--- title: "Writing a configuration" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Writing a configuration} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE, error = FALSE) ``` One YAML file describes a whole run, and `platypus_spec()` reads it. This is the walk through that file; the exhaustive list of fields lives on the engine's site, because the file is the engine's format and keeping a second copy here would mean two of them going out of step: [every configuration field](https://maju116.github.io/pyplatypus/config.html). **The same file works in both packages.** R reads it with `platypus_spec()`, Python with `from_yaml()`, and both hand it to the same validator. Nothing below is R-specific except the lines that run it. Arguments and YAML produce the same object and nothing downstream can tell which was used, so a configuration file and an R script describe the same experiment - and either can be handed to a colleague. ## One file, one run The shortest thing that trains: ```yaml task: semantic_segmentation data: train_path: data/train validation_path: data/validation colormap: [[0, 0, 0], [255, 255, 255]] models: - name: first_try input_shape: [256, 256] ``` ```{r} spec <- platypus_spec("run.yaml") fit <- platypus_fit(spec) evaluate(fit) ``` Four things are required and the rest have defaults: `task`, `data.train_path`, and a `name` and `input_shape` for each model. Everything else - the architecture, the loss, the optimiser, how many epochs - has a default that lives in the engine, so **a short configuration is a complete one** rather than an incomplete one. `?u_net` has a *Where the defaults live* section saying why they are not written into the R signatures. `colormap` says which colour in a mask means which class, darkest first. Masks stored as label maps rather than pictures use `labels: [0, 1]` instead, and giving both is an error: exactly one says how a mask is encoded. ## Several models at once `models:` is a list, and that is the reason this file format exists rather than only the code path. Each entry trains on the same data and lands in the same table, so comparing variants is one run rather than a loop somebody has to write. ```yaml task: semantic_segmentation data: train_path: data/train validation_path: data/validation colormap: [[0, 0, 0], [255, 255, 255]] models: - name: shallow input_shape: [256, 256] blocks: 3 filters: 16 - name: deep input_shape: [256, 256] blocks: 5 filters: 32 architecture: u_net_plus_plus ``` ```{r} fit <- platypus_fit(platypus_spec("run.yaml")) evaluate(fit) # one row per model training_history(fit) # one row per model per epoch ``` `name` has to be unique, because it is how a model is identified in every score table, in every record and in `predict(fit, model = ...)`. Two models called the same thing is refused rather than resolved, by name: `model names must be unique; repeated: shallow`. Two entries differing only in `blocks` is a legitimate configuration and a common one: this is for comparing variants, not only architectures. ## Dividing one folder With no `validation_path`, say how to divide `train_path`: ```yaml task: semantic_segmentation data: train_path: data/all split: fractions: [0.7, 0.15, 0.15] group_by: "^(patient\\d+)_" colormap: [[0, 0, 0], [255, 255, 255]] models: - name: by_patient input_shape: [256, 256] ``` Two fractions give train and validation, three add a test set. Nothing is written to disk - the samples are divided after they are discovered, so there are no new folders and no CSV. `split_dataset()` is still the tool when the CSVs themselves are the point. **`group_by` is required and may be `NULL`.** It is a regular expression whose first group is the thing that must not appear on both sides - a patient, a scan, a slide. The mistake it prevents leaves no trace: slices of one patient in both sets make validation measure memory rather than generalisation, and every number in the output still looks reasonable. An omitted key would be that choice made silently; `null` is the same choice made on purpose, for data where every sample is independent. Exactly one of `split` and `validation_path`. If a run genuinely has no validation set - a final fit on everything - say `validation: false`, which is the third answer rather than an absence, because "I have no validation set" and "I forgot" look identical otherwise. The same from R, where the argument answers the same question three ways: ```{r} segmentation_data("data/all", split = list(fractions = c(0.8, 0.2), group_by = NULL)) segmentation_data("data/train", validation = "data/validation") segmentation_data("data/all", validation = FALSE) ``` ## Augmentation, callbacks, and the rest of a model ```yaml task: semantic_segmentation data: train_path: data/train validation_path: data/validation colormap: [[0, 0, 0], [255, 255, 255]] models: - name: augmented input_shape: [256, 256] epochs: 40 loss: {name: dice} metrics: [{name: iou}, {name: dice}] optimizer: {name: adam, learning_rate: 0.001} callbacks: - {name: early_stopping, monitor: val_loss, patience: 10} - {name: model_checkpoint, monitor: val_iou, path: runs/best.safetensors} augmentation: - {name: HorizontalFlip, params: {p: 0.5}} - {name: RandomBrightnessContrast, params: {p: 0.3}} ``` Every one of those blocks has an R constructor that builds the same thing, and the help pages for them are where the measurements are: `?losses`, `?metrics`, `?optimizers`, `?callbacks`. ```{r} u_net( "augmented", input_shape = c(256, 256), epochs = 40, loss = loss_dice(), metrics = list(metric_iou(), metric_dice()), optimizer = optimizer_adam(learning_rate = 0.001), callbacks = list( callback_early_stopping(monitor = "val_loss", patience = 10), callback_model_checkpoint(monitor = "val_iou", path = "runs/best.safetensors") ), augmentation = list( augmentation_step("HorizontalFlip", p = 0.5), augmentation_step("RandomBrightnessContrast", p = 0.3) ) ) ``` **There is no `mode` on a callback that watches a number.** The direction is taken from the name: anything ending in `loss` is better lower, anything else better higher. So `monitor = "val_iou"` maximises and `monitor = "val_loss"` minimises, and there is nothing to set inconsistently. **Augmentation applies to the training data only** - never to validation, where the point is to measure the same thing every epoch. The names are albumentations' own, and `available_transforms()` lists what the installed version offers; at `rank = 3` the list is shorter, because support for volumes is uneven. Each step is probed at the model's own input size while the pipeline is built, so a transform that cannot do the job says so before the first epoch rather than during it. ## Instances instead of one mask `task: instance_segmentation` keeps the objects apart rather than uniting them. The data block gets **shorter** rather than longer: no `colormap` and no `labels`, because there are always exactly two classes - background and object - and what names the objects is the directory of per-object mask files itself. ```yaml task: instance_segmentation data: train_path: data/train validation_path: data/validation mode: nested_dirs models: - name: rim input_shape: [256, 256] separation: {name: rim, width: 2} loss: {name: cce_dice} decode: [{name: nearest_seed}, {name: connected_components}] - name: flows input_shape: [256, 256] separation: name: flows loss: {name: mse, where: foreground} ``` The same two models from R. `instance_data()` is what says which task this is - an instance data block declares nothing a segmentation one lacks, so the constructor is the word: ```{r} spec <- platypus_spec( data = instance_data("data/train", "data/validation"), models = list( u_net( "rim", input_shape = c(256, 256), separation = separation_rim(width = 2), loss = loss_cce_dice(), decode = list(decode_nearest_seed(), decode_connected_components()) ), u_net( "flows", input_shape = c(256, 256), separation = separation_flows(loss = loss_mse(where = "foreground")) ) ) ) fit <- platypus_fit(spec) evaluate(fit) ``` Two axes, and they are deliberately different shapes. **`separation` is one block per model and has no default**: it is what the network is trained to predict, so it sets the width of the head - three channels for `rim`, four for `flows` at two classes - and that width is derived rather than written anywhere. **`decode` is a list**, because several decodings share one forward pass: a second decoding of 134 images costs 1.41 s against an hour and a half for a second training run. `evaluate()` then returns a row per *model and decoding*. **Which mask column to read is a decision the table makes for you and it used to make it wrongly.** Every row carries `foreground_dice` - the binary overlap of anything the model called an object against anything that is one, the same arithmetic whatever the separation is - and that is the one to read *across* models. The `dice` beside it is the training metric, a mean over the class channels the head has, so three for `separation_rim()` against two for `separation_flows()`: read down one model's rows, never across two. On the Data Science Bowl's validation cases the class column showed two masks 0.09 apart that are 0.0062 apart, and in the other direction. **No value appears in both lists**, which is what settles which field a new idea belongs in: `separation` is a noun and `decode` is a verb. `separation_rim()` can be decoded by `decode_nearest_seed()` or `decode_watershed()`, `separation_flows()` by `decode_follow_flows()`, and `decode_connected_components()` reads no signal at all and works with every target. Leave `decode` out and it derives to the signal's own decoding plus `connected_components`; give a list and you get the list you gave. A model's own `loss` and `metrics` score the mask at either separation, so they are an overlap or a cross-entropy loss here as everywhere else. `separation_flows()` carries its own `loss` for the two vector channels, which is where `loss_mse()`, `loss_l1()` and `loss_smooth_l1()` belong: `where = "foreground"` averages the error over the voxels the target is defined on, and on these images that is 13.97% of them. **What this task is worth is measured, and it is not flattering.** On the Data Science Bowl the separation signal is worth +0.068 of instance average precision at best while a perfect mask is worth +0.2398, so the mask is the limit by a factor of 3.6 - and a *perfect* semantic mask decoded by `connected_components` still scores only 0.7655. That is what the yardstick row is for: a run that improved the mask and did nothing for the separation improves every row at once, and without the row that ignores the signal there is nothing to notice it against. **One thing is missing from R rather than from the engine, and it is missing on purpose rather than by oversight**: `predict()` on an instance fit refuses. The run and its scores work - `evaluate()` gives the row per model and decoding above - but there is no call yet that brings the objects themselves back into R as a label map, so `predict()` says so instead of returning a mask that is not what you asked for. `platypus#103`. ## Detection instead of segmentation `task: object_detection` changes which fields the file accepts. Boxes rather than masks, so `classes` and an annotation format rather than a colormap: ```yaml task: object_detection data: train_path: data/train validation_path: data/validation annotation_format: pascal_voc classes: [rbc, wbc, platelets] models: - name: cells input_shape: [416, 416] ``` **Leave `anchors` out and they are fitted to your own boxes** by k-means on an IoU distance, `anchors_per_grid` of them per grid. That is worth about 0.23 of mean overlap against COCO's nine on blood cells - measured, 0.65 against 0.88 - so the default is the useful one and naming anchors by hand is for reproducing a published model. `detection_anchors()` reports them and `plot_anchors()` draws them against the boxes they were fitted to. ## Not training at all ```yaml task: semantic_segmentation data: train_path: data/images validation_path: data/images colormap: [[0, 0, 0], [255, 255, 255]] models: - name: published input_shape: [256, 256] weights: dsbowl-unet fit: false ``` `weights` takes a registry name, a local path, or `hf://owner/repo/file@commit` - and the `hf://` form requires the commit, because weights that change under a stable name are the worst kind of irreproducibility. A registry name carries one already; `available_weights()` lists them. With `fit: false` nothing trains and `predict(fit)` works immediately. The weights are checked against the model they are being loaded into *before* torch is touched: a shape, a channel count or a class count that does not match is a refusal naming the field, not a tensor-size error from inside a loss. ## What is checked, and when Validation happens in this order, and the first two cost no epochs: 1. **The file, against the schema.** Unknown fields, missing required ones, a `name:` that no block has, a callback watching a number nothing will produce. Before any path is read - and before Python starts, for the checks R can make itself. 2. **The data on disk**, when the run begins: that the folders exist, that images and masks correspond, and that every class in `colormap` or `labels` actually appears in the masks, because a declared class appearing in none has a target channel of zeros, so the model is never shown what it is being asked to find. `inspect_masks()` runs that check on its own, and `platypus_fit(check_masks = FALSE)` skips it. 3. **Everything else**, during the run. A refusal names the field and says what to do. The engine's own messages are translated into R conditions rather than passed through as Python tracebacks, so `tryCatch(platypus_spec("run.yaml"), error = function(e) conditionMessage(e))` gives a sentence rather than a stack. ## The same run from code The file is not a second implementation. These describe the same experiment: ```{r} spec <- platypus_spec("run.yaml") spec <- platypus_spec( data = segmentation_data( "data/train", validation = "data/validation", colormap = list(c(0, 0, 0), c(255, 255, 255)) ), models = list(u_net("first_try", input_shape = c(256, 256))) ) ``` And `as.list(spec)` is the file, so a run built from arguments can be written out and kept: ```{r} yaml::write_yaml(as.list(spec), "run.yaml") ``` Which to use is a question about how many models and how often, not about what is possible. A file is better when there are several models, when the run is going to be repeated, or when somebody else has to see what was done; the arguments are better inside a loop over something the file cannot express, like a vector of datasets. Note that `as.list()` is also how the fields are reached: the object returned by `platypus_spec()` carries the specification on the Python side, so `spec$task` is `NULL` while `as.list(spec)$task` is what you want.