diff --git a/docs/intro/model_zoo.md b/docs/intro/model_zoo.md index faf7cd2..375b819 100644 --- a/docs/intro/model_zoo.md +++ b/docs/intro/model_zoo.md @@ -34,6 +34,7 @@ PaddleRS目前已支持的全部模型如下（标注\*的为遥感专用模型 | 目标检测 | YOLOv3 | 否 | | 图像分割 | BiSeNet V2 | 是 | | 图像分割 | DeepLab V3+ | 是 | +| 图像分割 | \*FactSeg | 是 | | 图像分割 | \*FarSeg | 是 | | 图像分割 | Fast-SCNN | 是 | | 图像分割 | HRNet | 是 | diff --git a/paddlers/models/ppgan/models/generators/generator_firstorder.py b/paddlers/models/ppgan/models/generators/generator_firstorder.py index 76151b7..0a01c90 100644 --- a/paddlers/models/ppgan/models/generators/generator_firstorder.py +++ b/paddlers/models/ppgan/models/generators/generator_firstorder.py @@ -131,8 +131,8 @@ class FirstOrderGenerator(nn.Layer): transformed_kp['jacobian'])) normed_driving = paddle.inverse(kp_driving['jacobian']) normed_transformed = jacobian_transformed - value = paddle.matmul( - *broadcast(normed_driving, normed_transformed)) + value = paddle.matmul(*broadcast(normed_driving, + normed_transformed)) eye = paddle.tensor.eye(2, dtype='float32').reshape( (1, 1, 2, 2)) eye = paddle.tile(eye, [1, value.shape[1], 1, 1]) diff --git a/paddlers/models/ppseg/models/losses/lovasz_loss.py b/paddlers/models/ppseg/models/losses/lovasz_loss.py index 16e755c..940af92 100644 --- a/paddlers/models/ppseg/models/losses/lovasz_loss.py +++ b/paddlers/models/ppseg/models/losses/lovasz_loss.py @@ -77,8 +77,8 @@ class LovaszHingeLoss(nn.Layer): """ if logits.shape[1] == 2: logits = binary_channel_to_unary(logits) - loss = lovasz_hinge_flat( - *flatten_binary_scores(logits, labels, self.ignore_index)) + loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, + self.ignore_index)) return loss diff --git a/paddlers/rs_models/seg/__init__.py b/paddlers/rs_models/seg/__init__.py index 1a73ed8..0236735 100644 --- a/paddlers/rs_models/seg/__init__.py +++ b/paddlers/rs_models/seg/__init__.py @@ -13,3 +13,4 @@ # limitations under the License. from .farseg import FarSeg +from .factseg import FactSeg diff --git a/paddlers/rs_models/seg/factseg.py b/paddlers/rs_models/seg/factseg.py new file mode 100644 index 0000000..4a7dd5c --- /dev/null +++ b/paddlers/rs_models/seg/factseg.py @@ -0,0 +1,141 @@ +# Copyright (c) 2022 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 paddle +import paddle.nn as nn +import paddle.nn.functional as F + +from paddlers.models.ppdet.modeling import \ + initializer as init +from paddlers.rs_models.seg.farseg import FPN, \ + ResNetEncoder,AsymmetricDecoder + + +def conv_with_kaiming_uniform(use_gn=False, use_relu=False): + def make_conv(in_channels, out_channels, kernel_size, stride=1, dilation=1): + conv = nn.Conv2D( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=dilation * (kernel_size - 1) // 2, + dilation=dilation, + bias_attr=False if use_gn else True) + + init.kaiming_uniform_(conv.weight, a=1) + if not use_gn: + init.constant_(conv.bias, 0) + module = [conv, ] + if use_gn: + raise NotImplementedError + if use_relu: + module.append(nn.ReLU()) + if len(module) > 1: + return nn.Sequential(*module) + return conv + + return make_conv + + +default_conv_block = conv_with_kaiming_uniform(use_gn=False, use_relu=False) + + +class FactSeg(nn.Layer): + """ + The FactSeg implementation based on PaddlePaddle. + + The original article refers to + A. Ma, J. Wang, Y. Zhong and Z. Zheng, "FactSeg: Foreground Activation + -Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing + Imagery,"in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, + pp. 1-16, 2022, Art no. 5606216. + + + Args: + in_channels (int): The number of image channels for the input model. + num_classes (int): The unique number of target classes. + backbone (str, optional): A backbone network, models available in + `paddle.vision.models.resnet`. Default: resnet50. + backbone_pretrained (bool, optional): Whether the backbone network uses + IMAGENET pretrained weights. Default: True. + """ + + def __init__(self, + in_channels, + num_classes, + backbone='resnet50', + backbone_pretrained=True): + super(FactSeg, self).__init__() + backbone = backbone.lower() + self.resencoder = ResNetEncoder( + backbone=backbone, + in_channels=in_channels, + pretrained=backbone_pretrained) + self.resencoder.resnet._sub_layers.pop('fc') + self.fgfpn = FPN(in_channels_list=[256, 512, 1024, 2048], + out_channels=256, + conv_block=default_conv_block) + self.bifpn = FPN(in_channels_list=[256, 512, 1024, 2048], + out_channels=256, + conv_block=default_conv_block) + self.fg_decoder = AsymmetricDecoder( + in_channels=256, + out_channels=128, + in_feature_output_strides=(4, 8, 16, 32), + out_feature_output_stride=4, + conv_block=nn.Conv2D) + self.bi_decoder = AsymmetricDecoder( + in_channels=256, + out_channels=128, + in_feature_output_strides=(4, 8, 16, 32), + out_feature_output_stride=4, + conv_block=nn.Conv2D) + self.fg_cls = nn.Conv2D(128, num_classes, kernel_size=1) + self.bi_cls = nn.Conv2D(128, 1, kernel_size=1) + self.config_loss = ['joint_loss'] + self.config_foreground = [] + self.fbattention_atttention = False + + def forward(self, x): + feat_list = self.resencoder(x) + if 'skip_decoder' in []: + fg_out = self.fgskip_deocder(feat_list) + bi_out = self.bgskip_deocder(feat_list) + else: + forefeat_list = list(self.fgfpn(feat_list)) + binaryfeat_list = self.bifpn(feat_list) + if self.fbattention_atttention: + for i in range(len(binaryfeat_list)): + forefeat_list[i] = self.fbatt_block_list[i]( + binaryfeat_list[i], forefeat_list[i]) + fg_out = self.fg_decoder(forefeat_list) + bi_out = self.bi_decoder(binaryfeat_list) + fg_pred = self.fg_cls(fg_out) + bi_pred = self.bi_cls(bi_out) + fg_pred = F.interpolate( + fg_pred, scale_factor=4.0, mode='bilinear', align_corners=True) + bi_pred = F.interpolate( + bi_pred, scale_factor=4.0, mode='bilinear', align_corners=True) + if self.training: + return [fg_pred] + else: + binary_prob = F.sigmoid(bi_pred) + cls_prob = F.softmax(fg_pred, axis=1) + cls_prob[:, 0, :, :] = cls_prob[:, 0, :, :] * ( + 1 - binary_prob).squeeze(axis=1) + cls_prob[:, 1:, :, :] = cls_prob[:, 1:, :, :] * binary_prob + z = paddle.sum(cls_prob, axis=1) + z = z.unsqueeze(axis=1) + cls_prob = paddle.divide(cls_prob, z) + return [cls_prob] diff --git a/paddlers/tasks/segmenter.py b/paddlers/tasks/segmenter.py index a9a60a7..0dea0c4 100644 --- a/paddlers/tasks/segmenter.py +++ b/paddlers/tasks/segmenter.py @@ -13,7 +13,6 @@ # limitations under the License. import math -import os import os.path as osp from collections import OrderedDict @@ -36,7 +35,9 @@ from .utils import seg_metrics as metrics from .utils.infer_nets import InferSegNet from .utils.slider_predict import slider_predict -__all__ = ["UNet", "DeepLabV3P", "FastSCNN", "HRNet", "BiSeNetV2", "FarSeg"] +__all__ = [ + "UNet", "DeepLabV3P", "FastSCNN", "HRNet", "BiSeNetV2", "FarSeg", "FactSeg" +] class BaseSegmenter(BaseModel): @@ -894,3 +895,19 @@ class FarSeg(BaseSegmenter): losses=losses, in_channels=in_channels, **params) + + +class FactSeg(BaseSegmenter): + def __init__(self, + in_channels=3, + num_classes=2, + use_mixed_loss=False, + losses=None, + **params): + super(FactSeg, self).__init__( + model_name='FactSeg', + num_classes=num_classes, + use_mixed_loss=use_mixed_loss, + losses=losses, + in_channels=in_channels, + **params) diff --git a/test_tipc/README.md b/test_tipc/README.md index 759cbd3..f581110 100644 --- a/test_tipc/README.md +++ b/test_tipc/README.md @@ -46,6 +46,7 @@ | 目标检测 | YOLOv3 | 支持 | - | - | - | | 图像分割 | BiSeNet V2 | 支持 | - | - | - | | 图像分割 | DeepLab V3+ | 支持 | - | - | - | +| 图像分割 | FactSeg | 支持 | - | - | - | | 图像分割 | FarSeg | 支持 | - | - | - | | 图像分割 | Fast-SCNN | 支持 | - | - | - | | 图像分割 | HRNet | 支持 | - | - | - | diff --git a/test_tipc/configs/seg/factseg/factseg_rsseg.yaml b/test_tipc/configs/seg/factseg/factseg_rsseg.yaml new file mode 100644 index 0000000..7a139a7 --- /dev/null +++ b/test_tipc/configs/seg/factseg/factseg_rsseg.yaml @@ -0,0 +1,11 @@ +# Configurations of FactSeg with RSSeg dataset + +_base_: ../_base_/rsseg.yaml + +save_dir: ./test_tipc/output/seg/factseg/ + +model: !Node + type: FactSeg + args: + in_channels: 3 + num_classes: 5 \ No newline at end of file diff --git a/test_tipc/configs/seg/factseg/train_infer_python.txt b/test_tipc/configs/seg/factseg/train_infer_python.txt new file mode 100644 index 0000000..800b81e --- /dev/null +++ b/test_tipc/configs/seg/factseg/train_infer_python.txt @@ -0,0 +1,53 @@ +===========================train_params=========================== +model_name:seg:factseg +python:python +gpu_list:0 +use_gpu:null|null +--precision:null +--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 +--save_dir:adaptive +--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 +--model_path:null +--config:lite_train_lite_infer=./test_tipc/configs/seg/factseg/factseg_rsseg.yaml|lite_train_whole_infer=./test_tipc/configs/seg/factseg/factseg_rsseg.yaml|whole_train_whole_infer=./test_tipc/configs/seg/factseg/factseg_rsseg.yaml +train_model_name:best_model +null:null +## +trainer:norm +norm_train:test_tipc/run_task.py train seg +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:null +null:null +## +===========================export_params=========================== +--save_dir:adaptive +--model_dir:adaptive +--fixed_input_shape:[-1,3,512,512] +norm_export:deploy/export/export_model.py +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +===========================infer_params=========================== +infer_model:null +infer_export:null +infer_quant:False +inference:test_tipc/infer.py +--device:cpu|gpu +--enable_mkldnn:True +--cpu_threads:6 +--batch_size:1 +--use_trt:False +--precision:fp32 +--model_dir:null +--config:null +--save_log_path:null +--benchmark:True +--model_name:factseg +null:null \ No newline at end of file diff --git a/test_tipc/docs/test_train_inference_python.md b/test_tipc/docs/test_train_inference_python.md index 30ec1a2..fdec9d8 100644 --- a/test_tipc/docs/test_train_inference_python.md +++ b/test_tipc/docs/test_train_inference_python.md @@ -33,6 +33,7 @@ Linux GPU/CPU 基础训练推理测试的主程序为`test_train_inference_pytho | 图像复原 | LESRCNN | 正常训练 | 正常训练 | PSNR=23.67 | | 图像分割 | BiSeNet V2 | 正常训练 | 正常训练 | mIoU=70.52% | | 图像分割 | DeepLab V3+ | 正常训练 | 正常训练 | mIoU=64.41% | +| 图像分割 | FactSeg | 正常训练 | 正常训练 | | | 图像分割 | FarSeg | 正常训练 | 正常训练 | mIoU=50.60% | | 图像分割 | Fast-SCNN | 正常训练 | 正常训练 | mIoU=49.27% | | 图像分割 | HRNet | 正常训练 | 正常训练 | mIoU=33.03% | @@ -68,6 +69,7 @@ Linux GPU/CPU 基础训练推理测试的主程序为`test_train_inference_pytho | 目标检测 | YOLOv3 | 支持 | 支持 | 1 | | 图像分割 | BiSeNet V2 | 支持 | 支持 | 1 | | 图像分割 | DeepLab V3+ | 支持 | 支持 | 1 | +| 图像分割 | FactSeg | 支持 | 支持 | 1 | | 图像分割 | FarSeg | 支持 | 支持 | 1 | | 图像分割 | Fast-SCNN | 支持 | 支持 | 1 | | 图像分割 | HRNet | 支持 | 支持 | 1 | diff --git a/tests/rs_models/test_seg_models.py b/tests/rs_models/test_seg_models.py index b4d6ab5..3789d3b 100644 --- a/tests/rs_models/test_seg_models.py +++ b/tests/rs_models/test_seg_models.py @@ -15,7 +15,7 @@ import paddlers from rs_models.test_model import TestModel -__all__ = ['TestFarSegModel'] +__all__ = ['TestFarSegModel', 'TestFactSegModel'] class TestSegModel(TestModel): @@ -70,3 +70,21 @@ class TestFarSegModel(TestSegModel): self.targets = [[self.get_zeros_array(2)], [self.get_zeros_array(10)], [self.get_zeros_array(2)], [self.get_zeros_array(2)], [self.get_zeros_array(2)]] + + +class TestFactSegModel(TestSegModel): + MODEL_CLASS = paddlers.rs_models.seg.FactSeg + + def set_specs(self): + base_spec = dict(in_channels=3, num_classes=2) + self.specs = [ + base_spec, + dict(in_channels=6, num_classes=10), + dict(**base_spec, + backbone='resnet50', + backbone_pretrained=False) + ] # yapf: disable + + def set_targets(self): + self.targets = [[self.get_zeros_array(2)], [self.get_zeros_array(10)], + [self.get_zeros_array(2)]] diff --git a/tutorials/train/README.md b/tutorials/train/README.md index 105f98a..a21daa1 100644 --- a/tutorials/train/README.md +++ b/tutorials/train/README.md @@ -29,6 +29,7 @@ |object_detection/yolov3.py | 目标检测 | YOLOv3 | |semantic_segmentation/bisenetv2.py | 图像分割 | BiSeNet V2 | |semantic_segmentation/deeplabv3p.py | 图像分割 | DeepLab V3+ | +|semantic_segmentation/factseg.py | 图像分割 | FactSeg | |semantic_segmentation/farseg.py | 图像分割 | FarSeg | |semantic_segmentation/fast_scnn.py | 图像分割 | Fast-SCNN | |semantic_segmentation/hrnet.py | 图像分割 | HRNet | diff --git a/tutorials/train/semantic_segmentation/factseg.py b/tutorials/train/semantic_segmentation/factseg.py new file mode 100644 index 0000000..fb26169 --- /dev/null +++ b/tutorials/train/semantic_segmentation/factseg.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python + +# 图像分割模型FactSeg训练示例脚本 +# 执行此脚本前，请确认已正确安装PaddleRS库 + +import paddlers as pdrs +from paddlers import transforms as T + +# 数据集存放目录 +DATA_DIR = './data/rsseg/' +# 训练集`file_list`文件路径 +TRAIN_FILE_LIST_PATH = './data/rsseg/train.txt' +# 验证集`file_list`文件路径 +EVAL_FILE_LIST_PATH = './data/rsseg/val.txt' +# 数据集类别信息文件路径 +LABEL_LIST_PATH = './data/rsseg/labels.txt' +# 实验目录，保存输出的模型权重和结果 +EXP_DIR = './output/factseg/' + +# 下载和解压多光谱地块分类数据集 +pdrs.utils.download_and_decompress( + 'https://paddlers.bj.bcebos.com/datasets/rsseg.zip', path='./data/') + +# 定义训练和验证时使用的数据变换（数据增强、预处理等） +# 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 +# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md +train_transforms = T.Compose([ + # 读取影像 + T.DecodeImg(), + # 选择前三个波段 + T.SelectBand([1, 2, 3]), + # 将影像缩放到512x512大小 + T.Resize(target_size=512), + # 以50%的概率实施随机水平翻转 + T.RandomHorizontalFlip(prob=0.5), + # 将数据归一化到[-1,1] + T.Normalize( + mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + T.ArrangeSegmenter('train') +]) + +eval_transforms = T.Compose([ + T.DecodeImg(), + # 验证阶段与训练阶段应当选择相同的波段 + T.SelectBand([1, 2, 3]), + T.Resize(target_size=512), + # 验证阶段与训练阶段的数据归一化方式必须相同 + T.Normalize( + mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + T.ReloadMask(), + T.ArrangeSegmenter('eval') +]) + +# 分别构建训练和验证所用的数据集 +train_dataset = pdrs.datasets.SegDataset( + data_dir=DATA_DIR, + file_list=TRAIN_FILE_LIST_PATH, + label_list=LABEL_LIST_PATH, + transforms=train_transforms, + num_workers=0, + shuffle=True) + +eval_dataset = pdrs.datasets.SegDataset( + data_dir=DATA_DIR, + file_list=EVAL_FILE_LIST_PATH, + label_list=LABEL_LIST_PATH, + transforms=eval_transforms, + num_workers=0, + shuffle=False) + +# 构建FactSeg模型 +# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md +# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py +model = pdrs.tasks.seg.FactSeg(num_classes=len(train_dataset.labels)) + +# 执行模型训练 +model.train( + num_epochs=10, + train_dataset=train_dataset, + train_batch_size=4, + eval_dataset=eval_dataset, + save_interval_epochs=5, + # 每多少次迭代记录一次日志 + log_interval_steps=4, + save_dir=EXP_DIR, + pretrain_weights=None, + # 初始学习率大小 + learning_rate=0.001, + # 是否使用early stopping策略，当精度不再改善时提前终止训练 + early_stop=False, + # 是否启用VisualDL日志功能 + use_vdl=True, + # 指定从某个检查点继续训练 + resume_checkpoint=None)