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dataset.py
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dataset.py
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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 os
import paddle
import numpy as np
from PIL import Image
from paddleseg.cvlibs import manager
from paddleseg.transforms import Compose
import paddleseg.transforms.functional as F
@manager.DATASETS.add_component
class Dataset(paddle.io.Dataset):
"""
Pass in a custom dataset that conforms to the format.
Args:
transforms (list): Transforms for image.
dataset_root (str): The dataset directory.
num_classes (int): Number of classes.
mode (str, optional): which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
train_path (str, optional): The train dataset file. When mode is 'train', train_path is necessary.
The contents of train_path file are as follow:
image1.jpg ground_truth1.png
image2.jpg ground_truth2.png
val_path (str. optional): The evaluation dataset file. When mode is 'val', val_path is necessary.
The contents is the same as train_path
test_path (str, optional): The test dataset file. When mode is 'test', test_path is necessary.
The annotation file is not necessary in test_path file.
separator (str, optional): The separator of dataset list. Default: ' '.
edge (bool, optional): Whether to compute edge while training. Default: False
Examples:
import paddleseg.transforms as T
from paddleseg.datasets import Dataset
transforms = [T.RandomPaddingCrop(crop_size=(512,512)), T.Normalize()]
dataset_root = 'dataset_root_path'
train_path = 'train_path'
num_classes = 2
dataset = Dataset(transforms = transforms,
dataset_root = dataset_root,
num_classes = 2,
train_path = train_path,
mode = 'train')
"""
def __init__(self,
mode,
dataset_root,
transforms,
num_classes,
img_channels=3,
train_path=None,
val_path=None,
test_path=None,
separator=' ',
ignore_index=255,
edge=False):
self.dataset_root = dataset_root
self.transforms = Compose(transforms, img_channels=img_channels)
self.file_list = list()
self.mode = mode.lower()
self.num_classes = num_classes
self.img_channels = img_channels
self.ignore_index = ignore_index
self.edge = edge
if self.mode not in ['train', 'val', 'test']:
raise ValueError(
"mode should be 'train', 'val' or 'test', but got {}.".format(
self.mode))
if not os.path.exists(self.dataset_root):
raise FileNotFoundError('there is not `dataset_root`: {}.'.format(
self.dataset_root))
if self.transforms is None:
raise ValueError("`transforms` is necessary, but it is None.")
if num_classes < 1:
raise ValueError(
"`num_classes` should be greater than 1, but got {}".format(
num_classes))
if img_channels not in [1, 3]:
raise ValueError("`img_channels` should in [1, 3], but got {}".
format(img_channels))
if self.mode == 'train':
if train_path is None:
raise ValueError(
'When `mode` is "train", `train_path` is necessary, but it is None.'
)
elif not os.path.exists(train_path):
raise FileNotFoundError('`train_path` is not found: {}'.format(
train_path))
else:
file_path = train_path
elif self.mode == 'val':
if val_path is None:
raise ValueError(
'When `mode` is "val", `val_path` is necessary, but it is None.'
)
elif not os.path.exists(val_path):
raise FileNotFoundError('`val_path` is not found: {}'.format(
val_path))
else:
file_path = val_path
else:
if test_path is None:
raise ValueError(
'When `mode` is "test", `test_path` is necessary, but it is None.'
)
elif not os.path.exists(test_path):
raise FileNotFoundError('`test_path` is not found: {}'.format(
test_path))
else:
file_path = test_path
with open(file_path, 'r') as f:
for line in f:
items = line.strip().split(separator)
if len(items) != 2:
if self.mode == 'train' or self.mode == 'val':
raise ValueError(
"File list format incorrect! In training or evaluation task it should be"
" image_name{}label_name\\n".format(separator))
image_path = os.path.join(self.dataset_root, items[0])
label_path = None
else:
image_path = os.path.join(self.dataset_root, items[0])
label_path = os.path.join(self.dataset_root, items[1])
self.file_list.append([image_path, label_path])
def __getitem__(self, idx):
data = {}
data['trans_info'] = []
image_path, label_path = self.file_list[idx]
data['img'] = image_path
data['label'] = label_path
# If key in gt_fields, the data[key] have transforms synchronous.
data['gt_fields'] = []
if self.mode == 'val':
data = self.transforms(data)
if data['label'].ndim == 2:
data['label'] = data['label'][np.newaxis, :, :]
else:
data['gt_fields'].append('label')
data = self.transforms(data)
if self.edge:
edge_mask = F.mask_to_binary_edge(
data['label'], radius=2, num_classes=self.num_classes)
data['edge'] = edge_mask
elif 'edge' in data: # for AddEdgeLabel
# F.mask_to_binary_edge is so slow
# AddEdgeLabel will faster
# But offline generation of edges might be better
data['edge'][data['edge'] == self.ignore_index] = 0
return data
def __len__(self):
return len(self.file_list)