You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
301 lines
13 KiB
301 lines
13 KiB
#!/usr/bin/env python
|
|
""" ImageNet Validation Script
|
|
|
|
This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
|
|
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
|
|
canonical PyTorch, standard Python style, and good performance. Repurpose as you see fit.
|
|
|
|
Hacked together by Ross Wightman (https://github.com/rwightman)
|
|
"""
|
|
import argparse
|
|
import os
|
|
import csv
|
|
import glob
|
|
import time
|
|
import logging
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.parallel
|
|
from collections import OrderedDict
|
|
|
|
try:
|
|
from apex import amp
|
|
has_apex = True
|
|
except ImportError:
|
|
has_apex = False
|
|
|
|
from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models
|
|
from timm.data import Dataset, DatasetTar, create_loader, resolve_data_config, RealLabelsImagenet
|
|
from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging
|
|
|
|
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=4, 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('--crop-pct', default=None, type=float,
|
|
metavar='N', help='Input image center crop pct')
|
|
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('--class-map', default='', type=str, metavar='FILENAME',
|
|
help='path to class to idx mapping file (default: "")')
|
|
parser.add_argument('--log-freq', default=10, type=int,
|
|
metavar='N', help='batch logging 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('--no-prefetcher', action='store_true', default=False,
|
|
help='disable fast prefetcher')
|
|
parser.add_argument('--pin-mem', action='store_true', default=False,
|
|
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
|
|
parser.add_argument('--amp', action='store_true', default=False,
|
|
help='Use AMP mixed precision')
|
|
parser.add_argument('--tf-preprocessing', action='store_true', default=False,
|
|
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')
|
|
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
|
|
help='convert model torchscript for inference')
|
|
parser.add_argument('--legacy-jit', dest='legacy_jit', action='store_true',
|
|
help='use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance')
|
|
parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
|
|
help='Output csv file for validation results (summary)')
|
|
parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
|
|
help='Real labels JSON file for imagenet evaluation')
|
|
|
|
|
|
def set_jit_legacy():
|
|
""" Set JIT executor to legacy w/ support for op fusion
|
|
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
|
|
in the JIT exectutor. These API are not supported so could change.
|
|
"""
|
|
#
|
|
assert hasattr(torch._C, '_jit_set_profiling_executor'), "Old JIT behavior doesn't exist!"
|
|
torch._C._jit_set_profiling_executor(False)
|
|
torch._C._jit_set_profiling_mode(False)
|
|
torch._C._jit_override_can_fuse_on_gpu(True)
|
|
#torch._C._jit_set_texpr_fuser_enabled(True)
|
|
|
|
|
|
def validate(args):
|
|
# might as well try to validate something
|
|
args.pretrained = args.pretrained or not args.checkpoint
|
|
args.prefetcher = not args.no_prefetcher
|
|
if args.legacy_jit:
|
|
set_jit_legacy()
|
|
|
|
# create model
|
|
model = create_model(
|
|
args.model,
|
|
pretrained=args.pretrained,
|
|
num_classes=args.num_classes,
|
|
in_chans=3,
|
|
scriptable=args.torchscript)
|
|
|
|
if args.checkpoint:
|
|
load_checkpoint(model, args.checkpoint, args.use_ema)
|
|
|
|
param_count = sum([m.numel() for m in model.parameters()])
|
|
logging.info('Model %s created, param count: %d' % (args.model, param_count))
|
|
|
|
data_config = resolve_data_config(vars(args), model=model)
|
|
model, test_time_pool = apply_test_time_pool(model, data_config, args)
|
|
|
|
if args.torchscript:
|
|
torch.jit.optimized_execution(True)
|
|
model = torch.jit.script(model)
|
|
|
|
if args.amp:
|
|
model = amp.initialize(model.cuda(), opt_level='O1')
|
|
else:
|
|
model = model.cuda()
|
|
|
|
if args.num_gpu > 1:
|
|
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu)))
|
|
|
|
criterion = nn.CrossEntropyLoss().cuda()
|
|
|
|
if os.path.splitext(args.data)[1] == '.tar' and os.path.isfile(args.data):
|
|
dataset = DatasetTar(args.data, load_bytes=args.tf_preprocessing, class_map=args.class_map)
|
|
else:
|
|
dataset = Dataset(args.data, load_bytes=args.tf_preprocessing, class_map=args.class_map)
|
|
|
|
if args.real_labels:
|
|
real_labels = RealLabelsImagenet(dataset.filenames(basename=True), real_json=args.real_labels)
|
|
else:
|
|
real_labels = None
|
|
|
|
crop_pct = 1.0 if test_time_pool else data_config['crop_pct']
|
|
loader = create_loader(
|
|
dataset,
|
|
input_size=data_config['input_size'],
|
|
batch_size=args.batch_size,
|
|
use_prefetcher=args.prefetcher,
|
|
interpolation=data_config['interpolation'],
|
|
mean=data_config['mean'],
|
|
std=data_config['std'],
|
|
num_workers=args.workers,
|
|
crop_pct=crop_pct,
|
|
pin_memory=args.pin_mem,
|
|
tf_preprocessing=args.tf_preprocessing)
|
|
|
|
batch_time = AverageMeter()
|
|
losses = AverageMeter()
|
|
top1 = AverageMeter()
|
|
top5 = AverageMeter()
|
|
|
|
model.eval()
|
|
with torch.no_grad():
|
|
# warmup, reduce variability of first batch time, especially for comparing torchscript vs non
|
|
input = torch.randn((args.batch_size,) + data_config['input_size']).cuda()
|
|
model(input)
|
|
end = time.time()
|
|
for batch_idx, (input, target) in enumerate(loader):
|
|
if args.no_prefetcher:
|
|
target = target.cuda()
|
|
input = input.cuda()
|
|
if args.fp16:
|
|
input = input.half()
|
|
|
|
# compute output
|
|
output = model(input)
|
|
loss = criterion(output, target)
|
|
|
|
if real_labels is not None:
|
|
real_labels.add_result(output)
|
|
|
|
# measure accuracy and record loss
|
|
acc1, acc5 = accuracy(output.data, target, topk=(1, 5))
|
|
losses.update(loss.item(), input.size(0))
|
|
top1.update(acc1.item(), input.size(0))
|
|
top5.update(acc5.item(), input.size(0))
|
|
|
|
# measure elapsed time
|
|
batch_time.update(time.time() - end)
|
|
end = time.time()
|
|
|
|
if batch_idx % args.log_freq == 0:
|
|
logging.info(
|
|
'Test: [{0:>4d}/{1}] '
|
|
'Time: {batch_time.val:.3f}s ({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s) '
|
|
'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f}) '
|
|
'Acc@1: {top1.val:>7.3f} ({top1.avg:>7.3f}) '
|
|
'Acc@5: {top5.val:>7.3f} ({top5.avg:>7.3f})'.format(
|
|
batch_idx, len(loader), batch_time=batch_time,
|
|
rate_avg=input.size(0) / batch_time.avg,
|
|
loss=losses, top1=top1, top5=top5))
|
|
|
|
if real_labels is not None:
|
|
real_top1 = real_labels.get_accuracy(k=1)
|
|
real_top5 = real_labels.get_accuracy(k=5)
|
|
results = OrderedDict(
|
|
top1=round(real_top1, 4), top1_err=round(100 - real_top1, 4),
|
|
top5=round(real_top5, 4), top5_err=round(100 - real_top5, 4),
|
|
top1_original=round(top1.avg, 4),
|
|
top5_original=round(top5.avg, 4))
|
|
else:
|
|
results = OrderedDict(
|
|
top1=round(top1.avg, 4), top1_err=round(100 - top1.avg, 4),
|
|
top5=round(top5.avg, 4), top5_err=round(100 - top5.avg, 4))
|
|
results.update(OrderedDict(
|
|
param_count=round(param_count / 1e6, 2),
|
|
img_size=data_config['input_size'][-1],
|
|
cropt_pct=crop_pct,
|
|
interpolation=data_config['interpolation']
|
|
))
|
|
logging.info(' * Acc@1 {:.3f} ({:.3f}) Acc@5 {:.3f} ({:.3f})'.format(
|
|
results['top1'], results['top1_err'], results['top5'], results['top5_err']))
|
|
|
|
return results
|
|
|
|
|
|
def main():
|
|
setup_default_logging()
|
|
args = parser.parse_args()
|
|
model_cfgs = []
|
|
model_names = []
|
|
if 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_names = list_models(args.model)
|
|
model_cfgs = [(args.model, c) for c in sorted(checkpoints, key=natural_key)]
|
|
else:
|
|
if args.model == 'all':
|
|
# validate all models in a list of names with pretrained checkpoints
|
|
args.pretrained = True
|
|
model_names = list_models(pretrained=True)
|
|
model_cfgs = [(n, '') for n in model_names]
|
|
elif not is_model(args.model):
|
|
# model name doesn't exist, try as wildcard filter
|
|
model_names = list_models(args.model)
|
|
model_cfgs = [(n, '') for n in model_names]
|
|
|
|
if len(model_cfgs):
|
|
results_file = args.results_file or './results-all.csv'
|
|
logging.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
|
|
results = []
|
|
try:
|
|
start_batch_size = args.batch_size
|
|
for m, c in model_cfgs:
|
|
batch_size = start_batch_size
|
|
args.model = m
|
|
args.checkpoint = c
|
|
result = OrderedDict(model=args.model)
|
|
r = {}
|
|
while not r and batch_size >= args.num_gpu:
|
|
try:
|
|
args.batch_size = batch_size
|
|
print('Validating with batch size: %d' % args.batch_size)
|
|
r = validate(args)
|
|
except RuntimeError as e:
|
|
if batch_size <= args.num_gpu:
|
|
print("Validation failed with no ability to reduce batch size. Exiting.")
|
|
raise e
|
|
batch_size = max(batch_size // 2, args.num_gpu)
|
|
print("Validation failed, reducing batch size by 50%")
|
|
torch.cuda.empty_cache()
|
|
result.update(r)
|
|
if args.checkpoint:
|
|
result['checkpoint'] = args.checkpoint
|
|
results.append(result)
|
|
except KeyboardInterrupt as e:
|
|
pass
|
|
results = sorted(results, key=lambda x: x['top1'], reverse=True)
|
|
if len(results):
|
|
write_results(results_file, results)
|
|
else:
|
|
validate(args)
|
|
|
|
|
|
def write_results(results_file, results):
|
|
with open(results_file, mode='w') as cf:
|
|
dw = csv.DictWriter(cf, fieldnames=results[0].keys())
|
|
dw.writeheader()
|
|
for r in results:
|
|
dw.writerow(r)
|
|
cf.flush()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|