Source code for paddle.fluid.layers.io

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from __future__ import print_function
from ..wrapped_decorator import signature_safe_contextmanager
import multiprocessing
import os
import six
import threading

from ..data_feeder import DataFeeder
from .control_flow import BlockGuard
from .layer_function_generator import templatedoc
from .. import core
from ..executor import global_scope
from ..framework import convert_np_dtype_to_dtype_, default_main_program, \
    default_startup_program, program_guard, Program, Variable
from ..layer_helper import LayerHelper
from ..unique_name import generate as unique_name

__all__ = [
    'data', 'open_files', 'read_file', 'shuffle', 'batch', 'double_buffer',
    'random_data_generator', 'py_reader', 'create_py_reader_by_data',
    'Preprocessor', 'load'
]


[docs]def data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=core.VarDesc.VarType.LOD_TENSOR, stop_gradient=True): """ **Data Layer** This function takes in the input and based on whether data has to be returned back as a minibatch, it creates the global variable by using the helper functions. The global variables can be accessed by all the following operators in the graph. All the input variables of this function are passed in as local variables to the LayerHelper constructor. Notice that paddle would only use :code:`shape` to infer the shapes of following variables in the network during compile-time. During run-time, paddle would not check whether the shape of the feeded data matches the :code:`shape` settings in this function. Args: name(str): The name/alias of the function shape(list): Tuple declaring the shape. If :code:`append_batch_size` is True and there is no -1 inside :code:`shape`, it should be considered as the shape of the each sample. Otherwise, it should be considered as the shape of the batched data. append_batch_size(bool): 1. If true, it prepends -1 to the shape. For example if shape=[1], the resulting shape is [-1, 1]. This will be useful to set different batch size at run time. 2. If shape contains -1, such as shape=[1, -1]. append_batch_size will be enforced to be be False (ineffective) because PaddlePaddle cannot set more than 1 unknown number on the shape. dtype(np.dtype|VarType|str): The type of data : float32, float16, int etc type(VarType): The output type. By default it is LOD_TENSOR. lod_level(int): The LoD Level. 0 means the input data is not a sequence. stop_gradient(bool): A boolean that mentions whether gradient should flow. Returns: Variable: The global variable that gives access to the data. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='x', shape=[784], dtype='float32') """ helper = LayerHelper('data', **locals()) shape = list(shape) for i in six.moves.range(len(shape)): if shape[i] is None: shape[i] = -1 append_batch_size = False elif shape[i] < 0: append_batch_size = False if append_batch_size: shape = [-1] + shape # append batch size as -1 data_var = helper.create_global_variable( name=name, shape=shape, dtype=dtype, type=type, stop_gradient=stop_gradient, lod_level=lod_level, is_data=True) return data_var
class BlockGuardServ(BlockGuard): """ BlockGuardServ class. BlockGuardServ class is used to create an op with a block in a program. """ def __init__(self, server): if not (isinstance(server, ListenAndServ)): raise TypeError("BlockGuardServ takes a ListenAndServ") super(BlockGuardServ, self).__init__(server.helper.main_program) self.server = server def __exit__(self, exc_type, exc_val, exc_tb): if exc_type is not None: return False self.server.complete_op() return super(BlockGuardServ, self).__exit__(exc_type, exc_val, exc_tb) class ListenAndServ(object): """ **ListenAndServ Layer** ListenAndServ is used to create a rpc server bind and listen on specific TCP port, this server will run the sub-block when received variables from clients. Args: endpoint(string): IP:port string which the server will listen on. inputs(list): a list of variables that the server will get from clients. fan_in(int): how many client are expected to report to this server, default: 1. optimizer_mode(bool): whether to run the server as a parameter server, default: True. Examples: .. code-block:: python import paddle.fluid as fluid with fluid.program_guard(main): serv = layers.ListenAndServ( "127.0.0.1:6170", ["X"], optimizer_mode=False) with serv.do(): x = layers.data( shape=[32, 32], dtype='float32', name="X", append_batch_size=False) fluid.initializer.Constant(value=1.0)(x, main.global_block()) layers.scale(x=x, scale=10.0, out=out_var) exe = fluid.Executor(place) exe.run(main) """ def __init__(self, endpoint, inputs, fan_in=1, optimizer_mode=True): self.helper = LayerHelper("listen_and_serv") self.inputs = inputs self.outputs = [] self.endpoint = endpoint self.fan_in = fan_in # FIXME(typhoonzero): add optimizer_mode is stupid, should make it more # general. self.optimizer_mode = optimizer_mode def do(self): return BlockGuardServ(self) def get_params_and_grads(self): main_program = self.helper.main_program current_block = main_program.current_block() parent_block = self.parent_block() # params and grads in the same order. params = list() grads = list() for op in current_block.ops: # FIXME(typhoonzero): op.inputs is None if it's cloned. if self.optimizer_mode: if "Grad" in op.inputs and "Param" in op.inputs: params.append(op.inputs["Param"].name) grads.append(op.inputs["Grad"].name) else: # simple recv mode, recv operators inputs. for iname in op.input_names: for in_var_name in op.input(iname): params.append(parent_block.var(in_var_name)) grads.append(parent_block.var(in_var_name)) return params, grads def parent_block(self): prog = self.helper.main_program parent_idx = prog.current_block().parent_idx assert parent_idx >= 0 parent_block = prog.block(parent_idx) return parent_block def complete_op(self): main_program = self.helper.main_program current_block = main_program.current_block() parent_block = self.parent_block() parent_block.append_op( type='listen_and_serv', inputs={"X": self.inputs}, outputs={}, attrs={ 'endpoint': self.endpoint, 'Fanin': self.fan_in, 'optimize_blocks': [ current_block ], # did not support multiple optimize blocks in layers 'sync_mode': True, # did not support async now in layers 'grad_to_block_id': [""] }) def Send(endpoints, send_vars, dummy_output=None, sync=True): """ Send variables to the server side, and get vars from server side when server have finished running server side program. Args: endpoints (str): comma seperated IP:PORT pairs in the order of send_vars to send send_vars (list): variables to send to server sync (bool): whether to wait the request finish """ assert (type(send_vars) == list) if dummy_output is None: dummy_output = [] elif isinstance(dummy_output, Variable): dummy_output = [dummy_output] assert (type(dummy_output) == list) epmap = endpoints.split(",") endpoints = list(set(epmap)) helper = LayerHelper("Send", **locals()) rpc_op_role_name = core.op_proto_and_checker_maker.kOpRoleAttrName() helper.append_op( type="send", inputs={"X": send_vars}, outputs={"Out": dummy_output}, attrs={ "endpoints": endpoints, "epmap": epmap, rpc_op_role_name: core.op_proto_and_checker_maker.OpRole.RPC }) if sync: helper.append_op( type="send_barrier", inputs={"X": dummy_output}, outputs={"Out": []}, attrs={"endpoints": endpoints}) def Recv(endpoints, get_vars, dummy_input=None, sync=True): """ Receive variables from server side Args: endpoints (str): comma seperated IP:PORT pairs in the order of send_vars to send get_vars (list): vars to get from server after send completes. sync (bool): whether to wait the request finish Returns: list: list of received variables """ assert (type(get_vars) == list) if dummy_input is None: dummy_input = [] elif isinstance(dummy_input, Variable): dummy_input = [dummy_input] assert (type(dummy_input) == list) epmap = endpoints.split(",") endpoints = list(set(epmap)) helper = LayerHelper("Recv", **locals()) helper.append_op( type="recv", inputs={"X": dummy_input}, outputs={"Out": get_vars}, attrs={"endpoints": endpoints, "epmap": epmap}) if sync: helper.append_op( type="fetch_barrier", outputs={"Out": get_vars}, attrs={"endpoints": endpoints}) return get_vars def monkey_patch_reader_methods(reader): def __get_reader__(): scope = global_scope() var = scope.find_var(reader.name) return var.get_reader() def reset(): return __get_reader__().reset() reader.reset = reset reader.stop_gradient = True reader.persistable = True return reader def _copy_reader_var_(block, var): new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER) new_var.desc.set_shapes(var.desc.shapes()) new_var.desc.set_dtypes(var.desc.dtypes()) new_var.desc.set_lod_levels(var.desc.lod_levels()) new_var.persistable = True return new_var def _copy_reader_create_op_(block, op): input_param_names = op.input_names new_input_map = {} for param_name in input_param_names: new_input_map[param_name] = [] arg_names = op.input(param_name) for arg_name in arg_names: new_input_map[param_name].append(block.var(arg_name)) output_param_names = op.output_names new_output_map = {} for param_name in output_param_names: new_output_map[param_name] = [] arg_names = op.output(param_name) for arg_name in arg_names: new_output_map[param_name].append(block.var(arg_name)) new_op = block.append_op( type=op.type, inputs=new_input_map, outputs=new_output_map, attrs=op.all_attrs()) return new_op @templatedoc(op_type='create_recordio_file_reader') def open_recordio_file(filename, shapes, lod_levels, dtypes, pass_num=1, for_parallel=True): """ ${comment} Args: filename(${filename_type}): ${filename_comment}. shapes(list): List of tuples which declaring data shapes. lod_levels(${lod_levels_type}): ${lod_levels_comment}. dtypes(list): List of strs which declaring data type. pass_num(int): Number of passes to run. for_parallel(Bool): Set it as True if you are going to run subsequent operators in parallel. Returns: ${out_comment}. Examples: >>> import paddle.fluid as fluid >>> reader = fluid.layers.io.open_recordio_file( >>> filename='./data.recordio', >>> shapes=[(3,224,224), (1,)], >>> lod_levels=[0, 0], >>> dtypes=['float32', 'int64']) >>> # Via the reader, we can use 'read_file' layer to get data: >>> image, label = fluid.layers.io.read_file(reader) """ dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes] shape_concat = [] ranks = [] for shape in shapes: shape_concat.extend(shape) ranks.append(len(shape)) var_name = unique_name('open_recordio_file') startup_blk = default_startup_program().current_block() startup_var = startup_blk.create_var(name=var_name) startup_blk.append_op( type='create_recordio_file_reader', outputs={'Out': [startup_var]}, attrs={ 'shape_concat': shape_concat, 'lod_levels': lod_levels, 'filename': filename, 'ranks': ranks }) startup_var.desc.set_dtypes(dtypes) startup_var.persistable = True main_prog_var = _copy_reader_var_(default_main_program().current_block(), startup_var) if pass_num > 1: main_prog_var = multi_pass(reader=main_prog_var, pass_num=pass_num) return monkey_patch_reader_methods(main_prog_var)
[docs]def random_data_generator(low, high, shapes, lod_levels, for_parallel=True): """ Create a uniform random data generator This layer returns a Reader Variable. Instead of opening a file and reading data from it, this Reader Variable generates float uniform random data by itself. It can be used as a dummy reader to test a network without opening a real file. Args: low(float): The lower bound of data's uniform distribution. high(float): The upper bound of data's uniform distribution. shapes(list): List of tuples which declaring data shapes. lod_levels(list): List of ints which declaring data lod_level. for_parallel(Bool): Set it as True if you are going to run subsequent operators in parallel. Returns: Variable: A Reader Variable from which we can get random data. Examples: .. code-block:: python import paddle.fluid as fluid reader = fluid.layers.random_data_generator( low=0.0, high=1.0, shapes=[[3,224,224], [1]], lod_levels=[0, 0]) # Via the reader, we can use 'read_file' layer to get data: image, label = fluid.layers.read_file(reader) """ dtypes = [core.VarDesc.VarType.FP32] * len(shapes) shape_concat = [] ranks = [] for shape in shapes: shape_concat.extend(shape) ranks.append(len(shape)) var_name = unique_name('random_data_generator') startup_blk = default_startup_program().current_block() startup_var = startup_blk.create_var(name=var_name) startup_blk.append_op( type='create_random_data_generator', outputs={'Out': [startup_var]}, attrs={ 'low': low, 'high': high, 'shape_concat': shape_concat, 'lod_levels': lod_levels, 'ranks': ranks }) startup_var.desc.set_dtypes(dtypes) startup_var.persistable = True main_prog_var = _copy_reader_var_(default_main_program().current_block(), startup_var) return monkey_patch_reader_methods(main_prog_var)
def _py_reader(capacity, shapes, dtypes, lod_levels=None, name=None, use_double_buffer=True, feed_list=None): if feed_list is not None: if not isinstance(feed_list, list): raise TypeError("feed_list should be a list of Variable" " instead of " + str(type(feed_list))) lod_levels = [] dtypes = [] shape_concat = [] ranks = [] shapes = [] for feed_data in 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) else: dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes] shape_concat = [] ranks = [] for shape in shapes: shape_concat.extend(shape) ranks.append(len(shape)) if lod_levels is None: lod_levels = [0] * len(shapes) if name is None: queue_name = unique_name('lod_tensor_blocking_queue') reader_name = unique_name('create_py_reader') double_buffer_name = unique_name('double_buffer') else: queue_name = "_".join([name, "queue"]) reader_name = "_".join([name, "reader"]) double_buffer_name = "_".join([name, "double_buffer"]) var = global_scope().var(queue_name) feed_queue = core.init_lod_tensor_blocking_queue(var, capacity) startup_blk = default_startup_program().current_block() startup_var = startup_blk.create_var(name=reader_name) startup_blk.append_op( type='create_py_reader', inputs={'blocking_queue': [queue_name]}, outputs={'Out': [startup_var]}, attrs={ 'shape_concat': shape_concat, 'lod_levels': lod_levels, 'ranks': ranks }) startup_var.desc.set_dtypes(dtypes) startup_var.persistable = True main_prog_var = _copy_reader_var_(default_main_program().current_block(), startup_var) reader = monkey_patch_reader_methods(main_prog_var) if 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 # monkey patch py_reader special methods reader.queue = feed_queue current_reset_method = reader.reset reader.thread = None reader.tensor_provider = None reader.exited = False def start_provide_thread(func): def __provider_thread__(): try: for tensors in func(): array = core.LoDTensorArray() for item in tensors: if not isinstance(item, core.LoDTensor): tmp = core.LoDTensor() tmp.set(item, core.CPUPlace()) item = tmp array.append(item) if reader.exited: break feed_queue.push(array) if reader.exited: break feed_queue.close() except Exception as ex: feed_queue.close() raise ex reader.thread = threading.Thread(target=__provider_thread__) reader.thread.daemon = True reader.thread.start() def __set_tensor_provider__(func): reader.tensor_provider = func def __set_paddle_reader__(paddle_reader): with program_guard(Program(), Program()): actual_feed_list = feed_list if actual_feed_list is None: actual_feed_list = [] counter = 0 for dtype, shape, lod_level in zip(dtypes, shapes, lod_levels): name = str(counter) actual_feed_list.append( data( name=name, dtype=dtype, shape=shape, lod_level=lod_level)) counter += 1 data_names = [feed_data.name for feed_data in actual_feed_list] feeder = DataFeeder( feed_list=actual_feed_list, place=core.CPUPlace()) paddle_reader = feeder.decorate_reader( paddle_reader, multi_devices=False) def __tensor_provider__(): for slots in paddle_reader(): yield [slots[data_name] for data_name in data_names] __set_tensor_provider__(__tensor_provider__) def __reset__(): current_reset_method() if reader.thread is not None and reader.tensor_provider is not None: reader.exited = True reader.thread.join() reader.exited = False def __start__(): start_provide_thread(reader.tensor_provider) reader.reset = __reset__ reader.decorate_tensor_provider = __set_tensor_provider__ reader.decorate_paddle_reader = __set_paddle_reader__ reader.decorate_batch_generator = __set_tensor_provider__ reader.decorate_sample_list_generator = __set_paddle_reader__ reader.start = __start__ return reader
[docs]def py_reader(capacity, shapes, dtypes, lod_levels=None, name=None, use_double_buffer=True): """ Create a Python reader for data feeding in Python This layer returns a Reader Variable. The Reader provides :code:`decorate_paddle_reader()` and :code:`decorate_tensor_provider()` to set a Python generator as the data source. More details :ref:`user_guide_use_py_reader_en` . When :code:`Executor::Run()` is invoked in C++ side, the data from the generator would be read automatically. Unlike :code:`DataFeeder.feed()`, the data reading process and :code:`Executor::Run()` process can run in parallel using :code:`py_reader`. The :code:`start()` method of the Reader should be called when each pass begins, while the :code:`reset()` method should be called when the pass ends and :code:`fluid.core.EOFException` raises. Note that :code:`Program.clone()` method cannot clone :code:`py_reader`. Args: capacity(int): The buffer capacity maintained by :code:`py_reader`. shapes(list|tuple): List of tuples which declaring data shapes. dtypes(list|tuple): List of strs which declaring data type. lod_levels(list|tuple): List of ints which declaring data lod_level. name(basestring): The prefix Python queue name and Reader name. None will be generated automatically. use_double_buffer(bool): Whether use double buffer or not. Returns: Variable: A Reader from which we can get feeding data. Examples: 1. The basic usage of :code:`py_reader` is as follows: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.dataset.mnist as mnist def network(image, label): # user defined network, here a softmax regresssion example predict = fluid.layers.fc(input=image, size=10, act='softmax') return fluid.layers.cross_entropy(input=predict, label=label) reader = fluid.layers.py_reader(capacity=64, shapes=[(-1, 1, 28, 28), (-1, 1)], dtypes=['float32', 'int64']) reader.decorate_paddle_reader( paddle.reader.shuffle(paddle.batch(mnist.train(), batch_size=5), buf_size=1000)) img, label = fluid.layers.read_file(reader) loss = network(img, label) fluid.Executor(fluid.CUDAPlace(0)).run(fluid.default_startup_program()) exe = fluid.ParallelExecutor(use_cuda=True) for epoch_id in range(10): reader.start() try: while True: exe.run(fetch_list=[loss.name]) except fluid.core.EOFException: reader.reset() fluid.io.save_inference_model(dirname='./model', feeded_var_names=[img.name, label.name], target_vars=[loss], executor=fluid.Executor(fluid.CUDAPlace(0))) 2. When training and testing are both performed, two different :code:`py_reader` should be created with different names, e.g.: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.dataset.mnist as mnist def network(reader): img, label = fluid.layers.read_file(reader) # User defined network. Here a simple regression as example predict = fluid.layers.fc(input=img, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=predict, label=label) return fluid.layers.mean(loss) # Create train_main_prog and train_startup_prog train_main_prog = fluid.Program() train_startup_prog = fluid.Program() with fluid.program_guard(train_main_prog, train_startup_prog): # Use fluid.unique_name.guard() to share parameters with test program with fluid.unique_name.guard(): train_reader = fluid.layers.py_reader(capacity=64, shapes=[(-1, 1, 28, 28), (-1, 1)], dtypes=['float32', 'int64'], name='train_reader') train_reader.decorate_paddle_reader( paddle.reader.shuffle(paddle.batch(mnist.train(), batch_size=5), buf_size=500)) train_loss = network(train_reader) # some network definition adam = fluid.optimizer.Adam(learning_rate=0.01) adam.minimize(train_loss) # Create test_main_prog and test_startup_prog test_main_prog = fluid.Program() test_startup_prog = fluid.Program() with fluid.program_guard(test_main_prog, test_startup_prog): # Use fluid.unique_name.guard() to share parameters with train program with fluid.unique_name.guard(): test_reader = fluid.layers.py_reader(capacity=32, shapes=[(-1, 1, 28, 28), (-1, 1)], dtypes=['float32', 'int64'], name='test_reader') test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512)) test_loss = network(test_reader) fluid.Executor(fluid.CUDAPlace(0)).run(train_startup_prog) fluid.Executor(fluid.CUDAPlace(0)).run(test_startup_prog) train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=train_loss.name, main_program=train_main_prog) test_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=test_loss.name, main_program=test_main_prog) for epoch_id in range(10): train_reader.start() try: while True: train_exe.run(fetch_list=[train_loss.name]) except fluid.core.EOFException: train_reader.reset() test_reader.start() try: while True: test_exe.run(fetch_list=[test_loss.name]) except fluid.core.EOFException: test_reader.reset() """ return _py_reader( capacity=capacity, shapes=shapes, dtypes=dtypes, lod_levels=lod_levels, name=name, use_double_buffer=use_double_buffer)
[docs]def create_py_reader_by_data(capacity, feed_list, name=None, use_double_buffer=True): """ Create a Python reader for data feeding in Python This layer returns a Reader Variable. Works much like py_reader except that it's input is feed_list instead of shapes, dtypes and lod_levels Args: capacity(int): The buffer capacity maintained by :code:`py_reader`. feed_list(list(Variable)): The data feed list. name(basestring): The prefix Python queue name and Reader name. None will be generated automatically. use_double_buffer(bool): Whether use double buffer or not. Returns: Variable: A Reader from which we can get feeding data. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.dataset.mnist as mnist def network(img, label): # User defined network. Here a simple regression as example predict = fluid.layers.fc(input=img, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=predict, label=label) return fluid.layers.mean(loss) image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') reader = fluid.layers.create_py_reader_by_data(capacity=64, feed_list=[image, label]) reader.decorate_paddle_reader( paddle.reader.shuffle(paddle.batch(mnist.train(), batch_size=5), buf_size=500)) img, label = fluid.layers.read_file(reader) loss = network(img, label) # some network definition fluid.Executor(fluid.CUDAPlace(0)).run(fluid.default_startup_program()) exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name) for epoch_id in range(10): reader.start() try: while True: exe.run(fetch_list=[loss.name]) except fluid.core.EOFException: reader.reset() """ return _py_reader( capacity=capacity, shapes=None, dtypes=None, lod_levels=None, name=name, use_double_buffer=use_double_buffer, feed_list=feed_list)
[docs]def open_files(filenames, shapes, lod_levels, dtypes, thread_num=None, buffer_size=None, pass_num=1, is_test=None): """ Open files This layer takes a list of files to read from and returns a Reader Variable. Via the Reader Variable, we can get data from given files. All files must have name suffixs to indicate their formats, e.g., '*.recordio'. Args: filenames(list): The list of file names. shapes(list): List of tuples which declaring data shapes. lod_levels(list): List of ints which declaring data lod_level. dtypes(list): List of strs which declaring data type. thread_num(None): The number of thread to read files. Default: min(len(filenames), cpu_number). buffer_size(None): The buffer size of reader. Default: 3 * thread_num pass_num(int): Number of passes to run. is_test(bool|None): Whether `open_files` used for testing or not. If it is used for testing, the order of data generated is same as the file order. Otherwise, it is not guaranteed the order of data is same between every epoch. [Default: False]. Returns: Variable: A Reader Variable via which we can get file data. Examples: .. code-block:: python import paddle.fluid. as fluid reader = fluid.layers.io.open_files(filenames=['./data1.recordio', './data2.recordio'], shapes=[(3,224,224), (1,)], lod_levels=[0, 0], dtypes=['float32', 'int64']) # Via the reader, we can use 'read_file' layer to get data: image, label = fluid.layers.io.read_file(reader) """ if thread_num is None: thread_num = min(len(filenames), multiprocessing.cpu_count()) else: thread_num = int(thread_num) if buffer_size is None: buffer_size = 3 * thread_num else: buffer_size = int(buffer_size) if isinstance(filenames, six.string_types): filenames = [filenames] dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes] shape_concat = [] ranks = [] for shape in shapes: shape_concat.extend(shape) ranks.append(len(shape)) multi_file_reader_name = unique_name('multi_file_reader') startup_blk = default_startup_program().current_block() startup_reader = startup_blk.create_var(name=multi_file_reader_name) attrs = { 'shape_concat': shape_concat, 'lod_levels': lod_levels, 'ranks': ranks, 'file_names': filenames, 'thread_num': thread_num, 'buffer_size': buffer_size } if is_test is not None: attrs['is_test'] = is_test startup_blk.append_op( type='open_files', outputs={'Out': [startup_reader]}, attrs=attrs) startup_reader.desc.set_dtypes(dtypes) startup_reader.persistable = True main_prog_reader = _copy_reader_var_(default_main_program().current_block(), startup_reader) if pass_num > 1: main_prog_reader = multi_pass( reader=main_prog_reader, pass_num=pass_num) return monkey_patch_reader_methods(main_prog_reader)
def __create_shared_decorated_reader__(op_type, reader, attrs): var_name = unique_name(op_type) startup_blk = default_startup_program().current_block() startup_var = startup_blk.create_var(name=var_name) startop_op = startup_blk.append_op( type=op_type, inputs={'UnderlyingReader': reader}, outputs={'Out': [startup_var]}, attrs=attrs) startup_var.persistable = True main_prog_block = default_main_program().current_block() main_prog_var = _copy_reader_var_(main_prog_block, startup_var) _copy_reader_create_op_(main_prog_block, startop_op) return monkey_patch_reader_methods(main_prog_var) def __create_unshared_decorated_reader__(op_type, reader, attrs, name=None): new_reader_name = name if name is not None else unique_name(op_type) main_blk = default_main_program().current_block() new_reader = main_blk.create_var(name=new_reader_name) main_blk.append_op( type=op_type, inputs={'UnderlyingReader': reader}, outputs={'Out': [new_reader]}, attrs=attrs) return monkey_patch_reader_methods(new_reader)
[docs]def shuffle(reader, buffer_size): """ Creates a data reader whose data output is shuffled. Output from the iterator that created by original reader will be buffered into shuffle buffer, and then shuffled. The size of shuffle buffer is determined by argument buf_size. Args: reader(callable): the original reader whose output will be shuffled. buf_size(int): shuffle buffer size. Returns: callable: the new reader whose output is shuffled. Examples: .. code-block:: python import paddle.fluid as fluid raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio', './data2.recordio'], shapes=[(3,224,224), (1,)], lod_levels=[0, 0], dtypes=['float32', 'int64'], thread_num=2, buffer_size=2) batch_reader = fluid.layers.batch(reader=raw_reader, batch_size=5) shuffle_reader = fluid.layers.shuffle(reader=batch_reader, buffer_size=5000) """ return __create_unshared_decorated_reader__( 'create_shuffle_reader', reader, {'buffer_size': int(buffer_size)})
[docs]def batch(reader, batch_size): """ This layer is a reader decorator. It takes a reader and adds 'batching' decoration on it. When reading with the result decorated reader, output data will be automatically organized to the form of batches. Args: reader(Variable): The reader to be decorated with 'batching'. batch_size(int): The batch size. Returns: Variable: The reader which has been decorated with 'batching'. Examples: .. code-block:: python import paddle.fluid as fluid raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio', './data2.recordio'], shapes=[(3,224,224), (1,)], lod_levels=[0, 0], dtypes=['float32', 'int64'], thread_num=2, buffer_size=2) batch_reader = fluid.layers.batch(reader=raw_reader, batch_size=5) # If we read data with the raw_reader: # data = fluid.layers.read_file(raw_reader) # We can only get data instance by instance. # # However, if we read data with the batch_reader: # data = fluid.layers.read_file(batch_reader) # Each 5 adjacent instances will be automatically combined together # to become a batch. So what we get('data') is a batch data instead # of an instance. """ return __create_unshared_decorated_reader__( 'create_batch_reader', reader, {'batch_size': int(batch_size)})
[docs]def double_buffer(reader, place=None, name=None): """ Wrap a double buffer reader. The data will copy to target place with a double buffer queue. If the target place is None, the place that executor perform on will be used. Args: reader(Variable): the reader variable need to be wrapped. place(Place): the place of target data. Default is the sample place of executor perform. name(str): Variable name. None if the user does not care. Returns: wrapped reader with double buffer. Examples: >>> import paddle.fluid as fluid >>> reader = fluid.layers.open_files(filenames=['mnist.recordio'], >>> shapes=[[-1, 784], [-1, 1]], >>> dtypes=['float32', 'int64']) >>> reader = fluid.layers.double_buffer(reader) >>> img, label = fluid.layers.read_file(reader) """ attrs = dict() if place is not None: attrs['place'] = str(place).upper() return __create_unshared_decorated_reader__( 'create_double_buffer_reader', reader, attrs, name=name)
def multi_pass(reader, pass_num): return __create_shared_decorated_reader__( 'create_multi_pass_reader', reader, {'pass_num': int(pass_num)})
[docs]def read_file(reader): """ Execute the given reader and get data via it. A reader is also a Variable. It can be a raw reader generated by `fluid.layers.open_files()` or a decorated one generated by `fluid.layers.double_buffer()` and so on. Args: reader(Variable): The reader to execute. Returns: Tuple[Variable]: Data read via the given reader. Examples: .. code-block:: python import paddle.fluid as fluid data_file = fluid.layers.open_files( filenames=['mnist.recordio'], shapes=[(-1, 748), (-1, 1)], lod_levels=[0, 0], dtypes=["float32", "int64"]) data_file = fluid.layers.double_buffer( fluid.layers.batch(data_file, batch_size=64)) input, label = fluid.layers.read_file(data_file) """ helper = LayerHelper('read_file') out = [ helper.create_variable_for_type_inference( stop_gradient=True, dtype='float32') for _ in range(len(reader.desc.shapes())) ] helper.append_op( type='read', inputs={'Reader': [reader]}, outputs={'Out': out}) if len(out) == 1: return out[0] else: return out
[docs]class Preprocessor(object): """ A block for data pre-processing in reader. Args: reader (Variable): A reader variable. name (str, default None): The name of the reader. Examples: .. code-block:: python reader = fluid.layers.io.open_files( filenames=['./data1.recordio', './data2.recordio'], shapes=[(3, 224, 224), (1, )], lod_levels=[0, 0], dtypes=['float32', 'int64']) preprocessor = fluid.layers.io.Preprocessor(reader=reader) with preprocessor.block(): img, lbl = preprocessor.inputs() img_out = img / 2 lbl_out = lbl + 1 preprocessor.outputs(img_out, lbl_out) data_file = fluid.layers.io.double_buffer(preprocessor()) """ BEFORE_SUB_BLOCK = 0 IN_SUB_BLOCK = 1 AFTER_SUB_BLOCK = 2 def __init__(self, reader, name=None): self.underlying_reader = reader new_reader_name = name if name is not None else unique_name( "create_custom_reader") self.main_prog = default_main_program() self.reader = self.main_prog.current_block().create_var( name=new_reader_name) self.sub_block = None self.source_var_names = None self.sink_var_names = None self.status = Preprocessor.BEFORE_SUB_BLOCK def _is_completed(self): return self.sub_block and self.source_var_names and self.sink_var_names @signature_safe_contextmanager def block(self): self.status = Preprocessor.IN_SUB_BLOCK self.sub_block = self.main_prog._create_block() yield self.main_prog._rollback() self.status = Preprocessor.AFTER_SUB_BLOCK if not self._is_completed(): raise RuntimeError( "The definition of preprocessor is incompleted! " "Please make sure that you have set input and output " "variables by invoking 'inputs' and 'outputs' in " "Preprocessor's sub-block.") def inputs(self): if self.status != Preprocessor.IN_SUB_BLOCK: raise RuntimeError( "Preprocessor.inputs() can only be invoked inside the sub-block." ) source_shapes = self.underlying_reader.desc.shapes() source_dtypes = self.underlying_reader.desc.dtypes() source_lod_levels = self.underlying_reader.desc.lod_levels() self.source_var_names = [ unique_name("preprocessor_source") for _ in six.moves.range(len(source_shapes)) ] source_vars = [] for var_name, shape, dtype, lod_level in zip( self.source_var_names, source_shapes, source_dtypes, source_lod_levels): source_vars.append(self.main_prog.current_block().create_var( name=var_name, shape=shape, dtype=dtype, lod_level=lod_level)) return source_vars def outputs(self, *outs): if self.status != Preprocessor.IN_SUB_BLOCK: raise RuntimeError( "Preprocessor.outputs() can only be invoked inside the sub-block." ) self.sink_var_names = [var.name for var in outs] def __call__(self, *args, **kwargs): if self.status != Preprocessor.AFTER_SUB_BLOCK: raise RuntimeError( "Preprocessor output can only be retrieved after rnn block.") self.main_prog.current_block().append_op( type="create_custom_reader", inputs={'UnderlyingReader': self.underlying_reader}, outputs={'Out': [self.reader]}, attrs={ "sub_block": self.sub_block, "source_var_names": self.source_var_names, "sink_var_names": self.sink_var_names }) return monkey_patch_reader_methods(self.reader)
@templatedoc()
[docs]def load(out, file_path, load_as_fp16=None): """ ${comment} >>> import paddle.fluid as fluid >>> tmp_tensor = fluid.layers.create_tensor(dtype='float32') >>> fluid.layers.load(tmp_tensor, "./tmp_tensor.bin") Args: out(${out_type}): ${out_comment}. file_path(${file_path_type}): ${file_path_comment}. load_as_fp16(${load_as_fp16_type}): ${load_as_fp16_comment}. Returns: None """ helper = LayerHelper("load", **locals()) attrs = {"file_path": file_path} if load_as_fp16 is not None: attrs['load_as_fp16'] = load_as_fp16 helper.append_op(type="load", inputs={}, output={"Out": out}, args=attrs)