-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
reader.py
1872 lines (1538 loc) · 74.4 KB
/
reader.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
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import core
import sys
import six
import numpy as np
import threading
import paddle
from .framework import Program, Variable, program_guard, default_main_program, default_startup_program, in_dygraph_mode, cpu_places, _current_expected_place
from .executor import global_scope
from .data_feeder import DataFeeder, BatchedTensorProvider
from .multiprocess_utils import multiprocess_queue_set, CleanupFuncRegistrar, _cleanup_mmap, _cleanup, _set_SIGCHLD_handler
from .dataloader import BatchSampler, Dataset, IterableDataset
from .dataloader.dataloader_iter import _DataLoaderIterSingleProcess, _DataLoaderIterMultiProcess, _DatasetKind, default_collate_fn
from .dataloader.batch_sampler import _InfiniteIterableSampler
from .layers.io import monkey_patch_reader_methods, _copy_reader_var_, double_buffer
from .unique_name import UniqueNameGenerator
from .framework import _get_paddle_place, _get_paddle_place_list
from paddle.fluid.framework import _set_expected_place, _current_expected_place
import logging
import warnings
### Dygraph DataLoader configs ###
import os
import multiprocessing
import signal
# NOTE: queue has a different name in python2 and python3
if six.PY2:
import Queue as queue
else:
import queue
# NOTE: [ avoid hanging & failed quickly ] These value is used in getting data from another process
QUEUE_GET_TIMEOUT = 60
__all__ = ['PyReader', 'DataLoader', 'default_collate_fn']
data_loader_unique_name_generator = UniqueNameGenerator()
KEEP_DATA_LOADER_ORDER = True
USE_PINNED_MEMORY = None
def keep_data_loader_order(*args):
global KEEP_DATA_LOADER_ORDER
if len(args) == 0:
return KEEP_DATA_LOADER_ORDER
else:
assert len(args) == 1 and isinstance(args[0], bool)
KEEP_DATA_LOADER_ORDER = args[0]
def use_pinned_memory(*args):
global USE_PINNED_MEMORY
if len(args) == 0:
return USE_PINNED_MEMORY
else:
assert len(args) == 1 and isinstance(args[0], bool)
USE_PINNED_MEMORY = args[0]
def _convert_places(places):
if not isinstance(places, (list, tuple)):
places = [places]
ret = []
for p in places:
if not isinstance(p, core.Place):
tmp = core.Place()
tmp.set_place(p)
p = tmp
ret.append(p)
return ret
# NOTE(chenweihang): _reader_process_loop must be top level method to be pickled
def _reader_process_loop(batch_reader, data_queue):
try:
# set signal handler
core._set_process_signal_handler()
# NOTE: [ mmap files clear ] When the child process exits unexpectedly,
# some shared memory objects may have been applied for but have not yet
# been put into the inter-process Queue. This part of the object needs
# to be cleaned up when the process ends.
CleanupFuncRegistrar.register(_cleanup_mmap)
for batch in batch_reader():
tensor_list = core._convert_to_tensor_list(batch)
data_queue.put(tensor_list)
core._remove_tensor_list_mmap_fds(tensor_list)
data_queue.put(None)
except KeyboardInterrupt:
# NOTE: Main process will raise KeyboardInterrupt anyways, ignore it in child process
pass
except:
six.reraise(*sys.exc_info())
class DataLoaderBase(object):
def __init__(self):
self._places = None
def __call__(self):
return self
def next(self):
'''
Get the next item in the DataLoader object. This method
should not be called by users directly. It is used for
implementing iterator protocol of Python 2.x inside
PaddlePaddle framework.
'''
return self.__next__()
def __iter__(self):
raise NotImplementedError()
def __next__(self):
raise NotImplementedError()
@classmethod
def _check_input_array(cls, item):
arr = np.asarray(item)
if arr.dtype == np.object:
raise TypeError(
"\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
"this means the input data contains nested lists with different lengths. "
"\n\t* Check the reader function passed to 'decorate_batch_generator'"
" to locate the data causes this issue.\n\t* Please consider using "
"'fluid.create_lod_tensor' to convert it to a LoD-Tensor.")
return arr
class DataLoader(object):
"""
DataLoader prodives an iterator which iterates given dataset
once by the batch_sampler.
DataLoader supports single-process and multi-prcess data loading,
multi-process workers will be used to load data asynchronously if
:attr:`num_workers` is set as a positive number.
DataLoader supports map-style dataset and iterable-style dataset.
For map-style datast(can get a sample from dataset with a given
index), please see :code:`paddle.io.Dataset`.
For iterable-style datast(get samples from dataset iteratively,
like a Python iterator), please see :code:`paddle.io.IterableDataset`.
For :code:`batch_sampler` please see :code:`paddle.io.BatchSampler`
**Disable automatic batching**
In certain cases such as some NLP tasks, instead of automatic batching,
handling batching manually in dataset is needed by users. For these
cases, automatic batching is disabled if both :attr:`batch_size` and
:attr:`batch_sampler` is set as None, each data got from :attr:`dataset`
should be batched data and will be processed with function define by
:attr:`collate_fn` or :attr:`default_collate_fn`.
.. note::
When automatic batching is disabled, :attr:`default_collate_fn` will
do nothing to data from dataset.
Args:
dataset(Dataset): the dataset to load data from, should be an
instance of subclass of :code:`paddle.io.Dataset` or
:code:`paddle.io.IterableDataset`.
feed_list (list(Tensor)|tuple(Tensor)): feed Tensor list.
The Tensors should be created by :code:`paddle.static.data()`.
:attr:`feed_list` must be set if :attr:`return_list` is
False. Default None.
places(list(Place)|tuple(Place)|list(str)|optional): a list of Place,
to put data onto, :attr:`places` can be None, if
:attr:`places` is None, default place(CPUPlace or CUDAPlace(0))
will be used. Default None. If ``places`` is list of string,
the string in the list can be ``cpu``, ``gpu:x`` and ``gpu_pinned``,
where ``x`` is the index of the GPUs.
return_list (bool): whether the return value on each device is
presented as a list. If :attr:`return_list=False`, the return
value on each device would be a dict of str -> Tensor, where
the key of the dict is the name of each fed Tensors. If
:attr:`return_list=True`, the return value on each device would
be a list(Tensor). :attr:`return_list` can only be True
in dynamic graph mode. Default True.
batch_sampler(BatchSampler): an instance of `paddle.io.BatchSampler`
to generate batch indices to draw samples from :attr:`dataset`
and combine a batch. Default None.
batch_size(int|None): sample number in a mini-batch, a substitution
parameter for :attr:`batch_sampler`, if :attr:`batch_sampler`
is not set, a default `paddle.io.BatchSampler` will be used
and initialize by :attr:`batch_size`, :attr:`shuffle` and
:attr:`drop_last`. Default 1.
shuffle(bool): whther to shuffle indices order before genrate
batch indices, a substitution parameter for :attr:`batch_sampler`
see :attr:`batch_size`. Default False.
drop_last(bool): whether drop the last incomplete batch dataset size
is not divisible by the batch size, a substitution parameter
for :attr:`batch_sampler`, see :attr:`batch_size`. Default False
collate_fn(callable): function to generate mini-batch data by merging
the sample list, None for only stack each fields of sample in axis
0(same as :attr::`np.stack(..., axis=0)`). Default None
num_workers(int): the number of subprocess to load data, 0 for no
subprocess used and loading data in main process. Default 0
use_buffer_reader (bool): whether to use bufferred reader.
If use_buffer_reader=True, the DataLoader would prefetch next
batch data asynchronously, so it would speed up data feeding
and occupies a little more CPU or GPU memory, i.e., the memory
of one batch input data. Default True.
use_shared_memory (bool): whether to use shared memory to speed up
putting data into inter-process queue, set :attr:`use_shared_memory`
as True only when the shared memory space on your machine(e.g.
space of '/dev/shm' on Linux operating sysytem) is large enough.
Shared memory will only be enabled in multi-process mode(num_workers
> 0). Default True.
timeout(int): the timeout value for getting data form output queue
of subprocesses. Default 0.
worker_init_fn(callable): init function which will be called with
worker id on each subproces starting if not set as None. Default
None.
Returns:
DataLoader: an iterable object for data iterating, each elemnet of the generated data is a Tensor.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.io import Dataset, BatchSampler, DataLoader
BATCH_NUM = 20
BATCH_SIZE = 16
EPOCH_NUM = 4
IMAGE_SIZE = 784
CLASS_NUM = 10
# define a random dataset
class RandomDataset(Dataset):
def __init__(self, num_samples):
self.num_samples = num_samples
def __getitem__(self, idx):
image = np.random.random([IMAGE_SIZE]).astype('float32')
label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
return image, label
def __len__(self):
return self.num_samples
dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
class SimpleNet(nn.Layer):
def __init__(self):
super(SimpleNet, self).__init__()
self.fc = nn.Linear(IMAGE_SIZE, CLASS_NUM)
def forward(self, image, label=None):
return self.fc(image)
simple_net = SimpleNet()
opt = paddle.optimizer.SGD(learning_rate=1e-3,
parameters=simple_net.parameters())
loader = DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=2)
for e in range(EPOCH_NUM):
for i, (image, label) in enumerate(loader()):
out = simple_net(image)
loss = F.cross_entropy(out, label)
avg_loss = paddle.mean(loss)
avg_loss.backward()
opt.minimize(avg_loss)
simple_net.clear_gradients()
print("Epoch {} batch {}: loss = {}".format(e, i, np.mean(loss.numpy())))
.. note::
For reading iterable dataset with multiprocess Dataloader,
please see :code:`paddle.io.IterableDataset`
"""
def __init__(self,
dataset,
feed_list=None,
places=None,
return_list=True,
batch_sampler=None,
batch_size=1,
shuffle=False,
drop_last=False,
collate_fn=None,
num_workers=0,
use_buffer_reader=True,
use_shared_memory=True,
timeout=0,
worker_init_fn=None):
self.return_list = return_list
self.collate_fn = collate_fn
self.use_buffer_reader = use_buffer_reader
self.worker_init_fn = worker_init_fn
assert isinstance(dataset, Dataset), \
"dataset should be subclass instance of paddle.io.Dataset"
self.dataset = dataset
if not return_list and not in_dygraph_mode():
assert feed_list is not None, \
"feed_list should be set when return_list=False"
self.feed_list = feed_list
if places is None:
places = _current_expected_place()
if isinstance(places, (list, tuple)):
places = _get_paddle_place_list(places)
else:
places = _get_paddle_place(places)
self.places = _convert_places(places)
assert num_workers >= 0, "num_workers should be a non-negative value"
if num_workers > 0 and (sys.platform == 'darwin' or
sys.platform == 'win32'):
warnings.warn(
"DataLoader with multi-process mode is not supported on MacOs and Windows currently." \
" Please use signle-process mode with num_workers = 0 instead")
num_workers = 0
self.num_workers = num_workers
self.use_shared_memory = use_shared_memory
if use_shared_memory and num_workers == 0:
self.use_shared_memory = False
assert timeout >= 0, "timeout should be a non-negative value"
self.timeout = timeout
if isinstance(dataset, IterableDataset):
self.dataset_kind = _DatasetKind.ITER
if shuffle:
raise ValueError(
"IterableDataset not support shuffle, but got shuffle={}".
format(shuffle))
if batch_sampler is not None:
raise ValueError(
"IterableDataset expect unspecified batch_sampler")
else:
self.dataset_kind = _DatasetKind.MAP
if batch_sampler is not None:
assert batch_size == 1 and not shuffle and not drop_last, \
"batch_size/shuffle/drop_last should not be set when " \
"batch_sampler is given"
self.batch_sampler = batch_sampler
self.batch_size = None
elif batch_size is None:
self.batch_sampler = None
self.batch_size = None
else:
assert batch_size > 0, \
"batch_size should be None or a positive value when " \
"batch_sampler is not given"
self.batch_size = batch_size
if isinstance(dataset, IterableDataset):
self.batch_sampler = _InfiniteIterableSampler(dataset,
batch_size)
else:
self.batch_sampler = BatchSampler(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
drop_last=drop_last)
self.auto_collate_batch = self.batch_sampler is not None
self.pin_memory = False
if in_dygraph_mode():
self.pin_memory = True if use_pinned_memory(
) is None else use_pinned_memory()
def __len__(self):
if self.dataset_kind == _DatasetKind.ITER:
raise ValueError("length of IterableDataset not supported")
else:
if self.auto_collate_batch:
return len(self.batch_sampler)
else:
return len(self.dataset)
def __iter__(self):
if self.num_workers == 0:
return _DataLoaderIterSingleProcess(self)
else:
return _DataLoaderIterMultiProcess(self)
def __call__(self):
return self.__iter__()
@staticmethod
def from_generator(feed_list=None,
capacity=None,
use_double_buffer=True,
iterable=True,
return_list=False,
use_multiprocess=False,
drop_last=True):
"""
.. warning::
This API will be deprecated in the future, it is recommended to use
:code:`paddle.io.DataLoader` which supports multi-processes acceleration.
.. note::
**The framework ensures that the data loading order of DataLoader is exactly the same as the user-defined data source.**
Create a DataLoader object for loading data from Python generator.
Data would be prefetched using Python thread and be pushed
into a queue asynchronously.
The created DataLoader object provides 3 methods to set the data source
:code:`set_sample_generator` , :code:`set_sample_list_generator` and
:code:`set_batch_generator` . Please see the following example codes
to know their usages.
If iterable = True, the created DataLoader object is a Python generator
object, which is iterable using for-range loop.
If iterable = False, the created DataLoader object provides
:code:`start()` and :code:`reset()` method to control the data reading
process.
Args:
feed_list (list(Tensor)|tuple(Tensor)): feed Tensor list.
The Tensors should be created by :code:`fluid.data()`.
capacity (int): capacity of the queue maintained in DataLoader.
The unit is batch number. Set larger capacity if your reader
is fast.
use_double_buffer (bool): whether to use double_buffer_reader.
If use_double_buffer=True, the DataLoader would prefetch next
batch data asynchronously, so it would speed up data feeding
and occupies a little more CPU or GPU memory, i.e., the memory
of one batch input data.
iterable (bool): whether the created DataLoader is iterable.
return_list (bool): whether the return value on each device is
presented as a list. It is only valid when iterable=True.
If return_list=False, the return value on each device would
be a dict of str -> LoDTensor, where the key of the dict is
the name of each fed Tensors. If return_list=True, the
return value on each device would be a list(LoDTensor). It is
recommended to use return_list=False in static graph mode and
use return_list=True in dygraph mode.
use_multiprocess (bool): whether to use multi-process to speed up
the data loading process in dygraph. Note: this parameter only
can be used in the dygraph mode. In the static graph mode,
whether this parameter is set or not has no effect.
The Default value is False.
drop_last (bool): whether to drop the last batches whose number is
less than the CPU core/GPU card number. The default value is
True. In training phase, users should not set drop_last=False,
because all CPU cores/GPU cards must read data from DataLoader.
In inference phase, users can set drop_last=False, so that the
last batches whose number is less than the CPU core/GPU card
number can be tested.
Returns:
loader (DataLoader): the created DataLoader object.
Examples 1:
.. code-block:: python
'''
Example in static graph mode
'''
import numpy as np
import paddle
import paddle.static as static
import paddle.nn.functional as F
BATCH_NUM = 10
BATCH_SIZE = 16
EPOCH_NUM = 4
CLASS_NUM = 10
ITERABLE = True # whether the created DataLoader object is iterable
USE_GPU = False # whether to use GPU
DATA_FORMAT = 'batch_generator' # data format of data source user provides
paddle.enable_static()
def simple_net(image, label):
fc_tmp = static.nn.fc(image, size=CLASS_NUM)
cross_entropy = F.softmax_with_cross_entropy(image, label)
loss = paddle.mean(cross_entropy)
sgd = paddle.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(loss)
return loss
def get_random_images_and_labels(image_shape, label_shape):
image = np.random.random(size=image_shape).astype('float32')
label = np.random.random(size=label_shape).astype('int64')
return image, label
# If the data generator yields one sample each time,
# use DataLoader.set_sample_generator to set the data source.
def sample_generator_creator():
def __reader__():
for _ in range(BATCH_NUM * BATCH_SIZE):
image, label = get_random_images_and_labels([784], [1])
yield image, label
return __reader__
# If the data generator yield list of samples each time,
# use DataLoader.set_sample_list_generator to set the data source.
def sample_list_generator_creator():
def __reader__():
for _ in range(BATCH_NUM):
sample_list = []
for _ in range(BATCH_SIZE):
image, label = get_random_images_and_labels([784], [1])
sample_list.append([image, label])
yield sample_list
return __reader__
# If the data generator yields a batch each time,
# use DataLoader.set_batch_generator to set the data source.
def batch_generator_creator():
def __reader__():
for _ in range(BATCH_NUM):
batch_image, batch_label = get_random_images_and_labels([BATCH_SIZE, 784], [BATCH_SIZE, 1])
yield batch_image, batch_label
return __reader__
# If DataLoader is iterable, use for loop to train the network
def train_iterable(exe, prog, loss, loader):
for _ in range(EPOCH_NUM):
for data in loader():
exe.run(prog, feed=data, fetch_list=[loss])
# If DataLoader is not iterable, use start() and reset() method to control the process
def train_non_iterable(exe, prog, loss, loader):
for _ in range(EPOCH_NUM):
loader.start() # call DataLoader.start() before each epoch starts
try:
while True:
exe.run(prog, fetch_list=[loss])
except paddle.core.EOFException:
loader.reset() # call DataLoader.reset() after catching EOFException
def set_data_source(loader, places):
if DATA_FORMAT == 'sample_generator':
loader.set_sample_generator(sample_generator_creator(), batch_size=BATCH_SIZE, drop_last=True, places=places)
elif DATA_FORMAT == 'sample_list_generator':
loader.set_sample_list_generator(sample_list_generator_creator(), places=places)
elif DATA_FORMAT == 'batch_generator':
loader.set_batch_generator(batch_generator_creator(), places=places)
else:
raise ValueError('Unsupported data format')
image = static.data(name='image', shape=[None, 784], dtype='float32')
label = static.data(name='label', shape=[None, 1], dtype='int64')
# Define DataLoader
loader = paddle.io.DataLoader.from_generator(feed_list=[image, label], capacity=16, iterable=ITERABLE)
# Define network
loss = simple_net(image, label)
# Set data source of DataLoader
#
# If DataLoader is iterable, places must be given and the number of places must be the same with device number.
# - If you are using GPU, call `paddle.static.cuda_places()` to get all GPU places.
# - If you are using CPU, call `paddle.static.cpu_places()` to get all CPU places.
#
# If DataLoader is not iterable, places can be None.
places = static.cuda_places() if USE_GPU else static.cpu_places()
set_data_source(loader, places)
exe = static.Executor(places[0])
exe.run(static.default_startup_program())
prog = static.CompiledProgram(static.default_main_program()).with_data_parallel(loss_name=loss.name)
if loader.iterable:
train_iterable(exe, prog, loss, loader)
else:
train_non_iterable(exe, prog, loss, loader)
Examples 2:
.. code-block:: python
'''
Example in dynamic graph mode.
'''
import numpy as np
import paddle
import paddle.nn as nn
import paddle.optimizer as opt
import paddle.distributed as dist
BATCH_SIZE = 16
BATCH_NUM = 4
EPOCH_NUM = 4
IMAGE_SIZE = 784
CLASS_NUM = 10
USE_GPU = False # whether to use GPU
def _get_random_images_and_labels(image_shape, label_shape):
image = np.random.random(size=image_shape).astype('float32')
label = np.random.random(size=label_shape).astype('int64')
return image, label
def __reader__():
for _ in range(BATCH_NUM):
batch_image, batch_label = _get_random_images_and_labels(
[BATCH_SIZE, IMAGE_SIZE], [BATCH_SIZE, CLASS_NUM])
yield batch_image, batch_label
def random_batch_reader():
return __reader__
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
@paddle.jit.to_static
def forward(self, x):
return self._linear(x)
# set device
paddle.set_device('gpu' if USE_GPU else 'cpu')
# create network
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.CrossEntropyLoss()
adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
# create data loader
loader = paddle.io.DataLoader.from_generator(capacity=5)
loader.set_batch_generator(random_batch_reader())
for epoch_id in range(EPOCH_NUM):
for batch_id, (image, label) in enumerate(loader()):
out = layer(image)
loss = loss_fn(out, label)
loss.backward()
adam.step()
adam.clear_grad()
print("Epoch {} batch {}: loss = {}".format(
epoch_id, batch_id, np.mean(loss.numpy())))
Examples 3:
.. code-block:: python
'''
Example of `drop_last` using in static graph multi-cards mode
'''
import paddle
import paddle.static as static
import numpy as np
import os
# We use 2 CPU cores to run inference network
os.environ['CPU_NUM'] = '2'
paddle.enable_static()
# The data source has only 3 batches, which can not be
# divided evenly to each CPU core
def batch_generator():
for i in range(3):
yield np.array([i+1]).astype('float32'),
x = static.data(name='x', shape=[None], dtype='float32')
y = x * x
def run_inference(drop_last):
loader = paddle.io.DataLoader.from_generator(feed_list=[x],
capacity=8, drop_last=drop_last)
loader.set_batch_generator(batch_generator, static.cpu_places())
exe = static.Executor(paddle.CPUPlace())
prog = static.CompiledProgram(static.default_main_program())
prog = prog.with_data_parallel()
result = []
for data in loader():
each_ret, = exe.run(prog, feed=data, fetch_list=[y])
result.extend(each_ret)
return result
# Set drop_last to True, so that the last batch whose
# number is less than CPU core number would be discarded.
print(run_inference(drop_last=True)) # [1.0, 4.0]
# Set drop_last to False, so that the last batch whose
# number is less than CPU core number can be tested.
print(run_inference(drop_last=False)) # [1.0, 4.0, 9.0]
"""
if in_dygraph_mode():
return DygraphGeneratorLoader(feed_list, capacity,
use_double_buffer, iterable,
return_list, use_multiprocess)
else:
return GeneratorLoader(feed_list, capacity, use_double_buffer,
iterable, return_list, drop_last)
@staticmethod
def from_dataset(dataset, places, drop_last=True):
"""
.. warning::
This API will be deprecated in the future, it is recommended to use
:code:`paddle.io.DataLoader` which supports multi-processes acceleration.
Create an iterable DataLoader object for loading data from Dataset.
Dataset is only supported in Linux system currently.
Args:
dataset (InMemoryDataset|QueueDataset): the dataset object.
places (list(CUDAPlace)|list(CPUPlace)|list(str)): places where the result
data should be converted. If places is list of string, the string in the list
can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where x is the index of the GPUs.
drop_last (bool): whether to drop the last batch whose sample
number is less than batch size. If drop_last = True, they
would be dropped. If drop_last = False, they would be kept.
Returns:
loader (DataLoader): the created DataLoader object, which can be
treated as a Python generator.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
image = static.data(name='image', shape=[None, 784], dtype='float32')
label = static.data(name='label', shape=[None, 1], dtype='int64')
dataset = paddle.distributed.QueueDataset()
dataset.init(
batch_size=32,
pipe_command='cat',
use_var=[image, label])
dataset.set_filelist(['a.txt', 'b.txt', 'c.txt'])
loader = paddle.io.DataLoader.from_dataset(dataset, static.cpu_places())
"""
return DatasetLoader(dataset, places, drop_last)
class DygraphGeneratorLoader(DataLoaderBase):
"""
The GeneratorLoader of dygraph
The multiprocess dygraph GeneratorLoader's most functions are different from
static graph GeneratorLoader, Separate implementation to keep code readable.
"""
def __init__(self,
feed_list=None,
capacity=None,
use_double_buffer=True,
iterable=True,
return_list=True,
use_multiprocess=False):
self._batch_reader = None
self._places = None
self._feed_list = feed_list
if not capacity:
raise ValueError("Please give value to capacity.")
self._capacity = capacity
self._use_double_buffer = use_double_buffer
if not iterable:
warnings.warn(
"Please NOTE: DygraphGeneratorLoader supports iterable mode only. Change to iterable mode."
)
self._iterable = True
if not return_list:
warnings.warn(
"Please NOTE: DygraphGeneratorLoader supports returning as list only. Change to return as list."
)
self._return_list = True
# NOTE: the multiprocessing in different platform is incompatible, we will solve it later
self._use_multiprocess = use_multiprocess
if self._use_multiprocess and (sys.platform == 'darwin' or
sys.platform == 'win32'):
warnings.warn(
"NOTE: DygraphGeneratorLoader with multiprocess mode is not currently supported on MacOs and Windows."
)
self._use_multiprocess = False
if self._use_multiprocess:
# NOTE: the multiprocessing.Queue used to save loading data in self._process
self._data_queue = None
# NOTE: this process is used to load data asynchronously from self._batch_reader
self._process = None
# NOTE: the C++ LoDTensorBlockingQueue instance
self._blocking_queue = None
# NOTE: 1. In multiprocess mode, this thread is used to get next batch data from
# self._data_queue, then push it into self._blocking_queue; 2. In singleprocess
# mode, this thread is used to get next batch data from self._batch_reader, then
# push it into self._blocking_queue
self._thread = None
self._pin_memory = True if use_pinned_memory(
) is None else use_pinned_memory()
@property
def queue(self):
return self._blocking_queue
@property
def iterable(self):
return self._iterable
def _clear_and_remove_data_queue(self):
if self._data_queue is not None:
while True:
try:
self._data_queue.get_nowait()
except queue.Empty:
break
global multiprocess_queue_set
multiprocess_queue_set.remove(self._data_queue)
def _wait_thread_ends(self):
thread = self._thread
if thread is not None:
self._blocking_queue.close()
thread.join()
def _wait_process_ends(self):
process = self._process
if process is not None:
process.join()
# erase process id
core._erase_process_pids(id(self))
def _init_iterable(self):
self._wait_thread_ends()
if self._use_multiprocess:
self._wait_process_ends()
self._var_names = []
self._shapes = []
self._dtypes = []
self._need_check_feed = []
self._blocking_queue = core.init_lod_tensor_blocking_queue(
core.Variable(), self._capacity, False)
self._reader = None
self._reader = core.create_py_reader(
self.queue, self._var_names, self._shapes, self._dtypes,
self._need_check_feed, self._places, self._use_double_buffer, True,
self._pin_memory)
def _start(self):
if self._use_multiprocess:
# clear old _data_queue and remove it from multiprocess_queue_set
self._clear_and_remove_data_queue()
# set data_queue and process
self._data_queue = multiprocessing.Queue(self._capacity)
# add _data_queue into global queue set
global multiprocess_queue_set
multiprocess_queue_set.add(self._data_queue)
self._process = multiprocessing.Process(
target=_reader_process_loop,
args=(self._batch_reader, self._data_queue))
self._process.daemon = True
self._process.start()
# Set child process signal handler
# NOTE: [ avoiding hang ] 1. if the child process dies due to bus error/segfault
# or just hang, the main process will hang waiting for data, so here need to deal
# with SIGSEGV and SIGBUS of child process; 2. if the main process end before child
# process, it shuts the all its daemonic children down with a SIGTERM (instead of
# joining them without a timeout), so here nedd to deal with SIGTERM.
core._set_process_pids(id(self), [self._process.pid])
_set_SIGCHLD_handler()
# Set reader_thread
self._thread_done_event = threading.Event()
self._thread = threading.Thread(
target=self._reader_thread_loop_for_multiprocess,
args=(_current_expected_place(), ))
self._thread.daemon = True
self._thread.start()
else:
self._thread = threading.Thread(
target=self._reader_thread_loop_for_singleprocess,
args=(_current_expected_place(), ))
self._thread.daemon = True
self._thread.start()
def _reset(self):
self._reader.reset()
self._wait_thread_ends()
if self._use_multiprocess:
self._wait_process_ends()
def __iter__(self):
assert self.iterable, "DataLoader is not iterable"
assert self._batch_reader is not None, \
"Data source of DataLoader has not set yet"
self._init_iterable()
self._start()
return self
def __next__(self):
try:
return self._reader.read_next_var_list()
except StopIteration:
self._reset()
six.reraise(*sys.exc_info())
def _exit_thread_expectedly(self):
self._thread_done_event.set()
self._blocking_queue.close()
def _exit_thread_unexpectedly(self):
self._thread_done_event.set()
self._blocking_queue.kill()
logging.error("DataLoader reader thread raised an exception!")
def _reader_thread_loop_for_multiprocess(self, legacy_expected_place):
# See _DataLoaderIterSingleProcess._thread_loop() for why set expected place here.
_set_expected_place(legacy_expected_place)
while not self._thread_done_event.is_set():
try:
# NOTE: [ avoid hanging ] Even with carefully designed data dependencies
# (i.e., a put() always corresponding to a get()), hanging on get() can
# still happen when data in queue is corrupted (e.g., due to
# Queue.cancel_join_thread or unexpected exit). So we set a timeout whenever
# we try to get data from `data_queue`
# NOTE: [ avoid failed quickly ] Here, the time setting of QUEUE_GET_TIMEOUT
# is relatively long, currently it is 60 seconds, because in some models,
# if the reader child process starts with a heavy burden, the child process
# has no enough time to put the data in the queue when the main process
# start trying to get data from queue. At this time, the child thread needs
# to wait slightly longer
tensor_list = self._data_queue.get(timeout=QUEUE_GET_TIMEOUT)
except:
# NOTE [ avoid handing ] After adding the shared memory mechanism, not only
# the queue.Empty exception will occur here, but other exceptions will also
# occur, such as mmap failure. If it is not handled here, it will hang.
self._exit_thread_unexpectedly()
logging.error(
"DataLoader reader thread failed to read data from the multiprocessing.Queue."
)
six.reraise(*sys.exc_info())