| |
| 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): |
| |
| 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') |
|
|
