def _get_imagenet_val(num_images):
_logger = logging.getLogger(fullname(_get_imagenet_val))
num_classes = 1000
num_images_per_class = (num_images - 1) // num_classes
base_indices = np.arange(num_images_per_class).astype(int)
indices = []
for i in range(num_classes):
indices.extend(50 * i + base_indices)
for i in range((num_images - 1) % num_classes + 1):
indices.extend(50 * i + np.array([num_images_per_class]).astype(int))
framework_home = os.path.expanduser(os.getenv('MT_HOME', '~/.model-tools'))
imagenet_filepath = os.getenv('MT_IMAGENET_PATH', os.path.join(framework_home, 'imagenet2012.hdf5'))
imagenet_dir = f"{imagenet_filepath}-files"
os.makedirs(imagenet_dir, exist_ok=True)
if not os.path.isfile(imagenet_filepath):
os.makedirs(os.path.dirname(imagenet_filepath), exist_ok=True)
_logger.debug(f"Downloading ImageNet validation to {imagenet_filepath}")
s3.download_file("imagenet2012-val.hdf5", imagenet_filepath)
filepaths = []
with h5py.File(imagenet_filepath, 'r') as f:
for index in indices:
imagepath = os.path.join(imagenet_dir, f"{index}.png")
if not os.path.isfile(imagepath):
image = np.array(f['val/images'][index])
Image.fromarray(image).save(imagepath)
filepaths.append(imagepath)
return filepaths
def __init__(self, layer_model):
self._logger = logging.getLogger(fullname(self))
self._layer_model = layer_model
self.region_layer_map = self._layer_model.region_layer_map
self.activations_model = self._layer_model.activations_model
self.start_task = self._layer_model.start_task
self._time_bins = None
def __init__(self, get_activations, preprocessing, identifier=False, batch_size=Defaults.batch_size):
"""
:param identifier: an activations identifier for the stored results file. False to disable saving.
"""
self._logger = logging.getLogger(fullname(self))
self._batch_size = batch_size
self.identifier = identifier
self.get_activations = get_activations
self.preprocess = preprocessing or (lambda x: x)
self._stimulus_set_hooks = {}
self._batch_activations_hooks = {}
def __init__(self, model, preprocessing, identifier=None, *args, **kwargs):
import torch
logger = logging.getLogger(fullname(self))
self._device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
logger.debug(f"Using device {self._device}")
self._model = model
self._model = self._model.to(self._device)
identifier = identifier or model.__class__.__name__
self._extractor = self._build_extractor(
identifier=identifier,
preprocessing=preprocessing,
get_activations=self.get_activations,
*args,
**kwargs)
self._extractor.insert_attrs(self)
def __init__(self, activations_extractor, n_components):
self._logger = logging.getLogger(fullname(self))
self._extractor = activations_extractor
self._n_components = n_components
self._layer_pcas = {}
def test_standard_commitment(self):
brain_model = ModelCommitment(identifier=fullname(self),
activations_model=None,
layers=['dummy'])
assert brain_model.visual_degrees() == 8
