text stringlengths 1 93.6k |
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cur_patience = 0
|
MI_1s = []
|
MI_2s = []
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AMI_1s = []
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AMI_2s = []
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top_1s = []
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top_5s = []
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top_10s = []
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mAPs = []
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Hi_MPC_losses = []
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mACT = []
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mRCL = []
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if dataset == 'KGBD' or dataset == 'KS20':
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if FLAGS.dataset == 'KS20':
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nb_nodes = 25
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X_train_J, X_train_P, X_train_B, _, _, y_train, X_gal_J, X_gal_P, X_gal_B, _, _, y_gal, \
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adj_J, biases_J, _, _, _, _, _, _, _, _, nb_classes, X_train_J_D, X_gal_J_D = \
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process.gen_train_data(dataset=dataset, split='gallery', time_step=time_step,
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nb_nodes=nb_nodes, nhood=nhood, global_att=global_att, batch_size=batch_size,
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norm=norm
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)
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nb_nodes = 20
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elif dataset == 'BIWI':
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if probe == 'Walking':
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X_train_J, X_train_P, X_train_B, _, _, y_train, X_gal_J, X_gal_P, X_gal_B, _, _, y_gal, \
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adj_J, biases_J, _, _, _, _, _, _, _, _, nb_classes, X_train_J_D, X_gal_J_D = \
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process.gen_train_data(dataset=dataset, split='Still', time_step=time_step,
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nb_nodes=nb_nodes, nhood=nhood, global_att=global_att,
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batch_size=batch_size, norm=norm
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)
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else:
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X_train_J, X_train_P, X_train_B, _, _, y_train, X_gal_J, X_gal_P, X_gal_B, _, _, y_gal, \
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adj_J, biases_J, _, _, _, _, _, _, _, _, nb_classes, X_train_J_D, X_gal_J_D = \
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process.gen_train_data(dataset=dataset, split='Walking', time_step=time_step,
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nb_nodes=nb_nodes, nhood=nhood, global_att=global_att,
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batch_size=batch_size, norm=norm
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)
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elif dataset == 'IAS':
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if probe == 'A':
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X_train_J, X_train_P, X_train_B, _, _, y_train, X_gal_J, X_gal_P, X_gal_B, _, _, y_gal, \
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adj_J, biases_J, _, _, _, _, _, _, _, _, nb_classes, X_train_J_D, X_gal_J_D = \
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process.gen_train_data(dataset=dataset, split='B', time_step=time_step,
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nb_nodes=nb_nodes, nhood=nhood, global_att=global_att,
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batch_size=batch_size, norm=norm
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)
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else:
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X_train_J, X_train_P, X_train_B, _, _, y_train, X_gal_J, X_gal_P, X_gal_B, _, _, y_gal, \
|
adj_J, biases_J, _, _, _, _, _, _, _, _, nb_classes, X_train_J_D, X_gal_J_D = \
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process.gen_train_data(dataset=dataset, split='A', time_step=time_step,
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nb_nodes=nb_nodes, nhood=nhood, global_att=global_att,
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batch_size=batch_size, norm=norm
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)
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elif dataset == 'CASIA_B':
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X_train_J, X_train_P, X_train_B, _, _, y_train, X_gal_J, X_gal_P, X_gal_B, _, _, y_gal, \
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adj_J, biases_J, _, _, _, _, _, _, _, _, nb_classes = \
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process.gen_train_data(dataset=dataset, split=probe, time_step=time_step,
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nb_nodes=nb_nodes, nhood=nhood, global_att=global_att, batch_size=batch_size,
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PG_type=FLAGS.probe_type.split('.')[1])
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del _
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gc.collect()
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for epoch in range(train_epochs):
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train_features_all_int, train_features_all, train_features_all_P, train_features_all_B, train_labels_all = train_loader(
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X_train_J, X_train_P, X_train_B, y_train)
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gal_features_all_int, gal_features_all, gal_features_all_P, gal_features_all_B, gal_labels_all = gal_loader(
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X_gal_J, X_gal_P, X_gal_B, y_gal)
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mAP_int, top_1_int, top_5_int, top_10_int, mAP, top_1, top_5, top_10, mAP_P, top_1_P, top_5_P, top_10_P, \
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mAP_B, top_1_B, top_5_B, top_10_B, = evaluation()
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cur_patience += 1
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if epoch > 0 and top_1 > max_acc_2:
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max_acc_1 = mAP
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max_acc_2 = top_1
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top_5_max = top_5
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top_10_max = top_10
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if epoch > 0 and top_1_int > max_acc_2_int:
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max_acc_1_int = mAP_int
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max_acc_2_int = top_1_int
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top_5_max_int = top_5_int
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top_10_max_int = top_10_int
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if FLAGS.mode == 'Train':
|
if FLAGS.dataset != 'CASIA_B':
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checkpt_file = pre_dir + dataset + '/' + probe + change + '/' + 'best.ckpt'
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elif FLAGS.dataset == 'CASIA_B':
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checkpt_file = pre_dir + dataset + '/' + probe + change + '/' + FLAGS.probe_type + '_best.ckpt'
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print(checkpt_file)
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if FLAGS.save_model == '1':
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saver.save(sess, checkpt_file)
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cur_patience = 0
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if epoch > 0 and top_1_P > max_acc_2_P:
|
max_acc_1_P = mAP_P
|
max_acc_2_P = top_1_P
|
top_5_max_P = top_5_P
|
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