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import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import VGG
from mmcv.runner import BaseModule, Sequential
from ..builder import BACKBONES
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detection.
Args:
input_size (int): width and height of input, from {300, 512}.
depth (int): Depth of vgg, from {11, 13, 16, 19}.
out_indices (Sequence[int]): Output from which stages.
pretrained (str, optional): model pretrained path. Default: None
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
Example:
>>> self = SSDVGG(input_size=300, depth=11)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 300, 300)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 1024, 19, 19)
(1, 512, 10, 10)
(1, 256, 5, 5)
(1, 256, 3, 3)
(1, 256, 1, 1)
"""
extra_setting = {
300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256),
512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128),
}
def __init__(self,
input_size,
depth,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indices=(22, 34),
l2_norm_scale=20.,
pretrained=None,
init_cfg=None):
# TODO: in_channels for mmcv.VGG
super(SSDVGG, self).__init__(
depth,
with_last_pool=with_last_pool,
ceil_mode=ceil_mode,
out_indices=out_indices)
assert input_size in (300, 512)
self.input_size = input_size
self.features.add_module(
str(len(self.features)),
nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
self.features.add_module(
str(len(self.features)),
nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6))
self.features.add_module(
str(len(self.features)), nn.ReLU(inplace=True))
self.features.add_module(
str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1))
self.features.add_module(
str(len(self.features)), nn.ReLU(inplace=True))
self.out_feature_indices = out_feature_indices
self.inplanes = 1024
self.extra = self._make_extra_layers(self.extra_setting[input_size])
self.l2_norm = L2Norm(
self.features[out_feature_indices[0] - 1].out_channels,
l2_norm_scale)
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
'please use "init_cfg" instead')
self.init_cfg = [dict(type='Pretrained', checkpoint=pretrained)]
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
dict(type='Kaiming', layer='Conv2d'),
dict(type='Constant', val=1, layer='BatchNorm2d'),
dict(type='Normal', std=0.01, layer='Linear'),
]
else:
raise TypeError('pretrained must be a str or None')
if init_cfg is None:
self.init_cfg += [
dict(
type='Xavier',
distribution='uniform',
override=dict(name='extra')),
dict(
type='Constant',
val=self.l2_norm.scale,
override=dict(name='l2_norm'))
]
def init_weights(self, pretrained=None):
super(VGG, self).init_weights()
def forward(self, x):
"""Forward function."""
outs = []
for i, layer in enumerate(self.features):
x = layer(x)
if i in self.out_feature_indices:
outs.append(x)
for i, layer in enumerate(self.extra):
x = F.relu(layer(x), inplace=True)
if i % 2 == 1:
outs.append(x)
outs[0] = self.l2_norm(outs[0])
if len(outs) == 1:
return outs[0]
else:
return tuple(outs)
def _make_extra_layers(self, outplanes):
layers = []
kernel_sizes = (1, 3)
num_layers = 0
outplane = None
for i in range(len(outplanes)):
if self.inplanes == 'S':
self.inplanes = outplane
continue
k = kernel_sizes[num_layers % 2]
if outplanes[i] == 'S':
outplane = outplanes[i + 1]
conv = nn.Conv2d(
self.inplanes, outplane, k, stride=2, padding=1)
else:
outplane = outplanes[i]
conv = nn.Conv2d(
self.inplanes, outplane, k, stride=1, padding=0)
layers.append(conv)
self.inplanes = outplanes[i]
num_layers += 1
if self.input_size == 512:
layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1))
return Sequential(*layers)
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20., eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
scale (float, optional): Defaults to 20..
eps (float, optional): Used to avoid division by zero.
Defaults to 1e-10.
"""
super(L2Norm, self).__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
def forward(self, x):
"""Forward function."""
# normalization layer convert to FP32 in FP16 training
x_float = x.float()
norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps
return (self.weight[None, :, None, None].float().expand_as(x_float) *
x_float / norm).type_as(x)
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