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import os
import numbers
import torch
import mxnet as mx
from PIL import Image
from torch.utils import data
from torchvision import transforms
import numpy as np
import PIL.Image as Image
""" Original mxnet dataset
"""
class MXFaceDataset(data.Dataset):
def __init__(self, root_dir, crop_param=(0, 0, 112, 112)):
super(MXFaceDataset, self,).__init__()
self.transform = transforms.Compose([
# transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
self.root_dir = root_dir
self.crop_param = crop_param
path_imgrec = os.path.join(root_dir, 'train.rec')
path_imgidx = os.path.join(root_dir, 'train.idx')
self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')
s = self.imgrec.read_idx(0)
header, _ = mx.recordio.unpack(s)
if header.flag > 0:
self.header0 = (int(header.label[0]), int(header.label[1]))
self.imgidx = np.array(range(1, int(header.label[0])))
else:
self.imgidx = np.array(list(self.imgrec.keys))
def __getitem__(self, index):
idx = self.imgidx[index]
s = self.imgrec.read_idx(idx)
header, img = mx.recordio.unpack(s)
label = header.label
if not isinstance(label, numbers.Number):
label = label[0]
label = torch.tensor(label, dtype=torch.long)
sample = mx.image.imdecode(img).asnumpy()
if self.transform is not None:
sample: Image = transforms.ToPILImage()(sample)
sample = sample.crop(self.crop_param)
sample = self.transform(sample)
return sample, label
def __len__(self):
return len(self.imgidx)
""" MXNet binary dataset reader.
Refer to https://github.com/deepinsight/insightface.
"""
import pickle
from typing import List
from mxnet import ndarray as nd
class ReadMXNet(object):
def __init__(self, val_targets, rec_prefix, image_size=(112, 112)):
self.ver_list: List[object] = []
self.ver_name_list: List[str] = []
self.rec_prefix = rec_prefix
self.val_targets = val_targets
def init_dataset(self, val_targets, data_dir, image_size):
for name in val_targets:
path = os.path.join(data_dir, name + ".bin")
if os.path.exists(path):
data_set = self.load_bin(path, image_size)
self.ver_list.append(data_set)
self.ver_name_list.append(name)
def load_bin(self, 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)) # (C, H, W)
img = nd.transpose(img, axes=(1, 2, 0)) # (H, W, C)
import PIL.Image as Image
fig = Image.fromarray(img.asnumpy(), mode='RGB')
data_list.append(fig)
# data_list[flip][idx][:] = torch.from_numpy(img.asnumpy())
if idx % 1000 == 0:
print('loading bin', idx)
# # save img to '/home/yuange/dataset/LFW/rgb-arcface'
# img = nd.transpose(img, axes=(1, 2, 0)) # (H, W, C)
# # save_name = 'ind_' + str(idx) + '.bmp'
# # import os
# # save_name = os.path.join('/home/yuange/dataset/LFW/rgb-arcface', save_name)
# import PIL.Image as Image
# fig = Image.fromarray(img.asnumpy(), mode='RGB')
# # fig.save(save_name)
print('load finished', len(data_list))
return data_list, issame_list
"""
Evaluation Benchmark
"""
class EvalDataset(data.Dataset):
def __init__(self,
target: str = 'lfw',
rec_folder: str = '',
transform = None,
crop_param = (0, 0, 112, 112)
):
print("=> Pre-loading images ...")
self.target = target
self.rec_folder = rec_folder
mx_reader = ReadMXNet(target, rec_folder)
path = os.path.join(rec_folder, target + ".bin")
all_img, issame_list = mx_reader.load_bin(path, (112, 112))
self.all_img = all_img
self.issame_list = []
for i in range(len(issame_list)):
flag = 0 if issame_list[i] else 1 # 0:is same
self.issame_list.append(flag)
self.transform = transform
if self.transform is None:
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
self.crop_param = crop_param
def __getitem__(self, index):
img1 = self.all_img[index * 2]
img2 = self.all_img[index * 2 + 1]
same = self.issame_list[index]
save_index = 11
if index == save_index:
img1.save('img1_ori.jpg')
img2.save('img2_ori.jpg')
img1 = img1.crop(self.crop_param)
img2 = img2.crop(self.crop_param)
if index == save_index:
img1.save('img1_crop.jpg')
img2.save('img2_crop.jpg')
img1 = self.transform(img1)
img2 = self.transform(img2)
return img1, img2, same
def __len__(self):
return len(self.issame_list)
if __name__ == '__main__':
import PIL.Image as Image
import time
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
torch.cuda.manual_seed_all(1)
mx.random.seed(1)
is_gray = False
train_set = FaceByRandOccMask(
root_dir='/tmp/train_tmp/casia',
local_rank=0,
use_norm=True,
is_gray=is_gray,
)
start = time.time()
for idx in range(100):
face, mask, label = train_set.__getitem__(idx)
if idx < 15:
face = ((face + 1) * 128).numpy().astype(np.uint8)
face = np.transpose(face, (1, 2, 0))
if is_gray:
face = Image.fromarray(face[:, :, 0], mode='L')
else:
face = Image.fromarray(face, mode='RGB')
face.save('face_{}.jpg'.format(idx))
print('time cost: %d ms' % (int((time.time() - start) * 1000))) |