diff --git a/bob/bio/face/extractor/MxNetModel.py~HEAD b/bob/bio/face/extractor/MxNetModel.py~HEAD
deleted file mode 100644
index 84e856862ee5a2e02eb2c832e4f7fcb4ad4bc83d..0000000000000000000000000000000000000000
--- a/bob/bio/face/extractor/MxNetModel.py~HEAD
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Yu Linghu & Xinyi Zhang <yu.linghu@uzh.ch, xinyi.zhang@uzh.ch>
-
-"""Feature extraction resnet models using mxnet interface"""
-from sklearn.base import TransformerMixin, BaseEstimator
-from sklearn.utils import check_array
-import numpy as np
-import pkg_resources
-import os
-import mxnet as mx
-from mxnet import gluon
-import warnings
-from bob.extension import rc
-
-mxnet_resnet_directory = rc["bob.extractor_model.mxnet"]
-mxnet_weight_directory = rc["bob.extractor_weights.mxnet"]
-
-
-class MxNetModel(TransformerMixin, BaseEstimator):
-
- """Extracts features using deep face recognition models under MxNet Interfaces.
-
- Users can download the pretrained face recognition models with MxNet Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model frame to :py:class:`bob.extractor_model.mxnet`, and set config of the parameters to :py:class:`bob.extractor_weights.mxnet`.
-
- .. code-block:: sh
-
- $ bob config set bob.extractor_model.mxnet /PATH/TO/MODEL/
- $ bob config set bob.extractor_weights.mxnet /PATH/TO/WEIGHTS/
-
- Examples: (Pretrained ResNet models): `LResNet100E-IR,ArcFace@ms1m-refine-v2 <https://github.com/deepinsight/insightface>`_
-
- The extracted features can be combined with different the algorithms.
-
- **Parameters:**
- use_gpu: True or False.
- """
-
- def __init__(self, use_gpu=False, **kwargs):
- super().__init__(**kwargs)
- self.model = None
- self.use_gpu = use_gpu
-
- internal_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", "resnet"),
- )
-
- checkpoint_path = (
- internal_path
- if rc["bob.bio.face.models.mxnet_resnet"] is None
- else rc["bob.bio.face.models.mxnet_resnet"]
- )
-
- self.checkpoint_path = checkpoint_path
-
- def _load_model(self):
-
- ctx = mx.gpu() if self.use_gpu else mx.cpu()
-
- with warnings.catch_warnings():
- warnings.simplefilter("ignore")
- deserialized_net = gluon.nn.SymbolBlock.imports(
- mxnet_resnet_directory, ["data"], mxnet_weight_directory, ctx=ctx
- )
-
- self.model = deserialized_net
-
- def transform(self, X):
- """__call__(image) -> feature
-
- Extracts the features from the given image.
-
- **Parameters:**
-
- image : 2D :py:class:`numpy.ndarray` (floats)
- The image to extract the features from.
-
- **Returns:**
-
- feature : 2D, 3D, or 4D :py:class:`numpy.ndarray` (floats)
- The list of features extracted from the image.
- """
-
- if self.model is None:
- self._load_model()
-
- X = check_array(X, allow_nd=True)
- X = mx.nd.array(X)
-
- return self.model(X,).asnumpy()
-
- def __getstate__(self):
- # Handling unpicklable objects
-
- d = self.__dict__.copy()
- d["model"] = None
- return d
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
diff --git a/bob/bio/face/extractor/OpenCVModel.py~HEAD b/bob/bio/face/extractor/OpenCVModel.py~HEAD
deleted file mode 100644
index 728b2b7ca94c451ada6b09411e9627964bc8df5d..0000000000000000000000000000000000000000
--- a/bob/bio/face/extractor/OpenCVModel.py~HEAD
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Yu Linghu & Xinyi Zhang <yu.linghu@uzh.ch, xinyi.zhang@uzh.ch>
-
-import bob.bio.base
-from bob.bio.face.preprocessor import FaceCrop
-
-from bob.bio.base.transformers.preprocessor import PreprocessorTransformer
-
-import cv2
-import numpy as np
-
-from bob.learn.tensorflow.utils.image import to_channels_last
-from sklearn.base import TransformerMixin, BaseEstimator
-from sklearn.utils import check_array
-
-from bob.extension import rc
-from functools import partial
-import pkg_resources
-import os
-
-from PIL import Image
-
-opencv_model_directory = rc["bob.extractor_model.opencv"]
-opencv_model_prototxt = rc["bob.extractor_weights.opencv"]
-
-
-class OpenCVModel(TransformerMixin, BaseEstimator):
- """Extracts features using deep face recognition models under OpenCV Interface
-
- Users can download the pretrained face recognition models with OpenCV Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model frame to :py:class:`bob.extractor_model.opencv`, and set config of the parameters to :py:class:`bob.extractor_weights.opencv`.
-
- .. code-block:: sh
-
- $ bob config set bob.extractor_model.opencv /PATH/TO/MODEL/
- $ bob config set bob.extractor_weights.opencv /PATH/TO/WEIGHTS/
-
- The extracted features can be combined with different the algorithms.
-
- .. note::
- This structure only can be used for CAFFE pretrained model.
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self.model = None
-
- internal_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", "opencv_model"),
- )
-
- checkpoint_path = (
- internal_path
- if rc["bob.bio.face.models.opencv"] is None
- else rc["bob.bio.face.models.opencv"]
- )
-
- self.checkpoint_path = checkpoint_path
-
- def _load_model(self):
-
- net = cv2.dnn.readNetFromCaffe(opencv_model_prototxt, opencv_model_directory)
-
- self.model = net
-
- def transform(self, X):
- """__call__(image) -> feature
-
- Extracts the features from the given image.
-
- **Parameters:**
-
- image : 2D :py:class:`numpy.ndarray` (floats)
- The image to extract the features from.
-
- **Returns:**
-
- feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
- The list of features extracted from the image.
- """
-
- if self.model is None:
- self._load_model()
-
- img = np.array(X)
- img = img / 255
-
- self.model.setInput(img)
-
- return self.model.forward()
-
- def __getstate__(self):
- # Handling unpicklable objects
-
- d = self.__dict__.copy()
- d["model"] = None
- return d
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
diff --git a/bob/bio/face/extractor/PyTorchModel.py~HEAD b/bob/bio/face/extractor/PyTorchModel.py~HEAD
deleted file mode 100644
index 85473ac4d2626f27cd0edf9f3fe0a2ac238631eb..0000000000000000000000000000000000000000
--- a/bob/bio/face/extractor/PyTorchModel.py~HEAD
+++ /dev/null
@@ -1,183 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Yu Linghu & Xinyi Zhang <yu.linghu@uzh.ch, xinyi.zhang@uzh.ch>
-
-import torch
-from bob.learn.tensorflow.utils.image import to_channels_last
-from sklearn.base import TransformerMixin, BaseEstimator
-from sklearn.utils import check_array
-from bob.extension import rc
-from functools import partial
-import pkg_resources
-import os
-import numpy as np
-import imp
-
-pytorch_model_directory = rc["bob.extractor_model.pytorch"]
-pytorch_weight_directory = rc["bob.extractor_weights.pytorch"]
-
-
-class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
- """Extracts features using deep face recognition models under PyTorch Interface, especially for the models and weights that need to load by hand.
-
- Users can download the pretrained face recognition models with PyTorch Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model frame to :py:class:`bob.extractor_model.pytorch`, and set config of the parameters to :py:class:`bob.extractor_weights.pytorch`.
-
- .. code-block:: sh
-
- $ bob config set bob.extractor_model.pytorch /PATH/TO/MODEL/
- $ bob config set bob.extractor_weights.pytorch /PATH/TO/WEIGHTS/
-
- The extracted features can be combined with different the algorithms.
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-<<<<<<< HEAD:bob/bio/face/extractor/PyTorchModel.py~HEAD
- self.model = None
-
- internal_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", "resnet"),
- )
-
- checkpoint_path = (
- internal_path
- if rc["bob.bio.face.models.pytorchmodel"] is None
- else rc["bob.bio.face.models.pytorchmodel"]
- )
-=======
- self.weights = weights
- self.config = config
-
- if self.config == None and self.weights == None:
- internal_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", "AFFFE-42a53f19", ""),
- )
-
- checkpoint_path = (
- internal_path
- if rc["bob.bio.face.models.AFFFE"] is None
- else rc["bob.bio.face.models.AFFFE"]
- )
-
- urls = [
- "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/pytorch/AFFFE-42a53f19.tar.gz"
- ]
-
- download_model(checkpoint_path, urls, "AFFFE-42a53f19.tar.gz")
->>>>>>> Update bob/bio/face/extractor/__init__.py, bob/bio/face/test/test_extractors.py, bob/bio/face/embeddings/PyTorchModel.py files:bob/bio/face/embeddings/PyTorchModel.py
-
- self.checkpoint_path = checkpoint_path
- self.device = None
-
- def _load_model(self):
-
- MainModel = imp.load_source("MainModel", pytorch_model_directory)
- network = torch.load(pytorch_weight_directory)
- network.eval()
-
- self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
- network.to(self.device)
-
- self.model = network
-
- def transform(self, X):
- """__call__(image) -> feature
-
- Extracts the features from the given image.
-
- **Parameters:**
-
- image : 2D :py:class:`numpy.ndarray` (floats)
- The image to extract the features from.
-
- **Returns:**
-
- feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
- The list of features extracted from the image.
- """
- if self.model is None:
- self._load_model()
-
- X = torch.Tensor(X)
- X = X / 255
-
- return self.model(X).detach().numpy()
-
- def __getstate__(self):
- # Handling unpicklable objects
-
- d = self.__dict__.copy()
- d["model"] = None
- return d
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
-
-
-class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
- """Extracts features using deep face recognition with registered model frames in the PyTorch Library.
-
- Users can import the pretrained face recognition models from PyTorch library. The model should be called in the pipeline. Example: `facenet_pytorch <https://github.com/timesler/facenet-pytorch>`_
-
- The extracted features can be combined with different the algorithms.
-
- **Parameters:**
- model: pytorch model calling from library.
- """
-
- def __init__(self, model=None, **kwargs):
- super().__init__(**kwargs)
- self.model = model
-
- internal_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", "resnet"),
- )
-
- checkpoint_path = (
- internal_path
- if rc["bob.bio.face.models.pytorchmodel"] is None
- else rc["bob.bio.face.models.pytorchmodel"]
- )
-
- self.checkpoint_path = checkpoint_path
- self.device = None
-
- def _load_model(self):
-
- self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- self.model.to(self.device)
-
- def transform(self, X):
- """__call__(image) -> feature
-
- Extracts the features from the given image.
-
- **Parameters:**
-
- image : 2D :py:class:`numpy.ndarray` (floats)
- The image to extract the features from.
-
- **Returns:**
-
- feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
- The list of features extracted from the image.
- """
-
- if self.model is None:
- self._load_model()
-
- X = torch.Tensor(X)
- X = X / 255
-
- return self.model(X).detach().numpy()
-
- def __getstate__(self):
- # Handling unpicklable objects
-
- d = self.__dict__.copy()
- d["model"] = None
- return d
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
diff --git a/bob/bio/face/extractor/TensorFlowModel.py~HEAD b/bob/bio/face/extractor/TensorFlowModel.py~HEAD
deleted file mode 100644
index 697afec88bf8bd382390d023744be332ac8c345d..0000000000000000000000000000000000000000
--- a/bob/bio/face/extractor/TensorFlowModel.py~HEAD
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Yu Linghu & Xinyi Zhang <yu.linghu@uzh.ch, xinyi.zhang@uzh.ch>
-
-import tensorflow as tf
-from bob.extension import rc
-from bob.learn.tensorflow.utils.image import to_channels_last
-from sklearn.base import TransformerMixin, BaseEstimator
-from sklearn.utils import check_array
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers.experimental import preprocessing
-from functools import partial
-import pkg_resources
-import os
-import numpy as np
-from tensorflow import keras
-
-tf_model_directory = rc["bob.extractor_model.tf"]
-
-
-class TensorFlowModel(TransformerMixin, BaseEstimator):
- """Extracts features using deep face recognition models under TensorFlow Interface.
-
- Users can download the pretrained face recognition models with TensorFlow Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model to :py:class:`bob.extractor_model.tf`.
-
- .. code-block:: sh
-
- $ bob config set bob.extractor_model.tf /PATH/TO/MODEL/
-
- The extracted features can be combined with different the algorithms.
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self.model = None
-
- internal_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", "resnet"),
- )
-
- checkpoint_path = (
- internal_path
- if rc["bob.bio.face.models.tfmodel"] is None
- else rc["bob.bio.face.models.tfmodel"]
- )
-
- self.checkpoint_path = checkpoint_path
-
- def _load_model(self):
-
- model = tf.keras.models.load_model(tf_model_directory)
-
- self.model = model
-
- def transform(self, X):
- """__call__(image) -> feature
-
- Extracts the features from the given image.
-
- **Parameters:**
-
- image : 2D :py:class:`numpy.ndarray` (floats)
- The image to extract the features from.
-
- **Returns:**
-
- feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
- The list of features extracted from the image.
- """
-
- if self.model is None:
- self._load_model()
-
- X = check_array(X, allow_nd=True)
- X = tf.convert_to_tensor(X)
- X = to_channels_last(X)
-
- X = X / 255
- predict = self.model.predict(X)
-
- return predict
-
- def __getstate__(self):
- # Handling unpicklable objects
-
- d = self.__dict__.copy()
- d["model"] = None
- return d
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}