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evaluate.py
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evaluate.py
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from __future__ import print_function
import argparse
import os
import shutil
import time
import json
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import pyvision.dataloaders as dataloaders
import pyvision.models as models
import pyvision.optim as optim
parser = argparse.ArgumentParser(description='PyTorch Classifier Training')
parser.add_argument('--data', dest='data_config', required=True, metavar='DATA_CONFIG', help='Dataset config file')
parser.add_argument('--model', dest='model_config', required=True, metavar='MODEL_CONFIG', help='Model config file')
parser.add_argument('--checkpoint', dest='checkpoint', required=True, metavar='CHECKPOINT_FILE', help='Checkpoint file')
parser.add_argument('--print-freq', dest='print_freq', default=10, type=int, metavar='N', help='Print frequency (default: 10)')
best_prec1 = 0
last_epoch = -1
def main():
global args, best_prec1, last_epoch
args = parser.parse_args()
with open(args.data_config, 'r') as json_file:
data_config = json.load(json_file)
with open(args.model_config, 'r') as json_file:
model_config = json.load(json_file)
if not os.path.exists(args.checkpoint):
raise RuntimeError('checkpoint `{}` does not exist.'.format(args.checkpoint))
# create model
print('==> Creating model `{}`...'.format(model_config['name']))
model = models.get_model(data_config['name'], model_config)
checkpoint = torch.load(args.checkpoint)
print('==> Checkpoint name is `{}`.'.format(checkpoint['name']))
model.load_state_dict(checkpoint['state_dict'])
model = torch.nn.DataParallel(model).cuda()
print('==> Creating model completed.')
print('==> Creating dataloaders...')
train_loader, valid_loader = dataloaders.get_dataloader(data_config)
print('==> Creating dataloaders completed.')
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss().cuda()
validate(valid_loader, model, criterion)
def validate(val_loader, model, criterion, epoch=0):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i + 1, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print('[Valid Summary]\t'
'Epoch: [{0}]\t'
'Loss {loss.avg: 3f}\t'
'Prec@1 {top1.avg:.3f}\t'
'Prec@5 {top5.avg:.3f}'.format(
epoch, loss=losses, top1=top1, top5=top5))
return top1.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 precision@k for the specified values of k"""
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
if __name__ == '__main__':
main()