<reponame>deepsphere/deepsphere_v2_code
#!/usr/bin/env python3
# coding: utf-8

import os
import shutil
import sys
sys.path.append('../..')

os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # change to chosen GPU to use, nothing if work on CPU

import numpy as np

from deepsphere import models
from ClimateDataLoader import EquiangularDataset
from pygsp.graphs import SphereEquiangular

from sklearn.metrics import accuracy_score, average_precision_score
from sklearn.preprocessing import label_binarize

def accuracy(pred_cls, true_cls, nclass=3):
    accu = []
    tot_int = 0
    tot_cl = 0
    for i in range(3):
        intersect = np.sum(((pred_cls == i) * (true_cls == i)))
        thiscls = np.sum(true_cls == i)
        accu.append(intersect / thiscls * 100)
        tot_int += intersect
        tot_cl += thiscls
    return np.array(accu), np.mean(accu) # , tot_int/tot_cl * 100

def average_precision(score_cls, true_cls, nclass=3):
    score = score_cls
    true = label_binarize(true_cls.reshape(-1), classes=[0, 1, 2])
    score = score.reshape(-1, nclass)
    return average_precision_score(true, score, None)

if __name__ == '__main__':
    sampling = 'equiangular'
    filepath = 'results_equiangular'
    
    restore = True
    
    path = '../../../Climate/'
    g = SphereEquiangular(bandwidth=(384, 576), sampling='SOFT')
    glong, glat = np.rad2deg(g.lon), np.rad2deg(g.lat)
    del g
    
    training = EquiangularDataset(path, 'train', s3=False)
    validation = EquiangularDataset(path, 'val', s3=False)
    test = EquiangularDataset(path, 'test', s3=False)
    
    EXP_NAME = 'Climate_pooling_{}_6layers_k4_initial'.format(sampling)
    print(EXP_NAME)

    # Cleanup before running again.
    if not restore:
        shutil.rmtree('../../summaries/{}/'.format(EXP_NAME), ignore_errors=True)
        shutil.rmtree('../../checkpoints/{}/'.format(EXP_NAME), ignore_errors=True)
    
    import tensorflow as tf
    
    params = {'nsides': [(384, 576), (384//8, 576//8), (384//16, 576//16), (384//32, 576//32),
                         (384//64, 576//64),(384//64, 576//64), (384//64, 576//64)],
              'F': [16, 32, 64, 128, 256, 512],
              'K': [4]*6,
              'batch_norm': [True]*6}
    params['sampling'] = sampling
    params['dir_name'] = EXP_NAME
    params['num_feat_in'] = 16
    params['conv'] = 'chebyshev5'
    params['pool'] = 'average'
    params['activation'] = 'relu'
    params['statistics'] = None # 'mean'
    params['regularization'] = 0
    params['dropout'] = 1
    params['num_epochs'] = 15  # Number of passes through the training data.
    params['batch_size'] = 1
    params['scheduler'] = lambda step: tf.train.exponential_decay(1e-3, step, decay_steps=2000, decay_rate=0.99)
    params['optimizer'] = lambda lr: tf.train.AdamOptimizer(lr, beta1=0.9, beta2=0.999, epsilon=1e-8)
    n_evaluations = 90
    params['eval_frequency'] = int(params['num_epochs'] * (training.N) / params['batch_size'] / n_evaluations)
    params['M'] = []
    params['Fseg'] = 3
    params['dense'] = True
    params['weighted'] = False
    params['dtype'] = tf.float32
    params['restore'] = True
    params['tf_dataset'] = training.get_tf_dataset(params['batch_size'], dtype=np.float32)
    
    model = models.deepsphere(**params)
    
    acc_val, loss_val, loss_train, t_step, t_batch = model.fit(training, validation, 
                                                               use_tf_dataset=True, cache='TF', restore=restore)
    
    probabilities, _, _ = model.probs(test, 3, cache='TF')
    predictions, labels_test, loss = model.predict(test, cache='TF')
    
    AP = average_precision(probabilities, labels_test)
    mAP = np.mean(AP[1:])
    acc, macc = accuracy(predictions, labels_test)
    
    if os.path.isfile(filepath+'.npz'):
        file = np.load(filepath+'.npz')
        tb = file['tbatch'].tolist()
        avprec = file['AP'].tolist()
        accuracy = file['acc'].tolist()
    else:
        tb = []
        avprec = []
        accuracy = []
    tb.append(t_batch)
    avprec.append([*AP, mAP])
    accuracy.append([*acc, macc])
    np.savez(filepath, AP=avprec, acc=accuracy, tbatch=tb)

#!/usr/bin/env python3
# coding: utf-8

import os
import shutil
import sys
sys.path.append('../..')

os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # change to chosen GPU to use, nothing if work on CPU

import numpy as np

from deepsphere import models
from ClimateDataLoader import EquiangularDataset
from pygsp.graphs import SphereEquiangular

from sklearn.metrics import accuracy_score, average_precision_score
from sklearn.preprocessing import label_binarize

def accuracy(pred_cls, true_cls, nclass=3):
    accu = []
    tot_int = 0
    tot_cl = 0
    for i in range(3):
        intersect = np.sum(((pred_cls == i) * (true_cls == i)))
        thiscls = np.sum(true_cls == i)
        accu.append(intersect / thiscls * 100)
        tot_int += intersect
        tot_cl += thiscls
    return np.array(accu), np.mean(accu) # , tot_int/tot_cl * 100

def average_precision(score_cls, true_cls, nclass=3):
    score = score_cls
    true = label_binarize(true_cls.reshape(-1), classes=[0, 1, 2])
    score = score.reshape(-1, nclass)
    return average_precision_score(true, score, None)

if __name__ == '__main__':
    sampling = 'equiangular'
    filepath = 'results_equiangular'
    
    restore = True
    
    path = '../../../Climate/'
    g = SphereEquiangular(bandwidth=(384, 576), sampling='SOFT')
    glong, glat = np.rad2deg(g.lon), np.rad2deg(g.lat)
    del g
    
    training = EquiangularDataset(path, 'train', s3=False)
    validation = EquiangularDataset(path, 'val', s3=False)
    test = EquiangularDataset(path, 'test', s3=False)
    
    EXP_NAME = 'Climate_pooling_{}_6layers_k4_initial'.format(sampling)
    print(EXP_NAME)

    # Cleanup before running again.
    if not restore:
        shutil.rmtree('../../summaries/{}/'.format(EXP_NAME), ignore_errors=True)
        shutil.rmtree('../../checkpoints/{}/'.format(EXP_NAME), ignore_errors=True)
    
    import tensorflow as tf
    
    params = {'nsides': [(384, 576), (384//8, 576//8), (384//16, 576//16), (384//32, 576//32),
                         (384//64, 576//64),(384//64, 576//64), (384//64, 576//64)],
              'F': [16, 32, 64, 128, 256, 512],
              'K': [4]*6,
              'batch_norm': [True]*6}
    params['sampling'] = sampling
    params['dir_name'] = EXP_NAME
    params['num_feat_in'] = 16
    params['conv'] = 'chebyshev5'
    params['pool'] = 'average'
    params['activation'] = 'relu'
    params['statistics'] = None # 'mean'
    params['regularization'] = 0
    params['dropout'] = 1
    params['num_epochs'] = 15  # Number of passes through the training data.
    params['batch_size'] = 1
    params['scheduler'] = lambda step: tf.train.exponential_decay(1e-3, step, decay_steps=2000, decay_rate=0.99)
    params['optimizer'] = lambda lr: tf.train.AdamOptimizer(lr, beta1=0.9, beta2=0.999, epsilon=1e-8)
    n_evaluations = 90
    params['eval_frequency'] = int(params['num_epochs'] * (training.N) / params['batch_size'] / n_evaluations)
    params['M'] = []
    params['Fseg'] = 3
    params['dense'] = True
    params['weighted'] = False
    params['dtype'] = tf.float32
    params['restore'] = True
    params['tf_dataset'] = training.get_tf_dataset(params['batch_size'], dtype=np.float32)
    
    model = models.deepsphere(**params)
    
    acc_val, loss_val, loss_train, t_step, t_batch = model.fit(training, validation, 
                                                               use_tf_dataset=True, cache='TF', restore=restore)
    
    probabilities, _, _ = model.probs(test, 3, cache='TF')
    predictions, labels_test, loss = model.predict(test, cache='TF')
    
    AP = average_precision(probabilities, labels_test)
    mAP = np.mean(AP[1:])
    acc, macc = accuracy(predictions, labels_test)
    
    if os.path.isfile(filepath+'.npz'):
        file = np.load(filepath+'.npz')
        tb = file['tbatch'].tolist()
        avprec = file['AP'].tolist()
        accuracy = file['acc'].tolist()
    else:
        tb = []
        avprec = []
        accuracy = []
    tb.append(t_batch)
    avprec.append([*AP, mAP])
    accuracy.append([*acc, macc])
    np.savez(filepath, AP=avprec, acc=accuracy, tbatch=tb)
#!/usr/bin/env python3 # coding: utf-8 # change to chosen GPU to use, nothing if work on CPU # , tot_int/tot_cl * 100 # Cleanup before running again. # 'mean' # Number of passes through the training data.