forked from mindspore-Ecosystem/mindspore
148 lines
5.7 KiB
Python
148 lines
5.7 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 jit
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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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class RLBufferAppend(nn.Cell):
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def __init__(self, capcity, shapes, types):
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super(RLBufferAppend, self).__init__()
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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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@jit
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def construct(self, buffer, exps):
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return self.buffer_append(buffer, exps, self.count, self.head)
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class RLBufferGet(nn.Cell):
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def __init__(self, capcity, shapes, types):
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super(RLBufferGet, self).__init__()
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self._capacity = capcity
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self.count = Parameter(Tensor(5, ms.int32), name="count")
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self.head = Parameter(Tensor(0, ms.int32), name="head")
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self.buffer_get = P.BufferGetItem(self._capacity, shapes, types)
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@jit
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def construct(self, buffer, index):
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return self.buffer_get(buffer, self.count, self.head, index)
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class RLBufferSample(nn.Cell):
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def __init__(self, capcity, batch_size, shapes, types):
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super(RLBufferSample, self).__init__()
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self._capacity = capcity
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self.count = Parameter(Tensor(5, ms.int32), name="count")
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self.head = Parameter(Tensor(0, ms.int32), name="head")
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self.buffer_sample = P.BufferSample(
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self._capacity, batch_size, shapes, types)
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@jit
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def construct(self, buffer):
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return self.buffer_sample(buffer, self.count, self.head)
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states = Tensor(np.arange(4*5).reshape(5, 4).astype(np.float32)/10.0)
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actions = Tensor(np.arange(2*5).reshape(5, 2).astype(np.int32))
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rewards = Tensor(np.ones((5, 1)).astype(np.int32))
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states_ = Tensor(np.arange(4*5).reshape(5, 4).astype(np.float32))
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b = [states, actions, rewards, states_]
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s = Tensor(np.array([2, 2, 2, 2]), ms.float32)
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a = Tensor(np.array([0, 0]), ms.int32)
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r = Tensor(np.array([0]), ms.int32)
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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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c = [Tensor(np.array([[6, 6, 6, 6], [6, 6, 6, 6]]), ms.float32),
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Tensor(np.array([[6, 6], [6, 6]]), ms.int32),
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Tensor(np.array([[6], [6]]), ms.int32),
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Tensor(np.array([[6, 6, 6, 6], [6, 6, 6, 6]]), ms.float32)]
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@ pytest.mark.level1
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@ pytest.mark.platform_x86_gpu_training
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@ pytest.mark.env_onecard
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def test_BufferSample():
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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buffer_sample = RLBufferSample(capcity=5, batch_size=3, shapes=[(4,), (2,), (1,), (4,)], types=[
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ms.float32, ms.int32, ms.int32, ms.float32])
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ss, aa, rr, ss_ = buffer_sample(b)
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print(ss, aa, rr, ss_)
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@ pytest.mark.level1
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@ pytest.mark.platform_x86_gpu_training
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@ pytest.mark.env_onecard
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def test_BufferGet():
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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buffer_get = RLBufferGet(capcity=5, shapes=[(4,), (2,), (1,), (4,)], types=[
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ms.float32, ms.int32, ms.int32, ms.float32])
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ss, aa, rr, ss_ = buffer_get(b, 1)
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expect_s = [0.4, 0.5, 0.6, 0.7]
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expect_a = [2, 3]
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expect_r = [1]
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expect_s_ = [4, 5, 6, 7]
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np.testing.assert_almost_equal(ss.asnumpy(), expect_s)
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np.testing.assert_almost_equal(aa.asnumpy(), expect_a)
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np.testing.assert_almost_equal(rr.asnumpy(), expect_r)
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np.testing.assert_almost_equal(ss_.asnumpy(), expect_s_)
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@ pytest.mark.level1
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@ pytest.mark.platform_x86_gpu_training
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@ pytest.mark.env_onecard
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def test_BufferAppend():
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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buffer_append = RLBufferAppend(capcity=5, shapes=[(4,), (2,), (1,), (4,)], types=[
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ms.float32, ms.int32, ms.int32, ms.float32])
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buffer_append(b, exp)
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buffer_append(b, exp)
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buffer_append(b, exp)
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buffer_append(b, exp)
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buffer_append(b, exp)
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buffer_append(b, exp1)
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expect_s = [[3, 3, 3, 3], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]]
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expect_a = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]
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expect_r = [[0], [0], [0], [0], [0]]
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expect_s_ = [[2, 2, 2, 2], [3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3]]
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np.testing.assert_almost_equal(b[0].asnumpy(), expect_s)
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np.testing.assert_almost_equal(b[1].asnumpy(), expect_a)
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np.testing.assert_almost_equal(b[2].asnumpy(), expect_r)
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np.testing.assert_almost_equal(b[3].asnumpy(), expect_s_)
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buffer_append(b, exp1)
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buffer_append(b, c)
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buffer_append(b, c)
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expect_s2 = [[6, 6, 6, 6], [3, 3, 3, 3], [6, 6, 6, 6], [6, 6, 6, 6], [6, 6, 6, 6]]
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expect_a2 = [[6, 6], [0, 0], [6, 6], [6, 6], [6, 6]]
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expect_r2 = [[6], [0], [6], [6], [6]]
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expect_s2_ = [[6, 6, 6, 6], [2, 2, 2, 2], [6, 6, 6, 6], [6, 6, 6, 6], [6, 6, 6, 6]]
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np.testing.assert_almost_equal(b[0].asnumpy(), expect_s2)
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np.testing.assert_almost_equal(b[1].asnumpy(), expect_a2)
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np.testing.assert_almost_equal(b[2].asnumpy(), expect_r2)
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np.testing.assert_almost_equal(b[3].asnumpy(), expect_s2_)
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