mirror of
https://github.com/deepinsight/insightface.git
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130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
from easydict import EasyDict as edict
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config = edict()
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# loss
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config.embedding_size = 512
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config.bn_mom = 0.9
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config.workspace = 256
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config.net_se = 0
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config.net_act = 'prelu'
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config.net_unit = 3
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config.net_input = 1
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config.net_output = 'FC'
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config.frequent = 20
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config.verbose = 2000
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config.image_size = 112
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config.memonger = False
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config.debug = 0
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config.fp16 = False
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config.batch_size = 64
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config.backbone_lr = 0.1
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config.memory_bank_lr = config.backbone_lr
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config.sample_ratio = 1.0
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def generate_config(loss_name, dataset, network):
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# loss
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if loss_name == 'arcface':
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config.loss_s = 64.0
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config.loss_m1 = 1.0
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config.loss_m2 = 0.5
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config.loss_m3 = 0.0
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elif loss_name == 'cosface':
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config.loss_s = 64.0
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config.loss_m1 = 1.0
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config.loss_m2 = 0.0
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config.loss_m3 = 0.4
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# dataset
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if dataset == 'webface':
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config.lr_steps = '20000,28000'
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config.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
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config.rec = '/anxiang/datasets/webface/train.rec'
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config.rec = '/train_tmp/webface/train.rec'
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config.num_classes = 10575
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config.max_update = 32000
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# glint360k 17091657
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# md5sum:
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# 5d9cd9f262ec87a5ca2eac5e703f7cdf train.idx
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# 8483be5af6f9906e19f85dee49132f8e train.rec
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# make training faster
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# our RAM is 256G
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# mount -t tmpfs -o size=140G tmpfs /train_tmp
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elif dataset == 'glint360k_8GPU':
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config.lr_steps = '200000,400000,500000,550000'
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config.val_targets = [
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'agedb_30', 'calfw', 'cfp_ff', 'cfp_fp', 'cplfw', 'lfw', 'vgg2_fp'
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]
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config.rec = '/train_tmp/glint360k/train.rec'
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config.num_classes = 360232
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config.batch_size = 64
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config.max_update = 600000
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elif dataset == 'glint360k_16GPU':
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config.lr_steps = '200000,280000,360000'
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config.val_targets = ['agedb_30', 'cfp_fp', 'lfw']
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config.rec = '/train_tmp/glint360k/train.rec'
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config.num_classes = 360232
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config.max_update = 400000
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elif dataset == 'emore':
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config.lr_steps = '100000,160000'
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config.val_targets = ['agedb_30', 'cfp_fp', 'lfw']
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config.rec = '/anxiang/datasets/faces_emore/train.rec'
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config.rec = '/train_tmp/faces_emore/train.rec'
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config.num_classes = 85742
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config.batch_size = 64
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config.max_update = 180000
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elif dataset == '100w':
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config.debug = 1
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config.num_classes = 100 * 10000
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config.lr_steps = '20000,28000'
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config.max_update = 32000
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elif dataset == '1000w':
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config.debug = 1
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config.num_classes = 1000 * 10000
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config.lr_steps = '20000,28000'
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config.max_update = 32000
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elif dataset == '2000w':
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config.debug = 1
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config.num_classes = 2000 * 10000
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config.lr_steps = '20000,28000'
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config.max_update = 32000
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elif dataset == '3000w':
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config.debug = 1
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config.num_classes = 3000 * 10000
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config.lr_steps = '20000,28000'
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config.max_update = 32000
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elif dataset == '10000w':
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config.debug = 1
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config.num_classes = 10000 * 10000
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config.lr_steps = '20000,28000'
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config.max_update = 32000
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# network
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if network == 'r100':
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config.net_name = 'resnet'
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config.num_layers = 100
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elif network == 'r122':
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config.net_name = 'resnet'
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config.num_layers = 122
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elif network == 'r50':
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config.net_name = 'resnet'
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config.num_layers = 50
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elif network == 'rx101':
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config.net_name = 'fresnext'
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config.num_layers = 101
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elif network == 'rx50':
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config.net_name = 'fresnext'
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config.num_layers = 50
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