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961 lines
31 KiB
961 lines
31 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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from . import framework
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import numpy as np
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from .wrapped_decorator import signature_safe_contextmanager
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from .core import VarDesc
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from . import unique_name
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__all__ = [
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'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
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'MSRA', 'force_init_on_cpu', 'init_on_cpu', 'ConstantInitializer',
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'UniformInitializer', 'NormalInitializer', 'TruncatedNormalInitializer',
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'XavierInitializer', 'BilinearInitializer', 'MSRAInitializer',
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'NumpyArrayInitializer'
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]
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_force_init_on_cpu_ = False
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def force_init_on_cpu():
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"""
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The flag of whether force to init variables on CPU.
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Returns:
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bool: the state if we should force init on CPU.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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if fluid.initializer.force_init_on_cpu():
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step = fluid.layers.create_global_var(
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shape=[2,3], value=1.0, dtype='float32')
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"""
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return _force_init_on_cpu_
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@signature_safe_contextmanager
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def init_on_cpu():
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"""
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Force the variable to be inited on CPU.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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with fluid.initializer.init_on_cpu():
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step = fluid.layers.create_global_var(
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shape=[2,3], value=1.0, dtype='float32')
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"""
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global _force_init_on_cpu_
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pre_state = force_init_on_cpu()
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_force_init_on_cpu_ = True
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yield
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_force_init_on_cpu_ = pre_state
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class Initializer(object):
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"""Base class for variable initializers
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Defines the common interface of variable initializers.
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They add operations to the init program that are used
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to initialize variables. Users should not use this class
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directly, but need to use one of its implementations.
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"""
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def __init__(self):
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pass
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def __call__(self, param, block):
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"""Add corresponding initialization operations to the network
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"""
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raise NotImplementedError()
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def _compute_fans(self, var):
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"""Compute the fan_in and the fan_out for layers
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This method computes the fan_in and the fan_out
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for neural network layers, if not specified. It is
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not possible to perfectly estimate fan_in and fan_out.
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This method will estimate it correctly for matrix multiply and
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convolutions.
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Args:
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var: variable for which fan_in and fan_out have to be computed
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Returns:
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tuple of two integers (fan_in, fan_out)
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"""
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shape = var.shape
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if not shape or len(shape) == 0:
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fan_in = fan_out = 1
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elif len(shape) == 1:
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fan_in = fan_out = shape[0]
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elif len(shape) == 2:
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# This is the case for simple matrix multiply
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fan_in = shape[0]
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fan_out = shape[1]
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else:
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# Assume this to be a convolutional kernel
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# In PaddlePaddle, the shape of the kernel is like:
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# [num_filters, num_filter_channels, ...] where the remaining
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# dimensions are the filter_size
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receptive_field_size = np.prod(shape[2:])
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fan_in = shape[1] * receptive_field_size
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fan_out = shape[0] * receptive_field_size
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return (fan_in, fan_out)
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class ConstantInitializer(Initializer):
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"""Implements the constant initializer
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Args:
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value (float): constant value to initialize the variable
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
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fc = fluid.layers.fc(input=x, size=10,
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param_attr=fluid.initializer.Constant(value=2.0))
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"""
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def __init__(self, value=0.0, force_cpu=False):
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assert value is not None
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super(ConstantInitializer, self).__init__()
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self._value = value
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self._force_cpu = force_cpu
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def __call__(self, var, block):
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"""Add constant initialization ops for a variable
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Args:
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var: Variable that needs to be initialized
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block: The block in which initialization ops
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should be added
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Returns:
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the initialization op
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"""
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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# to be compatible of fp16 initializers
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if var.dtype == VarDesc.VarType.FP16:
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(".".join(
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['constant_init', var.name, 'tmp'])),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.LOD_TENSOR,
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persistable=False)
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else:
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out_dtype = var.dtype
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out_var = var
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# Initialization Ops should be prepended and not appended
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op = block._prepend_op(
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type="fill_constant",
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outputs={"Out": out_var},
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attrs={
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"shape": var.shape,
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"dtype": int(out_dtype),
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"value": float(self._value),
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'force_cpu': self._force_cpu or force_init_on_cpu()
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},
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stop_gradient=True)
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if var.dtype == VarDesc.VarType.FP16:
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype,
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"out_dtype": var.dtype})
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if not framework.in_dygraph_mode():
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var.op = op
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return op
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class UniformInitializer(Initializer):
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"""Implements the random uniform distribution initializer
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Args:
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low (float): lower boundary of the uniform distribution
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high (float): upper boundary of the uniform distribution
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seed (int): random seed
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diag_num (int): the number of diagonal elements to initialize.
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If set to 0, diagonal initialization will be not performed.
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diag_step (int): Step size between two diagonal elements,
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which is generally the width of the square matrix.
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diag_val (float): the value of the diagonal element to be initialized,
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default 1.0. It takes effect only if the diag_num is greater than 0.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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x = fluid.layers.data(name='x', shape=[1], dtype='float32')
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fc = fluid.layers.fc(input=x, size=10,
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param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5))
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"""
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def __init__(self,
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low=-1.0,
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high=1.0,
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seed=0,
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diag_num=0,
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diag_step=0,
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diag_val=1.0):
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assert low is not None
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assert high is not None
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assert high >= low
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assert seed is not None
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assert diag_num is not None
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assert diag_step is not None
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assert diag_val is not None
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if diag_num > 0 or diag_step > 0:
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assert (diag_num > 0 and diag_step > 0)
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super(UniformInitializer, self).__init__()
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self._low = low
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self._high = high
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self._seed = seed
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self._diag_num = diag_num
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self._diag_step = diag_step
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self._diag_val = diag_val
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def __call__(self, var, block):
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"""Add uniform distribution initialization ops for a variable
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Args:
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var: Variable that needs to be initialized
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block: The block in which initialization ops
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should be added
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Returns:
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the initialization op
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"""
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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# Initialization Ops should be prepended and not appended
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if self._seed == 0:
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self._seed = block.program.random_seed
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# to be compatible of fp16 initializers
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if var.dtype == VarDesc.VarType.FP16:
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(".".join(
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['uniform_random', var.name, 'tmp'])),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.LOD_TENSOR,
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persistable=False)
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else:
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out_dtype = var.dtype
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out_var = var
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op = block._prepend_op(
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type="uniform_random",
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outputs={"Out": out_var},
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attrs={
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"shape": var.shape,
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"dtype": out_dtype,
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"min": self._low,
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"max": self._high,
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"seed": self._seed,
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"diag_num": self._diag_num,
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"diag_step": self._diag_step,
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"diag_val": self._diag_val
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},
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stop_gradient=True)
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if var.dtype == VarDesc.VarType.FP16:
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype,
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"out_dtype": var.dtype})
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if not framework.in_dygraph_mode():
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var.op = op
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return op
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class NormalInitializer(Initializer):
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"""Implements the Random Normal(Gaussian) distribution initializer
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Args:
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loc (float): mean of the normal distribution
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scale (float): standard deviation of the normal distribution
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seed (int): random seed
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
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fc = fluid.layers.fc(input=x, size=10,
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param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0))
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"""
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def __init__(self, loc=0.0, scale=1.0, seed=0):
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assert loc is not None
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assert scale is not None
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assert seed is not None
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super(NormalInitializer, self).__init__()
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self._mean = loc
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self._std_dev = scale
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self._seed = seed
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def __call__(self, var, block):
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"""Add normal distribution initialization ops for a variable
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Args:
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var: Variable that needs to be initialized
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block: The block in which initialization ops
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should be added
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Returns:
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the initialization op
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"""
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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# Initialization Ops should be prepended and not appended
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if self._seed == 0:
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self._seed = block.program.random_seed
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# to be compatible of fp16 initalizers
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if var.dtype == VarDesc.VarType.FP16:
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(".".join(
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['gaussian_random', var.name, 'tmp'])),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.LOD_TENSOR,
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persistable=False)
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else:
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out_dtype = var.dtype
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out_var = var
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op = block._prepend_op(
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type="gaussian_random",
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outputs={"Out": out_var},
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attrs={
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"shape": var.shape,
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"dtype": out_dtype,
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"mean": self._mean,
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"std": self._std_dev,
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"seed": self._seed,
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"use_mkldnn": False
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},
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stop_gradient=True)
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if var.dtype == VarDesc.VarType.FP16:
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype,
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"out_dtype": var.dtype})
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if not framework.in_dygraph_mode():
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var.op = op
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return op
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class TruncatedNormalInitializer(Initializer):
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"""Implements the Random TruncatedNormal(Gaussian) distribution initializer
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Args:
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loc (float): mean of the normal distribution
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scale (float): standard deviation of the normal distribution
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seed (int): random seed
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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x = fluid.layers.data(name='x', shape=[1], dtype='float32')
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fc = fluid.layers.fc(input=x, size=10,
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param_attr=fluid.initializer.TruncatedNormal(loc=0.0, scale=2.0))
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"""
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def __init__(self, loc=0.0, scale=1.0, seed=0):
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assert loc is not None
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assert scale is not None
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assert seed is not None
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super(TruncatedNormalInitializer, self).__init__()
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self._mean = loc
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self._std_dev = scale
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self._seed = seed
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def __call__(self, var, block):
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"""Add truncated normal distribution initialization ops for a variable
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Args:
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var: Variable that needs to be initialized
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block: The block in which initialization ops
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should be added
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Returns:
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the initialization op
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"""
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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# Initialization Ops should be prepended and not appended
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if self._seed == 0:
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self._seed = block.program.random_seed
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# to be compatible of fp16 initalizers
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if var.dtype == VarDesc.VarType.FP16:
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(".".join(
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['truncated_gaussian_random', var.name, 'tmp'])),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.LOD_TENSOR,
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persistable=False)
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else:
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out_dtype = var.dtype
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out_var = var
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op = block._prepend_op(
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type="truncated_gaussian_random",
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outputs={"Out": out_var},
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attrs={
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"shape": var.shape,
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"dtype": out_dtype,
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"mean": self._mean,
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"std": self._std_dev,
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"seed": self._seed
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},
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stop_gradient=True)
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if var.dtype == VarDesc.VarType.FP16:
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype,
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"out_dtype": var.dtype})
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if not framework.in_dygraph_mode():
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var.op = op
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return op
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class XavierInitializer(Initializer):
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"""
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This class implements the Xavier weight initializer from the paper
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`Understanding the difficulty of training deep feedforward neural
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networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
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by Xavier Glorot and Yoshua Bengio.
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This initializer is designed to keep the scale of the gradients
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approximately same in all the layers. In case of Uniform distribution,
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the range is [-x, x], where
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.. math::
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x = \sqrt{\\frac{6.0}{fan\_in + fan\_out}}
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In case of Normal distribution, the mean is 0 and the standard deviation
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is
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.. math::
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\sqrt{\\frac{2.0}{fan\_in + fan\_out}}
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Args:
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uniform (bool): whether to use uniform or normal distribution
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fan_in (float): fan_in for Xavier initialization. If None, it is
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inferred from the variable.
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fan_out (float): fan_out for Xavier initialization. If None, it is
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inferred from the variable.
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seed (int): random seed
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Note:
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It is recommended to set fan_in and fan_out to None for most cases.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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queries = fluid.layers.data(name='x', shape=[1], dtype='float32')
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fc = fluid.layers.fc(
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input=queries, size=10,
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param_attr=fluid.initializer.Xavier(uniform=False))
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"""
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def __init__(self, uniform=True, fan_in=None, fan_out=None, seed=0):
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assert uniform is not None
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assert seed is not None
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super(XavierInitializer, self).__init__()
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self._uniform = uniform
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self._fan_in = fan_in
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self._fan_out = fan_out
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self._seed = seed
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def __call__(self, var, block):
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"""Add xavier initialization ops for a variable
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Args:
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var: Variable that needs to be initialized
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block: The block in which initialization ops
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should be added
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Returns:
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the initialization op
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"""
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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f_in, f_out = self._compute_fans(var)
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# If fan_in and fan_out are passed, use them
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fan_in = f_in if self._fan_in is None else self._fan_in
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fan_out = f_out if self._fan_out is None else self._fan_out
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if self._seed == 0:
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self._seed = block.program.random_seed
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|
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# to be compatible of fp16 initalizers
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|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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.param_attr.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
|
|
|
|
|
|
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
|