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109 lines
3.4 KiB
109 lines
3.4 KiB
# Copyright 2019 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 cases for gumbel_cdf"""
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.bijector as msb
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from mindspore import Tensor
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from mindspore import dtype
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Net(nn.Cell):
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"""
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Test class: forward pass of bijector.
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"""
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def __init__(self, loc, scale):
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super(Net, self).__init__()
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self.bijector = msb.GumbelCDF(loc, scale)
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def construct(self, x_):
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return self.bijector.forward(x_)
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def test_forward():
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loc = np.array([0.0])
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scale = np.array([[1.0], [2.0]])
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forward = Net(loc, scale)
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x = np.array([-2., -1., 0., 1., 2.]).astype(np.float32)
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ans = forward(Tensor(x, dtype=dtype.float32))
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tol = 1e-6
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expected = np.exp(-np.exp(-(x - loc)/scale))
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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class Net1(nn.Cell):
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"""
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Test class: backward pass of bijector.
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"""
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def __init__(self, loc, scale):
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super(Net1, self).__init__()
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self.bijector = msb.GumbelCDF(loc, scale)
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def construct(self, x_):
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return self.bijector.inverse(x_)
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def test_backward():
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loc = np.array([0.0])
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scale = np.array([[1.0], [2.0]])
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backward = Net1(loc, scale)
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x = np.array([0.1, 0.25, 0.5, 0.75, 0.9]).astype(np.float32)
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ans = backward(Tensor(x, dtype=dtype.float32))
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tol = 1e-6
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expected = loc - scale * np.log(-np.log(x))
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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class Net2(nn.Cell):
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"""
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Test class: Forward Jacobian.
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"""
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def __init__(self, loc, scale):
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super(Net2, self).__init__()
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self.bijector = msb.GumbelCDF(loc, scale)
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def construct(self, x_):
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return self.bijector.forward_log_jacobian(x_)
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def test_forward_jacobian():
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loc = np.array([0.0])
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scale = np.array([[1.0], [2.0]])
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forward_jacobian = Net2(loc, scale)
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x = np.array([-2., -1., 0., 1., 2.]).astype(np.float32)
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ans = forward_jacobian(Tensor(x))
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z = (x - loc) / scale
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expected = -z - np.exp(-z) - np.log(scale)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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class Net3(nn.Cell):
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"""
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Test class: Backward Jacobian.
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"""
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def __init__(self, loc, scale):
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super(Net3, self).__init__()
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self.bijector = msb.GumbelCDF(loc, scale)
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def construct(self, x_):
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return self.bijector.inverse_log_jacobian(x_)
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def test_backward_jacobian():
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loc = np.array([0.0])
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scale = np.array([[1.0], [2.0]])
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backward_jacobian = Net3(loc, scale)
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x = np.array([0.1, 0.2, 0.5, 0.75, 0.9]).astype(np.float32)
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ans = backward_jacobian(Tensor(x))
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expected = np.log(scale / (-x * np.log(x)))
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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