isbi2019cancer-master/submission.py

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')