--- a
+++ b/extract_features_fp.py
@@ -0,0 +1,128 @@
+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_FP
+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
+import openslide
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+def compute_w_loader(file_path, output_path, wsi, model,
+ 	batch_size = 8, verbose = 0, print_every=20, pretrained=True, 
+	custom_downsample=1, 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
+		custom_downsample: custom defined downscale factor of image patches
+		target_patch_size: custom defined, rescaled image size before embedding
+	"""
+	dataset = Whole_Slide_Bag_FP(file_path=file_path, wsi=wsi, pretrained=pretrained, 
+		custom_downsample=custom_downsample, 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)
+			
+			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_h5_dir', type=str, default=None)
+parser.add_argument('--data_slide_dir', type=str, default=None)
+parser.add_argument('--slide_ext', type=str, default= '.svs')
+parser.add_argument('--csv_path', type=str, default=None)
+parser.add_argument('--feat_dir', type=str, default=None)
+parser.add_argument('--batch_size', type=int, default=256)
+parser.add_argument('--no_auto_skip', default=False, action='store_true')
+parser.add_argument('--custom_downsample', type=int, default=1)
+parser.add_argument('--target_patch_size', type=int, default=-1)
+args = parser.parse_args()
+
+
+if __name__ == '__main__':
+
+	print('initializing dataset')
+	csv_path = args.csv_path
+	if csv_path is None:
+		raise NotImplementedError
+
+	bags_dataset = Dataset_All_Bags(csv_path)
+	
+	os.makedirs(args.feat_dir, exist_ok=True)
+	os.makedirs(os.path.join(args.feat_dir, 'pt_files'), exist_ok=True)
+	os.makedirs(os.path.join(args.feat_dir, 'h5_files'), exist_ok=True)
+	dest_files = os.listdir(os.path.join(args.feat_dir, 'pt_files'))
+
+	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'
+		h5_file_path = os.path.join(args.data_h5_dir, 'patches', bag_name)
+		slide_file_path = os.path.join(args.data_slide_dir, slide_id+args.slide_ext)
+		print('\nprogress: {}/{}'.format(bag_candidate_idx, total))
+		print(slide_id)
+
+		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)
+		time_start = time.time()
+		wsi = openslide.open_slide(slide_file_path)
+		output_file_path = compute_w_loader(h5_file_path, output_path, wsi, 
+		model = model, batch_size = args.batch_size, verbose = 1, print_every = 20, 
+		custom_downsample=args.custom_downsample, 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'))
+
+
+