| '''VGGFace models for Keras. | |
| # Notes: | |
| - Utility functions are modified versions of Keras functions [Keras](https://keras.io) | |
| ''' | |
| import numpy as np | |
| from keras import backend as K | |
| from keras.utils.data_utils import get_file | |
| V1_LABELS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_labels_v1.npy' | |
| V2_LABELS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_labels_v2.npy' | |
| VGG16_WEIGHTS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_vgg16.h5' | |
| VGG16_WEIGHTS_PATH_NO_TOP = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_vgg16.h5' | |
| RESNET50_WEIGHTS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_resnet50.h5' | |
| RESNET50_WEIGHTS_PATH_NO_TOP = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_resnet50.h5' | |
| SENET50_WEIGHTS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_senet50.h5' | |
| SENET50_WEIGHTS_PATH_NO_TOP = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_senet50.h5' | |
| VGGFACE_DIR = 'models/vggface' | |
| def preprocess_input(x, data_format=None, version=1): | |
| x_temp = np.copy(x) | |
| if data_format is None: | |
| data_format = K.image_data_format() | |
| assert data_format in {'channels_last', 'channels_first'} | |
| if version == 1: | |
| if data_format == 'channels_first': | |
| x_temp = x_temp[:, ::-1, ...] | |
| x_temp[:, 0, :, :] -= 93.5940 | |
| x_temp[:, 1, :, :] -= 104.7624 | |
| x_temp[:, 2, :, :] -= 129.1863 | |
| else: | |
| x_temp = x_temp[..., ::-1] | |
| x_temp[..., 0] -= 93.5940 | |
| x_temp[..., 1] -= 104.7624 | |
| x_temp[..., 2] -= 129.1863 | |
| elif version == 2: | |
| if data_format == 'channels_first': | |
| x_temp = x_temp[:, ::-1, ...] | |
| x_temp[:, 0, :, :] -= 91.4953 | |
| x_temp[:, 1, :, :] -= 103.8827 | |
| x_temp[:, 2, :, :] -= 131.0912 | |
| else: | |
| x_temp = x_temp[..., ::-1] | |
| x_temp[..., 0] -= 91.4953 | |
| x_temp[..., 1] -= 103.8827 | |
| x_temp[..., 2] -= 131.0912 | |
| else: | |
| raise NotImplementedError | |
| return x_temp | |
| def decode_predictions(preds, top=5): | |
| LABELS = None | |
| if len(preds.shape) == 2: | |
| if preds.shape[1] == 2622: | |
| fpath = get_file('rcmalli_vggface_labels_v1.npy', | |
| V1_LABELS_PATH, | |
| cache_subdir=VGGFACE_DIR) | |
| LABELS = np.load(fpath) | |
| elif preds.shape[1] == 8631: | |
| fpath = get_file('rcmalli_vggface_labels_v2.npy', | |
| V2_LABELS_PATH, | |
| cache_subdir=VGGFACE_DIR) | |
| LABELS = np.load(fpath) | |
| else: | |
| raise ValueError('`decode_predictions` expects ' | |
| 'a batch of predictions ' | |
| '(i.e. a 2D array of shape (samples, 2622)) for V1 or ' | |
| '(samples, 8631) for V2.' | |
| 'Found array with shape: ' + str(preds.shape)) | |
| else: | |
| raise ValueError('`decode_predictions` expects ' | |
| 'a batch of predictions ' | |
| '(i.e. a 2D array of shape (samples, 2622)) for V1 or ' | |
| '(samples, 8631) for V2.' | |
| 'Found array with shape: ' + str(preds.shape)) | |
| results = [] | |
| for pred in preds: | |
| top_indices = pred.argsort()[-top:][::-1] | |
| result = [[str(LABELS[i].encode('utf8')), pred[i]] for i in top_indices] | |
| result.sort(key=lambda x: x[1], reverse=True) | |
| results.append(result) | |
| return results | |