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| from collections import OrderedDict | |
| import khandy | |
| import numpy as np | |
| def convert_feature_dict_to_array(feature_dict): | |
| one_feature = khandy.get_dict_first_item(feature_dict)[1] | |
| num_features = sum([len(item) for item in feature_dict.values()]) | |
| key_list = [] | |
| start_index = 0 | |
| feature_array = np.empty((num_features, one_feature.shape[-1]), one_feature.dtype) | |
| for key, value in feature_dict.items(): | |
| feature_array[start_index: start_index + len(value)]= value | |
| key_list += [key] * len(value) | |
| start_index += len(value) | |
| return key_list, feature_array | |
| def convert_feature_array_to_dict(key_list, feature_array): | |
| assert len(key_list) == len(feature_array) | |
| feature_dict = OrderedDict() | |
| for key, feat in zip(key_list, feature_array): | |
| feature_dict.setdefault(key, []).append(feat) | |
| for label in feature_dict.keys(): | |
| feature_dict[label] = np.vstack(feature_dict[label]) | |
| return feature_dict | |
| def pairwise_distances(x, y, squared=True): | |
| """Compute pairwise (squared) Euclidean distances. | |
| References: | |
| [2016 CVPR] Deep Metric Learning via Lifted Structured Feature Embedding | |
| `euclidean_distances` from sklearn | |
| """ | |
| assert isinstance(x, np.ndarray) and x.ndim == 2 | |
| assert isinstance(y, np.ndarray) and y.ndim == 2 | |
| assert x.shape[1] == y.shape[1] | |
| x_square = np.expand_dims(np.einsum('ij,ij->i', x, x), axis=1) | |
| if x is y: | |
| y_square = x_square.T | |
| else: | |
| y_square = np.expand_dims(np.einsum('ij,ij->i', y, y), axis=0) | |
| distances = np.dot(x, y.T) | |
| # use inplace operation to accelerate | |
| distances *= -2 | |
| distances += x_square | |
| distances += y_square | |
| # result maybe less than 0 due to floating point rounding errors. | |
| np.maximum(distances, 0, distances) | |
| if x is y: | |
| # Ensure that distances between vectors and themselves are set to 0.0. | |
| # This may not be the case due to floating point rounding errors. | |
| distances.flat[::distances.shape[0] + 1] = 0.0 | |
| if not squared: | |
| np.sqrt(distances, distances) | |
| return distances | |