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xrv_test.py
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xrv_test.py
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import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
import csv
import pickle
import PIL
import pprint
import random
import argparse
import os,sys,inspect
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from glob import glob
from os.path import exists, join
from tqdm import tqdm as tqdm_base
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision, torchvision.transforms
import skimage.transform
import sklearn.metrics
import sklearn, sklearn.model_selection
from sklearn.metrics import roc_auc_score, accuracy_score
import torchxrayvision as xrv
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
parser = argparse.ArgumentParser(description='X-RAY Pathology Detection')
parser.add_argument('--seed', type=int, default=0, help='')
parser.add_argument('--dataset_dir', type=str, default="./data/")
parser.add_argument('--dataset_name', type=str, default="nih")
### Data loader
parser.add_argument('--cuda', type=bool, default=True, help='')
parser.add_argument('--batch_size', type=int, default=64, help='')
parser.add_argument('--shuffle', type=bool, default=False, help='')
parser.add_argument('--num_workers', type=int, default=0, help='')
parser.add_argument('--num_batches', type=int, default=430, help='')
### Data Augmentation
parser.add_argument('--data_aug_rot', type=int, default=45, help='')
parser.add_argument('--data_aug_trans', type=float, default=0.15, help='')
parser.add_argument('--data_aug_scale', type=float, default=0.15, help='')
cfg = parser.parse_args()
print(cfg)
def tqdm(*args, **kwargs):
if hasattr(tqdm_base, '_instances'):
for instance in list(tqdm_base._instances):
tqdm_base._decr_instances(instance)
return tqdm_base(*args, **kwargs)
device = 'cuda' if cfg.cuda else 'cpu'
if not torch.cuda.is_available() and cfg.cuda:
device = 'cpu'
print("WARNING: cuda was requested but is not available, using cpu instead.")
print(f'Using device: {device}')
transforms = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(), xrv.datasets.XRayResizer(112)])
if "nih" in cfg.dataset_name:
### Load NIH Dataset ###
NIH_dataset = xrv.datasets.NIH_Dataset(
imgpath=cfg.dataset_dir + "/images-224-NIH",
csvpath=cfg.dataset_dir + "/Data_Entry_2017_v2020.csv.gz",
bbox_list_path=cfg.dataset_dir + "/BBox_List_2017.csv.gz",
transform=transforms, data_aug=None, unique_patients=False)
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, NIH_dataset)
test_data = NIH_dataset
if "mc" in cfg.dataset_name:
# ### Load MIMIC_CH Dataset ###
MIMIC_CH_dataset = xrv.datasets.MIMIC_Dataset(
imgpath=cfg.dataset_dir + "/images-224-MIMIC/files",
csvpath=cfg.dataset_dir + "/MIMICCXR-2.0/mimic-cxr-2.0.0-chexpert.csv.gz",
metacsvpath=cfg.dataset_dir + "/MIMICCXR-2.0/mimic-cxr-2.0.0-metadata.csv.gz",
transform=transforms, data_aug=None, unique_patients=False)
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, MIMIC_CH_dataset)
test_data = MIMIC_CH_dataset
if "cx" in cfg.dataset_name:
## Load CHEXPERT Dataset ###
CHEX_dataset = xrv.datasets.CheX_Dataset(
imgpath=cfg.dataset_dir + "/CheXpert-v1.0-small",
csvpath=cfg.dataset_dir + "/CheXpert-v1.0-small/train.csv",
transform=transforms, data_aug=None, unique_patients=False)
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, CHEX_dataset)
test_data = CHEX_dataset
if "pc" in cfg.dataset_name:
### Load PADCHEST Dataset ###
PC_dataset = xrv.datasets.PC_Dataset(
imgpath=cfg.dataset_dir + "/PC/images-224",
csvpath=cfg.dataset_dir + "/PC/PADCHEST_chest_x_ray_images_labels_160K_01.02.19.csv",
transform=transforms, data_aug=None, unique_patients=False)
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, PC_dataset)
test_data = PC_dataset
if "gg" in cfg.dataset_name:
### Load GOOGLE Dataset ###
GOOGLE_dataset = xrv.datasets.NIH_Google_Dataset(
imgpath=cfg.dataset_dir + "/images-224-NIH",
csvpath=cfg.dataset_dir + "/google2019_nih-chest-xray-labels.csv.gz",
transform=transforms, data_aug=None
)
xrv.datasets.default_pathologies = ['Pneumothorax', 'Fracture']
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, GOOGLE_dataset)
test_data = GOOGLE_dataset
if "op" in cfg.dataset_name:
### Load OPENI Dataset ###
OPENI_dataset = xrv.datasets.Openi_Dataset(
imgpath=cfg.dataset_dir + "/images-openi/",
xmlpath=cfg.dataset_dir + "/NLMCXR_reports.tgz",
dicomcsv_path=cfg.dataset_dir + "/nlmcxr_dicom_metadata.csv.gz",
tsnepacsv_path=cfg.dataset_dir + "/nlmcxr_tsne_pa.csv.gz",
transform=transforms, data_aug=None
)
xrv.datasets.default_pathologies = ['Effusion', 'Cardiomegaly', 'Edema']
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, OPENI_dataset)
test_data = OPENI_dataset
if "rs" in cfg.dataset_name:
### Load RSNA Dataset ###
RSNA_dataset = xrv.datasets.RSNA_Pneumonia_Dataset(
imgpath=cfg.dataset_dir + "/kaggle-pneumonia-jpg/stage_2_train_images_jpg",
csvpath=cfg.dataset_dir + "/kaggle-pneumonia-jpg/stage_2_train_labels.csv",
dicomcsvpath=cfg.dataset_dir + "/kaggle_stage_2_train_images_dicom_headers.csv.gz",
transform=transforms, data_aug=None, unique_patients=False
)
xrv.datasets.default_pathologies = ['Lung Opacity', 'Pneumonia']
xrv.datasets.relabel_dataset(xrv.datasets.default_pathologies, RSNA_dataset)
test_data = RSNA_dataset
print(f"Common pathologies among all train and validation datasets: {xrv.datasets.default_pathologies}")
np.random.seed(cfg.seed)
random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
if cfg.cuda:
torch.cuda.manual_seed_all(cfg.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
test_loader = DataLoader(test_data,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True)
###################################### Test ######################################
def inference(name, model, device, data_loader, criterion, limit=None):
model.eval()
avg_loss = []
task_outputs={}
task_targets={}
for task in range(data_loader.dataset[0]["lab"].shape[0]):
task_outputs[task] = []
task_targets[task] = []
with torch.no_grad():
t = tqdm(data_loader)
for batch_idx, samples in enumerate(t):
if limit and (batch_idx >= limit):
print("breaking out")
break
images = samples["img"].to(device)
targets = samples["lab"].to(device)
outputs = model(images)
loss = torch.zeros(1).to(device).double()
for task in range(targets.shape[1]):
task_output = outputs[:,task]
task_target = targets[:,task]
mask = ~torch.isnan(task_target)
task_output = task_output[mask]
task_target = task_target[mask]
if len(task_target) > 0:
loss += criterion(task_output.double(), task_target.double())
task_outputs[task].append(task_output.detach().cpu().numpy())
task_targets[task].append(task_target.detach().cpu().numpy())
loss = loss.sum()
avg_loss.append(loss.detach().cpu().numpy())
for task in range(len(task_targets)):
task_outputs[task] = np.concatenate(task_outputs[task])
task_targets[task] = np.concatenate(task_targets[task])
task_aucs = []
for task in range(len(task_targets)):
if len(np.unique(task_targets[task]))> 1:
task_auc = sklearn.metrics.roc_auc_score(task_targets[task], task_outputs[task])
task_aucs.append(task_auc)
else:
task_aucs.append(np.nan)
task_aucs = np.asarray(task_aucs)
auc = np.mean(task_aucs[~np.isnan(task_aucs)])
print(f'{name} - Avg AUC = {auc:4.4f}')
return auc, np.mean(avg_loss), task_aucs
model = xrv.models.DenseNet(weights="all")
model = model.to(device)
criterion = torch.nn.BCEWithLogitsLoss()
test_auc, test_loss, task_aucs = inference(name='Test',
model=model,
device=device,
data_loader=test_loader,
criterion=criterion,
limit=cfg.num_batches//2)
print(f"Average AUC for all pathologies {test_auc:4.4f}")
print(f"Test loss: {test_loss:4.4f}")
print(f"AUC for each task {[round(x, 4) for x in task_aucs]}")