def test_verify_cross_layer_for_multiple_pairs(self):
# Get trained MNIST model
model = MyModel()
# Call API
consecutive_layer_list = [(model.conv1, model.conv2),
(model.conv3, model.conv4)]
w1 = model.conv1.weight.detach().numpy()
w2 = model.conv2.weight.detach().numpy()
w3 = model.conv3.weight.detach().numpy()
CrossLayerScaling.scale_cls_sets(consecutive_layer_list)
# check if weights are updating
assert not np.allclose(model.conv1.weight.detach().numpy(), w1)
assert not np.allclose(model.conv2.weight.detach().numpy(), w2)
assert not np.allclose(model.conv3.weight.detach().numpy(), w3)
def test_verify_cross_layer_scaling_depthwise_separable_layer_multiple_triplets(
self):
torch.manual_seed(10)
model = MockMobileNetV1()
model = model.eval()
consecutive_layer_list = [
(model.model[0][0], model.model[1][0], model.model[1][3]),
(model.model[1][3], model.model[2][0], model.model[2][3])
]
w1 = model.model[0][0].weight.detach().numpy()
w2 = model.model[1][3].weight.detach().numpy()
w3 = model.model[2][3].weight.detach().numpy()
CrossLayerScaling.scale_cls_sets(consecutive_layer_list)
assert not np.allclose(model.model[0][0].weight.detach().numpy(), w1)
assert not np.allclose(model.model[1][3].weight.detach().numpy(), w2)
assert not np.allclose(model.model[2][3].weight.detach().numpy(), w3)