-
Notifications
You must be signed in to change notification settings - Fork 451
/
rpn.py
160 lines (137 loc) · 5.37 KB
/
rpn.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
import time
import numpy as np
import math
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.models import resnet
from torch.nn.modules.batchnorm import _BatchNorm
from det3d.torchie.cnn import constant_init, kaiming_init, xavier_init
from det3d.torchie.trainer import load_checkpoint
from det3d.models.utils import Empty, GroupNorm, Sequential
from det3d.models.utils import change_default_args
from .. import builder
from ..registry import NECKS
from ..utils import build_norm_layer
@NECKS.register_module
class RPN(nn.Module):
def __init__(
self,
layer_nums,
ds_layer_strides,
ds_num_filters,
us_layer_strides,
us_num_filters,
num_input_features,
norm_cfg=None,
name="rpn",
logger=None,
**kwargs
):
super(RPN, self).__init__()
self._layer_strides = ds_layer_strides
self._num_filters = ds_num_filters
self._layer_nums = layer_nums
self._upsample_strides = us_layer_strides
self._num_upsample_filters = us_num_filters
self._num_input_features = num_input_features
if norm_cfg is None:
norm_cfg = dict(type="BN", eps=1e-3, momentum=0.01)
self._norm_cfg = norm_cfg
assert len(self._layer_strides) == len(self._layer_nums)
assert len(self._num_filters) == len(self._layer_nums)
assert len(self._num_upsample_filters) == len(self._upsample_strides)
self._upsample_start_idx = len(self._layer_nums) - len(self._upsample_strides)
must_equal_list = []
for i in range(len(self._upsample_strides)):
# print(upsample_strides[i])
must_equal_list.append(
self._upsample_strides[i]
/ np.prod(self._layer_strides[: i + self._upsample_start_idx + 1])
)
for val in must_equal_list:
assert val == must_equal_list[0]
in_filters = [self._num_input_features, *self._num_filters[:-1]]
blocks = []
deblocks = []
for i, layer_num in enumerate(self._layer_nums):
block, num_out_filters = self._make_layer(
in_filters[i],
self._num_filters[i],
layer_num,
stride=self._layer_strides[i],
)
blocks.append(block)
if i - self._upsample_start_idx >= 0:
stride = (self._upsample_strides[i - self._upsample_start_idx])
if stride > 1:
deblock = Sequential(
nn.ConvTranspose2d(
num_out_filters,
self._num_upsample_filters[i - self._upsample_start_idx],
stride,
stride=stride,
bias=False,
),
build_norm_layer(
self._norm_cfg,
self._num_upsample_filters[i - self._upsample_start_idx],
)[1],
nn.ReLU(),
)
else:
stride = np.round(1 / stride).astype(np.int64)
deblock = Sequential(
nn.Conv2d(
num_out_filters,
self._num_upsample_filters[i - self._upsample_start_idx],
stride,
stride=stride,
bias=False,
),
build_norm_layer(
self._norm_cfg,
self._num_upsample_filters[i - self._upsample_start_idx],
)[1],
nn.ReLU(),
)
deblocks.append(deblock)
self.blocks = nn.ModuleList(blocks)
self.deblocks = nn.ModuleList(deblocks)
logger.info("Finish RPN Initialization")
@property
def downsample_factor(self):
factor = np.prod(self._layer_strides)
if len(self._upsample_strides) > 0:
factor /= self._upsample_strides[-1]
return factor
def _make_layer(self, inplanes, planes, num_blocks, stride=1):
block = Sequential(
nn.ZeroPad2d(1),
nn.Conv2d(inplanes, planes, 3, stride=stride, bias=False),
build_norm_layer(self._norm_cfg, planes)[1],
# nn.BatchNorm2d(planes, eps=1e-3, momentum=0.01),
nn.ReLU(),
)
for j in range(num_blocks):
block.add(nn.Conv2d(planes, planes, 3, padding=1, bias=False))
block.add(
build_norm_layer(self._norm_cfg, planes)[1],
# nn.BatchNorm2d(planes, eps=1e-3, momentum=0.01)
)
block.add(nn.ReLU())
return block, planes
# default init_weights for conv(msra) and norm in ConvModule
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution="uniform")
def forward(self, x):
ups = []
for i in range(len(self.blocks)):
x = self.blocks[i](x)
if i - self._upsample_start_idx >= 0:
ups.append(self.deblocks[i - self._upsample_start_idx](x))
if len(ups) > 0:
x = torch.cat(ups, dim=1)
return x