mirror of
https://github.com/ROCm/jax.git
synced 2025-04-19 05:16:06 +00:00
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
# 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.
|
|
|
|
|
|
import os
|
|
import sys
|
|
|
|
from absl.testing import absltest
|
|
from absl.testing import parameterized
|
|
|
|
import numpy as np
|
|
|
|
from jax import lax
|
|
from jax import test_util as jtu
|
|
from jax import random
|
|
import jax.numpy as jnp
|
|
|
|
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
|
from examples import kernel_lsq
|
|
from examples import resnet50
|
|
sys.path.pop()
|
|
|
|
from jax.config import config
|
|
config.parse_flags_with_absl()
|
|
FLAGS = config.FLAGS
|
|
|
|
|
|
def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):
|
|
jax_rng = random.PRNGKey(0)
|
|
result_shape, params = init_fun(jax_rng, input_shape)
|
|
rng = np.random.RandomState(0)
|
|
result = apply_fun(params, rng.randn(*input_shape).astype(dtype="float32"))
|
|
test_case.assertEqual(result.shape, result_shape)
|
|
|
|
|
|
class ExamplesTest(jtu.JaxTestCase):
|
|
|
|
@parameterized.named_parameters(
|
|
{"testcase_name": "_input_shape={}".format(input_shape),
|
|
"input_shape": input_shape}
|
|
for input_shape in [(2, 20, 25, 2)])
|
|
@jtu.skip_on_flag('jax_enable_x64', True)
|
|
def testIdentityBlockShape(self, input_shape):
|
|
init_fun, apply_fun = resnet50.IdentityBlock(2, (4, 3))
|
|
_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
|
|
|
|
@parameterized.named_parameters(
|
|
{"testcase_name": "_input_shape={}".format(input_shape),
|
|
"input_shape": input_shape}
|
|
for input_shape in [(2, 20, 25, 3)])
|
|
@jtu.skip_on_flag('jax_enable_x64', True)
|
|
def testConvBlockShape(self, input_shape):
|
|
init_fun, apply_fun = resnet50.ConvBlock(3, (2, 3, 4))
|
|
_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
|
|
|
|
@parameterized.named_parameters(
|
|
{"testcase_name": "_num_classes={}_input_shape={}"
|
|
.format(num_classes, input_shape),
|
|
"num_classes": num_classes, "input_shape": input_shape}
|
|
for num_classes in [5, 10]
|
|
for input_shape in [(224, 224, 3, 2)])
|
|
@jtu.skip_on_flag('jax_enable_x64', True)
|
|
def testResNet50Shape(self, num_classes, input_shape):
|
|
init_fun, apply_fun = resnet50.ResNet50(num_classes)
|
|
_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
|
|
|
|
def testKernelRegressionGram(self):
|
|
n, d = 100, 20
|
|
rng = np.random.RandomState(0)
|
|
xs = rng.randn(n, d)
|
|
kernel = lambda x, y: jnp.dot(x, y)
|
|
self.assertAllClose(kernel_lsq.gram(kernel, xs), jnp.dot(xs, xs.T),
|
|
check_dtypes=False)
|
|
|
|
def testKernelRegressionTrainAndPredict(self):
|
|
n, d = 100, 20
|
|
rng = np.random.RandomState(0)
|
|
truth = rng.randn(d)
|
|
xs = rng.randn(n, d)
|
|
ys = jnp.dot(xs, truth)
|
|
kernel = lambda x, y: jnp.dot(x, y, precision=lax.Precision.HIGH)
|
|
predict = kernel_lsq.train(kernel, xs, ys)
|
|
self.assertAllClose(predict(xs), ys, atol=1e-3, rtol=1e-3,
|
|
check_dtypes=False)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main(testLoader=jtu.JaxTestLoader())
|