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PLModels.py
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PLModels.py
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# from transformers import BertPreTrainedModel, BertModel, BertOnlyMLMHead
from transformers import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, MSELoss, BCELoss
from typing import Union, List
import numpy as np
from torch.autograd import Variable
def weights_init_normal(m):
"""Takes in a module and initializes all linear layers with weight
values taken from a normal distribution."""
classname = m.__class__.__name__
# for every Linear layer in a model
if classname.find("Linear") != -1:
try:
y = m.in_features
# m.weight.data shoud be taken from a normal distribution
m.weight.data.normal_(0.0, 1 / np.sqrt(y))
# m.bias.data should be 0
m.bias.data.fill_(0)
except:
# bound = 1 / math.sqrt(m.weight.size(-1))
# torch.nn.init.uniform_(m.weight.data, -bound, bound)
y = m.in_channels
# m.weight.data shoud be taken from a normal distribution
m.weight.data.normal_(0.0, 1 / np.sqrt(y))
print(m)
# raise
class FocalLoss(nn.Module):
r"""
This criterion is a implemenation of Focal Loss, which is proposed in
Focal Loss for Dense Object Detection.
Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])
The losses are averaged across observations for each minibatch.
Args:
alpha(1D Tensor, Variable) : the scalar factor for this criterion
gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5),
putting more focus on hard, misclassified examples
size_average(bool): size_average(bool): By default, the losses are averaged over observations for each minibatch.
However, if the field size_average is set to False, the losses are
instead summed for each minibatch.
"""
def __init__(self, class_num, alpha=None, gamma=2, size_average=True):
super(FocalLoss, self).__init__()
if alpha is None:
self.alpha = Variable(torch.ones(class_num, 1))
else:
if isinstance(alpha, Variable):
self.alpha = alpha
else:
self.alpha = Variable(alpha)
self.gamma = gamma
self.class_num = class_num
self.size_average = size_average
def forward(self, inputs, targets):
N = inputs.size(0)
C = inputs.size(1)
P = F.softmax(inputs)
class_mask = inputs.data.new(N, C).fill_(0)
class_mask = Variable(class_mask)
ids = targets.view(-1, 1)
class_mask.scatter_(1, ids.data, 1.)
if inputs.is_cuda and not self.alpha.is_cuda:
self.alpha = self.alpha.cuda()
alpha = self.alpha[ids.data.view(-1)]
probs = (P*class_mask).sum(1).view(-1,1)
log_p = probs.log()
batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p
if self.size_average:
loss = batch_loss.mean()
else:
loss = batch_loss.sum()
return loss
class BertBooleanQuestionYN(BertPreTrainedModel):
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_labels_YN = 2,
dataset = "spartun"# LM = "bert",# has_batch = False, # criterion = 'cross'
):
super().__init__(config)
#LM = "bert", #has_batch = False, #criterion = "cross"
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device2 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes = 2
self.num_labels = num_labels_YN
if dataset == "spartqa":
self.alphas = torch.tensor([[0.5, 0.5], [0.25, 0.75], [0.27, 0.73] ]).to(self.device2)
elif dataset == "human":
self.alphas = torch.tensor([[0.5, 0.5], [0.6, 0.4], [0.1, 0.9] ]).to(self.device2)
elif dataset == "spartun":
self.alphas = torch.tensor([[0.55, 0.45], [0.45, 0.55]]).to(self.device2)
else:
self.alphas = torch.tensor([[0.5, 0.5]]*self.num_labels_YN).to(self.device2)
# self.classifier = nn.Linear(config.hidden_size, self.num_classes)
classifiers = []
self.criterion = []
for item in range(self.num_labels):
classifiers.append(nn.Linear(config.hidden_size, self.num_classes))
self.criterion.append(FocalLoss(alpha=self.alphas[item], class_num=self.num_classes, gamma = 2))
self.classifiers = nn.ModuleList(classifiers)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
#@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
task = None,
multi_task = None
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = []
for ind in range(self.num_labels):
logit = self.classifiers[ind](pooled_output)
logits.append(logit)
# logits.append(logit.squeeze(0))
if labels is not None:
loss = 0
out_logits = []
for ind, logit in enumerate(logits):
loss += self.criterion[ind](logit, labels[:, ind])
# loss += self.criterion[ind](logit.unsqueeze(0), labels[ind].unsqueeze(0))
out_logits.append(self.softmax(logit))
outputs = (loss, torch.stack(out_logits),) + outputs[2:]
else:
out_logits = []
for ind, logit in enumerate(logits):
out_logits.append(self.softmax(logit))
outputs = (None, torch.stack(out_logits),) + outputs[2:]
return outputs
class BertBooleanQuestionFR(BertPreTrainedModel):
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_labels_FR = 7,
dataset = "spartun"# LM = "bert",# has_batch = False, # criterion = 'cross'
):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device1 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes = 2
self.num_labels = num_labels_FR
# self.classifier = nn.Linear(config.hidden_size, self.num_classes)
if dataset == "spartqa":
self.alphas = torch.tensor([[0.20, 0.8], [0.20, 0.8], [0.25, 0.75], [0.4, 0.6], [0.1, 0.9], [0.2, 0.98], [0.2, 0.98]]).to(self.device1)
# self.alphas = torch.tensor([[0.15, 0.85], [0.25, 0.75], [0.25, 0.75], [0.4, 0.6], [0.06, 0.94], [0.01, 0.99] ,[0.01, 0.99]]).to(self.device1)
# self.alphas = torch.tensor([[0.20, 0.8], [0.20, 0.8], [0.25, 0.75], [0.4, 0.6], [0.05, 0.95], [0.01, 0.99] ,[0.01, 0.99]]).to(self.device1)
# self.alphas = torch.tensor([[0.20, 0.8], [0.20, 0.8], [0.25, 0.75], [0.4, 0.6], [0.05, 0.95], [0.01, 0.99] ,[0.01, 0.99], [0.01, 0.99]]).to(self.device1)
elif dataset == "human":
self.alphas = torch.tensor([[0.35, 0.65], [0.25, 0.75], [0.25, 0.75], [0.2, 0.8], [0.25, 0.75], [0.45, 0.55], [0.05, 0.95]]).to(self.device1)
elif dataset == "spartun":
self.alphas = torch.tensor([[0.1, 0.9], [0.1, 0.9], [0.25, 0.75], [0.25, 0.75], [0.1, 0.9], [0.1, 0.9], [0.04, 0.96], [0.15, 0.85], [0.24, 0.76], [0.07, 0.93], [0.02, 0.98], [0.05, 0.95], [0.12, 0.88], [0.05, 0.95], [0.1, 0.9]]).to(self.device1)
elif dataset == "babi":
self.alphas = torch.tensor([[0.60, 0.4], [0.57, 0.43], [0.6, 0.4], [0.41, 0.59]]).to(self.device1)
else:
self.alphas = torch.tensor([[0.5, 0.5]]*self.num_labels).to(self.device1)
classifiers = []
self.criterion = []
for item in range(self.num_labels):
classifiers.append(nn.Linear(config.hidden_size, self.num_classes))
self.criterion.append(FocalLoss(alpha=self.alphas[item], class_num=self.num_classes, gamma = 2))
self.classifiers = nn.ModuleList(classifiers)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
task = None,
multi_task = None
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
# print(pooled_output.shape)
logits = []
for ind in range(self.num_labels):
logit = self.classifiers[ind](pooled_output)
# logit = self.classifiers[ind](pooled_output[ind])
logits.append(logit)
# for check on YN
# for ind in range(7):
# logit = self.classifiers[ind](pooled_output)
# logits.append(logit)
# print("FR",logits)
if labels is not None:
loss = 0
out_logits = []
for ind, logit in enumerate(logits):
loss += self.criterion[ind](logit, labels[:, ind])
# loss += self.criterion[ind](logit.unsqueeze(0), labels[ind].unsqueeze(0))
out_logits.append(self.softmax(logit))
outputs = (loss, torch.stack(out_logits),) + outputs[2:]
else:
out_logits = []
for ind, logit in enumerate(logits):
out_logits.append(self.softmax(logit))
outputs = (None, torch.stack(out_logits),) + outputs[2:]
return outputs # (loss), reshaped_logits, (hidden_st
class BertMultipleClass(BertPreTrainedModel):
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_classes = 3,
dataset = "spartun",
qtype = "FR"
):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device1 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes = num_classes
self.qtype = qtype
# self.classifier = nn.Linear(config.hidden_size, self.num_classes)
if self.qtype == 'YN':
if dataset == "spartqa":
self.alphas = torch.tensor([0.67, 1.3, 1.32]).to(self.device1)
else:
self.alphas = torch.tensor([1/self.num_classes]*self.num_classes).to(self.device1)
elif self.qtype == "FR":
# self.alphas = torch.tensor([0.125]*self.num_classes).to(self.device1)
self.alphas = torch.tensor([1/self.num_classes]*self.num_classes).to(self.device1)
# classifiers = []
# self.criterion = []
# for item in range(1):
self.classifiers= nn.Linear(config.hidden_size, self.num_classes)
self.criterion= FocalLoss(alpha=self.alphas, class_num=self.num_classes, gamma = 2)
# self.classifiers = nn.ModuleList(classifiers)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
#@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
# for ind in range(1):
logit = self.classifiers(pooled_output)
# logits = logit.squeeze(0)
out_logits = self.softmax(logit)
if labels is not None:
# loss = 0
# out_logits = []
#check labels
loss = self.criterion(logit, labels)
outputs = (loss, out_logits,) + outputs[2:]
else:
outputs = (None, out_logits,) + outputs[2:]
return outputs
class BertMultiTaskMultipleClass(BertPreTrainedModel):
#TODO add criterion to load
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_classes_YN = None,
num_classes_FR = None,
dataset = "stepgame",
LM = "bert",
has_batch = False,
criterion = 'focal'
):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device1 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes_YN = num_classes_YN
self.num_classes_FR = num_classes_FR
# self.qtype = qtype
if self.num_classes_YN:
if dataset == "spartqa":
self.alphasYN = torch.tensor([0.67, 1.3, 1.32]).to(self.device1)
elif dataset == "spartun":
self.alphasYN = torch.tensor([0.4, 0.6]).to(self.device1)
else:
self.alphasYN = torch.tensor([1/self.num_classes_YN]*self.num_classes_YN).to(self.device1)
#TODO test MLP, add batch
self.classifiers_YN = nn.Linear(config.hidden_size, self.num_classes_YN)
if criterion == "cross":
self.criterion_YN = nn.CrossEntropyLoss(weight=self.alphas)
else:
self.criterion_YN = FocalLoss(alpha=self.alphasYN, class_num=self.num_classes_YN, gamma = 2)
# self.init_weights()
# self.classifiers_YN.apply(weights_init_normal)
if self.num_classes_FR:
# if dataset == "spartqa":
# else:
#TODO compute for stepgame
self.alphasFR = torch.tensor([1/self.num_classes_FR]*self.num_classes_FR).to(self.device1)
self.classifiers_FR = nn.Linear(config.hidden_size, self.num_classes_FR)
if criterion == "cross":
self.criterion_FR = nn.CrossEntropyLoss(weight=self.alphas)
else:
self.criterion_FR =FocalLoss(alpha=self.alphasFR, class_num=self.num_classes_FR, gamma = 2, size_average=False)
# self.classifiers_FR.apply(weights_init_normal)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
task = "YN",
multi_task = False
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
if task == "YN":
logit = self.classifiers_YN(pooled_output)
elif task == "FR":
logit = self.classifiers_FR(pooled_output)
out_logits = self.softmax(logit)
if labels is not None:
if task == "YN":
loss = self.criterion_YN(logit, labels)
elif task == "FR":
loss = self.criterion_FR(logit, labels)
outputs = (loss, out_logits,) + outputs[2:]
else:
outputs = (None,out_logits,)+ outputs[2:]
return outputs
class BertMultiTaskMultipleClassLoad(BertPreTrainedModel):
#TODO add criterion to load
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_classes_YN = None,
num_classes_FR = None,
dataset = "stepgame",
LM = "bert",
has_batch = False,
criterion = 'focal'
):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device1 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes_YN = num_classes_YN
self.num_classes_FR = num_classes_FR
# self.qtype = qtype
if self.num_classes_YN:
if dataset == "spartqa":
self.alphasYN = torch.tensor([0.67, 1.3, 1.32]).to(self.device1)
elif dataset == "spartun":
self.alphasYN = torch.tensor([0.4, 0.6]).to(self.device1)
else:
self.alphasYN = torch.tensor([1/self.num_classes_YN]*self.num_classes_YN).to(self.device1)
#TODO test MLP, add batch
self.classifiers_YN_load = nn.Linear(config.hidden_size, self.num_classes_YN)
if criterion == "cross":
self.criterion_YN = nn.CrossEntropyLoss(weight=self.alphas)
else:
self.criterion_YN = FocalLoss(alpha=self.alphasYN, class_num=self.num_classes_YN, gamma = 2)
# self.init_weights()
# self.classifiers_YN.apply(weights_init_normal)
if self.num_classes_FR:
# if dataset == "spartqa":
# else:
#TODO compute for stepgame
self.alphasFR = torch.tensor([1/self.num_classes_FR]*self.num_classes_FR).to(self.device1)
self.classifiers_FR_load = nn.Linear(config.hidden_size, self.num_classes_FR)
if criterion == "cross":
self.criterion_FR = nn.CrossEntropyLoss(weight=self.alphas)
else:
self.criterion_FR =FocalLoss(alpha=self.alphasFR, class_num=self.num_classes_FR, gamma = 2, size_average=False)
# self.classifiers_FR.apply(weights_init_normal)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
task = "YN",
multi_task = False
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
if task == "YN":
logit = self.classifiers_YN_load(pooled_output)
elif task == "FR":
logit = self.classifiers_FR_load(pooled_output)
out_logits = self.softmax(logit)
if labels is not None:
if task == "YN":
loss = self.criterion_YN(logit, labels)
elif task == "FR":
loss = self.criterion_FR(logit, labels)
outputs = (loss, out_logits,) + outputs[2:]
else:
outputs = (None,out_logits,)+ outputs[2:]
return outputs
#TODO bertpretraining is added
class BertMultiTaskBooleanQuestion(BertPreTrainedModel):
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_labels_YN = None,
num_labels_FR = None,
dataset = "spartqa",
LM = "bert",
has_batch = False,
criterion = "focal"
):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device1 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes = 2
self.num_labels_YN = num_labels_YN
self.num_labels_FR = num_labels_FR
# self.qtype = qtype
if self.num_labels_YN:
if dataset == "spartqa":
self.alphasYN = torch.tensor([[0.5, 0.5], [0.25, 0.75], [0.27, 0.73] ]).to(self.device1)
elif dataset == "human":
self.alphasYN = torch.tensor([[0.5, 0.5], [0.6, 0.4], [0.1, 0.9] ]).to(self.device1)
elif dataset == "spartun":
self.alphasYN = torch.tensor([[0.55, 0.45], [0.45, 0.55]]).to(self.device1)
else:
self.alphasYN = torch.tensor([[0.5, 0.5]]*self.num_labels_YN).to(self.device1)
#initialize the classifier model
classifiers_YN = []
self.criterion_YN = []
for item in range(self.num_labels_YN):
classifiers_YN.append(nn.Linear(config.hidden_size, self.num_classes))
if criterion == "cross":
self.criterion_YN.append(nn.CrossEntropyLoss(weight=self.alphasYN[item]))
else:
self.criterion_YN.append(FocalLoss(alpha=self.alphasYN[item], class_num=self.num_classes, gamma = 2))
self.classifiers_YN = nn.ModuleList(classifiers_YN)
# self.sigma_YN = nn.Parameter(torch.ones(1))
if self.num_labels_FR:
if dataset == "spartqa":
self.alphasFR = torch.tensor([[0.20, 0.8], [0.20, 0.8], [0.25, 0.75], [0.4, 0.6], [0.1, 0.9], [0.02, 0.98], [0.02, 0.98]]).to(self.device1)
elif dataset == "human":
self.alphasFR = torch.tensor([[0.35, 0.65], [0.25, 0.75], [0.25, 0.75], [0.2, 0.8], [0.25, 0.75], [0.45, 0.55], [0.05, 0.95]]).to(self.device1)
elif dataset == "spartun":
self.alphasFR = torch.tensor([[0.1, 0.9], [0.1, 0.9], [0.27, 0.73], [0.26, 0.74], [0.1, 0.9], [0.1, 0.9], [0.02, 0.98], [0.15, 0.85], [0.26, 0.74], [0.07, 0.93], [0.002, 0.998], [0.02, 0.98], [0.1, 0.9], [0.03, 0.97], [0.08, 0.92]]).to(self.device1)
elif dataset == "babi":
self.alphasFR = torch.tensor([[0.60, 0.4], [0.57, 0.43], [0.6, 0.4], [0.41, 0.59]]).to(self.device1)
else:
self.alphasFR = torch.tensor([[0.5, 0.5]]*self.num_labels_FR)
classifiers_FR = []
self.criterion_FR = []
#TODO add batch
#TODO changed
for item in range(self.num_labels_FR):
classifiers_FR.append(nn.Linear(config.hidden_size, self.num_classes))
if criterion == "corss":
self.criterion_FR.append(nn.CrossEntropyLoss(weight=self.alphasFR[item]))
else:
self.criterion_FR.append(FocalLoss(alpha=self.alphasFR[item], class_num=self.num_classes, gamma = 2, size_average=True))
self.classifiers_FR = nn.ModuleList(classifiers_FR)
# self.sigma_FR = nn.Parameter(torch.ones(1))
# self.sigma = nn.Parameter(torch.ones(2))
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
# self.classifiers_FR.apply(weights_init_normal)
# self.classifiers_YN.apply(weights_init_normal)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
task = "YN",
multi_task = False
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
#select the cls token
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
# logits = []
out_logits = []
loss = 0
# TODO check teh batch siZe
# for ind, logit in enumerate(pooled_output):
if task == "YN":
for ind in range(self.num_labels_YN):
logit = self.classifiers_YN[ind](pooled_output)
if labels is not None:
loss += self.criterion_YN[ind](logit, labels[:, ind])
# logit = self.classifiers1[ind](pooled_output[ind])
# with torch.no_grad():
# logits.append(logit)
out_logits.append(self.softmax(logit))
# logit = self.classifiers1[ind](pooled_output[ind])
elif task == "FR":
for ind in range(self.num_labels_FR):
logit = self.classifiers_FR[ind](pooled_output)
if labels is not None:
loss += self.criterion_FR[ind](logit, labels[:, ind])
# logit = self.classifiers1[ind](pooled_output[ind])
# with torch.no_grad():
# logits.append(logit)
out_logits.append(self.softmax(logit))
# logits.append(logit.squeeze(0))
if labels is not None:
outputs = (loss, torch.stack(out_logits),) + outputs[2:]
else:
outputs = (None,torch.stack( out_logits),) + outputs[2:]
return outputs # (loss), reshaped_logits, (hidden_st
class BertMultiTaskBooleanQuestionLoad(BertPreTrainedModel):
def __init__(self, config,
device = 'cuda:0',
no_dropout = False,
num_labels_YN = None,
num_labels_FR = None,
dataset = "spartqa",
LM = "bert",
has_batch = False,
criterion = "focal"
):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
self.device1 = device
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.num_classes = 2
self.num_labels_YN = num_labels_YN
self.num_labels_FR = num_labels_FR
# self.qtype = qtype
if self.num_labels_YN:
if dataset == "spartqa":
self.alphasYN = torch.tensor([[0.5, 0.5], [0.25, 0.75], [0.27, 0.73] ]).to(self.device1)
elif dataset == "human":
self.alphasYN = torch.tensor([[0.5, 0.5], [0.6, 0.4], [0.1, 0.9] ]).to(self.device1)
elif dataset == "spartun":
self.alphasYN = torch.tensor([[0.55, 0.45], [0.45, 0.55]]).to(self.device1)
else:
self.alphasYN = torch.tensor([[0.5, 0.5]]*self.num_labels_YN).to(self.device1)
#initialize the classifier model
classifiers_YN = []
self.criterion_YN = []
for item in range(self.num_labels_YN):
classifiers_YN.append(nn.Linear(config.hidden_size, self.num_classes))
if criterion == "cross":
self.criterion_YN.append(nn.CrossEntropyLoss(weight=self.alphasYN[item]))
else:
self.criterion_YN.append(FocalLoss(alpha=self.alphasYN[item], class_num=self.num_classes, gamma = 2))
self.classifiers_YN_load = nn.ModuleList(classifiers_YN)
# self.sigma_YN = nn.Parameter(torch.ones(1))
if self.num_labels_FR:
if dataset == "spartqa":
self.alphasFR = torch.tensor([[0.20, 0.8], [0.20, 0.8], [0.25, 0.75], [0.4, 0.6], [0.1, 0.9], [0.02, 0.98], [0.02, 0.98]]).to(self.device1)
elif dataset == "human":
self.alphasFR = torch.tensor([[0.35, 0.65], [0.25, 0.75], [0.25, 0.75], [0.2, 0.8], [0.25, 0.75], [0.45, 0.55], [0.05, 0.95]]).to(self.device1)
elif dataset == "spartun":
self.alphasFR = torch.tensor([[0.1, 0.9], [0.1, 0.9], [0.27, 0.73], [0.26, 0.74], [0.1, 0.9], [0.1, 0.9], [0.02, 0.98], [0.15, 0.85], [0.26, 0.74], [0.07, 0.93], [0.002, 0.998], [0.02, 0.98], [0.1, 0.9], [0.03, 0.97], [0.08, 0.92]]).to(self.device1)
elif dataset == "babi":
self.alphasFR = torch.tensor([[0.60, 0.4], [0.57, 0.43], [0.6, 0.4], [0.41, 0.59]]).to(self.device1)
else:
self.alphasFR = torch.tensor([[0.5, 0.5]]*self.num_labels_FR)
classifiers_FR = []
self.criterion_FR = []
#TODO add batch
#TODO changed
for item in range(self.num_labels_FR):
classifiers_FR.append(nn.Linear(config.hidden_size, self.num_classes))
if criterion == "corss":
self.criterion_FR.append(nn.CrossEntropyLoss(weight=self.alphasFR[item]))
else:
self.criterion_FR.append(FocalLoss(alpha=self.alphasFR[item], class_num=self.num_classes, gamma = 2, size_average=True))
self.classifiers_FR_load = nn.ModuleList(classifiers_FR)
# self.sigma_FR = nn.Parameter(torch.ones(1))
# self.sigma = nn.Parameter(torch.ones(2))
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
# self.classifiers_FR.apply(weights_init_normal)
# self.classifiers_YN.apply(weights_init_normal)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
task = "YN",
multi_task = False
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
#select the cls token
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
# logits = []
out_logits = []
loss = 0
# TODO check teh batch siZe
# for ind, logit in enumerate(pooled_output):
if task == "YN":
for ind in range(self.num_labels_YN):
logit = self.classifiers_YN_load[ind](pooled_output)
if labels is not None:
loss += self.criterion_YN[ind](logit, labels[:, ind])
# logit = self.classifiers1[ind](pooled_output[ind])
# with torch.no_grad():
# logits.append(logit)
out_logits.append(self.softmax(logit))
# logit = self.classifiers1[ind](pooled_output[ind])
elif task == "FR":
for ind in range(self.num_labels_FR):
logit = self.classifiers_FR_load[ind](pooled_output)
if labels is not None:
loss += self.criterion_FR[ind](logit, labels[:, ind])
# logit = self.classifiers1[ind](pooled_output[ind])
# with torch.no_grad():
# logits.append(logit)
out_logits.append(self.softmax(logit))
# logits.append(logit.squeeze(0))
if labels is not None:
outputs = (loss, torch.stack(out_logits),) + outputs[2:]
else:
outputs = (None,torch.stack( out_logits),) + outputs[2:]
return outputs # (loss), reshaped_logits, (hidden_s
class BertForSpatialRelationExtraction(BertPreTrainedModel):
def __init__(self, config, num_labels = 12, device = 'cuda:0', no_dropout = False):
super().__init__(config)
if no_dropout:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
# self.device = device
self.num_labels = num_labels
self.num_classes = 2
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# self.classifier1 = nn.Linear(config.hidden_size, self.num_labels)
# self.classifier2 = nn.Linear(config.hidden_size, self.num_type_class)
# self.classifiers = nn.ModuleList([self.classifier1, self.classifier2])
self.alphas = torch.tensor([[0.5, 0.5]]*self.num_labels_FR)
classifiers = []
self.criterion = []
for item in range(self.num_labels):
classifiers.append(nn.Linear(config.hidden_size, self.num_classes))
# if criterion == "corss":
# self.criterion.append(nn.CrossEntropyLoss(weight=self.alphasFR[item]))
# else:
self.criterion.append(FocalLoss(alpha=self.alphas[item], class_num=self.num_classes, gamma = 2, size_average=True))
self.classifiers = nn.ModuleList(classifiers)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
loss = 0
out_logits = []
for ind in range(self.num_labels_FR):
logit = self.classifiers[ind](pooled_output)
if labels is not None:
loss += self.criterion[ind](logit, labels[:, ind])
out_logits.append(self.softmax(logit))
if labels is not None:
outputs = (loss, torch.stack(out_logits),) + outputs[2:]
else:
outputs = (None,torch.stack( out_logits),) + outputs[2:]
return outputs # (loss), reshaped_logits, (hidden_s
# if labels is not None:
# # label1 = labels[0].float()
# if self.num_labels == 1:
# # We are doing regression
# # loss_fct = MSELoss()
# loss_fct = BCELoss()
# out_logits1 = self.sigmoid(logits1)