261 lines
10 KiB
Python
261 lines
10 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import time
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import numpy as np
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import platform
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import paddle
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import sklearn
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from sklearn.model_selection import KFold
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from sklearn.decomposition import PCA
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from ppcls.utils.misc import AverageMeter
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from ppcls.utils import logger
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def fuse_features_with_norm(stacked_embeddings, stacked_norms):
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assert stacked_embeddings.ndim == 3 # (n_features_to_fuse, batch_size, channel)
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assert stacked_norms.ndim == 3 # (n_features_to_fuse, batch_size, 1)
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pre_norm_embeddings = stacked_embeddings * stacked_norms
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fused = pre_norm_embeddings.sum(axis=0)
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norm = paddle.norm(fused, 2, 1, True)
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fused = paddle.divide(fused, norm)
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return fused, norm
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def adaface_eval(engine, epoch_id=0):
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output_info = dict()
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time_info = {
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"batch_cost": AverageMeter(
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"batch_cost", '.5f', postfix=" s,"),
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"reader_cost": AverageMeter(
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"reader_cost", ".5f", postfix=" s,"),
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}
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print_batch_step = engine.config["Global"]["print_batch_step"]
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metric_key = None
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tic = time.time()
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unique_dict = {}
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for iter_id, batch in enumerate(engine.eval_dataloader):
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images, labels, dataname, image_index = batch
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if iter_id == 5:
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for key in time_info:
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time_info[key].reset()
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time_info["reader_cost"].update(time.time() - tic)
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batch_size = images.shape[0]
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batch[0] = paddle.to_tensor(images)
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embeddings = engine.model(images, labels)['features']
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norms = paddle.divide(embeddings, paddle.norm(embeddings, 2, 1, True))
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embeddings = paddle.divide(embeddings, norms)
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fliped_images = paddle.flip(images, axis=[3])
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flipped_embeddings = engine.model(fliped_images, labels)['features']
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flipped_norms = paddle.divide(
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flipped_embeddings, paddle.norm(flipped_embeddings, 2, 1, True))
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flipped_embeddings = paddle.divide(flipped_embeddings, flipped_norms)
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stacked_embeddings = paddle.stack(
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[embeddings, flipped_embeddings], axis=0)
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stacked_norms = paddle.stack([norms, flipped_norms], axis=0)
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embeddings, norms = fuse_features_with_norm(stacked_embeddings,
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stacked_norms)
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for out, nor, label, data, idx in zip(embeddings, norms, labels,
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dataname, image_index):
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unique_dict[int(idx.numpy())] = {
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'output': out,
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'norm': nor,
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'target': label,
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'dataname': data
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}
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# calc metric
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time_info["batch_cost"].update(time.time() - tic)
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if iter_id % print_batch_step == 0:
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time_msg = "s, ".join([
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"{}: {:.5f}".format(key, time_info[key].avg)
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for key in time_info
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])
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ips_msg = "ips: {:.5f} images/sec".format(
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batch_size / time_info["batch_cost"].avg)
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, output_info[key].val)
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for key in output_info
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])
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logger.info("[Eval][Epoch {}][Iter: {}/{}]{}, {}, {}".format(
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epoch_id, iter_id,
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len(engine.eval_dataloader), metric_msg, time_msg, ips_msg))
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tic = time.time()
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unique_keys = sorted(unique_dict.keys())
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all_output_tensor = paddle.stack(
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[unique_dict[key]['output'] for key in unique_keys], axis=0)
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all_norm_tensor = paddle.stack(
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[unique_dict[key]['norm'] for key in unique_keys], axis=0)
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all_target_tensor = paddle.stack(
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[unique_dict[key]['target'] for key in unique_keys], axis=0)
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all_dataname_tensor = paddle.stack(
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[unique_dict[key]['dataname'] for key in unique_keys], axis=0)
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eval_result = cal_metric(all_output_tensor, all_norm_tensor,
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all_target_tensor, all_dataname_tensor)
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, output_info[key].avg) for key in output_info
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])
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face_msg = ", ".join([
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"{}: {:.5f}".format(key, eval_result[key])
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for key in eval_result.keys()
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])
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logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg + ", " +
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face_msg))
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# return 1st metric in the dict
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return eval_result['all_test_acc']
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def cal_metric(all_output_tensor, all_norm_tensor, all_target_tensor,
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all_dataname_tensor):
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all_target_tensor = all_target_tensor.reshape([-1])
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all_dataname_tensor = all_dataname_tensor.reshape([-1])
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dataname_to_idx = {
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"agedb_30": 0,
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"cfp_fp": 1,
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"lfw": 2,
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"cplfw": 3,
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"calfw": 4
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}
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idx_to_dataname = {val: key for key, val in dataname_to_idx.items()}
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test_logs = {}
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# _, indices = paddle.unique(all_dataname_tensor, return_index=True, return_inverse=False, return_counts=False)
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for dataname_idx in all_dataname_tensor.unique():
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dataname = idx_to_dataname[dataname_idx.item()]
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# per dataset evaluation
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embeddings = all_output_tensor[all_dataname_tensor ==
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dataname_idx].numpy()
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labels = all_target_tensor[all_dataname_tensor == dataname_idx].numpy()
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issame = labels[0::2]
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tpr, fpr, accuracy, best_thresholds = evaluate_face(
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embeddings, issame, nrof_folds=10)
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acc, best_threshold = accuracy.mean(), best_thresholds.mean()
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num_test_samples = len(embeddings)
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test_logs[f'{dataname}_test_acc'] = acc
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test_logs[f'{dataname}_test_best_threshold'] = best_threshold
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test_logs[f'{dataname}_num_test_samples'] = num_test_samples
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test_acc = np.mean([
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test_logs[f'{dataname}_test_acc']
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for dataname in dataname_to_idx.keys()
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if f'{dataname}_test_acc' in test_logs
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])
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test_logs['all_test_acc'] = test_acc
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return test_logs
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def evaluate_face(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, best_thresholds = calculate_roc(
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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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return tpr, fpr, accuracy, best_thresholds
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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 = KFold(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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best_thresholds = np.zeros((nrof_folds))
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indices = np.arange(nrof_pairs)
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# print('pca', pca)
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dist = None
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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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# print('train_set', train_set)
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# print('test_set', test_set)
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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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# print(_embed_train.shape)
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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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# print(embed1.shape, embed2.shape)
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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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best_thresholds[fold_idx] = thresholds[best_threshold_index]
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for threshold_idx, threshold in enumerate(thresholds):
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tprs[fold_idx, threshold_idx], fprs[
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fold_idx, threshold_idx], _ = calculate_accuracy(
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threshold, dist[test_set], 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, best_thresholds
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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(
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np.logical_not(predict_issame), 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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