init

README.md

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+# C-NMC Challenge
+
+This is the code release for the paper:
+
+Prellberg J., Kramer O. (2019) Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks. In: Gupta A., Gupta R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore
+
+## Usage
+
+Use the script `main_manual.py` to train the model on the dataset. The expected training data layout is described below.
+
+Use the script `submission.py` to apply the trained model to the test data.
+
+## Data Layout
+
+The training data during the challenge was released in multiple steps which is why the data layout is a little peculiar.
+
+```
+data/fold_0/all/*.bmp
+data/fold_0/hem/*.bmp
+data/fold_1/...
+data/fold_2/...
+data/phase2/*.bmp
+data/phase3/*.bmp
+data/phase2.csv
+```
+
+The `fold_0` to `fold_2` folders contain the training images with two subdirectories for the two classes each. The directories `phase2` and `phase3` are the preliminary test-set and test-set respectively and contain images numbered starting from `1.bmp`. The labels for the preliminary test-set are specified in `phase2.csv` which looks as follows:
+
+```
+Patient_ID,new_names,labels
+UID_57_29_1_all.bmp,1.bmp,1
+UID_57_22_2_all.bmp,2.bmp,1
+UID_57_31_3_all.bmp,3.bmp,1
+UID_H49_35_1_hem.bmp,4.bmp,0
+```

dataset.py

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+import re
+from collections import defaultdict
+from glob import glob
+from os.path import join
+
+import pandas as pd
+import torch
+import torchvision.transforms.functional as TF
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+STD_RES = 450
+STD_CENTER_CROP = 300
+
+def file_iter(dataroot):
+    for file in glob(join(dataroot, '*', '*', '*')):
+        yield file
+
+
+def file_match_iter(dataroot):
+    pattern = re.compile(r'(?P<file>.*(?P<fold>[a-zA-Z0-9_]+)/'
+                         r'(?P<class>hem|all)/'
+                         r'UID_(?P<subject>H?\d+)_(?P<image>\d+)_(?P<cell>\d+)_(all|hem).bmp)')
+    for file in file_iter(dataroot):
+        match = pattern.match(file)
+        if match is not None:
+            yield file, match
+
+
+def to_dataframe(dataroot):
+    data = defaultdict(list)
+    keys = ['file', 'fold', 'subject', 'class', 'image', 'cell']
+
+    # Load data from the three training folds
+    for file, match in file_match_iter(dataroot):
+        for key in keys:
+            data[key].append(match.group(key))
+
+    # Load data from the phase2 validation set
+    phase2 = pd.read_csv(join(dataroot, 'phase2.csv'), header=0, names=['file_id', 'file', 'class'])
+    pattern = re.compile(r'UID_(?P<subject>H?\d+)_(?P<image>\d+)_(?P<cell>\d+)_(all|hem).bmp')
+    for i, row in phase2.iterrows():
+        match = pattern.match(row['file_id'])
+        data['file'].append(join(dataroot, f'phase2/{i+1}.bmp'))
+        data['fold'].append('3')
+        data['subject'].append(match.group('subject'))
+        data['class'].append('hem' if row['class'] == 0 else 'all')
+        data['image'].append(match.group('image'))
+        data['cell'].append(match.group('cell'))
+
+    # Convert to dataframe
+    df = pd.DataFrame(data)
+    df = df.apply(pd.to_numeric, errors='ignore')
+    return df
+
+
+class ISBI2019(Dataset):
+    def __init__(self, df, transform=None):
+        super().__init__()
+        self.transform = transform
+        self.df = df
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, index):
+        # Convert tensors to int because pandas screws up otherwise
+        index = int(index)
+        file, cls = self.df.iloc[index][['file', 'class']]
+        img = Image.open(file)#.convert('RGB')
+        cls = 0 if cls == 'hem' else 1
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, cls
+
+
+def get_class_weights(df):
+    class_weights = torch.FloatTensor([
+        df.loc[df['class'] == 'hem']['file'].count() / len(df),
+        df.loc[df['class'] == 'all']['file'].count() / len(df),
+    ]).to(dtype=torch.float32)
+    return class_weights
+
+
+def tf_rotation_stack(x, num_rotations=8):
+    xs = []
+    for i in range(num_rotations):
+        angle = 360 * i / num_rotations
+        xrot = TF.rotate(x, angle)
+        xrot = TF.to_tensor(xrot)
+        xs.append(xrot)
+    xs = torch.stack(xs)
+    return xs
+
+
+def get_tf_train_transform(res):
+    size_factor = int(STD_RES/res)
+    center_crop = int(STD_CENTER_CROP/size_factor)
+    tf_train = transforms.Compose([
+        transforms.Resize(res),
+        #transforms.CenterCrop(center_crop),
+        transforms.RandomVerticalFlip(),
+        transforms.RandomHorizontalFlip(),
+        transforms.RandomAffine(degrees=360, translate=(0.2, 0.2)),
+        # transforms.Lambda(tf_rotation_stack),
+        transforms.ToTensor(),
+    ])
+    return tf_train
+
+
+def get_tf_vaild_rot_transform(res):
+    size_factor = int(STD_RES/res)
+    center_crop = int(STD_CENTER_CROP/size_factor)
+    tf_valid_rot = transforms.Compose([
+        transforms.Resize(res),
+        #transforms.CenterCrop(center_crop),
+        transforms.Lambda(tf_rotation_stack),
+    ])
+    return tf_valid_rot
+    
+
+def get_tf_valid_norot_transform(res):
+    size_factor = int(STD_RES/res)
+    center_crop = int(STD_CENTER_CROP/size_factor)
+    tf_valid_norot = transforms.Compose([
+        transforms.Resize(res),
+        #transforms.CenterCrop(center_crop),
+        transforms.ToTensor(),
+    ])
+    return tf_valid_norot
+
+
+def get_dataset(dataroot, folds_train=(0, 1, 2), folds_valid=(3,), tf_train=None, tf_valid=None):
+    if tf_train is None or tf_valid is None:
+        sys.exit("Tranformation is None")
+    df = to_dataframe(dataroot)
+    df_trainset = df.loc[df['fold'].isin(folds_train)]
+    trainset = ISBI2019(df_trainset, transform=tf_train)
+    class_weights = get_class_weights(df_trainset)
+
+    if folds_valid is not None:
+        df_validset = df.loc[df['fold'].isin(folds_valid)]
+        validset_subjects = df_validset['subject'].values
+        validset = ISBI2019(df_validset, transform=tf_valid)
+        return trainset, validset, validset_subjects, class_weights
+    else:
+        return trainset, class_weights
+
+
+if __name__ == '__main__':
+    import math
+    from tqdm import tqdm
+
+    df = to_dataframe('data')
+    print(df)
+    print("Examples by fold and class")
+    print(df.groupby(['fold', 'class'])['file'].count())
+
+    dataset = ISBI2019(df)
+    mean_height, mean_width = 0, 0
+    weird_files = []
+    bound_left, bound_upper, bound_right, bound_lower = math.inf, math.inf, 0, 0
+    for i, (img, label) in tqdm(enumerate(dataset), total=len(dataset)):
+        left, upper, right, lower = img.getbbox()
+        if left == 0 or upper == 0 or right == 450 or lower == 450:
+            weird_files.append(df.iloc[i]['file'])
+        height = lower - upper
+        width = right - left
+        mean_height = mean_height + (height - mean_height) / (i + 1)
+        mean_width = mean_width + (width - mean_width) / (i + 1)
+        bound_left = min(bound_left, left)
+        bound_upper = min(bound_upper, upper)
+        bound_right = max(bound_right, right)
+        bound_lower = max(bound_lower, lower)
+    print(f"mean_height = {mean_height:.2f}")
+    print(f"mean_width  =  {mean_width:.2f}")
+    print(f"bound_left  =  {bound_left:d}")
+    print(f"bound_upper =  {bound_upper:d}")
+    print(f"bound_right =  {bound_right:d}")
+    print(f"bound_lower =  {bound_lower:d}")
+    print("Files that max out at least one border:")
+    for f in weird_files:
+        print(f)

main_manual.py

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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_train_transform, get_tf_vaild_rot_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)
+        bs, nrot, c, h, w = img.size()
+        with torch.no_grad():
+            pred = model(img.view(-1, c, h, w))
+            pred = pred.view(bs, nrot).mean(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_train=get_tf_train_transform(args.res),
+                                                                       tf_valid=get_tf_vaild_rot_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)
+
+        return f1
+
+    model = torch.nn.DataParallel(model)
+    train_loss = np.nan
+    best_val_f1 = 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)
+        val_f1 = logged_eval(e + 1)
+
+        if val_f1 > best_val_f1:
+            print(f"New best model at {val_f1:.6f}")
+            torch.save(model.state_dict(), f'{args.out}/model.pt')
+            best_val_f1 = val_f1
+
+    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 train_test(args):
+    model = get_model().to(args.device)
+    print("Model parameters:", count_parameters(model))
+
+    trainset, class_weights = get_dataset(args.dataroot, folds_train=(0, 1, 2, 3),
+                                          folds_valid=None,
+                                          tf_train=get_tf_train_transform(args.res),
+                                          tf_valid=get_tf_vaild_rot_transform(args.res))
+    class_weights = class_weights.to(args.device)
+    print(f"Trainset length: {len(trainset)}")
+    print(f"class_weights = {class_weights}")
+
+    train_loader = DataLoader(trainset, batch_size=args.batch_size, num_workers=6, shuffle=True, drop_last=True)
+
+    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)])
+
+    model = torch.nn.DataParallel(model)
+    for e in trange(args.epochs, desc='Epoch'):
+        scheduler.step(e)
+        train(model, opt, train_loader, class_weights, args.device)
+    torch.save(model.state_dict(), f'{args.out}/model.pt')
+
+
+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)

main_manual_abl_layerlr.py

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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_train_transform, get_tf_vaild_rot_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)
+        bs, nrot, c, h, w = img.size()
+        with torch.no_grad():
+            pred = model(img.view(-1, c, h, w))
+            pred = pred.view(bs, nrot).mean(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_train=get_tf_train_transform(args.res),
+                                                                       tf_valid=get_tf_vaild_rot_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': args.lr},
+        {'params': model.paramgroup234(), 'lr': args.lr},
+        {'params': model.parameters_classifier(), 'lr': args.lr},
+    ])
+    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)
+
+    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('--lr', type=float, default=1e-4)
+    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)

main_manual_abl_testrot.py

@@ -0,0 +1,208 @@
+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)

model.py

@@ -0,0 +1,183 @@
+# Code adapted from: https://github.com/Cadene/pretrained-models.pytorch
+import math
+from collections import OrderedDict
+from itertools import chain
+
+import torch.nn as nn
+from torch.utils import model_zoo
+
+from utils import Flatten
+
+
+class SEModule(nn.Module):
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)
+        self.relu = nn.ReLU(inplace=True)
+        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class SEResNeXtBottleneck(nn.Module):
+    """
+    ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
+    """
+    expansion = 4
+
+    def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4):
+        super(SEResNeXtBottleneck, self).__init__()
+        width = math.floor(planes * (base_width / 64)) * groups
+        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.se_module = SEModule(planes * 4, reduction=reduction)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out = self.se_module(out) + residual
+        out = self.relu(out)
+
+        return out
+
+
+class SENet(nn.Module):
+    def __init__(self, block, layers, groups, reduction, inplanes=128,
+                 downsample_kernel_size=3, downsample_padding=1):
+        super(SENet, self).__init__()
+        self.inplanes = inplanes
+
+        layer0_modules = [
+            ('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
+            ('bn1', nn.BatchNorm2d(inplanes)),
+            ('relu1', nn.ReLU(inplace=True)),
+            # To preserve compatibility with Caffe weights `ceil_mode=True`
+            # is used instead of `padding=1`.
+            ('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
+        ]
+        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
+        self.layer1 = self._make_layer(
+            block,
+            planes=64,
+            blocks=layers[0],
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=1,
+            downsample_padding=0
+        )
+        self.layer2 = self._make_layer(
+            block,
+            planes=128,
+            blocks=layers[1],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.layer3 = self._make_layer(
+            block,
+            planes=256,
+            blocks=layers[2],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.layer4 = self._make_layer(
+            block,
+            planes=512,
+            blocks=layers[3],
+            stride=2,
+            groups=groups,
+            reduction=reduction,
+            downsample_kernel_size=downsample_kernel_size,
+            downsample_padding=downsample_padding
+        )
+        self.cls = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            Flatten(),
+            nn.Linear(512 * block.expansion, 1)
+        )
+
+    def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
+                    downsample_kernel_size=1, downsample_padding=0):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=downsample_kernel_size, stride=stride,
+                          padding=downsample_padding, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups, reduction))
+
+        return nn.Sequential(*layers)
+
+    def paramgroup01(self):
+        return chain(
+            self.layer0.parameters(),
+            self.layer1.parameters(),
+        )
+
+    def paramgroup234(self):
+        return chain(
+            self.layer2.parameters(),
+            self.layer3.parameters(),
+            self.layer4.parameters(),
+        )
+
+    def parameters_classifier(self):
+        return self.cls.parameters()
+
+    def forward(self, x):
+        x = self.layer0(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        c = self.cls(x)
+        return c
+
+
+def get_model():
+    model = SENet(SEResNeXtBottleneck, [3, 4, 6, 3], groups=32, reduction=16, inplanes=64,
+                  downsample_kernel_size=1, downsample_padding=0)
+    checkpoint = model_zoo.load_url('http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth')
+    model.load_state_dict(checkpoint, strict=False)
+    return model

plot.py

@@ -0,0 +1,166 @@
+from glob import glob
+
+import numpy as np
+import matplotlib.pyplot as plt
+from os.path import join
+
+from scipy.stats import mannwhitneyu
+
+dataroots = {
+        'PROPOSAL' : 'results',
+    #'model_cnmc_res_128' : 'results/model_cnmc_res_128',
+    #'model_cnmc_res_224' : 'results/model_cnmc_res_224',
+    #'model_cnmc_res_256' : 'results/model_cnmc_res_256',
+    #'model_cnmc_res_450' : 'results/model_cnmc_res_450',
+    #'model_cnmc_res_450_blue_only' : 'results/model_cnmc_res_450_blue_only',
+    #'model_cnmc_res_450_green_only' : 'results/model_cnmc_res_450_green_only',
+    #'model_cnmc_res_450_red_only' : 'results/model_cnmc_res_450_red_only',
+    #'model_cnmc_res_450_no_blue' : 'results/model_cnmc_res_450_no_blue',
+    #'model_cnmc_res_450_no_green' : 'results/model_cnmc_res_450_no_green',
+    #'model_cnmc_res_450_no_red' : 'results/model_cnmc_res_450_no_red',
+    #'model_cnmc_res_450_grayscale' : 'results/model_cnmc_res_450_grayscale',
+}
+
+
+
+
+def get_values(dataroot, key):
+    npzs = list(glob(join(dataroot, '*', 'results.npz')))
+    vals = []
+    for f in npzs:
+        recorded_data = np.load(f)
+        val = recorded_data[key]
+        vals.append(val)
+    vals = np.stack(vals, 0)
+    return vals
+
+
+def plot_mean_std(dataroot, key, ax, **kwargs):
+    vals = get_values(dataroot, key)
+    mean = np.mean(vals, 0)
+    std = np.std(vals, 0)
+    epochs = np.arange(len(mean))
+
+    # Offset by 1 so that we have nicely zoomed plots
+    mean = mean[1:]
+    std = std[1:]
+    epochs = epochs[1:]
+
+    ax.plot(epochs, mean, **kwargs)
+    ax.fill_between(epochs, mean - std, mean + std, alpha=0.2)
+
+
+def plot3(key, ax):
+    for k, v in dataroots.items():
+        plot_mean_std(v, key, ax, label=k)
+
+
+def print_final_min_mean_max(dataroot, key, model_epochs):
+    vals = get_values(dataroot, key) * 100
+    vals = vals[np.arange(len(vals)), model_epochs]
+    min = np.min(vals)
+    mean = np.mean(vals)
+    std = np.std(vals)
+    max = np.max(vals)
+    print(f'{min:.2f}', f'{mean:.2f} ± {std:.2f}', f'{max:.2f}', sep='\t')
+
+
+def print_final_table(dataroot):
+    best_model_epochs = np.argmax(get_values(dataroot, 'f1'), axis=1)
+
+    print_final_min_mean_max(dataroot, 'acc', best_model_epochs)
+    print_final_min_mean_max(dataroot, 'acc_all', best_model_epochs)
+    print_final_min_mean_max(dataroot, 'acc_hem', best_model_epochs)
+    print_final_min_mean_max(dataroot, 'f1', best_model_epochs)
+    print_final_min_mean_max(dataroot, 'precision', best_model_epochs)
+    print_final_min_mean_max(dataroot, 'recall', best_model_epochs)
+
+
+def get_best_f1_scores(dataroot):
+    f1_scores = get_values(dataroot, 'f1')
+    best_model_epochs = np.argmax(f1_scores, axis=1)
+    return f1_scores[np.arange(len(f1_scores)), best_model_epochs]
+
+
+def is_statistically_greater(dataroot1, dataroot2):
+    # Tests if F1-score of dataroot1 is greater than dataroot2
+    a = get_best_f1_scores(dataroot1)
+    b = get_best_f1_scores(dataroot2)
+    u, p = mannwhitneyu(a, b, alternative='greater')
+    return u, p
+
+
+######
+
+for k, v in dataroots.items():
+    print(k)
+    print_final_table(v)
+    print()
+
+
+######
+
+#print("MWU-Test of PROPOSAL > NOSPECLR")
+#print(is_statistically_greater(dataroots['PROPOSAL'], dataroots['NOSPECLR']))
+#print()
+#print("MWU-Test of PROPOSAL > NOROT")
+#print(is_statistically_greater(dataroots['PROPOSAL'], dataroots['NOROT']))
+
+######
+
+fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(9, 5))
+
+ax[0, 0].set_title('Accuracy')
+plot3('acc', ax[0, 0])
+
+ax[0, 1].set_title('Sensitivity')
+plot3('acc_all', ax[0, 1])
+
+ax[0, 2].set_title('Specificity')
+plot3('acc_hem', ax[0, 2])
+
+ax[1, 0].set_title('F1 score')
+plot3('f1', ax[1, 0])
+
+ax[1, 1].set_title('Precision')
+plot3('precision', ax[1, 1])
+
+ax[1, 2].set_title('Recall')
+plot3('recall', ax[1, 2])
+
+fig.legend(loc='lower center', ncol=3)
+fig.tight_layout()
+fig.subplots_adjust(bottom=0.12)
+fig.savefig('results/plot_ablations.pdf')
+
+######
+npload= 'results/model_cnmc_res_128'
+npload_sub=npload + '/subj_acc.npz'
+npload_res=npload + '/results.npz'
+subj_acc = np.load(npload_sub)
+subj = list(sorted(subj_acc.keys()))
+acc = [subj_acc[k] for k in subj]
+fig, ax = plt.subplots(figsize=(9, 2))
+ax.bar(range(len(acc)), acc, width=0.3, tick_label=subj)
+fig.tight_layout()
+fig.savefig('results/plot_subj_acc.pdf')
+
+######
+
+data = np.load(npload_res)
+loss_train = data['loss_train']
+loss_valid = data['loss_valid'][1:]
+f1_valid = data['f1'][1:]
+fig, ax = plt.subplots(ncols=3, figsize=(9, 2))
+ax[0].plot(range(len(loss_train)), loss_train)
+ax[0].set_title("Training set loss")
+ax[1].plot(range(1, len(loss_valid) + 1), loss_valid)
+ax[1].set_title("Preliminary test set loss")
+ax[2].plot(range(1, len(f1_valid) + 1), f1_valid)
+ax[2].set_title("Preliminary test set F1-score")
+fig.tight_layout()
+fig.savefig('results/plot_curves.pdf')
+
+######
+
+plt.show()

run.ipynb

@@ -0,0 +1,679 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd8aaf96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pandas tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26bd5e25",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b753e6b8",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:41,  1.43it/s]                                                         \n",
+      "Positive: 1234\n",
+      "Negative: 633\n",
+      "AUC: 0.8797024225483345\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/20220216T154306Z.AZHL\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3246460b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC\n",
+    "# res     : 32\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a953a39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC\n",
+    "# res     : 128\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12c15b33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC\n",
+    "# res     : 224\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 224"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08ba15b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC\n",
+    "# res     : 256\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cf25ec3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73b9d9d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_Grayscale\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_grayscale\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce16353c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_no_red\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_red\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "959ab837",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_no_green\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_green\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "879beb46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_no_blue\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_blue\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d545dce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_red_only\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_red_only\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25480226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_green_only\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_green_only\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a064d169",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC_blue_only\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_blue_only\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d53828a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN\n",
+    "# dataset : CNMC\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "ea9c2f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROPOSAL\n",
+      "68.51\t83.57 ± 5.16\t89.61\n",
+      "84.33\t89.06 ± 2.09\t92.95\n",
+      "38.73\t73.26 ± 11.77\t84.72\n",
+      "66.76\t83.35 ± 5.61\t89.57\n",
+      "66.81\t83.36 ± 5.60\t89.55\n",
+      "68.51\t83.57 ± 5.16\t89.61\n",
+      "\n",
+      "Figure(900x500)\n",
+      "Figure(900x200)\n",
+      "Figure(900x200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# PLOT\n",
+    "# dataset : CNMC\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 plot.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c92073d",
+   "metadata": {},
+   "source": [
+    "# EVALUATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "b25a4267",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:05, 11.69it/s]                                                         \n",
+      "Positive: 1425\n",
+      "Negative: 442\n",
+      "AUC: 0.6153299354864846\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION \n",
+    "# dataset : CNMC\n",
+    "# res     : 32\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_32\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "b14e3e67",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:09,  6.24it/s]                                                         \n",
+      "Positive: 1315\n",
+      "Negative: 552\n",
+      "AUC: 0.7711131113339208\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC\n",
+    "# res     : 128\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_128\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "dfb25744",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:14,  4.19it/s]                                                         \n",
+      "Positive: 1262\n",
+      "Negative: 605\n",
+      "AUC: 0.8143717274835677\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC\n",
+    "# res     : 224\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_224\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 224"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "68600db4",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:41,  1.44it/s]                                                         \n",
+      "Positive: 1195\n",
+      "Negative: 672\n",
+      "AUC: 0.8400701597139936\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC\n",
+    "# res     : 256\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_256\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "71a5547e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:41,  1.42it/s]                                                         \n",
+      "Positive: 1241\n",
+      "Negative: 626\n",
+      "AUC: 0.8813918512441892\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "58450362",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:41,  1.42it/s]                                                         \n",
+      "Positive: 1261\n",
+      "Negative: 606\n",
+      "AUC: 0.8045073375262055\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_Grayscale\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_grayscale\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_grayscale/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "48c40f18",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:44,  1.33it/s]                                                         \n",
+      "Positive: 1178\n",
+      "Negative: 689\n",
+      "AUC: 0.8661869929814967\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_no_red\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_no_red\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_red/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "b6ad9232",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:52,  1.12it/s]                                                         \n",
+      "Positive: 1266\n",
+      "Negative: 601\n",
+      "AUC: 0.8018310900454735\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_no_green\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_no_green\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_green/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "1ba76d51",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:48,  1.23it/s]                                                         \n",
+      "Positive: 1248\n",
+      "Negative: 619\n",
+      "AUC: 0.8570821813062721\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_no_blue\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_no_blue\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_blue/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "05cfaf9c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:52,  1.12it/s]                                                         \n",
+      "Positive: 1239\n",
+      "Negative: 628\n",
+      "AUC: 0.8013924335875389\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_red_only\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_red_only\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_red_only/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "1ad09456",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:52,  1.13it/s]                                                         \n",
+      "Positive: 1221\n",
+      "Negative: 646\n",
+      "AUC: 0.8590070792695896\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_green_only\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_green_only\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_green_only/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "41e8d3a0",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [00:52,  1.12it/s]                                                         \n",
+      "Positive: 1255\n",
+      "Negative: 612\n",
+      "AUC: 0.8268636253152251\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC_blue_only\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_blue_only\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_blue_only/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "88bc18db",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model\n",
+      "Classifying\n",
+      "59it [01:24,  1.43s/it]                                                         \n",
+      "Positive: 1235\n",
+      "Negative: 632\n",
+      "AUC: 0.8588406050294211\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EVALUATION\n",
+    "# dataset : CNMC-blackborder\n",
+    "# res     : 450\n",
+    "# epochs  : 100\n",
+    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_w_blackborder\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 450"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec31125a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

submission.py

@@ -0,0 +1,92 @@
+import argparse
+import os
+import zipfile
+from os.path import join
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
+import numpy as np
+
+from model import get_model
+from dataset import get_tf_vaild_rot_transform
+
+from sklearn import metrics
+import matplotlib.pyplot as plt
+import csv
+from sklearn.metrics import roc_curve, roc_auc_score
+
+class OrderedImages(Dataset):
+    def __init__(self, root, transform):
+        super().__init__()
+        self.root = root
+        self.transform = transform
+
+    def __len__(self):
+        return 1867
+
+    def __getitem__(self, index):
+        img = Image.open(os.path.join(self.root, f'{index + 1}.bmp'))#.convert('RGB')
+        return self.transform(img)
+
+VALIDATION_ALL = 1219
+VALIDATION_HEM = 648
+    
+parser = argparse.ArgumentParser()
+parser.add_argument('--batch-size', type=int, default=64)
+parser.add_argument('--modelroot', default='results/20190313T101236Z.LGJL', help='path to model')
+parser.add_argument('--dataroot', default='data/phase3', help='path to dataset')
+parser.add_argument('--res', type=int, default='450', help='Desired input resolution')
+args = parser.parse_args()
+
+dataset = OrderedImages(args.dataroot, get_tf_vaild_rot_transform(args.res))
+
+print(f"Loading model")
+model = get_model().to('cuda:0')
+model = torch.nn.DataParallel(model)
+model.load_state_dict(torch.load(join(args.modelroot, 'model.pt')))
+model.eval()
+
+dataloader = DataLoader(dataset, batch_size=args.batch_size, num_workers=6)
+
+print("Classifying")
+all_labels = []
+for x in tqdm(dataloader, total=len(dataset) // args.batch_size):
+    x = x.to('cuda:0')
+    bs, nrot, c, h, w = x.size()
+    with torch.no_grad():
+        y = model(x.view(-1, c, h, w))
+        y = y.view(bs, nrot).mean(1)
+    labels = y > 0
+    all_labels.append(labels)
+
+all_labels = torch.cat(all_labels)
+print("Positive:", all_labels.sum().item())
+print("Negative:", len(all_labels) - all_labels.sum().item())
+
+file_w = open(r'/home/feoktistovar67431/data/resources/phase2_labels.csv', "r")
+true_labels = []
+reader = csv.reader(file_w, delimiter=',')
+for row in reader:
+    true_labels.append(row)
+
+print(f'AUC: {roc_auc_score(true_labels, all_labels.cpu())}') # Zeige Flaeche unter der Kurve an
+
+
+#print("Accuracy", metrics.accuracy_score(y_test, y_pred))
+#import matplotlib.pyplot as plt
+#import numpy as np
+#x = # false_positive_rate
+#y = # true_positive_rate 
+# This is the ROC curve
+#plt.plot(x,y)
+#plt.show() 
+# This is the AUC
+#auc = np.trapz(y,x)
+
+csv_path = join(args.modelroot, 'submission.csv')
+zip_path = join(args.modelroot, 'submission.zip')
+np.savetxt(csv_path, all_labels.cpu().numpy(), '%d')
+with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+    zipf.write(csv_path, 'isbi_valid.predict')

utils.py

@@ -0,0 +1,58 @@
+import pickle
+import random
+import string
+from datetime import datetime
+
+import torch
+import torch.nn as nn
+
+
+class IncrementalAverage:
+    def __init__(self):
+        self.value = 0
+        self.counter = 0
+
+    def update(self, x):
+        self.counter += 1
+        self.value += (x - self.value) / self.counter
+
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+
+class SizePrinter(nn.Module):
+    def forward(self, x):
+        print(x.size())
+        return x
+
+
+def count_parameters(model, grad_only=True):
+    return sum(p.numel() for p in model.parameters() if not grad_only or p.requires_grad)
+
+
+def to_device(device, *tensors):
+    return tuple(x.to(device) for x in tensors)
+
+
+def loop_iter(iter):
+    while True:
+        for item in iter:
+            yield item
+
+
+def unique_string():
+    return '{}.{}'.format(datetime.now().strftime('%Y%m%dT%H%M%SZ'),
+                          ''.join(random.choice(string.ascii_uppercase) for _ in range(4)))
+
+
+def set_seeds(seed):
+    random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def pickle_dump(obj, file):
+    with open(file, 'wb') as f:
+        pickle.dump(obj, f)