diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index 67d9365377c602ace96c6f3abf993c0d08c40d9f..e0a6a7059899b6efc8a5167973e88deae0686434 100644 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -1100,11 +1100,11 @@ paddle.fluid.ParamAttr ('paddle.fluid.param_attr.ParamAttr', ('document', '7b5bf paddle.fluid.ParamAttr.__init__ (ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, True)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.WeightNormParamAttr ('paddle.fluid.param_attr.WeightNormParamAttr', ('document', 'ea029ec9e0dea75f136211c433154f25')) paddle.fluid.WeightNormParamAttr.__init__ (ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.DataFeeder ('paddle.fluid.data_feeder.DataFeeder', ('document', 'd9e64be617bd5f49dbb08ac2bc8665e6')) +paddle.fluid.DataFeeder ('paddle.fluid.data_feeder.DataFeeder', ('document', '9e83e9c52fe5b234df4e29d07f382995')) paddle.fluid.DataFeeder.__init__ (ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'a0ed5ce816b5d603cb595aacb922335a')) -paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', 'ce65fe1d81dcd7067d5092a5667f35cc')) -paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '334c6af750941a4397a2dd2ea8a4d76f')) +paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', '982feeee2611898d312fdf12580409d7')) +paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', '69ee4aeeb5cd8c8e5922560457d318ba')) +paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '19fe07f2e40f938003f66f39798ec7d6')) paddle.fluid.clip.set_gradient_clip (ArgSpec(args=['clip', 'param_list', 'program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '7a0f76a77dd88a74f24485a103a22fc1')) paddle.fluid.clip.ErrorClipByValue ('paddle.fluid.clip.ErrorClipByValue', ('document', '629b07558971a8ab5e954d9a77457656')) paddle.fluid.clip.ErrorClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) diff --git a/python/paddle/fluid/data_feeder.py b/python/paddle/fluid/data_feeder.py index b377b32636c45e8eff4eca8bc22b49fe0afd8bc3..7ae5793317a097138078392f20c911f1a9ced742 100644 --- a/python/paddle/fluid/data_feeder.py +++ b/python/paddle/fluid/data_feeder.py @@ -152,53 +152,25 @@ class BatchedTensorProvider(object): class DataFeeder(object): """ DataFeeder converts the data that returned by a reader into a data - structure that can feed into Executor and ParallelExecutor. The reader - usually returns a list of mini-batch data entries. Each data entry in - the list is one sample. Each sample is a list or a tuple with one - feature or multiple features. - - The simple usage shows below: - - .. code-block:: python - - import paddle.fluid as fluid - place = fluid.CPUPlace() - img = fluid.layers.data(name='image', shape=[1, 28, 28]) - label = fluid.layers.data(name='label', shape=[1], dtype='int64') - feeder = fluid.DataFeeder([img, label], fluid.CPUPlace()) - result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])]) - - - If you want to feed data into GPU side separately in advance when you - use multi-GPU to train a model, you can use `decorate_reader` function. - - .. code-block:: python - - import paddle - import paddle.fluid as fluid - - place=fluid.CUDAPlace(0) - data = fluid.layers.data(name='data', shape=[3, 224, 224], dtype='float32') - label = fluid.layers.data(name='label', shape=[1], dtype='int64') - - feeder = fluid.DataFeeder(place=place, feed_list=[data, label]) - reader = feeder.decorate_reader( - paddle.batch(paddle.dataset.flowers.train(), batch_size=16), multi_devices=True) - - Args: - feed_list(list): The Variables or Variables'name that will - feed into model. - place(Place): place indicates feed data into CPU or GPU, if you want to - feed data into GPU, please using `fluid.CUDAPlace(i)` (`i` represents - the GPU id), or if you want to feed data into CPU, please using - `fluid.CPUPlace()`. - program(Program): The Program that will feed data into, if program - is None, it will use default_main_program(). Default None. + structure that can feed into Executor. The reader is usually a + python generator that returns a list of mini-batch data entries. + + Parameters: + feed_list (list): Variables or names of Variables that need + to feed. + place (:ref:`api_fluid_CPUPlace` | :ref:`api_fluid_CUDAPlace` ): + place indicates the device (CPU | GPU) the data will be fed into, if + you want to feed data into GPU, please using :code:`fluid.CUDAPlace(i)` + (:code:`i` represents the GPU id), or if you want to feed data into CPU, + please using :code:`fluid.CPUPlace()`. + program (:ref:`api_fluid_Program` , optional): The Program that will + feed data into, if program is None, it will use default_main_program(). + Default None. Raises: - ValueError: If some Variable is not in this Program. + :code:`ValueError` - If some Variables are not in this Program. - Examples: + Example: .. code-block:: python @@ -207,27 +179,34 @@ class DataFeeder(object): import paddle.fluid as fluid place = fluid.CPUPlace() - def reader(): - yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')], + for _ in range(4): + yield np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32'), main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program, startup_program): - data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2]) - data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3]) + data_1 = fluid.layers.data(name='data_1', shape=[-1, 2, 2]) + data_2 = fluid.layers.data(name='data_2', shape=[-1, 1, 3]) out = fluid.layers.fc(input=[data_1, data_2], size=2) # ... - feeder = fluid.DataFeeder([data_1, data_2], place) - + exe = fluid.Executor(place) exe.run(startup_program) - for data in reader(): - outs = exe.run(program=main_program, - feed=feeder.feed(data), - fetch_list=[out]) + + feed_data = feeder.feed(reader()) + + # print feed_data to view feed results + # print(feed_data['data_1']) + # print(feed_data['data_2']) + + outs = exe.run(program=main_program, + feed=feed_data, + fetch_list=[out]) + print(outs) + """ @@ -252,31 +231,42 @@ class DataFeeder(object): def feed(self, iterable): """ - According to feed_list and iterable, converters the input into - a data structure that can feed into Executor and ParallelExecutor. + According to :code:`feed_list` of :code:`DataFeeder` and :code:`iterable` , converts + the input into a data structure that can feed into Executor. - Args: - iterable(list|tuple): the input data. + Parameters: + iterable (generator): user defined python generator to read the raw input data - Returns: - dict: the result of conversion. + Returns: + :code:`dict`: a :code:`dict` that contains (variable name - converted tensor) pairs - Examples: + Example: .. code-block:: python - import numpy.random as random + # In this example, reader - generator will return a list of ndarray of 3 elements + # feed API will convert each ndarray input into a tensor + # the return result is a dict with keys: data_1, data_2, data_3 + # result['data_1'] a LoD-Tensor with shape of [5, 2, 1, 3]. 5 is batch size, and [2, 1, 3] is the real shape of data_1. + # result['data_2'], result['data_3'] are similar. + import numpy as np import paddle.fluid as fluid def reader(limit=5): - for i in range(limit): - yield random.random([784]).astype('float32'), random.random([1]).astype('int64'), random.random([256]).astype('float32') + for i in range(1, limit + 1): + yield np.ones([6]).astype('float32') * i , np.ones([1]).astype('int64') * i, np.random.random([9]).astype('float32') - data_1 = fluid.layers.data(name='data_1', shape=[1, 28, 28]) + data_1 = fluid.layers.data(name='data_1', shape=[2, 1, 3]) data_2 = fluid.layers.data(name='data_2', shape=[1], dtype='int64') - data_3 = fluid.layers.data(name='data_3', shape=[16, 16], dtype='float32') + data_3 = fluid.layers.data(name='data_3', shape=[3, 3], dtype='float32') feeder = fluid.DataFeeder(['data_1','data_2', 'data_3'], fluid.CPUPlace()) - result = feeder.feed(reader()) + + result = feeder.feed(reader()) + print(result['data_1']) + print(result['data_2']) + print(result['data_3']) + + """ converter = [] for lod_level, shape, dtype in six.moves.zip( @@ -303,46 +293,59 @@ class DataFeeder(object): def feed_parallel(self, iterable, num_places=None): """ - Takes multiple mini-batches. Each mini-batch will be feed on each - device in advance. + Similar with feed function, feed_parallel is used with multiple devices (CPU|GPU). + Here :code:`iterable` is a list of python generators. The data return by each + generator in the list will be fed into a seperate device. - Args: - iterable(list|tuple): the input data. - num_places(int): the number of devices. Default None. + Parameters: + iterable (list|tuple): list of user-defined python geneators. The element + number should match the :code:`num_places`. + num_places (int, optional): the number of devices. If not provided (None), + all available devices on the machine will be used. Default None. - Returns: - dict: the result of conversion. + Returns: + :code:`generator`: a :code:`generator` that generate dict which contains (variable name - converted tensor) pairs, + the total number of dicts will be generated matches with the :code:`num_places` - Notes: - The number of devices and number of mini-batches must be same. + .. note:: + The number of devices - :code:`num_places` should equal to the generator (element of :code:`iterable` ) number - Examples: + Example: .. code-block:: python - import numpy.random as random + + import numpy as np import paddle.fluid as fluid - def reader(limit=10): - for i in range(limit): - yield [random.random([784]).astype('float32'), random.random([1]).astype('float32')], + def generate_reader(batch_size, base=0, factor=1): + def _reader(): + for i in range(batch_size): + yield np.ones([4]) * factor + base, np.ones([4]) * factor + base + 5 + return _reader() - x = fluid.layers.data(name='x', shape=[1, 28, 28]) - y = fluid.layers.data(name='y', shape=[1], dtype='float32') + x = fluid.layers.data(name='x', shape=[-1, 2, 2]) + y = fluid.layers.data(name='y', shape=[-1, 2, 2], dtype='float32') - fluid.layers.elementwise_add(x, y) + z = fluid.layers.elementwise_add(x, y) feeder = fluid.DataFeeder(['x','y'], fluid.CPUPlace()) - place_num = 2 + place_num = 2 places = [fluid.CPUPlace() for x in range(place_num)] data = [] exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) program = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(places=places) - for item in reader(): - data.append(item) - if place_num == len(data): - exe.run(program=program, feed=list(feeder.feed_parallel(data, place_num)), fetch_list=[]) - data = [] + + # print sample feed_parallel r resultt + # for item in list(feeder.feed_parallel([generate_reader(5, 0, 1), generate_reader(3, 10, 2)], 2)): + # print(item['x']) + # print(item['y']) + + reader_list = [generate_reader(5, 0, 1), generate_reader(3, 10, 2)] + res = exe.run(program=program, feed=list(feeder.feed_parallel(reader_list, 2)), fetch_list=[z]) + print(res) + + """ if isinstance(self.place, core.CUDAPlace): places = [ @@ -383,52 +386,64 @@ class DataFeeder(object): num_places=None, drop_last=True): """ - Converter the input data into a data that returned by reader into - multiple mini-batches. Each mini-batch will be feed on each device. - - Args: - reader(function): the reader is the function which can generate data. - multi_devices(bool): whether to use multiple devices or not. - num_places(int): if multi_devices is True, you can specify the number - of GPU to use, if multi_devices is None, the function will use all the - GPU of the current machine. Default None. - drop_last(bool): whether to drop the last batch if the - size of the last batch is less than batch_size. Default True. - - Returns: - dict: the result of conversion. - + Decorate the reader (generator) to fit multiple devices. The reader generate + multiple mini-batches. Each mini-batch will be fed into a single device. + + Parameters: + reader(generator): a user defined python generator used to get :code:`mini-batch` of data. + A :code:`mini-batch` can be regarded as a python generator that returns batchs of input + entities, just like the below :code:`_mini_batch` in the code example. + multi_devices(bool): indicate whether to use multiple devices or not. + num_places(int, optional): if :code:`multi_devices` is True, you can specify the number + of devices(CPU|GPU) to use, if multi_devices is None, the function will use all the + devices of the current machine. Default None. + drop_last(bool, optional): whether to drop the last round of data if it is not enough to + feed all devices. Default True. + + Returns: + :code:`generator`: a new :code:`generator` which return converted dicts that can be fed into Executor + Raises: - ValueError: If drop_last is False and the data batch cannot fit for devices. + :code:`ValueError`: If drop_last is False and the data cannot fit devices perfectly. - Examples: + Example: .. code-block:: python - import numpy.random as random + import numpy as np import paddle import paddle.fluid as fluid import paddle.fluid.compiler as compiler - def reader(limit=10): - for i in range(limit): - yield (random.random([784]).astype('float32'), random.random([1]).astype('int64')), + def reader(): + def _mini_batch(batch_size): + for i in range(batch_size): + yield np.random.random([16]).astype('float32'), np.random.randint(10, size=[1]) + + for _ in range(10): + yield _mini_batch(np.random.randint(1, 10)) - place=fluid.CUDAPlace(0) - data = fluid.layers.data(name='data', shape=[1, 28, 28], dtype='float32') - label = fluid.layers.data(name='label', shape=[1], dtype='int64') + place_num = 3 + places = [fluid.CPUPlace() for _ in range(place_num)] + # a simple network sample + data = fluid.layers.data(name='data', shape=[-1, 4, 4], dtype='float32') + label = fluid.layers.data(name='label', shape=[-1, 1], dtype='int64') hidden = fluid.layers.fc(input=data, size=10) - feeder = fluid.DataFeeder(place=place, feed_list=[data, label]) - reader = feeder.decorate_reader(reader, multi_devices=True) + feeder = fluid.DataFeeder(place=places[0], feed_list=[data, label]) + reader = feeder.decorate_reader(reader, multi_devices=True, num_places=3, drop_last=True) - exe = fluid.Executor(place) + exe = fluid.Executor(places[0]) exe.run(fluid.default_startup_program()) compiled_prog = compiler.CompiledProgram( - fluid.default_main_program()).with_data_parallel() + fluid.default_main_program()).with_data_parallel(places=places) + for i,data in enumerate(reader()): - print('iteration : ', i + 1) + # print data if you like + # print(i, data) ret = exe.run(compiled_prog, feed=data, fetch_list=[hidden]) + print(ret) + """ def __reader_creator__():