mirror of
https://github.com/deepinsight/insightface.git
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135 lines
4.0 KiB
Python
135 lines
4.0 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 pickle
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import paddle
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import os
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import cv2
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import six
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import random
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import paddle
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import numpy as np
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import logging
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from PIL import Image
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from io import BytesIO
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from datasets.kv_helper import read_img_from_bin
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def transform(img):
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# random horizontal flip
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if random.randint(0, 1) == 0:
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img = cv2.flip(img, 1)
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# normalize to mean 0.5, std 0.5
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img = (img - 127.5) * 0.00784313725
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# BGR2RGB
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img = img[:, :, ::-1]
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img = img.transpose((2, 0, 1))
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return img
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class CommonDataset(paddle.io.Dataset):
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def __init__(self, root_dir, label_file, fp16=False, is_bin=True):
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super(CommonDataset, self).__init__()
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self.root_dir = root_dir
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self.label_file = label_file
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self.fp16 = fp16
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with open(label_file, "r") as fin:
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self.full_lines = fin.readlines()
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self.delimiter = "\t"
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self.is_bin = is_bin
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self.num_samples = len(self.full_lines)
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logging.info("read label file finished, total num: {}"
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.format(self.num_samples))
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def __getitem__(self, idx):
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line = self.full_lines[idx]
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img_path, label = line.strip().split(self.delimiter)
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img_path = os.path.join(self.root_dir, img_path)
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if self.is_bin:
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img = read_img_from_bin(img_path)
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else:
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img = cv2.imread(img_path)
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img = transform(img)
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img = img.astype('float16' if self.fp16 else 'float32')
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label = np.int32(label)
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return img, label
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def __len__(self):
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return self.num_samples
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class SyntheticDataset(paddle.io.Dataset):
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def __init__(self, num_classes, fp16=False):
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super(SyntheticDataset, self).__init__()
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self.num_classes = num_classes
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self.fp16 = fp16
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self.label_list = np.random.randint(
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0, num_classes, (5179510, ), dtype=np.int32)
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self.num_samples = len(self.label_list)
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def __getitem__(self, idx):
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label = self.label_list[idx]
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img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8)
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img = transform(img)
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img = img.astype('float16' if self.fp16 else 'float32')
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label = np.int32(label)
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return img, label
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def __len__(self):
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return self.num_samples
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# 返回为 numpy
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def load_bin(path, image_size):
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if six.PY2:
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bins, issame_list = pickle.load(open(path, 'rb'))
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else:
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bins, issame_list = pickle.load(open(path, 'rb'), encoding='bytes')
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data_list = []
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for flip in [0, 1]:
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data = np.empty(
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(len(issame_list) * 2, 3, image_size[0], image_size[1]))
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data_list.append(data)
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for i in range(len(issame_list) * 2):
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_bin = bins[i]
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if six.PY2:
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if not isinstance(_bin, six.string_types):
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_bin = _bin.tostring()
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img_ori = Image.open(StringIO(_bin))
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else:
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img_ori = Image.open(BytesIO(_bin))
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for flip in [0, 1]:
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img = img_ori.copy()
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if flip == 1:
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img = img.transpose(Image.FLIP_LEFT_RIGHT)
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img = np.array(img).astype('float32').transpose((2, 0, 1))
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img = (img - 127.5) * 0.00784313725
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data_list[flip][i][:] = img
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if i % 1000 == 0:
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print('loading bin', i)
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print(data_list[0].shape)
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return data_list, issame_list
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