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io.py
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# 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
import multiprocessing
import os
import six
import sys
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
import logging
from ..data_feeder import check_dtype, check_type
from paddle.fluid.framework import static_only
from ..framework import _get_paddle_place, _current_expected_place, _set_expected_place
__all__ = [
'data', 'read_file', 'double_buffer', 'py_reader',
'create_py_reader_by_data', 'load'
]
@static_only
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 operator creates the global variable. The global variables can be
accessed by all the following operators in the graph.
Note:
:code:`paddle.fluid.layers.data` is deprecated as it will be removed in
a later version. Please use :code:`paddle.fluid.data` .
This :code:`paddle.fluid.layers.data` set shape and dtype at compile
time but does NOT check the shape or the dtype of fed data, the
:code:`paddle.fluid.data` checks the shape and the dtype of data fed
by Executor or ParallelExecutor during run time.
To feed variable size inputs, users can feed variable size inputs
directly to this :code:`paddle.fluid.layers.data` and PaddlePaddle will
fit the size accordingly. Or set -1 on the variable dimension when using
:code:`paddle.fluid.data` .
The default :code:`stop_gradient` attribute of the Variable created by
this API is true, which means the gradient won't be passed backward
through the data Varaible. Set :code:`var.stop_gradient = False` If
user would like to pass backward gradient.
Args:
name(str): The name/alias of the variable, see :ref:`api_guide_Name`
for more details.
shape(list|tuple): 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 the data. Supported dtype: bool,
float16, float32, float64, int8, int16, int32, int64, uint8.
type(VarType): The output type. Supported dtype: VarType.LOD_TENSOR,
VarType.SELECTED_ROWS, VarType.NCCL_ID. Default: VarType.LOD_TENSOR.
lod_level(int): The LoD Level. 0 means the input data is not a sequence.
Default: 0.
stop_gradient(bool): A boolean that mentions whether gradient should flow.
Default: True.
Returns:
The global variable that gives access to the data.
Return Type:
Variable
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='x', shape=[784], dtype='float32')
"""
helper = LayerHelper('data', **locals())
check_type(name, 'name', (six.binary_type, six.text_type), 'data')
check_type(shape, 'shape', (list, tuple), 'data')
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):
from ..incubate.fleet.parameter_server.mode import DistributedMode
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
'distributed_mode':
DistributedMode.SYNC, # 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 separated 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 separated 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
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 = []
need_check_feed = []
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)
need_check_feed.append(int(feed_data.desc.need_check_feed()))
else:
dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
need_check_feed = [0 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)
dtype_int = [int(t) for t in dtypes]
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, False)
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,
'dtypes': dtype_int,
'need_check_feed': need_check_feed,
'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__(legacy_expected_place):
try:
# See _DataLoaderIterSingleProcess._thread_loop() for why set expected place here.
_set_expected_place(legacy_expected_place)
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.kill()
logging.warn('Your decorated reader has raised an exception!')
six.reraise(*sys.exc_info())
reader.thread = threading.Thread(
target=__provider_thread__, args=(_current_expected_place(), ))
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):
"""
:api_attr: Static Graph
Create a Python reader for data feeding in Python
This operator 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 and feed the data from the data source to the Reader Variable.
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:
:code:`Program.clone()` method cannot clone :code:`py_reader`. You can
refer to :ref:`api_fluid_Program` for more details.
The :code:`read_file` call needs to be in the program block of :code:`py_reader`.
You can refer to :ref:`api_fluid_layers_read_file` for more details.
Args:
capacity(int): The buffer capacity maintained by :code:`py_reader`.
shapes(list|tuple): List of tuples which declaring data shapes. shapes[i]
represents the i-th data shape.
dtypes(list|tuple): List of strings which declaring data type. Supported dtype:
bool, float16, float32, float64, int8, int16, int32, int64, uint8.
lod_levels(list|tuple): List of ints which declaring data lod_level.
name(basestring): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
use_double_buffer(bool): Whether use double buffer or not. The double buffer is
for pre-reading the data of the next batch and copy the data asynchronously
from CPU to GPU. Default is True.
Returns:
A Reader from which we can get feeding data.
Return Type:
Variable
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 regession 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()
"""
logging.warn(
'paddle.fluid.layers.py_reader() may be deprecated in the near future. '
'Please use paddle.fluid.io.DataLoader.from_generator() instead.')
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):
"""
:api_attr: Static Graph
The OP creates a Python reader for data feeding in Python, it is similar
to :ref:`api_fluid_layers_py_reader` except that it can read data from
the list of feed variables.
Parameters:
capacity (int): The buffer capacity maintained by :code:`py_reader`. Its unit
is batch number. Set larger :attr:`capacity` if the reader is fast.
feed_list (list(Variable)): The feed variables, are usually created by
:code:`fluid.data()`.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`. Default: None.
use_double_buffer (bool, optional): Whether use double buffer. If it's True,
the OP would prefetch next batch data asynchronously. Default: True.
Returns:
Reader: A Reader for data feeding. The data types of read data are the same as the data types of variables of :attr:`feed_list`.
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)
MEMORY_OPT = False
USE_CUDA = False
image = fluid.data(name='image', shape=[None, 1, 28, 28], dtype='float32')
label = fluid.data(name='label', shape=[None, 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) # The definition of custom network and the loss function
place = fluid.CUDAPlace(0) if USE_CUDA else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = True if MEMORY_OPT else False
exec_strategy = fluid.ExecutionStrategy()
compiled_prog = fluid.compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
for epoch_id in range(2):
reader.start()
try:
while True:
exe.run(compiled_prog, fetch_list=[loss.name])
except fluid.core.EOFException:
reader.reset()
"""
logging.warn(
'paddle.fluid.layers.create_py_reader_by_data() may be deprecated in the near future. '
'Please use paddle.fluid.io.DataLoader.from_generator() instead.')
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 __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 double_buffer(reader, place=None, name=None):
"""
Wrap a double buffer reader. The class Reader contains DecoratedReader and FileReader. Moreover, the DecoratedReader is inherited by CustomReader and BufferedReader. This function is related to BufferedReader. 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|str, optional): The place of target data, such as CPU, GPU, and if use GPU, it's necessary to point out which card is involved. Default is the sample place of executor perform.
if ``place`` is string, It can be ``cpu``, ``gpu:x``, where ``x`` is the ndex of the GPUs.
name (str, optional): Variable name. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns:
Variable(Reader): wrapped reader with double buffer.
Examples:
.. code-block:: python
import paddle.fluid as fluid
reader = fluid.layers.py_reader(capacity=64,
shapes=[(-1, 1, 28, 28), (-1, 1)],
dtypes=['float32', 'int64'],
use_double_buffer=False)
reader = fluid.layers.double_buffer(reader)
image, label = fluid.layers.read_file(reader)
"""
attrs = dict()
if place is not None:
attrs['place'] = str(_get_paddle_place(place)).upper()
return __create_unshared_decorated_reader__(
'create_double_buffer_reader', reader, attrs, name=name)
def read_file(reader):
"""
:api_attr: Static Graph
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()` .
Args:
reader(Variable): The reader to execute.
Returns:
Tuple[Variable]: Data read from the given reader.
Examples:
.. code-block:: python
import paddle.fluid as fluid
reader = fluid.layers.py_reader(capacity=64,
shapes=[(-1, 1, 28, 28), (-1, 1)],
dtypes=['float32', 'int64'])
image, label = fluid.layers.read_file(reader)
"""
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
def load(out, file_path, load_as_fp16=None):
"""
Load operator will load a LoDTensor / SelectedRows variable from disk file.
Args:
out(Variable): The LoDTensor / SelectedRows need to be loaded..
file_path(STRING): Variable will be loaded from "file_path".
load_as_fp16(BOOLEAN): If true, the tensor will be first loaded and then converted to float16 data type. Otherwise, the tensor will be directly loaded without data type conversion. Default is false..
Returns:
None
Examples:
.. code-block:: python
import paddle.fluid as fluid
tmp_tensor = fluid.layers.create_tensor(dtype='float32')
fluid.layers.load(tmp_tensor, "./tmp_tensor.bin")
"""
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={}, outputs={"Out": out}, attrs=attrs)