import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from sklearn.metrics import confusion_matrix class cross_entropy2d(nn.Module): def __init__(self, weight=None, size_average=True, ignore=-100): super(cross_entropy2d, self).__init__() self.nll_loss = nn.NLLLoss(weight=weight, size_average=size_average, ignore_index=ignore) self.ignore = ignore def forward(self, input, target, th=1.0): log_p = F.log_softmax(input, dim=1) if th < 1: # This is done while using Hardmining. Not used for our model training mask = F.softmax(input, dim=1) > th mask = mask.data new_target = target.data.clone() new_target[new_target == self.ignore] = 0 indx = torch.gather(mask, 1, new_target.unsqueeze(1)) indx = indx.squeeze(1) mod_target = target.clone() mod_target[indx] = self.ignore target = mod_target loss = self.nll_loss(log_p, target) total_valid_pixel = torch.sum(target.data != self.ignore) return loss, Variable(torch.FloatTensor([total_valid_pixel]).cuda()) def pixel_accuracy(outputs, labels, n_classes): lbl = labels.data mask = lbl < n_classes accuracy = [] for output in outputs: _, pred = output.data.max(dim=1) diff = pred[mask] - lbl[mask] accuracy += [torch.sum(diff == 0)] return accuracy def prediction_stat(outputs, labels, n_classes): lbl = labels.data valid = lbl < n_classes classwise_pixel_acc = [] classwise_gtpixels = [] classwise_predpixels = [] for output in outputs: _, pred = output.data.max(dim=1) for m in range(n_classes): mask1 = lbl == m mask2 = pred[valid] == m diff = pred[mask1] - lbl[mask1] classwise_pixel_acc += [torch.sum(diff == 0)] classwise_gtpixels += [torch.sum(mask1)] classwise_predpixels += [torch.sum(mask2)] return classwise_pixel_acc, classwise_gtpixels, classwise_predpixels def prediction_stat_confusion_matrix(logits, annotation, n_classes): labels = range(n_classes) # First we do argmax on gpu and then transfer it to cpu logits = logits.data annotation = annotation.data _, prediction = logits.max(1) prediction = prediction.squeeze(1) prediction_np = prediction.cpu().numpy().flatten() annotation_np = annotation.cpu().numpy().flatten() # Mask-out value is ignored by default in the sklearn # read sources to see how that was handled current_confusion_matrix = confusion_matrix(y_true=annotation_np, y_pred=prediction_np, labels=labels) return current_confusion_matrix