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fadb92b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | import native_rasterizer
import torch
import torch.nn as nn
from torch.autograd import Function
MODE_BOUNDARY = "boundary"
MODE_MASK = "mask"
MODE_HARD_MASK = "hard_mask"
MODE_MAPPING = {MODE_BOUNDARY: 0, MODE_MASK: 1, MODE_HARD_MASK: 2}
class SoftPolygonFunction(Function):
@staticmethod
def forward(ctx, vertices, width, height, inv_smoothness=1.0, mode=MODE_BOUNDARY):
ctx.width = width
ctx.height = height
ctx.inv_smoothness = inv_smoothness
ctx.mode = MODE_MAPPING[mode]
vertices = vertices.clone()
ctx.device = vertices.device
ctx.batch_size, ctx.number_vertices = vertices.shape[:2]
rasterized = torch.FloatTensor(ctx.batch_size, ctx.height, ctx.width).fill_(0.0).to(device=ctx.device)
contribution_map = torch.IntTensor(ctx.batch_size, ctx.height, ctx.width).fill_(0).to(device=ctx.device)
rasterized, contribution_map = native_rasterizer.forward_rasterize(
vertices, rasterized, contribution_map, width, height, inv_smoothness, ctx.mode
)
ctx.save_for_backward(vertices, rasterized, contribution_map)
return rasterized # , contribution_map
@staticmethod
def backward(ctx, grad_output):
vertices, rasterized, contribution_map = ctx.saved_tensors
grad_output = grad_output.contiguous()
# grad_vertices = torch.FloatTensor(
# ctx.batch_size, ctx.height, ctx.width, ctx.number_vertices, 2).fill_(0.0).to(device=ctx.device)
grad_vertices = torch.FloatTensor(ctx.batch_size, ctx.number_vertices, 2).fill_(0.0).to(device=ctx.device)
grad_vertices = native_rasterizer.backward_rasterize(
vertices,
rasterized,
contribution_map,
grad_output,
grad_vertices,
ctx.width,
ctx.height,
ctx.inv_smoothness,
ctx.mode,
)
return grad_vertices, None, None, None, None
class SoftPolygon(nn.Module):
MODES = [MODE_BOUNDARY, MODE_MASK, MODE_HARD_MASK]
def __init__(self, inv_smoothness=1.0, mode=MODE_BOUNDARY):
super(SoftPolygon, self).__init__()
self.inv_smoothness = inv_smoothness
if mode not in SoftPolygon.MODES:
raise ValueError("invalid mode: {0}".format(mode))
self.mode = mode
def forward(self, vertices, width, height, p, color=False):
return SoftPolygonFunction.apply(vertices, width, height, self.inv_smoothness, self.mode)
def pnp(vertices, width, height):
device = vertices.device
batch_size = vertices.size(0)
polygon_dimension = vertices.size(1)
y_index = torch.arange(0, height).to(device)
x_index = torch.arange(0, width).to(device)
grid_y, grid_x = torch.meshgrid(y_index, x_index)
xp = grid_x.unsqueeze(0).repeat(batch_size, 1, 1).float()
yp = grid_y.unsqueeze(0).repeat(batch_size, 1, 1).float()
result = torch.zeros((batch_size, height, width)).bool().to(device)
j = polygon_dimension - 1
for vn in range(polygon_dimension):
from_x = vertices[:, vn, 0].unsqueeze(-1).unsqueeze(-1).repeat(1, height, width)
from_y = vertices[:, vn, 1].unsqueeze(-1).unsqueeze(-1).repeat(1, height, width)
to_x = vertices[:, j, 0].unsqueeze(-1).unsqueeze(-1).repeat(1, height, width)
to_y = vertices[:, j, 1].unsqueeze(-1).unsqueeze(-1).repeat(1, height, width)
has_condition = torch.logical_and(
(from_y > yp) != (to_y > yp), xp < ((to_x - from_x) * (yp - from_y) / (to_y - from_y) + from_x)
)
if has_condition.any():
result[has_condition] = ~result[has_condition]
j = vn
signed_result = -torch.ones((batch_size, height, width), device=device)
signed_result[result] = 1.0
return signed_result
# used for verification purposes only.
class SoftPolygonPyTorch(nn.Module):
def __init__(self, inv_smoothness=1.0):
super(SoftPolygonPyTorch, self).__init__()
self.inv_smoothness = inv_smoothness
# vertices is N x P x 2
def forward(self, vertices, width, height, p, color=False):
device = vertices.device
batch_size = vertices.size(0)
polygon_dimension = vertices.size(1)
inside_outside = pnp(vertices, width, height)
# discrete points we will sample from.
y_index = torch.arange(0, height).to(device)
x_index = torch.arange(0, width).to(device)
grid_y, grid_x = torch.meshgrid(y_index, x_index)
grid_x = grid_x.unsqueeze(0).repeat(batch_size, 1, 1).float()
grid_y = grid_y.unsqueeze(0).repeat(batch_size, 1, 1).float()
# do this "per dimension"
distance_segments = []
over_segments = []
for from_index in range(polygon_dimension):
segment_result = torch.zeros((batch_size, height, width)).to(device)
from_vertex = vertices[:, from_index].unsqueeze(-1).unsqueeze(-1)
if from_index == (polygon_dimension - 1):
to_vertex = vertices[:, 0].unsqueeze(-1).unsqueeze(-1)
else:
to_vertex = vertices[:, from_index + 1].unsqueeze(-1).unsqueeze(-1)
x2_sub_x1 = to_vertex[:, 0] - from_vertex[:, 0]
y2_sub_y1 = to_vertex[:, 1] - from_vertex[:, 1]
square_segment_length = x2_sub_x1 * x2_sub_x1 + y2_sub_y1 * y2_sub_y1 + 0.00001
# figure out if this is a major/minor segment (todo?)
x_sub_x1 = grid_x - from_vertex[:, 0]
y_sub_y1 = grid_y - from_vertex[:, 1]
x_sub_x2 = grid_x - to_vertex[:, 0]
y_sub_y2 = grid_y - to_vertex[:, 1]
# dot between the given point and first vertex and first vertex and second vertex.
dot = ((x_sub_x1 * x2_sub_x1) + (y_sub_y1 * y2_sub_y1)) / square_segment_length
# needlessly computed sometimes.
x_proj = grid_x - (from_vertex[:, 0] + dot * x2_sub_x1)
y_proj = grid_y - (from_vertex[:, 1] + dot * y2_sub_y1)
from_closest = dot < 0
to_closest = dot > 1
interior_closest = (dot >= 0) & (dot <= 1)
segment_result[from_closest] = x_sub_x1[from_closest] ** 2 + y_sub_y1[from_closest] ** 2
segment_result[to_closest] = x_sub_x2[to_closest] ** 2 + y_sub_y2[to_closest] ** 2
segment_result[interior_closest] = x_proj[interior_closest] ** 2 + y_proj[interior_closest] ** 2
distance_map = -segment_result
distance_segments.append(distance_map)
signed_map = torch.sigmoid(-distance_map * inside_outside / self.inv_smoothness)
over_segments.append(signed_map)
F_max, F_arg = torch.max(torch.stack(distance_segments, dim=-1), dim=-1)
F_theta = torch.gather(torch.stack(over_segments, dim=-1), dim=-1, index=F_arg.unsqueeze(-1))[..., 0]
return F_theta
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