deepmialab/ai-ffpe

Deep Learning-based Frozen Section to FFPE Translation

ai-ffpe's Issues

Which case to be used for the FrozGan

Hi there, thanks a lot for the great work and congratulations!

I want to quickly adapt your method in my dataset, and sorry that I didn't have time to go through your article carefully. I guess the best models are stored in the FrozGanModels/wAtt_wLoss folder. Am I correct? In addition, I found that there are three cases there. Would you please give me some suggestions if I want to quickly apply your model? Which case should I use? Thanks a lot!

pre-trained model weight mismatched

hello

I really want to test your Algorithm. (for test only, not for training)

However, the pre-trained model does not match the defined model. (I used the CUT model for now, and I used the model in Lung/CUT from the pre-trained model you provided)

Check the test command line again.

Pre trained models not working

Hi, I tried to try out the CUT pretrained model, however I have the feeling that the pretrained models are outdated by the version of pytorch or that your code has changed so there is a mismatch in loading the models.

Here is the config and the error I get. Could you maybe help me with this?

user@5789a7f2eba2:~/AI-FFPE$ python3 test.py --dataroot /home/user/patches_png/ --results_dir /home/user/results --direction AtoB --dataset_mode single --name Lung/CUT --epoch 5 
----------------- Options ---------------
                 CUT_mode: CUT                           
               batch_size: 1                             
          checkpoints_dir: ./checkpoints                 
                crop_size: 512                           
                 dataroot: /home/user/patches_png/              [default: placeholder]
             dataset_mode: single                               [default: unaligned]
                direction: AtoB                          
          display_winsize: 512                           
               easy_label: experiment_name               
                    epoch: 5                                    [default: latest]
                     eval: False                         
        flip_equivariance: False                         
                  gpu_ids: 0                             
                init_gain: 0.02                          
                init_type: xavier                        
                 input_nc: 3                             
                  isTrain: False                                [default: None]
               lambda_GAN: 1.0                           
               lambda_NCE: 1.0                           
                load_size: 512                           
         max_dataset_size: inf                           
                    model: cut                           
               n_layers_D: 3                             
                     name: Lung/CUT                             [default: experiment_name]
                    nce_T: 0.07                          
                  nce_idt: True                          
nce_includes_all_negatives_from_minibatch: False                         
               nce_layers: 0,4,8,12,16                   
                      ndf: 64                            
                     netD: basic                         
                     netF: mlp_sample                    
                  netF_nc: 256                           
                     netG: resnet_9blocks                
                      ngf: 64                            
             no_antialias: False                         
          no_antialias_up: False                         
               no_dropout: True                          
                  no_flip: False                         
                    normD: instance                      
                    normG: instance                      
              num_patches: 256                           
                 num_test: 50                            
              num_threads: 4                             
                output_nc: 3                             
                    phase: test                          
                pool_size: 0                             
               preprocess: none                          
         random_scale_max: 3.0                           
              results_dir: /home/user/results                   [default: ./results/]
      self_regularization: 0.03                          
           serial_batches: False                         
stylegan2_G_num_downsampling: 1                             
                   suffix:                               
                  verbose: False                         
----------------- End -------------------
dataset [SingleDataset] was created
dataset [SingleDataset] was created
model [CUTModel] was created
creating web directory /home/user/results/test_5
loading the model from ./checkpoints/Lung/CUT/5_net_G.pth
Traceback (most recent call last):
  File "/home/user/AI-FFPE/test.py", line 57, in <module>
    model.setup(opt)               # regular setup: load and print networks; create schedulers
  File "/home/user/AI-FFPE/models/base_model.py", line 99, in setup
    self.load_networks(load_suffix)
  File "/home/user/AI-FFPE/models/base_model.py", line 225, in load_networks
    net.load_state_dict(state_dict)
  File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ResnetGenerator:
        Missing key(s) in state_dict: "SAB.conv1.weight", "SAB.conv2.weight", "SAB.conv3.weight", "model.4.conv1.weight", "model.4.conv2.weight", "model.4.conv3.weight", "model.5.weight", "model.5.bias", "model.8.filt", "model.9.weight", "model.9.bias", "model.12.filt", "model.21.conv_block.1.weight", "model.21.conv_block.1.bias", "model.21.conv_block.5.weight", "model.21.conv_block.5.bias", "model.22.filt", "model.23.weight", "model.23.bias", "model.26.filt", "model.27.weight", "model.27.bias", "model.31.weight", "model.31.bias". 
        Unexpected key(s) in state_dict: "model.4.weight", "model.4.bias", "model.7.filt", "model.8.weight", "model.8.bias", "model.11.filt", "model.12.conv_block.1.weight", "model.12.conv_block.1.bias", "model.12.conv_block.5.weight", "model.12.conv_block.5.bias", "model.21.filt", "model.22.weight", "model.22.bias", "model.25.filt", "model.26.weight", "model.26.bias", "model.30.weight", "model.30.bias".