53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class YoloLoss(nn.Module):
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def __init__(self):
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super(YoloLoss, self).__init__()
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def forward(self, predictions, targets):
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pred_boxes = predictions[..., :4]
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pred_conf = predictions[..., 4]
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pred_classes = predictions[..., 5:]
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target_boxes = targets[..., :4]
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target_conf = targets[..., 4]
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target_classes = targets[..., 5:]
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obj_mask = targets[..., 4] == 1
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noobj_mask = targets[..., 4] == 0
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if obj_mask.any():
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box_loss = F.mse_loss(
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pred_boxes[obj_mask],
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target_boxes[obj_mask],
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reduction="mean"
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)
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else:
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box_loss = torch.tensor(0.0, device=predictions.device)
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obj_loss = F.mse_loss(
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pred_conf[obj_mask],
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target_conf[obj_mask],
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reduction="mean"
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) if obj_mask.any() else torch.tensor(0.0, device=predictions.device)
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noobj_loss = F.mse_loss(
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pred_conf[noobj_mask],
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target_conf[noobj_mask],
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reduction="mean"
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) if noobj_mask.any() else torch.tensor(0.0, device=predictions.device)
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# The model already applies a sigmoid, so these are probabilities.
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class_loss = F.binary_cross_entropy(
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pred_classes[obj_mask],
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target_classes[obj_mask]
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) if obj_mask.any() else torch.tensor(0.0, device=predictions.device)
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total_loss = box_loss + obj_loss + noobj_loss + class_loss
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return total_loss |