- 遥感影像超分辨率重建的意义：遥感影像的分辨率对应用来说十分重要，无论是变化检测、目标检测还是地物分类等领域，高质量的遥感影像对于下游任务的结果有比较大的影响。
- 迁移学习的意义：是一种机器学习方法，是把一个领域（即源领域）的知识，迁移到另外一个领域（即目标领域），使得目标领域能够取得更好的学习效果。可以归纳为三点：
- 更高的起点：在微调之前，模型的初始性能更高；
- 更高的斜率：训练过程中，模型提升的速率更快；
- 更高的渐进：训练结束后，得到的模型收敛更好。
- 本项目目的：PaddleGAN的图像超分辨率的模型库比较丰富，而且都已经在DIV2K数据集上训练好，权重可自行下载，不需要自己复现代码。但是由于本项目的目标数据是遥感影像，所以直接使用该权重不能达到最优效果。如果使用PaddleGAN重新从头训练，消耗的算力太多了，你说是吧？因此想到使用训练好的超分模型权重进行迁移学习，减少训练所用时间的同时达到令人满意的效果。
- 看看放大四倍的效果：
| 低分辨率 | 超分辨率 | 高分辨率 |
|---|---|---|
 |
 |
 |
-
在进行训练之前，首先要克隆PaddleGAN，然后下载PaddleGAN提供的模型权重，再要准备数据，然后安装环境依赖。
-
项目所用数据集地址：RS_SR_Data ，简介有介绍该数据集如何产生，这里不做重复
-
从高分辨率影像生成对应低分辨率影像的代码放入work文件夹中，名称为：Prepare_TestData_HR_LR.m
# 从gitee克隆PaddleGAN的仓库
# clone好后便不用执行
!git clone https://gitee.com/paddlepaddle/PaddleGAN.git #从码云上clone正克隆到 'PaddleGAN'...
remote: Enumerating objects: 5600, done.�[K
remote: Counting objects: 100% (1656/1656), done.�[K
remote: Compressing objects: 100% (768/768), done.�[K
接收对象中: 24% (1353/5600), 7.82 MiB | 281.00 KiB/s
- PaddleGAN中的超分辨率模型在以下数据集的 RGB 通道上进行评估，并在评估之前裁剪每个边界的尺度像素。 度量指标为 PSNR / SSIM。
- 可以看到模型库中指标最高的模型是DRN，论文为CVPR2020的Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution，故选用该模型的参数做为迁移学习的初始值
| 模型 | Set5 | Set14 | DIV2K |
|---|---|---|---|
| realsr_df2k | 28.4385 / 0.8106 | 24.7424 / 0.6678 | 26.7306 / 0.7512 |
| realsr_dped | 20.2421 / 0.6158 | 19.3775 / 0.5259 | 20.5976 / 0.6051 |
| realsr_merge | 24.8315 / 0.7030 | 23.0393 / 0.5986 | 24.8510 / 0.6856 |
| lesrcnn_x4 | 31.9476 / 0.8909 | 28.4110 / 0.7770 | 30.231 / 0.8326 |
| esrgan_psnr_x4 | 32.5512 / 0.8991 | 28.8114 / 0.7871 | 30.7565 / 0.8449 |
| esrgan_x4 | 28.7647 / 0.8187 | 25.0065 / 0.6762 | 26.9013 / 0.7542 |
| pan_x4 | 30.4574 / 0.8643 | 26.7204 / 0.7434 | 28.9187 / 0.8176 |
| drns_x4 | 32.6684 / 0.8999 | 28.9037 / 0.7885 | - |
# 获取PaddleGAN训练好的DRN模型权重，并保存到work/pretrain文件夹中，若已存在则不用运行下列命令
# 事先已经准备好，可不用执行
!wget https://paddlegan.bj.bcebos.com/models/DRNSx4.pdparams
!mkdir work/pretrain
!mv DRNSx4.pdparams work/pretrain/--2022-03-21 10:57:43-- https://paddlegan.bj.bcebos.com/models/DRNSx4.pdparams
Resolving paddlegan.bj.bcebos.com (paddlegan.bj.bcebos.com)... 182.61.200.195, 182.61.200.229, 2409:8c04:1001:1002:0:ff:b001:368a
Connecting to paddlegan.bj.bcebos.com (paddlegan.bj.bcebos.com)|182.61.200.195|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 19266672 (18M) [application/octet-stream]
Saving to: ‘DRNSx4.pdparams’
DRNSx4.pdparams 100%[===================>] 18.37M 15.0MB/s in 1.2s
2022-03-21 10:57:44 (15.0 MB/s) - ‘DRNSx4.pdparams’ saved [19266672/19266672]
mkdir: cannot create directory ‘work/pretrain’: File exists
# 解压数据集到指定文件夹中，大概一分钟
# 执行过一次第二次不用执行
!unzip -oq data/data129011/RSdata_for_SR.zip -d PaddleGAN/data/!pwd/home/aistudio
# 安装依赖
%cd /home/aistudio/PaddleGAN
!pip install -r requirements.txt /home/aistudio/PaddleGAN
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- 迁移学习：遥感影像超分辨率的迁移学习其实就是加载预训练模型微调，所以只需要设置好训练的配置文件，运行时使用命令load一下预训练模型即可
- 下面介绍配置文件drn_psnr_x4_rssr.yaml，其如下所示。其中：
- total_iters是训练的迭代次数
- output_dir为训练输出的文件夹名称
- gt_folder为高分辨率影像所在文件夹，lq_folder为低分辨率所在文件夹，图片名要相互对应
- scale为影像超分辨率的倍数，DRN只能选择2或者4，本项目选择4
- 文件的最后snapshot_config: interval: 1000指的是每迭代1000次保存一次权重
- 由于是迁移学习，所以不用训练太久，设置为20000，大概一个半小时就能达到好的效果
total_iters: 20000
output_dir: output_dir
# tensor range for function tensor2img
min_max:
(0., 255.)
model:
name: DRN
generator:
name: DRNGenerator
scale: (2, 4)
n_blocks: 30
n_feats: 16
n_colors: 3
rgb_range: 255
negval: 0.2
pixel_criterion:
name: L1Loss
dataset:
train:
name: SRDataset
gt_folder: data/RSdata_for_SR/trian_HR
lq_folder: data/RSdata_for_SR/train_LR/x4
num_workers: 4
batch_size: 8
scale: 4
preprocess:
- name: LoadImageFromFile
key: lq
- name: LoadImageFromFile
key: gt
- name: Transforms
input_keys: [lq, gt]
output_keys: [lq, lqx2, gt]
pipeline:
- name: SRPairedRandomCrop
gt_patch_size: 192
scale: 4
scale_list: True
keys: [image, image]
- name: PairedRandomHorizontalFlip
keys: [image, image, image]
- name: PairedRandomVerticalFlip
keys: [image, image, image]
- name: PairedRandomTransposeHW
keys: [image, image, image]
- name: Transpose
keys: [image, image, image]
- name: Normalize
mean: [0., 0., 0.]
std: [1., 1., 1.]
keys: [image, image, image]
test:
name: SRDataset
gt_folder: data/RSdata_for_SR/test_HR
lq_folder: data/RSdata_for_SR/test_LR/x4
scale: 4
preprocess:
- name: LoadImageFromFile
key: lq
- name: LoadImageFromFile
key: gt
- name: Transforms
input_keys: [lq, gt]
pipeline:
- name: Transpose
keys: [image, image]
- name: Normalize
mean: [0., 0., 0.]
std: [1., 1., 1.]
keys: [image, image]
lr_scheduler:
name: CosineAnnealingRestartLR
learning_rate: 0.0001
periods: [1000000]
restart_weights: [1]
eta_min: !!float 1e-7
optimizer:
optimG:
name: Adam
net_names:
- generator
weight_decay: 0.0
beta1: 0.9
beta2: 0.999
optimD:
name: Adam
net_names:
- dual_model_0
- dual_model_1
weight_decay: 0.0
beta1: 0.9
beta2: 0.999
validate:
interval: 1000
save_img: True
metrics:
psnr: # metric name, can be arbitrary
name: PSNR
crop_border: 4
test_y_channel: True
ssim:
name: SSIM
crop_border: 4
test_y_channel: True
log_config:
interval: 10
visiual_interval: 500
snapshot_config:
interval: 1000
- .yaml文件已放入work文件夹中，运行以下命令即可加载预训练模型开始训练
!python -u tools/main.py --config-file ../work/drn_psnr_x4_rssr.yaml --load ../work/pretrain/DRNSx4.pdparams[05/29 18:26:09] ppgan.engine.trainer INFO: Iter: 13100/20000 lr: 9.996e-05 loss_promary: 8.114 loss_dual: 1.006 loss_total: 9.120 batch_cost: 0.19771 sec reader_cost: 0.00029 sec ips: 40.46246 images/s eta: 0:22:44
[05/29 18:26:11] ppgan.engine.trainer INFO: Iter: 13110/20000 lr: 9.996e-05 loss_promary: 7.907 loss_dual: 0.949 loss_total: 8.856 batch_cost: 0.20322 sec reader_cost: 0.00029 sec ips: 39.36712 images/s eta: 0:23:20
[05/29 18:26:13] ppgan.engine.trainer INFO: Iter: 13120/20000 lr: 9.996e-05 loss_promary: 9.708 loss_dual: 1.156 loss_total: 10.864 batch_cost: 0.20787 sec reader_cost: 0.00029 sec ips: 38.48474 images/s eta: 0:23:50
[05/29 18:26:15] ppgan.engine.trainer INFO: Iter: 13130/20000 lr: 9.996e-05 loss_promary: 8.144 loss_dual: 0.938 loss_total: 9.082 batch_cost: 0.20754 sec reader_cost: 0.00030 sec ips: 38.54682 images/s eta: 0:23:45
[05/29 18:26:17] ppgan.engine.trainer INFO: Iter: 13140/20000 lr: 9.996e-05 loss_promary: 8.536 loss_dual: 1.009 loss_total: 9.545 batch_cost: 0.19794 sec reader_cost: 0.00028 sec ips: 40.41711 images/s eta: 0:22:37
[05/29 18:26:19] ppgan.engine.trainer INFO: Iter: 13150/20000 lr: 9.996e-05 loss_promary: 8.673 loss_dual: 1.008 loss_total: 9.681 batch_cost: 0.20917 sec reader_cost: 0.00030 sec ips: 38.24591 images/s eta: 0:23:52
[05/29 18:26:21] ppgan.engine.trainer INFO: Iter: 13160/20000 lr: 9.996e-05 loss_promary: 8.257 loss_dual: 1.067 loss_total: 9.324 batch_cost: 0.21111 sec reader_cost: 0.00030 sec ips: 37.89580 images/s eta: 0:24:03
[05/29 18:26:23] ppgan.engine.trainer INFO: Iter: 13170/20000 lr: 9.996e-05 loss_promary: 9.440 loss_dual: 1.060 loss_total: 10.500 batch_cost: 0.20126 sec reader_cost: 0.00030 sec ips: 39.74935 images/s eta: 0:22:54
[05/29 18:26:25] ppgan.engine.trainer INFO: Iter: 13180/20000 lr: 9.996e-05 loss_promary: 8.957 loss_dual: 1.071 loss_total: 10.029 batch_cost: 0.20106 sec reader_cost: 0.00029 sec ips: 39.78908 images/s eta: 0:22:51
[05/29 18:26:27] ppgan.engine.trainer INFO: Iter: 13190/20000 lr: 9.996e-05 loss_promary: 9.151 loss_dual: 1.066 loss_total: 10.216 batch_cost: 0.19976 sec reader_cost: 0.00028 sec ips: 40.04885 images/s eta: 0:22:40
[05/29 18:26:29] ppgan.engine.trainer INFO: Iter: 13200/20000 lr: 9.996e-05 loss_promary: 9.996 loss_dual: 1.128 loss_total: 11.124 batch_cost: 0.19811 sec reader_cost: 0.00028 sec ips: 40.38181 images/s eta: 0:22:27
[05/29 18:26:31] ppgan.engine.trainer INFO: Iter: 13210/20000 lr: 9.996e-05 loss_promary: 9.234 loss_dual: 1.084 loss_total: 10.318 batch_cost: 0.20192 sec reader_cost: 0.00030 sec ips: 39.61950 images/s eta: 0:22:51
[05/29 18:26:33] ppgan.engine.trainer INFO: Iter: 13220/20000 lr: 9.996e-05 loss_promary: 7.686 loss_dual: 0.965 loss_total: 8.651 batch_cost: 0.20269 sec reader_cost: 0.00030 sec ips: 39.46951 images/s eta: 0:22:54
[05/29 18:26:35] ppgan.engine.trainer INFO: Iter: 13230/20000 lr: 9.996e-05 loss_promary: 9.290 loss_dual: 1.104 loss_total: 10.393 batch_cost: 0.20376 sec reader_cost: 0.00029 sec ips: 39.26184 images/s eta: 0:22:59
[05/29 18:26:37] ppgan.engine.trainer INFO: Iter: 13240/20000 lr: 9.996e-05 loss_promary: 8.780 loss_dual: 1.118 loss_total: 9.898 batch_cost: 0.20370 sec reader_cost: 0.00029 sec ips: 39.27380 images/s eta: 0:22:56
[05/29 18:26:39] ppgan.engine.trainer INFO: Iter: 13250/20000 lr: 9.996e-05 loss_promary: 12.926 loss_dual: 1.469 loss_total: 14.395 batch_cost: 0.20172 sec reader_cost: 0.00029 sec ips: 39.65977 images/s eta: 0:22:41
[05/29 18:26:41] ppgan.engine.trainer INFO: Iter: 13260/20000 lr: 9.996e-05 loss_promary: 8.717 loss_dual: 1.101 loss_total: 9.818 batch_cost: 0.20217 sec reader_cost: 0.00029 sec ips: 39.57052 images/s eta: 0:22:42
[05/29 18:26:43] ppgan.engine.trainer INFO: Iter: 13270/20000 lr: 9.996e-05 loss_promary: 9.768 loss_dual: 1.161 loss_total: 10.929 batch_cost: 0.20252 sec reader_cost: 0.00029 sec ips: 39.50201 images/s eta: 0:22:42
[05/29 18:26:45] ppgan.engine.trainer INFO: Iter: 13280/20000 lr: 9.996e-05 loss_promary: 7.958 loss_dual: 1.005 loss_total: 8.963 batch_cost: 0.20274 sec reader_cost: 0.00028 sec ips: 39.45911 images/s eta: 0:22:42
[05/29 18:26:47] ppgan.engine.trainer INFO: Iter: 13290/20000 lr: 9.996e-05 loss_promary: 8.377 loss_dual: 1.051 loss_total: 9.428 batch_cost: 0.20718 sec reader_cost: 0.00030 sec ips: 38.61365 images/s eta: 0:23:10
[05/29 18:26:49] ppgan.engine.trainer INFO: Iter: 13300/20000 lr: 9.996e-05 loss_promary: 7.114 loss_dual: 0.842 loss_total: 7.956 batch_cost: 0.20045 sec reader_cost: 0.00028 sec ips: 39.91092 images/s eta: 0:22:22
[05/29 18:26:51] ppgan.engine.trainer INFO: Iter: 13310/20000 lr: 9.996e-05 loss_promary: 9.479 loss_dual: 1.067 loss_total: 10.546 batch_cost: 0.22380 sec reader_cost: 0.00030 sec ips: 35.74647 images/s eta: 0:24:57
[05/29 18:26:54] ppgan.engine.trainer INFO: Iter: 13320/20000 lr: 9.996e-05 loss_promary: 7.826 loss_dual: 0.953 loss_total: 8.779 batch_cost: 0.20307 sec reader_cost: 0.00030 sec ips: 39.39436 images/s eta: 0:22:36
[05/29 18:26:56] ppgan.engine.trainer INFO: Iter: 13330/20000 lr: 9.996e-05 loss_promary: 6.486 loss_dual: 0.843 loss_total: 7.329 batch_cost: 0.20350 sec reader_cost: 0.00031 sec ips: 39.31203 images/s eta: 0:22:37
[05/29 18:26:58] ppgan.engine.trainer INFO: Iter: 13340/20000 lr: 9.996e-05 loss_promary: 6.840 loss_dual: 0.893 loss_total: 7.733 batch_cost: 0.20175 sec reader_cost: 0.00030 sec ips: 39.65279 images/s eta: 0:22:23
[05/29 18:27:00] ppgan.engine.trainer INFO: Iter: 13350/20000 lr: 9.996e-05 loss_promary: 8.526 loss_dual: 1.110 loss_total: 9.636 batch_cost: 0.20039 sec reader_cost: 0.00028 sec ips: 39.92216 images/s eta: 0:22:12
[05/29 18:27:02] ppgan.engine.trainer INFO: Iter: 13360/20000 lr: 9.996e-05 loss_promary: 11.089 loss_dual: 1.210 loss_total: 12.299 batch_cost: 0.20248 sec reader_cost: 0.00029 sec ips: 39.51033 images/s eta: 0:22:24
[05/29 18:27:04] ppgan.engine.trainer INFO: Iter: 13370/20000 lr: 9.996e-05 loss_promary: 8.228 loss_dual: 0.996 loss_total: 9.224 batch_cost: 0.20478 sec reader_cost: 0.00030 sec ips: 39.06600 images/s eta: 0:22:37
[05/29 18:27:06] ppgan.engine.trainer INFO: Iter: 13380/20000 lr: 9.996e-05 loss_promary: 13.335 loss_dual: 1.455 loss_total: 14.790 batch_cost: 0.21494 sec reader_cost: 0.00031 sec ips: 37.21909 images/s eta: 0:23:42
[05/29 18:27:08] ppgan.engine.trainer INFO: Iter: 13390/20000 lr: 9.996e-05 loss_promary: 9.435 loss_dual: 1.117 loss_total: 10.552 batch_cost: 0.20596 sec reader_cost: 0.00030 sec ips: 38.84204 images/s eta: 0:22:41
[05/29 18:27:10] ppgan.engine.trainer INFO: Iter: 13400/20000 lr: 9.996e-05 loss_promary: 9.004 loss_dual: 1.076 loss_total: 10.080 batch_cost: 0.20937 sec reader_cost: 0.00030 sec ips: 38.21073 images/s eta: 0:23:01
[05/29 18:27:12] ppgan.engine.trainer INFO: Iter: 13410/20000 lr: 9.996e-05 loss_promary: 7.771 loss_dual: 0.923 loss_total: 8.694 batch_cost: 0.21066 sec reader_cost: 0.00032 sec ips: 37.97649 images/s eta: 0:23:08
[05/29 18:27:14] ppgan.engine.trainer INFO: Iter: 13420/20000 lr: 9.996e-05 loss_promary: 13.621 loss_dual: 1.461 loss_total: 15.082 batch_cost: 0.20568 sec reader_cost: 0.00029 sec ips: 38.89595 images/s eta: 0:22:33
[05/29 18:27:16] ppgan.engine.trainer INFO: Iter: 13430/20000 lr: 9.996e-05 loss_promary: 10.626 loss_dual: 1.263 loss_total: 11.889 batch_cost: 0.21171 sec reader_cost: 0.00032 sec ips: 37.78746 images/s eta: 0:23:10
[05/29 18:27:18] ppgan.engine.trainer INFO: Iter: 13440/20000 lr: 9.996e-05 loss_promary: 10.421 loss_dual: 1.182 loss_total: 11.603 batch_cost: 0.20445 sec reader_cost: 0.00041 sec ips: 39.12934 images/s eta: 0:22:21
[05/29 18:27:21] ppgan.engine.trainer INFO: Iter: 13450/20000 lr: 9.996e-05 loss_promary: 10.531 loss_dual: 1.316 loss_total: 11.847 batch_cost: 0.26532 sec reader_cost: 0.03661 sec ips: 30.15270 images/s eta: 0:28:57
[05/29 18:27:23] ppgan.engine.trainer INFO: Iter: 13460/20000 lr: 9.996e-05 loss_promary: 8.166 loss_dual: 1.046 loss_total: 9.212 batch_cost: 0.22524 sec reader_cost: 0.00031 sec ips: 35.51769 images/s eta: 0:24:33
[05/29 18:27:25] ppgan.engine.trainer INFO: Iter: 13470/20000 lr: 9.996e-05 loss_promary: 9.353 loss_dual: 1.137 loss_total: 10.490 batch_cost: 0.21019 sec reader_cost: 0.00031 sec ips: 38.06067 images/s eta: 0:22:52
[05/29 18:27:27] ppgan.engine.trainer INFO: Iter: 13480/20000 lr: 9.996e-05 loss_promary: 9.157 loss_dual: 1.090 loss_total: 10.247 batch_cost: 0.20709 sec reader_cost: 0.00031 sec ips: 38.63017 images/s eta: 0:22:30
[05/29 18:27:29] ppgan.engine.trainer INFO: Iter: 13490/20000 lr: 9.996e-05 loss_promary: 11.711 loss_dual: 1.406 loss_total: 13.117 batch_cost: 0.20611 sec reader_cost: 0.00031 sec ips: 38.81388 images/s eta: 0:22:21
[05/29 18:27:32] ppgan.engine.trainer INFO: Iter: 13500/20000 lr: 9.996e-05 loss_promary: 10.701 loss_dual: 1.233 loss_total: 11.934 batch_cost: 0.20874 sec reader_cost: 0.00030 sec ips: 38.32572 images/s eta: 0:22:36
[05/29 18:27:34] ppgan.engine.trainer INFO: Iter: 13510/20000 lr: 9.996e-05 loss_promary: 8.681 loss_dual: 1.016 loss_total: 9.698 batch_cost: 0.20839 sec reader_cost: 0.00032 sec ips: 38.38898 images/s eta: 0:22:32
[05/29 18:27:36] ppgan.engine.trainer INFO: Iter: 13520/20000 lr: 9.995e-05 loss_promary: 9.182 loss_dual: 1.082 loss_total: 10.264 batch_cost: 0.20747 sec reader_cost: 0.00030 sec ips: 38.55977 images/s eta: 0:22:24
[05/29 18:27:38] ppgan.engine.trainer INFO: Iter: 13530/20000 lr: 9.995e-05 loss_promary: 10.831 loss_dual: 1.236 loss_total: 12.066 batch_cost: 0.21590 sec reader_cost: 0.00031 sec ips: 37.05399 images/s eta: 0:23:16
[05/29 18:27:40] ppgan.engine.trainer INFO: Iter: 13540/20000 lr: 9.995e-05 loss_promary: 8.416 loss_dual: 1.012 loss_total: 9.428 batch_cost: 0.20908 sec reader_cost: 0.00032 sec ips: 38.26257 images/s eta: 0:22:30
[05/29 18:27:42] ppgan.engine.trainer INFO: Iter: 13550/20000 lr: 9.995e-05 loss_promary: 11.829 loss_dual: 1.312 loss_total: 13.141 batch_cost: 0.21299 sec reader_cost: 0.00032 sec ips: 37.56046 images/s eta: 0:22:53
[05/29 18:27:44] ppgan.engine.trainer INFO: Iter: 13560/20000 lr: 9.995e-05 loss_promary: 8.136 loss_dual: 1.135 loss_total: 9.272 batch_cost: 0.20810 sec reader_cost: 0.00031 sec ips: 38.44379 images/s eta: 0:22:20
[05/29 18:27:46] ppgan.engine.trainer INFO: Iter: 13570/20000 lr: 9.995e-05 loss_promary: 7.574 loss_dual: 0.983 loss_total: 8.557 batch_cost: 0.21011 sec reader_cost: 0.00032 sec ips: 38.07456 images/s eta: 0:22:31
[05/29 18:27:48] ppgan.engine.trainer INFO: Iter: 13580/20000 lr: 9.995e-05 loss_promary: 7.313 loss_dual: 0.951 loss_total: 8.264 batch_cost: 0.20649 sec reader_cost: 0.00030 sec ips: 38.74299 images/s eta: 0:22:05
[05/29 18:27:50] ppgan.engine.trainer INFO: Iter: 13590/20000 lr: 9.995e-05 loss_promary: 8.691 loss_dual: 1.103 loss_total: 9.793 batch_cost: 0.20709 sec reader_cost: 0.00032 sec ips: 38.63110 images/s eta: 0:22:07
[05/29 18:27:53] ppgan.engine.trainer INFO: Iter: 13600/20000 lr: 9.995e-05 loss_promary: 11.410 loss_dual: 1.323 loss_total: 12.732 batch_cost: 0.20717 sec reader_cost: 0.00031 sec ips: 38.61481 images/s eta: 0:22:05
[05/29 18:27:55] ppgan.engine.trainer INFO: Iter: 13610/20000 lr: 9.995e-05 loss_promary: 8.096 loss_dual: 0.993 loss_total: 9.089 batch_cost: 0.24286 sec reader_cost: 0.00035 sec ips: 32.94138 images/s eta: 0:25:51
[05/29 18:27:57] ppgan.engine.trainer INFO: Iter: 13620/20000 lr: 9.995e-05 loss_promary: 11.664 loss_dual: 1.264 loss_total: 12.928 batch_cost: 0.21826 sec reader_cost: 0.00032 sec ips: 36.65298 images/s eta: 0:23:12
[05/29 18:27:59] ppgan.engine.trainer INFO: Iter: 13630/20000 lr: 9.995e-05 loss_promary: 10.959 loss_dual: 1.150 loss_total: 12.109 batch_cost: 0.21381 sec reader_cost: 0.00032 sec ips: 37.41655 images/s eta: 0:22:41
[05/29 18:28:01] ppgan.engine.trainer INFO: Iter: 13640/20000 lr: 9.995e-05 loss_promary: 10.572 loss_dual: 1.295 loss_total: 11.867 batch_cost: 0.21675 sec reader_cost: 0.00033 sec ips: 36.90861 images/s eta: 0:22:58
[05/29 18:28:04] ppgan.engine.trainer INFO: Iter: 13650/20000 lr: 9.995e-05 loss_promary: 7.363 loss_dual: 0.925 loss_total: 8.288 batch_cost: 0.20766 sec reader_cost: 0.00032 sec ips: 38.52531 images/s eta: 0:21:58
[05/29 18:28:06] ppgan.engine.trainer INFO: Iter: 13660/20000 lr: 9.995e-05 loss_promary: 6.841 loss_dual: 0.976 loss_total: 7.817 batch_cost: 0.20999 sec reader_cost: 0.00033 sec ips: 38.09705 images/s eta: 0:22:11
[05/29 18:28:08] ppgan.engine.trainer INFO: Iter: 13670/20000 lr: 9.995e-05 loss_promary: 8.813 loss_dual: 1.140 loss_total: 9.953 batch_cost: 0.20655 sec reader_cost: 0.00032 sec ips: 38.73218 images/s eta: 0:21:47
[05/29 18:28:10] ppgan.engine.trainer INFO: Iter: 13680/20000 lr: 9.995e-05 loss_promary: 7.700 loss_dual: 1.022 loss_total: 8.722 batch_cost: 0.20673 sec reader_cost: 0.00030 sec ips: 38.69695 images/s eta: 0:21:46
[05/29 18:28:12] ppgan.engine.trainer INFO: Iter: 13690/20000 lr: 9.995e-05 loss_promary: 7.564 loss_dual: 0.977 loss_total: 8.542 batch_cost: 0.20574 sec reader_cost: 0.00031 sec ips: 38.88379 images/s eta: 0:21:38
[05/29 18:28:14] ppgan.engine.trainer INFO: Iter: 13700/20000 lr: 9.995e-05 loss_promary: 8.168 loss_dual: 0.965 loss_total: 9.133 batch_cost: 0.20478 sec reader_cost: 0.00030 sec ips: 39.06679 images/s eta: 0:21:30
[05/29 18:28:16] ppgan.engine.trainer INFO: Iter: 13710/20000 lr: 9.995e-05 loss_promary: 9.061 loss_dual: 1.119 loss_total: 10.180 batch_cost: 0.21466 sec reader_cost: 0.00031 sec ips: 37.26893 images/s eta: 0:22:30
[05/29 18:28:18] ppgan.engine.trainer INFO: Iter: 13720/20000 lr: 9.995e-05 loss_promary: 8.155 loss_dual: 1.001 loss_total: 9.157 batch_cost: 0.21070 sec reader_cost: 0.00030 sec ips: 37.96813 images/s eta: 0:22:03
[05/29 18:28:20] ppgan.engine.trainer INFO: Iter: 13730/20000 lr: 9.995e-05 loss_promary: 10.906 loss_dual: 1.245 loss_total: 12.151 batch_cost: 0.20568 sec reader_cost: 0.00031 sec ips: 38.89616 images/s eta: 0:21:29
[05/29 18:28:22] ppgan.engine.trainer INFO: Iter: 13740/20000 lr: 9.995e-05 loss_promary: 8.745 loss_dual: 1.028 loss_total: 9.773 batch_cost: 0.20831 sec reader_cost: 0.00030 sec ips: 38.40487 images/s eta: 0:21:44
[05/29 18:28:24] ppgan.engine.trainer INFO: Iter: 13750/20000 lr: 9.995e-05 loss_promary: 10.325 loss_dual: 1.155 loss_total: 11.480 batch_cost: 0.20767 sec reader_cost: 0.00031 sec ips: 38.52268 images/s eta: 0:21:37
[05/29 18:28:27] ppgan.engine.trainer INFO: Iter: 13760/20000 lr: 9.995e-05 loss_promary: 8.100 loss_dual: 0.979 loss_total: 9.078 batch_cost: 0.21436 sec reader_cost: 0.00031 sec ips: 37.32085 images/s eta: 0:22:17
[05/29 18:28:29] ppgan.engine.trainer INFO: Iter: 13770/20000 lr: 9.995e-05 loss_promary: 9.072 loss_dual: 0.995 loss_total: 10.067 batch_cost: 0.21200 sec reader_cost: 0.00030 sec ips: 37.73625 images/s eta: 0:22:00
[05/29 18:28:31] ppgan.engine.trainer INFO: Iter: 13780/20000 lr: 9.995e-05 loss_promary: 16.154 loss_dual: 1.721 loss_total: 17.875 batch_cost: 0.21072 sec reader_cost: 0.00032 sec ips: 37.96423 images/s eta: 0:21:50
[05/29 18:28:33] ppgan.engine.trainer INFO: Iter: 13790/20000 lr: 9.995e-05 loss_promary: 12.826 loss_dual: 1.321 loss_total: 14.148 batch_cost: 0.20472 sec reader_cost: 0.00032 sec ips: 39.07693 images/s eta: 0:21:11
[05/29 18:28:35] ppgan.engine.trainer INFO: Iter: 13800/20000 lr: 9.995e-05 loss_promary: 7.397 loss_dual: 0.861 loss_total: 8.257 batch_cost: 0.20613 sec reader_cost: 0.00030 sec ips: 38.81024 images/s eta: 0:21:18
[05/29 18:28:37] ppgan.engine.trainer INFO: Iter: 13810/20000 lr: 9.995e-05 loss_promary: 11.764 loss_dual: 1.234 loss_total: 12.997 batch_cost: 0.20559 sec reader_cost: 0.00032 sec ips: 38.91277 images/s eta: 0:21:12
[05/29 18:28:39] ppgan.engine.trainer INFO: Iter: 13820/20000 lr: 9.995e-05 loss_promary: 9.112 loss_dual: 0.982 loss_total: 10.095 batch_cost: 0.20895 sec reader_cost: 0.00032 sec ips: 38.28670 images/s eta: 0:21:31
[05/29 18:28:41] ppgan.engine.trainer INFO: Iter: 13830/20000 lr: 9.995e-05 loss_promary: 10.599 loss_dual: 1.218 loss_total: 11.817 batch_cost: 0.20826 sec reader_cost: 0.00031 sec ips: 38.41319 images/s eta: 0:21:24
[05/29 18:28:43] ppgan.engine.trainer INFO: Iter: 13840/20000 lr: 9.995e-05 loss_promary: 8.198 loss_dual: 1.054 loss_total: 9.252 batch_cost: 0.21309 sec reader_cost: 0.00041 sec ips: 37.54304 images/s eta: 0:21:52
[05/29 18:28:45] ppgan.engine.trainer INFO: Iter: 13850/20000 lr: 9.995e-05 loss_promary: 12.623 loss_dual: 1.399 loss_total: 14.022 batch_cost: 0.21703 sec reader_cost: 0.00032 sec ips: 36.86104 images/s eta: 0:22:14
[05/29 18:28:47] ppgan.engine.trainer INFO: Iter: 13860/20000 lr: 9.995e-05 loss_promary: 8.312 loss_dual: 1.033 loss_total: 9.345 batch_cost: 0.20320 sec reader_cost: 0.00031 sec ips: 39.36926 images/s eta: 0:20:47
[05/29 18:28:50] ppgan.engine.trainer INFO: Iter: 13870/20000 lr: 9.995e-05 loss_promary: 7.620 loss_dual: 0.947 loss_total: 8.566 batch_cost: 0.20544 sec reader_cost: 0.00030 sec ips: 38.94060 images/s eta: 0:20:59
[05/29 18:28:52] ppgan.engine.trainer INFO: Iter: 13880/20000 lr: 9.995e-05 loss_promary: 9.986 loss_dual: 1.303 loss_total: 11.289 batch_cost: 0.20658 sec reader_cost: 0.00031 sec ips: 38.72685 images/s eta: 0:21:04
[05/29 18:28:54] ppgan.engine.trainer INFO: Iter: 13890/20000 lr: 9.995e-05 loss_promary: 11.980 loss_dual: 1.307 loss_total: 13.286 batch_cost: 0.20650 sec reader_cost: 0.00031 sec ips: 38.74033 images/s eta: 0:21:01
[05/29 18:28:56] ppgan.engine.trainer INFO: Iter: 13900/20000 lr: 9.995e-05 loss_promary: 13.141 loss_dual: 1.456 loss_total: 14.597 batch_cost: 0.21294 sec reader_cost: 0.00056 sec ips: 37.56929 images/s eta: 0:21:38
[05/29 18:28:58] ppgan.engine.trainer INFO: Iter: 13910/20000 lr: 9.995e-05 loss_promary: 10.052 loss_dual: 1.148 loss_total: 11.200 batch_cost: 0.21782 sec reader_cost: 0.00032 sec ips: 36.72768 images/s eta: 0:22:06
[05/29 18:29:00] ppgan.engine.trainer INFO: Iter: 13920/20000 lr: 9.995e-05 loss_promary: 11.005 loss_dual: 1.246 loss_total: 12.251 batch_cost: 0.23435 sec reader_cost: 0.00032 sec ips: 34.13701 images/s eta: 0:23:44
[05/29 18:29:02] ppgan.engine.trainer INFO: Iter: 13930/20000 lr: 9.995e-05 loss_promary: 8.656 loss_dual: 1.054 loss_total: 9.710 batch_cost: 0.20403 sec reader_cost: 0.00030 sec ips: 39.21020 images/s eta: 0:20:38
[05/29 18:29:05] ppgan.engine.trainer INFO: Iter: 13940/20000 lr: 9.995e-05 loss_promary: 9.264 loss_dual: 1.051 loss_total: 10.315 batch_cost: 0.21598 sec reader_cost: 0.00033 sec ips: 37.04033 images/s eta: 0:21:48
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[05/29 18:56:57] ppgan.engine.trainer INFO: Iter: 19880/20000 lr: 9.990e-05 loss_promary: 8.408 loss_dual: 0.954 loss_total: 9.362 batch_cost: 0.20465 sec reader_cost: 0.00030 sec ips: 39.09138 images/s eta: 0:00:24
[05/29 18:56:59] ppgan.engine.trainer INFO: Iter: 19890/20000 lr: 9.990e-05 loss_promary: 9.830 loss_dual: 1.168 loss_total: 10.999 batch_cost: 0.20324 sec reader_cost: 0.00030 sec ips: 39.36155 images/s eta: 0:00:22
[05/29 18:57:02] ppgan.engine.trainer INFO: Iter: 19900/20000 lr: 9.990e-05 loss_promary: 7.590 loss_dual: 0.877 loss_total: 8.467 batch_cost: 0.20522 sec reader_cost: 0.00030 sec ips: 38.98199 images/s eta: 0:00:20
[05/29 18:57:04] ppgan.engine.trainer INFO: Iter: 19910/20000 lr: 9.990e-05 loss_promary: 11.218 loss_dual: 1.188 loss_total: 12.406 batch_cost: 0.21352 sec reader_cost: 0.00030 sec ips: 37.46644 images/s eta: 0:00:19
[05/29 18:57:06] ppgan.engine.trainer INFO: Iter: 19920/20000 lr: 9.990e-05 loss_promary: 8.219 loss_dual: 0.984 loss_total: 9.203 batch_cost: 0.20869 sec reader_cost: 0.00030 sec ips: 38.33524 images/s eta: 0:00:16
[05/29 18:57:08] ppgan.engine.trainer INFO: Iter: 19930/20000 lr: 9.990e-05 loss_promary: 7.666 loss_dual: 0.877 loss_total: 8.543 batch_cost: 0.20001 sec reader_cost: 0.00029 sec ips: 39.99859 images/s eta: 0:00:14
[05/29 18:57:10] ppgan.engine.trainer INFO: Iter: 19940/20000 lr: 9.990e-05 loss_promary: 8.462 loss_dual: 0.967 loss_total: 9.430 batch_cost: 0.20230 sec reader_cost: 0.00030 sec ips: 39.54459 images/s eta: 0:00:12
[05/29 18:57:12] ppgan.engine.trainer INFO: Iter: 19950/20000 lr: 9.990e-05 loss_promary: 10.219 loss_dual: 1.161 loss_total: 11.380 batch_cost: 0.20357 sec reader_cost: 0.00032 sec ips: 39.29935 images/s eta: 0:00:10
[05/29 18:57:14] ppgan.engine.trainer INFO: Iter: 19960/20000 lr: 9.990e-05 loss_promary: 10.733 loss_dual: 1.337 loss_total: 12.069 batch_cost: 0.20929 sec reader_cost: 0.00030 sec ips: 38.22524 images/s eta: 0:00:08
[05/29 18:57:16] ppgan.engine.trainer INFO: Iter: 19970/20000 lr: 9.990e-05 loss_promary: 10.525 loss_dual: 1.171 loss_total: 11.695 batch_cost: 0.20607 sec reader_cost: 0.00032 sec ips: 38.82246 images/s eta: 0:00:06
[05/29 18:57:18] ppgan.engine.trainer INFO: Iter: 19980/20000 lr: 9.990e-05 loss_promary: 7.848 loss_dual: 0.932 loss_total: 8.781 batch_cost: 0.20411 sec reader_cost: 0.00031 sec ips: 39.19393 images/s eta: 0:00:04
[05/29 18:57:20] ppgan.engine.trainer INFO: Iter: 19990/20000 lr: 9.990e-05 loss_promary: 10.081 loss_dual: 1.056 loss_total: 11.137 batch_cost: 0.20616 sec reader_cost: 0.00030 sec ips: 38.80536 images/s eta: 0:00:02
[05/29 18:57:22] ppgan.engine.trainer INFO: Iter: 20000/20000 lr: 9.990e-05 loss_promary: 8.080 loss_dual: 0.925 loss_total: 9.005 batch_cost: 0.20322 sec reader_cost: 0.00030 sec ips: 39.36626 images/s eta: 0:00:00
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[05/29 18:58:34] ppgan.engine.trainer INFO: Metric psnr: 29.4280
[05/29 18:58:34] ppgan.engine.trainer INFO: Metric ssim: 0.8011
- 训练达到预期效果后，可以使用模型来测试。我已经提前将权重文件放在work/weight文件夹下，运行下行命令对测试集test_LR进行测试
- 注意：训练过程中保存的权重是放在 PaddleGAN/output_dir/drn_psnr_x4_div2k-{time}/文件夹下，这里的time是和你开始训练的时间有关的，所以为了能让新手全流程跑通，不用修改路径的，就提前将权重拿出来放到weight文件夹下
- 如果想测试自己训练出的模型，更改--load 后面的模型权重路径即可
- 测试的结果会在运行结束后保存在 PaddleGAN/output_dir/drn_psnr_x4_div2k-{time}/visual_test 文件夹下，测试的结果：PSNR:29.4236 SSIM:0.8002
!python -u tools/main.py --config-file ../work/drn_psnr_x4_rssr.yaml --evaluate-only --load /home/aistudio/PaddleGAN/output_dir/drn_psnr_x4_rssr-2023-05-29-17-24/iter_20000_weight.pdparams[05/30 20:16:23] ppgan INFO: Configs:
dataset:
test:
gt_folder: data/RSdata_for_SR/test_HR
lq_folder: data/RSdata_for_SR/test_LR/x4
name: SRDataset
preprocess:
- key: lq
name: LoadImageFromFile
- key: gt
name: LoadImageFromFile
- input_keys:
- lq
- gt
name: Transforms
pipeline:
- keys:
- image
- image
name: Transpose
- keys:
- image
- image
mean:
- 0.0
- 0.0
- 0.0
name: Normalize
std:
- 1.0
- 1.0
- 1.0
scale: 4
train:
batch_size: 8
gt_folder: data/RSdata_for_SR/trian_HR
lq_folder: data/RSdata_for_SR/train_LR/x4
name: SRDataset
num_workers: 4
preprocess:
- key: lq
name: LoadImageFromFile
- key: gt
name: LoadImageFromFile
- input_keys:
- lq
- gt
name: Transforms
output_keys:
- lq
- lqx2
- gt
pipeline:
- gt_patch_size: 192
keys:
- image
- image
name: SRPairedRandomCrop
scale: 4
scale_list: true
- keys:
- image
- image
- image
name: PairedRandomHorizontalFlip
- keys:
- image
- image
- image
name: PairedRandomVerticalFlip
- keys:
- image
- image
- image
name: PairedRandomTransposeHW
- keys:
- image
- image
- image
name: Transpose
- keys:
- image
- image
- image
mean:
- 0.0
- 0.0
- 0.0
name: Normalize
std:
- 1.0
- 1.0
- 1.0
scale: 4
is_train: false
log_config:
interval: 10
visiual_interval: 500
lr_scheduler:
eta_min: 1.0e-07
learning_rate: 0.0001
name: CosineAnnealingRestartLR
periods:
- 1000000
restart_weights:
- 1
min_max: !!python/tuple
- 0.0
- 255.0
model:
generator:
n_blocks: 30
n_colors: 3
n_feats: 16
name: DRNGenerator
negval: 0.2
rgb_range: 255
scale: !!python/tuple
- 2
- 4
name: DRN
pixel_criterion:
name: L1Loss
optimizer:
optimD:
beta1: 0.9
beta2: 0.999
name: Adam
net_names:
- dual_model_0
- dual_model_1
weight_decay: 0.0
optimG:
beta1: 0.9
beta2: 0.999
name: Adam
net_names:
- generator
weight_decay: 0.0
output_dir: output_dir/drn_psnr_x4_rssr-2023-05-30-20-16
profiler_options: null
snapshot_config:
interval: 1000
timestamp: -2023-05-30-20-16
total_iters: 20000
validate:
interval: 1000
metrics:
psnr:
crop_border: 4
name: PSNR
test_y_channel: true
ssim:
crop_border: 4
name: SSIM
test_y_channel: true
save_img: true
W0530 20:16:23.638520 1875 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0530 20:16:23.643198 1875 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[05/30 20:16:28] ppgan.engine.trainer INFO: Loaded pretrained weight for net generator
[05/30 20:16:28] ppgan.engine.trainer INFO: Loaded pretrained weight for net dual_model_0
[05/30 20:16:28] ppgan.engine.trainer INFO: Loaded pretrained weight for net dual_model_1
[05/30 20:16:28] ppgan.engine.trainer INFO: Test iter: [0/420]
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[05/30 20:17:38] ppgan.engine.trainer INFO: Metric psnr: 29.4280
[05/30 20:17:38] ppgan.engine.trainer INFO: Metric ssim: 0.8011
- 定义一个预测类，快速调用训练好的模型权重对指定文件夹的低分辨率影像做4倍的超分，然后使用matplotlib可视化
- 接下来的示例，是对work/example/inputs文件夹下的图像进行重建，inputs文件夹中的文件为测试集中的图像。
- 运行下列命令，从测试集中复制图像到work/example/inputs文件夹中
%cd /home/aistudio/work/example
!unzip NV10-dataset.zip/home/aistudio/work/example
Archive: NV10-dataset.zip
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# 运行这步会报已经存在文件夹的错，因为已经存在文件夹所以可以不执行这步，也可以删掉重新执行
!mkdir work/example
!mkdir work/example/inputs
# 复制影像
!cp PaddleGAN/data/RSdata_for_SR/test_LR/x4/runway16.png work/example/inputs/
!cp PaddleGAN/data/RSdata_for_SR/test_LR/x4/harbor13.png work/example/inputs/
!cp PaddleGAN/data/RSdata_for_SR/test_LR/x4/intersection16.png work/example/inputs/
!cp PaddleGAN/data/RSdata_for_SR/test_LR/x4/parkinglot61.png work/example/inputs/
!cp PaddleGAN/data/RSdata_for_SR/test_LR/x4/overpass63.png work/example/inputs/
!cp PaddleGAN/data/RSdata_for_SR/test_LR/x4/tenniscourt19.png work/example/inputs//home/aistudio
# 定义使用DRN模型预测的类DRNPredictor，需要输入参数：
# output: 模型输出保存的文件夹
# weight_path: 模型权重文件所在的路径
%cd /home/aistudio/PaddleGAN/
import cv2
import glob
import numpy as np
from PIL import Image
from tqdm import tqdm
import paddle
from ppgan.models.generators import DRNGenerator
from ppgan.apps.base_predictor import BasePredictor
from ppgan.utils.logger import get_logger
class DRNPredictor(BasePredictor):
def __init__(self, output='../work/example/output', weight_path=None):
self.input = input
self.output = os.path.join(output, 'DRN') #定义超分的结果保存的路径，为output路径+模型名所在文件夹
self.model = DRNGenerator((2, 4)) # 实例化模型
state_dict = paddle.load(weight_path) #加载权重
state_dict = state_dict['generator']
self.model.load_dict(state_dict)
self.model.eval()
# 标准化
def norm(self, img):
img = np.array(img).transpose([2, 0, 1]).astype('float32') / 1.0
return img.astype('float32')
# 去标准化
def denorm(self, img):
img = img.transpose((1, 2, 0))
return (img * 1).clip(0, 255).astype('uint8')
# 对图片输入进行预测，输入可以是图像路径，也可以是cv2读取的矩阵，或者PIL读取的图像文件
def run_image(self, img):
if isinstance(img, str):
ori_img = Image.open(img).convert('RGB')
elif isinstance(img, np.ndarray):
ori_img = Image.fromarray(img).convert('RGB')
elif isinstance(img, Image.Image):
ori_img = img
img = self.norm(ori_img) #图像标准化
x = paddle.to_tensor(img[np.newaxis, ...]) #转成tensor
with paddle.no_grad():
out = self.model(x)[2] # 执行预测，DRN模型会输出三个tensor，第一个是原始低分辨率影像，第二个是放大两倍，第三个才是我们所需要的最后的结果
pred_img = self.denorm(out.numpy()[0]) #tensor转成numpy的array并去标准化
pred_img = Image.fromarray(pred_img) # array转图像
return pred_img
#输入图像文件路径
def run(self, input):
# 如果输出路径不存在则新建一个
if not os.path.exists(self.output):
os.makedirs(self.output)
pred_img = self.run_image(input) #对输入的图片进行预测
out_path = None
if self.output:
try:
base_name = os.path.splitext(os.path.basename(input))[0]
except:
base_name = 'result'
out_path = os.path.join(self.output, base_name + '.png') #保存路径
pred_img.save(out_path) #保存输出图片
logger = get_logger()
logger.info('Image saved to {}'.format(out_path))
return pred_img, out_path/home/aistudio/PaddleGAN
# png转成jpg
import os
import shutil
# 待修改后缀的文件夹路径
folder_path = '/home/aistudio/work/example/inputs'
# 将PNG文件路径列表赋值给变量png_files
png_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.png')]
# 遍历png_files列表，将每个PNG文件修改后缀为JPG，并保存到同文件夹下
for file in png_files:
newfile = os.path.splitext(file)[0] + '.jpg'
shutil.move(file, newfile)-
定义好预测的类之后，接下来实例化预测类并对文件夹下的图像进行预测
-
在预测的过程中，展示输入的低分辨率图像与预测的图像
-
预测的结果保存在指定的文件夹的DRN文件夹中
import matplotlib.pyplot as plt
import os
import numpy as np
%matplotlib inline
# 输出预测结果的文件夹
output = r'../work/example/output'
# 模型路径
weight_path = r"../work/weight/drn_x4_rssr.pdparams"
# 待输入的低分辨率影像位置
input_dir = r"/home/aistudio/work/example/inputs"
paddle.device.set_device("gpu:0") # 若是cpu环境，则替换为 paddle.device.set_device("cpu")
predictor = DRNPredictor(output, weight_path) # 实例化
filenames = [f for f in os.listdir(input_dir) if f.endswith('.jpg')]
for filename in filenames:
imgPath = os.path.join(input_dir, filename)
outImg, _ = predictor.run(imgPath) # 预测
# 可视化
image = Image.open(imgPath)
plt.figure(figsize=(10, 6))
plt.subplot(1,2,1), plt.title('Input')
plt.imshow(image), plt.axis('off')
plt.subplot(1,2,2), plt.title('Output')
plt.imshow(outImg), plt.axis('off')
plt.show()[05/30 19:04:57] ppgan INFO: Image saved to ../work/example/output/DRN/airplane40.png
[05/30 19:04:58] ppgan INFO: Image saved to ../work/example/output/DRN/freeway12.png
[05/30 19:04:58] ppgan INFO: Image saved to ../work/example/output/DRN/intersection28.png
[05/30 19:04:59] ppgan INFO: Image saved to ../work/example/output/DRN/intersection99.png
[05/30 19:04:59] ppgan INFO: Image saved to ../work/example/output/DRN/airplane86.png
[05/30 19:05:00] ppgan INFO: Image saved to ../work/example/output/DRN/intersection32.png
[05/30 19:05:00] ppgan INFO: Image saved to ../work/example/output/DRN/freeway00.png
[05/30 19:05:00] ppgan INFO: Image saved to ../work/example/output/DRN/airplane87.png
[05/30 19:05:01] ppgan INFO: Image saved to ../work/example/output/DRN/mediumresidential19.png
[05/30 19:05:01] ppgan INFO: Image saved to ../work/example/output/DRN/freeway76.png
[05/30 19:05:02] ppgan INFO: Image saved to ../work/example/output/DRN/freeway64.png
[05/30 19:05:02] ppgan INFO: Image saved to ../work/example/output/DRN/intersection40.png
[05/30 19:05:02] ppgan INFO: Image saved to ../work/example/output/DRN/airplane17.png
[05/30 19:05:03] ppgan INFO: Image saved to ../work/example/output/DRN/intersection10.png
[05/30 19:05:03] ppgan INFO: Image saved to ../work/example/output/DRN/intersection50.png
[05/30 19:05:04] ppgan INFO: Image saved to ../work/example/output/DRN/airplane75.png
[05/30 19:05:04] ppgan INFO: Image saved to ../work/example/output/DRN/intersection76.png
[05/30 19:05:05] ppgan INFO: Image saved to ../work/example/output/DRN/intersection87.png
[05/30 19:05:05] ppgan INFO: Image saved to ../work/example/output/DRN/intersection26.png
[05/30 19:05:06] ppgan INFO: Image saved to ../work/example/output/DRN/intersection89.png
[05/30 19:05:06] ppgan INFO: Image saved to ../work/example/output/DRN/freeway78.png
[05/30 19:05:06] ppgan INFO: Image saved to ../work/example/output/DRN/airplane65.png
[05/30 19:05:07] ppgan INFO: Image saved to ../work/example/output/DRN/airplane48.png
[05/30 19:05:07] ppgan INFO: Image saved to ../work/example/output/DRN/freeway07.png
[05/30 19:05:08] ppgan INFO: Image saved to ../work/example/output/DRN/airplane09.png
[05/30 19:05:08] ppgan INFO: Image saved to ../work/example/output/DRN/freeway75.png
[05/30 19:05:09] ppgan INFO: Image saved to ../work/example/output/DRN/freeway93.png
[05/30 19:05:09] ppgan INFO: Image saved to ../work/example/output/DRN/intersection19.png
[05/30 19:05:10] ppgan INFO: Image saved to ../work/example/output/DRN/airplane32.png
[05/30 19:05:10] ppgan INFO: Image saved to ../work/example/output/DRN/freeway44.png
[05/30 19:05:11] ppgan INFO: Image saved to ../work/example/output/DRN/intersection30.png
[05/30 19:05:11] ppgan INFO: Image saved to ../work/example/output/DRN/mediumresidential15.png
[05/30 19:05:11] ppgan INFO: Image saved to ../work/example/output/DRN/airplane55.png
[05/30 19:05:12] ppgan INFO: Image saved to ../work/example/output/DRN/airplane71.png
[05/30 19:05:12] ppgan INFO: Image saved to ../work/example/output/DRN/airplane04.png
[05/30 19:05:13] ppgan INFO: Image saved to ../work/example/output/DRN/airplane28.png
[05/30 19:05:13] ppgan INFO: Image saved to ../work/example/output/DRN/freeway67.png
[05/30 19:05:13] ppgan INFO: Image saved to ../work/example/output/DRN/airplane25.png
[05/30 19:05:14] ppgan INFO: Image saved to ../work/example/output/DRN/intersection77.png
[05/30 19:05:14] ppgan INFO: Image saved to ../work/example/output/DRN/freeway55.png
[05/30 19:05:15] ppgan INFO: Image saved to ../work/example/output/DRN/airplane80.png
[05/30 19:05:15] ppgan INFO: Image saved to ../work/example/output/DRN/freeway03.png
[05/30 19:05:15] ppgan INFO: Image saved to ../work/example/output/DRN/airplane85.png
[05/30 19:05:16] ppgan INFO: Image saved to ../work/example/output/DRN/freeway26.png
[05/30 19:05:16] ppgan INFO: Image saved to ../work/example/output/DRN/intersection70.png
[05/30 19:05:17] ppgan INFO: Image saved to ../work/example/output/DRN/airplane81.png
[05/30 19:05:17] ppgan INFO: Image saved to ../work/example/output/DRN/airplane74.png
[05/30 19:05:17] ppgan INFO: Image saved to ../work/example/output/DRN/freeway25.png
[05/30 19:05:18] ppgan INFO: Image saved to ../work/example/output/DRN/intersection16.png
[05/30 19:05:18] ppgan INFO: Image saved to ../work/example/output/DRN/airplane30.png
[05/30 19:05:18] ppgan INFO: Image saved to ../work/example/output/DRN/freeway77.png
[05/30 19:05:19] ppgan INFO: Image saved to ../work/example/output/DRN/intersection43.png
- 使用PaddleGAN进行迁移学习对遥感影像进行超分辨率，只需要2个小时即可达到上述效果，对于算力不够的小伙伴可以尝试。
- DRN网络的论文原文实验结果上来看，效果比RCAN略高，有兴趣做对比的，可以结合以RCAN模型对遥感图像超分辨率重建，可以直接体验！项目做一个对比，视觉效果上看是差不多的。