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case_covir.py
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'''
@Author: Yingshi Chen
https://github.com/lindawangg/COVID-Net/blob/master/create_COVIDx_v2.ipynb
@Date: 2020-04-06 15:50:21
@
# Description:
'''
import numpy as np
import pandas as pd
import os
import random
from shutil import copyfile
import pydicom as dicom
import cv2
from torch.utils.data import Dataset,DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.nn import CrossEntropyLoss
from PIL import Image
import logging
import sys
import time
ONNET_DIR = os.path.abspath("./python-package/")
sys.path.append(ONNET_DIR) # To find local version of the onnet
#sys.path.append(os.path.abspath("./python-package/cnn_models/"))
from cnn_models.COVIDNext50 import COVIDNext50
from onnet import *
import torch
from torch.optim import Adam
from torchvision import transforms
from sklearn.metrics import f1_score, precision_score, recall_score,accuracy_score,classification_report
isONN=True
class COVID_set(Dataset):
def __init__(self, config,img_dir, labels_file, transforms):
self.config = config
self.img_pths, self.labels = self._prepare_data(img_dir, labels_file)
self.transforms = transforms
def _prepare_data(self, img_dir, labels_file):
with open(labels_file, 'r') as f:
labels_raw = f.readlines()
labels, img_pths = [], []
for i in range(len(labels_raw)):
data = labels_raw[i].split()
img_pth = data[1]
#img_name = data[1]
#img_pth = os.path.join(img_dir, img_name)
img_pths.append(img_pth)
labels.append(self.config.mapping[data[2]])
return img_pths, labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
img = Image.open(self.img_pths[idx]).convert("RGB")
img_tensor = self.transforms(img)
label = self.labels[idx]
label_tensor = torch.tensor(label, dtype=torch.long)
return img_tensor, label_tensor
def train_test_split():
seed = 0
np.random.seed(seed) # Reset the seed so all runs are the same.
random.seed(seed)
MAXVAL = 255 # Range [0 255]
# path to covid-19 dataset from https://github.com/ieee8023/covid-chestxray-dataset
imgpath = 'E:/Insegment/covid-chestxray-dataset-master/images'
csvpath = 'E:/Insegment/covid-chestxray-dataset-master/metadata.csv'
# path to https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
kaggle_datapath = 'F:/Datasets/rsna-pneumonia-detection-challenge/'
kaggle_csvname = 'stage_2_detailed_class_info.csv' # get all the normal from here
kaggle_csvname2 = 'stage_2_train_labels.csv' # get all the 1s from here since 1 indicate pneumonia
kaggle_imgpath = 'stage_2_train_images'
# parameters for COVIDx dataset
train = []
test = []
test_count = {'normal': 0, 'pneumonia': 0, 'COVID-19': 0}
train_count = {'normal': 0, 'pneumonia': 0, 'COVID-19': 0}
mapping = dict()
mapping['COVID-19'] = 'COVID-19'
mapping['SARS'] = 'pneumonia'
mapping['MERS'] = 'pneumonia'
mapping['Streptococcus'] = 'pneumonia'
mapping['Normal'] = 'normal'
mapping['Lung Opacity'] = 'pneumonia'
mapping['1'] = 'pneumonia'
train_file = open("train_split_v2.txt","a")
test_file = open("test_split_v2.txt", "a")
# train/test split
split = 0.1
csv = pd.read_csv(csvpath, nrows=None)
idx_pa = csv["view"] == "PA" # Keep only the PA view
csv = csv[idx_pa]
pneumonias = ["COVID-19", "SARS", "MERS", "ARDS", "Streptococcus"]
pathologies = ["Pneumonia","Viral Pneumonia", "Bacterial Pneumonia", "No Finding"] + pneumonias
pathologies = sorted(pathologies)
filename_label = {'normal': [], 'pneumonia': [], 'COVID-19': []}
count = {'normal': 0, 'pneumonia': 0, 'COVID-19': 0}
for index, row in csv.iterrows():
f = row['finding']
if f in mapping:
count[mapping[f]] += 1
entry = [int(row['patientid']), row['filename'], mapping[f]]
filename_label[mapping[f]].append(entry)
print('Data distribution from covid-chestxray-dataset:')
print(count)
for key in filename_label.keys():
arr = np.array(filename_label[key])
if arr.size == 0:
continue
# split by patients
# num_diff_patients = len(np.unique(arr[:,0]))
# num_test = max(1, round(split*num_diff_patients))
# select num_test number of random patients
if key == 'pneumonia':
test_patients = ['8', '31']
elif key == 'COVID-19':
test_patients = ['19', '20', '36', '42', '86'] # random.sample(list(arr[:,0]), num_test)
else:
test_patients = []
print('Key: ', key)
print('Test patients: ', test_patients)
# go through all the patients
for patient in arr:
info = f"{str(patient[0])} {imgpath}\{patient[1]} {patient[2]}\n"
if patient[0] in test_patients:
#copyfile(os.path.join(imgpath, patient[1]), os.path.join(savepath, 'test', patient[1]))
test.append(patient); test_count[patient[2]] += 1
train_file.write(info)
else:
#copyfile(os.path.join(imgpath, patient[1]), os.path.join(savepath, 'train', patient[1]))
train.append(patient); train_count[patient[2]] += 1
test_file.write(info)
csv_normal = pd.read_csv(os.path.join(kaggle_datapath, kaggle_csvname), nrows=None)
csv_pneu = pd.read_csv(os.path.join(kaggle_datapath, kaggle_csvname2), nrows=None)
patients = {'normal': [], 'pneumonia': []}
for index, row in csv_normal.iterrows():
if row['class'] == 'Normal':
patients['normal'].append(row['patientId'])
for index, row in csv_pneu.iterrows():
if int(row['Target']) == 1:
patients['pneumonia'].append(row['patientId'])
for key in patients.keys():
arr = np.array(patients[key])
if arr.size == 0:
continue
# split by patients
num_diff_patients = len(np.unique(arr))
num_test = max(1, round(split*num_diff_patients))
#test_patients = np.load('rsna_test_patients_{}.npy'.format(key)) #
test_patients = random.sample(list(arr), num_test) #, download the .npy files from the repo.
np.save('rsna_test_patients_{}.npy'.format(key), np.array(test_patients))
for patient in arr:
ds = dicom.dcmread(os.path.join(kaggle_datapath, kaggle_imgpath, patient + '.dcm'))
pixel_array_numpy = ds.pixel_array
imgname = patient + '.png'
if patient in test_patients:
path = os.path.join(kaggle_datapath, 'test', imgname)
cv2.imwrite(path, pixel_array_numpy)
test.append([patient, imgname, key]); test_count[key] += 1
test_file.write(f"{patient} {path} {key}\n" )
if test_count[key]%50==0:
test_file.flush()
else:
path = os.path.join(kaggle_datapath, 'train', imgname)
cv2.imwrite(path, pixel_array_numpy)
train_file.write(f"{patient} {path} {key}\n")
if train_count[key]%20==0:
train_file.flush()
train.append([patient, imgname, key]); train_count[key] += 1
print(f"\r@{path}",end="")
print('Final stats')
print('Train count: ', train_count)
print('Test count: ', test_count)
print('Total length of train: ', len(train))
print('Total length of test: ', len(test))
train_file.close()
test_file.close()
log = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
def save_model(model, config):
if isinstance(model, torch.nn.DataParallel):
# Save without the DataParallel module
model_dict = model.module.state_dict()
else:
model_dict = model.state_dict()
state = {
"state_dict": model_dict,
"global_step": config['global_step'],
"clf_report": config['clf_report']
}
f1_macro = config['clf_report']['macro avg']['f1-score'] * 100
name = "{}_F1_{:.2f}_step_{}.pth".format(config['name'],
f1_macro,
config['global_step'])
model_path = os.path.join(config['save_dir'], name)
torch.save(state, model_path)
log.info("Saved model to {}".format(model_path))
def validate(data_loader, model, best_score, global_step, cfg):
model.eval()
gts, predictions = [], []
log.info("Validation started...")
for data in data_loader:
imgs, labels = data
imgs = to_device(imgs, gpu=cfg.gpu)
with torch.no_grad():
logits = model(imgs)
if isONN:
preds = net.predict(logits).cpu().numpy()
else:
probs = model.module.probability(logits)
preds = torch.argmax(probs, dim=1).cpu().numpy()
labels = labels.cpu().detach().numpy()
predictions.extend(preds)
gts.extend(labels)
predictions = np.array(predictions, dtype=np.int32)
gts = np.array(gts, dtype=np.int32)
acc, f1, prec, rec = clf_metrics(predictions=predictions,targets=gts,average="macro")
report = classification_report(gts, predictions, output_dict=True)
log.info("\n====== VALIDATION | Accuracy {:.4f} | F1 {:.4f} | Precision {:.4f} | Recall {:.4f}".format(acc, f1, prec, rec))
if f1 > best_score:
save_config = {
'name': config.name,
'save_dir': config.ckpts_dir,
'global_step': global_step,
'clf_report': report
}
#save_model(model=model, config=save_config)
best_score = f1
#log.info("Validation end")
model.train()
return best_score
def train_transforms(width, height):
trans_list = [
transforms.Resize((height, width)),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply([
transforms.RandomAffine(degrees=20,
translate=(0.15, 0.15),
scale=(0.8, 1.2),
shear=5)], p=0.5),
transforms.RandomApply([
transforms.ColorJitter(brightness=0.3, contrast=0.3)], p=0.5),
transforms.Grayscale(),
transforms.ToTensor()
]
return transforms.Compose(trans_list)
def val_transforms(width, height):
trans_list = [
transforms.Resize((height, width)),
transforms.Grayscale(),
transforms.ToTensor()
]
return transforms.Compose(trans_list)
def to_device(tensor, gpu=False):
return tensor.cuda() if gpu else tensor.cpu()
def clf_metrics(predictions, targets, average='macro'):
f1 = f1_score(targets, predictions, average=average)
precision = precision_score(targets, predictions, average=average)
recall = recall_score(targets, predictions, average=average)
acc = accuracy_score(targets, predictions)
return acc, f1, precision, recall
def main(model):
if config.gpu and not torch.cuda.is_available():
raise ValueError("GPU not supported or enabled on this system.")
use_gpu = config.gpu
log.info("Loading train dataset")
train_dataset = COVID_set(config,config.train_imgs, config.train_labels,train_transforms(config.width,config.height))
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,shuffle=True,drop_last=True, num_workers=config.n_threads,pin_memory=use_gpu)
log.info("Number of training examples {}".format(len(train_dataset)))
log.info("Loading val dataset")
val_dataset = COVID_set(config,config.val_imgs, config.val_labels,val_transforms(config.width,config.height))
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.n_threads,
pin_memory=use_gpu)
log.info("Number of validation examples {}".format(len(val_dataset)))
if use_gpu:
model.cuda()
#model = torch.nn.DataParallel(model)
optim_layers = filter(lambda p: p.requires_grad, model.parameters())
# optimizer and lr scheduler
optimizer = Adam(optim_layers,
lr=config.lr,
weight_decay=config.weight_decay)
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=config.lr_reduce_factor,
patience=config.lr_reduce_patience,
mode='max',
min_lr=1e-7)
# Load the last global_step from the checkpoint if existing
global_step = 0 if state is None else state['global_step'] + 1
class_weights = to_device(torch.FloatTensor(config.loss_weights),gpu=use_gpu)
loss_fn = CrossEntropyLoss(reduction='mean', weight=class_weights)
# Reset the best metric score
best_score = -1
t0=time.time()
for epoch in range(config.epochs):
log.info("\nStarted epoch {}/{}".format(epoch + 1,config.epochs))
for data in train_loader:
imgs, labels = data
imgs = to_device(imgs, gpu=use_gpu)
labels = to_device(labels, gpu=use_gpu)
logits = model(imgs)
loss = loss_fn(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if global_step % config.log_steps == 0 and global_step > 0:
if isONN:
preds = net.predict(logits).cpu().numpy()
else:
probs = model.module.probability(logits)
preds = torch.argmax(probs, dim=1).detach().cpu().numpy()
labels = labels.cpu().detach().numpy()
acc, f1, _, _ = clf_metrics(preds, labels)
lr = optimizer.param_groups[0]['lr'] #get_learning_rate(optimizer)
print(f"\r{global_step} | batch: Loss={loss.item():.3f} | F1={f1:.3f} | Accuracy={acc:.4f} | LR={lr:.2e}\tT={time.time()-t0:.4f}",end="")
if global_step % config.eval_steps == 0 and global_step > 0:
best_score = validate(val_loader, model,best_score=best_score,global_step=global_step,cfg=config)
scheduler.step(best_score)
global_step += 1
def UpdateConfig(config):
config.name = "COVIDNext50_NewData"
config.gpu = True
config.batch_size = 16
config.n_threads = 4
config.random_seed = 1337
config.weights = "E:/Insegment/COVID-Next-Pytorch-master/COVIDNext50_NewData_F1_92.98_step_10800.pth"
config.lr = 1e-4
config.weight_decay = 1e-3
config.lr_reduce_factor = 0.7
config.lr_reduce_patience = 5
# Data
config.train_imgs = None#"/data/ssd/datasets/covid/COVIDxV2/data/train"
config.train_labels = "E:/ONNet/data/covid_train_split_v2.txt" #"/data/ssd/datasets/covid/COVIDxV2/data/train_COVIDx.txt"
config.val_imgs = None#"/data/ssd/datasets/covid/COVIDxV2/data/test"
config.val_labels = "E:/ONNet/data/covid_test_split_v2.txt" #"/data/ssd/datasets/covid/COVIDxV2/data/test_COVIDx.txt"
# Categories mapping
config.mapping = {
'normal': 0,
'pneumonia': 1,
'COVID-19': 2
}
# Loss weigths order follows the order in the category mapping dict
config.loss_weights = [0.05, 0.05, 1.0]
config.width = 256
config.height = 256
config.n_classes = len(config.mapping)
# Training
config.epochs = 300
config.log_steps = 5
config.eval_steps = 400
config.ckpts_dir = "./experiments/ckpts"
return config
IMG_size = (256, 256)
if __name__ == '__main__':
config_0 = NET_config("DNet",'covid',IMG_size,0.01,batch_size=16, nClass=3, nLayer=5)
#config_0 = RGBO_CNN_config("RGBO_CNN",'covid',IMG_size,0.01,batch_size=16, nClass=3, nLayer=5)
if isONN:
env_title, net = DNet_instance(config_0)
#env_title, net = RGBO_CNN_instance(config_0)
config = net.config
config = UpdateConfig(config)
config.batch_size = 64
config.log_steps = 10
config.lr = 0.001
state = None
else:
config = UpdateConfig(config_0)
if config.weights:
state = torch.load(config.weights)
log.info("Loaded model weights from: {}".format(config.weights))
else:
state = None
state_dict = state["state_dict"] if state else None
net = COVIDNext50(n_classes=config.n_classes)
if state_dict:
net = load_model_weights(model=net, state_dict=state_dict,log=log)
print(net)
Net_dump(net)
seed_everything(config.random_seed)
main(net)