Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
refactor(encoders): code style inheritance refactoring
- Loading branch information
Showing
20 changed files
with
220 additions
and
293 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| __copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved." | ||
| __license__ = "Apache-2.0" | ||
|
|
||
| import numpy as np | ||
|
|
||
| from ..frameworks import BaseTorchEncoder, BasePaddleEncoder | ||
| from ...decorators import batching, as_ndarray | ||
|
|
||
|
|
||
| class BaseCVTorchEncoder(BaseTorchEncoder): | ||
| """" | ||
| :class:`BaseTorchEncoder` implements the common part for :class:`ImageTorchEncoder` and :class:`VideoTorchEncoder`. | ||
| ..warning:: | ||
| :class:`BaseTorchEncoder` is not intented to be used to do the real encoding. | ||
| """ | ||
|
|
||
| def __init__(self, channel_axis: int = 1, *args, **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
| self.channel_axis = channel_axis | ||
| self._default_channel_axis = 1 | ||
|
|
||
| @batching | ||
| @as_ndarray | ||
| def encode(self, data: 'np.ndarray', *args, **kwargs) -> 'np.ndarray': | ||
| if self.channel_axis != self._default_channel_axis: | ||
| data = np.moveaxis(data, self.channel_axis, self._default_channel_axis) | ||
| import torch | ||
| _input = torch.from_numpy(data.astype('float32')) | ||
| if self.on_gpu: | ||
| _input = _input.cuda() | ||
| _feature = self._get_features(_input).detach() | ||
| if self.on_gpu: | ||
| _feature = _feature.cpu() | ||
| _feature = _feature.numpy() | ||
| return self._get_pooling(_feature) | ||
|
|
||
| def _get_features(self, data): | ||
| raise NotImplementedError | ||
|
|
||
| def _get_pooling(self, feature_map): | ||
| return feature_map | ||
|
|
||
|
|
||
| class BaseCVPaddleEncoder(BasePaddleEncoder): | ||
| """ | ||
| :class:`BaseCVPaddleEncoder` implements the common parts for :class:`ImagePaddlehubEncoder` and | ||
| :class:`VideoPaddleEncoder`. | ||
| ..warning:: | ||
| :class:`BaseCVPaddleEncoder` is not intented to be used to do the real encoding. | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| model_name: str, | ||
| output_feature: str = None, | ||
| pool_strategy: str = None, | ||
| channel_axis: int = -3, | ||
| *args, | ||
| **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
| self.pool_strategy = pool_strategy | ||
| self.outputs_name = output_feature | ||
| self.inputs_name = None | ||
| self.channel_axis = channel_axis | ||
| self._default_channel_axis = -3 | ||
|
|
||
| def post_init(self): | ||
| super().post_init() | ||
| import paddlehub as hub | ||
| module = hub.Module(name=self.model_name) | ||
| inputs, outputs, self.model = module.context(trainable=False) | ||
| self.get_inputs_and_outputs_name(inputs, outputs) | ||
| self.exe = self.to_device() | ||
|
|
||
| def close(self): | ||
| self.exe.close() | ||
|
|
||
| def get_inputs_and_outputs_name(self, input_dict, output_dict): | ||
| raise NotImplementedError | ||
|
|
||
| @batching | ||
| @as_ndarray | ||
| def encode(self, data: 'np.ndarray', *args, **kwargs) -> 'np.ndarray': | ||
| """ | ||
| :param data: a `B x T x (Channel x Height x Width)` numpy ``ndarray``, `B` is the size of the batch, `T` is the | ||
| number of frames | ||
| :return: a `B x D` numpy ``ndarray``, `D` is the output dimension | ||
| """ | ||
| if self.channel_axis != self._default_channel_axis: | ||
| data = np.moveaxis(data, self.channel_axis, self._default_channel_axis) | ||
| feature_map, *_ = self.exe.run( | ||
| program=self.model, | ||
| fetch_list=[self.outputs_name], | ||
| feed={self.inputs_name: data.astype('float32')}, | ||
| return_numpy=True | ||
| ) | ||
| if feature_map.ndim == 2 or self.pool_strategy is None: | ||
| return feature_map | ||
| return self.get_pooling(feature_map) | ||
|
|
||
| def get_pooling(self, data: 'np.ndarray', axis=None) -> 'np.ndarray': | ||
| _reduce_axis = tuple((i for i in range(len(data.shape)) if i > 1)) | ||
| return getattr(np, self.pool_strategy)(data, axis=_reduce_axis) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file was deleted.
Oops, something went wrong.
Oops, something went wrong.