def coco_evaluate(model, data_loader, device):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(coco, iou_types)
for image, targets in metric_logger.log_every(data_loader, 100, header):
image = list(img.to(device) for img in image)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
torch.cuda.synchronize()
model_time = time.time()
outputs = model(image)
outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
model_time = time.time() - model_time
res = {target["image_id"].item(): output for target, output in zip(targets, outputs)}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
return coco_evaluator
def val_dataloader(self):
"""TODO Add missing docstring."""
valid_loader = torch.utils.data.DataLoader(
self.valid_dataset,
batch_size=self.cfg.data.batch_size,
num_workers=self.cfg.data.num_workers,
shuffle=False,
collate_fn=collate_fn,
)
# prepare coco evaluator
coco = get_coco_api_from_dataset(valid_loader.dataset)
iou_types = get_iou_types(self.model)
self.coco_evaluator = CocoEvaluator(coco, iou_types)
return valid_loader
class XrayDetection(pl.LightningModule):
"""Xray object detection pytorch module."""
def __init__(self, hparams: DictConfig, cfg: DictConfig, model: nn.Module):
super().__init__()
self.cfg = cfg
self.hparams = hparams
self.model = model
def configure_optimizers(self):
"""TODO Add missing docstring."""
if "decoder_lr" in self.cfg.optimizer.params.keys():
params = [
{
"params": self.model.decoder.parameters(),
"lr": self.cfg.optimizer.params.lr,
},
{
"params": self.model.encoder.parameters(),
"lr": self.cfg.optimizer.params.decoder_lr,
},
]
optimizer = load_obj(self.cfg.optimizer.class_name)(params)
else:
optimizer = load_obj(self.cfg.optimizer.class_name)(
self.model.parameters(), **self.cfg.optimizer.params)
scheduler = load_obj(self.cfg.scheduler.class_name)(
optimizer, **self.cfg.scheduler.params)
return (
[optimizer],
[{
"scheduler": scheduler,
"interval": self.cfg.scheduler.step,
"monitor": self.cfg.scheduler.monitor,
}],
)
def forward(self, x, *args, **kwargs):
"""TODO Add missing docstring."""
return self.model(x)
def get_callbacks(self) -> Dict[str, Callback]:
"""
Get a list of pytorch callbacks for this model.
Returns
-------
Dict[str, Callback]
List of callbacks
"""
early_stopping = EarlyStopping(
**self.cfg.callbacks.early_stopping.params)
model_checkpoint = ModelCheckpoint(
**self.cfg.callbacks.model_checkpoint.params)
return {
"early_stopping": early_stopping,
"model_checkpoint": model_checkpoint,
}
def get_loggers(self) -> List:
"""TODO Add missing docstring."""
return [TensorBoardLogger(save_dir=self.cfg.logging.logs_dir)]
def prepare_data(self):
"""TODO Add missing docstring."""
get_logger().info("Loading training dataset...")
datasets = get_training_dataset(self.cfg)
self.train_dataset = datasets["train"]
self.valid_dataset = datasets["valid"]
def train_dataloader(self):
"""TODO Add missing docstring."""
train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_size=self.cfg.data.batch_size,
num_workers=self.cfg.data.num_workers,
shuffle=True,
collate_fn=collate_fn,
)
return train_loader
def training_step(self, batch, batch_idx):
"""TODO Add missing docstring."""
images, targets, image_ids = batch
targets = [{k: v for k, v in t.items()} for t in targets]
# separate losses
loss_dict = self.model(images, targets)
# total loss
loss = sum(loss for loss in loss_dict.values())
return {"loss": loss, "log": loss_dict, "progress_bar": loss_dict}
def val_dataloader(self):
"""TODO Add missing docstring."""
valid_loader = torch.utils.data.DataLoader(
self.valid_dataset,
batch_size=self.cfg.data.batch_size,
num_workers=self.cfg.data.num_workers,
shuffle=False,
collate_fn=collate_fn,
)
# prepare coco evaluator
coco = get_coco_api_from_dataset(valid_loader.dataset)
iou_types = get_iou_types(self.model)
self.coco_evaluator = CocoEvaluator(coco, iou_types)
return valid_loader
def validation_epoch_end(self, outputs):
"""TODO Add missing docstring."""
self.coco_evaluator.accumulate()
self.coco_evaluator.summarize()
# coco main metric
metric = self.coco_evaluator.coco_eval["bbox"].stats[0]
metric = torch.as_tensor(metric, dtype=torch.float32)
tensorboard_logs = {"main_score": metric}
return {
"val_loss": metric,
"log": tensorboard_logs,
"progress_bar": tensorboard_logs,
}
def validation_step(self, batch, batch_idx):
"""TODO Add missing docstring."""
images, targets, image_ids = batch
targets = [{k: v for k, v in t.items()} for t in targets]
outputs = self.model(images, targets)
res = {
target["image_id"].item(): output
for target, output in zip(targets, outputs)
}
self.coco_evaluator.update(res)
return {}