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| """Helper for evaluation on the Labeled Faces in the Wild dataset | |
| """ | |
| # MIT License | |
| # | |
| # Copyright (c) 2016 David Sandberg | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| import datetime | |
| import os | |
| import pickle | |
| import mxnet as mx | |
| import numpy as np | |
| import sklearn | |
| import torch | |
| from mxnet import ndarray as nd | |
| from scipy import interpolate | |
| from sklearn.decomposition import PCA | |
| from sklearn.model_selection import KFold | |
| class LFold: | |
| def __init__(self, n_splits=2, shuffle=False): | |
| self.n_splits = n_splits | |
| if self.n_splits > 1: | |
| self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle) | |
| def split(self, indices): | |
| if self.n_splits > 1: | |
| return self.k_fold.split(indices) | |
| else: | |
| return [(indices, indices)] | |
| def calculate_roc(thresholds, | |
| embeddings1, | |
| embeddings2, | |
| actual_issame, | |
| nrof_folds=10, | |
| pca=0): | |
| assert (embeddings1.shape[0] == embeddings2.shape[0]) | |
| assert (embeddings1.shape[1] == embeddings2.shape[1]) | |
| nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) | |
| nrof_thresholds = len(thresholds) | |
| k_fold = LFold(n_splits=nrof_folds, shuffle=False) | |
| tprs = np.zeros((nrof_folds, nrof_thresholds)) | |
| fprs = np.zeros((nrof_folds, nrof_thresholds)) | |
| accuracy = np.zeros((nrof_folds)) | |
| indices = np.arange(nrof_pairs) | |
| if pca == 0: | |
| diff = np.subtract(embeddings1, embeddings2) | |
| dist = np.sum(np.square(diff), 1) | |
| for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): | |
| if pca > 0: | |
| print('doing pca on', fold_idx) | |
| embed1_train = embeddings1[train_set] | |
| embed2_train = embeddings2[train_set] | |
| _embed_train = np.concatenate((embed1_train, embed2_train), axis=0) | |
| pca_model = PCA(n_components=pca) | |
| pca_model.fit(_embed_train) | |
| embed1 = pca_model.transform(embeddings1) | |
| embed2 = pca_model.transform(embeddings2) | |
| embed1 = sklearn.preprocessing.normalize(embed1) | |
| embed2 = sklearn.preprocessing.normalize(embed2) | |
| diff = np.subtract(embed1, embed2) | |
| dist = np.sum(np.square(diff), 1) | |
| # Find the best threshold for the fold | |
| acc_train = np.zeros((nrof_thresholds)) | |
| for threshold_idx, threshold in enumerate(thresholds): | |
| _, _, acc_train[threshold_idx] = calculate_accuracy( | |
| threshold, dist[train_set], actual_issame[train_set]) | |
| best_threshold_index = np.argmax(acc_train) | |
| for threshold_idx, threshold in enumerate(thresholds): | |
| tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy( | |
| threshold, dist[test_set], | |
| actual_issame[test_set]) | |
| _, _, accuracy[fold_idx] = calculate_accuracy( | |
| thresholds[best_threshold_index], dist[test_set], | |
| actual_issame[test_set]) | |
| tpr = np.mean(tprs, 0) | |
| fpr = np.mean(fprs, 0) | |
| return tpr, fpr, accuracy | |
| def calculate_accuracy(threshold, dist, actual_issame): | |
| predict_issame = np.less(dist, threshold) | |
| tp = np.sum(np.logical_and(predict_issame, actual_issame)) | |
| fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) | |
| tn = np.sum( | |
| np.logical_and(np.logical_not(predict_issame), | |
| np.logical_not(actual_issame))) | |
| fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) | |
| tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) | |
| fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) | |
| acc = float(tp + tn) / dist.size | |
| return tpr, fpr, acc | |
| def calculate_val(thresholds, | |
| embeddings1, | |
| embeddings2, | |
| actual_issame, | |
| far_target, | |
| nrof_folds=10): | |
| assert (embeddings1.shape[0] == embeddings2.shape[0]) | |
| assert (embeddings1.shape[1] == embeddings2.shape[1]) | |
| nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) | |
| nrof_thresholds = len(thresholds) | |
| k_fold = LFold(n_splits=nrof_folds, shuffle=False) | |
| val = np.zeros(nrof_folds) | |
| far = np.zeros(nrof_folds) | |
| diff = np.subtract(embeddings1, embeddings2) | |
| dist = np.sum(np.square(diff), 1) | |
| indices = np.arange(nrof_pairs) | |
| for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): | |
| # Find the threshold that gives FAR = far_target | |
| far_train = np.zeros(nrof_thresholds) | |
| for threshold_idx, threshold in enumerate(thresholds): | |
| _, far_train[threshold_idx] = calculate_val_far( | |
| threshold, dist[train_set], actual_issame[train_set]) | |
| if np.max(far_train) >= far_target: | |
| f = interpolate.interp1d(far_train, thresholds, kind='slinear') | |
| threshold = f(far_target) | |
| else: | |
| threshold = 0.0 | |
| val[fold_idx], far[fold_idx] = calculate_val_far( | |
| threshold, dist[test_set], actual_issame[test_set]) | |
| val_mean = np.mean(val) | |
| far_mean = np.mean(far) | |
| val_std = np.std(val) | |
| return val_mean, val_std, far_mean | |
| def calculate_val_far(threshold, dist, actual_issame): | |
| predict_issame = np.less(dist, threshold) | |
| true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) | |
| false_accept = np.sum( | |
| np.logical_and(predict_issame, np.logical_not(actual_issame))) | |
| n_same = np.sum(actual_issame) | |
| n_diff = np.sum(np.logical_not(actual_issame)) | |
| # print(true_accept, false_accept) | |
| # print(n_same, n_diff) | |
| val = float(true_accept) / float(n_same) | |
| far = float(false_accept) / float(n_diff) | |
| return val, far | |
| def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0): | |
| # Calculate evaluation metrics | |
| thresholds = np.arange(0, 4, 0.01) | |
| embeddings1 = embeddings[0::2] | |
| embeddings2 = embeddings[1::2] | |
| tpr, fpr, accuracy = calculate_roc(thresholds, | |
| embeddings1, | |
| embeddings2, | |
| np.asarray(actual_issame), | |
| nrof_folds=nrof_folds, | |
| pca=pca) | |
| thresholds = np.arange(0, 4, 0.001) | |
| val, val_std, far = calculate_val(thresholds, | |
| embeddings1, | |
| embeddings2, | |
| np.asarray(actual_issame), | |
| 1e-3, | |
| nrof_folds=nrof_folds) | |
| return tpr, fpr, accuracy, val, val_std, far | |
| def load_bin(path, image_size): | |
| try: | |
| with open(path, 'rb') as f: | |
| bins, issame_list = pickle.load(f) # py2 | |
| except UnicodeDecodeError as e: | |
| with open(path, 'rb') as f: | |
| bins, issame_list = pickle.load(f, encoding='bytes') # py3 | |
| data_list = [] | |
| for flip in [0, 1]: | |
| data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1])) | |
| data_list.append(data) | |
| for idx in range(len(issame_list) * 2): | |
| _bin = bins[idx] | |
| img = mx.image.imdecode(_bin) | |
| if img.shape[1] != image_size[0]: | |
| img = mx.image.resize_short(img, image_size[0]) | |
| img = nd.transpose(img, axes=(2, 0, 1)) | |
| for flip in [0, 1]: | |
| if flip == 1: | |
| img = mx.ndarray.flip(data=img, axis=2) | |
| data_list[flip][idx][:] = torch.from_numpy(img.asnumpy()) | |
| if idx % 1000 == 0: | |
| print('loading bin', idx) | |
| print(data_list[0].shape) | |
| return data_list, issame_list | |
| def test(data_set, backbone, batch_size, nfolds=10): | |
| print('testing verification..') | |
| data_list = data_set[0] | |
| issame_list = data_set[1] | |
| embeddings_list = [] | |
| time_consumed = 0.0 | |
| for i in range(len(data_list)): | |
| data = data_list[i] | |
| embeddings = None | |
| ba = 0 | |
| while ba < data.shape[0]: | |
| bb = min(ba + batch_size, data.shape[0]) | |
| count = bb - ba | |
| _data = data[bb - batch_size: bb] | |
| time0 = datetime.datetime.now() | |
| img = ((_data / 255) - 0.5) / 0.5 | |
| net_out: torch.Tensor = backbone(img) | |
| _embeddings = net_out.detach().cpu().numpy() | |
| time_now = datetime.datetime.now() | |
| diff = time_now - time0 | |
| time_consumed += diff.total_seconds() | |
| if embeddings is None: | |
| embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) | |
| embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] | |
| ba = bb | |
| embeddings_list.append(embeddings) | |
| _xnorm = 0.0 | |
| _xnorm_cnt = 0 | |
| for embed in embeddings_list: | |
| for i in range(embed.shape[0]): | |
| _em = embed[i] | |
| _norm = np.linalg.norm(_em) | |
| _xnorm += _norm | |
| _xnorm_cnt += 1 | |
| _xnorm /= _xnorm_cnt | |
| acc1 = 0.0 | |
| std1 = 0.0 | |
| embeddings = embeddings_list[0] + embeddings_list[1] | |
| embeddings = sklearn.preprocessing.normalize(embeddings) | |
| print(embeddings.shape) | |
| print('infer time', time_consumed) | |
| _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds) | |
| acc2, std2 = np.mean(accuracy), np.std(accuracy) | |
| return acc1, std1, acc2, std2, _xnorm, embeddings_list | |
| def dumpR(data_set, | |
| backbone, | |
| batch_size, | |
| name='', | |
| data_extra=None, | |
| label_shape=None): | |
| print('dump verification embedding..') | |
| data_list = data_set[0] | |
| issame_list = data_set[1] | |
| embeddings_list = [] | |
| time_consumed = 0.0 | |
| for i in range(len(data_list)): | |
| data = data_list[i] | |
| embeddings = None | |
| ba = 0 | |
| while ba < data.shape[0]: | |
| bb = min(ba + batch_size, data.shape[0]) | |
| count = bb - ba | |
| _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb) | |
| time0 = datetime.datetime.now() | |
| if data_extra is None: | |
| db = mx.io.DataBatch(data=(_data,), label=(_label,)) | |
| else: | |
| db = mx.io.DataBatch(data=(_data, _data_extra), | |
| label=(_label,)) | |
| model.forward(db, is_train=False) | |
| net_out = model.get_outputs() | |
| _embeddings = net_out[0].asnumpy() | |
| time_now = datetime.datetime.now() | |
| diff = time_now - time0 | |
| time_consumed += diff.total_seconds() | |
| if embeddings is None: | |
| embeddings = np.zeros((data.shape[0], _embeddings.shape[1])) | |
| embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :] | |
| ba = bb | |
| embeddings_list.append(embeddings) | |
| embeddings = embeddings_list[0] + embeddings_list[1] | |
| embeddings = sklearn.preprocessing.normalize(embeddings) | |
| actual_issame = np.asarray(issame_list) | |
| outname = os.path.join('temp.bin') | |
| with open(outname, 'wb') as f: | |
| pickle.dump((embeddings, issame_list), | |
| f, | |
| protocol=pickle.HIGHEST_PROTOCOL) | |
| # if __name__ == '__main__': | |
| # | |
| # parser = argparse.ArgumentParser(description='do verification') | |
| # # general | |
| # parser.add_argument('--data-dir', default='', help='') | |
| # parser.add_argument('--model', | |
| # default='../model/softmax,50', | |
| # help='path to load model.') | |
| # parser.add_argument('--target', | |
| # default='lfw,cfp_ff,cfp_fp,agedb_30', | |
| # help='test targets.') | |
| # parser.add_argument('--gpu', default=0, type=int, help='gpu id') | |
| # parser.add_argument('--batch-size', default=32, type=int, help='') | |
| # 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) | |