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  • 156d586cc390bbd2048c3579b4dd4c9adc39c7a5
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Created with Raphaël 2.2.021Nov1411754324Oct23211514131087632130Sep2924232220181127Aug26251915141312114330Jul29282725241817141110876326Jun1918171110765427May261915128Apr2423221722Mar2111Feb7328Jan22Dec20329Nov221312876430Oct252416151411874224Sep20181076527Aug129876230Jul2625Jun20730May23222120171614109872130Apr29242215Mar1429Feb22765130Jan2323Dec22211915628Nov24231030Oct262019127630Aug291728Jul2019181711330Jun23Mar17141018Feb175Dec16Nov23Sep1326Aug19Jul17Jun27May2423rm pos_name_stem from yotch; fixed cutoffdevdevrefactor(dataloader): null_projections attributechange: correct_buds efficient; "None"|"null"change(image_utils): tighter image translation fnschange: added mother_docs(dataloader)fix: wrongly placed linedocs: NumPy formatfix(training): wandb steppingfix(training): for resample_cellschange(dataset): optional one cell per imagefix(inference): get most recent checkpointfeature(dataset): added outlinesrefactor(dataset): removed caching of masksrefactor(inference):generate_predictions-> predictchange(training.py): save wandb namechange(inference.py): returns best checkpointfix(training_setup.setup_normalisation): averagingchange(torch): added training_setup.pyremove: range_dicts independentfix: typingfix(data_preparation): both ways of creating valrefactor: major restructuring of wela.torchchange(torch.position_dataset): masks now appliedrefactor(wela.torch)fix(position_dataset): compute_and_transform_bothfeature(position_dataset): rescale_targets boolchange(im_rescale): with tensorsfix: missing bracketchange(wela.torch): persistent_workers; resumechange(position_dataset): one position->test & valfeature(image_function.plot_rgb): ax as parameterfix(downloader): channel orderfix(dataloader): merging b_df with r_dfrefactor(torch.train_test)change(tposition_dataset): get_dataloader_dictfix(position_dataset.prepare_data): None transformfix(dataloader.load_h5): merge r_df and b_dfchange(position_dataset.prepare_data): transformdocs: better typing