utils.py

# Copyright (c) 2022 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 sys
import os
import os.path as osp
import time
import math
import imghdr
import chardet
import json
import platform

import numpy as np
import paddle

from . import logging
import paddlers


def seconds_to_hms(seconds):
    h = math.floor(seconds / 3600)
    m = math.floor((seconds - h * 3600) / 60)
    s = int(seconds - h * 3600 - m * 60)
    hms_str = "{}:{}:{}".format(h, m, s)
    return hms_str


def get_encoding(path):
    f = open(path, 'rb')
    data = f.read()
    file_encoding = chardet.detect(data).get('encoding')
    f.close()
    return file_encoding


def get_single_card_bs(batch_size):
    card_num = paddlers.env_info['num']
    place = paddlers.env_info['place']
    if batch_size % card_num == 0:
        return int(batch_size // card_num)
    elif batch_size == 1:
        # Evaluation of detection task only supports single card with batch size 1
        return batch_size
    else:
        raise ValueError("Please support correct batch_size, \
                        which can be divided by available cards({}) in {}"
                         .format(card_num, place))


def dict2str(dict_input):
    out = ''
    for k, v in dict_input.items():
        try:
            v = '{:8.6f}'.format(float(v))
        except:
            pass
        out = out + '{}={}, '.format(k, v)
    return out.strip(', ')


def norm_path(path):
    win_sep = "\\"
    other_sep = "/"
    if platform.system() == "Windows":
        path = win_sep.join(path.split(other_sep))
    else:
        path = other_sep.join(path.split(win_sep))
    return path


def is_pic(img_path):
    valid_suffix = [
        'JPEG', 'jpeg', 'JPG', 'jpg', 'BMP', 'bmp', 'PNG', 'png', 'npy'
    ]
    suffix = img_path.split('.')[-1]
    if suffix in valid_suffix:
        return True
    img_format = imghdr.what(img_path)
    _, ext = osp.splitext(img_path)
    if img_format == 'tiff' or ext == '.img':
        return True
    return False


class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return super(MyEncoder, self).default(obj)


class EarlyStop:
    def __init__(self, patience, thresh):
        self.patience = patience
        self.counter = 0
        self.score = None
        self.max = 0
        self.thresh = thresh
        if patience < 1:
            raise ValueError("Argument patience should be a positive integer.")

    def __call__(self, current_score):
        if self.score is None:
            self.score = current_score
            return False
        elif current_score > self.max:
            self.counter = 0
            self.score = current_score
            self.max = current_score
            return False
        else:
            if (abs(self.score - current_score) < self.thresh or
                    current_score < self.score):
                self.counter += 1
                self.score = current_score
                logging.debug("EarlyStopping: %i / %i" %
                              (self.counter, self.patience))
                if self.counter >= self.patience:
                    logging.info("EarlyStopping: Stop training")
                    return True
                return False
            else:
                self.counter = 0
                self.score = current_score
                return False


class DisablePrint(object):
    def __enter__(self):
        self._original_stdout = sys.stdout
        sys.stdout = open(os.devnull, 'w')

    def __exit__(self, exc_type, exc_val, exc_tb):
        sys.stdout.close()
        sys.stdout = self._original_stdout


class Times(object):
    def __init__(self):
        self.time = 0.
        # Start time
        self.st = 0.
        # End time
        self.et = 0.

    def start(self):
        self.st = time.time()

    def end(self, iter_num=1, accumulative=True):
        self.et = time.time()
        if accumulative:
            self.time += (self.et - self.st) / iter_num
        else:
            self.time = (self.et - self.st) / iter_num

    def reset(self):
        self.time = 0.
        self.st = 0.
        self.et = 0.

    def value(self):
        return round(self.time, 4)


class Timer(Times):
    def __init__(self):
        super(Timer, self).__init__()
        self.preprocess_time_s = Times()
        self.inference_time_s = Times()
        self.postprocess_time_s = Times()
        self.img_num = 0
        self.repeats = 0

    def info(self, average=False):
        total_time = self.preprocess_time_s.value(
        ) * self.img_num + self.inference_time_s.value(
        ) + self.postprocess_time_s.value() * self.img_num
        total_time = round(total_time, 4)
        print("------------------ Inference Time Info ----------------------")
        print("total_time(ms): {}, img_num: {}, batch_size: {}".format(
            total_time * 1000, self.img_num, self.img_num))
        preprocess_time = round(
            self.preprocess_time_s.value() / self.repeats,
            4) if average else self.preprocess_time_s.value()
        postprocess_time = round(
            self.postprocess_time_s.value() / self.repeats,
            4) if average else self.postprocess_time_s.value()
        inference_time = round(self.inference_time_s.value() / self.repeats,
                               4) if average else self.inference_time_s.value()

        average_latency = total_time / self.repeats
        print("average latency time(ms): {:.2f}, QPS: {:2f}".format(
            average_latency * 1000, 1 / average_latency))
        print("preprocess_time_per_im(ms): {:.2f}, "
              "inference_time_per_batch(ms): {:.2f}, "
              "postprocess_time_per_im(ms): {:.2f}".format(
                  preprocess_time * 1000, inference_time * 1000,
                  postprocess_time * 1000))

    def report(self, average=False):
        dic = {}
        dic['preprocess_time_s'] = round(
            self.preprocess_time_s.value() / self.repeats,
            4) if average else self.preprocess_time_s.value()
        dic['postprocess_time_s'] = round(
            self.postprocess_time_s.value() / self.repeats,
            4) if average else self.postprocess_time_s.value()
        dic['inference_time_s'] = round(
            self.inference_time_s.value() / self.repeats,
            4) if average else self.inference_time_s.value()
        dic['img_num'] = self.img_num
        total_time = self.preprocess_time_s.value(
        ) + self.inference_time_s.value() + self.postprocess_time_s.value()
        dic['total_time_s'] = round(total_time, 4)
        dic['batch_size'] = self.img_num / self.repeats
        return dic

    def reset(self):
        self.preprocess_time_s.reset()
        self.inference_time_s.reset()
        self.postprocess_time_s.reset()
        self.img_num = 0
        self.repeats = 0


def to_data_parallel(layers, *args, **kwargs):
    from paddlers.tasks.utils.res_adapters import GANAdapter
    if isinstance(layers, GANAdapter):
        layers = GANAdapter(
            [to_data_parallel(g, *args, **kwargs) for g in layers.generators], [
                to_data_parallel(d, *args, **kwargs)
                for d in layers.discriminators
            ])
    else:
        layers = paddle.DataParallel(layers, *args, **kwargs)
    return layers


def scheduler_step(optimizer, loss=None):
    from paddlers.tasks.utils.res_adapters import OptimizerAdapter
    if not isinstance(optimizer, OptimizerAdapter):
        optimizer = [optimizer]
    for optim in optimizer:
        if isinstance(optim._learning_rate, paddle.optimizer.lr.LRScheduler):
            # If ReduceOnPlateau is used as the scheduler, use the loss value as the metric.
            if isinstance(optim._learning_rate,
                          paddle.optimizer.lr.ReduceOnPlateau):
                optim._learning_rate.step(loss.item())
            else:
                optim._learning_rate.step()