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301 lines
13 KiB
301 lines
13 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 / Copyright 2020 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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try:
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from apex import amp
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has_apex = True
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except ImportError:
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has_apex = False
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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, RealLabelsImagenet
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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('--class-map', default='', type=str, metavar='FILENAME',
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help='path to class to idx mapping file (default: "")')
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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('--amp', action='store_true', default=False,
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help='Use AMP mixed precision')
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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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parser.add_argument('--legacy-jit', dest='legacy_jit', action='store_true',
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help='use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance')
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parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
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help='Output csv file for validation results (summary)')
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parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
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help='Real labels JSON file for imagenet evaluation')
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def set_jit_legacy():
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""" Set JIT executor to legacy w/ support for op fusion
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This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
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in the JIT exectutor. These API are not supported so could change.
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"""
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#
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assert hasattr(torch._C, '_jit_set_profiling_executor'), "Old JIT behavior doesn't exist!"
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torch._C._jit_set_profiling_executor(False)
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torch._C._jit_set_profiling_mode(False)
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torch._C._jit_override_can_fuse_on_gpu(True)
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#torch._C._jit_set_texpr_fuser_enabled(True)
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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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if args.legacy_jit:
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set_jit_legacy()
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# create model
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model = create_model(
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args.model,
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pretrained=args.pretrained,
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num_classes=args.num_classes,
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in_chans=3,
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scriptable=args.torchscript)
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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.amp:
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model = amp.initialize(model.cuda(), opt_level='O1')
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else:
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model = model.cuda()
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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)))
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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, class_map=args.class_map)
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else:
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dataset = Dataset(args.data, load_bytes=args.tf_preprocessing, class_map=args.class_map)
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if args.real_labels:
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real_labels = RealLabelsImagenet(dataset.filenames(basename=True), real_json=args.real_labels)
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else:
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real_labels = None
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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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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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with torch.no_grad():
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# warmup, reduce variability of first batch time, especially for comparing torchscript vs non
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input = torch.randn((args.batch_size,) + data_config['input_size']).cuda()
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model(input)
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end = time.time()
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for batch_idx, (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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if real_labels is not None:
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real_labels.add_result(output)
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# measure accuracy and record loss
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acc1, acc5 = accuracy(output.data, target, topk=(1, 5))
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losses.update(loss.item(), input.size(0))
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top1.update(acc1.item(), input.size(0))
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top5.update(acc5.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 batch_idx % 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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'Acc@1: {top1.val:>7.3f} ({top1.avg:>7.3f}) '
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'Acc@5: {top5.val:>7.3f} ({top5.avg:>7.3f})'.format(
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batch_idx, 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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if real_labels is not None:
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real_top1 = real_labels.get_accuracy(k=1)
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real_top5 = real_labels.get_accuracy(k=5)
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results = OrderedDict(
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top1=round(real_top1, 4), top1_err=round(100 - real_top1, 4),
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top5=round(real_top5, 4), top5_err=round(100 - real_top5, 4),
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top1_original=round(top1.avg, 4),
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top5_original=round(top5.avg, 4))
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else:
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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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results.update(OrderedDict(
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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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))
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logging.info(' * Acc@1 {:.3f} ({:.3f}) Acc@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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results_file = args.results_file or './results-all.csv'
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logging.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
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results = []
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try:
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start_batch_size = args.batch_size
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for m, c in model_cfgs:
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batch_size = start_batch_size
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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 = {}
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while not r and batch_size >= args.num_gpu:
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try:
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args.batch_size = batch_size
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print('Validating with batch size: %d' % args.batch_size)
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r = validate(args)
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except RuntimeError as e:
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if batch_size <= args.num_gpu:
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print("Validation failed with no ability to reduce batch size. Exiting.")
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raise e
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batch_size = max(batch_size // 2, args.num_gpu)
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print("Validation failed, reducing batch size by 50%")
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torch.cuda.empty_cache()
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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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results.append(result)
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except KeyboardInterrupt as e:
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pass
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results = sorted(results, key=lambda x: x['top1'], reverse=True)
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if len(results):
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write_results(results_file, results)
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else:
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validate(args)
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def write_results(results_file, results):
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with open(results_file, mode='w') as cf:
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dw = csv.DictWriter(cf, fieldnames=results[0].keys())
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dw.writeheader()
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for r in results:
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dw.writerow(r)
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cf.flush()
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if __name__ == '__main__':
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main()
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