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778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import torch
import torch.nn as nn
import torch.nn.functional as F
# CrossEntropyLoss = softmax + log + NLLLoss
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=0.5, reduction=None):
super(FocalLoss, self).__init__()
self.weight = weight
self.gamma = gamma
self.reduction = reduction
def forward(self, input_tensor, target_tensor):
assert input_tensor.shape[0] == target_tensor.shape[0]
prob = F.softmax(input_tensor, dim = -1)
log_prob = torch.log(prob + 1e-8)
loss = F.nll_loss(
((1 - prob) ** self.gamma) * log_prob,
target_tensor,
weight=self.weight,
reduction=self.reduction
)
return loss
class ClassifierLoss():
def __init__(self, alpha, gamma):
weight = torch.FloatTensor([1-alpha, alpha])
if torch.cuda.is_available():
weight = weight.cuda()
self.focal_loss = FocalLoss(
weight = weight,
gamma = gamma,
reduction = 'mean'
)
# self.ce_loss = nn.CrossEntropyLoss(weight = weight, reduction = "mean")
def compute_batch_loss(self, batch_logits, batch_labels, batch_size):
loss = 0
for logits, labels in zip(batch_logits, batch_labels):
loss += self.focal_loss(logits, labels)
return loss/batch_size
def compute_loss(
self,
batch_table_name_cls_logits,
batch_table_labels,
batch_column_info_cls_logits,
batch_column_labels
):
batch_size = len(batch_table_labels)
table_loss = self.compute_batch_loss(batch_table_name_cls_logits, batch_table_labels, batch_size)
column_loss = self.compute_batch_loss(batch_column_info_cls_logits, batch_column_labels, batch_size)
return table_loss + column_loss |