detrex/modeling/backbone/resnet.py

# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/resnet.py
# ------------------------------------------------------------------------------------------------

import numpy as np
from typing import List
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn

from detectron2.layers import (
    CNNBlockBase,
    Conv2d,
    DeformConv,
    ModulatedDeformConv,
    ShapeSpec,
    get_norm,
)
from detectron2.modeling.backbone import Backbone

__all__ = [
    "ResNetBlockBase",
    "BasicBlock",
    "BottleneckBlock",
    "DeformBottleneckBlock",
    "BasicStem",
    "ResNet",
    "make_stage",
]


class BasicBlock(CNNBlockBase):
    """
    The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`,
    with two 3x3 conv layers and a projection shortcut if needed.

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        stride (int): Stride for the first conv.
        norm (str or callable): normalization for all conv layers.
            See :func:`detectron2.layers.get_norm` for supported format.
    """

    def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"):
        super().__init__(in_channels, out_channels, stride)

        if in_channels != out_channels:
            self.shortcut = Conv2d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=stride,
                bias=False,
                norm=get_norm(norm, out_channels),
            )
        else:
            self.shortcut = None

        self.conv1 = Conv2d(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False,
            norm=get_norm(norm, out_channels),
        )

        self.conv2 = Conv2d(
            out_channels,
            out_channels,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=False,
            norm=get_norm(norm, out_channels),
        )

        for layer in [self.conv1, self.conv2, self.shortcut]:
            if layer is not None:  # shortcut can be None
                weight_init.c2_msra_fill(layer)

    def forward(self, x):
        out = self.conv1(x)
        out = F.relu_(out)
        out = self.conv2(out)

        if self.shortcut is not None:
            shortcut = self.shortcut(x)
        else:
            shortcut = x

        out += shortcut
        out = F.relu_(out)
        return out


class BottleneckBlock(CNNBlockBase):
    """
    The standard bottleneck residual block used by ResNet-50, 101 and 152
    defined in :paper:`ResNet`.  It contains 3 conv layers with kernels
    1x1, 3x3, 1x1, and a projection shortcut if needed.

    Args:
        bottleneck_channels (int): number of output channels for the 3x3
            "bottleneck" conv layers.
        num_groups (int): number of groups for the 3x3 conv layer.
        norm (str or callable): normalization for all conv layers.
            See :func:`layers.get_norm` for supported format.
        stride_in_1x1 (bool): when stride>1, whether to put stride in the
            first 1x1 convolution or the bottleneck 3x3 convolution.
        dilation (int): the dilation rate of the 3x3 conv layer.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        *,
        bottleneck_channels,
        stride=1,
        num_groups=1,
        norm="BN",
        stride_in_1x1=False,
        dilation=1,
    ):
        super().__init__(in_channels, out_channels, stride)

        if in_channels != out_channels:
            self.shortcut = Conv2d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=stride,
                bias=False,
                norm=get_norm(norm, out_channels),
            )
        else:
            self.shortcut = None

        # 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,
            norm=get_norm(norm, bottleneck_channels),
        )

        self.conv2 = Conv2d(
            bottleneck_channels,
            bottleneck_channels,
            kernel_size=3,
            stride=stride_3x3,
            padding=1 * dilation,
            bias=False,
            groups=num_groups,
            dilation=dilation,
            norm=get_norm(norm, bottleneck_channels),
        )

        self.conv3 = Conv2d(
            bottleneck_channels,
            out_channels,
            kernel_size=1,
            bias=False,
            norm=get_norm(norm, out_channels),
        )

        for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
            if layer is not None:  # shortcut can be None
                weight_init.c2_msra_fill(layer)

        # Zero-initialize the last normalization in each residual branch,
        # so that at the beginning, the residual branch starts with zeros,
        # and each residual block behaves like an identity.
        # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
        # "For BN layers, the learnable scaling coefficient γ is initialized
        # to be 1, except for each residual block's last BN
        # where γ is initialized to be 0."

        # nn.init.constant_(self.conv3.norm.weight, 0)
        # TODO this somehow hurts performance when training GN models from scratch.
        # Add it as an option when we need to use this code to train a backbone.

    def forward(self, x):
        """Forward function of `BottleneckBlock`."""
        out = self.conv1(x)
        out = F.relu_(out)

        out = self.conv2(out)
        out = F.relu_(out)

        out = self.conv3(out)

        if self.shortcut is not None:
            shortcut = self.shortcut(x)
        else:
            shortcut = x

        out += shortcut
        out = F.relu_(out)
        return out


class DeformBottleneckBlock(CNNBlockBase):
    """
    Similar to :class:`BottleneckBlock`, but with
    paper `Deformable Convolutional Networks
    <https://arxiv.org/pdf/1703.06211.pdf>`_ in the 3x3 convolution.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        *,
        bottleneck_channels,
        stride=1,
        num_groups=1,
        norm="BN",
        stride_in_1x1=False,
        dilation=1,
        deform_modulated=False,
        deform_num_groups=1,
    ):
        super().__init__(in_channels, out_channels, stride)
        self.deform_modulated = deform_modulated

        if in_channels != out_channels:
            self.shortcut = Conv2d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=stride,
                bias=False,
                norm=get_norm(norm, out_channels),
            )
        else:
            self.shortcut = None

        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,
            norm=get_norm(norm, bottleneck_channels),
        )

        if deform_modulated:
            deform_conv_op = ModulatedDeformConv
            # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size
            offset_channels = 27
        else:
            deform_conv_op = DeformConv
            offset_channels = 18

        self.conv2_offset = Conv2d(
            bottleneck_channels,
            offset_channels * deform_num_groups,
            kernel_size=3,
            stride=stride_3x3,
            padding=1 * dilation,
            dilation=dilation,
        )
        self.conv2 = deform_conv_op(
            bottleneck_channels,
            bottleneck_channels,
            kernel_size=3,
            stride=stride_3x3,
            padding=1 * dilation,
            bias=False,
            groups=num_groups,
            dilation=dilation,
            deformable_groups=deform_num_groups,
            norm=get_norm(norm, bottleneck_channels),
        )

        self.conv3 = Conv2d(
            bottleneck_channels,
            out_channels,
            kernel_size=1,
            bias=False,
            norm=get_norm(norm, out_channels),
        )

        for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
            if layer is not None:  # shortcut can be None
                weight_init.c2_msra_fill(layer)

        nn.init.constant_(self.conv2_offset.weight, 0)
        nn.init.constant_(self.conv2_offset.bias, 0)

    def forward(self, x):
        """Forward function of `DeformBottleneckBlock`."""
        out = self.conv1(x)
        out = F.relu_(out)

        if self.deform_modulated:
            offset_mask = self.conv2_offset(out)
            offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1)
            offset = torch.cat((offset_x, offset_y), dim=1)
            mask = mask.sigmoid()
            out = self.conv2(out, offset, mask)
        else:
            offset = self.conv2_offset(out)
            out = self.conv2(out, offset)
        out = F.relu_(out)

        out = self.conv3(out)

        if self.shortcut is not None:
            shortcut = self.shortcut(x)
        else:
            shortcut = x

        out += shortcut
        out = F.relu_(out)
        return out


class BasicStem(CNNBlockBase):
    """
    The standard ResNet stem (layers before the first residual block),
    with a conv, relu and max_pool.

    Args:
        norm (str or callable): norm after the first conv layer.
            See :func:`detectron2.layers.get_norm` for supported format.
    """

    def __init__(self, in_channels=3, out_channels=64, norm="BN"):
        super().__init__(in_channels, out_channels, 4)
        self.in_channels = in_channels
        self.conv1 = Conv2d(
            in_channels,
            out_channels,
            kernel_size=7,
            stride=2,
            padding=3,
            bias=False,
            norm=get_norm(norm, out_channels),
        )
        weight_init.c2_msra_fill(self.conv1)

    def forward(self, x):
        """Forward function of `BasicStem`."""
        x = self.conv1(x)
        x = F.relu_(x)
        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        return x


class ResNet(Backbone):
    """
    Implement paper `Deep Residual Learning for Image Recognition
    <https://arxiv.org/pdf/1512.03385.pdf>`_.

    Args:
        stem (nn.Module): a stem module.
        stages (list[list[detectron2.layers.CNNBlockBase]]): several (typically 4) stages,
            each contains multiple :class:`detectron2.layers.CNNBlockBase`.
        num_classes (None or int): if None, will not perform classification.
            Otherwise, will create a linear layer.
        out_features (list[str]): name of the layers whose outputs should
            be returned in forward. Can be anything in "stem", "linear", or "res2" ...
            If None, will return the output of the last layer.
        freeze_at (int): The number of stages at the beginning to freeze.
            see :meth:`freeze` for detailed explanation.
    """

    def __init__(self, stem, stages, num_classes=None, out_features=None, freeze_at=0):
        super().__init__()
        self.stem = stem
        self.num_classes = num_classes

        current_stride = self.stem.stride
        self._out_feature_strides = {"stem": current_stride}
        self._out_feature_channels = {"stem": self.stem.out_channels}

        self.stage_names, self.stages = [], []

        if out_features is not None:
            # Avoid keeping unused layers in this module. They consume extra memory
            # and may cause allreduce to fail
            num_stages = max(
                [{"res2": 1, "res3": 2, "res4": 3, "res5": 4}.get(f, 0) for f in out_features]
            )
            stages = stages[:num_stages]
        for i, blocks in enumerate(stages):
            assert len(blocks) > 0, len(blocks)
            for block in blocks:
                assert isinstance(block, CNNBlockBase), block

            name = "res" + str(i + 2)
            stage = nn.Sequential(*blocks)

            self.add_module(name, stage)
            self.stage_names.append(name)
            self.stages.append(stage)

            self._out_feature_strides[name] = current_stride = int(
                current_stride * np.prod([k.stride for k in blocks])
            )
            self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels
        self.stage_names = tuple(self.stage_names)  # Make it static for scripting

        if num_classes is not None:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
            self.linear = nn.Linear(curr_channels, num_classes)

            # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
            # "The 1000-way fully-connected layer is initialized by
            # drawing weights from a zero-mean Gaussian with standard deviation of 0.01."
            nn.init.normal_(self.linear.weight, std=0.01)
            name = "linear"

        if out_features is None:
            out_features = [name]
        self._out_features = out_features
        assert len(self._out_features)
        children = [x[0] for x in self.named_children()]
        for out_feature in self._out_features:
            assert out_feature in children, "Available children: {}".format(", ".join(children))
        self.freeze(freeze_at)

    def forward(self, x):
        """
        Args:
            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.

        Returns:
            dict[str->Tensor]: names and the corresponding features
        """
        assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!"
        outputs = {}
        x = self.stem(x)
        if "stem" in self._out_features:
            outputs["stem"] = x
        for name, stage in zip(self.stage_names, self.stages):
            x = stage(x)
            if name in self._out_features:
                outputs[name] = x
        if self.num_classes is not None:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.linear(x)
            if "linear" in self._out_features:
                outputs["linear"] = x
        return outputs

    def output_shape(self):
        return {
            name: ShapeSpec(
                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
            )
            for name in self._out_features
        }

    def freeze(self, freeze_at=0):
        """
        Freeze the first several stages of the ResNet. Commonly used in
        fine-tuning.
        Layers that produce the same feature map spatial size are defined as one
        "stage" by paper `Feature Pyramid Networks for Object Detection
        <https://arxiv.org/pdf/1612.03144.pdf>`_.

        Args:
            freeze_at (int): number of stages to freeze.
                `1` means freezing the stem. `2` means freezing the stem and
                one residual stage, etc.

        Returns:
            nn.Module: this ResNet itself
        """
        if freeze_at >= 1:
            self.stem.freeze()
        for idx, stage in enumerate(self.stages, start=2):
            if freeze_at >= idx:
                for block in stage.children():
                    block.freeze()
        return self

    @staticmethod
    def make_stage(block_class, num_blocks, *, in_channels, out_channels, **kwargs):
        """
        Create a list of blocks of the same type that forms one ResNet stage.

        Args:
            block_class (type): a subclass of ``detectron2.layers.CNNBlockBase`` that's
                used to create all blocks in this stage. A module of this type
                must not change spatial resolution of inputs unless its stride != 1.
            num_blocks (int): number of blocks in this stage
            in_channels (int): input channels of the entire stage.
            out_channels (int): output channels of **every block** in the stage.
            kwargs: other arguments passed to the constructor of
                `block_class`. If the argument name is "xx_per_block", the
                argument is a list of values to be passed to each block in the
                stage. Otherwise, the same argument is passed to every block
                in the stage.

        Returns:
            list[detectron2.layers.CNNBlockBase]: a list of block module.

        Examples:
        ::
            stage = ResNet.make_stage(
                BottleneckBlock, 3, in_channels=16, out_channels=64,
                bottleneck_channels=16, num_groups=1,
                stride_per_block=[2, 1, 1],
                dilations_per_block=[1, 1, 2]
            )

        Usually, layers that produce the same feature map spatial size are defined as one
        "stage" (in paper `Feature Pyramid Networks for Object Detection
        <https://arxiv.org/pdf/1612.03144.pdf>`_).
        Under such definition, ``stride_per_block[1:]`` should all be 1.
        """
        blocks = []
        for i in range(num_blocks):
            curr_kwargs = {}
            for k, v in kwargs.items():
                if k.endswith("_per_block"):
                    assert len(v) == num_blocks, (
                        f"Argument '{k}' of make_stage should have the "
                        f"same length as num_blocks={num_blocks}."
                    )
                    newk = k[: -len("_per_block")]
                    assert newk not in kwargs, f"Cannot call make_stage with both {k} and {newk}!"
                    curr_kwargs[newk] = v[i]
                else:
                    curr_kwargs[k] = v

            blocks.append(
                block_class(in_channels=in_channels, out_channels=out_channels, **curr_kwargs)
            )
            in_channels = out_channels
        return blocks

    @staticmethod
    def make_default_stages(depth, block_class=None, **kwargs):
        """
        Created list of ResNet stages from pre-defined depth (one of 18, 34, 50, 101, 152).
        If it doesn't create the ResNet variant you need, please use :meth:`make_stage`
        instead for fine-grained customization.

        Args:
            depth (int): depth of ResNet
            block_class (type): the CNN block class. Has to accept
                `bottleneck_channels` argument for depth > 50.
                By default it is BasicBlock or BottleneckBlock, based on the
                depth.
            kwargs:
                other arguments to pass to `make_stage`. Should not contain
                stride and channels, as they are predefined for each depth.

        Returns:
            list[list[detectron2.layers.CNNBlockBase]]: modules in all stages; see arguments of
            :class:`ResNet`.
        """
        num_blocks_per_stage = {
            18: [2, 2, 2, 2],
            34: [3, 4, 6, 3],
            50: [3, 4, 6, 3],
            101: [3, 4, 23, 3],
            152: [3, 8, 36, 3],
        }[depth]
        if block_class is None:
            block_class = BasicBlock if depth < 50 else BottleneckBlock
        if depth < 50:
            in_channels = [64, 64, 128, 256]
            out_channels = [64, 128, 256, 512]
        else:
            in_channels = [64, 256, 512, 1024]
            out_channels = [256, 512, 1024, 2048]
        ret = []
        for (n, s, i, o) in zip(num_blocks_per_stage, [1, 2, 2, 2], in_channels, out_channels):
            if depth >= 50:
                kwargs["bottleneck_channels"] = o // 4
            ret.append(
                ResNet.make_stage(
                    block_class=block_class,
                    num_blocks=n,
                    stride_per_block=[s] + [1] * (n - 1),
                    in_channels=i,
                    out_channels=o,
                    **kwargs,
                )
            )
        return ret


ResNetBlockBase = CNNBlockBase
"""
Alias for backward compatibiltiy.
"""


def make_stage(
    depth: int = 50,
    norm: float = "FrozenBN",
    num_groups: int = 1,
    width_per_group: int = 64,
    in_channels: int = 64,
    out_channels: int = 256,
    stride_in_1x1: bool = False,
    res5_dilation: int = 1,
    deform_on_per_stage: List[bool] = [False, False, False, False],
    deform_modulated: bool = False,
    deform_num_groups: int = 1,
):
    """
    Modified from `detectron2.modeling.backbone.build_resnet_backbone
    <https://github.com/facebookresearch/detectron2/blob/717ab9f0aeca216a2f800e43d705766251ba3a55/detectron2/modeling/backbone/resnet.py#L614>`_

    Create a list of blocks of the same type that forms one ResNet stage.

    Args:
        depth (int): The depth of ResNet. Default: 50.
        norm (str or callable): Normalization for all conv layers.
            See :func:`detectron2.layers.get_norm` for supported format.
            Default: `FrozenBN`.
        num_groups (int): The number of groups for the 3x3 conv layer. Default: 1.
        width_per_group (int): Baseline width of each group.
            Scaling this parameters will scale the width of all bottleneck layers.
            Default: 64.
        in_channels (int): Output feature channels of the `Stem` Block. Needs
            to be set to 64 for R18 and R34. Default: 64.
        out_channels (int): Output width of res2. Scaling this parameters
            will scale the width of all 1x1 convs in ResNet. Default: 256.
        stride_in_1x1 (bool): Place the stride 2 conv on the 1x1 filter.
            Use True only for the original MSRA ResNet;
            use False for C2 and Torch models. Default: False.
        res5_dilation (int): Apply dilation in stage "res5". Default: 1.
        deform_on_per_stage (List[bool]): Apply Deformable Convolution in stages.
            Specify if apply `deform_conv` on Res2, Res3, Res4, Res5.
            Default: `[False, False, False, False]`.
        deform_modulated: Use True to use modulated deform_conv
            (DeformableV2, https://arxiv.org/abs/1811.11168); Use False for DeformableV1.
            Default: False.
        deform_num_groups (int): Number of groups in deformable conv. Default: 1.

    Returns:
        list[detectron2.layers.CNNBlockBase]: a list of block module.

    Examples:
    ::
        from detrex.modeling.backbone import make_stage, ResNet, BasicStem

        resnet50_dc5 = ResNet(
            stem=BasicStem(in_channels=3, out_channels=64, norm="FrozenBN"),
            stages=make_stage(
                depth=50,
                norm="FrozenBN",
                in_channels=64,
                out_channels=256,
                res5_dilation=2,
            ),
            out_features=["res2", "res3", "res4", "res5"],
            freeze_at=1,
        )
    """
    bottleneck_channels = num_groups * width_per_group
    assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)
    num_blocks_per_stage = {
        18: [2, 2, 2, 2],
        34: [3, 4, 6, 3],
        50: [3, 4, 6, 3],
        101: [3, 4, 23, 3],
        152: [3, 8, 36, 3],
    }[depth]

    if depth in [18, 34]:
        assert out_channels == 64, "Must set out_channels = 64 for R18/R34"
        assert not any(deform_on_per_stage), "deform_on_per_stage unsupported for R18/R34"
        assert res5_dilation == 1, "Must set res5_dilation = 1 for R18/R34"
        assert num_groups == 1, "Must set num_groups = 1 for R18/R34"

    stages = []
    for idx, stage_idx in enumerate(range(2, 6)):
        dilation = res5_dilation if stage_idx == 5 else 1
        first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
        stage_kargs = {
            "num_blocks": num_blocks_per_stage[idx],
            "stride_per_block": [first_stride] + [1] * (num_blocks_per_stage[idx] - 1),
            "in_channels": in_channels,
            "out_channels": out_channels,
            "norm": norm,
        }
        # Use BasicBlock for R18 and R34.
        if depth in [18, 34]:
            stage_kargs["block_class"] = BasicBlock
        else:
            stage_kargs["bottleneck_channels"] = bottleneck_channels
            stage_kargs["stride_in_1x1"] = stride_in_1x1
            stage_kargs["dilation"] = dilation
            stage_kargs["num_groups"] = num_groups
            if deform_on_per_stage[idx]:
                stage_kargs["block_class"] = DeformBottleneckBlock
                stage_kargs["deform_modulated"] = deform_modulated
                stage_kargs["deform_num_groups"] = deform_num_groups
            else:
                stage_kargs["block_class"] = BottleneckBlock
        blocks = ResNet.make_stage(**stage_kargs)
        in_channels = out_channels
        out_channels *= 2
        bottleneck_channels *= 2
        stages.append(blocks)

    return stages