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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
)
OR:
model = ResNet(
"StemWithGN",
"BottleneckWithGN",
"ResNet50StagesTo4",
)
Custom implementations may be written in user code and hooked in via the
`register_*` functions.
"""
from collections import namedtuple
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import BatchNorm2d, SyncBatchNorm
from maskrcnn_benchmark.layers import FrozenBatchNorm2d, NaiveSyncBatchNorm2d
from maskrcnn_benchmark.layers import Conv2d, DFConv2d, SELayer
from maskrcnn_benchmark.modeling.make_layers import group_norm
from maskrcnn_benchmark.utils.registry import Registry
# ResNet stage specification
StageSpec = namedtuple(
"StageSpec",
[
"index", # Index of the stage, eg 1, 2, ..,. 5
"block_count", # Number of residual blocks in the stage
"return_features", # True => return the last feature map from this stage
],
)
# -----------------------------------------------------------------------------
# Standard ResNet models
# -----------------------------------------------------------------------------
# ResNet-50 (including all stages)
ResNet50StagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, False), (4, 3, True))
)
# ResNet-50 up to stage 4 (excludes stage 5)
ResNet50StagesTo4 = tuple(
StageSpec(index=i, block_count=c, return_features=r) for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, True))
)
# ResNet-101 (including all stages)
ResNet101StagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, False), (4, 3, True))
)
# ResNet-101 up to stage 4 (excludes stage 5)
ResNet101StagesTo4 = tuple(
StageSpec(index=i, block_count=c, return_features=r) for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, True))
)
# ResNet-50-FPN (including all stages)
ResNet50FPNStagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 6, True), (4, 3, True))
)
# ResNet-101-FPN (including all stages)
ResNet101FPNStagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 23, True), (4, 3, True))
)
# ResNet-152-FPN (including all stages)
ResNet152FPNStagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, True), (2, 8, True), (3, 36, True), (4, 3, True))
)
class ResNet(nn.Module):
def __init__(self, cfg):
super(ResNet, self).__init__()
# If we want to use the cfg in forward(), then we should make a copy
# of it and store it for later use:
# self.cfg = cfg.clone()
# Translate string names to implementations
norm_level = None
stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC]
stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY]
transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC]
if cfg.MODEL.BACKBONE.USE_BN:
stem_module = StemWithBatchNorm
transformation_module = BottleneckWithBatchNorm
norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL
elif cfg.MODEL.BACKBONE.USE_NSYNCBN:
stem_module = StemWithNaiveSyncBatchNorm
transformation_module = BottleneckWithNaiveSyncBatchNorm
norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL
elif cfg.MODEL.BACKBONE.USE_SYNCBN:
stem_module = StemWithSyncBatchNorm
transformation_module = BottleneckWithSyncBatchNorm
norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL
# Construct the stem module
self.stem = stem_module(cfg)
# Constuct the specified ResNet stages
num_groups = cfg.MODEL.RESNETS.NUM_GROUPS
width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
stage2_bottleneck_channels = num_groups * width_per_group
stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
with_se = cfg.MODEL.RESNETS.WITH_SE
self.stages = []
self.out_channels = []
self.return_features = {}
for stage_spec in stage_specs:
name = "layer" + str(stage_spec.index)
stage2_relative_factor = 2 ** (stage_spec.index - 1)
bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
out_channels = stage2_out_channels * stage2_relative_factor
stage_with_dcn = cfg.MODEL.RESNETS.STAGE_WITH_DCN[stage_spec.index - 1]
if cfg.MODEL.RESNETS.USE_AVG_DOWN:
avg_down_stride = 1 if stage_spec.index == 1 else 2
else:
avg_down_stride = 0
module = _make_stage(
transformation_module,
in_channels,
bottleneck_channels,
out_channels,
stage_spec.block_count,
num_groups,
cfg.MODEL.RESNETS.STRIDE_IN_1X1,
first_stride=int(stage_spec.index > 1) + 1,
dcn_config={
"stage_with_dcn": stage_with_dcn,
"with_modulated_dcn": cfg.MODEL.RESNETS.WITH_MODULATED_DCN,
"deformable_groups": cfg.MODEL.RESNETS.DEFORMABLE_GROUPS,
},
norm_level=norm_level,
with_se=with_se,
avg_down_stride=avg_down_stride,
)
in_channels = out_channels
self.add_module(name, module)
self.stages.append(name)
self.out_channels.append(out_channels)
self.return_features[name] = stage_spec.return_features
# Optionally freeze (requires_grad=False) parts of the backbone
self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
def _freeze_backbone(self, freeze_at):
if freeze_at < 0:
return
for stage_index in range(freeze_at):
if stage_index == 0:
m = self.stem # stage 0 is the stem
else:
m = getattr(self, "layer" + str(stage_index))
for p in m.parameters():
p.requires_grad = False
def forward(self, x):
outputs = []
x = self.stem(x)
for stage_name in self.stages:
x = getattr(self, stage_name)(x)
if self.return_features[stage_name]:
outputs.append(x)
return outputs
class ResNetHead(nn.Module):
def __init__(
self,
block_module,
stages,
num_groups=1,
width_per_group=64,
stride_in_1x1=True,
stride_init=None,
res2_out_channels=256,
dilation=1,
dcn_config=None,
):
super(ResNetHead, self).__init__()
stage2_relative_factor = 2 ** (stages[0].index - 1)
stage2_bottleneck_channels = num_groups * width_per_group
out_channels = res2_out_channels * stage2_relative_factor
in_channels = out_channels // 2
bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
block_module = _TRANSFORMATION_MODULES[block_module]
self.stages = []
stride = stride_init
for stage in stages:
name = "layer" + str(stage.index)
if not stride:
stride = int(stage.index > 1) + 1
module = _make_stage(
block_module,
in_channels,
bottleneck_channels,
out_channels,
stage.block_count,
num_groups,
stride_in_1x1,
first_stride=stride,
dilation=dilation,
dcn_config=dcn_config,
)
stride = None
self.add_module(name, module)
self.stages.append(name)
self.out_channels = out_channels
def forward(self, x):
for stage in self.stages:
x = getattr(self, stage)(x)
return x
def _make_stage(
transformation_module,
in_channels,
bottleneck_channels,
out_channels,
block_count,
num_groups,
stride_in_1x1,
first_stride,
dilation=1,
dcn_config=None,
norm_level=None,
**kwargs
):
blocks = []
stride = first_stride
for li in range(block_count):
if norm_level is not None:
layer_module = BottleneckWithFixedBatchNorm
if norm_level >= 1 and li == 0:
layer_module = transformation_module
if norm_level >= 2 and li == block_count - 1:
layer_module = transformation_module
if norm_level >= 3:
layer_module = transformation_module
else:
layer_module = transformation_module
blocks.append(
layer_module(
in_channels,
bottleneck_channels,
out_channels,
num_groups,
stride_in_1x1,
stride,
dilation=dilation,
dcn_config=dcn_config,
**kwargs
)
)
stride = 1
in_channels = out_channels
return nn.Sequential(*blocks)
class Bottleneck(nn.Module):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups,
stride_in_1x1,
stride,
dilation,
norm_func,
dcn_config,
with_se=False,
avg_down_stride=0,
):
super(Bottleneck, self).__init__()
self.downsample = None
if in_channels != out_channels:
down_stride = stride if dilation == 1 else 1
if avg_down_stride > 0:
self.downsample = nn.Sequential(
nn.AvgPool2d(
kernel_size=avg_down_stride, stride=avg_down_stride, ceil_mode=True, count_include_pad=False
),
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
norm_func(out_channels),
)
else:
self.downsample = nn.Sequential(
Conv2d(in_channels, out_channels, kernel_size=1, stride=down_stride, bias=False),
norm_func(out_channels),
)
for modules in [
self.downsample,
]:
for l in modules.modules():
if isinstance(l, Conv2d):
nn.init.kaiming_uniform_(l.weight, a=1)
if dilation > 1:
stride = 1 # reset to be 1
# The original MSRA ResNet models have stride in the first 1x1 conv
# The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
# stride in the 3x3 conv
stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
self.conv1 = Conv2d(
in_channels,
bottleneck_channels,
kernel_size=1,
stride=stride_1x1,
bias=False,
)
self.bn1 = norm_func(bottleneck_channels)
# TODO: specify init for the above
with_dcn = dcn_config.get("stage_with_dcn", False)
if with_dcn:
deformable_groups = dcn_config.get("deformable_groups", 1)
with_modulated_dcn = dcn_config.get("with_modulated_dcn", False)
self.conv2 = DFConv2d(
bottleneck_channels,
bottleneck_channels,
with_modulated_dcn=with_modulated_dcn,
kernel_size=3,
stride=stride_3x3,
groups=num_groups,
dilation=dilation,
deformable_groups=deformable_groups,
bias=False,
)
else:
self.conv2 = Conv2d(
bottleneck_channels,
bottleneck_channels,
kernel_size=3,
stride=stride_3x3,
padding=dilation,
bias=False,
groups=num_groups,
dilation=dilation,
)
nn.init.kaiming_uniform_(self.conv2.weight, a=1)
self.bn2 = norm_func(bottleneck_channels)
self.conv3 = Conv2d(bottleneck_channels, out_channels, kernel_size=1, bias=False)
self.bn3 = norm_func(out_channels)
self.se = SELayer(out_channels) if with_se and not with_dcn else None
for l in [
self.conv1,
self.conv3,
]:
nn.init.kaiming_uniform_(l.weight, a=1)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = F.relu_(out)
out = self.conv2(out)
out = self.bn2(out)
out = F.relu_(out)
out0 = self.conv3(out)
out = self.bn3(out0)
if self.se:
out = self.se(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = F.relu_(out)
return out
class BaseStem(nn.Module):
def __init__(self, cfg, norm_func):
super(BaseStem, self).__init__()
out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
self.stem_3x3 = cfg.MODEL.RESNETS.USE_STEM3X3
if self.stem_3x3:
self.conv1 = Conv2d(3, out_channels, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = norm_func(out_channels)
self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False)
self.bn2 = norm_func(out_channels)
for l in [self.conv1, self.conv2]:
nn.init.kaiming_uniform_(l.weight, a=1)
else:
self.conv1 = Conv2d(3, out_channels, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_func(out_channels)
for l in [
self.conv1,
]:
nn.init.kaiming_uniform_(l.weight, a=1)
def forward(self, x):
if self.stem_3x3:
x = self.conv1(x)
x = self.bn1(x)
x = F.relu_(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu_(x)
else:
x = self.conv1(x)
x = self.bn1(x)
x = F.relu_(x)
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
return x
class BottleneckWithFixedBatchNorm(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config=None,
**kwargs
):
super(BottleneckWithFixedBatchNorm, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=FrozenBatchNorm2d,
dcn_config=dcn_config,
**kwargs
)
class StemWithFixedBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithFixedBatchNorm, self).__init__(cfg, norm_func=FrozenBatchNorm2d)
class BottleneckWithBatchNorm(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config=None,
**kwargs
):
super(BottleneckWithBatchNorm, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=BatchNorm2d,
dcn_config=dcn_config,
**kwargs
)
class StemWithBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithBatchNorm, self).__init__(cfg, norm_func=BatchNorm2d)
class BottleneckWithNaiveSyncBatchNorm(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config=None,
**kwargs
):
super(BottleneckWithNaiveSyncBatchNorm, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=NaiveSyncBatchNorm2d,
dcn_config=dcn_config,
**kwargs
)
class StemWithNaiveSyncBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithNaiveSyncBatchNorm, self).__init__(cfg, norm_func=NaiveSyncBatchNorm2d)
class BottleneckWithSyncBatchNorm(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config=None,
**kwargs
):
super(BottleneckWithSyncBatchNorm, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=SyncBatchNorm,
dcn_config=dcn_config,
**kwargs
)
class StemWithSyncBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithSyncBatchNorm, self).__init__(cfg, norm_func=SyncBatchNorm)
class BottleneckWithGN(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config=None,
**kwargs
):
super(BottleneckWithGN, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=group_norm,
dcn_config=dcn_config,
**kwargs
)
class StemWithGN(BaseStem):
def __init__(self, cfg):
super(StemWithGN, self).__init__(cfg, norm_func=group_norm)
_TRANSFORMATION_MODULES = Registry(
{
"BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
"BottleneckWithGN": BottleneckWithGN,
}
)
_STEM_MODULES = Registry(
{
"StemWithFixedBatchNorm": StemWithFixedBatchNorm,
"StemWithGN": StemWithGN,
}
)
_STAGE_SPECS = Registry(
{
"R-50-C4": ResNet50StagesTo4,
"R-50-C5": ResNet50StagesTo5,
"R-50-RETINANET": ResNet50StagesTo5,
"R-101-C4": ResNet101StagesTo4,
"R-101-C5": ResNet101StagesTo5,
"R-101-RETINANET": ResNet101StagesTo5,
"R-50-FPN": ResNet50FPNStagesTo5,
"R-50-FPN-RETINANET": ResNet50FPNStagesTo5,
"R-50-FPN-FCOS": ResNet50FPNStagesTo5,
"R-101-FPN": ResNet101FPNStagesTo5,
"R-101-FPN-RETINANET": ResNet101FPNStagesTo5,
"R-101-FPN-FCOS": ResNet101FPNStagesTo5,
"R-152-FPN": ResNet152FPNStagesTo5,
}
)
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