DL之MaskR-CNN：基于類MaskR-CNN算法(RetinaNet+mask head)訓(xùn)練自己的數(shù)據(jù)集(.h5文件)從而實(shí)現(xiàn)圖像分割daiding

相關(guān)文章
DL之MaskR-CNN：基于類MaskR-CNN算法(RetinaNet+mask head)利用數(shù)據(jù)集(resnet50_coco_v0.2.0.h5)實(shí)現(xiàn)圖像分割

輸出結(jié)果

更新……

設(shè)計(jì)思路

參考文章：DL之MaskR-CNN：Mask R-CNN算法的簡介(論文介紹)、架構(gòu)詳解、案例應(yīng)用等配圖集合之詳細(xì)攻略

核心代碼

1、train.py

#!/usr/bin/env python

"""
Copyright 2017-2018 Fizyr (https://)

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./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 argparse
import os
import sys

import keras
import keras.preprocessing.image
import tensorflow as tf

import keras_retinanet.losses
from keras_retinanet.callbacks import RedirectModel
from keras_retinanet.utils.config import read_config_file, parse_anchor_parameters
from keras_retinanet.utils.transform import random_transform_generator
from keras_retinanet.utils.keras_version import check_keras_version
from keras_retinanet.utils.model import freeze as freeze_model

# Allow relative imports when being executed as script.
if __name__ == "__main__" and __package__ is None:
    sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
    import keras_maskrcnn.bin
    __package__ = "keras_maskrcnn.bin"

# Change these to absolute imports if you copy this script outside the keras_retinanet package.
from .. import losses
from .. import models
from ..callbacks.eval import Evaluate


def get_session():
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    return tf.Session(config=config)


def model_with_weights(model, weights, skip_mismatch):
    if weights is not None:
        model.load_weights(weights, by_name=True, skip_mismatch=skip_mismatch)
    return model


def create_models(backbone_retinanet, num_classes, weights, freeze_backbone=False, class_specific_filter=True, anchor_params=None):
    modifier = freeze_model if freeze_backbone else None

    model            = model_with_weights(
        backbone_retinanet(
            num_classes,
            nms=True,
            class_specific_filter=class_specific_filter,
            modifier=modifier,
            anchor_params=anchor_params
        ), weights=weights, skip_mismatch=True)
    training_model   = model
    prediction_model = model

    # compile model
    training_model.compile(
        loss={
            'regression'    : keras_retinanet.losses.smooth_l1(),
            'classification': keras_retinanet.losses.focal(),
            'masks'         : losses.mask(),
        },
        optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001)
    )

    return model, training_model, prediction_model


def create_callbacks(model, training_model, prediction_model, validation_generator, args):
    callbacks = []

    # save the prediction model
    if args.snapshots:
        # ensure directory created first; otherwise h5py will error after epoch.
        os.makedirs(args.snapshot_path, exist_ok=True)
        checkpoint = keras.callbacks.ModelCheckpoint(
            os.path.join(
                args.snapshot_path,
                '{backbone}_{dataset_type}_{{epoch:02d}}.h5'.format(backbone=args.backbone, dataset_type=args.dataset_type)
            ),
            verbose=1
        )
        checkpoint = RedirectModel(checkpoint, prediction_model)
        callbacks.append(checkpoint)

    tensorboard_callback = None

    if args.tensorboard_dir:
        tensorboard_callback = keras.callbacks.TensorBoard(
            log_dir                = args.tensorboard_dir,
            histogram_freq         = 0,
            batch_size             = args.batch_size,
            write_graph            = True,
            write_grads            = False,
            write_images           = False,
            embeddings_freq        = 0,
            embeddings_layer_names = None,
            embeddings_metadata    = None
        )
        callbacks.append(tensorboard_callback)

    if args.evaluation and validation_generator:
        if args.dataset_type == 'coco':
            from ..callbacks.coco import CocoEval

            # use prediction model for evaluation
            evaluation = CocoEval(validation_generator)
        else:
            evaluation = Evaluate(validation_generator, tensorboard=tensorboard_callback, weighted_average=args.weighted_average)
        evaluation = RedirectModel(evaluation, prediction_model)
        callbacks.append(evaluation)

    callbacks.append(keras.callbacks.ReduceLROnPlateau(
        monitor  = 'loss',
        factor   = 0.1,
        patience = 2,
        verbose  = 1,
        mode     = 'auto',
        epsilon  = 0.0001,
        cooldown = 0,
        min_lr   = 0
    ))

    return callbacks


def create_generators(args):
    # create random transform generator for augmenting training data
    transform_generator = random_transform_generator(flip_x_chance=0.5)

    if args.dataset_type == 'coco':
        # import here to prevent unnecessary dependency on cocoapi
        from ..preprocessing.coco import CocoGenerator

        train_generator = CocoGenerator(
            args.coco_path,
            'train2017',
            transform_generator=transform_generator,
            batch_size=args.batch_size,
            config=args.config
        )

        validation_generator = CocoGenerator(
            args.coco_path,
            'val2017',
            batch_size=args.batch_size,
            config=args.config
        )
    elif args.dataset_type == 'csv':
        from ..preprocessing.csv_generator import CSVGenerator

        train_generator = CSVGenerator(
            args.annotations,
            args.classes,
            transform_generator=transform_generator,
            batch_size=args.batch_size,
            config=args.config
        )

        if args.val_annotations:
            validation_generator = CSVGenerator(
                args.val_annotations,
                args.classes,
                batch_size=args.batch_size,
                config=args.config
            )
        else:
            validation_generator = None
    else:
        raise ValueError('Invalid data type received: {}'.format(args.dataset_type))

    return train_generator, validation_generator


def check_args(parsed_args):
    """
    Function to check for inherent contradictions within parsed arguments.
    For example, batch_size < num_gpus
    Intended to raise errors prior to backend initialisation.

    :param parsed_args: parser.parse_args()
    :return: parsed_args
    """

    return parsed_args


def parse_args(args):
    parser     = argparse.ArgumentParser(description='Simple training script for training a RetinaNet mask network.')
    subparsers = parser.add_subparsers(help='Arguments for specific dataset types.', dest='dataset_type')
    subparsers.required = True

    coco_parser = subparsers.add_parser('coco')
    coco_parser.add_argument('coco_path', help='Path to dataset directory (ie. /tmp/COCO).')

    csv_parser = subparsers.add_parser('csv')
    csv_parser.add_argument('annotations', help='Path to CSV file containing annotations for training.')
    csv_parser.add_argument('classes', help='Path to a CSV file containing class label mapping.')
    csv_parser.add_argument('--val-annotations', help='Path to CSV file containing annotations for validation (optional).')

    group = parser.add_mutually_exclusive_group()
    group.add_argument('--snapshot',          help='Resume training from a snapshot.')
    group.add_argument('--imagenet-weights',  help='Initialize the model with pretrained imagenet weights. This is the default behaviour.', action='store_const', const=True, default=True)
    group.add_argument('--weights',           help='Initialize the model with weights from a file.')
    group.add_argument('--no-weights',        help='Don\'t initialize the model with any weights.', dest='imagenet_weights', action='store_const', const=False)

    parser.add_argument('--backbone',         help='Backbone model used by retinanet.', default='resnet50', type=str)
    parser.add_argument('--batch-size',       help='Size of the batches.', default=1, type=int)
    parser.add_argument('--gpu',              help='Id of the GPU to use (as reported by nvidia-smi).')
    parser.add_argument('--epochs',           help='Number of epochs to train.', type=int, default=50)
    parser.add_argument('--steps',            help='Number of steps per epoch.', type=int, default=10000)
    parser.add_argument('--snapshot-path',    help='Path to store snapshots of models during training (defaults to \'./snapshots\')', default='./snapshots')
    parser.add_argument('--tensorboard-dir',  help='Log directory for Tensorboard output', default='./logs')
    parser.add_argument('--no-snapshots',     help='Disable saving snapshots.', dest='snapshots', action='store_false')
    parser.add_argument('--no-evaluation',    help='Disable per epoch evaluation.', dest='evaluation', action='store_false')
    parser.add_argument('--freeze-backbone',  help='Freeze training of backbone layers.', action='store_true')
    parser.add_argument('--no-class-specific-filter', help='Disables class specific filtering.', dest='class_specific_filter', action='store_false')
    parser.add_argument('--config',           help='Path to a configuration parameters .ini file.')
    parser.add_argument('--weighted-average', help='Compute the mAP using the weighted average of precisions among classes.', action='store_true')

    return check_args(parser.parse_args(args))


def main(args=None):
    # parse arguments
    if args is None:
        args = sys.argv[1:]
    args = parse_args(args)

    # make sure keras is the minimum required version
    check_keras_version()

    # create object that stores backbone information
    backbone = models.backbone(args.backbone)

    # optionally choose specific GPU
    if args.gpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    keras.backend.tensorflow_backend.set_session(get_session())

    # optionally load config parameters
    if args.config:
        args.config = read_config_file(args.config)

    # create the generators
    train_generator, validation_generator = create_generators(args)

    # create the model
    if args.snapshot is not None:
        print('Loading model, this may take a second...')
        model            = models.load_model(args.snapshot, backbone_name=args.backbone)
        training_model   = model
        prediction_model = model
    else:
        weights = args.weights
        # default to imagenet if nothing else is specified
        if weights is None and args.imagenet_weights:
            weights = backbone.download_imagenet()

        anchor_params = None
        if args.config and 'anchor_parameters' in args.config:
            anchor_params = parse_anchor_parameters(args.config)

        print('Creating model, this may take a second...')
        model, training_model, prediction_model = create_models(
            backbone_retinanet=backbone.maskrcnn,
            num_classes=train_generator.num_classes(),
            weights=weights,
            freeze_backbone=args.freeze_backbone,
            class_specific_filter=args.class_specific_filter,
            anchor_params=anchor_params
        )

    # print model summary
    print(model.summary())

    # create the callbacks
    callbacks = create_callbacks(
        model,
        training_model,
        prediction_model,
        validation_generator,
        args,
    )

    # start training
    training_model.fit_generator(
        generator=train_generator,
        steps_per_epoch=args.steps,
        epochs=args.epochs,
        verbose=1,
        callbacks=callbacks,
        max_queue_size=1,
    )

if __name__ == '__main__':
    main()