def load_components(class_name, inst):
global components, state, use_named_latents
config = args.from_dict({'output_class': class_name})
dump_name = get_or_compute(config, inst)
data = np.load(dump_name, allow_pickle=False)
X_comp = data['act_comp']
X_mean = data['act_mean']
X_stdev = data['act_stdev']
Z_comp = data['lat_comp']
Z_mean = data['lat_mean']
Z_stdev = data['lat_stdev']
random_stdev_act = np.mean(data['random_stdevs'])
n_comp = X_comp.shape[0]
data.close()
# Transfer to GPU
components = SimpleNamespace(
X_comp=torch.from_numpy(X_comp).cuda().float(),
X_mean=torch.from_numpy(X_mean).cuda().float(),
X_stdev=torch.from_numpy(X_stdev).cuda().float(),
Z_comp=torch.from_numpy(Z_comp).cuda().float(),
Z_stdev=torch.from_numpy(Z_stdev).cuda().float(),
Z_mean=torch.from_numpy(Z_mean).cuda().float(),
names=[f'Component {i}' for i in range(n_comp)],
latent_types=[model.latent_space_name()] * n_comp,
ranges=[(0, model.get_max_latents())] * n_comp,
)
state.component_class = class_name # invalidates cache
use_named_latents = False
print('Loaded components for', class_name, 'from', dump_name)
latent_shape = model.get_latent_shape()
print('Feature shape:', feature_shape)
# Layout of activations
if len(feature_shape) != 4: # non-spatial
axis_mask = np.ones(len(feature_shape), dtype=np.int32)
else:
axis_mask = np.array(
[0, 1, 1, 1]) # only batch fixed => whole activation volume used
# Shape of sample passed to PCA
sample_shape = feature_shape * axis_mask
sample_shape[sample_shape == 0] = 1
# Load or compute components
dump_name = get_or_compute(args, inst)
data = np.load(dump_name,
allow_pickle=False) # does not contain object arrays
X_comp = data['act_comp']
X_global_mean = data['act_mean']
X_stdev = data['act_stdev']
X_var_ratio = data['var_ratio']
X_stdev_random = data['random_stdevs']
Z_global_mean = data['lat_mean']
Z_comp = data['lat_comp']
Z_stdev = data['lat_stdev']
n_comp = X_comp.shape[0]
data.close()
# Transfer components to device
tensors = SimpleNamespace(
