cosmic-cortex / pytorch-unet Goto Github PK

After the prediction code is run, only the output image is obtained. How to use these outputs to obtain the dice coefficient? Did you calculate the dice coefficient of the prediction set?How do you calculate that?

After the prediction code is run, only the output image is obtained. How to use these outputs to obtain the dice coefficient? Did you calculate the dice coefficient of the prediction set?How do you calculate that?

Hello, I got val_loss, f1 and train_loss by using my own data training volume networkIs it convenient to provide your training results?I want to make a reference.

How do i solve this kind of problem，Is it because of my own data set?

Hello, I have read your code, which is very helpful to me. However, I reported such an error when using my own data set. Is there any solution?Thanks a million!

Hello please kindly look at this error. Whenever I try to train my dataset by using train.py I keep getting this error. As I am new to pytorch a little guidance would be helpful.
RuntimeError: 1only batches of spatial targets supported (non-empty 3D tensors) but got targets of size: : [1, 512, 512, 3]

Thank you in advance.

File ".../unet/model.py", line 212, in fit_dataset **val_logs, **train_logs} UnboundLocalError: local variable 'val_logs' referenced before assignment

The val_dataset argument to fit_dataset is optional. However, if none is provided, val_logs is not assigned. Anyway it is referenced, leading to this error.

Suggested fix: Add val_logs = {} to the else clause of if val_dataset is not None: in fit_dataset

Hi,
Thank you for your project. It's helpful. I'm tried to launch your train.py script on a new dataset and i have encounter the following error

File "src/train.py", line 60, in
metric_list=metric_list, verbose=True)
File "/workspace/src/unet/model.py", line 186, in fit_dataset
train_logs = self.fit_epoch(dataset, n_batch=n_batch, shuffle=shuffle)
File "/workspace/src/unet/model.py", line 99, in fit_epoch
training_loss = self.loss(y_out, y_batch)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/workspace/src/unet/metrics.py", line 25, in forward
ignore_index=self.ignore_index)
File "/usr/local/lib/python3.6/site-packages/torch/nn/functional.py", line 2056, in cross_entropy
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
File "/usr/local/lib/python3.6/site-packages/torch/nn/functional.py", line 1873, in nll_loss
ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at /pytorch/aten/src/THNN/generic/SpatialClassNLLCriterion.c:109

As i search in the internet for the cause, i find that maybe it's the dimensions of the output layer. The problem is thta i did not change anything and i used a similar dataset

Thank you for your help

Is this package public domain or is the license missing?

I ran:

python train.py --train_dataset images_folder/ --device 'cuda:0' --epochs 10 --save_freq 1 --save_model 1 --model_name round1 --crop 2560 --learning_rate 0.0001 --checkpoint_path checkpoint

Error output:

Traceback (most recent call last):
  File "train.py", line 40, in <module>
    val_dataset = ImageToImage2D(args.val_dataset, tf_val)
  File "/unet/pytorch-UNet/unet/dataset.py", line 122, in __init__
    self.input_path = os.path.join(dataset_path, 'images')
  File "/anaconda3/lib/python3.7/posixpath.py", line 80, in join
    a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType

The diagram says images_folder, but there is none.
The instructions repeat itself for diff flag.

I wish there was standards in docs, you can reduce the amount of stuff in readme, with a simple step by step to recreate so ppl can just take it from there.

I have a new question. In my training results, the value of val_loss is very unstable, but the result of train_loss is good. What is the reason for this?

val_loss

0.121626744
0.064643869
1.067177767
1.446711094
1.281244414
0.085074004
0.033228467
0.07067455
0.057629818
0.058747965
1.917625544
0.041074573
0.15646584
1.15546266
1.469133962
0.040493928
1.874986998
1.004034303
0.048311408
0.100535154
1.64835466
0.054501295
0.055335191
0.10456412
0.276266118
0.068388779
0.715268846
0.098956134
2.583597354
0.538678182
1.748115192
0.060186028
1.108167116
0.309113808
0.101157197
0.124394018
0.066762745
0.055764281
0.460467963
2.378881313
0.066589679
0.471164338
0.078409711
0.065184287
0.086847927
0.101323177
0.069627192
1.949108887
0.56550861
1.750528685
1.873376575
1.489622217
0.994260068
0.066217561
0.065698464
0.072816557
0.073953711
0.071316366
0.421468448
0.85330223
0.076412463
1.056728009
0.092980409
0.074839294
0.63967285
1.866399809
1.322311701
0.07251732
0.076650151
0.075991224
0.592627899
0.178139341
0.874911083
0.086961034
2.873830932
0.082318357
0.086678351
0.09311628
0.110618592
0.797192226
0.08203448
0.144572049
0.083447114
0.103286757
0.089605071
0.136912736
0.149895869
0.079707029
0.091769812
0.987303428
0.093305247
0.089239203
1.865140941
1.418952992
0.174026518
0.094368358
0.095077354
0.629811309
0.090620203
1.349854706

train_loss

0.230814192
0.028987918
0.024291774
0.017069094
0.014445363
0.013398573
0.017434838
0.010788895
0.010943851
0.010186778
0.008584953
0.01008794
0.008166692
0.00812426
0.007191894
0.006891105
0.007163724
0.006250243
0.007437692
0.005759141
0.005480012
0.007539251
0.005302504
0.005195955
0.005868624
0.004928016
0.004689556
0.00476825
0.004551805
0.004410871
0.004312812
0.005318373
0.003946777
0.00389254
0.004088193
0.004916692
0.003619704
0.003698593
0.003596091
0.003551835
0.003599238
0.003506489
0.003349912
0.004076907
0.003196912
0.003090051
0.003097881
0.003223821
0.003074504
0.003073007
0.002930183
0.002931052
0.002878063
0.002818113
0.004682063
0.002902587
0.00261943
0.002597745
0.002628764
0.002631262
0.003391225
0.002469422
0.002454039
0.002499085
0.002512494
0.00248172
0.002464812
0.002398441
0.002394155
0.002346173
0.002301199
0.002282861
0.002268845
0.002215712
0.002189308
0.002250406
0.002511505
0.002021703
0.00206177
0.002084404
0.002078039
0.00203906
0.002020018
0.001995647
0.001981924
0.001943689
0.001929661
0.001958759
0.001993959
0.001822729
0.001834546
0.001916094
0.001810232
0.001773823
0.001764436
0.001750559
0.001739222
0.002303985
0.001640475
0.001577288

What are middle_channels?