mirror of
https://github.com/deepinsight/insightface.git
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134 lines
4.9 KiB
Python
134 lines
4.9 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 typing import List
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from utils.verification import evaluate
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from datasets import load_bin
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@paddle.no_grad()
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def test(data_set, backbone, batch_size, fp16=False, nfolds=10):
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print('testing verification..')
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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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time_consumed = 0.0
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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 = data[bb - batch_size:bb]
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# 将numpy转Tensor
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img = paddle.to_tensor(
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_data, dtype='float16' if fp16 else 'float32')
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net_out: paddle.Tensor = backbone(img)
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_embeddings = net_out.detach().cpu().numpy()
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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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for i in range(embed.shape[0]):
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_em = embed[i]
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_norm = np.linalg.norm(_em)
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_xnorm += _norm
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_xnorm_cnt += 1
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_xnorm /= _xnorm_cnt
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embeddings = embeddings_list[0].copy()
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try:
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embeddings = sklearn.preprocessing.normalize(embeddings)
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except:
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print(embeddings)
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acc1 = 0.0
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std1 = 0.0
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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=nfolds)
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acc2, std2 = np.mean(accuracy), np.std(accuracy)
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return acc1, std1, acc2, std2, _xnorm, embeddings_list
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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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val_targets,
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rec_prefix,
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fp16=False,
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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.fp16 = fp16
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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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if self.rank == 0:
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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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def ver_test(self, backbone: paddle.nn.Layer, 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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acc1, std1, acc2, std2, xnorm, embeddings_list = test(
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self.ver_list[i],
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backbone,
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self.batch_size,
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fp16=self.fp16,
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nfolds=10)
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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, backbone: paddle.nn.Layer):
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if self.rank == 0 and num_update > 0 and num_update % self.frequent == 0:
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backbone.eval()
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with paddle.no_grad():
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self.ver_test(backbone, num_update)
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backbone.train()
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