def evaluate(model, criterion, postprocessors, data_loader, base_ds, device,
output_dir):
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
iou_types = tuple(k for k in ('segm', 'bbox')
if k in postprocessors.keys())
coco_evaluator = CocoEvaluator(base_ds, iou_types)
# coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75]
panoptic_evaluator = None
if 'panoptic' in postprocessors.keys():
panoptic_evaluator = PanopticEvaluator(
data_loader.dataset.ann_file,
data_loader.dataset.ann_folder,
output_dir=os.path.join(output_dir, "panoptic_eval"),
)
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
outputs = model(samples)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items() if k in weight_dict
}
loss_dict_reduced_unscaled = {
f'{k}_unscaled': v
for k, v in loss_dict_reduced.items()
}
metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()),
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled)
metric_logger.update(class_error=loss_dict_reduced['class_error'])
orig_target_sizes = torch.stack([t["orig_size"] for t in targets],
dim=0)
results = postprocessors['bbox'](outputs, orig_target_sizes)
if 'segm' in postprocessors.keys():
target_sizes = torch.stack([t["size"] for t in targets], dim=0)
results = postprocessors['segm'](results, outputs,
orig_target_sizes, target_sizes)
res = {
target['image_id'].item(): output
for target, output in zip(targets, results)
}
if coco_evaluator is not None:
coco_evaluator.update(res)
if panoptic_evaluator is not None:
res_pano = postprocessors["panoptic"](outputs, target_sizes,
orig_target_sizes)
for i, target in enumerate(targets):
image_id = target["image_id"].item()
file_name = f"{image_id:012d}.png"
res_pano[i]["image_id"] = image_id
res_pano[i]["file_name"] = file_name
panoptic_evaluator.update(res_pano)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
if coco_evaluator is not None:
coco_evaluator.synchronize_between_processes()
if panoptic_evaluator is not None:
panoptic_evaluator.synchronize_between_processes()
# accumulate predictions from all images
if coco_evaluator is not None:
coco_evaluator.accumulate()
coco_evaluator.summarize()
panoptic_res = None
if panoptic_evaluator is not None:
panoptic_res = panoptic_evaluator.summarize()
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
if coco_evaluator is not None:
if 'bbox' in postprocessors.keys():
stats['coco_eval_bbox'] = coco_evaluator.coco_eval[
'bbox'].stats.tolist()
if 'segm' in postprocessors.keys():
stats['coco_eval_masks'] = coco_evaluator.coco_eval[
'segm'].stats.tolist()
if panoptic_res is not None:
stats['PQ_all'] = panoptic_res["All"]
stats['PQ_th'] = panoptic_res["Things"]
stats['PQ_st'] = panoptic_res["Stuff"]
return stats, coco_evaluator
def train_one_epoch(model: torch.nn.Module,
criterion: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
max_norm: float = 0):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter(
'class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
for samples, targets in metric_logger.log_every(data_loader, print_freq,
header):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
outputs = model(samples)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys()
if k in weight_dict)
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = {
f'{k}_unscaled': v
for k, v in loss_dict_reduced.items()
}
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items() if k in weight_dict
}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
metric_logger.update(loss=loss_value,
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled)
metric_logger.update(class_error=loss_dict_reduced['class_error'])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}