def backward(grad_img):
scene = ctx.scene
width = ctx.width
height = ctx.height
num_samples_x = ctx.num_samples_x
num_samples_y = ctx.num_samples_y
seed = ctx.seed
output_type = ctx.output_type
use_prefiltering = ctx.use_prefiltering
start = time.time()
with tf.device(pydiffvg.get_device_name()):
diffvg.render(
scene,
diffvg.float_ptr(0), # background_image
diffvg.float_ptr(0), # render_image
diffvg.float_ptr(0), # render_sdf
width,
height,
num_samples_x,
num_samples_y,
seed,
diffvg.float_ptr(0), # d_background_image
diffvg.float_ptr(
pydiffvg.data_ptr(grad_img) if output_type ==
OutputType.color else 0),
diffvg.float_ptr(
pydiffvg.data_ptr(grad_img) if output_type ==
OutputType.sdf else 0),
diffvg.float_ptr(0), # d_translation
use_prefiltering,
diffvg.float_ptr(0), # eval_positions
0) # num_eval_positions (automatically set to entire raster))
time_elapsed = time.time() - start
global print_timing
if print_timing:
print('Backward pass, time: %.5f s' % time_elapsed)
with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())):
d_args = []
d_args.append(None) # width
d_args.append(None) # height
d_args.append(None) # num_samples_x
d_args.append(None) # num_samples_y
d_args.append(None) # seed
d_args.append(None) # canvas_width
d_args.append(None) # canvas_height
d_args.append(None) # num_shapes
d_args.append(None) # num_shape_groups
d_args.append(None) # output_type
d_args.append(None) # use_prefiltering
for shape_id in range(scene.num_shapes):
d_args.append(None) # type
d_shape = scene.get_d_shape(shape_id)
if d_shape.type == diffvg.ShapeType.circle:
d_circle = d_shape.as_circle()
radius = tf.constant(d_circle.radius)
d_args.append(radius)
c = d_circle.center
c = tf.constant((c.x, c.y))
d_args.append(c)
elif d_shape.type == diffvg.ShapeType.ellipse:
d_ellipse = d_shape.as_ellipse()
r = d_ellipse.radius
r = tf.constant((d_ellipse.radius.x, d_ellipse.radius.y))
d_args.append(r)
c = d_ellipse.center
c = tf.constant((c.x, c.y))
d_args.append(c)
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_args.append(None) # num_control_points
d_args.append(points)
d_args.append(None) # is_closed
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))
p_max = tf.constant((d_rect.p_max.x, d_rect.p_max.y))
d_args.append(p_min)
d_args.append(p_max)
else:
assert (False)
w = tf.constant((d_shape.stroke_width))
d_args.append(w)
for group_id in range(scene.num_shape_groups):
d_shape_group = scene.get_d_shape_group(group_id)
d_args.append(None) # shape_ids
d_args.append(None) # fill_color_type
if d_shape_group.has_fill_color():
if d_shape_group.fill_color_type == diffvg.ColorType.constant:
d_constant = d_shape_group.fill_color_as_constant()
c = d_constant.color
d_args.append(tf.constant((c.x, c.y, c.z, c.w)))
elif d_shape_group.fill_color_type == diffvg.ColorType.linear_gradient:
d_linear_gradient = d_shape_group.fill_color_as_linear_gradient(
)
beg = d_linear_gradient.begin
d_args.append(tf.constant((beg.x, beg.y)))
end = d_linear_gradient.end
d_args.append(tf.constant((end.x, end.y)))
offsets = tf.zeros((d_linear_gradient.num_stops),
dtype=tf.float32)
stop_colors = tf.zeros(
(d_linear_gradient.num_stops, 4), dtype=tf.float32)
# HACK: tensorflow's eager mode uses a cache to store scalar
# constants to avoid memory copy. If we pass scalar tensors
# into the C++ code and modify them, we would corrupt the
# cache, causing incorrect result in future scalar constant
# creations. Thus we force tensorflow to copy by plusing a zero.
# (also see https://github.com/tensorflow/tensorflow/issues/11186
# for more discussion regarding copying tensors)
if offsets.shape.num_elements() == 1:
offsets = offsets + 0
d_linear_gradient.copy_to(\
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
d_args.append(offsets)
d_args.append(stop_colors)
elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient:
d_radial_gradient = d_shape_group.fill_color_as_radial_gradient(
)
center = d_radial_gradient.center
d_args.append(tf.constant((center.x, center.y)))
radius = d_radial_gradient.radius
d_args.append(tf.constant((radius.x, radius.y)))
offsets = tf.zeros((d_radial_gradient.num_stops))
if offsets.shape.num_elements() == 1:
offsets = offsets + 0
stop_colors = tf.zeros(
(d_radial_gradient.num_stops, 4))
d_radial_gradient.copy_to(\
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
d_args.append(offsets)
d_args.append(stop_colors)
else:
assert (False)
d_args.append(None) # stroke_color_type
if d_shape_group.has_stroke_color():
if d_shape_group.stroke_color_type == diffvg.ColorType.constant:
d_constant = d_shape_group.stroke_color_as_constant()
c = d_constant.color
d_args.append(tf.constant((c.x, c.y, c.z, c.w)))
elif d_shape_group.stroke_color_type == diffvg.ColorType.linear_gradient:
d_linear_gradient = d_shape_group.stroke_color_as_linear_gradient(
)
beg = d_linear_gradient.begin
d_args.append(tf.constant((beg.x, beg.y)))
end = d_linear_gradient.end
d_args.append(tf.constant((end.x, end.y)))
offsets = tf.zeros((d_linear_gradient.num_stops))
stop_colors = tf.zeros(
(d_linear_gradient.num_stops, 4))
if offsets.shape.num_elements() == 1:
offsets = offsets + 0
d_linear_gradient.copy_to(\
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
d_args.append(offsets)
d_args.append(stop_colors)
elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient:
d_radial_gradient = d_shape_group.stroke_color_as_radial_gradient(
)
center = d_radial_gradient.center
d_args.append(tf.constant((center.x, center.y)))
radius = d_radial_gradient.radius
d_args.append(tf.constant((radius.x, radius.y)))
offsets = tf.zeros((d_radial_gradient.num_stops))
stop_colors = tf.zeros(
(d_radial_gradient.num_stops, 4))
if offsets.shape.num_elements() == 1:
offsets = offsets + 0
d_radial_gradient.copy_to(\
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
d_args.append(offsets)
d_args.append(stop_colors)
else:
assert (False)
d_args.append(None) # use_even_odd_rule
d_shape_to_canvas = tf.zeros((3, 3), dtype=tf.float32)
d_shape_group.copy_to(
diffvg.float_ptr(pydiffvg.data_ptr(d_shape_to_canvas)))
d_args.append(d_shape_to_canvas)
d_args.append(None) # filter_type
d_args.append(tf.constant(scene.get_d_filter_radius()))
return d_args
def backward(ctx,
grad_img):
if not grad_img.is_contiguous():
grad_img = grad_img.contiguous()
assert(torch.isfinite(grad_img).all())
scene = ctx.scene
width = ctx.width
height = ctx.height
num_samples_x = ctx.num_samples_x
num_samples_y = ctx.num_samples_y
seed = ctx.seed
output_type = ctx.output_type
use_prefiltering = ctx.use_prefiltering
eval_positions = ctx.eval_positions
background_image = ctx.background_image
if background_image is not None:
d_background_image = torch.zeros_like(background_image)
else:
d_background_image = None
start = time.time()
diffvg.render(scene,
diffvg.float_ptr(background_image.data_ptr() if background_image is not None else 0),
diffvg.float_ptr(0), # render_image
diffvg.float_ptr(0), # render_sdf
width,
height,
num_samples_x,
num_samples_y,
seed,
diffvg.float_ptr(d_background_image.data_ptr() if background_image is not None else 0),
diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.color else 0),
diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.sdf else 0),
diffvg.float_ptr(0), # d_translation
use_prefiltering,
diffvg.float_ptr(eval_positions.data_ptr()),
eval_positions.shape[0])
time_elapsed = time.time() - start
global print_timing
if print_timing:
print('Backward pass, time: %.5f s' % time_elapsed)
d_args = []
d_args.append(None) # width
d_args.append(None) # height
d_args.append(None) # num_samples_x
d_args.append(None) # num_samples_y
d_args.append(None) # seed
d_args.append(d_background_image)
d_args.append(None) # canvas_width
d_args.append(None) # canvas_height
d_args.append(None) # num_shapes
d_args.append(None) # num_shape_groups
d_args.append(None) # output_type
d_args.append(None) # use_prefiltering
d_args.append(None) # eval_positions
for shape_id in range(scene.num_shapes):
d_args.append(None) # type
d_shape = scene.get_d_shape(shape_id)
use_thickness = False
if d_shape.type == diffvg.ShapeType.circle:
d_circle = d_shape.as_circle()
radius = torch.tensor(d_circle.radius)
assert(torch.isfinite(radius).all())
d_args.append(radius)
c = d_circle.center
c = torch.tensor((c.x, c.y))
assert(torch.isfinite(c).all())
d_args.append(c)
elif d_shape.type == diffvg.ShapeType.ellipse:
d_ellipse = d_shape.as_ellipse()
r = d_ellipse.radius
r = torch.tensor((d_ellipse.radius.x, d_ellipse.radius.y))
assert(torch.isfinite(r).all())
d_args.append(r)
c = d_ellipse.center
c = torch.tensor((c.x, c.y))
assert(torch.isfinite(c).all())
d_args.append(c)
elif d_shape.type == diffvg.ShapeType.path:
d_path = d_shape.as_path()
points = torch.zeros((d_path.num_points, 2))
thickness = None
if d_path.has_thickness():
use_thickness = True
thickness = torch.zeros(d_path.num_points)
d_path.copy_to(diffvg.float_ptr(points.data_ptr()), diffvg.float_ptr(thickness.data_ptr()))
else:
d_path.copy_to(diffvg.float_ptr(points.data_ptr()), diffvg.float_ptr(0))
assert(torch.isfinite(points).all())
if thickness is not None:
assert(torch.isfinite(thickness).all())
d_args.append(None) # num_control_points
d_args.append(points)
d_args.append(thickness)
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 = torch.tensor((d_rect.p_min.x, d_rect.p_min.y))
p_max = torch.tensor((d_rect.p_max.x, d_rect.p_max.y))
assert(torch.isfinite(p_min).all())
assert(torch.isfinite(p_max).all())
d_args.append(p_min)
d_args.append(p_max)
else:
assert(False)
if use_thickness:
d_args.append(None)
else:
w = torch.tensor((d_shape.stroke_width))
assert(torch.isfinite(w).all())
d_args.append(w)
for group_id in range(scene.num_shape_groups):
d_shape_group = scene.get_d_shape_group(group_id)
d_args.append(None) # shape_ids
d_args.append(None) # fill_color_type
if d_shape_group.has_fill_color():
if d_shape_group.fill_color_type == diffvg.ColorType.constant:
d_constant = d_shape_group.fill_color_as_constant()
c = d_constant.color
d_args.append(torch.tensor((c.x, c.y, c.z, c.w)))
elif d_shape_group.fill_color_type == diffvg.ColorType.linear_gradient:
d_linear_gradient = d_shape_group.fill_color_as_linear_gradient()
beg = d_linear_gradient.begin
d_args.append(torch.tensor((beg.x, beg.y)))
end = d_linear_gradient.end
d_args.append(torch.tensor((end.x, end.y)))
offsets = torch.zeros((d_linear_gradient.num_stops))
stop_colors = torch.zeros((d_linear_gradient.num_stops, 4))
d_linear_gradient.copy_to(
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
assert(torch.isfinite(stop_colors).all())
d_args.append(offsets)
d_args.append(stop_colors)
elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient:
d_radial_gradient = d_shape_group.fill_color_as_radial_gradient()
center = d_radial_gradient.center
d_args.append(torch.tensor((center.x, center.y)))
radius = d_radial_gradient.radius
d_args.append(torch.tensor((radius.x, radius.y)))
offsets = torch.zeros((d_radial_gradient.num_stops))
stop_colors = torch.zeros((d_radial_gradient.num_stops, 4))
d_radial_gradient.copy_to(
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
assert(torch.isfinite(stop_colors).all())
d_args.append(offsets)
d_args.append(stop_colors)
else:
assert(False)
d_args.append(None) # stroke_color_type
if d_shape_group.has_stroke_color():
if d_shape_group.stroke_color_type == diffvg.ColorType.constant:
d_constant = d_shape_group.stroke_color_as_constant()
c = d_constant.color
d_args.append(torch.tensor((c.x, c.y, c.z, c.w)))
elif d_shape_group.stroke_color_type == diffvg.ColorType.linear_gradient:
d_linear_gradient = d_shape_group.stroke_color_as_linear_gradient()
beg = d_linear_gradient.begin
d_args.append(torch.tensor((beg.x, beg.y)))
end = d_linear_gradient.end
d_args.append(torch.tensor((end.x, end.y)))
offsets = torch.zeros((d_linear_gradient.num_stops))
stop_colors = torch.zeros((d_linear_gradient.num_stops, 4))
d_linear_gradient.copy_to(
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
assert(torch.isfinite(stop_colors).all())
d_args.append(offsets)
d_args.append(stop_colors)
elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient:
d_radial_gradient = d_shape_group.stroke_color_as_radial_gradient()
center = d_radial_gradient.center
d_args.append(torch.tensor((center.x, center.y)))
radius = d_radial_gradient.radius
d_args.append(torch.tensor((radius.x, radius.y)))
offsets = torch.zeros((d_radial_gradient.num_stops))
stop_colors = torch.zeros((d_radial_gradient.num_stops, 4))
d_radial_gradient.copy_to(
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
assert(torch.isfinite(stop_colors).all())
d_args.append(offsets)
d_args.append(stop_colors)
else:
assert(False)
d_args.append(None) # use_even_odd_rule
d_shape_to_canvas = torch.zeros((3, 3))
d_shape_group.copy_to(diffvg.float_ptr(d_shape_to_canvas.data_ptr()))
assert(torch.isfinite(d_shape_to_canvas).all())
d_args.append(d_shape_to_canvas)
d_args.append(None) # filter_type
d_args.append(torch.tensor(scene.get_d_filter_radius()))
return tuple(d_args)
def forward(width, height, num_samples_x, num_samples_y, seed, *args):
"""
Forward rendering pass: given a serialized scene and output an image.
"""
# Unpack arguments
with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())):
current_index = 0
canvas_width = int(args[current_index])
current_index += 1
canvas_height = int(args[current_index])
current_index += 1
num_shapes = int(args[current_index])
current_index += 1
num_shape_groups = int(args[current_index])
current_index += 1
output_type = OutputType(int(args[current_index]))
current_index += 1
use_prefiltering = bool(args[current_index])
current_index += 1
shapes = []
shape_groups = []
shape_contents = [] # Important to avoid GC deleting the shapes
color_contents = [] # Same as above
for shape_id in range(num_shapes):
shape_type = ShapeType.asShapeType(args[current_index])
current_index += 1
if shape_type == diffvg.ShapeType.circle:
radius = args[current_index]
current_index += 1
center = args[current_index]
current_index += 1
shape = diffvg.Circle(
float(radius),
diffvg.Vector2f(float(center[0]), float(center[1])))
elif shape_type == diffvg.ShapeType.ellipse:
radius = args[current_index]
current_index += 1
center = args[current_index]
current_index += 1
shape = diffvg.Ellipse(
diffvg.Vector2f(float(radius[0]), float(radius[1])),
diffvg.Vector2f(float(center[0]), float(center[1])))
elif shape_type == diffvg.ShapeType.path:
num_control_points = args[current_index]
current_index += 1
points = args[current_index]
current_index += 1
is_closed = 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)),
num_control_points.shape[0], points.shape[0], is_closed)
elif shape_type == diffvg.ShapeType.rect:
p_min = args[current_index]
current_index += 1
p_max = args[current_index]
current_index += 1
shape = diffvg.Rect(
diffvg.Vector2f(float(p_min[0]), float(p_min[1])),
diffvg.Vector2f(float(p_max[0]), float(p_max[1])))
else:
assert (False)
stroke_width = args[current_index]
current_index += 1
shapes.append(diffvg.Shape(\
shape_type, shape.get_ptr(), float(stroke_width)))
shape_contents.append(shape)
for shape_group_id in range(num_shape_groups):
shape_ids = args[current_index]
current_index += 1
fill_color_type = ColorType.asColorType(args[current_index])
current_index += 1
if fill_color_type == diffvg.ColorType.constant:
color = args[current_index]
current_index += 1
fill_color = diffvg.Constant(\
diffvg.Vector4f(color[0], color[1], color[2], color[3]))
elif fill_color_type == diffvg.ColorType.linear_gradient:
beg = args[current_index]
current_index += 1
end = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert (offsets.shape[0] == stop_colors.shape[0])
fill_color = diffvg.LinearGradient(
diffvg.Vector2f(float(beg[0]), float(beg[1])),
diffvg.Vector2f(float(end[0]), float(end[1])),
offsets.shape[0],
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
elif fill_color_type == diffvg.ColorType.radial_gradient:
center = args[current_index]
current_index += 1
radius = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert (offsets.shape[0] == stop_colors.shape[0])
fill_color = diffvg.RadialGradient(
diffvg.Vector2f(float(center[0]), float(center[1])),
diffvg.Vector2f(float(radius[0]), float(radius[1])),
offsets.shape[0],
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
elif fill_color_type is None:
fill_color = None
else:
assert (False)
stroke_color_type = ColorType.asColorType(args[current_index])
current_index += 1
if stroke_color_type == diffvg.ColorType.constant:
color = args[current_index]
current_index += 1
stroke_color = diffvg.Constant(\
diffvg.Vector4f(float(color[0]),
float(color[1]),
float(color[2]),
float(color[3])))
elif stroke_color_type == diffvg.ColorType.linear_gradient:
beg = args[current_index]
current_index += 1
end = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert (offsets.shape[0] == stop_colors.shape[0])
stroke_color = diffvg.LinearGradient(\
diffvg.Vector2f(float(beg[0]), float(beg[1])),
diffvg.Vector2f(float(end[0]), float(end[1])),
offsets.shape[0],
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(stop_colors.data_ptr()))
elif stroke_color_type == diffvg.ColorType.radial_gradient:
center = args[current_index]
current_index += 1
radius = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert (offsets.shape[0] == stop_colors.shape[0])
stroke_color = diffvg.RadialGradient(\
diffvg.Vector2f(float(center[0]), float(center[1])),
diffvg.Vector2f(float(radius[0]), float(radius[1])),
offsets.shape[0],
diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
elif stroke_color_type is None:
stroke_color = None
else:
assert (False)
use_even_odd_rule = bool(args[current_index])
current_index += 1
shape_to_canvas = args[current_index]
current_index += 1
if fill_color is not None:
color_contents.append(fill_color)
if stroke_color is not None:
color_contents.append(stroke_color)
shape_groups.append(diffvg.ShapeGroup(\
diffvg.int_ptr(pydiffvg.data_ptr(shape_ids)),
shape_ids.shape[0],
diffvg.ColorType.constant if fill_color_type is None else fill_color_type,
diffvg.void_ptr(0) if fill_color is None else fill_color.get_ptr(),
diffvg.ColorType.constant if stroke_color_type is None else stroke_color_type,
diffvg.void_ptr(0) if stroke_color is None else stroke_color.get_ptr(),
use_even_odd_rule,
diffvg.float_ptr(pydiffvg.data_ptr(shape_to_canvas))))
filter_type = FilterType.asFilterType(args[current_index])
current_index += 1
filter_radius = args[current_index]
current_index += 1
filt = diffvg.Filter(filter_type, filter_radius)
device_name = pydiffvg.get_device_name()
device_spec = tf.DeviceSpec.from_string(device_name)
use_gpu = device_spec.device_type == 'GPU'
gpu_index = device_spec.device_index if device_spec.device_index is not None else 0
start = time.time()
scene = diffvg.Scene(canvas_width, canvas_height, shapes, shape_groups,
filt, use_gpu, gpu_index)
time_elapsed = time.time() - start
global print_timing
if print_timing:
print('Scene construction, time: %.5f s' % time_elapsed)
with tf.device(device_name):
if output_type == OutputType.color:
rendered_image = tf.zeros((int(height), int(width), 4),
dtype=tf.float32)
else:
assert (output_type == OutputType.sdf)
rendered_image = tf.zeros((int(height), int(width), 1),
dtype=tf.float32)
start = time.time()
diffvg.render(
scene,
diffvg.float_ptr(0), # background image
diffvg.float_ptr(
pydiffvg.data_ptr(rendered_image) if output_type ==
OutputType.color else 0),
diffvg.float_ptr(
pydiffvg.data_ptr(rendered_image) if output_type ==
OutputType.sdf else 0),
width,
height,
int(num_samples_x),
int(num_samples_y),
seed,
diffvg.float_ptr(0), # d_background_image
diffvg.float_ptr(0), # d_render_image
diffvg.float_ptr(0), # d_render_sdf
diffvg.float_ptr(0), # d_translation
use_prefiltering,
diffvg.float_ptr(0), # eval_positions
0) # num_eval_positions (automatically set to entire raster)
time_elapsed = time.time() - start
if print_timing:
print('Forward pass, time: %.5f s' % time_elapsed)
ctx = Context()
ctx.scene = scene
ctx.shape_contents = shape_contents
ctx.color_contents = color_contents
ctx.filter = filt
ctx.width = width
ctx.height = height
ctx.num_samples_x = num_samples_x
ctx.num_samples_y = num_samples_y
ctx.seed = seed
ctx.output_type = output_type
ctx.use_prefiltering = use_prefiltering
return rendered_image, ctx
def render_grad(grad_img,
width,
height,
num_samples_x,
num_samples_y,
seed,
background_image,
*args):
if not grad_img.is_contiguous():
grad_img = grad_img.contiguous()
assert(torch.isfinite(grad_img).all())
# Unpack arguments
current_index = 0
canvas_width = args[current_index]
current_index += 1
canvas_height = args[current_index]
current_index += 1
num_shapes = args[current_index]
current_index += 1
num_shape_groups = args[current_index]
current_index += 1
output_type = args[current_index]
current_index += 1
use_prefiltering = args[current_index]
current_index += 1
eval_positions = args[current_index]
current_index += 1
shapes = []
shape_groups = []
shape_contents = [] # Important to avoid GC deleting the shapes
color_contents = [] # Same as above
for shape_id in range(num_shapes):
shape_type = args[current_index]
current_index += 1
if shape_type == diffvg.ShapeType.circle:
radius = args[current_index]
current_index += 1
center = args[current_index]
current_index += 1
shape = diffvg.Circle(radius, diffvg.Vector2f(center[0], center[1]))
elif shape_type == diffvg.ShapeType.ellipse:
radius = args[current_index]
current_index += 1
center = args[current_index]
current_index += 1
shape = diffvg.Ellipse(diffvg.Vector2f(radius[0], radius[1]),
diffvg.Vector2f(center[0], center[1]))
elif shape_type == diffvg.ShapeType.path:
num_control_points = args[current_index]
current_index += 1
points = args[current_index]
current_index += 1
thickness = args[current_index]
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(num_control_points.data_ptr()),
diffvg.float_ptr(points.data_ptr()),
diffvg.float_ptr(thickness.data_ptr() if thickness is not None else 0),
num_control_points.shape[0],
points.shape[0],
is_closed,
use_distance_approx)
elif shape_type == diffvg.ShapeType.rect:
p_min = args[current_index]
current_index += 1
p_max = args[current_index]
current_index += 1
shape = diffvg.Rect(diffvg.Vector2f(p_min[0], p_min[1]),
diffvg.Vector2f(p_max[0], p_max[1]))
else:
assert(False)
stroke_width = args[current_index]
current_index += 1
shapes.append(diffvg.Shape(
shape_type, shape.get_ptr(), stroke_width.item()))
shape_contents.append(shape)
for shape_group_id in range(num_shape_groups):
shape_ids = args[current_index]
current_index += 1
fill_color_type = args[current_index]
current_index += 1
if fill_color_type == diffvg.ColorType.constant:
color = args[current_index]
current_index += 1
fill_color = diffvg.Constant(
diffvg.Vector4f(color[0], color[1], color[2], color[3]))
elif fill_color_type == diffvg.ColorType.linear_gradient:
beg = args[current_index]
current_index += 1
end = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert(offsets.shape[0] == stop_colors.shape[0])
fill_color = diffvg.LinearGradient(diffvg.Vector2f(beg[0], beg[1]),
diffvg.Vector2f(end[0], end[1]),
offsets.shape[0],
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
elif fill_color_type == diffvg.ColorType.radial_gradient:
center = args[current_index]
current_index += 1
radius = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert(offsets.shape[0] == stop_colors.shape[0])
fill_color = diffvg.RadialGradient(diffvg.Vector2f(center[0], center[1]),
diffvg.Vector2f(radius[0], radius[1]),
offsets.shape[0],
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
elif fill_color_type is None:
fill_color = None
else:
assert(False)
stroke_color_type = args[current_index]
current_index += 1
if stroke_color_type == diffvg.ColorType.constant:
color = args[current_index]
current_index += 1
stroke_color = diffvg.Constant(
diffvg.Vector4f(color[0], color[1], color[2], color[3]))
elif stroke_color_type == diffvg.ColorType.linear_gradient:
beg = args[current_index]
current_index += 1
end = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert(offsets.shape[0] == stop_colors.shape[0])
stroke_color = diffvg.LinearGradient(diffvg.Vector2f(beg[0], beg[1]),
diffvg.Vector2f(end[0], end[1]),
offsets.shape[0],
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
elif stroke_color_type == diffvg.ColorType.radial_gradient:
center = args[current_index]
current_index += 1
radius = args[current_index]
current_index += 1
offsets = args[current_index]
current_index += 1
stop_colors = args[current_index]
current_index += 1
assert(offsets.shape[0] == stop_colors.shape[0])
stroke_color = diffvg.RadialGradient(diffvg.Vector2f(center[0], center[1]),
diffvg.Vector2f(radius[0], radius[1]),
offsets.shape[0],
diffvg.float_ptr(offsets.data_ptr()),
diffvg.float_ptr(stop_colors.data_ptr()))
elif stroke_color_type is None:
stroke_color = None
else:
assert(False)
use_even_odd_rule = args[current_index]
current_index += 1
shape_to_canvas = args[current_index]
current_index += 1
if fill_color is not None:
color_contents.append(fill_color)
if stroke_color is not None:
color_contents.append(stroke_color)
shape_groups.append(diffvg.ShapeGroup(
diffvg.int_ptr(shape_ids.data_ptr()),
shape_ids.shape[0],
diffvg.ColorType.constant if fill_color_type is None else fill_color_type,
diffvg.void_ptr(0) if fill_color is None else fill_color.get_ptr(),
diffvg.ColorType.constant if stroke_color_type is None else stroke_color_type,
diffvg.void_ptr(0) if stroke_color is None else stroke_color.get_ptr(),
use_even_odd_rule,
diffvg.float_ptr(shape_to_canvas.data_ptr())))
filter_type = args[current_index]
current_index += 1
filter_radius = args[current_index]
current_index += 1
filt = diffvg.Filter(filter_type, filter_radius)
scene = diffvg.Scene(canvas_width, canvas_height,
shapes, shape_groups, filt, pydiffvg.get_use_gpu(),
pydiffvg.get_device().index if pydiffvg.get_device().index is not None else -1)
if output_type == OutputType.color:
assert(grad_img.shape[2] == 4)
else:
assert(grad_img.shape[2] == 1)
if background_image is not None:
background_image = background_image.to(pydiffvg.get_device())
if background_image.shape[2] == 3:
background_image = torch.cat((
background_image, torch.ones(background_image.shape[0], background_image.shape[1], 1,
device=background_image.device)), dim=2)
background_image = background_image.contiguous()
# assert(background_image.shape[0] == rendered_image.shape[0])
# assert(background_image.shape[1] == rendered_image.shape[1])
assert(background_image.shape[2] == 4)
translation_grad_image = \
torch.zeros(height, width, 2, device=pydiffvg.get_device())
start = time.time()
diffvg.render(scene,
diffvg.float_ptr(background_image.data_ptr() if background_image is not None else 0),
diffvg.float_ptr(0), # render_image
diffvg.float_ptr(0), # render_sdf
width,
height,
num_samples_x,
num_samples_y,
seed,
diffvg.float_ptr(0), # d_background_image
diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.color else 0),
diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.sdf else 0),
diffvg.float_ptr(translation_grad_image.data_ptr()),
use_prefiltering,
diffvg.float_ptr(eval_positions.data_ptr()),
eval_positions.shape[0])
time_elapsed = time.time() - start
if print_timing:
print('Gradient pass, time: %.5f s' % time_elapsed)
assert(torch.isfinite(translation_grad_image).all())
return translation_grad_image
