254 lines
9.0 KiB
Python
254 lines
9.0 KiB
Python
# All modification made by Kneron Corp.: Copyright (c) 2022 Kneron Corp.
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from mmcv.cnn import build_conv_layer, build_norm_layer
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from mmcv.runner import BaseModule
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcv.cnn import ConvModule
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from ..builder import BACKBONES
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class Focus(BaseModule):
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"""Focus width and height information into channel space.
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Args:
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in_channels (int): The input channels of this Module.
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out_channels (int): The output channels of this Module.
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kernel_size (int): The kernel size of the convolution. Default: 1
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stride (int): The stride of the convolution. Default: 1
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conv_cfg (dict): Config dict for convolution layer. Default: None,
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which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='BN', momentum=0.03, eps=0.001).
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act_cfg (dict): Config dict for activation layer.
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Default: dict(type='Swish').
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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conv_cfg=None,
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
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init_cfg=None):
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super().__init__()
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self.conv = ConvModule(
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in_channels * 4,
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out_channels,
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kernel_size,
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stride,
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padding=(kernel_size - 1) // 2,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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def forward(self, x):
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# shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
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patch_top_left = x[..., ::2, ::2]
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patch_top_right = x[..., ::2, 1::2]
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patch_bot_left = x[..., 1::2, ::2]
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patch_bot_right = x[..., 1::2, 1::2]
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x = torch.cat(
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(
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patch_top_left,
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patch_bot_left,
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patch_top_right,
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patch_bot_right,
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),
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dim=1,
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)
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return self.conv(x)
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class VggLayer(BaseModule):
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def __init__(self,
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inplanes,
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outplanes,
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kernel_size=3,
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stride=1,
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dilation=1,
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with_cp=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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init_cfg=None):
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super(VggLayer, self).__init__(init_cfg)
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self.inplanes = inplanes
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self.stride = stride
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self.dilation = dilation
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self.with_cp = with_cp
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.conv1_stride = stride
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, outplanes, postfix=1)
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self.conv1 = build_conv_layer(
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conv_cfg,
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inplanes,
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outplanes,
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kernel_size=kernel_size,
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stride=self.conv1_stride,
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padding=(kernel_size - 1) // 2)
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self.add_module(self.norm1_name, norm1)
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self.LeakyRelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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@property
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def norm1(self):
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"""nn.Module: normalization layer after the first convolution layer"""
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return getattr(self, self.norm1_name)
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def forward(self, x):
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"""Forward function."""
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def _inner_forward(x):
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.LeakyRelu(out)
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return out
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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return out
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@BACKBONES.register_module()
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class FCOS_VGG(BaseModule):
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"""
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Args:
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stem_channels (int | None): Number of stem channels. If not specified,
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it will be the same as `base_channels`. Default: None.
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base_channels (int): Number of base channels of res layer. Default: 64.
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in_channels (int): Number of input image channels. Default: 3.
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num_stages (int): Resnet stages. Default: 4.
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strides (Sequence[int]): Strides of the first block of each stage.
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dilations (Sequence[int]): Dilation of each stage.
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out_indices (Sequence[int]): Output from which stages.
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deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottleneck.
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frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
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-1 means not freezing any parameters.
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norm_cfg (dict): Dictionary to construct and config norm layer.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only.
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plugins (list[dict]): List of plugins for stages, each dict contains:
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- cfg (dict, required): Cfg dict to build plugin.
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- position (str, required): Position inside block to insert
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plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
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- stages (tuple[bool], optional): Stages to apply plugin, length
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should be same as 'num_stages'.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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zero_init_residual (bool): Whether to use zero init for last norm layer
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in resblocks to let them behave as identity.
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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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"""
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def __init__(self,
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in_channels=3,
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stem_channels=None,
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base_channels=32,
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strides=(2, 1, 2, 2, 2),
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dilations=(1, 1, 1, 1, 1),
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out_indices=(0, 1, 2, 3),
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out_planes=(64, 64, 128, 196, 128),
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avg_down=False,
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frozen_stages=-1,
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conv_cfg=None,
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norm_cfg=dict(type='BN', requires_grad=True),
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act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
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norm_eval=True,
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plugins=None,
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with_cp=False,
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zero_init_residual=True,
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init_cfg=None):
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super(FCOS_VGG, self).__init__(init_cfg)
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self.zero_init_residual = zero_init_residual
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if stem_channels is None:
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stem_channels = base_channels
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self.stem_channels = stem_channels
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self.base_channels = base_channels
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self.strides = strides
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self.dilations = dilations
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self.out_indices = out_indices
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self.avg_down = avg_down
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self.frozen_stages = frozen_stages
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.with_cp = with_cp
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self.norm_eval = norm_eval
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self.act_cfg = act_cfg
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self.inplanes = stem_channels
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self.stem = Focus(
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in_channels,
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self.base_channels,
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kernel_size=3,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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self.vgg_layers = []
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for i in range(len(out_planes)):
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stride = strides[i]
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dilation = dilations[i]
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out_plane = out_planes[i]
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vgg_layer = VggLayer(
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inplanes=self.inplanes,
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outplanes=out_plane,
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stride=stride,
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dilation=dilation,
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with_cp=with_cp,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg)
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self.inplanes = out_plane
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layer_name = f'layer{i + 1}'
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self.add_module(layer_name, vgg_layer)
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self.vgg_layers.append(layer_name)
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self._freeze_stages()
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@property
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def norm1(self):
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"""nn.Module: the normalization layer named "norm1" """
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return getattr(self, self.norm1_name)
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def _freeze_stages(self):
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for i in range(1, self.frozen_stages + 1):
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m = getattr(self, f'layer{i}')
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m.eval()
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for param in m.parameters():
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param.requires_grad = False
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def forward(self, x):
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"""Forward function."""
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x = self.stem(x)
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outs = []
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for i, layer_name in enumerate(self.vgg_layers):
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vgg_layer = getattr(self, layer_name)
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x = vgg_layer(x)
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if i in self.out_indices:
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outs.append(x)
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return tuple(outs)
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def train(self, mode=True):
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"""Convert the model into training mode while keep normalization layer
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freezed."""
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super(FCOS_VGG, self).train(mode)
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self._freeze_stages()
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if mode and self.norm_eval:
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for m in self.modules():
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# trick: eval have effect on BatchNorm only
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if isinstance(m, _BatchNorm):
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m.eval()
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