wehub-resource-sync
486 lines
16 KiB
Python
486 lines
16 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import get_device_place
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import paddle
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def ref_histogramdd(x, bins, ranges, weights, density):
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D = x.shape[-1]
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x = x.reshape(-1, D)
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if ranges is not None:
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ranges = np.array(ranges, dtype=x.dtype).reshape(D, 2).tolist()
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if weights is not None:
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weights = weights.reshape(-1)
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ref_hist, ref_edges = np.histogramdd(x, bins, ranges, density, weights)
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return ref_hist, ref_edges
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# inputs, bins, ranges, weights, density
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class TestHistogramddAPI(unittest.TestCase):
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def setUp(self):
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self.ranges = None
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self.weights = None
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self.density = False
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self.init_input()
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self.set_expect_output()
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self.place = get_device_place()
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def init_input(self):
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# self.sample = np.array([[0.0, 1.0], [1.0, 0.0], [2.0, 0.0], [2.0, 2.0]])
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self.sample = np.random.randn(
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4,
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2,
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).astype(np.float64)
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self.bins = [3, 3]
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self.weights = np.array([1.0, 2.0, 4.0, 8.0], dtype=self.sample.dtype)
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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def test_static_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(
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'x', self.sample.shape, dtype=self.sample.dtype
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)
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if self.weights is not None:
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weights = paddle.static.data(
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'weights', self.weights.shape, dtype=self.weights.dtype
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)
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out_0, out_1 = paddle.histogramdd(
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x,
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bins=self.bins,
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weights=weights,
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ranges=self.ranges,
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density=self.density,
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)
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else:
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out_0, out_1 = paddle.histogramdd(
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x, bins=self.bins, ranges=self.ranges, density=self.density
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)
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exe = paddle.static.Executor(self.place)
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if self.weights is not None:
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res = exe.run(
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feed={'x': self.sample, 'weights': self.weights},
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fetch_list=[out_0, out_1],
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)
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else:
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res = exe.run(
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feed={'x': self.sample}, fetch_list=[out_0, out_1]
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)
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hist_out, edges_out = res[0], res[1:]
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np.testing.assert_allclose(
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hist_out,
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self.expect_hist,
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)
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for idx, edge_out in enumerate(edges_out):
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expect_edge = np.array(self.expect_edges[idx])
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np.testing.assert_allclose(
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edge_out,
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expect_edge,
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)
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def test_dygraph_api(self):
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paddle.disable_static(self.place)
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self.sample_dy = paddle.to_tensor(self.sample, dtype=self.sample.dtype)
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self.weights_dy = None
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if self.weights is not None:
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self.weights_dy = paddle.to_tensor(self.weights)
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if isinstance(self.bins, tuple):
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self.bins = tuple([paddle.to_tensor(bin) for bin in self.bins])
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hist, edges = paddle.histogramdd(
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self.sample_dy,
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bins=self.bins,
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weights=self.weights_dy,
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ranges=self.ranges,
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density=self.density,
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)
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np.testing.assert_allclose(
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hist.numpy(),
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self.expect_hist,
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)
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for idx, edge in enumerate(edges):
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edge = edge.numpy()
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expect_edge = np.array(self.expect_edges[idx])
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np.testing.assert_allclose(
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edge,
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expect_edge,
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)
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paddle.enable_static()
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def test_error(self):
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pass
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class TestHistogramddAPICase1ForDensity(TestHistogramddAPI):
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def init_input(self):
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# self.sample = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 2.0]])
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self.sample = np.random.randn(4, 2).astype(np.float64)
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self.bins = [2, 2]
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self.ranges = [0.0, 1.0, 0.0, 1.0]
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self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase2ForMultiDimsAndDensity(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.bins = [3, 4]
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self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase3ForMultiDimsNotDensity(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.bins = [3, 4]
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# self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase4ForRangesAndDensity(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.bins = [3, 4]
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# [leftmost_1, rightmost_1, leftmost_2, rightmost_2,..., leftmost_D, rightmost_D]
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self.ranges = [1.0, 10.0, 1.0, 100.0]
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self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase5ForRangesNotDensity(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.bins = [3, 4]
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# [leftmost_1, rightmost_1, leftmost_2, rightmost_2,..., leftmost_D, rightmost_D]
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self.ranges = [1.0, 10.0, 1.0, 100.0]
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# self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase6NotRangesAndDensityAndWeights(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.bins = [3, 4]
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# [leftmost_1, rightmost_1, leftmost_2, rightmost_2,..., leftmost_D, rightmost_D]
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# self.ranges = [1., 10., 1., 100.]
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self.density = True
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self.weights = np.array(
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[
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[1.0, 2.0],
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[3.0, 4.0],
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[5.0, 6.0],
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[7.0, 8.0],
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],
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dtype=self.sample.dtype,
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)
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase7ForRangesAndDensityAndWeights(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.bins = [3, 4]
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# [leftmost_1, rightmost_1, leftmost_2, rightmost_2,..., leftmost_D, rightmost_D]
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self.ranges = [1.0, 10.0, 1.0, 100.0]
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self.density = True
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self.weights = np.array(
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[
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[1.0, 2.0],
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[3.0, 4.0],
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[5.0, 6.0],
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[7.0, 8.0],
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],
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dtype=self.sample.dtype,
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)
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase8MoreInnermostDim(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,4]
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self.sample = np.random.randn(4, 2, 4).astype(np.float64)
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self.bins = [1, 2, 3, 4]
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# [leftmost_1, rightmost_1, leftmost_2, rightmost_2,..., leftmost_D, rightmost_D]
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self.density = False
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self.weights = np.array(
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[
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[1.0, 2.0],
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[3.0, 4.0],
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[5.0, 6.0],
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[7.0, 8.0],
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],
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dtype=self.sample.dtype,
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)
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase8MoreInnermostDimAndDensity(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,4]
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self.sample = np.random.randn(4, 2, 4).astype(np.float64)
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self.bins = [1, 2, 3, 4]
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# [leftmost_1, rightmost_1, leftmost_2, rightmost_2,..., leftmost_D, rightmost_D]
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self.density = True
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self.weights = np.array(
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[
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[1.0, 2.0],
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[3.0, 4.0],
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[5.0, 6.0],
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[7.0, 8.0],
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],
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dtype=self.sample.dtype,
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)
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase9ForIntBin(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.weights = np.array(
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[
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[1.0, 2.0],
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[3.0, 4.0],
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[5.0, 6.0],
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[7.0, 8.0],
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],
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dtype=self.sample.dtype,
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)
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self.bins = 5
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self.density = True
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self.ranges = [1.0, 10.0, 1.0, 100.0]
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase10ForTensorBin(TestHistogramddAPI):
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def init_input(self):
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# shape: [4,2,2]
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self.sample = np.random.randn(4, 2, 2).astype(np.float64)
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self.weights = np.array(
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[
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[1.0, 2.0],
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[3.0, 4.0],
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[5.0, 6.0],
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[7.0, 8.0],
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],
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dtype=self.sample.dtype,
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)
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self.bins = (
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np.array([1.0, 2.0, 10.0, 15.0, 20.0]),
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np.array([0.0, 20.0, 100.0]),
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)
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self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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class TestHistogramddAPICase10ForFloat32(TestHistogramddAPI):
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def init_input(self):
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# self.sample = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 2.0]])
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self.sample = np.random.randn(4, 2).astype(np.float32)
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self.bins = [2, 2]
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self.ranges = [0.0, 1.0, 0.0, 1.0]
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self.density = True
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def set_expect_output(self):
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self.expect_hist, self.expect_edges = ref_histogramdd(
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self.sample, self.bins, self.ranges, self.weights, self.density
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)
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# histogramdd(sample, bins=10, ranges=None, density=False, weights=None, name=None):
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class TestHistogramddAPI_check_sample_type_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor([[False, True], [True, False]])
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with self.assertRaises(TypeError):
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paddle.histogramdd(sample)
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class TestHistogramddAPI_check_bins_element_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor(
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[
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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]
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)
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bins = [3.4, 4.5]
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with self.assertRaises(ValueError):
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paddle.histogramdd(sample, bins=bins)
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class TestHistogramddAPI_check_ranges_type_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor(
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[
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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]
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)
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ranges = 10
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with self.assertRaises(TypeError):
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paddle.histogramdd(sample, ranges=ranges)
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class TestHistogramddAPI_check_density_type_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor(
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[
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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]
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)
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density = 10
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with self.assertRaises(TypeError):
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paddle.histogramdd(sample, density=density)
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class TestHistogramddAPI_check_weights_type_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor(
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[
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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]
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)
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weights = 10
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with self.assertRaises(AttributeError):
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paddle.histogramdd(sample, weights=weights)
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class TestHistogramddAPI_sample_weights_shape_mismatch_error(
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TestHistogramddAPI
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):
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def test_error(self):
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sample = paddle.to_tensor(
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[ # shape: [4,2]
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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]
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)
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weights = paddle.to_tensor(
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[2.0, 3.0, 4.0], dtype=self.sample.dtype
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) # shape: [3,]
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with self.assertRaises(AssertionError):
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paddle.histogramdd(sample, weights=weights)
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class TestHistogramddAPI_sample_weights_type_mismatch_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor(
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[ # float32
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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],
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dtype=paddle.float32,
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)
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weights = paddle.to_tensor(
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[2.0, 3.0, 4.0], dtype=paddle.float64
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) # float64
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with self.assertRaises(AssertionError):
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paddle.histogramdd(sample, weights=weights)
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class TestHistogramddAPI_check_bins_type_error(TestHistogramddAPI):
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def test_error(self):
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sample = paddle.to_tensor(
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[
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[[1.0, 2.0], [3.0, 4.0]],
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[[5.0, 6.0], [7.0, 8.0]],
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[[9.0, 10.0], [11.0, 12.0]],
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[[13.0, 14.0], [15.0, 16.0]],
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]
|
|
)
|
|
bins = 2.0
|
|
with self.assertRaises(ValueError):
|
|
paddle.histogramdd(sample, bins=bins)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|