410 lines
16 KiB
Python
410 lines
16 KiB
Python
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"""Helper for evaluation on the Labeled Faces in the Wild dataset
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"""
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# MIT License
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#
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# Copyright (c) 2016 David Sandberg
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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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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# Find the best threshold for the fold
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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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# Find the threshold that gives FAR = far_target
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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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# print(true_accept, false_accept)
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# print(n_same, n_diff)
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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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# Calculate evaluation metrics
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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) # py2
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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') # py3
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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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embeddings = embeddings_list[0].copy()
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embeddings = sklearn.preprocessing.normalize(embeddings)
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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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# if __name__ == '__main__':
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#
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# parser = argparse.ArgumentParser(description='do verification')
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# # general
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# parser.add_argument('--data-dir', default='', help='')
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# parser.add_argument('--model',
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# default='../model/softmax,50',
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# help='path to load model.')
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# parser.add_argument('--target',
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# default='lfw,cfp_ff,cfp_fp,agedb_30',
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# help='test targets.')
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# parser.add_argument('--gpu', default=0, type=int, help='gpu id')
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# parser.add_argument('--batch-size', default=32, type=int, help='')
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# parser.add_argument('--max', default='', type=str, help='')
|
||
|
|
# parser.add_argument('--mode', default=0, type=int, help='')
|
||
|
|
# parser.add_argument('--nfolds', default=10, type=int, help='')
|
||
|
|
# args = parser.parse_args()
|
||
|
|
# image_size = [112, 112]
|
||
|
|
# print('image_size', image_size)
|
||
|
|
# ctx = mx.gpu(args.gpu)
|
||
|
|
# nets = []
|
||
|
|
# vec = args.model.split(',')
|
||
|
|
# prefix = args.model.split(',')[0]
|
||
|
|
# epochs = []
|
||
|
|
# if len(vec) == 1:
|
||
|
|
# pdir = os.path.dirname(prefix)
|
||
|
|
# for fname in os.listdir(pdir):
|
||
|
|
# if not fname.endswith('.params'):
|
||
|
|
# continue
|
||
|
|
# _file = os.path.join(pdir, fname)
|
||
|
|
# if _file.startswith(prefix):
|
||
|
|
# epoch = int(fname.split('.')[0].split('-')[1])
|
||
|
|
# epochs.append(epoch)
|
||
|
|
# epochs = sorted(epochs, reverse=True)
|
||
|
|
# if len(args.max) > 0:
|
||
|
|
# _max = [int(x) for x in args.max.split(',')]
|
||
|
|
# assert len(_max) == 2
|
||
|
|
# if len(epochs) > _max[1]:
|
||
|
|
# epochs = epochs[_max[0]:_max[1]]
|
||
|
|
#
|
||
|
|
# else:
|
||
|
|
# epochs = [int(x) for x in vec[1].split('|')]
|
||
|
|
# print('model number', len(epochs))
|
||
|
|
# time0 = datetime.datetime.now()
|
||
|
|
# for epoch in epochs:
|
||
|
|
# print('loading', prefix, epoch)
|
||
|
|
# sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
|
||
|
|
# # arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
|
||
|
|
# all_layers = sym.get_internals()
|
||
|
|
# sym = all_layers['fc1_output']
|
||
|
|
# model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
|
||
|
|
# # model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
|
||
|
|
# model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0],
|
||
|
|
# image_size[1]))])
|
||
|
|
# model.set_params(arg_params, aux_params)
|
||
|
|
# nets.append(model)
|
||
|
|
# time_now = datetime.datetime.now()
|
||
|
|
# diff = time_now - time0
|
||
|
|
# print('model loading time', diff.total_seconds())
|
||
|
|
#
|
||
|
|
# ver_list = []
|
||
|
|
# ver_name_list = []
|
||
|
|
# for name in args.target.split(','):
|
||
|
|
# path = os.path.join(args.data_dir, name + ".bin")
|
||
|
|
# if os.path.exists(path):
|
||
|
|
# print('loading.. ', name)
|
||
|
|
# data_set = load_bin(path, image_size)
|
||
|
|
# ver_list.append(data_set)
|
||
|
|
# ver_name_list.append(name)
|
||
|
|
#
|
||
|
|
# if args.mode == 0:
|
||
|
|
# for i in range(len(ver_list)):
|
||
|
|
# results = []
|
||
|
|
# for model in nets:
|
||
|
|
# acc1, std1, acc2, std2, xnorm, embeddings_list = test(
|
||
|
|
# ver_list[i], model, args.batch_size, args.nfolds)
|
||
|
|
# print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
|
||
|
|
# print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
|
||
|
|
# print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
|
||
|
|
# results.append(acc2)
|
||
|
|
# print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
|
||
|
|
# elif args.mode == 1:
|
||
|
|
# raise ValueError
|
||
|
|
# else:
|
||
|
|
# model = nets[0]
|
||
|
|
# dumpR(ver_list[0], model, args.batch_size, args.target)
|