-
Notifications
You must be signed in to change notification settings - Fork 666
/
create_db.py
791 lines (669 loc) · 25.1 KB
/
create_db.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
#!/usr/bin/env python2
# Copyright (c) 2014-2016, NVIDIA CORPORATION. All rights reserved.
import argparse
from collections import Counter
import logging
import math
import os
import Queue
import random
import re
import shutil
import sys
import threading
import time
#import utils
# Find the best implementation available
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
#import h5py
import lmdb
import numpy as np
import PIL.Image
# must call digits.config.load_config() before caffe to set the path
import caffe.io
from caffe.proto import caffe_pb2
from utils import image_processor
class Logger:
def __init__(self):
foo = 1
def info(self, s):
print "Info ::",s
def error(self, s):
print "Error ::", s
def debug(self, s):
print "Debug ::", s
def warning(self, s):
print "Warning :: ", s
logger = Logger()
class Error(Exception):
pass
class BadInputFileError(Error):
"""Input file is empty"""
pass
class ParseLineError(Error):
"""Failed to parse a line in the input file"""
pass
class LoadError(Error):
"""Failed to load image[s]"""
pass
class WriteError(Error):
"""Failed to write image[s]"""
pass
class Hdf5DatasetExtendError(Error):
"""Failed to extend an hdf5 dataset"""
pass
class DbWriter(object):
"""
Abstract class for writing to databases
"""
def __init__(self, output_dir, image_height, image_width, image_channels):
self._dir = output_dir
os.makedirs(output_dir)
self._image_height = image_height
self._image_width = image_width
self._image_channels = image_channels
self._count = 0
def write_batch(self, batch):
raise NotImplementedError
def count(self):
return self._count
class LmdbWriter(DbWriter):
# TODO
pass
class Hdf5Writer(DbWriter):
"""
A class for writing to HDF5 files
"""
LIST_FILENAME = 'list.txt'
DTYPE = 'float32'
def __init__(self, **kwargs):
"""
Keyword arguments:
compression -- the type of dataset compression
dset_limit -- the dataset size limit
"""
self._compression = kwargs.pop('compression', None)
self._dset_limit = kwargs.pop('dset_limit', None)
super(Hdf5Writer, self).__init__(**kwargs)
self._db = None
if self._dset_limit is not None:
self._max_count = self._dset_limit / (
self._image_height * self._image_width * self._image_channels)
else:
self._max_count = None
def write_batch(self, batch):
# convert batch to numpy arrays
if batch[0][0].ndim == 2:
# add channel axis for grayscale images
data_batch = np.array([i[0][...,np.newaxis] for i in batch])
else:
data_batch = np.array([i[0] for i in batch])
# Transpose to (channels, height, width)
data_batch = data_batch.transpose((0,3,1,2))
label_batch = np.array([i[1] for i in batch])
# first batch
if self._db is None:
self._create_new_file(len(batch))
self._db['data'][:] = data_batch
self._db['label'][:] = label_batch
self._count += len(batch)
return
current_count = self._db['data'].len()
# will fit in current dataset
if current_count + len(batch) <= self._max_count:
self._db['data'].resize(current_count+len(batch),axis=0)
self._db['label'].resize(current_count+len(batch),axis=0)
self._db['data'][-len(batch):] = data_batch
self._db['label'][-len(batch):] = label_batch
self._count += len(batch)
return
# calculate how many will fit in current dataset
split = self._max_count - current_count
if split > 0:
# put what we can into the current dataset
self._db['data'].resize(self._max_count,axis=0)
self._db['label'].resize(self._max_count,axis=0)
self._db['data'][-split:] = data_batch[:split]
self._db['label'][-split:] = label_batch[:split]
self._count += split
self._create_new_file(len(batch) - split)
self._db['data'][:] = data_batch[split:]
self._db['label'][:] = label_batch[split:]
self._count += len(batch) - split
def _create_new_file(self, initial_count):
assert self._max_count is None or initial_count <= self._max_count, \
'Your batch size is too large for your dataset limit - %d vs %d' % \
(initial_count, self._max_count)
# close the old file
if self._db is not None:
self._db.close()
mode = 'a'
else:
mode = 'w'
# get the filename
filename = self._new_filename()
logger.info('Creating HDF5 database at "%s" ...' %
os.path.join(*filename.split(os.sep)[-2:]))
# update the list
with open(self._list_filename(), mode) as outfile:
outfile.write('%s\n' % filename)
# create the new file
self._db = h5py.File(os.path.join(self._dir, filename), 'w')
# initialize the datasets
self._db.create_dataset('data',
(initial_count,self._image_channels,
self._image_height,self._image_width),
maxshape=(self._max_count,self._image_channels,
self._image_height,self._image_width),
chunks=True, compression=self._compression, dtype=self.DTYPE)
self._db.create_dataset('label',
(initial_count,),
maxshape=(self._max_count,),
chunks=True, compression=self._compression, dtype=self.DTYPE)
def _list_filename(self):
return os.path.join(self._dir, self.LIST_FILENAME)
def _new_filename(self):
return '%s.h5' % self.count()
def create_db(input_file, output_dir,
image_width, image_height, image_channels,
backend,
resize_mode = None,
image_folder = None,
shuffle = True,
mean_files = None,
**kwargs):
"""
Create a database of images from a list of image paths
Raises exceptions on errors
Arguments:
input_file -- a textfile containing labelled image paths
output_dir -- the location to store the created database
image_width -- image resize width
image_height -- image resize height
image_channels -- image channels
backend -- the DB format (lmdb/hdf5)
Keyword arguments:
resize_mode -- passed to utils.image.resize_image()
shuffle -- if True, shuffle the images in the list before creating
mean_files -- a list of mean files to save
"""
### Validate arguments
if not os.path.exists(input_file):
raise ValueError('input_file does not exist')
if os.path.exists(output_dir):
logger.warning('removing existing database')
if os.path.isdir(output_dir):
shutil.rmtree(output_dir, ignore_errors=True)
else:
os.remove(output_dir)
if image_width <= 0:
raise ValueError('invalid image width')
if image_height <= 0:
raise ValueError('invalid image height')
if image_channels not in [1,3]:
raise ValueError('invalid number of channels')
if resize_mode not in [None, 'crop', 'squash', 'fill', 'half_crop']:
raise ValueError('invalid resize_mode')
if image_folder is not None and not os.path.exists(image_folder):
raise ValueError('image_folder does not exist')
if mean_files:
for mean_file in mean_files:
if os.path.exists(mean_file):
logger.warning('overwriting existing mean file "%s"!' % mean_file)
else:
dirname = os.path.dirname(mean_file)
if not dirname:
dirname = '.'
if not os.path.exists(dirname):
raise ValueError('Cannot save mean file at "%s"' % mean_file)
compute_mean = bool(mean_files)
### Load lines from input_file into a load_queue
load_queue = Queue.Queue()
image_count = _fill_load_queue(input_file, load_queue, shuffle)
# Start some load threads
batch_size = _calculate_batch_size(image_count,
bool(backend=='hdf5'), kwargs.get('hdf5_dset_limit'),
image_channels, image_height, image_width)
num_threads = _calculate_num_threads(batch_size, shuffle)
write_queue = Queue.Queue(2*batch_size)
summary_queue = Queue.Queue()
for _ in xrange(num_threads):
p = threading.Thread(target=_load_thread,
args=(load_queue, write_queue, summary_queue,
image_width, image_height, image_channels,
resize_mode, image_folder, compute_mean),
kwargs={'backend': backend,
'encoding': kwargs.get('encoding', None)},
)
p.daemon = True
p.start()
start = time.time()
if backend == 'lmdb':
_create_lmdb(image_count, write_queue, batch_size, output_dir,
summary_queue, num_threads,
mean_files, **kwargs)
elif backend == 'hdf5':
_create_hdf5(image_count, write_queue, batch_size, output_dir,
image_width, image_height, image_channels,
summary_queue, num_threads,
mean_files, **kwargs)
else:
raise ValueError('invalid backend')
logger.info('Database created after %d seconds.' % (time.time() - start))
def _create_lmdb(image_count, write_queue, batch_size, output_dir,
summary_queue, num_threads,
mean_files = None,
encoding = None,
lmdb_map_size = None,
**kwargs):
"""
Create an LMDB
Keyword arguments:
encoding -- image encoding format
lmdb_map_size -- the initial LMDB map size
"""
wait_time = time.time()
threads_done = 0
images_loaded = 0
images_written = 0
image_sum = None
batch = []
compute_mean = bool(mean_files)
db = lmdb.open(output_dir,
map_size=lmdb_map_size,
map_async=True,
max_dbs=0)
while (threads_done < num_threads) or not write_queue.empty():
# Send update every 2 seconds
if time.time() - wait_time > 2:
logger.debug('Processed %d/%d' % (images_written, image_count))
wait_time = time.time()
processed_something = False
if not summary_queue.empty():
result_count, result_sum = summary_queue.get()
images_loaded += result_count
# Update total_image_sum
if compute_mean and result_count > 0 and result_sum is not None:
if image_sum is None:
image_sum = result_sum
else:
image_sum += result_sum
threads_done += 1
processed_something = True
if not write_queue.empty():
datum = write_queue.get()
batch.append(datum)
if len(batch) == batch_size:
_write_batch_lmdb(db, batch, images_written)
images_written += len(batch)
batch = []
processed_something = True
if not processed_something:
time.sleep(0.2)
if len(batch) > 0:
_write_batch_lmdb(db, batch, images_written)
images_written += len(batch)
if images_loaded == 0:
raise LoadError('no images loaded from input file')
logger.debug('%s images loaded' % images_loaded)
if images_written == 0:
raise WriteError('no images written to database')
logger.info('%s images written to database' % images_written)
if compute_mean:
_save_means(image_sum, images_written, mean_files)
db.close()
def _create_hdf5(image_count, write_queue, batch_size, output_dir,
image_width, image_height, image_channels,
summary_queue, num_threads,
mean_files = None,
compression = None,
hdf5_dset_limit = None,
**kwargs):
"""
Create an HDF5 file
Keyword arguments:
compression -- dataset compression format
"""
wait_time = time.time()
threads_done = 0
images_loaded = 0
images_written = 0
image_sum = None
batch = []
compute_mean = bool(mean_files)
writer = Hdf5Writer(
output_dir = output_dir,
image_height = image_height,
image_width = image_width,
image_channels = image_channels,
dset_limit = hdf5_dset_limit,
compression = compression,
)
while (threads_done < num_threads) or not write_queue.empty():
# Send update every 2 seconds
if time.time() - wait_time > 2:
logger.debug('Processed %d/%d' % (images_written, image_count))
wait_time = time.time()
processed_something = False
if not summary_queue.empty():
result_count, result_sum = summary_queue.get()
images_loaded += result_count
# Update total_image_sum
if compute_mean and result_count > 0 and result_sum is not None:
if image_sum is None:
image_sum = result_sum
else:
image_sum += result_sum
threads_done += 1
processed_something = True
if not write_queue.empty():
batch.append(write_queue.get())
if len(batch) == batch_size:
writer.write_batch(batch)
images_written += len(batch)
batch = []
processed_something = True
if not processed_something:
time.sleep(0.2)
if len(batch) > 0:
writer.write_batch(batch)
images_written += len(batch)
assert images_written == writer.count()
if images_loaded == 0:
raise LoadError('no images loaded from input file')
logger.debug('%s images loaded' % images_loaded)
if images_written == 0:
raise WriteError('no images written to database')
logger.info('%s images written to database' % images_written)
if compute_mean:
_save_means(image_sum, images_written, mean_files)
def _fill_load_queue(filename, queue, shuffle):
"""
Fill the queue with data from the input file
Print the category distribution
Returns the number of lines added to the queue
NOTE: This can be slow on a large input file, but we need the total image
count in order to report the progress, so we might as well read it all
"""
total_lines = 0
valid_lines = 0
distribution = Counter()
with open(filename) as infile:
if shuffle:
lines = infile.readlines() # less memory efficient
random.shuffle(lines)
for line in lines:
total_lines += 1
try:
result = _parse_line(line, distribution)
valid_lines += 1
queue.put(result)
except ParseLineError:
pass
else:
for line in infile: # more memory efficient
total_lines += 1
try:
result = _parse_line(line, distribution)
valid_lines += 1
queue.put(result)
except ParseLineError:
pass
logger.debug('%s total lines in file' % total_lines)
if valid_lines == 0:
raise BadInputFileError('No valid lines in input file')
logger.info('%s valid lines in file' % valid_lines)
for key in sorted(distribution):
logger.debug('Category %s has %d images.' % (key, distribution[key]))
return valid_lines
def _parse_line(line, distribution):
"""
Parse a line in the input file into (path, label)
"""
line = line.strip()
if not line:
raise ParseLineError
# Expect format - [/]path/to/file.jpg 123
match = re.match(r'(.+)\s+(\d+)\s*$', line)
if match is None:
raise ParseLineError
path = match.group(1)
label = int(match.group(2))
distribution[label] += 1
return path, label
def _calculate_batch_size(image_count, is_hdf5=False, hdf5_dset_limit=None,
image_channels=None, image_height=None, image_width=None):
"""
Calculates an appropriate batch size for creating this database
"""
if is_hdf5 and hdf5_dset_limit is not None:
return min(100, image_count, hdf5_dset_limit/(image_channels*image_height*image_width))
else:
return min(100, image_count)
def _calculate_num_threads(batch_size, shuffle):
"""
Calculates an appropriate number of threads for creating this database
"""
if shuffle:
return min(10, int(round(math.sqrt(batch_size))))
else:
#XXX This is the only way to preserve order for now
# This obviously hurts performance considerably
return 1
def _load_thread(load_queue, write_queue, summary_queue,
image_width, image_height, image_channels,
resize_mode, image_folder, compute_mean,
backend=None, encoding=None):
"""
Consumes items in load_queue
Produces items to write_queue
Stores cumulative results in summary_queue
"""
images_added = 0
if compute_mean:
image_sum = _initial_image_sum(image_width, image_height, image_channels)
else:
image_sum = None
while not load_queue.empty():
try:
path, label = load_queue.get(True, 0.05)
except Queue.Empty:
continue
# prepend path with image_folder, if appropriate
#if not utils.is_url(path) and image_folder and not os.path.isabs(path):
# path = os.path.join(image_folder, path)
try:
image = image_processor.load_image(path)
except Exception as e:
logger.warning('[%s] %s: %s' % (path, type(e).__name__, e) )
continue
image = image_processor.resize_image(image,
image_height, image_width,
channels = image_channels,
resize_mode = resize_mode,
)
#image = image.resize((256, 256), PIL.Image.ANTIALIAS)
if compute_mean:
image_sum += image
if backend == 'lmdb':
datum = _array_to_datum(image, label, encoding)
write_queue.put(datum)
else:
write_queue.put((image, label))
images_added += 1
summary_queue.put((images_added, image_sum))
def _initial_image_sum(width, height, channels):
"""
Returns an array of zeros that will be used to store the accumulated sum of images
"""
if channels == 1:
return np.zeros((height, width), np.float64)
else:
return np.zeros((height, width, channels), np.float64)
def _array_to_datum(image, label, encoding):
"""
Create a caffe Datum from a numpy.ndarray
"""
if not encoding:
# Transform to caffe's format requirements
if image.ndim == 3:
# Transpose to (channels, height, width)
image = image.transpose((2,0,1))
if image.shape[0] == 3:
# channel swap
# XXX see issue #59
image = image[[2,1,0],...]
elif image.ndim == 2:
# Add a channels axis
image = image[np.newaxis,:,:]
else:
raise Exception('Image has unrecognized shape: "%s"' % image.shape)
datum = caffe.io.array_to_datum(image, label)
else:
datum = caffe_pb2.Datum()
if image.ndim == 3:
datum.channels = image.shape[2]
else:
datum.channels = 1
datum.height = image.shape[0]
datum.width = image.shape[1]
datum.label = label
s = StringIO()
if encoding == 'png':
PIL.Image.fromarray(image).save(s, format='PNG')
elif encoding == 'jpg':
PIL.Image.fromarray(image).save(s, format='JPEG', quality=90)
else:
raise ValueError('Invalid encoding type')
datum.data = s.getvalue()
datum.encoded = True
return datum
def _write_batch_lmdb(db, batch, image_count):
"""
Write a batch to an LMDB database
"""
try:
with db.begin(write=True) as lmdb_txn:
for i, datum in enumerate(batch):
key = '%08d_%d' % (image_count + i, datum.label)
lmdb_txn.put(key, datum.SerializeToString())
except lmdb.MapFullError:
# double the map_size
curr_limit = db.info()['map_size']
new_limit = curr_limit*2
logger.debug('Doubling LMDB map size to %sMB ...' % (new_limit>>20,))
try:
db.set_mapsize(new_limit) # double it
except AttributeError as e:
version = tuple(int(x) for x in lmdb.__version__.split('.'))
if version < (0,87):
raise Error('py-lmdb is out of date (%s vs 0.87)' % lmdb.__version__)
else:
raise e
# try again
_write_batch_lmdb(db, batch, image_count)
def _save_means(image_sum, image_count, mean_files):
"""
Save mean[s] to file
"""
mean = np.around(image_sum / image_count).astype(np.uint8)
for mean_file in mean_files:
if mean_file.lower().endswith('.npy'):
np.save(mean_file, mean)
elif mean_file.lower().endswith('.binaryproto'):
data = mean
# Transform to caffe's format requirements
if data.ndim == 3:
# Transpose to (channels, height, width)
data = data.transpose((2,0,1))
if data.shape[0] == 3:
# channel swap
# XXX see issue #59
data = data[[2,1,0],...]
elif mean.ndim == 2:
# Add a channels axis
data = data[np.newaxis,:,:]
blob = caffe_pb2.BlobProto()
blob.num = 1
blob.channels, blob.height, blob.width = data.shape
blob.data.extend(data.astype(float).flat)
with open(mean_file, 'wb') as outfile:
outfile.write(blob.SerializeToString())
elif mean_file.lower().endswith(('.jpg', '.jpeg', '.png')):
image = PIL.Image.fromarray(mean)
image.save(mean_file)
else:
logger.warning('Unrecognized file extension for mean file: "%s"' % mean_file)
continue
logger.info('Mean saved at "%s"' % mean_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Create-Db tool - DIGITS')
### Positional arguments
parser.add_argument('input_file',
help='An input file of labeled images')
parser.add_argument('output_dir',
help='Path to the output database')
parser.add_argument('width',
type=int,
help='width of resized images'
)
parser.add_argument('height',
type=int,
help='height of resized images'
)
### Optional arguments
parser.add_argument('-c', '--channels',
type=int,
default=3,
help='channels of resized images (1 for grayscale, 3 for color [default])'
)
parser.add_argument('-r', '--resize_mode',
help='resize mode for images (must be "crop", "squash" [default], "fill" or "half_crop")'
)
parser.add_argument('-m', '--mean_file', action='append',
help="location to output the image mean (doesn't save mean if not specified)")
parser.add_argument('-f', '--image_folder',
help='folder containing the images (if the paths in input_file are not absolute)')
parser.add_argument('-s', '--shuffle',
action='store_true',
help='Shuffle images before saving'
)
parser.add_argument('-e', '--encoding',
help = 'Image encoding format (jpg/png)'
)
parser.add_argument('-C', '--compression',
help = 'Database compression format (gzip)'
)
parser.add_argument('-b', '--backend',
default='lmdb',
help = 'The database backend - lmdb[default] or hdf5')
parser.add_argument('--lmdb_map_size',
type=int,
help = 'The initial map size for LMDB (in MB)')
parser.add_argument('--hdf5_dset_limit',
type=int,
default=2**31,
help = 'The size limit for HDF5 datasets')
args = vars(parser.parse_args())
if args['lmdb_map_size']:
# convert from MB to B
args['lmdb_map_size'] <<= 20
try:
create_db(args['input_file'], args['output_dir'],
args['width'], args['height'], args['channels'],
args['backend'],
resize_mode = args['resize_mode'],
image_folder = args['image_folder'],
shuffle = args['shuffle'],
mean_files = args['mean_file'],
encoding = args['encoding'],
compression = args['compression'],
lmdb_map_size = args['lmdb_map_size'],
hdf5_dset_limit = args['hdf5_dset_limit'],
)
except Exception as e:
logger.error('%s: %s' % (type(e).__name__, e.message))
raise