mirror of
https://github.com/deepinsight/insightface.git
synced 2025-12-30 08:02:27 +00:00
225 lines
5.6 KiB
Python
225 lines
5.6 KiB
Python
import numpy as np
|
|
import os
|
|
from easydict import EasyDict as edict
|
|
|
|
config = edict()
|
|
|
|
config.bn_mom = 0.9
|
|
config.workspace = 256
|
|
config.emb_size = 512
|
|
config.ckpt_embedding = True
|
|
config.net_se = 0
|
|
config.net_act = 'prelu'
|
|
config.net_unit = 3
|
|
config.net_input = 1
|
|
config.net_blocks = [1, 4, 6, 2]
|
|
config.net_output = 'E'
|
|
config.net_multiplier = 1.0
|
|
config.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
|
|
config.ce_loss = True
|
|
config.fc7_lr_mult = 1.0
|
|
config.fc7_wd_mult = 1.0
|
|
config.fc7_no_bias = False
|
|
config.max_steps = 0
|
|
config.data_rand_mirror = True
|
|
config.data_cutoff = False
|
|
config.data_color = 0
|
|
config.data_images_filter = 0
|
|
config.count_flops = True
|
|
config.memonger = False #not work now
|
|
|
|
config.loss_K = 3
|
|
|
|
config.flip_noise_prob = 0.0
|
|
|
|
# network settings
|
|
network = edict()
|
|
|
|
network.r100 = edict()
|
|
network.r100.net_name = 'fresnet'
|
|
network.r100.num_layers = 100
|
|
|
|
network.r100fc = edict()
|
|
network.r100fc.net_name = 'fresnet'
|
|
network.r100fc.num_layers = 100
|
|
network.r100fc.net_output = 'FC'
|
|
|
|
network.r50 = edict()
|
|
network.r50.net_name = 'fresnet'
|
|
network.r50.num_layers = 50
|
|
|
|
network.r50v1 = edict()
|
|
network.r50v1.net_name = 'fresnet'
|
|
network.r50v1.num_layers = 50
|
|
network.r50v1.net_unit = 1
|
|
|
|
network.d169 = edict()
|
|
network.d169.net_name = 'fdensenet'
|
|
network.d169.num_layers = 169
|
|
network.d169.per_batch_size = 64
|
|
network.d169.densenet_dropout = 0.0
|
|
|
|
network.d201 = edict()
|
|
network.d201.net_name = 'fdensenet'
|
|
network.d201.num_layers = 201
|
|
network.d201.per_batch_size = 64
|
|
network.d201.densenet_dropout = 0.0
|
|
|
|
network.y1 = edict()
|
|
network.y1.net_name = 'fmobilefacenet'
|
|
network.y1.emb_size = 128
|
|
network.y1.net_output = 'GDC'
|
|
|
|
network.y2 = edict()
|
|
network.y2.net_name = 'fmobilefacenet'
|
|
network.y2.emb_size = 256
|
|
network.y2.net_output = 'GDC'
|
|
network.y2.net_blocks = [2, 8, 16, 4]
|
|
|
|
network.m1 = edict()
|
|
network.m1.net_name = 'fmobilenet'
|
|
network.m1.emb_size = 256
|
|
network.m1.net_output = 'GDC'
|
|
network.m1.net_multiplier = 1.0
|
|
|
|
network.m05 = edict()
|
|
network.m05.net_name = 'fmobilenet'
|
|
network.m05.emb_size = 256
|
|
network.m05.net_output = 'GDC'
|
|
network.m05.net_multiplier = 0.5
|
|
|
|
network.mnas = edict()
|
|
network.mnas.net_name = 'fmnasnet'
|
|
network.mnas.emb_size = 256
|
|
network.mnas.net_output = 'GDC'
|
|
network.mnas.net_multiplier = 1.0
|
|
|
|
network.mnas05 = edict()
|
|
network.mnas05.net_name = 'fmnasnet'
|
|
network.mnas05.emb_size = 256
|
|
network.mnas05.net_output = 'GDC'
|
|
network.mnas05.net_multiplier = 0.5
|
|
|
|
network.mnas025 = edict()
|
|
network.mnas025.net_name = 'fmnasnet'
|
|
network.mnas025.emb_size = 256
|
|
network.mnas025.net_output = 'GDC'
|
|
network.mnas025.net_multiplier = 0.25
|
|
|
|
network.vargfacenet = edict()
|
|
network.vargfacenet.net_name = 'vargfacenet'
|
|
network.vargfacenet.net_multiplier = 1.25
|
|
network.vargfacenet.emb_size = 512
|
|
network.vargfacenet.net_output = 'J'
|
|
|
|
# dataset settings
|
|
dataset = edict()
|
|
|
|
dataset.emore = edict()
|
|
dataset.emore.dataset = 'emore'
|
|
dataset.emore.dataset_path = '../datasets/faces_emore'
|
|
dataset.emore.num_classes = 85742
|
|
dataset.emore.image_shape = (112, 112, 3)
|
|
dataset.emore.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
|
|
|
|
dataset.retina = edict()
|
|
dataset.retina.dataset = 'retina'
|
|
dataset.retina.dataset_path = '../datasets/ms1m-retinaface-t1'
|
|
dataset.retina.num_classes = 93431
|
|
dataset.retina.image_shape = (112, 112, 3)
|
|
dataset.retina.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
|
|
|
|
loss = edict()
|
|
loss.softmax = edict()
|
|
loss.softmax.loss_name = 'softmax'
|
|
|
|
loss.nsoftmax = edict()
|
|
loss.nsoftmax.loss_name = 'margin_softmax'
|
|
loss.nsoftmax.loss_s = 64.0
|
|
loss.nsoftmax.loss_m1 = 1.0
|
|
loss.nsoftmax.loss_m2 = 0.0
|
|
loss.nsoftmax.loss_m3 = 0.0
|
|
|
|
loss.arcface = edict()
|
|
loss.arcface.loss_name = 'margin_softmax'
|
|
loss.arcface.loss_s = 64.0
|
|
loss.arcface.loss_m1 = 1.0
|
|
loss.arcface.loss_m2 = 0.5
|
|
loss.arcface.loss_m3 = 0.0
|
|
|
|
loss.cosface = edict()
|
|
loss.cosface.loss_name = 'margin_softmax'
|
|
loss.cosface.loss_s = 64.0
|
|
loss.cosface.loss_m1 = 1.0
|
|
loss.cosface.loss_m2 = 0.0
|
|
loss.cosface.loss_m3 = 0.35
|
|
|
|
loss.combined = edict()
|
|
loss.combined.loss_name = 'margin_softmax'
|
|
loss.combined.loss_s = 64.0
|
|
loss.combined.loss_m1 = 1.0
|
|
loss.combined.loss_m2 = 0.3
|
|
loss.combined.loss_m3 = 0.2
|
|
|
|
loss.triplet = edict()
|
|
loss.triplet.loss_name = 'triplet'
|
|
loss.triplet.images_per_identity = 5
|
|
loss.triplet.triplet_alpha = 0.3
|
|
loss.triplet.triplet_bag_size = 7200
|
|
loss.triplet.triplet_max_ap = 0.0
|
|
loss.triplet.per_batch_size = 60
|
|
loss.triplet.lr = 0.05
|
|
|
|
loss.atriplet = edict()
|
|
loss.atriplet.loss_name = 'atriplet'
|
|
loss.atriplet.images_per_identity = 5
|
|
loss.atriplet.triplet_alpha = 0.35
|
|
loss.atriplet.triplet_bag_size = 7200
|
|
loss.atriplet.triplet_max_ap = 0.0
|
|
loss.atriplet.per_batch_size = 60
|
|
loss.atriplet.lr = 0.05
|
|
|
|
# default settings
|
|
default = edict()
|
|
|
|
# default network
|
|
default.network = 'r100'
|
|
default.pretrained = ''
|
|
default.pretrained_epoch = 1
|
|
# default dataset
|
|
default.dataset = 'emore'
|
|
default.loss = 'arcface'
|
|
default.frequent = 20
|
|
default.verbose = 2000
|
|
default.kvstore = 'device'
|
|
|
|
default.end_epoch = 10000
|
|
default.lr = 0.1
|
|
default.wd = 0.0005
|
|
default.mom = 0.9
|
|
default.per_batch_size = 128
|
|
default.ckpt = 3
|
|
default.lr_steps = '100000,160000,220000'
|
|
default.models_root = './models'
|
|
|
|
|
|
def generate_config(_network, _dataset, _loss):
|
|
for k, v in loss[_loss].items():
|
|
config[k] = v
|
|
if k in default:
|
|
default[k] = v
|
|
for k, v in network[_network].items():
|
|
config[k] = v
|
|
if k in default:
|
|
default[k] = v
|
|
for k, v in dataset[_dataset].items():
|
|
config[k] = v
|
|
if k in default:
|
|
default[k] = v
|
|
config.loss = _loss
|
|
config.network = _network
|
|
config.dataset = _dataset
|
|
config.num_workers = 1
|
|
if 'DMLC_NUM_WORKER' in os.environ:
|
|
config.num_workers = int(os.environ['DMLC_NUM_WORKER'])
|