io.py

#   Copyright (c) 2018 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 __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'
]


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

          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

            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)


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

            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


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)


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)


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)


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

            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)})


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

            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)})


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)})


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


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

           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']) 

            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()
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)