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- # 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 akg.utils import kernel_exec as utils
- from tests.common.test_op import prelu_grad
- from tests.common.tensorio import compare_tensor
- from tests.common.gen_random import random_gaussian
-
- def prelu_grad_run(shape, w_shape, dtype, rtol, attrs):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(prelu_grad.prelu_grad, [shape, shape, w_shape], [dtype, dtype, dtype],
- kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- dy, expect_dA, expect_dw, input_data, output_dA, output_dw, w_data = gen_data(dtype, shape, w_shape)
- return mod, (expect_dA, expect_dw), {"args": (dy, input_data, w_data, output_dA, output_dw),
- 'outputs': (-2, -1), 'tuning': False}
- else:
- return mod
- else:
- mod = utils.op_build_test(prelu_grad.prelu_grad, [shape, shape, w_shape], [dtype, dtype, dtype],
- kernel_name='prelu_grad', attrs=attrs)
- dy, expect_dA, expect_dw, input_data, output_dA, output_dw, w_data = gen_data(dtype, shape, w_shape)
- output_dA, output_dw = utils.mod_launch(mod, (dy, input_data, w_data, output_dA, output_dw), outputs=(-2, -1)
- , expect=(expect_dw, expect_dA))
- return (input_data, dy, w_data), (output_dw, output_dA), (expect_dw, expect_dA), \
- compare_tensor(output_dw, expect_dw, rtol=rtol) and compare_tensor(output_dA, expect_dA, rtol=rtol)
-
-
- def gen_data(dtype, shape, w_shape):
- # input_data = -0.01 * np.ones(shape).astype(dtype)
- # dy = -0.01 * np.ones(shape).astype(dtype)
- input_data = random_gaussian(shape, miu=0, sigma=0.001).astype(dtype.lower())
- dy = random_gaussian(shape, miu=-0.01, sigma=0.001).astype(dtype.lower())
- # w_data = random_gaussian(w_shape, miu=0, sigma=1).astype(dtype.lower())
- # input_data = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(dtype)
- # dy = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(dtype)
- w_data = np.random.uniform(low=0, high=1.0, size=w_shape).astype(dtype)
- w_reshape = w_data.reshape(1, w_shape[0], 1, 1)
- w_broadcast = np.broadcast_to(w_reshape, shape)
- expect_dA = dy * (input_data >= 0) + dy * (input_data < 0) * w_broadcast
- # expect_dA = (dy.astype("float32") * (input_data >= 0) + dy.astype("float32") * (input_data < 0) * w_broadcast.astype("float32")).astype(dtype.lower())
- dw_intermediate = dy * (input_data < 0) * input_data
- if w_shape[0] == 1:
- # expect_dw = np.sum(dw_intermediate, keepdims = True, dtype=dtype)
- expect_dw = (np.sum(dw_intermediate, dtype="float32")).astype(dtype.lower())
- # expect_dw = np.sum(dw_intermediate, dtype=dtype)
- expect_dw = expect_dw.reshape(1)
- else:
- expect_dw = (np.sum(dw_intermediate, axis=(0, 2, 3), dtype="float32")).astype(dtype.lower())
- # expect_dw = np.sum(dw_intermediate, axis=(0,2,3), dtype=dtype)
- # expect_dw = np.sum(dw_intermediate.astype("float32"), axis=(0,2,3))
- # expect_dw = expect_dw.astype(dtype)
- output_dA = np.full(expect_dA.shape, np.nan, dtype)
- output_dw = np.full(expect_dw.shape, np.nan, dtype)
- return dy, expect_dA, expect_dw, input_data, output_dA, output_dw, w_data
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