Comments (7)
Hi, I guess you only need to add the mode (fine or coarse) make sure you use bigger base and crop sizes given the size of the CS images; here is an example:
"train_loader": {
"type": "CityScapes",
"args":{
"data_dir": "/data/Cityscapes",
"batch_size": 8,
"base_size": 1024,
"crop_size": 512,
"augment": true,
"shuffle": true,
"scale": true,
"flip": true,
"rotate": true,
"blur": false,
"split": "train",
"mode": "fine",
"num_workers": 8
}
},
"val_loader": {
"type": "CityScapes",
"args":{
"data_dir": "/data/Cityscapes",
"batch_size": 1,
"val": true,
"split": "val",
"mode": "fine",
"num_workers": 4
}
}
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Hi Yassine,
Thanks for the quick reply. I see the original image size is 2018 * 1024, so here what's the input size and patch size for training? For validation are you using the original size?
from pytorch-segmentation.
from pytorch-segmentation.
First step is to resize the bigger size to 1024 (so 1024 x 512) then a scaling then a random crop of 512 x 512, for validation you can either take crops of 1024 with a bigger batch for quick processing, or use the original size with a batch of 1, accurate but slow.
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Thanks!
from pytorch-segmentation.
from pytorch-segmentation.
With 4 GPUs, maybe the batch size is say 7, in this case each GPU will have 2 Images but the last one with one image, and his will give you an error, try a batch size = GPUs * 2 at least.
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