import os.path as osp

import numpy as np

import argparse

import torch.utils.data

from torchvision import transforms, datasets

from proxyless_nas.utils import AverageMeter

from proxyless_nas_tensorflow import tf_model_zoo

model_names = sorted(name for name in tf_model_zoo.__dict__

if name.islower() and not name.startswith("__")

and callable(tf_model_zoo.__dict__[name]))

parser = argparse.ArgumentParser()

parser.add_argument("-p", '--path', help='The path of imagenet', type=str, default="/ssd/dataset/imagenet")

parser.add_argument("-b", "--batch-size", help="The batch on every device for validation", type=int, default=64)

parser.add_argument("-j", "--workers", help="The batch on every device for validation", type=int, default=4)

parser.add_argument('-a', '--arch', metavar='ARCH', default='proxyless_mobile_14',

choices=model_names,

help='model architecture: ' +

' | '.join(model_names) +

' (default: proxyless_mobile_14)')

parser.add_argument('--manual_seed', default=0, type=int)

args = parser.parse_args()

net = tf_model_zoo.__dict__[args.arch](pretrained=True)

data_loader = torch.utils.data.DataLoader(

)

losses = AverageMeter()

top1 = AverageMeter()

for i, (_input, target) in enumerate(data_loader):

images = _input.numpy()

images = np.transpose(images, axes=[0, 2, 3, 1])

labels = net.labels_to_one_hot(1000, target.numpy())

feed_dict = {

net.images: images,

net.labels: labels,

net.is_training: False,

}

fetches = [net.cross_entropy, net.accuracy]

loss, accuracy = net.sess.run(fetches, feed_dict=feed_dict)

losses.update(loss, images.shape[0])

top1.update(accuracy * 100, images.shape[0])

if i % 50 == 0:

print(i, '\tLoss {loss.val:.4f} ({loss.avg:.4f})'.format(loss=losses),

'\tTop 1-acc {top1.val:.3f} ({top1.avg:.3f})'.format(top1=top1))

print('Loss: %.4f' % losses.avg, '\tTop-1: %.3f' % top1.avg)