mirror of https://github.com/open-mmlab/mmcv.git
add cnn module
Kai Chen
parent
f4550cd319
commit
e363982ada
|
|
@ -0,0 +1,3 @@
|
|||
from .resnet import ResNet, make_res_layer
|
||||
|
||||
__all__ = ['ResNet', 'make_res_layer']
|
||||
|
|
@ -0,0 +1,314 @@
|
|||
import logging
|
||||
import math
|
||||
|
||||
import torch.nn as nn
|
||||
import torch.utils.checkpoint as cp
|
||||
|
||||
from ..runner import load_checkpoint
|
||||
|
||||
|
||||
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'):
|
||||
super(BasicBlock, self).__init__()
|
||||
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
|
||||
|
||||
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 i 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('invalid depth {} for resnet'.format(depth))
|
||||
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 = 'layer{}'.format(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()
|
||||
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||||
elif pretrained is None:
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
nn.init.normal_(m.weight, 0, math.sqrt(2. / n))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
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, 'layer{}'.format(i))
|
||||
mod.eval()
|
||||
for param in mod.parameters():
|
||||
param.requires_grad = False
|
||||
Loading…
Reference in New Issue