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import numpy as np
import torch
import torch.nn as nn
from transformers import AdamW, get_linear_schedule_with_warmup
device='cuda'
import random
import time
import torch.nn as nn
# Specify loss function
loss_fn = nn.CrossEntropyLoss()
class PretrainedBert(nn.Module):
"""Bert Model for Classification Tasks.
"""
def __init__(self, freeze_bert=False):
"""
@param bert: a BertModel object
@param classifier: a torch.nn.Module classifier
@param freeze_bert (bool): Set `False` to fine-tune the BERT model
"""
super(PretrainedBert, self).__init__()
# Specify hidden size of BERT, hidden size of our classifier, and number of labels
D_in, H, D_out = 768, 50, 14
# Instantiate BERT model
from transformers import BertConfig
config = BertConfig(
# we align this to the tokenizer vocab_size
max_position_embeddings=5000,
hidden_size=768,
num_attention_heads=2,
num_hidden_layers=2,
type_vocab_size=1
)
from transformers import BertForMaskedLM
self.bert =BertModel(config)
# Instantiate an one-layer feed-forward classifier
self.classifier = nn.Sequential(
nn.Linear(D_in, H),
nn.ReLU(),
#nn.Dropout(0.5),
nn.Linear(H, D_out)
)
# Freeze the BERT model
if freeze_bert:
for param in self.bert.parameters():
param.requires_grad = False
def forward(self, input_ids, attention_mask):
"""
Feed input to BERT and the classifier to compute logits.
@param input_ids (torch.Tensor): an input tensor with shape (batch_size,
max_length)
@param attention_mask (torch.Tensor): a tensor that hold attention mask
information with shape (batch_size, max_length)
@return logits (torch.Tensor): an output tensor with shape (batch_size,
num_labels)
"""
# Feed input to BERT
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask)
# Extract the last hidden state of the token `[CLS]` for classification task
last_hidden_state_cls = outputs[0][:, 0, :]
# Feed input to classifier to compute logits
logits = self.classifier(last_hidden_state_cls)
return logits
from transformers import AdamW, get_linear_schedule_with_warmup
device='cuda'
def valid_evaluate(model, val_dataloader):
"""After the completion of each training epoch, measure the model's performance
on our validation set.
"""
# Put the model into the evaluation mode. The dropout layers are disabled during
# the test time.
model.eval()
# Tracking variables
val_accuracy = []
val_loss = []
# For each batch in our validation set...
for batch in val_dataloader:
# Load batch to GPU
b_input_ids, b_attn_mask, b_labels = tuple(t.to(device) for t in batch)
# Compute logits
with torch.no_grad():
logits = model(b_input_ids, b_attn_mask)
# Compute loss
loss = loss_fn(logits, b_labels)
val_loss.append(loss.item())
# Get the predictions
preds = torch.argmax(logits, dim=1).flatten()
# Calculate the accuracy rate
accuracy = (preds == b_labels).cpu().numpy().mean() * 100
val_accuracy.append(accuracy)
# Compute the average accuracy and loss over the validation set.
val_loss = np.mean(val_loss)
val_accuracy = np.mean(val_accuracy)
return val_loss, val_accuracy
import torch
import torch.nn as nn
from transformers import BertModel
# Create the BertClassfier class
class FinetunningBert(nn.Module):
"""Bert Model for Classification Tasks.
"""
def __init__(self, freeze_bert=False):
"""
@param bert: a BertModel object
@param classifier: a torch.nn.Module classifier
@param freeze_bert (bool): Set `False` to fine-tune the BERT model
"""
super(FinetunningBert, self).__init__()
# Specify hidden size of BERT, hidden size of our classifier, and number of labels
D_in, H, D_out = 768, 50, 7
# Instantiate BERT model
from transformers import BertConfig
from transformers import BertForMaskedLM
bert_classifier = PretrainedBert(freeze_bert=False)
bert_classifier.load_state_dict(torch.load('/home/user/app/virBERT.pt'))
self.bert =bert_classifier.bert.to(device)
# Instantiate an one-layer feed-forward classifier
self.classifier = nn.Sequential(
nn.Linear(D_in, H),
nn.ReLU(),
#nn.Dropout(0.5),
nn.Linear(H, D_out)
)
# Freeze the BERT model
if freeze_bert:
for param in self.bert.parameters():
param.requires_grad = False
def forward(self, input_ids, attention_mask):
"""
Feed input to BERT and the classifier to compute logits.
@param input_ids (torch.Tensor): an input tensor with shape (batch_size,
max_length)
@param attention_mask (torch.Tensor): a tensor that hold attention mask
information with shape (batch_size, max_length)
@return logits (torch.Tensor): an output tensor with shape (batch_size,
num_labels)
"""
# Feed input to BERT
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask)
# Extract the last hidden state of the token `[CLS]` for classification task
last_hidden_state_cls = outputs[0][:, 0, :]
# Feed input to classifier to compute logits
logits = self.classifier(last_hidden_state_cls)
return logits
from transformers import AdamW, get_linear_schedule_with_warmup
device='cuda'
def initialize_finetunningBert(train_dataloader,epochs=4):
"""Initialize the Bert Classifier, the optimizer and the learning rate scheduler.
"""
# Instantiate Bert Classifier
bert_classifier = FinetunningBert(freeze_bert=False)
# Tell PyTorch to run the model on GPU
bert_classifier.to(device)
# Create the optimizer
optimizer = AdamW(bert_classifier.parameters(),
lr=5e-5, # Default learning rate
eps=1e-8 # Default epsilon value
)
# Total number of training steps
total_steps = len(train_dataloader) * epochs
# Set up the learning rate scheduler
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=0, # Default value
num_training_steps=total_steps)
return bert_classifier, optimizer, scheduler
import random
import time
import torch.nn as nn
# Specify loss function
loss_fn = nn.CrossEntropyLoss()
def finetunningBert_training(model, optimizer, scheduler, train_dataloader, val_dataloader=None, epochs=4, evaluation=False):
"""Train the BertClassifier model.
"""
# Start training loop
print("Start training...\n")
for epoch_i in range(epochs):
# =======================================
# Training
# =======================================
# Print the header of the result table
print(f"{'Epoch':^7} | {'Batch':^7} | {'Train Loss':^12} | {'Val Loss':^10} | {'Val Acc':^9} | {'Elapsed':^9}")
print("-"*70)
# Measure the elapsed time of each epoch
t0_epoch, t0_batch = time.time(), time.time()
# Reset tracking variables at the beginning of each epoch
total_loss, batch_loss, batch_counts = 0, 0, 0
# Put the model into the training mode
model.train()
# For each batch of training data...
for step, batch in enumerate(train_dataloader):
batch_counts +=1
# Load batch to GPU
b_input_ids, b_attn_mask, b_labels = tuple(t.to(device) for t in batch)
# Zero out any previously calculated gradients
model.zero_grad()
# Perform a forward pass. This will return logits.
logits = model(b_input_ids, b_attn_mask)
# Compute loss and accumulate the loss values
loss = loss_fn(logits, b_labels)
batch_loss += loss.item()
total_loss += loss.item()
# Perform a backward pass to calculate gradients
loss.backward()
# Clip the norm of the gradients to 1.0 to prevent "exploding gradients"
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and the learning rate
optimizer.step()
scheduler.step()
# Print the loss values and time elapsed for every 20 batches
if (step % 20 == 0 and step != 0) or (step == len(train_dataloader) - 1):
# Calculate time elapsed for 20 batches
time_elapsed = time.time() - t0_batch
# Print training results
print(f"{epoch_i + 1:^7} | {step:^7} | {batch_loss / batch_counts:^12.6f} | {'-':^10} | {'-':^9} | {time_elapsed:^9.2f}")
# Reset batch tracking variables
batch_loss, batch_counts = 0, 0
t0_batch = time.time()
# Calculate the average loss over the entire training data
avg_train_loss = total_loss / len(train_dataloader)
torch.save(model.state_dict(), '{}model.pt'.format("VirDNA4Baltimore"))
print("-"*70)
# =======================================
# Evaluation
# =======================================
if evaluation == True:
# After the completion of each training epoch, measure the model's performance
# on our validation set.
val_loss, val_accuracy = valid_evaluate(model, val_dataloader)
# Print performance over the entire training data
time_elapsed = time.time() - t0_epoch
print(f"{epoch_i + 1:^7} | {'-':^7} | {avg_train_loss:^12.6f} | {val_loss:^10.6f} | {val_accuracy:^9.2f} | {time_elapsed:^9.2f}")
print("-"*70)
print("\n")
print("Training complete!")
def bertPredictions(torch,model, val_dataloader):
"""After the completion of each training epoch, measure the model's performance
on our validation set.
"""
# Put the model into the evaluation mode. The dropout layers are disabled during
# the test time.
model.eval()
device = 0
print("working3")
# Tracking variables
val_accuracy = []
val_loss = []
pred=[]
actual=[]
# For each batch in our validation set...
for batch in val_dataloader:
device = 0
# Load batch to GPU
b_input_ids, b_attn_mask, b_labels = tuple(t for t in batch)
# Compute logits
with torch.no_grad():
logits = model(b_input_ids, b_attn_mask)
# Compute loss
#loss = loss_fn(logits, b_labels)
#val_loss.append(loss.item())
# Get the predictions
preds = torch.argmax(logits, dim=1).flatten()
# Calculate the accuracy rate
#accuracy = (preds == b_labels).cpu().numpy().mean() * 100
#val_accuracy.append(accuracy)
pred.append(preds.cpu())
#actual.append(b_labels.cpu())
# Compute the average accuracy and loss over the validation set.
#val_loss = np.mean(val_loss)
#val_accuracy = np.mean(val_accuracy)
return pred
import torch
import torch.nn as nn
from transformers import BertModel
# Create the BertClassfier class
class ScratchBert(nn.Module):
"""Bert Model for Classification Tasks.
"""
def __init__(self, freeze_bert=False):
"""
@param bert: a BertModel object
@param classifier: a torch.nn.Module classifier
@param freeze_bert (bool): Set `False` to fine-tune the BERT model
"""
super(ScratchBert, self).__init__()
# Specify hidden size of BERT, hidden size of our classifier, and number of labels
D_in, H, D_out = 768, 50, 2
# Instantiate BERT model
from transformers import BertConfig
config = BertConfig(
# we align this to the tokenizer vocab_size
max_position_embeddings=5000,
hidden_size=768,
num_attention_heads=2,
num_hidden_layers=2,
type_vocab_size=1
)
from transformers import BertForMaskedLM
self.bert =BertModel(config)
# Instantiate an one-layer feed-forward classifier
self.classifier = nn.Sequential(
nn.Linear(D_in, H),
nn.ReLU(),
#nn.Dropout(0.5),
nn.Linear(H, D_out)
)
# Freeze the BERT model
if freeze_bert:
for param in self.bert.parameters():
param.requires_grad = False
def forward(self, input_ids, attention_mask):
"""
Feed input to BERT and the classifier to compute logits.
@param input_ids (torch.Tensor): an input tensor with shape (batch_size,
max_length)
@param attention_mask (torch.Tensor): a tensor that hold attention mask
information with shape (batch_size, max_length)
@return logits (torch.Tensor): an output tensor with shape (batch_size,
num_labels)
"""
# Feed input to BERT
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask)
# Extract the last hidden state of the token `[CLS]` for classification task
last_hidden_state_cls = outputs[0][:, 0, :]
# Feed input to classifier to compute logits
logits = self.classifier(last_hidden_state_cls)
return logits
from transformers import AdamW, get_linear_schedule_with_warmup
device='cuda'
def initialize_model(train_dataloader,epochs=4):
"""Initialize the Bert Classifier, the optimizer and the learning rate scheduler.
"""
# Instantiate Bert Classifier
bert_classifier = ScratchBert(freeze_bert=False)
# Tell PyTorch to run the model on GPU
bert_classifier.to(device)
# Create the optimizer
optimizer = AdamW(bert_classifier.parameters(),
lr=5e-5, # Default learning rate
eps=1e-8 # Default epsilon value
)
# Total number of training steps
total_steps = len(train_dataloader) * epochs
# Set up the learning rate scheduler
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=0, # Default value
num_training_steps=total_steps)
return bert_classifier, optimizer, scheduler
import random
import time
import torch.nn as nn
# Specify loss function
loss_fn = nn.CrossEntropyLoss()
def train(model,optimizer, scheduler, train_dataloader, val_dataloader=None, epochs=4, evaluation=False):
"""Train the BertClassifier model.
"""
# Start training loop
print("Start training...\n")
for epoch_i in range(epochs):
# =======================================
# Training
# =======================================
# Print the header of the result table
print(f"{'Epoch':^7} | {'Batch':^7} | {'Train Loss':^12} | {'Val Loss':^10} | {'Val Acc':^9} | {'Elapsed':^9}")
print("-"*70)
# Measure the elapsed time of each epoch
t0_epoch, t0_batch = time.time(), time.time()
# Reset tracking variables at the beginning of each epoch
total_loss, batch_loss, batch_counts = 0, 0, 0
# Put the model into the training mode
model.train()
# For each batch of training data...
for step, batch in enumerate(train_dataloader):
batch_counts +=1
# Load batch to GPU
b_input_ids, b_attn_mask, b_labels = tuple(t.to(device) for t in batch)
# Zero out any previously calculated gradients
model.zero_grad()
# Perform a forward pass. This will return logits.
logits = model(b_input_ids, b_attn_mask)
# Compute loss and accumulate the loss values
loss = loss_fn(logits, b_labels)
batch_loss += loss.item()
total_loss += loss.item()
# Perform a backward pass to calculate gradients
loss.backward()
# Clip the norm of the gradients to 1.0 to prevent "exploding gradients"
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and the learning rate
optimizer.step()
scheduler.step()
# Print the loss values and time elapsed for every 20 batches
if (step % 20 == 0 and step != 0) or (step == len(train_dataloader) - 1):
# Calculate time elapsed for 20 batches
time_elapsed = time.time() - t0_batch
# Print training results
print(f"{epoch_i + 1:^7} | {step:^7} | {batch_loss / batch_counts:^12.6f} | {'-':^10} | {'-':^9} | {time_elapsed:^9.2f}")
# Reset batch tracking variables
batch_loss, batch_counts = 0, 0
t0_batch = time.time()
# Calculate the average loss over the entire training data
avg_train_loss = total_loss / len(train_dataloader)
torch.save(model.state_dict(), '{}model.pt'.format("VirDNA"))
print("-"*70)
# =======================================
# Evaluation
# =======================================
if evaluation == True:
# After the completion of each training epoch, measure the model's performance
# on our validation set.
val_loss, val_accuracy = valid_evaluate(model, val_dataloader)
# Print performance over the entire training data
time_elapsed = time.time() - t0_epoch
print(f"{epoch_i + 1:^7} | {'-':^7} | {avg_train_loss:^12.6f} | {val_loss:^10.6f} | {val_accuracy:^9.2f} | {time_elapsed:^9.2f}")
print("-"*70)
print("\n")
print("Training complete!") |