evaluator.py

#   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

from . import layers
from .framework import Program, Variable, program_guard
from . import unique_name
from .layer_helper import LayerHelper
from .initializer import Constant
from .layers import detection

__all__ = [
    'DetectionMAP',
]


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


class DetectionMAP(Evaluator):
    """
    Warning: This would be deprecated in the future. Please use fluid.metrics.DetectionMAP
    instead.
    Calculate the detection mean average precision (mAP).

    The general steps are as follows:
    1. calculate the true positive and false positive according to the input
        of detection and labels.
    2. calculate mAP value, support two versions: '11 point' and 'integral'.

    Please get more information from the following articles:
      https://sanchom.wordpress.com/tag/average-precision/
      https://arxiv.org/abs/1512.02325

    Args:
        input (Variable): The detection results, which is a LoDTensor with shape
            [M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax].
        gt_label (Variable): The ground truth label index, which is a LoDTensor
            with shape [N, 1].
        gt_box (Variable): The ground truth bounding box (bbox), which is a
            LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax].
        gt_difficult (Variable|None): Whether this ground truth is a difficult
            bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
            it means all the ground truth labels are not difficult bbox.
        class_num (int): The class number.
        background_label (int): The index of background label, the background
            label will be ignored. If set to -1, then all categories will be
            considered, 0 by default.
        overlap_threshold (float): The threshold for deciding true/false
            positive, 0.5 by default.
        evaluate_difficult (bool): Whether to consider difficult ground truth
            for evaluation, True by default. This argument does not work when
            gt_difficult is None.
        ap_version (string): The average precision calculation ways, it must be
            'integral' or '11point'. Please check
            https://sanchom.wordpress.com/tag/average-precision/ for details.
            - 11point: the 11-point interpolated average precision.
            - integral: the natural integral of the precision-recall curve.

    Examples:
        .. code-block:: python

            exe = fluid.executor(place)
            map_evaluator = fluid.Evaluator.DetectionMAP(input,
                gt_label, gt_box, gt_difficult)
            cur_map, accum_map = map_evaluator.get_map_var()
            fetch = [cost, cur_map, accum_map]
            for epoch in PASS_NUM:
                map_evaluator.reset(exe)
                for data in batches:
                    loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch)

        In the above example:

        'cur_map_v' is the mAP of current mini-batch.
        'accum_map_v' is the accumulative mAP of one pass.
    """

    def __init__(
        self,
        input,
        gt_label,
        gt_box,
        gt_difficult=None,
        class_num=None,
        background_label=0,
        overlap_threshold=0.5,
        evaluate_difficult=True,
        ap_version='integral',
    ):
        super().__init__("map_eval")

        gt_label = layers.cast(x=gt_label, dtype=gt_box.dtype)
        if gt_difficult:
            gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
            label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
        else:
            label = layers.concat([gt_label, gt_box], axis=1)

        # calculate mean average precision (mAP) of current mini-batch
        map = detection.detection_map(
            input,
            label,
            class_num,
            background_label,
            overlap_threshold=overlap_threshold,
            evaluate_difficult=evaluate_difficult,
            ap_version=ap_version,
        )

        self._create_state(dtype='int32', shape=None, suffix='accum_pos_count')
        self._create_state(dtype='float32', shape=None, suffix='accum_true_pos')
        self._create_state(
            dtype='float32', shape=None, suffix='accum_false_pos'
        )

        self.has_state = None
        var = self.helper.create_variable(
            persistable=True, dtype='int32', shape=[1]
        )
        self.helper.set_variable_initializer(
            var, initializer=Constant(value=int(0))
        )
        self.has_state = var

        # calculate accumulative mAP
        accum_map = detection.detection_map(
            input,
            label,
            class_num,
            background_label,
            overlap_threshold=overlap_threshold,
            evaluate_difficult=evaluate_difficult,
            has_state=self.has_state,
            input_states=self.states,
            out_states=self.states,
            ap_version=ap_version,
        )

        layers.fill_constant(
            shape=self.has_state.shape,
            value=1,
            dtype=self.has_state.dtype,
            out=self.has_state,
        )

        self.cur_map = map
        self.accum_map = accum_map

    def get_map_var(self):
        return self.cur_map, self.accum_map

    def reset(self, executor, reset_program=None):
        if reset_program is None:
            reset_program = Program()
        with program_guard(main_program=reset_program):
            var = _clone_var_(reset_program.current_block(), self.has_state)
            layers.fill_constant(
                shape=var.shape, value=0, dtype=var.dtype, out=var
            )
        executor.run(reset_program)