| Your predictions need to be converted to | |
| logits first, and then reshaped to match the size of the labels before you can call [~evaluate.EvaluationModule.compute]: | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| def compute_metrics(eval_pred): | |
| with torch.no_grad(): | |
| logits, labels = eval_pred | |
| logits_tensor = torch.from_numpy(logits) | |
| logits_tensor = nn.functional.interpolate( | |
| logits_tensor, | |
| size=labels.shape[-2:], | |
| mode="bilinear", | |
| align_corners=False, | |
| ).argmax(dim=1) | |
| pred_labels = logits_tensor.detach().cpu().numpy() | |
| metrics = metric.compute( | |
| predictions=pred_labels, | |
| references=labels, | |
| num_labels=num_labels, | |
| ignore_index=255, | |
| reduce_labels=False, | |
| ) | |
| for key, value in metrics.items(): | |
| if isinstance(value, np.ndarray): | |
| metrics[key] = value.tolist() | |
| return metrics | |
| def compute_metrics(eval_pred): | |
| logits, labels = eval_pred | |
| logits = tf.transpose(logits, perm=[0, 2, 3, 1]) | |
| logits_resized = tf.image.resize( | |
| logits, | |
| size=tf.shape(labels)[1:], | |
| method="bilinear", | |
| ) | |
| pred_labels = tf.argmax(logits_resized, axis=-1) | |
| metrics = metric.compute( | |
| predictions=pred_labels, | |
| references=labels, | |
| num_labels=num_labels, | |
| ignore_index=-1, | |
| reduce_labels=image_processor.do_reduce_labels, | |
| ) | |
| per_category_accuracy = metrics.pop("per_category_accuracy").tolist() | |
| per_category_iou = metrics.pop("per_category_iou").tolist() | |
| metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)}) | |
| metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)}) | |
| return {"val_" + k: v for k, v in metrics.items()} | |
| Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. |