# Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for Stax library.""" from absl.testing import absltest from absl.testing import parameterized import numpy as np from jax._src import test_util as jtu from jax import random from jax.example_libraries import stax from jax import dtypes from jax.config import config config.parse_flags_with_absl() def random_inputs(rng, input_shape): if type(input_shape) is tuple: return rng.randn(*input_shape).astype(dtypes.canonicalize_dtype(np.float_)) elif type(input_shape) is list: return [random_inputs(rng, shape) for shape in input_shape] else: raise TypeError(type(input_shape)) def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape): rng_key = random.PRNGKey(0) rng_key, init_key = random.split(rng_key) result_shape, params = init_fun(init_key, input_shape) inputs = random_inputs(test_case.rng(), input_shape) result = apply_fun(params, inputs, rng=rng_key) test_case.assertEqual(result.shape, result_shape) # stax makes use of implicit rank promotion, so we allow it in the tests. @jtu.with_config(jax_numpy_rank_promotion="allow") class StaxTest(jtu.JaxTestCase): @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_shape={shape}", "shape": shape} for shape in [(2, 3), (5,)])) def testRandnInitShape(self, shape): key = random.PRNGKey(0) out = stax.randn()(key, shape) self.assertEqual(out.shape, shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_shape={shape}", "shape": shape} for shape in [(2, 3), (2, 3, 4)])) def testGlorotInitShape(self, shape): key = random.PRNGKey(0) out = stax.glorot()(key, shape) self.assertEqual(out.shape, shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_channels={}_filter_shape={}_padding={}_strides={}_input_shape={}" .format(channels, filter_shape, padding, strides, input_shape), "channels": channels, "filter_shape": filter_shape, "padding": padding, "strides": strides, "input_shape": input_shape} for channels in [2, 3] for filter_shape in [(1, 1), (2, 3)] for padding in ["SAME", "VALID"] for strides in [None, (2, 1)] for input_shape in [(2, 10, 11, 1)])) def testConvShape(self, channels, filter_shape, padding, strides, input_shape): init_fun, apply_fun = stax.Conv(channels, filter_shape, strides=strides, padding=padding) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_channels={}_filter_shape={}_padding={}_strides={}_input_shape={}" .format(channels, filter_shape, padding, strides, input_shape), "channels": channels, "filter_shape": filter_shape, "padding": padding, "strides": strides, "input_shape": input_shape} for channels in [2, 3] for filter_shape in [(1, 1), (2, 3), (3, 3)] for padding in ["SAME", "VALID"] for strides in [None, (2, 1), (2, 2)] for input_shape in [(2, 10, 11, 1)])) def testConvTransposeShape(self, channels, filter_shape, padding, strides, input_shape): init_fun, apply_fun = stax.ConvTranspose(channels, filter_shape, # 2D strides=strides, padding=padding) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_channels={}_filter_shape={}_padding={}_strides={}_input_shape={}" .format(channels, filter_shape, padding, strides, input_shape), "channels": channels, "filter_shape": filter_shape, "padding": padding, "strides": strides, "input_shape": input_shape} for channels in [2, 3] for filter_shape in [(1,), (2,), (3,)] for padding in ["SAME", "VALID"] for strides in [None, (1,), (2,)] for input_shape in [(2, 10, 1)])) def testConv1DTransposeShape(self, channels, filter_shape, padding, strides, input_shape): init_fun, apply_fun = stax.Conv1DTranspose(channels, filter_shape, strides=strides, padding=padding) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_out_dim={}_input_shape={}" .format(out_dim, input_shape), "out_dim": out_dim, "input_shape": input_shape} for out_dim in [3, 4] for input_shape in [(2, 3), (3, 4)])) def testDenseShape(self, out_dim, input_shape): init_fun, apply_fun = stax.Dense(out_dim) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_input_shape={}_nonlinear={}" .format(input_shape, nonlinear), "input_shape": input_shape, "nonlinear": nonlinear} for input_shape in [(2, 3), (2, 3, 4)] for nonlinear in ["Relu", "Sigmoid", "Elu", "LeakyRelu"])) def testNonlinearShape(self, input_shape, nonlinear): init_fun, apply_fun = getattr(stax, nonlinear) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_window_shape={}_padding={}_strides={}_input_shape={}" "_maxpool={}_spec={}" .format(window_shape, padding, strides, input_shape, max_pool, spec), "window_shape": window_shape, "padding": padding, "strides": strides, "input_shape": input_shape, "max_pool": max_pool, "spec": spec} for window_shape in [(1, 1), (2, 3)] for padding in ["VALID"] for strides in [None, (2, 1)] for input_shape in [(2, 5, 6, 4)] for max_pool in [False, True] for spec in ["NHWC", "NCHW", "WHNC", "WHCN"])) def testPoolingShape(self, window_shape, padding, strides, input_shape, max_pool, spec): layer = stax.MaxPool if max_pool else stax.AvgPool init_fun, apply_fun = layer(window_shape, padding=padding, strides=strides, spec=spec) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_shape={input_shape}", "input_shape": input_shape} for input_shape in [(2, 3), (2, 3, 4)])) def testFlattenShape(self, input_shape): init_fun, apply_fun = stax.Flatten _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_input_shape={input_shape}_spec={i}", "input_shape": input_shape, "spec": spec} for input_shape in [(2, 5, 6, 1)] for i, spec in enumerate([ [stax.Conv(3, (2, 2))], [stax.Conv(3, (2, 2)), stax.Flatten, stax.Dense(4)]]))) def testSerialComposeLayersShape(self, input_shape, spec): init_fun, apply_fun = stax.serial(*spec) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_input_shape={input_shape}", "input_shape": input_shape} for input_shape in [(3, 4), (2, 5, 6, 1)])) def testDropoutShape(self, input_shape): init_fun, apply_fun = stax.Dropout(0.9) _CheckShapeAgreement(self, init_fun, apply_fun, input_shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_input_shape={input_shape}", "input_shape": input_shape} for input_shape in [(3, 4), (2, 5, 6, 1)])) def testFanInSum(self, input_shape): init_fun, apply_fun = stax.FanInSum _CheckShapeAgreement(self, init_fun, apply_fun, [input_shape, input_shape]) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": f"_inshapes={input_shapes}_axis={axis}", "input_shapes": input_shapes, "axis": axis} for input_shapes, axis in [ ([(2, 3), (2, 1)], 1), ([(2, 3), (2, 1)], -1), ([(1, 2, 4), (1, 1, 4)], 1), ])) def testFanInConcat(self, input_shapes, axis): init_fun, apply_fun = stax.FanInConcat(axis) _CheckShapeAgreement(self, init_fun, apply_fun, input_shapes) def testIssue182(self): key = random.PRNGKey(0) init_fun, apply_fun = stax.Softmax input_shape = (10, 3) inputs = np.arange(30.).astype("float32").reshape(input_shape) out_shape, params = init_fun(key, input_shape) out = apply_fun(params, inputs) assert out_shape == out.shape assert np.allclose(np.sum(np.asarray(out), -1), 1.) def testBatchNormNoScaleOrCenter(self): key = random.PRNGKey(0) axes = (0, 1, 2) init_fun, apply_fun = stax.BatchNorm(axis=axes, center=False, scale=False) input_shape = (4, 5, 6, 7) inputs = random_inputs(self.rng(), input_shape) out_shape, params = init_fun(key, input_shape) out = apply_fun(params, inputs) means = np.mean(out, axis=(0, 1, 2)) std_devs = np.std(out, axis=(0, 1, 2)) assert np.allclose(means, np.zeros_like(means), atol=1e-4) assert np.allclose(std_devs, np.ones_like(std_devs), atol=1e-4) def testBatchNormShapeNHWC(self): key = random.PRNGKey(0) init_fun, apply_fun = stax.BatchNorm(axis=(0, 1, 2)) input_shape = (4, 5, 6, 7) inputs = random_inputs(self.rng(), input_shape) out_shape, params = init_fun(key, input_shape) out = apply_fun(params, inputs) self.assertEqual(out_shape, input_shape) beta, gamma = params self.assertEqual(beta.shape, (7,)) self.assertEqual(gamma.shape, (7,)) self.assertEqual(out_shape, out.shape) def testBatchNormShapeNCHW(self): key = random.PRNGKey(0) # Regression test for https://github.com/google/jax/issues/461 init_fun, apply_fun = stax.BatchNorm(axis=(0, 2, 3)) input_shape = (4, 5, 6, 7) inputs = random_inputs(self.rng(), input_shape) out_shape, params = init_fun(key, input_shape) out = apply_fun(params, inputs) self.assertEqual(out_shape, input_shape) beta, gamma = params self.assertEqual(beta.shape, (5,)) self.assertEqual(gamma.shape, (5,)) self.assertEqual(out_shape, out.shape) if __name__ == "__main__": absltest.main(testLoader=jtu.JaxTestLoader())