diff --git a/ppdet/modeling/initializer.py b/ppdet/modeling/initializer.py
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+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import paddle
+import paddle.nn as nn
+
+__all__ = [
+    'uniform_',
+    'normal_',
+    'constant_',
+    'ones_',
+    'zeros_',
+    'xavier_uniform_',
+    'xavier_normal_',
+    'kaiming_uniform_',
+    'kaiming_normal_',
+    'reset_initialized_parameter',
+]
+
+
+def _no_grad_uniform_(tensor, a, b):
+    with paddle.no_grad():
+        tensor.set_value(
+            paddle.uniform(
+                shape=tensor.shape, dtype=tensor.dtype, min=a, max=b))
+    return tensor
+
+
+def _no_grad_normal_(tensor, mean=0., std=1.):
+    with paddle.no_grad():
+        tensor.set_value(paddle.normal(mean=mean, std=std, shape=tensor.shape))
+    return tensor
+
+
+def _no_grad_fill_(tensor, value=0):
+    with paddle.no_grad():
+        v = paddle.rand(shape=tensor.shape, dtype=tensor.dtype)
+        v[...] = value
+        tensor.set_value(v)
+    return tensor
+
+
+def uniform_(tensor, a, b):
+    """
+    Modified tensor inspace using uniform_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        a (float|int): min value.
+        b (float|int): max value.
+    Return:
+        tensor
+    """
+    return _no_grad_uniform_(tensor, a, b)
+
+
+def normal_(tensor, mean=0., std=1.):
+    """
+    Modified tensor inspace using normal_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        mean (float|int): mean value.
+        std (float|int): std value.
+    Return:
+        tensor
+    """
+    return _no_grad_normal_(tensor, mean, std)
+
+
+def constant_(tensor, value=0):
+    """
+    Modified tensor inspace using constant_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        value (float|int): value to fill tensor.
+    Return:
+        tensor
+    """
+    return _no_grad_fill_(tensor, value)
+
+
+def ones_(tensor):
+    """
+    Modified tensor inspace using ones_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+    Return:
+        tensor
+    """
+    return _no_grad_fill_(tensor, 1)
+
+
+def zeros_(tensor):
+    """
+    Modified tensor inspace using zeros_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+    Return:
+        tensor
+    """
+    return _no_grad_fill_(tensor, 0)
+
+
+def _calculate_fan_in_and_fan_out(tensor, reverse=False):
+    """
+    Calculate (fan_in, _fan_out) for tensor
+
+    Args:
+        tensor (Tensor): paddle.Tensor
+        reverse (bool: False): tensor data format order, False by default as [fout, fin, ...]. e.g. : conv.weight [cout, cin, kh, kw] is False; linear.weight [cin, cout] is True
+
+    Return:
+        Tuple[fan_in, fan_out]
+    """
+    if tensor.ndim < 2:
+        raise ValueError(
+            "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions"
+        )
+
+    if reverse:
+        num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1]
+    else:
+        num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0]
+
+    receptive_field_size = 1
+    if tensor.ndim > 2:
+        receptive_field_size = math.prod(tensor.shape[2:])
+
+    fan_in = num_input_fmaps * receptive_field_size
+    fan_out = num_output_fmaps * receptive_field_size
+
+    return fan_in, fan_out
+
+
+def xavier_uniform_(tensor, gain=1., reverse=False):
+    """
+    Modified tensor inspace using xavier_uniform_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        gain (str): super parameter, 1. default.
+        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
+    Return:
+        tensor
+    """
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse)
+    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
+    k = math.sqrt(3.0) * std
+    return _no_grad_uniform_(tensor, -k, k)
+
+
+def xavier_normal_(tensor, gain=1., reverse=False):
+    """
+    Modified tensor inspace using xavier_normal_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        gain (str): super parameter, 1. default.
+        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
+    Return:
+        tensor
+    """
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse)
+    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
+    return _no_grad_normal_(tensor, 0, std)
+
+
+# reference: https://pytorch.org/docs/stable/_modules/torch/nn/init.html
+def _calculate_correct_fan(tensor, mode, reverse=False):
+    mode = mode.lower()
+    valid_modes = ['fan_in', 'fan_out']
+    if mode not in valid_modes:
+        raise ValueError("Mode {} not supported, please use one of {}".format(
+            mode, valid_modes))
+
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse)
+
+    return fan_in if mode == 'fan_in' else fan_out
+
+
+def _calculate_gain(nonlinearity, param=None):
+    linear_fns = [
+        'linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d',
+        'conv_transpose2d', 'conv_transpose3d'
+    ]
+    if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
+        return 1
+    elif nonlinearity == 'tanh':
+        return 5.0 / 3
+    elif nonlinearity == 'relu':
+        return math.sqrt(2.0)
+    elif nonlinearity == 'leaky_relu':
+        if param is None:
+            negative_slope = 0.01
+        elif not isinstance(param, bool) and isinstance(
+                param, int) or isinstance(param, float):
+            # True/False are instances of int, hence check above
+            negative_slope = param
+        else:
+            raise ValueError("negative_slope {} not a valid number".format(
+                param))
+        return math.sqrt(2.0 / (1 + negative_slope**2))
+    elif nonlinearity == 'selu':
+        return 3.0 / 4
+    else:
+        raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
+
+
+def kaiming_uniform_(tensor,
+                     a=0,
+                     mode='fan_in',
+                     nonlinearity='leaky_relu',
+                     reverse=False):
+    """
+    Modified tensor inspace using kaiming_uniform method
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        mode (str): ['fan_in', 'fan_out'], 'fin_in' defalut
+        nonlinearity (str): nonlinearity method name
+        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
+    Return:
+        tensor
+    """
+    fan = _calculate_correct_fan(tensor, mode, reverse)
+    gain = _calculate_gain(nonlinearity, a)
+    std = gain / math.sqrt(fan)
+    k = math.sqrt(3.0) * std
+    return _no_grad_uniform_(tensor, -k, k)
+
+
+def kaiming_normal_(tensor,
+                    a=0,
+                    mode='fan_in',
+                    nonlinearity='leaky_relu',
+                    reverse=False):
+    """
+    Modified tensor inspace using kaiming_normal_
+    Args:
+        tensor (paddle.Tensor): paddle Tensor
+        mode (str): ['fan_in', 'fan_out'], 'fin_in' defalut
+        nonlinearity (str): nonlinearity method name
+        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
+    Return:
+        tensor
+    """
+    fan = _calculate_correct_fan(tensor, mode, reverse)
+    gain = _calculate_gain(nonlinearity, a)
+    std = gain / math.sqrt(fan)
+    return _no_grad_normal_(tensor, 0, std)
+
+
+@paddle.no_grad()
+def reset_initialized_parameter(model, include_self=True):
+    """
+    Reset initialized parameter using following method for [conv, linear, embedding, bn]
+
+    Args:
+        model (paddle.Layer): paddle Layer
+        include_self (bool: False): include_self for Layer.named_sublayers method. Indicate whether including itself
+    Return:
+        None
+    """
+    for _, m in model.named_sublayers(include_self=include_self):
+        if isinstance(m, nn.Conv2D):
+            k = float(m._groups) / (m._in_channels * m._kernel_size[0] *
+                                    m._kernel_size[1])
+            k = math.sqrt(k)
+            _no_grad_uniform_(m.weight, -k, k)
+            if hasattr(m, 'bias') and getattr(m, 'bias') is not None:
+                _no_grad_uniform_(m.bias, -k, k)
+
+        elif isinstance(m, nn.Linear):
+            k = math.sqrt(1. / m.weight.shape[0])
+            _no_grad_uniform_(m.weight, -k, k)
+            if hasattr(m, 'bias') and getattr(m, 'bias') is not None:
+                _no_grad_uniform_(m.bias, -k, k)
+
+        elif isinstance(m, nn.Embedding):
+            _no_grad_normal_(m.weight, mean=0., std=1.)
+
+        elif isinstance(m, (nn.BatchNorm2D, nn.LayerNorm)):
+            _no_grad_fill_(m.weight, 1.)
+            if hasattr(m, 'bias') and getattr(m, 'bias') is not None:
+                _no_grad_fill_(m.bias, 0)