bupt-ai-cz / bci Goto Github PK

/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)
----------------- Options ---------------
             aspect_ratio: 1.0                           
               batch_size: 2                             
          checkpoints_dir: ./checkpoints                 
                crop_size: 256                           
                 dataroot: /kaggle/input/bci-img2img/BCI_for_GrandChallenge-20230210T115047Z-001/BCI_for_GrandChallenge/test	[default: ./datasets/BCI]
             dataset_mode: aligned                       
                direction: AtoB                          
          display_winsize: 256                           
                    epoch: latest                        
                     eval: False                         
                  gpu_ids: 0                             
                init_gain: 0.02                          
                init_type: normal                        
                 input_nc: 3                             
                  isTrain: False                         	[default: None]
                load_iter: 0                             	[default: 0]
                load_size: 320                           
         max_dataset_size: inf                           
                    model: pix2pix                       
               n_layers_D: 3                             
                     name: pyramidpix2pix                
                      ndf: 64                            
                     netD: basic                         
                     netG: resnet_9blocks                
                      ngf: 64                            
               no_dropout: False                         
                  no_flip: False                         
                     norm: batch                         
                 num_test: 1000                          
              num_threads: 4                             
                output_nc: 3                             
                  pattern: L1_L2_L3_L4                   
                    phase: test                          
               preprocess: scale_width_and_midcrop       
              results_dir: ./results/                    
           serial_batches: False                         
                   suffix:                               
                  verbose: False                         
----------------- End -------------------
dataset [AlignedDataset] was created
initialize network with normal
model [Pix2PixModel] was created
loading the model from ./checkpoints/pyramidpix2pix/latest_net_G.pth
Traceback (most recent call last):
  File "test.py", line 47, in <module>
    model.setup(opt)               # regular setup: load and print networks; create schedulers
  File "/kaggle/working/BCI-main/PyramidPix2pix/models/base_model.py", line 88, in setup
    self.load_networks(load_suffix)
  File "/kaggle/working/BCI-main/PyramidPix2pix/models/base_model.py", line 198, in load_networks
    self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
  File "/kaggle/working/BCI-main/PyramidPix2pix/models/base_model.py", line 174, in __patch_instance_norm_state_dict
    self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
  File "/kaggle/working/BCI-main/PyramidPix2pix/models/base_model.py", line 174, in __patch_instance_norm_state_dict
    self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1208, in __getattr__
    type(self).__name__, name))
AttributeError: 'Sequential' object has no attribute 'model'

    In paper "Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review", it is menntioned that "The dataset used in this challenge consists of 3396 pairs of the training set images, 500 pairs of the validation set images", but I can't  find any information for the val set both on the official website https://bci.grand-challenge.org/ and the dataset

    So, which part in the train fold is for validation? Or just split the dataset randomly