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Merge pull request #13 from weinman/tf_updates

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Tensorflow Renderer Path Updates
This commit is contained in:
Tzu-Mao Li
2020-12-23 13:25:55 -05:00
committed by GitHub
parent 2c936f4030 69610cfffd
commit fd38f71527
4 changed files with 229 additions and 4 deletions
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109
apps/single_curve_tf.py Normal file
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@@ -0,0 +1,109 @@
import pydiffvg_tensorflow as pydiffvg
import tensorflow as tf
import skimage
import numpy as np
canvas_width, canvas_height = 256, 256
num_control_points = tf.constant([2, 2, 2])
points = tf.constant([[120.0, 30.0], # base
[150.0, 60.0], # control point
[ 90.0, 198.0], # control point
[ 60.0, 218.0], # base
[ 90.0, 180.0], # control point
[200.0, 65.0], # control point
[210.0, 98.0], # base
[220.0, 70.0], # control point
[130.0, 55.0]]) # control point
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
is_closed = True)
shapes = [path]
path_group = pydiffvg.ShapeGroup( shape_ids = tf.constant([0], dtype=tf.int32),
fill_color = tf.constant([0.3, 0.6, 0.3, 1.0]))
shape_groups = [path_group]
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
render = pydiffvg.render
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(0), # seed
*scene_args)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img, 'results/single_curve_tf/target.png', gamma=2.2)
target = tf.identity(img)
# Move the path to produce initial guess
# normalize points for easier learning rate
points_n = tf.Variable([[100.0/256.0, 40.0/256.0], # base
[155.0/256.0, 65.0/256.0], # control point
[100.0/256.0, 180.0/256.0], # control point
[ 65.0/256.0, 238.0/256.0], # base
[100.0/256.0, 200.0/256.0], # control point
[170.0/256.0, 55.0/256.0], # control point
[220.0/256.0, 100.0/256.0], # base
[210.0/256.0, 80.0/256.0], # control point
[140.0/256.0, 60.0/256.0]]) # control point
color = tf.Variable([0.3, 0.2, 0.5, 1.0])
path.points = points_n * 256
path_group.fill_color = color
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(1), # seed
*scene_args)
pydiffvg.imwrite(img, 'results/single_curve_tf/init.png', gamma=2.2)
optimizer = tf.compat.v1.train.AdamOptimizer(1e-2)
for t in range(100):
print('iteration:', t)
with tf.GradientTape() as tape:
# Forward pass: render the image.
path.points = points_n * 256
path_group.fill_color = color
# Important to use a different seed every iteration, otherwise the result
# would be biased.
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(t+1), # seed,
*scene_args)
loss_value = tf.reduce_sum(tf.square(img - target))
print(f"loss_value: {loss_value}")
pydiffvg.imwrite(img, 'results/single_curve_tf/iter_{}.png'.format(t))
grads = tape.gradient(loss_value, [points_n, color])
print(grads)
optimizer.apply_gradients(zip(grads, [points_n, color]))
# Render the final result.
path.points = points_n * 256
path_group.fill_color = color
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(101), # seed
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img, 'results/single_curve_tf/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/single_curve_tf/iter_%d.png", "-vb", "20M",
"results/single_curve_tf/out.mp4"])

109
apps/single_stroke_tf.py Normal file
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@@ -0,0 +1,109 @@
import pydiffvg_tensorflow as pydiffvg
import tensorflow as tf
import skimage
import numpy as np
canvas_width, canvas_height = 256, 256
num_control_points = tf.constant([2])
points = tf.constant([[120.0, 30.0], # base
[150.0, 60.0], # control point
[ 90.0, 198.0], # control point
[ 60.0, 218.0]]) # base
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
is_closed = False,
stroke_width = tf.constant(15.0))
shapes = [path]
path_group = pydiffvg.ShapeGroup( shape_ids = tf.constant([0], dtype=tf.int32),
fill_color = tf.constant([0.0, 0.0, 0.0, 0.0]),
stroke_color = tf.constant([0.6, 0.3, 0.6, 0.8]))
shape_groups = [path_group]
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
render = pydiffvg.render
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(0), # seed
*scene_args)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img, 'results/single_stroke_tf/target.png', gamma=2.2)
target = tf.identity(img)
# Move the path to produce initial guess
# normalize points for easier learning rate
points_n = tf.Variable([[100.0/256.0, 40.0/256.0], # base
[155.0/256.0, 65.0/256.0], # control point
[100.0/256.0, 180.0/256.0], # control point
[ 65.0/256.0, 238.0/256.0]] # base
)
stroke_color = tf.Variable([0.4, 0.7, 0.5, 0.5])
stroke_width_n = tf.Variable(5.0 / 100.0)
path.points = points_n * 256
path.stroke_width = stroke_width_n * 100
path_group.stroke_color = stroke_color
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(1), # seed
*scene_args)
pydiffvg.imwrite(img, 'results/single_stroke_tf/init.png', gamma=2.2)
optimizer = tf.compat.v1.train.AdamOptimizer(1e-2)
for t in range(100):
print('iteration:', t)
with tf.GradientTape() as tape:
# Forward pass: render the image.
path.points = points_n * 256
path.stroke_width = stroke_width_n * 100
path_group.stroke_color = stroke_color
# Important to use a different seed every iteration, otherwise the result
# would be biased.
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(t+1), # seed,
*scene_args)
loss_value = tf.reduce_sum(tf.square(img - target))
print(f"loss_value: {loss_value}")
pydiffvg.imwrite(img, 'results/single_stroke_tf/iter_{}.png'.format(t))
grads = tape.gradient(loss_value, [points_n, stroke_width_n, stroke_color])
print(grads)
optimizer.apply_gradients(zip(grads, [points_n, stroke_width_n, stroke_color]))
# Render the final result.
path.points = points_n * 256
path_group.stroke_color = stroke_color
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(101), # seed
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img, 'results/single_stroke_tf/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/single_stroke_tf/iter_%d.png", "-vb", "20M",
"results/single_curve_tf/out.mp4"])

12
pydiffvg_tensorflow/render_tensorflow.py
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@@ -133,9 +133,10 @@ def serialize_scene(canvas_width,
elif isinstance(shape, pydiffvg.Path):
assert(shape.points.shape[1] == 2)
args.append(ShapeType.asTensor(diffvg.ShapeType.path))
args.append(tf.identity(shape.num_control_points, type=tf.int32))
args.append(tf.identity(shape.num_control_points))
args.append(tf.identity(shape.points))
args.append(tf.constant(shape.is_closed))
args.append(tf.constant(shape.use_distance_approx))
elif isinstance(shape, pydiffvg.Polygon):
assert(shape.points.shape[1] == 2)
args.append(ShapeType.asTensor(diffvg.ShapeType.path))
@@ -260,11 +261,15 @@ def forward(width,
current_index += 1
is_closed = args[current_index]
current_index += 1
use_distance_approx = args[current_index]
current_index += 1
shape = diffvg.Path(diffvg.int_ptr(pydiffvg.data_ptr(num_control_points)),
diffvg.float_ptr(pydiffvg.data_ptr(points)),
diffvg.float_ptr(0), # thickness
num_control_points.shape[0],
points.shape[0],
is_closed)
is_closed,
use_distance_approx)
elif shape_type == diffvg.ShapeType.rect:
p_min = args[current_index]
current_index += 1
@@ -545,10 +550,11 @@ def render(*x):
elif d_shape.type == diffvg.ShapeType.path:
d_path = d_shape.as_path()
points = tf.zeros((d_path.num_points, 2), dtype=tf.float32)
d_path.copy_to(diffvg.float_ptr(points.data_ptr()))
d_path.copy_to(diffvg.float_ptr(pydiffvg.data_ptr(points)),diffvg.float_ptr(0))
d_args.append(None) # num_control_points
d_args.append(points)
d_args.append(None) # is_closed
d_args.append(None) # use_distance_approx
elif d_shape.type == diffvg.ShapeType.rect:
d_rect = d_shape.as_rect()
p_min = tf.constant((d_rect.p_min.x, d_rect.p_min.y))

3
pydiffvg_tensorflow/shape.py
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@@ -16,12 +16,13 @@ class Ellipse:
self.id = id
class Path:
def __init__(self, num_control_points, points, is_closed, stroke_width = tf.constant(1.0), id = ''):
def __init__(self, num_control_points, points, is_closed, stroke_width = tf.constant(1.0), id = '', use_distance_approx = False):
self.num_control_points = num_control_points
self.points = points
self.is_closed = is_closed
self.stroke_width = stroke_width
self.id = id
self.use_distance_approx = use_distance_approx
class Polygon:
def __init__(self, points, is_closed, stroke_width = tf.constant(1.0), id = ''):