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eval_mobilenet_torch.py
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eval_mobilenet_torch.py
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'''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import os
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import argparse
from torch.autograd import Variable
from models import mobilenet_v1, mobilenet_v2
from utils import measure_model, AverageMeter, progress_bar, accuracy, process_state_dict
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--model', default='mobilenet_0.5flops', type=str, help='name of the model to test')
parser.add_argument('--imagenet_path', default=None, type=str, help='Directory of ImageNet')
parser.add_argument('--n_gpu', default=1, type=int, help='name of the job')
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--n_worker', default=32, type=int, help='number of data loader worker')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
def get_dataset():
# lazy import
import torchvision.datasets as datasets
import torchvision.transforms as transforms
if not args.imagenet_path:
raise Exception('Please provide valid ImageNet path!')
print('=> Preparing data..')
valdir = os.path.join(args.imagenet_path, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
input_size = 224
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(int(input_size / 0.875)),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.n_worker, pin_memory=True)
n_class = 1000
return val_loader, n_class
def get_model(n_class):
print('=> Building model {}...'.format(args.model))
if args.model == 'mobilenet_0.5flops':
net = mobilenet_v1.MobileNet(n_class, profile='0.5flops')
checkpoint_path = './checkpoints/torch/mobilenet_imagenet_0.5flops_70.5.pth.tar'
elif args.model == 'mobilenet_0.5time':
net = mobilenet_v1.MobileNet(n_class, profile='0.5time')
checkpoint_path = './checkpoints/torch/mobilenet_imagenet_0.5time_70.2.pth.tar'
elif args.model == 'mobilenetv2':
net = mobilenet_v2.MobileNetV2(n_class, profile='normal')
checkpoint_path = './checkpoints/torch/mobilenetv2_imagenet_71.814.pth.tar'
elif args.model == 'mobilenetv2_0.7flops':
net = mobilenet_v2.MobileNetV2(n_class, profile='0.7flops')
checkpoint_path = './checkpoints/torch/mobilenetv2_imagenet_0.7amc_70.854.pth.tar'
else:
raise NotImplementedError
print('=> Loading checkpoints..')
checkpoint = torch.load(checkpoint_path)
if 'state_dict' in checkpoint:
checkpoint = checkpoint['state_dict'] # get state_dict
net.load_state_dict(process_state_dict(checkpoint)) # remove .module
return net
def evaluate():
# build dataset
val_loader, n_class = get_dataset()
# build model
net = get_model(n_class) # for measure
n_flops, n_params = measure_model(net, 224, 224)
print('=> Model Parameter: {:.3f} M, FLOPs: {:.3f}M'.format(n_params / 1e6, n_flops / 1e6))
del net
net = get_model(n_class)
criterion = nn.CrossEntropyLoss()
if use_cuda:
net = net.cuda()
net = torch.nn.DataParallel(net, list(range(args.n_gpu)))
cudnn.benchmark = True
# begin eval
net.eval()
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(val_loader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
# timing
batch_time.update(time.time() - end)
end = time.time()
progress_bar(batch_idx, len(val_loader), 'Loss: {:.3f} | Acc1: {:.3f}% | Acc5: {:.3f}%'
.format(losses.avg, top1.avg, top5.avg))
if __name__ == '__main__':
evaluate()