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fine_tuning.py
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fine_tuning.py
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from evaluation_utils import evaluate_target_regression_epoch, model_save_check
from collections import defaultdict
from itertools import chain
from mlp import MLP
from multi_out_mlp import MoMLP
from encoder_decoder import EncoderDecoder
from torch.nn import functional as F
from loss_and_metrics import masked_mse, masked_simse
import os
import torch
def classification_train_step(model, batch, loss_fn, device, optimizer, history, scheduler=None, clip=None):
model.zero_grad()
model.train()
x = batch[0].to(device)
y = batch[1].to(device)
loss = loss_fn(model(x), y.double().unsqueeze(1))
optimizer.zero_grad()
loss.backward()
if clip is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
if scheduler is not None:
scheduler.step()
history['bce'].append(loss.cpu().detach().item())
return history
def regression_train_step(model, batch, device, optimizer, history, scheduler=None, clip=None):
# gc.collect()
# torch.cuda.empty_cache()
model.zero_grad()
model.train()
x = batch[0].to(device)
y = batch[1].to(device)
# mse_loss = sum([F.mse_loss(torch.where(torch.isnan(y[i, :]), torch.zeros_like(y[i, :]), y[i, :]),
# torch.where(torch.isnan(y[i, :]), torch.zeros_like(y[i, :]), model(x)[i, :]))
# for i in range(y.shape[0])])
# penalty_term = sum([torch.square(torch.sum(
# torch.where(torch.isnan(y[i, :]), torch.zeros_like(y[i, :]), y[i, :]) -
# torch.where(torch.isnan(y[i, :]), torch.zeros_like(y[i, :]), model(x)[i, :]))) / torch.square(
# (~torch.isnan(y[i, :])).sum())
# for i in range(y.shape[0])])
#
# loss = (mse_loss-penalty_term) / y.shape[0]
loss = masked_simse(preds=model(x), labels=y)
optimizer.zero_grad()
loss.backward()
if clip is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
# with torch.no_grad():
# mask_module_indices = [i for i in range(len(list(model.decoder.modules())))
# if str(list(model.decoder.modules())[i]).startswith('MaskedLinear')]
# for index in mask_module_indices:
# list(model.decoder.modules())[index].linear.weight.grad.mul_(list(model.decoder.modules())[index].mask)
optimizer.step()
if scheduler is not None:
scheduler.step()
history['loss'].append(loss.cpu().detach().item())
return history
def fine_tune_encoder(encoder, train_dataloader, val_dataloader, seed, task_save_folder, test_dataloader=None,
metric_name='cpearsonr',
normalize_flag=False, **kwargs):
target_decoder = MoMLP(input_dim=kwargs['latent_dim'],
output_dim=kwargs['output_dim'],
hidden_dims=kwargs['regressor_hidden_dims'],
out_fn=torch.nn.Sigmoid).to(kwargs['device'])
target_regressor = EncoderDecoder(encoder=encoder,
decoder=target_decoder,
normalize_flag=normalize_flag).to(kwargs['device'])
target_regression_train_history = defaultdict(list)
target_regression_eval_train_history = defaultdict(list)
target_regression_eval_val_history = defaultdict(list)
target_regression_eval_test_history = defaultdict(list)
encoder_module_indices = [i for i in range(len(list(encoder.modules())))
if str(list(encoder.modules())[i]).startswith('Linear')]
reset_count = 1
lr = kwargs['lr']
target_regression_params = [target_regressor.decoder.parameters()]
target_regression_optimizer = torch.optim.AdamW(chain(*target_regression_params),
lr=lr)
for epoch in range(kwargs['train_num_epochs']):
if epoch % 50 == 0:
print(f'Fine tuning epoch {epoch}')
for step, batch in enumerate(train_dataloader):
target_regression_train_history = regression_train_step(model=target_regressor,
batch=batch,
device=kwargs['device'],
optimizer=target_regression_optimizer,
history=target_regression_train_history)
target_regression_eval_train_history = evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=train_dataloader,
device=kwargs['device'],
history=target_regression_eval_train_history)
target_regression_eval_val_history = evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=val_dataloader,
device=kwargs['device'],
history=target_regression_eval_val_history)
if test_dataloader is not None:
target_regression_eval_test_history = evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=test_dataloader,
device=kwargs['device'],
history=target_regression_eval_test_history)
save_flag, stop_flag = model_save_check(history=target_regression_eval_val_history,
metric_name=metric_name,
tolerance_count=10,
reset_count=reset_count)
if save_flag:
torch.save(target_regressor.state_dict(),
os.path.join(task_save_folder, f'target_regressor_{seed}.pt'))
torch.save(target_regressor.encoder.state_dict(),
os.path.join(task_save_folder, f'ft_encoder_{seed}.pt'))
if stop_flag:
try:
ind = encoder_module_indices.pop()
print(f'Unfreezing {epoch}')
target_regressor.load_state_dict(
torch.load(os.path.join(task_save_folder, f'target_regressor_{seed}.pt')))
target_regression_params.append(list(target_regressor.encoder.modules())[ind].parameters())
lr = lr * kwargs['decay_coefficient']
target_regression_optimizer = torch.optim.AdamW(chain(*target_regression_params), lr=lr)
reset_count += 1
except IndexError:
break
target_regressor.load_state_dict(
torch.load(os.path.join(task_save_folder, f'target_regressor_{seed}.pt')))
evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=val_dataloader,
device=kwargs['device'],
history=None,
seed=seed,
cv_flag=True,
output_folder=kwargs['model_save_folder'])
evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=test_dataloader,
device=kwargs['device'],
history=None,
seed=seed,
output_folder=kwargs['model_save_folder'])
return target_regressor, (target_regression_train_history, target_regression_eval_train_history,
target_regression_eval_val_history, target_regression_eval_test_history)
def fine_tune_encoder_new(encoder, train_dataloader, val_dataloader, seed, task_save_folder, test_dataloader=None,
normalize_flag=False, **kwargs):
target_decoder = MoMLP(input_dim=kwargs['latent_dim'],
output_dim=kwargs['output_dim'],
hidden_dims=kwargs['regressor_hidden_dims'],
out_fn=torch.nn.Sigmoid).to(kwargs['device'])
target_regressor = EncoderDecoder(encoder=encoder,
decoder=target_decoder,
normalize_flag=normalize_flag).to(kwargs['device'])
target_regression_train_history = defaultdict(list)
target_regression_eval_train_history = defaultdict(list)
target_regression_eval_val_history = defaultdict(list)
target_regression_eval_test_history = defaultdict(list)
encoder_module_indices = [i for i in range(len(list(encoder.modules())))
if str(list(encoder.modules())[i]).startswith('Linear')]
lr = kwargs['lr']
target_regression_params = [target_regressor.decoder.parameters()]
target_regression_optimizer = torch.optim.AdamW(chain(*target_regression_params),
lr=lr)
gu_flag = True
for epoch in range(kwargs['train_num_epochs']):
if epoch % 50 == 0:
print(f'Fine tuning epoch {epoch}')
for step, batch in enumerate(train_dataloader):
target_regression_train_history = regression_train_step(model=target_regressor,
batch=batch,
device=kwargs['device'],
optimizer=target_regression_optimizer,
history=target_regression_train_history)
target_regression_eval_train_history = evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=train_dataloader,
device=kwargs['device'],
history=target_regression_eval_train_history)
target_regression_eval_val_history = evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=val_dataloader,
device=kwargs['device'],
history=target_regression_eval_val_history)
if test_dataloader is not None:
target_regression_eval_test_history = evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=test_dataloader,
device=kwargs['device'],
history=target_regression_eval_test_history)
if epoch >= 0.5 * kwargs['train_num_epochs'] and epoch % 10 == 0 and gu_flag:
try:
ind = encoder_module_indices.pop()
print(f'Unfreezing {epoch}')
target_regression_params.append(list(target_regressor.encoder.modules())[ind].parameters())
lr = lr * kwargs['decay_coefficient']
target_regression_optimizer = torch.optim.AdamW(chain(*target_regression_params), lr=lr)
except IndexError:
gu_flag = False
torch.save(target_regressor.state_dict(),
os.path.join(task_save_folder, f'target_regressor_{seed}.pt'))
evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=val_dataloader,
device=kwargs['device'],
history=None,
seed=seed,
cv_flag=True,
output_folder=kwargs['model_save_folder'])
if test_dataloader is not None:
evaluate_target_regression_epoch(regressor=target_regressor,
dataloader=test_dataloader,
device=kwargs['device'],
history=None,
seed=seed,
output_folder=kwargs['model_save_folder'])
return target_regressor, (target_regression_train_history, target_regression_eval_train_history,
target_regression_eval_val_history, target_regression_eval_test_history)