268 lines
11 KiB
Python
268 lines
11 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import mmcv
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from mmcv.cnn import ConvModule
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from mmcv.cnn.bricks import Conv2dAdaptivePadding
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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 ..builder import BACKBONES
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from ..utils import InvertedResidualV3 as InvertedResidual
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@BACKBONES.register_module()
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class MobileNetV3(BaseModule):
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"""MobileNetV3 backbone.
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This backbone is the improved implementation of `Searching for MobileNetV3
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<https://ieeexplore.ieee.org/document/9008835>`_.
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Args:
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arch (str): Architecture of mobilnetv3, from {'small', 'large'}.
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Default: 'small'.
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conv_cfg (dict): Config dict for convolution layer.
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Default: None, which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='BN').
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out_indices (tuple[int]): Output from which layer.
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Default: (0, 1, 12).
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frozen_stages (int): Stages to be frozen (all param fixed).
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Default: -1, which means not freezing any parameters.
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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. Default: False.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save
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some memory while slowing down the training speed.
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Default: False.
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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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# Parameters to build each block:
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# [kernel size, mid channels, out channels, with_se, act type, stride]
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arch_settings = {
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'small': [[3, 16, 16, True, 'ReLU', 2], # block0 layer1 os=4
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[3, 72, 24, False, 'ReLU', 2], # block1 layer2 os=8
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[3, 88, 24, False, 'ReLU', 1],
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[5, 96, 40, True, 'HSwish', 2], # block2 layer4 os=16
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[5, 240, 40, True, 'HSwish', 1],
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[5, 240, 40, True, 'HSwish', 1],
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[5, 120, 48, True, 'HSwish', 1], # block3 layer7 os=16
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[5, 144, 48, True, 'HSwish', 1],
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[5, 288, 96, True, 'HSwish', 2], # block4 layer9 os=32
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[5, 576, 96, True, 'HSwish', 1],
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[5, 576, 96, True, 'HSwish', 1]],
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'large': [[3, 16, 16, False, 'ReLU', 1], # block0 layer1 os=2
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[3, 64, 24, False, 'ReLU', 2], # block1 layer2 os=4
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[3, 72, 24, False, 'ReLU', 1],
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[5, 72, 40, True, 'ReLU', 2], # block2 layer4 os=8
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[5, 120, 40, True, 'ReLU', 1],
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[5, 120, 40, True, 'ReLU', 1],
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[3, 240, 80, False, 'HSwish', 2], # block3 layer7 os=16
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[3, 200, 80, False, 'HSwish', 1],
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[3, 184, 80, False, 'HSwish', 1],
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[3, 184, 80, False, 'HSwish', 1],
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[3, 480, 112, True, 'HSwish', 1], # block4 layer11 os=16
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[3, 672, 112, True, 'HSwish', 1],
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[5, 672, 160, True, 'HSwish', 2], # block5 layer13 os=32
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[5, 960, 160, True, 'HSwish', 1],
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[5, 960, 160, True, 'HSwish', 1]]
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} # yapf: disable
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def __init__(self,
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arch='small',
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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out_indices=(0, 1, 12),
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frozen_stages=-1,
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reduction_factor=1,
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norm_eval=False,
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with_cp=False,
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pretrained=None,
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init_cfg=None):
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super(MobileNetV3, self).__init__(init_cfg)
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self.pretrained = pretrained
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assert not (init_cfg and pretrained), \
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'init_cfg and pretrained cannot be setting at the same time'
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if isinstance(pretrained, str):
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warnings.warn('DeprecationWarning: pretrained is a 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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if init_cfg is None:
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self.init_cfg = [
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dict(type='Kaiming', layer='Conv2d'),
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dict(
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type='Constant',
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val=1,
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layer=['_BatchNorm', 'GroupNorm'])
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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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assert arch in self.arch_settings
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assert isinstance(reduction_factor, int) and reduction_factor > 0
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assert mmcv.is_tuple_of(out_indices, int)
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for index in out_indices:
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if index not in range(0, len(self.arch_settings[arch]) + 2):
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raise ValueError(
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'the item in out_indices must in '
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f'range(0, {len(self.arch_settings[arch])+2}). '
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f'But received {index}')
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if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
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raise ValueError('frozen_stages must be in range(-1, '
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f'{len(self.arch_settings[arch])+2}). '
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f'But received {frozen_stages}')
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self.arch = arch
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.out_indices = out_indices
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self.frozen_stages = frozen_stages
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self.reduction_factor = reduction_factor
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self.norm_eval = norm_eval
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self.with_cp = with_cp
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self.layers = self._make_layer()
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def _make_layer(self):
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layers = []
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# build the first layer (layer0)
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in_channels = 16
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layer = ConvModule(
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in_channels=3,
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out_channels=in_channels,
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kernel_size=3,
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stride=2,
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padding=1,
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conv_cfg=dict(type='Conv2dAdaptivePadding'),
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norm_cfg=self.norm_cfg,
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act_cfg=dict(type='HSwish'))
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self.add_module('layer0', layer)
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layers.append('layer0')
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layer_setting = self.arch_settings[self.arch]
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for i, params in enumerate(layer_setting):
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(kernel_size, mid_channels, out_channels, with_se, act,
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stride) = params
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if self.arch == 'large' and i >= 12 or self.arch == 'small' and \
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i >= 8:
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mid_channels = mid_channels // self.reduction_factor
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out_channels = out_channels // self.reduction_factor
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if with_se:
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se_cfg = dict(
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channels=mid_channels,
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ratio=4,
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act_cfg=(dict(type='ReLU'),
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dict(type='HSigmoid', bias=3.0, divisor=6.0)))
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else:
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se_cfg = None
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layer = InvertedResidual(
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in_channels=in_channels,
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out_channels=out_channels,
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mid_channels=mid_channels,
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kernel_size=kernel_size,
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stride=stride,
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se_cfg=se_cfg,
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with_expand_conv=(in_channels != mid_channels),
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=dict(type=act),
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with_cp=self.with_cp)
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in_channels = out_channels
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layer_name = 'layer{}'.format(i + 1)
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self.add_module(layer_name, layer)
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layers.append(layer_name)
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# build the last layer
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# block5 layer12 os=32 for small model
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# block6 layer16 os=32 for large model
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layer = ConvModule(
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in_channels=in_channels,
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out_channels=576 if self.arch == 'small' else 960,
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kernel_size=1,
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stride=1,
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dilation=4,
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padding=0,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=dict(type='HSwish'))
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layer_name = 'layer{}'.format(len(layer_setting) + 1)
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self.add_module(layer_name, layer)
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layers.append(layer_name)
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# next, convert backbone MobileNetV3 to a semantic segmentation version
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if self.arch == 'small':
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self.layer4.depthwise_conv.conv.stride = (1, 1)
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self.layer9.depthwise_conv.conv.stride = (1, 1)
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for i in range(4, len(layers)):
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layer = getattr(self, layers[i])
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if isinstance(layer, InvertedResidual):
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modified_module = layer.depthwise_conv.conv
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else:
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modified_module = layer.conv
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if i < 9:
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modified_module.dilation = (2, 2)
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pad = 2
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else:
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modified_module.dilation = (4, 4)
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pad = 4
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if not isinstance(modified_module, Conv2dAdaptivePadding):
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# Adjust padding
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pad *= (modified_module.kernel_size[0] - 1) // 2
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modified_module.padding = (pad, pad)
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else:
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self.layer7.depthwise_conv.conv.stride = (1, 1)
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self.layer13.depthwise_conv.conv.stride = (1, 1)
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for i in range(7, len(layers)):
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layer = getattr(self, layers[i])
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if isinstance(layer, InvertedResidual):
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modified_module = layer.depthwise_conv.conv
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else:
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modified_module = layer.conv
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if i < 13:
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modified_module.dilation = (2, 2)
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pad = 2
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else:
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modified_module.dilation = (4, 4)
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pad = 4
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if not isinstance(modified_module, Conv2dAdaptivePadding):
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# Adjust padding
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pad *= (modified_module.kernel_size[0] - 1) // 2
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modified_module.padding = (pad, pad)
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return layers
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def forward(self, x):
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outs = []
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for i, layer_name in enumerate(self.layers):
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layer = getattr(self, layer_name)
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x = layer(x)
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if i in self.out_indices:
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outs.append(x)
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return outs
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def _freeze_stages(self):
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for i in range(self.frozen_stages + 1):
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layer = getattr(self, f'layer{i}')
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layer.eval()
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for param in layer.parameters():
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param.requires_grad = False
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def train(self, mode=True):
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super(MobileNetV3, 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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if isinstance(m, _BatchNorm):
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m.eval()
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