add code for few-shot baseline

engine/crossdomain.py

@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+from torchvision.datasets import ImageFolder
+
+import ignite.distributed as idist
+from ignite.contrib.metrics.gpu_info import GpuInfo
+from ignite.contrib.handlers.tensorboard_logger import TensorboardLogger, global_step_from_engine, OutputHandler, \
+    WeightsScalarHandler, GradsHistHandler, WeightsHistHandler, GradsScalarHandler
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
+from ignite.metrics import Accuracy, Loss, RunningAverage
+from ignite.contrib.engines.common import save_best_model_by_val_score
+from ignite.contrib.handlers import ProgressBar
+
+from util.build import build_model, build_optimizer
+from util.handler import setup_common_handlers
+from data.transform import transform_pipeline
+
+
+def baseline_trainer(config, logger, val_loader):
+    model = build_model(config.model, config.distributed.model)
+    optimizer = build_optimizer(model.parameters(), config.baseline.optimizers)
+    loss_fn = nn.CrossEntropyLoss()
+    trainer = create_supervised_trainer(model, optimizer, loss_fn, idist.device(), non_blocking=True)
+    trainer.logger = logger
+    RunningAverage(output_transform=lambda x: x).attach(trainer, "loss")
+    ProgressBar(ncols=0).attach(trainer)
+
+    val_metrics = {
+        "accuracy": Accuracy(),
+        "nll": Loss(loss_fn)
+    }
+    evaluator = create_supervised_evaluator(model, val_metrics, idist.device())
+    ProgressBar(ncols=0).attach(evaluator)
+
+    @trainer.on(Events.EPOCH_COMPLETED)
+    def log_training_loss(engine):
+        logger.info(f"Epoch[{engine.state.epoch}] Loss: {engine.state.output:.2f}")
+        evaluator.run(val_loader)
+        metrics = evaluator.state.metrics
+        logger.info("Training Results - Avg accuracy: {:.2f} Avg loss: {:.2f}"
+                    .format(trainer.state.epoch, metrics["accuracy"], metrics["nll"]))
+
+    if idist.get_rank() == 0:
+        GpuInfo().attach(trainer, name='gpu')
+
+        tb_logger = TensorboardLogger(log_dir=config.output_dir)
+        tb_logger.attach(
+            evaluator,
+            log_handler=OutputHandler(
+                tag="val",
+                metric_names='all',
+                global_step_transform=global_step_from_engine(trainer),
+            ),
+            event_name=Events.EPOCH_COMPLETED
+        )
+        tb_logger.attach(trainer, log_handler=WeightsScalarHandler(model),
+                         event_name=Events.EPOCH_COMPLETED(every=10))
+
+        tb_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=25))
+
+        tb_logger.attach(trainer, log_handler=GradsScalarHandler(model),
+                         event_name=Events.EPOCH_COMPLETED(every=10))
+
+        tb_logger.attach(trainer, log_handler=GradsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=25))
+
+        @trainer.on(Events.COMPLETED)
+        def _():
+            tb_logger.close()
+
+    to_save = dict(model=model, optimizer=optimizer, trainer=trainer)
+    setup_common_handlers(trainer, config.output_dir, print_interval_event=Events.EPOCH_COMPLETED, to_save=to_save,
+                          save_interval_event=Events.EPOCH_COMPLETED(every=25), n_saved=5,
+                          metrics_to_print=["loss"])
+    save_best_model_by_val_score(config.output_dir, evaluator, model, "accuracy", 1, trainer)
+    return trainer
+
+
+def run(task, config, logger):
+    assert torch.backends.cudnn.enabled
+    torch.backends.cudnn.benchmark = True
+    logger.info(f"start task {task}")
+    if task == "baseline":
+        train_dataset = ImageFolder(config.baseline.data.dataset.train.path,
+                                    transform=transform_pipeline(config.baseline.data.dataset.train.pipeline))
+        val_dataset = ImageFolder(config.baseline.data.dataset.val.path,
+                                  transform=transform_pipeline(config.baseline.data.dataset.val.pipeline))
+        logger.info(f"train with dataset:\n{train_dataset}")
+        train_data_loader = idist.auto_dataloader(train_dataset, **config.baseline.data.dataloader)
+        val_data_loader = idist.auto_dataloader(val_dataset, **config.baseline.data.dataloader)
+        trainer = baseline_trainer(config, logger, val_data_loader)
+        try:
+            trainer.run(train_data_loader, max_epochs=400)
+        except Exception:
+            import traceback
+            print(traceback.format_exc())
+    else:
+        return NotImplemented(f"invalid task: {task}")