'''
Written by SeuTao
'''
from setting import parse_opts
from datasets.TReNDs import TReNDsDataset
from model import generate_model
import torch
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
import time
import os
from tqdm import tqdm
def metric(y_true, y_pred):
return np.mean(np.sum(np.abs(y_true - y_pred), axis=0) / np.sum(y_true, axis=0))
def weighted_nae(inp, targ):
W = torch.FloatTensor([0.3, 0.175, 0.175, 0.175, 0.175])
return torch.mean(torch.matmul(torch.abs(inp - targ), W.cuda() / torch.mean(targ, axis=0)))
def valid(data_loader, model, sets):
# settings
print("validation")
model.eval()
y_pred = []
y_true = []
loss_ave = []
with torch.no_grad():
for batch_data in tqdm(data_loader):
# getting data batch
volumes, fea, label = batch_data
if not sets.no_cuda:
volumes = volumes.cuda()
label = label.cuda()
fea = fea.cuda()
logits = model(volumes, fea)
# calculating loss
loss_value = weighted_nae(logits, label)
y_pred.append(logits.data.cpu().numpy())
y_true.append(label.data.cpu().numpy())
loss_ave.append(loss_value.data.cpu().numpy())
print('valid loss', np.mean(loss_ave))
y_pred = np.concatenate(y_pred,axis=0)
y_true = np.concatenate(y_true,axis=0)
domain = ['age', 'domain1_var1', 'domain1_var2', 'domain2_var1', 'domain2_var2']
w = [0.3, 0.175, 0.175, 0.175, 0.175]
m_all = 0
for i in range(5):
m = metric(y_true[:,i], y_pred[:,i])
print(domain[i],'metric:', m)
m_all += m*w[i]
print('all_metric:', m_all)
model.train()
return np.mean(loss_ave)
def test(data_loader, model, sets):
# settings
print("validation")
model.eval()
y_pred = []
ids_all = []
with torch.no_grad():
for batch_data in tqdm(data_loader):
# getting data batch
ids, volumes, fea = batch_data
if not sets.no_cuda:
volumes = volumes.cuda()
fea = fea.cuda()
logits = model(volumes, fea)
# calculating loss
y_pred.append(logits.data.cpu().numpy())
ids_all += ids
print(ids_all)
exit()
def train(train_loader,valid_loader, model, optimizer, ajust_lr, total_epochs, save_interval, save_folder, sets):
f = open(os.path.join(save_folder,'log.txt'),'w')
# settings
batches_per_epoch = len(train_loader)
print("Current setting is:")
print(sets)
print("\n\n")
model.train()
train_time_sp = time.time()
valid_loss = 99999
min_loss = 99999
for epoch in range(total_epochs):
rate = ajust_lr(optimizer, epoch)
# log.info('lr = {}'.format(scheduler.get_lr()))
for batch_id, batch_data in enumerate(train_loader):
# getting data batch
batch_id_sp = epoch * batches_per_epoch
volumes, fea, label = batch_data
if not sets.no_cuda:
volumes = volumes.cuda()
label = label.cuda()
fea = fea.cuda()
optimizer.zero_grad()
logits = model(volumes,fea)
# calculating loss
loss = weighted_nae(logits, label)
loss.backward()
optimizer.step()
avg_batch_time = (time.time() - train_time_sp) / (1 + batch_id_sp)
log_ = 'Batch: {}-{} ({}), ' \
'lr = {:.5f}, ' \
'train loss = {:.3f}, ' \
'valid loss = {:.3f}, ' \
'avg_batch_time = {:.3f} '.format(epoch, batch_id, batch_id_sp, rate, loss.item(), valid_loss, avg_batch_time)
print(log_)
f.write(log_ + '\n')
f.flush()
if 1:
valid_loss = valid(valid_loader,model,sets)
if valid_loss < min_loss:
min_loss = valid_loss
model_save_path = '{}/epoch_{}_batch_{}_loss_{}.pth.tar'.format(save_folder, epoch, batch_id, valid_loss)
model_save_dir = os.path.dirname(model_save_path)
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
log_ = 'Save checkpoints: epoch = {}, batch_id = {}'.format(epoch, batch_id)
print(log_)
f.write(log_ + '\n')
torch.save({'ecpoch': epoch,
'batch_id': batch_id,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()},
model_save_path)
print('Finished training')
f.close()
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '7'
sets = parse_opts()
sets.no_cuda = False
sets.resume_path = '/mnt/group-ai-medical/private/shentao/CASP/Protein/trn/models_resnet_50_A_fold_1/epoch_1_batch_134_loss_133.458740234375.pth.tar'
sets.resume_path = None
sets.pretrain_path = r'/mnt/group-ai-medical/private/shentao/CASP/Protein/MedicalNet_pytorch_files/pretrain/resnet_10_23dataset.pth'
sets.num_workers = 16
sets.model_depth = 10
sets.resnet_shortcut = 'A'
sets.fold_index = 1
sets.n_epochs = 50
sets.save_folder = r'/mnt/group-ai-medical/private/shentao/CASP/Protein/trn/AddFea_weighted_nae_23dataset_pretrain_MONAI/' \
r'models_{}_{}_{}_fold_{}'.format('resnet',sets.model_depth,sets.resnet_shortcut,sets.fold_index)
if not os.path.exists(sets.save_folder):
os.makedirs(sets.save_folder)
# getting model
torch.manual_seed(sets.manual_seed)
model, parameters = generate_model(sets)
def get_optimizer(net):
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=3e-4, betas=(0.9, 0.999), eps=1e-08)
def ajust_lr(optimizer, epoch):
if epoch < 24 :
lr = 3e-4
elif epoch < 36:
lr = 1e-4
else:
lr = 1e-5
for p in optimizer.param_groups:
p['lr'] = lr
return lr
rate = ajust_lr(optimizer, 0)
return optimizer, ajust_lr
optimizer, ajust_lr = get_optimizer(model)
# train from resume
if sets.resume_path:
if os.path.isfile(sets.resume_path):
print("=> loading checkpoint '{}'".format(sets.resume_path))
checkpoint = torch.load(sets.resume_path)
model.load_state_dict(checkpoint['state_dict'])
# getting data
sets.phase = 'train'
if sets.no_cuda:
sets.pin_memory = False
else:
sets.pin_memory = True
# train_dataset = TReNDsDataset(mode='train', fold_index=sets.fold_index)
# train_loader = DataLoader(train_dataset, batch_size=sets.batch_size,
# shuffle=True, num_workers=sets.num_workers,
# pin_memory=sets.pin_memory,drop_last=True)
#
# valid_dataset = TReNDsDataset(mode='valid', fold_index=sets.fold_index)
# valid_loader = DataLoader(valid_dataset, batch_size=sets.batch_size,
# shuffle=False, num_workers=sets.num_workers,
# pin_memory=sets.pin_memory, drop_last=False)
#
# # exit(-1)
# # # training
# train(train_loader, valid_loader,model, optimizer,ajust_lr,
# total_epochs=sets.n_epochs,
# save_interval=sets.save_intervals,
# save_folder=sets.save_folder, sets=sets)
# # validate
# valid(valid_loader,model,sets)
test_dataset = TReNDsDataset(mode='test', fold_index=sets.fold_index)
test_loader = DataLoader(test_dataset, batch_size=sets.batch_size,
shuffle=False, num_workers=sets.num_workers,
pin_memory=sets.pin_memory, drop_last=False)
test(test_loader, model, sets)
Datasets
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