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# Copyright (c) OpenMMLab. All rights reserved.
import logging
import torch.nn as nn
import torch.utils.checkpoint as cp
from .utils import constant_init, kaiming_init
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False):
super(BasicBlock, self).__init__()
assert style in ['pytorch', 'caffe']
self.conv1 = conv3x3(inplanes, planes, stride, dilation)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
assert not with_cp
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False):
"""Bottleneck block.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe']
if style == 'pytorch':
conv1_stride = 1
conv2_stride = stride
else:
conv1_stride = stride
conv2_stride = 1
self.conv1 = nn.Conv2d(
inplanes, planes, kernel_size=1, stride=conv1_stride, bias=False)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.with_cp = with_cp
def forward(self, x):
def _inner_forward(x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
def make_res_layer(block,
inplanes,
planes,
blocks,
stride=1,
dilation=1,
style='pytorch',
with_cp=False):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(
block(
inplanes,
planes,
stride,
dilation,
downsample,
style=style,
with_cp=with_cp))
inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(inplanes, planes, 1, dilation, style=style, with_cp=with_cp))
return nn.Sequential(*layers)
class ResNet(nn.Module):
"""ResNet backbone.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
num_stages (int): Resnet stages, normally 4.
strides (Sequence[int]): Strides of the first block of each stage.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
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.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters.
bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
running stats (mean and var).
bn_frozen (bool): Whether to freeze weight and bias of BN layers.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
"""
arch_settings = {
18: (BasicBlock, (2, 2, 2, 2)),
34: (BasicBlock, (3, 4, 6, 3)),
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3))
}
def __init__(self,
depth,
num_stages=4,
strides=(1, 2, 2, 2),
dilations=(1, 1, 1, 1),
out_indices=(0, 1, 2, 3),
style='pytorch',
frozen_stages=-1,
bn_eval=True,
bn_frozen=False,
with_cp=False):
super(ResNet, self).__init__()
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for resnet')
assert num_stages >= 1 and num_stages <= 4
block, stage_blocks = self.arch_settings[depth]
stage_blocks = stage_blocks[:num_stages]
assert len(strides) == len(dilations) == num_stages
assert max(out_indices) < num_stages
self.out_indices = out_indices
self.style = style
self.frozen_stages = frozen_stages
self.bn_eval = bn_eval
self.bn_frozen = bn_frozen
self.with_cp = with_cp
self.inplanes = 64
self.conv1 = nn.Conv2d(
3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.res_layers = []
for i, num_blocks in enumerate(stage_blocks):
stride = strides[i]
dilation = dilations[i]
planes = 64 * 2**i
res_layer = make_res_layer(
block,
self.inplanes,
planes,
num_blocks,
stride=stride,
dilation=dilation,
style=self.style,
with_cp=with_cp)
self.inplanes = planes * block.expansion
layer_name = f'layer{i + 1}'
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
self.feat_dim = block.expansion * 64 * 2**(len(stage_blocks) - 1)
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
from ..runner import load_checkpoint
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(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)
if len(outs) == 1:
return outs[0]
else:
return tuple(outs)
def train(self, mode=True):
super(ResNet, self).train(mode)
if self.bn_eval:
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
if self.bn_frozen:
for params in m.parameters():
params.requires_grad = False
if mode and self.frozen_stages >= 0:
for param in self.conv1.parameters():
param.requires_grad = False
for param in self.bn1.parameters():
param.requires_grad = False
self.bn1.eval()
self.bn1.weight.requires_grad = False
self.bn1.bias.requires_grad = False
for i in range(1, self.frozen_stages + 1):
mod = getattr(self, f'layer{i}')
mod.eval()
for param in mod.parameters():
param.requires_grad = False
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