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test_two_stream.py
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test_two_stream.py
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import os
import time
import datetime
import argparse
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import f1_score, roc_auc_score, recall_score, precision_score, accuracy_score
from torchvision import transforms
from tqdm import tqdm
from data.base_dataset import Preproc, Rescale, ToTensor, Resize
from data.csv_dataset import TwoStreamDataset
from utils.utils import calc_kappa
from utils.draw import draw_roc, draw_ConfusionMatrix
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
cols = ['Neovascular AMD', 'PCV', 'Others']
classCount = len(cols)
data_dir = 'AMD_processed/'
list_dir = 'AMD_processed/label/new_two_stream/'
mean = {
224 : [0.485, 0.456, 0.406],
299 : [0.5, 0.5, 0.5]
}
std = {
224 : [0.229, 0.224, 0.225],
299 : [0.5, 0.5, 0.5]
}
def get_parser():
parser = argparse.ArgumentParser(description='Input hyperparameter of model:')
parser.add_argument('--root_path', type=str, default='/home/hejiawen/datasets',
help='The root path of dataset')
parser.add_argument('--fundus_model', type=str, default='resnet50',
choices=['resnet18', 'resnet34', 'resnet50', 'resnest50', 'scnet50', 'inceptionv3', 'vgg16', 'vgg19'],
help='The backbone model for Color fundus image')
parser.add_argument('--oct_model', type=str, default='resnet50',
choices=['resnet18', 'resnet34', 'resnet50', 'resnest50', 'scnet50', 'inceptionv3', 'vgg16', 'vgg19'],
help='The backbone model for OCT image')
parser.add_argument('--two_stream_path', type=str, help='the model file path of two stream model', required=True)
parser.add_argument('--fundus_size', type=int, default=224, help='The input size for Color fundus image')
parser.add_argument('--oct_size', type=int, default=224, help='The input size for OCT image')
parser.add_argument('--epoch', type=int, default=500, help = 'The number of training epoch')
parser.add_argument('--batch_size', type=int, default=64, help='The size of batch')
parser.add_argument('--workers', type=int, default=1, help='The number of sub-processes to use for data loading')
parser.add_argument('--average', type=str, default='weighted',
choices=['micro', 'macro', 'weighted', 'samples'],
help='the type of averaging performed on the data')
parser.add_argument('--momentum', type=float, default=0.9, help='The momentum in optimizer')
parser.add_argument('--weight_decay', type=float, default=0.001, help='The weight_decay in optimizer')
parser.add_argument('--learning_rate', type=float, default=0.001, help='The learning_rate in optimizer')
parser.add_argument('--loss', type=str, default='bceloss', help='The loss function')
parser.add_argument('--use_gpu', type=int, default=0, choices=[0,1,2,3], help='The GPU on server used', required=True)
args = parser.parse_args()
return args
def pred2int(x):
out = []
for i in range(len(x)):
# print(x[i])
out.append([1 if y > 0.5 else 0 for y in x[i]])
return np.array(out)
def test(model, test_loader, criterion):
model.eval()
y_pred = []
y_true = []
tbar = tqdm(test_loader, desc='\r', ncols=100) # 进度条
loss_test = 0
loss_test_norm = 0
with torch.no_grad():
for batch_idx, (fundus, OCT, target) in enumerate(tbar):
fundus, OCT, target = fundus.cuda(), OCT.cuda(), target.cuda() # fundus.cuda(),target.cuda()
# target = torch.tensor(target, dtype=torch.long).clone().detach()
target = target.long()
output = model(fundus, OCT)
loss = criterion(output, target)
output_real = torch.argmax(output.cpu(), dim=1) # 单分类用softmax
output_one_hot = F.one_hot(output_real, classCount)
target_one_hot = F.one_hot(target, classCount)
y_pred.extend(output_one_hot.numpy())
y_true.extend(target_one_hot.data.cpu().numpy())
loss_test += loss.item()
loss_test_norm += 1
out_loss = loss_test / loss_test_norm
y_pred = np.array(y_pred)
y_true = np.array(y_true)
auroc = roc_auc_score(y_true, y_pred, average=args.average)
y_pred = pred2int(y_pred)
f1 = f1_score(y_true, y_pred, average=args.average)
precision = precision_score(y_true, y_pred, average=args.average)
recall = recall_score(y_true, y_pred, average=args.average)
kappa = calc_kappa(y_true, y_pred, cols)
acc = accuracy_score(y_true=y_true, y_pred=y_pred)
avg = (f1 + kappa + auroc + recall) / 4.0
print()
print('{:10s} {:10s} {:10s} {:10s} {:10s} {:10s} {:10s}'.
format('f1', 'auroc', 'recall', 'precision', 'acc', 'kappa', 'loss'))
print('{:10s} {:10s} {:10s} {:10s} {:10s} {:10s} {:10s}'.
format(str(round(f1,4)), str(round(auroc,4)), str(round(recall,4)), str(round(precision,4)),
str(round(acc,4)), str(round(kappa,4)), str(round(out_loss,4)) ))
draw_ConfusionMatrix(y_pred.argmax(axis=1), y_true.argmax(axis=1), cols)
return avg
def main():
test_OCT_tf = transforms.Compose([
Resize(args.oct_size),
ToTensor(),
transforms.Normalize(mean=mean[args.oct_size], std=std[args.oct_size])
])
test_fundus_tf = transforms.Compose([
Resize(args.fundus_size),
ToTensor(),
transforms.Normalize(mean=mean[args.fundus_size], std=std[args.fundus_size])
])
test_loader = torch.utils.data.DataLoader(
TwoStreamDataset(data_dir, 'test', test_fundus_tf, test_OCT_tf, classCount, list_dir=list_dir),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True, drop_last=False
)
model = torch.load(args.two_stream_path)
model = model.cuda()
criterion = nn.CrossEntropyLoss()
avg = test(model, test_loader, criterion)
if __name__ == '__main__':
args = get_parser()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.use_gpu)
data_dir = os.path.join(args.root_path, data_dir)
list_dir = os.path.join(args.root_path, list_dir)
# writer = SummaryWriter(os.path.join('runs', 'OCT/' + model_name[:-4]))
print("Test Two Stream ")
start = time.time()
main()
end = time.time()
print('Finish Two Stream, Time=', end - start)