82 lines
3.6 KiB
Python
82 lines
3.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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from mmcv.cnn import build_conv_layer, build_norm_layer
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from mmcls.registry import MODELS
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from .resnet import ResNet
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@MODELS.register_module()
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class ResNet_CIFAR(ResNet):
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"""ResNet backbone for CIFAR.
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Compared to standard ResNet, it uses `kernel_size=3` and `stride=1` in
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conv1, and does not apply MaxPoolinng after stem. It has been proven to
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be more efficient than standard ResNet in other public codebase, e.g.,
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`https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py`.
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Args:
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depth (int): Network depth, from {18, 34, 50, 101, 152}.
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in_channels (int): Number of input image channels. Default: 3.
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stem_channels (int): Output channels of the stem layer. Default: 64.
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base_channels (int): Middle channels of the first stage. Default: 64.
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num_stages (int): Stages of the network. Default: 4.
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strides (Sequence[int]): Strides of the first block of each stage.
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Default: ``(1, 2, 2, 2)``.
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dilations (Sequence[int]): Dilation of each stage.
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Default: ``(1, 1, 1, 1)``.
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out_indices (Sequence[int]): Output from which stages. If only one
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stage is specified, a single tensor (feature map) is returned,
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otherwise multiple stages are specified, a tuple of tensors will
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be returned. Default: ``(3, )``.
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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deep_stem (bool): This network has specific designed stem, thus it is
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asserted to be False.
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottleneck. Default: False.
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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. Default: -1.
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conv_cfg (dict | None): The config dict for conv layers. Default: None.
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norm_cfg (dict): The config dict for norm layers.
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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 some
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memory while slowing down the training speed. Default: False.
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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. Default: True.
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"""
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def __init__(self, depth, deep_stem=False, **kwargs):
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super(ResNet_CIFAR, self).__init__(
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depth, deep_stem=deep_stem, **kwargs)
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assert not self.deep_stem, 'ResNet_CIFAR do not support deep_stem'
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def _make_stem_layer(self, in_channels, base_channels):
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self.conv1 = build_conv_layer(
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self.conv_cfg,
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in_channels,
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base_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False)
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self.norm1_name, norm1 = build_norm_layer(
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self.norm_cfg, base_channels, postfix=1)
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self.add_module(self.norm1_name, norm1)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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x = self.conv1(x)
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x = self.norm1(x)
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x = self.relu(x)
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outs = []
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for i, layer_name in enumerate(self.res_layers):
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res_layer = getattr(self, layer_name)
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x = res_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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