def main(p: Parameters, o: Options):
profiler = Profiler()
profiler.event("start")
if o.verbose:
print(f"Experimenting with parameters: {p}")
accuracy_results = experiment(p, o)
profiler.event("end")
print(profiler.summary(human=True))
config.save_accuracy(accuracy_results)
def do_train():
model, optimizer = p.model.make_model_and_optimizer(
dataset.input_shape, dataset.num_classes, o.use_cuda)
def generate_epochs_callbacks():
epochs_callbacks = []
for epoch in p.savepoints:
def callback(epoch=epoch):
scores = training.eval_scores(
model, dataset, p.transformations,
TransformationStrategy.random_sample,
o.get_eval_options())
if o.verbose_general:
print(
f"Saving model {model.name} at epoch {epoch}/{p.epochs}."
)
training.save_model(p, o, model, scores,
experiment.model_path(p, epoch))
epochs_callbacks.append((epoch, callback))
return dict(epochs_callbacks)
epochs_callbacks = generate_epochs_callbacks()
# TRAINING
if 0 in p.savepoints:
scores = training.eval_scores(model, dataset, p.transformations,
TransformationStrategy.random_sample,
o.get_eval_options())
print(f"Saving model {model.name} at epoch {0} (before training).")
training.save_model(p, o, model, scores,
experiment.model_path(p, 0))
pr = Profiler()
pr.event("start")
scores, history = training.run(p,
o,
model,
optimizer,
dataset,
epochs_callbacks=epochs_callbacks)
pr.event("end")
print(pr.summary(human=True))
training.print_scores(scores)
return model, history, scores