-
Notifications
You must be signed in to change notification settings - Fork 58
/
face_ssd.py
287 lines (246 loc) · 10.4 KB
/
face_ssd.py
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
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from .utils import PriorBox
from ..box_utils import batched_decode
class FEM(nn.Module):
def __init__(self, channel_size):
super(FEM, self).__init__()
self.cs = channel_size
self.cpm1 = nn.Conv2d(self.cs, 256, kernel_size=3, padding=1)
self.cpm2 = nn.Conv2d(self.cs, 256, kernel_size=3, dilation=2, padding=2)
self.cpm3 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
self.cpm4 = nn.Conv2d(256, 128, kernel_size=3, dilation=2, padding=2)
self.cpm5 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
def forward(self, x):
x1_1 = self.cpm1(x).relu()
x1_2 = self.cpm2(x).relu()
x2_1 = self.cpm3(x1_2).relu()
x2_2 = self.cpm4(x1_2).relu()
x3_1 = self.cpm5(x2_2).relu()
return torch.cat([x1_1, x2_1, x3_1], dim=1)
class SSD(nn.Module):
"""Single Shot Multibox Architecture
The network is composed of a base VGG network followed by the
added multibox conv layers. Each multibox layer branches into
1) conv2d for class conf scores
2) conv2d for localization predictions
3) associated priorbox layer to produce default bounding
boxes specific to the layer's feature map size.
See: https://arxiv.org/pdf/1512.02325.pdf for more details.
Args:
phase: (string) Can be "test" or "train"
size: input image size
base: VGG16 layers for input, size of either 300 or 500
extras: extra layers that feed to multibox loc and conf layers
head: "multibox head" consists of loc and conf conv layers
"""
def __init__(self, cfg):
super(SSD, self).__init__()
self.num_classes = 2 # Background and face
self.cfg = cfg
resnet = torchvision.models.resnet152(pretrained=False)
self.layer1 = nn.Sequential(
resnet.conv1, resnet.bn1, resnet.relu,
resnet.maxpool, resnet.layer1)
self.layer2 = nn.Sequential(resnet.layer2)
self.layer3 = nn.Sequential(resnet.layer3)
self.layer4 = nn.Sequential(resnet.layer4)
self.layer5 = nn.Sequential(
nn.Conv2d(2048, 512, kernel_size=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=2),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True)
)
self.layer6 = nn.Sequential(
nn.Conv2d(512, 128, kernel_size=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True)
)
output_channels = [256, 512, 1024, 2048, 512, 256]
# Feature Pyramid Network
fpn_in = output_channels
self.latlayer3 = nn.Conv2d(fpn_in[3], fpn_in[2], kernel_size=1)
self.latlayer2 = nn.Conv2d(fpn_in[2], fpn_in[1], kernel_size=1)
self.latlayer1 = nn.Conv2d(fpn_in[1], fpn_in[0], kernel_size=1)
self.smooth3 = nn.Conv2d(fpn_in[2], fpn_in[2], kernel_size=1)
self.smooth2 = nn.Conv2d(fpn_in[1], fpn_in[1], kernel_size=1)
self.smooth1 = nn.Conv2d(fpn_in[0], fpn_in[0], kernel_size=1)
# Feature enhance module
cpm_in = output_channels
self.cpm3_3 = FEM(cpm_in[0])
self.cpm4_3 = FEM(cpm_in[1])
self.cpm5_3 = FEM(cpm_in[2])
self.cpm7 = FEM(cpm_in[3])
self.cpm6_2 = FEM(cpm_in[4])
self.cpm7_2 = FEM(cpm_in[5])
head = pa_multibox(output_channels, self.cfg['mbox'], self.num_classes)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
# Testing scenario
self.softmax = nn.Softmax(dim=-1)
# Cache to stop computing new priors per fowrard pass
self.prior_cache = {
}
def init_priors(self, feature_maps, image_size):
# Hacky key system, but works....
key = ".".join([str(item) for i in range(len(feature_maps)) for item in feature_maps[i]]) + \
"," + ".".join([str(_) for _ in image_size])
if key in self.prior_cache:
return self.prior_cache[key].clone()
priorbox = PriorBox(self.cfg, image_size, feature_maps)
prior = priorbox.forward()
self.prior_cache[key] = prior.clone()
return prior
def forward(self, x, confidence_threshold, nms_threshold):
"""Applies network layers and ops on input image(s) x.
Args:
x: input image or batch of images. Shape: [batch,3,300,300].
Return:
Depending on phase:
test:
Variable(tensor) of output class label predictions,
confidence score, and corresponding location predictions for
each object detected. Shape: [batch,topk,7]
train:
list of concat outputs from:
1: confidence layers, Shape: [batch*num_priors,num_classes]
2: localization layers, Shape: [batch,num_priors*4]
3: priorbox layers, Shape: [2,num_priors*4]
"""
image_size = [x.shape[2], x.shape[3]]
loc = list()
conf = list()
# ResNet152
conv3_3_x = self.layer1(x)
conv4_3_x = self.layer2(conv3_3_x)
conv5_3_x = self.layer3(conv4_3_x)
fc7_x = self.layer4(conv5_3_x)
conv6_2_x = self.layer5(fc7_x)
conv7_2_x = self.layer6(conv6_2_x)
# FPN
lfpn3 = self._upsample_product(
self.latlayer3(fc7_x), self.smooth3(conv5_3_x))
lfpn2 = self._upsample_product(
self.latlayer2(lfpn3), self.smooth2(conv4_3_x))
lfpn1 = self._upsample_product(
self.latlayer1(lfpn2), self.smooth1(conv3_3_x))
conv5_3_x = lfpn3
conv4_3_x = lfpn2
conv3_3_x = lfpn1
sources = [
self.cpm3_3(conv3_3_x),
self.cpm4_3(conv4_3_x),
self.cpm5_3(conv5_3_x),
self.cpm7(fc7_x),
self.cpm6_2(conv6_2_x),
self.cpm7_2(conv7_2_x)]
# Feature Enhance Module
# apply multibox head to source layers
featuremap_size = []
for (x, l, c) in zip(sources, self.loc, self.conf):
featuremap_size.append([x.shape[2], x.shape[3]])
loc.append(l(x).permute(0, 2, 3, 1).contiguous())
# Max in out
len_conf = len(conf)
out = self.mio_module(c(x), len_conf)
conf.append(out.permute(0, 2, 3, 1).contiguous())
# Progressive Anchor
mbox_num = self.cfg['mbox'][0]
face_loc = torch.cat([
o[:, :, :, :4*mbox_num].contiguous().view(o.size(0), -1)
for o in loc], dim=1)
face_conf = torch.cat([
o[:, :, :, :2*mbox_num].contiguous().view(o.size(0), -1)
for o in conf], dim=1)
# Test Phase
self.priors = self.init_priors(featuremap_size, image_size)
self.priors = self.priors.to(face_conf.device)
conf_preds = face_conf.view(
face_conf.size(0), -1, self.num_classes).softmax(dim=-1)
face_loc = face_loc.view(face_loc.size(0), -1, 4)
boxes = batched_decode(
face_loc, self.priors,
self.cfg["variance"]
)
scores = conf_preds.view(-1, self.priors.shape[0], 2)[:, :, 1:]
output = torch.cat((boxes, scores), dim=-1)
return output
def mio_module(self, each_mmbox, len_conf):
chunk = torch.chunk(each_mmbox, each_mmbox.shape[1], 1)
bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
if len_conf == 0:
out = torch.cat([bmax, chunk[3]], dim=1)
else:
out = torch.cat([chunk[3], bmax], dim=1)
if len(chunk) == 6:
out = torch.cat([out, chunk[4], chunk[5]], dim=1)
elif len(chunk) == 8:
out = torch.cat(
[out, chunk[4], chunk[5], chunk[6], chunk[7]], dim=1)
return out
def _upsample_product(self, x, y):
'''Upsample and add two feature maps.
Args:
x: (Variable) top feature map to be upsampled.
y: (Variable) lateral feature map.
Returns:
(Variable) added feature map.
Note in PyTorch, when input size is odd, the upsampled feature map
with `F.upsample(..., scale_factor=2, mode='nearest')`
maybe not equal to the lateral feature map size.
e.g.
original input size: [N,_,15,15] ->
conv2d feature map size: [N,_,8,8] ->
upsampled feature map size: [N,_,16,16]
So we choose bilinear upsample which supports arbitrary output sizes.
'''
# Deprecation warning. align_corners=False default in 0.4.0, but in 0.3.0 it was True
# Original code was written in 0.3.1, I guess this is correct.
return y * F.interpolate(
x, size=y.shape[2:], mode="bilinear", align_corners=True)
class DeepHeadModule(nn.Module):
def __init__(self, input_channels, output_channels):
super().__init__()
self._input_channels = input_channels
self._output_channels = output_channels
self._mid_channels = min(self._input_channels, 256)
self.conv1 = nn.Conv2d(
self._input_channels, self._mid_channels, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(
self._mid_channels, self._mid_channels, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(
self._mid_channels, self._mid_channels, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(
self._mid_channels, self._output_channels, kernel_size=1,)
def forward(self, x):
out = self.conv1(x).relu()
out = self.conv2(out).relu()
out = self.conv3(out).relu()
out = self.conv4(out)
return out
def pa_multibox(output_channels, mbox_cfg, num_classes):
loc_layers = []
conf_layers = []
for k, v in enumerate(output_channels):
input_channels = 512
if k == 0:
loc_output = 4
conf_output = 2
elif k == 1:
loc_output = 8
conf_output = 4
else:
loc_output = 12
conf_output = 6
loc_layers += [
DeepHeadModule(input_channels, mbox_cfg[k] * loc_output)]
conf_layers += [
DeepHeadModule(input_channels, mbox_cfg[k] * (2+conf_output))]
return (loc_layers, conf_layers)