-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmainDGER.py
102 lines (88 loc) · 5.18 KB
/
mainDGER.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
import warnings
def warn(*args, **kwargs): pass
warnings.warn = warn
import torch, random, numpy as np, os, pandas as pd
os.system('nvidia-smi -q -d Memory |grep -A6 GPU|grep Free >tmp')
memory_available = [int(x.split()[2]) for x in open('tmp', 'r').readlines()]
gpu_number = int(np.argmax(memory_available))
torch.cuda.set_device(gpu_number)
print(f"Cuda:{gpu_number}")
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(DEVICE)
#################DATASET#####################
dataset_type = "DailySports" #RotatedMNIST, FEMNIST, CIFAR10C, DailySports, WISDM
import sys, os
sys.path.insert(1, "./" + dataset_type)
import data, params, models
from trainingDGER import DGER, evaluate_dger
from utils import write_in_file
# For reproducibility
torch.random.manual_seed(params.seed)
random.seed(params.seed)
np.random.seed(params.seed)
final_mean_acc = []
for it in range(3):
PATH = "./" + dataset_type + "/Results" + str(it+1) + "/"
if not os.path.exists(PATH): os.makedirs(PATH)
print(f"DATASET {dataset_type}")
if dataset_type == "RotatedMNIST":
domains_train_idxs = random.sample(range(params.nb_domains_tot), params.nb_domains_train)
domains_test_idxs = list(set(range(params.nb_domains_tot)) - set(domains_train_idxs))
#domains_train_idxs = pd.read_pickle(PATH+"domains_train")
#domains_test_idxs = pd.read_pickle(PATH+"domains_test")
rotatedMnist = data.RotatedMNIST(print_output=False)
domains_train, domains_test = data.domain_generator(rotatedMnist, domains_train_idxs, domains_test_idxs)
dataset_train = data.RotatedMNIST(domains_idxs = domains_train_idxs, split='train', print_output=False)
dataset_test = data.RotatedMNIST(domains_idxs = domains_test_idxs, split='test', print_output=False)
#write_in_file(domains_train_idxs, PATH+"domains_train")
#write_in_file(domains_test_idxs, PATH+"domains_test")
elif dataset_type == "FEMNIST":
dataset_train = data.FEMNISTDataset(split="train")
domains_train = data.domain_generator(dataset_train, split="train")
dataset_test = data.FEMNISTDataset(split="test")
domains_test = data.domain_generator(dataset_test)
elif dataset_type == "CIFAR10C":
dataset_train = data.CIFARDataset(split='train')
domains_train = data.domain_generator(dataset_train)
dataset_test = data.CIFARDataset(split='test')
domains_test = data.domain_generator(dataset_test, test='True')
elif dataset_type == "DailySports":
root_dir = "DailySports/"
domains_train_idxs = random.sample(range(params.nb_domains_tot), params.nb_domains_train)
domains_test_idxs = list(set(range(params.nb_domains_tot)) - set(domains_train_idxs))
#domains_train_idxs = pd.read_pickle(PATH+"domains_train")
#domains_test_idxs = pd.read_pickle(PATH+"domains_test")
HAR_Dataset = data.HARDataset(root_dir, feature_reduction=params.feature_reduction, extract_features=params.extract_features)
domains_train, domains_test = data.domain_generator(HAR_Dataset, domains_train_idxs, domains_test_idxs)
dataset_train = data.HARDataset(root_dir, domains_train_idxs, feature_reduction=params.feature_reduction, extract_features=params.extract_features)
dataset_test = data.HARDataset(root_dir, domains_test_idxs, feature_reduction=params.feature_reduction, extract_features=params.extract_features)
elif dataset_type == "WISDM":
root_dir = "WISDM/"
domains_total = list(range(params.nb_domains_tot))
#Remove subjects with corrupted data
domains_total.remove(14)
domains_total.remove(18)
domains_total.remove(42)
domains_train_idxs = random.sample(domains_total, params.nb_domains_train)
domains_test_idxs = list(set(domains_total)-set(domains_train_idxs))
WISDM_Dataset = data.WISDMDataset(root_dir, domains_total, datatype = params.data_type, extract_features=params.extract_features)
domains_train, domains_test = data.domain_generator(WISDM_Dataset, domains_train_idxs, domains_test_idxs)
dataset_train = data.WISDMDataset(root_dir, domains_train_idxs, datatype=params.data_type, extract_features=params.extract_features)
dataset_test = data.WISDMDataset(root_dir, domains_test_idxs, datatype=params.data_type, extract_features=params.extract_features)
############### MODEL ########################
X_size = len(domains_train[0][0]) + len(domains_train[0][2])
if dataset_type=="CIFAR10C" or dataset_type=="FEMNIST":
n_domains = min(len(domains_train), 8)
else:
n_domains = len(domains_train)
dger_dger = DGER(n_domains=n_domains, X_size=X_size).fit(domains_train, steps=params.training_steps)
#torch.save(dger_dger, PATH + "model_dger.pkl")
############### EVALUATE ########################
test_accuracies = evaluate_dger(dger_dger.main_model, domains_test)
print(f"Mean accuracy: {np.mean(test_accuracies)}")
print(f"Std accuracy: {np.std(test_accuracies)}")
write_in_file(test_accuracies, PATH+"perf_DGER")
final_mean_acc.append(np.mean(test_accuracies))
print("FINAL RESULTS")
print(f"Mean accuracy: {np.mean(final_mean_acc)}")
print(f"Std accuracy: {np.std(final_mean_acc)}")