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Configuration error
Configuration error
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# 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 numpy as np
def config_check(cfg, train_dataset=None, val_dataset=None):
"""
To check config。
Args:
cfg (paddleseg.cvlibs.Config): An object of paddleseg.cvlibs.Config.
train_dataset (paddle.io.Dataset): Used to read and process training datasets.
val_dataset (paddle.io.Dataset, optional): Used to read and process validation datasets.
"""
num_classes_check(cfg, train_dataset, val_dataset)
def num_classes_check(cfg, train_dataset, val_dataset):
""""
Check that the num_classes in model, train_dataset and val_dataset is consistent.
"""
num_classes_set = set()
if train_dataset and hasattr(train_dataset, 'num_classes'):
num_classes_set.add(train_dataset.num_classes)
if val_dataset and hasattr(val_dataset, 'num_classes'):
num_classes_set.add(val_dataset.num_classes)
if cfg.dic.get('model', None) and cfg.dic['model'].get('num_classes', None):
num_classes_set.add(cfg.dic['model'].get('num_classes'))
if (not cfg.train_dataset) and (not cfg.val_dataset):
raise ValueError(
'One of `train_dataset` or `val_dataset should be given, but there are none.'
)
if len(num_classes_set) == 0:
raise ValueError(
'`num_classes` is not found. Please set it in model, train_dataset or val_dataset'
)
elif len(num_classes_set) > 1:
raise ValueError(
'`num_classes` is not consistent: {}. Please set it consistently in model or train_dataset or val_dataset'
.format(num_classes_set))
else:
num_classes = num_classes_set.pop()
if train_dataset:
train_dataset.num_classes = num_classes
if val_dataset:
val_dataset.num_classes = num_classes
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