Open apsthe opened 6 months ago
monxarat
commented
6 months ago @apsthe I think you should check if the size of the Image Train and Annotation image are the same, for example, if the Image Train is portrait but the Annotation is landscape.
And this relation with RandomResize and RandomCrop in the configs/_base_/datasets/xxx.py file
NiklasDHahn
commented
3 months ago @apsthe You encounter this error because of how the padding function is designed. When you prepare the data pipeline in mmsegmentation/configs/base/datasets/your_dataset.py you use the RandomResize method. You need to specife the parameters scale and ratio_range. Let's consider the case where you provide a tuple for both and let's say you define the size of your input images to be 512 x 512. This input size will be the base size and the ratio range will have a upper and lower limit (let's agree on 0.5 - 2.0) of the original size of the input image. Then you need to set scale accordingly. I.e. scale=(1024, 256). Once these values agree the padding function for the image resizing will make sure that all images have the same size and you will no longer encouter this error.
Saillxl
commented
3 months ago Do you have any solutions now？
Saillxl
commented
3 months ago @NiklasDHahn What should I do if my dataset image sizes are inconsistent?Thanks！ dict( keep_ratio=True, ratio_range=( 0.5, 2.0, ), scale=( 256, 1024, ), type='RandomResize'), dict( cat_max_ratio=0.75, crop_size=( 512, 512, ), type='RandomCrop'),
ZhimingNJ
commented
2 weeks ago the reason is that the size of input image is different from annotation image size
Traceback (most recent call last): File "train.py", line 104, in
main()
File "train.py", line 100, in main
runner.train()
File "/home/lyu-03/.conda/envs/lx_torch/lib/python3.7/site-packages/mmengine/runner/runner.py", line 1777, in train
model = self.train_loop.run() # type: ignore
File "/home/lyu-03/.conda/envs/lx_torch/lib/python3.7/site-packages/mmengine/runner/loops.py", line 286, in run
self.run_iter(data_batch)
File "/home/lyu-03/.conda/envs/lx_torch/lib/python3.7/site-packages/mmengine/runner/loops.py", line 310, in run_iter
data_batch, optim_wrapper=self.runner.optim_wrapper)
File "/home/lyu-03/.conda/envs/lx_torch/lib/python3.7/site-packages/mmengine/model/base_model/base_model.py", line 114, in train_step
losses = self._run_forward(data, mode='loss') # type: ignore
File "/home/lyu-03/.conda/envs/lx_torch/lib/python3.7/site-packages/mmengine/model/base_model/base_model.py", line 361, in _run_forward
results = self(*data, mode=mode)
File "/home/lyu-03/.conda/envs/lx_torch/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(input, **kwargs)
File "/home/lyu-03/lx_wp/mmsegmentation-main/mmsegmentation-main/mmseg/models/segmentors/base.py", line 94, in forward
return self.loss(inputs, data_samples)
File "/home/lyu-03/lx_wp/mmsegmentation-main/mmsegmentation-main/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss
loss_decode = self._decode_head_forward_train(x, data_samples)
File "/home/lyu-03/lx_wp/mmsegmentation-main/mmsegmentation-main/mmseg/models/segmentors/encoder_decoder.py", line 140, in _decode_head_forward_train
self.train_cfg)
File "/home/lyu-03/lx_wp/mmsegmentation-main/mmsegmentation-main/mmseg/models/decode_heads/decode_head.py", line 262, in loss
losses = self.loss_by_feat(seg_logits, batch_data_samples)
File "/home/lyu-03/lx_wp/mmsegmentation-main/mmsegmentation-main/mmseg/models/decode_heads/decode_head.py", line 305, in loss_by_feat
seg_label = self._stack_batch_gt(batch_data_samples)
File "/home/lyu-03/lx_wp/mmsegmentation-main/mmsegmentation-main/mmseg/models/decode_heads/decode_head.py", line 289, in _stack_batch_gt
return torch.stack(gt_semantic_segs, dim=0)
RuntimeError: stack expects each tensor to be equal size, but got [1, 677, 347] at entry 0 and [1, 512, 512] at entry 1