static Loss TrainStep(ObjectDetectionDataset.EntryBatch batch, Model model, IOptimizer optimizer, int classCount, ReadOnlySpan <int> strides, bool bench = false)
{
if (bench)
{
return(ComputeLosses(model, batch, classCount, strides));
}
var tape = new GradientTape();
Loss losses;
Tensor totalLoss;
using (tape.StartUsing()) {
losses = ComputeLosses(model, batch, classCount, strides);
totalLoss = losses.GIUO + losses.Conf + losses.Prob;
if (!tf.executing_eagerly() || !tf.logical_or(tf.is_inf(totalLoss), tf.is_nan(totalLoss)).numpy().any())
{
PythonList <Tensor> gradients = tape.gradient(totalLoss, model.trainable_variables);
optimizer.apply_gradients(gradients.Zip(
(PythonList <Variable>)model.trainable_variables, (g, v) => (g, v)));
}
else
{
Trace.TraceWarning("NaN/inf loss ignored");
}
}
return(losses);
}
void _minimize(GradientTape tape, OptimizerV2 optimizer, Tensor loss, List <IVariableV1> trainable_variables)
{
var gradients = tape.gradient(loss, trainable_variables);
gradients = optimizer._aggregate_gradients(zip(gradients, trainable_variables));
gradients = optimizer._clip_gradients(gradients);
optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable)),
experimental_aggregate_gradients: false);
}