wehub-resource-sync
448 lines
10 KiB
Python
448 lines
10 KiB
Python
# Copyright (c) 2021 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 math
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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convert_uint16_to_float,
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get_device,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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paddle.enable_static()
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paddle.seed(100)
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def output_hist(out, lam, a, b):
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prob = []
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bin = []
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for i in range(a, b + 1):
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prob.append((lam**i) * math.exp(-lam) / math.factorial(i))
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bin.append(i)
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bin.append(b + 0.1)
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hist, _ = np.histogram(out, bin)
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hist = hist.astype("float32")
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hist = hist / float(out.size)
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return hist, prob
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class TestPoissonOp1(OpTest):
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def setUp(self):
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self.op_type = "poisson"
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self.python_api = paddle.poisson
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self.init_dtype()
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self.config()
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self.attrs = {}
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self.inputs = {'X': np.full([2048, 1024], self.lam, dtype=self.dtype)}
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self.outputs = {'Out': np.ones([2048, 1024], dtype=self.dtype)}
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def init_dtype(self):
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self.dtype = "float64"
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def config(self):
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self.lam = 10
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self.a = 5
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self.b = 15
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def verify_output(self, outs):
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hist, prob = output_hist(np.array(outs[0]), self.lam, self.a, self.b)
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np.testing.assert_allclose(hist, prob, rtol=0.01)
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def test_check_output(self):
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self.check_output_customized(self.verify_output, check_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[np.zeros([2048, 1024], dtype=self.dtype)],
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user_defined_grad_outputs=[
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np.random.rand(2048, 1024).astype(self.dtype)
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],
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check_pir=True,
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)
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class TestPoissonOp2(TestPoissonOp1):
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def config(self):
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self.lam = 5
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self.a = 1
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self.b = 8
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self.dtype = "float32"
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class TestPoissonAPI(unittest.TestCase):
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def test_alias(self):
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with paddle.base.dygraph.base.guard():
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x_np = np.random.random((3, 3)).astype("float32")
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x = paddle.to_tensor(x_np)
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out_ref = paddle.poisson(x)
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out_alias = paddle.poisson(input=x)
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assert out_ref.shape == out_alias.shape
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assert out_ref.dtype == out_alias.dtype
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def test_static(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x_np = np.random.rand(10, 10)
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x = paddle.static.data(name="x", shape=[10, 10], dtype='float64')
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y = paddle.poisson(x)
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exe = paddle.static.Executor()
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y_np = exe.run(
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paddle.static.default_main_program(),
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feed={"x": x_np},
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fetch_list=[y],
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)
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self.assertTrue(np.min(y_np) >= 0)
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def test_dygraph(self):
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with paddle.base.dygraph.base.guard():
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x = paddle.randn([10, 10], dtype='float32')
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y = paddle.poisson(x)
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self.assertTrue(np.min(y.numpy()) >= 0)
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x = paddle.randn([10, 10], dtype='float32')
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x.stop_gradient = False
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y = paddle.poisson(x)
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y.backward()
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self.assertTrue(np.min(y.numpy()) >= 0)
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np.testing.assert_array_equal(np.zeros_like(x), x.gradient())
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def test_fixed_random_number(self):
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# Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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print("Test Fixed Random number on GPU------>")
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paddle.disable_static()
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paddle.set_device(get_device())
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paddle.seed(2021)
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x = paddle.full([32, 3, 1024, 768], 10.0, dtype="float32")
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y = paddle.poisson(x)
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y_np = y.numpy()
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expect = [
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13.0,
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13.0,
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11.0,
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8.0,
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12.0,
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6.0,
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9.0,
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15.0,
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16.0,
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6.0,
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13.0,
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12.0,
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9.0,
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15.0,
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17.0,
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8.0,
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11.0,
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16.0,
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11.0,
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10.0,
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]
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np.testing.assert_array_equal(y_np[0, 0, 0, 0:20], expect)
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expect = [
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15.0,
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7.0,
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12.0,
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8.0,
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14.0,
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10.0,
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10.0,
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11.0,
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11.0,
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11.0,
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21.0,
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6.0,
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9.0,
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13.0,
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13.0,
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11.0,
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6.0,
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9.0,
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12.0,
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12.0,
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]
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np.testing.assert_array_equal(y_np[8, 1, 300, 200:220], expect)
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expect = [
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10.0,
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15.0,
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9.0,
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6.0,
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4.0,
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13.0,
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10.0,
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10.0,
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13.0,
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12.0,
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9.0,
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7.0,
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10.0,
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14.0,
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7.0,
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10.0,
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8.0,
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5.0,
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10.0,
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14.0,
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]
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np.testing.assert_array_equal(y_np[16, 1, 600, 400:420], expect)
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expect = [
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10.0,
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9.0,
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14.0,
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12.0,
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8.0,
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9.0,
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7.0,
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8.0,
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11.0,
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10.0,
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13.0,
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8.0,
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12.0,
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9.0,
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7.0,
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8.0,
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11.0,
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11.0,
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12.0,
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5.0,
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]
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np.testing.assert_array_equal(y_np[24, 2, 900, 600:620], expect)
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expect = [
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15.0,
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5.0,
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11.0,
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13.0,
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12.0,
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12.0,
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13.0,
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16.0,
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9.0,
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9.0,
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7.0,
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9.0,
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13.0,
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11.0,
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15.0,
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6.0,
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11.0,
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9.0,
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10.0,
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10.0,
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]
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np.testing.assert_array_equal(y_np[31, 2, 1023, 748:768], expect)
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x = paddle.full([16, 1024, 1024], 5.0, dtype="float32")
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y = paddle.poisson(x)
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y_np = y.numpy()
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expect = [
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4.0,
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5.0,
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2.0,
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9.0,
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8.0,
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7.0,
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4.0,
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7.0,
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4.0,
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7.0,
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6.0,
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3.0,
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10.0,
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7.0,
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5.0,
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7.0,
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2.0,
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5.0,
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5.0,
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6.0,
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]
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np.testing.assert_array_equal(y_np[0, 0, 100:120], expect)
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expect = [
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1.0,
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4.0,
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8.0,
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11.0,
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6.0,
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5.0,
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4.0,
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4.0,
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7.0,
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4.0,
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4.0,
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7.0,
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11.0,
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6.0,
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5.0,
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3.0,
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4.0,
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6.0,
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3.0,
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3.0,
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]
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np.testing.assert_array_equal(y_np[4, 300, 300:320], expect)
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expect = [
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7.0,
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5.0,
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4.0,
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6.0,
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8.0,
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5.0,
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6.0,
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7.0,
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7.0,
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7.0,
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3.0,
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10.0,
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5.0,
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10.0,
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4.0,
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5.0,
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8.0,
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7.0,
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5.0,
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7.0,
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]
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np.testing.assert_array_equal(y_np[8, 600, 600:620], expect)
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expect = [
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8.0,
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6.0,
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7.0,
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4.0,
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3.0,
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0.0,
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4.0,
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6.0,
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6.0,
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4.0,
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3.0,
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10.0,
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5.0,
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1.0,
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3.0,
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8.0,
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8.0,
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2.0,
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1.0,
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4.0,
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]
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np.testing.assert_array_equal(y_np[12, 900, 900:920], expect)
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expect = [
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2.0,
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1.0,
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14.0,
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3.0,
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6.0,
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5.0,
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2.0,
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2.0,
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6.0,
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5.0,
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7.0,
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4.0,
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8.0,
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4.0,
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8.0,
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4.0,
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5.0,
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7.0,
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1.0,
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7.0,
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]
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np.testing.assert_array_equal(y_np[15, 1023, 1000:1020], expect)
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paddle.enable_static()
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class TestPoissonFP16OP(TestPoissonOp1):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestPoissonBF16Op(OpTest):
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def setUp(self):
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self.op_type = "poisson"
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self.python_api = paddle.poisson
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self.__class__.op_type = self.op_type
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self.config()
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x = np.full([2048, 1024], self.lam, dtype="float32")
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out = np.ones([2048, 1024], dtype="float32")
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self.attrs = {}
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def config(self):
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self.lam = 10
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self.a = 5
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self.b = 15
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self.dtype = np.uint16
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def verify_output(self, outs):
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hist, prob = output_hist(
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convert_uint16_to_float(np.array(outs[0])), self.lam, self.a, self.b
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)
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np.testing.assert_allclose(hist, prob, rtol=0.01)
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place_customized(
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self.verify_output, place, check_pir=True
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)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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user_defined_grads=[np.zeros([2048, 1024], dtype="float32")],
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user_defined_grad_outputs=[
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np.random.rand(2048, 1024).astype("float32")
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],
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check_pir=True,
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)
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if __name__ == "__main__":
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unittest.main()
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