kongdebug
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License.. |
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from itertools import repeat |
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import collections.abc |
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import paddle |
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import paddle.nn as nn |
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""" |
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Droppath, reimplement from https://github.com/yueatsprograms/Stochastic_Depth |
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""" |
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class DropPath(nn.Layer): |
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"""DropPath class""" |
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def __init__(self, drop_prob=None): |
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super().__init__() |
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self.drop_prob = drop_prob |
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def drop_path(self, inputs): |
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"""drop path op |
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Args: |
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input: tensor with arbitrary shape |
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drop_prob: float number of drop path probability, default: 0.0 |
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training: bool, if current mode is training, default: False |
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Returns: |
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output: output tensor after drop path |
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""" |
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# if prob is 0 or eval mode, return original input |
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if self.drop_prob == 0. or not self.training: |
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return inputs |
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keep_prob = 1 - self.drop_prob |
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keep_prob = paddle.to_tensor(keep_prob, dtype='float32') |
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shape = (inputs.shape[0], ) + (1, ) * (inputs.ndim - 1 |
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) # shape=(N, 1, 1, 1) |
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random_tensor = keep_prob + paddle.rand(shape, dtype=inputs.dtype) |
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random_tensor = random_tensor.floor() # mask |
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output = inputs.divide( |
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keep_prob) * random_tensor # divide to keep same output expectation |
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return output |
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def forward(self, inputs): |
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return self.drop_path(inputs) |
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def _ntuple(n): |
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def parse(x): |
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if isinstance(x, collections.abc.Iterable): |
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return x |
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return tuple(repeat(x, n)) |
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return parse |
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to_1tuple = _ntuple(1) |
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to_2tuple = _ntuple(2) |
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to_3tuple = _ntuple(3) |
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to_4tuple = _ntuple(4) |
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to_ntuple = _ntuple |
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# Basic configurations of ChangeFormer |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/changeformer/ |
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model: !Node |
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type: ChangeFormer |
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===========================train_params=========================== |
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model_name:cd:changeformer |
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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=5|lite_train_whole_infer=5|whole_train_whole_infer=10 |
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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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train_model_name:best_model |
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train_infer_file_list:./test_tipc/data/airchange/:./test_tipc/data/airchange/eval.txt |
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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 cd --config ./test_tipc/configs/cd/changeformer/changeformer.yaml |
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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:[1,3,256,256] |
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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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--file_list:null:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:changeformer |
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null:null |
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#!/usr/bin/env python |
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# 变化检测模型ChangeFormer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/airchange/' |
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# 训练集`file_list`文件路径 |
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TRAIN_FILE_LIST_PATH = './data/airchange/train.txt' |
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# 验证集`file_list`文件路径 |
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EVAL_FILE_LIST_PATH = './data/airchange/eval.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/changeformer/' |
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# 下载和解压AirChange数据集 |
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pdrs.utils.download_and_decompress( |
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'https://paddlers.bj.bcebos.com/datasets/airchange.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/transforms.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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# 随机裁剪 |
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T.RandomCrop( |
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# 裁剪区域将被缩放到256x256 |
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crop_size=256, |
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# 裁剪区域的横纵比在0.5-2之间变动 |
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aspect_ratio=[0.5, 2.0], |
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# 裁剪区域相对原始影像长宽比例在一定范围内变动，最小不低于原始长宽的1/5 |
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scaling=[0.2, 1.0]), |
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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.ArrangeChangeDetector('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.ArrangeChangeDetector('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.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=TRAIN_FILE_LIST_PATH, |
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label_list=None, |
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transforms=train_transforms, |
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num_workers=0, |
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shuffle=True, |
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with_seg_labels=False, |
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binarize_labels=True) |
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eval_dataset = pdrs.datasets.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=EVAL_FILE_LIST_PATH, |
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label_list=None, |
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transforms=eval_transforms, |
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num_workers=0, |
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shuffle=False, |
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with_seg_labels=False, |
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binarize_labels=True) |
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# 使用默认参数构建ChangeFormer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/apis/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/change_detector.py |
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model = pdrs.tasks.cd.ChangeFormer() |
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# 执行模型训练 |
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model.train( |
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num_epochs=5, |
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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=3, |
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# 每多少次迭代记录一次日志 |
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log_interval_steps=50, |
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save_dir=EXP_DIR, |
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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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