wehub-resource-sync
284 lines
8.3 KiB
Python
284 lines
8.3 KiB
Python
# Copyright (c) 2020 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 (
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OpTest,
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convert_float_to_uint16,
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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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np.random.seed(10)
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def logit(x, eps):
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if eps:
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x_min = np.minimum(x, 1.0 - eps)
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x_max = np.maximum(x_min, eps)
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return np.log(x_max / (1.0 - x_max))
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else:
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return np.where(
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(x < 0.0) | (x > 1.0),
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np.array(np.nan, dtype=x.dtype),
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np.log(x / (1.0 - x)),
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)
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def logit_grad(x, eps=1e-8):
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if eps:
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tmp_x = np.select(
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[x < eps, x > (1.0 - eps)], [x * 0.0, x * 0.0], default=-1.0
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)
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x_1 = 1.0 - x
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_x = np.select([tmp_x == -1.0], [np.reciprocal(x * x_1)], default=0.0)
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else:
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tmp_x = np.select(
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[x < 0.0, x > 1.0],
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[np.array(np.nan, dtype=x.dtype), np.array(np.nan, dtype=x.dtype)],
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default=-1.0,
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)
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x_1 = 1.0 - x
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_x = np.select(
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[tmp_x == -1.0],
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[np.reciprocal(x * x_1)],
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default=np.array(np.nan, dtype=x.dtype),
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)
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if _x.size == 0:
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dout = np.full_like(x, fill_value=0.0)
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else:
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dout = np.full_like(x, fill_value=1.0 / _x.size)
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dx = dout * _x
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return dx
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class TestLogitOp(OpTest):
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def setUp(self):
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self.op_type = 'logit'
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self.python_api = paddle.logit
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self.set_attrs()
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x = np.random.uniform(-1.0, 1.0, self.shape).astype(self.dtype)
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out = logit(x, self.eps)
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self.x_grad = logit_grad(x, self.eps)
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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self.attrs = {'eps': self.eps}
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def set_attrs(self):
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self.dtype = np.float64
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self.shape = [120]
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self.eps = 1e-8
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(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=[self.x_grad],
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check_pir=True,
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)
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class TestLogitOpFp32(TestLogitOp):
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def set_attrs(self):
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self.dtype = np.float32
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self.shape = [120]
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self.eps = 1e-8
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def test_check_output(self):
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self.check_output(check_pir=True)
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class TestLogitOpFp16(TestLogitOp):
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def set_attrs(self):
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self.dtype = np.float16
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self.shape = [120]
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self.eps = 1e-8
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def test_check_output(self):
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self.check_output(check_pir=True)
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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 TestLogitOpBf16(OpTest):
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def setUp(self):
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self.op_type = 'logit'
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self.python_api = paddle.logit
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self.set_attrs()
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x = np.random.uniform(-0.5, 0.5, self.shape).astype(np.float32)
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out = logit(x, self.eps)
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self.x_grad = logit_grad(x, self.eps)
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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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self.attrs = {'eps': self.eps}
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def set_attrs(self):
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self.dtype = np.uint16
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self.shape = [120]
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self.eps = 1e-8
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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self.check_output_with_place(
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place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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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=[self.x_grad],
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check_pir=True,
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)
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class TestLogitShape(TestLogitOp):
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def set_attrs(self):
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self.dtype = np.float64
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self.shape = [2, 60]
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self.eps = 1e-8
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class TestLogitEps(TestLogitOp):
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def set_attrs(self):
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self.dtype = np.float32
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self.shape = [120]
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self.eps = 1e-8
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class TestLogit_ZeroSize(TestLogitOp):
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def set_attrs(self):
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self.dtype = np.float64
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self.shape = [2, 0]
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self.eps = 1e-8
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class TestLogitAPI(unittest.TestCase):
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def init_data(self):
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self.x_shape = [120]
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self.x_dtype = "float32"
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def setUp(self):
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self.init_data()
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self.x = np.random.uniform(-1.0, 1.0, self.x_shape).astype(self.x_dtype)
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self.place = get_device_place()
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def check_api(self, eps=1e-8):
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ref_out = logit(self.x, eps)
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# test static api
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(
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name='x', shape=self.x_shape, dtype=self.x_dtype
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)
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y = paddle.logit(x, eps)
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exe = paddle.static.Executor(self.place)
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out = exe.run(feed={'x': self.x}, fetch_list=[y])
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np.testing.assert_allclose(out[0], ref_out, rtol=1e-05)
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# test dygrapg api
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paddle.disable_static()
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x = paddle.to_tensor(self.x, dtype=self.x_dtype)
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y = paddle.logit(x, eps)
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np.testing.assert_allclose(y.numpy(), ref_out, rtol=1e-05)
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paddle.enable_static()
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def check_api_grad(self, eps=1e-8):
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ref_grad = logit_grad(self.x, eps)
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numpy_tensor = np.ones(self.x_shape).astype(self.x_dtype)
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# test dygrapg api
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paddle.disable_static()
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paddle_outgrad = paddle.to_tensor(numpy_tensor / numpy_tensor.size)
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x = paddle.to_tensor(self.x, dtype=self.x_dtype)
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x.stop_gradient = False
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y = paddle.logit(x, eps)
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x_grad = paddle.grad([y], [x], [paddle_outgrad])
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np.testing.assert_allclose(x_grad[0].numpy(), ref_grad, rtol=1e-05)
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paddle.enable_static()
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def test_check_api(self):
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paddle.enable_static()
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for eps in [1e-6, 0.0]:
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self.check_api(eps)
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self.check_api_grad(eps)
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def test_errors(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(name='X1', shape=[100], dtype='bool')
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self.assertRaises(TypeError, paddle.logit, x)
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x = paddle.static.data(name='X2', shape=[100], dtype='float32')
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self.assertRaises(TypeError, paddle.logit, x, dtype='int32')
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class TestLogitAPI_NAN_Val(unittest.TestCase):
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def setUp(self):
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self.init_input_output()
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self.place = [paddle.CPUPlace()]
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if core.is_compiled_with_cuda() or is_custom_device():
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self.place.append(get_device_place())
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def init_input_output(self):
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self.x = [-0.1, 1.1, 2]
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self.expect_out = [np.nan, np.nan, np.nan]
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self.expect_x_grad = [np.nan, np.nan, np.nan]
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def test_nan_val(self):
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def _test_nan_val_with_place(place):
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with paddle.base.dygraph.guard():
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x = paddle.to_tensor(self.x, stop_gradient=False, place=place)
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y = paddle.logit(x)
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loss = y.sum()
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loss.backward()
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np.testing.assert_allclose(
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y.numpy(), self.expect_out, rtol=1e-05
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)
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np.testing.assert_allclose(
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x.grad.numpy(), self.expect_x_grad, rtol=1e-05
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)
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for place in self.place:
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_test_nan_val_with_place(place)
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class TestLogitAPICase1(unittest.TestCase):
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def init_data(self):
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self.x_shape = [120]
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self.x_dtype = "float64"
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class TestLogitAPICase2(unittest.TestCase):
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def init_data(self):
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self.x_shape = [120]
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self.x_dtype = "float16"
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if __name__ == "__main__":
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unittest.main()
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