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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import torch.nn as nn
from mmcv.cnn import VGG
from mmengine.model import BaseModule
from mmdet.registry import MODELS
from ..necks import ssd_neck
@MODELS.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detection.
Args:
depth (int): Depth of vgg, from {11, 13, 16, 19}.
with_last_pool (bool): Whether to add a pooling layer at the last
of the model
ceil_mode (bool): When True, will use `ceil` instead of `floor`
to compute the output shape.
out_indices (Sequence[int]): Output from which stages.
out_feature_indices (Sequence[int]): Output from which feature map.
pretrained (str, optional): model pretrained path. Default: None
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
input_size (int, optional): Deprecated argumment.
Width and height of input, from {300, 512}.
l2_norm_scale (float, optional) : Deprecated argumment.
L2 normalization layer init scale.
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,
depth,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indices=(22, 34),
pretrained=None,
init_cfg=None,
input_size=None,
l2_norm_scale=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)
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
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be specified at the same time'
if init_cfg is not None:
self.init_cfg = init_cfg
elif isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained 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 input_size is not None:
warnings.warn('DeprecationWarning: input_size is deprecated')
if l2_norm_scale is not None:
warnings.warn('DeprecationWarning: l2_norm_scale in VGG is '
'deprecated, it has been moved to SSDNeck.')
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)
if len(outs) == 1:
return outs[0]
else:
return tuple(outs)
class L2Norm(ssd_neck.L2Norm):
def __init__(self, **kwargs):
super(L2Norm, self).__init__(**kwargs)
warnings.warn('DeprecationWarning: L2Norm in ssd_vgg.py '
'is deprecated, please use L2Norm in '
'mmdet/models/necks/ssd_neck.py instead')
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