text stringlengths 1 93.6k |
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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, \
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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='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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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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# print(
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# '[Evaluation - J-level] %s - %s | Top-1: %.4f | Top-5: %.4f | Top-10: %.4f | mAP: %.4f ' % (
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# FLAGS.dataset, FLAGS.probe,
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# top_1, top_5, top_10, mAP))
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# print(
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# '[Evaluation - C-level] %s - %s | Top-1: %.4f | Top-5: %.4f | Top-10: %.4f | mAP: %.4f ' % (
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# FLAGS.dataset, FLAGS.probe,
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# top_1_P, top_5_P, top_10_P, mAP_P,))
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# print(
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# '[Evaluation - L-level] %s - %s | Top-1: %.4f | Top-5: %.4f | Top-10: %.4f | mAP: %.4f ' % (
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# FLAGS.dataset, FLAGS.probe,
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# top_1_B, top_5_B, top_10_B, mAP_B,))
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print(
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'[Evaluation - MSMR] %s - %s | Top-1: %.4f | Top-5: %.4f | Top-10: %.4f | mAP: %.4f ' % (
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FLAGS.dataset, FLAGS.probe,
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top_1_int, top_5_int, top_10_int, mAP_int))
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sess.close()
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exit()
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print('End')
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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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print('Probe: ' + FLAGS.probe)
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# <FILESEP>
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#!/usr/bin/env python
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# coding: utf-8
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# # Imports
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import argparse
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import collections
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import math
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import time
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import numpy as np
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import scipy.io as sio
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from sklearn import metrics, preprocessing
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from sklearn.decomposition import PCA
|
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