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import torch
import torch.nn as nn
from time import time
import os
from torch.utils.data import DataLoader
from UltraFlow import dataset, commons, losses
import numpy as np
import pandas as pd
class DefaultRunner(object):
def __init__(self,train_set, val_set, test_set, model, optimizer, scheduler, config):
self.train_set = train_set
self.val_set = val_set
self.test_set = test_set
self.config = config
self.device = config.train.device
self.batch_size = self.config.train.batch_size
self._model = model
self._optimizer = optimizer
self._scheduler = scheduler
self.best_matric = -1
self.start_epoch = 0
if self.device.type == 'cuda':
self._model = self._model.cuda(self.device)
self.get_loss_fn()
if self.config.train.ranking_loss in ['pairwise_v1', 'pairwise_v2']:
self.use_collate_fn = dataset.collate_affinity_pair_wise
else:
self.use_collate_fn = dataset.collate_affinity
def save(self, checkpoint, epoch=None, var_list={}):
state = {
**var_list,
"model": self._model.state_dict(),
"optimizer": self._optimizer.state_dict(),
"scheduler": self._scheduler.state_dict(),
"config": self.config
}
epoch = str(epoch) if epoch is not None else ''
checkpoint = os.path.join(checkpoint, 'checkpoint%s' % epoch)
torch.save(state, checkpoint)
def load(self, checkpoint, epoch=None, load_optimizer=False, load_scheduler=False):
epoch = str(epoch) if epoch is not None else ''
checkpoint = os.path.join(checkpoint, 'checkpoint%s' % epoch)
print("Load checkpoint from %s" % checkpoint)
state = torch.load(checkpoint, map_location=self.device)
self._model.load_state_dict(state["model"])
#self._model.load_state_dict(state["model"], strict=False)
self.best_matric = state['best_matric']
self.start_epoch = state['cur_epoch'] + 1
if load_optimizer:
self._optimizer.load_state_dict(state["optimizer"])
if self.device.type == 'cuda':
for state in self._optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda(self.device)
if load_scheduler:
self._scheduler.load_state_dict(state["scheduler"])
def get_loss_fn(self):
self.loss_fn = nn.MSELoss()
if self.config.train.ranking_loss == 'pairwise_v1':
self.ranking_fn = losses.pair_wise_ranking_loss().to(self.device)
elif self.config.train.ranking_loss == 'pairwise_v2':
self.ranking_fn = losses.pair_wise_ranking_loss_v2(self.config).to(self.device)
def trans_device(self,batch):
return [x if isinstance(x, list) else x.to(self.device) for x in batch]
@torch.no_grad()
def evaluate(self, split, verbose=0, logger=None, visualize=True):
"""
Evaluate the model.
Parameters:
split (str): split to evaluate. Can be ``train``, ``val`` or ``test``.
"""
if split not in ['train', 'val', 'test']:
raise ValueError('split should be either train, val, or test.')
test_set = getattr(self, "%s_set" % split)
dataloader = DataLoader(test_set, batch_size=self.config.train.batch_size,
shuffle=False, collate_fn=dataset.collate_affinity,
num_workers=self.config.train.num_workers)
y_preds = torch.tensor([]).to(self.device)
y = torch.tensor([]).to(self.device)
eval_start = time()
model = self._model
model.eval()
for batch in dataloader:
if self.device.type == "cuda":
batch = self.trans_device(batch)
y_pred, x_output = model(batch)
y_preds = torch.cat([y_preds, y_pred])
y = torch.cat([y, batch[-2]])
np_y = np.array(y.cpu())
np_f = np.array(y_preds.cpu())
metics_dict = commons.get_sbap_matric_dict(np_y,np_f)
result_str = commons.get_matric_output_str(metics_dict)
result_str += 'Time: %.4f'%(time() - eval_start)
if verbose:
if logger is not None:
logger.info(result_str)
else:
print(result_str)
if visualize:
result_d = {'pred_y': np_f.flatten().tolist(), 'y': np_y.flatten().tolist()}
pd.DataFrame(result_d).to_csv(os.path.join(self.config.train.save_path, 'pred_values_pw_2.csv'))
return metics_dict['Spearman']
def train(self, verbose=1):
self.logger = self.config.logger
self.logger.info(self.config)
train_start = time()
num_epochs = self.config.train.epochs
dataloader = DataLoader(self.train_set, batch_size=self.config.train.batch_size,
shuffle=self.config.train.shuffle, collate_fn=self.use_collate_fn,
num_workers=self.config.train.num_workers)
model = self._model
self.logger.info('trainable params in model: {:.2f}M'.format( sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6))
train_losses = []
val_matric = []
best_matric = self.best_matric
start_epoch = self.start_epoch
print('start training...')
early_stop = 0
pair_wise_sample_num_total = [0] * len(self.train_set)
for epoch in range(num_epochs):
# train
model.train()
epoch_start = time()
batch_losses, batch_ranking_losses = [], []
batch_cnt = 0
pair_wise_sample_num_epoch = [0] * len(self.train_set)
for batch in dataloader:
batch_cnt += 1
if self.device.type == "cuda":
batch = self.trans_device(batch)
y_pred, x_output = model(batch)
loss = self.loss_fn(y_pred,batch[-2])
if self.config.train.ranking_loss is not None:
if self.config.train.ranking_loss == 'pairwise_v1':
ranking_loss = self.ranking_fn(y_pred, batch[-2])
elif self.config.train.ranking_loss == 'pairwise_v2':
ranking_loss = self.ranking_fn(x_output, batch[-2])
batch_ranking_losses.append(ranking_loss.item())
loss += self.config.train.ranking_loss_lambda * ranking_loss
if not loss.requires_grad:
raise RuntimeError("loss doesn't require grad")
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
batch_losses.append(loss.item())
for index in batch[-1]:
pair_wise_sample_num_epoch[index] += 1
pair_wise_sample_num_total[index] += 1
train_losses.append(sum(batch_losses))
if verbose:
self.logger.info('Epoch: %d | Train Loss: %.4f | Ranking Loss: %.4f | Lr: %.4f | Time: %.4f' % (
epoch + start_epoch, sum(batch_losses), sum(batch_ranking_losses), self._optimizer.param_groups[0]['lr'], time() - epoch_start))
# evaluate
if self.config.train.eval:
eval_rmse = self.evaluate('val', verbose=1, logger=self.logger)
val_matric.append(eval_rmse)
if self.config.train.scheduler.type == "plateau":
self._scheduler.step(train_losses[-1])
else:
self._scheduler.step()
if val_matric[-1] > best_matric:
early_stop = 0
best_matric = val_matric[-1]
if self.config.train.save:
print('saving checkpoint')
val_list = {
'cur_epoch': epoch + start_epoch,
'best_matric': best_matric,
}
self.save(self.config.train.save_path, epoch + start_epoch, val_list)
test_spearman = self.evaluate('test', verbose=1, logger=self.logger)
else:
early_stop += 1
if early_stop >= self.config.train.early_stop:
break
# record sample times
if self.config.train.ranking_loss in ['pairwise_v1', 'pairwise_v2']:
data_sample_epoch_d = {'data_index':list(range(len(self.train_set))),'sample_times':pair_wise_sample_num_epoch}
data_sample_total_d = {'data_index':list(range(len(self.train_set))),'sample_times':pair_wise_sample_num_total}
pd.DataFrame(data_sample_epoch_d).to_csv(os.path.join(self.config.train.save_path, f'epoch_{epoch + start_epoch}_sample_times.csv'))
pd.DataFrame(data_sample_total_d).to_csv(os.path.join(self.config.train.save_path, f'total_sample_times.csv'))
self.best_matric = best_matric
self.start_epoch = start_epoch + num_epochs
print('optimization finished.')
print('Total time elapsed: %.5fs' % (time() - train_start))
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