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NNablaをmacで動かしてみた
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| # rpath で実行時解決？だと mac は怒られるからパスをそのまま書き込んであげる | |
| install_name_tool -id /Users/yamachu/.anyenv/envs/pyenv/versions/anaconda3-4.3.1/envs/py27/lib/python2.7/site-packages/nnabla/libnnabla.dylib libnnabla.dylib | |
| for ss in `ls *.so`; do | |
| install_name_tool -change "@rpath/libnnabla.dylib" /Users/yamachu/.anyenv/envs/pyenv/versions/anaconda3-4.3.1/envs/py27/lib/python2.7/site-packages/nnabla/libnnabla.dylib $ss | |
| done |
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| ============================= test session starts ============================== | |
| platform darwin -- Python 2.7.13, pytest-3.1.2, py-1.4.34, pluggy-0.4.0 | |
| rootdir: /Users/yamachu/tmp/nnabla/python, inifile: pytest.ini | |
| collected 873 items | |
| python/test/test_array_preference.py . | |
| python/test/test_graph.py ..... | |
| python/test/test_imperative.py ... | |
| python/test/test_nd_array.py . | |
| python/test/test_parameter.py .. | |
| python/test/test_python_function.py . | |
| python/test/test_save_load_parameters.py . | |
| python/test/test_variable.py ......... | |
| python/test/function/test_abs.py . | |
| python/test/function/test_add2.py ....... | |
| python/test/function/test_add_scalar.py ... | |
| python/test/function/test_affine.py .... | |
| python/test/function/test_arithmetic_ops.py ............................. | |
| python/test/function/test_average_pooling.py ................ | |
| python/test/function/test_batch_normalization.py ...... | |
| python/test/function/test_binary_activation.py .. | |
| python/test/function/test_binary_connect_affine.py .... | |
| python/test/function/test_binary_connect_convolution.py .... | |
| python/test/function/test_binary_cross_entropy.py . | |
| python/test/function/test_binary_error.py . | |
| python/test/function/test_binary_weight_affine.py .... | |
| python/test/function/test_binary_weight_convolution.py .... | |
| python/test/function/test_broadcast.py ............................................................... | |
| python/test/function/test_categorical_cross_entropy.py ... | |
| python/test/function/test_celu.py ......... | |
| python/test/function/test_concatenate.py ............ | |
| python/test/function/test_confusion_matrix.py ... | |
| python/test/function/test_convolution.py ........ | |
| python/test/function/test_crelu.py ... | |
| python/test/function/test_deconvolution.py ................................................................ | |
| python/test/function/test_dropout.py ........ | |
| python/test/function/test_elu.py ... | |
| python/test/function/test_embed.py ............ | |
| python/test/function/test_exp.py . | |
| python/test/function/test_flip.py .... | |
| python/test/function/test_huber_loss.py ... | |
| python/test/function/test_identity.py . | |
| python/test/function/test_image_augmentation.py . | |
| python/test/function/test_kl_multinomial.py ... | |
| python/test/function/test_log.py . | |
| python/test/function/test_logical.py .............................. | |
| python/test/function/test_max_pooling.py ........ | |
| python/test/function/test_maximum_scalar.py ... | |
| python/test/function/test_mean_subtraction.py ... | |
| python/test/function/test_minimum_scalar.py ... | |
| python/test/function/test_mul_scalar.py ... | |
| python/test/function/test_one_hot.py . | |
| python/test/function/test_pow_scalar.py ... | |
| python/test/function/test_prelu.py .. | |
| python/test/function/test_r_div_scalar.py ... | |
| python/test/function/test_r_pow_scalar.py ... | |
| python/test/function/test_r_sub_scalar.py ... | |
| python/test/function/test_random_crop.py ... | |
| python/test/function/test_random_flip.py ... | |
| python/test/function/test_random_shift.py ........ | |
| python/test/function/test_reduce.py .. | |
| python/test/function/test_reduction.py ......F......F........................................................ | |
| python/test/function/test_relu.py .. | |
| python/test/function/test_reshape.py .. | |
| python/test/function/test_shift.py ........ | |
| python/test/function/test_sigmoid.py . | |
| python/test/function/test_sigmoid_cross_entropy.py . | |
| python/test/function/test_sign.py .. | |
| python/test/function/test_slice.py .... | |
| python/test/function/test_softmax.py ... | |
| python/test/function/test_softmax_cross_entropy.py ... | |
| python/test/function/test_split.py ... | |
| python/test/function/test_squared_error.py . | |
| python/test/function/test_stack.py ...... | |
| python/test/function/test_sum_pooling.py ........ | |
| python/test/function/test_tanh.py . | |
| python/test/function/test_top_n_error.py ...... | |
| python/test/function/test_transform_binary.py .......................................... | |
| python/test/function/test_transpose.py .... | |
| python/test/function/test_unlink.py .. | |
| python/test/function/test_unpooling.py ............ | |
| python/test/function/test_vat_noise.py ...... | |
| python/test/solver/test_adadelta.py .... | |
| python/test/solver/test_adagrad.py .. | |
| python/test/solver/test_adam.py .... | |
| python/test/solver/test_adamax.py .... | |
| python/test/solver/test_momentum.py .... | |
| python/test/solver/test_nesterov.py .... | |
| python/test/solver/test_rmsprop.py .... | |
| python/test/solver/test_sgd.py .. | |
| python/test/utils/test_data_iterator.py .................................................................................................................................................................................................................................................................... | |
| python/test/utils/test_data_source.py ...... | |
| =================================== FAILURES =================================== | |
| ________ test_reduction_forward_backward[sum-ctx0-Sum-False-axis6-313] _________ | |
| op = 'sum', seed = 313, axis = (1, 2, 3), keepdims = False | |
| ctx = Context(backend='cpu', array_class='', device_id='0', compute_backend='default') | |
| func_name = 'Sum' | |
| @pytest.mark.parametrize("seed", [313]) | |
| @pytest.mark.parametrize("axis", [None, 0, 1, 2, 3, (0, 2), (1, 2, 3)]) | |
| @pytest.mark.parametrize("keepdims", [False, True]) | |
| @pytest.mark.parametrize("op, ctx, func_name", list_ctx_and_func_name(['sum', 'mean', 'max', 'min', 'prod'])) | |
| def test_reduction_forward_backward(op, seed, axis, keepdims, ctx, func_name): | |
| from nbla_test_utils import function_tester | |
| func = getattr(F, op) | |
| ref_func = getattr(np, op) | |
| rng = np.random.RandomState(seed) | |
| inputs = [rng.randn(2, 3, 4, 5).astype(np.float32)] | |
| function_tester(rng, func, ref_func, inputs, | |
| func_args=[axis], | |
| func_kwargs=dict(keepdims=keepdims), | |
| ctx=ctx, func_name=func_name, | |
| > atol_b=3e-3) | |
| python/test/function/test_reduction.py:40: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| rng = <mtrand.RandomState object at 0x10c0fa140> | |
| func = <function sum at 0x10a0fbde8>, ref_func = <function sum at 0x10496a9b0> | |
| inputs = [array([[[[ 0.18527888, -1.2367872 , 0.32600349, 0.99755579, 0.08737291], | |
| ...3105891, -0.11142748, -2.01562786, 1.57058263, 1.40829337]]]], dtype=float32)] | |
| func_args = [(1, 2, 3)], func_kwargs = {'keepdims': False}, atol_f = 1e-06 | |
| atol_b = 0.003, dstep = 0.001, backward = [True] | |
| ctx = Context(backend='cpu', array_class='', device_id='0', compute_backend='default') | |
| func_name = 'Sum', ref_grad = None | |
| def function_tester(rng, func, ref_func, inputs, | |
| func_args=[], func_kwargs={}, | |
| atol_f=1e-6, atol_b=1e-3, dstep=1e-3, backward=None, | |
| ctx=None, func_name=None, ref_grad=None): | |
| """ Automatic testing of forward/backward pass of `func` by comparing it | |
| to the reference implementation in `ref_func`. | |
| Syntax of `ref_func`: inputs, parametes | |
| Syntax of `ref_grad`: inputs, output grads, parameters | |
| """ | |
| if ctx is None: | |
| ctx = nn.Context() | |
| if backward is None: | |
| backward = [True for _ in inputs] | |
| # Create Variables | |
| # print 'create_variable' | |
| def create_variables(inputs, backward): | |
| vinputs = [] | |
| for i, b in zip(inputs, backward): | |
| if i is None: | |
| vinputs += [None] | |
| continue | |
| vinputs += [nn.Variable(i.shape, need_grad=b)] | |
| vinputs[-1].data.cast(i.dtype)[...] = i | |
| return vinputs | |
| vinputs = create_variables(inputs, backward) | |
| # Checking forward | |
| # print 'checking forward' | |
| with nn.context_scope(ctx), nn.auto_forward(): | |
| o = func(*(vinputs + func_args), **func_kwargs) | |
| rinputs = copy.deepcopy(inputs) # inputs for ref_func | |
| refs = ref_func(*(rinputs + func_args), **func_kwargs) | |
| def force_tuple(x): | |
| if isinstance(x, tuple): | |
| return x | |
| return (x,) | |
| refs = force_tuple(refs) | |
| o = force_tuple(o) | |
| assert len(o) == len(refs) | |
| for i, ref in enumerate(refs): | |
| res = o[i].d | |
| assert np.allclose(ref, res, atol=atol_f) | |
| # Checking function name | |
| # print 'checking function name' | |
| if func_name is not None: | |
| assert o[0].parent.name == func_name | |
| # Checking backward | |
| # print 'checking backward' | |
| if not True in backward: | |
| return | |
| # NNabla backward | |
| for v in vinputs: | |
| if v is None: | |
| continue | |
| if len(v.shape) == 0: | |
| v.g = rng.randn() | |
| continue | |
| v.g = rng.randn(*v.shape).astype(v.data.dtype) | |
| # Verify grad | |
| vinputs = create_variables(inputs, backward) | |
| rinputs = copy.deepcopy(inputs) | |
| rinputs = [rinput if test else None for rinput, | |
| test in zip(rinputs, backward)] | |
| vgrads = [rng.randn(*o_.shape) for o_ in o] | |
| agrads, ngrads = compute_analytical_and_numerical_grad( | |
| o[0].parent, vinputs, o, rinputs, vgrads, epsilon=dstep, | |
| rng=rng) | |
| if ref_grad is not None: | |
| rinputs = copy.deepcopy(inputs) | |
| doutputs = [o_.g for o_ in o] | |
| ngrads = ref_grad(*(rinputs + doutputs + func_args), **func_kwargs) | |
| > assert np.allclose(ngrads, agrads, atol=atol_b) | |
| E AssertionError | |
| python/test/nbla_test_utils.py:249: AssertionError | |
| _________ test_reduction_forward_backward[sum-ctx0-Sum-True-axis6-313] _________ | |
| op = 'sum', seed = 313, axis = (1, 2, 3), keepdims = True | |
| ctx = Context(backend='cpu', array_class='', device_id='0', compute_backend='default') | |
| func_name = 'Sum' | |
| @pytest.mark.parametrize("seed", [313]) | |
| @pytest.mark.parametrize("axis", [None, 0, 1, 2, 3, (0, 2), (1, 2, 3)]) | |
| @pytest.mark.parametrize("keepdims", [False, True]) | |
| @pytest.mark.parametrize("op, ctx, func_name", list_ctx_and_func_name(['sum', 'mean', 'max', 'min', 'prod'])) | |
| def test_reduction_forward_backward(op, seed, axis, keepdims, ctx, func_name): | |
| from nbla_test_utils import function_tester | |
| func = getattr(F, op) | |
| ref_func = getattr(np, op) | |
| rng = np.random.RandomState(seed) | |
| inputs = [rng.randn(2, 3, 4, 5).astype(np.float32)] | |
| function_tester(rng, func, ref_func, inputs, | |
| func_args=[axis], | |
| func_kwargs=dict(keepdims=keepdims), | |
| ctx=ctx, func_name=func_name, | |
| > atol_b=3e-3) | |
| python/test/function/test_reduction.py:40: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| rng = <mtrand.RandomState object at 0x10c177050> | |
| func = <function sum at 0x10a0fbde8>, ref_func = <function sum at 0x10496a9b0> | |
| inputs = [array([[[[ 0.18527888, -1.2367872 , 0.32600349, 0.99755579, 0.08737291], | |
| ...3105891, -0.11142748, -2.01562786, 1.57058263, 1.40829337]]]], dtype=float32)] | |
| func_args = [(1, 2, 3)], func_kwargs = {'keepdims': True}, atol_f = 1e-06 | |
| atol_b = 0.003, dstep = 0.001, backward = [True] | |
| ctx = Context(backend='cpu', array_class='', device_id='0', compute_backend='default') | |
| func_name = 'Sum', ref_grad = None | |
| def function_tester(rng, func, ref_func, inputs, | |
| func_args=[], func_kwargs={}, | |
| atol_f=1e-6, atol_b=1e-3, dstep=1e-3, backward=None, | |
| ctx=None, func_name=None, ref_grad=None): | |
| """ Automatic testing of forward/backward pass of `func` by comparing it | |
| to the reference implementation in `ref_func`. | |
| Syntax of `ref_func`: inputs, parametes | |
| Syntax of `ref_grad`: inputs, output grads, parameters | |
| """ | |
| if ctx is None: | |
| ctx = nn.Context() | |
| if backward is None: | |
| backward = [True for _ in inputs] | |
| # Create Variables | |
| # print 'create_variable' | |
| def create_variables(inputs, backward): | |
| vinputs = [] | |
| for i, b in zip(inputs, backward): | |
| if i is None: | |
| vinputs += [None] | |
| continue | |
| vinputs += [nn.Variable(i.shape, need_grad=b)] | |
| vinputs[-1].data.cast(i.dtype)[...] = i | |
| return vinputs | |
| vinputs = create_variables(inputs, backward) | |
| # Checking forward | |
| # print 'checking forward' | |
| with nn.context_scope(ctx), nn.auto_forward(): | |
| o = func(*(vinputs + func_args), **func_kwargs) | |
| rinputs = copy.deepcopy(inputs) # inputs for ref_func | |
| refs = ref_func(*(rinputs + func_args), **func_kwargs) | |
| def force_tuple(x): | |
| if isinstance(x, tuple): | |
| return x | |
| return (x,) | |
| refs = force_tuple(refs) | |
| o = force_tuple(o) | |
| assert len(o) == len(refs) | |
| for i, ref in enumerate(refs): | |
| res = o[i].d | |
| assert np.allclose(ref, res, atol=atol_f) | |
| # Checking function name | |
| # print 'checking function name' | |
| if func_name is not None: | |
| assert o[0].parent.name == func_name | |
| # Checking backward | |
| # print 'checking backward' | |
| if not True in backward: | |
| return | |
| # NNabla backward | |
| for v in vinputs: | |
| if v is None: | |
| continue | |
| if len(v.shape) == 0: | |
| v.g = rng.randn() | |
| continue | |
| v.g = rng.randn(*v.shape).astype(v.data.dtype) | |
| # Verify grad | |
| vinputs = create_variables(inputs, backward) | |
| rinputs = copy.deepcopy(inputs) | |
| rinputs = [rinput if test else None for rinput, | |
| test in zip(rinputs, backward)] | |
| vgrads = [rng.randn(*o_.shape) for o_ in o] | |
| agrads, ngrads = compute_analytical_and_numerical_grad( | |
| o[0].parent, vinputs, o, rinputs, vgrads, epsilon=dstep, | |
| rng=rng) | |
| if ref_grad is not None: | |
| rinputs = copy.deepcopy(inputs) | |
| doutputs = [o_.g for o_ in o] | |
| ngrads = ref_grad(*(rinputs + doutputs + func_args), **func_kwargs) | |
| > assert np.allclose(ngrads, agrads, atol=atol_b) | |
| E AssertionError | |
| python/test/nbla_test_utils.py:249: AssertionError | |
| ==================== 2 failed, 871 passed in 19.59 seconds ===================== |
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