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train.py
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from typing import overload
import torch
from numpy.lib import save
from util import Logger, accuracy, TotalMeter
import numpy as np
from pathlib import Path
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_recall_fscore_support
from util.prepossess import mixup_criterion, mixup_data
from util.loss import mixup_cluster_loss
from sklearn.metrics import roc_auc_score, confusion_matrix
from datetime import datetime
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class BasicTrain:
def __init__(self, train_config, model, optimizers, dataloaders, log_folder) -> None:
self.logger = Logger()
self.model = model.to(device)
self.train_dataloader, self.val_dataloader, self.test_dataloader = dataloaders
self.epochs = train_config['epochs']
self.optimizers = optimizers
self.best_acc = 0
self.best_model = None
self.best_acc_val = 0
self.best_auc_val = 0
self.loss_fn = torch.nn.CrossEntropyLoss(reduction='mean')
self.group_loss = train_config['group_loss']
self.sparsity_loss = train_config['sparsity_loss']
self.sparsity_loss_weight = train_config['sparsity_loss_weight']
self.save_path = log_folder
self.save_learnable_graph = True
self.init_meters()
def init_meters(self):
self.train_loss, self.val_loss, self.test_loss, self.train_accuracy, \
self.val_accuracy, self.test_accuracy, self.edges_num = [
TotalMeter() for _ in range(7)]
self.loss1, self.loss2, self.loss3 = [TotalMeter() for _ in range(3)]
def reset_meters(self):
for meter in [self.train_accuracy, self.val_accuracy, self.test_accuracy,
self.train_loss, self.val_loss, self.test_loss, self.edges_num,
self.loss1, self.loss2, self.loss3]:
meter.reset()
def train_per_epoch(self, optimizer):
self.model.train()
for data_in, pearson, label, _ in self.train_dataloader:
label = label.long()
data_in, pearson, label = data_in.to(
device), pearson.to(device), label.to(device)
inputs, nodes, targets_a, targets_b, lam = mixup_data(
data_in, pearson, label, 1, device)
output, learnable_matrix, edge_variance = self.model(inputs, nodes)
loss = 2 * mixup_criterion(
self.loss_fn, output, targets_a, targets_b, lam)
if self.group_loss:
loss += mixup_cluster_loss(learnable_matrix,
targets_a, targets_b, lam)
if self.sparsity_loss:
sparsity_loss = self.sparsity_loss_weight * \
torch.norm(learnable_matrix, p=1)
loss += sparsity_loss
self.train_loss.update_with_weight(loss.item(), label.shape[0])
optimizer.zero_grad()
loss.backward()
optimizer.step()
top1 = accuracy(output, label)[0]
self.train_accuracy.update_with_weight(top1, label.shape[0])
self.edges_num.update_with_weight(edge_variance, label.shape[0])
def test_per_epoch(self, dataloader, loss_meter, acc_meter):
labels = []
result = []
self.model.eval()
for data_in, pearson, label, _ in dataloader:
label = label.long()
data_in, pearson, label = data_in.to(
device), pearson.to(device), label.to(device)
output, _, _ = self.model(data_in, pearson)
loss = self.loss_fn(output, label)
loss_meter.update_with_weight(
loss.item(), label.shape[0])
top1 = accuracy(output, label)[0]
acc_meter.update_with_weight(top1, label.shape[0])
result += F.softmax(output, dim=1)[:, 1].tolist()
labels += label.tolist()
auc = roc_auc_score(labels, result)
result = np.array(result)
result[result > 0.5] = 1
result[result <= 0.5] = 0
metric = precision_recall_fscore_support(
labels, result, average='micro')
con_matrix = confusion_matrix(labels, result)
return [auc] + list(metric), con_matrix
def generate_save_learnable_matrix(self):
learable_matrixs = []
labels = []
for data_in, nodes, label, _ in self.test_dataloader:
label = label.long()
data_in, nodes, label = data_in.to(
device), nodes.to(device), label.to(device)
_, learable_matrix, _ = self.model(data_in, nodes)
learable_matrixs.append(learable_matrix.cpu().detach().numpy())
labels += label.tolist()
self.save_path.mkdir(exist_ok=True, parents=True)
np.save(self.save_path / "learnable_matrix.npy", {'matrix': np.vstack(
learable_matrixs), "label": np.array(labels)}, allow_pickle=True)
def save_result(self, results, txt):
self.save_path.mkdir(exist_ok=True, parents=True)
np.save(self.save_path/"training_process.npy",
results, allow_pickle=True)
with open(self.save_path / "training_info.txt", 'a', encoding='utf-8') as f:
f.write(txt)
torch.save(self.best_model.state_dict(), self.save_path/f"model_{self.best_acc}%.pt")
def train(self):
training_process = []
txt = ''
for epoch in range(self.epochs):
self.reset_meters()
self.train_per_epoch(self.optimizers[0])
val_result, _ = self.test_per_epoch(self.val_dataloader,
self.val_loss, self.val_accuracy)
test_result, con_matrix = self.test_per_epoch(self.test_dataloader,
self.test_loss, self.test_accuracy)
if self.best_acc <= self.test_accuracy.avg:
self.best_acc = self.test_accuracy.avg
self.best_model = self.model
if (con_matrix[0][0] + con_matrix[1][0]) != 0:
SEN = con_matrix[0][0] / (con_matrix[0][0] + con_matrix[1][0])
else:
SEN = 0
if (con_matrix[1][1] + con_matrix[0][1]) != 0:
SPE = con_matrix[1][1] / (con_matrix[1][1] + con_matrix[0][1])
else:
SPE = 0
self.logger.info(" | ".join([
f'Epoch[{epoch}/{self.epochs}]',
f'Train Loss:{self.train_loss.avg: .3f}',
f'Train ACC:{self.train_accuracy.avg: .3f}%',
f'Val ACC:{self.val_accuracy.avg: .2f}%',
f'Val AUC:{val_result[0]:.2f}',
f'Test ACC:{self.test_accuracy.avg: .2f}%',
f'Test AUC:{test_result[0]:.4f}',
f'Test SEN:{SEN:.4f}',
f'Test SPE:{SPE:.4f}',
f'Test F1:{test_result[-4]:.4f}',
]))
txt += f'Epoch[{epoch}/{self.epochs}] '+f'Train Loss:{self.train_loss.avg: .3f} '+f'Train ACC:{self.train_accuracy.avg: .3f}% '+f'Val ACC:{self.val_accuracy.avg: .3f}% '+ f'Val AUC:{val_result[0]:.3f} '+f'Test ACC:{self.test_accuracy.avg: .3f}% '+f'Test AUC:{test_result[0]:.4f} '+f'Test SEN:{SEN:.4f} '+f'Test SPE:{SPE:.4f} '+f'Test F1:{test_result[-4]:.4f}'+'\n'
training_process.append([self.train_accuracy.avg, self.train_loss.avg,
self.val_loss.avg, self.test_loss.avg]
+ val_result + test_result)
now = datetime.now()
date_time = now.strftime("%m-%d-%H-%M-%S")
self.save_path = self.save_path/Path(f"{self.best_acc: .3f}%_{date_time}")
self.logger.info(" | ".join([
f'Best_ACC[{self.best_acc}]'
]))
if self.save_learnable_graph:
self.generate_save_learnable_matrix()
self.save_result(training_process, txt)