# 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 from .executor import global_scope from .data_feeder import DataFeeder, BatchedTensorProvider from .layers.io import monkey_patch_reader_methods, _copy_reader_var_, double_buffer from .unique_name import UniqueNameGenerator import logging from .dataset import DatasetBase, InMemoryDataset ### Dygraph DataLoader configs ### import multiprocessing import signal # NOTE: queue has a different name in python2 and python3 if sys.version_info[0] == 2: import Queue as queue else: import queue # NOTE: [ avoid hanging ] This value is used in getting data from another process MP_CHECK_TIMEOUT = 10 __all__ = ['PyReader', 'DataLoader'] data_loader_unique_name_generator = UniqueNameGenerator() 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 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() class DataLoader(object): @staticmethod def from_generator(feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False, use_multiprocess=False, keep_order=False): """ 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. This mode is designed to be compatible with the :code:`fluid.layers.py_reader` interface. Users can migrate the codes from :code:`fluid.layers.py_reader` to :code:`fluid.io.DataLoader` easily when using iterable=False. Args: feed_list (list(Variable)|tuple(Variable)): feed variable list. The variables 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 feeded variables. 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. keep_order (bool): whether to assign the data to CPU cores or GPU cards in order. Supposing that there are 2 batches and we use 2 GPU cards to run the network. If keep_order=True, GPU 0 would get batch 0 and GPU 1 would get batch 1 exactly. If keep_order=False, GPU 0 may get batch 0 or may get batch 1, and GPU 1 may get the rest of the data, which is uncertain. If keep_order=True, the framework may do some synchronization to keep the reading order, which may be slower. The default value is False. Returns: loader (DataLoader): the created DataLoader object. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np 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 def simple_net(image, label): fc_tmp = fluid.layers.fc(image, size=CLASS_NUM) cross_entropy = fluid.layers.softmax_with_cross_entropy(image, label) loss = fluid.layers.reduce_mean(cross_entropy) sgd = fluid.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 fluid.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 = fluid.data(name='image', shape=[None, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') # Define DataLoader loader = fluid.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 `fluid.cuda_places()` to get all GPU places. # - If you are using CPU, call `fluid.cpu_places()` to get all CPU places. # # If DataLoader is not iterable, places can be None. places = fluid.cuda_places() if USE_GPU else fluid.cpu_places() set_data_source(loader, places) exe = fluid.Executor(places[0]) exe.run(fluid.default_startup_program()) prog = fluid.CompiledProgram(fluid.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) ''' Users can use return_list = True in dygraph mode. ''' with fluid.dygraph.guard(places[0]): loader = fluid.io.DataLoader.from_generator(capacity=2, return_list=True) set_data_source(loader, places[0]) for image, label in loader(): relu = fluid.layers.relu(image) assert image.shape == [BATCH_SIZE, 784] assert label.shape == [BATCH_SIZE, 1] assert relu.shape == [BATCH_SIZE, 784] """ if in_dygraph_mode(): # Dygraph only support multiprocess training when using multi GPUs. # So in each process, we only use 1 GPU card to train the network, # so `keep_order` would also be True. 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, keep_order) @staticmethod def from_dataset(dataset, places, drop_last=True): """ 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)): places where the result data should be converted. 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.fluid as fluid image = fluid.data(name='image', shape=[None, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') dataset = fluid.DatasetFactory().create_dataset("QueueDataset") dataset.set_batch_size(32) dataset.set_filelist(['a.txt', 'b.txt', 'c.txt']) dataset.set_use_var([image, label]) dataset.set_pipe_command('cat') loader = fluid.io.DataLoader.from_dataset(dataset, fluid.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: logging.warning( "Please NOTE: dygraph can support iterable mode only. Change to iterable mode." ) self._iterable = True if not return_list: logging.warning( "Please NOTE: dygraph can support return 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'): logging.warning( "NOTE: The multiprocess mode does not currently support 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 @property def queue(self): return self._blocking_queue @property def iterable(self): return self._iterable 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: self._data_queue.cancel_join_thread() self._data_queue.close() process.join() # erase process id core._erase_process_pid(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 = core.create_py_reader( self.queue, self._var_names, self._shapes, self._dtypes, self._need_check_feed, self._places, self._use_double_buffer) def _start(self): if self._use_multiprocess: # Set data_queue and process self._data_queue = multiprocessing.Queue(self._capacity) self._process = multiprocessing.Process( target=self._reader_process_loop) 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. self._set_child_signal_handler() # Set reader_thread self._thread_done_event = threading.Event() self._thread = threading.Thread( target=self._reader_thread_loop_with_process) self._thread.daemon = True self._thread.start() else: self._thread = threading.Thread(target=self._reader_thread_loop) 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()) @classmethod def _check_input_array(cls, item): arr = np.array(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.") def _set_child_signal_handler(self): core._set_process_pid(id(self), self._process.pid) current_handler = signal.getsignal(signal.SIGCHLD) if not callable(current_handler): current_handler = None def __handler__(signum, frame): core._throw_error_if_process_failed() if current_handler is not None: current_handler(signum, frame) signal.signal(signal.SIGCHLD, __handler__) def _reader_process_loop(self): try: # set signal handler core._set_process_signal_handler() for sample in self._batch_reader(): if sample is None: raise ValueError( "Sample in reader is None. Please check whether your dataset is valid." ) self._data_queue.put(sample) self._data_queue.put(None) except KeyboardInterrupt: # NOTE: Main process will raise KeyboardInterrupt anyways, ignore it in child process pass except: self._data_queue.cancel_join_thread() self._data_queue.close() six.reraise(*sys.exc_info()) def _reader_thread_loop_with_process(self): 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` sample = self._data_queue.get(timeout=MP_CHECK_TIMEOUT) except queue.Empty: self._thread_done_event.set() logging.error("The reader has not read data for a long time.") if not self._thread_done_event.is_set(): if sample is not None: try: array = core.LoDTensorArray() for item in sample: if not isinstance(item, core.LoDTensor): self._check_input_array(item) tmp = core.LoDTensor() tmp.set(item, core.CPUPlace()) item = tmp array.append(item) if not self._blocking_queue.push(array): self._blocking_queue.close() except: self._thread_done_event.set() self._blocking_queue.kill() self._data_queue.close() logging.warning( "DygraphDataLoader reader thread raised an exception." ) six.reraise(*sys.exc_info()) else: self._thread_done_event.set() self._blocking_queue.close() self._data_queue.close() else: self._blocking_queue.kill() self._data_queue.close() def _reader_thread_loop(self): try: for sample in self._batch_reader(): array = core.LoDTensorArray() for item in sample: if not isinstance(item, core.LoDTensor): self._check_input_array(item) tmp = core.LoDTensor() tmp.set(item, core.CPUPlace()) item = tmp array.append(item) if not self._blocking_queue.push(array): break self._blocking_queue.close() self._thread = None except Exception: self._blocking_queue.kill() self._thread = None logging.warning( "DygraphDataLoader reader thread raised an exception.") six.reraise(*sys.exc_info()) def set_sample_generator(self, reader, batch_size, drop_last=True, places=None): assert batch_size > 0, "batch_size must be larger than 0" self.set_sample_list_generator( paddle.batch( reader, batch_size=batch_size, drop_last=drop_last), places=places) return self def set_sample_list_generator(self, reader, places=None): def __batch_reader_impl__(): for batch in reader(): slots = [] for items in batch: for i, item in enumerate(items): if len(slots) < len(items): slots.append([item]) else: slots[i].append(item) yield slots self.set_batch_generator(__batch_reader_impl__, places) return self def set_batch_generator(self, reader, places=None): self._batch_reader = reader assert places is not None, "Places cannot be None when DataLoader is iterable" self._places = _convert_places(places) assert len(self._places) == 1, \ "Number of places must be 1 in dygraph mode" return self class GeneratorLoader(DataLoaderBase): def __init__(self, feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False, keep_order=False): self._tensor_reader = None self._places = None self._thread = None self._queue = None self._feed_list = feed_list self._exited = False if not capacity: raise ValueError("Please give value to capacity.") self._iterable = iterable self._return_list = return_list if not self._feed_list: raise Exception("Feed list must be given under static mode.") self._use_double_buffer = use_double_buffer self._capacity = capacity self._keep_order = keep_order if not self._iterable: self._init_non_iterable() def _wait_thread_ends(self): # Get self._thread first to prevent data race, because __thread_main__ # would set self._thread be None at the end thread = self._thread if thread is not None and self._iterable: self._queue.close() thread.join() def _init_iterable(self): self._wait_thread_ends() self._var_names = [v.name for v in self._feed_list] self._shapes = [v.shape for v in self._feed_list] self._dtypes = [v.dtype for v in self._feed_list] self._need_check_feed = [ v.desc.need_check_feed() for v in self._feed_list ] self._queue = core.init_lod_tensor_blocking_queue( core.Variable(), self._capacity, self._keep_order) 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) def _init_non_iterable(self): lod_levels = [] dtypes = [] shape_concat = [] ranks = [] shapes = [] need_check_feed = [] for feed_data in self._feed_list: dtypes.append(feed_data.dtype) shape_concat.extend(feed_data.shape) ranks.append(len(feed_data.shape)) shapes.append(feed_data.shape) lod_levels.append(feed_data.lod_level) need_check_feed.append(int(feed_data.desc.need_check_feed())) queue_name = data_loader_unique_name_generator( 'lod_tensor_blocking_queue') reader_name = data_loader_unique_name_generator('create_py_reader') double_buffer_name = data_loader_unique_name_generator('double_buffer') var = global_scope().var(queue_name) self._queue = core.init_lod_tensor_blocking_queue(var, self._capacity, self._keep_order) if self._keep_order: block = default_main_program().current_block() else: block = default_startup_program().current_block() reader_var = block.create_var(name=reader_name) dtype_int = [int(t) for t in dtypes] block.append_op( type='create_py_reader', inputs={'blocking_queue': [queue_name]}, outputs={'Out': [reader_var]}, attrs={ 'shape_concat': shape_concat, 'lod_levels': lod_levels, 'dtypes': dtype_int, 'need_check_feed': need_check_feed, 'ranks': ranks }) reader_var.desc.set_dtypes(dtypes) reader_var.persistable = True reader_var.stop_gradient = True if self._keep_order: main_prog_var = reader_var reader = main_prog_var reader.reset = self._queue.reset else: main_prog_var = _copy_reader_var_( default_main_program().current_block(), reader_var) main_prog_var.stop_gradient = True main_prog_var.persistable = True reader = monkey_patch_reader_methods(main_prog_var) if self._use_double_buffer: double_buffer_reader = double_buffer( reader, name=double_buffer_name) # we return a double buffer reader. However, the reset method comes from # py_reader. double_buffer_reader.reset = reader.reset reader = double_buffer_reader self._reader = reader default_main_program().current_block().append_op( type='read', inputs={'Reader': [self._reader]}, outputs={'Out': self._feed_list}) @property def queue(self): return self._queue @property def iterable(self): return self._iterable def __iter__(self): assert self.iterable, "DataLoader is not iterable" assert self._tensor_reader is not None, \ "Data source of DataLoader has not set yet" self._init_iterable() self._start() return self def __next__(self): try: if self._return_list: return self._reader.read_next_list() else: return self._reader.read_next() except StopIteration: self._queue.close() self._reset() six.reraise(*sys.exc_info()) def start(self): assert not self._iterable, "start() cannot be called when DataLoader is iterable" self._start() def reset(self): assert not self._iterable, "reset() cannot be called when DataLoader is iterable" self._reset() @classmethod def _check_input_array(cls, item): arr = np.array(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 def _start(self): def __thread_main__(): try: while not self._queue.wait_for_inited(1): if self._exited: return for tensors in self._tensor_reader(): array = core.LoDTensorArray() for item in tensors: if not isinstance(item, core.LoDTensor): item = self._check_input_array(item) tmp = core.LoDTensor() tmp.set(item, core.CPUPlace()) item = tmp array.append(item) if not self._queue.push(array): break self._queue.close() self._thread = None except Exception as ex: self._queue.kill() self._thread = None logging.warn('Your reader has raised an exception!') six.reraise(*sys.exc_info()) self._thread = threading.Thread(target=__thread_main__) self._thread.daemon = True self._thread.start() def _reset(self): self._queue.close() self._exited = True thread = self._thread if thread is not None: thread.join() self._exited = False self._reader.reset() def set_sample_generator(self, reader, batch_size, drop_last=True, places=None): assert batch_size > 0, "batch_size must be larger than 0" has_lod = False for f in self._feed_list: if f.lod_level != 0: has_lod = True break if has_lod: self.set_sample_list_generator( paddle.batch( reader, batch_size=batch_size, drop_last=drop_last), places=places) else: reader = BatchedTensorProvider( feed_list=self._feed_list, place=core.CPUPlace(), batch_size=batch_size, generator=reader, drop_last=drop_last) self.set_batch_generator(reader, places=places) return self def set_sample_list_generator(self, reader, places=None): with program_guard(Program(), Program()): feeder = DataFeeder( feed_list=self._feed_list, place=core.CPUPlace()) paddle_reader = feeder.decorate_reader(reader, multi_devices=False) def __tensor_reader_impl__(): for slots in paddle_reader(): yield [slots[var.name] for var in self._feed_list] self.set_batch_generator(__tensor_reader_impl__, places) return self def set_batch_generator(self, reader, places=None): self._tensor_reader = reader if self._iterable: assert places is not None, "Places cannot be None when DataLoader is iterable" self._places = _convert_places(places) else: if places is not None: logging.info( 'places would be ommited when DataLoader is not iterable') return self class PyReader(DataLoaderBase): """ Create a reader object for data feeding in Python. Data would be prefetched using Python thread and be pushed into a queue asynchronously. Data in the queue would be extracted automatically when `Executor.run(...)` is called. Args: feed_list (list(Variable)|tuple(Variable)): feed variable list. The variables should be created by :code:`fluid.layers.data()`. capacity (int): capacity of the queue maintained in PyReader. 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, PyReader 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 PyReader 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 feeded variables. 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. Returns: the created reader object. Return type: reader(Reader) Examples: 1. If iterable = False, the created PyReader object is almost the same as :code:`fluid.layers.py_reader()`. Operators would be inserted into the program. User should call :code:`start()` before each epoch and catch :code:`fluid.core.EOFException` thrown by :code:`Executor.run()` when epoch ends. Once the exception is caught, user should call :code:`reset()` to reset the reader manually. .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np EPOCH_NUM = 3 ITER_NUM = 5 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = fluid.layers.fc(input=image, size=10, act='softmax') return fluid.layers.cross_entropy(input=predict, label=label) def reader_creator_random_image_and_label(height, width): def reader(): for i in range(ITER_NUM): fake_image = np.random.uniform(low=0, high=255, size=[height, width]) fake_label = np.ones([1]) yield fake_image, fake_label return reader image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=False) user_defined_reader = reader_creator_random_image_and_label(784, 784) reader.decorate_sample_list_generator( paddle.batch(user_defined_reader, batch_size=BATCH_SIZE)) loss = network(image, label) executor = fluid.Executor(fluid.CPUPlace()) executor.run(fluid.default_startup_program()) for i in range(EPOCH_NUM): reader.start() while True: try: executor.run(feed=None) except fluid.core.EOFException: reader.reset() break 2. If iterable=True, the created PyReader object is decoupled with the program. No operator would be inserted into the program. In this case, the created reader is a Python generator, which is iterable. User should feed the data yielded from PyReader object into :code:`Executor.run(feed=...)`. .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np EPOCH_NUM = 3 ITER_NUM = 5 BATCH_SIZE = 10 def network(image, label): # User-defined network, here is an example of softmax regression. predict = fluid.layers.fc(input=image, size=10, act='softmax') return fluid.layers.cross_entropy(input=predict, label=label) def reader_creator_random_image(height, width): def reader(): for i in range(ITER_NUM): fake_image = np.random.uniform(low=0, high=255, size=[height, width]) fake_label = np.ones([1]) yield fake_image, fake_label return reader image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True, return_list=False) user_defined_reader = reader_creator_random_image(784, 784) reader.decorate_sample_list_generator( paddle.batch(user_defined_reader, batch_size=BATCH_SIZE), fluid.core.CPUPlace()) loss = network(image, label) executor = fluid.Executor(fluid.CPUPlace()) executor.run(fluid.default_startup_program()) for _ in range(EPOCH_NUM): for data in reader(): executor.run(feed=data, fetch_list=[loss]) 3. If return_list=True, the return values would be presented as list instead of dict. This is usually used in dygraph mode. .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np ITER_NUM = 5 BATCH_SIZE = 10 def reader_creator_random_image(height, width): def reader(): for i in range(ITER_NUM): yield np.random.uniform(low=0, high=255, size=[height, width]), \ np.random.random_integers(low=0, high=9, size=[1]) return reader place = fluid.CPUPlace() with fluid.dygraph.guard(place): py_reader = fluid.io.PyReader(capacity=2, return_list=True) user_defined_reader = reader_creator_random_image(784, 784) py_reader.decorate_sample_list_generator( paddle.batch(user_defined_reader, batch_size=BATCH_SIZE), place) for image, label in py_reader(): relu = fluid.layers.relu(image) """ def __init__(self, feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False): self._loader = DataLoader.from_generator( feed_list, capacity, use_double_buffer, iterable, return_list) @property def queue(self): return self._loader.queue @property def iterable(self): return self._loader.iterable def __iter__(self): return self._loader.__iter__() def __next__(self): return self._loader.__next__() def start(self): ''' Start the data feeding thread. Can only call when the reader object is not iterable. Example: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np BATCH_SIZE = 10 def generator(): for i in range(5): yield np.random.uniform(low=0, high=255, size=[784, 784]), image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=False) reader.decorate_sample_list_generator( paddle.batch(generator, batch_size=BATCH_SIZE)) executor = fluid.Executor(fluid.CPUPlace()) executor.run(fluid.default_startup_program()) for i in range(3): reader.start() while True: try: executor.run(feed=None) except fluid.core.EOFException: reader.reset() break ''' self._loader.start() def reset(self): ''' Reset the reader object when :code:`fluid.core.EOFException` raises. Can only call when the reader object is not iterable. Example: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np BATCH_SIZE = 10 def generator(): for i in range(5): yield np.random.uniform(low=0, high=255, size=[784, 784]), image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=False) reader.decorate_sample_list_generator( paddle.batch(generator, batch_size=BATCH_SIZE)) executor = fluid.Executor(fluid.CPUPlace()) executor.run(fluid.default_startup_program()) for i in range(3): reader.start() while True: try: executor.run(feed=None) except fluid.core.EOFException: reader.reset() break ''' self._loader.reset() def decorate_sample_generator(self, sample_generator, batch_size, drop_last=True, places=None): ''' Set the data source of the PyReader object. The provided :code:`sample_generator` should be a Python generator, which yields list(numpy.ndarray)-typed data of each sample. :code:`places` must be set when the PyReader object is iterable. If all inputs have no lods, this method is faster than :code:`decorate_sample_list_generator(paddle.batch(sample_generator, ...))` . Args: sample_generator (generator): Python generator that yields list(numpy.ndarray)-typed sample data. batch_size (int): batch size. Must be larger than 0. drop_last (bool): Whether to drop the last batch when sample number is less than batch_size. places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must be provided when PyReader is iterable. Example: .. code-block:: python import paddle.fluid as fluid import numpy as np EPOCH_NUM = 3 ITER_NUM = 15 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = fluid.layers.fc(input=image, size=10, act='softmax') return fluid.layers.cross_entropy(input=predict, label=label) def random_image_and_label_generator(height, width): def generator(): for i in range(ITER_NUM): fake_image = np.random.uniform(low=0, high=255, size=[height, width]) fake_label = np.array([1]) yield fake_image, fake_label return generator image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True) user_defined_generator = random_image_and_label_generator(784, 784) reader.decorate_sample_generator(user_defined_generator, batch_size=BATCH_SIZE, places=[fluid.CPUPlace()]) loss = network(image, label) executor = fluid.Executor(fluid.CPUPlace()) executor.run(fluid.default_startup_program()) for _ in range(EPOCH_NUM): for data in reader(): executor.run(feed=data, fetch_list=[loss]) ''' self._loader.set_sample_generator(sample_generator, batch_size, drop_last, places) def decorate_sample_list_generator(self, reader, places=None): ''' Set the data source of the PyReader object. The provided :code:`reader` should be a Python generator, which yields list(numpy.ndarray) typed batched data. :code:`places` must be set when the PyReader object is iterable. Args: reader (generator): Python generator that yields list(numpy.ndarray)-typed batched data. places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must be provided when PyReader is iterable. Example: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np EPOCH_NUM = 3 ITER_NUM = 15 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = fluid.layers.fc(input=image, size=10, act='softmax') return fluid.layers.cross_entropy(input=predict, label=label) def random_image_and_label_generator(height, width): def generator(): for i in range(ITER_NUM): fake_image = np.random.uniform(low=0, high=255, size=[height, width]) fake_label = np.ones([1]) yield fake_image, fake_label return generator image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True) user_defined_generator = random_image_and_label_generator(784, 784) reader.decorate_sample_list_generator( paddle.batch(user_defined_generator, batch_size=BATCH_SIZE), fluid.core.CPUPlace()) loss = network(image, label) executor = fluid.Executor(fluid.core.CPUPlace()) executor.run(fluid.default_startup_program()) for _ in range(EPOCH_NUM): for data in reader(): executor.run(feed=data, fetch_list=[loss]) ''' self._loader.set_sample_list_generator(reader, places) def decorate_batch_generator(self, reader, places=None): ''' Set the data source of the PyReader object. The provided :code:`reader` should be a Python generator, which yields numpy.ndarray-typed or LoDTensor-typed batched data. :code:`places` must be set when the PyReader object is iterable. Args: reader (generator): Python generator that yields LoDTensor-typed batched data. places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must be provided when PyReader is iterable. Example: .. code-block:: python import paddle.fluid as fluid import numpy as np EPOCH_NUM = 3 ITER_NUM = 15 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = fluid.layers.fc(input=image, size=10, act='softmax') return fluid.layers.cross_entropy(input=predict, label=label) def random_image_and_label_generator(height, width): def generator(): for i in range(ITER_NUM): batch_image = np.random.uniform(low=0, high=255, size=[BATCH_SIZE, height, width]) batch_label = np.ones([BATCH_SIZE, 1]) batch_image = batch_image.astype('float32') batch_label = batch_label.astype('int64') yield batch_image, batch_label return generator image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True) user_defined_generator = random_image_and_label_generator(784, 784) reader.decorate_batch_generator(user_defined_generator, fluid.CPUPlace()) loss = network(image, label) executor = fluid.Executor(fluid.CPUPlace()) executor.run(fluid.default_startup_program()) for _ in range(EPOCH_NUM): for data in reader(): executor.run(feed=data, fetch_list=[loss]) ''' self._loader.set_batch_generator(reader, places) class DatasetLoader(DataLoaderBase): def __init__(self, dataset, places, drop_last): assert isinstance(dataset, DatasetBase), "dataset must be type of DatasetBase" assert not in_dygraph_mode( ), "DatasetLoader is not supported in dygraph mode yet" thread_num = len(places) assert len(dataset.filelist) >= thread_num, \ "Filelist number of dataset {} must be not less than place number {}".format(len(dataset.filelist), thread_num) if dataset.thread_num != 0 and dataset.thread_num != thread_num: logging.warn('thread_num {} which is set in Dataset is ignored'. format(dataset.thread_num)) dataset.set_thread(thread_num) if isinstance(dataset, InMemoryDataset) and dataset.queue_num > thread_num: logging.warn("queue_num {} which is set in Dataset is ignored". format(dataset.queue_num)) dataset.set_queue_num(thread_num) self._dataset = dataset use_slots = [ slot.name for slot in dataset.proto_desc.multi_slot_desc.slots if slot.is_used ] self._iterable_dataset = core.IterableDatasetWrapper( dataset.dataset, use_slots, _convert_places(places), dataset.proto_desc.batch_size, drop_last) def __iter__(self): self._dataset._finish_to_run() self._dataset._prepare_to_run() self._iterable_dataset._start() return self def __next__(self): return self._iterable_dataset._next()