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fadb92b ef36c4f 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 | import copy
import itertools
import math
import numpy as np
import torch
from torch import nn
class _Registry:
"""Minimal replacement for detectron2.utils.registry.Registry."""
def __init__(self, name):
self._name = name
self._obj_map = {}
def register(self, obj=None):
if obj is None:
def decorator(func_or_class):
self._obj_map[func_or_class.__name__] = func_or_class
return func_or_class
return decorator
self._obj_map[obj.__name__] = obj
return obj
def get(self, name):
return self._obj_map[name]
POLY_LOSS_REGISTRY = _Registry("POLY_LOSS")
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1)
def box_xyxy_to_cxcywh(x):
x0, y0, x1, y1 = x.unbind(-1)
b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
return torch.stack(b, dim=-1)
def clip_and_normalize_polygons(polys, inf_value=2.01):
min_x, _ = polys[:, :, 0].min(dim=-1)
min_y, _ = polys[:, :, 1].min(dim=-1)
polys[torch.isinf(polys)] = -np.inf
max_x, _ = polys[:, :, 0].max(dim=-1)
max_y, _ = polys[:, :, 1].max(dim=-1)
polys[torch.isinf(polys)] = inf_value
min_xy = torch.stack((min_x, min_y), dim=-1)
max_xy = torch.stack((max_x, max_y), dim=-1) - min_xy
polys = (polys - min_xy.unsqueeze(1)) / max_xy.unsqueeze(1)
return polys
def pad_polygons(polys):
count = len(polys)
max_vertices = max([len(p) for p in polys])
pad_count = [max_vertices - len(p) for p in polys]
# add between the first and second vertices.
xs = [np.linspace(polys[i][0][0] + 0.00001, polys[i][1][0] - 0.00001, num=pad_count[i]) for i in range(count)]
ys = [np.linspace(polys[i][0][1] + 0.00001, polys[i][1][1] - 0.00001, num=pad_count[i]) for i in range(count)]
xys = [np.stack((xs[i], ys[i]), axis=-1) for i in range(count)]
polys = [np.concatenate((polys[i][:1], xys[i], polys[i][1:])) for i in range(count)]
return np.stack(polys)
def rasterize_instances(rasterizer, instances, shape, offset=0.0):
if shape[0] != shape[1]:
raise ValueError("expected square")
device = instances[0].gt_boxes.device
all_polygons = clip_and_normalize_polygons(
torch.from_numpy(
pad_polygons(
list(
itertools.chain.from_iterable(
[[p[0].reshape(-1, 2) for p in inst.gt_masks.polygons] for inst in instances]
)
)
)
)
.float()
.to(device)
)
# to me it seems the offset would need to be in _pixel_ space?
return rasterizer(all_polygons * float(shape[1].item()) + offset, shape[1].item(), shape[0].item(), 1.0)
def get_union_box(p, box):
# compute the enclosing box.
all_points = torch.cat((p, box.view(-1, 2, 2)), dim=-2)
min_xy = torch.min(all_points, dim=-2)[0]
max_xy = torch.max(all_points, dim=-2)[0]
return torch.cat((min_xy, max_xy), dim=-1)
def sample_ellipse_fast(x, y, r1, r2, count=32, dt=0.01):
batch_size, num_el = r1.shape
device = r1.device
num_integrals = int(round(2 * math.pi / dt))
thetas = dt * torch.arange(num_integrals, device=device).unsqueeze(0).unsqueeze(0).repeat(batch_size, num_el, 1)
thetas_c = torch.cumsum(thetas, dim=-1)
dpt = torch.sqrt((r1.unsqueeze(-1) * torch.sin(thetas_c)) ** 2 + (r2.unsqueeze(-1) * torch.cos(thetas_c)) ** 2)
circumference = dpt.sum(dim=-1)
run = torch.cumsum(
torch.sqrt(
(r1.unsqueeze(-1) * torch.sin(thetas + dt)) ** 2 + (r2.unsqueeze(-1) * torch.cos(thetas + dt)) ** 2
),
dim=-1,
)
sub = (count * run) / circumference.unsqueeze(-1)
# OK, now find the smallest point >= 0..count-1
counts = (
torch.arange(count, device=device)
.unsqueeze(0)
.unsqueeze(0)
.unsqueeze(0)
.repeat(batch_size, num_el, num_integrals, 1)
)
diff = sub.unsqueeze(dim=-1) - counts
diff[diff < 0] = 10000.0
idx = diff.argmin(dim=2)
thetas = torch.gather(thetas + dt, -1, idx)
xy = torch.stack(
(
x.unsqueeze(-1) + r1.unsqueeze(-1) * torch.cos(thetas),
y.unsqueeze(-1) + r2.unsqueeze(-1) * torch.sin(thetas),
),
dim=-1,
)
return xy
def inverse_sigmoid(x, eps=1e-5):
x = x.clamp(min=0, max=1)
x1 = x.clamp(min=eps)
x2 = (1 - x).clamp(min=eps)
return torch.log(x1 / x2)
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
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