maskrcnn_benchmark/modeling/backbone/resnet.py

# 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,
    }
)