Spaces:
Runtime error
Runtime error
File size: 3,711 Bytes
cc0dd3c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmpretrain.registry import MODELS
from .resnet import ResNet
@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.,
`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)
|