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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. |