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datasets.py
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import torchvision, torchvision.transforms
import sys, os
sys.path.insert(0,"../torchxrayvision/")
import torchxrayvision as xrv
import matplotlib.pyplot as plt
import torch
from torch.nn import functional as F
import glob
import numpy as np
import skimage, skimage.filters
import captum, captum.attr
import torch, torch.nn
import pickle
import attribution
import pandas as pd
def get_data(dataset_str, transforms=True, size=224):
dataset_dir = "/home/groups/akshaysc/joecohen/"
if transforms:
transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),
xrv.datasets.XRayResizer(size)])
else:
transform = None
datasets = []
if "covid" in dataset_str:
dataset = xrv.datasets.COVID19_Dataset(
imgpath=dataset_dir + "/covid-chestxray-dataset/images",
csvpath=dataset_dir + "/covid-chestxray-dataset/metadata.csv",
transform=transform)
datasets.append(dataset)
if "pc" in dataset_str:
dataset = xrv.datasets.PC_Dataset(
imgpath=dataset_dir + "/PC/images-224",
transform=transform, unique_patients=False)
datasets.append(dataset)
if "rsna" in dataset_str:
dataset = xrv.datasets.RSNA_Pneumonia_Dataset(
imgpath=dataset_dir + "/kaggle-pneumonia-jpg/stage_2_train_images_jpg",
transform=transform,unique_patients=False, pathology_masks=True)
datasets.append(dataset)
if "nih" in dataset_str:
dataset = xrv.datasets.NIH_Dataset(
imgpath=dataset_dir + "/NIH/images-224",
transform=transform, unique_patients=False, pathology_masks=True)
datasets.append(dataset)
if "nilarge" in dataset_str:
dataset = xrv.datasets.NIH_Dataset(
imgpath=dataset_dir + "/NIH/ChestXray-NIHCC/images",
transform=transform, unique_patients=False, pathology_masks=True)
datasets.append(dataset)
if "siim" in dataset_str:
dataset = xrv.datasets.SIIM_Pneumothorax_Dataset(
imgpath=dataset_dir + "SIIM_TRAIN_TEST/dicom-images-train/",
csvpath=dataset_dir + "SIIM_TRAIN_TEST/train-rle.csv",
transform=transform, unique_patients=False, masks=True)
datasets.append(dataset)
if "chex" in dataset_str:
dataset = xrv.datasets.CheX_Dataset(
imgpath=dataset_dir + "/CheXpert-v1.0-small",
csvpath=dataset_dir + "/CheXpert-v1.0-small/train.csv",
transform=transform, unique_patients=False)
datasets.append(dataset)
if "google" in dataset_str:
dataset = xrv.datasets.NIH_Google_Dataset(
imgpath=dataset_dir + "/NIH/images-224",
transform=transform)
datasets.append(dataset)
if "mimic_ch" in dataset_str:
dataset = xrv.datasets.MIMIC_Dataset(
imgpath=dataset_dir + "/images-224-MIMIC/files",
csvpath=dataset_dir + "/MIMICCXR-2.0/mimic-cxr-2.0.0-chexpert.csv.gz",
metacsvpath=dataset_dir + "/MIMICCXR-2.0/mimic-cxr-2.0.0-metadata.csv.gz",
transform=transform, unique_patients=False)
datasets.append(dataset)
if "openi" in dataset_str:
dataset = xrv.datasets.Openi_Dataset(
imgpath=dataset_dir + "/OpenI/images/",
transform=transform)
datasets.append(dataset)
if "vin" in dataset_str:
dataset = xrv.datasets.VinBrain_Dataset(
imgpath=dataset_dir + "vinbigdata-chest-xray-abnormalities-detection/train",
csvpath=dataset_dir + "vinbigdata-chest-xray-abnormalities-detection/train.csv",
pathology_masks=True,
transform=transform)
datasets.append(dataset)
newlabels = set()
for d in datasets:
newlabels = newlabels.union(d.pathologies)
newlabels = sorted(newlabels)
#newlabels.remove("Support Devices")
#print("labels",list(newlabels))
for d in datasets:
xrv.datasets.relabel_dataset(list(newlabels), d, silent=True)
if len(datasets) > 1:
dmerge = xrv.datasets.Merge_Dataset(datasets)
else:
dmerge = datasets[0]
print(dmerge.string())
return dmerge