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* [doc] add doc of installation, kd, nas, pruning * [doc] add user guide part * fix name of mmcv-full to mmcv * [doc] fix mmcv based on zaida 's suggestion * [doc] fix index error based on shiguang's comments
125 lines
3.0 KiB
Markdown
125 lines
3.0 KiB
Markdown
# Knowledge Distillation
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Here we show how to develop new KD algorithms with an example of `SingleTeacherDistill`.
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1. Register a new algorithm
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Create a new file `mmrazor/models/algorithms/distill/configurable/single_teacher_distill.py`, class `SingleTeacherDistill` inherits from class `BaseAlgorithm`
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```Python
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from mmrazor.registry import MODELS
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from ..base import BaseAlgorithm
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@ALGORITHMS.register_module()
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class SingleTeacherDistill(BaseAlgorithm):
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def __init__(self, use_gt, **kwargs):
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super(Distillation, self).__init__(**kwargs)
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pass
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def train_step(self, data, optimizer):
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pass
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```
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2. Develop connectors (Optional) .
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Take ConvModuleConnector as an example.
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```python
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from mmrazor.registry import MODELS
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from .base_connector import BaseConnector
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@MODELS.register_module()
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class ConvModuleConncetor(BaseConnector):
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def __init__(self, in_channel, out_channel, kernel_size = 1, stride = 1):
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...
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def forward_train(self, feature):
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...
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```
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3. Develop distiller.
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Take `ConfigurableDistiller` as an example.
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```python
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from .base_distiller import BaseDistiller
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from mmrazor.registry import MODELS
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@MODELS.register_module()
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class ConfigurableDistiller(BaseDistiller):
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def __init__(self,
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student_recorders = None,
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teacher_recorders = None,
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distill_deliveries = None,
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connectors = None,
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distill_losses = None,
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loss_forward_mappings = None):
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...
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def build_connectors(self, connectors):
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...
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def build_distill_losses(self, losses):
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...
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def compute_distill_losses(self):
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...
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```
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4. Develop custom loss (Optional).
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Here we take `L1Loss` as an example. Create a new file in `mmrazor/models/losses/l1_loss.py`.
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```python
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from mmrazor.registry import MODELS
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@MODELS.register_module()
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class L1Loss(nn.Module):
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def __init__(
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self,
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loss_weight: float = 1.0,
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size_average: Optional[bool] = None,
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reduce: Optional[bool] = None,
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reduction: str = 'mean',
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) -> None:
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super().__init__()
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...
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def forward(self, s_feature, t_feature):
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loss = F.l1_loss(s_feature, t_feature, self.size_average, self.reduce,
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self.reduction)
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return self.loss_weight * loss
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```
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5. Import the class
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You can either add the following line to `mmrazor/models/algorithms/__init__.py`
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```Python
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from .single_teacher_distill import SingleTeacherDistill
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__all__ = [..., 'SingleTeacherDistill']
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```
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or alternatively add
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```Python
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custom_imports = dict(
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imports=['mmrazor.models.algorithms.distill.configurable.single_teacher_distill'],
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allow_failed_imports=False)
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```
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to the config file to avoid modifying the original code.
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6. Use the algorithm in your config file
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```Python
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algorithm = dict(
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type='Distill',
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distiller=dict(type='SingleTeacherDistill', ...),
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# you can also use your new algorithm components here
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...
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)
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```
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