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import warnings |
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import torch.nn as nn |
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from mmcv.cnn import VGG |
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from mmengine.model import BaseModule |
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from mmdet.registry import MODELS |
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from ..necks import ssd_neck |
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@MODELS.register_module() |
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class SSDVGG(VGG, BaseModule): |
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"""VGG Backbone network for single-shot-detection. |
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Args: |
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depth (int): Depth of vgg, from {11, 13, 16, 19}. |
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with_last_pool (bool): Whether to add a pooling layer at the last |
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of the model |
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ceil_mode (bool): When True, will use `ceil` instead of `floor` |
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to compute the output shape. |
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out_indices (Sequence[int]): Output from which stages. |
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out_feature_indices (Sequence[int]): Output from which feature map. |
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pretrained (str, optional): model pretrained path. Default: None |
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init_cfg (dict or list[dict], optional): Initialization config dict. |
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Default: None |
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input_size (int, optional): Deprecated argumment. |
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Width and height of input, from {300, 512}. |
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l2_norm_scale (float, optional) : Deprecated argumment. |
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L2 normalization layer init scale. |
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Example: |
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>>> self = SSDVGG(input_size=300, depth=11) |
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>>> self.eval() |
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>>> inputs = torch.rand(1, 3, 300, 300) |
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>>> level_outputs = self.forward(inputs) |
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>>> for level_out in level_outputs: |
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... print(tuple(level_out.shape)) |
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(1, 1024, 19, 19) |
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(1, 512, 10, 10) |
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(1, 256, 5, 5) |
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(1, 256, 3, 3) |
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(1, 256, 1, 1) |
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""" |
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extra_setting = { |
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300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256), |
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512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128), |
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} |
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def __init__(self, |
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depth, |
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inplanes =3, |
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with_last_pool=False, |
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ceil_mode=True, |
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out_indices=(3, 4), |
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out_feature_indices=(22, 34), |
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pretrained=None, |
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init_cfg=None, |
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input_size=None, |
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l2_norm_scale=None): |
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super(SSDVGG, self).__init__( |
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depth, |
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with_last_pool=with_last_pool, |
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inplanes=inplanes, |
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ceil_mode=ceil_mode, |
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out_indices=out_indices) |
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self.features.add_module( |
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str(len(self.features)), |
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1)) |
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self.features.add_module( |
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str(len(self.features)), |
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nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)) |
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self.features.add_module( |
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str(len(self.features)), nn.ReLU(inplace=True)) |
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self.features.add_module( |
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str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1)) |
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self.features.add_module( |
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str(len(self.features)), nn.ReLU(inplace=True)) |
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self.out_feature_indices = out_feature_indices |
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assert not (init_cfg and pretrained), \ |
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'init_cfg and pretrained cannot be specified at the same time' |
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if init_cfg is not None: |
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self.init_cfg = init_cfg |
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elif isinstance(pretrained, str): |
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warnings.warn('DeprecationWarning: pretrained is deprecated, ' |
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'please use "init_cfg" instead') |
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self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) |
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elif pretrained is None: |
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self.init_cfg = [ |
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dict(type='Kaiming', layer='Conv2d'), |
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dict(type='Constant', val=1, layer='BatchNorm2d'), |
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dict(type='Normal', std=0.01, layer='Linear'), |
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] |
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else: |
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raise TypeError('pretrained must be a str or None') |
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if input_size is not None: |
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warnings.warn('DeprecationWarning: input_size is deprecated') |
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if l2_norm_scale is not None: |
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warnings.warn('DeprecationWarning: l2_norm_scale in VGG is ' |
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'deprecated, it has been moved to SSDNeck.') |
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def init_weights(self, pretrained=None): |
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super(VGG, self).init_weights() |
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def forward(self, x): |
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"""Forward function.""" |
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outs = [] |
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for i, layer in enumerate(self.features): |
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x = layer(x) |
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if i in self.out_feature_indices: |
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outs.append(x) |
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if len(outs) == 1: |
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return outs[0] |
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else: |
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return tuple(outs) |
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class L2Norm(ssd_neck.L2Norm): |
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def __init__(self, **kwargs): |
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super(L2Norm, self).__init__(**kwargs) |
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warnings.warn('DeprecationWarning: L2Norm in ssd_vgg.py ' |
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'is deprecated, please use L2Norm in ' |
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'mmdet/models/necks/ssd_neck.py instead') |
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