@ptrblck the criterion is defined as:
criterion = MultiboxLoss(config.priors, iou_threshold=0.5, neg_pos_ratio=3,
center_variance=0.1, size_variance=0.2, device=DEVICE)
WHereas Multibox is defined as a class as shown here:
import torch.nn as nn
import torch.nn.functional as F
import torch
from ..utils import box_utils
class MultiboxLoss(nn.Module):
def __init__(self, priors, iou_threshold, neg_pos_ratio,
center_variance, size_variance, device):
"""Implement SSD Multibox Loss.
Basically, Multibox loss combines classification loss
and Smooth L1 regression loss.
"""
super(MultiboxLoss, self).__init__()
self.iou_threshold = iou_threshold
self.neg_pos_ratio = neg_pos_ratio
self.center_variance = center_variance
self.size_variance = size_variance
self.priors = priors
self.priors.to(device)
def forward(self, confidence, predicted_locations, labels, gt_locations):
"""Compute classification loss and smooth l1 loss.
Args:
confidence (batch_size, num_priors, num_classes): class predictions.
locations (batch_size, num_priors, 4): predicted locations.
labels (batch_size, num_priors): real labels of all the priors.
boxes (batch_size, num_priors, 4): real boxes corresponding all the priors.
"""
num_classes = confidence.size(2)
with torch.no_grad():
# derived from cross_entropy=sum(log(p))
loss = -F.log_softmax(confidence, dim=2)[:, :, 0]
mask = box_utils.hard_negative_mining(loss, labels, self.neg_pos_ratio)
confidence = confidence[mask, :]
classification_loss = F.cross_entropy(confidence.reshape(-1, num_classes), labels[mask], reduction='sum')
pos_mask = labels > 0
predicted_locations = predicted_locations[pos_mask, :].reshape(-1, 4)
gt_locations = gt_locations[pos_mask, :].reshape(-1, 4)
smooth_l1_loss = F.smooth_l1_loss(predicted_locations, gt_locations, reduction=False)
num_pos = gt_locations.size(0)
return smooth_l1_loss/num_pos, classification_loss/num_pos
I will check out the input and target to the criterion