def validation_step(self, batch, batch_idx):
images, targets = batch
# Retinanet takes only images for eval() mode
outs = self.model(images)
iou = torch.stack([_evaluate_iou(t, o) for t, o in zip(targets, outs)]).mean()
giou = torch.stack([_evaluate_giou(t, o) for t, o in zip(targets, outs)]).mean()
return {"val_iou": iou, "val_giou": giou}
def val_step(model, val_loader, device, num_batches=None,
log_interval: int = 100):
"""
Performs one step of validation. Calculates loss, forward pass and returns metrics.
Args:
model : PyTorch FasterRCNN Model.
val_loader : Validation loader.
device : "cuda" or "cpu"
num_batches : (optional) Integer To limit validation to certain number of batches.
log_interval : (optional) Defualt 100. Integer to Log after specified batch ids in every batch.
"""
model = model.to(device)
start_val_step = time.time()
last_idx = len(val_loader) - 1
batch_time_m = utils.AverageMeter()
cnt = 0
model.eval()
batch_start = time.time()
metrics = OrderedDict()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(val_loader):
last_batch = batch_idx == last_idx
images = list(image.to(device) for image in inputs)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
out = model(images)
iou = torch.stack([_evaluate_iou(t, o) for t, o in zip(targets, out)]).mean()
giou = torch.stack([_evaluate_giou(t, o) for t, o in zip(targets, out)]).mean()
cnt += 1
batch_time_m.update(time.time() - batch_start)
batch_start = time.time()
if last_batch or batch_idx % log_interval == 0: # If we reach the log intervel
print("Batch Validation Time: {batch_time.val:.3f} ({batch_time.avg:.3f}) ".format(
batch_time=batch_time_m,))
if num_batches is not None:
if cnt >= num_batches:
avg_iou = torch.stack([iou]).mean()
avg_giou = torch.stack([giou]).mean()
metrics["iou"] = avg_iou
metrics["giou"] = avg_giou
print(f"Done till {num_batches} Validation batches")
end_val_step = time.time()
print(f"Time taken for validation step = {end_val_step - start_val_step} sec")
return metrics
avg_iou = torch.stack([iou]).mean()
avg_giou = torch.stack([giou]).mean()
metrics["iou"] = avg_iou
metrics["giou"] = avg_giou
end_val_step = time.time()
print(f"Time taken for validation step = {end_val_step - start_val_step} sec")
return metrics
