Misc changes
This commit is contained in:
James Betker
parent
46f550e42b
commit
2c145c39b6
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@ -22,21 +22,21 @@ class HighToLowResNet(nn.Module):
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# All sub-modules must be explicit members. Make it so. Then add them to a list.
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self.trunk1 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf), 4)
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self.trunk2 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*4), 8)
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self.trunk3 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*8), 16)
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self.trunk4 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*16), 32)
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self.trunk2 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*2), 6)
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self.trunk3 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*4), 12)
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self.trunk4 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*8), 12)
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self.trunks = [self.trunk1, self.trunk2, self.trunk3, self.trunk4]
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self.trunkshapes = [4, 8, 16, 32]
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self.trunkshapes = [4, 6, 12, 12]
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self.r1 = nn.Conv2d(nf, nf*4, 3, stride=2, padding=1, bias=True)
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self.r2 = nn.Conv2d(nf*4, nf*8, 3, stride=2, padding=1, bias=True)
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self.r3 = nn.Conv2d(nf*8, nf*16, 3, stride=2, padding=1, bias=True)
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self.r1 = nn.Conv2d(nf, nf*2, 3, stride=2, padding=1, bias=True)
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self.r2 = nn.Conv2d(nf*2, nf*4, 3, stride=2, padding=1, bias=True)
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self.r3 = nn.Conv2d(nf*4, nf*8, 3, stride=2, padding=1, bias=True)
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self.reducers = [self.r1, self.r2, self.r3]
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self.pixel_shuffle = nn.PixelShuffle(2)
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self.a1 = nn.Conv2d(nf*4, nf*8, 3, stride=1, padding=1, bias=True)
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self.a2 = nn.Conv2d(nf*2, nf*4, 3, stride=1, padding=1, bias=True)
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self.a1 = nn.Conv2d(nf*2, nf*4, 3, stride=1, padding=1, bias=True)
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self.a2 = nn.Conv2d(nf, nf*4, 3, stride=1, padding=1, bias=True)
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self.a3 = nn.Conv2d(nf, nf, 3, stride=1, padding=1, bias=True)
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self.assemblers = [self.a1, self.a2, self.a3]
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@ -45,7 +45,7 @@ class HighToLowResNet(nn.Module):
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elif self.downscale == 2:
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nf_last = nf * 4
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elif self.downscale == 4:
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nf_last = nf * 8
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nf_last = nf * 4
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self.conv_last = nn.Conv2d(nf_last, out_nc, 3, stride=1, padding=1, bias=True)
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@ -1,5 +1,5 @@
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#### general settings
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name: corruptGAN_4k_lqprn_closeup
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name: corruptGAN_4k_lqprn_closeup_flat_net
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use_tb_logger: true
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model: corruptgan
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distortion: downsample
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@ -13,11 +13,11 @@ datasets:
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name: blacked
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mode: downsample
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dataroot_GT: K:\\4k6k\\4k_closeup\\hr
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dataroot_LQ: E:\\4k6k\\adrianna\\for_training\\lr
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dataroot_LQ: E:\\4k6k\\datasets\\ultra_lowq\\for_training
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mismatched_Data_OK: true
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use_shuffle: true
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n_workers: 4 # per GPU
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batch_size: 16
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batch_size: 32
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target_size: 64
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use_flip: false
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use_rot: false
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@ -26,16 +26,18 @@ datasets:
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name: blacked_val
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mode: downsample
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target_size: 64
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dataroot_GT: ../datasets/blacked/val/hr
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dataroot_LQ: ../datasets/blacked/val/lr
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dataroot_GT: E:\\4k6k\\datasets\\blacked\\val\\hr
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dataroot_LQ: E:\\4k6k\\datasets\\blacked\\val\\lr
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#### network structures
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network_G:
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which_model_G: HighToLowResNet
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which_model_G: FlatProcessorNet
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in_nc: 3
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out_nc: 3
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nf: 64
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nb: 64
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nf: 32
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ra_blocks: 5
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assembler_blocks: 3
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network_D:
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which_model_D: discriminator_vgg_128
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in_nc: 3
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@ -43,17 +45,18 @@ network_D:
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#### path
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path:
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pretrain_model_G: ~
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resume_state: ~
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pretrain_model_G: ../experiments/corrupt_flatnet_G.pth
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pretrain_model_D: ../experiments/corrupt_flatnet_D.pth
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resume_state: ../experiments/corruptGAN_4k_lqprn_closeup_flat_net/training_state/3000.state
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strict_load: true
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#### training settings: learning rate scheme, loss
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train:
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lr_G: !!float 1e-4
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lr_G: !!float 1e-5
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weight_decay_G: 0
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beta1_G: 0.9
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beta2_G: 0.99
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lr_D: !!float 1e-4
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lr_D: !!float 4e-5
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weight_decay_D: 0
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beta1_D: 0.9
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beta2_D: 0.99
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@ -68,11 +71,11 @@ train:
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pixel_weight: !!float 1e-2
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feature_criterion: l1
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feature_weight: 0
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gan_type: gan # gan | ragan
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gan_type: ragan # gan | ragan
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gan_weight: !!float 1e-1
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D_update_ratio: 2
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D_init_iters: 500
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D_update_ratio: 1
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D_init_iters: 0
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manual_seed: 10
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val_freq: !!float 5e2
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@ -5,7 +5,7 @@ model: corruptgan
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distortion: downsample
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scale: 1
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gpu_ids: [0]
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amp_opt_level: O1
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amp_opt_level: O0
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#### datasets
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datasets:
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@ -15,6 +15,7 @@ datasets:
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dataroot_GT: K:\\4k6k\\4k_closeup\\hr
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dataroot_LQ: E:\\4k6k\\adrianna\\for_training\\hr
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mismatched_Data_OK: true
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doCrop: false
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use_shuffle: true
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n_workers: 4 # per GPU
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batch_size: 16
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@ -34,12 +35,11 @@ network_G:
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which_model_G: HighToLowResNet
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in_nc: 3
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out_nc: 3
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nf: 64
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nb: 56
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nf: 16
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network_D:
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which_model_D: discriminator_vgg_128
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in_nc: 3
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nf: 128
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nf: 96
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#### path
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path:
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@ -71,7 +71,7 @@ train:
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gan_type: ragan # gan | ragan
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gan_weight: !!float 1e-1
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D_update_ratio: 2
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D_update_ratio: 1
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D_init_iters: 0
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manual_seed: 10
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@ -1,255 +0,0 @@
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There are three kinds of datasets: training dataset, validation dataset, and testing dataset. Usually, we do not explicitly distinguish between the validation and testing datasets in image/video restoration. So we use the validation/testing dataset in our description. <br/>
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We recommend to use [LMDB](https://lmdb.readthedocs.io/en/release/) (Lightning Memory-Mapped Database) formats for the training datasets, and directly read images (using image folder) during validation/testing. So there is no need to prepare LMDB files for evaluation/testing datasets.
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---
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We organize the training datasets in LMDB format for **faster training IO speed**. If you do not want to use LMDB, you can also use the **image folder**.<br/>
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Besides the standard LMDB folder, we add an extra `meta_info.pkl` file to record the **meta information** of the dataset, such as the dataset name, keys and resolution of each image in the dataset.
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Take the DIV2K dataset in LMDB for example, the folder structure and meta information are as follows:
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#### folder structure
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```
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- DIV2K800_sub.lmdb
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|--- data.mdb
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|--- lock.mdb
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|--- meta_info.pkl
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```
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#### meta information in `meta_info.pkl`
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`meta_info.pkl` is a python-pickled dict.
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| Key | Value |
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|:----------:|:---------------------------------------------------------:|
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| name | `DIV2K800_sub_GT` |
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| keys | [ `0001_s001`, `0001_s002`, ..., `0800_s040` ] |
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| resolution | [ `3_480_480` ] |
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If all the images in the LMDB file have the same resolution, only one copy of `resolution` is stored. Otherwise, each key has its corresponding `resolution`.
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----
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## Table of Contents
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1. [Prepare DIV2K](#prepare-div2k)
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1. [Common Image SR Datasets](#common-image-sr-datasets)
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1. [Prepare Vimeo90K](#prepare-vimeo90k)
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1. [Prepare REDS](#prepare-reds)
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The following shows how to prepare the datasets in detail.<br/>
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It is recommended to symlink the dataset root to $MMSR/datasets. If your folder structure is different, you may need to change the corresponding paths in config files.
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## Prepare DIV2K
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[DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) is a widely-used dataset in image super-resolution. In many research works, a MATLAB bicubic downsampling kernel is assumed. It may not be practical because the MATLAB bicubic downsampling kernel is not a good approximation for the implicit degradation kernels in real-world scenarios. And there is another topic named **blind restoration** that deals with this gap.
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We provide a demo script for DIV2K X4 datasets preparation.
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```
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cd codes/data_scripts
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bash prepare_DIV2K_x4_dataset.sh
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```
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The specific steps are as follows:
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**Step 1**: Download the GT images and corresponding LR images from the [official DIV2K website](https://data.vision.ee.ethz.ch/cvl/DIV2K/).<br/>
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Here are shortcuts for the download links:
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| Name | links (training) | links (validation)|
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|:----------:|:----------:|:----------:|
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|Ground-Truth|[DIV2K_train_HR](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip)|[DIV2K_valid_HR](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_HR.zip)|
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|LRx2 (MATLAB bicubic)|[DIV2K_train_LR_bicubic_X2](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip)|[DIV2K_valid_LR_bicubic_X2](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X2.zip)|
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|LRx3 (MATLAB bicubic)|[DIV2K_train_LR_bicubic_X3](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X3.zip)|[DIV2K_valid_LR_bicubic_X3](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X3.zip)|
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|LRx4 (MATLAB bicubic)|[DIV2K_train_LR_bicubic_X4](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X4.zip)|[DIV2K_valid_LR_bicubic_X4](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X4.zip)|
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|LRx8 (MATLAB bicubic)|[DIV2K_train_LR_bicubic_X8](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_x8.zip)|[DIV2K_valid_LR_bicubic_X8](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_x8.zip)|
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**Step 2**: Rename the downloaded LR images to have the same name as those of GT.<br/> Run the script `data_scripts/rename.py`. Remember to modify the folder path.
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**Step 3 (optional)**: Generate low-resolution counterparts. <br/>If you have downloaded the LR datasets, skip this step. Otherwise, you can use the script `data_scripts/generate_mod_LR_bic.m` or `data_scripts/generate_mod_LR_bic.py` to generate LR images. Make sure the LR and GT pairs have the same name.
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**Step 4**: Crop to sub-images. <br/>DIV2K has 2K resolution (e.g., 2048 × 1080) images but the training patches are usually very small (e.g., 128x128). So there is a waste if reading the whole image but only using a very small part of it. In order to accelerate the IO speed during training, we crop the 2K resolution images to sub-images (here, we crop to 480x480 sub-images). You can skip this step if your have a high IO speed.<br/>
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Note that the size of sub-images is different from the training patch size (`GT_size`) defined in the config file. Specifically, the sub-images with 480x480 are stored in the LMDB files. The dataloader will further randomly crop the sub-images to `GT_size x GT_size` patches for training. <br/>
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Use the script `data_scripts/extract_subimages.py` with `mode = 'pair'`. Remember to modify the following configurations if you have different settings:
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```
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GT_folder = '../../datasets/DIV2K/DIV2K800'
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LR_folder = '../../datasets/DIV2K/DIV2K800_bicLRx4'
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save_GT_folder = '../../datasets/DIV2K/DIV2K800_sub'
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save_LR_folder = '../../datasets/DIV2K/DIV2K800_sub_bicLRx4'
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scale_ratio = 4
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```
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**Step 5**: Create LMDB files. <br/>You need to run the script `data_scripts/create_lmdb.py` separately for GT and LR images.<br/>
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**Step 6**: Test the dataloader with the script `data_scripts/test_dataloader.py`.
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This procedure is also applied to other datasets, such as 291 images, or your custom datasets.
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```
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@InProceedings{Agustsson_2017_CVPR_Workshops,
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author = {Agustsson, Eirikur and Timofte, Radu},
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title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
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booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
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month = {July},
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year = {2017}
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}
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```
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## Common Image SR Datasets
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We provide a list of common image super-resolution datasets. You can download the images from the official website or Google Drive or Baidu Drive.
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<table>
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<tr>
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<th>Name</th>
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<th>Datasets</th>
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<th>Short Description</th>
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<th>Google Drive</th>
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<th>Baidu Drive</th>
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</tr>
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<tr>
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<td rowspan="3">Classical SR Training</td>
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<td>T91</td>
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<td><sub>91 images for training</sub></td>
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<td rowspan="9"><a href="https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU?usp=sharing">Google Drive</a></td>
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<td rowspan="9"><a href="https://pan.baidu.com/s/1q_1ERCMqALH0xFwjLM0pTg">Baidu Drive</a></td>
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</tr>
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<tr>
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<td><a href="https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/">BSDS200</a></td>
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<td><sub>A subset (train) of BSD500 for training</sub></td>
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</tr>
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<tr>
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<td><a href="http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html">General100</a></td>
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<td><sub>100 images for training</sub></td>
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</tr>
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<tr>
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<td rowspan="6">Classical SR Testing</td>
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<td>Set5</td>
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<td><sub>Set5 test dataset</sub></td>
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</tr>
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<tr>
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<td>Set14</td>
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<td><sub>Set14 test dataset</sub></td>
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</tr>
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<tr>
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<td><a href="https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/">BSDS100</a></td>
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<td><sub>A subset (test) of BSD500 for testing</sub></td>
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</tr>
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<tr>
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<td><a href="https://sites.google.com/site/jbhuang0604/publications/struct_sr">urban100</a></td>
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<td><sub>100 building images for testing (regular structures)</sub></td>
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</tr>
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<tr>
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<td><a href="http://www.manga109.org/en/">manga109</a></td>
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<td><sub>109 images of Japanese manga for testing</sub></td>
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</tr>
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<tr>
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<td>historical</td>
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<td><sub>10 gray LR images without the ground-truth</sub></td>
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</tr>
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<tr>
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<td rowspan="3">2K Resolution</td>
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<td><a href="https://data.vision.ee.ethz.ch/cvl/DIV2K/">DIV2K</a></td>
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<td><sub>proposed in <a href="http://www.vision.ee.ethz.ch/ntire17/">NTIRE17</a> (800 train and 100 validation)</sub></td>
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<td rowspan="3"><a href="https://drive.google.com/drive/folders/1B-uaxvV9qeuQ-t7MFiN1oEdA6dKnj2vW?usp=sharing">Google Drive</a></td>
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<td rowspan="3"><a href="https://pan.baidu.com/s/1CFIML6KfQVYGZSNFrhMXmA">Baidu Drive</a></td>
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</tr>
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<tr>
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<td><a href="https://github.com/LimBee/NTIRE2017">Flickr2K</a></td>
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<td><sub>2650 2K images from Flickr for training</sub></td>
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</tr>
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<tr>
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<td>DF2K</td>
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<td><sub>A merged training dataset of DIV2K and Flickr2K</sub></td>
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</tr>
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<tr>
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<td rowspan="2">OST (Outdoor Scenes)</td>
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<td>OST Training</td>
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<td><sub>7 categories images with rich textures</sub></td>
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<td rowspan="2"><a href="https://drive.google.com/drive/u/1/folders/1iZfzAxAwOpeutz27HC56_y5RNqnsPPKr">Google Drive</a></td>
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<td rowspan="2"><a href="https://pan.baidu.com/s/1neUq5tZ4yTnOEAntZpK_rQ#list/path=%2Fpublic%2FSFTGAN&parentPath=%2Fpublic">Baidu Drive</a></td>
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</tr>
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<tr>
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<td>OST300</td>
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<td><sub>300 test images of outdoor scences</sub></td>
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</tr>
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<tr>
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<td >PIRM</td>
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<td>PIRM</td>
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<td><sub>PIRM self-val, val, test datasets</sub></td>
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<td rowspan="2"><a href="https://drive.google.com/drive/folders/17FmdXu5t8wlKwt8extb_nQAdjxUOrb1O?usp=sharing">Google Drive</a></td>
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<td rowspan="2"><a href="https://pan.baidu.com/s/1gYv4tSJk_RVCbCq4B6UxNQ">Baidu Drive</a></td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
## Prepare Vimeo90K
|
||||
The description of the Vimeo90K can be found in [Open-VideoRestoration](https://xinntao.github.io/open-videorestoration/rst_src/datasets_sr.html#vimeo90k) and [the official webpage](http://toflow.csail.mit.edu/).<br/>
|
||||
|
||||
**Step 1**: Download the dataset<br/>
|
||||
Download the [`Septuplets dataset --> The original training + test set (82GB)`](http://data.csail.mit.edu/tofu/dataset/vimeo_septuplet.zip). This is the Ground-Truth (GT). There is a `sep_trainlist.txt` file recording the training samples in the download zip file.
|
||||
|
||||
**Step 2**: Generate the low-resolution images<br/>
|
||||
The low-resolution images in the Vimeo90K test dataset are generated with the MATLAB bicubic downsampling kernel. Use the script `data_scripts/generate_LR_Vimeo90K.m` (run in MATLAB) to generate the low-resolution images.
|
||||
|
||||
**Step 3**: Create LMDB files<br/>
|
||||
Use the script `data_scripts/create_lmdb.py` to generate the lmdb files separately for GT and LR images. You need to modify the configurations in the script:
|
||||
1) For GT
|
||||
```
|
||||
dataset = 'vimeo90K'
|
||||
mode = 'GT'
|
||||
```
|
||||
2) For LR
|
||||
```
|
||||
dataset = 'vimeo90K'
|
||||
mode = 'LR'
|
||||
```
|
||||
|
||||
**Step 4**: Test the dataloader with the script `data_scripts/test_dataloader.py`.
|
||||
|
||||
```
|
||||
@Article{xue2017video,
|
||||
author = {Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},
|
||||
title = {Video enhancement with task-oriented flow},
|
||||
journal = {International Journal of Computer Vision},
|
||||
year = {2017}
|
||||
}
|
||||
```
|
||||
|
||||
## Prepare REDS
|
||||
We re-group the REDS training and validation sets as follows:
|
||||
|
||||
| name | from | total number |
|
||||
|:----------:|:----------:|:----------:|
|
||||
| REDS training | the original training (except 4 clips) and validation sets | 266 clips |
|
||||
| REDS4 testing | 000, 011, 015 and 020 clips from the *original training set* | 4 clips |
|
||||
|
||||
The description of the REDS dataset can be found in [Open-VideoRestoration](https://xinntao.github.io/open-videorestoration/rst_src/datasets_sr.html#reds) and the [official website](https://seungjunnah.github.io/Datasets/reds.html).
|
||||
|
||||
**Step 1**: Download the datasets<br/>
|
||||
You can download the REDS datasets from the [official website](https://seungjunnah.github.io/Datasets/reds.html). The download links are also sorted as follows:
|
||||
|
||||
| track | links (training) | links (validation)|links (testing)|
|
||||
|:----------:|:----------:|:----------:|:----------:|
|
||||
| Ground-truth| [train_sharp - part1](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_sharp_part1.zip), [part2](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_sharp_part2.zip), [part3](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_sharp_part3.zip) |[val_sharp](https://cv.snu.ac.kr/~snah/Deblur/dataset/REDS/val_sharp.zip) | Not Available |- |
|
||||
| SR-clean | [train_sharp_bicubic](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_sharp_bicubic.zip) | [val_sharp_bicubic](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/val_sharp_bicubic.zip) |[test_sharp_bicubic](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/test_sharp_bicubic.zip) |
|
||||
| SR-blur) | [train_blur_bicubic](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_bicubic.zip) | [val_blur_bicubic](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/val_blur_bicubic.zip) |[test_blur_bicubic](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/test_blur_bicubic.zip) |
|
||||
| Deblurring | [train_blur - part1](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_part1.zip), [part2](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_part2.zip), [part3](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_part3.zip) | [val_blur](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/val_blur.zip) |[test_blur](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/test_blur.zip) |
|
||||
| Deblurring - Compression | [train_blur_comp - part1](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_comp_part1.zip), [part2](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_comp_part2.zip), [part3](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/train_blur_comp_part3.zip) | [val_blur_comp](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/val_blur_comp.zip) |[test_blur_comp](https://data.vision.ee.ethz.ch/timofter/NTIRE19video/test_blur_comp.zip) |
|
||||
|
||||
**Step 2**: Re-group the datasets<br/>
|
||||
We rename the clips in the original validation set, starting from 240 ... It can be accomplished by `data_scripts/regroup_REDS.py`.
|
||||
Note that the REDS4 will be excluded in the data loader, so there is no need to remove the REDS4 explicitly.
|
||||
|
||||
**Step 3**: Create LMDB files<br/>
|
||||
Use the script `data_scripts/create_lmdb.py` to generate the lmdb files separately for GT and LR frames. You need to modify the configurations in the script:
|
||||
1) For GT (train_sharp)
|
||||
```
|
||||
dataset = 'REDS'
|
||||
mode = 'train_sharp'
|
||||
```
|
||||
2) For LR (train_sharp_bicubic)
|
||||
```
|
||||
dataset = 'REDS'
|
||||
mode = 'train_sharp_bicubic'
|
||||
```
|
||||
**Step 4**: Test the dataloader with the script `data_scripts/test_dataloader.py`.
|
||||
|
||||
```
|
||||
@InProceedings{nah2019reds,
|
||||
author = {Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},
|
||||
title = {NTIRE 2019 challenges on video deblurring and super-resolution: Dataset and study},
|
||||
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
|
||||
year = {2019}
|
||||
}
|
||||
```
|
||||
Loading…
Reference in New Issue
Block a user