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99 lines
2.4 KiB
Python
99 lines
2.4 KiB
Python
import numpy as np
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from easydict import EasyDict as edict
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config = edict()
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#default training/dataset config
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config.num_classes = 68
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config.record_img_size = 384
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config.base_scale = 256
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config.input_img_size = 128
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config.output_label_size = 64
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config.label_xfirst = False
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config.losstype = 'heatmap'
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config.net_coherent = False
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config.multiplier = 1.0
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config.gaussian = 0
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# network settings
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network = edict()
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network.hourglass = edict()
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network.hourglass.net_coherent = False
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network.hourglass.net_sta = 0
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network.hourglass.net_n = 3
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network.hourglass.net_dcn = 0
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network.hourglass.net_stacks = 2
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network.hourglass.net_block = 'resnet'
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network.hourglass.net_binarize = False
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network.hourglass.losstype = 'heatmap'
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network.sdu = edict()
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network.sdu.net_coherent = False
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network.sdu.net_sta = 1
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network.sdu.net_n = 3
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network.sdu.net_dcn = 3
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network.sdu.net_stacks = 2
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network.sdu.net_block = 'cab'
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network.sdu.net_binarize = False
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network.sdu.losstype = 'heatmap'
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# dataset settings
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dataset = edict()
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dataset.i2d = edict()
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dataset.i2d.dataset = '2D'
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dataset.i2d.landmark_type = '2d'
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dataset.i2d.dataset_path = './data_2d'
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dataset.i2d.num_classes = 68
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dataset.i2d.record_img_size = 384
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dataset.i2d.base_scale = 256
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dataset.i2d.input_img_size = 128
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dataset.i2d.output_label_size = 64
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dataset.i2d.label_xfirst = False
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dataset.i2d.val_targets = ['ibug', 'cofw_testset', '300W']
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dataset.i3d = edict()
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dataset.i3d.dataset = '3D'
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dataset.i3d.landmark_type = '3d'
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dataset.i3d.dataset_path = './data_3d'
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dataset.i3d.num_classes = 68
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dataset.i3d.record_img_size = 384
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dataset.i3d.base_scale = 256
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dataset.i3d.input_img_size = 128
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dataset.i3d.output_label_size = 64
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dataset.i3d.label_xfirst = False
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dataset.i3d.val_targets = ['AFLW2000-3D']
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# default settings
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default = edict()
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# default network
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default.network = 'hourglass'
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default.pretrained = ''
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default.pretrained_epoch = 0
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# default dataset
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default.dataset = 'i2d'
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default.frequent = 20
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default.verbose = 200
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default.kvstore = 'device'
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default.prefix = 'model/A'
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default.end_epoch = 10000
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default.lr = 0.00025
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default.wd = 0.0
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default.per_batch_size = 20
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default.lr_step = '16000,24000,30000'
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def generate_config(_network, _dataset):
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for k, v in network[_network].items():
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config[k] = v
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default[k] = v
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for k, v in dataset[_dataset].items():
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config[k] = v
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default[k] = v
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config.network = _network
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config.dataset = _dataset
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