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0ff234049b
These docstrings do not make the tests any more clear and typically just duplicate the test module name. PiperOrigin-RevId: 737611977
233 lines
8.0 KiB
Python
233 lines
8.0 KiB
Python
# Copyright 2018 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from absl.testing import absltest
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import numpy as np
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import jax
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from jax._src import test_util as jtu
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from jax import random
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from jax.example_libraries import stax
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from jax import dtypes
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jax.config.parse_flags_with_absl()
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def random_inputs(rng, input_shape):
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if type(input_shape) is tuple:
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return rng.randn(*input_shape).astype(dtypes.canonicalize_dtype(float))
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elif type(input_shape) is list:
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return [random_inputs(rng, shape) for shape in input_shape]
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else:
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raise TypeError(type(input_shape))
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def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):
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rng_key = random.PRNGKey(0)
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rng_key, init_key = random.split(rng_key)
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result_shape, params = init_fun(init_key, input_shape)
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inputs = random_inputs(test_case.rng(), input_shape)
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if params:
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inputs = inputs.astype(np.dtype(params[0]))
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result = apply_fun(params, inputs, rng=rng_key)
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test_case.assertEqual(result.shape, result_shape)
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# stax makes use of implicit rank promotion, so we allow it in the tests.
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@jtu.with_config(jax_numpy_rank_promotion="allow",
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jax_legacy_prng_key="allow")
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class StaxTest(jtu.JaxTestCase):
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@jtu.sample_product(shape=[(2, 3), (5,)])
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def testRandnInitShape(self, shape):
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key = random.PRNGKey(0)
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out = stax.randn()(key, shape)
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self.assertEqual(out.shape, shape)
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@jtu.sample_product(shape=[(2, 3), (2, 3, 4)])
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def testGlorotInitShape(self, shape):
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key = random.PRNGKey(0)
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out = stax.glorot()(key, shape)
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self.assertEqual(out.shape, shape)
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@jtu.sample_product(
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channels=[2, 3],
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filter_shape=[(1, 1), (2, 3)],
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padding=["SAME", "VALID"],
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strides=[None, (2, 1)],
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input_shape=[(2, 10, 11, 1)],
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)
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def testConvShape(self, channels, filter_shape, padding, strides,
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input_shape):
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init_fun, apply_fun = stax.Conv(channels, filter_shape, strides=strides,
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padding=padding)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(
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channels=[2, 3],
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filter_shape=[(1, 1), (2, 3), (3, 3)],
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padding=["SAME", "VALID"],
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strides=[None, (2, 1), (2, 2)],
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input_shape=[(2, 10, 11, 1)],
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)
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def testConvTransposeShape(self, channels, filter_shape, padding, strides,
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input_shape):
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init_fun, apply_fun = stax.ConvTranspose(channels, filter_shape, # 2D
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strides=strides, padding=padding)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(
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channels=[2, 3],
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filter_shape=[(1,), (2,), (3,)],
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padding=["SAME", "VALID"],
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strides=[None, (1,), (2,)],
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input_shape=[(2, 10, 1)],
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)
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def testConv1DTransposeShape(self, channels, filter_shape, padding, strides,
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input_shape):
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init_fun, apply_fun = stax.Conv1DTranspose(channels, filter_shape,
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strides=strides, padding=padding)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(
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out_dim=[3, 4],
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input_shape=[(2, 3), (3, 4)],
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)
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def testDenseShape(self, out_dim, input_shape):
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init_fun, apply_fun = stax.Dense(out_dim)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(
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input_shape=[(2, 3), (2, 3, 4)],
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nonlinear=["Relu", "Sigmoid", "Elu", "LeakyRelu"],
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)
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def testNonlinearShape(self, input_shape, nonlinear):
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init_fun, apply_fun = getattr(stax, nonlinear)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(
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window_shape=[(1, 1), (2, 3)],
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padding=["VALID"],
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strides=[None, (2, 1)],
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input_shape=[(2, 5, 6, 4)],
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max_pool=[False, True],
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spec=["NHWC", "NCHW", "WHNC", "WHCN"],
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)
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def testPoolingShape(self, window_shape, padding, strides, input_shape,
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max_pool, spec):
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layer = stax.MaxPool if max_pool else stax.AvgPool
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init_fun, apply_fun = layer(window_shape, padding=padding, strides=strides,
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spec=spec)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(input_shape=[(2, 3), (2, 3, 4)])
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def testFlattenShape(self, input_shape):
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init_fun, apply_fun = stax.Flatten
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(
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input_shape=[(2, 5, 6, 1)],
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spec=[
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[stax.Conv(3, (2, 2))],
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[stax.Conv(3, (2, 2)), stax.Flatten, stax.Dense(4)],
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],
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)
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def testSerialComposeLayersShape(self, input_shape, spec):
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init_fun, apply_fun = stax.serial(*spec)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(input_shape=[(3, 4), (2, 5, 6, 1)])
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def testDropoutShape(self, input_shape):
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init_fun, apply_fun = stax.Dropout(0.9)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
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@jtu.sample_product(input_shape=[(3, 4), (2, 5, 6, 1)])
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def testFanInSum(self, input_shape):
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init_fun, apply_fun = stax.FanInSum
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_CheckShapeAgreement(self, init_fun, apply_fun, [input_shape, input_shape])
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@jtu.sample_product(
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[dict(input_shapes=input_shapes, axis=axis)
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for input_shapes, axis in [
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([(2, 3), (2, 1)], 1),
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([(2, 3), (2, 1)], -1),
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([(1, 2, 4), (1, 1, 4)], 1),
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]
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],
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)
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def testFanInConcat(self, input_shapes, axis):
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init_fun, apply_fun = stax.FanInConcat(axis)
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_CheckShapeAgreement(self, init_fun, apply_fun, input_shapes)
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def testIssue182(self):
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key = random.PRNGKey(0)
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init_fun, apply_fun = stax.Softmax
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input_shape = (10, 3)
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inputs = np.arange(30.).astype("float32").reshape(input_shape)
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out_shape, params = init_fun(key, input_shape)
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out = apply_fun(params, inputs)
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assert out_shape == out.shape
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assert np.allclose(np.sum(np.asarray(out), -1), 1.)
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def testBatchNormNoScaleOrCenter(self):
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key = random.PRNGKey(0)
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axes = (0, 1, 2)
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init_fun, apply_fun = stax.BatchNorm(axis=axes, center=False, scale=False)
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input_shape = (4, 5, 6, 7)
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inputs = random_inputs(self.rng(), input_shape)
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out_shape, params = init_fun(key, input_shape)
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out = apply_fun(params, inputs)
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means = np.mean(out, axis=(0, 1, 2))
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std_devs = np.std(out, axis=(0, 1, 2))
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assert np.allclose(means, np.zeros_like(means), atol=1e-4)
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assert np.allclose(std_devs, np.ones_like(std_devs), atol=1e-4)
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def testBatchNormShapeNHWC(self):
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key = random.PRNGKey(0)
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init_fun, apply_fun = stax.BatchNorm(axis=(0, 1, 2))
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input_shape = (4, 5, 6, 7)
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out_shape, params = init_fun(key, input_shape)
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inputs = random_inputs(self.rng(), input_shape).astype(params[0].dtype)
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out = apply_fun(params, inputs)
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self.assertEqual(out_shape, input_shape)
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beta, gamma = params
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self.assertEqual(beta.shape, (7,))
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self.assertEqual(gamma.shape, (7,))
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self.assertEqual(out_shape, out.shape)
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def testBatchNormShapeNCHW(self):
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key = random.PRNGKey(0)
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# Regression test for https://github.com/jax-ml/jax/issues/461
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init_fun, apply_fun = stax.BatchNorm(axis=(0, 2, 3))
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input_shape = (4, 5, 6, 7)
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out_shape, params = init_fun(key, input_shape)
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inputs = random_inputs(self.rng(), input_shape).astype(params[0].dtype)
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out = apply_fun(params, inputs)
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self.assertEqual(out_shape, input_shape)
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beta, gamma = params
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self.assertEqual(beta.shape, (5,))
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self.assertEqual(gamma.shape, (5,))
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self.assertEqual(out_shape, out.shape)
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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