# Copyright (c) Alibaba, Inc. and its affiliates.
from typing import List
import torch
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from torch.nn.modules.batchnorm import _BatchNorm
from easycv.framework.errors import KeyError
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
[docs]class BasicBlock(nn.Module):
expansion = 1
[docs] def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
super(BasicBlock, self).__init__()
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
conv_cfg, planes, planes, 3, padding=1, bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
assert not with_cp
@property
def norm1(self):
return getattr(self, self.norm1_name)
@property
def norm2(self):
return getattr(self, self.norm2_name)
[docs] def forward(self, x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.norm2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
[docs]class Bottleneck(nn.Module):
expansion = 4
[docs] def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
"""Bottleneck block for ResNet.
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']
self.inplanes = inplanes
self.planes = planes
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
norm_cfg, planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg,
planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
@property
def norm1(self):
return getattr(self, 'bn1')
@property
def norm2(self):
return getattr(self, 'bn2')
@property
def norm3(self):
return getattr(self, 'bn3')
def _inner_forward(self, x):
identity = x
out = self.conv1(x)
out = getattr(self, 'bn1')(out)
out = self.relu(out)
out = self.conv2(out)
out = getattr(self, 'bn2')(out)
out = self.relu(out)
out = self.conv3(out)
out = getattr(self, 'bn3')(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
[docs] def forward(self, x):
out = self._inner_forward(x)
out = self.relu(out)
return out
[docs]def make_res_layer(block,
inplanes,
planes,
blocks,
stride=1,
dilation=1,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1],
)
layers = []
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
dilation=dilation,
downsample=downsample,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
dilation=dilation,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
return nn.Sequential(*layers)
[docs]@BACKBONES.register_module
class ResNetJIT(nn.Module):
"""ResNet backbone.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int): Number of input image channels. Normally 3.
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 (stop grad and set eval mode).
-1 means not freezing any parameters.
norm_cfg (dict): dictionary to construct and config norm layer.
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.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
Example:
>>> from easycv.models import ResNet
>>> import torch
>>> self = ResNet(depth=18)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 64, 8, 8)
(1, 128, 4, 4)
(1, 256, 2, 2)
(1, 512, 1, 1)
"""
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))
}
[docs] def __init__(self,
depth,
in_channels=3,
num_stages=4,
strides=(1, 2, 2, 2),
dilations=(1, 1, 1, 1),
out_indices=(0, 1, 2, 3, 4),
style='pytorch',
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=False,
with_cp=False,
zero_init_residual=False):
super(ResNetJIT, self).__init__()
if depth not in self.arch_settings:
raise KeyError('invalid depth {} for resnet'.format(depth))
self.depth = depth
self.num_stages = num_stages
assert num_stages >= 1 and num_stages <= 4
self.strides = strides
self.dilations = dilations
assert len(strides) == len(dilations) == num_stages
self.out_indices = out_indices
assert max(out_indices) < num_stages + 1
self.style = style
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.with_cp = with_cp
self.norm_eval = norm_eval
self.zero_init_residual = zero_init_residual
self.block, stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
self.inplanes = 64
self._make_stem_layer(in_channels)
self.res_layers = nn.ModuleList()
for i, num_blocks in enumerate(self.stage_blocks):
stride = strides[i]
dilation = dilations[i]
planes = 64 * 2**i
res_layer = make_res_layer(
self.block,
self.inplanes,
planes,
num_blocks,
stride=stride,
dilation=dilation,
style=self.style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1)
self.res_layers.add_module(layer_name, res_layer)
self._freeze_stages()
self.feat_dim = self.block.expansion * 64 * 2**(
len(self.stage_blocks) - 1)
@property
def norm1(self):
return getattr(self, 'bn1')
def _make_stem_layer(self, in_channels):
self.conv1 = build_conv_layer(
self.conv_cfg,
in_channels,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.bn1 = norm1
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
def _freeze_stages(self):
if self.frozen_stages >= 0:
self.norm1.eval()
for m in [self.conv1, self.norm1]:
for param in m.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
m = getattr(self, 'layer{}'.format(i))
m.eval()
for param in m.parameters():
param.requires_grad = False
[docs] def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m, mode='fan_in', nonlinearity='relu')
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
[docs] def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
outs = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x) # r50: 64x128x128
if 0 in self.out_indices:
outs.append(x)
x = self.maxpool(x) # r50: 64x56x56
for i, res_layer in enumerate(self.res_layers):
x = res_layer(x)
if i + 1 in self.out_indices:
outs.append(x)
# r50: 1-256x56x56; 2-512x28x28; 3-1024x14x14; 4-2048x7x7
return outs
[docs] def train(self, mode=True):
super(ResNetJIT, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
if isinstance(m, _BatchNorm):
m.eval()