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resnet.py
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resnet.py
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# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Optional, Sequence, Tuple, Union
import torch.nn as nn
import torch.utils.checkpoint as cp
from torch import Tensor
from .utils import constant_init, kaiming_init
def conv3x3(in_planes: int,
out_planes: int,
stride: int = 1,
dilation: int = 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: int,
planes: int,
stride: int = 1,
dilation: int = 1,
downsample: Optional[nn.Module] = None,
style: str = 'pytorch',
with_cp: bool = False):
super().__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: Tensor) -> Tensor:
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: int,
planes: int,
stride: int = 1,
dilation: int = 1,
downsample: Optional[nn.Module] = None,
style: str = 'pytorch',
with_cp: bool = 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().__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: Tensor) -> Tensor:
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: nn.Module,
inplanes: int,
planes: int,
blocks: int,
stride: int = 1,
dilation: int = 1,
style: str = 'pytorch',
with_cp: bool = False) -> nn.Module:
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: int,
num_stages: int = 4,
strides: Sequence[int] = (1, 2, 2, 2),
dilations: Sequence[int] = (1, 1, 1, 1),
out_indices: Sequence[int] = (0, 1, 2, 3),
style: str = 'pytorch',
frozen_stages: int = -1,
bn_eval: bool = True,
bn_frozen: bool = False,
with_cp: bool = False):
super().__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] # type: ignore
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: int = 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 # type: ignore
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**( # type: ignore
len(stage_blocks) - 1)
def init_weights(self, pretrained: Optional[str] = None) -> 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: Tensor) -> Union[Tensor, Tuple[Tensor]]:
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: bool = True) -> None:
super().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