Semi-supervised-learning/eval.py at main · microsoft/Semi-supervised-learning · 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
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.


import os
import numpy as np
import torch
from torch.utils.data import DataLoader
from semilearn.core.utils import get_net_builder, get_dataset


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()

    parser.add_argument('--load_path', type=str, required=True)

    '''
    Backbone Net Configurations
    '''
    parser.add_argument('--net', type=str, default='wrn_28_2')
    parser.add_argument('--net_from_name', type=bool, default=False)

    '''
    Data Configurations
    '''
    parser.add_argument('--batch_size', type=int, default=128)
    parser.add_argument('--data_dir', type=str, default='./data')
    parser.add_argument('--dataset', type=str, default='cifar10')
    parser.add_argument('--num_classes', type=int, default=10)
    parser.add_argument('--img_size', type=int, default=32)
    parser.add_argument('--crop_ratio', type=int, default=0.875)
    parser.add_argument('--max_length', type=int, default=512)
    parser.add_argument('--max_length_seconds', type=float, default=4.0)
    parser.add_argument('--sample_rate', type=int, default=16000)

    args = parser.parse_args()

    checkpoint_path = os.path.join(args.load_path)
    checkpoint = torch.load(checkpoint_path)
    load_model = checkpoint['ema_model']
    load_state_dict = {}
    for key, item in load_model.items():
        if key.startswith('module'):
            new_key = '.'.join(key.split('.')[1:])
            load_state_dict[new_key] = item
        else:
            load_state_dict[key] = item
    save_dir = '/'.join(checkpoint_path.split('/')[:-1])
    args.save_dir = save_dir
    args.save_name = ''

    net = get_net_builder(args.net, args.net_from_name)(num_classes=args.num_classes)
    keys = net.load_state_dict(load_state_dict)
    if torch.cuda.is_available():
        net.cuda()
    net.eval()

    # specify these arguments manually
    args.num_labels = 40
    args.ulb_num_labels = 49600
    args.lb_imb_ratio = 1
    args.ulb_imb_ratio = 1
    args.seed = 0
    args.epoch = 1
    args.num_train_iter = 1024
    dataset_dict = get_dataset(args, 'fixmatch', args.dataset, args.num_labels, args.num_classes, args.data_dir, False)
    eval_dset = dataset_dict['eval']
    eval_loader = DataLoader(eval_dset, batch_size=args.batch_size, drop_last=False, shuffle=False, num_workers=4)

    acc = 0.0
    test_feats = []
    test_preds = []
    test_probs = []
    test_labels = []
    with torch.no_grad():
        for data in eval_loader:
            image = data['x_lb']
            target = data['y_lb']

            image = image.type(torch.FloatTensor).cuda()
            feat = net(image, only_feat=True)
            logit = net(feat, only_fc=True)
            prob = logit.softmax(dim=-1)
            pred = prob.argmax(1)

            acc += pred.cpu().eq(target).numpy().sum()

            test_feats.append(feat.cpu().numpy())
            test_preds.append(pred.cpu().numpy())
            test_probs.append(prob.cpu().numpy())
            test_labels.append(target.cpu().numpy())
    test_feats = np.concatenate(test_feats)
    test_preds = np.concatenate(test_preds)
    test_probs = np.concatenate(test_probs)
    test_labels = np.concatenate(test_labels)

    print(f"Test Accuracy: {acc/len(eval_dset)}")