Merge pull request #32 from Bobholamovic/farseg_in_chns
[Feat] Add multi-channel support to FarSegown
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2 years ago
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17 changed files with 336 additions and 112 deletions
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# Configurations of FarSeg with RSSeg dataset |
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_base_: ../_base_/rsseg.yaml |
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save_dir: ./test_tipc/output/seg/farseg/ |
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model: !Node |
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type: FarSeg |
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args: |
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in_channels: 10 |
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num_classes: 5 |
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===========================train_params=========================== |
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model_name:seg:farseg |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/seg/farseg/farseg_rsseg.yaml|lite_train_whole_infer=./test_tipc/configs/seg/farseg/farseg_rsseg.yaml|whole_train_whole_infer=./test_tipc/configs/seg/farseg/farseg_rsseg.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train seg |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,10,512,512] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:farseg |
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null:null |
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#!/usr/bin/env python |
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# 图像分割模型FarSeg训练示例脚本 |
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# 执行此脚本前，请确认已正确安装PaddleRS库 |
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import paddlers as pdrs |
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from paddlers import transforms as T |
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# 数据集存放目录 |
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DATA_DIR = './data/rsseg/' |
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# 训练集`file_list`文件路径 |
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TRAIN_FILE_LIST_PATH = './data/rsseg/train.txt' |
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# 验证集`file_list`文件路径 |
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EVAL_FILE_LIST_PATH = './data/rsseg/val.txt' |
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# 数据集类别信息文件路径 |
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LABEL_LIST_PATH = './data/rsseg/labels.txt' |
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# 实验目录，保存输出的模型权重和结果 |
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EXP_DIR = './output/farseg/' |
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# 下载和解压多光谱地块分类数据集 |
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pdrs.utils.download_and_decompress( |
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'https://paddlers.bj.bcebos.com/datasets/rsseg.zip', path='./data/') |
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# 定义训练和验证时使用的数据变换（数据增强、预处理等） |
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# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行 |
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# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md |
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train_transforms = T.Compose([ |
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# 读取影像 |
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T.DecodeImg(), |
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# 选择前三个波段 |
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T.SelectBand([1, 2, 3]), |
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# 将影像缩放到512x512大小 |
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T.Resize(target_size=512), |
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# 以50%的概率实施随机水平翻转 |
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T.RandomHorizontalFlip(prob=0.5), |
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# 将数据归一化到[-1,1] |
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T.Normalize( |
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mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), |
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T.ArrangeSegmenter('train') |
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]) |
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eval_transforms = T.Compose([ |
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T.DecodeImg(), |
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# 验证阶段与训练阶段应当选择相同的波段 |
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T.SelectBand([1, 2, 3]), |
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T.Resize(target_size=512), |
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# 验证阶段与训练阶段的数据归一化方式必须相同 |
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T.Normalize( |
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mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), |
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T.ReloadMask(), |
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T.ArrangeSegmenter('eval') |
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]) |
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# 分别构建训练和验证所用的数据集 |
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train_dataset = pdrs.datasets.SegDataset( |
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data_dir=DATA_DIR, |
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file_list=TRAIN_FILE_LIST_PATH, |
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label_list=LABEL_LIST_PATH, |
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transforms=train_transforms, |
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num_workers=0, |
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shuffle=True) |
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eval_dataset = pdrs.datasets.SegDataset( |
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data_dir=DATA_DIR, |
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file_list=EVAL_FILE_LIST_PATH, |
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label_list=LABEL_LIST_PATH, |
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transforms=eval_transforms, |
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num_workers=0, |
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shuffle=False) |
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# 构建FarSeg模型 |
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# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md |
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# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py |
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model = pdrs.tasks.seg.FarSeg(num_classes=len(train_dataset.labels)) |
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# 执行模型训练 |
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model.train( |
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num_epochs=10, |
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train_dataset=train_dataset, |
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train_batch_size=4, |
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eval_dataset=eval_dataset, |
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save_interval_epochs=5, |
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# 每多少次迭代记录一次日志 |
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log_interval_steps=4, |
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save_dir=EXP_DIR, |
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pretrain_weights=None, |
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# 初始学习率大小 |
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learning_rate=0.001, |
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# 是否使用early stopping策略，当精度不再改善时提前终止训练 |
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early_stop=False, |
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# 是否启用VisualDL日志功能 |
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use_vdl=True, |
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# 指定从某个检查点继续训练 |
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resume_checkpoint=None) |
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