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os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu
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dataset = FLAGS.dataset
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# optimal paramters
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if dataset == 'KGBD':
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FLAGS.lr = '0.00035'
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if FLAGS.min_samples == '':
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FLAGS.min_samples = '4'
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elif dataset == 'CASIA_B':
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FLAGS.lr = '0.00035'
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if FLAGS.min_samples == '':
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FLAGS.min_samples = '2'
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if FLAGS.eps == '':
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FLAGS.eps = '0.75'
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else:
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FLAGS.lr = '0.00035'
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if dataset == 'KS20' or dataset == 'IAS':
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if FLAGS.eps == '':
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FLAGS.eps = '0.8'
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elif dataset == 'BIWI':
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if FLAGS.probe == 'Walking':
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if FLAGS.eps == '':
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FLAGS.eps = '0.8'
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T_eps = FLAGS.eps
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T_min_a = FLAGS.min_samples
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if FLAGS.eps == '':
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FLAGS.eps = '0.6'
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if FLAGS.min_samples == '':
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FLAGS.min_samples = '2'
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eps = float(FLAGS.eps)
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min_samples = int(FLAGS.min_samples)
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time_step = int(FLAGS.length)
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probe = FLAGS.probe
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patience = int(FLAGS.patience)
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batch_size = int(FLAGS.batch_size)
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# not used
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global_att = False
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nhood = 1
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residual = False
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nonlinearity = tf.nn.elu
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pre_dir = 'ReID_Models/'
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# Customize the [directory] to save models with different hyper-parameters
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change = '_Hi-MPC_Formal'
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# [directory] = [pre_dir] + [dataset] + '/' + [probe] + [change] + '/' + 'best.ckpt'
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# e.g., ReID_Models/BIWI/Walking_Hi-MPC/best.ckpt
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if FLAGS.probe_type != '':
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change += '_CME'
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try:
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os.mkdir(pre_dir)
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except:
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pass
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if dataset == 'KS20':
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nb_nodes = 25
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if dataset == 'CASIA_B':
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nb_nodes = 14
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if FLAGS.dataset == 'CASIA_B':
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FLAGS.length = '40'
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print('----- Model hyperparams -----')
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print('batch_size: ' + str(batch_size))
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print('M: ' + FLAGS.M)
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print('H: ' + FLAGS.H)
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print('eps: ' + FLAGS.eps)
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print('min_samples: ' + FLAGS.min_samples)
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print('seqence_length: ' + str(time_step))
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print('patience: ' + FLAGS.patience)
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print('Mode: ' + FLAGS.mode)
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if FLAGS.mode == 'Train':
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print('----- Dataset Information -----')
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print('Dataset: ' + dataset)
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if dataset == 'CASIA_B':
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print('Probe.Gallery: ', FLAGS.probe_type.split('.')[0], FLAGS.probe_type.split('.')[1])
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else:
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print('Probe: ' + FLAGS.probe)
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"""
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Codes from our project of SPC-MGR
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We use joint-level (J), component-level (P), and limb-level (B) skeleton data
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"""
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norm = True
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