未验证 提交 4050ca0e 编写于 作者: L LoneRanger 提交者: GitHub

remove entry_attr.py and evaluatory.py in fluid (#51330)

* move entry_attr

* remove evaluator

* fix evaluator
上级 30e0409c
...@@ -51,7 +51,6 @@ from .data import * ...@@ -51,7 +51,6 @@ from .data import *
from . import trainer_desc from . import trainer_desc
from . import io from . import io
from . import evaluator
from . import initializer from . import initializer
from .initializer import set_global_initializer from .initializer import set_global_initializer
from . import layers from . import layers
......
# 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.
__all__ = ['ProbabilityEntry', 'CountFilterEntry']
class EntryAttr:
"""
Examples:
.. code-block:: python
import paddle.fluid as fluid
"""
def __init__(self):
self._name = None
def _to_attr(self):
"""
Returns the attributes of this parameter.
Returns:
Parameter attributes(map): The attributes of this parameter.
"""
raise NotImplementedError("EntryAttr is base class")
class ProbabilityEntry(EntryAttr):
def __init__(self, probability):
super().__init__()
if not isinstance(probability, float):
raise ValueError("probability must be a float in (0,1)")
if probability <= 0 or probability >= 1:
raise ValueError("probability must be a float in (0,1)")
self._name = "probability_entry"
self._probability = probability
def _to_attr(self):
return ":".join([self._name, str(self._probability)])
class CountFilterEntry(EntryAttr):
def __init__(self, count_filter):
super().__init__()
if not isinstance(count_filter, int):
raise ValueError(
"count_filter must be a valid integer greater than 0"
)
if count_filter < 0:
raise ValueError(
"count_filter must be a valid integer greater or equal than 0"
)
self._name = "count_filter_entry"
self._count_filter = count_filter
def _to_attr(self):
return ":".join([self._name, str(self._count_filter)])
# 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.
import warnings
import numpy as np
import paddle
from . import layers
from .framework import Program, Variable, program_guard
from . import unique_name
from .layer_helper import LayerHelper
def _clone_var_(block, var):
assert isinstance(var, Variable)
return block.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True,
)
class Evaluator:
"""
Warning: better to use the fluid.metrics.* things, more
flexible support via pure Python and Operator, and decoupled
with executor. Short doc are intended to urge new user
start from Metrics.
Base Class for all evaluators.
Args:
name(str): The name of evaluator. such as, "accuracy". Used for generate
temporary variable name.
main_program(Program, optional): The evaluator should be added to this
main_program. Default default_main_program()
startup_program(Program, optional):The parameter should be added to this
startup_program. Default default_startup_program()
Attributes:
states(list): The list of state variables. states will be reset to zero
when `reset` is invoked.
metrics(list): The list of metrics variables. They will be calculate
every mini-batch
"""
def __init__(self, name, **kwargs):
warnings.warn(
"The %s is deprecated, because maintain a modified program inside evaluator cause bug easily, please use fluid.metrics.%s instead."
% (self.__class__.__name__, self.__class__.__name__),
Warning,
)
self.states = []
self.metrics = []
self.helper = LayerHelper(name, **kwargs)
def reset(self, executor, reset_program=None):
"""
reset metric states at the begin of each pass/user specified batch
Args:
executor(Executor|ParallelExecutor): a executor for executing the reset_program
reset_program(Program): a single Program for reset process
"""
if reset_program is None:
reset_program = Program()
with program_guard(main_program=reset_program):
for var in self.states:
assert isinstance(var, Variable)
g_var = _clone_var_(reset_program.current_block(), var)
layers.fill_constant(
shape=g_var.shape, value=0.0, dtype=g_var.dtype, out=g_var
)
executor.run(reset_program)
def eval(self, executor, eval_program=None):
"""
Evaluate the statistics merged by multiple mini-batches.
Args:
executor(Executor|ParallelExecutor): a executor for executing the eval_program
eval_program(Program): a single Program for eval process
"""
raise NotImplementedError()
def _create_state(self, suffix, dtype, shape):
"""
Create state variable.
Args:
suffix(str): the state suffix.
dtype(str|core.VarDesc.VarType): the state data type
shape(tuple|list): the shape of state
Returns: State variable
"""
state = self.helper.create_variable(
name="_".join([unique_name.generate(self.helper.name), suffix]),
persistable=True,
dtype=dtype,
shape=shape,
)
self.states.append(state)
return state
...@@ -27,7 +27,6 @@ import math ...@@ -27,7 +27,6 @@ import math
import paddle import paddle
from paddle.fluid import layers from paddle.fluid import layers
from paddle.fluid.executor import Executor, global_scope from paddle.fluid.executor import Executor, global_scope
from paddle.fluid.evaluator import Evaluator
from paddle.fluid.framework import ( from paddle.fluid.framework import (
Program, Program,
Parameter, Parameter,
......
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