repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
insightface | recognition/arcface_oneflow/configs/ms1mv3_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "cosface"
config.network = "r50"
config.resume = False
config.output = "partial_fc"
config.embedding_size = 512
config.model_parallel = True
config.partial_fc ... | 31 | 760 |
insightface | recognition/arcface_oneflow/configs/ms1mv3_r18.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "arcface"
config.network = "r18"
config.resume = False
config.output = None
config.embedding_size = 512
config.model_parallel = True
config.partial_fc = 1
conf... | 30 | 741 |
insightface | recognition/arcface_oneflow/configs/speed.py | .py | from easydict import EasyDict as edict
# configs for test speed
config = edict()
config.loss = "arcface"
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.model_parallel = True
config.sample_rate = 1.0
config.fp16 = False
config.momentum = 0.9
config.weight_decay = 5... | 25 | 543 |
insightface | recognition/arcface_oneflow/configs/ms1mv3_r34.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "arcface"
config.network = "r34"
config.resume = False
config.output = None
config.embedding_size = 512
config.model_parallel = True
config.partial_fc = 1
conf... | 31 | 742 |
insightface | recognition/arcface_oneflow/configs/base.py | .py | from pickle import TRUE
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "arcface"
config.network = "r50"
config.resume = False
config.output = "ms1mv3_arcface_r50"
config.dataset = "ms1m-retinaface-t1"
conf... | 69 | 1,936 |
insightface | recognition/arcface_oneflow/configs/glint360k_r34.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "cosface"
config.network = "r34"
config.resume = False
config.output = None
config.embedding_size = 512
config.partial_fc = 1
config.sample_rate = 0.1
config.m... | 31 | 769 |
insightface | recognition/arcface_oneflow/configs/ms1mv3_mbf.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "arcface"
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.model_parallel = True
config.partial_fc = 1
conf... | 30 | 741 |
insightface | recognition/arcface_oneflow/configs/glint360k_r18.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.loss = "cosface"
config.network = "r18"
config.resume = False
config.output = None
config.embedding_size = 512
config.partial_fc = 1
config.sample_rate = 0.1
config.m... | 31 | 769 |
insightface | recognition/idmmd/utils.py | .py | import os
import numpy as np
import torch
import torch.nn.functional as F
def ort_loss(x, y):
loss = torch.abs((x * y).sum(dim=1)).sum()
loss = loss / float(x.size(0))
return loss
def ang_loss(x, y):
loss = (x * y).sum(dim=1).sum()
loss = loss / float(x.size(0))
return loss
def MMD_Loss(fc_... | 151 | 4,338 |
insightface | recognition/idmmd/losses.py | .py | import torch
from torch import nn
import torch.nn.functional as F
class IDMMD(nn.Module):
def __init__(self, kernel_type='rbf', kernel_mul=2.0, kernel_num=5):
super(IDMMD, self).__init__()
self.kernel_num = kernel_num
self.kernel_mul = kernel_mul
self.fix_sigma = None
self.... | 106 | 3,406 |
insightface | recognition/idmmd/train.py | .py | import os
import argparse
import random
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from utils import *
from network.lightcnn112 import LightCNN_29Layers_cosface
from losses import IDMMD, CosFace
from dataset_mix import Real_Dataset_112_paired, IdentitySampler, GenIdx
p... | 200 | 7,415 |
insightface | recognition/idmmd/dataset_mix.py | .py | from PIL import Image
import os
import torch.utils.data as data
import torchvision.transforms as transforms
class Real_Dataset_112(data.Dataset):
def __init__(self, args):
super(Real_Dataset_112, self).__init__()
self.img_root = args.img_root_R
self.img_list, self.num_classes = se... | 348 | 11,889 |
insightface | recognition/idmmd/evaluate/eval_lamp_112.py | .py | import numpy as np
import os,sys
sys.path.append(os.getcwd())
import argparse
from PIL import Image
import torch
from network.lightcnn112 import LightCNN_29Layers
from evaluate import evaluate2
fars = [10 ** -4, 10 ** -3, 10 ** -2]
parser = argparse.ArgumentParser()
parser.add_argument('--test_fold_id', default=1,... | 169 | 5,494 |
insightface | recognition/idmmd/evaluate/eval_ops.py | .py | import numpy as np
from sklearn.metrics import roc_curve
def evaluate2(gallery_feat, query_feat, labels, fars = [10**-5, 10**-4, 10**-3, 10**-2]):
query_num = query_feat.shape[0]
similarity = np.dot(query_feat, gallery_feat.T)
top_inds = np.argsort(-similarity)
labels = labels.T
# calculate t... | 55 | 1,635 |
insightface | recognition/idmmd/evaluate/eval_buaa_112.py | .py | import numpy as np
import os,sys
sys.path.append(os.getcwd())
import argparse
import torch
from PIL import Image
from network.lightcnn112 import LightCNN_29Layers
from evaluate import evaluate2
fars = [10 ** -4, 10 ** -3, 10 ** -2]
parser = argparse.ArgumentParser()
parser.add_argument('--test_fold_id', default=1, t... | 136 | 4,284 |
insightface | recognition/idmmd/evaluate/eval_casia_112.py | .py | import numpy as np
import pandas as pd
import os,sys
sys.path.append(os.getcwd())
import argparse
from PIL import Image
import torch
from network.lightcnn112 import LightCNN_29Layers
from evaluate import evaluate2
fars = [10 ** -4, 10 ** -3, 10 ** -2]
parser = argparse.ArgumentParser()
parser.add_argument('--test_f... | 190 | 6,193 |
insightface | recognition/idmmd/evaluate/eval_oulu_112.py | .py | import numpy as np
import pandas as pd
import os,sys
sys.path.append(os.getcwd())
print(sys.path)
import argparse
import torch
from PIL import Image
from network.lightcnn112 import LightCNN_29Layers
from evaluate import evaluate2
fars = [10 ** -4, 10 ** -3, 10 ** -2]
parser = argparse.ArgumentParser()
parser.add_ar... | 160 | 5,163 |
insightface | recognition/idmmd/network/lightcnn112.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv... | 182 | 6,004 |
insightface | recognition/arcface_torch/partial_fc_v2.py | .py |
import math
from typing import Callable
import torch
from torch import distributed
from torch.nn.functional import linear, normalize
class PartialFC_V2(torch.nn.Module):
"""
https://arxiv.org/abs/2203.15565
A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).
When sample ... | 261 | 8,925 |
insightface | recognition/arcface_torch/onnx_ijbc.py | .py | import argparse
import os
import pickle
import timeit
import cv2
import mxnet as mx
import numpy as np
import pandas as pd
import prettytable
import skimage.transform
import torch
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from torch.utils.data import DataLoader
from onnx_helper ... | 270 | 10,317 |
insightface | recognition/arcface_torch/inference.py | .py | import argparse
import cv2
import numpy as np
import torch
from backbones import get_model
@torch.no_grad()
def inference(weight, name, img):
if img is None:
img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8)
else:
img = cv2.imread(img)
img = cv2.resize(img, (112, 11... | 36 | 1,033 |
insightface | recognition/arcface_torch/losses.py | .py | import torch
import math
class CombinedMarginLoss(torch.nn.Module):
def __init__(self,
s,
m1,
m2,
m3,
interclass_filtering_threshold=0):
super().__init__()
self.s = s
self.m1 = m1
self.m2 = m2
... | 101 | 3,397 |
insightface | recognition/arcface_torch/torch2onnx.py | .py | import numpy as np
import onnx
import torch
def convert_onnx(net, path_module, output, opset=11, simplify=False):
assert isinstance(net, torch.nn.Module)
img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)
img = img.astype(np.float)
img = (img / 255. - 0.5) / 0.5 # torch style norm
... | 54 | 2,236 |
insightface | recognition/arcface_torch/onnx_helper.py | .py | from __future__ import division
import datetime
import os
import os.path as osp
import glob
import numpy as np
import cv2
import sys
import onnxruntime
import onnx
import argparse
from onnx import numpy_helper
from insightface.data import get_image
class ArcFaceORT:
def __init__(self, model_path, cpu=False):
... | 251 | 10,422 |
insightface | recognition/arcface_torch/flops.py | .py | from ptflops import get_model_complexity_info
from backbones import get_model
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument('n', type=str, default="r100")
args = parser.parse_args()
net = get_model(args.n)
macs, params = get_model_co... | 21 | 686 |
insightface | recognition/arcface_torch/eval_ijbc.py | .py | # coding: utf-8
import os
import pickle
import matplotlib
import pandas as pd
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
import cv2
import numpy as np
import torch
from skimage import transform as trans
from backbones import get_model
from sklearn.metrics impor... | 484 | 17,284 |
insightface | recognition/arcface_torch/lr_scheduler.py | .py | from torch.optim.lr_scheduler import _LRScheduler
from torch.optim import SGD
import torch
import warnings
class PolynomialLRWarmup(_LRScheduler):
def __init__(self, optimizer, warmup_iters, total_iters=5, power=1.0, last_epoch=-1, verbose=False):
super().__init__(optimizer, last_epoch=last_epoch, verbose=... | 87 | 2,983 |
insightface | recognition/arcface_torch/dataset.py | .py | import numbers
import os
import queue as Queue
import threading
from typing import Iterable
import mxnet as mx
import numpy as np
import torch
from functools import partial
from torch import distributed
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from torchvision.datasets import... | 284 | 9,663 |
insightface | recognition/arcface_torch/train_v2.py | .py | import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from backbones import get_model
from dataset import get_dataloader
from losses import CombinedMarginLoss
from lr_scheduler import PolynomialLRWarmup
from partial_fc_v2 import PartialFC_V2
from torch import distribute... | 258 | 9,483 |
insightface | recognition/arcface_torch/backbones/iresnet2060.py | .py | import torch
from torch import nn
assert torch.__version__ >= "1.8.1"
from torch.utils.checkpoint import checkpoint_sequential
__all__ = ['iresnet2060']
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out... | 177 | 6,708 |
insightface | recognition/arcface_torch/backbones/__init__.py | .py | from .iresnet import iresnet18, iresnet34, iresnet50, iresnet100, iresnet200
from .mobilefacenet import get_mbf
def get_model(name, **kwargs):
# resnet
if name == "r18":
return iresnet18(False, **kwargs)
elif name == "r34":
return iresnet34(False, **kwargs)
elif name == "r50":
... | 93 | 4,033 |
insightface | recognition/arcface_torch/backbones/iresnet.py | .py | import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
using_ckpt = False
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
... | 195 | 7,431 |
insightface | recognition/arcface_torch/backbones/mobilefacenet.py | .py | '''
Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
Original author cavalleria
'''
import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
import torch
class Flatten(Module):
def forward(self, x):
ret... | 148 | 5,591 |
insightface | recognition/arcface_torch/backbones/vit.py | .py | import torch
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from typing import Optional, Callable
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
super().__init__()
out_features = o... | 281 | 10,780 |
insightface | recognition/arcface_torch/eval/verification.py | .py | """Helper for evaluation on the Labeled Faces in the Wild dataset
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restricti... | 410 | 16,196 |
insightface | recognition/arcface_torch/scripts/shuffle_rec.py | .py | import argparse
import multiprocessing
import os
import time
import mxnet as mx
import numpy as np
def read_worker(args, q_in):
path_imgidx = os.path.join(args.input, "train.idx")
path_imgrec = os.path.join(args.input, "train.rec")
imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, "r")
... | 82 | 2,428 |
insightface | recognition/arcface_torch/utils/utils_distributed_sampler.py | .py | import math
import os
import random
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data import DistributedSampler as _DistributedSampler
def setup_seed(seed, cuda_deterministic=True):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
rand... | 127 | 4,264 |
insightface | recognition/arcface_torch/utils/plot.py | .py | import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
from prettytable import PrettyTable
from sklearn.metrics import roc_curve, auc
with open(sys.argv[1], "r") as f:
files = f.readlines()
files = [x.st... | 72 | 2,176 |
insightface | recognition/arcface_torch/utils/utils_logging.py | .py | import logging
import os
import sys
class AverageMeter(object):
"""Computes and stores the average and current value
"""
def __init__(self):
self.val = None
self.avg = None
self.sum = None
self.count = None
self.reset()
def reset(self):
self.val = 0
... | 42 | 1,110 |
insightface | recognition/arcface_torch/utils/utils_callbacks.py | .py | import logging
import os
import time
from typing import List
import torch
from eval import verification
from utils.utils_logging import AverageMeter
from torch.utils.tensorboard import SummaryWriter
from torch import distributed
class CallBackVerification(object):
def __init__(self, val_targets, rec_prefix... | 126 | 5,546 |
insightface | recognition/arcface_torch/utils/utils_config.py | .py | import importlib
import os.path as osp
def get_config(config_file):
assert config_file.startswith('configs/'), 'config file setting must start with configs/'
temp_config_name = osp.basename(config_file)
temp_module_name = osp.splitext(temp_config_name)[0]
config = importlib.import_module("configs.base... | 16 | 571 |
insightface | recognition/arcface_torch/configs/wf4m_mbf.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 642 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_t.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_t_dp005_mask0"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
con... | 28 | 659 |
insightface | recognition/arcface_torch/configs/glint360k_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 643 |
insightface | recognition/arcface_torch/configs/wf12m_pfc02_r100.py | .py |
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.intercla... | 30 | 664 |
insightface | recognition/arcface_torch/configs/ms1mv2_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tr... | 28 | 644 |
insightface | recognition/arcface_torch/configs/glint360k_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tr... | 28 | 644 |
insightface | recognition/arcface_torch/configs/wf12m_flip_pfc01_filter04_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.1
config.interclass... | 29 | 692 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_8gpus_r50_bs4k.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tru... | 28 | 646 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_b.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_b_dp005_mask_005"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
... | 28 | 662 |
insightface | recognition/arcface_torch/configs/wf12m_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.interclass... | 29 | 662 |
insightface | recognition/arcface_torch/configs/wf4m_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 642 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tr... | 28 | 647 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_l.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_l_dp005_mask_005"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
... | 28 | 662 |
insightface | recognition/arcface_torch/configs/ms1mv2_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 643 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_16gpus_r50_bs8k.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tru... | 28 | 646 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_32gpus_r50_bs4k.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tru... | 28 | 646 |
insightface | recognition/arcface_torch/configs/3millions.py | .py | from easydict import EasyDict as edict
# configs for test speed
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.1
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_... | 24 | 566 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_16gpus_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tr... | 28 | 646 |
insightface | recognition/arcface_torch/configs/wf42m_pfc0008_32gpu_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0
config.fp16 = True... | 28 | 665 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_vit_h.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_h"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.5
config.fp16 = T... | 29 | 668 |
insightface | recognition/arcface_torch/configs/ms1mv3_r50_onegpu.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 651 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_16gpus_mbf_bs8k.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tru... | 28 | 619 |
insightface | recognition/arcface_torch/configs/glint360k_mbf.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 643 |
insightface | recognition/arcface_torch/configs/ms1mv3_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 650 |
insightface | recognition/arcface_torch/configs/wf12m_flip_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.interclass... | 29 | 690 |
insightface | recognition/arcface_torch/configs/wf12m_mbf.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.interclass... | 29 | 662 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_40epoch_8gpu_vit_b.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_b_dp005_mask_005"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
... | 29 | 717 |
insightface | recognition/arcface_torch/configs/ms1mv2_mbf.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 643 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_32gpu_r18.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r18"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
config.fp16 = Tru... | 28 | 666 |
insightface | recognition/arcface_torch/configs/wf4m_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tr... | 28 | 643 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_32gpu_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
config.fp16 = Tr... | 28 | 667 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_40epoch_64gpu_vit_s.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_s_dp005_mask_0"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
co... | 28 | 660 |
insightface | recognition/arcface_torch/configs/ms1mv3_r100.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tr... | 28 | 651 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_32gpu_r200.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r200"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
config.fp16 = Tr... | 28 | 667 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_32gpu_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
config.fp16 = Tru... | 28 | 666 |
insightface | recognition/arcface_torch/configs/wf12m_conflict_r50_pfc03_filter04.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
config.interclass... | 29 | 695 |
insightface | recognition/arcface_torch/configs/base.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
# Margin Base Softmax
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.save_all_states = False
config.output = "ms1mv3_arcface_r50"
... | 60 | 1,291 |
insightface | recognition/arcface_torch/configs/wf12m_conflict_r50.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.interclass... | 29 | 693 |
insightface | recognition/arcface_torch/configs/ms1mv3_mbf.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = Tru... | 28 | 650 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_r100_32gpus.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tr... | 28 | 668 |
insightface | recognition/arcface_torch/configs/wf42m_pfc03_40epoch_8gpu_vit_t.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "vit_t_dp005_mask0"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.3
con... | 28 | 659 |
insightface | recognition/arcface_torch/configs/wf42m_pfc02_r100_16gpus.py | .py | from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.2
config.fp16 = Tr... | 28 | 668 |
insightface | recognition/arcface_torch/configs/wf12m_r100.py | .py |
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.intercla... | 30 | 664 |
insightface | recognition/partial_fc/unpack_glint360k.py | .py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import cv2
import mxnet as mx
def main(args):
include_datasets = args.include.split(',')
rec_list = []
for ds in include_datasets:
path_imgrec = os.path.join(ds,... | 59 | 1,957 |
insightface | recognition/partial_fc/mxnet/memory_scheduler.py | .py | from default import config
import mxnet as mx
def get_scheduler():
step = [int(x) for x in config.lr_steps.split(',')]
backbone_lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(
step=step, factor=0.1, base_lr=config.backbone_lr)
memory_bank_lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(
... | 13 | 441 |
insightface | recognition/partial_fc/mxnet/default.py | .py | from easydict import EasyDict as edict
config = edict()
# loss
config.embedding_size = 512
config.bn_mom = 0.9
config.workspace = 256
config.net_se = 0
config.net_act = 'prelu'
config.net_unit = 3
config.net_input = 1
config.net_output = 'FC'
config.frequent = 20
config.verbose = 2000
config.image_size = 112
config.me... | 130 | 3,753 |
insightface | recognition/partial_fc/mxnet/memory_bank.py | .py | import os
import numpy as np
from mxnet import nd
import mxnet as mx
from memory_samplers import WeightIndexSampler
class MemoryBank(object):
def __init__(self,
num_sample,
num_local,
rank,
local_rank,
embedding_size,
... | 118 | 3,835 |
insightface | recognition/partial_fc/mxnet/callbacks.py | .py | import logging
import os
import sys
import time
import horovod.mxnet as hvd
import mxnet as mx
from mxboard import SummaryWriter
from mxnet import nd
from default import config
from evaluation import verification
class MetricNdarray(object):
def __init__(self):
self.sum = None
self.count = 0
... | 222 | 7,486 |
insightface | recognition/partial_fc/mxnet/memory_module.py | .py | import logging
import warnings
from collections import namedtuple
import horovod.mxnet as hvd
import mxnet as mx
import mxnet.ndarray as nd
import numpy as np
from default import config
from optimizer import DistributedOptimizer
class SampleDistributeModule(object):
"""
Large-scale distributed sampling face... | 509 | 20,288 |
insightface | recognition/partial_fc/mxnet/image_iter.py | .py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import logging
import sys
import numbers
import math
import datetime
import numpy as np
import cv2
import mxnet as mx
from mxnet import ndarray as nd
from mxnet import io
from mxnet imp... | 349 | 12,516 |
insightface | recognition/partial_fc/mxnet/memory_samplers.py | .py | import numpy as np
class CenterPositiveClassGet(object):
""" Get the corresponding center of the positive class
"""
def __init__(self, num_sample, num_local, rank):
self.num_sample = num_sample
self.num_local = num_local
self.rank = rank
self.rank_class_start = self.rank * ... | 78 | 2,824 |
insightface | recognition/partial_fc/mxnet/train_memory.py | .py | import argparse
import logging
import os
import sys
import time
import horovod.mxnet as hvd
import mxnet as mx
import default
from callbacks import CallBackModelSave, CallBackLogging, CallBackCenterSave, CallBackVertification
from default import config
from image_iter import FaceImageIter, DummyIter
from memory_modul... | 174 | 5,878 |
insightface | recognition/partial_fc/mxnet/memory_softmax.py | .py | import mxnet as mx
from mxnet import autograd
from mxnet import nd
class MarginLoss(object):
""" Default is Arcface loss
"""
def __init__(self, margins=(1.0, 0.5, 0.0), loss_s=64, embedding_size=512):
"""
"""
# margins
self.loss_m1 = margins[0]
self.loss_m2 = margin... | 54 | 1,914 |
insightface | recognition/partial_fc/mxnet/optimizer.py | .py | import horovod.mxnet as hvd
import mxnet as mx
from mxnet import nd
# This is where Horovod's DistributedOptimizer wrapper for MXNet goes
class DistributedOptimizer(mx.optimizer.Optimizer):
def __init__(self, optimizer, prefix=""):
self._optimizer = optimizer
self._prefix = prefix
def __getat... | 72 | 2,412 |
insightface | recognition/partial_fc/mxnet/evaluation/ijb.py | .py | import argparse
import os
import pickle
import timeit
import warnings
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import mxnet as mx
import numpy as np
import pandas as pd
import sklearn
from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
from mxnet.gluon.... | 461 | 16,877 |
insightface | recognition/partial_fc/mxnet/evaluation/align_ijb.py | .py | import os
import cv2
import numpy as np
from skimage import transform as trans
src = np.array([[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366],
[33.5493, 92.3655], [62.7299, 92.2041]],
dtype=np.float32)
src[:, 0] += 8.0
img_path = '/data/anxiang/datasets/IJB_release/IJBC/loo... | 43 | 1,540 |
insightface | recognition/partial_fc/mxnet/evaluation/verification.py | .py | """Helper for evaluation on the Labeled Faces in the Wild dataset
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restricti... | 680 | 26,321 |
insightface | recognition/partial_fc/mxnet/evaluation/lfw.py | .py | """Helper for evaluation on the Labeled Faces in the Wild dataset
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restricti... | 326 | 12,802 |
insightface | recognition/partial_fc/mxnet/symbol/resnet.py | .py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 1,185 | 46,669 |
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