arcface

arcface_torch/eval/verification.py

@@ -0,0 +1,409 @@
+"""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
+
+    embeddings = embeddings_list[0].copy()
+    embeddings = sklearn.preprocessing.normalize(embeddings)
+    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)