from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import csv import glob import time import torch import torch.nn as nn import torch.nn.parallel from collections import OrderedDict from models import create_model, apply_test_time_pool, load_checkpoint from data import Dataset, create_loader, resolve_data_config from utils import accuracy, AverageMeter, natural_key torch.backends.cudnn.benchmark = True parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation') parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92', help='model architecture (default: dpn92)') parser.add_argument('-j', '--workers', default=2, type=int, metavar='N', help='number of data loading workers (default: 2)') parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)') parser.add_argument('--img-size', default=None, type=int, metavar='N', help='Input image dimension, uses model default if empty') parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', help='Override mean pixel value of dataset') parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', help='Override std deviation of of dataset') parser.add_argument('--interpolation', default='', type=str, metavar='NAME', help='Image resize interpolation type (overrides model)') parser.add_argument('--num-classes', type=int, default=1000, help='Number classes in dataset') parser.add_argument('--print-freq', '-p', default=10, type=int, metavar='N', help='print frequency (default: 10)') parser.add_argument('--checkpoint', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model') parser.add_argument('--num-gpu', type=int, default=1, help='Number of GPUS to use') parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true', help='disable test time pool') parser.add_argument('--tf-preprocessing', dest='tf_preprocessing', action='store_true', help='Use Tensorflow preprocessing pipeline (require CPU TF installed') parser.add_argument('--use-ema', dest='use_ema', action='store_true', help='use ema version of weights if present') def validate(args): # create model model = create_model( args.model, num_classes=args.num_classes, in_chans=3, pretrained=args.pretrained) if args.checkpoint and not args.pretrained: load_checkpoint(model, args.checkpoint, args.use_ema) else: args.pretrained = True # might as well try to validate something... param_count = sum([m.numel() for m in model.parameters()]) print('Model %s created, param count: %d' % (args.model, param_count)) data_config = resolve_data_config(model, args) model, test_time_pool = apply_test_time_pool(model, data_config, args) if args.num_gpu > 1: model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda() else: model = model.cuda() criterion = nn.CrossEntropyLoss().cuda() loader = create_loader( Dataset(args.data, load_bytes=args.tf_preprocessing), input_size=data_config['input_size'], batch_size=args.batch_size, use_prefetcher=True, interpolation=data_config['interpolation'], mean=data_config['mean'], std=data_config['std'], num_workers=args.workers, crop_pct=1.0 if test_time_pool else data_config['crop_pct'], tf_preprocessing=args.tf_preprocessing) batch_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() top5 = AverageMeter() model.eval() end = time.time() with torch.no_grad(): for i, (input, target) in enumerate(loader): target = target.cuda() input = input.cuda() # compute output output = model(input) loss = criterion(output, target) # measure accuracy and record loss prec1, prec5 = accuracy(output.data, target, topk=(1, 5)) losses.update(loss.item(), input.size(0)) top1.update(prec1.item(), input.size(0)) top5.update(prec5.item(), input.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: print('Test: [{0}/{1}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s) \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, len(loader), batch_time=batch_time, rate_avg=input.size(0) / batch_time.avg, loss=losses, top1=top1, top5=top5)) results = OrderedDict( top1=round(top1.avg, 3), top1_err=round(100 - top1.avg, 3), top5=round(top5.avg, 3), top5_err=round(100 - top5.avg, 3), param_count=round(param_count / 1e6, 2)) print(' * Prec@1 {:.3f} ({:.3f}) Prec@5 {:.3f} ({:.3f})'.format( results['top1'], results['top1_err'], results['top5'], results['top5_err'])) return results def main(): args = parser.parse_args() if args.model == 'all': # validate all models in a list of names with pretrained checkpoints args.pretrained = True # FIXME just an example list, need to add model name collections for # batch testing of various pretrained combinations by arg string models = ['tf_efficientnet_b0', 'tf_efficientnet_b1', 'tf_efficientnet_b2', 'tf_efficientnet_b3'] model_cfgs = [(n, '') for n in models] elif os.path.isdir(args.checkpoint): # validate all checkpoints in a path with same model checkpoints = glob.glob(args.checkpoint + '/*.pth.tar') checkpoints += glob.glob(args.checkpoint + '/*.pth') model_cfgs = [(args.model, c) for c in sorted(checkpoints, key=natural_key)] else: model_cfgs = [] if len(model_cfgs): header_written = False with open('./results-all.csv', mode='w') as cf: for m, c in model_cfgs: args.model = m args.checkpoint = c result = OrderedDict(model=args.model) result.update(validate(args)) if args.checkpoint: result['checkpoint'] = args.checkpoint dw = csv.DictWriter(cf, fieldnames=result.keys()) if not header_written: dw.writeheader() header_written = True dw.writerow(result) cf.flush() else: validate(args) if __name__ == '__main__': main()