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chexpert.disease.py
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chexpert.disease.py
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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
import pandas as pd
import numpy as np
import torchvision
import torchvision.transforms as T
from torchvision import models
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks import ModelCheckpoint
from skimage.io import imread
from skimage.io import imsave
from tqdm import tqdm
from argparse import ArgumentParser
image_size = (224, 224)
num_classes = 14
batch_size = 150
epochs = 20
num_workers = 4
img_data_dir = '<path_to_data>/CheXpert-v1.0/'
class CheXpertDataset(Dataset):
def __init__(self, csv_file_img, image_size, augmentation=False, pseudo_rgb = True):
self.data = pd.read_csv(csv_file_img)
self.image_size = image_size
self.do_augment = augmentation
self.pseudo_rgb = pseudo_rgb
self.labels = [
'No Finding',
'Enlarged Cardiomediastinum',
'Cardiomegaly',
'Lung Opacity',
'Lung Lesion',
'Edema',
'Consolidation',
'Pneumonia',
'Atelectasis',
'Pneumothorax',
'Pleural Effusion',
'Pleural Other',
'Fracture',
'Support Devices']
self.augment = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.RandomApply(transforms=[T.RandomAffine(degrees=15, scale=(0.9, 1.1))], p=0.5),
])
self.samples = []
for idx, _ in enumerate(tqdm(range(len(self.data)), desc='Loading Data')):
img_path = img_data_dir + self.data.loc[idx, 'path_preproc']
img_label = np.zeros(len(self.labels), dtype='float32')
for i in range(0, len(self.labels)):
img_label[i] = np.array(self.data.loc[idx, self.labels[i].strip()] == 1, dtype='float32')
sample = {'image_path': img_path, 'label': img_label}
self.samples.append(sample)
def __len__(self):
return len(self.data)
def __getitem__(self, item):
sample = self.get_sample(item)
image = torch.from_numpy(sample['image']).unsqueeze(0)
label = torch.from_numpy(sample['label'])
if self.do_augment:
image = self.augment(image)
if self.pseudo_rgb:
image = image.repeat(3, 1, 1)
return {'image': image, 'label': label}
def get_sample(self, item):
sample = self.samples[item]
image = imread(sample['image_path']).astype(np.float32)
return {'image': image, 'label': sample['label']}
class CheXpertDataModule(pl.LightningDataModule):
def __init__(self, csv_train_img, csv_val_img, csv_test_img, image_size, pseudo_rgb, batch_size, num_workers):
super().__init__()
self.csv_train_img = csv_train_img
self.csv_val_img = csv_val_img
self.csv_test_img = csv_test_img
self.image_size = image_size
self.batch_size = batch_size
self.num_workers = num_workers
self.train_set = CheXpertDataset(self.csv_train_img, self.image_size, augmentation=True, pseudo_rgb=pseudo_rgb)
self.val_set = CheXpertDataset(self.csv_val_img, self.image_size, augmentation=False, pseudo_rgb=pseudo_rgb)
self.test_set = CheXpertDataset(self.csv_test_img, self.image_size, augmentation=False, pseudo_rgb=pseudo_rgb)
print('#train: ', len(self.train_set))
print('#val: ', len(self.val_set))
print('#test: ', len(self.test_set))
def train_dataloader(self):
return DataLoader(self.train_set, self.batch_size, shuffle=True, num_workers=self.num_workers)
def val_dataloader(self):
return DataLoader(self.val_set, self.batch_size, shuffle=False, num_workers=self.num_workers)
def test_dataloader(self):
return DataLoader(self.test_set, self.batch_size, shuffle=False, num_workers=self.num_workers)
class ResNet(pl.LightningModule):
def __init__(self, num_classes):
super().__init__()
self.num_classes = num_classes
self.model = models.resnet34(pretrained=True)
# freeze_model(self.model)
num_features = self.model.fc.in_features
self.model.fc = nn.Linear(num_features, self.num_classes)
def remove_head(self):
num_features = self.model.fc.in_features
id_layer = nn.Identity(num_features)
self.model.fc = id_layer
def forward(self, x):
return self.model.forward(x)
def configure_optimizers(self):
params_to_update = []
for param in self.parameters():
if param.requires_grad == True:
params_to_update.append(param)
optimizer = torch.optim.Adam(params_to_update, lr=0.001)
return optimizer
def unpack_batch(self, batch):
return batch['image'], batch['label']
def process_batch(self, batch):
img, lab = self.unpack_batch(batch)
out = self.forward(img)
prob = torch.sigmoid(out)
loss = F.binary_cross_entropy(prob, lab)
return loss
def training_step(self, batch, batch_idx):
loss = self.process_batch(batch)
self.log('train_loss', loss)
grid = torchvision.utils.make_grid(batch['image'][0:4, ...], nrow=2, normalize=True)
self.logger.experiment.add_image('images', grid, self.global_step)
return loss
def validation_step(self, batch, batch_idx):
loss = self.process_batch(batch)
self.log('val_loss', loss)
def test_step(self, batch, batch_idx):
loss = self.process_batch(batch)
self.log('test_loss', loss)
class DenseNet(pl.LightningModule):
def __init__(self, num_classes):
super().__init__()
self.num_classes = num_classes
self.model = models.densenet121(pretrained=True)
# freeze_model(self.model)
num_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(num_features, self.num_classes)
def remove_head(self):
num_features = self.model.classifier.in_features
id_layer = nn.Identity(num_features)
self.model.classifier = id_layer
def forward(self, x):
return self.model.forward(x)
def configure_optimizers(self):
params_to_update = []
for param in self.parameters():
if param.requires_grad == True:
params_to_update.append(param)
optimizer = torch.optim.Adam(params_to_update, lr=0.001)
return optimizer
def unpack_batch(self, batch):
return batch['image'], batch['label']
def process_batch(self, batch):
img, lab = self.unpack_batch(batch)
out = self.forward(img)
prob = torch.sigmoid(out)
loss = F.binary_cross_entropy(prob, lab)
return loss
def training_step(self, batch, batch_idx):
loss = self.process_batch(batch)
self.log('train_loss', loss)
grid = torchvision.utils.make_grid(batch['image'][0:4, ...], nrow=2, normalize=True)
self.logger.experiment.add_image('images', grid, self.global_step)
return loss
def validation_step(self, batch, batch_idx):
loss = self.process_batch(batch)
self.log('val_loss', loss)
def test_step(self, batch, batch_idx):
loss = self.process_batch(batch)
self.log('test_loss', loss)
def freeze_model(model):
for param in model.parameters():
param.requires_grad = False
def test(model, data_loader, device):
model.eval()
logits = []
preds = []
targets = []
with torch.no_grad():
for index, batch in enumerate(tqdm(data_loader, desc='Test-loop')):
img, lab = batch['image'].to(device), batch['label'].to(device)
out = model(img)
pred = torch.sigmoid(out)
logits.append(out)
preds.append(pred)
targets.append(lab)
logits = torch.cat(logits, dim=0)
preds = torch.cat(preds, dim=0)
targets = torch.cat(targets, dim=0)
counts = []
for i in range(0,num_classes):
t = targets[:, i] == 1
c = torch.sum(t)
counts.append(c)
print(counts)
return preds.cpu().numpy(), targets.cpu().numpy(), logits.cpu().numpy()
def embeddings(model, data_loader, device):
model.eval()
embeds = []
targets = []
with torch.no_grad():
for index, batch in enumerate(tqdm(data_loader, desc='Test-loop')):
img, lab = batch['image'].to(device), batch['label'].to(device)
emb = model(img)
embeds.append(emb)
targets.append(lab)
embeds = torch.cat(embeds, dim=0)
targets = torch.cat(targets, dim=0)
return embeds.cpu().numpy(), targets.cpu().numpy()
def main(hparams):
# sets seeds for numpy, torch, python.random and PYTHONHASHSEED.
pl.seed_everything(42, workers=True)
# data
data = CheXpertDataModule(csv_train_img='../datafiles/chexpert/chexpert.sample.train.csv',
csv_val_img='../datafiles/chexpert/chexpert.sample.val.csv',
csv_test_img='../datafiles/chexpert/chexpert.sample.test.csv',
image_size=image_size,
pseudo_rgb=True,
batch_size=batch_size,
num_workers=num_workers)
# model
model_type = DenseNet
model = model_type(num_classes=num_classes)
# Create output directory
out_name = 'densenet-all'
out_dir = 'chexpert/disease/' + out_name
if not os.path.exists(out_dir):
os.makedirs(out_dir)
temp_dir = os.path.join(out_dir, 'temp')
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
for idx in range(0,5):
sample = data.train_set.get_sample(idx)
imsave(os.path.join(temp_dir, 'sample_' + str(idx) + '.jpg'), sample['image'].astype(np.uint8))
checkpoint_callback = ModelCheckpoint(monitor="val_loss", mode='min')
# train
trainer = pl.Trainer(
callbacks=[checkpoint_callback],
log_every_n_steps = 5,
max_epochs=epochs,
gpus=hparams.gpus,
logger=TensorBoardLogger('chexpert/disease', name=out_name),
)
trainer.logger._default_hp_metric = False
trainer.fit(model, data)
model = model_type.load_from_checkpoint(trainer.checkpoint_callback.best_model_path, num_classes=num_classes)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:" + str(hparams.dev) if use_cuda else "cpu")
model.to(device)
cols_names_classes = ['class_' + str(i) for i in range(0,num_classes)]
cols_names_logits = ['logit_' + str(i) for i in range(0, num_classes)]
cols_names_targets = ['target_' + str(i) for i in range(0, num_classes)]
print('VALIDATION')
preds_val, targets_val, logits_val = test(model, data.val_dataloader(), device)
df = pd.DataFrame(data=preds_val, columns=cols_names_classes)
df_logits = pd.DataFrame(data=logits_val, columns=cols_names_logits)
df_targets = pd.DataFrame(data=targets_val, columns=cols_names_targets)
df = pd.concat([df, df_logits, df_targets], axis=1)
df.to_csv(os.path.join(out_dir, 'predictions.val.csv'), index=False)
print('TESTING')
preds_test, targets_test, logits_test = test(model, data.test_dataloader(), device)
df = pd.DataFrame(data=preds_test, columns=cols_names_classes)
df_logits = pd.DataFrame(data=logits_test, columns=cols_names_logits)
df_targets = pd.DataFrame(data=targets_test, columns=cols_names_targets)
df = pd.concat([df, df_logits, df_targets], axis=1)
df.to_csv(os.path.join(out_dir, 'predictions.test.csv'), index=False)
print('EMBEDDINGS')
model.remove_head()
embeds_val, targets_val = embeddings(model, data.val_dataloader(), device)
df = pd.DataFrame(data=embeds_val)
df_targets = pd.DataFrame(data=targets_val, columns=cols_names_targets)
df = pd.concat([df, df_targets], axis=1)
df.to_csv(os.path.join(out_dir, 'embeddings.val.csv'), index=False)
embeds_test, targets_test = embeddings(model, data.test_dataloader(), device)
df = pd.DataFrame(data=embeds_test)
df_targets = pd.DataFrame(data=targets_test, columns=cols_names_targets)
df = pd.concat([df, df_targets], axis=1)
df.to_csv(os.path.join(out_dir, 'embeddings.test.csv'), index=False)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--gpus', default=1)
parser.add_argument('--dev', default=0)
args = parser.parse_args()
main(args)