# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import paddle import numpy as np from op_test import OpTest import math import os from paddle.fluid.framework import _test_eager_guard paddle.enable_static() paddle.seed(100) def output_hist(out, lam, a, b): prob = [] bin = [] for i in range(a, b + 1): prob.append((lam**i) * math.exp(-lam) / math.factorial(i)) bin.append(i) bin.append(b + 0.1) hist, _ = np.histogram(out, bin) hist = hist.astype("float32") hist = hist / float(out.size) return hist, prob class TestPoissonOp1(OpTest): def setUp(self): self.op_type = "poisson" self.config() self.attrs = {} self.inputs = {'X': np.full([1024, 1024], self.lam, dtype=self.dtype)} self.outputs = {'Out': np.ones([1024, 1024], dtype=self.dtype)} def config(self): self.lam = 10 self.a = 5 self.b = 15 self.dtype = "float64" def verify_output(self, outs): hist, prob = output_hist(np.array(outs[0]), self.lam, self.a, self.b) self.assertTrue( np.allclose( hist, prob, rtol=0.01), "actual: {}, expected: {}".format(hist, prob)) def test_check_output(self): self.check_output_customized(self.verify_output) def test_check_grad_normal(self): self.check_grad( ['X'], 'Out', user_defined_grads=[np.zeros( [1024, 1024], dtype=self.dtype)], user_defined_grad_outputs=[ np.random.rand(1024, 1024).astype(self.dtype) ]) class TestPoissonOp2(TestPoissonOp1): def config(self): self.lam = 5 self.a = 1 self.b = 9 self.dtype = "float32" class TestPoissonAPI(unittest.TestCase): def test_static(self): with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): x_np = np.random.rand(10, 10) x = paddle.static.data(name="x", shape=[10, 10], dtype='float64') y = paddle.poisson(x) exe = paddle.static.Executor() y_np = exe.run(paddle.static.default_main_program(), feed={"x": x_np}, fetch_list=[y]) self.assertTrue(np.min(y_np) >= 0) def test_dygraph(self): with paddle.fluid.dygraph.base.guard(): x = paddle.randn([10, 10], dtype='float32') y = paddle.poisson(x) self.assertTrue(np.min(y.numpy()) >= 0) with _test_eager_guard(): x = paddle.randn([10, 10], dtype='float32') x.stop_gradient = False y = paddle.poisson(x) y.backward() self.assertTrue(np.min(y.numpy()) >= 0) self.assertTrue(np.array_equal(np.zeros_like(x), x.gradient())) def test_fixed_random_number(self): # Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t' if not paddle.is_compiled_with_cuda(): return if os.getenv("FLAGS_use_curand", None) in ('0', 'False', None): return print("Test Fixed Random number on GPU------>") paddle.disable_static() paddle.set_device('gpu') paddle.seed(2021) x = paddle.full([32, 3, 1024, 768], 10., dtype="float32") y = paddle.poisson(x) y_np = y.numpy() expect = [ 13., 13., 11., 8., 12., 6., 9., 15., 16., 6., 13., 12., 9., 15., 17., 8., 11., 16., 11., 10. ] self.assertTrue(np.array_equal(y_np[0, 0, 0, 0:20], expect)) expect = [ 15., 7., 12., 8., 14., 10., 10., 11., 11., 11., 21., 6., 9., 13., 13., 11., 6., 9., 12., 12. ] self.assertTrue(np.array_equal(y_np[8, 1, 300, 200:220], expect)) expect = [ 10., 15., 9., 6., 4., 13., 10., 10., 13., 12., 9., 7., 10., 14., 7., 10., 8., 5., 10., 14. ] self.assertTrue(np.array_equal(y_np[16, 1, 600, 400:420], expect)) expect = [ 10., 9., 14., 12., 8., 9., 7., 8., 11., 10., 13., 8., 12., 9., 7., 8., 11., 11., 12., 5. ] self.assertTrue(np.array_equal(y_np[24, 2, 900, 600:620], expect)) expect = [ 15., 5., 11., 13., 12., 12., 13., 16., 9., 9., 7., 9., 13., 11., 15., 6., 11., 9., 10., 10. ] self.assertTrue(np.array_equal(y_np[31, 2, 1023, 748:768], expect)) x = paddle.full([16, 1024, 1024], 5., dtype="float32") y = paddle.poisson(x) y_np = y.numpy() expect = [ 4., 5., 2., 9., 8., 7., 4., 7., 4., 7., 6., 3., 10., 7., 5., 7., 2., 5., 5., 6. ] self.assertTrue(np.array_equal(y_np[0, 0, 100:120], expect)) expect = [ 1., 4., 8., 11., 6., 5., 4., 4., 7., 4., 4., 7., 11., 6., 5., 3., 4., 6., 3., 3. ] self.assertTrue(np.array_equal(y_np[4, 300, 300:320], expect)) expect = [ 7., 5., 4., 6., 8., 5., 6., 7., 7., 7., 3., 10., 5., 10., 4., 5., 8., 7., 5., 7. ] self.assertTrue(np.array_equal(y_np[8, 600, 600:620], expect)) expect = [ 8., 6., 7., 4., 3., 0., 4., 6., 6., 4., 3., 10., 5., 1., 3., 8., 8., 2., 1., 4. ] self.assertTrue(np.array_equal(y_np[12, 900, 900:920], expect)) expect = [ 2., 1., 14., 3., 6., 5., 2., 2., 6., 5., 7., 4., 8., 4., 8., 4., 5., 7., 1., 7. ] self.assertTrue(np.array_equal(y_np[15, 1023, 1000:1020], expect)) paddle.enable_static() if __name__ == "__main__": unittest.main()