# 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 numpy as np import threading import paddle import time import copy from .framework import ( Program, Variable, program_guard, default_main_program, default_startup_program, _non_static_mode, cpu_places, _current_expected_place, _in_eager_without_dygraph_check, ) 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, Subset 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_, __create_unshared_decorated_reader__, ) 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 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 # AutoTune Flags USE_AUTOTUNE = False TUNING_STEPS = 500 def set_autotune_config(use_autotune, tuning_steps=500): global USE_AUTOTUNE USE_AUTOTUNE = use_autotune global TUNING_STEPS TUNING_STEPS = tuning_steps 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: raise class DataLoaderBase: def __init__(self): self._places = None def __call__(self): return self 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 AuToTune: def __init__(self, loader): self.loader = loader self.max_num_worker = multiprocessing.cpu_count() / 2 def __call__(self): # use default loader if (not USE_AUTOTUNE) or (not self.need_autotune()): return self.loader.num_workers # get autotune loader auto_tune_loader = self.get_autotune_loader() if auto_tune_loader is None: return self.loader.num_workers # pick the best num_workers auto_tune_start = time.time() logging.debug("========= DataLoader Auto Tune =========") logging.debug( "User config for DataLoader: " + str(self.loader.num_workers) ) best_num_workers = 0 min_cost = float("inf") logging.debug( "Tuning Range for num_workers: 0 ~ " + str(self.max_num_worker) ) num_workers = 0 while num_workers < self.max_num_worker: auto_tune_loader.num_workers = num_workers avg_cost = self.evaluate_reader_cost(auto_tune_loader) if min_cost * 0.75 > avg_cost: min_cost = avg_cost best_num_workers = num_workers else: update_num = self.is_best( auto_tune_loader, best_num_workers, min_cost, self.max_num_worker, ) if update_num == best_num_workers: break else: best_num_workers = update_num logging.debug( "num_workers: " + str(num_workers) + " avg_cost: " + str(avg_cost) ) num_workers += 2 logging.info( "auto_tune dataLoader best_num_workers: " + str(best_num_workers) ) logging.debug( "AutoTuning Cost for DataLoader: " + str(time.time() - auto_tune_start) + ' seconds' ) # tune the default loader's num_workers return best_num_workers def need_autotune(self): if sys.platform == 'darwin' or sys.platform == 'win32': return False else: return True def get_sub_dataset(self, dataset, batch_size): num_samples = min(batch_size * TUNING_STEPS, len(dataset)) sub_dataset = Subset(dataset, indices=list(range(num_samples))) return sub_dataset def get_autotune_loader(self): loader = copy.copy(self.loader) batch_size = self.loader.batch_sampler.batch_size if isinstance( self.loader.batch_sampler, paddle.io.DistributedBatchSampler ): dataset = self.loader.batch_sampler.dataset sub_dataset = self.get_sub_dataset(dataset, batch_size) loader.batch_sampler = paddle.io.DistributedBatchSampler( dataset=sub_dataset, batch_size=batch_size, num_replicas=self.loader.batch_sampler.nranks, rank=self.loader.batch_sampler.local_rank, shuffle=self.loader.batch_sampler.shuffle, drop_last=self.loader.batch_sampler.drop_last, ) elif isinstance(self.loader.batch_sampler, paddle.io.BatchSampler): dataset = self.loader.batch_sampler.sampler.data_source sub_dataset = self.get_sub_dataset(dataset, batch_size) loader.batch_sampler = paddle.io.BatchSampler( dataset=sub_dataset, batch_size=batch_size, drop_last=self.loader.batch_sampler.drop_last, ) else: loader = None return loader def evaluate_reader_cost(self, reader): costs = [] avg_cost = 0 start = time.time() for i, data in enumerate(reader): costs.append(time.time() - start) start = time.time() if len(costs) > 2: avg_cost = sum(costs[2:]) / len(costs[2:]) else: avg_cost = sum(costs[0:]) / len(costs[0:]) return avg_cost def is_best(self, reader, best_workers, best_time, num_work_boundary): step = 0 num_workers = best_workers + 1 boundary = 1 while num_workers < num_work_boundary and step < 5: self.loader.num_workers = num_workers time = self.evaluate_reader_cost(reader) logging.debug( "for back num_workers: " + str(num_workers) + " avg_cost: " + str(time) ) step += 1 if time < best_time * 0.70 * boundary: return num_workers else: num_workers += 1 boundary *= 0.80 return best_workers class DataLoader: """ 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` .. note:: GPU tensor operation is not supported in subprocess currently, please don't use GPU tensor operations in pipeline which will be performed in subprocess, such as dataset transforms, collte_fn, etc. Numpy array and CPU tensor operation is supported. **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), optional): 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, optional): 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, optional): 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, optional): 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, optional): 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, optional): 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, optional): 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, optional): the number of subprocess to load data, 0 for no subprocess used and loading data in main process. Default 0 use_buffer_reader (bool, optional): whether to use bufferred reader. If use_buffer_reader=True, the DataLoader would prefetch 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. prefetch_factor (int, optional): Number of batch data the DataLoader would prefetch if use_buffer_reader=True. Default 2. use_shared_memory (bool, optional): 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, optional): the timeout value for getting data form output queue of subprocesses. Default 0. worker_init_fn(callable, optional): 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().__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, prefetch_factor=2, use_shared_memory=True, timeout=0, worker_init_fn=None, persistent_workers=False, ): self.return_list = return_list self.collate_fn = collate_fn self.use_buffer_reader = use_buffer_reader self.prefetch_factor = prefetch_factor self.worker_init_fn = worker_init_fn self.dataset = dataset if not return_list and not _non_static_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 assert prefetch_factor > 0, "prefetch_factor should be a positive value" 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.drop_last = drop_last self.auto_collate_batch = self.batch_sampler is not None self.pin_memory = False if _non_static_mode(): self.pin_memory = ( True if use_pinned_memory() is None else use_pinned_memory() ) self._persistent_workers = persistent_workers self._iterator = None self.num_workers = AuToTune(self).__call__() 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) elif self._persistent_workers: if self._iterator is None: self._iterator = _DataLoaderIterMultiProcess(self) else: self._iterator._reset() return self._iterator 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:`paddle.static.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, optional): 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, optional): whether the created DataLoader is iterable. return_list (bool, optional): 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, optional): 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, optional): 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) 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()) 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().__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()))) """ if _non_static_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, optional): 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: if _in_eager_without_dygraph_check(): return core.eager.read_next_tensor_list( self._reader.read_next_list()[0] ) else: return self._reader.read_next_var_list() except StopIteration: self._reset() raise 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. core.set_current_thread_name("Dataloader_" + str(id(self))) _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 Exception as e: # 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." ) raise e if not self._thread_done_event.is_set(): if tensor_list is not None: try: array = core.LoDTensorArray() for tensor in tensor_list: array.append(tensor) if not self._blocking_queue.push(array): self._blocking_queue.close() except Exception as e: self._exit_thread_unexpectedly() raise e else: self._exit_thread_expectedly() def _reader_thread_loop_for_singleprocess(self, legacy_expected_place): try: # See _DataLoaderIterSingleProcess._thread_loop() for why set expected place here. core.set_current_thread_name("Dataloader_" + str(id(self))) _set_expected_place(legacy_expected_place) for sample in self._batch_reader(): array = core.LoDTensorArray() for item in sample: 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._blocking_queue.push(array): break self._blocking_queue.close() self._thread = None except Exception as e: self._blocking_queue.kill() self._thread = None logging.warning( "DygraphDataLoader reader thread raised an exception." ) raise e def set_sample_generator( self, reader, batch_size, drop_last=True, places=None ): assert batch_size > 0, "batch_size must be larger than 0" if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) 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): if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) 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): if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) self._batch_reader = reader if places is None: places = _current_expected_place() self._places = _convert_places(places) assert ( len(self._places) == 1 ), "Number of places must be 1 in imperative mode" return self class GeneratorLoader(DataLoaderBase): def __init__( self, feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False, drop_last=True, ): self._tensor_reader = None self._places = None self._thread = None self._queue = None self._feed_list = feed_list self._exited = False self._drop_last = drop_last self._keep_order = keep_data_loader_order() 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 graph mode.") self._use_double_buffer = use_double_buffer self._capacity = capacity 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 = 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, self._drop_last, False, ) 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 = __create_unshared_decorated_reader__( 'create_double_buffer_reader', 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}, attrs={'drop_last': self._drop_last}, ) @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: data = self._reader.read_next_list() for i in range(len(data)): data[i] = data[i]._move_to_list() return data else: return self._reader.read_next() except StopIteration: self._queue.close() self._reset() raise 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() def _start(self): def __thread_main__(legacy_expected_place): try: # See _DataLoaderIterSingleProcess._thread_loop() for why set expected place here. core.set_current_thread_name("Dataloader_" + str(id(self))) _set_expected_place(legacy_expected_place) 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 e: self._queue.kill() self._thread = None logging.warning('Your reader has raised an exception!') raise e self._thread = threading.Thread( target=__thread_main__, args=(_current_expected_place(),) ) 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" if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) 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): if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) 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): if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) 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): r""" 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:`paddle.static.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 fed 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 paddle.enable_static() EPOCH_NUM = 3 ITER_NUM = 5 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = paddle.static.nn.fc(x=image, size=10, activation='softmax') return paddle.nn.functional.cross_entropy( input=predict, label=label, reduction='none', use_softmax=False ) 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 = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32') label = paddle.static.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 paddle.enable_static() EPOCH_NUM = 3 ITER_NUM = 5 BATCH_SIZE = 10 def network(image, label): # User-defined network, here is an example of softmax regression. predict = paddle.static.nn.fc(x=image, size=10, activation='softmax') return paddle.nn.functional.cross_entropy( input=predict, label=label, reduction='none', use_softmax=False ) 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 = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32') label = paddle.static.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 = paddle.nn.functional.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 = paddle.static.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 = paddle.static.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 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 = paddle.static.nn.fc(x=image, size=10, activation='softmax') return paddle.nn.functional.cross_entropy( input=predict, label=label, reduction='none', use_softmax=False ) 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 = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32') label = paddle.static.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 paddle.enable_static() EPOCH_NUM = 3 ITER_NUM = 15 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = paddle.static.nn.fc(x=image, size=10, activation='softmax') return paddle.nn.functional.cross_entropy( input=predict, label=label, reduction='none', use_softmax=False ) 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 = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32') label = paddle.static.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 import paddle.fluid as fluid import numpy as np paddle.enable_static() EPOCH_NUM = 3 ITER_NUM = 15 BATCH_SIZE = 3 def network(image, label): # User-defined network, here is an example of softmax regression. predict = paddle.static.nn.fc(x=image, size=10, activation='softmax') return paddle.nn.functional.cross_entropy( input=predict, label=label, reduction='none', use_softmax=False ) 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 = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32') label = paddle.static.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, paddle.distributed.fleet.dataset.DatasetBase ), "dataset must be type of DatasetBase" assert ( not _non_static_mode() ), "DatasetLoader is not supported in dygraph mode yet" if isinstance(places, (list, tuple)): places = _get_paddle_place_list(places) else: places = _get_paddle_place(places) 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, paddle.distributed.fleet.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()