forked from mindspore-Ecosystem/mindspore
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# http://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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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import mindspore as ms
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def create_tensor(capcity, shapes, dtypes):
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buffer = []
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for i in range(len(shapes)):
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buffer.append(Tensor(np.zeros(((capcity,)+shapes[i])), dtypes[i]))
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return buffer
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class RLBuffer(nn.Cell):
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def __init__(self, batch_size, capcity, shapes, types):
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super(RLBuffer, self).__init__()
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self.buffer = create_tensor(capcity, shapes, types)
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self._capacity = capcity
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self.count = Parameter(Tensor(0, ms.int32), name="count")
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self.head = Parameter(Tensor(0, ms.int32), name="head")
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self.buffer_append = P.BufferAppend(self._capacity, shapes, types)
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self.buffer_get = P.BufferGetItem(self._capacity, shapes, types)
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self.buffer_sample = P.BufferSample(
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self._capacity, batch_size, shapes, types)
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self.randperm = P.Randperm(max_length=capcity, pad=-1)
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self.reshape = P.Reshape()
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@ms_function
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def append(self, exps):
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return self.buffer_append(self.buffer, exps, self.count, self.head)
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@ms_function
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def get(self, index):
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return self.buffer_get(self.buffer, self.count, self.head, index)
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@ms_function
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def sample(self):
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return self.buffer_sample(self.buffer, self.count, self.head)
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s = Tensor(np.array([2, 2, 2, 2]), ms.float32)
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a = Tensor(np.array([0, 1]), ms.int32)
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r = Tensor(np.array([1]), ms.float32)
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s_ = Tensor(np.array([3, 3, 3, 3]), ms.float32)
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exp = [s, a, r, s_]
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exp1 = [s_, a, r, s]
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@ pytest.mark.level0
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@ pytest.mark.platform_x86_gpu_training
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@ pytest.mark.env_onecard
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def test_Buffer():
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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buffer = RLBuffer(batch_size=32, capcity=100, shapes=[(4,), (2,), (1,), (4,)], types=[
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ms.float32, ms.int32, ms.float32, ms.float32])
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print("init buffer:\n", buffer.buffer)
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for _ in range(0, 110):
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buffer.append(exp)
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buffer.append(exp1)
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print("buffer append:\n", buffer.buffer)
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b = buffer.get(-1)
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print("buffer get:\n", b)
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bs = buffer.sample()
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print("buffer sample:\n", bs)
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