forked from huawei/mindspore2022
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
# Copyright 2022 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 mindspore as ms
|
|
from mindspore import context
|
|
from mindspore.ops import operations as P
|
|
from mindspore.common.api import ms_function
|
|
from mindspore.common.tensor import Tensor
|
|
import mindspore.nn as nn
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
|
|
class MAPPOCriticNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.linear1_actor = nn.Dense(54, # input local obs shape
|
|
64,
|
|
weight_init='XavierUniform',
|
|
# paper uses orthogonal with gain 5/3 for every dense123
|
|
has_bias=False,
|
|
activation=nn.Tanh())
|
|
|
|
def construct(self, x):
|
|
# Feature Extraction
|
|
x = self.linear1_actor(x)
|
|
|
|
return x
|
|
|
|
|
|
class MAPPOActor(nn.Cell):
|
|
|
|
def __init__(self, actor_net):
|
|
super().__init__()
|
|
self.actor_net = actor_net
|
|
|
|
def construct(self, inputs_data):
|
|
_, global_obs = inputs_data
|
|
out = self.actor_net(global_obs)
|
|
|
|
return out
|
|
|
|
|
|
class TestClass(nn.Cell):
|
|
def __init__(self, actor_list):
|
|
super().__init__()
|
|
self.zero = Tensor(0, ms.int32)
|
|
self.actor_list = actor_list
|
|
self.less = P.Less()
|
|
self.zeros = P.Zeros()
|
|
|
|
def train(self):
|
|
state = Tensor(np.random.random((3, 128, 18)), ms.float32)
|
|
init_global_obs = self.zeros((128, 54), ms.float32)
|
|
out = self.test(state, init_global_obs)
|
|
return out
|
|
|
|
@ms_function
|
|
def test(self, state, init_global_obs):
|
|
num_agent = self.zero
|
|
while self.less(num_agent, 3):
|
|
samples = (state[num_agent], init_global_obs)
|
|
self.actor_list[num_agent](samples)
|
|
num_agent += 1
|
|
|
|
return num_agent
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net():
|
|
"""
|
|
Feature: Tuple arg transform.
|
|
Description: Test the pass: transform tuple arg to tensor arg.
|
|
Expectation: Compile done without error.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=False, save_graphs_path="./graph_ir")
|
|
actor_list = nn.CellList()
|
|
for _ in range(3):
|
|
net = MAPPOCriticNet()
|
|
actor = MAPPOActor(net)
|
|
actor_list.append(actor)
|
|
test = TestClass(actor_list)
|
|
graph_out = test.train()
|
|
|
|
assert np.allclose(graph_out.asnumpy(), graph_out.asnumpy(), 0.0001, 0.0001)
|