#   Copyright (c) 2018 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 math
import unittest

import numpy as np

import paddle
import paddle.fluid as fluid
import paddle.fluid.framework as framework
from paddle.fluid.core import VarDesc
from paddle.regularizer import L2Decay

DELTA = 0.00001


def check_cast_op(op):
    return (
        op.type == 'cast'
        and op.attr('in_dtype') == VarDesc.VarType.FP32
        and op.attr('out_dtype') in [VarDesc.VarType.FP16, VarDesc.VarType.BF16]
    )


def output_hist(out):
    hist, _ = np.histogram(out, range=(-1, 1))
    hist = hist.astype("float32")
    hist /= float(out.size)
    prob = 0.1 * np.ones((10))
    return hist, prob


class TestConstantInitializer(unittest.TestCase):
    def test_calculate_gain(self):
        self.assertEqual(paddle.nn.initializer.calculate_gain('sigmoid'), 1)
        self.assertEqual(paddle.nn.initializer.calculate_gain('linear'), 1)
        self.assertEqual(paddle.nn.initializer.calculate_gain('conv2d'), 1)
        self.assertEqual(paddle.nn.initializer.calculate_gain('tanh'), 5.0 / 3)
        self.assertEqual(
            paddle.nn.initializer.calculate_gain('relu'), math.sqrt(2.0)
        )
        self.assertEqual(
            paddle.nn.initializer.calculate_gain('leaky_relu', 1), 1
        )
        self.assertEqual(paddle.nn.initializer.calculate_gain('selu'), 3.0 / 4)

    def test_constant_initializer_default_value(self, dtype="float32"):
        """Test the constant initializer with default value"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.Constant(),
            )
        num_ops = 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'fill_constant')
        self.assertAlmostEqual(init_op.attr('value'), 0.0, delta=DELTA)
        return block

    def test_constant_initializer(self, dtype="float32"):
        """Test constant initializer with supplied value"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.Constant(2.3),
            )
        num_ops = 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'fill_constant')
        self.assertAlmostEqual(init_op.attr('value'), 2.3, delta=DELTA)
        return block

    def test_constant_initializer_fp16(self):
        """Test constant initializer with float16"""
        self.test_constant_initializer_default_value("float16")
        self.test_constant_initializer("float16")

    def test_constant_initializer_bf16(self):
        """Test constant initializer with bfloat16
        No cast operator has been added here
        """
        self.test_constant_initializer_default_value("uint16")
        self.test_constant_initializer("uint16")


class TestUniformInitializer(unittest.TestCase):
    def test_uniform_initializer_default_value(self, dtype="float32"):
        """Test the uniform initializer with default value"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.Uniform(),
            )
        num_ops = 2 if dtype == "float16" else 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        self.assertAlmostEqual(init_op.attr('min'), -1.0, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), 1.0, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)
        return block

    def test_uniform_initializer_random_seed(self):
        """Test the uniform initializer with manually setting seed"""
        program = framework.Program()
        program.random_seed = 123
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param1",
                initializer=paddle.nn.initializer.Uniform(),
            )
            block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param2",
                initializer=paddle.nn.initializer.UniformInitializer(seed=456),
            )
        init_op = block.ops[1]
        self.assertEqual(init_op.attr("seed"), 456)
        init_op1 = block.ops[0]
        self.assertEqual(init_op1.attr("seed"), 123)

    def test_uniform_initializer(self, dtype="float32"):
        """Test uniform initializer with supplied attributes"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.UniformInitializer(
                    -4.2, 3.1, 123
                ),
            )
        num_ops = 2 if dtype == "float16" else 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        self.assertAlmostEqual(init_op.attr('min'), -4.2, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), 3.1, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 123)
        return block

    def test_uniform_initializer_two_op(self, dtype="float32"):
        """Test uniform initializer with supplied attributes"""
        program = framework.Program()
        block = program.global_block()
        for i in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.UniformInitializer(
                    -4.2, float(i), 123
                ),
            )
        num_ops = 2 if dtype == "float16" else 1
        self.assertEqual(len(block.ops), num_ops)
        init_op0 = block.ops[0]
        self.assertEqual(init_op0.type, 'uniform_random')
        self.assertAlmostEqual(init_op0.attr('min'), -4.2, delta=DELTA)
        self.assertAlmostEqual(init_op0.attr('max'), 0.0, delta=DELTA)
        self.assertEqual(init_op0.attr('seed'), 123)
        return block

    def test_uniform_initializer_fp16(self):
        """Test uniform initializer with float16"""
        block = self.test_uniform_initializer_default_value("float16")
        self.assertTrue(check_cast_op(block.ops[1]))
        block = self.test_uniform_initializer(dtype="float16")
        self.assertTrue(check_cast_op(block.ops[1]))
        block = self.test_uniform_initializer_two_op("float16")
        self.assertTrue(check_cast_op(block.ops[1]))

    def test_uniform_initializer_bf16(self):
        """Test uniform initializer with bfloat16
        No cast operator has been added here
        """
        block = self.test_uniform_initializer_default_value("uint16")
        block = self.test_uniform_initializer(dtype="uint16")
        block = self.test_uniform_initializer_two_op("uint16")


class TestNormalInitializer(unittest.TestCase):
    def test_normal_initializer_default_value(self):
        """Test the normal initializer with default value"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.Normal(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'gaussian_random')
        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_normal_initializer(self, dtype="float32"):
        """Test normal initializer with supplied attributes"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.NormalInitializer(
                    2.3, 1.9, 123
                ),
            )
        num_ops = 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'gaussian_random')
        self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 123)
        return block

    def test_normal_initializer_fp16(self):
        """Test normal initializer with float16"""
        self.test_normal_initializer("float16")

    def test_normal_initializer_bf16(self):
        """Test normal initializer with bfloat16"""
        self.test_normal_initializer("uint16")


class TestXavierInitializer(unittest.TestCase):
    def test_uniform_xavier_initializer(self):
        """Test Xavier initializer with uniform distribution on
        for matrix multiply.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.XavierUniform(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        limit = np.sqrt(6.0 / (param.shape[0] + param.shape[1]))
        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_uniform_xavier_initializer_conv(self):
        """Test Xavier initializer with uniform distribution on
        for convolutions.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10, 15, 20],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.XavierUniform(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        receptive_field_size = float(15 * 20)
        limit = np.sqrt(
            6.0 / ((param.shape[0] + param.shape[1]) * receptive_field_size)
        )
        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_normal_xavier_initializer(self):
        """Test Xavier initializer with normal distribution on
        for matrix multiply.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.XavierNormal(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'gaussian_random')
        std = np.sqrt(2.0 / (param.shape[0] + param.shape[1]))
        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_normal_xavier_initializer_conv(self):
        """Test Xavier initializer with normal distribution on
        for convolutions.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10, 15, 20],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.XavierNormal(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'gaussian_random')
        receptive_field_size = float(15 * 20)
        std = np.sqrt(
            2.0 / ((param.shape[0] + param.shape[1]) * receptive_field_size)
        )
        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_xavier_initializer_supplied_arguments(
        self, dtype="float32", uniform=True
    ):
        """Test the Xavier initializer with supplied arguments"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.XavierInitializer(
                    uniform=uniform, fan_in=12, fan_out=23, seed=134
                ),
            )
        num_ops = (
            2
            if (dtype == "float16" or (dtype == "uint16" and not uniform))
            else 1
        )
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        if uniform:
            self.assertEqual(init_op.type, 'uniform_random')
            limit = np.sqrt(6.0 / (12 + 23))
            self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
            self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
        else:
            self.assertEqual(init_op.type, 'gaussian_random')
        self.assertEqual(init_op.attr('seed'), 134)
        return block

    def test_xavier_initializer_fp16(self):
        """Test the Xavier initializer with float16"""
        block = self.test_xavier_initializer_supplied_arguments("float16")

    def test_xavier_initializer_bf16(self):
        """Test the Xavier initializer with bfloat16"""
        block_uniform = self.test_xavier_initializer_supplied_arguments(
            "uint16"
        )
        self.assertEqual(len(block_uniform.ops), 1)
        block_gaussian = self.test_xavier_initializer_supplied_arguments(
            "uint16", False
        )


class TestMSRAInitializer(unittest.TestCase):
    def test_uniform_msra_initializer(self):
        """Test MSRA initializer with uniform distribution on
        for matrix multiply.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.KaimingUniform(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        limit = np.sqrt(6.0 / param.shape[0])
        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_uniform_msra_initializer_conv(self):
        """Test MSRA initializer with uniform distribution on
        for convolutions.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10, 15, 20],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.KaimingUniform(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        receptive_field_size = float(15 * 20)
        limit = np.sqrt(6.0 / (param.shape[1] * receptive_field_size))
        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_normal_msra_initializer(self):
        """Test MSRA initializer with normal distribution on
        for matrix multiply.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.KaimingNormal(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'gaussian_random')
        std = np.sqrt(2.0 / param.shape[0])
        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_normal_msra_initializer_conv(self):
        """Test MSRA initializer with normal distribution on
        for convolutions.
        """
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            param = block.create_parameter(
                dtype="float32",
                shape=[5, 10, 15, 20],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.KaimingNormal(),
            )
        self.assertEqual(len(block.ops), 1)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'gaussian_random')
        receptive_field_size = float(15 * 20)
        std = np.sqrt(2.0 / (param.shape[1] * receptive_field_size))
        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 0)

    def test_msra_initializer_supplied_arguments(self, dtype="float32"):
        """Test the MSRA initializer with supplied arguments"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[5, 10],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.MSRAInitializer(
                    fan_in=12, seed=134
                ),
            )
        num_ops = 2 if dtype == "float16" else 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'uniform_random')
        limit = np.sqrt(6.0 / 12)
        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
        self.assertEqual(init_op.attr('seed'), 134)
        return block

    def test_msra_initializer_fp16(self):
        """Test the MSRA initializer with float16"""
        block = self.test_msra_initializer_supplied_arguments("float16")
        self.assertTrue(check_cast_op(block.ops[1]))

    def test_msra_initializer_bf16(self):
        """Test the MSRA initializer with bfloat16"""
        block = self.test_msra_initializer_supplied_arguments("uint16")


class TestBilinearInitializer(unittest.TestCase):
    def test_bilinear_initializer(self, dtype="float32"):
        """Test the bilinear initializer with supplied arguments"""
        program = framework.Program()
        block = program.global_block()
        for _ in range(2):
            block.create_parameter(
                dtype=dtype,
                shape=[8, 1, 3, 3],
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.Bilinear(),
            )
        num_ops = 2 if dtype in ["float16", "uint16", "float64"] else 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'assign_value')
        return block

    def test_bilinear_initializer_fp64(self):
        self.test_bilinear_initializer(dtype='float64')

    def test_bilinear_initializer_fp16(self):
        """Test the bilinear initializer with supplied arguments"""
        block = self.test_bilinear_initializer("float16")
        self.assertTrue(check_cast_op(block.ops[1]))

    def test_bilinear_initializer_bf16(self):
        """Test the bilinear initializer with supplied arguments"""
        block = self.test_bilinear_initializer("uint16")
        self.assertTrue(check_cast_op(block.ops[1]))

    def test_type_error(self):
        self.assertRaises(TypeError, self.test_bilinear_initializer, 'int32')


class TestBilinearInitializerDygraphAPI(unittest.TestCase):
    def func_test_case(self):
        factor = 2
        C = 2
        B = 8
        H = W = 32
        w_attr = paddle.ParamAttr(
            learning_rate=0.0,
            regularizer=L2Decay(0.0),
            initializer=paddle.nn.initializer.Bilinear(),
        )
        data = paddle.rand([B, 3, H, W], dtype='float32')
        conv_up = paddle.nn.Conv2DTranspose(
            3,
            out_channels=C,
            kernel_size=2 * factor - factor % 2,
            padding=int(math.ceil((factor - 1) / 2.0)),
            stride=factor,
            weight_attr=w_attr,
            bias_attr=False,
        )
        x = conv_up(data)
        return x

    def func_test_case_fp16(self):
        paddle.set_default_dtype("float16")
        paddle.seed(1234)
        w_attr = paddle.ParamAttr(
            learning_rate=0.0,
            regularizer=L2Decay(0.0),
            initializer=paddle.nn.initializer.Bilinear(),
        )
        conv2d = paddle.nn.Conv2D(1, 2, 3, weight_attr=w_attr)
        paddle.set_default_dtype("float32")
        return conv2d.weight

    def test_bilinear_initializer(self):
        paddle.disable_static()
        eager_x = self.func_test_case()
        legacy_x = self.func_test_case()
        self.assertEqual(eager_x.numpy().all(), legacy_x.numpy().all())
        paddle.enable_static()

    def test_bilinear_initializer_fp16(self):
        paddle.disable_static()
        eager_x = self.func_test_case_fp16()
        legacy_x = self.func_test_case_fp16()
        self.assertEqual(eager_x.numpy().all(), legacy_x.numpy().all())
        paddle.enable_static()


class TestNumpyArrayInitializer(unittest.TestCase):
    def test_numpy_array_initializer(self, dtype="float32"):
        """Test the numpy array initializer with supplied arguments"""
        import numpy

        program = framework.Program()
        block = program.global_block()
        np_array = numpy.random.random((10000)).astype(dtype)
        for _ in range(2):
            block.create_parameter(
                dtype=np_array.dtype,
                shape=np_array.shape,
                lod_level=0,
                name="param",
                initializer=paddle.nn.initializer.Assign(np_array),
            )
        num_ops = 2 if dtype in ["float16", "uint16"] else 1
        self.assertEqual(len(block.ops), num_ops)
        init_op = block.ops[0]
        self.assertEqual(init_op.type, 'assign_value')
        assert (init_op.attr('fp32_values') == np_array).all()
        return block

    def test_numpy_array_initializer_fp16(self):
        """Test the numpy array initializer with float16"""
        block = self.test_numpy_array_initializer("float16")
        self.assertTrue(block.ops[1])

    def test_numpy_array_initializer_bf16(self):
        """Test the numpy array initializer with bfloat16"""
        block = self.test_numpy_array_initializer("uint16")
        self.assertTrue(block.ops[1])


class TestSetGlobalInitializer(unittest.TestCase):
    def test_set_global_weight_initilizer(self):
        """Test Set Global Param initilizer with UniformInitializer"""
        main_prog = framework.Program()
        startup_prog = framework.Program()
        fluid.set_global_initializer(
            paddle.nn.initializer.Uniform(low=-0.5, high=0.5)
        )
        with fluid.program_guard(main_prog, startup_prog):
            x = paddle.static.data(name="x", shape=[1, 3, 32, 32])
            # default initilizer of param in layers.conv2d is NormalInitializer
            conv = paddle.static.nn.conv2d(x, 5, 3)

        block = startup_prog.global_block()
        self.assertEqual(len(block.ops), 2)

        # init weight is the first op, and bias is the second
        bias_init_op = block.ops[1]
        self.assertEqual(bias_init_op.type, 'fill_constant')
        self.assertAlmostEqual(bias_init_op.attr('value'), 0.0, delta=DELTA)

        param_init_op = block.ops[0]
        self.assertEqual(param_init_op.type, 'uniform_random')
        self.assertAlmostEqual(param_init_op.attr('min'), -0.5, delta=DELTA)
        self.assertAlmostEqual(param_init_op.attr('max'), 0.5, delta=DELTA)
        self.assertEqual(param_init_op.attr('seed'), 0)
        fluid.set_global_initializer(None)

    def test_set_global_bias_initilizer(self):
        """Test Set Global Bias initilizer with NormalInitializer"""
        main_prog = framework.Program()
        startup_prog = framework.Program()
        fluid.set_global_initializer(
            paddle.nn.initializer.Uniform(low=-0.5, high=0.5),
            bias_init=paddle.nn.initializer.Normal(0.0, 2.0),
        )
        with fluid.program_guard(main_prog, startup_prog):
            x = paddle.static.data(name="x", shape=[1, 3, 32, 32])
            # default initilizer of bias in layers.conv2d is ConstantInitializer
            conv = paddle.static.nn.conv2d(x, 5, 3)

        block = startup_prog.global_block()
        self.assertEqual(len(block.ops), 2)

        # init weight is the first op, and bias is the second
        bias_init_op = block.ops[1]
        self.assertEqual(bias_init_op.type, 'gaussian_random')
        self.assertAlmostEqual(bias_init_op.attr('mean'), 0.0, delta=DELTA)
        self.assertAlmostEqual(bias_init_op.attr('std'), 2.0, delta=DELTA)
        self.assertEqual(bias_init_op.attr('seed'), 0)

        param_init_op = block.ops[0]
        self.assertEqual(param_init_op.type, 'uniform_random')
        self.assertAlmostEqual(param_init_op.attr('min'), -0.5, delta=DELTA)
        self.assertAlmostEqual(param_init_op.attr('max'), 0.5, delta=DELTA)
        self.assertEqual(param_init_op.attr('seed'), 0)
        fluid.set_global_initializer(None)


class TestUniformInitializerDygraph(unittest.TestCase):
    def test_uniform_initializer(self, dtype="float32"):
        """
        In dygraph mode, we can use initializer directly to initialize a tensor.
        """
        paddle.disable_static()

        tensor = paddle.zeros([1024, 1024, 16])
        tensor.stop_gradient = False
        np.testing.assert_allclose(
            np.zeros((1024, 1024, 16)), tensor.numpy(), rtol=1e-05
        )

        uniform_ = paddle.nn.initializer.Uniform()
        uniform_(tensor)

        self.assertEqual(
            tensor.stop_gradient, False
        )  # stop_gradient is not changed

        hist, prob = output_hist(tensor.numpy())

        np.testing.assert_allclose(hist, prob, rtol=0, atol=0.001)

        paddle.enable_static()


class TestXavierInitializerDygraph(unittest.TestCase):
    def test_xvarier_initializer(self, dtype="float32"):
        """
        In dygraph mode, we can use initializer directly to initialize a tensor.
        """
        paddle.disable_static()

        tensor = paddle.zeros([1024, 1024, 16])
        tensor.stop_gradient = False

        xavier_ = paddle.nn.initializer.XavierNormal(fan_in=3, fan_out=5)
        xavier_(tensor)

        hist, _ = output_hist(tensor.numpy())

        hist2, _ = output_hist(
            np.random.normal(0, np.sqrt(2.0 / (3 + 5)), [1024, 1024, 16])
        )

        np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.01)
        paddle.enable_static()


class TestMSRAInitializerDygraph(unittest.TestCase):
    def test_msra_initializer(self, dtype="float32"):
        """
        In dygraph mode, we can use initializer directly to initialize a tensor.
        """
        paddle.disable_static()

        tensor = paddle.zeros([1024, 1024, 16])
        tensor.stop_gradient = False

        msra_ = paddle.nn.initializer.KaimingNormal(fan_in=4)
        msra_(tensor)

        hist, _ = output_hist(tensor.numpy())

        hist2, _ = output_hist(
            np.random.normal(0, np.sqrt(2.0 / (4)), [1024, 1024, 16])
        )

        np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.01)
        paddle.enable_static()


class TesetconsistencyOfDynamicAndStaticGraph(unittest.TestCase):
    def test_order(self):
        paddle.set_device('cpu')
        SEED = 123
        weight_attr = paddle.framework.ParamAttr(
            name="linear_weight",
            learning_rate=1.0,
            trainable=False,
            regularizer=None,
            initializer=paddle.nn.initializer.TruncatedNormal(
                mean=0.0, std=2.0
            ),
        )
        bias_attr = paddle.framework.ParamAttr(
            name="linear_bias",
            learning_rate=1.0,
            trainable=False,
            regularizer=None,
            initializer=paddle.nn.initializer.TruncatedNormal(
                mean=0.0, std=2.0
            ),
        )

        def run_dynamic_graph():
            paddle.disable_static()
            paddle.seed(SEED)
            linear = paddle.nn.Linear(
                1, 1, weight_attr=weight_attr, bias_attr=bias_attr
            )
            return linear.weight.numpy(), linear.bias.numpy()
            paddle.enable_static()

        def run_static_graph():
            paddle.enable_static()
            exe = paddle.static.Executor(paddle.CPUPlace())
            paddle.seed(SEED)
            linear = paddle.nn.Linear(
                1, 1, weight_attr=weight_attr, bias_attr=bias_attr
            )
            res = exe.run(
                paddle.static.default_startup_program(),
                fetch_list=['linear_weight', 'linear_bias'],
            )
            return res[0], res[1]

        dynamic_res = run_dynamic_graph()
        static_res = run_static_graph()

        np.testing.assert_array_equal(dynamic_res[0], static_res[0])
        np.testing.assert_array_equal(dynamic_res[1], static_res[1])


# 2-D Parameter with shape: [10, 15]
class TestOrthogonalInitializer1(unittest.TestCase):
    """
    case 1
    """

    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Orthogonal(gain=3.0)
        )
        self.dtype = "float64"
        self.in_features = 10
        self.out_features = 15
        self.num_ops = 9

    def check_result(self, a, b):
        np.testing.assert_array_equal(a, b)
        np.testing.assert_allclose(
            np.matmul(a, a.T), 9 * np.eye(10), rtol=1e-5, atol=1e-8
        )

    def test_orthogonal(self):
        self.config()
        paddle.set_default_dtype(self.dtype)

        paddle.disable_static()
        paddle.seed(2021)
        linear = paddle.nn.Linear(
            self.in_features, self.out_features, weight_attr=self.weight_attr
        )
        res_dygraph = linear.weight.numpy()

        paddle.enable_static()
        paddle.seed(2021)
        start_prog = paddle.static.Program()
        main_prog = paddle.static.Program()
        with paddle.static.program_guard(main_prog, start_prog):
            linear = paddle.nn.Linear(
                self.in_features,
                self.out_features,
                weight_attr=self.weight_attr,
            )

            block = start_prog.global_block()
            self.assertEqual(len(block.ops), self.num_ops)
            self.assertEqual(block.ops[0].type, 'gaussian_random')
            self.assertEqual(block.ops[1].type, 'qr')
            self.assertEqual(block.ops[2].type, 'diag_v2')
            self.assertEqual(block.ops[3].type, 'sign')
            self.assertEqual(block.ops[4].type, 'elementwise_mul')
            self.assertEqual(block.ops[-3].type, 'reshape2')
            self.assertEqual(block.ops[-2].type, 'scale')

            exe = paddle.static.Executor()
            res_static = exe.run(start_prog, fetch_list=[linear.weight])[0]

        self.check_result(res_dygraph, res_static)


# 2-D Parameter with shape: [15, 10]
class TestOrthogonalInitializer2(TestOrthogonalInitializer1):
    """
    case 2
    """

    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Orthogonal(gain=2.0)
        )
        self.dtype = "float64"
        self.in_features = 15
        self.out_features = 10
        self.num_ops = 8

    def check_result(self, a, b):
        np.testing.assert_array_equal(a, b)
        np.testing.assert_allclose(
            np.matmul(a.T, a), 4 * np.eye(10), rtol=1e-5, atol=1e-8
        )


# 2-D Parameter with shape: [10, 10]
class TestOrthogonalInitializer3(TestOrthogonalInitializer1):
    """
    case 3
    """

    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Orthogonal()
        )
        self.dtype = "float32"
        self.in_features = 10
        self.out_features = 10
        self.num_ops = 8

    def check_result(self, a, b):
        np.testing.assert_array_equal(a, b)
        np.testing.assert_allclose(
            np.matmul(a.T, a), np.eye(10), rtol=1e-05, atol=1e-06
        )
        np.testing.assert_allclose(
            np.matmul(a, a.T), np.eye(10), rtol=1e-05, atol=1e-06
        )

    def test_error(self):
        self.config()
        with self.assertRaises(AssertionError):
            paddle.nn.Linear(10, 10, bias_attr=self.weight_attr)


# 4-D Parameter with shape: [6, 4, 3, 3]
class TestOrthogonalInitializer4(unittest.TestCase):
    """
    case 4
    """

    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Orthogonal(gain=3.0)
        )
        self.dtype = "float64"
        self.in_features = 4
        self.out_features = 6
        self.kernel_size = (3, 3)

    def check_result(self, a, b):
        np.testing.assert_array_equal(a, b)
        a = a.reshape(6, -1)
        np.testing.assert_allclose(
            np.matmul(a, a.T), 9 * np.eye(6), rtol=1e-5, atol=1e-8
        )

    def test_orthogonal(self):
        self.config()
        paddle.set_default_dtype(self.dtype)

        paddle.disable_static()
        paddle.seed(2021)
        conv2d = paddle.nn.Conv2D(
            self.in_features,
            self.out_features,
            self.kernel_size,
            weight_attr=self.weight_attr,
        )
        res_dygraph = conv2d.weight.numpy()

        paddle.enable_static()
        paddle.seed(2021)
        start_prog = paddle.static.Program()
        main_prog = paddle.static.Program()
        with paddle.static.program_guard(main_prog, start_prog):
            conv2d = paddle.nn.Conv2D(
                self.in_features,
                self.out_features,
                self.kernel_size,
                weight_attr=self.weight_attr,
            )
            exe = paddle.static.Executor()
            res_static = exe.run(
                paddle.static.default_startup_program(),
                fetch_list=[conv2d.weight],
            )[0]
        self.check_result(res_dygraph, res_static)


# 4-D Parameter with shape: [50, 4, 3, 3]
class TestOrthogonalInitializer5(TestOrthogonalInitializer4):
    """
    case 5
    """

    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Orthogonal(gain=2.0)
        )
        self.dtype = "float64"
        self.in_features = 4
        self.out_features = 50
        self.kernel_size = (3, 3)

    def check_result(self, a, b):
        np.testing.assert_array_equal(a, b)
        a = a.reshape(50, -1)
        np.testing.assert_allclose(
            np.matmul(a.T, a), 4 * np.eye(36), rtol=1e-5, atol=1e-8
        )


# 4-D Parameter with shape: [36, 4, 3, 3]
class TestOrthogonalInitializer6(TestOrthogonalInitializer4):
    """
    case 6
    """

    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Orthogonal()
        )
        self.dtype = "float32"
        self.in_features = 4
        self.out_features = 36
        self.kernel_size = (3, 3)

    def check_result(self, a, b):
        np.testing.assert_array_equal(a, b)
        a = a.reshape(36, -1)
        np.testing.assert_allclose(
            np.matmul(a.T, a), np.eye(36), rtol=1e-05, atol=1e-06
        )
        np.testing.assert_allclose(
            np.matmul(a, a.T), np.eye(36), rtol=1e-05, atol=1e-06
        )


# initialize Conv1D weight
class TestDiracInitializer1(unittest.TestCase):
    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Dirac()
        )
        self.dtype = "float64"
        self.in_channels = 3
        self.out_channels = 2
        self.kernel_size = 3
        self.input_shape = [8, self.in_channels, 10]
        self.conv_layer = paddle.nn.Conv1D
        self.num_ops = (
            8  # fill_constant*2, reshape*2, assign_value*2, scatter, cast
        )

    def check_result(self, w_dygraph, w_static, conv_in, conv_out):
        np.testing.assert_array_equal(w_dygraph, w_static)
        np.testing.assert_array_equal(conv_out, conv_in[:, 0:2, 1:9])

    def test_dirac(self):
        self.config()
        paddle.set_default_dtype(self.dtype)

        paddle.disable_static()
        conv = self.conv_layer(
            self.in_channels,
            self.out_channels,
            self.kernel_size,
            weight_attr=self.weight_attr,
        )
        weight_dygraph = conv.weight.numpy()

        paddle.enable_static()
        start_prog = paddle.static.Program()
        main_prog = paddle.static.Program()
        with paddle.static.program_guard(main_prog, start_prog):
            inp = paddle.rand(self.input_shape)
            conv = self.conv_layer(
                self.in_channels,
                self.out_channels,
                self.kernel_size,
                weight_attr=self.weight_attr,
            )

            output = conv(inp)
            block = start_prog.global_block()
            self.assertEqual(len(block.ops), self.num_ops)
            self.assertEqual(block.ops[0].type, 'fill_constant')
            self.assertEqual(block.ops[1].type, 'reshape2')
            self.assertEqual(block.ops[2].type, 'assign_value')
            self.assertEqual(block.ops[3].type, 'assign_value')
            self.assertEqual(block.ops[4].type, 'scatter')
            self.assertEqual(block.ops[5].type, 'reshape2')

            exe = paddle.static.Executor()
            exe.run(start_prog)
            fetch = exe.run(main_prog, fetch_list=[inp, output, conv.weight])
            conv_input = fetch[0]
            conv_output = fetch[1]
            weight_static = fetch[2]

        self.check_result(
            weight_dygraph, weight_static, conv_input, conv_output
        )


# initialize Conv2D weight
class TestDiracInitializer2(TestDiracInitializer1):
    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Dirac(groups=1)
        )
        self.dtype = "float64"
        self.in_channels = 4
        self.out_channels = 8
        self.kernel_size = (3, 3)
        self.input_shape = [8, self.in_channels, 10, 10]
        self.conv_layer = paddle.nn.Conv2D
        self.num_ops = 8

    def check_result(self, w_dygraph, w_static, conv_in, conv_out):
        np.testing.assert_array_equal(w_dygraph, w_static)
        np.testing.assert_array_equal(
            conv_out[:, 0:4, :, :], conv_in[:, :, 1:9, 1:9]
        )
        np.testing.assert_array_equal(
            conv_out[:, 4:8, :, :], np.zeros([8, 4, 8, 8])
        )


# initialize Conv3D weight
class TestDiracInitializer3(TestDiracInitializer1):
    def config(self):
        self.weight_attr = paddle.ParamAttr(
            initializer=paddle.nn.initializer.Dirac(groups=2)
        )
        self.dtype = "float32"
        self.in_channels = 5
        self.out_channels = 10
        self.kernel_size = (3, 3, 3)
        self.input_shape = [8, self.in_channels, 10, 10, 10]
        self.conv_layer = paddle.nn.Conv3D
        self.num_ops = 7

    def check_result(self, w_dygraph, w_static, conv_in, conv_out):
        np.testing.assert_array_equal(w_dygraph, w_static)
        np.testing.assert_array_equal(
            conv_out[:, 0:5, :, :, :], conv_in[:, :, 1:9, 1:9, 1:9]
        )
        np.testing.assert_array_equal(
            conv_out[:, 5:10, :, :, :], conv_in[:, :, 1:9, 1:9, 1:9]
        )

    def test_error(self):
        self.config()
        with self.assertRaises(AssertionError):
            paddle.nn.Linear(10, 10, weight_attr=self.weight_attr)

        with self.assertRaises(AssertionError):
            paddle.nn.Conv2D(5, 9, (3, 3), weight_attr=self.weight_attr)


class TestKaimingUniform(unittest.TestCase):
    def func_kaiminguniform_initializer_fan_in_zero(self):
        paddle.enable_static()
        x = paddle.static.data(name='x', shape=[1, 0, 0], dtype='float32')

        kaiming = paddle.nn.initializer.KaimingUniform(0)
        param_attr = paddle.ParamAttr(initializer=kaiming)

        paddle.static.nn.prelu(x, 'all', param_attr=param_attr)

    def test_type_error(self):
        self.assertRaises(
            ZeroDivisionError, self.func_kaiminguniform_initializer_fan_in_zero
        )


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()
