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# -*- coding: utf-8 -*-
# Author: ximing
# Description: DiffVG painter and optimizer
# Copyright (c) 2023, XiMing Xing.
# License: MPL-2.0 License
import copy
import random
from typing import List
import omegaconf
import numpy as np
import pydiffvg
import torch
from torch.optim.lr_scheduler import LambdaLR
from pytorch_svgrender.diffvg_warp import DiffVGState
class Painter(DiffVGState):
def __init__(
self,
target_img: torch.Tensor,
diffvg_cfg: omegaconf.DictConfig,
canvas_size: List,
path_type: str = 'unclosed',
max_width: float = 3.0,
device: torch.device = None,
):
super(Painter, self).__init__(device, print_timing=diffvg_cfg.print_timing,
canvas_width=canvas_size[0], canvas_height=canvas_size[1])
self.target_img = target_img
self.path_type: str = path_type
self.max_width = max_width
self.train_stroke: bool = path_type == 'unclosed'
self.strokes_counter: int = 0 # counts the number of calls to "get_path"
def init_image(self, num_paths=0):
for i in range(num_paths):
path = self.get_path()
self.shapes.append(path)
self.shapes.append(path)
fill_color_init = torch.FloatTensor(np.random.uniform(size=[4]))
stroke_color_init = torch.FloatTensor(np.random.uniform(size=[4]))
path_group = pydiffvg.ShapeGroup(
shape_ids=torch.tensor([len(self.shapes) - 1]),
fill_color=None if self.train_stroke else fill_color_init,
stroke_color=stroke_color_init if self.train_stroke else None
)
self.shape_groups.append(path_group)
self.shape_groups.append(path_group)
img = self.render_warp()
img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=self.device) \
* (1 - img[:, :, 3:4])
img = img.unsqueeze(0) # convert img from HWC to NCHW
img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW
return img
def get_image(self, step: int = 0):
img = self.render_warp(seed=step)
img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=self.device) \
* (1 - img[:, :, 3:4])
img = img.unsqueeze(0) # convert img from HWC to NCHW
img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW
return img
def get_path(self):
if self.path_type == 'unclosed':
num_segments = random.randint(1, 3)
num_control_points = torch.zeros(num_segments, dtype=torch.int32) + 2
points = []
p0 = (random.random(), random.random())
points.append(p0)
for j in range(num_segments):
radius = 0.05
p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5))
p2 = (p1[0] + radius * (random.random() - 0.5), p1[1] + radius * (random.random() - 0.5))
p3 = (p2[0] + radius * (random.random() - 0.5), p2[1] + radius * (random.random() - 0.5))
points.append(p1)
points.append(p2)
points.append(p3)
p0 = p3
points = torch.tensor(points)
points[:, 0] *= self.canvas_width
points[:, 1] *= self.canvas_height
# points = torch.rand(3 * num_segments + 1, 2) * min(canvas_width, canvas_height)
path = pydiffvg.Path(num_control_points=num_control_points,
points=points,
stroke_width=torch.tensor(1.0),
is_closed=False)
elif self.path_type == 'closed':
num_segments = random.randint(3, 5)
num_control_points = torch.zeros(num_segments, dtype=torch.int32) + 2
points = []
p0 = (random.random(), random.random())
points.append(p0)
for j in range(num_segments):
radius = 0.05
p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5))
p2 = (p1[0] + radius * (random.random() - 0.5), p1[1] + radius * (random.random() - 0.5))
p3 = (p2[0] + radius * (random.random() - 0.5), p2[1] + radius * (random.random() - 0.5))
points.append(p1)
points.append(p2)
if j < num_segments - 1:
points.append(p3)
p0 = p3
points = torch.tensor(points)
points[:, 0] *= self.canvas_width
points[:, 1] *= self.canvas_height
path = pydiffvg.Path(num_control_points=num_control_points,
points=points,
stroke_width=torch.tensor(1.0),
is_closed=True)
self.strokes_counter += 1
return path
def clip_curve_shape(self):
if self.train_stroke: # open-form path
for path in self.shapes:
path.stroke_width.data.clamp_(1.0, self.max_width)
for group in self.shape_groups:
group.stroke_color.data.clamp_(0.0, 1.0)
else: # closed-form path
for group in self.shape_groups:
group.fill_color.data.clamp_(0.0, 1.0)
def set_parameters(self):
# stroke`s location optimization
self.point_vars = []
for i, path in enumerate(self.shapes):
path.points.requires_grad = True
self.point_vars.append(path.points)
if self.train_stroke:
path.stroke_width.requires_grad = True
self.width_vars.append(path.stroke_width)
# for stroke' color optimization
self.color_vars = []
for i, group in enumerate(self.shape_groups):
if self.train_stroke:
group.stroke_color.requires_grad = True
self.color_vars.append(group.stroke_color)
else:
group.fill_color.requires_grad = True
self.color_vars.append(group.fill_color)
def get_point_parameters(self):
return self.point_vars
def get_color_parameters(self):
return self.color_vars
def get_stroke_parameters(self):
return self.width_vars, self.get_color_parameters()
def save_svg(self, fpath):
pydiffvg.save_svg(f'{fpath}', self.canvas_width, self.canvas_height, self.shapes, self.shape_groups)
class LinearDecayLR:
def __init__(self, decay_every, decay_ratio):
self.decay_every = decay_every
self.decay_ratio = decay_ratio
def __call__(self, n):
decay_time = n // self.decay_every
decay_step = n % self.decay_every
lr_s = self.decay_ratio ** decay_time
lr_e = self.decay_ratio ** (decay_time + 1)
r = decay_step / self.decay_every
lr = lr_s * (1 - r) + lr_e * r
return lr
class PainterOptimizer:
def __init__(self,
renderer: Painter,
num_iter: int,
lr_config: omegaconf.DictConfig,
trainable_stroke: bool = False):
self.renderer = renderer
self.num_iter = num_iter
self.trainable_stroke = trainable_stroke
self.lr_base = {
'point': lr_config.point,
'color': lr_config.color,
'stroke_width': lr_config.stroke_width,
'stroke_color': lr_config.stroke_color,
}
self.learnable_params = [] # list[Dict]
self.optimizer = None
self.scheduler = None
def init_optimizer(self):
# optimizers
params = {}
self.renderer.set_parameters()
params['point'] = self.renderer.get_point_parameters()
if self.trainable_stroke:
params['stroke_width'], params['stroke_color'] = self.renderer.get_stroke_parameters()
else:
params['color'] = self.renderer.get_color_parameters()
self.learnable_params = [
{'params': params[ki], 'lr': self.lr_base[ki]} for ki in sorted(params.keys())
]
self.optimizer = torch.optim.Adam(self.learnable_params)
# lr schedule
lr_lambda_fn = LinearDecayLR(self.num_iter, 0.4)
self.scheduler = LambdaLR(self.optimizer, lr_lambda=lr_lambda_fn, last_epoch=-1)
def update_params(self, name: str, value: torch.tensor):
for param_group in self.learnable_params:
if param_group.get('_id') == name:
param_group['params'] = value
def update_lr(self):
self.scheduler.step()
def zero_grad_(self):
self.optimizer.zero_grad()
def step_(self):
self.optimizer.step()
def get_lr(self):
return self.optimizer.param_groups[0]['lr']
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