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Added - Root parameter for image and numpy loader of the meta dataset. `root` is prepended to the given paths and thus allows for smaller label arrays - Category loader allows to convert a given label into a more expressive category, which is specifed in the dataset's `meta.yaml` - Debug options: `debug/disable_integrations=True`, `debug/max_examples=5 batches`. - Epoch and Batch step are restored. - Added option to save checkpoint zero with `--ckpt_zero True`. - Added support for `project` and `entity` in `integrations/wandb`. - Logging figures using tensorboard now possible using log_tensorboard_figures. - Added support for `eval_functor` in test mode. - use `-p <rundir/configs/config.yaml>` as shortcut for `-b <rundir/configs/config.yaml> -p <rundir>` - Log tmux target containing current run. - Support for tensorboard logging. Enable with `--tensorboard_logging True`. - Support for wandb logging. Enable with `--wandb_logging True`. - Support for flow visualizations in edexplore and improved index selection. - Git integration adds all .py and .yaml files not just tracked ones. - Support for validation batches in train mode. MinimalLoggingHook used in TemplateIterator logs them automatically under `root/train/validation`. - `-d/--debug` flag to enable post-mortem debugging. Uses `pudb` if available, otherwise `pdb`. - Logging of commandline used to start, logging root, git tag if applicable, hostname. - Classes with fixed splits for included datasets. - Added `edexplore` for dataset exploration with streamlit: `edexplore -b <config.yaml>` - Added Late Loading. You can now return functions in your examples, which will only be evaluated at the end of you data processing pipeline, allowing you to stack many filter operations on top of each other. - Added MetaView Dataset, which allows to store views on a base dataset without the need to recalculate the labels everytime. - `TFBaseEvaluator` now parses config file for `fcond` flag to filter checkpoints, e.g.`edflow -e xxx --fcond "lambda c: any([str(n) in c for n in [240000, 320000]])"` will only evaluate checkpoint 240k and 320k - Added MetaDataset for easy Dataloading - Added CelebA dataset. - Added CIFAR10 dataset. - Environment variable EDFLOW\_GIT enables git integration. - Minimal logger now supports list of handlers. - pdb support. Use `import edflow.fpdb as pdb; pdb.set_trace()` instead of `import pdb; pdb.set_trace()`. - TFMultiStageTrainer and TFMultiStageModel - adds interface for model and trainer to derive from to support multiple stages. For example pretraining and training. - example can be found in `examples/multistage_trainer` - `edlist` shows process group id (`pgid`), see FAQ. - method `probs_to_mu_sigma` in `tf_nn.py` to estimate spatial mean and covariance across multiple channels. - CHANGELOG.md to document notable changes. Changed - Changed usage from tensorboardX to tensorboard, due to native intergration in pytorch. - EvalPipeline defaults to keypath/labels for finding labels. - A `datasets` dict is now preferred over `dataset` and `validation_dataset` (backwards compatible default: `dataset` -> `datasets/train` and `validation_dataset` -> `datasets/validation`). - Eval Pipeline now stores data compatible with MetaDataset specifications. Previously exported data cannot be read again using edeval after this change. - Changed configuration of integrations: `EDFLOWGIT` now `integrations/git`, `wandb_logging` now `integrations/wandb`, `tensorboard_logging` now `--integrations/tensorboard`. - ProjectManager is now `edflow.run` and initialized with `edflow.run.init(...)`. - Saved config files use `-` instead of `:` in filename to be consistent. - No more `-e/--evaluation <config>` and `-t/--train <config>` options. Specify all configs under `-b/--base <config1> <config2>`. Default to evaluation mode, specify `-t/--train` for training mode. - Specifying model in config is optional. - Code root determined by import path of iterator not model. - When setting the `DatasetMixin` attribute `append_labels = True` the labels are not added to the example directly but behind the key `labels_`. - Changed tiling background color to white - Changed interface of `edflow.data.dataset.RandomlyJoinedDataset` to improve it. - Removed colons from log directory names and replaced them by hyphens. - `LambdaCheckpointHook` uses global step and doesn't save on first step. - Switched opencv2 functions with manual ones to get rid of the dependency. - `edeval` now allows for differnet callback interface via the config. Callbacks are now entered as `dicts`, which allows to also pass keyword arguments to the callbacks from the config. - `make_batches` now produces deep batches of examples. See documentation of `deep_lod2dol` or the section "Datasets and Batching" in the documention. - `make_var` was broken for period variables because subcommands lacked `**kwargs` in definition. This is fixed now. Removed - `get_implementations_from_config` superseded by `get_obj_from_str`. - Environment variable EDFLOWGIT is now ignored. - Cannot start training and (multiple) evaluations at the same time anymore. Simplifies a lot and was almost never used. - No single '-' possible for commandline specification of config parameters. Use '--'. - It is no longer possible to pass callbacks as list via the config Fixed - Show correct `edeval` command. - In debug mode of existing project, only move `latest_eval` folder to `eval_runs`. - Callbacks in eval pipeline config are not overwritten by loading them. - Image outputs in `template_pytorch` example. - Negative numbers as values for keyword arguments are now properly parsed.