Factseg paddle (#54)
* fact-seg paddle * fact-paddle * Coding modify * Add files via upload * Passed testing code * Use offical hook for pre-commit * Use the official .style.yarp file and passed the yarp test locally * Restore the file which was changed by error * Revert "Use the official .style.yarp file and passed the yarp test locally" This reverts commit 6a294fa21b74166bc6be94b858fcf486ff13d4ea. * Fix code style * Code Modify * Code Modify according to reviewer * solve conflict * Solve conflict * solve yarp * yarp * Fix code style * [Feat] Make FactSeg public Co-authored-by: Bobholamovic <mhlin425@whu.edu.cn>own
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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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|
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import paddle |
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import paddle.nn as nn |
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import paddle.nn.functional as F |
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from paddlers.models.ppdet.modeling import \ |
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initializer as init |
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from paddlers.rs_models.seg.farseg import FPN, \ |
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ResNetEncoder,AsymmetricDecoder |
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|
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def conv_with_kaiming_uniform(use_gn=False, use_relu=False): |
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def make_conv(in_channels, out_channels, kernel_size, stride=1, dilation=1): |
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conv = nn.Conv2D( |
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in_channels, |
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out_channels, |
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kernel_size=kernel_size, |
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stride=stride, |
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padding=dilation * (kernel_size - 1) // 2, |
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dilation=dilation, |
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bias_attr=False if use_gn else True) |
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|
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init.kaiming_uniform_(conv.weight, a=1) |
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if not use_gn: |
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init.constant_(conv.bias, 0) |
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module = [conv, ] |
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if use_gn: |
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raise NotImplementedError |
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if use_relu: |
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module.append(nn.ReLU()) |
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if len(module) > 1: |
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return nn.Sequential(*module) |
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return conv |
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return make_conv |
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default_conv_block = conv_with_kaiming_uniform(use_gn=False, use_relu=False) |
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class FactSeg(nn.Layer): |
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""" |
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The FactSeg implementation based on PaddlePaddle. |
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|
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The original article refers to |
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A. Ma, J. Wang, Y. Zhong and Z. Zheng, "FactSeg: Foreground Activation |
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-Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing |
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Imagery,"in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, |
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pp. 1-16, 2022, Art no. 5606216. |
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Args: |
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in_channels (int): The number of image channels for the input model. |
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num_classes (int): The unique number of target classes. |
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backbone (str, optional): A backbone network, models available in |
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`paddle.vision.models.resnet`. Default: resnet50. |
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backbone_pretrained (bool, optional): Whether the backbone network uses |
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IMAGENET pretrained weights. Default: True. |
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""" |
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def __init__(self, |
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in_channels, |
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num_classes, |
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backbone='resnet50', |
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backbone_pretrained=True): |
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super(FactSeg, self).__init__() |
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backbone = backbone.lower() |
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self.resencoder = ResNetEncoder( |
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backbone=backbone, |
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in_channels=in_channels, |
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pretrained=backbone_pretrained) |
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self.resencoder.resnet._sub_layers.pop('fc') |
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self.fgfpn = FPN(in_channels_list=[256, 512, 1024, 2048], |
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out_channels=256, |
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conv_block=default_conv_block) |
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self.bifpn = FPN(in_channels_list=[256, 512, 1024, 2048], |
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out_channels=256, |
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conv_block=default_conv_block) |
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self.fg_decoder = AsymmetricDecoder( |
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in_channels=256, |
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out_channels=128, |
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in_feature_output_strides=(4, 8, 16, 32), |
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out_feature_output_stride=4, |
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conv_block=nn.Conv2D) |
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self.bi_decoder = AsymmetricDecoder( |
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in_channels=256, |
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out_channels=128, |
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in_feature_output_strides=(4, 8, 16, 32), |
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out_feature_output_stride=4, |
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conv_block=nn.Conv2D) |
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self.fg_cls = nn.Conv2D(128, num_classes, kernel_size=1) |
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self.bi_cls = nn.Conv2D(128, 1, kernel_size=1) |
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self.config_loss = ['joint_loss'] |
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self.config_foreground = [] |
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self.fbattention_atttention = False |
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def forward(self, x): |
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feat_list = self.resencoder(x) |
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if 'skip_decoder' in []: |
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fg_out = self.fgskip_deocder(feat_list) |
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bi_out = self.bgskip_deocder(feat_list) |
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else: |
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forefeat_list = list(self.fgfpn(feat_list)) |
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binaryfeat_list = self.bifpn(feat_list) |
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if self.fbattention_atttention: |
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for i in range(len(binaryfeat_list)): |
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forefeat_list[i] = self.fbatt_block_list[i]( |
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binaryfeat_list[i], forefeat_list[i]) |
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fg_out = self.fg_decoder(forefeat_list) |
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bi_out = self.bi_decoder(binaryfeat_list) |
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fg_pred = self.fg_cls(fg_out) |
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bi_pred = self.bi_cls(bi_out) |
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fg_pred = F.interpolate( |
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fg_pred, scale_factor=4.0, mode='bilinear', align_corners=True) |
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bi_pred = F.interpolate( |
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bi_pred, scale_factor=4.0, mode='bilinear', align_corners=True) |
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if self.training: |
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return [fg_pred] |
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else: |
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binary_prob = F.sigmoid(bi_pred) |
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cls_prob = F.softmax(fg_pred, axis=1) |
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cls_prob[:, 0, :, :] = cls_prob[:, 0, :, :] * ( |
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1 - binary_prob).squeeze(axis=1) |
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cls_prob[:, 1:, :, :] = cls_prob[:, 1:, :, :] * binary_prob |
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z = paddle.sum(cls_prob, axis=1) |
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z = z.unsqueeze(axis=1) |
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cls_prob = paddle.divide(cls_prob, z) |
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return [cls_prob] |
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# Configurations of FactSeg with RSSeg dataset |
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_base_: ../_base_/rsseg.yaml |
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save_dir: ./test_tipc/output/seg/factseg/ |
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model: !Node |
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type: FactSeg |
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args: |
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in_channels: 3 |
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num_classes: 5 |
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===========================train_params=========================== |
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model_name:seg:factseg |
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python:python |
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gpu_list:0 |
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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/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 |
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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:[-1,3,512,512] |
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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:factseg |
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null:null |
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#!/usr/bin/env python |
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# 图像分割模型FactSeg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/factseg/' |
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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FactSeg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.FactSeg(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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