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