Updated this script to run on up to date PyTorch: https://github.com/szagoruyko/functional-zoo/blob/master/imagenet-validation.py

imagenet-validation.py

# dependencies
import re
import os
import argparse
import torch
from tqdm import tqdm
#import cv2
import numpy as np
import torch.utils.data
import torchnet as tnt
from torchvision import transforms as cvtransforms
import torchvision.datasets as datasets
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils import model_zoo

# input arguments
parser = argparse.ArgumentParser(description = 'PyTorch ImageNet validation')
parser.add_argument('--imagenetpath', metavar='PATH', required=True,
                    help='path to dataset')
parser.add_argument('--numthreads', default=4, type=int, metavar='N',
                    help='number of data loading threads (default: 4)')
parser.add_argument('--model', metavar='PATH', required=True,
                    help='path to model')

# functional model definition from functional zoo: https://github.com/szagoruyko/functional-zoo/blob/master/imagenet-validation.py#L27-L47
def define_model(params):
    def conv2d(input, params, base, stride=1, pad=0):
        return F.conv2d(input, params[base + '.weight'],
                        params[base + '.bias'], stride, pad)

    def group(input, params, base, stride, n):
        o = input
        for i in range(0,n):
            b_base = ('%s.block%d.conv') % (base, i)
            x = o
            o = conv2d(x, params, b_base + '0')
            o = F.relu(o)
            o = conv2d(o, params, b_base + '1', stride=i==0 and stride or 1, pad=1)
            o = F.relu(o)
            o = conv2d(o, params, b_base + '2')
            if i == 0:
                o += conv2d(x, params, b_base + '_dim', stride=stride)
            else:
                o += x
            o = F.relu(o)
        return o
    
    # determine network size by parameters
    blocks = [sum([re.match('group%d.block\d+.conv0.weight'%j, k) is not None
                   for k in params.keys()]) for j in range(4)]

    def f(input, params):
        o = F.conv2d(input, params['conv0.weight'], params['conv0.bias'], 2, 3)
        o = F.relu(o)
        o = F.max_pool2d(o, 3, 2, 1)
        o_g0 = group(o, params, 'group0', 1, blocks[0])
        o_g1 = group(o_g0, params, 'group1', 2, blocks[1])
        o_g2 = group(o_g1, params, 'group2', 2, blocks[2])
        o_g3 = group(o_g2, params, 'group3', 2, blocks[3])
        o = F.avg_pool2d(o_g3, 7, 1, 0)
        o = o.view(o.size(0), -1)
        o = F.linear(o, params['fc.weight'], params['fc.bias'])
        return o

    return f

def main():

    # parse input arguments
    args = parser.parse_args()

    #def cvload(path):
    #    img = cv2.imread(path, cv2.IMREAD_COLOR)
    #    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    #    return img

    # set up data loader
    print("| setting up data loader...")
    valdir = os.path.join(args.imagenetpath, 'val')
    ds = datasets.ImageFolder(valdir, tnt.transform.compose([
            cvtransforms.Scale(256),
            cvtransforms.CenterCrop(224),
            #lambda x: x.astype(np.float32) / 255.0,
            cvtransforms.ToTensor(),
            cvtransforms.Normalize(mean=[0.485, 0.456, 0.406],
                                   std=[0.229, 0.224, 0.225]),
            #lambda x: x.transpose(2,0,1).astype(np.float32),
            #torch.from_numpy,
            ]))#, loader = cvload)
    train_loader = torch.utils.data.DataLoader(ds,
        batch_size=256, shuffle=False,
        num_workers=args.numthreads, pin_memory=False)

    params = model_zoo.load_url('https://s3.amazonaws.com/modelzoo-networks/wide-resnet-50-2-export-5ae25d50.pth')
    params = {k:params[k].cuda() for k in params}

    f = define_model(params)

    class_err = tnt.meter.ClassErrorMeter(topk=[1,5], accuracy=True)

    for sample in tqdm(train_loader):
        with torch.no_grad():
            inputs = Variable(sample[0].cuda())
            targets = sample[1]
            class_err.add(f(inputs, params).data, targets)

    print('Validation top1/top5 accuracy:')
    print(class_err.value())

if __name__ == '__main__':
    main()