def test_dataset() -> None:
rows = read_train_rows("/store/points.json")
dataset = CharDataset(rows=rows, transforms=test_transforms)
for i in range(10):
id, img, points, labels = dataset[2]
plot = DetectionPlot(inv_normalize(img))
plot.draw_points(points, color="red", size=0.5)
plot.save(f"/store/test-{i}.jpg")
def eval_step() -> None:
model.eval()
loss_meter = MeanMeter()
metrics = MeanAveragePrecision(iou_threshold=0.3,
num_classes=config.num_classes)
for ids, image_batch, gt_point_batch, gt_label_batch in tqdm(
test_loader):
image_batch = image_batch.to(device)
gt_point_batch = [x.to(device) for x in gt_point_batch]
gt_label_batch = [x.to(device) for x in gt_label_batch]
_, _, h, w = image_batch.shape
netout = model(image_batch)
_, _, hm_h, hm_w = netout.shape
gt_hms = config.mkmaps(gt_point_batch,
gt_label_batch,
w=hm_w,
h=hm_h)
loss = config.hmloss(
netout,
gt_hms,
)
point_batch, confidence_batch, label_batch = config.to_points(
netout, h=h, w=w)
loss_meter.update(loss.item())
for (
points,
gt_points,
labels,
gt_labels,
confidences,
image,
gt_hm,
id,
) in zip(
point_batch,
gt_point_batch,
label_batch,
gt_label_batch,
confidence_batch,
image_batch,
gt_hms,
ids,
):
plot = DetectionPlot(inv_normalize(image))
plot.draw_points(points, color="blue", size=w / 100)
plot.draw_points(gt_points, color="red", size=w / 150)
plot.save(f"{config.out_dir}/{id}-points-.png")
logs["test_loss"] = loss_meter.get_value()
model_loader.save_if_needed(
model,
loss.item(),
)