def try_to_load_broden(directory, imgsize, broden_version, perturbation,
download, size):
# Load broden dataset
ds_resolution = (224 if max(imgsize) <= 224 else
227 if max(imgsize) <= 227 else 384)
if not os.path.isfile(os.path.join(directory,
'broden%d_%d' % (broden_version, ds_resolution), 'index.csv')):
return None
return BrodenDataset(directory,
resolution=ds_resolution,
download=download,
broden_version=broden_version,
transform=transforms.Compose([
transforms.Resize(imgsize),
AddPerturbation(perturbation),
transforms.ToTensor(),
transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
size=size)
def test_dissection():
verbose_progress(True)
from torchvision.models import alexnet
from torchvision import transforms
model = InstrumentedModel(alexnet(pretrained=True))
model.eval()
# Load an alexnet
model.retain_layers([('features.0', 'conv1'), ('features.3', 'conv2'),
('features.6', 'conv3'), ('features.8', 'conv4'),
('features.10', 'conv5')])
# load broden dataset
bds = BrodenDataset('dataset/broden',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)
]),
size=100)
# run dissect
dissect('dissect/test', model, bds, examples_per_unit=10)
