
 saved_models_epochs = [int(model.split('_')[-1].split('.')[0].replace('epoch', '')) for model in saved_models]
 saved_models = zip(saved_models_epochs, saved_models)
 saved_models = sorted(saved_models, key=lambda x: x[0], reverse=True)
 
 if len(saved_models) > 0:
 latest_model_path = f'.{os.sep}models{os.sep}{saved_models[0][1]}'
 model.load_state_dict(torch.load(latest_model_path,map_location=device))
 load_epoch=saved_models[0][0]
 print(f'Loaded latest model from {latest_model_path}')
 else:
 load_epoch=0
 print('No saved models found')
 
 
 
 try:
 with open(f'.{os.sep}models{os.sep}log_{train_dataset_hash}_on_{val_dataset_hash}.log','r') as f:
 log_lines = f.readlines()
 except FileNotFoundError:
 log_lines = []
 
 train_record_reg = re.compile(r'epoch:(\d+),train_loss:(\d+\.\d+),val_loss:(\d+\.\d+),val_acc:(\d+\.\d+)%,lr:(.+)')
 
 
 lr_last = 1e-5
 for i in range(len(log_lines)-1, -1, -1):
 matched_record = re.search(train_record_reg, log_lines[i])
 if matched_record:
 if int(matched_record.group(1)) == load_epoch:
 lr_last = float(matched_record.group(5))
 print(f'Using last lr: {lr_last}')
 break
 
 
 best_epoch = 0
 best_val_loss_avg_last=float('inf')
 for i in range(len(log_lines)-1, -1, -1):
 
 if 'saved best model' in log_lines[i]:
 
 for j in range(i, -1, -1):
 matched_record = re.search(train_record_reg, log_lines[j])
 if matched_record:
 if int(matched_record.group(1)) > load_epoch:
 break
 else:
 best_epoch = int(matched_record.group(1))
 best_val_loss_avg_last = float(matched_record.group(3))
 print(f'Using last best val loss avg: {best_val_loss_avg_last}')
 break
 
 if best_val_loss_avg_last < float('inf'):
 break
 
 model.to(device)
 
 def criterion(outputs, targets, train: bool = True):
 loss_main = nn.functional.cross_entropy(outputs['out'], targets)
 
 if not train:
 return loss_main
 
 return loss_main + 0.5 * nn.functional.cross_entropy(outputs['aux'], targets)
 
 optimizer = optim.Adam(model.parameters(), lr=lr_last, weight_decay=1e-4)
 
 scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.5, patience=10, min_lr=1e-7)
 
 num_epochs_train = 1000
 
 print(f"Training for {num_epochs_train} epochs")
 
 best_val_loss_avg = best_val_loss_avg_last
 
 early_stopping_patience = 50
 patience_counter = max(0,load_epoch - best_epoch)
 if patience_counter > 0:
 print(f'Using last patience counter: {patience_counter}')
 
 for epoch in range(load_epoch + 1, num_epochs_train + load_epoch + 1):
 model.train()
 running_loss = 0.0
 for inputs, targets in tqdm(train_loader, desc=f'Epoch {epoch}/{num_epochs_train + load_epoch}'):
 inputs, targets = inputs.to(device), targets.to(device)
 
 targets = targets.squeeze(1).long()
 
 optimizer.zero_grad()
 outputs = model(inputs)
 loss = criterion(outputs, targets, train=True)
 loss.backward()
 optimizer.step()
 running_loss += loss.item()
 
 model.eval()
 val_loss = 0.0
 tn, fp, fn, tp = 0, 0, 0, 0
 
 with torch.no_grad():
 for inputs, targets in tqdm(val_loader, desc=f"Epoch {epoch}/{num_epochs_train + load_epoch}"):
 inputs, targets = inputs.to(device), targets.to(device)
 
 targets = targets.squeeze(1).long()
 
 outputs = model(inputs)
 val_loss += criterion(outputs, targets, train=False).item()
 
 predicts = outputs['out'].argmax(dim=1)
 
 predicts_flat = predicts.flatten().cpu().numpy()
 targets_flat = targets.flatten().cpu().numpy()
 
 tn_batch, fp_batch, fn_batch, tp_batch = confusion_matrix(targets_flat, predicts_flat).ravel()
 tn += tn_batch
 fp += fp_batch
 fn += fn_batch
 tp += tp_batch
 
 val_loss_avg = val_loss/len(val_loader)
 
 scheduler.step(val_loss_avg)
 
 
 print(f"Epoch {epoch}/{num_epochs_train + load_epoch}, Loss: {running_loss/len(train_loader):.3f}, Val Loss: {val_loss_avg:.3f}, Val Accuracy: {100*(tp+tn)/(tp+fp+fn+tn):.3f}%, lr: {optimizer.param_groups[0]['lr']}")
 print(f"Epoch {epoch}/{num_epochs_train + load_epoch}, Sensitivity(Recall): {tp/(tp+fn):.3f}, Specificity: {tn/(tn+fp):.3f}, Precision: {tp/(tp+fp):.3f}")
 with open(f'.{os.sep}models{os.sep}log_{train_dataset_hash}_on_{val_dataset_hash}.log','a') as f:
 f.write(f"epoch:{epoch},train_loss:{running_loss/len(train_loader)},val_loss:{val_loss_avg},val_acc:{100*(tp+tn)/(tp+fp+fn+tn)}%,lr:{optimizer.param_groups[0]['lr']}\n")
 f.write(f"epoch:{epoch},sensitivity(Recall):{tp/(tp+fn)},specificity:{tn/(tn+fp)},precision:{tp/(tp+fp)}\n")
 
 if epoch % save_epoch == 0:
 torch.save(model.state_dict(), f'.{os.sep}models{os.sep}model_{train_dataset_hash}_on_{val_dataset_hash}_epoch{epoch}.pth')
 
 if val_loss_avg < best_val_loss_avg:
 best_val_loss_avg = val_loss_avg
 torch.save(model.state_dict(), f'.{os.sep}models{os.sep}best_model_{train_dataset_hash}_on_{val_dataset_hash}.pth')
 print(f'epoch:{epoch},saved best model')
 with open(f'.{os.sep}models{os.sep}log_{train_dataset_hash}_on_{val_dataset_hash}.log','a') as f:
 f.write(f'epoch:{epoch},saved best model\n')
 
 patience_counter=0
 else:
 patience_counter+=1
 
 if patience_counter >= early_stopping_patience:
 print(f'Early stopping triggered at epoch {epoch}')
 with open(f'.{os.sep}models{os.sep}log_{train_dataset_hash}_on_{val_dataset_hash}.log','a') as f:
 f.write(f'epoch:{epoch},early stopping\n')
 break
 
 |