utils.py

# Copyright (c) 2020 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 contextlib
import os
import numpy as np
import math
import cv2
import tempfile
import paddle.fluid as fluid
from urllib.parse import urlparse, unquote

import filelock

import paddleseg.env as segenv
from paddleseg.utils import logger
from paddleseg.utils.download import download_file_and_uncompress


@contextlib.contextmanager
def generate_tempdir(directory: str = None, **kwargs):
    '''Generate a temporary directory'''
    directory = segenv.TMP_HOME if not directory else directory
    with tempfile.TemporaryDirectory(dir=directory, **kwargs) as _dir:
        yield _dir


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 load_entire_model(model, pretrained):

    if pretrained is not None:
        if os.path.exists(pretrained):
            load_pretrained_model(model, pretrained)
        else:
            raise Exception(
                'Pretrained model is not found: {}'.format(pretrained))
    else:
        logger.warning('Not all pretrained params of {} to load, '\
             'training from scratch or a pretrained backbone'.format(model.__class__.__name__))


def load_pretrained_model(model, pretrained_model):
    if pretrained_model is not None:
        logger.info('Load pretrained model from {}'.format(pretrained_model))
        # download pretrained model from url
        if urlparse(pretrained_model).netloc:
            pretrained_model = unquote(pretrained_model)
            savename = pretrained_model.split('/')[-1].split('.')[0]
            with generate_tempdir() as _dir:
                with filelock.FileLock(os.path.join(segenv.TMP_HOME, savename)):
                    pretrained_model = download_file_and_uncompress(
                        pretrained_model,
                        savepath=_dir,
                        extrapath=segenv.PRETRAINED_MODEL_HOME,
                        extraname=savename)

        if os.path.exists(pretrained_model):
            ckpt_path = os.path.join(pretrained_model, 'model')
            try:
                para_state_dict, _ = fluid.load_dygraph(ckpt_path)
            except:
                para_state_dict = fluid.load_program_state(pretrained_model)

            model_state_dict = model.state_dict()
            keys = model_state_dict.keys()
            num_params_loaded = 0
            for k in keys:
                if k not in para_state_dict:
                    logger.warning("{} is not in pretrained model".format(k))
                elif list(para_state_dict[k].shape) != list(
                        model_state_dict[k].shape):
                    logger.warning(
                        "[SKIP] Shape of pretrained params {} doesn't match.(Pretrained: {}, Actual: {})"
                        .format(k, para_state_dict[k].shape,
                                model_state_dict[k].shape))
                else:
                    model_state_dict[k] = para_state_dict[k]
                    num_params_loaded += 1
            model.set_dict(model_state_dict)
            logger.info("There are {}/{} variables are loaded into {}.".format(
                num_params_loaded, len(model_state_dict),
                model.__class__.__name__))

        else:
            raise ValueError(
                'The pretrained model directory is not Found: {}'.format(
                    pretrained_model))
    else:
        logger.info(
            'No pretrained model to load, {} will be train from scratch.'.
            format(model.__class__.__name__))


def resume(model, optimizer, resume_model):
    if resume_model is not None:
        logger.info('Resume model from {}'.format(resume_model))
        if os.path.exists(resume_model):
            resume_model = os.path.normpath(resume_model)
            ckpt_path = os.path.join(resume_model, 'model')
            para_state_dict, opti_state_dict = fluid.load_dygraph(ckpt_path)
            model.set_dict(para_state_dict)
            optimizer.set_dict(opti_state_dict)
            epoch = resume_model.split('_')[-1]
            if epoch.isdigit():
                epoch = int(epoch)
            return epoch
        else:
            raise ValueError(
                'The resume model directory is not Found: {}'.format(
                    resume_model))
    else:
        logger.info('No model need to resume')


def visualize(image, result, save_dir=None, weight=0.6):
    """
    Convert segment result to color image, and save added image.
    Args:
        image: the path of origin image
        result: the predict result of image
        save_dir: the directory for saving visual image
        weight: the image weight of visual image, and the result weight is (1 - weight)
    """
    color_map = get_color_map_list(256)
    color_map = np.array(color_map).astype("uint8")
    # Use OpenCV LUT for color mapping
    c1 = cv2.LUT(result, color_map[:, 0])
    c2 = cv2.LUT(result, color_map[:, 1])
    c3 = cv2.LUT(result, color_map[:, 2])
    pseudo_img = np.dstack((c1, c2, c3))

    im = cv2.imread(image)
    vis_result = cv2.addWeighted(im, weight, pseudo_img, 1 - weight, 0)

    if save_dir is not None:
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        image_name = os.path.split(image)[-1]
        out_path = os.path.join(save_dir, image_name)
        cv2.imwrite(out_path, vis_result)
    else:
        return vis_result


def get_color_map_list(num_classes):
    """ Returns the color map for visualizing the segmentation mask,
        which can support arbitrary number of classes.
    Args:
        num_classes: Number of classes
    Returns:
        The color map
    """
    num_classes += 1
    color_map = num_classes * [0, 0, 0]
    for i in range(0, num_classes):
        j = 0
        lab = i
        while lab:
            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
            j += 1
            lab >>= 3
    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
    color_map = color_map[1:]
    return color_map