mmcls.models.backbones.resnet_cifar 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer

from mmcls.registry import MODELS
from .resnet import ResNet

[文档]@MODELS.register_module() class ResNet_CIFAR(ResNet): """ResNet backbone for CIFAR. Compared to standard ResNet, it uses `kernel_size=3` and `stride=1` in conv1, and does not apply MaxPoolinng after stem. It has been proven to be more efficient than standard ResNet in other public codebase, e.g., ``. Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. base_channels (int): Middle channels of the first stage. Default: 64. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): This network has specific designed stem, thus it is asserted to be False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. """ def __init__(self, depth, deep_stem=False, **kwargs): super(ResNet_CIFAR, self).__init__( depth, deep_stem=deep_stem, **kwargs) assert not self.deep_stem, 'ResNet_CIFAR do not support deep_stem' def _make_stem_layer(self, in_channels, base_channels): self.conv1 = build_conv_layer( self.conv_cfg, in_channels, base_channels, kernel_size=3, stride=1, padding=1, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, base_channels, postfix=1) self.add_module(self.norm1_name, norm1) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv1(x) x = self.norm1(x) x = self.relu(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs)
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