def create_loss(self, input_dict=None):
if self.loss_flag == 0:
loss = CrossEntropy2d(ignore_index=250)
self.add_block_list(LossType.CrossEntropy2d, loss,
self.block_out_channels[-1])
self.lossList.append(loss)
elif self.loss_flag == 1:
loss = CrossEntropy2d(ignore_index=250)
self.add_block_list(LossType.CrossEntropy2d, loss,
self.block_out_channels[-1])
self.lossList.append(loss)
def __init__(self, num_class,
se_loss=False, se_weight=0.2,
aux=False, aux_weight=0.4,
weight=None, reduction='mean', ignore_index=250):
super().__init__(LossType.EncNetLoss)
self.num_class = num_class
self.se_loss = se_loss
self.aux = aux
self.se_weight = se_weight
self.aux_weight = aux_weight
self.ce = CrossEntropy2d(0, weight, True,
reduction, ignore_index)
self.bce = BinaryCrossEntropy2d(0, weight, True,
reduction, ignore_index)
def create_loss(self, module_def):
if module_def['type'] == LossType.YoloLoss:
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
# Extract anchors
anchors = [float(x) for x in module_def['anchors'].split(',')]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
num_classes = int(module_def['classes'])
# Define detection layer
yolo_layer = YoloLoss(num_classes, anchors=anchors,
anchors_mask=anchor_idxs, smoothLabel=False,
focalLoss=False)
self.addBlockList(LossType.YoloLoss, yolo_layer, self.filters)
self.input_channels = self.filters
elif module_def["type"] == LossType.CrossEntropy2d:
weight_type = int(module_def.get("weight_type", 0))
weight = module_def.get("weight", None)
reduce = module_def.get("reduce", None)
reduction = module_def.get("reduction", 'mean')
ignore_index = int(module_def.get("ignore_index", 250))
layer = CrossEntropy2d(weight_type=weight_type,
weight=weight,
reduce=reduce,
reduction=reduction,
ignore_index=ignore_index)
self.addBlockList(LossType.CrossEntropy2d, layer, self.filters)
self.input_channels = self.filters
elif module_def["type"] == LossType.OhemCrossEntropy2d:
ignore_index = int(module_def.get("ignore_index", 250))
layer = OhemCrossEntropy2d(ignore_index=ignore_index)
self.addBlockList(LossType.OhemCrossEntropy2d, layer, self.filters)
self.input_channels = self.filters
elif module_def["type"] == LossType.BinaryCrossEntropy2d:
weight_type = int(module_def.get("weight_type", 0))
weight = module_def.get("weight", None)
reduce = module_def.get("reduce", None)
reduction = module_def.get("reduction", 'mean')
layer = BinaryCrossEntropy2d(weight_type=weight_type,
weight=weight,
reduce=reduce,
reduction=reduction)
self.addBlockList(LossType.BinaryCrossEntropy2d, layer, self.filters)
self.input_channels = self.filters