-
Notifications
You must be signed in to change notification settings - Fork 2
/
test.py
130 lines (107 loc) · 4.52 KB
/
test.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
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
"""
Testing code.
Written by Matej Ulicny.
Based on PyTorch ImageNet example training script:
https://github.com/pytorch/examples/tree/master/imagenet
"""
import argparse
import os
import time
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from resnet import resnet50
from mobilenet import mobilenet_v2
import numpy as np
def validate(val_loader, model):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
input = input.cuda()
target = target.cuda()
# compute output
output = model(input)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % 10 == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time,
top1=top1, top5=top5))
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg, top5.avg
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
model_names = ['resnet50', 'mobilenet_v2']
modes = ['uniform', 'progressive']
parser = argparse.ArgumentParser(description='Testing script')
parser.add_argument('data', type=str, metavar='PATH', help='path to ImageNet')
parser.add_argument('checkpoint', type=str, metavar='PATH', help='name of the model file')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', choices=model_names, help='model architecture: '+' | '.join(model_names)+' (default: resnet50)')
parser.add_argument('-g', '--groups', default=4, type=int)
parser.add_argument('-r', '--compression-rate', default=2.0, type=float)
parser.add_argument('--progressive', action='store_true', help='compression mode: '+' | '.join(modes)+' (default: uniform)')
args = parser.parse_args()
model = resnet50(pretrained=False, g=args.groups, r=args.compression_rate, progressive=args.progressive) if args.arch == 'resnet50' else \
mobilenet_v2(pretrained=False, g=args.groups, r=args.compression_rate, progressive=args.progressive)
checkpoint = torch.load(args.checkpoint)
checkpoint = checkpoint['state_dict'] if 'state_dict' in checkpoint.keys() else checkpoint
state_dict = model.state_dict()
loaded_dict = {k: v for k, v in checkpoint.items() if k in state_dict}
if not bool(loaded_dict): # empty dictionary if model was trained in parallel
loaded_dict = {k[7:]: v for k, v in checkpoint.items() if k[7:] in state_dict}
state_dict.update(loaded_dict)
model.load_state_dict(state_dict)
model.cuda()
model = nn.DataParallel(model)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(os.path.join(args.data, "val"), transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])),
batch_size=256, shuffle=False,
num_workers=4, pin_memory=True)
top1, top5 = validate(val_loader, model)