def test_can_infer_magnitude_sparse_linear(weight_importance, threshold,
ref_output):
nncf_module = NNCFLinear(4, 1)
sparse_model = TestModel(nncf_module, weight_importance)
sparsifier = sparse_model.sparsifier
fill_linear_weight(nncf_module, 9)
fill_bias(nncf_module, 0)
if threshold is not None:
sparsifier.threshold = threshold
act_output = sparse_model(torch.ones([1, 4]))
assert act_output.item() == ref_output
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 test_can_infer_magnitude_sparse_conv(weight_importance, threshold,
ref_output):
nncf_module = NNCFConv2d(1, 1, 2)
sparse_model = TestModel(nncf_module)
sparsifier = sparse_model.sparsifier
fill_conv_weight(nncf_module, 9)
fill_bias(nncf_module, 0)
if threshold is not None:
sparsifier.binary_mask = calc_magnitude_binary_mask(
sparse_model.layer.weight, weight_importance, threshold)
act_output = sparse_model(torch.ones([1, 1, 2, 2]))
assert act_output.item() == ref_output
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)
def test_apply_filter_binary_mask(mask, reference_weight, reference_bias):
"""
Test that apply_filter_binary_mask not 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)
original_weight = nncf_module.weight.data.detach().clone()
original_bias = nncf_module.bias.data.detach().clone()
result = apply_filter_binary_mask(mask, nncf_module.weight.data)
assert torch.allclose(nncf_module.weight, original_weight)
assert torch.allclose(result, reference_weight)
result_bias = apply_filter_binary_mask(mask, nncf_module.bias.data)
assert torch.allclose(result_bias, reference_bias)
assert torch.allclose(nncf_module.bias, original_bias)
def test_can_infer_magnitude_pruned_conv(weights_val, bias_val):
"""
Check that NNCFConv2d with FilterPruningBlock as pre ops working exactly the same as
normal nn.Conv2d.
:param weights_val: value for filling weights
:param bias_val: value for filling biases
"""
nncf_module = NNCFConv2d(1, 1, 2)
pytorch_module = nn.Conv2d(1, 1, 2)
sparse_model = TestFilterPruningBlockModel(nncf_module)
fill_conv_weight(nncf_module, weights_val)
fill_bias(nncf_module, bias_val)
fill_conv_weight(pytorch_module, weights_val)
fill_bias(pytorch_module, bias_val)
act_output = sparse_model(torch.ones([1, 1, 2, 2]))
ref_output = pytorch_module(torch.ones([1, 1, 2, 2]))
assert act_output.item() == ref_output