# 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 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 __all__ = [ 'data', 'read_file', 'double_buffer', 'py_reader', 'create_py_reader_by_data', '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 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 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() logging.warn('Your decorated reader has raised an exception!') six.reraise(*sys.exc_info()) 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() """ 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): """ 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 import paddle.fluid.compiler as compiler 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.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 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 compiled_prog = 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 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: .. 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(place).upper() return __create_unshared_decorated_reader__( 'create_double_buffer_reader', reader, attrs, name=name) 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 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 @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}, attrs=attrs)