pose_estimation/models/arcface_torch/eval/verification.py

"""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

@torch.no_grad()
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

@torch.no_grad()
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)