Source code for paddle.fluid.initializer

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from __future__ import print_function

from . import framework
import numpy as np
from .wrapped_decorator import signature_safe_contextmanager
from .core import VarDesc
from . import unique_name

__all__ = [
    'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
    'MSRA', 'force_init_on_cpu', 'init_on_cpu', 'ConstantInitializer',
    'UniformInitializer', 'NormalInitializer', 'TruncatedNormalInitializer',
    'XavierInitializer', 'BilinearInitializer', 'MSRAInitializer',
    'NumpyArrayInitializer'
]

_force_init_on_cpu_ = False


[docs]def force_init_on_cpu(): """ The flag of whether force to init variables on CPU. Returns: bool: the state if we should force init on CPU. Examples: .. code-block:: python import paddle.fluid as fluid if fluid.initializer.force_init_on_cpu(): step = fluid.layers.create_global_var( shape=[2,3], value=1.0, dtype='float32') """ return _force_init_on_cpu_
@signature_safe_contextmanager
[docs]def init_on_cpu(): """ Force the variable to be inited on CPU. Examples: .. code-block:: python import paddle.fluid as fluid with fluid.initializer.init_on_cpu(): step = fluid.layers.create_global_var( shape=[2,3], value=1.0, dtype='float32') """ global _force_init_on_cpu_ pre_state = force_init_on_cpu() _force_init_on_cpu_ = True yield _force_init_on_cpu_ = pre_state
class Initializer(object): """Base class for variable initializers Defines the common interface of variable initializers. They add operations to the init program that are used to initialize variables. Users should not use this class directly, but need to use one of its implementations. """ def __init__(self): pass def __call__(self, param, block): """Add corresponding initialization operations to the network """ raise NotImplementedError() def _compute_fans(self, var): """Compute the fan_in and the fan_out for layers This method computes the fan_in and the fan_out for neural network layers, if not specified. It is not possible to perfectly estimate fan_in and fan_out. This method will estimate it correctly for matrix multiply and convolutions. Args: var: variable for which fan_in and fan_out have to be computed Returns: tuple of two integers (fan_in, fan_out) """ shape = var.shape if not shape or len(shape) == 0: fan_in = fan_out = 1 elif len(shape) == 1: fan_in = fan_out = shape[0] elif len(shape) == 2: # This is the case for simple matrix multiply fan_in = shape[0] fan_out = shape[1] else: # Assume this to be a convolutional kernel # In PaddlePaddle, the shape of the kernel is like: # [num_filters, num_filter_channels, ...] where the remaining # dimensions are the filter_size receptive_field_size = np.prod(shape[2:]) fan_in = shape[1] * receptive_field_size fan_out = shape[0] * receptive_field_size return (fan_in, fan_out)
[docs]class ConstantInitializer(Initializer): """Implements the constant initializer Args: value (float): constant value to initialize the variable Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.Constant(value=2.0)) """ def __init__(self, value=0.0, force_cpu=False): assert value is not None super(ConstantInitializer, self).__init__() self._value = value self._force_cpu = force_cpu def __call__(self, var, block): """Add constant initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) # to be compatible of fp16 initializers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['constant_init', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var # Initialization Ops should be prepended and not appended op = block._prepend_op( type="fill_constant", outputs={"Out": out_var}, attrs={ "shape": var.shape, "dtype": int(out_dtype), "value": float(self._value), 'force_cpu': self._force_cpu or force_init_on_cpu() }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class UniformInitializer(Initializer): """Implements the random uniform distribution initializer Args: low (float): lower boundary of the uniform distribution high (float): upper boundary of the uniform distribution seed (int): random seed Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[1], dtype='float32') fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5)) """ def __init__(self, low=-1.0, high=1.0, seed=0): assert low is not None assert high is not None assert high >= low assert seed is not None super(UniformInitializer, self).__init__() self._low = low self._high = high self._seed = seed def __call__(self, var, block): """Add uniform distribution initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) # Initialization Ops should be prepended and not appended if self._seed == 0: self._seed = block.program.random_seed # to be compatible of fp16 initializers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['uniform_random', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var op = block._prepend_op( type="uniform_random", outputs={"Out": out_var}, attrs={ "shape": var.shape, "dtype": out_dtype, "min": self._low, "max": self._high, "seed": self._seed }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class NormalInitializer(Initializer): """Implements the Random Normal(Gaussian) distribution initializer Args: loc (float): mean of the normal distribution scale (float): standard deviation of the normal distribution seed (int): random seed Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0)) """ def __init__(self, loc=0.0, scale=1.0, seed=0): assert loc is not None assert scale is not None assert seed is not None super(NormalInitializer, self).__init__() self._mean = loc self._std_dev = scale self._seed = seed def __call__(self, var, block): """Add normal distribution initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) # Initialization Ops should be prepended and not appended if self._seed == 0: self._seed = block.program.random_seed # to be compatible of fp16 initalizers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['gaussian_random', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var op = block._prepend_op( type="gaussian_random", outputs={"Out": out_var}, attrs={ "shape": var.shape, "dtype": out_dtype, "mean": self._mean, "std": self._std_dev, "seed": self._seed, "use_mkldnn": False }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class TruncatedNormalInitializer(Initializer): """Implements the Random TruncatedNormal(Gaussian) distribution initializer Args: loc (float): mean of the normal distribution scale (float): standard deviation of the normal distribution seed (int): random seed Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[1], dtype='float32') fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.TruncatedNormal(loc=0.0, scale=2.0)) """ def __init__(self, loc=0.0, scale=1.0, seed=0): assert loc is not None assert scale is not None assert seed is not None super(TruncatedNormalInitializer, self).__init__() self._mean = loc self._std_dev = scale self._seed = seed def __call__(self, var, block): """Add truncated normal distribution initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) # Initialization Ops should be prepended and not appended if self._seed == 0: self._seed = block.program.random_seed # to be compatible of fp16 initalizers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['truncated_gaussian_random', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var op = block._prepend_op( type="truncated_gaussian_random", outputs={"Out": out_var}, attrs={ "shape": var.shape, "dtype": out_dtype, "mean": self._mean, "std": self._std_dev, "seed": self._seed }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class XavierInitializer(Initializer): """ This class implements the Xavier weight initializer from the paper `Understanding the difficulty of training deep feedforward neural networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_ by Xavier Glorot and Yoshua Bengio. This initializer is designed to keep the scale of the gradients approximately same in all the layers. In case of Uniform distribution, the range is [-x, x], where .. math:: x = \sqrt{\\frac{6.0}{fan\_in + fan\_out}} In case of Normal distribution, the mean is 0 and the standard deviation is .. math:: \sqrt{\\frac{2.0}{fan\_in + fan\_out}} Args: uniform (bool): whether to use uniform or normal distribution fan_in (float): fan_in for Xavier initialization. If None, it is inferred from the variable. fan_out (float): fan_out for Xavier initialization. If None, it is inferred from the variable. seed (int): random seed Note: It is recommended to set fan_in and fan_out to None for most cases. Examples: .. code-block:: python import paddle.fluid as fluid queries = fluid.layers.data(name='x', shape=[1], dtype='float32') fc = fluid.layers.fc( input=queries, size=10, param_attr=fluid.initializer.Xavier(uniform=False)) """ def __init__(self, uniform=True, fan_in=None, fan_out=None, seed=0): assert uniform is not None assert seed is not None super(XavierInitializer, self).__init__() self._uniform = uniform self._fan_in = fan_in self._fan_out = fan_out self._seed = seed def __call__(self, var, block): """Add xavier initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) f_in, f_out = self._compute_fans(var) # If fan_in and fan_out are passed, use them fan_in = f_in if self._fan_in is None else self._fan_in fan_out = f_out if self._fan_out is None else self._fan_out if self._seed == 0: self._seed = block.program.random_seed # to be compatible of fp16 initalizers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['xavier_init', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var if self._uniform: limit = np.sqrt(6.0 / float(fan_in + fan_out)) op = block._prepend_op( type="uniform_random", outputs={"Out": out_var}, attrs={ "shape": out_var.shape, "dtype": out_dtype, "min": -limit, "max": limit, "seed": self._seed }, stop_gradient=True) else: std = np.sqrt(2.0 / float(fan_in + fan_out)) op = block._prepend_op( type="gaussian_random", outputs={"Out": out_var}, attrs={ "shape": out_var.shape, "dtype": out_dtype, "mean": 0.0, "std": std, "seed": self._seed }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class MSRAInitializer(Initializer): """Implements the MSRA initializer a.k.a. Kaiming Initializer This class implements the weight initialization from the paper `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification <https://arxiv.org/abs/1502.01852>`_ by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a robust initialization method that particularly considers the rectifier nonlinearities. In case of Uniform distribution, the range is [-x, x], where .. math:: x = \sqrt{\\frac{6.0}{fan\_in}} In case of Normal distribution, the mean is 0 and the standard deviation is .. math:: \sqrt{\\frac{2.0}{fan\_in}} Args: uniform (bool): whether to use uniform or normal distribution fan_in (float): fan_in for MSRAInitializer. If None, it is\ inferred from the variable. seed (int): random seed Note: It is recommended to set fan_in to None for most cases. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.MSRA(uniform=False)) """ def __init__(self, uniform=True, fan_in=None, seed=0): """Constructor for MSRAInitializer """ assert uniform is not None assert seed is not None super(MSRAInitializer, self).__init__() self._uniform = uniform self._fan_in = fan_in self._seed = seed def __call__(self, var, block): """Add MSRA initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) f_in, f_out = self._compute_fans(var) # If fan_in is passed, use it fan_in = f_in if self._fan_in is None else self._fan_in if self._seed == 0: self._seed = block.program.random_seed # to be compatible of fp16 initalizers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['masra_init', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var if self._uniform: limit = np.sqrt(6.0 / float(fan_in)) op = block._prepend_op( type="uniform_random", outputs={"Out": out_var}, attrs={ "shape": out_var.shape, "dtype": int(out_dtype), "min": -limit, "max": limit, "seed": self._seed }, stop_gradient=True) else: std = np.sqrt(2.0 / float(fan_in)) op = block._prepend_op( type="gaussian_random", outputs={"Out": out_var}, attrs={ "shape": out_var.shape, "dtype": int(out_dtype), "mean": 0.0, "std": std, "seed": self._seed }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class BilinearInitializer(Initializer): """ This initializer can be used in transposed convolution operator to act as upsampling. Users can upsample a feature map with shape of (B, C, H, W) by any integer factor. The usage is: Examples: .. code-block:: python import paddle.fluid as fluid factor = 2 C = 2 w_attr = fluid.initializer.ParamAttr( learning_rate=0., regularizer=fluid.regularizer.L2Decay(0.), initializer=fluid.initializer.Bilinear()) x = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32") conv_up = fluid.layers.conv2d_transpose( input=x, num_filters=C, output_size=None, filter_size=2 * factor - factor % 2, padding=int(math.ceil((factor - 1) / 2.)), stride=factor, groups=C, param_attr=w_attr, bias_attr=False) Where, `num_filters=C` and `groups=C` means this is channel-wise transposed convolution. The filter shape will be (C, 1, K, K) where K is `filer_size`, This initializer will set a (K, K) interpolation kernel for every channel of the filter identically. The resulting shape of the output feature map will be (B, C, factor * H, factor * W). Note that the learning rate and the weight decay are set to 0 in order to keep coefficient values of bilinear interpolation unchanged during training. """ def __init__(self): """Constructor for BilinearInitializer. """ super(BilinearInitializer, self).__init__() def __call__(self, var, block): """Add biliear initialization ops for a variable Args: var (Variable): Variable that needs to be initialized. block (Block): The block in which initialization ops should be added. Returns: Operator: the initialization op Raises: ValueError: If type of `var` and `block` is not right. If the shape of `var` size is not 4 and var.shape[2] != var.shape[3]. """ if not isinstance(var, framework.Variable): raise ValueError("var must be framework.Variable.") if not isinstance(block, framework.Block): raise ValueError("block must be framework.Block.") shape = var.shape if len(shape) != 4: raise ValueError("the length of shape must be 4.") if shape[2] != shape[3]: raise ValueError("shape[2] must be equal to shape[3].") weight = np.zeros(np.prod(var.shape), dtype='float32') size = shape[3] # factor f = np.ceil(size / 2.) # center c = (2 * f - 1 - f % 2) / (2. * f) for i in range(np.prod(shape)): x = i % size y = (i / size) % size weight[i] = (1 - abs(x / f - c)) * (1 - abs(y / f - c)) weight = np.reshape(weight, shape) # to be compatible of fp16 initalizers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 out_var = block.create_var( name=unique_name.generate(".".join( ['bilinear_init', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_dtype = var.dtype out_var = var if out_dtype == VarDesc.VarType.FP32: value_name = "fp32_values" values = [float(v) for v in weight.flat] else: raise ValueError("Unsupported dtype %s", input.dtype) if np.prod(shape) > 1024 * 1024: raise ValueError("The size of input is too big. ") op = block.append_op( type='assign_value', outputs={'Out': [out_var]}, attrs={ 'dtype': out_dtype, 'shape': list(shape), value_name: values }) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
[docs]class NumpyArrayInitializer(Initializer): """Init an parameter with an numpy array Args: value (numpy): numpy array to initialize the variable Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.layers.data(name="x", shape=[5], dtype='float32') fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.NumpyArrayInitializer(numpy.array([1,2]))) """ def __init__(self, value): import numpy assert isinstance(value, numpy.ndarray) super(NumpyArrayInitializer, self).__init__() self._value = value def __call__(self, var, block): """Add constant initialization ops for a variable Args: var: Variable that needs to be initialized block: The block in which initialization ops should be added Returns: the initialization op """ assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) # to be compatible of fp16 initalizers if var.dtype == VarDesc.VarType.FP16: out_dtype = VarDesc.VarType.FP32 np_value = self._value.astype("float32") out_var = block.create_var( name=unique_name.generate(".".join( ['numpy_array_init', var.name, 'tmp'])), shape=var.shape, dtype=out_dtype, type=VarDesc.VarType.LOD_TENSOR, persistable=False) else: out_var = var out_dtype = var.dtype np_value = self._value # Initialization Ops should be prepended and not appended if out_dtype == VarDesc.VarType.FP32: value_name = "fp32_values" values = [float(v) for v in np_value.flat] elif out_dtype == VarDesc.VarType.INT32: value_name = "int32_values" values = [int(v) for v in np_value.flat] else: raise ValueError("Unsupported dtype %s", self._value.dtype) if self._value.size > 1024 * 1024 * 1024: raise ValueError("The size of input is too big. Please consider " "saving it to file and 'load_op' to load it") op = block._prepend_op( type='assign_value', outputs={'Out': out_var}, attrs={ 'dtype': out_dtype, 'shape': list(self._value.shape), value_name: values }, stop_gradient=True) if var.dtype == VarDesc.VarType.FP16: block.append_op( type="cast", inputs={"X": out_var}, outputs={"Out": var}, attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype}) if not framework.in_dygraph_mode(): var.op = op return op
# We short the class name, since users will use the initializer with the package # name. The sample code: # # import paddle.fluid as fluid # # hidden = fluid.layers.fc(..., # param_attr=ParamAttr(fluid.initializer.Xavier())) # # It is no need to add an `Initializer` as the class suffix Constant = ConstantInitializer Uniform = UniformInitializer Normal = NormalInitializer TruncatedNormal = TruncatedNormalInitializer Xavier = XavierInitializer MSRA = MSRAInitializer Bilinear = BilinearInitializer