forked from binli123/dsmil-wsi
/
compute_feats.py
138 lines (121 loc) · 5.61 KB
/
compute_feats.py
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import dsmil as mil
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.models as models
import torchvision.transforms.functional as VF
from torchvision import transforms
import sys, argparse, os, glob
import pandas as pd
import numpy as np
from PIL import Image
from collections import OrderedDict
class BagDataset():
def __init__(self, csv_file, transform=None):
self.files_list = csv_file
self.transform = transform
def __len__(self):
return len(self.files_list)
def __getitem__(self, idx):
temp_path = self.files_list[idx]
img = os.path.join(temp_path)
img = Image.open(img)
sample = {'input': img}
if self.transform:
sample = self.transform(sample)
return sample
class ToTensor(object):
def __call__(self, sample):
img = sample['input']
img = VF.to_tensor(img)
return {'input': img}
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
def bag_dataset(args, csv_file_path):
transformed_dataset = BagDataset(csv_file=csv_file_path,
transform=Compose([
ToTensor()
]))
dataloader = DataLoader(transformed_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, drop_last=False)
return dataloader, len(transformed_dataset)
def compute_feats(args, bags_list, i_classifier, save_path=None):
num_bags = len(bags_list)
Tensor = torch.FloatTensor
for i in range(0, num_bags):
feats_list = []
if args.magnification == '20x':
csv_file_path = glob.glob(os.path.join(bags_list[i], '*/*.jpg'))
else:
csv_file_path = glob.glob(os.path.join(bags_list[i], '*.jpg'))
dataloader, bag_size = bag_dataset(args, csv_file_path)
with torch.no_grad():
for iteration, batch in enumerate(dataloader):
patches = batch['input'].float().cuda()
feats, classes = i_classifier(patches)
feats = feats.cpu().numpy()
feats_list.extend(feats)
df = pd.DataFrame(feats_list)
os.makedirs(os.path.join(save_path, bags_list[i].split(os.path.sep)[-3]), exist_ok=True)
df.to_csv(os.path.join(save_path, bags_list[i].split(os.path.sep)[-3], bags_list[i].split(os.path.sep)[-2]+'.csv'), index=False, float_format='%.4f')
sys.stdout.write('\r Computed: {}/{}'.format(i+1, num_bags))
def main():
parser = argparse.ArgumentParser(description='Compute TCGA features from SimCLR embedder')
parser.add_argument('--num_classes', default=2, type=int, help='Number of output classes')
parser.add_argument('--num_feats', default=512, type=int, help='Feature size')
parser.add_argument('--batch_size', default=128, type=int, help='Batch size of dataloader')
parser.add_argument('--num_workers', default=0, type=int, help='Number of threads for datalodaer')
parser.add_argument('--dataset', default='wsi-tcga-lung', type=str, help='Nanme of dataset')
parser.add_argument('--backbone', default='resnet18', type=str, help='Embedder backbone')
parser.add_argument('--magnification', default='20x', type=str, help='Magnification to compute features')
parser.add_argument('--weights', default=None, type=str, help='Folder of the pretrained weights, simclr/runs/*')
args = parser.parse_args()
if args.backbone == 'resnet18':
resnet = models.resnet18(pretrained=False, norm_layer=nn.InstanceNorm2d)
num_feats = 512
if args.backbone == 'resnet34':
resnet = models.resnet34(pretrained=False, norm_layer=nn.InstanceNorm2d)
num_feats = 512
if args.backbone == 'resnet50':
resnet = models.resnet50(pretrained=False, norm_layer=nn.InstanceNorm2d)
num_feats = 2048
if args.backbone == 'resnet101':
resnet = models.resnet101(pretrained=False, norm_layer=nn.InstanceNorm2d)
num_feats = 2048
for param in resnet.parameters():
param.requires_grad = False
resnet.fc = nn.Identity()
i_classifier = mil.IClassifier(resnet, num_feats, output_class=args.num_classes).cuda()
if args.weights is not None:
weight_path = os.path.join('simclr', 'runs', args.weights, 'checkpoints', 'model.pth')
else:
weight_path = glob.glob('simclr/runs/*/checkpoints/*.pth')[-1]
state_dict_weights = torch.load(weight_path)
try:
state_dict_weights.pop('module.l1.weight')
state_dict_weights.pop('module.l1.bias')
state_dict_weights.pop('module.l2.weight')
state_dict_weights.pop('module.l2.bias')
except:
state_dict_weights.pop('l1.weight')
state_dict_weights.pop('l1.bias')
state_dict_weights.pop('l2.weight')
state_dict_weights.pop('l2.bias')
state_dict_init = i_classifier.state_dict()
new_state_dict = OrderedDict()
for (k, v), (k_0, v_0) in zip(state_dict_weights.items(), state_dict_init.items()):
name = k_0
new_state_dict[name] = v
i_classifier.load_state_dict(new_state_dict, strict=False)
if args.dataset == 'wsi-tcga-lung':
bags_path = os.path.join('WSI', 'TCGA-lung', 'pyramid', '*', '*')
feats_path = os.path.join('datasets', args.dataset)
os.makedirs(feats_path, exist_ok=True)
bags_list = glob.glob(bags_path+os.path.sep)
compute_feats(args, bags_list, i_classifier, feats_path)
if __name__ == '__main__':
main()