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"""Helper for evaluation on the Labeled Faces in the Wild dataset |
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""" |
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import datetime |
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import os |
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import pickle |
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import mxnet as mx |
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import numpy as np |
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import sklearn |
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import torch |
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from mxnet import ndarray as nd |
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from scipy import interpolate |
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from sklearn.decomposition import PCA |
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from sklearn.model_selection import KFold |
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class LFold: |
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def __init__(self, n_splits=2, shuffle=False): |
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self.n_splits = n_splits |
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if self.n_splits > 1: |
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self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle) |
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def split(self, indices): |
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if self.n_splits > 1: |
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return self.k_fold.split(indices) |
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else: |
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return [(indices, indices)] |
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def calculate_roc(thresholds, |
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embeddings1, |
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embeddings2, |
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actual_issame, |
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nrof_folds=10, |
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pca=0): |
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assert (embeddings1.shape[0] == embeddings2.shape[0]) |
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assert (embeddings1.shape[1] == embeddings2.shape[1]) |
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nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) |
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nrof_thresholds = len(thresholds) |
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k_fold = LFold(n_splits=nrof_folds, shuffle=False) |
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tprs = np.zeros((nrof_folds, nrof_thresholds)) |
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fprs = np.zeros((nrof_folds, nrof_thresholds)) |
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accuracy = np.zeros((nrof_folds)) |
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indices = np.arange(nrof_pairs) |
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if pca == 0: |
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diff = np.subtract(embeddings1, embeddings2) |
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dist = np.sum(np.square(diff), 1) |
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): |
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if pca > 0: |
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print('doing pca on', fold_idx) |
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embed1_train = embeddings1[train_set] |
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embed2_train = embeddings2[train_set] |
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_embed_train = np.concatenate((embed1_train, embed2_train), axis=0) |
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pca_model = PCA(n_components=pca) |
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pca_model.fit(_embed_train) |
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embed1 = pca_model.transform(embeddings1) |
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embed2 = pca_model.transform(embeddings2) |
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embed1 = sklearn.preprocessing.normalize(embed1) |
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embed2 = sklearn.preprocessing.normalize(embed2) |
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diff = np.subtract(embed1, embed2) |
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dist = np.sum(np.square(diff), 1) |
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acc_train = np.zeros((nrof_thresholds)) |
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for threshold_idx, threshold in enumerate(thresholds): |
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_, _, acc_train[threshold_idx] = calculate_accuracy( |
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threshold, dist[train_set], actual_issame[train_set]) |
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best_threshold_index = np.argmax(acc_train) |
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for threshold_idx, threshold in enumerate(thresholds): |
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tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy( |
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threshold, dist[test_set], |
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actual_issame[test_set]) |
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_, _, accuracy[fold_idx] = calculate_accuracy( |
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thresholds[best_threshold_index], dist[test_set], |
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actual_issame[test_set]) |
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tpr = np.mean(tprs, 0) |
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fpr = np.mean(fprs, 0) |
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return tpr, fpr, accuracy |
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def calculate_accuracy(threshold, dist, actual_issame): |
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predict_issame = np.less(dist, threshold) |
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tp = np.sum(np.logical_and(predict_issame, actual_issame)) |
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fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) |
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tn = np.sum( |
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np.logical_and(np.logical_not(predict_issame), |
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np.logical_not(actual_issame))) |
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fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) |
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tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) |
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fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) |
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acc = float(tp + tn) / dist.size |
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return tpr, fpr, acc |
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def calculate_val(thresholds, |
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embeddings1, |
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embeddings2, |
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actual_issame, |
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far_target, |
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nrof_folds=10): |
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assert (embeddings1.shape[0] == embeddings2.shape[0]) |
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assert (embeddings1.shape[1] == embeddings2.shape[1]) |
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nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) |
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nrof_thresholds = len(thresholds) |
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k_fold = LFold(n_splits=nrof_folds, shuffle=False) |
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val = np.zeros(nrof_folds) |
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far = np.zeros(nrof_folds) |
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diff = np.subtract(embeddings1, embeddings2) |
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dist = np.sum(np.square(diff), 1) |
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indices = np.arange(nrof_pairs) |
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for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): |
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far_train = np.zeros(nrof_thresholds) |
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for threshold_idx, threshold in enumerate(thresholds): |
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_, far_train[threshold_idx] = calculate_val_far( |
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threshold, dist[train_set], actual_issame[train_set]) |
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if np.max(far_train) >= far_target: |
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f = interpolate.interp1d(far_train, thresholds, kind='slinear') |
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threshold = f(far_target) |
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else: |
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threshold = 0.0 |
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val[fold_idx], far[fold_idx] = calculate_val_far( |
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threshold, dist[test_set], actual_issame[test_set]) |
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val_mean = np.mean(val) |
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far_mean = np.mean(far) |
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val_std = np.std(val) |
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return val_mean, val_std, far_mean |
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def calculate_val_far(threshold, dist, actual_issame): |
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predict_issame = np.less(dist, threshold) |
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true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) |
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false_accept = np.sum( |
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np.logical_and(predict_issame, np.logical_not(actual_issame))) |
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n_same = np.sum(actual_issame) |
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n_diff = np.sum(np.logical_not(actual_issame)) |
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val = float(true_accept) / float(n_same) |
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far = float(false_accept) / float(n_diff) |
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return val, far |
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def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0): |
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thresholds = np.arange(0, 4, 0.01) |
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embeddings1 = embeddings[0::2] |
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embeddings2 = embeddings[1::2] |
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tpr, fpr, accuracy = calculate_roc(thresholds, |
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embeddings1, |
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embeddings2, |
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np.asarray(actual_issame), |
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nrof_folds=nrof_folds, |
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pca=pca) |
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thresholds = np.arange(0, 4, 0.001) |
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val, val_std, far = calculate_val(thresholds, |
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embeddings1, |
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embeddings2, |
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np.asarray(actual_issame), |
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1e-3, |
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nrof_folds=nrof_folds) |
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return tpr, fpr, accuracy, val, val_std, far |
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@torch.no_grad() |
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def load_bin(path, image_size): |
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try: |
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with open(path, 'rb') as f: |
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bins, issame_list = pickle.load(f) |
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except UnicodeDecodeError as e: |
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with open(path, 'rb') as f: |
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bins, issame_list = pickle.load(f, encoding='bytes') |
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data_list = [] |
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for flip in [0, 1]: |
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data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1])) |
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data_list.append(data) |
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for idx in range(len(issame_list) * 2): |
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_bin = bins[idx] |
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img = mx.image.imdecode(_bin) |
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if img.shape[1] != image_size[0]: |
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img = mx.image.resize_short(img, image_size[0]) |
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img = nd.transpose(img, axes=(2, 0, 1)) |
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for flip in [0, 1]: |
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if flip == 1: |
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img = mx.ndarray.flip(data=img, axis=2) |
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data_list[flip][idx][:] = torch.from_numpy(img.asnumpy()) |
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if idx % 1000 == 0: |
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print('loading bin', idx) |
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print(data_list[0].shape) |
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return data_list, issame_list |
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@torch.no_grad() |
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def test(data_set, backbone, batch_size, nfolds=10): |
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print('testing verification..') |
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data_list = data_set[0] |
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issame_list = data_set[1] |
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embeddings_list = [] |
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time_consumed = 0.0 |
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for i in range(len(data_list)): |
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data = data_list[i] |
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embeddings = None |
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ba = 0 |
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while ba < data.shape[0]: |
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bb = min(ba + batch_size, data.shape[0]) |
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count = bb - ba |
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_data = data[bb - batch_size: bb] |
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time0 = datetime.datetime.now() |
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img = ((_data / 255) - 0.5) / 0.5 |
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net_out: torch.Tensor = backbone(img) |
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_embeddings = net_out.detach().cpu().numpy() |
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time_now = datetime.datetime.now() |
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diff = time_now - time0 |
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time_consumed += diff.total_seconds() |
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if embeddings is None: |
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embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) |
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embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] |
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ba = bb |
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embeddings_list.append(embeddings) |
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_xnorm = 0.0 |
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_xnorm_cnt = 0 |
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for embed in embeddings_list: |
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for i in range(embed.shape[0]): |
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_em = embed[i] |
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_norm = np.linalg.norm(_em) |
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_xnorm += _norm |
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_xnorm_cnt += 1 |
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_xnorm /= _xnorm_cnt |
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acc1 = 0.0 |
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std1 = 0.0 |
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embeddings = embeddings_list[0] + embeddings_list[1] |
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embeddings = sklearn.preprocessing.normalize(embeddings) |
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print(embeddings.shape) |
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print('infer time', time_consumed) |
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_, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds) |
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acc2, std2 = np.mean(accuracy), np.std(accuracy) |
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return acc1, std1, acc2, std2, _xnorm, embeddings_list |
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def dumpR(data_set, |
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backbone, |
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batch_size, |
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name='', |
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data_extra=None, |
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label_shape=None): |
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print('dump verification embedding..') |
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data_list = data_set[0] |
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issame_list = data_set[1] |
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embeddings_list = [] |
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time_consumed = 0.0 |
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for i in range(len(data_list)): |
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data = data_list[i] |
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embeddings = None |
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ba = 0 |
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while ba < data.shape[0]: |
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bb = min(ba + batch_size, data.shape[0]) |
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count = bb - ba |
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_data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb) |
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time0 = datetime.datetime.now() |
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if data_extra is None: |
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db = mx.io.DataBatch(data=(_data,), label=(_label,)) |
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else: |
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db = mx.io.DataBatch(data=(_data, _data_extra), |
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label=(_label,)) |
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model.forward(db, is_train=False) |
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net_out = model.get_outputs() |
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_embeddings = net_out[0].asnumpy() |
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time_now = datetime.datetime.now() |
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diff = time_now - time0 |
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time_consumed += diff.total_seconds() |
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if embeddings is None: |
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embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) |
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embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] |
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ba = bb |
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embeddings_list.append(embeddings) |
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embeddings = embeddings_list[0] + embeddings_list[1] |
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embeddings = sklearn.preprocessing.normalize(embeddings) |
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actual_issame = np.asarray(issame_list) |
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outname = os.path.join('temp.bin') |
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with open(outname, 'wb') as f: |
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pickle.dump((embeddings, issame_list), |
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f, |
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protocol=pickle.HIGHEST_PROTOCOL) |
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