runtimeerror when using other dataset?

[mrgreen3325]

Hi.
I want to train the model with my own dataset.
However, it keeps reporting
RuntimeError: stack expects each tensor to be equal size, but got [3, 256, 256] at entry 0 and [3, 256, 252] at entry 1
Do I have any wrong setting?
Thanks.

The part of json:
"datasets": {
"train": {
"name": "train_dataset" // just name
, "dataset_type": "sr" // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"
, "dataroot_H": "HR" // path of H training dataset. DIV2K (800 training images)
, "dataroot_L": "LR" // path of L training dataset

  , "H_size": 256                   // 96/144|192/384 | 128/192/256/512. LR patch size is set to 48 or 64 when compared with RCAN or RRDB.

  , "dataloader_shuffle": true
  , "dataloader_num_workers": 16
  , "dataloader_batch_size": 8      // batch size 1 | 16 | 32 | 48 | 64 | 128. Total batch size =4x8=32 in SwinIR
}
, "test": {
  "name": "test_dataset"            // just name
  , "dataset_type": "sr"         // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"
  , "dataroot_H": "testsets/Set5/HR"  // path of H testing dataset
  , "dataroot_L": "testsets/Set5/LR_bicubic/X4"              // path of L testing dataset

}

}

, "netG": {
"net_type": "swinir"
, "upscale": 4 // 2 | 3 | 4 | 8
, "in_chans": 3
, "img_size": 64 // For fair comparison, LR patch size is set to 48 or 64 when compared with RCAN or RRDB.
, "window_size": 8
, "img_range": 1.0
, "depths": [6, 6, 6, 6, 6, 6]
, "embed_dim": 180
, "num_heads": [6, 6, 6, 6, 6, 6]
, "mlp_ratio": 2
, "upsampler": "pixelshuffle" // "pixelshuffle" | "pixelshuffledirect" | "nearest+conv" | null
, "resi_connection": "1conv" // "1conv" | "3conv"

, "init_type": "default"

}

[Jingyun Liang]

Could you provide the complete error log? Is there a problem in training or testing? I guess one image is less than 256x256 in your training set.

[mrgreen3325]

Thanks for the reply.
I think I find the problem.
It is related to the datasets pixel boundary problem.
I will fixed the images in dataset.
Thanks.

[mrgreen3325]

Also, may I ask that when I load the pretrain_G, and pretrain E,
, "path": {
"root": "superresolution" // "denoising" | "superresolution" | "dejpeg"
, "pretrained_netG": "supersolution/swinir_sr_classical_patch64_x4_l1_test/model/5000_G.pth" // path of pretrained model. We fine-tune X3/X4/X8 models from X2 model, so that G_optimizer_lr and G_scheduler_milestones can be halved to save time.
, "pretrained_netE": "supersolution/swinir_sr_classical_patch64_x4_l1_test/model/5000_E.pth" // path of pretrained model
}
However, the printed status on the terminal mentioned pretrained_netG= null pretrained_netE=null
Is that I loaded the pretrain model?
Thanks.

[mrgreen3325]

Could you provide the complete error log? Is there a problem in training or testing? I guess one image is less than 256x256 in your training set.

Thanks for reply.
After I fixed the pixel problem of the image, there is another error comes out.
Do u have any idea for that?
Thanks.

data = [self.dataset[idx] for idx in possibly_batched_index]

File "/data1/anaconda3/envs/py37_pytorch1.6/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/data1/KAIR/data/dataset_sr.py", line 43, in getitem
img_H = util.imread_uint(H_path, self.n_channels)
File "/data1/KAIR/utils/utils_image.py", line 193, in imread_uint
if img.ndim == 2:
AttributeError: 'NoneType' object has no attribute 'ndim'

[Jingyun Liang]

For 'NoneType' object has no attribute 'ndim', it's always due to that the image path in invalid.

[mrgreen3325]

For 'NoneType' object has no attribute 'ndim', it's always due to that the image path in invalid.

Thanks I got it.
May I ask that when I load the pretrain_G, and pretrain E,
, "path": {
"root": "superresolution" // "denoising" | "superresolution" | "dejpeg"
, "pretrained_netG": "supersolution/swinir_sr_classical_patch64_x4_l1_test/model/5000_G.pth" // path of pretrained model. We fine-tune X3/X4/X8 models from X2 model, so that G_optimizer_lr and G_scheduler_milestones can be halved to save time.
, "pretrained_netE": "supersolution/swinir_sr_classical_patch64_x4_l1_test/model/5000_E.pth" // path of pretrained model
}
However, the printed status on the terminal mentioned pretrained_netG= null pretrained_netE=null
Is that I loaded the pretrain model?
Thanks.

[Jingyun Liang]

For loading pretrained models, see #24 (comment) for a temporary solution. We will work on it later.