Created
July 31, 2023 14:23
-
-
Save Tony363/5c4cfe166c2b67edef8d1b4f864d34fd to your computer and use it in GitHub Desktop.
train_epoch.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
""" train model """ | |
def train_epoch(config, epoch, model_transformer, model_backbone, criterion, optimizer, scheduler, train_loader): | |
losses = [] | |
model_transformer.train() | |
model_backbone.train() | |
# input mask (batch_size x len_sqe+1) | |
mask_inputs = torch.ones(config.batch_size, config.n_enc_seq+1).to(config.device) | |
# save data for one epoch | |
pred_epoch = [] | |
labels_epoch = [] | |
for data in tqdm(train_loader): | |
# labels: batch size | |
# d_img_org: 3 x 768 x 1024 | |
# d_img_scale_1: 3 x 288 x 384 | |
# d_img_scale_2: 3 x 160 x 224 | |
d_img_org = data['d_img_org'].to(config.device) | |
d_img_scale_1 = data['d_img_scale_1'].to(config.device) | |
d_img_scale_2 = data['d_img_scale_2'].to(config.device) | |
labels = data['score'] | |
labels = torch.squeeze(labels.type(torch.FloatTensor)).to(config.device) | |
# backbone feature map (dis) | |
# feat_dis_org: 2048 x 24 x 32 | |
# feat_dis_scale_1: 2048 x 9 x 12 | |
# feat_dis_scale_2: 2048 x 5 x 7 | |
feat_dis_org = model_backbone(d_img_org) | |
feat_dis_scale_1 = model_backbone(d_img_scale_1) | |
feat_dis_scale_2 = model_backbone(d_img_scale_2) | |
# this value should be extracted from backbone network | |
# enc_inputs_embed: batch x len_seq x n_feat | |
# weight update | |
optimizer.zero_grad() | |
pred = model_transformer(mask_inputs, feat_dis_org, feat_dis_scale_1, feat_dis_scale_2) | |
loss = criterion(torch.squeeze(pred), labels) | |
loss_val = loss.item() | |
losses.append(loss_val) | |
loss.backward() | |
optimizer.step() | |
scheduler.step() | |
# save results in one epoch | |
pred_batch_numpy = pred.data.cpu().numpy() | |
labels_batch_numpy = labels.data.cpu().numpy() | |
pred_epoch = np.append(pred_epoch, pred_batch_numpy) | |
labels_epoch = np.append(labels_epoch, labels_batch_numpy) | |
# compute correlation coefficient | |
rho_s, _ = spearmanr(np.squeeze(pred_epoch), np.squeeze(labels_epoch)) | |
rho_p, _ = pearsonr(np.squeeze(pred_epoch), np.squeeze(labels_epoch)) | |
print('[train] epoch:%d / loss:%f / SROCC:%4f / PLCC:%4f' % (epoch+1, loss.item(), rho_s, rho_p)) | |
# save weights | |
if (epoch+1) % config.save_freq == 0: | |
weights_file_name = "epoch%d.pth" % (epoch+1) | |
weights_file = os.path.join(config.snap_path, weights_file_name) | |
torch.save({ | |
'epoch': epoch, | |
'model_backbone_state_dict': model_backbone.state_dict(), | |
'model_transformer_state_dict': model_transformer.state_dict(), | |
'optimizer_state_dict': optimizer.state_dict(), | |
'scheduler_state_dict': scheduler.state_dict(), | |
'loss': loss | |
}, weights_file) | |
print('save weights of epoch %d' % (epoch+1)) | |
return np.mean(losses), rho_s, rho_p |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment