bbox_metric = metric.RPNL1LossMetric()
rceval_metric = metric.RCNNAccMetric(config)
rccls_metric = metric.RCNNLogLossMetric(config)
rcbbox_metric = metric.RCNNL1LossCRCNNMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
eval_metrics.add(eval_metric)
eval_metrics.add(cls_metric)
eval_metrics.add(bbox_metric)
eval_metrics.add(rceval_metric)
eval_metrics.add(rccls_metric)
eval_metrics.add(rcbbox_metric)
if config.TRAIN.WITH_MASK:
mask_metric = metric.MaskLogLossMetric(config)
eval_metrics.add(mask_metric)
optimizer_params = get_optim_params(config, len(train_iter), batch_size)
print ('Optimizer params: {}'.format(optimizer_params))
# Checkpointing
prefix = os.path.join(output_path, args.save_prefix)
batch_end_callback = mx.callback.Speedometer(batch_size, args.display)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True),
eval('{}.checkpoint_callback'.format(config.symbol))(sym_inst.get_bbox_param_names(), prefix, bbox_means, bbox_stds)]
train_iter = PrefetchingIter(train_iter)
mod.fit(train_iter, optimizer='sgd', optimizer_params=optimizer_params,
eval_metric=eval_metrics, num_epoch=config.TRAIN.end_epoch, kvstore=config.default.kvstore,
batch_end_callback=batch_end_callback,
epoch_end_callback=epoch_end_callback, arg_params=arg_params, aux_params=aux_params)
bbox_metric = metric.RPNL1LossMetric()
rceval_metric = metric.RCNNAccMetric(config)
rccls_metric = metric.RCNNLogLossMetric(config)
rcbbox_metric = metric.RCNNL1LossCRCNNMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
eval_metrics.add(eval_metric)
eval_metrics.add(cls_metric)
eval_metrics.add(bbox_metric)
eval_metrics.add(rceval_metric)
eval_metrics.add(rccls_metric)
eval_metrics.add(rcbbox_metric)
if config.TRAIN.WITH_MASK:
mask_metric = metric.MaskLogLossMetric(config)
eval_metrics.add(mask_metric)
optimizer_params = get_optim_params(config, len(train_iter), batch_size)
print ('Optimizer params: {}'.format(optimizer_params))
# Checkpointing
prefix = os.path.join(output_path, args.save_prefix)
batch_end_callback = mx.callback.Speedometer(batch_size, args.display)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True),
eval('{}.checkpoint_callback'.format(config.symbol))(sym_inst.get_bbox_param_names(), prefix, bbox_means, bbox_stds)]
train_iter = PrefetchingIter(train_iter)
mod.fit(train_iter, optimizer='sgd', optimizer_params=optimizer_params,
eval_metric=eval_metrics, num_epoch=config.TRAIN.end_epoch, kvstore=config.default.kvstore,
batch_end_callback=batch_end_callback,
epoch_end_callback=epoch_end_callback, arg_params=arg_params, aux_params=aux_params)
