chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2022 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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from __future__ import annotations
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import numpy as np
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import paddle
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from paddle import _C_ops, pir
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from ...base import core, framework, unique_name
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from ...base.framework import (
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_current_expected_place,
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in_dygraph_mode,
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in_pir_mode,
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)
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from .initializer import Initializer
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__all__ = []
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class Bilinear(Initializer):
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"""
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This initializer can be used in transposed convolution operator to
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act as upsampling. Users can upsample a feature map with shape of
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(B, C, H, W) by any integer factor.
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Returns:
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Bilinear initializer instance objects.
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Examples:
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.. code-block:: pycon
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>>> import math
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>>> import paddle
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>>> import paddle.nn as nn
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>>> from paddle.regularizer import L2Decay
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>>> factor = 2
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>>> C = 2
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>>> B = 8
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>>> H = W = 32
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>>> w_attr = paddle.ParamAttr(
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... learning_rate=0.0,
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... regularizer=L2Decay(0.0),
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... initializer=nn.initializer.Bilinear(),
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... )
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>>> data = paddle.rand([B, 3, H, W], dtype='float32')
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>>> conv_up = nn.Conv2DTranspose(
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... 3,
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... out_channels=C,
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... kernel_size=2 * factor - factor % 2,
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... padding=int(math.ceil((factor - 1) / 2.0)),
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... stride=factor,
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... weight_attr=w_attr,
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... bias_attr=False,
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... )
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>>> x = conv_up(data)
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Where, `out_channels=C` and `groups=C` means this is channel-wise transposed
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convolution. The filter shape will be (C, 1, K, K) where K is `kernel_size`,
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This initializer will set a (K, K) interpolation kernel for every channel
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of the filter identically. The resulting shape of the output feature map
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will be (B, C, factor * H, factor * W). Note that the learning rate and the
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weight decay are set to 0 in order to keep coefficient values of bilinear
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interpolation unchanged during training.
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"""
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def __init__(self) -> None:
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"""Constructor for BilinearInitializer."""
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super().__init__()
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def forward(
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self, var: paddle.Tensor, block: pir.Block | None = None
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) -> paddle.Tensor | None:
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"""Initialize the input tensor with Bilinear initialization.
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Args:
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var(Tensor): Tensor that needs to be initialized.
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block(Block|None, optional): The block in which initialization ops
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should be added. Used in static graph only, default None.
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Returns:
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The initialization op
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"""
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assert not (
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isinstance(var, framework.EagerParamBase) and var.is_dist()
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), (
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"Currently, Bilinear initializer not support lazy init for dist param."
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)
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block = self._check_block(block)
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if not isinstance(var, (framework.Variable, pir.core.ParameterMeta)):
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raise ValueError(
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"var must be framework.Variable or pir.core.ParameterMeta."
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)
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if not isinstance(block, (framework.Block, pir.Block)):
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raise ValueError("block must be framework.Block or pir.Block.")
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shape = var.shape
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if len(shape) != 4:
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raise ValueError("the length of shape must be 4.")
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if shape[2] != shape[3]:
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raise ValueError("shape[2] must be equal to shape[3].")
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weight = np.zeros(np.prod(var.shape), dtype='float32')
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size = shape[3]
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# factor
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f = np.ceil(size / 2.0)
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# center
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c = (2 * f - 1 - f % 2) / (2.0 * f)
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for i in range(np.prod(shape)):
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x = i % size
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y = (i / size) % size
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weight[i] = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
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weight = np.reshape(weight, shape)
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# to be compatible of fp16 initializers
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if var.dtype in [
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core.VarDesc.VarType.FP16,
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core.VarDesc.VarType.BF16,
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core.VarDesc.VarType.FP64,
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]:
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out_dtype = core.VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(
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".".join(['bilinear_init', var.name, 'tmp'])
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),
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shape=var.shape,
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dtype=out_dtype,
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type=core.VarDesc.VarType.DENSE_TENSOR,
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persistable=False,
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)
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elif var.dtype in [
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core.DataType.FLOAT16,
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core.DataType.BFLOAT16,
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core.DataType.FLOAT64,
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]:
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out_dtype = core.DataType.FLOAT32
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out_var = var
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else:
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out_dtype = var.dtype
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out_var = var
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if out_dtype in (core.VarDesc.VarType.FP32, core.DataType.FLOAT32):
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value_name = "values"
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values = [float(v) for v in weight.flat]
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else:
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raise TypeError(f"Unsupported dtype {var.dtype}")
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if np.prod(shape) > 1024 * 1024:
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raise ValueError("The size of input is too big. ")
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if in_dygraph_mode():
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_C_ops.assign_value_(
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out_var,
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list(shape),
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out_dtype,
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values,
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_current_expected_place(),
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)
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if var.dtype in [
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core.VarDesc.VarType.FP16,
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core.VarDesc.VarType.BF16,
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core.VarDesc.VarType.FP64,
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]:
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var_tmp = _C_ops.cast(out_var, var.dtype)
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var_tmp._share_underline_tensor_to(var)
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else:
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out_var._share_underline_tensor_to(var)
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return None
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elif in_pir_mode():
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out_var = _C_ops.assign_value(
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list(shape),
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out_dtype,
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values,
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_current_expected_place(),
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)
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if var.dtype in [
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core.DataType.FLOAT16,
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core.DataType.BFLOAT16,
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core.DataType.FLOAT64,
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]:
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out_var = _C_ops.cast(out_var, var.dtype)
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return out_var
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else:
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op = block.append_op(
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type='assign_value',
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outputs={'Out': [out_var]},
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attrs={
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'dtype': out_dtype,
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'shape': list(shape),
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value_name: values,
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},
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)
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if var.dtype in [
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core.VarDesc.VarType.FP16,
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core.VarDesc.VarType.BF16,
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core.VarDesc.VarType.FP64,
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]:
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype},
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)
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var.op = op
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return op
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