| '''VGGFace models for Keras. | |
| # Reference: | |
| - [Deep Face Recognition](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf) | |
| - [VGGFace2: A dataset for recognising faces across pose and age](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/vggface2.pdf) | |
| ''' | |
| from __future__ import print_function | |
| from keras_vggface.models import RESNET50, VGG16, SENET50 | |
| def VGGFace(include_top=True, model='vgg16', weights='vggface', | |
| input_tensor=None, input_shape=None, | |
| pooling=None, | |
| classes=None): | |
| """Instantiates the VGGFace architectures. | |
| Optionally loads weights pre-trained | |
| on VGGFace datasets. Note that when using TensorFlow, | |
| for best performance you should set | |
| `image_data_format="channels_last"` in your Keras config | |
| at ~/.keras/keras.json. | |
| The model and the weights are compatible with both | |
| TensorFlow and Theano. The data format | |
| convention used by the model is the one | |
| specified in your Keras config file. | |
| # Arguments | |
| include_top: whether to include the 3 fully-connected | |
| layers at the top of the network. | |
| weights: one of `None` (random initialization) | |
| or "vggface" (pre-training on VGGFACE datasets). | |
| input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) | |
| to use as image input for the model. | |
| model: selects the one of the available architectures | |
| vgg16, resnet50 or senet50 default is vgg16. | |
| input_shape: optional shape tuple, only to be specified | |
| if `include_top` is False (otherwise the input shape | |
| has to be `(224, 224, 3)` (with `channels_last` data format) | |
| or `(3, 224, 244)` (with `channels_first` data format). | |
| It should have exactly 3 inputs channels, | |
| and width and height should be no smaller than 48. | |
| E.g. `(200, 200, 3)` would be one valid value. | |
| pooling: Optional pooling mode for feature extraction | |
| when `include_top` is `False`. | |
| - `None` means that the output of the model will be | |
| the 4D tensor output of the | |
| last convolutional layer. | |
| - `avg` means that global average pooling | |
| will be applied to the output of the | |
| last convolutional layer, and thus | |
| the output of the model will be a 2D tensor. | |
| - `max` means that global max pooling will | |
| be applied. | |
| classes: optional number of classes to classify images | |
| into, only to be specified if `include_top` is True, and | |
| if no `weights` argument is specified. | |
| # Returns | |
| A Keras model instance. | |
| # Raises | |
| ValueError: in case of invalid argument for `weights`, | |
| or invalid input shape. | |
| """ | |
| if weights not in {'vggface', None}: | |
| raise ValueError('The `weights` argument should be either ' | |
| '`None` (random initialization) or `vggface`' | |
| '(pre-training on VGGFace Datasets).') | |
| if model == 'vgg16': | |
| if classes is None: | |
| classes = 2622 | |
| if weights == 'vggface' and include_top and classes != 2622: | |
| raise ValueError( | |
| 'If using `weights` as vggface original with `include_top`' | |
| ' as true, `classes` should be 2622') | |
| return VGG16(include_top=include_top, input_tensor=input_tensor, | |
| input_shape=input_shape, pooling=pooling, | |
| weights=weights, | |
| classes=classes) | |
| if model == 'resnet50': | |
| if classes is None: | |
| classes = 8631 | |
| if weights == 'vggface' and include_top and classes != 8631: | |
| raise ValueError( | |
| 'If using `weights` as vggface original with `include_top`' | |
| ' as true, `classes` should be 8631') | |
| return RESNET50(include_top=include_top, input_tensor=input_tensor, | |
| input_shape=input_shape, pooling=pooling, | |
| weights=weights, | |
| classes=classes) | |
| if model == 'senet50': | |
| if classes is None: | |
| classes = 8631 | |
| if weights == 'vggface' and include_top and classes != 8631: | |
| raise ValueError( | |
| 'If using `weights` as vggface original with `include_top`' | |
| ' as true, `classes` should be 8631') | |
| return SENET50(include_top=include_top, input_tensor=input_tensor, | |
| input_shape=input_shape, pooling=pooling, | |
| weights=weights, | |
| classes=classes) |