|
- # Copyright 2019 Huawei Technologies Co., Ltd
- #
- # 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
- #
- # http://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 numpy as np
- from tests.common.tensorio import compare_tensor
- from akg.utils import kernel_exec as utils
- from tests.common.test_op.crossentropyloss_ad import crossentropyloss_ad
-
-
- def GenData(shape, dtype):
- """Generate data for testing the op."""
- class_num = shape[1]
- labels_int = np.random.randint(low=0, high=shape[1] - 1, size=shape[0])
- labels = np.eye(class_num)[labels_int].astype(dtype)
- logits = np.random.random(shape).astype(dtype)
- logits = np.where(logits < 0.001, 0.001, logits)
- head_np = np.random.uniform(low=0, high=1.0, size=shape[0]).astype(dtype)
- head_np = head_np.reshape([shape[0], 1])
- loss_ad_temp = labels / logits
- loss_ad_temp = loss_ad_temp * head_np
- loss_ad = loss_ad_temp * -1
- return labels, logits, head_np, loss_ad
-
-
- def crossentropyloss_ad_run(shape, dtype, kernel_name, attrs):
- if (len(shape) != 2):
- raise RuntimeError("shape must be 2-d")
- shape_head = [shape[0], 1]
-
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(crossentropyloss_ad, [shape_head, shape, shape], [dtype, dtype, dtype],
- kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- labels, logits, head_np, expect_loss_ad = GenData(shape, dtype)
- res_loss_ad = np.full(shape, 0, dtype)
- return mod, expect_loss_ad, (head_np, labels, logits, res_loss_ad)
- else:
- return mod
- else:
- labels, logits, head_np, expect_loss_ad = GenData(shape, dtype)
- res_loss_ad = np.full(shape, 0, dtype)
- mod = utils.op_build_test(crossentropyloss_ad, [shape_head, shape, shape], [dtype, dtype, dtype],
- kernel_name=kernel_name, attrs=attrs)
- res_loss_ad = utils.mod_launch(mod, (head_np, labels, logits, res_loss_ad), expect=expect_loss_ad)
- cpr_loss_ad = compare_tensor(res_loss_ad, expect_loss_ad, rtol=5e-03, atol=5e-03, equal_nan=True)
-
- return logits, res_loss_ad, expect_loss_ad, cpr_loss_ad
|
