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from typing import Mapping
import os
from tqdm import tqdm
from easydict import EasyDict as edict
import matplotlib.pyplot as plt
import torch
from torch.optim.lr_scheduler import LambdaLR
import pydiffvg
import save_svg
from losses import SDSLoss, ToneLoss, ConformalLoss
from config import set_config
from utils import (
check_and_create_dir,
get_data_augs,
save_image,
preprocess,
learning_rate_decay,
combine_word,
create_video)
import wandb
import warnings
warnings.filterwarnings("ignore")
pydiffvg.set_print_timing(False)
gamma = 1.0
def init_shapes(svg_path, trainable: Mapping[str, bool]):
svg = f'{svg_path}.svg'
canvas_width, canvas_height, shapes_init, shape_groups_init = pydiffvg.svg_to_scene(svg)
parameters = edict()
# path points
if trainable.point:
parameters.point = []
for path in shapes_init:
path.points.requires_grad = True
parameters.point.append(path.points)
return shapes_init, shape_groups_init, parameters
if __name__ == "__main__":
cfg = set_config()
# use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())
device = pydiffvg.get_device()
print("preprocessing")
preprocess(cfg.font, cfg.word, cfg.optimized_letter, cfg.script, cfg.level_of_cc)
if cfg.loss.use_sds_loss:
sds_loss = SDSLoss(cfg, device)
h, w = cfg.render_size, cfg.render_size
data_augs = get_data_augs(cfg.cut_size)
render = pydiffvg.RenderFunction.apply
# initialize shape
print('initializing shape')
shapes, shape_groups, parameters = init_shapes(svg_path=cfg.target, trainable=cfg.trainable)
scene_args = pydiffvg.RenderFunction.serialize_scene(w, h, shapes, shape_groups)
img_init = render(w, h, 2, 2, 0, None, *scene_args)
img_init = img_init[:, :, 3:4] * img_init[:, :, :3] + \
torch.ones(img_init.shape[0], img_init.shape[1], 3, device=device) * (1 - img_init[:, :, 3:4])
img_init = img_init[:, :, :3]
if cfg.use_wandb:
plt.imshow(img_init.detach().cpu())
wandb.log({"init": wandb.Image(plt)}, step=0)
plt.close()
if cfg.loss.tone.use_tone_loss:
tone_loss = ToneLoss(cfg)
tone_loss.set_image_init(img_init)
if cfg.save.init:
print('saving init')
filename = os.path.join(
cfg.experiment_dir, "svg-init", "init.svg")
check_and_create_dir(filename)
save_svg.save_svg(filename, w, h, shapes, shape_groups)
num_iter = cfg.num_iter
pg = [{'params': parameters["point"], 'lr': cfg.lr_base["point"]}]
optim = torch.optim.Adam(pg, betas=(0.9, 0.9), eps=1e-6)
if cfg.loss.conformal.use_conformal_loss:
conformal_loss = ConformalLoss(parameters, device, cfg.optimized_letter, shape_groups)
lr_lambda = lambda step: learning_rate_decay(step, cfg.lr.lr_init, cfg.lr.lr_final, num_iter,
lr_delay_steps=cfg.lr.lr_delay_steps,
lr_delay_mult=cfg.lr.lr_delay_mult) / cfg.lr.lr_init
scheduler = LambdaLR(optim, lr_lambda=lr_lambda, last_epoch=-1) # lr.base * lrlambda_f
print("start training")
# training loop
t_range = tqdm(range(num_iter))
for step in t_range:
if cfg.use_wandb:
wandb.log({"learning_rate": optim.param_groups[0]['lr']}, step=step)
optim.zero_grad()
# render image
scene_args = pydiffvg.RenderFunction.serialize_scene(w, h, shapes, shape_groups)
img = render(w, h, 2, 2, step, None, *scene_args)
# compose image with white background
img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=device) * (1 - img[:, :, 3:4])
img = img[:, :, :3]
if cfg.save.video and (step % cfg.save.video_frame_freq == 0 or step == num_iter - 1):
save_image(img, os.path.join(cfg.experiment_dir, "video-png", f"iter{step:04d}.png"), gamma)
filename = os.path.join(
cfg.experiment_dir, "video-svg", f"iter{step:04d}.svg")
check_and_create_dir(filename)
save_svg.save_svg(
filename, w, h, shapes, shape_groups)
if cfg.use_wandb:
plt.imshow(img.detach().cpu())
wandb.log({"img": wandb.Image(plt)}, step=step)
plt.close()
x = img.unsqueeze(0).permute(0, 3, 1, 2) # HWC -> NCHW
x = x.repeat(cfg.batch_size, 1, 1, 1)
x_aug = data_augs.forward(x)
# compute diffusion loss per pixel
loss = sds_loss(x_aug)
if cfg.use_wandb:
wandb.log({"sds_loss": loss.item()}, step=step)
if cfg.loss.tone.use_tone_loss:
tone_loss_res = tone_loss(x, step)
if cfg.use_wandb:
wandb.log({"dist_loss": tone_loss_res}, step=step)
loss = loss + tone_loss_res
if cfg.loss.conformal.use_conformal_loss:
loss_angles = conformal_loss()
loss_angles = cfg.loss.conformal.angeles_w * loss_angles
if cfg.use_wandb:
wandb.log({"loss_angles": loss_angles}, step=step)
loss = loss + loss_angles
t_range.set_postfix({'loss': loss.item()})
loss.backward()
optim.step()
scheduler.step()
filename = os.path.join(
cfg.experiment_dir, "output-svg", "output.svg")
check_and_create_dir(filename)
save_svg.save_svg(
filename, w, h, shapes, shape_groups)
# combine_word(cfg.word, cfg.optimized_letter, cfg.font, cfg.experiment_dir)
if cfg.save.image:
filename = os.path.join(
cfg.experiment_dir, "output-png", "output.png")
check_and_create_dir(filename)
imshow = img.detach().cpu()
pydiffvg.imwrite(imshow, filename, gamma=gamma)
if cfg.use_wandb:
plt.imshow(img.detach().cpu())
wandb.log({"img": wandb.Image(plt)}, step=step)
plt.close()
if cfg.save.video:
print("saving video")
create_video(cfg.num_iter, cfg.experiment_dir, cfg.save.video_frame_freq)
if cfg.use_wandb:
wandb.finish()
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