DL之MaskR-CNN：基于类MaskR-CNN算法(RetinaNet+mask head)训练自己的数据集(.h5文件)从而实现图像分割daiding
DL之MaskR-CNN：基于类MaskR-CNN算法(RetinaNet+mask head)训练自己的数据集(.h5文件)从而实现图像分割daiding
相关文章
DL之MaskR-CNN：基于类MaskR-CNN算法(RetinaNet+mask head)利用数据集(resnet50_coco_v0.2.0.h5)实现图像分割
输出结果
更新……
设计思路
参考文章：DL之MaskR-CNN：Mask R-CNN算法的简介(论文介绍)、架构详解、案例应用等配图集合之详细攻略
核心代码
1、train.py
#!/usr/bin/env python
"""
Copyright 2017-2018 Fizyr (https://fizyr.com)
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 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()
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