def load_model():
map_location = None if FLAGS.ngpus > 0 else 'cpu'
print('Getting VOneNet')
model = get_model(map_location=map_location,
model_arch=FLAGS.model_arch,
pretrained=False,
visual_degrees=FLAGS.visual_degrees,
stride=FLAGS.stride,
ksize=FLAGS.ksize,
sf_corr=FLAGS.sf_corr,
sf_max=FLAGS.sf_max,
sf_min=FLAGS.sf_min,
rand_param=FLAGS.rand_param,
gabor_seed=FLAGS.gabor_seed,
simple_channels=FLAGS.simple_channels,
complex_channels=FLAGS.simple_channels,
noise_mode=FLAGS.noise_mode,
noise_scale=FLAGS.noise_scale,
noise_level=FLAGS.noise_level,
k_exc=FLAGS.k_exc)
if FLAGS.ngpus > 0 and torch.cuda.device_count() > 1:
print('We have multiple GPUs detected')
model = model.to(device)
elif FLAGS.ngpus > 0 and torch.cuda.device_count() is 1:
print('We run on GPU')
model = model.to(device)
else:
print('No GPU detected!')
model = model.module
return model
def voneresnet(model_name='resnet50'):
from vonenet import get_model
model = get_model(model_name)
model = model.module
from model_tools.activations.pytorch import load_preprocess_images
preprocessing = functools.partial(load_preprocess_images, image_size=224,
normalize_mean=(0.5, 0.5, 0.5), normalize_std=(0.5, 0.5, 0.5))
wrapper = PytorchWrapper(identifier='vone'+model_name, model=model, preprocessing=preprocessing)
wrapper.image_size = 224
return wrapper
def vonecornet(model_name='cornets'):
from vonenet import get_model
model = get_model(model_name)
model = model.module
from model_tools.activations.pytorch import load_preprocess_images
preprocessing = functools.partial(load_preprocess_images,
image_size=224,
normalize_mean=(0.5, 0.5, 0.5),
normalize_std=(0.5, 0.5, 0.5))
from candidate_models.base_models.stochastic import StochasticTemporalPytorchWrapper
wrapper = StochasticTemporalPytorchWrapper(identifier='vone' + model_name,
model=model,
preprocessing=preprocessing)
wrapper.image_size = 224
return wrapper
def val():
model = get_model(model_arch=FLAGS.model_arch, pretrained=True)
if FLAGS.ngpus == 0:
print('Running on CPU')
if FLAGS.ngpus > 0 and torch.cuda.device_count() > 1:
print('Running on multiple GPUs')
model = model.to(device)
elif FLAGS.ngpus > 0 and torch.cuda.device_count() is 1:
print('Running on single GPU')
model = model.to(device)
else:
print('No GPU detected!')
model = model.module
validator = ImageNetVal(model)
record = validator()
print(record['top1'])
print(record['top5'])
return