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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 | |
| 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') | |