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Batch validation batch size adjustment, tweak L2 crop pct

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This commit is contained in:
Ross Wightman 2020-02-15 20:37:04 -08:00
parent 08553e16b3
commit 53c47479c4
2 changed files with 15 additions and 2 deletions
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2
timm/models/efficientnet.py
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@ -194,7 +194,7 @@ default_cfgs = {
input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936), input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936),
'tf_efficientnet_l2_ns': _cfg( 'tf_efficientnet_l2_ns': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth',
input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961), input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96),
'tf_efficientnet_es': _cfg( 'tf_efficientnet_es': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),

13
validate.py
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@ -211,11 +211,24 @@ def main():
logging.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names))) logging.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
results = [] results = []
try: try:
start_batch_size = args.batch_size
for m, c in model_cfgs: for m, c in model_cfgs:
batch_size = start_batch_size
args.model = m args.model = m
args.checkpoint = c args.checkpoint = c
result = OrderedDict(model=args.model) 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) 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%")
result.update(r) result.update(r)
if args.checkpoint: if args.checkpoint:
result['checkpoint'] = args.checkpoint result['checkpoint'] = args.checkpoint