segformer_plusplus/model/weight_init.py

import copy
import math
import warnings
import inspect
from typing import Any, Optional, Union
import torch
import torch.nn as nn
from torch import Tensor

from ..configs.config.config import Config, ConfigDict
from ..utils.registry import Registry
from ..utils.manager import ManagerMixin


WEIGHT_INITIALIZERS = Registry('weight initializer')

@WEIGHT_INITIALIZERS.register_module(name='Pretrained')
class PretrainedInit:
    """Initialize module by loading a pretrained model.

    Args:
        checkpoint (str): the checkpoint file of the pretrained model should
            be load.
        prefix (str, optional): the prefix of a sub-module in the pretrained
            model. it is for loading a part of the pretrained model to
            initialize. For example, if we would like to only load the
            backbone of a detector model, we can set ``prefix='backbone.'``.
            Defaults to None.
        map_location (str): map tensors into proper locations. Defaults to cpu.
    """

    def __init__(self, checkpoint, prefix=None, map_location='cpu'):
        self.checkpoint = checkpoint
        self.prefix = prefix
        self.map_location = map_location

    def __call__(self, module):
        from mmengine.runner.checkpoint import (_load_checkpoint_with_prefix,
                                                load_checkpoint,
                                                load_state_dict)
        if self.prefix is None:
            load_checkpoint(
                module,
                self.checkpoint,
                map_location=self.map_location,
                strict=False,
                logger='current')
        else:
            state_dict = _load_checkpoint_with_prefix(
                self.prefix, self.checkpoint, map_location=self.map_location)
            load_state_dict(module, state_dict, strict=False, logger='current')

        if hasattr(module, '_params_init_info'):
            update_init_info(module, init_info=self._get_init_info())

    def _get_init_info(self):
        info = f'{self.__class__.__name__}: load from {self.checkpoint}'
        return info
    

def update_init_info(module, init_info):
    """Update the `_params_init_info` in the module if the value of parameters
    are changed.

    Args:
        module (obj:`nn.Module`): The module of PyTorch with a user-defined
            attribute `_params_init_info` which records the initialization
            information.
        init_info (str): The string that describes the initialization.
    """
    assert hasattr(
        module,
        '_params_init_info'), f'Can not find `_params_init_info` in {module}'
    for name, param in module.named_parameters():

        assert param in module._params_init_info, (
            f'Find a new :obj:`Parameter` '
            f'named `{name}` during executing the '
            f'`init_weights` of '
            f'`{module.__class__.__name__}`. '
            f'Please do not add or '
            f'replace parameters during executing '
            f'the `init_weights`. ')

        # The parameter has been changed during executing the
        # `init_weights` of module
        mean_value = param.data.mean().cpu()
        if module._params_init_info[param]['tmp_mean_value'] != mean_value:
            module._params_init_info[param]['init_info'] = init_info
            module._params_init_info[param]['tmp_mean_value'] = mean_value


def initialize(module, init_cfg):
    r"""Initialize a module.

    Args:
        module (``torch.nn.Module``): the module will be initialized.
        init_cfg (dict | list[dict]): initialization configuration dict to
            define initializer. OpenMMLab has implemented 6 initializers
            including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
            ``Kaiming``, and ``Pretrained``.

    Example:
        >>> module = nn.Linear(2, 3, bias=True)
        >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
        >>> initialize(module, init_cfg)
        >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
        >>> # define key ``'layer'`` for initializing layer with different
        >>> # configuration
        >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
                dict(type='Constant', layer='Linear', val=2)]
        >>> initialize(module, init_cfg)
        >>> # define key``'override'`` to initialize some specific part in
        >>> # module
        >>> class FooNet(nn.Module):
        >>>     def __init__(self):
        >>>         super().__init__()
        >>>         self.feat = nn.Conv2d(3, 16, 3)
        >>>         self.reg = nn.Conv2d(16, 10, 3)
        >>>         self.cls = nn.Conv2d(16, 5, 3)
        >>> model = FooNet()
        >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
        >>>     override=dict(type='Constant', name='reg', val=3, bias=4))
        >>> initialize(model, init_cfg)
        >>> model = ResNet(depth=50)
        >>> # Initialize weights with the pretrained model.
        >>> init_cfg = dict(type='Pretrained',
                checkpoint='torchvision://resnet50')
        >>> initialize(model, init_cfg)
        >>> # Initialize weights of a sub-module with the specific part of
        >>> # a pretrained model by using "prefix".
        >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
        >>>     'retinanet_r50_fpn_1x_coco/'\
        >>>     'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
        >>> init_cfg = dict(type='Pretrained',
                checkpoint=url, prefix='backbone.')
    """
    if not isinstance(init_cfg, (dict, list)):
        raise TypeError(f'init_cfg must be a dict or a list of dict, \
                but got {type(init_cfg)}')

    if isinstance(init_cfg, dict):
        init_cfg = [init_cfg]

    for cfg in init_cfg:
        # should deeply copy the original config because cfg may be used by
        # other modules, e.g., one init_cfg shared by multiple bottleneck
        # blocks, the expected cfg will be changed after pop and will change
        # the initialization behavior of other modules
        cp_cfg = copy.deepcopy(cfg)
        override = cp_cfg.pop('override', None)
        _initialize(module, cp_cfg)

        if override is not None:
            cp_cfg.pop('layer', None)
            _initialize_override(module, override, cp_cfg)
        else:
            # All attributes in module have same initialization.
            pass


def _initialize(module, cfg, wholemodule=False):
    func = build_from_cfg(cfg, WEIGHT_INITIALIZERS)
    # wholemodule flag is for override mode, there is no layer key in override
    # and initializer will give init values for the whole module with the name
    # in override.
    func.wholemodule = wholemodule
    func(module)


def _initialize_override(module, override, cfg):
    if not isinstance(override, (dict, list)):
        raise TypeError(f'override must be a dict or a list of dict, \
                but got {type(override)}')

    override = [override] if isinstance(override, dict) else override

    for override_ in override:

        cp_override = copy.deepcopy(override_)
        name = cp_override.pop('name', None)
        if name is None:
            raise ValueError('`override` must contain the key "name",'
                             f'but got {cp_override}')
        # if override only has name key, it means use args in init_cfg
        if not cp_override:
            cp_override.update(cfg)
        # if override has name key and other args except type key, it will
        # raise error
        elif 'type' not in cp_override.keys():
            raise ValueError(
                f'`override` need "type" key, but got {cp_override}')

        if hasattr(module, name):
            _initialize(getattr(module, name), cp_override, wholemodule=True)
        else:
            raise RuntimeError(f'module did not have attribute {name}, '
                               f'but init_cfg is {cp_override}.')
        

def build_from_cfg(
        cfg: Union[dict, ConfigDict, Config],
        registry: Registry,
        default_args: Optional[Union[dict, ConfigDict, Config]] = None) -> Any:
    """Build a module from config dict when it is a class configuration, or
    call a function from config dict when it is a function configuration.

    If the global variable default scope (:obj:`DefaultScope`) exists,
    :meth:`build` will firstly get the responding registry and then call
    its own :meth:`build`.

    At least one of the ``cfg`` and ``default_args`` contains the key "type",
    which should be either str or class. If they all contain it, the key
    in ``cfg`` will be used because ``cfg`` has a high priority than
    ``default_args`` that means if a key exists in both of them, the value of
    the key will be ``cfg[key]``. They will be merged first and the key "type"
    will be popped up and the remaining keys will be used as initialization
    arguments.

    Examples:
        >>> from mmengine import Registry, build_from_cfg
        >>> MODELS = Registry('models')
        >>> @MODELS.register_module()
        >>> class ResNet:
        >>>     def __init__(self, depth, stages=4):
        >>>         self.depth = depth
        >>>         self.stages = stages
        >>> cfg = dict(type='ResNet', depth=50)
        >>> model = build_from_cfg(cfg, MODELS)
        >>> # Returns an instantiated object
        >>> @MODELS.register_module()
        >>> def resnet50():
        >>>     pass
        >>> resnet = build_from_cfg(dict(type='resnet50'), MODELS)
        >>> # Return a result of the calling function

    Args:
        cfg (dict or ConfigDict or Config): Config dict. It should at least
            contain the key "type".
        registry (:obj:`Registry`): The registry to search the type from.
        default_args (dict or ConfigDict or Config, optional): Default
            initialization arguments. Defaults to None.

    Returns:
        object: The constructed object.
    """
    if not isinstance(cfg, (dict, ConfigDict, Config)):
        raise TypeError(
            f'cfg should be a dict, ConfigDict or Config, but got {type(cfg)}')

    if 'type' not in cfg:
        if default_args is None or 'type' not in default_args:
            raise KeyError(
                '`cfg` or `default_args` must contain the key "type", '
                f'but got {cfg}\n{default_args}')

    if not isinstance(registry, Registry):
        raise TypeError('registry must be a mmengine.Registry object, '
                        f'but got {type(registry)}')

    if not (isinstance(default_args,
                       (dict, ConfigDict, Config)) or default_args is None):
        raise TypeError(
            'default_args should be a dict, ConfigDict, Config or None, '
            f'but got {type(default_args)}')

    args = cfg.copy()
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)

    # Instance should be built under target scope, if `_scope_` is defined
    # in cfg, current default scope should switch to specified scope
    # temporarily.
    scope = args.pop('_scope_', None)
    with registry.switch_scope_and_registry(scope) as registry:
        obj_type = args.pop('type')
        if isinstance(obj_type, str):
            obj_cls = registry.get(obj_type)
            if obj_cls is None:
                raise KeyError(
                    f'{obj_type} is not in the {registry.scope}::{registry.name} registry. '  # noqa: E501
                    f'Please check whether the value of `{obj_type}` is '
                    'correct or it was registered as expected. More details '
                    'can be found at '
                    'https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module'  # noqa: E501
                )
        # this will include classes, functions, partial functions and more
        elif callable(obj_type):
            obj_cls = obj_type
        else:
            raise TypeError(
                f'type must be a str or valid type, but got {type(obj_type)}')

        # If `obj_cls` inherits from `ManagerMixin`, it should be
        # instantiated by `ManagerMixin.get_instance` to ensure that it
        # can be accessed globally.
        if inspect.isclass(obj_cls) and \
                issubclass(obj_cls, ManagerMixin):  # type: ignore
            obj = obj_cls.get_instance(**args)  # type: ignore
        else:
            obj = obj_cls(**args)  # type: ignore
        return obj
    

def constant_init(module, val, bias=0):
    if hasattr(module, 'weight') and module.weight is not None:
        nn.init.constant_(module.weight, val)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def normal_init(module, mean=0, std=1, bias=0):
    if hasattr(module, 'weight') and module.weight is not None:
        nn.init.normal_(module.weight, mean, std)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def trunc_normal_init(module: nn.Module,
                      mean: float = 0,
                      std: float = 1,
                      a: float = -2,
                      b: float = 2,
                      bias: float = 0) -> None:
    if hasattr(module, 'weight') and module.weight is not None:
        trunc_normal_(module.weight, mean, std, a, b)  # type: ignore
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)  # type: ignore


def kaiming_init(module,
                 a=0,
                 mode='fan_out',
                 nonlinearity='relu',
                 bias=0,
                 distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if hasattr(module, 'weight') and module.weight is not None:
        if distribution == 'uniform':
            nn.init.kaiming_uniform_(
                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
        else:
            nn.init.kaiming_normal_(
                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def trunc_normal_(tensor: Tensor,
                  mean: float = 0.,
                  std: float = 1.,
                  a: float = -2.,
                  b: float = 2.) -> Tensor:
    r"""Fills the input Tensor with values drawn from a truncated normal
    distribution. The values are effectively drawn from the normal distribution
    :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside
    :math:`[a, b]` redrawn until they are within the bounds. The method used
    for generating the random values works best when :math:`a \leq \text{mean}
    \leq b`.

    Modified from
    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py

    Args:
        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
        mean (float): the mean of the normal distribution.
        std (float): the standard deviation of the normal distribution.
        a (float): the minimum cutoff value.
        b (float): the maximum cutoff value.
    """
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float,
                           b: float) -> Tensor:
    # Method based on
    # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    # Modified from
    # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
            'The distribution of values may be incorrect.',
            stacklevel=2)

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        lower = norm_cdf((a - mean) / std)
        upper = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [lower, upper], then translate
        # to [2lower-1, 2upper-1].
        tensor.uniform_(2 * lower - 1, 2 * upper - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor