-
Notifications
You must be signed in to change notification settings - Fork 0
/
hrnet_trans.py
218 lines (187 loc) · 9.99 KB
/
hrnet_trans.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
import torch
from torch import nn
from modules import BasicBlock, Bottleneck
from ptflops import get_model_complexity_info
class StageModule(nn.Module):
def __init__(self, stage, output_branches, c, bn_momentum):
super(StageModule, self).__init__()
self.stage = stage
self.output_branches = output_branches
self.branches = nn.ModuleList()
for i in range(self.stage):
w = c * (2 ** i)
branch = nn.Sequential(
BasicBlock(w, w, bn_momentum=bn_momentum),
BasicBlock(w, w, bn_momentum=bn_momentum),
BasicBlock(w, w, bn_momentum=bn_momentum),
BasicBlock(w, w, bn_momentum=bn_momentum),
)
self.branches.append(branch)
self.fuse_layers = nn.ModuleList()
# for each output_branches (i.e. each branch in all cases but the very last one)
for i in range(self.output_branches):
self.fuse_layers.append(nn.ModuleList())
for j in range(self.stage): # for each branch
if i == j:
self.fuse_layers[-1].append(nn.Sequential()) # Used in place of "None" because it is callable
elif i < j:
self.fuse_layers[-1].append(nn.Sequential(
nn.Conv2d(c * (2 ** j), c * (2 ** i), kernel_size=(1, 1), stride=(1, 1), bias=False),
nn.BatchNorm2d(c * (2 ** i), eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
nn.Upsample(scale_factor=(2.0 ** (j - i)), mode='nearest'),
))
elif i > j:
ops = []
for k in range(i - j - 1):
ops.append(nn.Sequential(
nn.Conv2d(c * (2 ** j), c * (2 ** j), kernel_size=(3, 3), stride=(2, 2), padding=(1, 1),
bias=False),
nn.BatchNorm2d(c * (2 ** j), eps=1e-05, momentum=0.1, affine=True,
track_running_stats=True),
nn.ReLU(inplace=True),
))
ops.append(nn.Sequential(
nn.Conv2d(c * (2 ** j), c * (2 ** i), kernel_size=(3, 3), stride=(2, 2), padding=(1, 1),
bias=False),
nn.BatchNorm2d(c * (2 ** i), eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
))
self.fuse_layers[-1].append(nn.Sequential(*ops))
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
assert len(self.branches) == len(x)
x = [branch(b) for branch, b in zip(self.branches, x)]
x_fused = []
for i in range(len(self.fuse_layers)):
for j in range(0, len(self.branches)):
if j == 0:
x_fused.append(self.fuse_layers[i][0](x[0]))
else:
x_fused[i] = x_fused[i] + self.fuse_layers[i][j](x[j])
for i in range(len(x_fused)):
x_fused[i] = self.relu(x_fused[i])
return x_fused
class HRNet(nn.Module):
def __init__(self, c=48, nof_joints=17, bn_momentum=0.1,in_c = 64):
super(HRNet, self).__init__()
# Input (stem net)
# self.conv1 = nn.Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
# self.bn1 = nn.BatchNorm2d(64, eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True)
self.conv2 = nn.Conv2d(in_c, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
self.bn2 = nn.BatchNorm2d(256, eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True)
self.relu = nn.ReLU(inplace=True)
# Stage 1 (layer1) - First group of bottleneck (resnet) modules
# downsample = nn.Sequential(
# nn.Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False),
# nn.BatchNorm2d(256, eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True),
# )
# self.layer1 = nn.Sequential(
# Bottleneck(64, 64, downsample=downsample),
# # Bottleneck(256, 64),
# # Bottleneck(256, 64),
# # Bottleneck(256, 64),
# )
# Fusion layer 1 (transition1) - Creation of the first two branches (one full and one half resolution)
self.transition1 = nn.ModuleList([
nn.Sequential(
nn.Conv2d(256, c, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),
nn.BatchNorm2d(c, eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True),
nn.ReLU(inplace=True),
),
nn.Sequential(nn.Sequential( # Double Sequential to fit with official pretrained weights
nn.Conv2d(256, c * (2 ** 1), kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),
nn.BatchNorm2d(c * (2 ** 1), eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True),
nn.ReLU(inplace=True),
)),
])
# Stage 2 (stage2) - Second module with 1 group of bottleneck (resnet) modules. This has 2 branches
self.stage2 = nn.Sequential(
StageModule(stage=2, output_branches=2, c=c, bn_momentum=bn_momentum),
)
# Fusion layer 2 (transition2) - Creation of the third branch (1/4 resolution)
self.transition2 = nn.ModuleList([
nn.Sequential(), # None, - Used in place of "None" because it is callable
nn.Sequential(), # None, - Used in place of "None" because it is callable
nn.Sequential(nn.Sequential( # Double Sequential to fit with official pretrained weights
nn.Conv2d(c * (2 ** 1), c * (2 ** 2), kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),
nn.BatchNorm2d(c * (2 ** 2), eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True),
nn.ReLU(inplace=True),
)), # ToDo Why the new branch derives from the "upper" branch only?
])
# Stage 3 (stage3) - Third module with 4 groups of bottleneck (resnet) modules. This has 3 branches
self.stage3 = nn.Sequential(
StageModule(stage=3, output_branches=3, c=c, bn_momentum=bn_momentum),
StageModule(stage=3, output_branches=3, c=c, bn_momentum=bn_momentum),
StageModule(stage=3, output_branches=3, c=c, bn_momentum=bn_momentum),
StageModule(stage=3, output_branches=3, c=c, bn_momentum=bn_momentum),
)
#
# # Fusion layer 3 (transition3) - Creation of the fourth branch (1/8 resolution)
# self.transition3 = nn.ModuleList([
# nn.Sequential(), # None, - Used in place of "None" because it is callable
# nn.Sequential(), # None, - Used in place of "None" because it is callable
# nn.Sequential(), # None, - Used in place of "None" because it is callable
# nn.Sequential(nn.Sequential( # Double Sequential to fit with official pretrained weights
# nn.Conv2d(c * (2 ** 2), c * (2 ** 3), kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),
# nn.BatchNorm2d(c * (2 ** 3), eps=1e-05, momentum=bn_momentum, affine=True, track_running_stats=True),
# nn.ReLU(inplace=True),
# )), # ToDo Why the new branch derives from the "upper" branch only?
# ])
#
# # Stage 4 (stage4) - Fourth module with 3 groups of bottleneck (resnet) modules. This has 4 branches
# self.stage4 = nn.Sequential(
# StageModule(stage=4, output_branches=4, c=c, bn_momentum=bn_momentum),
# StageModule(stage=4, output_branches=4, c=c, bn_momentum=bn_momentum),
# StageModule(stage=4, output_branches=1, c=c, bn_momentum=bn_momentum),
# )
# Final layer (final_layer)
self.final_layer = nn.Conv2d(c, nof_joints, kernel_size=(1, 1), stride=(1, 1))
def forward(self, x): #[4,3,224,224]
# x = self.conv1(x) #[4,64,112,112]
# x = self.bn1(x)
# x = self.relu(x)
x = self.conv2(x) #[4,64,56,56]
x = self.bn2(x)
x = self.relu(x)
# x = self.layer1(x) #一堆残差边
x = [trans(x) for trans in self.transition1] # Since now, x is a list (# == nof branches)
x = self.stage2(x)
# x = [trans(x[-1]) for trans in self.transition2] # New branch derives from the "upper" branch only
x = [
self.transition2[0](x[0]),
self.transition2[1](x[1]),
self.transition2[2](x[-1])
] # New branch derives from the "upper" branch only
x = self.stage3(x)
# x = [trans(x) for trans in self.transition3] # New branch derives from the "upper" branch only
# x = [
# self.transition3[0](x[0]),
# self.transition3[1](x[1]),
# self.transition3[2](x[2]),
# self.transition3[3](x[-1])
# ] # New branch derives from the "upper" branch only
#
# x = self.stage4(x)
x = self.final_layer(x[0])
return x
if __name__ == '__main__':
# model = HRNet(48, 17, 0.1)
model = HRNet(16, 64, 0.1)
ops, params = get_model_complexity_info(model, (64, 64, 64), as_strings=True,
print_per_layer_stat=True, verbose=True)
# print(model)
#
# model.load_state_dict(
# # torch.load('./weights/pose_hrnet_w48_384x288.pth')
# torch.load('./weights/pose_hrnet_w32_256x192.pth')
# )
# print('ok!!')
if torch.cuda.is_available() and False:
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
print(device)
model = model.to(device)
y = model(torch.ones(4, 64, 64, 64).to(device))
print(y.shape)
print(torch.min(y).item(), torch.mean(y).item(), torch.max(y).item())