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| # 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 | |
| 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., | |
| `https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py`. | |
| 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) | |