forked from mindspore-Ecosystem/mindspore
129 lines
3.8 KiB
Python
129 lines
3.8 KiB
Python
# Copyright 2020 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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""" test_net_infer """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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import mindspore.ops.operations as op
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def test_net_infer():
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""" test_net_infer """
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')
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self.bn = nn.BatchNorm2d(64)
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self.fc = nn.Dense(64, 10)
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self.relu = nn.ReLU()
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv(x)
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x = self.relu(x)
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x = self.flatten(x)
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out = self.fc(x)
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return out
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Tensor(np.random.randint(0, 255, [1, 3, 224, 224]))
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Net()
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def test_assign_in_while():
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context.set_context(device_target="Ascend")
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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def __init__(self, input_shape):
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super().__init__()
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self.assign = op.Assign()
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self.inputdata = Parameter(initializer(1, input_shape), name="global_step")
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def construct(self, x, y, z):
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out = z
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while x < y:
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inputdata = self.inputdata
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x = x + 1
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out = self.assign(inputdata, z)
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return out
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x = Tensor(np.array(1).astype(np.int32))
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y = Tensor(np.array(3).astype(np.int32))
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input_shape = (1024, 512)
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z = Tensor(np.random.randn(*input_shape).astype(np.float32))
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net = Net(input_shape)
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net(x, y, z)
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def test_dup_context():
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''' different func_with_fv in net1 and net2 should produce 2 different FuncGraphAbstractClosure and
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Evaluator.
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'''
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x):
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def identity(f):
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return f
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def func_with_fv():
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return x
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def net1():
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local_func = identity(func_with_fv)
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out = local_func() + 20.0
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return out
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def net2():
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local_func = identity(func_with_fv)
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out = local_func() + 15.0
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return out
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return net1() + net2()
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Net()(5.0)
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def test_maybe_poly_func():
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''' different func_with_fv in net1 and net2 may produce poly node. '''
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x, y, z):
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def identity(f, inp):
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return f(inp)
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def func_with_fv(yy):
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return (x, yy)
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def make_call():
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out1 = identity(func_with_fv, y)
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out2 = identity(func_with_fv, z)
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return (out1, out2)
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return make_call()
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y_input = Tensor(np.array([1, 2]).astype(np.int32))
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z_input = Tensor(np.array([[2, 2], [3, 3]]).astype(np.int32))
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Net()(1, y_input, z_input)
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