mirror of
https://github.com/deepinsight/insightface.git
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131 lines
5.0 KiB
Python
131 lines
5.0 KiB
Python
# Copyright (c) 2021 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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import time
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import os
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import numpy as np
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import sklearn
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import paddle
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import logging
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from utils.verification import evaluate
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from datasets import load_bin
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def test(rank, batch_size, data_set, executor, test_program, data_feeder,
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fetch_list):
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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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# data_list[0] for normalize
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# data_list[1] for flip_left_right
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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 = []
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for k in range(bb - batch_size, bb):
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_data.append((data[k], ))
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[_embeddings] = executor.run(test_program,
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fetch_list=fetch_list,
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feed=data_feeder.feed(_data),
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use_program_cache=True)
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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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xnorm += np.sqrt((embed * embed).sum(axis=1)).sum(axis=0)
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xnorm_cnt += embed.shape[0]
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xnorm /= xnorm_cnt
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embeddings = embeddings_list[0] + embeddings_list[1]
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embeddings = sklearn.preprocessing.normalize(embeddings)
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_, _, accuracy, val, val_std, far = evaluate(
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embeddings, issame_list, nrof_folds=10)
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acc, std = np.mean(accuracy), np.std(accuracy)
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return acc, std, xnorm
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class CallBackVerification(object):
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def __init__(self,
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frequent,
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rank,
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batch_size,
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test_program,
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feed_list,
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fetch_list,
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val_targets,
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rec_prefix,
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image_size=(112, 112)):
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self.frequent: int = frequent
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self.rank: int = rank
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self.batch_size: int = batch_size
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self.test_program: paddle.static.Program = test_program
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self.feed_list: List[paddle.fluid.framework.Variable] = feed_list
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self.fetch_list: List[paddle.fluid.framework.Variable] = fetch_list
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self.highest_acc_list: List[float] = [0.0] * len(val_targets)
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self.ver_list: List[object] = []
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self.ver_name_list: List[str] = []
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self.init_dataset(
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val_targets=val_targets,
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data_dir=rec_prefix,
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image_size=image_size)
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gpu_id = int(os.getenv("FLAGS_selected_gpus", 0))
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place = paddle.CUDAPlace(gpu_id)
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self.executor = paddle.static.Executor(place)
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self.data_feeder = paddle.fluid.DataFeeder(
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place=place, feed_list=self.feed_list, program=self.test_program)
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def ver_test(self, global_step: int):
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for i in range(len(self.ver_list)):
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test_start = time.time()
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acc2, std2, xnorm = test(
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self.rank, self.batch_size, self.ver_list[i], self.executor,
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self.test_program, self.data_feeder, self.fetch_list)
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logging.info('[%s][%d]XNorm: %f' %
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(self.ver_name_list[i], global_step, xnorm))
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logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' %
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(self.ver_name_list[i], global_step, acc2, std2))
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if acc2 > self.highest_acc_list[i]:
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self.highest_acc_list[i] = acc2
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logging.info('[%s][%d]Accuracy-Highest: %1.5f' % (
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self.ver_name_list[i], global_step, self.highest_acc_list[i]))
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test_end = time.time()
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logging.info("test time: {:.4f}".format(test_end - test_start))
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def init_dataset(self, val_targets, data_dir, image_size):
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for name in val_targets:
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path = os.path.join(data_dir, name + ".bin")
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if os.path.exists(path):
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data_set = load_bin(path, image_size)
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self.ver_list.append(data_set)
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self.ver_name_list.append(name)
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def __call__(self, num_update):
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if self.rank == 0 and num_update > 0 and num_update % self.frequent == 0:
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self.ver_test(num_update)
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