main.py's bug? about gsv-cities

[amaralibey]

Hi @libenchong,
This is not really a bug, but actually a feature in Pytorch Lightning. If you keep fast_dev_run=True this means you just want to feed the network with one batch and see if the feedforward step is working right (they call it dev mode). In this case, Pytorch Lightning will only pass one batch from the train dataset, and one batch from each validation set (to make sure your neural network is working right before you start training).
In the above example you are using batch size of 60, this is why we see that there are 60 elements in r_list (in validation we start with references then queries sequentially), which means PytorchLightning only fed one batch and called the get_recalls method (which, of course, will not be able to calculate recalls if there are no queries).

If you want to start training, just comment the following line:
fast_dev_run=True # comment if you want to start training the network and saving checkpoints

[amaralibey]

I'll add a condition in validation_epoch_end to make sure we don't run get_validation_recalls if we are in dev mode.
Thank you

[libenchong]

Hi,@amaralibey
Thanks for your guilding. When I comment the following line:
fast_dev_run=True # comment if you want to start training the network and saving checkpoint,
I find the same bug that the length of feats is far less than num_ References results in utils.get_ validation_ Recall() divisor is 0 and appearing NAN

[libenchong]

this bug appears MixVPR code, MixVPR code has commented fast_dev_run=True

[amaralibey]

Can you tell me what's the output of print(len(val_dataset)) when you put it bellow the line feats = torch.concat(val_step_outputs[i], dim=0) ?

[libenchong]

Hi,@amaralibey , pitts30k len(val_dataset) = 17608 ,feats.shape = [8804,8192] ;msls_val len(val_dataset)=19611,,feats.shape = [9806,8192].
Because I'm not familiar with pytorch_ Lightning code，so I wrote the code to get features with pytorch。Can you help me see if my writing is correct
` for i, (val_set_name, val_dataset) in enumerate(zip(dm.val_set_names, dm.val_datasets)):
feats = torch.concat(val_step_outputs[i], dim=0)

        # libenchong write code  begin
        val_dataloader = DataLoader(val_dataset,batch_size=20,num_workers=4)
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        Feat = torch.empty((len(val_dataset), 8192))
        for iteration, (places, labels) in enumerate(val_dataloader, 1):
            places = places.to(device)
            # calculate descriptors
            descriptors = self(places)
            descriptors.detach().cpu()
            Feat[labels.detach(),:] = descriptors.detach().cpu()
        # libenchong write code end
        
        if 'pitts' in val_set_name:
            # split to ref and queries
            num_references = val_dataset.dbStruct.numDb
            num_queries = len(val_dataset)-num_references
            print("*****")
            positives = val_dataset.getPositives()
        elif 'msls' in val_set_name:
            # split to ref and queries
            num_references = val_dataset.num_references
            num_queries = len(val_dataset)-num_references
            positives = val_dataset.pIdx
        else:
            print(f'Please implement validation_epoch_end for {val_set_name}')
            raise NotImplemented

        # r_list = feats[ : num_references]
        # q_list = feats[num_references : ]

        r_list = Feat[ : num_references]
        q_list = Feat[num_references : ]
        pitts_dict = utils.get_validation_recalls(r_list=r_list, 
                                            q_list=q_list,
                                            k_values=[1, 5, 10, 15, 20, 25],
                                            gt=positives,
                                            print_results=True,
                                            dataset_name=val_set_name,
                                            faiss_gpu=self.faiss_gpu
                                            )
        del r_list, q_list, feats, num_references, positives,Feat

        self.log(f'{val_set_name}/R1', pitts_dict[1], prog_bar=False, logger=True)
        self.log(f'{val_set_name}/R5', pitts_dict[5], prog_bar=False, logger=True)
        self.log(f'{val_set_name}/R10', pitts_dict[10], prog_bar=False, logger=True)
    print('\n\n')`

[libenchong]

[libenchong]

Hi,@amaralibey，I think I have found the reason. I used two GPUs to run the code, resulting in feats len being half of that of a single GPU
trainer = pl.Trainer( accelerator='gpu', devices=[0,1], default_root_dir=f'./LOGS/{model.encoder_arch}'
a single GPU

two GPUS
#6 (comment)