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254 lines
7.2 KiB
254 lines
7.2 KiB
# 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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"""
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@File : test_parse.py
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@Author:
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@Date : 2019-01-23 17:13
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@Desc :
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"""
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import logging
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import pytest
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops import composite as C
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from mindspore.common.api import ms_function, _executor
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from mindspore.ops._grad.grad_base import bprop_getters
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from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
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from mindspore.ops.functional import tensor_add
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from ...ut_filter import non_graph_engine
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# pylint: disable=W0613,W0612
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# W0613: unused-argument
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log = logging.getLogger("test")
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log.setLevel(level=logging.ERROR)
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context.set_context(mode=context.GRAPH_MODE)
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# Test case: use the parse obj interface use default parameter
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self, dim):
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super(Net, self).__init__()
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self.softmax1 = nn.Softmax(dim)
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self.softmax2 = nn.Softmax(dim + 1)
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def construct(self, input_data, input1=ms.Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))):
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return self.softmax1(input_data)
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@non_graph_engine
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def test_parse_defalut_parameter_case2():
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""" test_parse_defalut_parameter_case2 """
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log.debug("begin test_parse_defalut_parameter_case2")
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net = Net(0)
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npd = np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')
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log.debug("input value is: %r", npd)
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input_data = ms.Tensor(npd)
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input_data.set_dtype(ms.float32)
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log.debug("start run")
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output = net(input_data)
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value = output.asnumpy()
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log.debug("output value = %r", value)
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# Test case: use the variable parameter for parse object
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class Net1(nn.Cell):
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""" Net1 definition """
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def __init__(self):
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super(Net1, self).__init__()
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def construct(self, *args):
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x = args[0]
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return x
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def test_var_parameter_case2():
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""" test_var_parameter_case2 """
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log.debug("begin test_var_parameter_case2")
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net = Net1()
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npd = np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')
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log.debug("input value is: %r", npd)
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input_data = ms.Tensor(npd)
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input_data.set_dtype(ms.float32)
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np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input1 = ms.Tensor(np1)
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np2 = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input2 = ms.Tensor(np2)
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_executor.compile(net, input_data, input1, input2)
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# Test case: test the global flag
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g_x = Tensor(np.ones([3, 3]).astype(np.float32))
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@ms_function
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def tensor_add_global(x):
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""" tensor_add_global """
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global g_x
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res = tensor_add(x, g_x)
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return res
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@non_graph_engine
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def test_global_flag():
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""" test_global_flag """
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log.debug("begin test_global_flag")
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x = Tensor(np.ones([3, 3]).astype(np.float32))
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res = tensor_add_global(x)
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log.debug("finished test_global_flag, ret = %r", res)
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class NetWithNDarray(nn.Cell):
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""" NetWithNDarray definition """
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def __init__(self, dim):
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super(NetWithNDarray, self).__init__()
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self.softmax = nn.Softmax(dim)
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self.x = ms.Tensor(np.ones(shape=(1)).astype(np.float32))
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def construct(self, input_data):
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return self.softmax(input_data) * self.x
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@non_graph_engine
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def test_net_with_ndarray():
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""" test_net_with_ndarray """
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net = NetWithNDarray(0)
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input_data = np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32')
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net(ms.Tensor(input_data))
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def test_bprop_with_wrong_output_num():
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context.set_context(check_bprop=True)
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class BpropWithWrongOutputNum(PrimitiveWithInfer):
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@prim_attr_register
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def __init__(self):
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super(BpropWithWrongOutputNum, self).__init__('BpropWithWrongOutputNum')
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def __call__(self, x, y):
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return x
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def infer_shape(self, x_shape, yshape):
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return x_shape
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def infer_dtype(self, x_type, y_type):
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return x_type
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@bprop_getters.register(BpropWithWrongOutputNum)
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def get_bprop_with_wrong_output_num(self):
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"""Generate bprop for BpropWithWrongOutputNum"""
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def bprop(x, y, out, dout):
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return (dout,)
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return bprop
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class BpropWithWrongOutputNumCell(nn.Cell):
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def __init__(self):
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super(BpropWithWrongOutputNumCell, self).__init__()
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def construct(self, x, y):
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return BpropWithWrongOutputNum()(x, y)
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with pytest.raises(TypeError):
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C.grad_all(BpropWithWrongOutputNumCell())(1, 2)
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def test_bprop_with_wrong_output_type():
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context.set_context(check_bprop=True)
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class BpropWithWrongOutputType(PrimitiveWithInfer):
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@prim_attr_register
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def __init__(self):
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super(BpropWithWrongOutputType, self).__init__('BpropWithWrongOutputType')
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def __call__(self, x):
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return x
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_type):
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return x_type
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@bprop_getters.register(BpropWithWrongOutputType)
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def get_bprop_with_wrong_output_type(self):
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"""Generate bprop for BpropWithWrongOutputType"""
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def bprop(x, out, dout):
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return (1,)
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return bprop
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class BpropWithWrongOutputTypeCell(nn.Cell):
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def __init__(self):
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super(BpropWithWrongOutputTypeCell, self).__init__()
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def construct(self, x):
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return BpropWithWrongOutputType()(x)
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with pytest.raises(TypeError):
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C.grad_all(BpropWithWrongOutputTypeCell())(Tensor(np.ones([64, 10]).astype(np.int32)))
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def test_bprop_with_wrong_output_shape():
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context.set_context(check_bprop=True)
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class BpropWithWrongOutputShape(PrimitiveWithInfer):
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@prim_attr_register
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def __init__(self):
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super(BpropWithWrongOutputShape, self).__init__('BpropWithWrongOutputShape')
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def __call__(self, x):
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return x
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_type):
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return x_type
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@bprop_getters.register(BpropWithWrongOutputShape)
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def get_bprop_with_wrong_output_shape(self):
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"""Generate bprop for BpropWithWrongOutputShape"""
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ones = Tensor(np.ones([2,]).astype(np.int32))
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def bprop(x, out, dout):
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return (ones,)
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return bprop
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class BpropWithWrongOutputShapeCell(nn.Cell):
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def __init__(self):
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super(BpropWithWrongOutputShapeCell, self).__init__()
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def construct(self, x):
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return BpropWithWrongOutputShape()(x)
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with pytest.raises(TypeError):
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net = BpropWithWrongOutputShapeCell()
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net.set_grad()
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C.grad_all(net)(Tensor(np.ones([64, 10]).astype(np.int32)))
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