wehub-resource-sync
977 lines
29 KiB
Python
977 lines
29 KiB
Python
# Copyright (c) 2018 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 gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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from op_test import (
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OpTest,
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OpTestTool,
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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skip_check_grad_ci,
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)
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from test_sum_op import TestReduceOPTensorAxisBase
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import paddle
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from paddle import base
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from paddle.base import Program, core, program_guard
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np.random.seed(10)
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def mean_wrapper(x, axis=None, keepdim=False, reduce_all=False):
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if reduce_all:
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return paddle.mean(x, list(range(len(x.shape))), keepdim)
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return paddle.mean(x, axis, keepdim)
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def reduce_mean_wrapper(x, axis=0, keepdim=False, reduce_all=False):
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if reduce_all:
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return paddle.mean(x, list(range(len(x.shape))), keepdim)
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return paddle.mean(x, axis, keepdim)
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class TestMeanOp(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.public_python_api = paddle.mean
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self.init_dtype_type()
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self.init_prim_type()
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self.init_shape()
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self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def init_dtype_type(self):
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self.dtype = np.float64
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def init_shape(self):
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self.shape = [10, 10]
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_checkout_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True)
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class TestMeanOpPrim(TestMeanOp):
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def init_prim_type(self):
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self.prim_op_type = "prim"
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class TestMeanOp_ZeroDim(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.dtype = np.float64
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self.public_python_api = paddle.mean
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self.init_prim_type()
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self.inputs = {'X': np.random.random([]).astype(self.dtype)}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_checkout_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True)
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class TestMeanOp_float64ZeroSize(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.dtype = np.float64
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self.public_python_api = paddle.mean
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self.init_prim_type()
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self.inputs = {'X': np.array([]).astype(self.dtype)}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_checkout_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True)
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class TestMeanOp_float64ZeroSize3D(TestMeanOp_float64ZeroSize):
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def setUp(self):
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self.op_type = 'mean'
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self.python_api = paddle.mean
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self.dtype = np.float64
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self.public_python_api = paddle.mean
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self.init_prim_type()
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self.shape = [2, 0, 4]
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x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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self.inputs = {'X': x_np}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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class TestMeanOp_Complex64ZeroSize(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.public_python_api = paddle.mean
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self.init_prim_type()
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self.inputs = {'X': np.array([]).astype("complex64")}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_checkout_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True)
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@skip_check_grad_ci(
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reason="[skip float64 Nan check] Input nan, gradient is also nan"
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)
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class TestMeanOp_RealValuedNanInput(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.public_python_api = paddle.mean
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self.dtype = np.float64
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self.init_prim_type()
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data = np.arange(1, 100, dtype="float64")
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data = np.append(data, np.nan).astype(self.dtype)
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self.inputs = {'X': data}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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self.no_need_check_grad = True
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_check_grad(self):
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place = get_device_place()
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with paddle.base.dygraph.guard():
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data = np.arange(1, 100, dtype="float64")
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x_np = np.append(data, np.nan).astype(self.dtype)
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x = paddle.to_tensor(x_np)
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x.stop_gradient = False
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y = paddle.mean(x)
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dx = paddle.grad(y, x)[0].numpy()
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dx_expected = self.dtype(1.0 / np.prod(x_np.shape)) * np.ones(
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x_np.shape
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).astype(self.dtype)
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np.testing.assert_array_equal(dx, dx_expected)
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class TestMeanOp_RealNanInput(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.public_python_api = paddle.mean
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self.dtype = np.complex64
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self.init_prim_type()
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self.inputs = {
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'X': np.array([1 + 2j, 2 + 1j, np.nan + 1j]).astype("complex64")
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}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_checkout_grad(self):
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place = get_device_place()
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with paddle.base.dygraph.guard():
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x_np = np.array([1 + 1j, 2 + 2j, 1 + np.nan * 1j]).astype(
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self.dtype
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)
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x = paddle.to_tensor(x_np)
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x.stop_gradient = False
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y = paddle.mean(x)
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dx = paddle.grad(y, x)[0].numpy()
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dx_expected = self.dtype(1.0 / np.prod(x_np.shape)) * np.ones(
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x_np.shape
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).astype(self.dtype)
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np.testing.assert_array_equal(dx, dx_expected)
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class TestMeanOp_ImagNanInput(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.python_api = paddle.mean
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self.dtype = np.float64
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self.public_python_api = paddle.mean
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self.init_prim_type()
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self.inputs = {
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'X': np.array([1 + 1j, 2 + 2j, 1 + np.nan * 1j]).astype("complex64")
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}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def test_check_output(self):
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self.check_output(check_pir=True, equal_nan=True)
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def test_checkout_grad(self):
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place = get_device_place()
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with paddle.base.dygraph.guard():
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x_np = np.array([1 + 1j, 2 + 2j, 1 + np.nan * 1j]).astype(
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self.dtype
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)
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x = paddle.to_tensor(x_np)
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x.stop_gradient = False
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y = paddle.mean(x)
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dx = paddle.grad(y, x)[0].numpy()
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dx_expected = self.dtype(1.0 / np.prod(x_np.shape)) * np.ones(
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x_np.shape
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).astype(self.dtype)
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np.testing.assert_array_equal(dx, dx_expected)
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class TestMeanOp_ZeroDim_Prim(TestMeanOp_ZeroDim):
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def init_prim_type(self):
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self.prim_op_type = "prim"
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class TestMeanOpError(unittest.TestCase):
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def setUp(self):
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self.x_shape = [2, 3, 4, 5]
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self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.int32)
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self.place = (
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get_device_place()
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if (core.is_compiled_with_cuda() or is_custom_device())
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else paddle.CPUPlace()
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)
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def test_errors(self):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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# The input type of mean_op must be Variable.
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input1 = 12
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self.assertRaises(TypeError, paddle.mean, input1)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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input3 = paddle.static.data(
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name='input3', shape=[-1, 4], dtype="float16"
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)
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paddle.nn.functional.softmax(input3)
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paddle.disable_static()
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFP16MeanOp(TestMeanOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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self.__class__.no_need_check_grad = True
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def test_check_output(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(place, check_pir=True)
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def test_checkout_grad(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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with base.dygraph.guard():
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x_np = np.random.random((10, 10)).astype(self.dtype)
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x = paddle.to_tensor(x_np)
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x.stop_gradient = False
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y = paddle.mean(x)
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dx = paddle.grad(y, x)[0].numpy()
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dx_expected = self.dtype(1.0 / np.prod(x_np.shape)) * np.ones(
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x_np.shape
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).astype(self.dtype)
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np.testing.assert_array_equal(dx, dx_expected)
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@OpTestTool.skip_if_not_cpu_bf16()
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class TestBF16MeanOp(TestMeanOp):
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def init_dtype_type(self):
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self.dtype = np.uint16
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def test_check_output(self):
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paddle.enable_static()
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self.check_output_with_place(core.CPUPlace(), check_pir=True)
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def test_checkout_grad(self):
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place = core.CPUPlace()
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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def ref_reduce_mean(x, axis=None, keepdim=False, reduce_all=False):
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if isinstance(axis, list):
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axis = tuple(axis)
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if reduce_all:
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axis = None
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return np.mean(x, axis=axis, keepdims=keepdim)
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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_float16_supported(get_device_place()),
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"core is not compiled with CUDA",
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)
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class TestReduceMeanOp(OpTest):
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def setUp(self):
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self.op_type = 'reduce_mean'
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self.python_api = reduce_mean_wrapper
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self.public_python_api = reduce_mean_wrapper
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self.init_prim_type()
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self.dtype = 'float64'
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self.init_shapes()
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self.axis = [0]
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if self.shape == []:
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self.axis = []
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self.keepdim = False
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self.set_attrs()
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self.if_enable_cinn()
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np.random.seed(10)
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x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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if not hasattr(self, "reduce_all") and not x_np.shape == ():
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self.reduce_all = (not self.axis) or len(self.axis) == len(x_np)
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if x_np.shape == ():
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self.reduce_all = True
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out_np = ref_reduce_mean(x_np, self.axis, self.keepdim, self.reduce_all)
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self.inputs = {'X': x_np}
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self.outputs = {'Out': out_np}
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self.attrs = {
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'dim': self.axis,
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'keep_dim': self.keepdim,
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'reduce_all': self.reduce_all,
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}
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def init_prim_type(self):
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self.prim_op_type = "comp"
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def init_shapes(self):
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self.shape = [2, 3, 4, 5]
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def set_attrs(self):
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pass
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def if_enable_cinn(self):
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pass
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def test_check_output(self):
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if self.dtype != 'float16':
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self.check_output(
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check_prim=False, check_prim_pir=False, check_pir=True
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)
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else:
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place = get_device_place()
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self.check_output_with_place(
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place=place,
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check_prim=False,
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check_prim_pir=False,
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check_pir=True,
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)
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def test_check_grad(self):
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if self.dtype != 'float16':
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self.check_grad(
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['X'],
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['Out'],
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check_prim=False,
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check_prim_pir=False,
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check_pir=True,
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)
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else:
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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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numeric_grad_delta=0.5,
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check_prim=False,
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check_prim_pir=False,
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check_pir=True,
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)
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class TestReduceMeanOpPrim(TestReduceMeanOp):
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def init_prim_type(self):
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self.prim_op_type = "prim"
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def test_check_output(self):
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if self.dtype != 'float16':
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self.check_output(check_prim_pir=True, check_pir=True)
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else:
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place = get_device_place()
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self.check_output_with_place(
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place=place,
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check_prim_pir=True,
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check_pir=True,
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)
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def test_check_grad(self):
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if self.dtype != 'float16':
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self.check_grad(
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['X'],
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['Out'],
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check_prim_pir=True,
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check_pir=True,
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)
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else:
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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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numeric_grad_delta=0.5,
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check_prim_pir=True,
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check_pir=True,
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)
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class TestReduceMeanOp_ZeroDim(TestReduceMeanOp):
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def init_shapes(self):
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self.shape = []
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self.enable_cinn = False
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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 do not support bfloat16",
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)
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class TestReduceMeanBF16Op(OpTest):
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def setUp(self):
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self.op_type = 'reduce_mean'
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self.python_api = reduce_mean_wrapper
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self.public_python_api = reduce_mean_wrapper
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self.prim_op_type = "comp"
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self.dtype = np.uint16
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self.shape = [2, 3, 4, 5]
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self.axis = [0]
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self.keepdim = False
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self.set_attrs()
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self.if_enable_cinn()
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np.random.seed(10)
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x_np = np.random.uniform(-1, 1, self.shape).astype(np.float32)
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if not hasattr(self, "reduce_all"):
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self.reduce_all = (not self.axis) or len(self.axis) == len(x_np)
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out_np = ref_reduce_mean(x_np, self.axis, self.keepdim, self.reduce_all)
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self.inputs = {'X': convert_float_to_uint16(x_np)}
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self.outputs = {'Out': convert_float_to_uint16(out_np)}
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self.attrs = {
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'dim': self.axis,
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'keep_dim': self.keepdim,
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'reduce_all': self.reduce_all,
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}
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def if_enable_cinn(self):
|
|
self.enable_cinn = False
|
|
|
|
def set_attrs(self):
|
|
pass
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_output_with_place(place, check_prim=True)
|
|
|
|
def test_check_grad(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['X'],
|
|
['Out'],
|
|
numeric_grad_delta=0.05,
|
|
check_prim=True,
|
|
check_prim_pir=True,
|
|
)
|
|
|
|
|
|
class TestReduceMeanOpDefaultAttrs(TestReduceMeanOp):
|
|
def setUp(self):
|
|
self.op_type = 'reduce_mean'
|
|
self.python_api = reduce_mean_wrapper
|
|
self.public_python_api = reduce_mean_wrapper
|
|
self.prim_op_type = "comp"
|
|
self.dtype = 'float64'
|
|
self.shape = [2, 3, 4, 5]
|
|
|
|
x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
out_np = np.mean(x_np, axis=0)
|
|
self.inputs = {'X': x_np}
|
|
self.outputs = {'Out': out_np}
|
|
|
|
|
|
class TestReduceMeanOpDefaultAttrsForPrim(TestReduceMeanOpPrim):
|
|
def setUp(self):
|
|
self.op_type = 'reduce_mean'
|
|
self.python_api = reduce_mean_wrapper
|
|
self.public_python_api = reduce_mean_wrapper
|
|
self.init_prim_type()
|
|
self.dtype = 'float64'
|
|
self.shape = [2, 3, 4, 5]
|
|
|
|
x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
out_np = np.mean(x_np, axis=0)
|
|
self.inputs = {'X': x_np}
|
|
self.outputs = {'Out': out_np}
|
|
|
|
|
|
class TestReduceMeanOpFloat32(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestReduceMeanOpFloat32Prim(TestReduceMeanOpPrim):
|
|
def set_attrs(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestReduceMeanOpFloat16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpFloat16Prim(TestReduceMeanOpPrim):
|
|
def set_attrs(self):
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpShape1D(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.shape = [100]
|
|
|
|
|
|
class TestReduceMeanOpShape1DFP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.shape = [100]
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpShape6D(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.shape = [2, 3, 4, 5, 6, 7]
|
|
|
|
|
|
class TestReduceMeanOpShape6DBF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.shape = [2, 3, 4, 5, 6, 7]
|
|
|
|
|
|
class TestReduceMeanOpShape6DFP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.shape = [2, 3, 4, 5, 6, 7]
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpAxisAll(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
|
|
|
|
class TestReduceMeanOpAxisAllPrim(TestReduceMeanOpPrim):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
|
|
|
|
class TestReduceMeanOpAxisAllFP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpAxisAllFP16Prim(TestReduceMeanOpPrim):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpAxisAllBF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
|
|
|
|
class TestReduceMeanOpAxisTuple(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = (0, 1, 2)
|
|
|
|
|
|
class TestReduceMeanOpAxisTupleFP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = (0, 1, 2)
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpAxisTupleBF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.axis = (0, 1, 2)
|
|
|
|
|
|
class TestReduceMeanOpAxisNegative(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [-2, -1]
|
|
|
|
|
|
class TestReduceMeanOpAxisNegativeFP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [-2, -1]
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpAxisNegativeFP16Prim(TestReduceMeanOpPrim):
|
|
def set_attrs(self):
|
|
self.axis = [-2, -1]
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpAxisNegativeBF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.axis = [-2, -1]
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue1(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.keepdim = True
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue1FP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.keepdim = True
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue1BF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.keepdim = True
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue2(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
self.keepdim = True
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue2FP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
self.keepdim = True
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue2BF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
self.keepdim = True
|
|
|
|
|
|
class TestReduceMeanOpReduceAllTrue(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.reduce_all = True
|
|
|
|
|
|
class TestReduceMeanOpReduceAllTrueFP16(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.reduce_all = True
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestReduceMeanOpReduceAllTrueBF16(TestReduceMeanBF16Op):
|
|
def set_attrs(self):
|
|
self.reduce_all = True
|
|
|
|
|
|
class TestMeanAPI(unittest.TestCase):
|
|
# test paddle.tensor.stat.mean
|
|
|
|
def setUp(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
|
|
self.place = (
|
|
get_device_place()
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else paddle.CPUPlace()
|
|
)
|
|
|
|
def test_api_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('X', self.x_shape)
|
|
out1 = paddle.mean(x)
|
|
out2 = paddle.tensor.mean(x)
|
|
out3 = paddle.tensor.stat.mean(x)
|
|
axis = np.arange(len(self.x_shape)).tolist()
|
|
out4 = paddle.mean(x, axis)
|
|
out5 = paddle.mean(x, tuple(axis))
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
res = exe.run(
|
|
feed={'X': self.x}, fetch_list=[out1, out2, out3, out4, out5]
|
|
)
|
|
out_ref = np.mean(self.x)
|
|
for out in res:
|
|
np.testing.assert_allclose(out, out_ref, rtol=0.0001)
|
|
|
|
def test_api_dygraph(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
def test_case(x, axis=None, keepdim=False):
|
|
x_tensor = paddle.to_tensor(x)
|
|
out = paddle.mean(x_tensor, axis, keepdim)
|
|
if isinstance(axis, list):
|
|
axis = tuple(axis)
|
|
if len(axis) == 0:
|
|
axis = None
|
|
out_ref = np.mean(x, axis, keepdims=keepdim)
|
|
np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.0001)
|
|
|
|
test_case(self.x)
|
|
test_case(self.x, [])
|
|
test_case(self.x, -1)
|
|
test_case(self.x, keepdim=True)
|
|
test_case(self.x, 2, keepdim=True)
|
|
test_case(self.x, [0, 2])
|
|
test_case(self.x, (0, 2))
|
|
test_case(self.x, [0, 1, 2, 3])
|
|
paddle.enable_static()
|
|
|
|
def test_base_api(self):
|
|
with base.program_guard(base.Program(), base.Program()):
|
|
x = paddle.static.data("x", shape=[10, 10], dtype="float32")
|
|
out = paddle.mean(x=x, axis=1)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
x_np = np.random.rand(10, 10).astype(np.float32)
|
|
res = exe.run(feed={"x": x_np}, fetch_list=[out])
|
|
np.testing.assert_allclose(res[0], np.mean(x_np, axis=1), rtol=1e-05)
|
|
|
|
with base.dygraph.guard():
|
|
x_np = np.random.rand(10, 10).astype(np.float32)
|
|
x = paddle.to_tensor(x_np)
|
|
out = paddle.mean(x=x, axis=1)
|
|
np.testing.assert_allclose(
|
|
out.numpy(), np.mean(x_np, axis=1), rtol=1e-05
|
|
)
|
|
|
|
def test_errors(self):
|
|
paddle.disable_static()
|
|
x = np.random.uniform(-1, 1, [10, 12]).astype('float32')
|
|
x = paddle.to_tensor(x)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"\(InvalidArgument\) The reduce dim index 0 should ",
|
|
paddle.mean,
|
|
x,
|
|
-3,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"\(InvalidArgument\) The reduce dim index 0 should be in the range",
|
|
paddle.mean,
|
|
x,
|
|
2,
|
|
)
|
|
|
|
with self.assertRaises(Exception) as context:
|
|
paddle.mean(x, axis=[0, 0])
|
|
self.assertTrue(
|
|
"Axis contains duplicate dimensions" in str(context.exception)
|
|
)
|
|
with self.assertRaises(Exception) as context:
|
|
paddle.mean(x, axis=(1, 1))
|
|
self.assertTrue(
|
|
"Axis contains duplicate dimensions" in str(context.exception)
|
|
)
|
|
with self.assertRaises(Exception) as context:
|
|
paddle.mean(x, axis=[-2, -2])
|
|
self.assertTrue(
|
|
"Axis contains duplicate dimensions" in str(context.exception)
|
|
)
|
|
with self.assertRaises(Exception) as context:
|
|
paddle.mean(x, axis=[0, -2])
|
|
self.assertTrue(
|
|
"Axis contains duplicate dimensions" in str(context.exception)
|
|
)
|
|
|
|
|
|
class TestMeanAPIInt32(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.dtype = "int32"
|
|
self.x_np = np.random.randint(-1, 10000, self.x_shape).astype(
|
|
self.dtype
|
|
)
|
|
self.places = get_places()
|
|
|
|
def test_dygraph(self):
|
|
for place in self.places:
|
|
with base.dygraph.guard(place):
|
|
x = paddle.to_tensor(self.x_np)
|
|
out = paddle.mean(x=x)
|
|
np.testing.assert_equal(
|
|
out.numpy(),
|
|
np.mean(self.x_np.astype("float32")).astype(self.dtype),
|
|
)
|
|
|
|
def test_static(self):
|
|
paddle.enable_static()
|
|
for place in self.places:
|
|
with base.program_guard(base.Program(), base.Program()):
|
|
x = paddle.static.data(
|
|
"x", shape=self.x_shape, dtype=self.dtype
|
|
)
|
|
out = paddle.mean(x=x)
|
|
exe = base.Executor(place)
|
|
res = exe.run(feed={"x": self.x_np}, fetch_list=[out])
|
|
np.testing.assert_equal(
|
|
res[0], np.mean(self.x_np.astype("float32")).astype(self.dtype)
|
|
)
|
|
|
|
|
|
class TestMeanAPIInt64(TestMeanAPIInt32):
|
|
def setUp(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.dtype = "int64"
|
|
self.x_np = np.random.randint(-1, 10000, self.x_shape).astype(
|
|
self.dtype
|
|
)
|
|
self.places = get_places()
|
|
|
|
|
|
class TestMeanAPIBool(TestMeanAPIInt32):
|
|
def setUp(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.dtype = "bool"
|
|
self.x_np = np.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
|
|
self.places = get_places()
|
|
|
|
|
|
class TestMeanWithTensorAxis1(TestReduceOPTensorAxisBase):
|
|
def init_data(self):
|
|
self.pd_api = paddle.mean
|
|
self.np_api = np.mean
|
|
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
|
|
self.np_axis = np.array([1, 2], dtype='int64')
|
|
self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')
|
|
|
|
|
|
class TestMeanWithTensorAxis2(TestReduceOPTensorAxisBase):
|
|
def init_data(self):
|
|
self.pd_api = paddle.mean
|
|
self.np_api = np.mean
|
|
self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
|
|
self.np_axis = np.array([0, 1, 2], dtype='int64')
|
|
self.tensor_axis = [
|
|
0,
|
|
paddle.to_tensor([1], 'int64'),
|
|
paddle.to_tensor([2], 'int64'),
|
|
]
|
|
|
|
|
|
class TestMeanDoubleGradCheck(unittest.TestCase):
|
|
def mean_wrapper(self, x):
|
|
return paddle.mean(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [3, 4, 5], dtype)
|
|
data.persistable = True
|
|
out = paddle.mean(data)
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.mean_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestMeanTripleGradCheck(unittest.TestCase):
|
|
def mean_wrapper(self, x):
|
|
return paddle.mean(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
eps = 0.005
|
|
dtype = np.float32
|
|
|
|
data = paddle.static.data('data', [3, 4, 5], dtype)
|
|
data.persistable = True
|
|
out = paddle.mean(data)
|
|
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
|
|
|
|
gradient_checker.triple_grad_check(
|
|
[data], out, x_init=[data_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.mean_wrapper, [data], out, x_init=[data_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestMeanOp_ZeroSize1(TestMeanOp):
|
|
def init_shape(self):
|
|
self.shape = [0]
|
|
|
|
|
|
class TestMeanOp_ZeroSize2(TestMeanOp):
|
|
def init_shape(self):
|
|
self.shape = [0, 2]
|
|
|
|
|
|
class TestMeanOp_ZeroSize3(TestMeanOp):
|
|
def init_shape(self):
|
|
self.shape = [1, 100, 0]
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|