Closed ycwsilent closed 1 month ago
在运行demo时候,加载5k的权重,但是是运行不起来, 运行命令为
python demo.py --input_dir ../datasets/FiveK/test/low --result_dir ./visual/FiveK/B1 --weights ../checkpoints/5k/W aveNet_B_5k.pth
报错为如下;感觉就是问题2一样
RuntimeError: Error(s) in loading state_dict for WaveNet: Missing key(s) in state_dict: "asff.fcs.0.0.weight", "asff.fcs.0.1.weight", "asff.fcs.1.0.weight", "asff.fcs.1.1.weight", "asff.conv.weight". During handling of the above exception, another exception occurred: Traceback (most recent call last): File "demo.py", line 252, in <module> load_checkpoint(model, args.weights, device) File "demo.py", line 226, in load_checkpoint state_dict = checkpoint["state_dict"] KeyError: 'state_dict'
是有什么地方不对吗
你好,加载WaveNet_B_5k权重,需在WaveNet_B模型参数里设置use_asff:False.
您好,感谢您的回复;好像和问题9一样,但是我修改之后,它会报错什么尺寸不一样
修改
运行
python demo.py --input_dir ../datasets/FiveK/test/low --result_dir ./visual/FiveK/B1 --weights ../checkpoints/5k/W
aveNet_B_5k.pth
报错
D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py:235: UserWarning: Overwriting WaveNet_T in registry with models.archs.WaveNet_arch.WaveNet_T. This is
because the name being registered conflicts with an existing name. Please check if this is not expected.
def WaveNet_T(pretrained=False, **kwargs):
D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py:249: UserWarning: Overwriting WaveNet_S in registry with models.archs.WaveNet_arch.WaveNet_S. This is
because the name being registered conflicts with an existing name. Please check if this is not expected.
def WaveNet_S(pretrained=False, **kwargs):
D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py:263: UserWarning: Overwriting WaveNet_B in registry with models.archs.WaveNet_arch.WaveNet_B. This is
because the name being registered conflicts with an existing name. Please check if this is not expected.
def WaveNet_B(pretrained=False, **kwargs):
==> Build the model
enhancing images......
Traceback (most recent call last):
File "demo.py", line 272, in <module>
retored = model(restored.cuda())
File "D:\Ananconda\envs\Retinexformer\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py", line 225, in forward
embed_x = self.forward_tokens(conv_1x)
File "D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py", line 216, in forward_tokens
x= block(x,enc_maps[i])
File "D:\Ananconda\envs\Retinexformer\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py", line 148, in forward
x=self.up(x)+y
RuntimeError: The size of tensor a (764) must match the size of tensor b (765) at non-singleton dimension 3
这个有办法解决吗
您好,感谢您的回复;好像和问题9一样,但是我修改之后,它会报错什么尺寸不一样 修改
运行
python demo.py --input_dir ../datasets/FiveK/test/low --result_dir ./visual/FiveK/B1 --weights ../checkpoints/5k/W aveNet_B_5k.pth
报错
D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py:235: UserWarning: Overwriting WaveNet_T in registry with models.archs.WaveNet_arch.WaveNet_T. This is because the name being registered conflicts with an existing name. Please check if this is not expected. def WaveNet_T(pretrained=False, **kwargs): D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py:249: UserWarning: Overwriting WaveNet_S in registry with models.archs.WaveNet_arch.WaveNet_S. This is because the name being registered conflicts with an existing name. Please check if this is not expected. def WaveNet_S(pretrained=False, **kwargs): D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py:263: UserWarning: Overwriting WaveNet_B in registry with models.archs.WaveNet_arch.WaveNet_B. This is because the name being registered conflicts with an existing name. Please check if this is not expected. def WaveNet_B(pretrained=False, **kwargs): ==> Build the model enhancing images...... Traceback (most recent call last): File "demo.py", line 272, in <module> retored = model(restored.cuda()) File "D:\Ananconda\envs\Retinexformer\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py", line 225, in forward embed_x = self.forward_tokens(conv_1x) File "D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py", line 216, in forward_tokens x= block(x,enc_maps[i]) File "D:\Ananconda\envs\Retinexformer\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "D:\yan\WaveNet-1\basicsr\models\archs\WaveNet_arch.py", line 148, in forward x=self.up(x)+y RuntimeError: The size of tensor a (764) must match the size of tensor b (765) at non-singleton dimension 3
这个有办法解决吗
这是因为上下采样造成的大小不一致,你在demo.py文件里 取消注释 102 和113行,并注释掉114行即可。
好的;可以了,相当感谢作者耐心的回复;;我还有一个不太恰当的问题,我用单张3090和你yml一样的设置,在训练lol这个数据集时候和你25.4左右的结果差很大,我只有22.8左右,这个应该是我哪里操作有问题吗? 下面这个是配置文件
# general settings
name: WaveNet_B_LOL
model_type: ImageCleanModel
scale: 1
num_gpu: 1 # set num_gpu: 0 for cpu mode
manual_seed: 5
# dataset and data loader settings
#### datasets
datasets:
train:
name: TrainSet
type: Dataset_PairedImage
dataroot_gt: /data/coding/WaveNet/datasets/LOL/train/high/
dataroot_lq: /data/coding/WaveNet/datasets/LOL/train/low
geometric_augs: true
filename_tmpl: '{}'
io_backend:
type: disk
# data loader
use_shuffle: true
num_worker_per_gpu: 8
batch_size_per_gpu: 8
GT_size: 256 # size you want to crop out as input sample.
LQ_size: 256
use_flip: false
use_rot: false
color: RGB
### -------------Multi-scale Training Strategy--------------------------
batch_sizes: [1,1,1,2] # Batch size per gpu
min_patch_size: 192
step: 3
sort: 0 #1 represents descending and 0 represents ascending
stride: 32
patience: 19
state: 1 # 0 means start with crop 1 means start with resize
### ------------------------------------------------------------
dataset_enlarge_ratio: 1
prefetch_mode: ~
val:
name: ValSet
type: Dataset_PairedImage
dataroot_gt: /data/coding/WaveNet/datasets/LOL/test/high/
dataroot_lq: /data/coding/WaveNet/datasets/LOL/test/low/
io_backend:
type: disk
val_crop:
# network structures
network_g:
type: WaveNet_B
# path
path:
pretrain_network_g: ~
strict_load_g: true
resume_state: ~
# training settings
train:
total_epochs: 1000
total_iter: 145000
warmup_iter: -1 # no warm up
use_grad_clip: true
# Split 300k iterations into two cycles.
# 1st cycle: fixed 3e-4 LR for 92k iters.
# 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
scheduler:
type: CosineAnnealingRestartCyclicLR
periods: [450, 550,1500]
restart_weights: [1,1]
eta_mins: [0.000125,0.000001,0.000001]
mixing_augs:
mixup: true
mixup_beta: 1.2
use_identity: true
optim_g:
type: AdamW
lr: !!float 1.25e-4
# weight_decay: !!float 1e-4
betas: [0.9, 0.999]
# losses
pixel_opt:
type: ULoss
loss_weight: 1
reduction: mean
# validation settings
val:
window_size: 4
val_freq: !!float 1e2
save_img: false
rgb2bgr: true
use_image: false
max_minibatch: 8
val_crop:
metrics:
psnr: # metric name, can be arbitrary
type: calculate_psnr
crop_border: 0
test_y_channel: false
ssim:
type: calculate_ssim
crop_border: 0
test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 1e2
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
这个是我最终的结果
相应的结果趋势图
您的训练日志
它不单长得不一样,而且甚至图的个数不一样,请问一下,这个应该也是我什么地方操作有问题吧 特别感谢并期待您的回答
好的;可以了,相当感谢作者耐心的回复;;我还有一个不太恰当的问题,我用单张3090和你yml一样的设置,在训练lol这个数据集时候和你25.4左右的结果差很大,我只有22.8左右,这个应该是我哪里操作有问题吗? 下面这个是配置文件
# general settings name: WaveNet_B_LOL model_type: ImageCleanModel scale: 1 num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 5 # dataset and data loader settings #### datasets datasets: train: name: TrainSet type: Dataset_PairedImage dataroot_gt: /data/coding/WaveNet/datasets/LOL/train/high/ dataroot_lq: /data/coding/WaveNet/datasets/LOL/train/low geometric_augs: true filename_tmpl: '{}' io_backend: type: disk # data loader use_shuffle: true num_worker_per_gpu: 8 batch_size_per_gpu: 8 GT_size: 256 # size you want to crop out as input sample. LQ_size: 256 use_flip: false use_rot: false color: RGB ### -------------Multi-scale Training Strategy-------------------------- batch_sizes: [1,1,1,2] # Batch size per gpu min_patch_size: 192 step: 3 sort: 0 #1 represents descending and 0 represents ascending stride: 32 patience: 19 state: 1 # 0 means start with crop 1 means start with resize ### ------------------------------------------------------------ dataset_enlarge_ratio: 1 prefetch_mode: ~ val: name: ValSet type: Dataset_PairedImage dataroot_gt: /data/coding/WaveNet/datasets/LOL/test/high/ dataroot_lq: /data/coding/WaveNet/datasets/LOL/test/low/ io_backend: type: disk val_crop: # network structures network_g: type: WaveNet_B # path path: pretrain_network_g: ~ strict_load_g: true resume_state: ~ # training settings train: total_epochs: 1000 total_iter: 145000 warmup_iter: -1 # no warm up use_grad_clip: true # Split 300k iterations into two cycles. # 1st cycle: fixed 3e-4 LR for 92k iters. # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters. scheduler: type: CosineAnnealingRestartCyclicLR periods: [450, 550,1500] restart_weights: [1,1] eta_mins: [0.000125,0.000001,0.000001] mixing_augs: mixup: true mixup_beta: 1.2 use_identity: true optim_g: type: AdamW lr: !!float 1.25e-4 # weight_decay: !!float 1e-4 betas: [0.9, 0.999] # losses pixel_opt: type: ULoss loss_weight: 1 reduction: mean # validation settings val: window_size: 4 val_freq: !!float 1e2 save_img: false rgb2bgr: true use_image: false max_minibatch: 8 val_crop: metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 0 test_y_channel: false ssim: type: calculate_ssim crop_border: 0 test_y_channel: false # logging settings logger: print_freq: 100 save_checkpoint_freq: !!float 1e2 use_tb_logger: true wandb: project: ~ resume_id: ~ # dist training settings dist_params: backend: nccl port: 29500
这个是我最终的结果
相应的结果趋势图
您的训练日志
它不单长得不一样,而且甚至图的个数不一样,请问一下,这个应该也是我什么地方操作有问题吧 特别感谢并期待您的回答
你好,当时训练WaveNet的时候用的我另外一个repo:LPF的train.py文件,后面这个repo为了统一到BasicSR代码下,才修改的。你可以按照LPF下的train.py文件训练一下,配置文件也可以参考LPF/config下的配置文件设置。batch_size可以设置到占满gpu memory。原始设置如图
学习率任然是1.25*10-4。
特别感谢,我用LPF下的train.py的话,应该是需要修改什么的吧,它报错说没有utils,就是LPF下的utils文件夹,那我应该移动过来吧,同样的,刚刚你说到LPF/config下的文件配置,是能不能直接修改到现有的WaveNet_B_LOL.yml里面吗?不然感觉操作起来很复杂
特别感谢,我用LPF下的train.py的话,应该是需要修改什么的吧,它报错说没有utils,就是LPF下的utils文件夹,那我应该移动过来吧,同样的,刚刚你说到LPF/config下的文件配置,是能不能直接修改到现有的WaveNet_B_LOL.yml里面吗?不然感觉操作起来很复杂
最简单的方法你直接复制WaveNet_arch.py里面的代码 到新建 LPF/model/WaveNet.py 然后导入包的路径将“from models.archs.arch_util import LayerNorm,Mlp”改为 “from model.utils import LayerNorm,Mlp” 即可。yaml文件新建一个 复制LPF的就行,然后修改上述的参数,和学习率就行了。
特别感谢,我用LPF下的train.py的话,应该是需要修改什么的吧,它报错说没有utils,就是LPF下的utils文件夹,那我应该移动过来吧,同样的,刚刚你说到LPF/config下的文件配置,是能不能直接修改到现有的WaveNet_B_LOL.yml里面吗?不然感觉操作起来很复杂
最简单的方法你直接复制WaveNet_arch.py里面的代码 到新建 LPF/model/WaveNet.py 然后导入包的路径将“from models.archs.arch_util import LayerNorm,Mlp”改为 “from model.utils import LayerNorm,Mlp” 即可。yaml文件新建一个 复制LPF的就行,然后修改上述的参数,和学习率就行了。
当然你是需要clone LPF repo 在LPF文件目录下运行 “python train.py” 修改train.py里面的加载模型 和加载配置文件的代码(18、30、58行代码),如果直接把train.py文件搬运到WaveNet repo下是无法运行的 涉及到很多文件的更改。所以建议你用上述方法来运行。
特别感谢,我用LPF下的train.py的话,应该是需要修改什么的吧,它报错说没有utils,就是LPF下的utils文件夹,那我应该移动过来吧,同样的,刚刚你说到LPF/config下的文件配置,是能不能直接修改到现有的WaveNet_B_LOL.yml里面吗?不然感觉操作起来很复杂
最简单的方法你直接复制WaveNet_arch.py里面的代码 到新建 LPF/model/WaveNet.py 然后导入包的路径将“from models.archs.arch_util import LayerNorm,Mlp”改为 “from model.utils import LayerNorm,Mlp” 即可。yaml文件新建一个 复制LPF的就行,然后修改上述的参数,和学习率就行了。
当然你是需要clone LPF repo 在LPF文件目录下运行 “python train.py” 修改train.py里面的加载模型 和加载配置文件的代码(18、30、58行代码),如果直接把train.py文件搬运到WaveNet repo下是无法运行的 涉及到很多文件的更改。所以建议你用上述方法来运行。
就是需要换一个仓库,然后再来将wave模型换到LPF里面吧,好的十分感谢,真的太谢谢了;在复现的道路上真的艰难,特别感谢
很抱歉,这么晚还打扰你,运行报错,你们当时是自己写了dataset类吗
(torch) root@cuxrpufetckupgim-snow-8657547bf9-47xwb:/data/coding/LPF# python train.py
Traceback (most recent call last):
File "train.py", line 16, in <module>
from dataset import DataLoaderTrain, DataLoaderVal
ImportError: cannot import name 'DataLoaderTrain' from 'dataset' (/data/miniconda/envs/torch/lib/python3.8/site-packages/dataset/__init__.py)
很抱歉,这么晚还打扰你,运行报错,你们当时是自己写了dataset类吗
(torch) root@cuxrpufetckupgim-snow-8657547bf9-47xwb:/data/coding/LPF# python train.py Traceback (most recent call last): File "train.py", line 16, in <module> from dataset import DataLoaderTrain, DataLoaderVal ImportError: cannot import name 'DataLoaderTrain' from 'dataset' (/data/miniconda/envs/torch/lib/python3.8/site-packages/dataset/__init__.py)
抱歉,dataset文件遗漏上传了,已经在LPF repo上传LPF/dataset.py文件。
很抱歉,这么晚还打扰你,运行报错,你们当时是自己写了dataset类吗
(torch) root@cuxrpufetckupgim-snow-8657547bf9-47xwb:/data/coding/LPF# python train.py Traceback (most recent call last): File "train.py", line 16, in <module> from dataset import DataLoaderTrain, DataLoaderVal ImportError: cannot import name 'DataLoaderTrain' from 'dataset' (/data/miniconda/envs/torch/lib/python3.8/site-packages/dataset/__init__.py)
抱歉,dataset文件遗漏上传了,已经在LPF repo上传LPF/dataset.py文件。
好的,谢谢,很晚还打扰你们,谢谢
在模型加载时候是不是还需要其他操作,比如传参到模型中呢?
(torch) root@uvcgrowilrtdjnaj-snow-6b96db7fbf-lrdgm:/data/coding/LPF# python train.py
==> Build the model
Traceback (most recent call last):
File "train.py", line 61, in <module>
model_restored=WaveNet()
TypeError: __init__() missing 2 required positional arguments: 'encode_layers' and 'decode_layers'
特别感谢,我用LPF下的train.py的话,应该是需要修改什么的吧,它报错说没有utils,就是LPF下的utils文件夹,那我应该移动过来吧,同样的,刚刚你说到LPF/config下的文件配置,是能不能直接修改到现有的WaveNet_B_LOL.yml里面吗?不然感觉操作起来很复杂
最简单的方法你直接复制WaveNet_arch.py里面的代码 到新建 LPF/model/WaveNet.py 然后导入包的路径将“from models.archs.arch_util import LayerNorm,Mlp”改为 “from model.utils import LayerNorm,Mlp” 即可。yaml文件新建一个 复制LPF的就行,然后修改上述的参数,和学习率就行了。
当然你是需要clone LPF repo 在LPF文件目录下运行 “python train.py” 修改train.py里面的加载模型 和加载配置文件的代码(18、30、58行代码),如果直接把train.py文件搬运到WaveNet repo下是无法运行的 涉及到很多文件的更改。所以建议你用上述方法来运行。
在模型加载时候是不是还需要其他操作,比如传参到模型中呢?
(torch) root@uvcgrowilrtdjnaj-snow-6b96db7fbf-lrdgm:/data/coding/LPF# python train.py ==> Build the model Traceback (most recent call last): File "train.py", line 61, in <module> model_restored=WaveNet() TypeError: __init__() missing 2 required positional arguments: 'encode_layers' and 'decode_layers'
特别感谢,我用LPF下的train.py的话,应该是需要修改什么的吧,它报错说没有utils,就是LPF下的utils文件夹,那我应该移动过来吧,同样的,刚刚你说到LPF/config下的文件配置,是能不能直接修改到现有的WaveNet_B_LOL.yml里面吗?不然感觉操作起来很复杂
最简单的方法你直接复制WaveNet_arch.py里面的代码 到新建 LPF/model/WaveNet.py 然后导入包的路径将“from models.archs.arch_util import LayerNorm,Mlp”改为 “from model.utils import LayerNorm,Mlp” 即可。yaml文件新建一个 复制LPF的就行,然后修改上述的参数,和学习率就行了。
当然你是需要clone LPF repo 在LPF文件目录下运行 “python train.py” 修改train.py里面的加载模型 和加载配置文件的代码(18、30、58行代码),如果直接把train.py文件搬运到WaveNet repo下是无法运行的 涉及到很多文件的更改。所以建议你用上述方法来运行。
直接调用具体的model_restored=WaveNet_B(),import 也直接是from model.WaveNet import WaveNet_T, WaveNet_S, WaveNet_B
1.好的,这个LOL训练问题,终于在您的帮助下运行成功了:
但是它可能并没有自动占满gpu;还是说它自己已经占得很大了
2.对于5k数据集,我用demo.py得到了增强后的图片;但是在计算指标值时候报错
输入图像尺寸不一样,后面我去看了对应的数据,确实图片尺寸不一样,请问一下,您有遇到过吗,如何处理呢
Traceback (most recent call last):
File "evaluation.py", line 134, in <module>
measure_dirs(dirA, dirB, use_gpu=use_gpu, verbose=True)
File "evaluation.py", line 107, in measure_dirs
result['psnr'], result['ssim'], result['lpips'] = measure.measure(imread(pathA), imread(pathB))
File "evaluation.py", line 32, in measure
return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]]
File "evaluation.py", line 32, in <listcomp>
return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]]
File "evaluation.py", line 49, in psnr
psnr_val = psnr(imgA, imgB, data_range=255)
File "D:\Ananconda\envs\Retinexformer\lib\site-packages\skimage\metrics\simple_metrics.py", line 142, in peak_signal_noise_ratio
check_shape_equality(image_true, image_test)
File "D:\Ananconda\envs\Retinexformer\lib\site-packages\skimage\_shared\utils.py", line 569, in check_shape_equality
raise ValueError('Input images must have the same dimensions.')
ValueError: Input images must have the same dimensions.
1.好的,这个LOL训练问题,终于在您的帮助下运行成功了:
但是它可能并没有自动占满gpu;还是说它自己已经占得很大了
2.对于5k数据集,我用demo.py得到了增强后的图片;但是在计算指标值时候报错 输入图像尺寸不一样,后面我去看了对应的数据,确实图片尺寸不一样,请问一下,您有遇到过吗,如何处理呢
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Traceback (most recent call last): File "evaluation.py", line 134, in <module> measure_dirs(dirA, dirB, use_gpu=use_gpu, verbose=True) File "evaluation.py", line 107, in measure_dirs result['psnr'], result['ssim'], result['lpips'] = measure.measure(imread(pathA), imread(pathB)) File "evaluation.py", line 32, in measure return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]] File "evaluation.py", line 32, in <listcomp> return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]] File "evaluation.py", line 49, in psnr psnr_val = psnr(imgA, imgB, data_range=255) File "D:\Ananconda\envs\Retinexformer\lib\site-packages\skimage\metrics\simple_metrics.py", line 142, in peak_signal_noise_ratio check_shape_equality(image_true, image_test) File "D:\Ananconda\envs\Retinexformer\lib\site-packages\skimage\_shared\utils.py", line 569, in check_shape_equality raise ValueError('Input images must have the same dimensions.') ValueError: Input images must have the same dimensions.
你需要注释掉 demo.py的114行。
1.好的,这个LOL训练问题,终于在您的帮助下运行成功了:
但是它可能并没有自动占满gpu;还是说它自己已经占得很大了
2.对于5k数据集,我用demo.py得到了增强后的图片;但是在计算指标值时候报错 输入图像尺寸不一样,后面我去看了对应的数据,确实图片尺寸不一样,请问一下,您有遇到过吗,如何处理呢
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Traceback (most recent call last): File "evaluation.py", line 134, in <module> measure_dirs(dirA, dirB, use_gpu=use_gpu, verbose=True) File "evaluation.py", line 107, in measure_dirs result['psnr'], result['ssim'], result['lpips'] = measure.measure(imread(pathA), imread(pathB)) File "evaluation.py", line 32, in measure return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]] File "evaluation.py", line 32, in <listcomp> return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]] File "evaluation.py", line 49, in psnr psnr_val = psnr(imgA, imgB, data_range=255) File "D:\Ananconda\envs\Retinexformer\lib\site-packages\skimage\metrics\simple_metrics.py", line 142, in peak_signal_noise_ratio check_shape_equality(image_true, image_test) File "D:\Ananconda\envs\Retinexformer\lib\site-packages\skimage\_shared\utils.py", line 569, in check_shape_equality raise ValueError('Input images must have the same dimensions.') ValueError: Input images must have the same dimensions.
你需要注释掉 demo.py的114行。
这个在上面你好像回答过我;不好意思,又网络,谢谢您
在运行demo时候,加载5k的权重,但是是运行不起来, 运行命令为
报错为如下;感觉就是问题2一样
是有什么地方不对吗