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https://github.com/open-mmlab/mmsegmentation.git
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c46cc85cba
Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Support depth estimation algorithm [VPD](https://github.com/wl-zhao/VPD) ## Modification 1. add VPD backbone 2. add VPD decoder head for depth estimation 3. add a new segmentor `DepthEstimator` based on `EncoderDecoder` for depth estimation 4. add an integrated metric that calculate common metrics in depth estimation 5. add SiLog loss for depth estimation 6. add config for VPD ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 7. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 8. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 9. The documentation has been modified accordingly, like docstring or example tutorials.
256 lines
12 KiB
Python
256 lines
12 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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from typing import List, Optional, Union
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import torch
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import torch.nn as nn
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from mmengine.logging import print_log
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from mmengine.optim import DefaultOptimWrapperConstructor
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from mmengine.utils.dl_utils import mmcv_full_available
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from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm, _InstanceNorm
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from torch.nn import GroupNorm, LayerNorm
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from mmseg.registry import OPTIM_WRAPPER_CONSTRUCTORS
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@OPTIM_WRAPPER_CONSTRUCTORS.register_module()
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class ForceDefaultOptimWrapperConstructor(DefaultOptimWrapperConstructor):
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"""Default constructor with forced optimizer settings.
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This constructor extends the default constructor to add an option for
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forcing default optimizer settings. This is useful for ensuring that
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certain parameters or layers strictly adhere to pre-defined default
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settings, regardless of any custom settings specified.
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By default, each parameter share the same optimizer settings, and we
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provide an argument ``paramwise_cfg`` to specify parameter-wise settings.
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It is a dict and may contain various fields like 'custom_keys',
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'bias_lr_mult', etc., as well as the additional field
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`force_default_settings` which allows for enforcing default settings on
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optimizer parameters.
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- ``custom_keys`` (dict): Specified parameters-wise settings by keys. If
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one of the keys in ``custom_keys`` is a substring of the name of one
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parameter, then the setting of the parameter will be specified by
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``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will
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be ignored. It should be noted that the aforementioned ``key`` is the
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longest key that is a substring of the name of the parameter. If there
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are multiple matched keys with the same length, then the key with lower
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alphabet order will be chosen.
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``custom_keys[key]`` should be a dict and may contain fields ``lr_mult``
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and ``decay_mult``. See Example 2 below.
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- ``bias_lr_mult`` (float): It will be multiplied to the learning
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rate for all bias parameters (except for those in normalization
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layers and offset layers of DCN).
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- ``bias_decay_mult`` (float): It will be multiplied to the weight
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decay for all bias parameters (except for those in
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normalization layers, depthwise conv layers, offset layers of DCN).
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- ``norm_decay_mult`` (float): It will be multiplied to the weight
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decay for all weight and bias parameters of normalization
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layers.
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- ``flat_decay_mult`` (float): It will be multiplied to the weight
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decay for all one-dimensional parameters
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- ``dwconv_decay_mult`` (float): It will be multiplied to the weight
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decay for all weight and bias parameters of depthwise conv
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layers.
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- ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning
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rate for parameters of offset layer in the deformable convs
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of a model.
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- ``bypass_duplicate`` (bool): If true, the duplicate parameters
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would not be added into optimizer. Defaults to False.
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- ``force_default_settings`` (bool): If true, this will override any
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custom settings defined by ``custom_keys`` and enforce the use of
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default settings for optimizer parameters like ``bias_lr_mult``.
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This is particularly useful when you want to ensure that certain layers
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or parameters adhere strictly to the pre-defined default settings.
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Note:
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1. If the option ``dcn_offset_lr_mult`` is used, the constructor will
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override the effect of ``bias_lr_mult`` in the bias of offset layer.
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So be careful when using both ``bias_lr_mult`` and
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``dcn_offset_lr_mult``. If you wish to apply both of them to the offset
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layer in deformable convs, set ``dcn_offset_lr_mult`` to the original
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``dcn_offset_lr_mult`` * ``bias_lr_mult``.
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2. If the option ``dcn_offset_lr_mult`` is used, the constructor will
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apply it to all the DCN layers in the model. So be careful when the
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model contains multiple DCN layers in places other than backbone.
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3. When the option ``force_default_settings`` is true, it will override
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any custom settings provided in ``custom_keys``. This ensures that the
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default settings for the optimizer parameters are used.
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Args:
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optim_wrapper_cfg (dict): The config dict of the optimizer wrapper.
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Required fields of ``optim_wrapper_cfg`` are
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- ``type``: class name of the OptimizerWrapper
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- ``optimizer``: The configuration of optimizer.
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Optional fields of ``optim_wrapper_cfg`` are
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- any arguments of the corresponding optimizer wrapper type,
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e.g., accumulative_counts, clip_grad, etc.
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Required fields of ``optimizer`` are
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- `type`: class name of the optimizer.
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Optional fields of ``optimizer`` are
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- any arguments of the corresponding optimizer type, e.g.,
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lr, weight_decay, momentum, etc.
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paramwise_cfg (dict, optional): Parameter-wise options.
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Example 1:
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>>> model = torch.nn.modules.Conv1d(1, 1, 1)
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>>> optim_wrapper_cfg = dict(
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>>> dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01,
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>>> momentum=0.9, weight_decay=0.0001))
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>>> paramwise_cfg = dict(norm_decay_mult=0.)
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>>> optim_wrapper_builder = DefaultOptimWrapperConstructor(
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>>> optim_wrapper_cfg, paramwise_cfg)
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>>> optim_wrapper = optim_wrapper_builder(model)
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Example 2:
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>>> # assume model have attribute model.backbone and model.cls_head
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>>> optim_wrapper_cfg = dict(type='OptimWrapper', optimizer=dict(
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>>> type='SGD', lr=0.01, weight_decay=0.95))
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>>> paramwise_cfg = dict(custom_keys={
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>>> 'backbone': dict(lr_mult=0.1, decay_mult=0.9)})
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>>> optim_wrapper_builder = DefaultOptimWrapperConstructor(
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>>> optim_wrapper_cfg, paramwise_cfg)
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>>> optim_wrapper = optim_wrapper_builder(model)
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>>> # Then the `lr` and `weight_decay` for model.backbone is
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>>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for
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>>> # model.cls_head is (0.01, 0.95).
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"""
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def add_params(self,
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params: List[dict],
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module: nn.Module,
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prefix: str = '',
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is_dcn_module: Optional[Union[int, float]] = None) -> None:
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"""Add all parameters of module to the params list.
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The parameters of the given module will be added to the list of param
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groups, with specific rules defined by paramwise_cfg.
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Args:
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params (list[dict]): A list of param groups, it will be modified
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in place.
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module (nn.Module): The module to be added.
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prefix (str): The prefix of the module
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is_dcn_module (int|float|None): If the current module is a
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submodule of DCN, `is_dcn_module` will be passed to
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control conv_offset layer's learning rate. Defaults to None.
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"""
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# get param-wise options
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custom_keys = self.paramwise_cfg.get('custom_keys', {})
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# first sort with alphabet order and then sort with reversed len of str
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sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True)
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bias_lr_mult = self.paramwise_cfg.get('bias_lr_mult', None)
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bias_decay_mult = self.paramwise_cfg.get('bias_decay_mult', None)
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norm_decay_mult = self.paramwise_cfg.get('norm_decay_mult', None)
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dwconv_decay_mult = self.paramwise_cfg.get('dwconv_decay_mult', None)
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flat_decay_mult = self.paramwise_cfg.get('flat_decay_mult', None)
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bypass_duplicate = self.paramwise_cfg.get('bypass_duplicate', False)
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dcn_offset_lr_mult = self.paramwise_cfg.get('dcn_offset_lr_mult', None)
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force_default_settings = self.paramwise_cfg.get(
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'force_default_settings', False)
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# special rules for norm layers and depth-wise conv layers
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is_norm = isinstance(module,
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(_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm))
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is_dwconv = (
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isinstance(module, torch.nn.Conv2d)
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and module.in_channels == module.groups)
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for name, param in module.named_parameters(recurse=False):
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param_group = {'params': [param]}
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if bypass_duplicate and self._is_in(param_group, params):
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print_log(
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f'{prefix} is duplicate. It is skipped since '
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f'bypass_duplicate={bypass_duplicate}',
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logger='current',
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level=logging.WARNING)
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continue
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if not param.requires_grad:
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params.append(param_group)
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continue
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# if the parameter match one of the custom keys, ignore other rules
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is_custom = False
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for key in sorted_keys:
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if key in f'{prefix}.{name}':
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is_custom = True
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lr_mult = custom_keys[key].get('lr_mult', 1.)
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param_group['lr'] = self.base_lr * lr_mult
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if self.base_wd is not None:
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decay_mult = custom_keys[key].get('decay_mult', 1.)
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param_group['weight_decay'] = self.base_wd * decay_mult
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# add custom settings to param_group
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for k, v in custom_keys[key].items():
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param_group[k] = v
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break
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if not is_custom or force_default_settings:
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# bias_lr_mult affects all bias parameters
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# except for norm.bias dcn.conv_offset.bias
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if name == 'bias' and not (
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is_norm or is_dcn_module) and bias_lr_mult is not None:
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param_group['lr'] = self.base_lr * bias_lr_mult
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if (prefix.find('conv_offset') != -1 and is_dcn_module
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and dcn_offset_lr_mult is not None
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and isinstance(module, torch.nn.Conv2d)):
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# deal with both dcn_offset's bias & weight
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param_group['lr'] = self.base_lr * dcn_offset_lr_mult
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# apply weight decay policies
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if self.base_wd is not None:
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# norm decay
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if is_norm and norm_decay_mult is not None:
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param_group[
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'weight_decay'] = self.base_wd * norm_decay_mult
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# bias lr and decay
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elif (name == 'bias' and not is_dcn_module
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and bias_decay_mult is not None):
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param_group[
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'weight_decay'] = self.base_wd * bias_decay_mult
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# depth-wise conv
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elif is_dwconv and dwconv_decay_mult is not None:
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param_group[
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'weight_decay'] = self.base_wd * dwconv_decay_mult
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# flatten parameters except dcn offset
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elif (param.ndim == 1 and not is_dcn_module
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and flat_decay_mult is not None):
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param_group[
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'weight_decay'] = self.base_wd * flat_decay_mult
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params.append(param_group)
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for key, value in param_group.items():
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if key == 'params':
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continue
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full_name = f'{prefix}.{name}' if prefix else name
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print_log(
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f'paramwise_options -- {full_name}:{key}={value}',
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logger='current')
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if mmcv_full_available():
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from mmcv.ops import DeformConv2d, ModulatedDeformConv2d
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is_dcn_module = isinstance(module,
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(DeformConv2d, ModulatedDeformConv2d))
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else:
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is_dcn_module = False
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for child_name, child_mod in module.named_children():
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child_prefix = f'{prefix}.{child_name}' if prefix else child_name
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self.add_params(
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params,
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child_mod,
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prefix=child_prefix,
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is_dcn_module=is_dcn_module)
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