If sparsity is 0.6, the expected masks should mask out filter 0,1,2, this can be verified through:
`all(torch.sum(mask2, (1, 2, 3)).numpy() == np.array([0., 0., 0., 27., 27.]))`
w = np.array([np.zeros((3, 3, 3)), np.ones((3, 3, 3)), np.ones((3, 3, 3)) * 2,
np.ones((3, 3, 3)) * 3, np.ones((3, 3, 3)) * 4])
model = TorchModel()
config_list = [{"sparsity": 0.2, "op_types": ["Conv2d"], "op_names": ["conv1"]},
{"sparsity": 0.6, "op_types": ["Conv2d"], "op_names": ["conv2"]}]
pruner = torch_compressor.L1FilterPruner(model, config_list)
After Change
If sparsity is 0.6 for conv2, the expected masks should mask out filter 0,1,2, this can be verified through:
`all(torch.sum(mask2, (1, 2, 3)).numpy() == np.array([0., 0., 0., 0., 0., 0., 125., 125., 125., 125.]))`
w1 = np.array([np.ones((1, 5, 5))*i for i in range(5)]).astype(np.float32)
w2 = np.array([np.ones((5, 5, 5))*i for i in range(10)]).astype(np.float32)
model = TorchModel()
config_list = [{"sparsity": 0.2, "op_types": ["Conv2d"], "op_names": ["conv1"]},
