Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,955 Bytes
17cd746 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .core.coord_conv import CoordConvTh
from external.landmark_detection.lib.dataset import get_decoder
class Activation(nn.Module):
def __init__(self, kind: str = 'relu', channel=None):
super().__init__()
self.kind = kind
if '+' in kind:
norm_str, act_str = kind.split('+')
else:
norm_str, act_str = 'none', kind
self.norm_fn = {
'in': F.instance_norm,
'bn': nn.BatchNorm2d(channel),
'bn_noaffine': nn.BatchNorm2d(channel, affine=False, track_running_stats=True),
'none': None
}[norm_str]
self.act_fn = {
'relu': F.relu,
'softplus': nn.Softplus(),
'exp': torch.exp,
'sigmoid': torch.sigmoid,
'tanh': torch.tanh,
'none': None
}[act_str]
self.channel = channel
def forward(self, x):
if self.norm_fn is not None:
x = self.norm_fn(x)
if self.act_fn is not None:
x = self.act_fn(x)
return x
def extra_repr(self):
return f'kind={self.kind}, channel={self.channel}'
class ConvBlock(nn.Module):
def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True, groups=1):
super(ConvBlock, self).__init__()
self.inp_dim = inp_dim
self.conv = nn.Conv2d(inp_dim, out_dim, kernel_size,
stride, padding=(kernel_size - 1) // 2, groups=groups, bias=True)
self.relu = None
self.bn = None
if relu:
self.relu = nn.ReLU()
if bn:
self.bn = nn.BatchNorm2d(out_dim)
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class ResBlock(nn.Module):
def __init__(self, inp_dim, out_dim, mid_dim=None):
super(ResBlock, self).__init__()
if mid_dim is None:
mid_dim = out_dim // 2
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm2d(inp_dim)
self.conv1 = ConvBlock(inp_dim, mid_dim, 1, relu=False)
self.bn2 = nn.BatchNorm2d(mid_dim)
self.conv2 = ConvBlock(mid_dim, mid_dim, 3, relu=False)
self.bn3 = nn.BatchNorm2d(mid_dim)
self.conv3 = ConvBlock(mid_dim, out_dim, 1, relu=False)
self.skip_layer = ConvBlock(inp_dim, out_dim, 1, relu=False)
if inp_dim == out_dim:
self.need_skip = False
else:
self.need_skip = True
def forward(self, x):
if self.need_skip:
residual = self.skip_layer(x)
else:
residual = x
out = self.bn1(x)
out = self.relu(out)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn3(out)
out = self.relu(out)
out = self.conv3(out)
out += residual
return out
class Hourglass(nn.Module):
def __init__(self, n, f, increase=0, up_mode='nearest',
add_coord=False, first_one=False, x_dim=64, y_dim=64):
super(Hourglass, self).__init__()
nf = f + increase
Block = ResBlock
if add_coord:
self.coordconv = CoordConvTh(x_dim=x_dim, y_dim=y_dim,
with_r=True, with_boundary=True,
relu=False, bn=False,
in_channels=f, out_channels=f,
first_one=first_one,
kernel_size=1,
stride=1, padding=0)
else:
self.coordconv = None
self.up1 = Block(f, f)
# Lower branch
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.low1 = Block(f, nf)
self.n = n
# Recursive hourglass
if self.n > 1:
self.low2 = Hourglass(n=n - 1, f=nf, increase=increase, up_mode=up_mode, add_coord=False)
else:
self.low2 = Block(nf, nf)
self.low3 = Block(nf, f)
self.up2 = nn.Upsample(scale_factor=2, mode=up_mode)
def forward(self, x, heatmap=None):
if self.coordconv is not None:
x = self.coordconv(x, heatmap)
up1 = self.up1(x)
pool1 = self.pool1(x)
low1 = self.low1(pool1)
low2 = self.low2(low1)
low3 = self.low3(low2)
up2 = self.up2(low3)
return up1 + up2
class E2HTransform(nn.Module):
def __init__(self, edge_info, num_points, num_edges):
super().__init__()
e2h_matrix = np.zeros([num_points, num_edges])
for edge_id, isclosed_indices in enumerate(edge_info):
is_closed, indices = isclosed_indices
for point_id in indices:
e2h_matrix[point_id, edge_id] = 1
e2h_matrix = torch.from_numpy(e2h_matrix).float()
# pn x en x 1 x 1.
self.register_buffer('weight', e2h_matrix.view(
e2h_matrix.size(0), e2h_matrix.size(1), 1, 1))
# some keypoints are not coverred by any edges,
# in these cases, we must add a constant bias to their heatmap weights.
bias = ((e2h_matrix @ torch.ones(e2h_matrix.size(1)).to(
e2h_matrix)) < 0.5).to(e2h_matrix)
# pn x 1.
self.register_buffer('bias', bias)
def forward(self, edgemaps):
# input: batch_size x en x hw x hh.
# output: batch_size x pn x hw x hh.
return F.conv2d(edgemaps, weight=self.weight, bias=self.bias)
class StackedHGNetV1(nn.Module):
def __init__(self, config, classes_num, edge_info,
nstack=4, nlevels=4, in_channel=256, increase=0,
add_coord=True, decoder_type='default'):
super(StackedHGNetV1, self).__init__()
self.cfg = config
self.coder_type = decoder_type
self.decoder = get_decoder(decoder_type=decoder_type)
self.nstack = nstack
self.add_coord = add_coord
self.num_heats = classes_num[0]
if self.add_coord:
convBlock = CoordConvTh(x_dim=self.cfg.width, y_dim=self.cfg.height,
with_r=True, with_boundary=False,
relu=True, bn=True,
in_channels=3, out_channels=64,
kernel_size=7,
stride=2, padding=3)
else:
convBlock = ConvBlock(3, 64, 7, 2, bn=True, relu=True)
pool = nn.MaxPool2d(kernel_size=2, stride=2)
Block = ResBlock
self.pre = nn.Sequential(
convBlock,
Block(64, 128),
pool,
Block(128, 128),
Block(128, in_channel)
)
self.hgs = nn.ModuleList(
[Hourglass(n=nlevels, f=in_channel, increase=increase, add_coord=self.add_coord, first_one=(_ == 0),
x_dim=int(self.cfg.width / self.nstack), y_dim=int(self.cfg.height / self.nstack))
for _ in range(nstack)])
self.features = nn.ModuleList([
nn.Sequential(
Block(in_channel, in_channel),
ConvBlock(in_channel, in_channel, 1, bn=True, relu=True)
) for _ in range(nstack)])
self.out_heatmaps = nn.ModuleList(
[ConvBlock(in_channel, self.num_heats, 1, relu=False, bn=False)
for _ in range(nstack)])
if self.cfg.use_AAM:
self.num_edges = classes_num[1]
self.num_points = classes_num[2]
self.e2h_transform = E2HTransform(edge_info, self.num_points, self.num_edges)
self.out_edgemaps = nn.ModuleList(
[ConvBlock(in_channel, self.num_edges, 1, relu=False, bn=False)
for _ in range(nstack)])
self.out_pointmaps = nn.ModuleList(
[ConvBlock(in_channel, self.num_points, 1, relu=False, bn=False)
for _ in range(nstack)])
self.merge_edgemaps = nn.ModuleList(
[ConvBlock(self.num_edges, in_channel, 1, relu=False, bn=False)
for _ in range(nstack - 1)])
self.merge_pointmaps = nn.ModuleList(
[ConvBlock(self.num_points, in_channel, 1, relu=False, bn=False)
for _ in range(nstack - 1)])
self.edgemap_act = Activation("sigmoid", self.num_edges)
self.pointmap_act = Activation("sigmoid", self.num_points)
self.merge_features = nn.ModuleList(
[ConvBlock(in_channel, in_channel, 1, relu=False, bn=False)
for _ in range(nstack - 1)])
self.merge_heatmaps = nn.ModuleList(
[ConvBlock(self.num_heats, in_channel, 1, relu=False, bn=False)
for _ in range(nstack - 1)])
self.nstack = nstack
self.heatmap_act = Activation("in+relu", self.num_heats)
self.inference = False
def set_inference(self, inference):
self.inference = inference
def forward(self, x):
x = self.pre(x)
y, fusionmaps = [], []
heatmaps = None
for i in range(self.nstack):
hg = self.hgs[i](x, heatmap=heatmaps)
feature = self.features[i](hg)
heatmaps0 = self.out_heatmaps[i](feature)
heatmaps = self.heatmap_act(heatmaps0)
if self.cfg.use_AAM:
pointmaps0 = self.out_pointmaps[i](feature)
pointmaps = self.pointmap_act(pointmaps0)
edgemaps0 = self.out_edgemaps[i](feature)
edgemaps = self.edgemap_act(edgemaps0)
mask = self.e2h_transform(edgemaps) * pointmaps
fusion_heatmaps = mask * heatmaps
else:
fusion_heatmaps = heatmaps
landmarks = self.decoder.get_coords_from_heatmap(fusion_heatmaps)
if i < self.nstack - 1:
x = x + self.merge_features[i](feature) + \
self.merge_heatmaps[i](heatmaps)
if self.cfg.use_AAM:
x += self.merge_pointmaps[i](pointmaps)
x += self.merge_edgemaps[i](edgemaps)
y.append(landmarks)
if self.cfg.use_AAM:
y.append(pointmaps)
y.append(edgemaps)
fusionmaps.append(fusion_heatmaps)
return y, fusionmaps, landmarks |