def test_match_flatten(self):
A = np.random.rand(10, 256, 6, 6)
flattened, indices = flatten(A, return_index=True)
for layer in range(A.shape[0]):
for i in range(np.prod(A.shape[1:])):
value = flattened[layer][i]
index = indices[i]
assert A[layer][tuple(index)] == value
def init_and_progress(layer, activations):
activations = flatten(activations)
if activations.shape[1] <= n_components:
self._logger.debug(f"Not computing principal components for {layer} "
f"activations {activations.shape} as shape is small enough already")
pca = None
else:
pca = PCA(n_components=n_components, random_state=0)
pca.fit(activations)
progress.update(1)
return pca
def apply_pca(layer, activations):
pca = self._layer_pcas[layer]
activations = flatten(activations)
if pca is None:
return activations
return pca.transform(activations)
def test_inverse(self):
A = np.random.rand(2560, 256, 6, 6)
flattened = flatten(A)
A_ = np.reshape(flattened, [flattened.shape[0], 256, 6, 6])
assert A.shape == A_.shape
assert (A == A_).all()
def test_indices_shape(self):
A = np.random.rand(2560, 256, 6, 6)
_, indices = flatten(A, return_index=True)
assert len(indices.shape) == 2
assert indices.shape[0] == np.prod(A.shape[1:])
assert indices.shape[1] == 3 # for 256, 6, 6
def test_flattened_shape(self):
A = np.random.rand(2560, 256, 6, 6)
flattened = flatten(A)
assert np.prod(flattened.shape) == np.prod(A.shape)
assert flattened.shape[0] == A.shape[0]
assert len(flattened.shape) == 2
