masonghao1
commented
1 year ago Hello, I would like to use the semantic segmentation images to improve my training.I have the RGB directory named as train_A,another RGB directory named as train_B,, and the semantic segmentation images named as train_inst.My semantic segmentation images have 2 channels,that N=2.
My parameters are:
------------ Options ------------- batchSize: 1 beta1: 0.5 checkpoints_dir: ./checkpoints continue_train: False data_type: 32 dataroot: ./datasets/test/ debug: False display_freq: 100 display_winsize: 512 feat_num: 2 fineSize: 512 fp16: False gpu_ids: [0] input_nc: 3 instance_feat: False isTrain: True label_feat: True label_nc: 0 lambda_feat: 10.0 loadSize: 1024 load_features: False load_pretrain: local_rank: 0 lr: 0.0002 max_dataset_size: inf model: pix2pixHD nThreads: 2 n_blocks_global: 9 n_blocks_local: 3 n_clusters: 2 n_downsample_E: 4 n_downsample_global: 4 n_layers_D: 3 n_local_enhancers: 1 name: test ndf: 64 nef: 16 netG: global ngf: 64 niter: 100 niter_decay: 100 niter_fix_global: 0 no_flip: False no_ganFeat_loss: False no_html: False no_instance: True no_lsgan: False no_vgg_loss: False norm: instance num_D: 2 output_nc: 3 phase: train pool_size: 0 print_freq: 100 resize_or_crop: scale_width save_epoch_freq: 10 save_latest_freq: 1000 serial_batches: False tf_log: False use_dropout: False verbose: False which_epoch: latestI met the following problem:
C:/cb/pytorch_1000000000000/work/aten/src/ATen/native/cuda/IndexKernel.cu:84: block: [1,0,0], thread: [123,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. C:/cb/pytorch_1000000000000/work/aten/src/ATen/native/cuda/IndexKernel.cu:84: block: [1,0,0], thread: [124,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. C:/cb/pytorch_1000000000000/work/aten/src/ATen/native/cuda/IndexKernel.cu:84: block: [1,0,0], thread: [125,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. C:/cb/pytorch_1000000000000/work/aten/src/ATen/native/cuda/IndexKernel.cu:84: block: [1,0,0], thread: [126,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. C:/cb/pytorch_1000000000000/work/aten/src/ATen/native/cuda/IndexKernel.cu:84: block: [1,0,0], thread: [127,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. Traceback (most recent call last): File "train_win10.py", line 148, in <module> train() File "train_win10.py", line 73, in train Variable(data['image']), Variable(data['feat']), infer=save_fake) File "C:\Users\*****\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "C:\Users\*****\anaconda3\envs\pytorch\lib\site-packages\torch\nn\parallel\data_parallel.py", line 159, in forward return self.module(*inputs[0], **kwargs[0]) File "C:\Users\*****\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "D:\Desktop\pix2pixHD-master\models\pix2pixHD_model.py", line 159, in forward feat_map = self.netE.forward(real_image, inst_map) File "D:\Desktop\pix2pixHD-master\models\networks.py", line 285, in forward indices = (inst[b:b+1] == int(i)).nonzero() # n x 4 RuntimeError: CUDA error: device-side assert triggeredwhat is my problem?Is it because of the problem of semantic segmentation images or the need to modify the code?If so, how to modify it?
Thanks for your time. hello，I wonder if your problem has been solved?
Hello, I would like to use the semantic segmentation images to improve my training.I have the RGB directory named as train_A,another RGB directory named as train_B,, and the semantic segmentation images named as train_inst.My semantic segmentation images have 2 channels,that N=2.
My parameters are:
I met the following problem:
what is my problem?Is it because of the problem of semantic segmentation images or the need to modify the code?If so, how to modify it?
Thanks for your time.