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227 lines
9.3 KiB
227 lines
9.3 KiB
#!/usr/bin/env python
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""" ImageNet Validation Script
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This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
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models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
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canonical PyTorch, standard Python style, and good performance. Repurpose as you see fit.
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Hacked together by Ross Wightman (https://github.com/rwightman)
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"""
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import argparse
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import os
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import csv
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import glob
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import time
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import logging
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import torch
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import torch.nn as nn
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import torch.nn.parallel
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from collections import OrderedDict
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from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models
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from timm.data import Dataset, DatasetTar, create_loader, resolve_data_config
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from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging
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torch.backends.cudnn.benchmark = True
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parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
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parser.add_argument('data', metavar='DIR',
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help='path to dataset')
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parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
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help='model architecture (default: dpn92)')
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parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
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help='number of data loading workers (default: 2)')
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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metavar='N', help='mini-batch size (default: 256)')
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parser.add_argument('--img-size', default=None, type=int,
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metavar='N', help='Input image dimension, uses model default if empty')
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parser.add_argument('--crop-pct', default=None, type=float,
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metavar='N', help='Input image center crop pct')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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help='Override mean pixel value of dataset')
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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help='Override std deviation of of dataset')
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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help='Image resize interpolation type (overrides model)')
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parser.add_argument('--num-classes', type=int, default=1000,
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help='Number classes in dataset')
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parser.add_argument('--log-freq', default=10, type=int,
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metavar='N', help='batch logging frequency (default: 10)')
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
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help='path to latest checkpoint (default: none)')
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parser.add_argument('--pretrained', dest='pretrained', action='store_true',
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help='use pre-trained model')
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parser.add_argument('--num-gpu', type=int, default=1,
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help='Number of GPUS to use')
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parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
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help='disable test time pool')
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parser.add_argument('--no-prefetcher', action='store_true', default=False,
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help='disable fast prefetcher')
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parser.add_argument('--pin-mem', action='store_true', default=False,
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help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
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parser.add_argument('--fp16', action='store_true', default=False,
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help='Use half precision (fp16)')
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parser.add_argument('--tf-preprocessing', action='store_true', default=False,
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help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
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parser.add_argument('--use-ema', dest='use_ema', action='store_true',
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help='use ema version of weights if present')
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parser.add_argument('--torchscript', dest='torchscript', action='store_true',
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help='convert model torchscript for inference')
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def validate(args):
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# might as well try to validate something
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args.pretrained = args.pretrained or not args.checkpoint
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args.prefetcher = not args.no_prefetcher
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# create model
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model = create_model(
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args.model,
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num_classes=args.num_classes,
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in_chans=3,
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pretrained=args.pretrained)
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if args.checkpoint:
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load_checkpoint(model, args.checkpoint, args.use_ema)
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param_count = sum([m.numel() for m in model.parameters()])
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logging.info('Model %s created, param count: %d' % (args.model, param_count))
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data_config = resolve_data_config(vars(args), model=model)
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model, test_time_pool = apply_test_time_pool(model, data_config, args)
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if args.torchscript:
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torch.jit.optimized_execution(True)
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model = torch.jit.script(model)
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if args.num_gpu > 1:
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model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
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else:
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model = model.cuda()
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if args.fp16:
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model = model.half()
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criterion = nn.CrossEntropyLoss().cuda()
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if os.path.splitext(args.data)[1] == '.tar' and os.path.isfile(args.data):
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dataset = DatasetTar(args.data, load_bytes=args.tf_preprocessing)
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else:
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dataset = Dataset(args.data, load_bytes=args.tf_preprocessing)
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crop_pct = 1.0 if test_time_pool else data_config['crop_pct']
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loader = create_loader(
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dataset,
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input_size=data_config['input_size'],
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batch_size=args.batch_size,
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use_prefetcher=args.prefetcher,
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interpolation=data_config['interpolation'],
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mean=data_config['mean'],
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std=data_config['std'],
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num_workers=args.workers,
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crop_pct=crop_pct,
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pin_memory=args.pin_mem,
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fp16=args.fp16,
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tf_preprocessing=args.tf_preprocessing)
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batch_time = AverageMeter()
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losses = AverageMeter()
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top1 = AverageMeter()
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top5 = AverageMeter()
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model.eval()
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end = time.time()
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with torch.no_grad():
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for i, (input, target) in enumerate(loader):
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if args.no_prefetcher:
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target = target.cuda()
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input = input.cuda()
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if args.fp16:
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input = input.half()
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# compute output
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output = model(input)
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loss = criterion(output, target)
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# measure accuracy and record loss
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prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
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losses.update(loss.item(), input.size(0))
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top1.update(prec1.item(), input.size(0))
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top5.update(prec5.item(), input.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if i % args.log_freq == 0:
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logging.info(
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'Test: [{0:>4d}/{1}] '
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'Time: {batch_time.val:.3f}s ({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s) '
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'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f}) '
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'Prec@1: {top1.val:>7.3f} ({top1.avg:>7.3f}) '
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'Prec@5: {top5.val:>7.3f} ({top5.avg:>7.3f})'.format(
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i, len(loader), batch_time=batch_time,
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rate_avg=input.size(0) / batch_time.avg,
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loss=losses, top1=top1, top5=top5))
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results = OrderedDict(
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top1=round(top1.avg, 4), top1_err=round(100 - top1.avg, 4),
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top5=round(top5.avg, 4), top5_err=round(100 - top5.avg, 4),
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param_count=round(param_count / 1e6, 2),
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img_size=data_config['input_size'][-1],
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cropt_pct=crop_pct,
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interpolation=data_config['interpolation'])
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logging.info(' * Prec@1 {:.3f} ({:.3f}) Prec@5 {:.3f} ({:.3f})'.format(
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results['top1'], results['top1_err'], results['top5'], results['top5_err']))
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return results
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def main():
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setup_default_logging()
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args = parser.parse_args()
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model_cfgs = []
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model_names = []
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if os.path.isdir(args.checkpoint):
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# validate all checkpoints in a path with same model
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checkpoints = glob.glob(args.checkpoint + '/*.pth.tar')
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checkpoints += glob.glob(args.checkpoint + '/*.pth')
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model_names = list_models(args.model)
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model_cfgs = [(args.model, c) for c in sorted(checkpoints, key=natural_key)]
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else:
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if args.model == 'all':
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# validate all models in a list of names with pretrained checkpoints
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args.pretrained = True
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model_names = list_models(pretrained=True)
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model_cfgs = [(n, '') for n in model_names]
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elif not is_model(args.model):
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# model name doesn't exist, try as wildcard filter
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model_names = list_models(args.model)
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model_cfgs = [(n, '') for n in model_names]
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if len(model_cfgs):
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logging.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
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header_written = False
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with open('./results-all.csv', mode='w') as cf:
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for m, c in model_cfgs:
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args.model = m
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args.checkpoint = c
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result = OrderedDict(model=args.model)
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r = validate(args)
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result.update(r)
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if args.checkpoint:
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result['checkpoint'] = args.checkpoint
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dw = csv.DictWriter(cf, fieldnames=result.keys())
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if not header_written:
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dw.writeheader()
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header_written = True
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dw.writerow(result)
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cf.flush()
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else:
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validate(args)
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if __name__ == '__main__':
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main()
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