def _apply_binary_mask_to_module_weight_and_bias(module, mask, module_name=""):
with torch.no_grad():
# Applying mask to weights
inplace_apply_filter_binary_mask(mask, module.weight, module_name)
# Applying mask to bias too (if exists)
if module.bias is not None:
inplace_apply_filter_binary_mask(mask, module.bias, module_name)
def test_assert_broadcastable_mask_and_weight_shape():
nncf_module = NNCFConv2d(1, 2, 2)
fill_conv_weight(nncf_module, 1)
fill_bias(nncf_module, 1)
mask = torch.zeros(10)
with pytest.raises(RuntimeError):
inplace_apply_filter_binary_mask(mask, nncf_module.weight.data)
with pytest.raises(RuntimeError):
apply_filter_binary_mask(mask, nncf_module.weight.data)
def _apply_binary_mask_to_module_weight_and_bias(
module, mask, module_scope):
with torch.no_grad():
dim = module.target_weight_dim_for_compression if isinstance(
module, _NNCFModuleMixin) else 0
# Applying mask to weights
inplace_apply_filter_binary_mask(mask, module.weight,
module_scope, dim)
# Applying mask to bias too (if exists)
if module.bias is not None:
inplace_apply_filter_binary_mask(mask, module.bias,
module_scope)
def test_inplace_apply_filter_binary_mask(mask, reference_weight,
reference_bias):
"""
Test that inplace_apply_filter_binary_mask changes the input weight and returns valid result.
"""
nncf_module = NNCFConv2d(1, 2, 2)
fill_conv_weight(nncf_module, 1)
fill_bias(nncf_module, 1)
result_weight = inplace_apply_filter_binary_mask(
mask, nncf_module.weight.data)
assert torch.allclose(result_weight, reference_weight)
assert torch.allclose(nncf_module.weight, reference_weight)
result_bias = inplace_apply_filter_binary_mask(mask,
nncf_module.bias.data)
assert torch.allclose(result_bias, reference_bias)
assert torch.allclose(nncf_module.bias, reference_bias)
