import torch
import torch.nn as nn
from math import floor
import os
import random
import numpy as np
import pdb
import time
from datasets.dataset_h5 import Dataset_All_Bags, Whole_Slide_Bag
from torch.utils.data import DataLoader
from models.resnet_custom import resnet50_baseline
import argparse
from utils.utils import print_network, collate_features
from utils.file_utils import save_hdf5
from PIL import Image
import h5py
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
def compute_w_loader(file_path, output_path, model, batch_size = 8, verbose = 0,
print_every=20, pretrained=True, target_patch_size=-1):
"""
args:
file_path: directory of bag (.h5 file)
output_path: directory to save computed features (.h5 file)
model: pytorch model
batch_size: batch_size for computing features in batches
verbose: level of feedback
pretrained: use weights pretrained on imagenet
"""
dataset = Whole_Slide_Bag(file_path=file_path, pretrained=pretrained,
target_patch_size=target_patch_size)
x, y = dataset[0]
kwargs = {'num_workers': 4, 'pin_memory': True} if device.type == "cuda" else {}
loader = DataLoader(dataset=dataset, batch_size=batch_size, **kwargs, collate_fn=collate_features)
if verbose > 0:
print('processing {}: total of {} batches'.format(file_path,len(loader)))
mode = 'w'
for count, (batch, coords) in enumerate(loader):
with torch.no_grad():
if count % print_every == 0:
print('batch {}/{}, {} files processed'.format(count, len(loader), count * batch_size))
batch = batch.to(device, non_blocking=True)
mini_bs = coords.shape[0]
features = model(batch)
features = features.cpu().numpy()
asset_dict = {'features': features, 'coords': coords}
save_hdf5(output_path, asset_dict, attr_dict= None, mode=mode)
mode = 'a'
return output_path
parser = argparse.ArgumentParser(description='Feature Extraction')
parser.add_argument('--data_dir', type=str)
parser.add_argument('--csv_path', type=str)
parser.add_argument('--feat_dir', type=str)
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--slide_ext', type=str, default= '.svs')
parser.add_argument('--no_auto_skip', default=False, action='store_true')
parser.add_argument('--target_patch_size', type=int, default=-1,
help='the desired size of patches for optional scaling before feature embedding')
args = parser.parse_args()
if __name__ == '__main__':
print('initializing dataset')
csv_path = args.csv_path
bags_dataset = Dataset_All_Bags(csv_path)
os.makedirs(args.feat_dir, exist_ok=True)
dest_files = os.listdir(args.feat_dir)
print('loading model checkpoint')
model = resnet50_baseline(pretrained=True)
model = model.to(device)
# print_network(model)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.eval()
total = len(bags_dataset)
for bag_candidate_idx in range(total):
slide_id = bags_dataset[bag_candidate_idx].split(args.slide_ext)[0]
bag_name = slide_id + '.h5'
bag_candidate = os.path.join(args.data_dir, 'patches', bag_name)
print('\nprogress: {}/{}'.format(bag_candidate_idx, total))
print(bag_name)
if not args.no_auto_skip and slide_id+'.pt' in dest_files:
print('skipped {}'.format(slide_id))
continue
output_path = os.path.join(args.feat_dir, 'h5_files', bag_name)
file_path = bag_candidate
time_start = time.time()
output_file_path = compute_w_loader(file_path, output_path,
model = model, batch_size = args.batch_size,
verbose = 1, print_every = 20,
target_patch_size=args.target_patch_size)
time_elapsed = time.time() - time_start
print('\ncomputing features for {} took {} s'.format(output_file_path, time_elapsed))
file = h5py.File(output_file_path, "r")
features = file['features'][:]
print('features size: ', features.shape)
print('coordinates size: ', file['coords'].shape)
features = torch.from_numpy(features)
bag_base, _ = os.path.splitext(bag_name)
torch.save(features, os.path.join(args.feat_dir, 'pt_files', bag_base+'.pt'))
Datasets
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