segformer_plusplus/utils/activation.py

import warnings
from typing import Dict, Tuple, Union, Any, Optional
import inspect
import torch.nn as nn
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.instancenorm import _InstanceNorm

from ..model.weight_init import constant_init, kaiming_init
from ..utils.registry import MODELS
from .build_functions import SyncBatchNorm


TORCH_VERSION = torch.__version__

# register norm-layers
MODELS.register_module('BN', module=nn.BatchNorm2d)
MODELS.register_module('BN1d', module=nn.BatchNorm1d)
MODELS.register_module('BN2d', module=nn.BatchNorm2d)
MODELS.register_module('BN3d', module=nn.BatchNorm3d)
MODELS.register_module('SyncBN', module=SyncBatchNorm)
MODELS.register_module('GN', module=nn.GroupNorm)
MODELS.register_module('LN', module=nn.LayerNorm)
MODELS.register_module('IN', module=nn.InstanceNorm2d)
MODELS.register_module('IN1d', module=nn.InstanceNorm1d)
MODELS.register_module('IN2d', module=nn.InstanceNorm2d)
MODELS.register_module('IN3d', module=nn.InstanceNorm3d)
# register conv-layers
MODELS.register_module('Conv1d', module=nn.Conv1d)
MODELS.register_module('Conv2d', module=nn.Conv2d)
MODELS.register_module('Conv3d', module=nn.Conv3d)
MODELS.register_module('Conv', module=nn.Conv2d)
# register activation-functions
MODELS.register_module('GELU', module=nn.GELU)
MODELS.register_module('ReLU', module=nn.ReLU)

def build_activation_layer(cfg: Dict) -> nn.Module:
    """Build activation layer.

    Args:
        cfg (dict): The activation layer config, which should contain:

            - type (str): Layer type.
            - layer args: Args needed to instantiate an activation layer.

    Returns:
        nn.Module: Created activation layer.
    """
    return MODELS.build(cfg)


def build_norm_layer(cfg: Dict,
                     num_features: int,
                     postfix: Union[int, str] = '') -> Tuple[str, nn.Module]:
    """Build normalization layer.

    Args:
        cfg (dict): The norm layer config, which should contain:

            - type (str): Layer type.
            - layer args: Args needed to instantiate a norm layer.
            - requires_grad (bool, optional): Whether stop gradient updates.
        num_features (int): Number of input channels.
        postfix (int | str): The postfix to be appended into norm abbreviation
            to create named layer.

    Returns:
        tuple[str, nn.Module]: The first element is the layer name consisting
        of abbreviation and postfix, e.g., bn1, gn. The second element is the
        created norm layer.
    """
    if not isinstance(cfg, dict):
        raise TypeError('cfg must be a dict')
    if 'type' not in cfg:
        raise KeyError('the cfg dict must contain the key "type"')
    cfg_ = cfg.copy()

    layer_type = cfg_.pop('type')

    # Switch registry to the target scope. If `norm_layer` cannot be found
    # in the registry, fallback to search `norm_layer` in the
    # mmengine.MODELS.
    with MODELS.switch_scope_and_registry(None) as registry:
        norm_layer = registry.get(layer_type)
    if norm_layer is None:
        raise KeyError(f'Cannot find {norm_layer} in registry under scope '
                       f'name {registry.scope}')
    abbr = infer_abbr(norm_layer)

    assert isinstance(postfix, (int, str))
    name = abbr + str(postfix)

    requires_grad = cfg_.pop('requires_grad', True)
    cfg_.setdefault('eps', 1e-5)
    if layer_type != 'GN':
        layer = norm_layer(num_features, **cfg_)
        if layer_type == 'SyncBN' and hasattr(layer, '_specify_ddp_gpu_num'):
            layer._specify_ddp_gpu_num(1)
    else:
        assert 'num_groups' in cfg_
        layer = norm_layer(num_channels=num_features, **cfg_)

    for param in layer.parameters():
        param.requires_grad = requires_grad

    return name, layer


def infer_abbr(class_type):
    """Infer abbreviation from the class name.

    When we build a norm layer with `build_norm_layer()`, we want to preserve
    the norm type in variable names, e.g, self.bn1, self.gn. This method will
    infer the abbreviation to map class types to abbreviations.

    Rule 1: If the class has the property "_abbr_", return the property.
    Rule 2: If the parent class is _BatchNorm, GroupNorm, LayerNorm or
    InstanceNorm, the abbreviation of this layer will be "bn", "gn", "ln" and
    "in" respectively.
    Rule 3: If the class name contains "batch", "group", "layer" or "instance",
    the abbreviation of this layer will be "bn", "gn", "ln" and "in"
    respectively.
    Rule 4: Otherwise, the abbreviation falls back to "norm".

    Args:
        class_type (type): The norm layer type.

    Returns:
        str: The inferred abbreviation.
    """
    if not inspect.isclass(class_type):
        raise TypeError(
            f'class_type must be a type, but got {type(class_type)}')
    if hasattr(class_type, '_abbr_'):
        return class_type._abbr_
    if issubclass(class_type, _InstanceNorm):  # IN is a subclass of BN
        return 'in'
    elif issubclass(class_type, _BatchNorm):
        return 'bn'
    elif issubclass(class_type, nn.GroupNorm):
        return 'gn'
    elif issubclass(class_type, nn.LayerNorm):
        return 'ln'
    else:
        class_name = class_type.__name__.lower()
        if 'batch' in class_name:
            return 'bn'
        elif 'group' in class_name:
            return 'gn'
        elif 'layer' in class_name:
            return 'ln'
        elif 'instance' in class_name:
            return 'in'
        else:
            return 'norm_layer'
        

def build_dropout(cfg: Dict, default_args: Optional[Dict] = None) -> Any:
    """Builder for drop out layers."""
    return MODELS.build(cfg, default_args=default_args)


def build_conv_layer(cfg: Optional[Dict], *args, **kwargs) -> nn.Module:
    """Build convolution layer.

    Args:
        cfg (None or dict): The conv layer config, which should contain:
            - type (str): Layer type.
            - layer args: Args needed to instantiate an conv layer.
        args (argument list): Arguments passed to the `__init__`
            method of the corresponding conv layer.
        kwargs (keyword arguments): Keyword arguments passed to the `__init__`
            method of the corresponding conv layer.

    Returns:
        nn.Module: Created conv layer.
    """
    if cfg is None:
        cfg_ = dict(type='Conv2d')
    else:
        if not isinstance(cfg, dict):
            raise TypeError('cfg must be a dict')
        if 'type' not in cfg:
            raise KeyError('the cfg dict must contain the key "type"')
        cfg_ = cfg.copy()

    layer_type = cfg_.pop('type')

    # Switch registry to the target scope. If `conv_layer` cannot be found
    # in the registry, fallback to search `conv_layer` in the
    # mmengine.MODELS.
    with MODELS.switch_scope_and_registry(None) as registry:
        conv_layer = registry.get(layer_type)
    if conv_layer is None:
        raise KeyError(f'Cannot find {conv_layer} in registry under scope '
                       f'name {registry.scope}')
    layer = conv_layer(*args, **kwargs, **cfg_)

    return layer


def build_padding_layer(cfg: Dict, *args, **kwargs) -> nn.Module:
    """Build padding layer.

    Args:
        cfg (dict): The padding layer config, which should contain:
            - type (str): Layer type.
            - layer args: Args needed to instantiate a padding layer.

    Returns:
        nn.Module: Created padding layer.
    """
    if not isinstance(cfg, dict):
        raise TypeError('cfg must be a dict')
    if 'type' not in cfg:
        raise KeyError('the cfg dict must contain the key "type"')

    cfg_ = cfg.copy()
    padding_type = cfg_.pop('type')

    # Switch registry to the target scope. If `padding_layer` cannot be found
    # in the registry, fallback to search `padding_layer` in the
    # mmengine.MODELS.
    with MODELS.switch_scope_and_registry(None) as registry:
        padding_layer = registry.get(padding_type)
    if padding_layer is None:
        raise KeyError(f'Cannot find {padding_layer} in registry under scope '
                       f'name {registry.scope}')
    layer = padding_layer(*args, **kwargs, **cfg_)

    return layer


@MODELS.register_module()
class ConvModule(nn.Module):
    """A conv block that bundles conv/norm/activation layers.

    This block simplifies the usage of convolution layers, which are commonly
    used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
    It is based upon three build methods: `build_conv_layer()`,
    `build_norm_layer()` and `build_activation_layer()`.

    Besides, we add some additional features in this module.
    1. Automatically set `bias` of the conv layer.
    2. Spectral norm is supported.
    3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
    supports zero and circular padding, and we add "reflect" padding mode.

    Args:
        in_channels (int): Number of channels in the input feature map.
            Same as that in ``nn._ConvNd``.
        out_channels (int): Number of channels produced by the convolution.
            Same as that in ``nn._ConvNd``.
        kernel_size (int | tuple[int]): Size of the convolving kernel.
            Same as that in ``nn._ConvNd``.
        stride (int | tuple[int]): Stride of the convolution.
            Same as that in ``nn._ConvNd``.
        padding (int | tuple[int]): Zero-padding added to both sides of
            the input. Same as that in ``nn._ConvNd``.
        dilation (int | tuple[int]): Spacing between kernel elements.
            Same as that in ``nn._ConvNd``.
        groups (int): Number of blocked connections from input channels to
            output channels. Same as that in ``nn._ConvNd``.
        bias (bool | str): If specified as `auto`, it will be decided by the
            norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise
            False. Default: "auto".
        conv_cfg (dict): Config dict for convolution layer. Default: None,
            which means using conv2d.
        norm_cfg (dict): Config dict for normalization layer. Default: None.
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='ReLU').
        inplace (bool): Whether to use inplace mode for activation.
            Default: True.
        with_spectral_norm (bool): Whether use spectral norm in conv module.
            Default: False.
        padding_mode (str): If the `padding_mode` has not been supported by
            current `Conv2d` in PyTorch, we will use our own padding layer
            instead. Currently, we support ['zeros', 'circular'] with official
            implementation and ['reflect'] with our own implementation.
            Default: 'zeros'.
        order (tuple[str]): The order of conv/norm/activation layers. It is a
            sequence of "conv", "norm" and "act". Common examples are
            ("conv", "norm", "act") and ("act", "conv", "norm").
            Default: ('conv', 'norm', 'act').
    """

    _abbr_ = 'conv_block'

    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 kernel_size: Union[int, Tuple[int, int]],
                 stride: Union[int, Tuple[int, int]] = 1,
                 padding: Union[int, Tuple[int, int]] = 0,
                 dilation: Union[int, Tuple[int, int]] = 1,
                 groups: int = 1,
                 bias: Union[bool, str] = 'auto',
                 conv_cfg: Optional[Dict] = None,
                 norm_cfg: Optional[Dict] = None,
                 act_cfg: Optional[Dict] = dict(type='ReLU'),
                 inplace: bool = True,
                 with_spectral_norm: bool = False,
                 padding_mode: str = 'zeros',
                 order: tuple = ('conv', 'norm', 'act')):
        super().__init__()
        assert conv_cfg is None or isinstance(conv_cfg, dict)
        assert norm_cfg is None or isinstance(norm_cfg, dict)
        assert act_cfg is None or isinstance(act_cfg, dict)
        official_padding_mode = ['zeros', 'circular']
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
        self.inplace = inplace
        self.with_spectral_norm = with_spectral_norm
        self.with_explicit_padding = padding_mode not in official_padding_mode
        self.order = order
        assert isinstance(self.order, tuple) and len(self.order) == 3
        assert set(order) == {'conv', 'norm', 'act'}

        self.with_norm = norm_cfg is not None
        self.with_activation = act_cfg is not None
        # if the conv layer is before a norm layer, bias is unnecessary.
        if bias == 'auto':
            bias = not self.with_norm
        self.with_bias = bias

        if self.with_explicit_padding:
            pad_cfg = dict(type=padding_mode)
            self.padding_layer = build_padding_layer(pad_cfg, padding)

        # reset padding to 0 for conv module
        conv_padding = 0 if self.with_explicit_padding else padding

        print(f"ConvModule init - received bias: {bias}, final self.with_bias: {self.with_bias}")

        # build convolution layer
        self.conv = build_conv_layer(
            conv_cfg,
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=conv_padding,
            dilation=dilation,
            groups=groups,
            bias=bias)
        # export the attributes of self.conv to a higher level for convenience
        self.in_channels = self.conv.in_channels
        self.out_channels = self.conv.out_channels
        self.kernel_size = self.conv.kernel_size
        self.stride = self.conv.stride
        self.padding = padding
        self.dilation = self.conv.dilation
        self.transposed = self.conv.transposed
        self.output_padding = self.conv.output_padding
        self.groups = self.conv.groups

        if self.with_spectral_norm:
            self.conv = nn.utils.spectral_norm(self.conv)

        # build normalization layers
        if self.with_norm:
            # norm layer is after conv layer
            if order.index('norm') > order.index('conv'):
                norm_channels = out_channels
            else:
                norm_channels = in_channels
            self.norm_name, norm = build_norm_layer(
                norm_cfg, norm_channels)  # type: ignore
            self.add_module(self.norm_name, norm)
            if self.with_bias:
                if isinstance(norm, (_BatchNorm, _InstanceNorm)):
                    warnings.warn(
                        'Unnecessary conv bias before batch/instance norm')
        else:
            self.norm_name = None  # type: ignore

        # build activation layer
        if self.with_activation:
            act_cfg_ = act_cfg.copy()  # type: ignore
            # nn.Tanh has no 'inplace' argument
            if act_cfg_['type'] not in [
                    'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish', 'GELU'
            ]:
                act_cfg_.setdefault('inplace', inplace)
            self.activate = build_activation_layer(act_cfg_)

        # Use msra init by default
        self.init_weights()

    @property
    def norm(self):
        if self.norm_name:
            return getattr(self, self.norm_name)
        else:
            return None

    def init_weights(self):
        # 1. It is mainly for customized conv layers with their own
        #    initialization manners by calling their own ``init_weights()``,
        #    and we do not want ConvModule to override the initialization.
        # 2. For customized conv layers without their own initialization
        #    manners (that is, they don't have their own ``init_weights()``)
        #    and PyTorch's conv layers, they will be initialized by
        #    this method with default ``kaiming_init``.
        # Note: For PyTorch's conv layers, they will be overwritten by our
        #    initialization implementation using default ``kaiming_init``.
        if not hasattr(self.conv, 'init_weights'):
            if self.with_activation and self.act_cfg['type'] == 'LeakyReLU':
                nonlinearity = 'leaky_relu'
                a = self.act_cfg.get('negative_slope', 0.01)
            else:
                nonlinearity = 'relu'
                a = 0
            kaiming_init(self.conv, a=a, nonlinearity=nonlinearity)
        if self.with_norm:
            constant_init(self.norm, 1, bias=0)

    def forward(self,
                x: torch.Tensor,
                activate: bool = True,
                norm: bool = True) -> torch.Tensor:
        for layer in self.order:
            if layer == 'conv':
                if self.with_explicit_padding:
                    x = self.padding_layer(x)
                x = self.conv(x)
            elif layer == 'norm' and norm and self.with_norm:
                x = self.norm(x)
            elif layer == 'act' and activate and self.with_activation:
                x = self.activate(x)
        return x