def _setup_costs(self, log):
if not self._total_cost is None:
log.print3("ERROR: Problem in Trainer. It was called to setup the total cost, but it was not None."+\
"\n\t This should not happen. Setup should be called only once.\n Exiting!")
exit(1)
# Cost functions
cost = 0
y_gt = self._net._output_gt_tensor_feeds['train']['y_gt']
if "xentr" in self._losses_and_weights and self._losses_and_weights["xentr"] is not None:
log.print3("COST: Using cross entropy with weight: " +str(self._losses_and_weights["xentr"]))
w_per_cl_vec = self._compute_w_per_class_vector_for_xentr( self._net.num_classes, y_gt )
cost += self._losses_and_weights["xentr"] * cfs.x_entr( self._net.finalTargetLayer.p_y_given_x_train, y_gt, w_per_cl_vec )
if "iou" in self._losses_and_weights and self._losses_and_weights["iou"] is not None:
log.print3("COST: Using iou loss with weight: " +str(self._losses_and_weights["iou"]))
cost += self._losses_and_weights["iou"] * cfs.iou( self._net.finalTargetLayer.p_y_given_x_train, y_gt )
if "dsc" in self._losses_and_weights and self._losses_and_weights["dsc"] is not None:
log.print3("COST: Using dsc loss with weight: " +str(self._losses_and_weights["dsc"]))
cost += self._losses_and_weights["dsc"] * cfs.dsc( self._net.finalTargetLayer.p_y_given_x_train, y_gt )
cost_L1_reg = self._L1_reg_weight * self._net._get_L1_cost()
cost_L2_reg = self._L2_reg_weight * self._net._get_L2_cost()
cost = cost + cost_L1_reg + cost_L2_reg
self._total_cost = cost
def _setup_costs(self, log):
if not self._total_cost is None:
log.print3("ERROR: Problem in Trainer. It was called to setup the total cost, but it was not None."+\
"\n\t This should not happen. Setup should be called only once.\n Exiting!")
exit(1)
# Cost functions
cost = 0
y_gtmix0 = self._net._output_gt_tensor_feeds['train']['y_gt0']
y_gtmix1 = self._net._output_gt_tensor_feeds['train']['y_gt1']
mixup_lambda = self._net._output_gt_tensor_feeds['train'][
'mixup_lambda']
y_gt = y_gtmix0
if "xentr" in self._losses_and_weights and self._losses_and_weights[
"xentr"] is not None:
log.print3("COST: Using cross entropy with weight: " +
str(self._losses_and_weights["xentr"]))
w_per_cl_vec = self._compute_w_per_class_vector_for_xentr(
self._net.num_classes, y_gt)
cost += self._losses_and_weights["xentr"] * cfs.x_entr(
self._net.finalTargetLayer.p_y_given_x_train, y_gt,
w_per_cl_vec)
if "iou" in self._losses_and_weights and self._losses_and_weights[
"iou"] is not None:
log.print3("COST: Using iou loss with weight: " +
str(self._losses_and_weights["iou"]))
cost += self._losses_and_weights["iou"] * cfs.iou(
self._net.finalTargetLayer.p_y_given_x_train, y_gt)
if "dsc" in self._losses_and_weights and self._losses_and_weights[
"dsc"] is not None:
log.print3("COST: Using dsc loss with weight: " +
str(self._losses_and_weights["dsc"]))
cost += self._losses_and_weights["dsc"] * cfs.dsc(
self._net.finalTargetLayer.p_y_given_x_train, y_gt)
if "focaloneside" in self._losses_and_weights and self._losses_and_weights[
"focaloneside"] is not None:
log.print3(
"COST: Using focal loss one side cross entropy with weight: " +
str(self._losses_and_weights["focaloneside"]))
log.print3(
"COST: Using focal loss one side cross entropy with gama: " +
str(self._losses_and_weights["focalonesidegama"]))
w_per_cl_vec = self._compute_w_per_class_vector_for_xentr(
self._net.num_classes, y_gtmix0, y_gtmix1)
cost += self._losses_and_weights["focaloneside"] * cfs.focaloneside(
self._net.finalTargetLayer.network_output, y_gtmix0, y_gtmix1,
self._losses_and_weights["focalonesidegama"], w_per_cl_vec,
self._mixup_biasmargin, self._marginm, mixup_lambda)
cost_L1_reg = self._L1_reg_weight * self._net._get_L1_cost()
cost_L2_reg = self._L2_reg_weight * self._net._get_L2_cost()
cost = cost + cost_L1_reg + cost_L2_reg
self._total_cost = cost
def compute_costs(
self, log, p_y_given_x
): # Needs to be run with initialized self._num_epochs_trained_tfv
if not self._total_cost is None:
log.print3("ERROR: Problem in Trainer. It was called to setup the total cost, but it was not None."+\
"\n\t This should not happen. Setup should be called only once.\n Exiting!")
exit(1)
# Cost functions
cost = 0
y_gt = self._net._output_gt_tensor_feeds['train']['y_gt']
if "xentr" in self._losses_and_weights and self._losses_and_weights[
"xentr"] is not None:
log.print3("COST: Using cross entropy with weight: " +
str(self._losses_and_weights["xentr"]))
w_per_cl_vec = self._compute_w_per_class_vector_for_xentr(
self._net.num_classes, y_gt)
cost += self._losses_and_weights["xentr"] * cfs.x_entr(
p_y_given_x, y_gt, w_per_cl_vec)
if "iou" in self._losses_and_weights and self._losses_and_weights[
"iou"] is not None:
log.print3("COST: Using iou loss with weight: " +
str(self._losses_and_weights["iou"]))
cost += self._losses_and_weights["iou"] * cfs.iou(
p_y_given_x, y_gt)
if "dsc" in self._losses_and_weights and self._losses_and_weights[
"dsc"] is not None:
log.print3("COST: Using dsc loss with weight: " +
str(self._losses_and_weights["dsc"]))
cost += self._losses_and_weights["dsc"] * cfs.dsc(
p_y_given_x, y_gt)
cost_L1_reg = self._L1_reg_weight * cfs.cost_L1(
self._net.params_for_L1_L2_reg())
cost_L2_reg = self._L2_reg_weight * cfs.cost_L2(
self._net.params_for_L1_L2_reg())
cost = cost + cost_L1_reg + cost_L2_reg
self._total_cost = cost