wehub-resource-sync
366 lines
12 KiB
Python
366 lines
12 KiB
Python
# Copyright (c) 2019 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, is_custom_device
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import paddle
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from paddle import base
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class TestHistogramOpAPI(unittest.TestCase):
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"""Test histogram api."""
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def test_static_graph(self):
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startup_program = paddle.static.Program()
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train_program = paddle.static.Program()
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with paddle.static.program_guard(train_program, startup_program):
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inputs = paddle.static.data(
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name='input', dtype='int64', shape=[2, 3]
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)
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output = paddle.histogram(inputs, bins=5, min=1, max=5)
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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img = np.array([[2, 4, 2], [2, 5, 4]]).astype(np.int64)
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res = exe.run(feed={'input': img}, fetch_list=[output])
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actual = np.array(res[0])
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expected = np.array([0, 3, 0, 2, 1]).astype(np.int64)
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self.assertTrue(
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(actual == expected).all(),
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msg='histogram output is wrong, out =' + str(actual),
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)
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def test_dygraph(self):
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with base.dygraph.guard():
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inputs_np = np.array([[2, 4, 2], [2, 5, 4]]).astype(np.int64)
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inputs = paddle.to_tensor(inputs_np)
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actual = paddle.histogram(inputs, bins=5, min=1, max=5)
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expected = np.array([0, 3, 0, 2, 1]).astype(np.int64)
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self.assertTrue(
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(actual.numpy() == expected).all(),
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msg='histogram output is wrong, out =' + str(actual.numpy()),
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)
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inputs_np = np.array([[2, 4, 2], [2, 5, 4]]).astype(np.int64)
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inputs = paddle.to_tensor(inputs_np)
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actual = paddle.histogram(inputs, bins=5, min=1, max=5)
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self.assertTrue(
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(actual.numpy() == expected).all(),
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msg='histogram output is wrong, out =' + str(actual.numpy()),
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)
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class TestHistogramOpError(unittest.TestCase):
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"""Test histogram op error."""
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def run_network(self, net_func):
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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net_func()
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exe = base.Executor()
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exe.run(main_program)
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def test_bins_error(self):
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"""Test bins should be greater than or equal to 1."""
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def net_func():
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input_value = paddle.tensor.fill_constant(
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shape=[3, 4], dtype='float32', value=3.0
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)
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paddle.histogram(input=input_value, bins=-1, min=1, max=5)
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with self.assertRaises(ValueError):
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self.run_network(net_func)
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def test_min_max_error(self):
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"""Test max must be larger or equal to min."""
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def net_func():
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input_value = paddle.tensor.fill_constant(
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shape=[3, 4], dtype='float32', value=3.0
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)
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paddle.histogram(input=input_value, bins=1, min=5, max=1)
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with self.assertRaises(ValueError):
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self.run_network(net_func)
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def test_min_max_range_error(self):
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"""Test range of min, max is not finite"""
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def net_func():
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input_value = paddle.tensor.fill_constant(
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shape=[3, 4], dtype='float32', value=3.0
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)
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paddle.histogram(input=input_value, bins=1, min=-np.inf, max=5)
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with self.assertRaises(ValueError):
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self.run_network(net_func)
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def test_input_range_error(self):
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"""Test range of input is out of bound"""
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def net_func():
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input_value = paddle.to_tensor(
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[
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-7095538316670326452,
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-6102192280439741006,
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2040176985344715288,
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-6276983991026997920,
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-6570715756420355710,
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-5998045007776667296,
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-6763099356862306438,
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3166073479842736625,
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],
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dtype=paddle.int64,
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)
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paddle.histogram(input=input_value, bins=1, min=0, max=0)
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with self.assertRaises(ValueError):
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self.run_network(net_func)
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def test_type_errors(self):
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with paddle.static.program_guard(paddle.static.Program()):
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# The input type must be Variable.
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self.assertRaises(
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TypeError, paddle.histogram, 1, bins=5, min=1, max=5
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)
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# The input type must be 'int32', 'int64', 'float32', 'float64'
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x_bool = paddle.static.data(
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name='x_bool', shape=[4, 3], dtype='bool'
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)
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self.assertRaises(
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TypeError, paddle.histogram, x_bool, bins=5, min=1, max=5
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)
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class TestHistogram(unittest.TestCase):
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"""Test histogram api."""
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def setUp(self):
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self.init_test_case()
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self.input_np = np.random.uniform(
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low=0.0, high=20.0, size=self.in_shape
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).astype(np.float32)
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self.weight_np = np.random.uniform(
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low=0.0, high=1.0, size=self.in_shape
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).astype(np.float32)
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def init_test_case(self):
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self.in_shape = (10, 12)
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = False
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self.is_weight = True
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def test_static_graph(self):
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startup_program = paddle.static.Program()
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train_program = paddle.static.Program()
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with paddle.static.program_guard(train_program, startup_program):
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inputs = paddle.static.data(
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name='input', dtype='float32', shape=self.in_shape
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)
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if self.is_weight:
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weight = paddle.static.data(
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name='weight', dtype='float32', shape=self.in_shape
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)
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output = paddle.histogram(
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inputs,
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bins=self.bins,
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min=self.min,
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max=self.max,
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weight=weight,
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density=self.density,
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)
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else:
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output = paddle.histogram(
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inputs,
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bins=self.bins,
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min=self.min,
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max=self.max,
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density=self.density,
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)
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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if self.is_weight:
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res = exe.run(
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feed={
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'input': self.input_np,
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'weight': self.weight_np,
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},
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fetch_list=[output],
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)
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else:
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res = exe.run(
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feed={'input': self.input_np}, fetch_list=[output]
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)
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actual = np.array(res[0])
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Out, _ = np.histogram(
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self.input_np,
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bins=self.bins,
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range=(self.min, self.max),
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density=self.density,
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weights=self.weight_np if self.is_weight else None,
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)
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np.testing.assert_allclose(actual, Out, rtol=1e-58, atol=1e-5)
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def test_dygraph(self):
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with base.dygraph.guard():
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inputs_np = np.random.uniform(
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low=0.0, high=20.0, size=self.in_shape
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).astype(np.float32)
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inputs = paddle.to_tensor(inputs_np)
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weight_np = np.random.uniform(
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low=0.0, high=1.0, size=self.in_shape
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).astype(np.float32)
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weight = paddle.to_tensor(weight_np)
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actual = paddle.histogram(
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inputs,
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bins=5,
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min=1,
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max=5,
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weight=weight if self.is_weight else None,
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density=self.density,
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)
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Out, _ = np.histogram(
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inputs_np,
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bins=5,
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range=(1, 5),
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weights=weight_np if self.is_weight else None,
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density=self.density,
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)
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np.testing.assert_allclose(
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actual.numpy(), Out, rtol=1e-58, atol=1e-5
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)
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class TestHistogramOpAPIWithDensity(TestHistogram):
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def init_test_case(self):
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self.in_shape = (10, 12)
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = True
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self.is_weight = False
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class TestHistogramOpAPIWithWeight(TestHistogram):
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def init_test_case(self):
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self.in_shape = (10, 12)
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = False
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self.is_weight = True
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class TestHistogramOpAPIWithWeightAndDensity(TestHistogram):
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def init_test_case(self):
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self.in_shape = (10, 12)
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = True
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self.is_weight = True
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class TestHistogramOpAPIWithFloat32(TestHistogram):
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def init_test_case(self):
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self.in_shape = (10, 12)
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = False
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self.is_weight = False
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class TestHistogramOp_ZeroDim(TestHistogram):
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def init_test_case(self):
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self.in_shape = []
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = False
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self.is_weight = False
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class TestHistogramOpAPIWithFloatminMax(TestHistogram):
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def init_test_case(self):
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self.in_shape = (10, 12)
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self.bins = 4
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self.min = 2.2
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self.max = 4.5
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self.density = False
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self.is_weight = False
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class TestHistogram_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.init_test_case()
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self.input_np = np.random.uniform(
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low=0.0, high=20.0, size=self.in_shape
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).astype(np.float32)
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self.weight_np = np.random.uniform(
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low=0.0, high=1.0, size=self.in_shape
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).astype(np.float32)
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def init_test_case(self):
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self.in_shape = (0, 12)
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self.bins = 5
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self.min = 1
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self.max = 5
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self.density = False
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self.is_weight = True
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def test_dygraph(self):
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with base.dygraph.guard():
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inputs_np = np.random.uniform(
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low=0.0, high=20.0, size=self.in_shape
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).astype(np.float32)
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inputs = paddle.to_tensor(inputs_np)
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weight_np = np.random.uniform(
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low=0.0, high=1.0, size=self.in_shape
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).astype(np.float32)
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weight = paddle.to_tensor(weight_np)
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actual = paddle.histogram(
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inputs,
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bins=5,
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min=1,
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max=5,
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weight=weight if self.is_weight else None,
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density=self.density,
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)
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Out, _ = np.histogram(
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inputs_np,
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bins=5,
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range=(1, 5),
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weights=weight_np if self.is_weight else None,
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density=self.density,
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)
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np.testing.assert_allclose(
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actual.numpy(), Out, rtol=1e-58, atol=1e-5
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)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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