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train.py
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train.py
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import os
import random
import numpy as np
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam, AdamW
from torchvision.utils import save_image
from tensorboardX import SummaryWriter
from dataloader import get_loader
from models import util_funcs
from models.model_main import ModelMain
from options import get_parser_main_model
from data_utils.svg_utils import render
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def train_main_model(opts):
setup_seed(1111)
dir_exp = os.path.join("./experiments", opts.name_exp)
dir_sample = os.path.join(dir_exp, "samples")
dir_ckpt = os.path.join(dir_exp, "checkpoints")
dir_log = os.path.join(dir_exp, "logs")
logfile_train = open(os.path.join(dir_log, "train_loss_log.txt"), 'w')
logfile_val = open(os.path.join(dir_log, "val_loss_log.txt"), 'w')
train_loader = get_loader(opts.data_root, opts.img_size, opts.language, opts.char_num, opts.max_seq_len, opts.dim_seq, opts.batch_size, opts.mode)
val_loader = get_loader(opts.data_root, opts.img_size, opts.language, opts.char_num, opts.max_seq_len, opts.dim_seq, opts.batch_size_val, 'test')
model_main = ModelMain(opts)
if torch.cuda.is_available() and opts.multi_gpu:
model_main = torch.nn.DataParallel(model_main)
model_main.cuda()
parameters_all = [{"params": model_main.img_encoder.parameters()}, {"params": model_main.img_decoder.parameters()},
{"params": model_main.modality_fusion.parameters()}, {"params": model_main.transformer_main.parameters()},
{"params": model_main.transformer_seqdec.parameters()}]
optimizer = Adam(parameters_all, lr=opts.lr, betas=(opts.beta1, opts.beta2), eps=opts.eps, weight_decay=opts.weight_decay)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.997)
if opts.tboard:
writer = SummaryWriter(dir_log)
for epoch in range(opts.init_epoch, opts.n_epochs):
for idx, data in enumerate(train_loader):
for key in data: data[key] = data[key].cuda()
ret_dict, loss_dict = model_main(data)
loss = opts.loss_w_l1 * loss_dict['img']['l1'] + opts.loss_w_pt_c * loss_dict['img']['vggpt'] + opts.kl_beta * loss_dict['kl'] \
+ loss_dict['svg']['total'] + loss_dict['svg_para']['total']
# perform optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
batches_done = epoch * len(train_loader) + idx + 1
message = (
f"Epoch: {epoch}/{opts.n_epochs}, Batch: {idx}/{len(train_loader)}, "
f"Loss: {loss.item():.6f}, "
f"img_l1_loss: {opts.loss_w_l1 * loss_dict['img']['l1'].item():.6f}, "
f"img_pt_c_loss: {opts.loss_w_pt_c * loss_dict['img']['vggpt']:.6f}, "
f"svg_total_loss: {loss_dict['svg']['total'].item():.6f}, "
f"svg_cmd_loss: {opts.loss_w_cmd * loss_dict['svg']['cmd'].item():.6f}, "
f"svg_args_loss: {opts.loss_w_args * loss_dict['svg']['args'].item():.6f}, "
f"svg_smooth_loss: {opts.loss_w_smt * loss_dict['svg']['smt'].item():.6f}, "
f"svg_aux_loss: {opts.loss_w_aux * loss_dict['svg']['aux'].item():.6f}, "
f"lr: {optimizer.param_groups[0]['lr']:.6f}, "
f"Step: {batches_done}"
)
if batches_done % opts.freq_log == 0:
logfile_train.write(message + '\n')
print(message)
if opts.tboard:
writer.add_scalar('Loss/loss', loss.item(), batches_done)
loss_img_items = ['l1', 'vggpt']
loss_svg_items = ['total', 'cmd', 'args', 'aux', 'smt']
for item in loss_img_items:
writer.add_scalar(f'Loss/img_{item}', loss_dict['img'][item].item(), batches_done)
for item in loss_svg_items:
writer.add_scalar(f'Loss/svg_{item}', loss_dict['svg'][item].item(), batches_done)
for item in loss_svg_items:
writer.add_scalar(f'Loss/svg_para_{item}', loss_dict['svg_para'][item].item(), batches_done)
writer.add_scalar('Loss/img_kl_loss', opts.kl_beta * loss_dict['kl'].item(), batches_done)
writer.add_image('Images/trg_img', ret_dict['img']['trg'][0], batches_done)
writer.add_image('Images/img_output', ret_dict['img']['out'][0], batches_done)
if opts.freq_sample > 0 and batches_done % opts.freq_sample == 0:
img_sample = torch.cat((ret_dict['img']['trg'].data, ret_dict['img']['out'].data), -2)
save_file = os.path.join(dir_sample, f"train_epoch_{epoch}_batch_{batches_done}.png")
save_image(img_sample, save_file, nrow=8, normalize=True)
if opts.freq_val > 0 and batches_done % opts.freq_val == 0:
with torch.no_grad():
model_main.eval()
loss_val = {'img':{'l1':0.0, 'vggpt':0.0}, 'svg':{'total':0.0, 'cmd':0.0, 'args':0.0, 'aux':0.0},
'svg_para':{'total':0.0, 'cmd':0.0, 'args':0.0, 'aux':0.0}}
for val_idx, val_data in enumerate(val_loader):
for key in val_data: val_data[key] = val_data[key].cuda()
ret_dict_val, loss_dict_val = model_main(val_data, mode='val')
for loss_cat in ['img', 'svg']:
for key, _ in loss_val[loss_cat].items():
loss_val[loss_cat][key] += loss_dict_val[loss_cat][key]
for loss_cat in ['img', 'svg']:
for key, _ in loss_val[loss_cat].items():
loss_val[loss_cat][key] /= len(val_loader)
if opts.tboard:
for loss_cat in ['img', 'svg']:
for key, _ in loss_val[loss_cat].items():
writer.add_scalar(f'VAL/loss_{loss_cat}_{key}', loss_val[loss_cat][key], batches_done)
val_msg = (
f"Epoch: {epoch}/{opts.n_epochs}, Batch: {idx}/{len(train_loader)}, "
f"Val loss img l1: {loss_val['img']['l1']: .6f}, "
f"Val loss img pt: {loss_val['img']['vggpt']: .6f}, "
f"Val loss total: {loss_val['svg']['total']: .6f}, "
f"Val loss cmd: {loss_val['svg']['cmd']: .6f}, "
f"Val loss args: {loss_val['svg']['args']: .6f}, "
)
logfile_val.write(val_msg + "\n")
print(val_msg)
scheduler.step()
if epoch % opts.freq_ckpt == 0:
if opts.multi_gpu:
torch.save({'model':model_main.module.state_dict(), 'opt':optimizer.state_dict(), 'n_epoch':epoch, 'n_iter':batches_done}, f'{dir_ckpt}/{epoch}_{batches_done}.ckpt')
else:
torch.save({'model':model_main.state_dict(), 'opt':optimizer.state_dict(), 'n_epoch':epoch, 'n_iter':batches_done}, f'{dir_ckpt}/{epoch}_{batches_done}.ckpt')
logfile_train.close()
logfile_val.close()
def backup_code(name_exp):
os.makedirs(os.path.join('experiments', name_exp, 'code'), exist_ok=True)
shutil.copy('models/transformers.py', os.path.join('experiments', name_exp, 'code', 'transformers.py') )
shutil.copy('models/model_main.py', os.path.join('experiments', name_exp, 'code', 'model_main.py'))
shutil.copy('models/image_encoder.py', os.path.join('experiments', name_exp, 'code', 'image_encoder.py'))
shutil.copy('models/image_decoder.py', os.path.join('experiments', name_exp, 'code', 'image_decoder.py'))
shutil.copy('./train.py', os.path.join('experiments', name_exp, 'code', 'train.py'))
shutil.copy('./options.py', os.path.join('experiments', name_exp, 'code', 'options.py'))
def train(opts):
if opts.model_name == 'main_model':
train_main_model(opts)
elif opts.model_name == 'others':
train_others(opts)
else:
raise NotImplementedError
def main():
opts = get_parser_main_model().parse_args()
opts.name_exp = opts.name_exp + '_' + opts.model_name
os.makedirs("./experiments", exist_ok=True)
debug = True
# Create directories
experiment_dir = os.path.join("./experiments", opts.name_exp)
backup_code(opts.name_exp)
os.makedirs(experiment_dir, exist_ok=debug) # False to prevent multiple train run by mistake
os.makedirs(os.path.join(experiment_dir, "samples"), exist_ok=True)
os.makedirs(os.path.join(experiment_dir, "checkpoints"), exist_ok=True)
os.makedirs(os.path.join(experiment_dir, "results"), exist_ok=True)
os.makedirs(os.path.join(experiment_dir, "logs"), exist_ok=True)
print(f"Training on experiment {opts.name_exp}...")
# Dump options
with open(os.path.join(experiment_dir, "opts.txt"), "w") as f:
for key, value in vars(opts).items():
f.write(str(key) + ": " + str(value) + "\n")
train(opts)
if __name__ == "__main__":
main()