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- import argparse
- import os
- from collections import defaultdict
-
- import numpy as np
- import torch
- import torch.nn.functional as F
- from sklearn.metrics import roc_auc_score, confusion_matrix, precision_recall_fscore_support, accuracy_score
- from tensorboardX import SummaryWriter
- from torch.optim.lr_scheduler import StepLR, LambdaLR
- from torch.utils.data import DataLoader
- from tqdm import tqdm, trange
-
- from dataset import get_dataset, get_tf_valid_norot_transform, get_tf_train_transform
- from model import get_model
- from utils import IncrementalAverage, to_device, set_seeds, unique_string, count_parameters
-
-
- def evaluate(model, valid_loader, class_weights, device):
- model.eval()
-
- all_labels = []
- all_preds = []
- loss_avg = IncrementalAverage()
- for img, label in tqdm(valid_loader, leave=False):
- img, label = to_device(device, img, label)
- with torch.no_grad():
- pred = model(img).view(-1)
- loss = lossfn(pred, label.to(pred.dtype), class_weights)
- all_labels.append(label.cpu())
- all_preds.append(pred.cpu())
- loss_avg.update(loss.item())
-
- all_labels = torch.cat(all_labels).numpy()
- all_preds = torch.cat(all_preds).numpy()
- all_preds_binary = all_preds > 0
-
- cm = confusion_matrix(all_labels, all_preds_binary)
- auc = roc_auc_score(all_labels, all_preds)
- prec, rec, f1, _ = precision_recall_fscore_support(all_labels, all_preds_binary, average='weighted')
- return loss_avg.value, cm, auc, prec, rec, f1
-
-
- def train(model, opt, train_loader, class_weights, device):
- model.train()
- loss_avg = IncrementalAverage()
- for img, label in tqdm(train_loader, leave=False):
- img, label = to_device(device, img, label)
- pred = model(img)
- pred = pred.view(-1)
- loss = lossfn(pred, label.to(pred.dtype), class_weights)
- loss_avg.update(loss.item())
-
- opt.zero_grad()
- loss.backward()
- opt.step()
- return loss_avg.value
-
-
- def lossfn(prediction, target, class_weights):
- pos_weight = (class_weights[0] / class_weights[1]).expand(len(target))
- return F.binary_cross_entropy_with_logits(prediction, target, pos_weight=pos_weight)
-
-
- def schedule(epoch):
- if epoch < 2:
- ub = 1
- elif epoch < 4:
- ub = 0.1
- else:
- ub = 0.01
- return ub
-
-
- def train_validate(args):
- model = get_model().to(args.device)
- print("Model parameters:", count_parameters(model))
-
- trainset, validset, validset_subjects, class_weights = get_dataset(args.dataroot,
- tf_valid=get_tf_valid_norot_transform(args.res),
- tf_train=get_tf_train_transform(args.res))
- class_weights = class_weights.to(args.device)
- print(f"Trainset length: {len(trainset)}")
- print(f"Validset length: {len(validset)}")
- print(f"class_weights = {class_weights}")
-
- train_loader = DataLoader(trainset, batch_size=args.batch_size, num_workers=6, shuffle=True, drop_last=True)
- valid_loader = DataLoader(validset, batch_size=args.batch_size, num_workers=6, shuffle=False)
-
- opt = torch.optim.Adam([
- {'params': model.paramgroup01(), 'lr': 1e-6},
- {'params': model.paramgroup234(), 'lr': 1e-4},
- {'params': model.parameters_classifier(), 'lr': 1e-2},
- ])
- scheduler = LambdaLR(opt, lr_lambda=[lambda e: schedule(e),
- lambda e: schedule(e),
- lambda e: schedule(e)])
-
- summarywriter = SummaryWriter(args.out)
- recorded_data = defaultdict(list)
-
- def logged_eval(e):
- valid_loss, cm, auc, prec, rec, f1 = evaluate(model, valid_loader, class_weights, args.device)
-
- # Derive some accuracy metrics from confusion matrix
- tn, fp, fn, tp = cm.ravel()
- acc = (tp + tn) / cm.sum()
- acc_hem = tn / (tn + fp)
- acc_all = tp / (tp + fn)
-
- print(f"epoch={e} f1={f1:.4f}")
-
- summarywriter.add_scalar('loss/train', train_loss, e)
- summarywriter.add_scalar('loss/valid', valid_loss, e)
- summarywriter.add_scalar('cm/tn', tn, e)
- summarywriter.add_scalar('cm/fp', fp, e)
- summarywriter.add_scalar('cm/fn', fn, e)
- summarywriter.add_scalar('cm/tp', tp, e)
- summarywriter.add_scalar('metrics/precision', prec, e)
- summarywriter.add_scalar('metrics/recall', rec, e)
- summarywriter.add_scalar('metrics/f1', f1, e)
- summarywriter.add_scalar('metrics/auc', auc, e)
- summarywriter.add_scalar('acc/acc', acc, e)
- summarywriter.add_scalar('acc/hem', acc_hem, e)
- summarywriter.add_scalar('acc/all', acc_all, e)
-
- recorded_data['loss_train'].append(train_loss)
- recorded_data['loss_valid'].append(valid_loss)
- recorded_data['tn'].append(tn)
- recorded_data['tn'].append(tn)
- recorded_data['fp'].append(fp)
- recorded_data['fn'].append(fn)
- recorded_data['tp'].append(tp)
- recorded_data['precision'].append(prec)
- recorded_data['recall'].append(rec)
- recorded_data['f1'].append(f1)
- recorded_data['auc'].append(auc)
- recorded_data['acc'].append(acc)
- recorded_data['acc_hem'].append(acc_hem)
- recorded_data['acc_all'].append(acc_all)
- np.savez(f'{args.out}/results', **recorded_data)
-
- model = torch.nn.DataParallel(model)
- train_loss = np.nan
- logged_eval(0)
- for e in trange(args.epochs, desc='Epoch'):
- scheduler.step(e)
- train_loss = train(model, opt, train_loader, class_weights, args.device)
- logged_eval(e + 1)
-
- torch.save(model.state_dict(), f'{args.out}/model.pt')
- summarywriter.close()
-
- subj_acc = evaluate_subj_acc(model, validset, validset_subjects, args.device)
- np.savez(f'{args.out}/subj_acc', **subj_acc)
-
-
- def evaluate_subj_acc(model, dataset, subjects, device):
- model.eval()
-
- subj_pred = defaultdict(list)
- subj_label = defaultdict(list)
-
- dataloader = DataLoader(dataset, batch_size=1, num_workers=1, shuffle=False)
-
- for (img, cls), subj in tqdm(zip(dataloader, subjects), total=len(subjects), leave=False):
- img, cls = to_device(device, img, cls)
- bs, nrot, c, h, w = img.size()
- with torch.no_grad():
- cls_hat = model(img.view(-1, c, h, w))
- cls_hat = cls_hat.view(bs, nrot).mean(1)
- subj_label[subj].append(cls.cpu())
- subj_pred[subj].append(cls_hat.cpu())
-
- for k in subj_label:
- subj_label[k] = torch.cat(subj_label[k]).numpy()
- subj_pred[k] = torch.cat(subj_pred[k]).numpy() > 0
-
- subj_acc = {}
- for k in subj_label:
- subj_acc[k] = accuracy_score(subj_label[k], subj_pred[k])
-
- return subj_acc
-
-
- def parse_args():
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataroot', default='data', help='path to dataset')
- parser.add_argument('--batch-size', type=int, default=16)
- parser.add_argument('--epochs', type=int, default=6)
- parser.add_argument('--seed', default=1, type=int, help='random seed')
- parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
- parser.add_argument('--out', default='results', help='output folder')
- parser.add_argument('--res', type=int, default='450', help='Desired input resolution')
- args = parser.parse_args()
- args.out = os.path.join(args.out, unique_string())
- return args
-
-
- if __name__ == '__main__':
- args = parse_args()
- print(args)
-
- os.makedirs(args.out, exist_ok=True)
- set_seeds(args.seed)
- torch.backends.cudnn.benchmark = True
-
- train_validate(args)
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