pytorch_patternnet/utils.py at master · TNTLFreiburg/pytorch_patternnet · GitHub

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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
import torch
from torch.autograd import Variable
import time
import torchvision
import torchvision.transforms as transforms
import PIL


def load_data_mnist(batchsize):
    # load Cifar10 data set
    # define transform of data files
    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
    )

    # load trainset and define trainloader
    trainset = torchvision.datasets.MNIST(
        root="./data/mnist",
        train=True,
        download=True,
        transform=transform,
    )
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batchsize, shuffle=True, num_workers=2
    )

    # load testset and define testloader
    testset = torchvision.datasets.MNIST(
        root="./data/mnist",
        train=False,
        download=True,
        transform=transform,
    )
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=batchsize, shuffle=False, num_workers=2
    )

    classes = ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")

    return trainloader, testloader, classes


def train(net, num_epochs, trainloader, criterion, optimizer, gpu=False, opt_steps=100):
    if gpu:
        net = net.cuda()
    trainloader = trainloader
    criterion = criterion
    optimizer = optimizer

    start_time = time.time()
    if num_epochs == None:
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            if i <= opt_steps:
                # get the inputs
                inputs, labels = data

                # wrap them in Variable
                if gpu:
                    inputs, labels = Variable(inputs.cuda()), Variable(
                        labels.cuda()
                    )
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward + backward + optimize
                # net(inputs) calls forward from class Net
                outputs = net(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()

                # print statistics
                running_loss += loss.item()
                if i % 500 == 499:  # print every 2000 mini-batches
                    print(
                        "[%d, %5d] loss: %.3f"
                        % (epoch + 1, i + 1, running_loss / 500)
                    )
                    running_loss = 0.0
            else:
                break
    else:
        for epoch in range(num_epochs):  # loop over the dataset multiple times

            running_loss = 0.0
            for i, data in enumerate(trainloader, 0):
                # get the inputs
                inputs, labels = data

                # wrap them in Variable
                if gpu:
                    inputs, labels = Variable(inputs.cuda()), Variable(
                        labels.cuda()
                    )
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward + backward + optimize
                # net(inputs) calls forward from class Net
                outputs = net(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()

                # print statistics
                running_loss += loss.item()
                if i % 500 == 499:  # print every 2000 mini-batches
                    print(
                        "[%d, %5d] loss: %.3f"
                        % (epoch + 1, i + 1, running_loss / 500)
                    )
                    running_loss = 0.0

    end_time = time.time()
    time_needed = int(end_time - start_time)
    print("Finished Training in %i seconds" % time_needed)


def accuracy(net, iterator, num_steps=None, device=torch.device("cpu")):

    total = 0.
    correct = 0.
    if num_steps is None:
        for data in iterator:
            inp, labels = data
            inp, labels = inp.to(device), labels.to(device)
            out = net(Variable(inp))
            _, predictions = torch.max(out.data, 1)
            total += labels.size(0)
            correct += (predictions == labels).sum()
    else:
        for _ in range(num_steps):
            inp, labels = next(iterator.__iter__())
            inp, labels = inp.to(device), labels.to(device)
            out = net(Variable(inp))
            _, predictions = torch.max(out.data, 1)
            total += labels.size(0)
            correct += (predictions == labels).sum()

    print("Accuracy: %f" % (int(correct) / int(total)))

    return (int(correct) / int(total))