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training.clj
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training.clj
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(ns clj-djl.training
(:require [clj-djl.engine :as engine]
[clj-djl.ndarray :as nd])
(:import [ai.djl.training.util ProgressBar]
[ai.djl.training.dataset RandomAccessDataset]
[ai.djl.training DefaultTrainingConfig TrainingConfig ParameterStore]
[ai.djl.training.loss Loss]
[ai.djl.training EasyTrain]
[ai.djl.training.evaluator Accuracy TopKAccuracy BinaryAccuracy]
[ai.djl.training.listener TrainingListener LoggingTrainingListener]
[ai.djl.ndarray.types Shape]
[ai.djl.training.dataset Batch]
[ai.djl.ndarray NDList]
[ai.djl.engine Engine]
[ai.djl.metric Metric Metrics]))
(defn progress-bar []
(ProgressBar.))
(def new-progress-bar progress-bar)
(defn config [{:keys [loss devices data-manager initializer parameter optimizer evaluator listeners]}]
(cond-> (DefaultTrainingConfig. loss)
listeners (.addTrainingListeners (if (sequential? listeners)
(into-array TrainingListener listeners)
listeners))
evaluator (.addEvaluator evaluator)
devices (.optDevices devices)
data-manager (.optDataManager data-manager)
initializer (.optInitializer initializer parameter)
optimizer (.optOptimizer optimizer)))
(def training-config config)
(def default-training-config config)
(defn new-default-training-config [loss]
(DefaultTrainingConfig. loss))
(def new-training-config new-default-training-config)
(defn opt-initializer [config initializer parameter]
(.optInitializer config initializer parameter))
(defn opt-optimizer [config optimizer]
(.optOptimizer config optimizer))
(defn softmax-cross-entropy-loss []
(Loss/softmaxCrossEntropyLoss))
(defn add-evaluator [config evaluator]
(.addEvaluator config evaluator)
config)
(defn get-evaluators [trainer]
(.getEvaluators trainer))
(defn accuracy []
(Accuracy.))
(def new-accuracy accuracy)
(defn topk-accuracy
([topk]
(TopKAccuracy. topk))
([index topk]
(TopKAccuracy. topk))
([name index topk]
(TopKAccuracy. name topk)))
(def new-topk-accuracy topk-accuracy)
(defn binary-accuracy
([]
(BinaryAccuracy.))
([threshold]
(BinaryAccuracy. threshold))
([acc-name threshold ]
(BinaryAccuracy. acc-name threshold ))
([acc-name threshold axis]
(BinaryAccuracy. acc-name threshold axis)))
(def new-binary-accuracy binary-accuracy)
(defn add-accumulator
"Adds an accumulator to the accuracy for the results of the evaluation with the
given key."
[acc key]
(.addAccumulator acc key)
acc)
(defn update-accumulator
"Updates the accuracy with the given key based on a NDList of labels and
predictions."
[acc key label-list pred-list]
(.updateAccumulator acc key (nd/ndlist (nd/to-type (.head label-list) :int32 false)) pred-list)
acc)
(defn get-accumulator
"Returns the accumulated evaluator value."
[acc key]
(.getAccumulator acc key))
(defn add-training-listeners [config listener]
(.addTrainingListeners config listener)
config)
(defn training-listeners []
(into-array TrainingListener [(LoggingTrainingListener.)]))
(def new-default-training-listeners training-listeners)
(defn initialize
([trainer shapes]
(cond
(sequential? shapes) (.initialize trainer (into-array Shape shapes))
(instance? Shape shapes) (.initialize trainer (into-array Shape [shapes]))
:else (.initialize trainer (into-array Shape [shapes])))
trainer)
([trainer shape & shapes]
(.initialize trainer (into-array Shape (cons shape shapes)))
trainer))
(defn trainer
([model config]
(.newTrainer model config))
([{:keys [model loss devices data-manager initializer parameter optimizer listeners]}]
(.newTrainer model
(cond-> (DefaultTrainingConfig. loss)
listeners (.addTrainingListeners (if (sequential? listeners)
(into-array TrainingListener listeners)
listeners))
devices (.optDevices devices)
data-manager (.optDataManager data-manager)
initializer (.optInitializer initializer parameter)
optimizer (.optOptimizer optimizer)))))
(def new-trainer trainer)
(defn step [trainer]
(.step trainer))
(defn close [batch]
(.close batch))
(defn iter-seq
([iterable]
(iter-seq iterable (.iterator iterable)))
([iterable iter]
(lazy-seq
(when (.hasNext iter)
(cons (.next iter) (iter-seq iterable iter))))))
(defn iterate-dataset [trainer ds]
(iter-seq (.iterateDataset trainer ds)))
(defmacro as-consumer [f]
`(reify java.util.function.Consumer
(accept [this arg#]
(~f arg#))))
(defn notify-listeners [trainer callback]
(.notifyListeners trainer (as-consumer callback)))
(defn get-manager [trainer]
(.getManager trainer))
(defn forward [trainer input]
(.forward trainer (NDList. input)))
(defn metrics []
(Metrics.))
(defn set-metrics [trainer metrics]
(.setMetrics trainer metrics)
trainer)
(defn get-metrics
"Get metrics from trainer, put the metrics to seq of map:
[{\"train_progress_Accuracy\" {:timestamp 1607859588747 :value 0.68125 :unit \"count\"}}]"
[^ai.djl.training.Trainer trainer]
(let [metrics (.getMetrics trainer)
metric-names (.getMetricNames metrics)]
(into {}
(for [n metric-names]
{n (map (fn [m] {:timestamp (.getTimestamp m) :value (.getValue m) :unit (.getUnit m)})
(.getMetric metrics n))}))))
(defn parameter-store [manager copy]
(ParameterStore. manager copy))
(defn gradient-collector
([]
(engine/new-gradient-collector (engine/get-instance)))
([trainer]
(.newGradientCollector trainer)))
(def new-gradient-collector gradient-collector)
(defn fit
([trainer nepochs train-iter]
(EasyTrain/fit trainer nepochs train-iter nil))
([trainer nepochs train-iter test-iter]
(EasyTrain/fit trainer nepochs train-iter test-iter)))
(defn train-batch [trainer batch]
(EasyTrain/trainBatch trainer batch))
(defn validate-batch [trainer batch]
(EasyTrain/validateBatch trainer batch))
(defn set-requires-gradient
[ndarray requires-grad]
(.setRequiresGradient ndarray requires-grad))
(defn get-gradient
"Returns the gradient NDArray attached to this NDArray."
[ndarray]
(.getGradient ndarray))
(defn stop-gradient
[ndarray]
(.stopGradient ndarray))
(defn backward [gc target]
(.backward gc target))
(defn get-devices [config]
(vec (.getDevices config)))
(defn get-loss [trainer]
(.getLoss trainer))
(defn get-result [trainer]
(let [result (.getTrainingResult trainer)]
(assoc {}
:epochs (.getEpoch result)
:train-accuracy (.getTrainEvaluation result "Accuracy")
:train-loss (.getTrainLoss result)
:validate-accuracy (.getValidateEvaluation result "Accuracy")
:validate-loss (.getValidateLoss result))))
(def get-training-result get-result)
(defn get-model [trainer]
(.getModel trainer))