extract_features.py

# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import argparse, os, json
import h5py
import numpy as np
from scipy.misc import imread, imresize

import torch
import torchvision


parser = argparse.ArgumentParser()
parser.add_argument('--input_image_dir', required=True)
parser.add_argument('--max_images', default=None, type=int)
parser.add_argument('--output_h5_file', required=True)

parser.add_argument('--image_height', default=224, type=int)
parser.add_argument('--image_width', default=224, type=int)

parser.add_argument('--model', default='resnet101')
parser.add_argument('--model_stage', default=3, type=int)
parser.add_argument('--batch_size', default=128, type=int)


def build_model(args):
  if not hasattr(torchvision.models, args.model):
    raise ValueError('Invalid model "%s"' % args.model)
  if not 'resnet' in args.model:
    raise ValueError('Feature extraction only supports ResNets')
  cnn = getattr(torchvision.models, args.model)(pretrained=True)
  layers = [
    cnn.conv1,
    cnn.bn1,
    cnn.relu,
    cnn.maxpool,
  ]
  for i in range(args.model_stage):
    name = 'layer%d' % (i + 1)
    layers.append(getattr(cnn, name))
  model = torch.nn.Sequential(*layers)
  model.cuda()
  model.eval()
  return model


def run_batch(cur_batch, model):
  mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3, 1, 1)
  std = np.array([0.229, 0.224, 0.224]).reshape(1, 3, 1, 1)

  image_batch = np.concatenate(cur_batch, 0).astype(np.float32)
  image_batch = (image_batch / 255.0 - mean) / std
  image_batch = torch.FloatTensor(image_batch).cuda()
  image_batch = torch.autograd.Variable(image_batch, volatile=True)

  feats = model(image_batch)
  feats = feats.data.cpu().clone().numpy()

  return feats


def main(args):
  input_paths = []
  idx_set = set()
  for fn in os.listdir(args.input_image_dir):
    if not fn.endswith('.png'): continue
    idx = int(os.path.splitext(fn)[0].split('_')[-1])
    input_paths.append((os.path.join(args.input_image_dir, fn), idx))
    idx_set.add(idx)
  input_paths.sort(key=lambda x: x[1])
  assert len(idx_set) == len(input_paths)
  # assert min(idx_set) == 0 and max(idx_set) == len(idx_set) - 1
  if args.max_images is not None:
    input_paths = input_paths[:args.max_images]
  print(input_paths[0])
  print(input_paths[-1])

  model = build_model(args)

  img_size = (args.image_height, args.image_width)
  with h5py.File(args.output_h5_file, 'w') as f:
    feat_dset = None
    i0 = 0
    cur_batch = []
    for i, (path, idx) in enumerate(input_paths):
      img = imread(path, mode='RGB')
      img = imresize(img, img_size, interp='bicubic')
      img = img.transpose(2, 0, 1)[None]
      cur_batch.append(img)
      if len(cur_batch) == args.batch_size:
        feats = run_batch(cur_batch, model)
        if feat_dset is None:
          N = len(input_paths)
          _, C, H, W = feats.shape
          feat_dset = f.create_dataset('features', (N, C, H, W),
                                       dtype=np.float32)
        i1 = i0 + len(cur_batch)
        feat_dset[i0:i1] = feats
        i0 = i1
        print('Processed %d / %d images' % (i1, len(input_paths)))
        cur_batch = []
    if len(cur_batch) > 0:
      feats = run_batch(cur_batch, model)
      i1 = i0 + len(cur_batch)
      feat_dset[i0:i1] = feats
      print('Processed %d / %d images' % (i1, len(input_paths)))


if __name__ == '__main__':
  args = parser.parse_args()
  main(args)