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from sklearn.metrics import classification_report, f1_score, accuracy_score
import wandb
import time
import copy
import torchvision
import matplotlib.pyplot as plt
import numpy as np
class Trainer:
def __init__(self, method, model, criterion, optimizer, dataloaders, target_names, output_folders, num_epochs):
self.method = method
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.dataloaders = dataloaders
self.target_names = target_names
self.output_folders = output_folders
self.num_epochs = num_epochs
def training_loop(self):
train_loss_all = []
self.optimizer.zero_grad()
for i, (inputs, labels) in enumerate(self.dataloaders['train']):
self.optimizer.zero_grad()
inputs = torch.cat([inputs[0], inputs[1]], dim=0)
#print(inputs.shape)
inputs, labels = inputs.cuda(), labels.long().cuda()
bsz = labels.shape[0]
features = self.model(inputs)
f1, f2 = torch.split(features, [bsz, bsz], dim=0)
features = torch.cat([f1.unsqueeze(1), f2.unsqueeze(1)], dim=1)
if self.method == 'SupCon':
train_loss = self.criterion(features, labels)
elif self.method == 'SimCLR':
train_loss = self.criterion(features)
else:
raise ValueError('contrastive method not supported: {}'.
format(self.method))
train_loss_all.append(train_loss.cpu().data.item())
train_loss.backward()
self.optimizer.step()
return train_loss_all
def validation_loop(self):
validation_loss_all = []
self.model.eval()
with torch.no_grad():
for inputs_v, labels_v in self.dataloaders['valid']:
inputs_v = torch.cat([inputs_v[0], inputs_v[1]], dim=0)
inputs_v, labels_v = inputs_v.cuda(), labels_v.long().cuda()
bsz_v = labels_v.shape[0]
features_v = self.model(inputs_v)
f1_v, f2_v = torch.split(features_v, [bsz_v, bsz_v], dim=0)
features_v = torch.cat([f1_v.unsqueeze(1), f2_v.unsqueeze(1)], dim=1)
if self.method == 'SupCon':
validation_loss = self.criterion(features_v, labels_v)
elif self.method == 'SimCLR':
validation_loss = self.criterion(features_v)
else:
raise ValueError('contrastive method not supported: {}'.
format(self.method))
validation_loss_all.append(validation_loss.cpu().data.item())
return validation_loss_all
def main_loop(self):
training_losses = []
for epoch in range(self.num_epochs):
start = time.time()
train_loss_all = self.training_loop()
train_loss_avg = (sum(train_loss_all) / len(train_loss_all))
training_losses.append(train_loss_avg)
wandb.log({
"Train Loss": train_loss_avg})
end = time.time()
print('Epoch: {}. '
'Time: {:.2f} '
'train_loss: {:.4f} '.format(epoch,
end - start,
train_loss_avg
))
if train_loss_avg <= min(training_losses):
state = {
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'epoch': epoch,
}
torch.save(state, self.output_folders + 'least_training_loss_epoch.pth')
del state
wandb.run.summary["Least Train Loss"] = train_loss_avg
state = {
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'epoch': epoch,
}
torch.save(state, self.output_folders + 'last_epoch.pth')
return
class SupervisedTrainer:
def __init__(self, device, method, model, classifier, criterion, optimizer, dataloaders, target_names,
output_folders, num_epochs):
self.device = device
self.method = method
self.model = model
self.classifier = classifier
self.criterion = criterion
self.optimizer = optimizer
self.dataloaders = dataloaders
self.target_names = target_names
self.output_folders = output_folders
self.num_epochs = num_epochs
def training_loop(self):
train_loss_all = []
labels_true_train = []
labels_predicted_train = []
self.model.eval()
self.classifier.train()
for i, (inputs, labels) in enumerate(self.dataloaders['train']):
inputs, labels = inputs.to(self.device), labels.long().to(self.device)
self.optimizer.zero_grad()
bsz = labels.shape[0]
#labels = labels.unsqueeze(1)
with torch.no_grad():
if self.method =='SupConEF':
features = self.model.calculate_embedding(inputs)
elif self.method =='SupCon':
features = self.model.encoder(inputs)
train_output = self.classifier(features.detach())
train_loss = self.criterion(train_output, labels)
_, train_predicted = torch.max(train_output.data, 1)
labels_true_train.append(labels.tolist())
labels_predicted_train.append(train_predicted.tolist())
train_loss_all.append(train_loss.cpu().data.item())
train_loss.backward()
self.optimizer.step()
true_train = sum(labels_true_train, [])
pred_train = sum(labels_predicted_train, [])
train_f1 = f1_score(true_train, pred_train, average='macro')
train_accuracy = accuracy_score(true_train, pred_train)
return train_accuracy, train_f1, train_loss_all
def validation_loop(self):
validation_loss_all = []
labels_true = []
labels_predicted = []
self.model.eval()
self.classifier.eval()
with torch.no_grad():
for inputs_v, labels_v in self.dataloaders['valid']:
inputs_v, labels_v = inputs_v.to(self.device), labels_v.long().to(self.device)
#labels_v = labels_v.unsqueeze(1)
bsz_v = labels_v.shape[0]
if self.method =='SupConEF':
outputs_v = self.classifier(self.model.calculate_embedding(inputs_v))
elif self.method =='SupCon':
outputs_v = self.classifier(self.model.encoder(inputs_v))
validation_loss = self.criterion(outputs_v, labels_v)
_, predicted = torch.max(outputs_v.data, 1)
labels_true.append(labels_v.tolist())
labels_predicted.append(predicted.tolist())
validation_loss_all.append(validation_loss.cpu().data.item())
true = sum(labels_true, [])
pred = sum(labels_predicted, [])
valid_f1 = f1_score(true, pred, average='macro')
valid_accuracy = accuracy_score(true, pred)
return valid_accuracy, valid_f1, true, pred, validation_loss_all
def main_loop(self):
validation_losses = []
valids = []
f1s = []
for epoch in range(self.num_epochs):
start = time.time()
train_accuracy, train_f1, train_loss_all = self.training_loop()
valid_accuracy, valid_f1, true, pred, validation_loss_all = self.validation_loop()
validation_loss_avg = (sum(validation_loss_all) / len(validation_loss_all))
train_loss_avg = (sum(train_loss_all) / len(train_loss_all))
validation_losses.append(validation_loss_avg)
valids.append(valid_accuracy)
f1s.append(valid_f1)
wandb.log({
"Train Loss": train_loss_avg,
"Validation Loss": validation_loss_avg,
"Validation Accuracy": valid_accuracy,
"Validation F1": valid_f1})
print('Epoch: {}. '
'train_loss: {:.4f} '
'train_accuracy: {:.4f} '
'validation_loss: {:.4f} '
'validation_accuracy: {:.4f} '
'valid_f1:{:.4f}'.format(epoch,
train_loss_avg,
train_accuracy,
validation_loss_avg,
valid_accuracy,
valid_f1))
# torch.save(self.model.state_dict(), self.output_folders + 'epoch_' + str(epoch) +'.pth')
if validation_loss_avg <= min(validation_losses):
torch.save(copy.deepcopy(self.model.state_dict()),
self.output_folders + 'least_validation_loss_epoch.pth')
torch.save(copy.deepcopy(self.classifier.state_dict()),
self.output_folders + 'least_validation_loss_epoch_classifier.pth')
print('Minimum Validation Loss')
print(classification_report(true, pred, target_names=self.target_names))
wandb.run.summary["Least Validation Loss"] = validation_loss_avg
if valid_accuracy >= max(valids):
torch.save(copy.deepcopy(self.model.state_dict()), self.output_folders + 'max_accuracy_epoch.pth')
torch.save(copy.deepcopy(self.classifier.state_dict()),
self.output_folders + 'max_accuracy_epoch_classifier.pth')
print('Maximum Accuracy')
print(classification_report(true, pred, target_names=self.target_names))
wandb.run.summary["Best Validation Accuracy"] = valid_accuracy
if valid_f1 >= max(f1s):
torch.save(copy.deepcopy(self.model.state_dict()), self.output_folders + 'max_f1_epoch.pth')
torch.save(copy.deepcopy(self.classifier.state_dict()),
self.output_folders + 'max_f1_epoch_classifier.pth')
print('Maximum F1')
print(classification_report(true, pred, target_names=self.target_names))
wandb.run.summary["Best Validation F1"] = valid_f1
end = time.time()
print('Total time for One Epoch : {:.4f} Seconds ', end - start)
print('')
return
class FullySupervisedTrainer:
def __init__(self,device, method, model, criterion, optimizer, dataloaders, target_names, output_folders,
num_epochs):
self.device =device
self.method = method
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.dataloaders = dataloaders
self.target_names = target_names
self.output_folders = output_folders
self.num_epochs = num_epochs
def training_loop(self):
train_loss_all = []
labels_true_train = []
labels_predicted_train = []
self.model.train()
for i, (inputs, labels) in enumerate(self.dataloaders['train']):
#print(inputs.shape)
#grid_img = torchvision.utils.make_grid(inputs, nrow=5).permute(1, 2, 0)
#plt.imshow(grid_img)
#plt.show()
#plt.savefig('train_batch_'+str(i)+'.png')
#labels = labels.type(torch.LongTensor)
#print('i:',1)
inputs, labels = inputs.to(self.device), labels.long().to(self.device)
self.optimizer.zero_grad()
train_output = self.model(inputs)
train_loss = self.criterion(train_output, labels)
_, train_predicted = torch.max(train_output.data, 1)
#train_predicted = np.round(train_output.cpu().detach())
#labels = np.round(labels.cpu().detach())
labels_true_train.append(labels.tolist())
labels_predicted_train.append(train_predicted.tolist())
train_loss_all.append(train_loss.cpu().data.item())
train_loss.backward()
self.optimizer.step()
true_train = sum(labels_true_train, [])
pred_train = sum(labels_predicted_train, [])
train_f1 = f1_score(true_train, pred_train, average='macro')
train_accuracy = accuracy_score(true_train, pred_train)
return train_accuracy, train_f1, train_loss_all
def validation_loop(self):
validation_loss_all = []
labels_true = []
labels_predicted = []
self.model.eval()
with torch.no_grad():
#i=0
for inputs_v, labels_v in self.dataloaders['valid']:
#print(inputs.shape)
#i+=1
#grid_img = torchvision.utils.make_grid(inputs_v, nrow=5).permute(1, 2, 0)
#plt.imshow(grid_img)
#plt.savefig('valid_batch_'+str(i)+'.png')
#labels_v = labels_v.type(torch.LongTensor)
inputs_v, labels_v = inputs_v.to(self.device), labels_v.long().to(self.device)
outputs_v = self.model(inputs_v)
validation_loss = self.criterion(outputs_v, labels_v)
_, predicted = torch.max(outputs_v.data, 1)
#predicted = np.round(outputs_v.cpu().detach())
#labels_v = np.round(labels_v.cpu().detach())
labels_true.append(labels_v.tolist())
labels_predicted.append(predicted.tolist())
validation_loss_all.append(validation_loss.cpu().data.item())
true = sum(labels_true, [])
pred = sum(labels_predicted, [])
valid_f1 = f1_score(true, pred, average='macro')
valid_accuracy = accuracy_score(true, pred)
return valid_accuracy, valid_f1, true, pred, validation_loss_all
def main_loop(self):
validation_losses = []
valids = []
f1s = []
for epoch in range(self.num_epochs):
start = time.time()
train_accuracy, train_f1, train_loss_all = self.training_loop()
valid_accuracy, valid_f1, true, pred, validation_loss_all = self.validation_loop()
validation_loss_avg = (sum(validation_loss_all) / len(validation_loss_all))
train_loss_avg = (sum(train_loss_all) / len(train_loss_all))
validation_losses.append(validation_loss_avg)
valids.append(valid_accuracy)
f1s.append(valid_f1)
wandb.log({
"Train Loss": train_loss_avg,
"Train Accuracy": train_accuracy,
"Validation Loss": validation_loss_avg,
"Validation Accuracy": valid_accuracy,
"Validation F1": valid_f1})
print('Epoch: {}. '
'train_loss: {:.4f} '
'train_accuracy: {:.4f} '
'validation_loss: {:.4f} '
'validation_accuracy: {:.4f} '
'valid_f1:{:.4f}'.format(epoch,
train_loss_avg,
train_accuracy,
validation_loss_avg,
valid_accuracy,
valid_f1))
# torch.save(self.model.state_dict(), self.output_folders + 'epoch_' + str(epoch) +'.pth')
if validation_loss_avg <= min(validation_losses):
torch.save(copy.deepcopy(self.model.state_dict()),
self.output_folders + 'least_validation_loss_epoch.pth')
print('Minimum Validation Loss')
print(classification_report(true, pred, target_names=self.target_names))
wandb.run.summary["Least Validation Loss"] = validation_loss_avg
if valid_accuracy >= max(valids):
torch.save(copy.deepcopy(self.model.state_dict()), self.output_folders + 'max_accuracy_epoch.pth')
print('Maximum Accuracy')
print(classification_report(true, pred, target_names=self.target_names))
wandb.run.summary["Best Validation Accuracy"] = valid_accuracy
if valid_f1 >= max(f1s):
torch.save(copy.deepcopy(self.model.state_dict()), self.output_folders + 'max_f1_epoch.pth')
print('Maximum F1')
print(classification_report(true, pred, target_names=self.target_names))
wandb.run.summary["Best Validation F1"] = valid_f1
end = time.time()
print('Total time for One Epoch : {:.4f} Seconds ', end - start)
print('')
return
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