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# -*- coding: utf-8 -*-
# Copyright (c) XiMing Xing. All rights reserved.
# Author: XiMing Xing
# Description:
from PIL import Image
import torch
from tqdm.auto import tqdm
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from torchvision.datasets.folder import is_image_file
from pytorch_svgrender.libs.engine import ModelState
from pytorch_svgrender.painter.clipasso import Painter, PainterOptimizer, Loss
from pytorch_svgrender.painter.clipasso.sketch_utils import plot_attn, get_mask_u2net, fix_image_scale
from pytorch_svgrender.plt import plot_img, plot_couple, plot_img_title
class CLIPassoPipeline(ModelState):
def __init__(self, args):
logdir_ = f"sd{args.seed}" \
f"-im{args.x.image_size}" \
f"{'-mask' if args.x.mask_object else ''}" \
f"{'-XDoG' if args.x.xdog_intersec else ''}" \
f"-P{args.x.num_paths}W{args.x.width}{'OP' if args.x.force_sparse else 'BL'}" \
f"-tau{args.x.softmax_temp}"
super().__init__(args, log_path_suffix=logdir_)
# create log dir
self.png_logs_dir = self.result_path / "png_logs"
self.svg_logs_dir = self.result_path / "svg_logs"
if self.accelerator.is_main_process:
self.png_logs_dir.mkdir(parents=True, exist_ok=True)
self.svg_logs_dir.mkdir(parents=True, exist_ok=True)
# make video log
self.make_video = self.args.mv
if self.make_video:
self.frame_idx = 0
self.frame_log_dir = self.result_path / "frame_logs"
self.frame_log_dir.mkdir(parents=True, exist_ok=True)
def painterly_rendering(self, image_path):
loss_func = Loss(self.x_cfg, self.device)
# preprocess input image
inputs, mask = self.get_target(image_path,
self.x_cfg.image_size,
self.result_path,
self.x_cfg.u2net_path,
self.x_cfg.mask_object,
self.x_cfg.fix_scale,
self.device)
plot_img(inputs, self.result_path, fname="input")
# init renderer
renderer = self.load_renderer(inputs, mask)
img = renderer.init_image(stage=0)
self.print("init_image shape: ", img.shape)
plot_img(img, self.result_path, fname="init_img")
# init optimizer
optimizer = PainterOptimizer(renderer,
self.x_cfg.num_iter,
self.x_cfg.lr,
self.x_cfg.force_sparse, self.x_cfg.color_lr)
optimizer.init_optimizers()
best_loss, best_fc_loss = 100, 100
min_delta = 1e-5
total_step = self.x_cfg.num_iter
with tqdm(initial=self.step, total=total_step, disable=not self.accelerator.is_main_process) as pbar:
while self.step < total_step:
sketches = renderer.get_image().to(self.device)
if self.make_video and (self.step % self.args.framefreq == 0 or self.step == total_step - 1):
plot_img(sketches, self.frame_log_dir, fname=f"iter{self.frame_idx}")
self.frame_idx += 1
losses_dict = loss_func(sketches,
inputs.detach(),
renderer.get_color_parameters(),
renderer,
self.step,
optimizer)
loss = sum(list(losses_dict.values()))
optimizer.zero_grad_()
loss.backward()
optimizer.step_()
if self.x_cfg.lr_schedule:
optimizer.update_lr()
pbar.set_description(f"L_train: {loss.item():.5f}")
if self.step % self.args.save_step == 0 and self.accelerator.is_main_process:
plot_couple(inputs,
sketches,
self.step,
output_dir=self.png_logs_dir.as_posix(),
fname=f"iter{self.step}")
renderer.save_svg(self.svg_logs_dir.as_posix(), f"svg_iter{self.step}")
if self.step % self.args.eval_step == 0 and self.accelerator.is_main_process:
with torch.no_grad():
losses_dict_eval = loss_func(
sketches,
inputs,
renderer.get_color_parameters(),
renderer.get_point_parameters(),
self.step,
optimizer,
mode="eval"
)
loss_eval = sum(list(losses_dict_eval.values()))
cur_delta = loss_eval.item() - best_loss
if abs(cur_delta) > min_delta and cur_delta < 0:
best_loss = loss_eval.item()
best_iter = self.step
plot_couple(inputs,
sketches,
best_iter,
output_dir=self.result_path.as_posix(),
fname="best_iter")
renderer.save_svg(self.result_path.as_posix(), "best_iter")
if self.step == 0 and self.x_cfg.attention_init and self.accelerator.is_main_process:
plot_attn(renderer.get_attn(),
renderer.get_thresh(),
inputs,
renderer.get_inds(),
(self.result_path / "attention_map.png").as_posix(),
self.x_cfg.saliency_model)
self.step += 1
pbar.update(1)
# log final results
renderer.save_svg(self.result_path.as_posix(), "final_svg")
final_raster_sketch = renderer.get_image().to(self.device)
plot_img_title(final_raster_sketch,
title=f'final result - {self.step} step',
output_dir=self.result_path,
fname='final_render')
if self.make_video:
from subprocess import call
call([
"ffmpeg",
"-framerate", f"{self.args.framerate}",
"-i", (self.frame_log_dir / "iter%d.png").as_posix(),
"-vb", "20M",
(self.result_path / "clipasso_rendering.mp4").as_posix()
])
self.close(msg="painterly rendering complete.")
def load_renderer(self, target_im=None, mask=None):
renderer = Painter(method_cfg=self.x_cfg,
diffvg_cfg=self.args.diffvg,
num_strokes=self.x_cfg.num_paths,
canvas_size=self.x_cfg.image_size,
device=self.device,
target_im=target_im,
mask=mask)
return renderer
def get_target(self,
target_file,
image_size,
output_dir,
u2net_path,
mask_object,
fix_scale,
device):
if not is_image_file(target_file):
raise TypeError(f"{target_file} is not image file.")
target = Image.open(target_file)
if target.mode == "RGBA":
# Create a white rgba background
new_image = Image.new("RGBA", target.size, "WHITE")
# Paste the image on the background.
new_image.paste(target, (0, 0), target)
target = new_image
target = target.convert("RGB")
# U^2 net mask
masked_im, mask = get_mask_u2net(target, output_dir, u2net_path, device)
if mask_object:
target = masked_im
if fix_scale:
target = fix_image_scale(target)
transforms_ = []
if target.size[0] != target.size[1]:
transforms_.append(
transforms.Resize((image_size, image_size),
interpolation=InterpolationMode.BICUBIC)
)
else:
transforms_.append(transforms.Resize(image_size,
interpolation=InterpolationMode.BICUBIC))
transforms_.append(transforms.CenterCrop(image_size))
transforms_.append(transforms.ToTensor())
data_transforms = transforms.Compose(transforms_)
target_ = data_transforms(target).unsqueeze(0).to(self.device)
return target_, mask
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