def test_load_and_infer_improvement(nntxt_idx, parameter_format, dataset_sample_num):
'''This case tests improvement features, comparing legacy implementation,
legacy cannot load or infer successfully, while refactor-ed is OK.
'''
with generate_case_from_nntxt_str(NNTXT_IMPROVEMENT_CASES[nntxt_idx], parameter_format, dataset_sample_num) as nnp_file:
with pytest.raises(ValueError) as excinfo:
ref_info = ref_load(nnp_file)
ref_result = partial(
common_forward, forward_func=_ref_forward)(ref_info)
print(excinfo)
info = load.load(nnp_file)
result = partial(common_forward, forward_func=_forward)(info)
def test_load_and_infer_equivalence(nntxt_idx, parameter_format, dataset_sample_num):
'''These cases tends to test equivalence before and after
refactoring NNP load functions. The scope of refactor includes network part and load function.
This test firstly generated .nnp from nntxt_str, according to specified parameter_format
and replace dataset's uri with a temporarily generated random dataset, then performs inferring
operation similar to what is done in cli/forward.py.
'''
with generate_case_from_nntxt_str(NNTXT_EQUIVALENCE_CASES[nntxt_idx], parameter_format, dataset_sample_num) as nnp_file:
ref_info = ref_load(nnp_file)
ref_result = partial(
common_forward, forward_func=_ref_forward)(ref_info)
info = load.load(nnp_file)
result = partial(common_forward, forward_func=_forward)(info)
assert_tensor_equal(result, ref_result)
def test_load_and_train_equivalence(nntxt_idx, parameter_format,
dataset_sample_num, batch_size):
'''These cases tends to test equivalence before and after refactoring.
The operation is similar to what is done in cli/train.py.
'''
# for debugging
save_v = False
output_network_topology = False
verbose = False
m_iter = 10
class Callback:
pass
legacy_config = TrainConfig()
legacy_config.on_iter = None
legacy_config.save_optimizer_variable = False
legacy_config.save_evaluation_variable = False
legacy_config.start_iteration = 0
legacy_config.end_iteration = 10
legacy_config.enable_save_variable = save_v
legacy_cb = Callback()
legacy_cb.forward = lambda o: o.network.forward(o.forward_sequence)
legacy_cb.backward = lambda o, b: o.network.backward(
o.backward_sequence, b)
legacy_config.cb = legacy_cb
legacy_config.impl = "legacy"
new_config = TrainConfig()
new_config.on_iter = None
new_config.save_optimizer_variable = False
new_config.save_evaluation_variable = False
new_config.start_iteration = 0
new_config.end_iteration = 10
new_config.enable_save_variable = save_v
new_cb = Callback()
new_cb.forward = lambda x: x.target.forward(clear_no_need_grad=True)
new_cb.backward = lambda x, b: x.target.backward(clear_buffer=True)
new_config.cb = new_cb
new_config.impl = "new"
with generate_case_from_nntxt_str(NNTXT_EQUIVALENCE_CASES[nntxt_idx],
parameter_format, dataset_sample_num,
batch_size) as nnp_file:
ref_result = []
result = []
nn.clear_parameters()
info = ref_load(nnp_file, batch_size=batch_size)
for cost, error in partial(train, config=legacy_config)(info):
ref_result.append((cost, error))
nn.clear_parameters()
info = load.load(nnp_file, batch_size=batch_size)
if output_network_topology:
for n, opt in info.optimizers.items():
print(n)
opt.network.execute_on_proto(Verifier())
for cost, error in partial(train, config=new_config)(info):
result.append((cost, error))
for i, ((cost_ref, error_ref),
(cost, error)) in enumerate(zip(ref_result, result)):
if verbose:
print("{}: cost: {} <--> {}".format(i, cost_ref, cost))
print("{}: error: {} <--> {}".format(i, error_ref, error))
assert_allclose(np.array([cost_ref, error_ref]),
np.array([cost, error]),
rtol=1e-2,
atol=1e-3,
err_msg="Error: {}".format(nntxt_idx))