mmdet.models.backbones.ssd_vgg 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import warnings

import torch.nn as nn
from mmcv.cnn import VGG
from mmcv.runner import BaseModule

from ..builder import BACKBONES
from ..necks import ssd_neck

[文档]@BACKBONES.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.')
[文档] def init_weights(self, pretrained=None): super(VGG, self).init_weights()
[文档] 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 ' 'is deprecated, please use L2Norm in ' 'mmdet/models/necks/ instead')
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