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1. change h5 feature file to individual npy(npz) files. 2. Split prepro to prepro_feats and prepro_labels 3. Add shuffle to the dataloader
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| Preprocess a raw json dataset into hdf5/json files for use in data_loader.lua | ||
| Input: json file that has the form | ||
| [{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...] | ||
| example element in this list would look like | ||
| {'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a motor bike on a dirt road on the countryside.', u'A man riding on the back of a motorcycle.', u'A dirt path with a young person on a motor bike rests to the foreground of a verdant area with a bridge and a background of cloud-wreathed mountains. ', u'A man in a red shirt and a red hat is on a motorcycle on a hill side.'], 'file_path': u'val2014/COCO_val2014_000000391895.jpg', 'id': 391895} | ||
| This script reads this json, does some basic preprocessing on the captions | ||
| (e.g. lowercase, etc.), creates a special UNK token, and encodes everything to arrays | ||
| Output: a json file and an hdf5 file | ||
| The hdf5 file contains several fields: | ||
| /images is (N,3,256,256) uint8 array of raw image data in RGB format | ||
| /labels is (M,max_length) uint32 array of encoded labels, zero padded | ||
| /label_start_ix and /label_end_ix are (N,) uint32 arrays of pointers to the | ||
| first and last indices (in range 1..M) of labels for each image | ||
| /label_length stores the length of the sequence for each of the M sequences | ||
| The json file has a dict that contains: | ||
| - an 'ix_to_word' field storing the vocab in form {ix:'word'}, where ix is 1-indexed | ||
| - an 'images' field that is a list holding auxiliary information for each image, | ||
| such as in particular the 'split' it was assigned to. | ||
| """ | ||
|
|
||
| import os | ||
| import json | ||
| import argparse | ||
| from random import shuffle, seed | ||
| import string | ||
| # non-standard dependencies: | ||
| import h5py | ||
| from six.moves import cPickle | ||
| import numpy as np | ||
| import torch | ||
| import torchvision.models as models | ||
| from torch.autograd import Variable | ||
| import skimage.io | ||
|
|
||
| from torchvision import transforms as trn | ||
| preprocess = trn.Compose([ | ||
| #trn.ToTensor(), | ||
| trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | ||
| ]) | ||
|
|
||
| from misc.resnet_utils import myResnet | ||
| import misc.resnet as resnet | ||
|
|
||
| def main(params): | ||
| net = getattr(resnet, params['model'])() | ||
| net.load_state_dict(torch.load('/home-nfs/rluo/rluo/model/pytorch-resnet/'+params['model']+'.pth')) | ||
| my_resnet = myResnet(net) | ||
| my_resnet.cuda() | ||
| my_resnet.eval() | ||
|
|
||
| imgs = json.load(open(params['input_json'], 'r')) | ||
| imgs = imgs['images'] | ||
| N = len(imgs) | ||
|
|
||
| seed(123) # make reproducible | ||
|
|
||
| dir_fc = params['ourput_dir']+'_fc' | ||
| dir_att = params['ourput_dir']+'_att' | ||
| if not os.path.isdir(dir_fc): | ||
| os.mkdir(dir_fc) | ||
| if not os.path.isdir(dir_att): | ||
| os.mkdir(dir_att) | ||
|
|
||
| for i,img in enumerate(imgs): | ||
| # load the image | ||
| I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename'])) | ||
| # handle grayscale input images | ||
| if len(I.shape) == 2: | ||
| I = I[:,:,np.newaxis] | ||
| I = np.concatenate((I,I,I), axis=2) | ||
|
|
||
| I = I.astype('float32')/255.0 | ||
| I = torch.from_numpy(I.transpose([2,0,1])).cuda() | ||
| I = Variable(preprocess(I), volatile=True) | ||
| tmp_fc, tmp_att = my_resnet(I, params['att_size']) | ||
| # write to pkl | ||
| np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy()) | ||
| np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy()) | ||
|
|
||
| if i % 1000 == 0: | ||
| print 'processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N) | ||
| print 'wrote ', params['ourput_dir'] | ||
|
|
||
| if __name__ == "__main__": | ||
|
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||
| parser = argparse.ArgumentParser() | ||
|
|
||
| # input json | ||
| parser.add_argument('--input_json', required=True, help='input json file to process into hdf5') | ||
| parser.add_argument('--ourput_dir', default='data', help='output h5 file') | ||
|
|
||
| # options | ||
| parser.add_argument('--images_root', default='', help='root location in which images are stored, to be prepended to file_path in input json') | ||
| parser.add_argument('--att_size', default=14, type=int, help='14x14 or 7x7') | ||
| parser.add_argument('--model', default='resnet101', type=str, help='resnet101, resnet152') | ||
|
|
||
| args = parser.parse_args() | ||
| params = vars(args) # convert to ordinary dict | ||
| print 'parsed input parameters:' | ||
| print json.dumps(params, indent = 2) | ||
| main(params) |
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