def train(model_dir, sensitivities_file, eval_metric_loss):
# 下载和解压视盘分割数据集
optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
pdx.utils.download_and_decompress(optic_dataset, path='./')
# 定义训练和验证时的transforms
train_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ResizeRangeScaling(),
transforms.RandomPaddingCrop(crop_size=512),
transforms.Normalize()
])
eval_transforms = transforms.Compose([
transforms.ResizeByLong(long_size=512),
transforms.Padding(target_size=512),
transforms.Normalize()
])
# 定义训练和验证所用的数据集
train_dataset = pdx.datasets.SegDataset(
data_dir='optic_disc_seg',
file_list='optic_disc_seg/train_list.txt',
label_list='optic_disc_seg/labels.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.SegDataset(
data_dir='optic_disc_seg',
file_list='optic_disc_seg/val_list.txt',
label_list='optic_disc_seg/labels.txt',
transforms=eval_transforms)
if model_dir is None:
# 使用coco数据集上的预训练权重
pretrain_weights = "COCO"
else:
assert os.path.isdir(model_dir), "Path {} is not a directory".format(
model_dir)
pretrain_weights = model_dir
save_dir = "output/unet"
if sensitivities_file is not None:
if sensitivities_file != 'DEFAULT':
assert os.path.exists(
sensitivities_file), "Path {} not exist".format(
sensitivities_file)
save_dir = "output/unet_prune"
num_classes = len(train_dataset.labels)
model = pdx.seg.UNet(num_classes=num_classes)
model.train(
num_epochs=20,
train_dataset=train_dataset,
train_batch_size=4,
eval_dataset=eval_dataset,
learning_rate=0.01,
pretrain_weights=pretrain_weights,
save_dir=save_dir,
use_vdl=True,
sensitivities_file=sensitivities_file,
eval_metric_loss=eval_metric_loss)
def __init__(self):
super(BarometerReader, self).__init__()
self.detector = pdx.load_model(
os.path.join(self.directory, 'meter_det_inference_model'))
self.segmenter = pdx.load_model(
os.path.join(self.directory, 'meter_seg_inference_model'))
self.seg_transform = T.Compose([T.Normalize()])
def __init__(self, detector_dir, segmenter_dir):
if not osp.exists(detector_dir):
raise Exception(
"Model path {} does not exist".format(detector_dir))
if not osp.exists(segmenter_dir):
raise Exception(
"Model path {} does not exist".format(segmenter_dir))
self.detector = pdx.load_model(detector_dir)
self.segmenter = pdx.load_model(segmenter_dir)
# Because we will resize images with (METER_SHAPE, METER_SHAPE) before fed into the segmenter,
# here the transform is composed of normalization only.
self.seg_transforms = transforms.Compose([transforms.Normalize()])
self.ocr = OcrInference()
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import matplotlib
matplotlib.use('Agg')
import paddlex as pdx
from paddlex.seg import transforms
# 定义训练和验证时的transforms
train_transforms = transforms.Compose([
transforms.RandomPaddingCrop(crop_size=769),
transforms.RandomHorizontalFlip(prob=0.5),
transforms.RandomVerticalFlip(prob=0.5),
transforms.RandomBlur(prob=0.5),
transforms.RandomRotate(rotate_range=35),
transforms.RandomDistort(brightness_prob=0.5,
contrast_prob=0.5,
saturation_prob=0.5,
hue_prob=0.5),
transforms.Normalize()
])
eval_transforms = transforms.Compose(
[transforms.Padding(target_size=769),
transforms.Normalize()])
#定义数据集
train_dataset = pdx.datasets.SegDataset(data_dir='dataset',
file_list='dataset/train_list.txt',
label_list='dataset/labels.txt',
transforms=train_transforms,
# 设置使用0号GPU卡（如无GPU，执行此代码后仍然会使用CPU训练模型）
import matplotlib
import os
import paddlex as pdx
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from paddlex.seg import transforms
import imgaug.augmenters as iaa
train_transforms = transforms.Compose([
transforms.Resize(target_size=300),
transforms.RandomPaddingCrop(crop_size=256),
transforms.RandomBlur(prob=0.1),
transforms.RandomRotate(rotate_range=15),
# transforms.RandomDistort(brightness_range=0.5),
transforms.RandomHorizontalFlip(),
transforms.Normalize()
])
eval_transforms = transforms.Compose(
[transforms.Resize(256), transforms.Normalize()])
# !unzip data/data55723/img_testA.zip
# !unzip data/data55723/train_data.zip
# !unzip train_data/lab_train.zip
# !unzip train_data/img_train.zip
import numpy as np
import os
# 选择使用0号卡
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx
from paddlex.seg import transforms
# 下载和解压视盘分割数据集
optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
pdx.utils.download_and_decompress(optic_dataset, path='./')
# 定义训练和验证时的transforms
train_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize(target_size=512),
transforms.RandomPaddingCrop(crop_size=500),
transforms.Normalize()
])
eval_transforms = transforms.Compose(
[transforms.Resize(512), transforms.Normalize()])
# 定义训练和验证所用的数据集
train_dataset = pdx.datasets.SegDataset(
data_dir='optic_disc_seg',
file_list='optic_disc_seg/train_list.txt',
label_list='optic_disc_seg/labels.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.SegDataset(data_dir='optic_disc_seg',
file_list='optic_disc_seg/val_list.txt',
# f4.write('house')
# 设置使用0号GPU卡（如无GPU，执行此代码后仍然会使用CPU训练模型）
import matplotlib
matplotlib.use('Agg')
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx
target_size = 1024
data_dir = '/home/aistudio/data/data51222/'
train_list = 'E:\Workspace\PythonWorkSpace\Segmentation\dataset\dom\segmentation\data_train.txt'
val_list = 'E:\Workspace\PythonWorkSpace\Segmentation\dataset\dom\segmentation\data_val.txt'
test_list = 'E:\Workspace\PythonWorkSpace\Segmentation\dataset\dom\segmentation\data_test.txt'
label_list = 'E:\Workspace\PythonWorkSpace\Segmentation\dataset\dom\segmentation\data_label.txt'
from paddlex.seg import transforms
transforms = transforms.Compose(
[transforms.Resize(target_size=target_size),
transforms.Normalize()])
# 定义数据集
# train_dataset = pdx.datasets.SegDataset(
# data_dir=data_dir,
# file_list=train_list,
# label_list=label_list,
# transforms=transforms,
# shuffle=True)
val_dataset = pdx.datasets.SegDataset(data_dir=data_dir,
file_list=val_list,
label_list=label_list,
transforms=transforms)
# test_dataset = pdx.datasets.SegDataset(
# data_dir=data_dir,
import os
# 选择使用0号卡
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx
from paddlex.seg import transforms
# 下载和解压视盘分割数据集
optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
pdx.utils.download_and_decompress(optic_dataset, path='./')
# 定义训练和验证时的transforms
train_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ResizeRangeScaling(),
transforms.RandomPaddingCrop(crop_size=512),
transforms.Normalize()
])
eval_transforms = transforms.Compose([
transforms.ResizeByLong(long_size=512),
transforms.Padding(target_size=512),
transforms.Normalize()
])
# 定义训练和验证所用的数据集
train_dataset = pdx.datasets.SegDataset(
data_dir='optic_disc_seg',
file_list='optic_disc_seg/train_list.txt',
label_list='optic_disc_seg/labels.txt',
transforms=train_transforms,
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx
# 图像预处理流程
target_size = 512
log = 'test'
data_dir = '/home/aistudio/data/data51222/'
train_list = '/home/aistudio/data/data51222/data_train.txt'
val_list = '/home/aistudio/data/data51222/data_val.txt'
test_list = '/home/aistudio/data/data51222/data_test.txt'
label_list = '/home/aistudio/data/data51222/data_label.txt'
from paddlex.seg import transforms
transforms = transforms.Compose([
transforms.Resize(target_size=target_size, interp='NEAREST'),
transforms.Normalize()
])
# 定义数据集
train_dataset = pdx.datasets.SegDataset(data_dir=data_dir,
file_list=train_list,
label_list=label_list,
transforms=transforms,
shuffle=True)
val_dataset = pdx.datasets.SegDataset(data_dir=data_dir,
file_list=val_list,
label_list=label_list,
transforms=transforms)
test_dataset = pdx.datasets.SegDataset(data_dir=data_dir,
file_list=test_list,
label_list=label_list,
